+
+> İmge sahibi [Dmitry Soshnikov](http://soshnikov.com)
+
+Zaman ilerdikçe, hesaplama kaynakları daha ucuz hale geldi ve daha fazla veri kullanılabilir hale geldi, böylece sinir ağı yaklaşımları, bilgisayarla görme veya konuşmayı anlama gibi birçok alanda insanlarla rekabet etmede büyük başarım göstermeye başladı. Son on yılda, Yapay Zeka terimi çoğunlukla Sinir Ağları ile eşanlamlı olarak kullanılmıştır, çünkü duyduğumuz YZ başarılarının çoğu onlara dayanmaktadır.
+
+Yaklaşımların nasıl değiştiğini, mesela satranç oynayan bir bilgisayar programı yaratırken, gözlemleyebiliriz:
+
+* Evvelki satranç programları aramaya dayalıydı - bir program, belirli sayıda sonraki hamle için rakibin olası hamlelerini açıkça tahmin etmeye çalışırdı ve birkaç hamlede elde edilebilecek en uygun konuma dayalı olarak en uygun hamleyi seçerdi. Bu, [alfa-beta budama](https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning) denen arama algoritmasının geliştirilmesine yön verdi.
+* Arama stratejileri, arama alanının az sayıda olası hareketle sınırlı olduğu oyunun sonuna doğru iyi çalışır. Ancak oyunun başında arama alanı çok büyüktür ve algoritma, insan oyuncular arasındaki mevcut karşılamalardan öğrenilerek geliştirilebilir. Sonraki deneyler, programın bilgi tabanında oyundaki mevcut duruma çok benzeyen vakaları aradığı sözde [vakaya dayalı akıl yürütme](https://en.wikipedia.org/wiki/Case-based_reasoning)yi kullandı.
+* İnsan oyunculara karşı kazanan modern programlar, sinir ağları ve programların yalnızca kendisine karşı uzun süre oynayarak - tıpkı insanların satranç oynamayı öğrenirken yaptığı gibi - kendi hatalarından oynamayı öğrendiği [pekiştirmeli öğrenme](https://en.wikipedia.org/wiki/Reinforcement_learning) üzerine kuruludur. Ancak bir bilgisayar programı çok daha fazla oyunu çok daha kısa sürede oynayabilir ve böylece çok daha hızlı öğrenebilir.
+
+✅ YZ tarafından oynanan diğer oyunlar hakkında biraz araştırma yapın.
+
+Benzer şekilde, (Turing testini geçebilecek) “konuşan programlar” oluşturmaya yönelik yaklaşımın nasıl değiştiğini görebiliriz:
+
+* [Eliza](https://en.wikipedia.org/wiki/ELIZA) gibi bu türün ilk programları, çok basit dilbilgisi kurallarına ve girdi cümlesinin bir soru olarak yeniden formüle edilmesine dayanıyordu.
+* Cortana, Siri veya Google Asistan gibi modern asistanların tümü, konuşmayı metne dönüştürmek ve niyetimizi tanımak için Sinir ağlarını kullanan ve ardından gerekli eylemleri gerçekleştirmek için bazı akıl yürütmeler veya açık algoritmalar kullanan karma sistemlerdir.
+* Gelecekte, tamamen sinir tabanlı bir modelin diyaloğu tek başına idare etmesini bekleyebiliriz. En son GPT ve [Turing-NLG](https://turing.microsoft.com/) sinir ağları ailesi bu konuda büyük başarı gösteriyor.
+
+
+
+> İmge sahibi Dmitry Soshnikov, [fotoğraf](https://unsplash.com/photos/r8LmVbUKgns) ve [Marina Abrosimova](https://unsplash.com/@abrosimova_marina_foto), Unsplash
+
+## Yakın Zaman YZ Araştırmaları
+
+Sinir ağı araştırmalarındaki yakın zamanlardaki büyük büyüme, büyük halka açık veri kümelerinin kullanıma sunulmaya başladığı 2010 civarında başladı. Yaklaşık 14 milyon açıklamalı resim içeren [ImageNet](https://en.wikipedia.org/wiki/ImageNet) adlı devasa bir resim koleksiyonu, [ImageNet Büyük Ölçekli Görsel Tanıma Yarışmasını](https://image-net.org/challenges/LSVRC/) doğurdu.
+
+
+
+> İmge sahibi [Dmitry Soshnikov](http://soshnikov.com)
+
+2012 yılında, [Evrişimli Sinir Ağları](../../4-ComputerVision/07-ConvNets/README.tr.md) ilk olarak imge sınıflandırmada kullanıldı ve bu da sınıflandırma hatalarında önemli bir düşüşe neden oldu (neredeyse %30'dan %16.4'e). 2015 yılında, Microsoft Araştırma'nın ResNet mimarisi [insan düzeyinde doğruluk elde etti](https://doi.org/0.1109/ICCV.2015.123).
+
+O zamandan beri, Sinir Ağları birçok görevde çok başarılı davranışlar sergiledi:
+
+---
+
+Yıl | İnsana denklik elde edildi
+-----|--------
+2015 | [İmge Sınıflandırma](https://doi.org/10.1109/ICCV.2015.123)
+2016 | [Sohbetli Konuşma Tanıma](https://arxiv.org/abs/1610.05256)
+2018 | [Otomatik Makine Çevirisi](https://arxiv.org/abs/1803.05567) (Çince-İngilizce)
+2020 | [İmge Altyazısı](https://arxiv.org/abs/2009.13682)
+
+Geçtiğimiz birkaç yılda, BERT ve GPT-3 gibi büyük dil modelleriyle büyük başarılara tanık olduk. Bu, çoğunlukla, onları genel metin koleksiyonları üzerinde önceden eğitmemize ve daha sonra bu modelleri daha belirli görevler için özelleştirmemize olanak tanıyarak metinlerin yapısını ve anlamını yakalamak için modelleri eğitmemizi sağlayan çok sayıda genel metin verisi olduğu gerçeğinden kaynaklanmaktadır. Bu dersin ilerleyen bölümlerinde [Doğal Dil İşleme](../../5-NLP/README.tr.md) hakkında daha fazla bilgi edineceğiz.
+
+## 🚀 Kendini Sınama
+
+Yapay zekanın size göre en etkili nerede kullanıldığını belirlemek için internette bir tur yapın. Bir eşleme uygulamasında mı, yoksa bir konuşmadan metne hizmetinde mi ya da bir video oyununda mı? Sistemin nasıl inşa edildiği araştırın.
+
+## [Ders sonrası sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/201)
+
+## Gözden Geçirme ve Bireysel Çalışma
+
+[Bu dersi](https://github.com/microsoft/ML-For-Beginners/tree/main/1-Introduction/2-history-of-ML) okuyarak YZ ve MÖ'nün geçmişini inceleyin. O dersin veya bu dersin üstündeki eskiz notundan bir öğe alın ve evrimini bildiren kültürel bağlamı anlamak için daha derinlemesine araştırın.
+
+**Ödev**: [Oyun Özeti](assignment.tr.md)
diff --git a/lessons/1-Intro/translations/assignment.tr.md b/lessons/1-Intro/translations/assignment.tr.md
new file mode 100644
index 00000000..14fea9b4
--- /dev/null
+++ b/lessons/1-Intro/translations/assignment.tr.md
@@ -0,0 +1,3 @@
+# Oyun Özeti
+
+Oyunlar, YZ ve MÖ'deki gelişmelerden büyük ölçüde etkilenen bir alandır. Bu ödevde, YZ'nin evriminden etkilenmiş sevdiğiniz bir oyun hakkında kısa bir makale yazın. Birkaç tür bilgisayar işleme sisteminden etkilenecek kadar eski bir oyun olsun. İyi bir örnek Satranç veya Go'dur, ancak pong veya Pac-Man gibi video oyunlarına da bir göz atın. Oyunun geçmişini, bugününü ve yapay zekanın geleceğini tartışan bir makale yazın.
\ No newline at end of file
diff --git a/lessons/2-Symbolic/Animals.ipynb b/lessons/2-Symbolic/Animals.ipynb
index f511ed76..18f8edf2 100644
--- a/lessons/2-Symbolic/Animals.ipynb
+++ b/lessons/2-Symbolic/Animals.ipynb
@@ -1,463 +1,441 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": true
- },
- "source": [
- "# Implementing an Animal Expert System\n",
- "\n",
- "An example from [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n",
- "\n",
- "In this sample, we will implement a simple knowledge-based system to determine an animal based on some physical characteristics. The system can be represented by the following AND-OR tree (this is a part of the whole tree, we can easily add some more rules):\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Our own expert systems shell with backward inference\n",
- "\n",
- "Let's try to define a simple language for knowledge representation based on production rules. We will use Python classes as keywords to define rules. There would be essentially 3 types of classes:\n",
- "* `Ask` represents a question that needs to be asked to the user. It contains the set of possible answers.\n",
- "* `If` represents a rule, and it is just a syntactic sugar to store the content of the rule\n",
- "* `AND`/`OR` are classes to represent AND/OR branches of the tree. They just store the list of arguments inside. To simplify code, all functionality is defined in the parent class `Content`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "trusted": true
- },
- "outputs": [],
- "source": [
- "class Ask():\n",
- " def __init__(self,choices=['y','n']):\n",
- " self.choices = choices\n",
- " def ask(self):\n",
- " if max([len(x) for x in self.choices])>1:\n",
- " for i,x in enumerate(self.choices):\n",
- " print(\"{0}. {1}\".format(i,x),flush=True)\n",
- " x = int(input())\n",
- " return self.choices[x]\n",
- " else:\n",
- " print(\"/\".join(self.choices),flush=True)\n",
- " return input()\n",
- "\n",
- "class Content():\n",
- " def __init__(self,x):\n",
- " self.x=x\n",
- " \n",
- "class If(Content):\n",
- " pass\n",
- "\n",
- "class AND(Content):\n",
- " pass\n",
- "\n",
- "class OR(Content):\n",
- " pass"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In our system, working memory would contain the list of **facts** as **attribute-value pairs**. The knowledgebase can be defined as one big dictionary that maps actions (new facts that should be inserted into working memory) to conditions, expressed as AND-OR expressions. Also, some facts can be `Ask`-ed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "trusted": true
- },
- "outputs": [],
- "source": [
- "rules = {\n",
- " 'default': Ask(['y','n']),\n",
- " 'color' : Ask(['red-brown','black and white','other']),\n",
- " 'pattern' : Ask(['dark stripes','dark spots']),\n",
- " 'mammal': If(OR(['hair','gives milk'])),\n",
- " 'carnivor': If(OR([AND(['sharp teeth','claws','forward-looking eyes']),'eats meat'])),\n",
- " 'ungulate': If(['mammal',OR(['has hooves','chews cud'])]),\n",
- " 'bird': If(OR(['feathers',AND(['flies','lies eggs'])])),\n",
- " 'animal:monkey' : If(['mammal','carnivor','color:red-brown','pattern:dark spots']),\n",
- " 'animal:tiger' : If(['mammal','carnivor','color:red-brown','pattern:dark stripes']),\n",
- " 'animal:giraffe' : If(['ungulate','long neck','long legs','pattern:dark spots']),\n",
- " 'animal:zebra' : If(['ungulate','pattern:dark stripes']),\n",
- " 'animal:ostrich' : If(['bird','long nech','color:black and white','cannot fly']),\n",
- " 'animal:pinguin' : If(['bird','swims','color:black and white','cannot fly']),\n",
- " 'animal:albatross' : If(['bird','flies well'])\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To perform the backward inference, we will define `Knowledgebase` class. It will contain:\n",
- "* Working `memory` - a dictionary that maps attributes to values\n",
- "* Knowledgebase `rules` in the format as defined above\n",
- "\n",
- "Two main methods are:\n",
- "* `get` to obtain the value of an attribute, performing inference if necessary. For example, `get('color')` would get the value of a color slot (it will ask if necessary, and store the value for later usage in the working memory). If we ask `get('color:blue')`, it will ask for a color, and then return `y`/`n` value depending on the color.\n",
- "* `eval` performs the actual inference, i.e. traverses AND/OR tree, evaluates sub-goals, etc."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "trusted": true
- },
- "outputs": [],
- "source": [
- "class KnowledgeBase():\n",
- " def __init__(self,rules):\n",
- " self.rules = rules\n",
- " self.memory = {}\n",
- " \n",
- " def get(self,name):\n",
- " if ':' in name:\n",
- " k,v = name.split(':')\n",
- " vv = self.get(k)\n",
- " return 'y' if v==vv else 'n'\n",
- " if name in self.memory.keys():\n",
- " return self.memory[name]\n",
- " for fld in self.rules.keys():\n",
- " if fld==name or fld.startswith(name+\":\"):\n",
- " # print(\" + proving {}\".format(fld))\n",
- " value = 'y' if fld==name else fld.split(':')[1]\n",
- " res = self.eval(self.rules[fld],field=name)\n",
- " if res!='y' and res!='n' and value=='y':\n",
- " self.memory[name] = res\n",
- " return res\n",
- " if res=='y':\n",
- " self.memory[name] = value\n",
- " return value\n",
- " # field is not found, using default\n",
- " res = self.eval(self.rules['default'],field=name)\n",
- " self.memory[name]=res\n",
- " return res\n",
- " \n",
- " def eval(self,expr,field=None):\n",
- " # print(\" + eval {}\".format(expr))\n",
- " if isinstance(expr,Ask):\n",
- " print(field)\n",
- " return expr.ask()\n",
- " elif isinstance(expr,If):\n",
- " return self.eval(expr.x)\n",
- " elif isinstance(expr,AND) or isinstance(expr,list):\n",
- " expr = expr.x if isinstance(expr,AND) else expr\n",
- " for x in expr:\n",
- " if self.eval(x)=='n':\n",
- " return 'n'\n",
- " return 'y'\n",
- " elif isinstance(expr,OR):\n",
- " for x in expr.x:\n",
- " if self.eval(x)=='y':\n",
- " return 'y'\n",
- " return 'n'\n",
- " elif isinstance(expr,str):\n",
- " return self.get(expr)\n",
- " else:\n",
- " print(\"Unknown expr: {}\".format(expr))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's define our animal knowledgebase and perform the consultation. Note that this call will ask you questions. You can answer by typing `y`/`n` for yes-no questions, or by specifying number (0..N) for questions with longer multiple-choice answers."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "trusted": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "hair\n",
- "y/n\n",
- "sharp teeth\n",
- "y/n\n",
- "claws\n",
- "y/n\n",
- "eats meat\n",
- "y/n\n",
- "color\n",
- "0. red-brown\n",
- "1. black and white\n",
- "2. other\n",
- "pattern\n",
- "0. dark stripes\n",
- "1. dark spots\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'monkey'"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "kb = KnowledgeBase(rules)\n",
- "kb.get('animal')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Using PyKnow for Forward Inference\n",
- "\n",
- "In the next example, we will try to implement forward inference using one of the libraries for knowledge representation, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** is a library for creating forward inference systems in Python, which is designed to be similar to classical old system [CLIPS](http://www.clipsrules.net/index.html). \n",
- "\n",
- "We could have also implemented forward chaining ourselves without many problems, but naive implementations are usually not very efficient. For more effective rule matching a special algorithm [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) is used."
- ]
- },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Implementing an Animal Expert System\n",
+ "\n",
+ "An example from [AI for Beginners Curriculum](http://github.com/microsoft/ai-for-beginners).\n",
+ "\n",
+ "In this sample, we will implement a simple knowledge-based system to determine an animal based on some physical characteristics. The system can be represented by the following AND-OR tree (this is a part of the whole tree, we can easily add some more rules):\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Our own expert systems shell with backward inference\n",
+ "\n",
+ "Let's try to define a simple language for knowledge representation based on production rules. We will use Python classes as keywords to define rules. There would be essentially 3 types of classes:\n",
+ "* `Ask` represents a question that needs to be asked to the user. It contains the set of possible answers.\n",
+ "* `If` represents a rule, and it is just a syntactic sugar to store the content of the rule\n",
+ "* `AND`/`OR` are classes to represent AND/OR branches of the tree. They just store the list of arguments inside. To simplify code, all functionality is defined in the parent class `Content`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Ask():\n",
+ " def __init__(self,choices=['y','n']):\n",
+ " self.choices = choices\n",
+ " def ask(self):\n",
+ " if max([len(x) for x in self.choices])>1:\n",
+ " for i,x in enumerate(self.choices):\n",
+ " print(\"{0}. {1}\".format(i,x),flush=True)\n",
+ " x = int(input())\n",
+ " return self.choices[x]\n",
+ " else:\n",
+ " print(\"/\".join(self.choices),flush=True)\n",
+ " return input()\n",
+ "\n",
+ "class Content():\n",
+ " def __init__(self,x):\n",
+ " self.x=x\n",
+ " \n",
+ "class If(Content):\n",
+ " pass\n",
+ "\n",
+ "class AND(Content):\n",
+ " pass\n",
+ "\n",
+ "class OR(Content):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In our system, working memory would contain the list of **facts** as **attribute-value pairs**. The knowledgebase can be defined as one big dictionary that maps actions (new facts that should be inserted into working memory) to conditions, expressed as AND-OR expressions. Also, some facts can be `Ask`-ed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rules = {\n",
+ " 'default': Ask(['y','n']),\n",
+ " 'color' : Ask(['red-brown','black and white','other']),\n",
+ " 'pattern' : Ask(['dark stripes','dark spots']),\n",
+ " 'mammal': If(OR(['hair','gives milk'])),\n",
+ " 'carnivor': If(OR([AND(['sharp teeth','claws','forward-looking eyes']),'eats meat'])),\n",
+ " 'ungulate': If(['mammal',OR(['has hooves','chews cud'])]),\n",
+ " 'bird': If(OR(['feathers',AND(['flies','lies eggs'])])),\n",
+ " 'animal:monkey' : If(['mammal','carnivor','color:red-brown','pattern:dark spots']),\n",
+ " 'animal:tiger' : If(['mammal','carnivor','color:red-brown','pattern:dark stripes']),\n",
+ " 'animal:giraffe' : If(['ungulate','long neck','long legs','pattern:dark spots']),\n",
+ " 'animal:zebra' : If(['ungulate','pattern:dark stripes']),\n",
+ " 'animal:ostrich' : If(['bird','long neck','color:black and white','cannot fly']),\n",
+ " 'animal:pinguin' : If(['bird','swims','color:black and white','cannot fly']),\n",
+ " 'animal:albatross' : If(['bird','flies well'])\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To perform the backward inference, we will define `Knowledgebase` class. It will contain:\n",
+ "* Working `memory` - a dictionary that maps attributes to values\n",
+ "* Knowledgebase `rules` in the format as defined above\n",
+ "\n",
+ "Two main methods are:\n",
+ "* `get` to obtain the value of an attribute, performing inference if necessary. For example, `get('color')` would get the value of a color slot (it will ask if necessary, and store the value for later usage in the working memory). If we ask `get('color:blue')`, it will ask for a color, and then return `y`/`n` value depending on the color.\n",
+ "* `eval` performs the actual inference, i.e. traverses AND/OR tree, evaluates sub-goals, etc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class KnowledgeBase():\n",
+ " def __init__(self,rules):\n",
+ " self.rules = rules\n",
+ " self.memory = {}\n",
+ " \n",
+ " def get(self,name):\n",
+ " if ':' in name:\n",
+ " k,v = name.split(':')\n",
+ " vv = self.get(k)\n",
+ " return 'y' if v==vv else 'n'\n",
+ " if name in self.memory.keys():\n",
+ " return self.memory[name]\n",
+ " for fld in self.rules.keys():\n",
+ " if fld==name or fld.startswith(name+\":\"):\n",
+ " # print(\" + proving {}\".format(fld))\n",
+ " value = 'y' if fld==name else fld.split(':')[1]\n",
+ " res = self.eval(self.rules[fld],field=name)\n",
+ " if res!='y' and res!='n' and value=='y':\n",
+ " self.memory[name] = res\n",
+ " return res\n",
+ " if res=='y':\n",
+ " self.memory[name] = value\n",
+ " return value\n",
+ " # field is not found, using default\n",
+ " res = self.eval(self.rules['default'],field=name)\n",
+ " self.memory[name]=res\n",
+ " return res\n",
+ " \n",
+ " def eval(self,expr,field=None):\n",
+ " # print(\" + eval {}\".format(expr))\n",
+ " if isinstance(expr,Ask):\n",
+ " print(field)\n",
+ " return expr.ask()\n",
+ " elif isinstance(expr,If):\n",
+ " return self.eval(expr.x)\n",
+ " elif isinstance(expr,AND) or isinstance(expr,list):\n",
+ " expr = expr.x if isinstance(expr,AND) else expr\n",
+ " for x in expr:\n",
+ " if self.eval(x)=='n':\n",
+ " return 'n'\n",
+ " return 'y'\n",
+ " elif isinstance(expr,OR):\n",
+ " for x in expr.x:\n",
+ " if self.eval(x)=='y':\n",
+ " return 'y'\n",
+ " return 'n'\n",
+ " elif isinstance(expr,str):\n",
+ " return self.get(expr)\n",
+ " else:\n",
+ " print(\"Unknown expr: {}\".format(expr))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's define our animal knowledgebase and perform the consultation. Note that this call will ask you questions. You can answer by typing `y`/`n` for yes-no questions, or by specifying number (0..N) for questions with longer multiple-choice answers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "trusted": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Collecting git+https://github.com/buguroo/pyknow/\n",
- " Cloning https://github.com/buguroo/pyknow/ to c:\\users\\dmitryso\\appdata\\local\\temp\\pip-req-build-3iv4twpl\n",
- "Collecting frozendict==1.2\n",
- " Using cached frozendict-1.2.tar.gz (2.6 kB)\n",
- "Collecting schema==0.6.7\n",
- " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n",
- "Building wheels for collected packages: pyknow, frozendict\n",
- " Building wheel for pyknow (setup.py): started\n",
- " Building wheel for pyknow (setup.py): finished with status 'done'\n",
- " Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34580 sha256=334cc7a6eb47459f488db594e8537d7d33d2865c2dbcdd44854146c5c27608e3\n",
- " Stored in directory: C:\\Users\\dmitryso\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-l_g7bnq7\\wheels\\96\\36\\bd\\ee1de50bbcf2c7a323dead05584cf90db8898524cf7f57f488\n",
- " Building wheel for frozendict (setup.py): started\n",
- " Building wheel for frozendict (setup.py): finished with status 'done'\n",
- " Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3146 sha256=71e32ca6c8ad7e0413bdc9a38f5882a36ba0509e562564a69904fcc9c8b66a9b\n",
- " Stored in directory: c:\\users\\dmitryso\\appdata\\local\\pip\\cache\\wheels\\5b\\fa\\ab\\0a80360debb57b95f092356ee3a075bbbffc631b9813136599\n",
- "Successfully built pyknow frozendict\n",
- "Installing collected packages: schema, frozendict, pyknow\n",
- "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " Running command git clone -q https://github.com/buguroo/pyknow/ 'C:\\Users\\dmitryso\\AppData\\Local\\Temp\\pip-req-build-3iv4twpl'\n"
- ]
- }
- ],
- "source": [
- "import sys\n",
- "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hair\n",
+ "y/n\n",
+ "sharp teeth\n",
+ "y/n\n",
+ "claws\n",
+ "y/n\n",
+ "eats meat\n",
+ "y/n\n",
+ "color\n",
+ "0. red-brown\n",
+ "1. black and white\n",
+ "2. other\n",
+ "pattern\n",
+ "0. dark stripes\n",
+ "1. dark spots\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "trusted": true
- },
- "outputs": [],
- "source": [
- "from pyknow import *"
+ "data": {
+ "text/plain": [
+ "'monkey'"
]
- },
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kb = KnowledgeBase(rules)\n",
+ "kb.get('animal')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Using PyKnow for Forward Inference\n",
+ "\n",
+ "In the next example, we will try to implement forward inference using one of the libraries for knowledge representation, [PyKnow](https://github.com/buguroo/pyknow/). **PyKnow** is a library for creating forward inference systems in Python, which is designed to be similar to classical old system [CLIPS](http://www.clipsrules.net/index.html). \n",
+ "\n",
+ "We could have also implemented forward chaining ourselves without many problems, but naive implementations are usually not very efficient. For more effective rule matching a special algorithm [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) is used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We will define our system as a class that subclasses `KnowledgeEngine`. Each rule is defined by a separate function with `@Rule` annotation, which specifies when the rule should fire. Inside the rule, we can add new facts using `declare` function, and adding those facts will result in some more rules being called by forward inference engine. "
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting git+https://github.com/buguroo/pyknow/\n",
+ " Cloning https://github.com/buguroo/pyknow/ to c:\\users\\dmitryso\\appdata\\local\\temp\\pip-req-build-3iv4twpl\n",
+ "Collecting frozendict==1.2\n",
+ " Using cached frozendict-1.2.tar.gz (2.6 kB)\n",
+ "Collecting schema==0.6.7\n",
+ " Using cached schema-0.6.7-py2.py3-none-any.whl (14 kB)\n",
+ "Building wheels for collected packages: pyknow, frozendict\n",
+ " Building wheel for pyknow (setup.py): started\n",
+ " Building wheel for pyknow (setup.py): finished with status 'done'\n",
+ " Created wheel for pyknow: filename=pyknow-1.7.0-py3-none-any.whl size=34580 sha256=334cc7a6eb47459f488db594e8537d7d33d2865c2dbcdd44854146c5c27608e3\n",
+ " Stored in directory: C:\\Users\\dmitryso\\AppData\\Local\\Temp\\pip-ephem-wheel-cache-l_g7bnq7\\wheels\\96\\36\\bd\\ee1de50bbcf2c7a323dead05584cf90db8898524cf7f57f488\n",
+ " Building wheel for frozendict (setup.py): started\n",
+ " Building wheel for frozendict (setup.py): finished with status 'done'\n",
+ " Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3146 sha256=71e32ca6c8ad7e0413bdc9a38f5882a36ba0509e562564a69904fcc9c8b66a9b\n",
+ " Stored in directory: c:\\users\\dmitryso\\appdata\\local\\pip\\cache\\wheels\\5b\\fa\\ab\\0a80360debb57b95f092356ee3a075bbbffc631b9813136599\n",
+ "Successfully built pyknow frozendict\n",
+ "Installing collected packages: schema, frozendict, pyknow\n",
+ "Successfully installed frozendict-1.2 pyknow-1.7.0 schema-0.6.7\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "trusted": true
- },
- "outputs": [],
- "source": [
- "class Animals(KnowledgeEngine):\n",
- " @Rule(OR(\n",
- " AND(Fact('sharp teeth'),Fact('claws'),Fact('forward looking eyes')),\n",
- " Fact('eats meat')))\n",
- " def cornivor(self):\n",
- " self.declare(Fact('carnivor'))\n",
- " \n",
- " @Rule(OR(Fact('hair'),Fact('gives milk')))\n",
- " def mammal(self):\n",
- " self.declare(Fact('mammal'))\n",
- "\n",
- " @Rule(Fact('mammal'),\n",
- " OR(Fact('has hooves'),Fact('chews cud')))\n",
- " def hooves(self):\n",
- " self.declare('ungulate')\n",
- " \n",
- " @Rule(OR(Fact('feathers'),AND(Fact('flies'),Fact('lays eggs'))))\n",
- " def bird(self):\n",
- " self.declare('bird')\n",
- " \n",
- " @Rule(Fact('mammal'),Fact('carnivor'),\n",
- " Fact(color='red-brown'),\n",
- " Fact(pattern='dark spots'))\n",
- " def monkey(self):\n",
- " self.declare(Fact(animal='monkey'))\n",
- "\n",
- " @Rule(Fact('mammal'),Fact('carnivor'),\n",
- " Fact(color='red-brown'),\n",
- " Fact(pattern='dark stripes'))\n",
- " def tiger(self):\n",
- " self.declare(Fact(animal='tiger'))\n",
- "\n",
- " @Rule(Fact('ungulate'),\n",
- " Fact('long neck'),\n",
- " Fact('long legs'),\n",
- " Fact(pattern='dark spots'))\n",
- " def giraffe(self):\n",
- " self.declare(Fact(animal='giraffe'))\n",
- "\n",
- " @Rule(Fact('ungulate'),\n",
- " Fact(pattern='dark stripes'))\n",
- " def zebra(self):\n",
- " self.declare(Fact(animal='zebra'))\n",
- "\n",
- " @Rule(Fact('bird'),\n",
- " Fact('long neck'),\n",
- " Fact('cannot fly'),\n",
- " Fact(color='black and white'))\n",
- " def straus(self):\n",
- " self.declare(Fact(animal='ostrich'))\n",
- "\n",
- " @Rule(Fact('bird'),\n",
- " Fact('swims'),\n",
- " Fact('cannot fly'),\n",
- " Fact(color='black and white'))\n",
- " def pinguin(self):\n",
- " self.declare(Fact(animal='pinguin'))\n",
- "\n",
- " @Rule(Fact('bird'),\n",
- " Fact('flies well'))\n",
- " def albatros(self):\n",
- " self.declare(Fact(animal='albatross'))\n",
- " \n",
- " @Rule(Fact(animal=MATCH.a))\n",
- " def print_result(self,a):\n",
- " print('Animal is {}'.format(a))\n",
- " \n",
- " def factz(self,l):\n",
- " for x in l:\n",
- " self.declare(x)"
- ]
- },
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " Running command git clone -q https://github.com/buguroo/pyknow/ 'C:\\Users\\dmitryso\\AppData\\Local\\Temp\\pip-req-build-3iv4twpl'\n"
+ ]
+ }
+ ],
+ "source": [
+ "import sys\n",
+ "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyknow import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will define our system as a class that subclasses `KnowledgeEngine`. Each rule is defined by a separate function with `@Rule` annotation, which specifies when the rule should fire. Inside the rule, we can add new facts using `declare` function, and adding those facts will result in some more rules being called by forward inference engine. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Animals(KnowledgeEngine):\n",
+ " @Rule(OR(\n",
+ " AND(Fact('sharp teeth'),Fact('claws'),Fact('forward looking eyes')),\n",
+ " Fact('eats meat')))\n",
+ " def cornivor(self):\n",
+ " self.declare(Fact('carnivor'))\n",
+ " \n",
+ " @Rule(OR(Fact('hair'),Fact('gives milk')))\n",
+ " def mammal(self):\n",
+ " self.declare(Fact('mammal'))\n",
+ "\n",
+ " @Rule(Fact('mammal'),\n",
+ " OR(Fact('has hooves'),Fact('chews cud')))\n",
+ " def hooves(self):\n",
+ " self.declare('ungulate')\n",
+ " \n",
+ " @Rule(OR(Fact('feathers'),AND(Fact('flies'),Fact('lays eggs'))))\n",
+ " def bird(self):\n",
+ " self.declare('bird')\n",
+ " \n",
+ " @Rule(Fact('mammal'),Fact('carnivor'),\n",
+ " Fact(color='red-brown'),\n",
+ " Fact(pattern='dark spots'))\n",
+ " def monkey(self):\n",
+ " self.declare(Fact(animal='monkey'))\n",
+ "\n",
+ " @Rule(Fact('mammal'),Fact('carnivor'),\n",
+ " Fact(color='red-brown'),\n",
+ " Fact(pattern='dark stripes'))\n",
+ " def tiger(self):\n",
+ " self.declare(Fact(animal='tiger'))\n",
+ "\n",
+ " @Rule(Fact('ungulate'),\n",
+ " Fact('long neck'),\n",
+ " Fact('long legs'),\n",
+ " Fact(pattern='dark spots'))\n",
+ " def giraffe(self):\n",
+ " self.declare(Fact(animal='giraffe'))\n",
+ "\n",
+ " @Rule(Fact('ungulate'),\n",
+ " Fact(pattern='dark stripes'))\n",
+ " def zebra(self):\n",
+ " self.declare(Fact(animal='zebra'))\n",
+ "\n",
+ " @Rule(Fact('bird'),\n",
+ " Fact('long neck'),\n",
+ " Fact('cannot fly'),\n",
+ " Fact(color='black and white'))\n",
+ " def straus(self):\n",
+ " self.declare(Fact(animal='ostrich'))\n",
+ "\n",
+ " @Rule(Fact('bird'),\n",
+ " Fact('swims'),\n",
+ " Fact('cannot fly'),\n",
+ " Fact(color='black and white'))\n",
+ " def pinguin(self):\n",
+ " self.declare(Fact(animal='pinguin'))\n",
+ "\n",
+ " @Rule(Fact('bird'),\n",
+ " Fact('flies well'))\n",
+ " def albatros(self):\n",
+ " self.declare(Fact(animal='albatross'))\n",
+ " \n",
+ " @Rule(Fact(animal=MATCH.a))\n",
+ " def print_result(self,a):\n",
+ " print('Animal is {}'.format(a))\n",
+ " \n",
+ " def factz(self,l):\n",
+ " for x in l:\n",
+ " self.declare(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Once we have defined a knowledgebase, we populate our working memory with some initial facts, and then call `run()` method to perform the inference. You can see as a result that new inferred facts are added to the working memory, including the final fact about the animal (if we set up all the initial facts correctly)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once we have defined a knowledgebase, we populate our working memory with some initial facts, and then call `run()` method to perform the inference. You can see as a result that new inferred facts are added to the working memory, including the final fact about the animal (if we set up all the initial facts correctly)."
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Animal is tiger\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "trusted": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Animal is tiger\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "FactList([(0, InitialFact()),\n",
- " (1, Fact(color='red-brown')),\n",
- " (2, Fact(pattern='dark stripes')),\n",
- " (3, Fact('sharp teeth')),\n",
- " (4, Fact('claws')),\n",
- " (5, Fact('forward looking eyes')),\n",
- " (6, Fact('gives milk')),\n",
- " (7, Fact('mammal')),\n",
- " (8, Fact('carnivor')),\n",
- " (9, Fact(animal='tiger'))])"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ex1 = Animals()\n",
- "ex1.reset()\n",
- "ex1.factz([\n",
- " Fact(color='red-brown'),\n",
- " Fact(pattern='dark stripes'),\n",
- " Fact('sharp teeth'),\n",
- " Fact('claws'),\n",
- " Fact('forward looking eyes'),\n",
- " Fact('gives milk')])\n",
- "ex1.run()\n",
- "ex1.facts"
+ "data": {
+ "text/plain": [
+ "FactList([(0, InitialFact()),\n",
+ " (1, Fact(color='red-brown')),\n",
+ " (2, Fact(pattern='dark stripes')),\n",
+ " (3, Fact('sharp teeth')),\n",
+ " (4, Fact('claws')),\n",
+ " (5, Fact('forward looking eyes')),\n",
+ " (6, Fact('gives milk')),\n",
+ " (7, Fact('mammal')),\n",
+ " (8, Fact('carnivor')),\n",
+ " (9, Fact(animal='tiger'))])"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.7.4 64-bit (conda)",
- "metadata": {
- "interpreter": {
- "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
- }
- },
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.5"
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
}
+ ],
+ "source": [
+ "ex1 = Animals()\n",
+ "ex1.reset()\n",
+ "ex1.factz([\n",
+ " Fact(color='red-brown'),\n",
+ " Fact(pattern='dark stripes'),\n",
+ " Fact('sharp teeth'),\n",
+ " Fact('claws'),\n",
+ " Fact('forward looking eyes'),\n",
+ " Fact('gives milk')])\n",
+ "ex1.run()\n",
+ "ex1.facts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 2
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
}
diff --git a/lessons/2-Symbolic/translations/Animals.tr.ipynb b/lessons/2-Symbolic/translations/Animals.tr.ipynb
new file mode 100644
index 00000000..f2aa9d82
--- /dev/null
+++ b/lessons/2-Symbolic/translations/Animals.tr.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Bir Hayvan Uzman Sisteminin Uygulanması\n",
+ "\n",
+ "[Yeni Başlayanlar için YZ Müfredatı](http://github.com/microsoft/ai-for-beginners)'ndan bir örnek.\n",
+ "\n",
+ "Bu örnekte, bazı fiziksel özelliklere dayalı olarak bir hayvanı belirlemek için basit bir bilgiye dayalı sistemi uygulayacağız. Sistem aşağıdaki VE-VEYA ağacı ile temsil edilebilir (bu, tüm ağacın bir parçasıdır, kolayca daha fazla kural ekleyebiliriz):\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Geriye dönük çıkarsamalı kendi uzman sistemlerimizin kabuğu\n",
+ "\n",
+ "Üretme kurallarına dayalı bilgi temsili için basit bir dil tanımlamaya çalışalım. Kuralları tanımlamada anahtar kelimeler olarak Python sınıflarını kullanacağız. Temel olarak 3 tür sınıf olacaktır:\n",
+ "* `Sor`, kullanıcıya sorulması gereken bir soruyu temsil eder. Olası cevaplar kümesini içerir.\n",
+ "* `If` represents a rule, and it is just a syntactic sugar to store the content of the rule\n",
+ "* `Eger` bir kuralı temsil eder ve sadece kuralın içeriğini saklamak için sözdizimsel bir bileşendir.\n",
+ "* `VE`/`VEYA`, ağacın VE/VEYA dallarını temsil eden sınıflardır. Sadece argümanların listesini içeride saklarlar. Kodu basitleştirmek için, tüm işlevler `Icerik` üst sınıfında tanımlanmıştır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Sor():\n",
+ " def __init__(self,tercihler=['e','h']):\n",
+ " self.tercihler = tercihler\n",
+ " def sor(self):\n",
+ " if max([len(x) for x in self.tercihler])>1:\n",
+ " for i,x in enumerate(self.tercihler):\n",
+ " print(\"{0}. {1}\".format(i,x),flush=True)\n",
+ " x = int(input())\n",
+ " return self.tercihler[x]\n",
+ " else:\n",
+ " print(\"/\".join(self.tercihler),flush=True)\n",
+ " return input()\n",
+ "\n",
+ "class Icerik():\n",
+ " def __init__(self,x):\n",
+ " self.x=x\n",
+ " \n",
+ "class Eger(Icerik):\n",
+ " pass\n",
+ "\n",
+ "class VE(Icerik):\n",
+ " pass\n",
+ "\n",
+ "class VEYA(Icerik):\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sistemimizde, çalışan bellek, **nitelik-değer çiftleri** olarak **olguların (gerçeklerin)** listesini içerir. Bilgi tabanı, eylemleri (çalışan belleğe eklenmesi gereken yeni gerçekleri) VE-VEYA ifadeleri olarak ifade edilen koşullara eşleyen büyük bir sözlük olarak tanımlanabilir. Ayrıca, bazı gerçekler `Sor`-ulabilir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kurallar = {\n",
+ " 'varsayilan': Sor(['e','h']),\n",
+ " 'renk' : Sor(['kirmizi-kahverengi','siyah ve beyaz','diger']),\n",
+ " 'desen' : Sor(['koyu seritler','koyu benekler']),\n",
+ " 'memeli': Eger(VEYA(['killi','sut verir'])),\n",
+ " 'etcil': Eger(VEYA([VE(['sivri disler','penceler','ileriye bakan gozler']),'et yer'])),\n",
+ " 'toynakli': Eger(['memeli',VEYA(['toynaklari var','gevis getirir'])]),\n",
+ " 'kus': Eger(VEYA(['tuylu',VE(['ucar','yumurta verir'])])),\n",
+ " 'hayvan:maymun' : Eger(['memeli','etcil','renk:kirmizi-kahverengi','desen:koyu benekler']),\n",
+ " 'hayvan:kaplan' : Eger(['memeli','etcil','renk:kirmizi-kahverengi','desen:koyu seritler']),\n",
+ " 'hayvan:zurafa' : Eger(['toynakli','uzun boyun','uzun bacaklar','desen:koyu benekler']),\n",
+ " 'hayvan:zebra' : Eger(['toynakli','desen:koyu seritler']),\n",
+ " 'hayvan:devekusu' : Eger(['kus','uzun boyun','renk:siyah ve beyaz','ucamaz']),\n",
+ " 'hayvan:penguen' : Eger(['kus','yuzer','renk:siyah ve beyaz','ucamaz']),\n",
+ " 'hayvan:albatros' : Eger(['kus','iyi ucar'])\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Geriye dönük çıkarsama gerçekleştirmek için `BilgiTabani` sınıfını tanımlayacağız. Şunları içerecektir:\n",
+ "* Çalışan `bellek` - nitelikleri değerlerle eşleştiren bir sözlük\n",
+ "* Yukarıda tanımlanan biçimde `kurallar` bilgi tabanı\n",
+ "\n",
+ "İki ana yöntem şunlardır:\n",
+ "* Bir niteliğin değerini elde etmek için `getir`, gerekirse çıkarım yapar. Örneğin, `getir('renk')` bir renk yuvasının değerini alır (gerekirse sorar ve değeri daha sonra kullanmak üzere çalışan bellekte saklar). `getir('renk:mavi')` diye sorarsak, bir renk isteyecek ve ardından renge bağlı olarak `e`/`h` değerini döndürecektir.\n",
+ "* `degerlendir` gerçek çıkarsamayı gerçekleştirir, yani VE/VEYA ağacında ilerler, alt hedefleri değerlendirir, vb."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class BilgiTabani():\n",
+ " def __init__(self,kurallar):\n",
+ " self.kurallar = kurallar\n",
+ " self.bellek = {}\n",
+ " \n",
+ " def getir(self,name):\n",
+ " if ':' in name:\n",
+ " k,v = name.split(':')\n",
+ " vv = self.getir(k)\n",
+ " return 'e' if v==vv else 'h'\n",
+ " if name in self.bellek.keys():\n",
+ " return self.bellek[name]\n",
+ " for fld in self.kurallar.keys():\n",
+ " if fld==name or fld.startswith(name+\":\"):\n",
+ " # print(\" + proving {}\".format(fld))\n",
+ " value = 'e' if fld==name else fld.split(':')[1]\n",
+ " res = self.degerlendir(self.kurallar[fld],field=name)\n",
+ " if res!='e' and res!='h' and value=='e':\n",
+ " self.bellek[name] = res\n",
+ " return res\n",
+ " if res=='e':\n",
+ " self.bellek[name] = value\n",
+ " return value\n",
+ " # field is not found, using default\n",
+ " res = self.degerlendir(self.kurallar['varsayilan'],field=name)\n",
+ " self.bellek[name]=res\n",
+ " return res\n",
+ " \n",
+ " def degerlendir(self,expr,field=None):\n",
+ " # print(\" + eval {}\".format(expr))\n",
+ " if isinstance(expr,Sor):\n",
+ " print(field)\n",
+ " return expr.sor()\n",
+ " elif isinstance(expr,Eger):\n",
+ " return self.degerlendir(expr.x)\n",
+ " elif isinstance(expr,VE) or isinstance(expr,list):\n",
+ " expr = expr.x if isinstance(expr,VE) else expr\n",
+ " for x in expr:\n",
+ " if self.degerlendir(x)=='h':\n",
+ " return 'h'\n",
+ " return 'e'\n",
+ " elif isinstance(expr,VEYA):\n",
+ " for x in expr.x:\n",
+ " if self.degerlendir(x)=='e':\n",
+ " return 'e'\n",
+ " return 'h'\n",
+ " elif isinstance(expr,str):\n",
+ " return self.getir(expr)\n",
+ " else:\n",
+ " print(\"Bilinmeyen ifade: {}\".format(expr))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Şimdi hayvan bilgi tabanımızı tanımlayalım ve incelemeyi gerçekleştirelim. Bu aramanın size sorular soracağını unutmayın. Evet-hayır soruları için `e`/`h` yazarak veya daha uzun çoktan seçmeli cevaplar için sayı (0..N) belirterek cevap verebilirsiniz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kb = BilgiTabani(kurallar)\n",
+ "kb.getir('hayvan')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## İleri Çıkarsama için PyKnow'u Kullanma\n",
+ "\n",
+ "Sonraki örnekte, bilgi temsili kütüphanelerinden biri olan [PyKnow](https://github.com/buguroo/pyknow/) kullanarak ileriye çıkarsama uygulamaya çalışacağız. **PyKnow**, klasik eski \n",
+ "[CLIPS](http://www.clipsrules.net/index.html) sistemine benzer şekilde tasarlanmış, Python'da ileriye çıkarsama sistemleri oluşturmaya yönelik bir kütüphanedir.\n",
+ "\n",
+ "Kendimiz ileriye zincirlemeyi pek sorun yaşamadan da uygulayabilirdik, ancak saf uygulamalar genellikle çok verimli değildir. Daha etkili kural eşleştirmesi için özel bir algoritma olan [Rete](https://en.wikipedia.org/wiki/Rete_algorithm) kullanılır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "!{sys.executable} -m pip install git+https://github.com/buguroo/pyknow/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pyknow import *"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sistemimizi `KnowledgeEngine` (BilgiMotoru) altsınıfları olan bir sınıf olarak tanımlayacağız. Her kural, kuralın ne zaman tetikleneceğini belirten `@Rule` (Kural) ek açıklamalı ayrı bir işlevle tanımlanır. Kuralın içinde, `declare` (bildirme) işlevini kullanarak yeni gerçekler ekleyebiliriz ve bu gerçekleri eklemek, ileri çıkarsama motoru tarafından daha fazla kuralın çağrılmasıyla sonuçlanacaktır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Hayvanlar(KnowledgeEngine):\n",
+ " @Rule(OR(\n",
+ " AND(Fact('sivri disler'),Fact('penceler'),Fact('ileriye bakan gozler')),\n",
+ " Fact('et yer')))\n",
+ " def etcil(self):\n",
+ " self.declare(Fact('etcil'))\n",
+ " \n",
+ " @Rule(OR(Fact('killi'),Fact('sut verir')))\n",
+ " def memeli(self):\n",
+ " self.declare(Fact('memeli'))\n",
+ "\n",
+ " @Rule(Fact('memeli'),\n",
+ " OR(Fact('toynaklari var'),Fact('gevis getirir')))\n",
+ " def toynaklar(self):\n",
+ " self.declare('toynakli')\n",
+ " \n",
+ " @Rule(OR(Fact('tuylu'),AND(Fact('ucar'),Fact('yumurta verir'))))\n",
+ " def kus(self):\n",
+ " self.declare('kus')\n",
+ " \n",
+ " @Rule(Fact('memeli'),Fact('etcil'),\n",
+ " Fact(renk='kirmizi-kahverengi'),\n",
+ " Fact(desen='koyu benekler'))\n",
+ " def maymun(self):\n",
+ " self.declare(Fact(hayvan='maymun'))\n",
+ "\n",
+ " @Rule(Fact('memeli'),Fact('etcil'),\n",
+ " Fact(renk='kirmizi-kahverengi'),\n",
+ " Fact(desen='koyu seritler'))\n",
+ " def kaplan(self):\n",
+ " self.declare(Fact(hayvan='kaplan'))\n",
+ "\n",
+ " @Rule(Fact('toynakli'),\n",
+ " Fact('uzun boyun'),\n",
+ " Fact('uzun bacaklar'),\n",
+ " Fact(desen='koyu benekler'))\n",
+ " def zurafa(self):\n",
+ " self.declare(Fact(hayvan='zurafa'))\n",
+ "\n",
+ " @Rule(Fact('toynakli'),\n",
+ " Fact(desen='koyu seritler'))\n",
+ " def zebra(self):\n",
+ " self.declare(Fact(hayvan='zebra'))\n",
+ "\n",
+ " @Rule(Fact('kus'),\n",
+ " Fact('uzun boyun'),\n",
+ " Fact('ucamaz'),\n",
+ " Fact(renk='siyah ve beyaz'))\n",
+ " def devekusu(self):\n",
+ " self.declare(Fact(hayvan='devekusu'))\n",
+ "\n",
+ " @Rule(Fact('kus'),\n",
+ " Fact('yuzer'),\n",
+ " Fact('ucamaz'),\n",
+ " Fact(renk='siyah ve beyaz'))\n",
+ " def penguen(self):\n",
+ " self.declare(Fact(hayvan='penguen'))\n",
+ "\n",
+ " @Rule(Fact('kus'),\n",
+ " Fact('iyi ucar'))\n",
+ " def albatros(self):\n",
+ " self.declare(Fact(hayvan='albatros'))\n",
+ " \n",
+ " @Rule(Fact(hayvan=MATCH.a))\n",
+ " def print_result(self,a):\n",
+ " print('Hayvan {}'.format(a))\n",
+ " \n",
+ " def factz(self,l):\n",
+ " for x in l:\n",
+ " self.declare(x)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Bir bilgi tabanı tanımladıktan sonra, çalışan belleğimizi bazı ilk gerçeklerle doldururuz ve ardından çıkarsama gerçekleştirmek için `run()` yöntemini çağırırız. Sonuç olarak, hayvanla ilgili son gerçek de dahil olmak üzere (başlangıçtaki tüm gerçekleri doğru bir şekilde kurarsak) çalışan belleğe yeni çıkarsanan gerçeklerin eklendiğini görebilirsiniz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ex1 = Hayvanlar()\n",
+ "ex1.reset()\n",
+ "ex1.factz([\n",
+ " Fact(renk='kirmizi-kahverengi'),\n",
+ " Fact(desen='koyu seritler'),\n",
+ " Fact('sivri disler'),\n",
+ " Fact('penceler'),\n",
+ " Fact('ileriye bakan gozler'),\n",
+ " Fact('sut verir')])\n",
+ "ex1.run()\n",
+ "ex1.facts"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "7e1998ff7f8aa20ada591c520b972326324e5ea05489af9e422744c7c09f6dad"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/2-Symbolic/translations/FamilyOntology.tr.ipynb b/lessons/2-Symbolic/translations/FamilyOntology.tr.ipynb
new file mode 100644
index 00000000..a9ed7062
--- /dev/null
+++ b/lessons/2-Symbolic/translations/FamilyOntology.tr.ipynb
@@ -0,0 +1,343 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Aile İlişkileri Ontolojisi\n",
+ "\n",
+ "Bu örnek, [Yeni Başlayanlar için Yapay Zeka Müfredatı](http://github.com/microsoft/ai-for-beginners)'nın bir parçasıdır ve [bu blog gönderisinden](https://habr.com/posta/270857/) esinlenilmiştir.\n",
+ "\n",
+ "Bir ailedeki insanlar arasındaki farklı ilişkileri hatırlamakta her zaman zorlanırım. Bu örnekte, aile ilişkilerini ve gerçek soy ağacını tanımlayan bir ontoloji alacağız ve daha sonra tüm akrabaları bulmak için otomatik çıkarsamayı nasıl yapabileceğimizi göstereceğiz.\n",
+ "\n",
+ "### Soy Ağacını Almak\n",
+ "\n",
+ "Örnek olarak, [Romanov Çar Ailesinin](https://en.wikipedia.org/wiki/House_of_Romanov) soy ağacını alacağız. Aile ilişkilerini tanımlamanın en yaygın biçimi [GEDCOM](https://en.wikipedia.org/wiki/GEDCOM)'dur. Romanov soy ağacını GEDCOM formatında alacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!head -15 ../data/tsars.ged"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "GEDCOM dosyasını kullanmak için `python-gedcom` kütüphanesini kullanabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "!{sys.executable} -m pip install python-gedcom"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Bu kütüphane, dosya ayrıştırmayla ilgili bazı teknik sorunları ortadan kaldırır, ancak yine de bize ağaçtaki tüm bireylere ve ailelere oldukça düşük düzeyde erişim sağlar. Dosyayı şu şekilde ayrıştırabilir ve tüm bireylerin listesini gösterebiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from gedcom.parser import Parser\n",
+ "from gedcom.element.individual import IndividualElement\n",
+ "from gedcom.element.family import FamilyElement\n",
+ "g = Parser()\n",
+ "g.parse_file('../data/tsars.ged')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "d = g.get_element_dictionary()\n",
+ "[ (k,v.get_name()) for k,v in d.items() if isinstance(v,IndividualElement)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "İşte aileler hakkında bilgiyi böyle edinebiliriz. Bunun bize bir **tanımlayıcı** listesi verdiğini ve daha fazla netlik istiyorsak bunları adlara dönüştürmemiz gerektiğini unutmayın:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "d = g.get_element_dictionary()\n",
+ "[ (k,[x.get_value() for x in v.get_child_elements()]) for k,v in d.items() if isinstance(v,FamilyElement)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Aile Ontolojisini Alma\n",
+ "\n",
+ "Şimdi, Semantik Web üçlüleri kümesi olarak tanımlanan [aile ontolojisine](https://raw.githubusercontent.com/blokhin/genealogical-trees/master/data/header.ttl) bir göz atalım. Bu ontoloji, `isUncleOf` (Amcadır), `isCousinOf` (Kuzendir) ve diğerleri gibi ilişkileri tanımlar. Tüm bu ilişkiler `isMotherOf` (Annedir), `isFatherOf` (Babadır), `isBrotherOf` (Erkekkardeştir) ve `isSisterOf` (Kızkardeştir) temel yüklemleri cinsinden tanımlanır. Ontolojiyi kullanarak diğer tüm ilişkileri çıkarmak için otomatik akıl yürütmeyi kullanacağız.\n",
+ "\n",
+ "Burada, `isSisterOf` (Kızkardeştir) ve `isParentOf` (Ebeveyndir) (*Teyze/Hala kişinin ebeveyninin kız kardeşidir*) bileşimi olarak tanımlanan `isAuntOf` (Teyzedir) özelliğinin örnek bir tanımı verilmiştir.\n",
+ "\n",
+ "```\n",
+ "fhkb:isAuntOf a owl:ObjectProperty ;\n",
+ " rdfs:domain fhkb:Woman ;\n",
+ " rdfs:range fhkb:Person ;\n",
+ " owl:propertyChainAxiom ( fhkb:isSisterOf fhkb:isParentOf ) .\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!head -20 ../data/onto.ttl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Çıkarsama için Ontoloji Oluşturma\n",
+ "\n",
+ "Basit olması için, aile ontolojisinden orijinal kuralları ve GEDCOM dosyamızdan bireyler hakkındaki gerçekleri içerecek bir ontoloji dosyası oluşturacağız. GEDCOM dosyasını inceleyeceğiz ve aileler ve bireyler hakkında bilgi çıkaracağız ve bunları üçlülere dönüştüreceğiz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!cp ../data/onto.ttl .\n",
+ "\n",
+ "gedcom_dict = g.get_element_dictionary()\n",
+ "individuals, marriages = {}, {}\n",
+ "\n",
+ "def term2id(el):\n",
+ " return \"i\" + el.get_pointer().replace('@', '').lower()\n",
+ "\n",
+ "out = open(\"onto.ttl\",\"a\")\n",
+ "\n",
+ "for k, v in gedcom_dict.items():\n",
+ " if isinstance(v,IndividualElement):\n",
+ " children, siblings = set(), set()\n",
+ " idx = term2id(v)\n",
+ "\n",
+ " title = v.get_name()[0] + \" \" + v.get_name()[1]\n",
+ " title = title.replace('\"', '').replace('[', '').replace(']', '').replace('(', '').replace(')', '').strip()\n",
+ "\n",
+ " own_families = g.get_families(v, 'FAMS')\n",
+ " for fam in own_families:\n",
+ " children |= set(term2id(i) for i in g.get_family_members(fam, \"CHIL\"))\n",
+ "\n",
+ " parent_families = g.get_families(v, 'FAMC')\n",
+ " if len(parent_families):\n",
+ " for member in g.get_family_members(parent_families[0], \"CHIL\"): # NB adoptive families i.e len(parent_families)>1 are not considered (TODO?)\n",
+ " if member.get_pointer() == v.get_pointer():\n",
+ " continue\n",
+ " siblings.add(term2id(member))\n",
+ "\n",
+ " if idx in individuals:\n",
+ " children |= individuals[idx].get('children', set())\n",
+ " siblings |= individuals[idx].get('siblings', set())\n",
+ " individuals[idx] = {'sex': v.get_gender().lower(), 'children': children, 'siblings': siblings, 'title': title}\n",
+ "\n",
+ " elif isinstance(v,FamilyElement):\n",
+ " wife, husb, children = None, None, set()\n",
+ " children = set(term2id(i) for i in g.get_family_members(v, \"CHIL\"))\n",
+ "\n",
+ " try:\n",
+ " wife = g.get_family_members(v, \"WIFE\")[0]\n",
+ " wife = term2id(wife)\n",
+ " if wife in individuals: individuals[wife]['children'] |= children\n",
+ " else: individuals[wife] = {'children': children}\n",
+ " except IndexError: pass\n",
+ " try:\n",
+ " husb = g.get_family_members(v, \"HUSB\")[0]\n",
+ " husb = term2id(husb)\n",
+ " if husb in individuals: individuals[husb]['children'] |= children\n",
+ " else: individuals[husb] = {'children': children}\n",
+ " except IndexError: pass\n",
+ "\n",
+ " if wife and husb: marriages[wife + husb] = (term2id(v), wife, husb)\n",
+ "\n",
+ "for idx, val in individuals.items():\n",
+ " added_terms = ''\n",
+ " if val['sex'] == 'f':\n",
+ " parent_predicate, sibl_predicate = \"isMotherOf\", \"isSisterOf\"\n",
+ " else:\n",
+ " parent_predicate, sibl_predicate = \"isFatherOf\", \"isBrotherOf\"\n",
+ " if len(val['children']):\n",
+ " added_terms += \" ;\\n fhkb:\" + parent_predicate + \" \" + \", \".join([\"fhkb:\" + i for i in val['children']])\n",
+ " if len(val['siblings']):\n",
+ " added_terms += \" ;\\n fhkb:\" + sibl_predicate + \" \" + \", \".join([\"fhkb:\" + i for i in val['siblings']])\n",
+ " out.write(\"fhkb:%s a owl:NamedIndividual, owl:Thing%s ;\\n rdfs:label \\\"%s\\\" .\\n\" % (idx, added_terms, val['title']))\n",
+ "\n",
+ "for k, v in marriages.items():\n",
+ " out.write(\"fhkb:%s a owl:NamedIndividual, owl:Thing ;\\n fhkb:hasFemalePartner fhkb:%s ;\\n fhkb:hasMalePartner fhkb:%s .\\n\" % v)\n",
+ "\n",
+ "out.write(\"[] a owl:AllDifferent ;\\n owl:distinctMembers (\")\n",
+ "for idx in individuals.keys():\n",
+ " out.write(\" fhkb:\" + idx)\n",
+ "for k, v in marriages.items():\n",
+ " out.write(\" fhkb:\" + v[0])\n",
+ "out.write(\" ) .\")\n",
+ "out.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!tail onto.ttl"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Çıkarım Yapmak \n",
+ "\n",
+ "Şimdi bu ontolojiyi çıkarsama ve sorgulama için kullanabilmek istiyoruz. [RDFLib](https://github.com/RDFLib), RDF Çizgesinin farklı formatlarda okunması, sorgulanması vb. için kütüphane kullanacağız.\n",
+ "\n",
+ "Mantıksal çıkarım için, RDF Çizgesinin **Kapanışını** oluşturmamıza, yani tüm olası kavramları ve ilişkileri ekleyip çıkarsamamıza, yardımcı olan [OWL-RL](https://github.com/RDFLib/OWL-RL) kütüphanesini kullanacağız."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ontoloji dosyasını açalım ve kaç tane üçlü içerdiğini görelim:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import rdflib\n",
+ "from owlrl import DeductiveClosure, OWLRL_Extension\n",
+ "\n",
+ "g = rdflib.Graph()\n",
+ "g.parse(\"onto.ttl\", format=\"turtle\")\n",
+ "\n",
+ "print(\"Bununla ucluler:%d\" % len(g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Şimdi kapanışı oluşturalım ve üçlülerin sayısının nasıl arttığını görelim:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DeductiveClosure(OWLRL_Extension).expand(g)\n",
+ "print(\"Cikarsama sonrasi ucluler:%d\" % len(g))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Akrabaları Sorgulama\n",
+ "\n",
+ "Artık insanlar arasındaki farklı ilişkileri görmek için çizgeyi sorgulayabiliriz. **SPARQL** dilini `query` metodu ile birlikte kullanabiliriz. Bizim durumumuzda, soy ağacımızdaki tüm **amcaları** görelim:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "qres = g.query(\n",
+ " \"\"\"SELECT DISTINCT ?aname ?bname\n",
+ " WHERE {\n",
+ " ?a fhkb:isUncleOf ?b .\n",
+ " ?a rdfs:label ?aname .\n",
+ " ?b rdfs:label ?bname .\n",
+ " }\"\"\")\n",
+ "\n",
+ "for row in qres:\n",
+ " print(\"%s amcasidir %s\" % row)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Farklı diğer aile ilişkilerini denemekten çekinmeyin. Örneğin, belirli bir kişinin tüm atalarını yinelemeli tanımlayan `isAncestorOf` (Atadır) ilişkisine bakabilirsiniz.\n",
+ "\n",
+ "Son olarak, etrafı temizleyelim!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!rm onto.ttl"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/2-Symbolic/translations/MSConceptGraph.tr.ipynb b/lessons/2-Symbolic/translations/MSConceptGraph.tr.ipynb
new file mode 100644
index 00000000..367f7f68
--- /dev/null
+++ b/lessons/2-Symbolic/translations/MSConceptGraph.tr.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Microsoft Kavram Çizgesi\n",
+ "\n",
+ "[Microsoft Kavram Çizgesi](https://concept.research.microsoft.com/), kavramlar arasında `is-a` (bir örneğidir) ilişkileri olan, internetten çıkarılan geniş bir terimler sınıflandırmasıdır.\n",
+ "\n",
+ "Bağlam Grafiği iki biçimde mevcuttur:\n",
+ " * İndirmek için büyük metin dosyası\n",
+ " * REST API'si\n",
+ "\n",
+ "İstatistik:\n",
+ " * 5401933 eşsiz kavram, \n",
+ " * 12551613 eşsiz örnek,\n",
+ " * 87603947 `is-a` ilişkisi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Web Hizmetini Kullanma\n",
+ "\n",
+ "Web hizmeti, farklı gruplara ait bir kavramın olasılığını tahmin etmek için farklı çağrılar sunar. Daha fazla bilgiyi [burada](https://concept.research.microsoft.com/Home/Api) bulabilirsiniz.\n",
+ "İşte aranacak örnek URL: `https://concept.research.microsoft.com/api/Concept/ScoreByProb?instance=microsoft&topK=10`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import urllib\n",
+ "import json\n",
+ "import ssl\n",
+ "\n",
+ "def http(x):\n",
+ " ssl._create_default_https_context = ssl._create_unverified_context\n",
+ " response = urllib.request.urlopen(x)\n",
+ " data = response.read()\n",
+ " return data.decode('utf-8')\n",
+ "\n",
+ "def query(x):\n",
+ " return json.loads(http(\"https://concept.research.microsoft.com/api/Concept/ScoreByProb?instance={}&topK=10\".format(urllib.parse.quote(x))))\n",
+ "\n",
+ "query('microsoft')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Haber başlıklarını ana kavramlarını kullanarak kategorize etmeye çalışalım. Haber başlıklarını almak için [NewsApi.org](http://newsapi.org) hizmetini kullanacağız. Hizmeti kullanmak için kendi API anahtarınızı almanız gerekir - web sitesine gidin ve ücretsiz geliştirici planına kaydolun."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "newsapi_key = '
+
+*İmge [Wikipedia'dan](https://commons.wikimedia.org/w/index.php?curid=37705247), By Longlivetheux - Own work, CC BY-SA 4.0*
+
+Bu nedenle, **bilgi temsili** sorunu, bilgiyi bir bilgisayar içinde veri biçiminde temsil etmenin, otomatik olarak kullanılabilir hale getirmenin etkili bir yolunu bulmaktır. Bu bir spektrum olarak görülebilir:
+
+
+
+> İmge sahibi [Dmitry Soshnikov](http://soshnikov.com)
+
+* Solda, bilgisayarlar tarafından etkin bir şekilde kullanılabilecek çok basit bilgi temsili türleri vardır. En basit olanı, bilgi bir bilgisayar programı tarafından temsil edildiğinde algoritmiktir. Ancak bu, bilgiyi temsil etmenin en iyi yolu değildir, çünkü esnek değildir. Kafamızın içindeki bilgi genellikle algoritmik değildir.
+* Sağda doğal metin gibi temsiller vardır. En güçlüsüdür, ancak otomatik akıl yürütme için kullanılamaz.
+
+> ✅ Bir an için bilgiyi kafanızda nasıl temsil ettiğinizi düşünün ve onu notlara dönüştürün. Akılda tutmaya yardımcı olmada iyi çalışan belirli bir format var mı?
+
+## Bilgisayar Bilgi Temsillerini Sınıflandırma
+
+Farklı bilgisayar bilgisi temsil yöntemlerini aşağıdaki kategorilerde sınıflandırabiliriz:
+
+* **Ağ temsilleri** kafamızın içinde birbiriyle ilişkili kavramlardan oluşan bir ağımız olduğu gerçeğine dayanmaktadır. Aynı ağları bir bilgisayarın içindeki bir çizge olarak yeniden üretmeyi deneyebiliriz - sözde **anlamsal ağ**.
+
+1. **Nesne-Nitelik-Değer üçlüleri** veya **nitelik-değer çiftleri**. Bir öizge bir bilgisayar içinde düğümler ve kenarların bir listesi olarak gösterilebildiğinden, anlamsal bir ağı nesneler, nitelikler ve değerler içeren bir üçlüler listesiyle temsil edebiliriz. Örneğin, programlama dilleriyle ilgili aşağıdaki üçlüleri oluşturuyoruz:
+
+Nesne | Nitelik | Değer
+-------|-----------|------
+Python | olmak | türsüz dil
+Python | icat edilmek | Guido van Rossum
+Python | blok sözdizimi | girinti
+Türsüz dil | sahip olmamak | tür tanımları
+
+> ✅ Diğer bilgi türlerini temsil etmek için üçlülerin nasıl kullanılabileceğini düşünün.
+
+2. **Hiyerarşik temsiller**, genellikle kafamızın içinde bir nesneler hiyerarşisi oluşturduğumuz gerçeğini vurgular. Örneğin, kanarya bir kuştur ve tüm kuşların kanatları vardır. Ayrıca genellikle kanarya renginin ne olduğu ve uçuş hızlarının ne olduğu hakkında da bir fikrimiz var.
+
+ - **Çerçeve temsili**, her bir nesneyi veya nesne sınıfını **yuvalar** içeren bir **çerçeve** olarak temsil etmeye dayanır. Yuvaların, bir yuvanın değerini elde etmek için çağrılabilecek olası varsayılan değerleri, değer kısıtlamaları veya saklı yordamları vardır. Tüm çerçeveler, nesne yönelimli programlama dillerindeki nesne hiyerarşisine benzer bir hiyerarşi oluşturur.
+ - **Senaryolar**, zaman içinde ortaya çıkabilecek karmaşık durumları temsil eden özel tür çerçevelerdir.
+
+**Python**
+Yuva | Değer | Varsayılan Değer | Aralık |
+-----|-------|---------------|----------|
+İsim | Python | | |
+Örneği Olmak | Türsüz Dil | | |
+Değişken Harf Boyutu | | Deve Harf Boyutu (CamelCase) | |
+Program Uzunluğu | | | 5-5000 satır |
+Blok Sözdizimi | Girinti | | |
+
+3. **Yöntemsel temsiller**, belirli bir koşul oluştuğunda yürütülebilecek bir eylemler listesiyle bilgiyi temsil etmeye dayanır.
+ - Üretme kuralları, sonuç çıkarmamıza izin veren eğer-öyleyse ifadelerdir. Örneğin, bir doktorun, kan testinde **EĞER** bir hastanın ateşi yüksek **VEYA** yüksek düzeyde C-reaktif proteini varsa **O ZAMAN** iltihabı olduğunu söyleyen bir kuralı olabilir. Koşullardan biriyle karşılaştığımızda, iltihaplanma hakkında bir sonuca varabilir ve daha sonra bunu daha fazla akıl yürütmede kullanabiliriz.
+ - Algoritmalar, neredeyse hiçbir zaman doğrudan bilgi tabanlı sistemlerde kullanılmamalarına rağmen, yöntemsel temsilin başka bir biçimi olarak düşünülebilir.
+
+4. **Mantık** ilk olarak Aristo tarafından evrensel insan bilgisini temsil etmenin bir yolu olarak önerildi.
+ - Matematiksel bir teori olarak Yüklem Mantığı, hesaplanamayacak kadar zengindir, bu nedenle normal olarak Prolog'da kullanılan Horn cümlecikleri gibi bazı alt kümeleri kullanılır.
+ - Tanımlayıcı Mantık, *anlamsal ağ* gibi dağıtılmış bilgi temsilleri olan nesnelerin hiyerarşilerini temsil etmek ve bunlar hakkında akıl yürütmek için kullanılan bir mantıksal sistemler ailesidir.
+
+## Uzman Sistemler
+
+Simgesel YZ'nin ilk başarılarından biri, **uzman sistemler** olarak adlandırıldı - bazı sınırlı problem alanlarında uzman olarak davranmak üzere tasarlanmış bilgisayar sistemleridir. Bir veya daha fazla insan uzmanından alınan bir **bilgi tabanına** dayanıyorlardı ve bunun üzerinden bazı akıl yürütmeler yapan bir **çıkarsama motoru** içeriyorlardı.
+
+ | 
+---------------------------------------------|------------------------------------------------
+Bir insan sinir sisteminin basitleştirilmiş yapısı | Bilgi tabanlı bir sistemin mimarisi
+
+Uzman sistemler, **kısa süreli bellek** ve **uzun süreli bellek** içeren insan akıl yürütme sistemi gibi oluşturulmuştur. Benzer şekilde, bilgi tabanlı sistemlerde aşağıdaki bileşenleri fark edebiliriz:
+
+* **Problem belleği**: Şu anda çözülmekte olan problem hakkındaki bilgileri, yani bir hastanın ateşi veya tansiyonu, iltihabı olup olmadığı vb. bilgileri içerir. Bu bilgi aynı zamanda **statik bilgi** olarak da adlandırılır, çünkü sorun hakkında şu anda bildiklerimizin bir anlık görüntüsünü içerir - sözde *problem durumu*.
+* **Bilgi tabanı**: Bir problem alanı hakkında uzun vadeli bilgiyi temsil eder. İnsan uzmanlardan manuel olarak elde edilir ve muayeneden muayeneye değişmez. Bir problem durumundan diğerine geçmemize izin verdiği için **dinamik bilgi** olarak da adlandırılır.
+* **Çıkarsama motoru**: Gerektiğinde kullanıcıya sorular sorarak, problem durumu alanında tüm arama sürecini düzenler. Ayrıca her duruma uygulanacak doğru kuralları bulmaktan da sorumludur.
+
+Örnek olarak, bir hayvanın fiziksel özelliklerine göre belirleyen aşağıdaki uzman sistemi ele alalım:
+
+
+
+> İmge sahibi [Dmitry Soshnikov](http://soshnikov.com)
+
+Bu diyagrama **VE-VEYA ağacı** denir ve bir dizi üretme kuralının çizgesel bir temsilidir. Bir ağaç çizmek, uzmandan bilgi elde etmenin başlangıcında faydalıdır. Bilgisayardaki bilgiyi temsil etmek için kuralları kullanmak daha uygundur:
+
+```
+EĞER hayvan et yerse
+VEYA (hayvan keskin dişleri varsa
+ VE hayvanın pençeleri varsa
+ VE hayvanın ileriye bakan gözleri varsa
+)
+O ZAMAN hayvan bir etoburdur
+```
+
+Kuralın ve eylemin sol tarafındaki her koşulun esasen nesne-nitelik-değer (NND) üçlüleri olduğunu fark edebilirsiniz. **Çalışan bellek**, o anda çözülmekte olan probleme karşılık gelen NND üçlüler kümesini içerir. Bir **kural motoru**, bir koşulun sağlandığı kuralları arar ve bunları uygulayarak, çalışan belleğe başka bir üçlü ekler.
+
+> ✅ Beğendiğiniz bir konu üzerine kendi VE-VEYA ağacınızı yazın!
+
+### İleri ve Geri Çıkarsama
+
+Yukarıda açıklanan sürece **ileri çıkarsama** denir. Çalışma belleğinde bulunan problemle ilgili bazı başlangıç verileriyle başlar ve ardından aşağıdaki akıl yürütme döngüsünü yürütür:
+
+1. Çalışan bellekte hedef niteliği mevcutsa - durun ve sonucu döndürün.
+2. Şu anda koşulu sağlanan tüm kuralları arayın - kuralların **çatışma kümesini** edinin.
+3. **Çatışma gidermeyi** gerçekleştirin - bu adımda yürütülecek bir kural seçin. Farklı çatışma giderme stratejileri olabilir:
+ - Bilgi tabanındaki ilk geçerli kuralı seçin.
+ - Rastgele bir kural seçin.
+ - *Daha özgül* bir kural seçin, mesela "sol tarafta" en çok koşulu karşılayanı.
+4. Seçili kuralı uygula ve problem durumuna yeni bilgi parçası ekle.
+5. 1. adımdan itibaren tekrarlayın.
+
+Ancak bazı durumlarda problem hakkında boş bir bilgiyle başlamak ve sonuca varmamıza yardımcı olacak sorular sormak isteyebiliriz. Örneğin tıbbi teşhis yaparken genellikle hastaya teşhis koymadan önce tüm tıbbi analizleri yapmıyoruz. Bir karar verilmesi gerektiğinde analizler yapmayı tercih ediyoruz.
+
+Bu süreç **geriye çıkarsama** kullanılarak modellenebilir. Bulmak istediğimiz nitelik değeri olan **hedef** tarafından yönlendirilir:
+
+1. Bize bir hedefin değerini verebilecek tüm kuralları seçin (yani, ST'deki hedef ("sağ taraf") ile) - bir çatışma kümesi
+1. Bu nitelik için herhangi bir kural yoksa veya değeri kullanıcıdan istememiz gerektiğini söyleyen bir kural varsa - kullanıcıya sorun, aksi takdirde:
+1. *Hipotez* olarak kullanacağımız bir kuralı seçmek için çatışma giderme stratejisini kullanın - bunu kanıtlamaya çalışacağız
+1. Kuralın sol tarafındaki tüm nitelikler için süreci tekrarlayarak, bunları hedef olarak kanıtlamaya çalışın
+1. Herhangi bir noktada süreç başarısız olursa - 3. adımda başka bir kural kullanın.
+
+> ✅ Hangi durumlarda ileriye çıkarsama daha uygundur? Geriye çıkarsamaya ne demeli?
+
+### Uzman Sistemlerin Uygulanması
+
+Uzman sistemler farklı araçlar kullanılarak uygulanabilir:
+
+* Bunları doğrudan bazı üst düzey programlama dillerinde programlamak. Bu en iyi fikir değildir, çünkü bilgiye dayalı bir sistemin ana avantajı, bilginin çıkarsamadan ayrılmasıdır ve potansiyel olarak bir problem alanı uzmanı, çıkarsama sürecinin ayrıntılarını anlamadan kurallar yazabilmelidir.
+* **Uzman sistemler kabuğu**, yani bazı bilgi temsil dili kullanılarak bilgi tarafından doldurulmak üzere özel olarak tasarlanmış bir sistem kullanmak.
+
+## ✍️ Alıştırma: Hayvan Çıkarsama
+
+İleri ve geri çıkarsama uzman sistemi uygulama örneği için [Animals.tr.ipynb](Animals.tr.ipynb) bölümüne bakın.
+
+> **Not**: Bu örnek oldukça basittir ve yalnızca bir uzman sistemin nasıl göründüğü hakkında fikir verir. Böyle bir sistem oluşturmaya başladığınızda, yalnızca belirli sayıda kurala ulaştığınızda, yaklaşık 200'den fazlaysa, *akıllı* davranış fark edeceksiniz. Bir noktada, kurallar hepsini akılda tutamayacak kadar karmaşık hale gelir ve bu noktada bir sistemin neden belirli kararlar aldığını merak etmeye başlayabilirsiniz. Bununla birlikte, bilgi tabanlı sistemlerin önemli özelliği, kararlardan herhangi birinin nasıl verildiğini her zaman tam olarak *açıklayabilmenizdir*.
+
+## Ontolojiler ve Anlamsal Web
+
+20. yüzyılın sonunda, çok özel sorgulara karşılık gelen kaynakları bulmak mümkün olacak şekilde, İnternet kaynaklarına açıklama eklemek için bilgi temsilini kullanma girişimi vardı. Bu harekete **Anlamsal Web** adı verildi ve birkaç kavrama dayanıyordu:
+
+- **[Tanım mantığına](https://en.wikipedia.org/wiki/Description_logic)** (TM) dayalı özel bir bilgi temsili. Çerçeve bilgi temsiline benzerdir, çünkü özelliklere sahip bir nesneler hiyerarşisi oluşturur, ancak biçimsel mantıksal anlambilimi ve çıkarsama vardır. Anlamlılığın ve çıkarsamanın algoritmik karmaşıklığı arasında denge kuran bütün bir TM ailesi vardır.
+- Tüm kavramların küresel bir URI tanımlayıcısı tarafından temsil edildiği, interneti kapsayan bilgi hiyerarşileri oluşturmayı mümkün kılan dağıtılmış bilgi temsili.
+- Bilgi tanımı için bir XML tabanlı dil ailesi: RDF (Kaynak Tanım Çerçevesi - Resource Description Framework), RDFS (RDF Şeması), OWL (Ontoloji Web Dili).
+
+Anlamsal Web'deki temel bir kavram, **Ontoloji** kavramıdır. Bazı resmi bilgi temsillerini kullanarak bir problem alanının açık bir isterini ifade eder. En basit ontoloji, bir problem alanındaki nesnelerin sade bir hiyerarşisi olabilir, ancak daha karmaşık ontolojiler, çıkarsama için kullanılabilecek kuralları içerecektir.
+
+In the semantic web, all representations are based on triplets. Each object and each relation are uniquely identified by the URI. For example, if we want to state the fact that this AI Curriculum has been developed by Dmitry Soshnikov on Jan 1st, 2022 - here are the triplets we can use:
+
+Anlamsal ağda, tüm temsiller üçlülere dayanmaktadır. Her nesne ve her ilişki, URI tarafından benzersiz bir şekilde tanımlanır. Örneğin, bu YZ Müfredatının 1 Ocak 2022'de Dmitry Soshnikov tarafından geliştirildiğini belirtmek istersek - kullanabileceğimiz üçlüler böyledir:
+
+
+
+```
+http://github.com/microsoft/ai-for-beginners http://www.example.com/terms/creation-date “Jan 13, 2007”
+http://github.com/microsoft/ai-for-beginners http://purl.org/dc/elements/1.1/creator http://soshnikov.com
+```
+
+> ✅ Burada `http://www.example.com/terms/creation-date` ve `http://purl.org/dc/elements/1.1/creator` *yaratıcı* ve *oluşturma tarihi* kavramlarını ifade etmek için iyi bilinen ve evrensel olarak kabul edilen bazı URI'lerdir.
+
+Daha karmaşık bir durumda, bir yaratıcı listesi tanımlamak istersek, RDF'de tanımlanan bazı veri yapılarını kullanabiliriz.
+
+
+
+> Diyagramların sahibi [Dmitry Soshnikov](http://soshnikov.com)
+
+Anlamsal Web'i oluşturmanın ilerlemesi, arama motorlarının ve metinden yapılandırılmış verilerin çıkarılmasına izin veren doğal dil işleme tekniklerinin başarısıyla bir şekilde yavaşladı. Bununla birlikte, bazı alanlarda ontolojileri ve bilgi tabanlarını sürdürmek için hala önemli çabalar vardır. Kayda değer birkaç proje:
+
+* [WikiData](https://wikidata.org/), Wikipedia ile ilişkili makine tarafından okunabilen bilgi tabanlarının bir koleksiyonudur. Verilerin çoğu, Wikipedia sayfalarındaki yapılandırılmış içerik parçaları olan Wikipedia *Bilgi Kutularından* çıkarılır. Anlamsal Web için özel bir sorgu dili olan SPARQL ile wikidatada [sorgu](https://query.wikidata.org/) yapabilirsiniz. İşte insanlar arasında en popüler göz renklerini gösteren örnek bir sorgu:
+
+```sparql
+#defaultView:BubbleChart
+SELECT ?eyeColorLabel (COUNT(?human) AS ?count)
+WHERE
+{
+ ?human wdt:P31 wd:Q5. # human instance-of homo sapiens
+ ?human wdt:P1340 ?eyeColor. # human eye-color ?eyeColor
+ SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
+}
+GROUP BY ?eyeColorLabel
+```
+
+* [DBpedia](https://www.dbpedia.org/) WikiData'ya benzer başka bir çalışmadır.
+
+> ✅ Kendi ontolojilerinizi oluşturmayı veya mevcut olanları açmayı denemek istiyorsanız, [Protégé](https://protege.stanford.edu/) adında harika bir görsel ontoloji düzenleyicisi var. İndirin veya çevrimiçi kullanın.
+
+
+
+*Romanov Ailesi ontolojisi ile açık Web Protégé editörü. Dmitry Soshnikov'un ekran görüntüsü*
+
+## ✍️ Alıştırma: Bir Aile Ontolojisi
+
+Aile ilişkileri hakkında akıl yürütmede Anlamsal Web tekniklerini kullanma örneği için [FamilyOntology.tr.ipynb](FamilyOntology.tr.ipynb) bölümüne bakın. Ortak GEDCOM formatında temsil edilen bir aile ağacını ve aile ilişkilerinin ontolojisini alacağız ve belirli bir grup birey için tüm aile ilişkilerinin bir çizgesini oluşturacağız.
+
+## Microsoft Kavram Çizgesi
+
+Çoğu durumda, ontolojiler dikkatle elle oluşturulur. Bununla birlikte, örneğin doğal dil metinlerinden yapılandırılmamış verilerden ontolojiler **çıkarmak** da mümkündür.
+
+Böyle bir girişim Microsoft Research tarafından yapıldı ve [Microsoft Kavram Çizgesi](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-77998-cacaste) ile sonuçlandı.
+
+`is-a` (bir örneği olma) kalıtım ilişkisi kullanılarak gruplandırılmış geniş bir varlıklar topluluğudur. "Microsoft Nedir?" gibi soruların yanıtlanmasına olanak tanır - cevap, "0.87 olasılıkla bir şirket ve 0.75 olasılıkla bir marka" gibi bir şeydir.
+
+Çizge, ya REST API olarak ya da tüm varlık çiftlerini listeleyen büyük bir indirilebilir metin dosyası olarak mevcuttur.
+
+## ✍️ Alıştırma: Bir Kavram Çizgesi
+
+Haber makalelerini birkaç kategoride gruplandırmak için Microsoft Kavram Çizgesi'ni nasıl kullanabileceğimizi görmek için [MSConceptGraph.tr.ipynb](MSConceptGraph.tr.ipynb) not defterini deneyin.
+
+## Vargılar
+
+Günümüzde YZ, genellikle *Makine Öğrenmesi* veya *Sinir Ağları* ile eşanlamlı olarak kabul edilir. Bununla birlikte, bir insan aynı zamanda şu anda sinir ağları tarafından ele alınmayan açık bir akıl yürütme sergiler. Gerçek dünya projelerinde, açıklama gerektiren görevleri gerçekleştirmek veya sistemin davranışını kontrollü bir şekilde değiştirebilmek için açık akıl yürütme hala kullanılmaktadır.
+
+## 🚀 Kendini Sınama
+
+Bu dersle ilişkili Aile Ontolojisi defterinde, diğer aile ilişkilerini deneme fırsatı vardır. Soy ağacındaki insanlar arasında yeni bağlantılar keşfetmeye çalışın.
+
+## [Ders sonrası sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/202)
+
+## Gözden Geçirme ve Bireysel Çalışma
+
+İnsanların bilgiyi ölçmeye ve kodlamaya çalıştığı alanları keşfetmek için internette biraz araştırma yapın. Bloom'un Taksonomisine bir göz atın ve insanların dünyalarını nasıl anlamlandırmaya çalıştıklarını öğrenmek için tarihe bakın. Linnaeus'un bir organizma sınıflandırması yaratma çalışmalarını keşfedin ve Dmitri Mendeleev'in kimyasal elementlerin tanımlanması ve gruplandırılması için yarattığını yolu gözlemleyin. Başka hangi ilginç örnekleri bulabilirsiniz?
+
+**Ödev**: [Bir Ontoloji Oluşturun](assignment.tr.md)
diff --git a/lessons/2-Symbolic/translations/assignment.tr.md b/lessons/2-Symbolic/translations/assignment.tr.md
new file mode 100644
index 00000000..89664e69
--- /dev/null
+++ b/lessons/2-Symbolic/translations/assignment.tr.md
@@ -0,0 +1,3 @@
+# Bir Ontoloji Oluşturun
+
+Bir bilgi tabanı oluşturmak, bir konu hakkındaki gerçekleri temsil eden bir modeli kategorize etmekle ilgilidir. Bir konu seçin - bir kişi, bir yer veya bir nesne gibi - ve sonra o konunun bir modelini oluşturun. Bu derste açıklanan bazı teknikleri ve model kurma stratejilerini kullanın. Bir örnek, mobilyalar, ışıklar vb. ile bir oturma odasının ontolojisini oluşturmak olabilir. Oturma odası mutfaktan nasıl farklıdır? Ya banyo? Yemek odası değil de oturma odası olduğunu nereden biliyorsun? Ontolojinizi oluşturmak için [Protégé](https://protege.stanford.edu/)'i kullanın.
\ No newline at end of file
diff --git a/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
index c01be7ea..90e439a1 100644
--- a/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
+++ b/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
@@ -3,7 +3,6 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "slide"
}
@@ -213,7 +212,7 @@
" * $t_{n} \\in \\{-1, +1\\}$ for negative and positive training samples, respectively\n",
" * $\\mathcal{M}$ - a set of wrongly classified examples\n",
" \n",
- "We will use the process of **graident descent**. Starting with some initial random weights $\\mathbf{w}^{(0)}$, we will adjust weights on each step of the training using the gradient of $E$:\n",
+ "We will use the process of **gradient descent**. Starting with some initial random weights $\\mathbf{w}^{(0)}$, we will adjust weights on each step of the training using the gradient of $E$:\n",
"\n",
"$$\\mathbf{w}^{\\tau + 1}=\\mathbf{w}^{\\tau} - \\eta \\nabla E(\\mathbf{w}) = \\mathbf{w}^{\\tau} + \\eta \\mathbf{x}_{n} t_{n}$$\n",
"\n",
@@ -599,7 +598,6 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "slide"
}
@@ -986,7 +984,6 @@
"cell_type": "code",
"execution_count": 26,
"metadata": {
- "scrolled": false,
"slideshow": {
"slide_type": "slide"
}
@@ -1052,11 +1049,9 @@
],
"metadata": {
"celltoolbar": "Slideshow",
- "interpreter": {
- "hash": "16aeaa504b544176258e5caf576fc030dfd6fff62d0c15825e7863ff13e121ff"
- },
"kernelspec": {
- "display_name": "Python 3.8.0 64-bit (conda)",
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -1069,12 +1064,17 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.0"
+ "version": "3.8.13"
},
"livereveal": {
"start_slideshow_at": "selected"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+ }
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb b/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb
index 89d11985..b57f44d3 100644
--- a/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb
+++ b/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb
@@ -3,7 +3,6 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "slide"
}
@@ -109,7 +108,6 @@
{
"cell_type": "markdown",
"metadata": {
- "collapsed": true,
"slideshow": {
"slide_type": "slide"
}
@@ -193,7 +191,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Code to create *one-vs-other* dataset for two-digit classification. You need to modify this code to create *one-vs-all* dateset."
+ "Code to create *one-vs-other* dataset for two-digit classification. You need to modify this code to create *one-vs-all* dataset."
]
},
{
@@ -253,12 +251,17 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.8.13"
},
"livereveal": {
"start_slideshow_at": "selected"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+ }
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md b/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md
index f7248b20..a4737753 100644
--- a/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md
+++ b/lessons/3-NeuralNetworks/03-Perceptron/lab/README.md
@@ -14,6 +14,6 @@ Using the code we have developed in this lesson for binary classification of MNI
> **Hint**: If we combine weights of all 10 perceptrons into one matrix, we should be able to apply all 10 perceptrons to the input digits by one matrix multiplication. Most probable digit can then be found just by applying `argmax` operation on the output.
-## Stating Notebook
+## Starting Notebook
Start the lab by opening [PerceptronMultiClass.ipynb](PerceptronMultiClass.ipynb)
diff --git a/lessons/3-NeuralNetworks/03-Perceptron/lab/translations/PerceptronMultiClass.tr.ipynb b/lessons/3-NeuralNetworks/03-Perceptron/lab/translations/PerceptronMultiClass.tr.ipynb
new file mode 100644
index 00000000..4a0771de
--- /dev/null
+++ b/lessons/3-NeuralNetworks/03-Perceptron/lab/translations/PerceptronMultiClass.tr.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "# Algılayıcı ile Çok Sınıflı Sınıflandırma\n",
+ "\n",
+ "[Yeni Başlayanlar için Yapay Zeka Müfredatı](https://github.com/microsoft/ai-for-beginners)'ndan Laboratuvar Ödevi."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pickle\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dersten aşağıdaki algılayıcı eğitim kodunu kullanabilirsiniz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def train(positive_examples, negative_examples, num_iterations = 100):\n",
+ " num_dims = positive_examples.shape[1]\n",
+ " weights = np.zeros((num_dims,1)) # ağırlıkları ilkle\n",
+ " \n",
+ " pos_count = positive_examples.shape[0]\n",
+ " neg_count = negative_examples.shape[0]\n",
+ " \n",
+ " report_frequency = 10\n",
+ " \n",
+ " for i in range(num_iterations):\n",
+ " pos = random.choice(positive_examples)\n",
+ " neg = random.choice(negative_examples)\n",
+ "\n",
+ " z = np.dot(pos, weights) \n",
+ " if z < 0:\n",
+ " weights = weights + pos.reshape(weights.shape)\n",
+ "\n",
+ " z = np.dot(neg, weights)\n",
+ " if z >= 0:\n",
+ " weights = weights - neg.reshape(weights.shape)\n",
+ " \n",
+ " if i % report_frequency == 0: \n",
+ " pos_out = np.dot(positive_examples, weights)\n",
+ " neg_out = np.dot(negative_examples, weights) \n",
+ " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n",
+ " neg_correct = (neg_out < 0).sum() / float(neg_count)\n",
+ " print(\"Yineleme={}, pozitif doğruluk={}, negatif doğruluk={}\".format(i,pos_correct,neg_correct))\n",
+ "\n",
+ " return weights"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "fragment"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def accuracy(weights, test_x, test_labels):\n",
+ " res = np.dot(np.c_[test_x,np.ones(len(test_x))],weights)\n",
+ " return (res.reshape(test_labels.shape)*test_labels>=0).sum()/float(len(test_labels))\n",
+ "\n",
+ "accuracy(wts, test_x, test_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "### Veri Kümesini Okuma\n",
+ "\n",
+ "Bu kod, veri kümesini internetteki depodan indirir. AI Müfredat deposunun `/data` dizininden veri kümesini manuel olarak da kopyalayabilirsiniz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "!rm *.pkl\n",
+ "!wget https://raw.githubusercontent.com/microsoft/AI-For-Beginners/main/data/mnist.pkl.gz\n",
+ "!gzip -d mnist.pkl.gz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('mnist.pkl', 'rb') as mnist_pickle:\n",
+ " train_set, validation_set, test_set = pickle.load(mnist_pickle, encoding='bytes')\n",
+ "\n",
+ "MNIST = {}\n",
+ "MNIST['Train'] = {}\n",
+ "MNIST['Validation'] = {}\n",
+ "MNIST['Test'] = {} \n",
+ "MNIST['Train']['Features'], MNIST['Train']['Labels'] = train_set[0], train_set[1]\n",
+ "MNIST['Validation']['Features'], MNIST['Validation']['Labels'] = validation_set[0], validation_set[1]\n",
+ "MNIST['Test']['Features'], MNIST['Test']['Labels'] = test_set[0], test_set[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "print(MNIST['Train']['Features'][0][130:180])\n",
+ "print(MNIST['Train']['Labels'][0])\n",
+ "features = MNIST['Train']['Features'].astype(np.float32) / 256.0\n",
+ "labels = MNIST['Train']['Labels']\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "for i in range(10):\n",
+ " ax = fig.add_subplot(1,10,i+1)\n",
+ " plt.imshow(features[i].reshape(28,28))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "İki rakamlı sınıflandırma için *bire karşı diğeri* veri kümesi oluşturma kodu. *Bire karşı hepsi* veri kümesi oluşturmak için bu kodu değiştirmeniz gerekir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def set_mnist_pos_neg(positive_label, negative_label):\n",
+ " positive_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n",
+ " if j == positive_label]\n",
+ " negative_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n",
+ " if j == negative_label]\n",
+ "\n",
+ " positive_images = MNIST['Train']['Features'][positive_indices]\n",
+ " negative_images = MNIST['Train']['Features'][negative_indices]\n",
+ "\n",
+ " return positive_images, negative_images"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Şimdi yapmanız gerekenler:\n",
+ "1. Tüm rakamlar için 10 *bire karşı hepsi* veri kümesi oluşturun.\n",
+ "1. 10 algılayıcı eğitin.\n",
+ "1. Rakam sınıflandırmasını gerçekleştirmek için `classify` (sınıflandırma) işlevini tanımlayın.\n",
+ "1. Sınıflandırmanın doğruluğunu ölçün ve *hata matrisi*ni yazdırın.\n",
+ "1. [İsteğe bağlı] Bir matris çarpımı kullanarak sınıflandırmayı gerçekleştiren gelişmiş `classify` işlevini oluşturun."
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Slideshow",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.12"
+ },
+ "livereveal": {
+ "start_slideshow_at": "selected"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "d355c6d9fcfa2da36351d09a4957315c029537f44307b30fb3762ace87798487"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/3-NeuralNetworks/03-Perceptron/lab/translations/README.tr.md b/lessons/3-NeuralNetworks/03-Perceptron/lab/translations/README.tr.md
new file mode 100644
index 00000000..72c6b90e
--- /dev/null
+++ b/lessons/3-NeuralNetworks/03-Perceptron/lab/translations/README.tr.md
@@ -0,0 +1,21 @@
+# Algılayıcı ile Çok Sınıflı Sınıflandırma
+
+[Yeni Başlayanlar için Yapay Zeka Müfredatı](https://github.com/microsoft/ai-for-beginners)'ndan Laboratuvar Ödevi.
+
+## Görevs
+
+MNIST el yazısı rakamlarının ikili sınıflandırması için bu derste geliştirdiğimiz kodu kullanarak, herhangi bir rakamı tanıyabilecek çok sınıflı bir sınıflandırma oluşturun. Eğitim ve test veri kümesindeki sınıflandırma doğruluğunu hesaplayın ve hata matrisini yazdırın.
+
+## İpuçları
+
+1. Her rakam için, "bu rakam ve diğer tüm rakamlar" ikili sınıflandırıcısı için bir veri kümesi oluşturun.
+1. İkili sınıflandırma için 10 farklı algılayıcı eğitin (her rakam için bir tane).
+1. Bir girdi rakamını sınıflandıracak bir fonksiyon tanımlayın.
+
+> **Hint**: If we combine weights of all 10 perceptrons into one matrix, we should be able to apply all 10 perceptrons to the input digits by one matrix multiplication. Most probable digit can then be found just by applying `argmax` operation on the output.
+
+**İpucu**: Tüm 10 algılayıcının ağırlıklarını tek bir matriste birleştirirsek, 10 algılayıcının tümünü bir matris çarpımı ile girdi rakamlarına uygulayabilmeliyiz. En olası basamak, çıktıya yalnızca `argmax` işlemi uygulanarak bulunabilir.
+
+## Başlangıç Not Defteri
+
+[PerceptronMultiClass.tr.ipynb](PerceptronMultiClass.tr.ipynb) dosyasını açarak laboratuvarı başlatın.
diff --git a/lessons/3-NeuralNetworks/03-Perceptron/translations/Perceptron.tr.ipynb b/lessons/3-NeuralNetworks/03-Perceptron/translations/Perceptron.tr.ipynb
new file mode 100644
index 00000000..a81c0e5f
--- /dev/null
+++ b/lessons/3-NeuralNetworks/03-Perceptron/translations/Perceptron.tr.ipynb
@@ -0,0 +1,816 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Algılayıcı\n",
+ "\n",
+ "> Bu not defteri, [Yeni Başlayanlar İçin Yapay Zeka Müfredatı](http://github.com/microsoft/ai-for-beginners)'nın bir parçasıdır. Eksiksiz öğrenme materyallerinin tamamı için kod deposunu ziyaret edin.\n",
+ "\n",
+ "Tartıştığımız gibi, algılayıcı **ikili sınıflandırma problemini** çözmenize, yani girdi örneklerini iki sınıfa ayırmanıza izin verir - bunlara **pozitif** ve **negatif** diyebiliriz.\n",
+ "\n",
+ "İlk olarak, bazı gerekli kütüphaneleri içe aktaralım."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pylab\n",
+ "from matplotlib import gridspec\n",
+ "from sklearn.datasets import make_classification\n",
+ "import numpy as np\n",
+ "from ipywidgets import interact, interactive, fixed\n",
+ "import ipywidgets as widgets\n",
+ "import pickle\n",
+ "import os\n",
+ "import gzip\n",
+ "\n",
+ "# yeniden üretilebilirlik için tohumu (seed) seçin - rastgele varyasyonların etkilerini keşfetmek için değiştirin\n",
+ "np.random.seed(1)\n",
+ "import random"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Basit Örnek Problem\n",
+ "\n",
+ "Başlangıç olarak, iki girdi özniteliğimiz olan bir basit örnek problem ile başlayalım. Örneğin, tıpta tümörleri boyutuna ve yaşına bağlı olarak iyi huylu ve kötü huylu olarak sınıflandırmak isteyebiliriz.\n",
+ "\n",
+ "SciKit Learn kütüphanesinden `make_classification` fonksiyonunu kullanarak rastgele bir sınıflandırma veri kümesi oluşturacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "n = 50\n",
+ "X, Y = make_classification(n_samples = n, n_features=2,\n",
+ " n_redundant=0, n_informative=2, flip_y=0)\n",
+ "Y = Y*2-1 # ilk 0/1 değerlerini -1/1'e çevir\n",
+ "X = X.astype(np.float32); Y = Y.astype(np.int32) # öznitelikler - kayan virgüllü sayı, etiket - tam sayı\n",
+ "\n",
+ "# Veri kümesini eğitim ve test olarak ayırın\n",
+ "train_x, test_x = np.split(X, [ n*8//10])\n",
+ "train_labels, test_labels = np.split(Y, [n*8//10])\n",
+ "print(\"Features:\\n\",train_x[0:4])\n",
+ "print(\"Labels:\\n\",train_labels[0:4])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's also plot the dataset:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_dataset(suptitle, features, labels):\n",
+ " # prepare the plot\n",
+ " fig, ax = pylab.subplots(1, 1)\n",
+ " #pylab.subplots_adjust(bottom=0.2, wspace=0.4)\n",
+ " fig.suptitle(suptitle, fontsize = 16)\n",
+ " ax.set_xlabel('$x_i[0]$ -- (öznitelik 1)')\n",
+ " ax.set_ylabel('$x_i[1]$ -- (öznitelik 2)')\n",
+ "\n",
+ " colors = ['r' if l>0 else 'b' for l in labels]\n",
+ " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
+ " fig.show()\n",
+ "\n",
+ "plot_dataset('Eğitim verisi', train_x, train_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Algılayıcı\n",
+ "\n",
+ "Algılayıcı bir ikili sınıflandırıcı olduğundan, her bir $x$ girdi vektörü için algılayıcımızın çıktısı, sınıfa bağlı olarak +1 veya -1 olacaktır. Çıktı aşağıdaki formül kullanılarak hesaplanacaktır.\n",
+ "\n",
+ "$$y(\\mathbf{x}) = f(\\mathbf{w}^{\\mathrm{T}}\\mathbf{x})$$\n",
+ "\n",
+ "burada $\\mathbf{w}$ bir ağırlık vektörüdür, $f$ bir basamak etkinleştirme fonksiyonudur:\n",
+ "$$\n",
+ "f(x) = \\begin{cases}\n",
+ " +1 & x \\geq 0 \\\\\n",
+ " -1 & x < 0\n",
+ " \\end{cases} \\\\\n",
+ "$$\n",
+ "\n",
+ "Bununla birlikte, genel bir doğrusal modelin de bir ek girdisi olmalıdır, yani ideal olarak $y$'yi $y=f(\\mathbf{w}^{\\mathrm{T}}\\mathbf{x})+\\mathbf{b}$ olarak hesaplamalıyız. Modelimizi basitleştirmek için, girdi özniteliklerimize her zaman 1'e eşit olan bir boyut daha ekleyerek bu ek girdi teriminden kurtulabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "pos_examples = np.array([ [t[0], t[1], 1] for i,t in enumerate(train_x) \n",
+ " if train_labels[i]>0])\n",
+ "neg_examples = np.array([ [t[0], t[1], 1] for i,t in enumerate(train_x) \n",
+ " if train_labels[i]<0])\n",
+ "print(pos_examples[0:3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Eğitim Algoritması\n",
+ "\n",
+ "Algılayıcıyı eğitmek için, hatayı en aza indirecek $\\mathbf{w}$ ağırlıklarını bulmamız gerekiyor. Hata, **algılayıcı kriterleri** kullanılarak tanımlanır:\n",
+ "\n",
+ "$$E(\\mathbf{w}) = -\\sum_{n \\in \\mathcal{M}}\\mathbf{w}^{\\mathrm{T}}\\mathbf{x}_{n}t_{n}$$\n",
+ " \n",
+ " * $t_{n} \\in \\{-1, +1\\}$ for negative and positive training samples, respectively\n",
+ " * $\\mathcal{M}$ - a set of wrongly classified examples\n",
+ "\n",
+ "**Gradyan inişi** işlemini kullanacağız. Bazı rastgele ilk $\\mathbf{w}^{(0)}$ ağırlıkları ile başlayarak, $E$'nin gradyanını kullanarak eğitimin her adımındaki ağırlıkları ayarlayacağız:\n",
+ "\n",
+ "$$\\mathbf{w}^{\\tau + 1}=\\mathbf{w}^{\\tau} - \\eta \\nabla E(\\mathbf{w}) = \\mathbf{w}^{\\tau} + \\eta \\mathbf{x}_{n} t_{n}$$\n",
+ "\n",
+ "burada $\\eta$ **öğrenme oranı** ve $\\tau\\in\\mathbb{N}$ yineleme sayısıdır.\n",
+ "\n",
+ "Bu algoritmayı Python'da tanımlayalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def train(positive_examples, negative_examples, num_iterations = 100):\n",
+ " num_dims = positive_examples.shape[1]\n",
+ " \n",
+ " # Ağırlıkları ilkle.\n",
+ " # Basitlik için 0 ile ilkletiyoruz, ancak rastgele ilkleme de iyi bir fikirdir\n",
+ " weights = np.zeros((num_dims,1)) \n",
+ " \n",
+ " pos_count = positive_examples.shape[0]\n",
+ " neg_count = negative_examples.shape[0]\n",
+ " \n",
+ " report_frequency = 10\n",
+ " \n",
+ " for i in range(num_iterations):\n",
+ " # Bir pozitif ve bir negatif örnek seçin\n",
+ " pos = random.choice(positive_examples)\n",
+ " neg = random.choice(negative_examples)\n",
+ "\n",
+ " z = np.dot(pos, weights) \n",
+ " if z < 0: # pozitif örnek negatif olarak sınıflandırıldı\n",
+ " weights = weights + pos.reshape(weights.shape)\n",
+ "\n",
+ " z = np.dot(neg, weights)\n",
+ " if z >= 0: # negatif örnek pozitif olarak sınıflandırıldı\n",
+ " weights = weights - neg.reshape(weights.shape)\n",
+ " \n",
+ " # Periyodik olarak, tüm örneklerde mevcut doğruluğu yazdırın\n",
+ " if i % report_frequency == 0: \n",
+ " pos_out = np.dot(positive_examples, weights)\n",
+ " neg_out = np.dot(negative_examples, weights) \n",
+ " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n",
+ " neg_correct = (neg_out < 0).sum() / float(neg_count)\n",
+ " print(\"Yineleme={}, positif doğruluk={}, negatif doğruluk={}\".format(i,pos_correct,neg_correct))\n",
+ "\n",
+ " return weights"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Şimdi eğitimi veri kümemizde çalıştıralım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "wts = train(pos_examples,neg_examples)\n",
+ "print(wts.transpose())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Gördüğünüz gibi, ilk doğruluk %50 civarındadır, ancak hızla %90'a yakın daha yüksek değerlere çıkar.\n",
+ "\n",
+ "Sınıfların nasıl ayrıldığını görselleştirelim. Sınıflandırma fonksiyonumuz $\\mathbf{w}^Tx$ gibi görünüyor ve bir sınıf için 0'dan büyük, diğeri için 0'ın altında. Böylece, sınıf ayırma doğrusu $\\mathbf{w}^Tx = 0$ ile tanımlanır. Yalnızca $x_0$ ve $x_1$ boyutlarına sahip olduğumuz için, doğrunun denklemi $w_0x_0+w_1x_1+w_2 = 0$ olacaktır (ekstra bir boyut olarak $x_2=1$ tanımladığımızı unutmayın). Bu doğruyu çizelim:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_boundary(positive_examples, negative_examples, weights):\n",
+ " if np.isclose(weights[1], 0):\n",
+ " if np.isclose(weights[0], 0):\n",
+ " x = y = np.array([-6, 6], dtype = 'float32')\n",
+ " else:\n",
+ " y = np.array([-6, 6], dtype='float32')\n",
+ " x = -(weights[1] * y + weights[2])/weights[0]\n",
+ " else:\n",
+ " x = np.array([-6, 6], dtype='float32')\n",
+ " y = -(weights[0] * x + weights[2])/weights[1]\n",
+ "\n",
+ " pylab.xlim(-6, 6)\n",
+ " pylab.ylim(-6, 6) \n",
+ " pylab.plot(positive_examples[:,0], positive_examples[:,1], 'bo')\n",
+ " pylab.plot(negative_examples[:,0], negative_examples[:,1], 'ro')\n",
+ " pylab.plot(x, y, 'g', linewidth=2.0)\n",
+ " pylab.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plot_boundary(pos_examples,neg_examples,wts)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Test Veri Kümesinde Değerlendirme\n",
+ "\n",
+ "Başlangıçta, bazı verileri test veri kümesine ayırdık. Bu test veri kümesinde sınıflandırıcımızın ne kadar doğru olduğunu görelim. Bunu yapmak için ayrıca test veri kümesini ekstra bir boyutla genişletiyoruz, ağırlıklar matrisi ile çarpıyoruz ve elde edilen değerin etiketle (+1 veya -1) aynı işarette olduğundan emin oluyoruz. Daha sonra doğruluğu elde etmek için tüm boole değerlerini toplarız ve test örnekleminin uzunluğuna böleriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "fragment"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def accuracy(weights, test_x, test_labels):\n",
+ " res = np.dot(np.c_[test_x,np.ones(len(test_x))],weights)\n",
+ " return (res.reshape(test_labels.shape)*test_labels>=0).sum()/float(len(test_labels))\n",
+ "\n",
+ "accuracy(wts, test_x, test_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Eğitim sürecini gözlemlemek\n",
+ "\n",
+ "Eğitim sırasında doğruluğun nasıl azaldığını daha önce gördük. Eğitim sırasında ayırma doğrusunun nasıl davrandığını görmek güzel olurdu. Aşağıdaki kod, her şeyi tek bir grafikte görselleştirecektir ve kaydırıcıyı eğitim süreci boyunca \"zaman yolculuğu\"na hareket ettirebilmelisiniz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def train_graph(positive_examples, negative_examples, num_iterations = 100):\n",
+ " num_dims = positive_examples.shape[1]\n",
+ " weights = np.zeros((num_dims,1)) # ağırlıkları ilkle\n",
+ " \n",
+ " pos_count = positive_examples.shape[0]\n",
+ " neg_count = negative_examples.shape[0]\n",
+ " \n",
+ " report_frequency = 15;\n",
+ " snapshots = []\n",
+ " \n",
+ " for i in range(num_iterations):\n",
+ " pos = random.choice(positive_examples)\n",
+ " neg = random.choice(negative_examples)\n",
+ "\n",
+ " z = np.dot(pos, weights) \n",
+ " if z < 0:\n",
+ " weights = weights + pos.reshape(weights.shape)\n",
+ "\n",
+ " z = np.dot(neg, weights)\n",
+ " if z >= 0:\n",
+ " weights = weights - neg.reshape(weights.shape)\n",
+ " \n",
+ " if i % report_frequency == 0: \n",
+ " pos_out = np.dot(positive_examples, weights)\n",
+ " neg_out = np.dot(negative_examples, weights) \n",
+ " pos_correct = (pos_out >= 0).sum() / float(pos_count)\n",
+ " neg_correct = (neg_out < 0).sum() / float(neg_count)\n",
+ " snapshots.append((np.copy(weights),(pos_correct+neg_correct)/2.0))\n",
+ "\n",
+ " return np.array(snapshots)\n",
+ "\n",
+ "snapshots = train_graph(pos_examples,neg_examples)\n",
+ "\n",
+ "def plotit(pos_examples,neg_examples,snapshots,step):\n",
+ " fig = pylab.figure(figsize=(10,4))\n",
+ " fig.add_subplot(1, 2, 1)\n",
+ " plot_boundary(pos_examples, neg_examples, snapshots[step][0])\n",
+ " fig.add_subplot(1, 2, 2)\n",
+ " pylab.plot(np.arange(len(snapshots[:,1])), snapshots[:,1])\n",
+ " pylab.ylabel('Accuracy')\n",
+ " pylab.xlabel('Iteration')\n",
+ " pylab.plot(step, snapshots[step,1], \"bo\")\n",
+ " pylab.show()\n",
+ "def pl1(step): plotit(pos_examples,neg_examples,snapshots,step)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "interact(pl1, step=widgets.IntSlider(value=0, min=0, max=len(snapshots)-1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Algılayıcının Sınırları\n",
+ "\n",
+ "Yukarıda gördüğünüz gibi, algılayıcı bir **doğrusal sınıflandırıcıdır**. **Doğrusal olarak ayrılabilir** iseler, yani düz bir çizgi ile ayrılabilirlerse, iki sınıf arasında iyi bir ayrım yapabilir. Aksi takdirde, algılayıcı eğitim işlemi yakınsamayacaktır.\n",
+ "\n",
+ "Bir algılayıcı tarafından çözülemeyen bir problemin en bariz örneği **XOR (Dışlayıcı Veya) problemi** olarak adlandırılır. Algılayıcımızın aşağıdaki doğruluk tablosuna sahip olan XOR boole işlevini öğrenmesini istiyoruz:\n",
+ "\n",
+ "| | 0 | 1 |\n",
+ "|---|---|---|\n",
+ "| 0 | 0 | 1 | \n",
+ "| 1 | 1 | 0 |\n",
+ "\n",
+ "Hadi bunu deneyelim ve yapalım! Tüm pozitif ve negatif eğitim örneklerini manuel olarak dolduracağız ve ardından yukarıda tanımlanan eğitim fonksiyonumuzu çağıracağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "pos_examples_xor = np.array([[1,0,1],[0,1,1]])\n",
+ "neg_examples_xor = np.array([[1,1,1],[0,0,1]])\n",
+ "\n",
+ "snapshots_xor = train_graph(pos_examples_xor,neg_examples_xor,1000)\n",
+ "def pl2(step): plotit(pos_examples_xor,neg_examples_xor,snapshots_xor,step)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "interact(pl2, step=widgets.IntSlider(value=0, min=0, max=len(snapshots)-1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Yukarıdaki grafikten de görebileceğiniz gibi, doğruluk hiçbir zaman %75'in üzerine çıkmaz, çünkü olası tüm örnekleri doğru elde edecek şekilde düz bir çizgi çizmek imkansızdır.\n",
+ "\n",
+ "XOR problemi, algılayıcının sınırlarının klasik bir örneğidir ve 1969'da Marvin Minsky ve Seymour Papert tarafından [Algılayıcılar](https://en.wikipedia.org/wiki/Perceptrons_(book)) adlı kitaplarında belirtilmiştir. Bu gözlem, sinir ağları alanındaki araştırmayı neredeyse 10 yıllarca sınırlandırdı - ve bunu kursumuzun bir sonraki bölümünde göreceğiz - çok katmanlı algılayıcılar bu tür sorunları mükemmel bir şekilde çözme yeteneğine sahipler.\n",
+ "\n",
+ "## Karmaşık Örnek - MNIST\n",
+ "\n",
+ "Perceptron, XOR problemini çözemese de, el yazısı karakter tanıma gibi daha birçok karmaşık problemi çözebilir.\n",
+ "\n",
+ "Makine öğrenmesinde uzmanlaşırken sıklıkla kullanılan bir veri kümesine [MNIST](https://en.wikipedia.org/wiki/MNIST_database) denir. Modifiye Ulusal Standartlar ve Teknoloji Enstitüsü tarafından oluşturulmuştur ve yaklaşık 250 öğrenci ve enstitü çalışanından toplanan 60000 el yazısıyla yazılmış rakamdan bir eğitim kümesi içerir. Ayrıca farklı kişilerden toplanan 10000 rakamlık bir test veri kümesi de bulunmaktadır.\n",
+ "\n",
+ "Tüm rakamlar, 28x28 piksel boyutundaki gri tonlamalı imgelerle temsil edilir.\n",
+ "\n",
+ "> MNIST veri Kümesi, makine öğrenmesi yarışmalarına ve mücadelelerine ev sahipliği yapan bir site olan [Kaggle](https://www.kaggle.com/c/digit-recognizer) üzerinde bir eğitim yarışması olarak mevcuttur. MNIST rakamlarını nasıl sınıflandıracağınızı öğrendikten sonra, diğer katılımcılar arasında nasıl derecelendirildiğini görmek için çözümünüzü Kaggle'a gönderebilirsiniz.\n",
+ "\n",
+ "MNIST veri kümesini yükleyerek başlıyoruz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Bu not defterini klonlanmış bir depodan çalıştırmıyorsanız, önce ikili veri kümesi dosyasını almanız gerekebilir.\n",
+ "# !wget https://raw.githubusercontent.com/microsoft/AI-For-Beginners/main/data/mnist.pkl.gz\n",
+ "# Bu durumda aşağıdaki veri kümesinin bağlantısını da düzeltin.\n",
+ "MNIST = {}\n",
+ "\n",
+ "with gzip.open('../../../../data/mnist.pkl.gz', 'rb') as mnist_pickle:\n",
+ " train_set, validation_set, test_set = pickle.load(mnist_pickle, encoding='bytes')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MNIST['Train'] = {}\n",
+ "MNIST['Validation'] = {}\n",
+ "MNIST['Test'] = {} \n",
+ "MNIST['Train']['Features'], MNIST['Train']['Labels'] = train_set[0], train_set[1]\n",
+ "MNIST['Validation']['Features'], MNIST['Validation']['Labels'] = validation_set[0], validation_set[1]\n",
+ "MNIST['Test']['Features'], MNIST['Test']['Labels'] = test_set[0], test_set[1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Şimdi veri kümesini çizelim:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "print(MNIST['Train']['Features'][0][130:180])\n",
+ "print(MNIST['Train']['Labels'][0])\n",
+ "features = MNIST['Train']['Features'].astype(np.float32) / 256.0\n",
+ "labels = MNIST['Train']['Labels']\n",
+ "fig = pylab.figure(figsize=(10,5))\n",
+ "for i in range(10):\n",
+ " ax = fig.add_subplot(1,10,i+1)\n",
+ " pylab.imshow(features[i].reshape(28,28))\n",
+ "pylab.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Algılayıcı bir ikili sınıflandırıcı olduğundan, problemimizi sadece iki rakamı tanımakla sınırlayacağız. Aşağıdaki işlev, pozitif ve negatif örnek dizilerini verilen iki rakam ile dolduracaktır (ve ayrıca netlik için bu rakamların örneklerini de gösterecektir)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def set_mnist_pos_neg(positive_label, negative_label):\n",
+ " positive_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n",
+ " if j == positive_label]\n",
+ " negative_indices = [i for i, j in enumerate(MNIST['Train']['Labels']) \n",
+ " if j == negative_label]\n",
+ "\n",
+ " positive_images = MNIST['Train']['Features'][positive_indices]\n",
+ " negative_images = MNIST['Train']['Features'][negative_indices]\n",
+ "\n",
+ " fig = pylab.figure()\n",
+ " ax = fig.add_subplot(1, 2, 1)\n",
+ " pylab.imshow(positive_images[0].reshape(28,28), cmap='gray', interpolation='nearest')\n",
+ " ax.set_xticks([])\n",
+ " ax.set_yticks([])\n",
+ " ax = fig.add_subplot(1, 2, 2)\n",
+ " pylab.imshow(negative_images[0].reshape(28,28), cmap='gray', interpolation='nearest')\n",
+ " ax.set_xticks([])\n",
+ " ax.set_yticks([])\n",
+ " pylab.show()\n",
+ " \n",
+ " return positive_images, negative_images"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "0 ile 1 arasında sınıflandırma yapmaya çalışarak başlayacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "pos1,neg1 = set_mnist_pos_neg(1,0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plotit2(snapshots_mn,step):\n",
+ " fig = pylab.figure(figsize=(10,4))\n",
+ " ax = fig.add_subplot(1, 2, 1)\n",
+ " pylab.imshow(snapshots_mn[step][0].reshape(28, 28), interpolation='nearest')\n",
+ " ax.set_xticks([])\n",
+ " ax.set_yticks([])\n",
+ " pylab.colorbar()\n",
+ " ax = fig.add_subplot(1, 2, 2)\n",
+ " ax.set_ylim([0,1])\n",
+ " pylab.plot(np.arange(len(snapshots_mn[:,1])), snapshots_mn[:,1])\n",
+ " pylab.plot(step, snapshots_mn[step,1], \"bo\")\n",
+ " pylab.show()\n",
+ "def pl3(step): plotit2(snapshots_mn,step)\n",
+ "def pl4(step): plotit2(snapshots_mn2,step) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "snapshots_mn = train_graph(pos1,neg1,1000) \n",
+ "interact(pl3, step=widgets.IntSlider(value=0, min=0, max=len(snapshots_mn) - 1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lütfen doğruluğun çok hızlı bir şekilde neredeyse %100'e kadar çıktığına dikkat edin.\n",
+ "\n",
+ "Lütfen, kaydırıcıyı eğitimin sonuna doğru bir konuma getirin ve solda çizilen ağırlık matrisini gözlemleyin. Bu matris, algılayıcının gerçekte nasıl çalıştığını anlamanıza izin verecektir. Alanın ortasında tipik olarak 1 rakamı için mevcut olan piksellere karşılık gelen yüksek ağırlık değerlerini ve 0 rakamı bölümlerinin olduğu kenarlarda düşük negatif değerlerini görebilirsiniz. Yani, algılayıcıya sunulan rakam aslında 1 ise, orta kısmı yüksek değerlerle çarpılarak pozitif sonuç üretilecektir. Tersine, algılayıcı 0'ı gözlemlediğinde, karşılık gelen pikseller negatif sayılarla çarpılacaktır.\n",
+ "\n",
+ "> Algılayıcımıza yatay olarak hafifçe kaydırılmış bir 1 rakamı verirsek, öyleki pikselleri 0'ın dikey kısımlarının olduğu yeri doldursun, yanlış sonuç alabileceğimizi fark edebilirsiniz. MNIST veri kümemizin doğası gereği, tüm rakamlar ortalanmış ve düzgün bir şekilde konumlandırılmıştır ve algılayıcı, rakamları ayırt etmek için buna güvenir.\n",
+ "\n",
+ "Şimdi farklı rakamlar deneyelim:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "pos2,neg2 = set_mnist_pos_neg(2,5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "snapshots_mn2 = train_graph(pos2,neg2,1000)\n",
+ "interact(pl4, step=widgets.IntSlider(value=0, min=0, max=len(snapshots_mn2) - 1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Vargılar \n",
+ "\n",
+ "Nedense 2 ve 5 o kadar kolay ayrılamaz. Nispeten yüksek doğruluk elde etmemize rağmen (%85'in üzerinde), algılayıcının bir noktada öğrenmeyi nasıl durdurduğunu açıkça görebiliriz.\n",
+ "\n",
+ "Bunun neden olduğunu anlamak için [Temel Bileşen Analizi](https://en.wikipedia.org/wiki/Principal_component_analysis) (PCA) kullanmayı deneyebiliriz. Sınıflar arasında en iyi ayrılabilirliği elde edecek şekilde girdi veri kümesinin boyutunu azaltmak için kullanılan bir makine öğrenmesi tekniğidir.\n",
+ "\n",
+ "Bizim durumumuzda, bir girdi imgesi 784 piksele (girdi öznitelikleri) sahiptir ve parametre sayısını sadece 2'ye düşürmek için PCA'yı kullanmak istiyoruz, böylece bunları grafik üzerinde çizebiliriz. Bu iki parametre, orijinal özniteliklerin doğrusal bir birleşimi olacaktır ve bu prosedürü, sınıfları ayıran en iyi görünümü elde edene kadar orijinal 784 boyutlu uzayımızı \"döndürmek\" ve 2B uzayımıza izdüşümü gözlemlemek olarak görebiliriz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "fragment"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.decomposition import PCA\n",
+ "\n",
+ "def pca_analysis(positive_label, negative_label):\n",
+ " positive_images, negative_images = set_mnist_pos_neg(positive_label, negative_label)\n",
+ " M = np.append(positive_images, negative_images, 0)\n",
+ "\n",
+ " mypca = PCA(n_components=2)\n",
+ " mypca.fit(M)\n",
+ " \n",
+ " pos_points = mypca.transform(positive_images[:200])\n",
+ " neg_points = mypca.transform(negative_images[:200])\n",
+ "\n",
+ " pylab.plot(pos_points[:,0], pos_points[:,1], 'bo')\n",
+ " pylab.plot(neg_points[:,0], neg_points[:,1], 'ro')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "pca_analysis(1,0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "pca_analysis(2,5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Gördüğünüz gibi, 0 ve 1 düz bir çizgi ile net bir şekilde ayrılabilir. Bu, orijinal 784 boyutlu uzayda rakamlara karşılık gelen noktaların da doğrusal olarak ayrılabilir olduğunu gösterir. 2 ve 5 durumunda, rakamları net bir şekilde ayıracak iyi bir izdüşüm bulamıyoruz ve bu nedenle bazı yanlış sınıflandırma durumları var.\n",
+ "\n",
+ "> Bu kursun ilerleyen bölümlerinde, Sinir Ağlarını kullanarak doğrusal olmayan sınıflandırıcıların nasıl oluşturulacağını ve rakamların düzgün şekilde hizalanmaması sorunuyla nasıl başa çıkılacağını öğreneceğiz. Çok yakında 10 farklı sınıfa ayırarak MNIST rakam sınıflandırmasında %99'un üzerinde doğruluğa ulaşacağız.\n",
+ "\n",
+ "## Ana Fikirler\n",
+ " * En basit sinir ağı mimarisini öğrendik - tek katmanlı algılayıcı.\n",
+ " * Gradyan inişine dayalı basit eğitim prosedürünü kullanarak algılayıcıyı \"elle\" uyguladık.\n",
+ " * Basitliğine rağmen, tek katmanlı algılayıcı, el yazısı rakam tanımanın oldukça karmaşık problemlerini çözebilir.\n",
+ " * Tek katmanlı algılayıcı bir doğrusal sınıflandırıcıdır ve bu nedenle lojistik bağlanım (regresyon) ile aynı sınıflandırma gücünü sağlar.\n",
+ " * Örneklem uzayında, algılayıcı hiperdüzlem kullanarak iki sınıf girdi verisini ayırabilir."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Katkıda Bulunanlar\n",
+ "\n",
+ "Bu defter, [Yeni Başlayanlar için Yapay Zeka Müfredatı](http://github.com/microsoft/ai-for-beginners)'nın bir parçasıdır ve [Dmitry Soshnikov](http://soshnikov.com) tarafından hazırlanmıştır. Microsoft Research Cambridge'deki Neural Network Workshop'tan ilham almıştır. Bazı kodlar ve açıklayıcı materyaller [Katja Hoffmann](https://www.microsoft.com/en-us/research/people/kahofman/)'ın, [Matthew Johnson](https://www.microsoft.com/en-us/research/people/matjoh/)'ın ve [Ryoto Tomioka](https://www.microsoft.com/en-us/research/people/ryoto/)'nın sunumlarından ve [NeuroWorkshop](http://github.com/shwars/NeuroWorkshop) deposundan alınmıştır."
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Slideshow",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.6"
+ },
+ "livereveal": {
+ "start_slideshow_at": "selected"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/3-NeuralNetworks/03-Perceptron/translations/README.tr.md b/lessons/3-NeuralNetworks/03-Perceptron/translations/README.tr.md
new file mode 100644
index 00000000..52854986
--- /dev/null
+++ b/lessons/3-NeuralNetworks/03-Perceptron/translations/README.tr.md
@@ -0,0 +1,92 @@
+# Sinir Ağlarına Giriş: Algılayıcı
+
+## [Ders öncesi sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/103)
+
+Modern bir sinir ağına benzer bir şeyi uygulamaya yönelik ilk girişimlerden biri, 1957'de Cornell Havacılık Laboratuvarı'ndan Frank Rosenblatt tarafından yapıldı. Üçgenler, kareler ve daireler gibi ilkel geometrik şekilleri tanımak için tasarlanmış "Mark-1" adlı bir donanım uygulamasıydı.
+
+| | |
+|--------------|-----------|
+|
| 
|
+
+> İmgeler [Wikipedia'dan](https://en.wikipedia.org/wiki/Perceptron)
+
+Bir girdi imgesi 20x20'lik fotosel dizisi ile temsil edildi, bu nedenle sinir ağının 400 girdisi ve bir ikili çıktısı vardı. Basit bir ağ, **eşik mantık birimi** olarak da adlandırılan bir nöron içeriyordu. Sinir ağı ağırlıkları, eğitim aşamasında elle ayar gerektiren potansiyometreler gibi davrandı.
+
+> ✅ Potansiyometre, kullanıcının bir devrenin direncini ayarlamasını sağlayan bir cihazdır.
+
+> New York Times o dönemde algılayıcı hakkında şunları yazmıştı: *[Donanmanın] beklediği, yürüyebilecek, konuşabilecek, görebilecek, yazabilecek, kendini yeniden üretebilecek ve varlığının bilincinde olabilecek bir elektronik bilgisayarın embriyosu.*
+
+## Algılayıcı Modeli
+
+Modelimizde N tane özniteliğimiz olduğunu varsayalım, bu durumda girdi vektörü N boyutunda bir vektör olacaktır. Bir algılayıcı bir **ikili sınıflandırma** modelidir, yani iki girdi verisi sınıfını ayırt edebilir. Her x girdi vektörü için algılayıcımızın çıktısının sınıfa bağlı olarak +1 veya -1 olacağını varsayacağız. Çıktı, aşağıdaki formül kullanılarak hesaplanacaktır:
+
+y(x) = f(wTx)
+
+burada f basamak etkinleştirme fonksiyonudur.
+
+
+
+
+## Algılayıcıyı Eğitme
+
+Bir algılayıcıyı eğitmek için değerlerin çoğunu doğru şekilde sınıflandıran, yani en küçük **hata** ile sonuçlanan bir w ağırlık vektörü bulmamız gerekir. Bu hata, **algılayıcı kriteri** tarafından aşağıdaki şekilde tanımlanır:
+
+E(w) = -∑wTxiti
+
+burada:
+
+* yanlış sınıflandırmaya neden olan i eğitim veri noktalarının toplamı alınır.
+* xi girdi verisidir ve buna göre olumsuz ve olumlu örnekler için ti -1 veya +1'dir.
+
+Bu kriter w ağırlıklarının bir fonksiyonu olarak kabul edilir ve bunu en aza indirmemiz gerekir. Genellikle, bazı ilk ağırlıkları w(0) ile başladığımız ve ardından her adımda ağırlıkları aşağıdaki formüle göre güncellediğimiz **gradyan inişi** adlı bir yöntem kullanılır:
+
+w(t+1) = w(t) - η∇E(w)
+
+Burada η sözde **öğrenme oranı** ve ∇E(w) E'nin **gradyanını** belirtir. Gradyanı hesapladıktan sonra, aşağıdaki işleme varırız:
+
+w(t+1) = w(t) + ∑ηxiti
+
+Python'daki algoritma şöyle görünür:
+
+```python
+def train(positive_examples, negative_examples, num_iterations = 100, eta = 1):
+
+ weights = [0,0,0] # Ağırlıkları ilkle (neredeyse rastgele :))
+
+ for i in range(num_iterations):
+ pos = random.choice(positive_examples)
+ neg = random.choice(negative_examples)
+
+ z = np.dot(pos, weights) # algılayıcı çıktısını hesapla
+ if z < 0: # pozitif örnek negatif sınıflandırıldı
+ weights = weights + eta*weights.shape
+
+ z = np.dot(neg, weights)
+ if z >= 0: # negatif örnek pozitif sınıflandırıldı
+ weights = weights - eta*weights.shape
+
+ return weights
+```
+
+## Vargılar
+
+Bu derste, ikili sınıflandırma modeli olan algılayıcıyı ve ağırlık vektörü kullanarak onu nasıl eğiteceğinizi öğrendiniz.
+
+## 🚀 Kendini Sınama
+
+Kendi algılayıcınızı oluşturmaya çalışmak istiyorsanız, [Azure ML tasarımcısını (Azure ML designer)](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer?WT.mc_id=academic-77998-cacaste) kullanan [Microsoft Learn'deki bu laboratuvarı](https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/two-class-averaged-perceptron?WT.mc_id=academic-77998-cacaste) deneyin.
+
+## [Ders sonrası sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/203)
+
+## Gözden Geçirme ve Bireysel Çalışma
+
+Bir oyuncak probleminin yanı sıra gerçek hayattaki problemleri çözmek için algılayıcıyı nasıl kullanabileceğimizi görmek ve öğrenmeye devam etmek için [Algılayıcı](./Perceptron.tr.ipynb) not defterine gidin.
+
+İşte ilave olarak ilginç bir [algılayıcılar makalesi](https://towardsdatascience.com/what-is-a-perceptron-basics-of-neural-networks-c4cfea20c590).
+
+## [Ödev](../lab/translations/README.tr.md)
+
+Bu dersimizde ikili sınıflandırma görevi için bir algılayıcı uyguladık ve bunu iki el yazısı rakamı arasından sınıflandırmak için kullandık. Bu laboratuvarda, rakam sınıflandırma problemini tamamen çözmeniz, yani hangi rakamın belirli bir imgeye karşılık gelme olasılığının en yüksek olduğunu belirlemeniz istenmektedir.
+
+* [Talimatlar](../lab/translations/README.tr.md)
+* [Not Defteri](../lab/translations/PerceptronMultiClass.tr.ipynb)
diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb
index 35b62448..15b791d4 100644
--- a/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb
+++ b/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb
@@ -13,7 +13,7 @@
"\n",
"> This notebook is a part of [AI for Beginners Curricula](http://github.com/microsoft/ai-for-beginners). Visit the repository for complete set of learning materials.\n",
"\n",
- "In this notebook, we will gradually build our own neural framework capable of solving multi-class classification tasks as well as regression with multi-layered preceptrons.\n",
+ "In this notebook, we will gradually build our own neural framework capable of solving multi-class classification tasks as well as regression with multi-layered perceptrons.\n",
"\n",
"First, let's import some required libraries."
]
@@ -55,7 +55,6 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "scrolled": false,
"slideshow": {
"slide_type": "slide"
}
@@ -77,7 +76,6 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {
- "scrolled": false,
"slideshow": {
"slide_type": "skip"
}
@@ -101,7 +99,6 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
- "scrolled": false,
"slideshow": {
"slide_type": "slide"
}
@@ -708,7 +705,7 @@
"source": [
"To compute $\\partial\\mathcal{L}/\\partial W$ we can use the **chaining rule** for computing derivatives of a composite function, as you can see in the formulae above. It corresponds to the following idea:\n",
"\n",
- "* Suppose under given input we have obtanes loss $\\Delta\\mathcal{L}$\n",
+ "* Suppose under given input we have obtained loss $\\Delta\\mathcal{L}$\n",
"* To minimize it, we would have to adjust softmax output $p$ by value $\\Delta p = (\\partial\\mathcal{L}/\\partial p)\\Delta\\mathcal{L}$ \n",
"* This corresponds to the changes to node $z$ by $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$\n",
"* To minimize this error, we need to adjust parameters accordingly: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (and the same for $b$)\n",
@@ -1169,7 +1166,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- " Adding several layers make sense, because unlike one-layer network, multi-layered model will be able to accuratley classify sets that are not linearly separable. I.e., a model with several layers will be **reacher**.\n",
+ " Adding several layers make sense, because unlike one-layer network, multi-layered model will be able to accurately classify sets that are not linearly separable. I.e., a model with several layers will be **reacher**.\n",
"\n",
"> It can be demonstrated that with sufficient number of neurons a two-layered model is capable to classifying any convex set of data points, and three-layered network can classify virtually any set.\n",
"\n",
@@ -1212,7 +1209,6 @@
"cell_type": "code",
"execution_count": 40,
"metadata": {
- "scrolled": false,
"slideshow": {
"slide_type": "slide"
}
@@ -1306,11 +1302,9 @@
],
"metadata": {
"celltoolbar": "Slideshow",
- "interpreter": {
- "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
- },
"kernelspec": {
- "display_name": "Python 3.9.5 64-bit ('base': conda)",
+ "display_name": "Python 3.10.7 64-bit",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -1323,12 +1317,17 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.5"
+ "version": "3.10.7"
},
"livereveal": {
"start_slideshow_at": "selected"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+ }
}
},
"nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
}
diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/lab/translations/MyFW_MNIST.tr.ipynb b/lessons/3-NeuralNetworks/04-OwnFramework/lab/translations/MyFW_MNIST.tr.ipynb
new file mode 100644
index 00000000..c116870e
--- /dev/null
+++ b/lessons/3-NeuralNetworks/04-OwnFramework/lab/translations/MyFW_MNIST.tr.ipynb
@@ -0,0 +1,168 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Kendi Çerçevemiz ile MNIST Rakam Sınıflandırması\n",
+ "\n",
+ "Lab Assignment from [AI for Beginners Curriculum](https://github.com/microsoft/ai-for-beginners).\n",
+ "\n",
+ "[Yeni Başlayanlar için YZ Müfredatı](https://github.com/microsoft/ai-for-beginners)'ndan Laboratuvar Ödevi.\n",
+ "\n",
+ "### Veri Kümesini Okuma\n",
+ "\n",
+ "Bu kod, veri kümesini internetteki depodan indirir. YZ Müfredat deposunun `/data` dizininden veri kümesini elle de kopyalayabilirsiniz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " % Total % Received % Xferd Average Speed Time Time Time Current\n",
+ " Dload Upload Total Spent Left Speed\n",
+ "\n",
+ " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n",
+ "100 9.9M 100 9.9M 0 0 9.9M 0 0:00:01 --:--:-- 0:00:01 15.8M\n"
+ ]
+ }
+ ],
+ "source": [
+ "!rm *.pkl\n",
+ "!wget https://raw.githubusercontent.com/microsoft/AI-For-Beginners/main/data/mnist.pkl.gz\n",
+ "!gzip -d mnist.pkl.gz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "with open('mnist.pkl', 'rb') as mnist_pickle:\n",
+ " train_set, validation_set, test_set = pickle.load(mnist_pickle, encoding='bytes')\n",
+ "\n",
+ "MNIST = {}\n",
+ "MNIST['Train'] = {}\n",
+ "MNIST['Validation'] = {}\n",
+ "MNIST['Test'] = {} \n",
+ "MNIST['Train']['Features'], MNIST['Train']['Labels'] = train_set[0], train_set[1]\n",
+ "MNIST['Validation']['Features'], MNIST['Validation']['Labels'] = validation_set[0], validation_set[1]\n",
+ "MNIST['Test']['Features'], MNIST['Test']['Labels'] = test_set[0], test_set[1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "labels = MNIST['Train']['Labels']\n",
+ "data = MNIST['Train']['Features']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sahip olduğumuz verilerin şeklinin ne olduğunu görelim:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(50000, 784)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Verileri Bölme\n",
+ "\n",
+ "Verileri eğitim ve test veri kümesi arasında bölmek için Scikit Learn'ü kullanacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Eğitim örnekleri: 40000, test örnekleri: 10000\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "features_train, features_test, labels_train, labels_test = train_test_split(data,labels,test_size=0.2)\n",
+ "\n",
+ "print(f\"Eğitim örnekleri: {len(features_train)}, test örnekleri: {len(features_test)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Talimatlar\n",
+ "\n",
+ "1. Dersten çerçeve kodunu alın ve bu not defterine veya (hatta daha iyisi) ayrı bir Python modülüne yapıştırın.\n",
+ "1. Eğitim sırasında eğitim ve geçerleme doğruluğunu gözlemleyerek tek katmanlı algılayıcıyı tanımlayın ve eğitin.\n",
+ "1. Aşırı öğrenme olup olmadığını anlamaya çalışın ve doğruluğu artırmak için katman parametrelerini ayarlayın.\n",
+ "1. 2 ve 3 katmanlı algılayıcılar için önceki adımları tekrarlayın. Katmanlar arasında farklı etkinleştirme işlevleri denemeyi deneyin.\n",
+ "1. Aşağıdaki soruları yanıtlamaya çalışın:\n",
+ " - Katmanlar arası etkinleştirme işlevi ağ performansını etkiler mi?\n",
+ " - Bu görev için 2 veya 3 katmanlı ağa ihtiyacımız var mı?\n",
+ " - Ağı eğitirken herhangi bir sorun yaşadınız mı? Özellikle katman sayısı arttıkça.\n",
+ " - Eğitim sırasında ağın ağırlıkları nasıl davranıyor? İlişkiyi anlamak için dönem sayısına karşı ağırlıkların maksimum mutlak değerini çizebilirsiniz."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/lab/translations/README.tr.md b/lessons/3-NeuralNetworks/04-OwnFramework/lab/translations/README.tr.md
new file mode 100644
index 00000000..b886e745
--- /dev/null
+++ b/lessons/3-NeuralNetworks/04-OwnFramework/lab/translations/README.tr.md
@@ -0,0 +1,20 @@
+# Kendi Çerçevemizle MNIST Sınıflandırmasısı
+
+[Yeni Başlayanlar için YZ Müfredatı](https://github.com/microsoft/ai-for-beginners)'ndan Laboratuvar Ödevi.
+
+## Görev
+
+1, 2 ve 3 katmanlı algılayıcı kullanarak MNIST el yazısı rakam sınıflandırma problemini çözün. Derste geliştirdiğimiz sinir ağı çerçevesini kullanın.
+
+## Not Defterini Başlatmak
+
+[MyFW_MNIST.tr.ipynb](MyFW_MNIST.tr.ipynb) dosyasını açarak laboratuvarı başlatın.
+
+## Sorular
+
+Bu laboratuvarın sonucu olarak aşağıdaki soruları yanıtlamaya çalışın:
+
+- Katmanlar arası etkinleştirme işlevi ağ performansını etkiler mi?
+- Bu görev için 2 veya 3 katmanlı ağa ihtiyacımız var mı?
+- Ağı eğitirken herhangi bir sorun yaşadınız mı? Özellikle katman sayısı arttıkça.
+- Eğitim sırasında ağın ağırlıkları nasıl davranıyor? İlişkiyi anlamak için dönem sayısına karşı ağırlıkların maksimum mutlak değerini çizebilirsiniz.
\ No newline at end of file
diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/translations/OwnFramework.tr.ipynb b/lessons/3-NeuralNetworks/04-OwnFramework/translations/OwnFramework.tr.ipynb
new file mode 100644
index 00000000..c51f0bd2
--- /dev/null
+++ b/lessons/3-NeuralNetworks/04-OwnFramework/translations/OwnFramework.tr.ipynb
@@ -0,0 +1,1269 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Çok Katmanlı Algılayıcılar\n",
+ "## Kendi Sinirsel Çerçevemizi Oluşturmak\n",
+ "\n",
+ "> Bu not defteri, [Yeni Başlayanlar için YZ Müfredatı](http://github.com/microsoft/ai-for-beginners)'nın bir parçasıdır. Eksiksiz öğrenme materyalleri kümesi için kaynak deposunu ziyaret edin.\n",
+ "\n",
+ "Bu defterde, çok-katmanlı algılayıcılarla çok-sınıflı sınıflandırma görevlerini ve bağlanımı çözebilen kendi sinirsel çerçevemizi yavaş yavaş inşa edeceğiz.\n",
+ "\n",
+ "İlk olarak, bazı gerekli kütüphaneleri içe aktaralım."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib nbagg\n",
+ "import matplotlib.pyplot as plt \n",
+ "from matplotlib import gridspec\n",
+ "from sklearn.datasets import make_classification\n",
+ "import numpy as np\n",
+ "# tekrarlanabilirlik için tohumu (seed) seçin - rastgele değişğmlerin etkilerini keşfetmek için değiştirin\n",
+ "np.random.seed(0)\n",
+ "import random"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Numune Veri Kümesi\n",
+ "\n",
+ "Daha önce olduğu gibi, iki parametreli basit bir örnek veri kümesi ile başlayacağız."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "n = 100\n",
+ "X, Y = make_classification(n_samples = n, n_features=2,\n",
+ " n_redundant=0, n_informative=2, flip_y=0.2)\n",
+ "X = X.astype(np.float32)\n",
+ "Y = Y.astype(np.int32)\n",
+ "\n",
+ "# Eğitim ve test veri kümesine bölün\n",
+ "train_x, test_x = np.split(X, [n*8//10])\n",
+ "train_labels, test_labels = np.split(Y, [n*8//10])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_dataset(suptitle, features, labels):\n",
+ " # görseli hazırla\n",
+ " fig, ax = plt.subplots(1, 1)\n",
+ " #pylab.subplots_adjust(bottom=0.2, wspace=0.4)\n",
+ " fig.suptitle(suptitle, fontsize = 16)\n",
+ " ax.set_xlabel('$x_i[0]$ -- (öznitelik 1)')\n",
+ " ax.set_ylabel('$x_i[1]$ -- (öznitelik 2)')\n",
+ "\n",
+ " colors = ['r' if l else 'b' for l in labels]\n",
+ " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_l/jnklp1bj4cl95rc01tf5vx4h0000gn/T/ipykernel_23744/2900170536.py:11: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "plot_dataset('Eğitim verilerinin dağıtık grafiği', train_x, train_labels)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 1.3382818 -0.98613256]\n",
+ " [ 0.5128146 0.43299454]\n",
+ " [-0.4473693 -0.2680512 ]\n",
+ " [-0.9865851 -0.28692 ]\n",
+ " [-1.0693829 0.41718036]]\n",
+ "[1 1 0 0 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(train_x[:5])\n",
+ "print(train_labels[:5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Makine Öğrenmesi Problemi\n",
+ "\n",
+ "Diyelim ki $\\langle X,Y\\rangle$ veri setimiz var, burada $X$ bir dizi öznitelik ve $Y$ karşılık gelen etiketlerdir. Bağlanım problemi için $y_i\\in\\mathbb{R}$ ve sınıflandırma için $y_i\\in\\{0,\\dots,n\\}$ sınıf numarası ile temsil edilir.\n",
+ "\n",
+ "Herhangi bir makine öğrenmesi modeli, $f_\\theta(x)$ işleviyle temsil edilebilir, burada $\\theta$ bir **parametre** kümesidir. Amacımız, modelimizin veri kümesine en iyi şekilde uyduğu $\\theta$ parametrelerini bulmaktır. Kriterler **kayıp işlevi** $\\mathcal{L}$ tarafından tanımlanır ve en uygun değeri bulmamız gerekir\n",
+ "\n",
+ "$$\n",
+ "\\theta = \\mathrm{argmin}_\\theta \\mathcal{L}(f_\\theta(X),Y)\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Kayıp işlevi çözülen probleme bağlıdır.\n",
+ "\n",
+ "### Bağlanım için kayıp işlevleri\n",
+ "\n",
+ "Bağlanım için genellikle **mutlak hata** $\\mathcal{L}_{mutlak}(\\theta) = \\sum_{i=1}^n |y_i - f_{\\theta}(x_i)|$, veya **ortalama kare hatası** $\\mathcal{L}_{sq}(\\theta) = \\sum_{i=1}^n (y_i - f_{\\theta}(x_i))^2$ kullanırız."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# çeşitli kayıp fonksiyonlarını çizmek için yardımcı fonksiyon\n",
+ "def plot_loss_functions(suptitle, functions, ylabels, xlabel):\n",
+ " fig, ax = plt.subplots(1,len(functions), figsize=(9, 3))\n",
+ " plt.subplots_adjust(bottom=0.2, wspace=0.4)\n",
+ " fig.suptitle(suptitle)\n",
+ " for i, fun in enumerate(functions):\n",
+ " ax[i].set_xlabel(xlabel)\n",
+ " if len(ylabels) > i:\n",
+ " ax[i].set_ylabel(ylabels[i])\n",
+ " ax[i].plot(x, fun)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = np.linspace(-2, 2, 101)\n",
+ "plot_loss_functions(\n",
+ " suptitle = 'Bağlanım için ortak kayıp fonksiyonları',\n",
+ " functions = [np.abs(x), np.power(x, 2)],\n",
+ " ylabels = ['$\\mathcal{L}_{abs}}$ (mutlak kayıp)',\n",
+ " '$\\mathcal{L}_{sq}$ (kare kayıp)'],\n",
+ " xlabel = '$y - f(x_i)$')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "### Sınıflandırma için kayıp fonksiyonları\n",
+ "\n",
+ "Bir an için ikili sınıflandırmayı ele alalım. Bu durumda 0 ve 1 olarak numaralandırılmış iki sınıfımız var. $f_\\theta(x_i)\\in [0,1]$ ağının çıktısı esasen sınıf 1'in seçilme olasılığını tanımlar.\n",
+ "\n",
+ "**0-1 kaybı**\n",
+ "\n",
+ "0-1 kaybı, modelin doğruluğunu hesaplamakla aynıdır - doğru sınıflandırmaların sayısını hesaplarız:\n",
+ "\n",
+ "$$\\mathcal{L}_{0-1} = \\sum_{i=1}^n l_i \\quad l_i = \\begin{cases}\n",
+ " 0 & (f(x_i)<0.5 \\land y_i=0) \\lor (f(x_i)<0.5 \\land y_i=1) \\\\\n",
+ " 1 & \\mathrm{ diğer~türlü}\n",
+ " \\end{cases} \\\\\n",
+ "$$\n",
+ "\n",
+ "Ancak doğruluğun kendisi, doğru sınıflandırmadan ne kadar uzak olduğumuzu göstermez. Doğru sınıfı birazcık kaçırmış olabiliriz ve bu, bir bakımdan önemli ölçüde kaçırmaktan \"daha iyi\" (bir anlamda ağırlıkları çok daha az düzeltmemiz gerekiyor). Bu nedenle, genellikle bunu dikkate alan lojistik kayıp kullanılır.\n",
+ "\n",
+ "**Logistik Kayıp**\n",
+ "\n",
+ "$$\\mathcal{L}_{log} = \\sum_{i=1}^n -y\\log(f_{\\theta}(x_i)) - (1-y)\\log(1-f_\\theta(x_i))$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x = np.linspace(0,1,100)\n",
+ "def zero_one(d):\n",
+ " if d < 0.5:\n",
+ " return 0\n",
+ " return 1\n",
+ "zero_one_v = np.vectorize(zero_one)\n",
+ "\n",
+ "def logistic_loss(fx):\n",
+ " # assumes y == 1\n",
+ " return -np.log(fx)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_l/jnklp1bj4cl95rc01tf5vx4h0000gn/T/ipykernel_23744/331859503.py:10: RuntimeWarning: divide by zero encountered in log\n",
+ " return -np.log(fx)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_loss_functions(suptitle = 'Sınıflandırma için ortak kayıp fonksiyonları (sınıf=1)',\n",
+ " functions = [zero_one_v(x), logistic_loss(x)],\n",
+ " ylabels = ['$\\mathcal{L}_{0-1}}$ (0-1 kaybı)',\n",
+ " '$\\mathcal{L}_{log}$ (logistic kayıp)'],\n",
+ " xlabel = '$p$')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Lojistik kaybı anlamak için, beklenen çıktının iki durumunu göz önünde bulundurun:\n",
+ "* Çıktının 1 ($y=1$) olmasını beklersek, kayıp $-log f_\\theta(x_i)$ olur. Kayıp 0'dır, ağ 1 olasılıkla 1'i tahmin eder ve 1 olasılığı küçüldüğünde büyür.\n",
+ "* Çıktının 0 ($y=0$) olmasını beklersek, kayıp $-log(1-f_\\theta(x_i))$ olur. Burada, $1-f_\\theta(x_i)$, ağ tarafından tahmin edilen 0 olasılığıdır ve logaritmik kaybın anlamı, önceki durumda açıklananla aynıdır."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Sinir Ağı Mimarisi\n",
+ "\n",
+ "İkili sınıflandırma problemi için bir veri kümesi oluşturduk. Ancak, bunu en baştan çok sınıflı sınıflandırma olarak düşünelim, böylece kodumuzu kolayca çok sınıflı sınıflandırmaya çevirebiliriz. Bu durumda, tek katmanlı algılayıcımız aşağıdaki mimariye sahip olacaktır:\n",
+ "\n",
+ "![](\"../images/NeuroArch.png\")
\n",
+ "\n",
+ "Ağın iki çıktısı iki sınıfa karşılık gelir ve iki çıktı arasında en yüksek değere sahip sınıf doğru çözüme karşılık gelir.\n",
+ "\n",
+ "Model şöyle tanımlanır:\n",
+ "$$\n",
+ "f_\\theta(x) = W\\times x + b\n",
+ "$$\n",
+ "burada $$\\theta = \\langle W,b\\rangle$$ parametrelerdir.\n",
+ "\n",
+ "Bu doğrusal katmanı, hesaplamayı gerçekleştiren `forward` (ileri) işlevi olan bir Python sınıfı olarak tanımlayacağız. $x$ girdi değerini alır ve katmanın çıktısını üretir. `W` ve `b` parametreleri katman sınıfı içinde depolanır ve yaratıldığında sırasıyla rastgele değerler ve sıfırlarla ilklenir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.77202116, -0.25384488],\n",
+ " [ 0.28370828, -0.39610552],\n",
+ " [-0.30097433, 0.30513182],\n",
+ " [-0.8120485 , 0.56079421],\n",
+ " [-1.23519653, 0.3394973 ]])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "class Linear:\n",
+ " def __init__(self,nin,nout):\n",
+ " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\n",
+ " self.b = np.zeros((1,nout))\n",
+ " \n",
+ " def forward(self, x):\n",
+ " return np.dot(x, self.W.T) + self.b\n",
+ " \n",
+ "net = Linear(2,2)\n",
+ "net.forward(train_x[0:5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Çoğu durumda, tek bir girdi değeri üzerinde değil, girdi değerlerinin vektörü üzerinde çalışmak daha verimlidir. Numpy işlemlerini kullandığımız için, ağımıza bir girdi değerleri vektörü geçirebiliriz ve bu bize çıktı değerlerinin vektörünü verecektir.\n",
+ "\n",
+ "## Softmaks: Çıktıları Olasılıklara Dönüştürme\n",
+ "\n",
+ "Gördüğünüz gibi, çıktılarımız olasılık değil - herhangi bir değer alabilirler. Bunları olasılıklara dönüştürmek için tüm sınıflardaki değerleri normalleştirmemiz gerekir. Bu, **softmaks** işlevi kullanılarak yapılır: $$\\sigma(\\mathbf{z}_c) = \\frac{e^{z_c}}{\\sum_{j} e^{z_j}}, \\quad\\mathrm {öyleki}\\quad c\\in 1 .. |C|$$\n",
+ "\n",
+ "![](\"https://raw.githubusercontent.com/shwars/NeuroWorkshop/master/images/NeuroArch-softmax.PNG\")
\n",
+ "\n",
+ "> Ağın çıktısı $\\sigma(\\mathbf{z})$, $C$ sınıflar kümesinde olasılık dağılımı olarak yorumlanabilir: $q = \\sigma(\\mathbf{z}_c) = \\hat{p} (c | x)$\n",
+ "\n",
+ "`Softmax` (Softmaks) katmanını, `forward` (ileri) işlevli bir sınıfla aynı şekilde tanımlayacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.88348621, 0.11651379],\n",
+ " [0.66369714, 0.33630286],\n",
+ " [0.35294795, 0.64705205],\n",
+ " [0.20216095, 0.79783905],\n",
+ " [0.17154828, 0.82845172],\n",
+ " [0.24279153, 0.75720847],\n",
+ " [0.18915732, 0.81084268],\n",
+ " [0.17282951, 0.82717049],\n",
+ " [0.13897531, 0.86102469],\n",
+ " [0.72746882, 0.27253118]])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "class Softmax:\n",
+ " def forward(self,z):\n",
+ " zmax = z.max(axis=1,keepdims=True)\n",
+ " expz = np.exp(z-zmax)\n",
+ " Z = expz.sum(axis=1,keepdims=True)\n",
+ " return expz / Z\n",
+ "\n",
+ "softmax = Softmax()\n",
+ "softmax.forward(net.forward(train_x[0:10]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Artık çıktılar olarak olasılıklar aldığımızı görebilirsiniz, yani her çıktı vektörünün toplamı tam olarak 1'dir.\n",
+ "\n",
+ "2'den fazla sınıfımız olması durumunda, softmaks bunların hepsinde olasılıkları normalleştirir. İşte MNIST rakam sınıflandırması yapan bir ağ mimarisi diyagramı:\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Çapraz Entropi Kaybı\n",
+ "\n",
+ "Sınıflandırmadaki bir kayıp fonksiyonu, tipik olarak, **çapraz entropi kaybı** olarak genelleştirilebilen bir lojistik fonksiyondur. Çapraz entropi kaybı, iki rastgele olasılık dağılımı arasındaki benzerliği hesaplayabilen bir fonksiyondur. Bununla ilgili daha ayrıntılı tartışmayı [Wikipedia](https://en.wikipedia.org/wiki/Cross_entropy)'da bulabilirsiniz.\n",
+ "\n",
+ "Bizim durumumuzda, ilk dağılım ağımızın olasılıksal çıktısıdır ve ikincisi **bire bir** dağılım olarak adlandırılır, bu da belirli bir $c$ sınıfının 1'e karşılık gelen olasılığa sahip olduğunu belirtir (geri kalan her şey 0'dır). Böyle bir durumda çapraz entropi kaybı $-\\log p_c$ olarak hesaplanabilir, burada $c$ beklenen sınıftır ve $p_c$, sinir ağımız tarafından verilen bu sınıfa karşılık gelen olasılıktır.\n",
+ "\n",
+ "> Beklenen sınıf için ağ 1 olasılığını döndürürse, çapraz entropi kaybı 0 olur. Gerçek sınıfın olasılığı 0'a ne kadar yakınsa, çapraz entropi kaybı o kadar yüksek olur (ve sonsuza kadar gidebilir!)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_cross_ent():\n",
+ " p = np.linspace(0.01, 0.99, 101) # tahmin edilen olasılık p(y|x)\n",
+ " cross_ent_v = np.vectorize(cross_ent)\n",
+ " f3, ax = plt.subplots(1,1, figsize=(8, 3))\n",
+ " l1, = plt.plot(p, cross_ent_v(p, 1), 'r--')\n",
+ " l2, = plt.plot(p, cross_ent_v(p, 0), 'r-')\n",
+ " plt.legend([l1, l2], ['$y = 1$', '$y = 0$'], loc = 'upper center', ncol = 2)\n",
+ " plt.xlabel('$\\hat{p}(y|x)$', size=18)\n",
+ " plt.ylabel('$\\mathcal{L}_{CE}$', size=18)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "Ağın iki çıktısı iki sınıfa karşılık gelir ve iki çıktı arasında en yüksek değere sahip sınıf doğru çözüme karşılık gelir.\n",
+ "\n",
+ "Model şöyle tanımlanır:\n",
+ "$$\n",
+ "f_\\theta(x) = W\\times x + b\n",
+ "$$\n",
+ "burada $$\\theta = \\langle W,b\\rangle$$ parametrelerdir.\n",
+ "\n",
+ "Bu doğrusal katmanı, hesaplamayı gerçekleştiren `forward` (ileri) işlevi olan bir Python sınıfı olarak tanımlayacağız. $x$ girdi değerini alır ve katmanın çıktısını üretir. `W` ve `b` parametreleri katman sınıfı içinde depolanır ve yaratıldığında sırasıyla rastgele değerler ve sıfırlarla ilklenir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.77202116, -0.25384488],\n",
+ " [ 0.28370828, -0.39610552],\n",
+ " [-0.30097433, 0.30513182],\n",
+ " [-0.8120485 , 0.56079421],\n",
+ " [-1.23519653, 0.3394973 ]])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "class Linear:\n",
+ " def __init__(self,nin,nout):\n",
+ " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\n",
+ " self.b = np.zeros((1,nout))\n",
+ " \n",
+ " def forward(self, x):\n",
+ " return np.dot(x, self.W.T) + self.b\n",
+ " \n",
+ "net = Linear(2,2)\n",
+ "net.forward(train_x[0:5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Çoğu durumda, tek bir girdi değeri üzerinde değil, girdi değerlerinin vektörü üzerinde çalışmak daha verimlidir. Numpy işlemlerini kullandığımız için, ağımıza bir girdi değerleri vektörü geçirebiliriz ve bu bize çıktı değerlerinin vektörünü verecektir.\n",
+ "\n",
+ "## Softmaks: Çıktıları Olasılıklara Dönüştürme\n",
+ "\n",
+ "Gördüğünüz gibi, çıktılarımız olasılık değil - herhangi bir değer alabilirler. Bunları olasılıklara dönüştürmek için tüm sınıflardaki değerleri normalleştirmemiz gerekir. Bu, **softmaks** işlevi kullanılarak yapılır: $$\\sigma(\\mathbf{z}_c) = \\frac{e^{z_c}}{\\sum_{j} e^{z_j}}, \\quad\\mathrm {öyleki}\\quad c\\in 1 .. |C|$$\n",
+ "\n",
+ "\n",
+ "\n",
+ "> Ağın çıktısı $\\sigma(\\mathbf{z})$, $C$ sınıflar kümesinde olasılık dağılımı olarak yorumlanabilir: $q = \\sigma(\\mathbf{z}_c) = \\hat{p} (c | x)$\n",
+ "\n",
+ "`Softmax` (Softmaks) katmanını, `forward` (ileri) işlevli bir sınıfla aynı şekilde tanımlayacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.88348621, 0.11651379],\n",
+ " [0.66369714, 0.33630286],\n",
+ " [0.35294795, 0.64705205],\n",
+ " [0.20216095, 0.79783905],\n",
+ " [0.17154828, 0.82845172],\n",
+ " [0.24279153, 0.75720847],\n",
+ " [0.18915732, 0.81084268],\n",
+ " [0.17282951, 0.82717049],\n",
+ " [0.13897531, 0.86102469],\n",
+ " [0.72746882, 0.27253118]])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "class Softmax:\n",
+ " def forward(self,z):\n",
+ " zmax = z.max(axis=1,keepdims=True)\n",
+ " expz = np.exp(z-zmax)\n",
+ " Z = expz.sum(axis=1,keepdims=True)\n",
+ " return expz / Z\n",
+ "\n",
+ "softmax = Softmax()\n",
+ "softmax.forward(net.forward(train_x[0:10]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Artık çıktılar olarak olasılıklar aldığımızı görebilirsiniz, yani her çıktı vektörünün toplamı tam olarak 1'dir.\n",
+ "\n",
+ "2'den fazla sınıfımız olması durumunda, softmaks bunların hepsinde olasılıkları normalleştirir. İşte MNIST rakam sınıflandırması yapan bir ağ mimarisi diyagramı:\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Çapraz Entropi Kaybı\n",
+ "\n",
+ "Sınıflandırmadaki bir kayıp fonksiyonu, tipik olarak, **çapraz entropi kaybı** olarak genelleştirilebilen bir lojistik fonksiyondur. Çapraz entropi kaybı, iki rastgele olasılık dağılımı arasındaki benzerliği hesaplayabilen bir fonksiyondur. Bununla ilgili daha ayrıntılı tartışmayı [Wikipedia](https://en.wikipedia.org/wiki/Cross_entropy)'da bulabilirsiniz.\n",
+ "\n",
+ "Bizim durumumuzda, ilk dağılım ağımızın olasılıksal çıktısıdır ve ikincisi **bire bir** dağılım olarak adlandırılır, bu da belirli bir $c$ sınıfının 1'e karşılık gelen olasılığa sahip olduğunu belirtir (geri kalan her şey 0'dır). Böyle bir durumda çapraz entropi kaybı $-\\log p_c$ olarak hesaplanabilir, burada $c$ beklenen sınıftır ve $p_c$, sinir ağımız tarafından verilen bu sınıfa karşılık gelen olasılıktır.\n",
+ "\n",
+ "> Beklenen sınıf için ağ 1 olasılığını döndürürse, çapraz entropi kaybı 0 olur. Gerçek sınıfın olasılığı 0'a ne kadar yakınsa, çapraz entropi kaybı o kadar yüksek olur (ve sonsuza kadar gidebilir!)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_cross_ent():\n",
+ " p = np.linspace(0.01, 0.99, 101) # tahmin edilen olasılık p(y|x)\n",
+ " cross_ent_v = np.vectorize(cross_ent)\n",
+ " f3, ax = plt.subplots(1,1, figsize=(8, 3))\n",
+ " l1, = plt.plot(p, cross_ent_v(p, 1), 'r--')\n",
+ " l2, = plt.plot(p, cross_ent_v(p, 0), 'r-')\n",
+ " plt.legend([l1, l2], ['$y = 1$', '$y = 0$'], loc = 'upper center', ncol = 2)\n",
+ " plt.xlabel('$\\hat{p}(y|x)$', size=18)\n",
+ " plt.ylabel('$\\mathcal{L}_{CE}$', size=18)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def cross_ent(prediction, ground_truth):\n",
+ " t = 1 if ground_truth > 0.5 else 0\n",
+ " return -t * np.log(prediction) - (1 - t) * np.log(1 - prediction)\n",
+ "plot_cross_ent()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Çapraz entropi kaybı yine ayrı bir katman olarak tanımlanacak, ancak `forward` (ileri) işlevi iki girdi değerine sahip olacaktır: Ağın önceki katmanlarının çıktısı `p` ve beklenen sınıf `y`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1.429664938969559"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "class CrossEntropyLoss:\n",
+ " def forward(self,p,y):\n",
+ " self.p = p\n",
+ " self.y = y\n",
+ " p_of_y = p[np.arange(len(y)), y]\n",
+ " log_prob = np.log(p_of_y)\n",
+ " return -log_prob.mean() # tüm girdi örnekleri üzerinden ortalama\n",
+ "\n",
+ "cross_ent_loss = CrossEntropyLoss()\n",
+ "p = softmax.forward(net.forward(train_x[0:10]))\n",
+ "cross_ent_loss.forward(p,train_labels[0:10])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "> **ÖNEMLİ**: Kayıp işlevi, ağımızın ne kadar iyi (veya kötü) performans gösterdiğini gösteren bir sayı döndürür. Veri kümesinin tamamı veya veri kümesinin bir kısmı (minigrup) için bize bir sayı döndürmelidir. Bu nedenle, girdi vektörünün her bir bileşeni için çapraz entropi kaybını hesapladıktan sonra, tüm bileşenlerin ortalamasını almamız (veya toplamamız) gerekir - bu, `.mean()` çağrısıyla yapılır.\n",
+ "\n",
+ "## Hesaplamalı Çizge\n",
+ "\n",
+ "![](\"../images/ComputeGraph.png\")
\n",
+ "\n",
+ "Bu ana kadar ağın farklı katmanları için farklı sınıflar tanımladık. Bu katmanların bileşimi **hesaplamalı çizge** olarak gösterilebilir. Şimdi, belirli bir eğitim veri kümesi (veya bunun bir parçası) için kaybı aşağıdaki şekilde hesaplayabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.429664938969559\n"
+ ]
+ }
+ ],
+ "source": [
+ "z = net.forward(train_x[0:10])\n",
+ "p = softmax.forward(z)\n",
+ "loss = cross_ent_loss.forward(p,train_labels[0:10])\n",
+ "print(loss)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Kaybı En Aza İndirme Problemi ve Ağ Eğitimi\n",
+ "\n",
+ "Ağı $f_\\theta$ olarak tanımladıktan ve $\\mathcal{L}(Y,f_\\theta(X))$ kayıp fonksiyonu verildikten sonra, sabit eğitim veri kümemiz altında $\\mathcal{L}$'yi $\\theta$'nın bir fonksiyonu olarak düşünebiliriz. : $\\mathcal{L}(\\theta) = \\mathcal{L}(Y,f_\\theta(X))$\n",
+ "\n",
+ "Bu durumda, ağ eğitimi $\\theta$ bağımsız değişkeni altında $\\mathcal{L}$ en aza indirme (minimizasyon) problemi olacaktır:\n",
+ "$$\n",
+ "\\theta = \\mathrm{argmin}_{\\theta} \\mathcal{L}(Y,f_\\theta(X))\n",
+ "$$\n",
+ "\n",
+ "**Gradyan inişi** adı verilen iyi bilinen bir işlev eniyileme (optimizasyon) yöntemi vardır. Buradaki fikir, parametrelere göre kayıp fonksiyonunun bir türevini (çok boyutlu durumda **gradyan** olarak adlandırılır) hesaplayabilmemiz ve parametreleri, hatanın azalacağı şekilde değiştirebilmemizdir.\n",
+ "\n",
+ "Gradyan inişi şu şekilde çalışır:\n",
+ " * Parametreleri, $w^{(0)}$ ve $b^{(0)}$, bazı rasgele değerlerle ilkletin.\n",
+ " * Aşağıdaki adımı birçok kez tekrarlayın:\n",
+ "\n",
+ " $$\\begin{align}\n",
+ " W^{(i+1)}&=W^{(i)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial W}\\\\\n",
+ " b^{(i+1)}&=b^{(i)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial b}\n",
+ " \\end{align}\n",
+ " $$\n",
+ "\n",
+ "Eğitim esnasında eniyileme adımlarının tüm veri kümesi dikkate alınarak hesaplanması gerekir (kaybın tüm eğitim örnekleri üzerinden bir toplam/ortalama olarak hesaplandığını unutmayın). Bununla birlikte, gerçek hayatta **minigruplar** olarak adlandırılan veri kümesinin küçük kısımlarını alır ve bir veri alt kümesine dayalı olarak gradyanları hesaplarız. Alt küme her seferinde rastgele alındığından, bu yönteme **rasgele gradyan inişi** (SGD - RGİ) denir."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Geriye Yayma\n",
+ "\n",
+ "\n",
+ "\n",
+ "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n",
+ "\\begin{align}\n",
+ "\\zz{\\L}{W} =& \\zz{\\L}{p}\\zz{p}{z}\\zz{z}{W}\\cr\n",
+ "\\zz{\\L}{b} =& \\zz{\\L}{p}\\zz{p}{z}\\zz{z}{b}\n",
+ "\\end{align}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "$\\partial\\mathcal{L}/\\partial W$'ı hesaplamak için, yukarıdaki formüllerde görebileceğiniz gibi, bileşik bir fonksiyonun türevlerini hesaplamak için **zincirleme kuralını** kullanabiliriz. Aşağıdaki fikre karşılık gelir:\n",
+ "\n",
+ "* Verilen girdinin altında $\\Delta\\mathcal{L}$ kaybını elde ettiğimizi varsayalım.\n",
+ "* Bunu en aza indirmek için, softmaks çıktısı $p$'yı $\\Delta p = (\\partial\\mathcal{L}/\\partial p)\\Delta\\mathcal{L}$ değerine göre ayarlamamız gerekir.\n",
+ "* Bu, $z$ düğümünde $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$ tarafından yapılan değişikliklere karşılık gelir.\n",
+ "* Bu hatayı en aza indirmek için parametreleri buna göre ayarlamamız gerekiyor: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (ve $b$ için aynı)\n",
+ "\n",
+ "![](\"../images/ComputeGraphGrad.PNG\")
\n",
+ "\n",
+ "Bu işlem, kayıp hatasını ağın çıktısından parametrelerine geri dağıtmaya başlar. Bu nedenle işlem **geri yayma** olarak adlandırılır.\n",
+ "\n",
+ "Ağ eğitiminin bir geçişi iki bölümden oluşur:\n",
+ "* Belirli bir girdi minigrubu için kayıp fonksiyonunun değerini hesapladığımızda **ileriye geçiş** \n",
+ "* Bu hatayı hesaplamalı çizge aracılığıyla model parametrelerine geri dağıtarak en aza indirmeye çalıştığımızda **geriye geçiş**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Geri Yaymanın Uygulanması\n",
+ "\n",
+ "* Geriye geçiş sırasında türevi hesaplayacak ve hatayı yayacak olan düğümlerimizin her birine `backward` (geri) fonksiyonunu ekleyelim.\n",
+ "* Ayrıca yukarıda açıklanan prosedüre göre parametre güncellemelerini de uygulamamız gerekiyor.\n",
+ "\n",
+ "Her katman için türevleri elle hesaplamamız gerekir, örneğin doğrusal katman $z = x\\times W+b$ için:\n",
+ "$$\\begin{align}\n",
+ "\\frac{\\partial z}{\\partial W} &= x \\\\\n",
+ "\\frac{\\partial z}{\\partial b} &= 1 \\\\\n",
+ "\\end{align}$$\n",
+ "\n",
+ "Katmanın çıktısındaki $\\Delta z$ hatasını telafi etmemiz gerekirse, ağırlıkları buna göre güncellememiz gerekir:\n",
+ "$$\\begin{align}\n",
+ "\\Delta x &= \\Delta z \\times W \\\\\n",
+ "\\Delta W &= \\frac{\\partial z}{\\partial W} \\Delta z = \\Delta z \\times x \\\\\n",
+ "\\Delta b &= \\frac{\\partial z}{\\partial b} \\Delta z = \\Delta z \\\\\n",
+ "\\end{align}$$\n",
+ "\n",
+ "**ÖNEMLİ:** Hesaplamalar her eğitim örneği için bağımsız olarak değil, bir bütün **minigrup** için yapılır. Gerekli parametre güncellemeleri $\\Delta W$ ve $\\Delta b$ tüm minigrupta hesaplanır ve ilgili vektörlerin $x\\in\\mathbb{R}^{\\mathrm{minigrup}\\, \\times\\, \\mathrm{sınıfsayısı}}$ boyutları vardır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Linear:\n",
+ " def __init__(self,nin,nout):\n",
+ " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\n",
+ " self.b = np.zeros((1,nout))\n",
+ " self.dW = np.zeros_like(self.W)\n",
+ " self.db = np.zeros_like(self.b)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " self.x=x\n",
+ " return np.dot(x, self.W.T) + self.b\n",
+ " \n",
+ " def backward(self, dz):\n",
+ " dx = np.dot(dz, self.W)\n",
+ " dW = np.dot(dz.T, self.x)\n",
+ " db = dz.sum(axis=0)\n",
+ " self.dW = dW\n",
+ " self.db = db\n",
+ " return dx\n",
+ " \n",
+ " def update(self,lr):\n",
+ " self.W -= lr*self.dW\n",
+ " self.b -= lr*self.db"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Aynı şekilde, katmanlarımızın geri kalanı için `backward` (geri) işlevi tanımlayabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Softmax:\n",
+ " def forward(self,z):\n",
+ " self.z = z\n",
+ " zmax = z.max(axis=1,keepdims=True)\n",
+ " expz = np.exp(z-zmax)\n",
+ " Z = expz.sum(axis=1,keepdims=True)\n",
+ " return expz / Z\n",
+ " def backward(self,dp):\n",
+ " p = self.forward(self.z)\n",
+ " pdp = p * dp\n",
+ " return pdp - p * pdp.sum(axis=1, keepdims=True)\n",
+ " \n",
+ "class CrossEntropyLoss:\n",
+ " def forward(self,p,y):\n",
+ " self.p = p\n",
+ " self.y = y\n",
+ " p_of_y = p[np.arange(len(y)), y]\n",
+ " log_prob = np.log(p_of_y)\n",
+ " return -log_prob.mean()\n",
+ " def backward(self,loss):\n",
+ " dlog_softmax = np.zeros_like(self.p)\n",
+ " dlog_softmax[np.arange(len(self.y)), self.y] -= 1.0/len(self.y)\n",
+ " return dlog_softmax / self.p"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model Eğitimi\n",
+ "\n",
+ "Artık veri kümemiz üzerinden geçecek olan **eğitim döngüsü**nü yazmaya ve minigrup temelli eniyileme işlemini gerçekleştirmeye hazırız. Veri kümesinden tam bir geçişe genellikle **dönem** denir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "İlk doğruluk: 0.725\n",
+ "Nihai doğruluk: 0.825\n"
+ ]
+ }
+ ],
+ "source": [
+ "lin = Linear(2,2)\n",
+ "softmax = Softmax()\n",
+ "cross_ent_loss = CrossEntropyLoss()\n",
+ "\n",
+ "learning_rate = 0.1\n",
+ "\n",
+ "pred = np.argmax(lin.forward(train_x),axis=1)\n",
+ "acc = (pred==train_labels).mean()\n",
+ "print(\"İlk doğruluk: \",acc)\n",
+ "\n",
+ "batch_size=4\n",
+ "for i in range(0,len(train_x),batch_size):\n",
+ " xb = train_x[i:i+batch_size]\n",
+ " yb = train_labels[i:i+batch_size]\n",
+ " \n",
+ " # forward pass\n",
+ " z = lin.forward(xb)\n",
+ " p = softmax.forward(z)\n",
+ " loss = cross_ent_loss.forward(p,yb)\n",
+ " \n",
+ " # backward pass\n",
+ " dp = cross_ent_loss.backward(loss)\n",
+ " dz = softmax.backward(dp)\n",
+ " dx = lin.backward(dz)\n",
+ " lin.update(learning_rate)\n",
+ " \n",
+ "pred = np.argmax(lin.forward(train_x),axis=1)\n",
+ "acc = (pred==train_labels).mean()\n",
+ "print(\"Nihai doğruluk: \",acc)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Bir dönemde modelin doğruluğunu %50 civarından yaklaşık %80'e nasıl çıkarabileceğimizi görmek güzel.\n",
+ "\n",
+ "## Ağ Sınıfı\n",
+ "\n",
+ "Çoğu durumda sinir ağı yalnızca katmanların bir bileşimi olduğundan, bu mantığı açıkça programlamadan katmanları bir araya getirmemize ve bunlar arasında ileri ve geri geçişler yapmamıza izin verecek bir sınıf oluşturabiliriz. Katmanların listesini `Net` (Ağ) sınıfının içinde saklayacağız ve yeni katmanlar eklemek için `add()` (ekle()) işlevini kullanacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "class Net:\n",
+ " def __init__(self):\n",
+ " self.layers = []\n",
+ " \n",
+ " def add(self,l):\n",
+ " self.layers.append(l)\n",
+ " \n",
+ " def forward(self,x):\n",
+ " for l in self.layers:\n",
+ " x = l.forward(x)\n",
+ " return x\n",
+ " \n",
+ " def backward(self,z):\n",
+ " for l in self.layers[::-1]:\n",
+ " z = l.backward(z)\n",
+ " return z\n",
+ " \n",
+ " def update(self,lr):\n",
+ " for l in self.layers:\n",
+ " if 'update' in l.__dir__():\n",
+ " l.update(lr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Bu `Net` sınıfı ile model tanımımız ve eğitimimiz daha düzenli zarif geliyor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "İlk kayıp=0.6212072429381601, doğruluk=0.6875: \n",
+ "Nihai kayıp=0.44369925927417986, doğruluk=0.8: \n",
+ "Test kaybı=0.4767711377257787, doğruluk=0.85: \n"
+ ]
+ }
+ ],
+ "source": [
+ "net = Net()\n",
+ "net.add(Linear(2,2))\n",
+ "net.add(Softmax())\n",
+ "loss = CrossEntropyLoss()\n",
+ "\n",
+ "def get_loss_acc(x,y,loss=CrossEntropyLoss()):\n",
+ " p = net.forward(x)\n",
+ " l = loss.forward(p,y)\n",
+ " pred = np.argmax(p,axis=1)\n",
+ " acc = (pred==y).mean()\n",
+ " return l,acc\n",
+ "\n",
+ "print(\"İlk kayıp={}, doğruluk={}: \".format(*get_loss_acc(train_x,train_labels)))\n",
+ "\n",
+ "def train_epoch(net, train_x, train_labels, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\n",
+ " for i in range(0,len(train_x),batch_size):\n",
+ " xb = train_x[i:i+batch_size]\n",
+ " yb = train_labels[i:i+batch_size]\n",
+ "\n",
+ " p = net.forward(xb)\n",
+ " l = loss.forward(p,yb)\n",
+ " dp = loss.backward(l)\n",
+ " dx = net.backward(dp)\n",
+ " net.update(lr)\n",
+ " \n",
+ "train_epoch(net,train_x,train_labels)\n",
+ " \n",
+ "print(\"Nihai kayıp={}, doğruluk={}: \".format(*get_loss_acc(train_x,train_labels)))\n",
+ "print(\"Test kaybı={}, doğruluk={}: \".format(*get_loss_acc(test_x,test_labels)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Eğitim Sürecinin Görselleştirilmesi\n",
+ "\n",
+ "Ağın nasıl eğitildiğini görsel olarak görmek güzel olurdu! Bunun için bir `train_and_plot` (eğit ve çizdir) fonksiyonu tanımlayacağız. Ağın durumunu görselleştirmek için seviye haritası kullanacağız, yani farklı renkler kullanarak ağ çıktısının farklı değerlerini temsil edeceğiz.\n",
+ "\n",
+ "> Aşağıdaki çizim kodlarından bazılarını anlamadıysanız endişelenmeyin - altında yatan sinir ağı kavramlarını anlamak daha önemlidir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def train_and_plot(n_epoch, net, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\n",
+ " fig, ax = plt.subplots(2, 1)\n",
+ " ax[0].set_xlim(0, n_epoch + 1)\n",
+ " ax[0].set_ylim(0,1)\n",
+ "\n",
+ " train_acc = np.empty((n_epoch, 3))\n",
+ " train_acc[:] = np.NAN\n",
+ " valid_acc = np.empty((n_epoch, 3))\n",
+ " valid_acc[:] = np.NAN\n",
+ "\n",
+ " for epoch in range(1, n_epoch + 1):\n",
+ "\n",
+ " train_epoch(net,train_x,train_labels,loss,batch_size,lr)\n",
+ " tloss, taccuracy = get_loss_acc(train_x,train_labels,loss)\n",
+ " train_acc[epoch-1, :] = [epoch, tloss, taccuracy]\n",
+ " vloss, vaccuracy = get_loss_acc(test_x,test_labels,loss)\n",
+ " valid_acc[epoch-1, :] = [epoch, vloss, vaccuracy]\n",
+ " \n",
+ " ax[0].set_ylim(0, max(max(train_acc[:, 2]), max(valid_acc[:, 2])) * 1.1)\n",
+ "\n",
+ " plot_training_progress(train_acc[:, 0], (train_acc[:, 2],\n",
+ " valid_acc[:, 2]), fig, ax[0])\n",
+ " plot_decision_boundary(net, fig, ax[1])\n",
+ " fig.canvas.draw()\n",
+ " fig.canvas.flush_events()\n",
+ "\n",
+ " return train_acc, valid_acc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.cm as cm\n",
+ "\n",
+ "def plot_decision_boundary(net, fig, ax):\n",
+ " draw_colorbar = True\n",
+ " # önceki çizimi sildir\n",
+ " while ax.collections:\n",
+ " ax.collections.pop()\n",
+ " draw_colorbar = False\n",
+ "\n",
+ " # kontur ızgarası oluştur\n",
+ " x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 1\n",
+ " y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 1\n",
+ " xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
+ " np.arange(y_min, y_max, 0.1))\n",
+ " grid_points = np.c_[xx.ravel().astype('float32'), yy.ravel().astype('float32')]\n",
+ " n_classes = max(train_labels)+1\n",
+ " while train_x.shape[1] > grid_points.shape[1]:\n",
+ " # boyutları dolgula (yalnızca ilk ikisini çizin)\n",
+ " grid_points = np.c_[grid_points,\n",
+ " np.empty(len(xx.ravel())).astype('float32')]\n",
+ " grid_points[:, -1].fill(train_x[:, grid_points.shape[1]-1].mean())\n",
+ "\n",
+ " # tahminleri değerlendir\n",
+ " prediction = np.array(net.forward(grid_points))\n",
+ " # for two classes: prediction difference\n",
+ " if (n_classes == 2):\n",
+ " Z = np.array([0.5+(p[0]-p[1])/2.0 for p in prediction]).reshape(xx.shape)\n",
+ " else:\n",
+ " Z = np.array([p.argsort()[-1]/float(n_classes-1) for p in prediction]).reshape(xx.shape)\n",
+ " \n",
+ " # konturu çizdir\n",
+ " levels = np.linspace(0, 1, 40)\n",
+ " cs = ax.contourf(xx, yy, Z, alpha=0.4, levels = levels)\n",
+ " if draw_colorbar:\n",
+ " fig.colorbar(cs, ax=ax, ticks = [0, 0.5, 1])\n",
+ " c_map = [cm.jet(x) for x in np.linspace(0.0, 1.0, n_classes) ]\n",
+ " colors = [c_map[l] for l in train_labels]\n",
+ " ax.scatter(train_x[:, 0], train_x[:, 1], marker='o', c=colors, s=60, alpha = 0.5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_training_progress(x, y_data, fig, ax):\n",
+ " styles = ['k--', 'g-']\n",
+ " # önceki çizimi sildir\n",
+ " while ax.lines:\n",
+ " ax.lines.pop()\n",
+ " # güncellenmiş doğruları çiz\n",
+ " for i in range(len(y_data)):\n",
+ " ax.plot(x, y_data[i], styles[i])\n",
+ " ax.legend(ax.lines, ['eğitim doğruluğu', 'geçerleme doğruluğu'],\n",
+ " loc='upper center', ncol = 2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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wpbtKUVDQ0KhqrgIbHNh8AJ/qw6AzBI7Tl6PnTgQSjIgesdvt3Hbbbdxyyy2oqsopp5yCw+Fg9erVREZGcvXVV/OLX/yCJ554gkWLFnH99deTl5fHyy+/DHQ8BfLAgQMpKChg48aNZGRkYLfb2/w67C0LFizg8ccfZ+HChfziF78gNze3TY7JrbfeypQpU3jggQeYP38+33zzDU8//XSg73/EiBGcffbZ/OIXv+C5557DaDRy8803Y7Vau0yQnDZtGjabjTvvvJObbrqJtWvXdprjAnDmmWeycOFCPv74YwYPHsyf/vSnoEAjlPpYrVamT5/OH/7wh0D+0O9+97tuX7/ucPvclDaVUtNcE9iWYEsgNTK1Xzdt2+12rr76an7zm98QFxdHUlIS99xzDzqdLnA9hw0bxo9//GOuuuoqHn/8cSZOnEh1dTWff/45Y8eO5bzzzmPRokWMHTuWG264gQULFmAymVixYgU//OEPSUhI6PK7FKp7772XX/3qV0RFRTF37lzcbjfr16+nrq6OhQsX9vg6dPe4kZGRXHvttfzmN78hPj6e5ORk7rrrrqBW2qFDh3LRRRdx/fXX89xzz2G327njjjtIT08PdI3edNNNnHrqqTz55JNcdNFFfPrppyxdujSk5OMzzzyTV155hQsuuIDY2NjWxNdOhuEOGTKEzMxM7r33Xh588EH27NnTZuRce/VZ9sky9Do9w+OHU+woJmdWDq898xqLn17MWaeexSfvfcLWrVvbdLmJYBKMiB574IEHSEpKYvHixeTn5xMTE8OkSZO48847gdYkt3/961/ceuut/OlPf2LGjBncdddd3HDDDR0GGJdeeinvvfceZ5xxBvX19bz88stBw1Z704ABA3j33Xe55ZZbWLJkCVOnTuXhhx8OSjKdNGkSb7/9NnfffTcPPPAAqamp3H///UF1eu2117j22muZNWsWKSkpPPzww2zbti1otFN74uLi+Mc//sFvfvMbnn/+ec4++2zuvfdefv7znwfK+FU/XtXL/vr9AJx5yZlcsvYSfnLlT9Ab9Fz7f9cy7ZRpNHmaAmUe+stD3H7T7cycNZPEpER+8/vfsHnrZpyqM1DmgScf4Lc3/ZacyTkMGjKIO+67g6suuYrypvJAmd6iaip1LXVotDZdx1hiSLenYzV2Pb9Mf/DEE0+wYMECzj//fKKiovjtb39LUVFR0H/fl19+mQcffJBbb72VkpIS4uPjmTFjRmBCv2HDhrFs2TLuvPNOxo8fD8C5557Lj370I6Dr71KorrvuOmw2G48++ii//e1viYiIYOzYsd2e9bQ3jvvoo4/S1NTEhRdeiN1u59Zbb6WhoSGozMsvv8yvf/1rzj//fDweD6eeeipLly4NtCLMmjWLZ599lvvuu48777yTc889l1tuuYW//vWvXdZ50aJF5Ofnc/755xMdHc0DDzzQacuI0WjkzTff5P/+7/8YP348U6ZM4cEHHwzK9emsPnaznREJI0i9OJXtC7fz5INP8oj7EeZdPo958+exa/uudr9bfo+fmuYaXvr8Jep99V1+rnCIscTw6JxH+/QcinYcZJE5HA6io6NpaGggKioq3NXpVS6Xi4KCArKzs7t8eJ0IHnroIZ599lmKiorCXZU+U1xcTGZmJp9++mlgBEZ3+dXWCa3Km8qPep6NitIKzp9yPn/951+ZOjt8U1fbTXYyojJCGsXSnzmdTtLT03n88ce59tpru71/UVERV155Zb+Y0+V4dP3117Nz506++uqrcFcFaL8+mqZR01JDaWMpHr+n8wP4oLqkmgWrFnDAeaCPa9sz6fZ0ihcW92jfUJ/f0jIi+tSSJUuYMmUK8fHxrFq1ikcffZQbb7wx3NXqVZ9//jlNTU2MHTuWsrIyfvvb3zJw4EBOPfXUbh9L1VSqnFWUNZXhU31Aa15FrCX00TBff/k1TqeTkaNGUlFRwYO/f5DMrEzOP+f8sPVbR5giiDIfnz8k8vLy2LlzJ1OnTqWhoYH7778foM0oq1Ds3bsXt9vNunXrqK2tJS4urrere8J57LHHOOecc4iIiOC///0vr776atAQ6f5YH0VRSLAlEGeNo7alttPp5r0eLz6Lj1um34JT63i+qHCym/p+FmcJRkSf2rNnDw8++CC1tbUMGDCAW2+9lUWLFoW7Wr3K6/Vy5513kp+fj91uZ+bMmbz++uvdevC390vKYrCQbk/v9oRfUcYo7nvgvqD6vPPPdxgQN6DrnUW7HnvsMXbt2oXJZCInJ4evvvqKhISEbh/noYce4o033uDss8/ul8Ot+6O1a9fyyCOP0NjYyKBBg3jqqae47rrrjov66BRd6wibTrhcLpwWJ78Y+YuTonW8I9JNE2YnejdNe0PzxCGaplHvqqeksQSXzwWASW8iNTKVBFuCXDshTnAn+jNAumlE2DkcDkpKSsjKysJma53XoaGhgf3792M2m9t9GQyGk+YB7HA7KHGU4PS2Ns0adAZSIlNIikhCp8iyUUKIk0eP/sVbsmRJIIo72GTZmddff53x48djs9lITU3lpz/9KTU1NZ3uI45fTqeT3bt3s3v3bpxOJ6WlpYH33G43Xq+XpqYmampqKC0tpaCggJ07d7Jp06ag+6KlpYWKigrq6+tpaWk5ZvOP9DWnx8numt3srtmN0+tEp+hIjUxlTNIYUiJTJBARQpx0ut0y8tZbb3HzzTezZMkSZs2axXPPPcfcuXPZvn17YHKbw3399ddcddVV/OlPf+KCCy6gpKSEBQsWcN111/H+++/3yoc4ERwHvWVdcrlclJSUUFfXutqnoigkJiaSmpoaKBMfH09ERARut7vNy+PxBA35bWpqajPqxmg0YjabMZlMpKSkBFpcVFUNrPTbX7m8LkoaS6hzfXd9UEiMSCQ1MrVfTvglhOh7J8K//b2h2zkj06ZNY9KkSUEroY4cOZJ58+axePHiNuUfe+wxnnnmGfbt2xfY9pe//IVHHnkk5OGdJ3LOiN/vZ/fu3SQlJQXWsTgeFRcXU15eHvg7Pj6etLS0bk1YdmRA0dDQQHV1NS6PC7fXjeoPHuI6aNCgwMyltbW1lJaWYjKZ2n2ZzeawBSqqplLhrKC6+dDihPHWeNLsaf16wi9x/NM0DZ/P1+GyCAaDITARmKqqnU5b3p2yer0eg8HQp2U1TcPj6XjYbHfK6nS6QMJ5d8pCa2vv0ZRtbGyktLSU+Pj4oOUAioqKOvzvZjKZgn7kFRcXd9hybDAYgo5bWlra4TXW6/VBixmWlZWhqmqbZQq6o09yRjweD7m5udxxxx1B2+fMmcPq1avb3WfmzJncddddLF26lLlz51JZWcm//vUvvv/973d4noO/lA//MCcqvV5PTExMYG0Zmy30pc37k4N1ttvtJCcnY7FY0DQNl8vV42PqDXp0UTpcLa7AhFmHy2/Ih8PnUIoD93f/A8Dz3asfsZvsJEckYzFa0HxaIGlViJ5SVRWPx4PH48Hr9RIZGRn4EXAwSO9Ienp6YFRPY2MjBw50PM9Fampq4AeT0+nsdAKxpKQkkpKSAGhubiY/P7/DsomJiYHF+lwuF3v37u2wbHx8fOAh7PF42L17d4dlY2NjAw9Rr9fLrl27OiwbExMTeAirqsr27ds7LBsVFRXUC7B169YOy0ZERJCdnR34e9u2bW1aQlwuF8uWLWPlypVBc89Mnjy5wzXHcnJygqbFnz17dodrjo0YMSKw8CC0Pq+3bdvWbtkBAwYE3QPz5s2jvr6+02vXW7oVjFRXV+P3+wM3zkHJyclBv4oPd3CY4/z583G5XPh8Pi688EL+8pe/dHiexYsXt7uGw4kqJSUF6Hyxu/5EVVUcDgcmkynQTaJpGgaDAVVVg9aD6NHxNRWH24HD7ehwobIOabQbuBy+f1eNgX0RDJr1ZmIsMagGlbK6o7s+4uRz+Kg0j8eDw+HA5/Ph8/na/CKOj48PLNDncrmorq7u8J7WNC2wnEBLS0unuXwHv/cHj9tZWb/fj9PpDNS3s7I+n4/m5magNWjorKzH4wn8wPH5fJ2WPdj1e7A+nZV1uVyB1gJN0zote2T+WmdlD16Dw8seudBmTU0Nr7zyCiNGjAgqa7VaO1xg8cgWZ4vF0mHZI0fodKes2Ww+ZiN8utVNU1paSnp6OqtXrw5aGvqhhx7i73//Ozt37myzz/bt2zn77LO55ZZbOPfccykrK+M3v/kNU6ZM4cUXX2z3PO21jGRmZp6Q3TSH8/v9/Xp1R4/Hw9tvv82SJUuora1l4MCB/Oc//+m1ibQ8Pg9vb3ubZ3KfCaxhMipxFLdMv4VTBpzSa0HCihUrWLlyJUVFRRQXF1NSUhJ03T/77LPAL6o//elPPPfccx0ea/ny5YEF1B5//HFeeOGFdsvFxMTw/PPPB5ZuLygooLy8nMzMTFJSUgLNyeHS3NxMcXFx4FVSUsIdd9wRuOa///3veeeddzrcf+XKlYFfwg8//DCvvfZah2U/+eQTBg4cCHR9fd9///3AomnPPvssTz75ZIdl33zzzcD6H6+++mq73cYHvfjii8yaNQtozYO75557Oiz79NNPc/bZZwPwn//8h9/85jcdln3sscc4//zzAfj00087neDv/vvv57LLLgNac+s6mzvjD3/4A/PmzQNg1apVbWZ+jYiIIDMzk8zMTC677LLAgn1erxdN0zCZTB0eW4SXyWRqs7L6iaRPumkSEhLQ6/VtWkEqKyvbtJYctHjxYmbNmhX4Ao8bN46IiAhmz57Ngw8+GNTvddDBYZ4nG71e3+lCTuHi9/t5/fXXufvuuwNNeEOGDGHBggVEREQc9RfJr/p5Y8sb3P3F3YG1G4bEDeHBMx7kh6N/2OujS+bOncvcuXMPnd/vp6SkhPz8fPbt28fAgQMD/x0KCgo6bbrW6/WBXw6dNXMfOHCApKSkQNk33niDBx98MHCMrKwssrOzGTRoEIMGDeJnP/tZ4OHeG1RVDfyYOBhg/PnPf+att94iPz+fioqKNvssWLAg8P2sr6/v9DoYDIbAZ3M4HL1yzaC1lepgWafT2WlZTdMCZVtaWjotq6pqoKzb7e60rM/nC5T1eDydlvV4PIGyPp+v07JutztQVlXVTsvu2LGDyy+/HIDRo0fzi1/8InCvZGdnEx8f326wfiLOWyFOTD1KYM3JyQma/nbUqFFcdNFF7f4SufTSSzEYDLz11luBbd988w0zZ86kpKSEtLS0Ls95Iiew9nfLli3j5ptvDvQ5pqamcs899/Czn/3sqFtENE3jP7v/w52f3cm2qtY+zNTIVO457R5+NvFn/WaESVNTEy0tLe2+FxcXFwhcOiqnaRrl5eWMHj06UPaRRx7h5ZdfpqCgoN2ktvz8/EBf8x//+MdOp79evXp1oCXnwQcfbNM6o2kaFRUVeDweysvLAz8cbrvttqBVSaOjoxk8eHDgIbdw4cJA2cbGxk7zf0K5DgfFxsYGWoKcTmegif5oy8bExATuyebm5jZN5IeLjo4OtBa0tLTQ1NTUYdmoqKjAjyOXy0VjY2OHZe12e1CQ01m+W2RkZKC53OPxtFlE7nCHfzYhjid9NunZwoULufLKK5k8eTIzZszg+eefp7CwkAULFgCtKyWWlJQEmmkvuOACrr/+ep555plAN83NN9/M1KlTQwpExLF1MOn04D+SJpOJHTt2EBMTw6JFi7jxxhsDeSJH48v9X7Los0V8U/wN0Loq5KJTFnHj1BuxGY/++L0pMjIy0Aff03JHtnL89re/5be//W0gxyY/Pz/odbDrB2DLli0UFhZ2eN7D+6/r6+s7LGswGCguLg4EGD/+8Y+ZPn16oEWms+nJ7XY7dnto61OEer2gtXvh4Iio3ixrs9lCvk8765s/ksViCbm1wWw2k5iYGFJZk8kUclkhTkQ9mg5+yZIlPPLII5SVlTFmzBj+9Kc/BRYFu+aaa9i/f39QVvBf/vIXnn32WQoKCoiJieHMM8/kj3/8Y8jDhaRlpO/V1NTw97//neeff545c+YE+uY1TeOVV17hkksuITo6+qjPk1eWx52f38knez8BwGqwcvP0m/nNzN8Qa5W1OtqTn59PbW1th++PHTs28Mu9qKio3S6XhIQEMjIywp6bIoQ4uYT6/Ja1aU5imqaxcuVKnn/+ed59991Ad0FGRgb79+/v1fyVvbV7+f2K3/PPrf8EWqc+//mkn/O7U39Hqr1t3pAQQojjn6xNc4IobSzlL9/+hczoTK6deG2vTZL1wgsv8NhjjwWN058wYQI///nPueKKKwKBSGljKU99+xQljSU9PleTp4mPdn+ET/UBcMXYK7j/9PsZHDf46D6EEEKIE4IEI/1UXUsdf1z1R5769ilafK3JgI+seoT7z7ifH4/9MXpd91otjpyv4+DaMREREVxxxRX8/Oc/JycnJ/B+e+c/Wt8f+n0eOvMhxqeM75XjCSGEODFIN00/4/Q4eerbp/jjqj/S4G7Nrp+WPo3ChkLKmlonyxqdOJqHznyIC4df2OXcG6qq8pe//IWnn36aJUuWcM455wCwb98+Pv/8cy6//PKgxMT2zj8rcxYXj7j4qOb5mJExgxmZM7ouKIQQ4oQhOSPHGY/fw982/I0HVj5AeVPrPC5jksbw8JkPc/6w82nxtfD02qdZ/PVi6l31AEzPmM7isxZz+sDT2z2m1+vlmmuu4Y033gBaR0/84x//CPn8Y5PGsvisxZw39Lzjcop6IYQQ4SXByHFC1VT+ufWf/H7F78mva12/YWDMQB444wF+NOZHbbpj6lrqeHT1ozy55slA98m5g8/l4bMeZlLqpEC55uZmLrvsMj7++GMMBgOPPfYY1157bZshl+2dPzsmu/X8Y38ky9kLIYToMQlG+jlN0/jv3v+y6LNFbK7YDEByRDK/P/X3XJ9zPSZ959M3lzWW8eDKB3l+w/OBxND5o+fzwBkPkGRI4oILLuCrr77CYrHw7rvvct5557V7/js/u5NNFZsC57/7tLu5btJ1XZ5fCCGE6IoEI/3Y14Vfs+izRXxd+DUAUeYofjvzt/x6+q+JNIU2WdRB+2r3cc8X9/DGljfQ0NAremLyY6h5r4ZoXTQfffQRp5xySpfnv33W7fx62q+JMIU2qZQQQgjRFQlG+qHNFZu56/O7+Gj3RwBYDBZumnoTt8+6nXhb/FEde1P5Ju76/C4+3vNx6wYfXDPiGh6/5HHirHEdnv9XU3/F7afcHigjhBBC9BYJRvqR/Lp87l5xd1DrxbUTr+Xu0+4mPSq0WWhD9XXh19y+/HZWF68GINocza0zbmV37W5e3/x6n59fCCGEOEiCkX6gvKmcB1c+yHO5zwXyOi4bfRkPnPEAw+KH9dp51q9fz6uvvsqf//xndDodmqaxdM9S7vz8zkA+ykF9cX4hhBCiPTIDaxjVu+p5dNWjPPntkzR7W1cZnTN4DovPWhw04qU3rFixggsvvJCmpiYGDhzIrbfeiqIofH/Y95k7dC7/3PpP/vD1H8iMzuSBMx7o9fMLIYQQR0taRnpRi/fQXCB1rjqgdcKyxWct5ozsM3r9fB988AGXX345brebM888kw8++CDklVWFEEKIviYtI8eQ1+/l5Y0vc9+X91HaWArAqMRRPHTmQ1w0/KI+mTDslVde4dprr0VVVS6++GLeeOONkJc2F0IIIfoTCUaOgqqp/Gv7v/jd579jT+0eAAZED+D+0+/nJ+N+0u31Y0L15JNPcssttwDw05/+lOeff16WhhdCCHHckidYD2iaxrJ9y7jz8zvZULYBgERbInfNvosFkxf02sq67SkoKOD2228H4NZbb+XRRx+VqdqFEEIc1yQY6aY1xWtY9Nkivtj/BQB2k53bZt7GLdNvwW7u+3yN7Oxs/vnPf7Jz507uuOMOCUSEEEIc9yQYCdG2ym3c9fld/HvXvwEw6U38csovWXTKIhIjEvv03B6Ph9LSUgYOHAjAxRdf3KfnE0IIIY4lCUa6sL9+P/d+cS+vbXoNDQ2douPq8Vdz7+n3MiB6QJ+fv7m5mR/84Ads3ryZVatWkZWV1efnFEIIIY4lCUY6UOms5OGvHuaZ9c/g8XsAuGTkJTx4xoOMTBx5TOpQX1/P+eefz6pVq7Barezbt0+CESGEECccCUaO4HA7eHz14zyx5gmaPE0AnDHwDBaftZhpGdOOWT0qKio499xz2bRpEzExMXz00UfMmjXrmJ1fCCGEOFYkGPmOy+diybolPPzVw9S01ACQk5rD4rMWc/ags49pouj+/fs555xz2Lt3L8nJySxbtoxx48Yds/MLIYQQx9JJH4z4VB+vbXqNe7+4lyJHEQDD4ofx0JkPcenIS4/5aJXdu3dzxhlnBBJWly9fzpAhQ45pHYQQQohj6aQNRjRN470d7/G7Fb9jZ/VOANLt6dx3+n1cPeFqDLrwXJqkpCQSExOJjY3lf//7H+npsqquEEKIE5uuJzstWbKE7OxsLBYLOTk5fPXVV52Wd7vd3HXXXWRlZWE2mxk8eDAvvfRSjyrcGz7L/4xpf5vGD975ATurdxJnjeOxcx5jz017uHbStWELRABiYmL43//+x5dffimBiBBCiJNCt5+6b731FjfffDNLlixh1qxZPPfcc8ydO5ft27czYED7Q10vu+wyKioqePHFFxkyZAiVlZX4fL6jrnx3rStZx52f38mn+Z8CEGGM4Jbpt3DbzNuItkQf8/oc9P7771NYWMivf/1rAJKTk8NWFyGEEOJY6/aqvdOmTWPSpEk888wzgW0jR45k3rx5LF68uE35Tz75hMsvv5z8/Hzi4uJ6VMmjXbV3Z/VOfvf573h3x7sAGHVGFkxewF2z7yI5MrwP/pdeeonrr78eVVVZvnw5Z599dljrI4QQQvSWUJ/f3eqm8Xg85ObmMmfOnKDtc+bMYfXq1e3u8+GHHzJ58mQeeeQR0tPTGTZsGLfddhstLS0dnsftduNwOIJePfXu9ncZvWQ07+54FwWFq8Zfxa4bd/HU3KfCHog8/vjjgZV3r732Wk4//fSw1kcIIYQIh25101RXV+P3+9t0IyQnJ1NeXt7uPvn5+Xz99ddYLBbef/99qqurueGGG6itre0wb2Tx4sXcd9993alah87MPpMocxSnZp3KQ2c+xJikMb1y3KOhaRq/+93vePjhhwH4zW9+wx//+EdZZ0YIIcRJqUeZmkc+NDVN6/BBqqoqiqLw+uuvEx3dmpfxxBNP8IMf/IC//vWvWK3WNvssWrSIhQsXBv52OBxkZmb2pKrEWmPZ+cudYW8FOcjv93PjjTfy7LPPAvCHP/whsAqvEEIIcTLqVjCSkJCAXq9v0wpSWVnZYdJlamoq6enpgUAEWnNMNE2juLiYoUOHttnHbDZjNpu7U7VO9ZdABOB///sfzz77LIqi8Oyzz/Lzn/883FUSQgghwqpbOSMmk4mcnByWL18etH358uXMnDmz3X1mzZpFaWkpTU1NgW27d+9Gp9ORkZHRgyof38477zzuv/9+/vnPf0ogIoQQQtCD0TRvvfUWV155Jc8++ywzZszg+eef54UXXmDbtm1kZWWxaNEiSkpKeO211wBoampi5MiRTJ8+nfvuu4/q6mquu+46TjvtNF544YWQznm0o2nCra6uDkVRiImJCXdVhBBCiGMm1Od3t3NG5s+fT01NDffffz9lZWWMGTOGpUuXBlaTLSsro7CwMFA+MjKS5cuXc9NNNzF58mTi4+O57LLLePDBB3vwsY4/27Zt40c/+hFRUVEsW7YMm80W7ioJIYQQ/Uq3W0bC4XhsGfH5fDzyyCPcd999eDwekpOTWblyJcOGDQt31YQQQohjok/mGRGh2bJlC9OnT+euu+7C4/Fw/vnnk5ubK4GIEEII0Q4JRnqRz+fjgQceICcnh9zcXGJjY/n73//Ohx9+KOvMCCGEEB04aVft7Sv/+c9/8Hq9XHjhhTz77LOkpqaGu0pCCCFEvybByFHyeDxomobZbMZgMPDyyy+zadMmfvSjH8mMqkIIIUQIpJvmKOTl5TF16lTuvffewLbRo0dzxRVXSCAihBBChEiCkR7weDzcfffdTJ06lU2bNvHyyy/T2NgY7moJIYQQxyUJRrpp/fr15OTk8MADD+Dz+fjBD37A5s2bsdvt4a6aEEIIcVySYCREbrebu+66i+nTp7N161YSExN5++23eeedd0hKSgp39YQQQojjlgQjIaqoqOCpp57C7/czf/58tm3bxg9/+MNwV0sIIYQ47slomhANGDCAJUuWYLPZuPTSS8NdHSGEEOKEIS0jHWhubuanP/0pX331VWDblVdeKYGIEEII0cukZaQd+/fv5+KLL2bjxo0sX76cvXv3YrFYwl0tIYQQ4oQkLSNH+PTTT8nJyWHjxo0kJibyj3/8QwIRIYQQog9JMPIdTdN49NFHOffcc6mtrWXy5Mnk5uZy+umnh7tqQgghxAlNumloHbZ71VVX8fbbbwPw05/+lCVLlkiLiBBCCHEMSMsIYDKZUBQFg8HAkiVLePHFFyUQEUIIIY4RRdM0LdyV6IrD4SA6OpqGhgaioqJ67biapgXWkHE6nWzdupVp06b12vGFEEKIk1moz++TsmVEVVUeeughLr/8cg7GYhERERKICCGEEGFw0uWMOBwOrr76aj744AOgNT/ke9/7XngrJYQQQpzETqpgZNeuXcybN4+dO3diMpn461//KoGIEEIIEWYnTTDy4YcfcuWVV+JwOEhPT+fdd9+VbhkhhBCiHzgpckaefPJJLrroIhwOB7NnzyY3N1cCESGEEKKfOCmCkalTp2I0Grnpppv47LPPSE5ODneVhBBCCPGdHgUjS5YsITs7G4vFQk5OTtBicp1ZtWoVBoOBCRMm9OS0PTZz5ky2bdvGU089hdFoPKbnFkIIIUTnuh2MvPXWW9x8883cdddd5OXlMXv2bObOnUthYWGn+zU0NHDVVVdx1lln9biyR2Po0KFhOa8QQgghOtftSc+mTZvGpEmTeOaZZwLbRo4cybx581i8eHGH+11++eUMHToUvV7PBx98wMaNG0M+Z19NeiaEEEKIvtMnk555PB5yc3OZM2dO0PY5c+awevXqDvd7+eWX2bdvH/fcc09I53G73TgcjqCXEEIIIU5M3QpGqqur8fv9bRJAk5OTKS8vb3efPXv2cMcdd/D6669jMIQ2knjx4sVER0cHXpmZmd2pphBCCCGOIz1KYD24nstBh6/xcji/388VV1zBfffdx7Bhw0I+/qJFi2hoaAi8ioqKelJNIYQQQhwHujXpWUJCAnq9vk0rSGVlZbvDZRsbG1m/fj15eXnceOONQOu6MJqmYTAYWLZsGWeeeWab/cxmM2azuTtVE0IIIcRxqlstIyaTiZycHJYvXx60ffny5cycObNN+aioKLZs2cLGjRsDrwULFjB8+HA2btwoE48JIYQQovvTwS9cuJArr7ySyZMnM2PGDJ5//nkKCwtZsGAB0NrFUlJSwmuvvYZOp2PMmDFB+yclJWGxWNpsF0IIIcTJqdvByPz586mpqeH++++nrKyMMWPGsHTpUrKysgAoKyvrcs4RIYQQQoiDuj3PSDjIPCNCCCHE8adP5hkRQgghhOhtEowIIYQQIqwkGBFCCCFEWEkwIoQQQoiwkmBECCGEEGElwYgQQgghwkqCESGEEEKElQQjQgghhAgrCUaEEEIIEVYSjAghhBAirCQYEUIIIURYSTAihBBCiLCSYEQIIYQQYSXBiBBCCCHCSoIRIYQQQoSVBCNCCCGECCsJRoQQQggRVhKMCCGEECKsJBgRQgghRFhJMCKEEEKIsJJgRAghhBBhJcGIEEIIIcJKghEhhBBChFWPgpElS5aQnZ2NxWIhJyeHr776qsOy7733Hueccw6JiYlERUUxY8YM/ve///W4wkIIIYQ4sXQ7GHnrrbe4+eabueuuu8jLy2P27NnMnTuXwsLCdsuvXLmSc845h6VLl5Kbm8sZZ5zBBRdcQF5e3lFXXgghhBDHP0XTNK07O0ybNo1JkybxzDPPBLaNHDmSefPmsXjx4pCOMXr0aObPn8/dd98dUnmHw0F0dDQNDQ1ERUV1p7pCCCGECJNQn9/dahnxeDzk5uYyZ86coO1z5sxh9erVIR1DVVUaGxuJi4vrsIzb7cbhcAS9hBBCCHFi6lYwUl1djd/vJzk5OWh7cnIy5eXlIR3j8ccfx+l0ctlll3VYZvHixURHRwdemZmZ3ammEEIIIY4jPUpgVRQl6G9N09psa8+bb77Jvffey1tvvUVSUlKH5RYtWkRDQ0PgVVRU1JNqCiGEEOI4YOhO4YSEBPR6fZtWkMrKyjatJUd66623uPbaa3nnnXc4++yzOy1rNpsxm83dqZoQQgghjlPdahkxmUzk5OSwfPnyoO3Lly9n5syZHe735ptvcs011/DGG2/w/e9/v2c1FUIIIcQJqVstIwALFy7kyiuvZPLkycyYMYPnn3+ewsJCFixYALR2sZSUlPDaa68BrYHIVVddxZ///GemT58eaFWxWq1ER0f34kcRQgghxPGo28HI/Pnzqamp4f7776esrIwxY8awdOlSsrKyACgrKwuac+S5557D5/Pxy1/+kl/+8peB7VdffTWvvPLK0X8CIYQQQhzXuj3PSDjIPCNCCCHE8adP5hkRQgghhOhtEowIIYQQIqwkGBFCCCFEWEkwIoQQQoiwkmBECCGEEGElwYgQQgghwkqCESGEEEKElQQjQgghhAgrCUaEEEIIEVYSjAghhBAirCQYEUIIIURYSTAihBBCiLCSYEQIIYQQYSXBiBBCCCHCSoIRIYQQQoSVBCNCCCGECCsJRoQQQggRVhKMCCGEECKsJBgRQgghRFhJMCKEEEKIsJJgRAghhBBhJcGIEEIIIcKqR8HIkiVLyM7OxmKxkJOTw1dffdVp+S+//JKcnBwsFguDBg3i2Wef7VFlhRBCCHHi6XYw8tZbb3HzzTdz1113kZeXx+zZs5k7dy6FhYXtli8oKOC8885j9uzZ5OXlceedd/KrX/2Kd99996grL4QQQojjn6JpmtadHaZNm8akSZN45plnAttGjhzJvHnzWLx4cZvyt99+Ox9++CE7duwIbFuwYAGbNm3im2++CemcDoeD6OhoGhoaiIqK6k51hRBCCBEmoT6/u9Uy4vF4yM3NZc6cOUHb58yZw+rVq9vd55tvvmlT/txzz2X9+vV4vd7unF4IIYQQJyBDdwpXV1fj9/tJTk4O2p6cnEx5eXm7+5SXl7db3ufzUV1dTWpqapt93G43brc78HdDQwPQGmEJIYQQ4vhw8LndVSdMt4KRgxRFCfpb07Q227oq3972gxYvXsx9993XZntmZmZ3qyqEEEKIMGtsbCQ6OrrD97sVjCQkJKDX69u0glRWVrZp/TgoJSWl3fIGg4H4+Ph291m0aBELFy4M/F1fX09WVhaFhYWdfhjRPofDQWZmJkVFRZJz0wNy/Y6OXL+jI9fv6Mj1OzpHe/00TaOxsZG0tLROy3UrGDGZTOTk5LB8+XIuvvjiwPbly5dz0UUXtbvPjBkz+M9//hO0bdmyZUyePBmj0djuPmazGbPZ3GZ7dHS03ExHISoqSq7fUZDrd3Tk+h0duX5HR67f0Tma6xdKI0K3h/YuXLiQv/3tb7z00kvs2LGDW265hcLCQhYsWAC0tmpcddVVgfILFizgwIEDLFy4kB07dvDSSy/x4osvctttt3X31EIIIYQ4AXU7Z2T+/PnU1NRw//33U1ZWxpgxY1i6dClZWVkAlJWVBc05kp2dzdKlS7nlllv461//SlpaGk899RSXXnpp730KIYQQQhy3epTAesMNN3DDDTe0+94rr7zSZttpp53Ghg0benIqoLXb5p577mm360Z0Ta7f0ZHrd3Tk+h0duX5HR67f0TlW16/bk54JIYQQQvQmWShPCCGEEGElwYgQQgghwkqCESGEEEKElQQjQgghhAirfh+MLFmyhOzsbCwWCzk5OXz11VfhrtJx4d5770VRlKBXSkpKuKvVr61cuZILLriAtLQ0FEXhgw8+CHpf0zTuvfde0tLSsFqtnH766Wzbti08le2Hurp+11xzTZt7cvr06eGpbD+zePFipkyZgt1uJykpiXnz5rFr166gMnL/dSyU6yf3X8eeeeYZxo0bF5jYbMaMGfz3v/8NvH8s7r1+HYy89dZb3Hzzzdx1113k5eUxe/Zs5s6dGzSPiejY6NGjKSsrC7y2bNkS7ir1a06nk/Hjx/P000+3+/4jjzzCE088wdNPP826detISUnhnHPOobGx8RjXtH/q6voBfO973wu6J5cuXXoMa9h/ffnll/zyl79kzZo1LF++HJ/Px5w5c3A6nYEycv91LJTrB3L/dSQjI4M//OEPrF+/nvXr13PmmWdy0UUXBQKOY3Lvaf3Y1KlTtQULFgRtGzFihHbHHXeEqUbHj3vuuUcbP358uKtx3AK0999/P/C3qqpaSkqK9oc//CGwzeVyadHR0dqzzz4bhhr2b0deP03TtKuvvlq76KKLwlKf401lZaUGaF9++aWmaXL/ddeR10/T5P7rrtjYWO1vf/vbMbv3+m3LiMfjITc3lzlz5gRtnzNnDqtXrw5TrY4ve/bsIS0tjezsbC6//HLy8/PDXaXjVkFBAeXl5UH3o9ls5rTTTpP7sRu++OILkpKSGDZsGNdffz2VlZXhrlK/1NDQAEBcXBwg9193HXn9DpL7r2t+v59//vOfOJ1OZsyYcczuvX4bjFRXV+P3+9usBpycnNxmFWDR1rRp03jttdf43//+xwsvvEB5eTkzZ86kpqYm3FU7Lh285+R+7Lm5c+fy+uuv8/nnn/P444+zbt06zjzzTNxud7ir1q9omsbChQs55ZRTGDNmDCD3X3e0d/1A7r+ubNmyhcjISMxmMwsWLOD9999n1KhRx+ze69F08MeSoihBf2ua1mabaGvu3LmB/z927FhmzJjB4MGDefXVV1m4cGEYa3Z8k/ux5+bPnx/4/2PGjGHy5MlkZWXx8ccfc8kll4SxZv3LjTfeyObNm/n666/bvCf3X9c6un5y/3Vu+PDhbNy4kfr6et59912uvvpqvvzyy8D7fX3v9duWkYSEBPR6fZvIq7Kysk2EJroWERHB2LFj2bNnT7irclw6OBJJ7sfek5qaSlZWltyTh7npppv48MMPWbFiBRkZGYHtcv+FpqPr1x65/4KZTCaGDBnC5MmTWbx4MePHj+fPf/7zMbv3+m0wYjKZyMnJYfny5UHbly9fzsyZM8NUq+OX2+1mx44dpKamhrsqx6Xs7GxSUlKC7kePx8OXX34p92MP1dTUUFRUJPckrb8yb7zxRt577z0+//xzsrOzg96X+69zXV2/9sj91zlN03C73cfu3uu1VNg+8M9//lMzGo3aiy++qG3fvl27+eabtYiICG3//v3hrlq/d+utt2pffPGFlp+fr61Zs0Y7//zzNbvdLteuE42NjVpeXp6Wl5enAdoTTzyh5eXlaQcOHNA0TdP+8Ic/aNHR0dp7772nbdmyRfvRj36kpaamag6HI8w17x86u36NjY3arbfeqq1evVorKCjQVqxYoc2YMUNLT0+X66dp2v/93/9p0dHR2hdffKGVlZUFXs3NzYEycv91rKvrJ/df5xYtWqStXLlSKygo0DZv3qzdeeedmk6n05YtW6Zp2rG59/p1MKJpmvbXv/5Vy8rK0kwmkzZp0qSgoVqiY/Pnz9dSU1M1o9GopaWlaZdccom2bdu2cFerX1uxYoUGtHldffXVmqa1Dq+85557tJSUFM1sNmunnnqqtmXLlvBWuh/p7Po1Nzdrc+bM0RITEzWj0agNGDBAu/rqq7XCwsJwV7tfaO+6AdrLL78cKCP3X8e6un5y/3XuZz/7WeA5m5iYqJ111lmBQETTjs29p2iapvVeO4sQQgghRPf025wRIYQQQpwcJBgRQgghRFhJMCKEEEKIsJJgRAghhBBhJcGIEEIIIcJKghEhhBBChJUEI0IIIYQIKwlGhBBCiDBbuXIlF1xwAWlpaSiKwgcffNDlPl9++SU5OTlYLBYGDRrEs88+2/cV7SMSjAghhBBh5nQ6GT9+PE8//XRI5QsKCjjvvPOYPXs2eXl53HnnnfzqV7/i3Xff7eOa9g2ZgVUIIYToRxRF4f3332fevHkdlrn99tv58MMP2bFjR2DbggUL2LRpE998880xqGXvMoS7AqFQVZXS0lLsdjuKooS7OkIIIfoxTdNobGwkLS0Nna5vOgBcLhcej6fLehz5zDKbzZjN5qM+/zfffMOcOXOCtp177rm8+OKLeL1ejEbjUZ/jWDougpHS0lIyMzPDXQ0hhBDHkaKiIjIyMnr9uC6Xi+yBqZRX1HdaLjIykqampqBt99xzD/fee+9R16G8vJzk5OSgbcnJyfh8Pqqrq0lNTT3qcxxLx0UwYrfbASjY/RRRdmuPjuFxr2/9P6ac3qpWt7S416EzTemVYzncuVi6eaw690YAbCHsV+naAkCMeWKn5UpbthFvGd/h+0XNO0myjGn3vf3O3aTbRrbZvrdxLwADIka0eW+XYx+DIocFbdvhKGBo5JA2ZTc37GekfXDQtk11BxgdPTBo28aaIsbGZAVt21BdzIS4Q8FvblUJE+ODg+HcihJyEtMP/V1eSk5SelCZDaVl5KSkHfq7pDTob4ANxaVMSj20La+ojEnpwf+IbDxQxsTMQ9s2FpQBMDHr0LZN+WVMyAreb/O+MiZkH7FtT+u+4wcFb9+yq5RxQ4LrtnVXCQDjhgZvB9iyvYRxw9tu37qthLEj224H2La5mDGj0tt9r/X9IgDGjOm4DMC2vEJGjw3tAbM9dz+jxg8IqWxgn/X5AIyalNVFyRCO9e1eRuUMPOrj9NS2b3YzaurgrgueYJpbmrnm9h8Fnh29zePxUF5R3+kzydHYQvawX1FUVERUVFRge2+0ihx0ZKvLwayL47EH4bgIRg5e2Ci7lagoWw+Pcioe97dg6lkwc7QMbhP6Xjq35jJjMXfvOtiZSa0rl4gQ9rNHTaOiZSORls7LRhgsRFo7LmPTW4i0RrT/ns5ChK3te+Pt49nduJuIiLbvWTUrEZHB2yfbx7C1YR/D7cFBisVvw2YPLmv22rDZI4O3udvZ5greZm62sd1Vw+SEQw8nc5MNW+ShMrOHDGNtWTFTkw8FLeaI4DJmm42tjnqmpmUEbTu8zKyRQ1l/oIQpmenBZSIOlZk5Zii5+SVB22aMHcqGvSXkZB+2n9XGjvIGcgYf2jZ9wlDydgXve7DskdumThrO5u1FbbYDWCw2bLb2t+8pqGf86LbBwpTpI9iysZBxY9pv5ZwyfSRb8g60e9ygc5jbP3fHZdu/Bzsy+dSxbP12L7YO7t3usJis5G+pYMzUQUd9rJ6evzc+x/Gqrx/KoTyToqKigoKR3pKSkkJ5eXnQtsrKSgwGA/Hx8b1+vr4mo2lE2BQ3bw13FUJyeIBxNKZk9ry5eH1hSbf3mTi08xaGvjBurHSnHm7MzGFdFxKiB2bMmMHy5cuDti1btozJkycfd/kiIMHIMWMzT8fvWdNrx3O5e+9YR6O6Ja/T98tbNrW7fVBk2y6aUOxt2hly2W2OvUF/T4obyOb6/DblNtYVdLseU1MyWVtZ1GmZKenprC0r7vJY60o6L3N4a0dfmTAyg7zdXde1v9iysTDksls3HOjDmvR/W7/ZE+4qiBA0NTWxceNGNm7cCLQO3d24cSOFha33+qJFi7jqqqsC5RcsWMCBAwdYuHAhO3bs4KWXXuLFF1/ktttuC0f1j9rJF4x41oW7Bkct2tLz3BNnN4KYetf6Tt9Pt43t9P2siFEhn+tww+zD2O/c3mb7yKihIR9jQkx2SOVyEtrmBUxJHEBuzbF5gE0Z0LPWkpxB6azf3/3WkoM27A1934072wYp48dmsnFH3wQvmzd1HuSNnTww5GMdTffI1vXdD1L7m7GntM29Er3Ho2l4NLWDV/dmzVi/fj0TJ05k4sTWXL2FCxcyceJE7r77bgDKysoCgQlAdnY2S5cu5YsvvmDChAk88MADPPXUU1x66aW99wGPoZMqGDGZp4W7CmEVZwk9eTfZOqHvKtLLxkQPZlfj7j4/z/rqzgOUqakZrK3oorUkM4O1pZ0/xCdnpbOuqPuBxqQh6eQWBO83cWg6ufuO2DY89JaWcaN61u2yaVv7n3HshAFs3trxNRo78eiTRnvDmGltk6J7fKyZw9i6tm2LnBCHO/3009E0rc3rlVdeAeCVV17hiy++CNrntNNOY8OGDbjdbgoKCliwYMGxr3gvOamCEdH/nGx5I0ejJ3kj4SB5I0KI7pJg5Bg6GfNGsiJG9fu8kZyErLDmjUwZkCF5Iz0geSOhk7wR0d+dnMGI5I2EXPZo80Z66ljmjbTnWOaN9FRHeSNHdtV0JJx5I5111YDkjfQmyRvpO26t85cI3UkXjEjeiOSNHI3jIW/kSP0xb6QzkjcixMnnpAtG+oPe7Ko5EUjeSOgkb0QIcSKSYOQYs5mn9+rxJG+ka5I3Erqe5I301RDfUEjeSOgkb0T0ZydvMHIS543EWXIkbyQER5M30lVXTW85Vnkj40Zldpg30lOSN3LsSN5I3/BoOtyavt2XRzt5H689cVJerZM9b6Q7JG8k2NTkzJDyRrpyvOWN9JTkjRx2LMkbEaJDJ2Uw0h9I3sghgyJHhiVvpL2p4Xuqq64aIKSp4UNxvOeNaJqG6vUGVhg9EWiqir+lGb+rGU1Vw10dIY47EoyEgeSN9I6jzRs5Ukd5I1111UxN6bqrYkp6160QJ3reiKemirpvPqf45ScpeuFR/EtfpObLpbjKio4qMAln3oivuYmGLWspefdvFL75NIVvPE3Zh6/h2JmH3+3q1XP1BskbEf3VyR2MSN5IyOX7c96IpmlUFLWw8j8VLHumnj8/vJZPXi8mf1sjfp961HkjPRVK3khXXTVAl101x0PeSNO2PMrefI6azz/G56gDTUPztLD/fx9R9s6L1K/9st0Whf6cN+KurqDso9ep/PzfuGsq0OmNKHoDLaUHqFz2LhWfvIW3sb7H5+xtkjci+rM+D0YWL17MlClTsNvtJCUlMW/ePHbt2tXXp+2S5I2Erj/njfi8Kis+qODvjxew4v0KvMUWaop95H5Rw9tPF/DuswdoqPX06jl7M2+kK5OzetYS0p/yRjYuX0n18n+jetxYBw7BnJSGMTaelOGDITkLxWik9uv/0bgl+MdBf84b8Tkbqfz8fVzlRVjTsrAkpWOwR2O0x2BJycScnIFz/y6qVvwH1Xvo/pO8kROLWzN2+hKh6/Ng5Msvv+SXv/wla9asYfny5fh8PubMmYPT6ezrU/d7kjdySE/yRjRN48v/VLLq4ypMFh0DR0aQOtBKfIaBgSMiSUyzsCuvgaWvFuF2tt+PL3kjHeuNqeHHjk5H3ZmL392MKTkNRVGC3lcUBWNsAorBRH3u1/jdLUd9zlBomoarsoS69Sup/uoTatd8hrNgJ6rPG9L+zn3bcZUVYU3NQtEb2ryvM5qwJGfSXLiH5sK97RxBCHG4Pg9GPvnkE6655hpGjx7N+PHjefnllyksLCQ3N7evT92vnYh5I+m2sX2WN9JeV01tqZ8vPysgOtFITIKpzYPOYtOTOSSC/G2NuNbbeP/jTaz8oJSv/1PGztw6Rlvbdi8cz3kj7XXV9NSEkV232hypvbwRd1kRWlURpvjkNv99DmeMT8RTU0FLQc9yGrqTN7Jl1VYqPnmL0ndfovqrpdTnrqTmm08p++h1Sv71N5oL93W6v6b6ady5EZ3ZgqLXd1hOZzID0LRnS8h1OxYkb0T0R8c8Z6ShoQGAuLi4Y33qNkzmaZI3EmIQk2yd0GXeSF8ZZh/W7nbvnnhaGjWi49o2h1a7awBQFKipcPPO0/tZ+3YDq5dW8PWH5Xz4wgFef3QPBWud7N1Uz7J/HODdp/fynxfyKVjTRFPDoab14yVvpD3t5Y0c2VUDfZc34quvRfG60VltHR6zorIBncEImoa3oabN+72ZNzJidCK+vP/RuGMj+ohIrJmDsWYOwjZgMOaEFNyVpVQseyfQmqF63DTv342/dDebP/4Mn7MRv6sFb2M9Bltkl+fTWyNwV5f3m5FDkjci+qu27Yt9SNM0Fi5cyCmnnMKYMWM6LOd2u3G73YG/HQ7HsaieOM4U7m7GbKPNL+4kSyyVrjr8Po29WxppqPGgN+hIzTCQGd/6APF6VIr3NvHNFw1siHdijzNhMuvw+zSqVjVR880uZn4/jZFT49r9RT81OZO1FQeYnNBxXsPU1IyuZ17NzGBdUdfzjaw/0LP5Ro4MMiYOTSdvT9u8kbxdfdvtU1HVSEpSVJvtySnRVJQ3fPdX2+s8dmIWW/J6bwRMw6Y1qFUHsIwa2RoAHUZnMmNJG4CrtJDqrz8hcugYmnZtxlNbiU1VcdQ6Ka7Kw5Y1DNXrQWc0dX1CDY78XGNmDmPr6t1HlVAr+gePZsCttf8Y9XSwXbTvmLaM3HjjjWzevJk333yz03KLFy8mOjo68MrMPHHXuTgZ80Y6mxq+O3kjql9D0Xfc9F9d5qKqpIXIaCMmky4oqPB5VBqqPLgbVarrmkgeYCU5y0ba4AgSBpspq6/n87cK2bW+LvQPRu/ljXTVVQPHR96IISaOmIRYtC5yQTSfDwBjdN+1mPpbnDTt3IhisbcJRA5SFAVTfCKObeupXPYuPqcDc3I6tsxBKLGpaKpK/cbVeKrL8dRVh3DOJiwpGZ12UQkhjmEwctNNN/Hhhx+yYsUKMjI6749etGgRDQ0NgVdR0bGZWvtY6828kWjLlOMmb6SnjswbiUs24WnWqPW0fShomkZlcQs6nYKmahhMOtKjYqly1wJQvr+ZxgYv6RkR+DwatRWHWuLSI2OISTMBGt9+Uoa7xQ8c27yRrhwveSPm1EwsmQNRHG27Xw7nqa3CFJeIdWD7XXKh6CpvxF1ZirehDmzRVJY1dFjOW1eDt74WxWjCnJASCFwUnQ5TTALW9EGAhquiBL+345FaflcLik5H5JCOW4HDRfJGRH/T58GIpmnceOONvPfee3z++edkZ3c954PZbCYqKiro1VdOlLyRnjqe80ZG5UQRY7bjaWnbH+9p1qiudWK26XC7VBLSzBiMrbe72+WnutyF1aZHZ9Ch0yk0VLnbHCM+1UptuYuCbQ0nfN7IxOFtu3Q60p28EUWnIzpnFprBhLuyrN3cCa2pnvqqWqImzUJvsbZ7zt7IG9H8PjTVT1JmfKdl3FVlKHo9OkPbZvbK0jp0BgOWjMEoikLzgd1BQ3cP8rtbcFeVEDF4FNaM9rtjwjXEV/JGRH/U58HIL3/5S/7xj3/wxhtvYLfbKS8vp7y8nJaWYzOET5y4skdEMGhUBFX7ffi8wUN3Yw3RaKpKc6Mfi01PUvqhh1yzw4fHpWKyto6EUHTg97V9SBqMOlRVo7qkud3zH8/zjRxLtqGj0OWcRZ3DjevAXjxV5Xgb6vDUVNKyfy+xkQaU0bOIntDaUuitr8GxZR0NuatwbFnH8KyOk1+7Q2exoTOaUD1tA8+DfE0O/C1NKAYjiiE4JyQp41AXktEegzEuAaM9BndlCS1lB/DUV+Opq6KlpABvbSX24eNJPO38doOaMTN73gIk+g/3dzkjHb1E6Pr8aj3zzDMAnH766UHbX375Za655pq+Pv1xwe9Zg97Uu0N9+7vylk2kWMe32T4ociT5TVvJsHXdtG0w6vj+j1Mpa6qmaE8zlgg90XFGFJ1CU70Xt1PDbFcYMjaKiKhDt7qmtbbYHezGV/3g1redurvcVY+i6PH7uzcSYm1lEVOTOu+yWVtW3GWgsq6kmCnpnZdZX1jC5AF9G1zk7S5m4rDud9kcbsK889gUn0KsWoVz5yZUtxudwUTk1NlEjhhPY5WC39lI3Tef0bRnK/6mxtahUJqGPsKOz5yMN/uHGKNielwHS3I65qR03FWlQPsjYTSfD9XnQ28yY4yO7fBYiqKgM5qIHjsVS3IGjbs346mpAEWHbcAQIoeOwZoxuN1ARAjRVp9/U/rLkLb+ymaeTnMv5XpEW6bQ4FqDpZfnMOmudNtYSprzSLBObPf9rIhRHGhn3pBQ7HduZ+BheSexiSbO+mkk+Rs8VG2wUVftQVM1LBF6Bk+2oDVaiEs2B8qnWGLJV6oxGHT4vBo6PVj0RiKTgr8KqbZoSp31qCpERh/6hZxbc4Cc+E5G0KRksra88y6FKenprCvpvEtkyoAM1hV2Pd9Ie10w6/eXMHlg7wQoE0ZmhLTuzOE27ihuN99EiUkkbvREYmeeheb1oBiNhyYMK9xGxUdLaTmwB0NsIpbMQSg6HZqm4W9sQN23kYqPfCSf/6NOA5ItGws7XBFY0RuIGjOZyuXvEhdroLKsgaTU6KAymqaheVwY4pPRR3TRPayB3mIjcugYIoeOCfxbd3iyqt/dQvOBPXjrqtE0FUNEFLasoRijOg50jpWt3+xhzIyhXRcU4hg4udem+Y7kjRy/eSMAk9KHM/o0C1ffPoifLRrMTxcN5prbBzHiVBtV1Q7WflrFtrV1FO9z0uL0YY3SYY8z0tzoo6nOiz3aSGR828mrWhr8REQZyB7d+lDqKG+kq64aOLnzRo6k6PXoLNagmUvV7Wso37wZc0Y2xuhYFF3rP02KomCIikFJyKB861bqvvmsw+OGkjdiHzWR6Amz8NZVodaX43e7WgMQvw9PbRU+Rx3GmITWWWHbGQCTlBFHZWkdfldL6yyrhyUtK4oSCEQ0TcOxfQPF7zxPxf/eoXbNZ9Su/YLKFf+m+J0XqFm9DM3vlbwRIb4jwYjol7o7NTyA0aQjPsWMoij8+8ViCr9UUAwKjlovpQXN7NnoYNOqWqoKPNiiDLQ0+tDpFQaNjUJnCH7yuFv8NFX6GDoxlriU9pMqoTVvpCuSN3LIpm1tAxhvQx0xLaVgj+lwyG1yRgKKPQ7nvu0hDantiKLTk3DaXJLOuRRdVCLe2ipcxfm4yorQGU3En3IuiWdeiOpq7nBqeE3T8NSUY0nNxJLWfoDq2LqOqi//g9/ZiDk5E2vmYGwZg7CmZ6PodNSu+5Jk9240v6/Hn0WEn1s14FaNHbyki6475Gr1E5I3ckhr3siOHh3TUevlw5eLKdrrJH2QFcsAN1pNJOUHmmmo9dBU76W6ykf2MAtT5yRRV+WmrtKNx+6n2FJDvCGa+io3Po9GxkQbUaeGtlbJkY4mb0RTVTz792P4+ms21tWTlZCAaUAWllGj25TNyU5nfcHxkTcybmwmm7e0bSFyFeXjb2qAiI5HuQBgteNrqsZVXIApNqHH9VB0eqLGTMbQFE1amg7V1YKiN2BOTENvi8DnbMRbV01z4V5McUnoI+yBFg+/qxmtthRjdibxM+eg6Nq2qHnra6hd9wU6oxlTXFLwuRUdxug4dCYzjl0bUVPNgCSzCiEtI/1Af5lvJNSumlAcq/lGNE2jodZLXZmfjYXbyPu6hsI9TgYMs2Gy6AGFxDQLY6bHMm5mHBNPjWfUhDiUWA+X3zqEy349hJFTYonWImis9NFY6yElK4I5PxnAdQsmYLK1fdj05Xwj/vp6av/+GjV/e4GkjXkY9u2lZdNG6t/7F1V/fQp97jo0tf1F/w53rOYbaa+rBtpfp6YjmscDKChK5/8cKYqColNQPR3P7TF28sCQ16kZO30o+8oVIgaNxJY1FL0tAgBDhJ3kcy8jatRk/K5mWoryaSnaR3NRPt76GnQJGSSf+0Msye1fF2fBTnyN9RhjEzs8t97aei61bHfY8urGnjJC5hsR/Ya0jHzHZJ6Gx/0tmHq2zsvxLs6SQ60rtMULk60TqGhZT4xlch/Xqq1h9mHsbtyNqmrs2dLEljUNFOx04vVqOLwOGktc2GP0KLpD3S7V7hoSzPFExbYmokZGG8nb0MTK98sYf0o85/4kk5YmP+tL9jMmPouYRDN6w3cPxqbg809JHMC6qrYPu/XVnU8ND615I5126zQ3s/XNN0koLsGYnoEuIgK1wYEpKgpNVfFVVWH88jPyNI1JF1/c4WFyBqWTm99OjkhBSZvJ0nL3lZAz+NC2icPT2bCrpN2unSONG5XJ5u1tWzrGj81kUzstIB1RzGbQNDRVpaKygeSk6HbLJaZEU7m5kCSTud33e5MhMoqkOZfiqa2kpWgfqtuFojdgSclkUNoAtq0rYExy+/s6D+xBZ7Z2Oeuq0R6LVlzB1q+2MPbUcX3wKYQ4fkgwIvqt4ub2h/iqqsaKf1fxzbIafF6V2EQTkdEG6kuhqtRFY13rRGbZoyID69QAeFx+SguaqSp1UV3u4bO3i9mVW09ihpWxM+KIGmUkPrHj/JCOtK5T0/nDN5R1akZWVHBg7z5MI0aimILnuFB0OozJycQrUL1uDb7TT8MQG/rU6aGuU3MsbNpWzPjRh1oVrBnZ6O3RxOs1atuOsA7wOerBEoE1Y2Cf1xFaW2LM8cmY4zuIOjqgelxBybkdHl9vICYhEqfkjQgh3TT9yYm4Tk1nXTXQ+To1HdmxystXS6uwxxgYODyC6Dgj1gg9NrsOW6Qei01PcX4LJQWHJitzt/jZuaGBA7ud+P0a5kgFQ6yfxEwLdZUulv+zmI3/rievqu3y8RvrCkL8tMG6s06N5vXSsn49mtXaJhApPWyhSENCIoqjAdf2bYFtOdnpx8U6NdCaN3IkQ3QsEcPH4a2tRusoadTnw1dXhZI+BONR5IscC4bI6E4nVjtI9bhaF9sz9n1Lj+gbXk2PR23/5dXadvGKjkkw0k8cj3kjXQ3xTbeN7fT9nuSNeNwqW7/24NU3Eh0XPPIi2R6HX+dB0SkYTQrlB1zfzcyqUbCjifoqD9HxrYGLWTFhtimYzHqSB9hISLNQmwsFa51Bx8zpoOvlyLwRpcFB8+o1NH6ynKblnzOuygGezpNfD88b8VVX46uqRo0O7qJIiw6e60LR6cBowl3Q9ZDQg3kj9bUucleVsfLdYl79yxbefmkHed9W4Kjv+oF5UF/njcTOPBPb4OFQXYK3vjaQF6OpKt6GWlzF+VizhqAfObPLY3Unb2TM1EFs3dB7qwIDRA4aCX5flyNlvI312LJHoJi63xrXWyRvRPQXEowcRuYbyQm5bLJ1Qt9VpBMFO5vxVNqISWp761oj9EQl6lrnEonQ0+L0U1fpoaVRo7zcgc1uQKdT8Ho0DEYFe+KhpnSb3YDZqqNgbTNet7/TOhw+34jqduP48L+Yn/0HBa+8SuPH/8Px74+p+9urWF56HdemLUH7dtSdo/n8oKmg11PS6Gi3TKCsTkdxdecLz+UMSkfTNLbnVfP2Czv4/D8HaKhyU1heT3lJE5/+u4C3X9pByb6mo55vpD2hzDdyOEOknaQLrkA3fAo11Q24ivJpKdyHqygffD6ic04h+fwrGDdrTJfr1BwLY6YNYev69lvMbAOHYUpMxVVRhKa1n2zsqatCb7FhH9oasIdrvhEh+gvJGRH92pF5I40NPlQ/GM3tJwfGZxhw1Si0OP1oGrjdKnqXFZ+nBVOMDr9PpbnRR1KGBWv0EevZJJvZta+J0gInWSO6XpxR83hpeOffNK9aS1J8LBVx0ZgjW/M4VLcbXX4+9f98h2i/H+ukCZ3mjejtkSgWC8l6HRVdnDfOoKMqOqbL+pXtc1K4uhxFURgwKApFp1BW6yAlNhJV1agsc1K0UcVo0sHgLg/Xa8Z9l+B6eN4ItAYkE679GZtXbSE50vNdN4YZS0Y2xpjQ82PCTW+xkXja+VR++h4txfkYYxIwREYBCqqrGU9dNYrBQMLMOVjSshiTrrB19e5wV1uIsJKWkX6mN/NGetpV09t6M29E0YFGx8sM2ON1DB5tR0GhudGH0+GjpcmP36fRWO+lqcFHfLKZQaNa546octcG9jWZ9ag+jW3t5Hq0lzfSsnELzd/mYsxMx5CYALpDXyed2YyamUZ1cyONH/8Xf0PHrR1ry4rRx8RgGTkSX21tu2UO5o2orhYUgxF1SPDcFEfmjfh8Kntz6/D5NBJTbUGjiwB0OoXktAhanF72bKxDVUMbXtobeSNdUSKisY+eRPTEmdjH5BxXgchB1rQsUuZeTvSYqWheD66S/bSU5Ld2zQwYQvKcHxA1dmqXI25E/+ZRDZ2+ROgkGGlPmLpqejtvpKf6c95IUqoZi1WHq0mj3lPV5v1EczzG5CaGjIskPsWMxaqnucmH36sRGWVkyLgohk+KxmTRk2JpXR/E4/JTfqCZfVsa8FQqlGxpoa7yUD5Fu3kjqsreLz5vndo8ov1VZdMjo1FTkvBVVuPe1v5aPIfnjVinTEYfEYFSWRlU5mDeiObz4ikqwjxkCGpm+zN/HlSc76C+wo3X0nHegqIoxCdbqS5zUV7S1Ob9I7tq+jpvpDeFO28EwJyYStJZ88j4wfWkXnAlqef/hPSLf0bq+T8mctDIfhOISN6I6A8kGDmCyTwt3FUIq/6eN5KebWHgMBuealuHrSOapuFyqkw5I55f/XE4V/x6IBkjTcSP8pGaZcNgbL3tVVWjqsDD5lW17N3soHBXE84GH/lrnLz52C6+eLcYj6v9/JEJSiS6knL08cG/2kub64ML6nQoJiOuXa3N8FNTMzrMGzEPGkTUhRegKFC2dTP+ujpUjwfV5UKprMSTn4958BCi510Cen2n69TUVbuIslhau2AOkxoXRWndoVYaq82Iz6uyZnNw0DBxeOgzuvZW3sjhNm/tOC9k7MSsfp83cjhjTDwR2cOJHDQSS3J6YN2dI0neiDiZSTAi+r3D16nR6RRmz40jKsZI+f7W7pfD+f0a1YV+LBF6Tjkvkeg4ExNOiWX8hBQayv2BAEbTNIr3Oinf7UH1a9ijDRhNOgaOspMy0ozBqLBuWQVf/KsYv6+dJESvF/x+FOOhET3pEe1P1qUYDGjNLSF9VtuUKQz95Q34xo9H9XrwlZXiq6pEi4wk6vwLifvxlRji47tcp0bTaHeht/bE2q0c60lAx43NbHedGqDDVXdPZGNmypTw4uQmnVr9UG+uU+Nyr8HSi90/PVXdkkeCdWKH73dnnZrs4TYu/mkKb/zdQeGeZvRGBZNZh9ej4vVo2GJ1fP+KdAaPsQOgN+g4Y14yBWXVFO1pJinTgs+jUpLfjNEMXp0bT72B6HgTaQNtmKx6ypR6Esx2tq6pJXtMNGS05o1MiM0GQLFYwGhEc7nAaun8szvqyYhuu67MQVPS01lbcmidGvPgwXjPN5NosaE2NraOsGlxs9NsZkpUx4m1h69TExndOldJu4HUYTxuPzqdgi0y9H8KemOdGiFOBF5Vj1dtfz6RjraL9knLSEckbyTksuGYb2TY2EguvtnGjB97GT7eTlK6maFj7Fx0dRq33T0Rw9DSoPIDhkYw+8ooMgbbqC51sWN9PQ01HhSXAZ9bIzHdyrBJMZiselD9GNzNRBrd4POxY20tk+KDf63r42LwD8miqrTz7oI0sw00DfPo7n9GQ1wcpqwsTBkZTBnaveEuWUOiiEuy0FTvpbS+4+TZ2qoWktMiiE9tP6CSvJGuhdJV099J3ogINwlG2iF5I/07b+Sg8WkjGDTByJgpUcQmmPB6VPJ3NLFrUyPNDhVvdT3ObXtp2rIbV1E5SdkGcq73c9mN2cSnWEjNtjJ4bBTZU20MnxSDVXERsX8ziavfZcg3H5Dy1RuMKlpK86pvcZbVB51bURRGnHEmGA34aoJHwBzMG9FUFc+BItS0FMwjhwfe7yxv5HBrS7t+iHeUN2K2GBg/LRm7wUJLY3AS68G8kfpaF6oKE6Ylozfo2p1vJFS9nTcydsKA4yZvpDdJ3og4WUk3jTgutLdOTeG+Fj54yUlzWTEoYDLr8Hk19nyxn7jmTbijG7Gb3KCBPsJK3PBsqnNSyJ4ynKQMC1FxRhJSLZS7PBiaaond9CmmujJUg5kWgxFVb8LSVEvyni+ofbEGLpkJ33XTAFhGD8d3+nTUrzfhaWzCkJxEekQ0JU31+Ovq8VVWYUhOwvO909BZOu/KOdKUzAzWFXUejEzOSmf9gY6TWMdPS6a5ycfyT/ZRlO8gKtaMwajD6/ZTXdJCcryeWWelM3J8PIqiHPN1ajqab+RkNWbmMJlvRJy0pGWkH7KZp8t8I4dpb76RskIX775YRnWxn/RsKwOHRZCWZWVwXAOj674k5sAOCve6cEamYB0+EH1UBE0bd+B+ZRmOVblEx5lwt7TmU+g9Lkx5H2OqK8Mdl4Y3JglLZDwOgwGnJQF3XDrqgQPo313GxpJDQ3QVRcF/xnRifnIZxgEZ+CoqcO/Zi66gCNXlwjZjKrE/vZLJU6Z3uU7NlPT0LhfSA1hX0nmZw+cb0ekVZpydzrQL0hgzORFN1XA1e1EUGDQumnk/HsbkU1K7PcR0wsiMYzLfSG8JtasG6JMhvscT6aoR4SLBSGdOgKnhT9S8kdXLa6kscTN6ZBJOWqdGVzwtRG5eiaGpDltqGs2aSvEeJ5oKhmg71mEDQVGoeW8ZIzKa8XpU/D6NAfV12OoqcMemgu5Q0pmGhselkpARgWVYNnGldSjbg5vRpyRlsWNQHAk3XU/8DdcR97Of4LniYhJ+fQMxP56PMSMtqLzqduPavhPDpq205G3EV9V2rpSOTBnQ/RYEnU5h7hlDiZ9k5qqbxvLjG8Zw5U1jGX96EpnZUSEFIhv2lqBpGu6SIho3fEvjulWQvwN/S3Obsu3ljYwfm9lneSNdddWMnTww5GONmTqox/U4UfJGRPf4vktgbe/lkwTWbpFumg6YzNPwuL8NdzXCJs6SQ60rN6SyydYJVLRs7NsKHaaq3M2uzU4SU03oDptZ1LZ3I5bCHSiahrG+khTVj7feRnVUJolj0lFMRpKzB1OxZQtJzftJyhhKSX4T46t3oun0wcu+axouh0qk1UR8qhXFaACjEd2GbWhnf7/NQ1wxGDAPbX2Y+SuK2EgLkw8v4PPjXPk1zd+sxVdeTopfJd/dQnxCEpbRo4g4/bQ2n3NKZgZri4qZmtZxEDI5K511B0qYktl1fofFZsBia/2Mk4akk7u3hJzsQ/tNHJpO7p4ScgYftm14OnlfraNq/TJcBXtbZ4BVFHC0ULY3l8gJU4iadio6o5FxozLZvL338jjGThjA5o2FjBvTft7J2IlZbMkLf0vGmGlD2Prt3t451nddNUcTGAlxPJJgRPRLbpfK7i1OtqxrorrCQ6PWQsbQDcyeMZK6Gi9NDj9JaaZAeXPJHuwblqFvdqDaotAMZlTVi97ZgnvHHho9DUSOH4HOaoZYO94dO5nzo+l88s9yWjZVo5nMWL+bEt3rUWlp8qPTK2SNisIW1fo10UdFolTXg9cHJmN71QZganJmUIKq5vNh+t9nNGzcgc5mw5iZgWIy4Xc0oHj9NK34AtfW7ehOn42WnNbhpFjdsb6wdYjv0XIdKED59AOa/S0YE1MwpqSjKApKRS2qu4X6zz7GV19H/PcuQjF0fE06InkjQgiQYKTfspmn0+zuX/ON+Hwa+3a7qalqHZ2RkGRg8DAzen3oOQedzTeSFTGKA85N6OpG8v5rFRTsakHRgS1Ch+qP4tu99exdXURyhhlN1QKtE+a6Kuxb16DzuPEaI/CqRjQ/qIoev0FPZISJ5uIqmps1jCNH4NOMqG43mWl65v08i927rZSXN1Fd48SsM2Ew6khMt5CSZaPZ1gx8l3yqaSTYothUv58JSUND/swtuRtJ3rKL6igbaclp3x1KQ6muwVvXgK+2Ds/efcRv28bm9bkMPeMsrBPGB02odtCUARmsKyxmSnrHD++c7HRyC7qXjKppGt6yEpT1q6jZpkcxGDAmJdO4ZhVKYwPmUaOCWoOSk+OoqHaQmBRJU94aLBkDiJwwtVvnPJa2bCwMeTK1rRsOMGZSO0sAnCS2frOHMTNCv7+F6A0SjHTFsw5MPc+76A+iLVNocPUs/8XpXoPNNI0N61pYsayRA/kevF4NBTCaFLKHmDlzjp3kka15IzGWyR0eK902lpLmLZ2er7FOY/lL5RTsbiFriAWT+VArgTlZB016tuc20tzkJ32gGWuEgeSKCtwNdbh9VnSuZty6Q1O4+/wqNaoPo96ArqaC+ho7qt6Hy9oAn20haUQig2YNIGHLPipjrMQaozFbdFgjDaBA82F5Ef6GRqzjRoIx+GszJXEA66oOkBPf9gGm+f20rF0Pej1aZETrNlXFs2cvhr378OuNKDYr+oQE1MYmDDt2Ul9RhTt/H9EXX9zpteqOnEHprM8vYfLAtq0l/kYH9Z98SMuuHegqa2iKsYNfxe+ox1tVAWmDqaiuI17z4KmpQnO7UHR6MFhRsgei6A00blpPxNjWIeEbdxYzYURwsDR+bCYbtxR1Ok9JT23eVMS48R0PIR47eSBb1u8P6Vhjpg7q8fDaresLGDM5u+uCR1A9bpqL9uJrbABFQa1qQvVloutBS9PRGnvKCLZ8vfOYn/d4JZOe9R4JRjoheSOteSNfr3Dy7pv1+FWN1HQjVltrgNDsVNm3203Rfg8//MlQBk0++kz8bWv9FOxuIXu4FYPhiLwMRSEqzkh6tsaWtY0UF7gYPhiU/flUNdkw+RXilSYMBkBpnZFV9YOr2Y9m1RNl0EjwlEJzEy2qwp4/vU3jwIjWlpeKKmzxRmISE9utl9riAk0jYmrHs8geaX31Aca36PEUFmFITATNC4CvpBRPwQFsdjtORcFuMgOgOZ1oJjOG5GRavlmDPjqGKd87t1fzRg43aUg6uVv3MmD9F7Ts2IYxOYXk1HQqahtJjYuiecdWNK8PS3kBruICmo16UAC9AVQVWlw0NlRiSknHXVaMt6q8x3kjm7YVt9tVcyLnjWiqimP7Bho2r8FTW9k6hz8aNkXPxiXrGDp3LpFDxvSbBfWE6EsymkZ0qviAwn/ea8BkVhg0xBwIRKC1+2TwMDM6Pfz7nXoqjnKaCrdLZesalcgofZtA5CCHt5LYBCMRdj111V4aShpoqnHi8pvw22NR9UYMaut6Mz6vhl4PJosOn1fF73JjrC9Hjw9PWjr11jQOOGLQbBGozS5YvQVfTX2bc6rNLXj2F2EdPwrLqNDWEJma3PrwVFtc4PWiWL4LOPx+PIXFYNC3nXtEr0fxetFFRqKLiaFl3Xr8jo5nT+3KwSG+ndHt2krLzu2YBmShtwePrtFUFcVswtfQAPXV6CIi0EfHoo+0o4+KhshoUFXchQX4aipRPZ4e1XPcUSyod7zSNI263JVUffkffI0NmJPSsaZnY00fhDkhBa2pjsoVH+LYHloSuRDHOwlG+rH+MN/Iplw9DXV+UtI6bkRLyzBSV+NnW15ov+A6mm+krtqH3xGNwV7X7vuxpgSgtYUkJcNM6gAL1eVeWpwaeoOKR2fBYUoEnw/F3YxO0TCYFTRUdH4vmtuLhoI3Ph2PPQ57rJFmp0qjEk306dPAoKfm23W4duXjKavEU1ZJTGEtVQfyseWMJ+7yeejMJnISsthYF9pQTsVkbO2i8XhJj4ymrKQIrakRXURE28J+lejISEobHejj4/HX1uDe1XYSrCkDMkKab6QrmteLsmMLOqsN3XetM4fTGY34nU0otObnqG5X0PsRERZaFCOapuKtqQKt83Vwwq0/zTfiKj1A/YavMdjsmBNTg7pkdCYzuugkdAYjtWtX4K6t7NO6tEfmGxHHmgQjoThJ5xvx+zU25+qIiKrptKlYURTs0Tq2rFOoa+n5fCOqqoEGoQwmUXQKA4fZGDwjDZ/NjtXnxuX2Um9IoNGeDgYDkfoWbP5mzF4nNn8jGuCMTMWdNAB0OnRK68Rg1WVu9JE2Ek+dASlxWIYPxmCPxGCPxDJ5Ik0Xnof2vYvwGKwd1mdK4gBya9o+wIxpaRhSkvFXV7fW2+NpTV7VH+pPbvS4we8HnYIhLra1nF4PKKhNTV1fjBDlDEpn/f5DrSXe6kp09TXU6NrPTdDZItDc7tZuGZ0OV2NHdVFQFAVfQz3QOjW8zDfSuaa9W1HdLozRcR2WMcYm4mty4Mzf0WGZviDzjYTOpyr4VF0HL+le6w4JRrpwMq9T43ZrKL5E2vnR3IbZrEPnS8Dn7fn5omIM2CJ1OEPomXA1tw7tjU6OgMHDSY7zk5SmkZplxZqdTmXkUOoiBtBoSabRGEOLzkalIY2axFGg0xNhsNDsb8FgVPC4WhNeDdF2FIMe2+hh2G/8P4qmzmdpyViW/zeCfz65j9f/sJOv/l1KTVlLyJ8p11GKbepk1OYW1JYW0CmgtTbTA9jNZkDD73Cgs9vRJ8QftrcGel3rfCNdrFMzOSu9w3VqOqL5fMRGmIMmegNIiY+irNaBYjSBTo/m96HX69tv+fB5W9NIomLw1oY+gVt7Nm1r/zOGsk5NfxDqOjWq14OzYBf6yI5XYE7KjKeqtB69NeKYByNChIMEI6JDJpOC0aTDG0IqgMejYTIpHM0AgMgoPeOnRtJQqwUe1keKNSVQUVeByaJj1CQ71kg9tbFD8CdnEOmswqT4UHQKqt5AkykWhy0Vj8GGFxNF5qEoZlPQ8VSVwNBkRVFAp6OyuJn3ny1gxb9KcTZ6iYjT47G34PWofPNRGe8/k0/VPld71QtyMG/EOm0y1kkT8B4oQvP6wWhAc7deVM3rRdfgQGc2Yx4+LNBiora0gNGIMSW1x9cTOs8b0dsiUMwWcLf/WRS9Dn2kHcVgRPO4ac1e/Y6mojY7ocWJKSUdQ3Qsmt/f7nFCcTLljWg+L5rfF9JoGcVgxO9xo6n9uwtMiKMlwUg/F868EYNBYUKOmYY6pcPgAFp/5Tvq/UycYiXUxP+O8kZyZkQyIC2e3bvL2z2n26VSWaQyYnwkA4ZYGTzCBrZIakedTktiOobGGix1JcRSj9VZRVRzGQoa+/SDabSnEmU5FFlpmobXrRKXfKjpx+PT8dHXzZTtbyZzWCSJ6Vay4uMwWXXEpVgYMCISR42H0v/6WLU3tH51ncVC9OU/IPLsM0iMjaPF48FfXoG3qgp/QwOqzYZp5AgM8a1N9qn2SCry8zENGIBpUPtDRXsjb0QfF49l0FBoaD9HRzGZ0VmtGKKj0ZktoNPhq6/FX1+Lv6EeRa8nesQoHLEpABjs0V1dirDrD3kjitGEzmBE9XbdjKj5POjN1l6ZCK+7JG9EHEsSjITqJM0byZlsJcIOZaWlHZapKPMRHaNnwmQbydYJR7VOTdoAMz+4JgF7jMLe7S1UlLhpcvhw1Pk4sLeF4gI3Qyfq+f4Vyeh0CoNG2sjItlBUZcYz4xKKJ87ANXAMSnIyDZYU8mNy2Jp+PvstwxhgruKw2ePxNGtoJi9xKa3BiNriotGbRrUzgvTBNvTtjOhRdAppg2xUl7oo2RK8NktHeSMAOpuVqIsvIGHhjbguuxBDajK4PaBqKG4XdZs349q+A191Nd7CIrBZsZ9zNoqhd0ffH543oigKtok5KAY9Zbv3oHrcQQGg3h6F3h6Fv7ERc9YgtFGTcKVmYx06Etuo8UROmoYlaxA0O9Hbo7EOPbSgYV/kjXTWVQPHT96IzmAkYvBIfE0NXQb5/pZmIod0b92m3iB5I+JYk2AkBCdz3kj2YCMXXzyApiYo3O/B7T7UXOx2qRzI9+BqUTnv4igyBvTOJE0jxtr44Y1G5lwSjzXCQJNDpaVZZeBQG5ddl8zcn5qxR7c+pHV6hZnnxGKN1LN7u4tqcyqOcafhPucSKsfPZat3OKWNdmJTdWTpi8HvQ1U1mhw+9H4jSdlGbHZD60JwRWUUqpnooq0YjB1/NRSdgs1uoHBDMz5v583nU5MzWV99KEDRWawozhZ0UXZ0VgtoKha/Cs3NuLfvxPnVajS3G8/552EePjywX1/kjfga6vFWVxJt0qPbvQ3H58toWrsad0kRSTYD5fkFrfkgkXaMiUmkDMyAxBRM6QMwJqWgGE34W5rRHPVEjJmIMTa+y3N2pbO8kc4cb3kjkUPGoLdF4K2v7rBMjNVLo0tPRHbbVatF/+D36zp9idDJpGfHCb+n96aG7w5FUZgzNxLV5OObFQaK9ntRVVpHveghY4CRs75nZ8oMW69OzhSfqjB6UAKnfi+O5u/WibFH679bGC+a3bVbqN8+gI1rHJTsd9HU4KO+2kt5pZdau4PIKD0mi57MwTZUTUUlieqyFOz7i3FZYzDG2Bk4IhJ/nJu9BdBQ7MAQnUm1LQuTtf1fqxWuOpItraNdrJEG6ur9uFv8nQYuh9M0DcdH/8W4cSu16SmkjR6Fr7oGf1U1LY0OzBF2NAUUsxlNr2dtWTFTU49+xtL21qnxFBdS+/47eEuK0UXYULOHonc68NVU4ausQB8VhZY2iNgLfwhA09pVuAr2AmZUqwHN78NXV4um+mHYOArTx3C0oci4sZls3tJ7C+31Z+bkDOKmnEHNN8txlRdhiktEZ2qdd8bvasZTW4nObEE/bAammKMP8oTo745ZMLJkyRIeffRRysrKGD16NE8++SSzZ88+Vqc/rh1cp6a3dHedGkVRmHaKn1mnJLFzq5vamu/Wpkk0MHy0GZOp+78AWqeG73qdmhTreMyW4OO7WlQ+fdPD/vVl6PUKsYlGomMNxCcZ2Vvgwe13Mm50GjmzYxg82o7HpfLpmp20lM4idsterJX7segd7N5vZPPaKFp8NvSRCZgikikq9uM3eFH0lQzOTAqcM9UaQ1lLfeBvVdVItNrZ6jjAtKjgX8O5Ne1PDe+rqMSVt4nErCzKjUrr+i8pyRhTkqlpdGD5bnSFe28+g/IL2NtBvshBPV6nxtHAjg/fJq6lEdOgwa1Tu5ujiIyxozqbUJ1OvBWlEBdP9GmtXUXWQUNwbs6jft16fPW1KHo9loGDiRiXw4BR49i8t6LTuh5p447iPpkaPhT9YZ0aRVGIHj8dvS2Chs3f4qooAdUHWmtOiTU9m5iJM2kuDe/wUFmnRhwrx6Qd6a233uLmm2/mrrvuIi8vj9mzZzN37lwKC0NPJgs3k3naSZs3Aq1Tw/tYy/gcK2fMsXPGHDtjJ1rbDURCyRs5Gp/9u5qtq3wkp5sZMMRKhF2PTq9gi9STk5NMQoaeor3N6PQ6rBF6ouONXPr9seTMT+DUP13N2D8uIH/wuWzSxuNLTyN+ViLDvz+aQTlJJGdY8DYoFG91U13a8YiZxjovSZk2TLbgzz8lseOHnHvbDlRHE7qY9hM9S5paxzQbkhJw796DUtW2Cb+rrhqgy66aYU1VKJXlmLKyWwOR75TXN7Z2ySSnYBk+CqWsmI0rV6EoCpZBQ4mfdxnqpT+j+uzLSLnu1yT9+Doix+ega2dBP+g8b6SnTpS8Efhufp5h40i/+KekXXglSWddQtI5l5J24VWkXXQVEQNbu+l6ulbO0ZK8EXEsHZNg5IknnuDaa6/luuuuY+TIkTz55JNkZmbyzDPPHIvTixNITaWHvG8aiUlUMJgUSg+4yFvVwNov6lj7ZT3rv6rH5VRxu1Ryv6xrnUjtMIqiUFRjY3tFHCkT0hkxawSG5OjAaIXUgTYiY4z43CoHdjTibmk7XNXd4kf1a4yaGhtS19TBvBF/bR2KQd/uPun2Q3NO6CIiUFtaUBobg8pMyey6JWFyVucjaDSfl+YNuWC1BQUiKXHBc17ozBZiIiwoe7cHbZ84aQTEJmCMi28zwsNbW01j3rc4vl1J44Y1eKoqOk3Q7MiReSOa30/Lgb2kVObhW/EWpe+8SO3Xy3BXlgYd/2jyRvxuF427NlO39gvq1n5B485N+F2hzydzuFDzRg5S9AZsmYOJGjWJqJETsaYPDPy3GTMztOUHRHioqtLpS4Suz7tpPB4Pubm53HHHHUHb58yZw+rVq9vdx+1243a7A387jmJ9jhNJuPJGwqm8pbWr5qBdW5ppqPORNiCe3DXlNNca0RsUzJbWRdxczSrV23xERbrQVCg74CI9+9DMqZqmsXVtPX6fij227S/6qDgjyZlW9u1x46j3UlvuIjX70NTthdXVaFVmRkyOZdDYaLY0NbY5xkHNjV6a6r0oCnh9fjDog+aLKHE2kB7RTiuJpqEoOtDpQsobWVfSeVcNHMob8Tud+Bsb0GztTEd/BH1EBNR0PZGZz9EAXy1l24E9xOj9oLQOBddHREJkCt7U+RhjOp5p9HBH5o34mhxUL/uA5n070XwetGYVl7+B5n07aNiwCvvYKcSdcm6HrTNd0VQVx9Z1NOStPmyxOtAAU1wiPms22oTMsAytFeJk0ufBSHV1NX6/n+Tk5KDtycnJlJeXt7vP4sWLue+++/q6aseV3swbibZMocHVvbyRvhBa3kjwL3NHnRdNg11bnDRUqyQkGNHpD/0CMVt06Gw6HLXNOHerlBW2BAUjGwu3s+VbP06Hj23fNqDTg2r3Epnhx2JrbbXIHmnH4WumYreb/K2NKN+NB/a2GHAqHqZMj+OMH2ZgsujJsWSRW13AhNhD+R2OCg9//+9GPHt0tDS25tc4TW6MyUYSPX5Mfj/pkdGUNDW0+7n99fXoouxMGDOGDc7Op4M/mDfSmUmZyWzYs791LRoUDk5eVlbvIDWm41lA0TS6mjjG52ig+t1/EFO0hwajDfOAbJTvghF/YwPs3kTV+14SL/5Jm4Ckq7wR1dVC1X/fxbl7C+aUDPRWG47yBsxJ0YHj16/9EtCIP/38biVQb9lYyJjxmdTlfkXdqmUoJjPmlAHovhtKrfl9eGqrUfO/ZJPXzfj5PzxpV8+VvBFxLByzcP/IL7KmaR1+uRctWkRDQ0PgVVTUPzLsw5E3UlPjZ9WqFpZ/2syqr42Ul/uO6fkPF2fJwRliQNTbeSNNDh/lJW6cDhVHnZfaKi+RMUpQIHKQzWjDHqvD5fRzYPehuUBS3Fl89nITe7c0Ulnsoq7GQ3W5m+JtHr5dWUpJvrP1F71BYeKEZNJGW0gbZCNzaCQZgyOZck4Sp/48gbnXDMRmb/+XeOEuBwfeaWH3igZUn0ZskoXYJAtel59vttkoKtfTtK/jnI4SRz3+mlqsE8ajj4kGZzPufftw796Dt7wCNC3kvBFPaQkNnyyl8snHML36AuVPPoJjxaeg1xOntT/hVlndoVZIv9MJKenktlPfDXtbtzV8/Tmu/D2YBwyCyOjAd1pRFAxRMSSNHUPFtu3Uf/m/oC6VUPJGnLu30rx3O5b0LPRWW2B7RWVD4PjGuEQcm9fhLjv0b0SoeSPuimLq132JPuLgYnWHfpspegPmxBTiBqTh35eLq6z7+W2hrlMT0rEkb0Sc4Pq8ZSQhIQG9Xt+mFaSysrJNa8lBZrMZszmEBVFOYA6HyocfNrHmWxe1tSqKAj6flejYWiZMNHPhvAgSE0/8kdkVRSq5G6rYsr6RZqdKfY2Xgt0uTCaItXe8n9cFFpuO4n3N+H0aLU4/H7xUQukuH9ZIA0aTgsXW2i9v14zUNjRTsKO1FSJ9UASKoqA3wLCxMfzgxsGB426o3f/d8OK2GqrdfP5WIY5aDwlDzMTaLIH3RmQnUtRYz37HGAz7vsVoM0J08MJ7aRYrFTt3YswejGnkCBwfLcXy1dfUeP2tXTdWC4OHDqUgKwstNb3DYD5nQBobP15KTe46/I4GdFHRaAYD+Pw0fbsKtal1xAyDI4P2S4mLory2NRjxO5tQjEaGn3kaO46IfycOTydvVwm++lqad2zGEJeAYjQCbXMsFL0BYuJp3rsTb00lpoT2v/NH2rjlAElbclEMxqAVhZNToqkoP9SipI+MwltbhXPPFixpAxg7MYsteaHNnNq0Zyv+ZiemASkdljFGx0FBEY27t6I3W1G9HnQmM8aYtjkzhxszbQhbv90bUj26MmbmMLaubrt6swg/VdWhdjCfiKpK11539PnTzGQykZOTw/Lly7n44osD25cvX85FF13U16c/LjU2qjz3XD3rc90kJ+kZOcKITqfg8fmora1gxecJlJT4WHBDNElJJ25Asi3Pyb9e8tFQXUBKUjzxiQbsUXq2rm/C0aKi0xtRk5qIsAQ/VDVNo7lRI8KmY+fGRv7+pwM0Onzs39lE6kg9dTozlUWuQDCiKGCJ1EGLQvG+ZuJTLRhNOnweGDohpk29NtfnMy4meKTFxroCvHlWqktbGDDMTqmrbZ6TTqdgmjKZfd/6iK3LR19dSZm1nITIKLQWF2ga/vQ0Is46k8aPluI9cADFZKI6NprUqGhUpxPXps2YN+TRbDARMWNG4NiH5424d2zHuOJTNLsd05ChrUFLnYNKIG3QUDxVFbi2bEK3Ywtq9Iyghz20BiLe8lIicqZhHpAN+e13p7qL9uN3NGDOGnzwwlNeVEp8hBEUHXpbROs08jY7/qYqXIUFIQUj48Zmsunb7XhqKjBEtb3+h1MUBZ01gpai7rVCaJqKc98ODJFRnXa/aKoKqo/qz96ncUcuqH4UvRFLaib2EROJHDo6KBFYCNEzx+RJtnDhQq688komT57MjBkzeP755yksLGTBggXH4vTHnaVLneTmehg21ITFcugfSpMhg/j4YmLiTOza6eHdd5pYcEN0p/+YOhwq+wu8uN0aEREKgwYbsVh0/T5vpKTQzXt/r0bniiJrhAO7qXWBO2sEJKQYqSr30Njgw6ep2DK1QF6Hz6dSUeympUXD52nBYjKRv8NJ/vYm/H6NZr9GTHIzulIdrmZ/ICBpPbaBhhoPNWUuFEVhyIA4vAOr4bDpvCbFDWRD7f6guuYkZLGufD8719ViizQE6lLaUk+aNSaorN5ooDJ9ArtTx3DGJBf569dhMNnQx8RgGTeGoggzzf9dgbewENOQwaTq9ZQ6GlH0evRRUa2Tke3Lx/HfpRiSkjAPHhyUN6KpKo7ly1DKK/D7/a35JxERJCckUuFq7ZoxJSajDR5K8/79VO7cSVyUHSUiojVHpLwKX7SdiJxpxM69MLBwX3tUjwcUBUWn4K2pJLKihKbyCpoMCgoKitmMKTEZTDGteSSetisudpg3oqqtU+WHkDiq6JTuL9Kn+qkuryU+oePmNU3107x/D2pNKU40bINHore0to40F+6luXAvrrJCEmZ/r7UF6AQ19pQRbPl6p+SNiD51TL5B8+fPp6amhvvvv5+ysjLGjBnD0qVLycrqH1M4d4fJPA2P+1sw9Wy+jq40NPj55hsXCQm6oEDkcAaDQlq6ga1b3JQU+8jIbJu/4Gjws3x5M2u+cVFV6UfTQK+HjAwDp8y2cvqZtnaO3LU4Sw61rjVEhBDEJFsnUNGynhjL5G6fZ8M3TdRU+hg62oLDG3wdUgeYaXb68bo1mupVqo0eLBF6/F4NZ6Mft0sjIcmCR3WTmGAhIcVEcb4Oo1HBWaGi+n2kDYygpKAFn0fDGtn6wG32N6Oqegp3Oxk2IYqzLkujKaompPp6XSrNjV4ska1fqXRbNCXN7SeoWiIM1LQYiThzGq7Rg0lIOpQ/of9iBeU7tpM8ZEiHgYCWkED1gQNYNmzAPPhQF5Lm9VL35us0fvYpOo+XxqoKDH4/qCqKyYQuMRk1ZzI6swVTegYxXi81Q0ZjsZnwVZSDXoeWkEnV0BFMnDUj6Py5+0rIGXxo2PDE4enkfbyNOE3DVXQA9/59oPrBZMZgjwRNQ3W34CrcjyXSTj1GEo7oeh0/NpNNHc24arais0Xgb25qHZVzhIrKBpKTWkciqS3NmLIOPSjHTsxic94Bxo3vOC9l7NQh5H1hRvW4OyzjKi3EXVGMOdKKx28ItKLojCYMtkh8zkbqN63GEBVL7KRZbfY/2FUzZnLnk9eFauva/KOa+0SI/uyYhfM33HADN9xww7E63XFrzx4vVdV+hg3tfKhiTIyOslI/O3d62wQj9XV+nn+2gc2b3cTH6xky1IjBoOB2a1SU+3j9740UFnq56HKw9MPUnOYmP5vWOolNMLTb6pOSbqa8yIMpXqGq0oeq82CPsoNOw+dTiUkwYbXpqap2k5z53QfUwGTRYzLrqar0kJGuZ+g4O6X7W3A6fGiqHpffA14dqVlmLroui6zhkWysDy0YUfTKd7/QO55XIz0imhJnPRbVjN7QfqAxuKyKEjR0neRMpUVHURYdjXvHdvwNDeijo8Hvp+Hjj2ha+SWay4UB8HncYDCAXo/qdqEv2IfDUU/ElOkY4xNaWx2iokn44Q8PXVtak1MPD0QmDk0nb087SbepA0BRaNm9Hb3Vhs4eBc3u1gE4itK6zWLFW1UBOhOmbkxtrxhN1NgzsVetxZiQHHQfHJ43onpbW1sihne8+GK7x1cUlPQR+IvXtZtMr3o9uKvKUIwmNI8bxR7fpowhwo6/xUnjtvVEjc5Bb7bQVyRvRJzoJMOmn2lp0UBrbf3oiOYvbv2FprROjR70nqbxr3ca2bzZzbDhJlLTDIFjmc0KA7KMZGYZWPmli5Uremdhu97W6PDT0uwnIvLQ7dnkPTTdeGyCkZR0Ey1OFbvdQkyijkmnRBOXYEKv02G26Kiv8RKdqCMhxYzJ/P/tvXl4XGd59/8558y+aqTROtoly7Itr/ISO87iJGSDBGgKCVBISkJrlhbIS4HQXoVfr3Klv755eflBS0iAJG2gTQohpJQAcUv2xPESO953ydp3aUaafeac3x9jSZY1Go3kkUaSn891nSvxmeecuWfRnPs8z/f+3jKKTiIaUVF0ErIOetrCFJSaWHu1i1WbnCxba6d8jRFPlYVr7yikYvn43bimafR1htj9uy7+64lm9vzbAM8+d5D+zuBYhYjeJKEWhBkemr4t/IgvStmyca3Cnp7x2YH44BDahSWpi+kYnqhB0Uwm1FA4IUQF5JbztL36ciLxCAUTPWOMJmSjCdloRLHa0EwmogMDBN87QNw7lDjR5ZSrWu3IRlPCoO2iC/FI4KLZBk1LJDayTGxoIO1Tr1ldhlS5AkNeIeGOlqTmaVosRrj9PKayaiyVMzcHUzzL0DlcRHo7J50/5h1EDQXQ1DiywYRkT+6Tos/JIzLYS7AtO9UugiyjTrMJ0kYkIwsMk0kCCeJT3GHrlcTdpaZpqBoYL1nK6eiIc/BAmBKPDoMh+YXGZpOx2yUO7C7F681cz5vZ4rGspi94YOzfsiwhSQnZAIDTkD9hvCxD3WorngojoaCKb0Cluz1MW1OIUDDOiDeOu8hA5Wo9fvoxWRTyCo0E/XE0NAxmiUGvn3AwjqJI5BYaKa4wY82RceTqqVs/bkS2ylzBT58+wL8/cpqXn+vgxP4hRo7LHPovH8/8n1O8+lw70XCcjfmVlK23oGkQCoyXn3Rc1M8GIDQcx2CUqV2faLi3uWjiUoJkMEB84q9YiSOJrkFVkRQZ6UI5anVnB1okSnx4GCQJWTc50TSYTGiKQsw3RKj5XMKczOliX3P6nX4n4B8mHtOQXfnEhgZRgwEs5kQipWkaajBIzDuIPjcfcvI49fobSU9z8HjyUmXJkYf7lg+jszsJNp8h0t9DPBhIdAoe7CHUdg5TWTX5N//RJBFuOqzZsQFfSSPIMqH2ZuIB/1hSEvP7iAcDSJKMpaIWyWihp3Pyspus0yeEx4HUfjCLndXb6zny9ulshyFYwohkZBbMpd/IsmV68t0KPT2pBXneIRW7XaK+fuJd9LGjYbxeDZcr9UdbWKSjoyPG2dMzrwSYa7+RnDwd7kI9g/1Te6roDRL166yUVpmo36jgcOnQ6yVy8/Ws3mJn9SYHJTnusfFFZUb0Bhm/L45FZ0LTNLSLrvnRiEp/awxbTRhPTcKdNB7X+J/nOjj+sh+9Saai3oanxoqnxkrBMgMGs8LeXd28+st24nGNonoTdRtcdJ8P4vdFKTGPG4ppmoZvIMJITxRrg0xJdXLhpHFZDUQitPuGUr5H8tAQPVYrSm4uWjRK+OwZJDS0cBjZZkOLRkDTiMQueQ8VBUnViHZ0oFhtrL1ustYBmNxcD8b8RjRNo/msl74jXo4d6eFctIjOWC6+oTCh3n604SHi3kE0TcXoqcCyYjWuokLwT3arnc5vxFJVR9Fd9+HafhOyzkDMO0jMN4RkdTCybDtFH/wTDLn5k45bvb5iWr8RALm0nqLb7sZSWUd0eJBg6zkCrWeJB/woFivW2lUY84sp8LiSHq9pWmKZZ4qKmoYttUvCb0QgmGuWrgR8kZKTo7B1q4lfPj9Cbq6C0Th5diMWg/a2GFuvNlNaNvEjDAQ0ZHmyydylGAwS8Tj4/QvPVVKvl9i03c4vnupjxBcjFgVfVEOxqZit40mWdyBGkcfAbX+h4fb4KXmmgHff9JJfNPkuOcdtoHaVjTPHRhjsiSKZJFRNIxSIM9QXITgSZ8P6QmrviqFcMFJrOTnCoTcGcBYrOPMmJn2SJOHINaDoJA6/1U/tuhyUfJkb7y5Db5A59e4gfZ0hRqQwst5PJBTHbNdx0+2V6DdrU3qVGBtWkV9cTG//AExR1qqGQuTqdHStbkDS6VCDgcQ00oVqFV2Oi2hPDzpVJZakGkVTVaRoBMOyOmTTZJ3DhlrPmKnZKKO6EVXVeOe1Dva80YE0PESNJGM06AmaKvD5AxhVPxarhqvajSHHhWwyX3jOGOgmLz+lYs0FgevaVaXkXXcbOZuvI+4fBkmmwuHk6NEudLYUDrJpYqmsw1xeS7inneiFpaR4KED/ay+imMwpj40H/ShGU9r+KZeD0I0IljIiGVmA3HablebzUQ68G6awUMHtVpDlhMX2wIBKa7tCbU0vd31k5aSkw6CXSLK8Pol4XEOSQG+YeSOzuaIvOF7i66kwIEnwh98MYTDIqHIcnaGL4mInxeUGNBUG+2LccGcum+ryaPKfYGWjnQNveQmHVIymyRfhonITRovEsb3DYFXx9sWQZcgtMHDdnS4atuTQpI4bZh3bO0QspmG2J7/r7Q4NUuhwMdAZ5uS+Qdy3wYlIK+/7RCVrtudz9tAQB053UmZzkF9qoXZtDnnFZvb2TF6W2NPTyuaCMnR5udhuvpHef3+WaEcHusLCMTFph89LoQaxri7M69cTX7kSAMlgRLZYkKJRQrEoNksuOrebeH8/RMKoqpoQq2oaSixKTNFhKSrCtGz5jD+jIwd6efvVdmx2A87iUmL9+TijA8gOGyaTnXDYQoc3hDqkY3mBmfZBHb0+mVC3nXhNJZUDIVy5sxN6KmbLBCfWTCLJMqaiMkwXls00VSVw9jiB86cxlVYlTe41TSPa34O1ZiXGgtQNCgVLFFVKbFM9JkgbkYxcDpG9c1Li63DI7PxzJy/8p589e0IcP5FotqapGk6nwo03eLjltpMUFk7++Kpq9JhMMDKiYrNNvVTT1xcnL09hTf1aQuHZ+Y34w+mV+AIMhVKX+Cb8Rg4D0HIuxH880UcoFMfl1hMciYNmJOQPc+aon3MnAtSssHDrR9zceOd4lUPNCgtVyy2cPuKnss6MTp94/UORXnIM+WiaRnBEZfl6O3d8qoQepY0qew2ugoTIVVU1Am1xvJEIeoPE+ZMj2HP05JpsdIUGyDeOixiLzTl0XtCD2Fw6Wk4Oc9PHVnJwqBVJkiiqtFJUacXQm1hua8wrmfK1by4qY0/X+JKC5eqtRIYGkN89TOTsOZBl3JLEwPAIqjsfy1VX4bzzDhgYBEBSFMzrN2A7cgT/hWUDxWZDNhqJDA4iRSKJsltZRjJbiHvK0OXno3OOa2P2NbezsTL1BTUWVTmypwedTsbpSsw+DeXVkdfxJlIsjKYzYjQqWC0GmtolTgcs9I3oiQZDIHsIN1s59v/tZdtVZWy/vhSzefz7O12fmrli9cZKDu9rZvW68gn7JVnGtWUHkYEeQh0tGAs9FHhc9LQPUlDsRI1FCXd3JMp6N113RfStEX4jgrlEJCOzZMxvZI5wOhU+9UkHt99m5fjxCIGgitEgUVdnoKRERyCcfEajrk7PsjoDRw9HWL5Cn/RHMhrV6O2J8/4PWHHlKnhDM48v4TeyP62xCb+Rg2mN9Q/H+cVTfXR3RFi5LqHdGOyL0dMRod8bwaQzEYtp2JwKazbbJlQd6Q0yd36yiF8+0UnTyQA2pw5nbg5D0UEGvREGe6M4XDpu/1gxy9faUX06Cm1mImGVI+8McWj3EMfPDKKp+8iz5dBxLkBugQFILY6UZQlV1YjHJn8mm/LL2ds7ua/Jvr7zbHQn99mRJIlY4zqaqypY2TFArLcHZJmopuLeth1dSUnicx0YZE9HG5tLSjGvXo2hohJ/Vxeq349isyHp9WgOJ2FNw2o0oHq9KBYLYaMJU3kFhvJKABqrPew/l0Qj0tROY9V4gtLXEeT02X7ql42bwJlr19Hta6VouImYOZe40UZAtnJs0Ik5LlFo8qK3xDHXLkdf6KH5fD9793QxPBzh/XdWYzTpUvuNTMOhI62saUiuO0nHbyQV5tIqCm6+i75XXyTc2QKShOoNEVQHQdMw5BeTf+37x2ZTpkL4jQjS5Qc/+AH/+3//bzo7O1m1ahXf/e53ueaaa5KOfeWVV9ixY8ek/cePH6e+fvH1FBLJyALH7Va45prU69YXI8sSd/2xnd6eIU6eiFJWrsN6QWehaRo+r0pbW4wVKw3ccuv0beTnm2PvBWg7H6ZymWlMV5FXoCevQI83EsCmd6BpGmeOBTmyd4Ti0vEp/7bAEUqLGrjncx4OvuXlwFte+rsj+CJx8hwaW27KZd22nAldfIP+GC/+tINj+7zIikRhXg6DMR+xiEpve4je9hB6owLJ9YsAhAJx8opM6I0ypFFUsbmwjD3dU198I83nqT94iPNv72ZEZ0BSFIx1y1BLS9AVF48lmJvKStnbmljy0eXn4/roR+lvaiLe0Y6mqig2GzaDnhHfMFrQj2yxoisqhlAE6+atY5U4yUimGylx5qCqHegN48tWms5Ae/m12IZyMPeeRhlso8m7HC0aJTfei9HkxFS+DH1hIoGy2vTk2q2cPDFIWXkfm66aui8MTNSNXMrqdeUcPjjzBnYzwVKxDM9H/4xA8ymCLacZPtWKo7YUS8UyLBV1s6riuRyEbmTp8uyzz/KlL32JH/zgB1x99dU89thj3HbbbRw7dozy8vIpjzt58iQOx7h2Kj9/sqB7MSCSkUVMPLIbxTB5maS6Rs/Oz+Xw82eHOXMmQjisjZXK2u0yW7eZ+ejddnLzxi8qs12qyTRvvXkKnd41pc/KSLQbm74Qh0vHe+8Mc93tuRiMMtW2FZwbOQ6AI0fHtbfnsWVHDgO9Uc4ON1FfVI3DNbHcVdM0dv2ii8PvDFFSaR6zhg+FZNxGM8vWqhx9Z4gzh7wUrFUgiUaxKzBAOKBnxebcsSTh4GAT61wzvwve093CqjMdDL/4O1TfMBj1yA4zWjRGcM8+jG9FOdh0nnUf+3jSRMK0ciXhz/w52s+fxXL6NFGfD8loQJJk5Dw3ssmEbDBSedv7OeYqYKa+uFNoblF1RoaW3cBI6XqGm87TFwQIMuLOoWjdMmT9xPfdYFQwm3UcOdzH2g35GC4kN5qmEenpxH/yMKHzZ9BiMXQ5eWj6QqIeM7GhAbRYDNlixVjoSWlVn0kUkxl7/Vrs9WvpyTlHwYbF5xwtmBskVUKaQhsy1f6p+M53vsP999/PAw88AMB3v/tdfv/73/Poo4/y8MMPT3lcQUEBOTk5M3quhYhIRi6XOdKNTIfFeBWBFOW11TV6vvI1F6dORTl7OkI4kmgct2KlgfKKic6miT41sytVzrRuZKj/PcyW5FoXpyEfb6QXALNFJuhXCQZUDMbk441mheJyheFhhQFO42DlhMdzBsvY+85hikpyJ/SoGSWv2ERukZG+zjB53SZ6cybqRopMTo4c66Wq1EHt2hwg0admf9/krrH7+8/TmDf1RWxzURn7X/4ffL95Gdmgx7h8GdqwD2W0WiQvl3yvl963djPiKcV+ww1Jz9O4fRt7PR6qWlrw73mHaEcHwXAEY0EBpvp6zGvXY6ypheaOScdOpxtxuU3odBLBQAyz5ZKfDkkiZsml26QQNPnQ22XkfNOkRASgq89Hbq6V3p4gPd0BSsvsaPE4B555htyu48T9I8gWK5KsEGo/T7ytnTMvKehcech6A5LBmGhUt2YTmpo36fwzZSrdSDIaNlcnlkmu0IRE6EZmjs830bAwWVf6SCTC/v37+frXvz5h/80338xbb72V8vzr168nFAqxcuVK/uZv/ibp0s1iQPiMXAYG45Zsh5ASRZFYscLAB+60cdcf27n1disVlcl1JLMh19SY9thC87q0xun0Ca3lpWiaxlB/jHNHVfa97uPQnmFazgVpORec0iBulDp7cnfOk4eGCfk1bM7JOXlfuB+TWaF2tQObU8f5kyMMtEWJRlSiEZXBnjAtJ0aw5irc9LEyHLlTl61uyk9+kdt3UdKiqSr6Pe9CPIauaHwKpn1k/IdMcTrRrFYCb+8mPjK+HrSn45LqHEXBtv0aCr78vyj+m78l/Ok/o+2ue3Dd9VFMo118L6GxenISsqHWM8FvpKTcTm6RidNNAxMcS4vdDjoHEnHGYirRSBy7w4DFOvl9LcxPiGZ1Opl4XCMWTZi9VAyfgUNvJhKNymUYCz3o7E5iviHwDxMdHiI+7MVQWII+102oo4XeF3+Ox3eM9w5NTv5GSddvZK4RfiNXLmVlZTidzrEt2SxHX18f8XicwsKJ06+FhYV0dSXvml1cXMzjjz/Oc889xy9/+UuWL1/OjTfeyGuvvTYnr2OuETMjggVFzUp453cxNG08aYpGVE4eCdLVHiEUUTEbY4z44pitCs/8sJOGRjt3fDwf5Au6EUtDWs/V3xVBZ5zsyVJgctETSlSq5LgNrL7KxZlDPiKBGK2nR5AlCUeens03FyCtDFJWN3Xn16m4VDcSPd+K3NKOrnQ8cfHYHbRfagOf6yLe20f4+AksmzZO0I1ciiRJKDk5NK7PYd/5WbqsXoQsS9z5oXqe/skhujsCFBRbJvilqKpGwB9FkiQ8ZTbC2tTW+JFwHL1exmhUiA704dv3Jpht6HMuzHRoGoFzJ4l5B7EWFeIPhJFHhon0dmEur0bxlBPzDeHd8xpanQRrUs9UaPE4wfYmgufPEPOPoBiNmDyVWCqWIc9hT5m5QOhGFhetra0TNB2XzopczKW/Rcn6Jo2yfPlyli8fL8/funUrra2tPPLII1x77bWXGfX8I2ZGFjEW41XEI5mzcw+l6ao6l6zYIKGYvWPuq6qqcfJwkLbmMGaLTI5bQjYGMVlkGjbacBcaePdNHy883UOpfma+GbIsQRo2K7GIOjYTMBL3IysSOW4jheUWbG4dh4Ym36UeHJzZXXD8QgluJ9P0tlEUBkIh4kNDKYftbU+eoIzSWOVhX8vME5TKWifrryvAbNXR2uSjs22Evp4ggz0hWpuHyc0zUVJmw2xJLM909vuSnmdgIERRsZWCIguBU8eI+4bAnjP2eGzYS2yoH8VqB1kBSUY2Goj0dqLGEu+RzpGTqHA5dwQtPrVjsebro/OXT9H53JMMvP0Hho+9y+C+N+j6z5/R9sxj+M+dnPH7cKUjrOETSPHUG4DD4ZiwJUtG3G43iqJMmgXp6emZNFuSiquuuorTpxfnZyOSkUwwR9bw84nTNHvdS7rW8MC01vCbVqxm845EOW9vV5T+7iidbREcTgW9QUKLmBnxShSVGnEX6rHaFcqqjRzeP8LJQ/6U5272H5vw7+IKE2bNSn+od8pjulqCHN49iG8gisedQ26pntxCI53NAf7zx82892iII7/1suf3XZx6d5BwKE7jFCW7+/unXk5AknCZzEznWJfoU6NNrSYFNpXP3q8jnT41nhobH/v0Ct53ZxWecjs2u57cQjPve38F939hLavWuOnp8pPvSj5jFArG6B8M0LDGjaLIhDtbkfQGkGS6ehPJS3SwHy0WS+y/gGwwoYZDqMHg2D59bj7aQAfhnskaGIDoUD/xd36Dv+kE+twCLBW1mD2VWMprMBaVERnooef3P6fGHU27MqdhczVH3k3xWaYgk0s12WL19sVXNrrQMRgMNDY2smvXrgn7d+3axbZt29I+z4EDByguLs50ePOCWKa5TKbyGwmHNc6dixIKaRgMUF2tx2xeerlfpv1GJEniug9I5FpzefN/vBx+N8CINwboiMc1jEaZkiqoX20Zs203WRQkCQ687ePq+in8V+x1nBqeOLW9fK2dN37bh7c/QF4S3ea5th46j2hEIip1a53kl5joCgWx6vX4BiJ0twQ4dWAIUy60V6rIskResYmNNxWiVcYIHT9NpLkFLRKl3mrmpNuMlls+Ydp11G9El5+fEG2O+MGWM/b46FKN54KQVYtGQZLQuSeW7436jaRib2s7m8qmFqiO+o0Me8OcPT7EmWODBINR3gy1c+3WCmrqc7A7E3d1p/r6aNzsYf3m8bu2AyfbceWZ2HFrBYFfxmht9hGXY6iuhP19NBJnoD+EpOqpWWFh1eoLSzJqHEmWKSp00NXtu/A6w4mOiBcjjc5kjX/GstGE06bnxJEm1hVP9vsY2v8GtrgXnyGPokus3WW9HlNxOaGO8wy8uQut9uaU79/lMuo3kimE38jS4sEHH+STn/wkGzduZOvWrTz++OO0tLSwc+dOAB566CHa29v513/9VyBRbVNZWcmqVauIRCL89Kc/5bnnnuO5557L5suYNSIZyTDhsMbLrwR47bUg7e0xIhENvV6ipFjH9u0mbrjBgmWKahFBAkUncdOdLtZtsfKtL57HYpWx2WUsNoX8YgNxY2TMXTUUVImEVXQ6iZazITaHoU1KTzeSW2Bg845cXvjFEEPGCM68cZ1KvjGHs+e7GBmCfI+JwrKErkDToOXUCO1nRlD0Eq4CI/5ImHyPGb1Bpr8rxJuP72GZfi99+kgieUACTcWgg8E1p3B8+APo8lwTdCM6TzHG5bXIu3ejXfDkSEasuxs1Px/j8nFRbirdyCgbKzxp6UbaTw1z8FAvQ/0hDEYFnV6mfyDIrv9sYt+bJq67pZz1qxJ9aqbCnW/mzo/UsuflZva8fZ7zp6LIegOKXk9+oYU16/LBFEZREp+hzpWPGjk4QRQrybrxts2A1WrCPziE2aif4O2hxWNIsoykTP4pi/oG8Z88jN6VhxRMXgYsSRJGdxGhrjY0ZyuwOC7uQjey9Lj77rvp7+/n7/7u7+js7KShoYEXX3yRiorETGtnZyctLeOzd5FIhK985Su0t7djNptZtWoVv/nNb7j99tuz9RIuC5GMZJBQSOWJJ3y8/kYIm02ivEyP0ZjoX9bdHeNn/zbMmbMxPvOAY8yI7HIZLfFN5jcyGxaK30hf8AAu9zqKSw0UFIO7cLxE1BuR6OzpYqjDTm9XhFg0MXthMMpsed1D4eYeSLOFyfbb3LSMdND6lkrzcT9mu4KiSAz1RelvjeIusFK3zoH5QmWIfyDO+TMD2C1mjGYFTdMIB/Q0tfezcnkhZbYhrIdeJhIYpOuqUiqqEz8kmqah9XQS3H+Q+PAwufd9HMWVMxaHJElYr9uOu+k8XadOULRseaKfzAU0TSPe04MWi1Pz4Q+xb7CPzcXTzIS0t7HJM/WYxioP+5ra2ViemC05d2KI9/7Qi8tsprTKMSZOjepjFDnt9HT62fXrpoQJXAoiXR1oh/axquUQ9vgQg0ET9rx8curqWHZ9FZaCfA4dGxfvWpevwrfvDVT/eIWQzuEESUJT40hy4n0mEkZfUjJBcBobGkDvckNOweQ4ejqJjXgxlVZDMLl2BRKzK8RjaN4eDh9sSavEV5DQjYgS38zyuc99js997nNJH3vqqacm/PurX/0qX/3qV+chqvlB3KJnishefvu7AK+/HqKyUkdlhR6TSUrceRklysv1VFfpeWd3kBdeSK1tyBYLRTfisawGEqXJ7kIDI8MTxYnBHifH9qm0NYeQJbDaFXRKYgr/97/s5zc/CtHfHUl67lHdSCyqMtgbwTsQ5aMfbuCanSrX3VmIu8iEPUePp8pCXqmeddfkYnOOJ0JanwE1nvAwgUQSIckQDaqgqjhPvo2VAEGTh2Hv+J+XJEmUFJYw4MklfPIsI6+8OSk2Q001znv+GM3hIHL6LJGWVmLdPchd3UROnQYkHHfegXnT9HZlM9WNqHGNva91EIuoxCyxSV2FZVmisMRKKBBj7+sdqGry5bB3X3mD3mefwvfGy2iqRmV9KaUeA1W2QRzHXmLoV08T7kzM4hw8kfivoaQMa90qIj2dFOQY6Or1ocvJQ7HaUf3DaJpKfMQHegPGwvEeP2o0SmzEh23VeqQkTqhaLAYaY0ldd5c35XtQXpmb8vGLEboRoRsBID7NJkgbkYxkAINxC8PD8PprQVwuGdsUsx4Wi0x+vsLbu4MMDCydb+pc+I2MsmGrjVhUIxZNXPy8gzFOHA6gxjVceTrMVgVZSSyfLGuwUL3cROsplf/8adeYh8UodfY6Aj6Vd/6nn5883MTjf3+OH337HD/+dhNtx6OsvdrFpx+q4c/+dhk3311MSYGDofjg2PHxmMpgbxi96ZIlFA0kGYwD7RgGu4g43OiNCsO9sckvSKdDl5dL8MB7xL2Ju/WL/UZMq1YQuu8enHffhaG8DMlsorCigu5rryL3s3+O9Zqrp1zCmeQ3koS9rcmXV9qah+lqHWFZde6k8xfnOugY9CFJEu4CCx2tIwx0hdh/duK5GtxGpNd/R8znxVi1DH2eG9loBIMRQ34hpsoaIl3tDPzmF6yqGjcrkySJ3PfdiXXVOiKd7dDbTjwUwFBYghaNEu1uTyQUJTXo7E40TSM2Mky4rRlL9XIcazezel05h45M9BNRLFYknQ41EqagOGfK90S70FxQnqOOwBfTsKU2o+cTfiOCpYJIRjLE8RMK3T1xCgtTT2G73Qr9/SpHjya/cxdMZOVaC5W1JppPh4jHNDpbI4RDKlZH4iI2aoZmz9FRUGxAp5cprpI5eqSLc8cDE87V2xXmtz8K8JufdjHQE8Hq0GFz6hjxxXjnVwGe/adm2s4mjnEXGbE5dfgHxxOaeFxDUzWki/5qEjMEGkargmGoGykeRdMbkRVQY1rSymElL5d4/yDRljY2F04WXWpOB9bt28j7wk4KvvFV8h/8S6LXbENfPHUfl01l08+EbKxILl7d19LOYF+QeEzDaE69cmuy6IhGVIqsjkmPBY6+hzTYh6G0ImnCJMkKxtJKwh2tBE8fn/CYYrWRf8fduD94DxSWogYDyHo95urlmKrqMOQV4DTE6Tx8hFDzGeL+YWwNjRR84J5E+W8SjCXlGAtKiA5MXS0FiTJinc2BpWpmpeHZpmFbcjM/gWAxIpKRDDHiT/z4TtVTZRRFkZAk8PvVlONmwlL0G4GEbsRqV/jIffmUVRs5eTjAuVNBFJ1ELCoxODTCQG8Mu0Nh5TorRlPi61zoyEdVNY69O65BCAfjvPBUF51n4pQvM1NcYcbm0GG16ygsNVFcp6O3I8xvnm7H2x/BkatnZaOT4f742JKEopORdTJqHPyxRHlpcCSGyaqjtjwPX8jL6DU4HtNQDBLdoaFJr0u6INxMiFsns7mojD09qV1DN3k87OlMYyYkDb8RAG2GX8dLV2m0WBT/oXfBbJ2UiBS6HXRd8BuRdDrQ6fAfOTDpnLLRhH3NRqQb76b001/Cc98Xqfzy/0PNX/8fij/+57iufh/Sik3k3fABSj725xTc+TF0dueUMco6PY41m9GiEWIjyTUjaiRMdKAH67JVGFxugDlvvreUEH4jgkwhkpEModeDFhucUBGQDE3T0FTQ6TNjyZ5pFppuBKC4zMCf/mURN92ZaKCnqhpaxIjeCHUNFtZuceBwTbyjN5olBnrHZ59OHfbTdCrIyvp8RrT+Sc9XaHZjLQ/Q2RLkxIHEhWvDdbnkluhoO+0nFlUTGpZiI0pUh6apBEZiqHHw1FjRG2TiekPCJkRNLCtVludMep4Si5NOby8oCpJlbpcFZqIbsTkSlUSxqEqJy0HHUPKLdzQSR5YlbI6JPWfUYBA14AeTma6BqcWiAIrZQmywH03TxnQjF7NubQVH2kfQu/KQTWZkvQHr8gZyr78VpfFGcrbuwFjkSautgb1hI86N24kN9aP1d9B1vgstHkeNhAn3dBDqbMVat5q8a24BEn1q0kXoRoRuRFJTb4L0EclIhli+fA0Oh8bgYOpvoM+nYbVKVFdNbiC2EBjoj3PyuMLxo2G6OmPTJlejzKVuBCAnV8e2GxysWGth/RYbV13nYN12mco6M+YkGh01PnGW6tAeH4oCesPUX3lZkbDYFA69NUg8ppJfYuJPd67CUBKi/VyA1jN+JCkhfvV2JjQ/FfU2CssT/hUjuSWoBjOhPi8mq0JuUXKbcalvEH1JEYaqcXO0fUma613M5uLSCfbxydhUVjqtbmRjhSepbqS8xkleoYnBvlDS40Z1I4N9IdyFZsqrHKxf5hnXjSg6kGTczokJlhaPJ/rLDA8RG/ahqSpaXAWdwtpVs6taee9o8teYTDciKQp5195OwW0fpXD1Kgh4CXW0EOnrRu9wkX/DHRTe9lEUs3VWscwGoRsRCCYjSnszhKdEx7o1MV5+I4bTKY8Zcl2Mqmq0tcXYuMlIVdXCeuvbWqO8/Icg+/eF6Bswocj9WG0yq9YYueZ6C8vrp+6nMF+43HqKPEa62iPkFylEI8nvjDVNIxzUsFX0AmVomkZvRwSLbfqW81a7jmFvjFBAxeqQ8VRZuPlzTpTzRZx814tvMILJouNc8wBulwlXoXFMu+LFQYdUQkHkFIXLCjBaFJgoWyE+4odgCMvWTcjGhLvopX1qssGhni7WbC7g5V+fxzcUTjom4Iti0iTWbSlEb5j4XspmMwZPKcFTx8HqRovFiHS1E+3uIO73QyjCSJsRnc0Bmobr5jtmFeea1WUcOjyz90pSFGwr1xPOX05H7AAU27DbjRStqkBvWlx9aS5F+I0IlgoL64q4iJEkiTs/EOF8u54TJ6JUVOiw2cbvwv0BlfPNMTwehbv+yJaxzrmjXI7fyKmTEZ74sZf2thj5hQpVNSqS1EtgpIjdbwY5djjMxz7lZNMW8/QnyzB9wQO4zesB0OslNm238/Mne4mEVZzGfLyRbmz6ib0b+nuiFLtd1K5P3OWPlt+mM8kzGBnEKjkniFQNZpnlm3JYuSkHSCQ7//X6CU6/EWCoPUJvJAQaWCxWfNc0sj5owth9nmhnEKw6ukJDFEhmYj19qMEQ8U1rsG7bnDKOzUVl7OlqZXPBZIHrKJs8Hva0t6X0G9lUXsrelun9RvY3tbNmcyH+4Rj73+ikbyiITTOiM8hEIyq+wTDhcJzrby2hYUP+pHNIkoR1TSPBk8cgMEKg8xzR3m4knQ7ZYsVisxMYCWAc6EtoOAYH0OJJqo3mgO6eAHv2dnH2nI+uDhVHWxSdEqP45HnWr3WzakXupFLmmfiNHHn3PA0bUjfqW8oIvxFBJhDJSAYpKdH4/GfO89OfV3HyZITQeQ1ZkoirGiaTxMqVBj7+cTsVFQtnicbnU3n6X3x0d8dZscpw4Ue5hHCsA1euQo5LpuV8jGd/5qWoWEdZeerY/eHdWNM0TRsK7SPHNLVnhseymvbA4Qn71m22cuRdP8cOBvBUGmC8dQmqqjHQE8XnjXPzh/PILewZe6yyzsI7Lw9SUGIkz5hHf7iXHMPEi2q+MY/uwW6qGsyYrVPPokiSxB3XruDdVedw9RfjG4giSZCTb6TD3k2NspHhV98msOcguV39DIb8RC0O9CXFWLdupHTbZvYNddCYt7AuYLIisfUmD55KGyfe62f/u53kms0oepmGxnxUVxxDGZMu3KOYl6/E2rCOoReeYzgYwFpQmBCsjqKqSLKMwVNO4Mxxht99B6zlHDzRxrr6icnS2tVlHDzcyroVs++zA9DWPsJ//aaJ/oEQeXkmCvINSJJKjstGd0+A3/z+PEPeMNu3Fo/dIKzeWMnhfc1pnb9hc/Wsl0mO7GuiYWPVrI5dKKzeXs/hN05kO4ysIanjDfGSPSZIH5GMZBCDcQulpe/wV19xcfp0lOPHI/gDKhazTF2dnhUrDEmXb7LJwQMhzp+PUrfckPQiI0kS5RU6jh+LsH9PMGUykuk+Ncmw2BTuvj+f55/u48ThAIN+Fac1hKpqRMIaTpeOW+7K49pbczkfHE9GGjbaOfCmlxFfDJsj+dc+ElKJx2HNVTkTZq4anDUc8Z5iuX1iKaWsSJTW2ibs6xyQUBw2cu54H/Yd24icb6evt4X8whoMleVIhqnfv9E+NVOxubiUPZ2tScuBR9lUVsqe1tR9ajZWeNh7PnmfGlmWqKzLobIuB8cKHauKCjEYFSzWRNzvnpmoN1m/zMP+0+001niQ9XrsV1+P9eWXCIRDxIe9iWRES9i2o0oYK6swVdQQ7eli5MA7NNy3iSNnuqeMdSreO9rG2lWTX+PqdeUcOtjCmoYyQuEYu/67hUFvmPJyO7IsYbcb6OkcwmhUKCm2MuQNs3tPN0WFVpbVTF2Zk2ky2admdKlmLvrUxMNBgh3nUUMBJEWHscCDISdv+gMFghkikpE5QFEk6usN1Ncbph+cYeKRmS3V7H0nhMEgpSxJliQJl0th7zshbv2ADZMpu7pnp0vHJz9fyPkzIV7dPUJ0wIpOD+U1Zlass+IuHH/f2wKJPjWVyy2sucrB3leGKCoFLpEKBP1x2puDVDToqVub3Ldipig2K+ZVdWh9Royu1HfAC0E3sq9l3BoewGjRkZM7M01FtKMNfa4bLb8MUzxI3J8Q/cp2B2HJgslTiCRJ6PLyiXS1EW5tIm3v/gukqxtpavLR1ROgpNg65WxOjtOIzxfh6LF+aqsdGV8+Xayo0QhDh/cwfPwAUW8/aBoaGjqzDWtlHTnrt4+VQgsEmUBU0ywhLLPoKdPfr07ZJycWH78LtlgkggGVYCC96ppM0hec7EmhKBLVy8386b1ruOXPevnE50q45hbXhESk2rZiwvjb7y5g844chgaitJ6M0dUaorstRNNxP72dYRo2ObjmHiut8fS9E476Jt7dbsit5NDQxGn7RncFBwdnXsqZKb+RTeWlafuNXC7xwEiiu67NgalqGdaGdVhWrcNUXk1hWRHdA8MAyAYDqCrxwNy1Rjhz1osE6HSpf+Zycow0twzj8000IpyJ38hsS3wXImosSu9rL9L/1kuo4SDGglLMnirMJVVIeiPeo/vpeunnhAfGZx6F34jgchHJyFwQ2ZvtCNLGaIRYbHKCYdSVTPh3LAaykhCRpiLX1DhnfiOXi8mi8MFPFXHf/yrjtg+UY8j34S4ysvE6F5/4y3I+8udlNJal78K5Lmf26/2b8svZ3z+7C9h8zaA0VnvY1zy5DHh/09QdexPdcxPfp1R+I9qFjryj3XaT+Y2sXV3GwePTG7sl49CRVvz+6KSqn1Eu7lNjNCjEYirh8Pji/0z9RmbLQvQbGT5+gOETBzC6izDkFiBf0P1IkoTe7sTsqSLc10n/G79HU+PCb0SQEUQykmEMxi3ZDmFGNKwx4vNp0/qJ9PfFqV1mwGrL3DT2bPxGLhdZlqiotXD7PYX80YM27n+oijvuLaFmlQ0lxVJVg7OGk8NzX0KZjt/IdMzab0RVCbc0M7L7TYbffI3Agf0wMjxhyIbayTMoF/uNGIo8SHo9BbbUQue4bwjZasdQ7GHNyqk1MKlI5TcCYDIlkoxLubRPTSymosgSOv38/hxm0m+kYVtdRvxG1FgU34kDyAYTiin58pkkyxjzigl2nifUNbtkcakgTM8yh0hGliAzsYbfuNGEwyHR1zv1X87wsIokw5at5kW5pt4WODLvz3npUk06TCVMnW6pBkjLGj4VkZbz6F74OSe//12GfvUc3v96gYH/+Bn6Z/8F7+9fRA0nN0O7FGNFFcbSciJdHVPWUmtqnOhAL5blDehdsxNDrlk9fQJTWekgHlOJx1Mn2kPeMMXFVnKc2ffSyTbh3k4i/T3onak7GCsmM2o0QrCjeX4CEyx5RDKyxJipbqS8Qsctt1oZHIzT0R6b8MOtadDd3UFbS5St2800rM2eQVQy3cgoFdaVdAXfS/rYxbqRmXBmJP1yxWS6kUuZSjcy3VLN5qLpL7qbPNPrPVLpRsLNTQw88zPyutrQHE4M1bUYq2sxVFSDquJ7ZReDL/wCNTJ9c0dJp8O54xYUuwOtux01OvGYfJuerqNHMRSX4rzq2mnPB8x6qaa2JgeXy0RPb2DKMYFg4jvfkMRrBK483YgaCaHGY0j66cX3kiwTD46/t0I3Irgc5jQZaW5u5v7776eqqgqz2UxNTQ3f/OY3iaTxo7boWSS6EUmSeP8dVj56tw29XuLk8Sgnjkc4eSLC2ZNuImGJW95v454/cU6rFxllIetGLqbOXkez/9ik/SscUxs4XbpUc7m6kdmSKd2IFo3i/c2viQ8MYKipBbNlbPZLUhSKqysZsOYQOPgugXfHv9OpdCOmqlrcH/4Y5ObTc+oUoabThFuaCDWdJtrbDSWV5H/4E+jdBWPHrFlZNqVuZLaca+ri+ms9KIpEe/sIkci4JkRVNc6c6aOnJ8Cahjzql7smHX8l6kZknR5JUdBiyZs4XowWV1GMiRsUoRsRXC5zWtp74sQJVFXlscceo7a2liNHjvCZz3wGv9/PI488MpdPnVUMxi1Ewu9kO4y0URSJW2+3seUqMwcPhunoiBGPQWGhQvWKNsor5s5/YbZ+I9kg4Tdydk6fI1Him47fyDQVNGn6jbz7h1coamtF5ymdegnOZEKKh/Ef2Ie1cTMbaj0p/UYATDXLWPOVr/HeH97AoY6ghoIoVhum6joGg0YMBcUp40+XVH4jhw+2sKI+F0WReWt3J93dAeJxFSTQNAWIs31rMduuKpq24mauWGh+I8b8Egw5bqK+AYx5RVOOUyMhJL0OUxozd0sZKZ7C9GyK/YLkzGkycuutt3LrrbeO/bu6upqTJ0/y6KOPLulkZCEwU78RAFeuwo4bJorWvKH5L+XNNNW2FZwbSfiNzCeHhs6xJiczRlR7elJbw0NCN5KOwPVi5NYWtHgM2Tiul+jw+ihxOiaM0+W5iXZ1EunqwFiWnnOsbDRCdT25l4pej7dx4FQb6+suz101Xb+RumU5VFU6ON/io7cviKpqWK16wiM+rrpqYmxaPEawvZnY0ABIEmpvEC1eMlb1s9SRDUbsy9fS9+bvUCNhZMNkHY2maYR7OzEVV2D2LG4HWcHCYd5vB7xeL7m5qcVR4XAYn883YROkz2z8RlIRmsGSy1zhsayetW5kOpIt1UD6upF1OVWLVjcihUJIuvHKl2KX49LDAOgOhNDicbQMLLHOxuJ9trqRUfR6mdqaHLZuKebqrSWsW5OPxTyeYGiaxsipw7Q/+yM6f/EkPb9/jp7f/YLYm7/kwD9/h5FTh9PqYL0UdCOOlY3YalYR6m4j6h0YK8PWNI140E+ooxm9Mw/31TcjKRNLp4VuRDBb5jUZOXv2LN///vfZuXNnynEPP/wwTqdzbCsrW6RTgYtEN5IKp2nTrI6biW6k0LxuWt3IXFF3icX7KDPRjVwOc60bma7EVzOZGBgeTjmm2OWASDTR9O6iGZRkupHREt+LuXRJZyrmQjdy6Ejq9+jQe4nHfYf20P3bnxPubkPvLsRcXoO5vIb8umq0gS66X3qO4SOpv6NLRTeiGE0U7PggOeuvRtNUQh3NBNqbCLU3ER3xYqlYRuHNd2EqnJhUCt2I4HKYVTLyrW99K9EJNcW2b9/EP9yOjg5uvfVWPvKRj/DAAw+kPP9DDz2E1+sd21pbs2uTPRsWm9+IID0anDVz/hybC8sy5jcyHfVXbQZFQQ2lLt2VhgbQF3vQFyXM8KbyG5m0b3lmnF1TMZ3fyFSsXp9Ybgr3dDDw5kvIOj2mkooxUSYkLsxSXgmyoqP/zZcI93ZmLvALLES/EcVkJn/7rZTe9RkKbvwQ7m034772/Xju+CTFt38cU8Hcf66LAVlNvQnSZ1YLoV/4whe45557Uo6prKwc+/+Ojg527NjB1q1befzxx6c9v9FoxGgUNf+Xy2x0I0uVxaAb0TSNga4Qnc1+mnt8WAv7KK11YHclyiwzpRvZ297GJk9ijLG6BrWklGh7G4aqaiQ5cX9ysW4k7h+BWAzrho0Tu/BeJvOpG0nFyOmjxEZ8mMunTgr0eQUEW84ycuYoxvzMiG8XA3q7E/2KDdkOQ3AFMKuZEbfbTX19fcrNZErcXbS3t3P99dezYcMGnnzySWRZWJvMB5nUjThNm4RuZBouVzfy6skzvPR0Ez//7gle+mkTh3/dz+//9RzPfucYrz3fwmrb1JUNo6SrG7kYSa8nuuMm9AUFRM6dJe7zUpSTaBSoxaJEu7uIdXUQX7WWE67MXYSzoRtJhqapjJw8hM6aukleT7cPxWpLaEfU1Le8S0E3cjkI3YhgNsxpZtDR0cH1119PWVkZjzzyCL29vXR1ddHV1TWXT7tgMBi3LAndyGwRupH0qFEL2P/vvRx9ux+jRUfZMjvuahOeWjuSDPv/u5NdP2siEphcK5gR3UhRMa3Xvw/L+g2o/gCRs2eQW88TaW1Btlhw3HYHaz/9p5BkViQd3cj65ZNLgadi3nUj8Rh9nQNI+qnt6wtLcgCQ9Qa0SDilB8dC0Y1kYqlmNgjdiGC2zGm92ksvvcSZM2c4c+YMpaUT74TSUaYLBAuRTPqNaJrGGy+0MdgWYfVKF7KSuDv3WJ20+72UuHOw2vWcPTSIz25ie+X4sen6jextnb5PzT7AtWE9se4uou3t9LZ1kVdVgbG6BtmcvEfJVH4jB06nl3hMR8znJdLVhhqNolisaLHkTe9ger+RqVi9oZoD/61Pz+QrFkU2WSZUH2WKufAbEQgWE3M6M3LfffehaVrSTTA/zKRPzVJhdKlG0zS62sO88/IQr/9+kL79JRxpPjTv8STrUzO6VNPdEqD15DCOIv1YInIpeqOCzWWg/dAIbzQ3T/t86fSpSWYNL0kS+qJiLI0bUdes56g9d8pEJFMcODU5Dm3ES/9LL9Dx5PfoevYJen75NF3/9iO03/0U7743JyUO6fSpmQpJUZA8y4gN+1L+LmmaRmzEh23ZqjFdjUAwano21SZIH/FXtYS5UnUjAH3dEZ59rIvHHm7luae6+a9nevnFE10880iIXz3VhW8oNunY+dSNjNJ+ZoRgII7BKtMRHJry3M48I6aAnsGW1FUvs9GNJKOxKvl59jVnZtYDkutGogN98PKv8L71CgCm8irMlbUYikpgeJCzz/0bff/9a7T45M9vtiieOhSrnehg39SDRgbxhWWstekJoIVuROhGBDNDJCNzjNCNzL9uZKBb499/2Mn+t3xYbAo1K8zU1JuprjejN8I7Lw/y88c7GPaOX9CypRuJhuPIEpRac1KOU3QymgaxyOS790zoRgD2tqZONBqrkycomdKNaKrK4K7/xBHsx2vLR5/rRpITSzOywUjRijpwuBk+8DbDh2b2PUmpG3EV4i1cjRoKEOpqm9AQUI1ECHW1kWPXIS+/CmNBybTPNVvdSMOWWqEbEVyxiGREsKTQNI3X/zNGy9kQ1fVmnC7dWJWELEs4cmUqlpk5fdTP67/tn/XzZMpvRG9Uply69FiddASGAIjHVCQJdIaJSzmZ8hvZWDE734h0/UbSIdLRQrDpTGIWRJlCH2K2IukNDB/aO2m5ZrZ+I2s2VCIvayT/5j/C4HIT7mkn0HKWQMtZwj3tGFxuCm7+I5Sa9SkrbhYSDduSJ9cCwUJFJCNXAJnUjSyEpZpUdLZGOHdMxV7gQ0miwXAZ3ASlPnLdeo7sHcY3OL1wMRNMpRspXWbDZNERGE697ODtC+N0G3GVm9jTk8ZMyCx1I5eyryVzyzJTMaobCTadSTTUs9hSjtfn5hPp7iDcNR7b5ehGIKGXcaxqxPOxnRR/6FPk33gn+TfeSfGHPoXnY5/FvmrDoklEBPOH0IxkDpGMLHEyrRuZLeku1aRDKt1I0+kQUtCBdZpGwy63Hu9AjJYzwQn751s3UlBmoWy5nb6OEGpcS6obiYTjDA9FWL7Rzfbqyee6lMWqG1ED/gni0K6+5D2perxh1GgENRRM+vjlIOsNWGtWkLPhanI2XI21ZgXyhbLf1RsrU1bmXEzD5uorWjeyenu90I0IZoRIRuYBoRtpTHvs5epGImENSWbau1hZkUCCcHjcwCobuhFJkrjmQ6UUV1lR22SCQ7GxJRs1ruHvj3L8RA+1a1ysv65wyvMsBd2IZDCOGYoV5ifPJosKHaDGkRTdjEtsU+lGVq+vGOtTk02Wim5EIJgpIhkRLCmMJhlNnb58fCjUfWH81N4V05Ep3YirwMT7P11Fw9VuYmGN1tPDtJ720X5uhAKzjdrrcnjfJ6owWZPbAi0V3YixtAJJp0MNh1MPHB5En+vGWDT5OWarG1mKCN2IYDEhkpErhCvFb6R6uQm7U8fIEIxEu5OOcRncePs0XG49FcvM8xZbKr+RnHwTt36qiqv/rIhb/qSKGz5awW33VnHPV1aw8rZczLaJswDzpRtprPLMm27EXFWLsbiMSFf7lMmkGolAcATbqvXIpomf3eXqRgSCmSKrWspNkD4iGbkCWCh+I/OhGyks0VO/xkKg10YslvzHIBJW8fZrrNniwO6cPNuQDb+RUewFekJ1ftZeW0D9pjxsOYZJYzYXTX/RzZRuZCrmQjci6fS4bno/OoeT8PlzEAmN6UY0TSPmGyLc1gyltTRbsjPTIXQj6SN0I4KZIJKReULoRuZHNyJJErfdlUvtCjOtpzQGeqOoF+5Q4nGNvq4I50+HqF2rcPUtrknHZ7tPzab85BfZfX3TX9SWgm7EVFGD+0Mfx1hRhVMNQkczweYzhJrPoIZC2DdcxerP/BmSaXbOsEI3IhAsTEQyIlhyuPJ0/MlnC9l4o0wspnHuRJCzJ4I0nwohKRI77sjl1vuMDMrpzXSkIlO6kVRsLpx+JmSp6EYATBXVFP3Jn1P48Qdg0w24rruFvNv+iOJ7P4f7Ax9FZ3NMeeya1WVCN3IRQjciWCzMaaM8wcIiHtmNYsjcks1CoC94ALd5/aT9Obk6bvm4Hm//MMG2asIhDZNFpqrOjCNHB+RzbuT4nMZ21HeGVY7aCfsODZ1jTc5Eh86Dg02sc1XN+Px7elrZXJA6UdnT2TZtorK3vY1NnqnHNFZ52NfUzsby2SUX6XLgVBvr6y4s2Sg6zFXLkIImXPWzX04SCASLAzEzMt9kaalmMepGpluq8VhWp3y8wroSZ57EuqscbLneydrN9guJyPQk042scCybkW7kUtLVjQDs70+9LLNUdSPJOHgi+UzHwePTLznNBUI3kj5LXTcixTWk2BRbXAhYZ4JIRuYRg3FLtkPIKjPVjWSLqXQjqRC6kcRSTab61IyyZmXypGvtZVTOCN2IQLDwEMmI4IqmLXDkss8hdCNTM1vdyOUgdCMTEboRwWJAJCNXGEuxT81U1vCjdAXfS7q/2rZiLsKZwKUlvpDab2SmLEW/EYFAcOUhkpFssER0I7NlvnUjs0XoRqbncnQjly7VCN3I0mQp60YkVUu5CdJHJCPzjNCNCN1IOsxWN7K5uHTR6EbSJdO6kdXryoVuRCBYYIhkRHDFs5R0I+lwpetGrkSEbkSw0BHJyBWGxXiV0I1cRDZ0IxtyKzOmG9lcVDatbmSTxyN0IwuAK3mpBliySzWCzCCSkWyxBKzhl7pupM5eNye6kWQk041syi+fVjeSKWarG2ms9mRVN7J2ddmc6UamW6qZqW5ktmRyqSZbrN5en+0Q5oQpPUYubIL0EclIFhC6kcWhG5kpDc6ajOpGpmK+dCMbKzzT6kaSMZ+6kdmSjm5kIdCwpXb6Qemea1ud0I0IFiwiGRFkFJ83zhuvBnj0ewM88nAfj/3TIG+/GcA/omY7tJRkQjcyH8ynbmQqZrNUI3QjAoEgFSIZuQKZK93IgX1B/t+/7+fJHw3x3oEQLc1RDuwL8pNHh/jHv+/j+NFwxp4zGal0IxXWlUI3kiHdyFyzbkXpotKNpLtUA0I3InQjgqkQyUg2WUK6kSOHQjz9pJehgRh1y/XULjNQXqGnts5AbZ2e7u44T/1oiDOnI2PHzqduZLYI3cj0ZFo3koqFqBtJF6EbWXq6EUlVU26C9BHJSJZYSrqRWAxe/PUIfr9KZbUeRZEmPK7TSdTU6ujri/P734ygaZrQjVwmmdCNbCornVfdyPplnqzoRlJZwwvdiECwMBDJiOCyOXNKoelsBE+pDkmSko6RJAmPR+Hk8TAtzdF5jjA9rjTdyOUwHyW+mWDNZTTUEwgE84dIRq5QMqkb6eqQCQX7sVhSf53sDhm/X6W9PZaR503GfOtG0l2qgfR0I43uiqzqRjaVlwrdyCwQupH0EboRQTJEMpJtloBuxKQsgylmRC5GkiQkSUKNj+9b7LqRdElXN5KM+dSNzBahG0kPoRtZeroRQWYQyUgWWSq6EWeOjCRpxKYx+QmHNWQ5MR6E38jlInQj6SN0IxedS+hGMkdMTb0J0kYkI4LLZlWDkfwClZ7ueMpxXZ0xSsv01NUb5imymVFtWyF0IzNA6EYEAkGmEMnIFUymdCN2u8y2a2IMDvbi8yZPSAb644TDGtfdYMFonNuv3Wx1I7NF6EbmBqEbWboI3YjgUkQyshBYArqRD9+5lutvjNLTE+f0qQhDQ3ECAZXBgTinTkYYGlS57QM2rrneMunYpaobuXSpJlu6kXSs4TPBVLqRZNbwyUhXN7JmZZnQjSxihG5EkIx5S0bC4TDr1q1DkiQOHjw4X0+74FkquhG9XuKPPxbhgZ0uVq814h/R6O2JEwxqNG4y82efd/HBu+zI8kSh61LWjcwH6ehGpmOudSOXcrm6kdkidCMXnUvoRjJDTIVYfIpNaEZmgm6+nuirX/0qJSUlvPdeZqfIBZdPPLIbxXDVZZ9HUWDjFjONm03098UJhTRMZom8PGVK/5GFRrVtBedGjlBqaZjX5z00dI41OTO7Y95cWJZ01mNPTyubC1LrJPZ0tqWVqEzHvpZ2NpbPbSJx4FQb6+suL9Y1q8s4dHh+ZogEAsHMmZeZkd/+9re89NJLPPLII/PxdIIZYDFefhJyMaHwbiRJwp2vo7RMj9s9tRHaXOGxrF50upFLmUo3Mt1Szeai6cWamzzTJw+Xoxu5nBLfS1m3YuZJyFwt1aSD0I2kj9CNCC5mzpOR7u5uPvOZz/D0009jsUzWCyQjHA7j8/kmbEsZg3HLktCNjPapmSm5psa0dSOF5nXT6kbmioWgG5ktmdKNTLdU01idPEGZT93IbEm1VANCN5JJhG5EcClzmoxomsZ9993Hzp072bhxY9rHPfzwwzidzrGtrEyU5gkWH/OhG9lcWDavupHZsFh0I6kQuhFBUtQ4xKfY1NRWB4KJzCoZ+da3vjXmpjnVtm/fPr7//e/j8/l46KGHZnT+hx56CK/XO7a1toq13rkmU9bwi4lU1vDZ8Bu5tMT3cpiuxBeYtsQ3XebDbyQTJb7Cb0QgWLjMKhn5whe+wPHjx1NuDQ0N/OEPf2D37t0YjUZ0Oh21tYmsfuPGjdx7771Tnt9oNOJwOCZsgrkjk7oRp2kToRmU6s4V6ehGZkuypRoQupGLEbqR9BC6EaEbESSYVTLidrupr69PuZlMJr73ve/x3nvvcfDgQQ4ePMiLL74IwLPPPsu3v/3tjL6Qxc5S0Y3MlsWkG0nGUtGNTLdUA5nVjVy6VANCN5IOmdSNZGupRuhGBBczp5qR8vJyGhoaxra6usQPeU1NDaWll19WKBAsdBabbmQ6Mq0bmbRP6EamJdO6EYFgISAcWAVjCN3IOAtJNzIba3jInG5kuqUaELoRwRVKLJZ6E6TNvCYjlZWVaJrGunXr5vNpBWkgdCMz43J1I+tyqtLWjSRjPnUj0yF0I5MRupH0WL29XuhGBICYGVlQCN3IlaMbuRyEbmScK1U30rCldknoRgSCUUQyIhDMMUI3kkDoRhYmQjciWAiIZEQwAaEbGUfoRsYRuhGBYDJaPJ5ymyk/+MEPqKqqwmQy0djYyOuvv55y/KuvvkpjYyMmk4nq6mp++MMfzvalZB2RjAjGELqRmSF0I+NkWzeSbKkGsqcbWb2xUuhG0kToRhI8++yzfOlLX+Kv//qvOXDgANdccw233XYbLS3Jv0dNTU3cfvvtXHPNNRw4cIBvfOMb/OVf/iXPPffcPEeeGUQyssAQuhGhG0mHpa4bWb/cMyPdSDLmSjeyen3FtLqRmSB0IwKA73znO9x///088MADrFixgu9+97uUlZXx6KOPJh3/wx/+kPLycr773e+yYsUKHnjgAT796U8v2oa0umwHkA6apgHgGw5mOZL5IRIOgyE7rzUYjiBn6LmHw2Gi4cCMjxsJh1HTPG4kFEYXST3WHwxhik49JhAIMRL1J3/MH+LU8D48lhWTHgsOB/Grk48LDgfxaxP3V8lFHPc1EWDi/tBIgIA0cV94JEBAGZm8T5dkn2F8X4PFxZvnj7M+b/wiHPYHCIxcNMaew/6ujgn7Lh2zypXDu+2XjAlMHLMyz8mB1s7JY/yXxHjJvvpiJ++d65ywb3mJk0NnOycfG0xyviT7AEKhFPsDyfe/s/8Uq1eUTHqspi6Xo4fakh43dnw4+XlnOmbi2OTfwdTHBQkEZ37cpVSv93DsnTMZOddsCEUy8zpGCQQTf++j1465YvR5Uj12aaNXo9GI0WicsC8SibB//36+/vWvT9h/880389ZbbyU9/9tvv83NN988Yd8tt9zCT37yE6LRKHq9Pu3XsRBYFMnI8PAwAFV1f5nlSK4UfpTBcz2WwXMJBII55YksPvdPM3/K4eFhnE5nxs9rMBgoKirivq99LOU4m802qdHrN7/5Tb71rW9N2NfX10c8HqewsHDC/sLCQrq6upKeu6urK+n4WCxGX18fxcXFab6ahcGiSEZKSkpobW3FbrcjSVLSMT6fj7KyMlpbWxddL5vFGvtijRtE7Nlisca+WOOGKzN2TdMYHh6mpGTyzFcmMJlMNDU1EYlEpo3j0mvWpbMiF3Pp2GTHTzc+2f7FwKJIRmRZTts+fjE31lussS/WuEHEni0Wa+yLNW648mKfixmRizGZTJhMpoycy+12oyjKpFmQnp6eSbMfoxQVFSUdr9PpyMvLy0hc84kQsAoEAoFAkEUMBgONjY3s2rVrwv5du3axbdu2pMds3bp10viXXnqJjRs3Ljq9CIhkRCAQCASCrPPggw/y4x//mCeeeILjx4/z5S9/mZaWFnbu3AnAQw89xKc+9amx8Tt37uT8+fM8+OCDHD9+nCeeeIKf/OQnfOUrX8nWS7gsFsUyTToYjUa++c1vplyPW6gs1tgXa9wgYs8WizX2xRo3iNgXC3fffTf9/f383d/9HZ2dnTQ0NPDiiy9SUZHwGers7JzgOVJVVcWLL77Il7/8Zf75n/+ZkpISvve973HXXXdl6yVcFpI217VPAoFAIBAIBCkQyzQCgUAgEAiyikhGBAKBQCAQZBWRjAgEAoFAIMgqIhkRCAQCgUCQVZZ0MhIOh1m3bh2SJHHw4MFsh5MWd955J+Xl5ZhMJoqLi/nkJz9JR0dHtsOalubmZu6//36qqqowm83U1NTwzW9+c1qHwoXAt7/9bbZt24bFYiEnJyfb4aRkpi3GFwqvvfYad9xxByUlJUiSxK9+9atsh5QWDz/8MJs2bcJut1NQUMCHPvQhTp48me2w0uLRRx9lzZo1Y4ZhW7du5be//W22w5oxDz/8MJIk8aUvfSnboQjmkCWdjHz1q1+dMzvguWLHjh38x3/8BydPnuS5557j7Nmz/PEf/3G2w5qWEydOoKoqjz32GEePHuX//t//yw9/+EO+8Y1vZDu0aYlEInzkIx/hs5/9bLZDSclMW4wvJPx+P2vXruWf/umfsh3KjHj11Vf5/Oc/z+7du9m1axexWIybb74Zvz87DeVmQmlpKf/wD//Avn372LdvHzfccAMf/OAHOXr0aLZDS5u9e/fy+OOPs2bNmmyHIphrtCXKiy++qNXX12tHjx7VAO3AgQPZDmlWvPDCC5okSVokEsl2KDPmH//xH7Wqqqpsh5E2Tz75pOZ0OrMdxpRs3rxZ27lz54R99fX12te//vUsRTQ7AO3555/PdhizoqenRwO0V199NduhzAqXy6X9+Mc/znYYaTE8PKwtW7ZM27Vrl3bddddpX/ziF7MdkmAOWZIzI93d3XzmM5/h6aefxmKxZDucWTMwMMDPfvYztm3btijtfb1eL7m5udkOY0kw2mL80pbhqVqMCzKP1+sFWHTf63g8zjPPPIPf72fr1q3ZDictPv/5z/P+97+fm266KduhCOaBJZeMaJrGfffdx86dO9m4cWO2w5kVX/va17BareTl5dHS0sILL7yQ7ZBmzNmzZ/n+978/ZmUsuDxm02JckFk0TePBBx9k+/btNDQ0ZDuctDh8+DA2mw2j0cjOnTt5/vnnWblyZbbDmpZnnnmGd999l4cffjjboQjmiUWTjHzrW99CkqSU2759+/j+97+Pz+fjoYceynbIY6Qb+yh/9Vd/xYEDB3jppZdQFIVPfepTY62hF3rsAB0dHdx666185CMf4YEHHlg0cS8GZtpiXJA5vvCFL3Do0CH+/d//PduhpM3y5cs5ePAgu3fv5rOf/Sz33nsvx44dy3ZYKWltbeWLX/wiP/3pTzPWFVew8Fk0dvB9fX309fWlHFNZWck999zDr3/96wk/0PF4HEVR+MQnPsG//Mu/zHWok0g39mR/eG1tbZSVlfHWW29lZXp1prF3dHSwY8cOtmzZwlNPPYUsZyffnc17/tRTT/GlL32JoaGhOY5u5kQiESwWCz//+c/58Ic/PLb/i1/8IgcPHuTVV1/NYnQzQ5Iknn/+eT70oQ9lO5S0+Yu/+At+9atf8dprr1FVVZXtcGbNTTfdRE1NDY899li2Q5mSX/3qV3z4wx9GUZSxffF4HEmSkGWZcDg84THB0mDRNMpzu9243e5px33ve9/j7//+78f+3dHRwS233MKzzz7Lli1b5jLEKUk39mSM5orhcDiTIaXNTGJvb29nx44dNDY28uSTT2YtEYHLe88XIhe3GL84Gdm1axcf/OAHsxjZ0kbTNP7iL/6C559/nldeeWVRJyKQeD3Z+i1JlxtvvJHDhw9P2Penf/qn1NfX87WvfU0kIkuURZOMpEt5efmEf9tsNgBqamooLS3NRkhps2fPHvbs2cP27dtxuVycO3eOv/3bv6WmpmbBi846Ojq4/vrrKS8v55FHHqG3t3fssaKioixGNj0tLS0MDAzQ0tJCPB4f86Spra0d+/4sBB588EE++clPsnHjRrZu3crjjz8+ocX4QmZkZIQzZ86M/bupqYmDBw+Sm5s76W92IfH5z3+ef/u3f+OFF17AbreP6XOcTidmsznL0aXmG9/4BrfddhtlZWUMDw/zzDPP8Morr/C73/0u26GlxG63T9LkjGroFotWRzALslbHM080NTUtmtLeQ4cOaTt27NByc3M1o9GoVVZWajt37tTa2tqyHdq0PPnkkxqQdFvo3HvvvUnjfvnll7Md2iT++Z//WauoqNAMBoO2YcOGRVNi+vLLLyd9j++9995sh5aSqb7TTz75ZLZDm5ZPf/rTY9+V/Px87cYbb9ReeumlbIc1K0Rp79Jn0WhGBAKBQCAQLE0WTTWNQCAQCASCpYlIRgQCgUAgEGQVkYwIBAKBQCDIKiIZEQgEAoFAkFVEMiIQCAQCgSCriGREIBAIBAJBVhHJiEAgEAgEgqwikhGBQCAQCARZRSQjAoFAIBAIsopIRgQCgUAgEGQVkYwIBAKBQCDIKiIZEQgEAoFAkFX+fynQZ0y4pCVuAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "Bu ana kadar ağın farklı katmanları için farklı sınıflar tanımladık. Bu katmanların bileşimi **hesaplamalı çizge** olarak gösterilebilir. Şimdi, belirli bir eğitim veri kümesi (veya bunun bir parçası) için kaybı aşağıdaki şekilde hesaplayabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.429664938969559\n"
+ ]
+ }
+ ],
+ "source": [
+ "z = net.forward(train_x[0:10])\n",
+ "p = softmax.forward(z)\n",
+ "loss = cross_ent_loss.forward(p,train_labels[0:10])\n",
+ "print(loss)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Kaybı En Aza İndirme Problemi ve Ağ Eğitimi\n",
+ "\n",
+ "Ağı $f_\\theta$ olarak tanımladıktan ve $\\mathcal{L}(Y,f_\\theta(X))$ kayıp fonksiyonu verildikten sonra, sabit eğitim veri kümemiz altında $\\mathcal{L}$'yi $\\theta$'nın bir fonksiyonu olarak düşünebiliriz. : $\\mathcal{L}(\\theta) = \\mathcal{L}(Y,f_\\theta(X))$\n",
+ "\n",
+ "Bu durumda, ağ eğitimi $\\theta$ bağımsız değişkeni altında $\\mathcal{L}$ en aza indirme (minimizasyon) problemi olacaktır:\n",
+ "$$\n",
+ "\\theta = \\mathrm{argmin}_{\\theta} \\mathcal{L}(Y,f_\\theta(X))\n",
+ "$$\n",
+ "\n",
+ "**Gradyan inişi** adı verilen iyi bilinen bir işlev eniyileme (optimizasyon) yöntemi vardır. Buradaki fikir, parametrelere göre kayıp fonksiyonunun bir türevini (çok boyutlu durumda **gradyan** olarak adlandırılır) hesaplayabilmemiz ve parametreleri, hatanın azalacağı şekilde değiştirebilmemizdir.\n",
+ "\n",
+ "Gradyan inişi şu şekilde çalışır:\n",
+ " * Parametreleri, $w^{(0)}$ ve $b^{(0)}$, bazı rasgele değerlerle ilkletin.\n",
+ " * Aşağıdaki adımı birçok kez tekrarlayın:\n",
+ "\n",
+ " $$\\begin{align}\n",
+ " W^{(i+1)}&=W^{(i)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial W}\\\\\n",
+ " b^{(i+1)}&=b^{(i)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial b}\n",
+ " \\end{align}\n",
+ " $$\n",
+ "\n",
+ "Eğitim esnasında eniyileme adımlarının tüm veri kümesi dikkate alınarak hesaplanması gerekir (kaybın tüm eğitim örnekleri üzerinden bir toplam/ortalama olarak hesaplandığını unutmayın). Bununla birlikte, gerçek hayatta **minigruplar** olarak adlandırılan veri kümesinin küçük kısımlarını alır ve bir veri alt kümesine dayalı olarak gradyanları hesaplarız. Alt küme her seferinde rastgele alındığından, bu yönteme **rasgele gradyan inişi** (SGD - RGİ) denir."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Geriye Yayma\n",
+ "\n",
+ "\n",
+ "\n",
+ "$$\\def\\L{\\mathcal{L}}\\def\\zz#1#2{\\frac{\\partial#1}{\\partial#2}}\n",
+ "\\begin{align}\n",
+ "\\zz{\\L}{W} =& \\zz{\\L}{p}\\zz{p}{z}\\zz{z}{W}\\cr\n",
+ "\\zz{\\L}{b} =& \\zz{\\L}{p}\\zz{p}{z}\\zz{z}{b}\n",
+ "\\end{align}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "$\\partial\\mathcal{L}/\\partial W$'ı hesaplamak için, yukarıdaki formüllerde görebileceğiniz gibi, bileşik bir fonksiyonun türevlerini hesaplamak için **zincirleme kuralını** kullanabiliriz. Aşağıdaki fikre karşılık gelir:\n",
+ "\n",
+ "* Verilen girdinin altında $\\Delta\\mathcal{L}$ kaybını elde ettiğimizi varsayalım.\n",
+ "* Bunu en aza indirmek için, softmaks çıktısı $p$'yı $\\Delta p = (\\partial\\mathcal{L}/\\partial p)\\Delta\\mathcal{L}$ değerine göre ayarlamamız gerekir.\n",
+ "* Bu, $z$ düğümünde $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$ tarafından yapılan değişikliklere karşılık gelir.\n",
+ "* Bu hatayı en aza indirmek için parametreleri buna göre ayarlamamız gerekiyor: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (ve $b$ için aynı)\n",
+ "\n",
+ "\n",
+ "\n",
+ "Bu işlem, kayıp hatasını ağın çıktısından parametrelerine geri dağıtmaya başlar. Bu nedenle işlem **geri yayma** olarak adlandırılır.\n",
+ "\n",
+ "Ağ eğitiminin bir geçişi iki bölümden oluşur:\n",
+ "* Belirli bir girdi minigrubu için kayıp fonksiyonunun değerini hesapladığımızda **ileriye geçiş** \n",
+ "* Bu hatayı hesaplamalı çizge aracılığıyla model parametrelerine geri dağıtarak en aza indirmeye çalıştığımızda **geriye geçiş**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Geri Yaymanın Uygulanması\n",
+ "\n",
+ "* Geriye geçiş sırasında türevi hesaplayacak ve hatayı yayacak olan düğümlerimizin her birine `backward` (geri) fonksiyonunu ekleyelim.\n",
+ "* Ayrıca yukarıda açıklanan prosedüre göre parametre güncellemelerini de uygulamamız gerekiyor.\n",
+ "\n",
+ "Her katman için türevleri elle hesaplamamız gerekir, örneğin doğrusal katman $z = x\\times W+b$ için:\n",
+ "$$\\begin{align}\n",
+ "\\frac{\\partial z}{\\partial W} &= x \\\\\n",
+ "\\frac{\\partial z}{\\partial b} &= 1 \\\\\n",
+ "\\end{align}$$\n",
+ "\n",
+ "Katmanın çıktısındaki $\\Delta z$ hatasını telafi etmemiz gerekirse, ağırlıkları buna göre güncellememiz gerekir:\n",
+ "$$\\begin{align}\n",
+ "\\Delta x &= \\Delta z \\times W \\\\\n",
+ "\\Delta W &= \\frac{\\partial z}{\\partial W} \\Delta z = \\Delta z \\times x \\\\\n",
+ "\\Delta b &= \\frac{\\partial z}{\\partial b} \\Delta z = \\Delta z \\\\\n",
+ "\\end{align}$$\n",
+ "\n",
+ "**ÖNEMLİ:** Hesaplamalar her eğitim örneği için bağımsız olarak değil, bir bütün **minigrup** için yapılır. Gerekli parametre güncellemeleri $\\Delta W$ ve $\\Delta b$ tüm minigrupta hesaplanır ve ilgili vektörlerin $x\\in\\mathbb{R}^{\\mathrm{minigrup}\\, \\times\\, \\mathrm{sınıfsayısı}}$ boyutları vardır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Linear:\n",
+ " def __init__(self,nin,nout):\n",
+ " self.W = np.random.normal(0, 1.0/np.sqrt(nin), (nout, nin))\n",
+ " self.b = np.zeros((1,nout))\n",
+ " self.dW = np.zeros_like(self.W)\n",
+ " self.db = np.zeros_like(self.b)\n",
+ " \n",
+ " def forward(self, x):\n",
+ " self.x=x\n",
+ " return np.dot(x, self.W.T) + self.b\n",
+ " \n",
+ " def backward(self, dz):\n",
+ " dx = np.dot(dz, self.W)\n",
+ " dW = np.dot(dz.T, self.x)\n",
+ " db = dz.sum(axis=0)\n",
+ " self.dW = dW\n",
+ " self.db = db\n",
+ " return dx\n",
+ " \n",
+ " def update(self,lr):\n",
+ " self.W -= lr*self.dW\n",
+ " self.b -= lr*self.db"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Aynı şekilde, katmanlarımızın geri kalanı için `backward` (geri) işlevi tanımlayabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Softmax:\n",
+ " def forward(self,z):\n",
+ " self.z = z\n",
+ " zmax = z.max(axis=1,keepdims=True)\n",
+ " expz = np.exp(z-zmax)\n",
+ " Z = expz.sum(axis=1,keepdims=True)\n",
+ " return expz / Z\n",
+ " def backward(self,dp):\n",
+ " p = self.forward(self.z)\n",
+ " pdp = p * dp\n",
+ " return pdp - p * pdp.sum(axis=1, keepdims=True)\n",
+ " \n",
+ "class CrossEntropyLoss:\n",
+ " def forward(self,p,y):\n",
+ " self.p = p\n",
+ " self.y = y\n",
+ " p_of_y = p[np.arange(len(y)), y]\n",
+ " log_prob = np.log(p_of_y)\n",
+ " return -log_prob.mean()\n",
+ " def backward(self,loss):\n",
+ " dlog_softmax = np.zeros_like(self.p)\n",
+ " dlog_softmax[np.arange(len(self.y)), self.y] -= 1.0/len(self.y)\n",
+ " return dlog_softmax / self.p"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Model Eğitimi\n",
+ "\n",
+ "Artık veri kümemiz üzerinden geçecek olan **eğitim döngüsü**nü yazmaya ve minigrup temelli eniyileme işlemini gerçekleştirmeye hazırız. Veri kümesinden tam bir geçişe genellikle **dönem** denir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "İlk doğruluk: 0.725\n",
+ "Nihai doğruluk: 0.825\n"
+ ]
+ }
+ ],
+ "source": [
+ "lin = Linear(2,2)\n",
+ "softmax = Softmax()\n",
+ "cross_ent_loss = CrossEntropyLoss()\n",
+ "\n",
+ "learning_rate = 0.1\n",
+ "\n",
+ "pred = np.argmax(lin.forward(train_x),axis=1)\n",
+ "acc = (pred==train_labels).mean()\n",
+ "print(\"İlk doğruluk: \",acc)\n",
+ "\n",
+ "batch_size=4\n",
+ "for i in range(0,len(train_x),batch_size):\n",
+ " xb = train_x[i:i+batch_size]\n",
+ " yb = train_labels[i:i+batch_size]\n",
+ " \n",
+ " # forward pass\n",
+ " z = lin.forward(xb)\n",
+ " p = softmax.forward(z)\n",
+ " loss = cross_ent_loss.forward(p,yb)\n",
+ " \n",
+ " # backward pass\n",
+ " dp = cross_ent_loss.backward(loss)\n",
+ " dz = softmax.backward(dp)\n",
+ " dx = lin.backward(dz)\n",
+ " lin.update(learning_rate)\n",
+ " \n",
+ "pred = np.argmax(lin.forward(train_x),axis=1)\n",
+ "acc = (pred==train_labels).mean()\n",
+ "print(\"Nihai doğruluk: \",acc)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Bir dönemde modelin doğruluğunu %50 civarından yaklaşık %80'e nasıl çıkarabileceğimizi görmek güzel.\n",
+ "\n",
+ "## Ağ Sınıfı\n",
+ "\n",
+ "Çoğu durumda sinir ağı yalnızca katmanların bir bileşimi olduğundan, bu mantığı açıkça programlamadan katmanları bir araya getirmemize ve bunlar arasında ileri ve geri geçişler yapmamıza izin verecek bir sınıf oluşturabiliriz. Katmanların listesini `Net` (Ağ) sınıfının içinde saklayacağız ve yeni katmanlar eklemek için `add()` (ekle()) işlevini kullanacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "scrolled": true,
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "class Net:\n",
+ " def __init__(self):\n",
+ " self.layers = []\n",
+ " \n",
+ " def add(self,l):\n",
+ " self.layers.append(l)\n",
+ " \n",
+ " def forward(self,x):\n",
+ " for l in self.layers:\n",
+ " x = l.forward(x)\n",
+ " return x\n",
+ " \n",
+ " def backward(self,z):\n",
+ " for l in self.layers[::-1]:\n",
+ " z = l.backward(z)\n",
+ " return z\n",
+ " \n",
+ " def update(self,lr):\n",
+ " for l in self.layers:\n",
+ " if 'update' in l.__dir__():\n",
+ " l.update(lr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Bu `Net` sınıfı ile model tanımımız ve eğitimimiz daha düzenli zarif geliyor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "İlk kayıp=0.6212072429381601, doğruluk=0.6875: \n",
+ "Nihai kayıp=0.44369925927417986, doğruluk=0.8: \n",
+ "Test kaybı=0.4767711377257787, doğruluk=0.85: \n"
+ ]
+ }
+ ],
+ "source": [
+ "net = Net()\n",
+ "net.add(Linear(2,2))\n",
+ "net.add(Softmax())\n",
+ "loss = CrossEntropyLoss()\n",
+ "\n",
+ "def get_loss_acc(x,y,loss=CrossEntropyLoss()):\n",
+ " p = net.forward(x)\n",
+ " l = loss.forward(p,y)\n",
+ " pred = np.argmax(p,axis=1)\n",
+ " acc = (pred==y).mean()\n",
+ " return l,acc\n",
+ "\n",
+ "print(\"İlk kayıp={}, doğruluk={}: \".format(*get_loss_acc(train_x,train_labels)))\n",
+ "\n",
+ "def train_epoch(net, train_x, train_labels, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\n",
+ " for i in range(0,len(train_x),batch_size):\n",
+ " xb = train_x[i:i+batch_size]\n",
+ " yb = train_labels[i:i+batch_size]\n",
+ "\n",
+ " p = net.forward(xb)\n",
+ " l = loss.forward(p,yb)\n",
+ " dp = loss.backward(l)\n",
+ " dx = net.backward(dp)\n",
+ " net.update(lr)\n",
+ " \n",
+ "train_epoch(net,train_x,train_labels)\n",
+ " \n",
+ "print(\"Nihai kayıp={}, doğruluk={}: \".format(*get_loss_acc(train_x,train_labels)))\n",
+ "print(\"Test kaybı={}, doğruluk={}: \".format(*get_loss_acc(test_x,test_labels)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Eğitim Sürecinin Görselleştirilmesi\n",
+ "\n",
+ "Ağın nasıl eğitildiğini görsel olarak görmek güzel olurdu! Bunun için bir `train_and_plot` (eğit ve çizdir) fonksiyonu tanımlayacağız. Ağın durumunu görselleştirmek için seviye haritası kullanacağız, yani farklı renkler kullanarak ağ çıktısının farklı değerlerini temsil edeceğiz.\n",
+ "\n",
+ "> Aşağıdaki çizim kodlarından bazılarını anlamadıysanız endişelenmeyin - altında yatan sinir ağı kavramlarını anlamak daha önemlidir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def train_and_plot(n_epoch, net, loss=CrossEntropyLoss(), batch_size=4, lr=0.1):\n",
+ " fig, ax = plt.subplots(2, 1)\n",
+ " ax[0].set_xlim(0, n_epoch + 1)\n",
+ " ax[0].set_ylim(0,1)\n",
+ "\n",
+ " train_acc = np.empty((n_epoch, 3))\n",
+ " train_acc[:] = np.NAN\n",
+ " valid_acc = np.empty((n_epoch, 3))\n",
+ " valid_acc[:] = np.NAN\n",
+ "\n",
+ " for epoch in range(1, n_epoch + 1):\n",
+ "\n",
+ " train_epoch(net,train_x,train_labels,loss,batch_size,lr)\n",
+ " tloss, taccuracy = get_loss_acc(train_x,train_labels,loss)\n",
+ " train_acc[epoch-1, :] = [epoch, tloss, taccuracy]\n",
+ " vloss, vaccuracy = get_loss_acc(test_x,test_labels,loss)\n",
+ " valid_acc[epoch-1, :] = [epoch, vloss, vaccuracy]\n",
+ " \n",
+ " ax[0].set_ylim(0, max(max(train_acc[:, 2]), max(valid_acc[:, 2])) * 1.1)\n",
+ "\n",
+ " plot_training_progress(train_acc[:, 0], (train_acc[:, 2],\n",
+ " valid_acc[:, 2]), fig, ax[0])\n",
+ " plot_decision_boundary(net, fig, ax[1])\n",
+ " fig.canvas.draw()\n",
+ " fig.canvas.flush_events()\n",
+ "\n",
+ " return train_acc, valid_acc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.cm as cm\n",
+ "\n",
+ "def plot_decision_boundary(net, fig, ax):\n",
+ " draw_colorbar = True\n",
+ " # önceki çizimi sildir\n",
+ " while ax.collections:\n",
+ " ax.collections.pop()\n",
+ " draw_colorbar = False\n",
+ "\n",
+ " # kontur ızgarası oluştur\n",
+ " x_min, x_max = train_x[:, 0].min() - 1, train_x[:, 0].max() + 1\n",
+ " y_min, y_max = train_x[:, 1].min() - 1, train_x[:, 1].max() + 1\n",
+ " xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
+ " np.arange(y_min, y_max, 0.1))\n",
+ " grid_points = np.c_[xx.ravel().astype('float32'), yy.ravel().astype('float32')]\n",
+ " n_classes = max(train_labels)+1\n",
+ " while train_x.shape[1] > grid_points.shape[1]:\n",
+ " # boyutları dolgula (yalnızca ilk ikisini çizin)\n",
+ " grid_points = np.c_[grid_points,\n",
+ " np.empty(len(xx.ravel())).astype('float32')]\n",
+ " grid_points[:, -1].fill(train_x[:, grid_points.shape[1]-1].mean())\n",
+ "\n",
+ " # tahminleri değerlendir\n",
+ " prediction = np.array(net.forward(grid_points))\n",
+ " # for two classes: prediction difference\n",
+ " if (n_classes == 2):\n",
+ " Z = np.array([0.5+(p[0]-p[1])/2.0 for p in prediction]).reshape(xx.shape)\n",
+ " else:\n",
+ " Z = np.array([p.argsort()[-1]/float(n_classes-1) for p in prediction]).reshape(xx.shape)\n",
+ " \n",
+ " # konturu çizdir\n",
+ " levels = np.linspace(0, 1, 40)\n",
+ " cs = ax.contourf(xx, yy, Z, alpha=0.4, levels = levels)\n",
+ " if draw_colorbar:\n",
+ " fig.colorbar(cs, ax=ax, ticks = [0, 0.5, 1])\n",
+ " c_map = [cm.jet(x) for x in np.linspace(0.0, 1.0, n_classes) ]\n",
+ " colors = [c_map[l] for l in train_labels]\n",
+ " ax.scatter(train_x[:, 0], train_x[:, 1], marker='o', c=colors, s=60, alpha = 0.5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "skip"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def plot_training_progress(x, y_data, fig, ax):\n",
+ " styles = ['k--', 'g-']\n",
+ " # önceki çizimi sildir\n",
+ " while ax.lines:\n",
+ " ax.lines.pop()\n",
+ " # güncellenmiş doğruları çiz\n",
+ " for i in range(len(y_data)):\n",
+ " ax.plot(x, y_data[i], styles[i])\n",
+ " ax.legend(ax.lines, ['eğitim doğruluğu', 'geçerleme doğruluğu'],\n",
+ " loc='upper center', ncol = 2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib nbagg \n",
+ "%matplotlib inline\n",
+ "net = Net()\n",
+ "net.add(Linear(2,2))\n",
+ "net.add(Softmax())\n",
+ "\n",
+ "res = train_and_plot(30,net,lr=0.005)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "Yukarıdaki hücreyi çalıştırdıktan sonra, eğitim sırasında sınıflar arasındaki sınırın nasıl değiştiğini etkileşimli olarak görebilmelisiniz. Sürecin nasıl olduğunu görebilmemiz için çok küçük bir öğrenme oranı seçtiğimizi unutmayın.\n",
+ "\n",
+ "## Çok Katmanlı Modeller\n",
+ "\n",
+ "Yukarıdaki ağ birkaç katmandan oluşturulmuştur, ancak hala gerçek sınıflandırmayı yapan tek bir `Linear` (doğrusal) katmanımız vardı. Bu tür birkaç katman eklemeye karar verirsek ne olur?\n",
+ "\n",
+ "Şaşırtıcı bir şekilde, kodumuz çalışacak! Bununla birlikte, dikkat edilmesi gereken çok önemli bir nokta, doğrusal katmanlar arasında `tanh` gibi doğrusal olmayan bir **etkinleştirme fonksiyonuna** sahip olmamız gerektiğidir. Böyle bir doğrusal olmayanlık olmadan, birkaç doğrusal katman tek bir katmanla aynı ifade gücüne sahip olacaktır - çünkü doğrusal fonksiyonların bileşimi de doğrusaldır!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Tanh:\n",
+ " def forward(self,x):\n",
+ " y = np.tanh(x)\n",
+ " self.y = y\n",
+ " return y\n",
+ " def backward(self,dy):\n",
+ " return (1.0-self.y**2)*dy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Birkaç katman eklemek anlamlıdır, çünkü tek katmanlı ağdan farklı olarak çok katmanlı model, doğrusal olarak ayrılamayan kümeleri doğru bir şekilde sınıflandırabilecektir. Yani, birkaç katmana sahip bir model **rehber** olacaktır.\n",
+ "\n",
+ "> Yeterli sayıda nöronla, iki katmanlı bir modelin herhangi bir dışbükey veri noktası kümesini sınıflandırabileceği ve üç katmanlı ağın hemen hemen her kümeyi sınıflandırabileceği gösterilebilir.\n",
+ "\n",
+ "Matematiksel olarak, çok katmanlı algılayıcı, birkaç adımda hesaplanabilen daha karmaşık bir $f_\\theta$ işleviyle temsil edilir:\n",
+ "* $z_1 = W_1\\times x+b_1$\n",
+ "* $z_2 = W_2\\times\\alpha(z_1)+b_2$\n",
+ "* $f = \\sigma(z_2)$\n",
+ "\n",
+ "Burada $\\alpha$ bir **doğrusal olmayan etkinleştirme işlevidir**, $\\sigma$ bir softmaks işlevidir ve $\\theta=\\langle W_1,b_1,W_2,b_2\\rangle$ parametrelerdir.\n",
+ " \n",
+ "Gradyan inişi algoritması aynı kalacaktır, ancak gradyanları hesaplamak daha zor olacaktır. Zincir türev alma kuralı göz önüne alındığında, türevleri şu şekilde hesaplayabiliriz:\n",
+ "\n",
+ "$$\\begin{align}\n",
+ "\\frac{\\partial\\mathcal{L}}{\\partial W_2} &= \\color{red}{\\frac{\\partial\\mathcal{L}}{\\partial\\sigma}\\frac{\\partial\\sigma}{\\partial z_2}}\\color{black}{\\frac{\\partial z_2}{\\partial W_2}} \\\\\n",
+ "\\frac{\\partial\\mathcal{L}}{\\partial W_1} &= \\color{red}{\\frac{\\partial\\mathcal{L}}{\\partial\\sigma}\\frac{\\partial\\sigma}{\\partial z_2}}\\color{black}{\\frac{\\partial z_2}{\\partial\\alpha}\\frac{\\partial\\alpha}{\\partial z_1}\\frac{\\partial z_1}{\\partial W_1}}\n",
+ "\\end{align}\n",
+ "$$\n",
+ "\n",
+ "Tüm bu ifadelerin başlangıcının hala aynı olduğuna ve dolayısıyla hesaplamalı çizgede daha fazla ağırlık ayarlamak için bir doğrusal katmanın ötesine geri yaymaya devam edebileceğimize dikkat edin.\n",
+ "\n",
+ "Şimdi iki katmanlı ağ ile deney yapalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "net = Net()\n",
+ "net.add(Linear(2,10))\n",
+ "net.add(Tanh())\n",
+ "net.add(Linear(10,2))\n",
+ "net.add(Softmax())\n",
+ "loss = CrossEntropyLoss()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "res = train_and_plot(30,net,lr=0.01)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Neden Her Zaman Çok Katmanlı Model Kullanmayız?\n",
+ "\n",
+ "Çok katmanlı modelin tek katmanlı modelden daha *güçlü* ve *ifade edici* olduğunu gördük. Neden her zaman çok katmanlı model kullanmadığımızı merak ediyor olabilirsiniz. Bu sorunun cevabı **aşırı öğrenme**dir.\n",
+ "\n",
+ "Bu terimi sonraki bölümlerde daha fazla ele alacağız, ancak fikir şudur: **model ne kadar güçlüyse, eğitim verilerine o kadar iyi yaklaşıklayabilir ve** daha önce görmediği yeni veriler için **uygun şekilde genelleştirmek için o kadar fazla veriye ihtiyaç duyar**.\n",
+ "\n",
+ "**Doğrusal bir model:**\n",
+ "* Modelin tüm verileri doğru bir şekilde ayırmak için yeterli gücü olmadığında, **yetersiz öğrenme** olarak adlandırılan yüksek eğitim kaybı yaşamamız olasıdır.\n",
+ "* Geçerleme kaybı ve eğitim kaybı aşağı yukarı aynıdır. Modelin verileri test etmede iyi bir şekilde genellemesi muhtemeldir.\n",
+ "\n",
+ "**Karmaşık çok katmanlı model**\n",
+ "* Düşük eğitim kaybı olur - model, yeterli ifade gücüne sahip olduğu için eğitim verilerine iyi bir şekilde yaklaşıklayabilir.\n",
+ "* Geçerleme kaybı, eğitim kaybından çok daha yüksek olabilir ve eğitim sırasında artmaya başlayabilir - bunun nedeni, modelin eğitim noktalarını \"ezberlemesi\" ve \"genel resmi\" kaybetmesidir.\n",
+ "\n",
+ "\n",
+ "\n",
+ "> Bu resimde, `x` eğitim verisi, `o` - geçerleme verisi anlamına gelir. Sol - doğrusal model (tek katmanlı), verilerin doğasına oldukça iyi yaklaşıklar. Sağ - aşırı öğrenen model, model eğitim verilerine mükemmel bir şekilde yaklaşır, ancak diğer verilerle bir anlam ifade etmez (geçerleme hatası çok yüksektir)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "source": [
+ "## Ana Fikirler\n",
+ "\n",
+ "* Düşük sayıda parametreye (\"düşük kapasite\") sahip basit modellerin (daha az katman, daha az nöron) fazla öğrenme olasılığı daha düşüktür.\n",
+ "* Daha karmaşık modellerin (daha fazla katman, her katmanda daha fazla nöron, yüksek kapasite) olması muhtemeldir. Daha fazla eğitimle yükselmeye başlamadığından emin olmak için geçerleme hatasını izlememiz gerekiyor.\n",
+ "* Daha karmaşık modeller eğitime devam etmek için daha fazla veriye ihtiyaç duyar.\n",
+ "* Aşırı öğrenme sorununu aşağıdakilerden biri ile çözebilirsiniz:\n",
+ " - modelinizi basitleştirme\n",
+ " - eğitim verilerinin miktarını artırmak\n",
+ "* **Yanlılık-değişinti ödünleşmesi**, uzlaşmayı elde etmeniz gerektiğini gösteren bir terimdir:\n",
+ " - modelin gücü ile veri miktarı arasında\n",
+ " - aşırı ile yetersiz öğrenme arasında\n",
+ "* Kaç tane parametre katmanına ihtiyacınız olduğuna dair tek bir tarif yoktur - en iyi yol denemektir."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Katkıda Bulunanlar\n",
+ "\n",
+ "Bu defter, [Yeni Başlayanlar için YZ Müfredatı](http://github.com/microsoft/ai-for-beginners)'nın bir parçasıdır ve [Dmitry Soshnikov](http://soshnikov.com) tarafından hazırlanmıştır. Microsoft Research Cambridge'deki Sinir Ağı Çalıştay'ından ilham almıştır. Bazı kodlar ve açıklayıcı materyaller [Katja Hoffmann](https://www.microsoft.com/en-us/research/people/kahofman/), [Matthew Johnson](https://www.microsoft. com/en-us/research/people/matjoh/) ve [Ryoto Tomioka](https://www.microsoft.com/en-us/research/people/ryoto/) ve [NeuroWorkshop](http:/ /github.com/shwars/NeuroWorkshop) deposundan alınmıştır."
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Slideshow",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ },
+ "livereveal": {
+ "start_slideshow_at": "selected"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "d355c6d9fcfa2da36351d09a4957315c029537f44307b30fb3762ace87798487"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/3-NeuralNetworks/04-OwnFramework/translations/README.tr.md b/lessons/3-NeuralNetworks/04-OwnFramework/translations/README.tr.md
new file mode 100644
index 00000000..16dc7acf
--- /dev/null
+++ b/lessons/3-NeuralNetworks/04-OwnFramework/translations/README.tr.md
@@ -0,0 +1,86 @@
+# Sinir Ağlarına Giriş. Çok Katmanlı Algılayıcı
+
+Önceki bölümde, en basit sinir ağı modelini öğrendiniz - tek katmanlı algılayıcı, doğrusal iki sınıflı sınıflandırma modeli.
+
+Bu bölümde, bu modeli daha esnek bir çerçeveye genişleterek şunları yapmayı sağlayacağız:
+
+* İki sınıfa ek olarak **çok sınıflı sınıflandırma** gerçekleştirme
+* Sınıflandırmaya ek olarak **regresyon (bağlanım) problemlerini** çözme
+* Doğrusal olarak ayrılamayan sınıfları ayırma.
+
+Python'da farklı sinir ağı mimarileri oluşturmamıza izin verecek kendi modüler çerçevemizi de geliştireceğiz.
+
+## [Ders öncesi sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/104)
+
+## Makine Öğrenmesinin Formülleştirilmesi
+
+Makine Öğrenmesi problemini formülleştirerek başlayalım. **Y** etiketli bir **X** eğitim veri kümemiz olduğunu ve en doğru tahminleri yapacak bir *f* modeli oluşturmamız gerektiğini varsayalım. Tahminlerin kalitesi **kayıp işlevi** ℒ ile ölçülür. Aşağıdaki kayıp fonksiyonları sıklıkla kullanılır:
+
+* Bağlanım problemi için, bir sayıyı tahmin etmemiz gerektiğinde, **mutlak hata** ∑i|f(x(i))-y(i)| veya **kare hatası** ∑i(f(x(i))-y(i ))2 kullanabiliriz.
+* Sınıflandırma için **0-1 kaybı** (esas olarak modelin **doğruluğu** ile aynıdır) veya **lojistik kayıp** kullanırız.
+
+Tek katmanlı algılayıcı için *f* işlevi, *f(x)=wx+b* doğrusal işlevi olarak tanımlanmıştır (burada *w* ağırlık matrisidir, *x* girdi özniteliklerinin vektörüdür ve *b* ek girdi vektörüdür). Farklı sinir ağı mimarileri için bu işlev daha karmaşık bir biçim alabilir.
+
+> Sınıflandırma vakasında, genellikle ağ çıktısı olarak karşılık gelen sınıfların olasılıklarının alınması arzu edilir. Rastgele sayıları olasılıklara dönüştürmek için (örneğin çıktıyı normalleştirmek için), genellikle **softmaks** işlevi σ'yı kullanırız ve *f* işlevi *f(x)=σ(wx+b)* olur.
+
+Yukarıdaki *f* tanımında *w* ve *b*, θ=⟨*w,b*⟩ **parametreler**i olarak adlandırılır. Veri kümesi ⟨**X**,**Y**⟩ verildiğinde, θ parametrelerinin bir fonksiyonu olarak tüm veri kümesindeki genel bir hatayı hesaplayabiliriz.
+
+> ✅ **Sinir ağı eğitiminin amacı, θ parametrelerini değiştirerek hatayı en aza indirmektir.**
+
+## Gradyan İnişi Eniyilemesi
+
+**Gradyan iniş**i adı verilen iyi bilinen bir işlev eniyileme yöntemi vardır. Buradaki fikir, parametrelere göre kayıp fonksiyonunun bir türevini (çok boyutlu durumda **gradyan** olarak adlandırılır) hesaplayabilmemiz ve parametreleri, hatanın azalacağı şekilde değiştirebilmemizdir. Bu, aşağıdaki gibi formüle dökülebilir:
+
+* Parametreleri bazı rastgele değerlerle ilklet w(0), b(0)
+* Aşağıdaki adımı birçok kez tekrarlayın:
+ - w(i+1) = w(i)-η∂ℒ/∂w
+ - b(i+1) = b(i)-η∂ℒ/∂b
+
+Eğitim esnasında, eniyileme adımlarının tüm veri kümesi dikkate alınarak hesaplanması gerekir (kaybın tüm eğitim örneklerinin toplamı olarak hesaplandığını unutmayın). Bununla birlikte, gerçek hayatta **minigruplar** olarak adlandırılan veri kümesinin küçük kısımlarını alır ve bir veri alt kümesine dayalı olarak gradyanları hesaplarız. Alt küme her seferinde rastgele alındığından, bu yönteme **rasgele gradyan inişi** (SGD - RGİ) denir.
+
+## Çok Katmanlı Algılayıcılar ve Geri Yayma
+
+Tek katmanlı ağ, yukarıda gördüğümüz gibi, doğrusal olarak ayrılabilir sınıfları sınıflandırma yeteneğine sahiptir. Daha zengin bir model oluşturmak için ağın birkaç katmanını birleştirebiliriz. Matematiksel olarak bu, *f* fonksiyonunun daha karmaşık bir forma sahip olacağı ve birkaç adımda hesaplanacağı anlamına gelir:
+* z1=w1x+b1
+* z2=w2α(z1)+b2
+* f = σ(z2)
+
+Burada, α bir **doğrusal olmayan etkinleştirme işlevidir**, σ bir softmaks işlevidir ve θ=<*w1,b1,w2,b2*> parametreleridir.
+
+Gradyan inişi algoritması aynı kalacaktır, ancak gradyanları hesaplamak daha zor olacaktır. Zincir türev alma kuralı göz önüne alındığında, türevleri şu şekilde hesaplayabiliriz:
+
+* ∂ℒ/∂w2 = (∂ℒ/∂σ)(∂σ/∂z2)(∂z2/∂w2)
+* ∂ℒ/∂w1 = (∂ℒ/∂σ)(∂σ/∂z2)(∂z2/∂α)(∂α/∂z1)(∂z1/∂w1)
+
+> ✅ Parametrelere göre kayıp fonksiyonunun türevlerini hesaplamak için zincir türev alma kuralı kullanılır.
+
+Tüm bu ifadelerin en soldaki kısmının aynı olduğuna ve dolayısıyla kayıp fonksiyonundan başlayarak ve hesaplama çizgesi boyunca "geriye" doğru giden türevleri etkin bir şekilde hesaplayabileceğimize dikkat edin. Bu nedenle çok katmanlı bir algılayıcıyı eğitme yöntemine **geri yayma** veya 'geriyay' denir.
+
+
+
+> TODO: imge alıntısı
+
+> ✅ Geriyay'ı not defteri örneğimizde çok daha ayrıntılı olarak ele alacağız.
+
+## Vargılar
+
+Bu dersimizde kendi sinir ağı kütüphanemizi oluşturduk ve onu basit bir iki boyutlu sınıflandırma görevi için kullandık.
+
+## 🚀 Kendini Sınama
+
+Ekteki not defterinde, çok katmanlı algılayıcıları oluşturmak ve eğitmek için kendi çerçevenizi uygulayacaksınız. Modern sinir ağlarının nasıl çalıştığını ayrıntılı olarak görebileceksiniz.
+
+[OwnFramework (KendiCerveceniz)](OwnFramework.tr.ipynb) not defterine gidin ve üzerinde çalışın.
+
+## [Ders sonrası sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/204)
+
+## Gözden Geçirme ve Bireysel Çalışma
+
+Geri yayma, YZ ve makine öğrenmesinde kullanılan yaygın bir algoritmadır ve [daha ayrıntılı olarak](https://wikipedia.org/wiki/Backpropagation) incelemeye değerdir.
+
+## [Ödev](../lab/translations/README.tr.md)
+
+Bu laboratuvarda, MNIST el yazısı rakam sınıflandırmasını çözmek için bu derste oluşturduğunuz çerçeveyi kullanmanız istenmektedir.
+
+* [Talimatlar](../lab/translations/README.tr.md)
+* [Not Defteri](../lab/translations/MyFW_MNIST.tr.ipynb)
diff --git a/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb b/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb
index 9b634fc0..1346760a 100644
--- a/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb
+++ b/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb
@@ -1,733 +1,729 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "En2vX4FuwHlu"
- },
- "source": [
- "## Simplest Introduction to Neural Networks with Keras\n",
- "\n",
- "> This notebook is a part of [AI for Beginners Curricula](http://github.com/microsoft/ai-for-beginners). Visit the repository for complete set of learning materials.\n",
- "\n",
- "### Neural Frameworks\n",
- "\n",
- "There are several frameworks for training neural networks. However, if you want to get started fast and not go into much detail on how things work internally - you should consider using [Keras](https://keras.io/). This short tutorial will get you started, and if you want to get deeper into understanding how things work - look into [Introduction to Tensorflow and Keras](IntroKerasTF.ipynb) notebook."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8cACQoFMwHl3"
- },
- "source": [
- "### Getting things ready\n",
- "\n",
- "Keras is a part of Tensorflow 2.x framework. Let's make sure we have version 2.x.x of Tensorflow installed:\n",
- "```\n",
- "pip install tensorflow\n",
- "```\n",
- "or\n",
- "```\n",
- "conda install tensorflow\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "xwqVx9-bwHl3",
- "outputId": "2aa591b4-b647-441f-9c8e-4e0da2d517a0",
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Tensorflow version = 2.7.0\n",
- "Keras version = 2.7.0\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "import numpy as np\n",
- "from sklearn.datasets import make_classification\n",
- "import matplotlib.pyplot as plt\n",
- "print(f'Tensorflow version = {tf.__version__}')\n",
- "print(f'Keras version = {keras.__version__}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6tp2xGV7wHl4"
- },
- "source": [
- "## Basic Concepts: Tensor\n",
- "\n",
- "**Tensor** is a multi-dimensional array. It is very convenient to use tensors to represent different types of data:\n",
- "* 400x400 - black-and-white picture\n",
- "* 400x400x3 - color picture \n",
- "* 16x400x400x3 - minibatch of 16 color pictures\n",
- "* 25x400x400x3 - one second of 25-fps video\n",
- "* 8x25x400x400x3 - minibatch of 8 1-second videos\n",
- "\n",
- "Tensors give us a convenient way to represent input/output data, as well we weights inside the neural network."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "A10prCPowHl7"
- },
- "source": [
- "## Sample Problem\n",
- "\n",
- "Let's consider binary classification problem. A good example of such a problem would be a tumour classification between malignant and benign based on it's size and age. Let's start by generating some sample data:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "id": "j0OTPkGpwHl7",
- "scrolled": false,
- "trusted": true
- },
- "outputs": [],
- "source": [
- "np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations\n",
- "\n",
- "n = 100\n",
- "X, Y = make_classification(n_samples = n, n_features=2,\n",
- " n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n",
- "X = X.astype(np.float32)\n",
- "Y = Y.astype(np.int32)\n",
- "\n",
- "split = [ 70*n//100 ]\n",
- "train_x, test_x = np.split(X, split)\n",
- "train_labels, test_labels = np.split(Y, split)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "id": "c-_BjSHPwHl8",
- "scrolled": false,
- "trusted": true
- },
- "outputs": [],
- "source": [
- "def plot_dataset(features, labels, W=None, b=None):\n",
- " # prepare the plot\n",
- " fig, ax = plt.subplots(1, 1)\n",
- " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n",
- " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n",
- " colors = ['r' if l else 'b' for l in labels]\n",
- " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
- " if W is not None:\n",
- " min_x = min(features[:,0])\n",
- " max_x = max(features[:,1])\n",
- " min_y = min(features[:,1])*(1-.1)\n",
- " max_y = max(features[:,1])*(1+.1)\n",
- " cx = np.array([min_x,max_x],dtype=np.float32)\n",
- " cy = (0.5-W[0]*cx-b)/W[1]\n",
- " ax.plot(cx,cy,'g')\n",
- " ax.set_ylim(min_y,max_y)\n",
- " fig.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 283
- },
- "id": "tq0vFchQwHl8",
- "outputId": "9a5aa6a0-c92f-4d72-9e78-c0f615804bff",
- "scrolled": false,
- "trusted": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
- " fig.show()\n"
- ]
- },
- {
- "data": {
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",
- "text/plain": [
- "
+
+> TODO: imge alıntısı
+
+> ✅ Geriyay'ı not defteri örneğimizde çok daha ayrıntılı olarak ele alacağız.
+
+## Vargılar
+
+Bu dersimizde kendi sinir ağı kütüphanemizi oluşturduk ve onu basit bir iki boyutlu sınıflandırma görevi için kullandık.
+
+## 🚀 Kendini Sınama
+
+Ekteki not defterinde, çok katmanlı algılayıcıları oluşturmak ve eğitmek için kendi çerçevenizi uygulayacaksınız. Modern sinir ağlarının nasıl çalıştığını ayrıntılı olarak görebileceksiniz.
+
+[OwnFramework (KendiCerveceniz)](OwnFramework.tr.ipynb) not defterine gidin ve üzerinde çalışın.
+
+## [Ders sonrası sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/204)
+
+## Gözden Geçirme ve Bireysel Çalışma
+
+Geri yayma, YZ ve makine öğrenmesinde kullanılan yaygın bir algoritmadır ve [daha ayrıntılı olarak](https://wikipedia.org/wiki/Backpropagation) incelemeye değerdir.
+
+## [Ödev](../lab/translations/README.tr.md)
+
+Bu laboratuvarda, MNIST el yazısı rakam sınıflandırmasını çözmek için bu derste oluşturduğunuz çerçeveyi kullanmanız istenmektedir.
+
+* [Talimatlar](../lab/translations/README.tr.md)
+* [Not Defteri](../lab/translations/MyFW_MNIST.tr.ipynb)
diff --git a/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb b/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb
index 9b634fc0..1346760a 100644
--- a/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb
+++ b/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb
@@ -1,733 +1,729 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "En2vX4FuwHlu"
- },
- "source": [
- "## Simplest Introduction to Neural Networks with Keras\n",
- "\n",
- "> This notebook is a part of [AI for Beginners Curricula](http://github.com/microsoft/ai-for-beginners). Visit the repository for complete set of learning materials.\n",
- "\n",
- "### Neural Frameworks\n",
- "\n",
- "There are several frameworks for training neural networks. However, if you want to get started fast and not go into much detail on how things work internally - you should consider using [Keras](https://keras.io/). This short tutorial will get you started, and if you want to get deeper into understanding how things work - look into [Introduction to Tensorflow and Keras](IntroKerasTF.ipynb) notebook."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "8cACQoFMwHl3"
- },
- "source": [
- "### Getting things ready\n",
- "\n",
- "Keras is a part of Tensorflow 2.x framework. Let's make sure we have version 2.x.x of Tensorflow installed:\n",
- "```\n",
- "pip install tensorflow\n",
- "```\n",
- "or\n",
- "```\n",
- "conda install tensorflow\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "xwqVx9-bwHl3",
- "outputId": "2aa591b4-b647-441f-9c8e-4e0da2d517a0",
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Tensorflow version = 2.7.0\n",
- "Keras version = 2.7.0\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "from tensorflow import keras\n",
- "import numpy as np\n",
- "from sklearn.datasets import make_classification\n",
- "import matplotlib.pyplot as plt\n",
- "print(f'Tensorflow version = {tf.__version__}')\n",
- "print(f'Keras version = {keras.__version__}')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6tp2xGV7wHl4"
- },
- "source": [
- "## Basic Concepts: Tensor\n",
- "\n",
- "**Tensor** is a multi-dimensional array. It is very convenient to use tensors to represent different types of data:\n",
- "* 400x400 - black-and-white picture\n",
- "* 400x400x3 - color picture \n",
- "* 16x400x400x3 - minibatch of 16 color pictures\n",
- "* 25x400x400x3 - one second of 25-fps video\n",
- "* 8x25x400x400x3 - minibatch of 8 1-second videos\n",
- "\n",
- "Tensors give us a convenient way to represent input/output data, as well we weights inside the neural network."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "A10prCPowHl7"
- },
- "source": [
- "## Sample Problem\n",
- "\n",
- "Let's consider binary classification problem. A good example of such a problem would be a tumour classification between malignant and benign based on it's size and age. Let's start by generating some sample data:\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "id": "j0OTPkGpwHl7",
- "scrolled": false,
- "trusted": true
- },
- "outputs": [],
- "source": [
- "np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations\n",
- "\n",
- "n = 100\n",
- "X, Y = make_classification(n_samples = n, n_features=2,\n",
- " n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n",
- "X = X.astype(np.float32)\n",
- "Y = Y.astype(np.int32)\n",
- "\n",
- "split = [ 70*n//100 ]\n",
- "train_x, test_x = np.split(X, split)\n",
- "train_labels, test_labels = np.split(Y, split)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "id": "c-_BjSHPwHl8",
- "scrolled": false,
- "trusted": true
- },
- "outputs": [],
- "source": [
- "def plot_dataset(features, labels, W=None, b=None):\n",
- " # prepare the plot\n",
- " fig, ax = plt.subplots(1, 1)\n",
- " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n",
- " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n",
- " colors = ['r' if l else 'b' for l in labels]\n",
- " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
- " if W is not None:\n",
- " min_x = min(features[:,0])\n",
- " max_x = max(features[:,1])\n",
- " min_y = min(features[:,1])*(1-.1)\n",
- " max_y = max(features[:,1])*(1+.1)\n",
- " cx = np.array([min_x,max_x],dtype=np.float32)\n",
- " cy = (0.5-W[0]*cx-b)/W[1]\n",
- " ax.plot(cx,cy,'g')\n",
- " ax.set_ylim(min_y,max_y)\n",
- " fig.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 283
- },
- "id": "tq0vFchQwHl8",
- "outputId": "9a5aa6a0-c92f-4d72-9e78-c0f615804bff",
- "scrolled": false,
- "trusted": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
- " fig.show()\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_dataset(train_x, train_labels)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Normalizing Data\n",
- "\n",
- "Before training, it is common to bring our input features to the standard range of [0,1] (or [-1,1]). The exact reasons for that we will discuss later in the course, but in short the reason is the following. We want to avoid values that flow through our network getting too big or too small, and we normally agree to keep all values in the small range close to 0. Thus we initialize the weights with small random numbers, and we keep signals in the same range.\n",
- "\n",
- "When normalizing data, we need to subtract min value and divide by range. We compute min value and range using training data, and then normalize test/validation dataset using the same min/range values from the training set. This is because in real life we will only know the training set, and not all incoming new values that the network would be asked to predict. Occasionally, the new value may fall out of the [0,1] range, but that's not crucial. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "train_x_norm = (train_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))\n",
- "test_x_norm = (test_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "SjPlpf2-wHl8"
- },
- "source": [
- "## Training One-Layer Network (Perceptron)\n",
- "\n",
- "In many cases, a neural network would be a sequence of layers. It can be defined in Keras using `Sequential` model in the following manner:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential_2\"\n",
- "_________________________________________________________________\n",
- " Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- " dense_2 (Dense) (None, 1) 3 \n",
- " \n",
- " activation_1 (Activation) (None, 1) 0 \n",
- " \n",
- "=================================================================\n",
- "Total params: 3\n",
- "Trainable params: 3\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = keras.models.Sequential()\n",
- "model.add(keras.Input(shape=(2,)))\n",
- "model.add(keras.layers.Dense(1))\n",
- "model.add(keras.layers.Activation(keras.activations.sigmoid))\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here, we first create the model, and then add layers to it:\n",
- "* First `Input` layer (which is not strictly speaking a layer) contains the specification of network's input size\n",
- "* `Dense` layer is the actual perceptron that contains trainable weights\n",
- "* Finally, there is a layer with *sigmoid* `Activation` function to bring the result of the network into 0-1 range (to make it a probability).\n",
- "\n",
- "Input size, as well as activation function, can also be specified directly in the `Dense` layer for brevity: "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential_9\"\n",
- "_________________________________________________________________\n",
- " Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- " dense_9 (Dense) (None, 1) 3 \n",
- " \n",
- "=================================================================\n",
- "Total params: 3\n",
- "Trainable params: 3\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = keras.models.Sequential()\n",
- "model.add(keras.layers.Dense(1,input_shape=(2,),activation='sigmoid'))\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Before training the model, we need to **compile it**, which essentially mean specifying:\n",
- "* **Loss function**, which defines how loss is calculated. Because we have two-class classification problem, we will use *binary cross-entropy loss*.\n",
- "* **Optimizer** to use. The simplest option would be to use `sgd` for *stochastic gradient descent*, or you can use more sophisticated optimizers such as `adam`.\n",
- "* **Metrics** that we want to use to measure success of our training. Since it is classification task, a good metrics would be `Accuracy` (or `acc` for short)\n",
- "\n",
- "We can specify loss, metrics and optimizer either as strings, or by providing some objects from Keras framework. In our example, we need to specify `learning_rate` parameter to fine-tune learning speed of our model, and thus we provide full name of Keras SGD optimizer."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [],
- "source": [
- "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.2),loss='binary_crossentropy',metrics=['acc'])"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "En2vX4FuwHlu"
+ },
+ "source": [
+ "## Simplest Introduction to Neural Networks with Keras\n",
+ "\n",
+ "> This notebook is a part of [AI for Beginners Curricula](http://github.com/microsoft/ai-for-beginners). Visit the repository for complete set of learning materials.\n",
+ "\n",
+ "### Neural Frameworks\n",
+ "\n",
+ "There are several frameworks for training neural networks. However, if you want to get started fast and not go into much detail on how things work internally - you should consider using [Keras](https://keras.io/). This short tutorial will get you started, and if you want to get deeper into understanding how things work - look into [Introduction to Tensorflow and Keras](IntroKerasTF.ipynb) notebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8cACQoFMwHl3"
+ },
+ "source": [
+ "### Getting things ready\n",
+ "\n",
+ "Keras is a part of Tensorflow 2.x framework. Let's make sure we have version 2.x.x of Tensorflow installed:\n",
+ "```\n",
+ "pip install tensorflow\n",
+ "```\n",
+ "or\n",
+ "```\n",
+ "conda install tensorflow\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "xwqVx9-bwHl3",
+ "outputId": "2aa591b4-b647-441f-9c8e-4e0da2d517a0",
+ "tags": []
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "After compiling the model, we can do the actual training by calling `fit` method. The most important parameters are:\n",
- "* `x` and `y` specify training data, features and labels respectively\n",
- "* If we want validation to be performed on each epoch, we can specify `validation_data` parameter, which would be a tuple of features and labels\n",
- "* `epochs` specified the number of epochs\n",
- "* If we want training to happen in minibatches, we can speficu `batch_size` parameter. You can also pre-batch the data manually before passing it to `x`/`y`/`validation_data`, in which case you do not need `batch_size`"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Tensorflow version = 2.7.0\n",
+ "Keras version = 2.7.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "import numpy as np\n",
+ "from sklearn.datasets import make_classification\n",
+ "import matplotlib.pyplot as plt\n",
+ "print(f'Tensorflow version = {tf.__version__}')\n",
+ "print(f'Keras version = {keras.__version__}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6tp2xGV7wHl4"
+ },
+ "source": [
+ "## Basic Concepts: Tensor\n",
+ "\n",
+ "**Tensor** is a multi-dimensional array. It is very convenient to use tensors to represent different types of data:\n",
+ "* 400x400 - black-and-white picture\n",
+ "* 400x400x3 - color picture \n",
+ "* 16x400x400x3 - minibatch of 16 color pictures\n",
+ "* 25x400x400x3 - one second of 25-fps video\n",
+ "* 8x25x400x400x3 - minibatch of 8 1-second videos\n",
+ "\n",
+ "Tensors give us a convenient way to represent input/output data, as well we weights inside the neural network."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A10prCPowHl7"
+ },
+ "source": [
+ "## Sample Problem\n",
+ "\n",
+ "Let's consider binary classification problem. A good example of such a problem would be a tumour classification between malignant and benign based on it's size and age. Let's start by generating some sample data:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "j0OTPkGpwHl7"
+ },
+ "outputs": [],
+ "source": [
+ "np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations\n",
+ "\n",
+ "n = 100\n",
+ "X, Y = make_classification(n_samples = n, n_features=2,\n",
+ " n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n",
+ "X = X.astype(np.float32)\n",
+ "Y = Y.astype(np.int32)\n",
+ "\n",
+ "split = [ 70*n//100 ]\n",
+ "train_x, test_x = np.split(X, split)\n",
+ "train_labels, test_labels = np.split(Y, split)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "c-_BjSHPwHl8"
+ },
+ "outputs": [],
+ "source": [
+ "def plot_dataset(features, labels, W=None, b=None):\n",
+ " # prepare the plot\n",
+ " fig, ax = plt.subplots(1, 1)\n",
+ " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n",
+ " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n",
+ " colors = ['r' if l else 'b' for l in labels]\n",
+ " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
+ " if W is not None:\n",
+ " min_x = min(features[:,0])\n",
+ " max_x = max(features[:,1])\n",
+ " min_y = min(features[:,1])*(1-.1)\n",
+ " max_y = max(features[:,1])*(1+.1)\n",
+ " cx = np.array([min_x,max_x],dtype=np.float32)\n",
+ " cy = (0.5-W[0]*cx-b)/W[1]\n",
+ " ax.plot(cx,cy,'g')\n",
+ " ax.set_ylim(min_y,max_y)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283
},
+ "id": "tq0vFchQwHl8",
+ "outputId": "9a5aa6a0-c92f-4d72-9e78-c0f615804bff"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n",
- "70/70 [==============================] - 0s 4ms/step - loss: 0.3379 - acc: 0.9000 - val_loss: 0.3282 - val_acc: 0.9000\n",
- "Epoch 2/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.3270 - acc: 0.9429 - val_loss: 0.3336 - val_acc: 0.9000\n",
- "Epoch 3/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.3195 - acc: 0.9143 - val_loss: 0.3137 - val_acc: 0.9000\n",
- "Epoch 4/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.3087 - acc: 0.9286 - val_loss: 0.2970 - val_acc: 0.9333\n",
- "Epoch 5/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.3006 - acc: 0.9429 - val_loss: 0.3210 - val_acc: 0.9000\n",
- "Epoch 6/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.3003 - acc: 0.9000 - val_loss: 0.2985 - val_acc: 0.9000\n",
- "Epoch 7/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2956 - acc: 0.9286 - val_loss: 0.3037 - val_acc: 0.9000\n",
- "Epoch 8/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2891 - acc: 0.9429 - val_loss: 0.3035 - val_acc: 0.9000\n",
- "Epoch 9/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2809 - acc: 0.9000 - val_loss: 0.2815 - val_acc: 0.9000\n",
- "Epoch 10/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2809 - acc: 0.9286 - val_loss: 0.2907 - val_acc: 0.9000\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)"
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "s4_Atvn5K4K9"
- },
- "source": [
- "You can try to experiment with different training parameters to see how they affect the training:\n",
- "* Setting `batch_size` to be too large (or not specifying it at all) may result in less stable training, because with low-dimensional data small batch sizes provide more precise direction of the gradient for each specific case\n",
- "* Too high `learning_rate` may result in overfitting, or in less stable results, while too low learning rate means it will take more epochs to achieve the result\n",
- "\n",
- "> Note that you can call `fit` function several times in a row to further train the network. If you want to start training from scratch - you need to re-run the cell with the model definition. \n",
- "\n",
- "To make sure our training worked, let's plot the line that separates two classes. Separation line is defined by the equation $W\\times x + b = 0.5$"
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
]
- },
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_dataset(train_x, train_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Normalizing Data\n",
+ "\n",
+ "Before training, it is common to bring our input features to the standard range of [0,1] (or [-1,1]). The exact reasons for that we will discuss later in the course, but in short the reason is the following. We want to avoid values that flow through our network getting too big or too small, and we normally agree to keep all values in the small range close to 0. Thus we initialize the weights with small random numbers, and we keep signals in the same range.\n",
+ "\n",
+ "When normalizing data, we need to subtract min value and divide by range. We compute min value and range using training data, and then normalize test/validation dataset using the same min/range values from the training set. This is because in real life we will only know the training set, and not all incoming new values that the network would be asked to predict. Occasionally, the new value may fall out of the [0,1] range, but that's not crucial. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_x_norm = (train_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))\n",
+ "test_x_norm = (test_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SjPlpf2-wHl8"
+ },
+ "source": [
+ "## Training One-Layer Network (Perceptron)\n",
+ "\n",
+ "In many cases, a neural network would be a sequence of layers. It can be defined in Keras using `Sequential` model in the following manner:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 283
- },
- "id": "PgRTHttLwHl9",
- "outputId": "e4407e1b-edf5-48e5-fdc2-da28120a3c6b",
- "trusted": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
- " fig.show()\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_dataset(train_x,train_labels,model.layers[0].weights[0],model.layers[0].weights[1])"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_2\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " dense_2 (Dense) (None, 1) 3 \n",
+ " \n",
+ " activation_1 (Activation) (None, 1) 0 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 3\n",
+ "Trainable params: 3\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = keras.models.Sequential()\n",
+ "model.add(keras.Input(shape=(2,)))\n",
+ "model.add(keras.layers.Dense(1))\n",
+ "model.add(keras.layers.Activation(keras.activations.sigmoid))\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here, we first create the model, and then add layers to it:\n",
+ "* First `Input` layer (which is not strictly speaking a layer) contains the specification of network's input size\n",
+ "* `Dense` layer is the actual perceptron that contains trainable weights\n",
+ "* Finally, there is a layer with *sigmoid* `Activation` function to bring the result of the network into 0-1 range (to make it a probability).\n",
+ "\n",
+ "Input size, as well as activation function, can also be specified directly in the `Dense` layer for brevity: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "dvAiaj_JndyP"
- },
- "source": [
- "## Plotting the training graphs\n",
- "\n",
- "`fit` function returns `history` object as a result, which can be used to observe loss and metrics on each epoch. In the example below, we will re-start the training with small learning rate, and will observe how the loss and accuracy behave.\n",
- "\n",
- "> **Note** that we are using slightly different syntax for defining `Sequential` model. Instead of `add`-ing layers one by one, we can also specify the list of layers right when creating the model in the first place - this is a bit shorter syntax, and you may prefer to use it."
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_9\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " dense_9 (Dense) (None, 1) 3 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 3\n",
+ "Trainable params: 3\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = keras.models.Sequential()\n",
+ "model.add(keras.layers.Dense(1,input_shape=(2,),activation='sigmoid'))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Before training the model, we need to **compile it**, which essentially mean specifying:\n",
+ "* **Loss function**, which defines how loss is calculated. Because we have two-class classification problem, we will use *binary cross-entropy loss*.\n",
+ "* **Optimizer** to use. The simplest option would be to use `sgd` for *stochastic gradient descent*, or you can use more sophisticated optimizers such as `adam`.\n",
+ "* **Metrics** that we want to use to measure success of our training. Since it is classification task, a good metrics would be `Accuracy` (or `acc` for short)\n",
+ "\n",
+ "We can specify loss, metrics and optimizer either as strings, or by providing some objects from Keras framework. In our example, we need to specify `learning_rate` parameter to fine-tune learning speed of our model, and thus we provide full name of Keras SGD optimizer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.2),loss='binary_crossentropy',metrics=['acc'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After compiling the model, we can do the actual training by calling `fit` method. The most important parameters are:\n",
+ "* `x` and `y` specify training data, features and labels respectively\n",
+ "* If we want validation to be performed on each epoch, we can specify `validation_data` parameter, which would be a tuple of features and labels\n",
+ "* `epochs` specified the number of epochs\n",
+ "* If we want training to happen in minibatches, we can speficy `batch_size` parameter. You can also pre-batch the data manually before passing it to `x`/`y`/`validation_data`, in which case you do not need `batch_size`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n",
- "70/70 [==============================] - 1s 5ms/step - loss: 0.6600 - acc: 0.6143 - val_loss: 0.6351 - val_acc: 0.8000\n",
- "Epoch 2/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.6384 - acc: 0.7143 - val_loss: 0.6187 - val_acc: 0.8333\n",
- "Epoch 3/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.6188 - acc: 0.7571 - val_loss: 0.6001 - val_acc: 0.8667\n",
- "Epoch 4/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.6022 - acc: 0.7714 - val_loss: 0.5837 - val_acc: 0.9000\n",
- "Epoch 5/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.5860 - acc: 0.8571 - val_loss: 0.5673 - val_acc: 0.9000\n",
- "Epoch 6/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.5702 - acc: 0.8571 - val_loss: 0.5597 - val_acc: 0.8667\n",
- "Epoch 7/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.5568 - acc: 0.8286 - val_loss: 0.5458 - val_acc: 0.9000\n",
- "Epoch 8/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.5430 - acc: 0.8714 - val_loss: 0.5325 - val_acc: 0.9000\n",
- "Epoch 9/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.5308 - acc: 0.8714 - val_loss: 0.5234 - val_acc: 0.9000\n",
- "Epoch 10/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.5175 - acc: 0.9143 - val_loss: 0.5170 - val_acc: 0.8667\n"
- ]
- }
- ],
- "source": [
- "model = keras.models.Sequential([\n",
- " keras.layers.Dense(1,input_shape=(2,),activation='sigmoid')])\n",
- "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.05),loss='binary_crossentropy',metrics=['acc'])\n",
- "hist = model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "70/70 [==============================] - 0s 4ms/step - loss: 0.3379 - acc: 0.9000 - val_loss: 0.3282 - val_acc: 0.9000\n",
+ "Epoch 2/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.3270 - acc: 0.9429 - val_loss: 0.3336 - val_acc: 0.9000\n",
+ "Epoch 3/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.3195 - acc: 0.9143 - val_loss: 0.3137 - val_acc: 0.9000\n",
+ "Epoch 4/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.3087 - acc: 0.9286 - val_loss: 0.2970 - val_acc: 0.9333\n",
+ "Epoch 5/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.3006 - acc: 0.9429 - val_loss: 0.3210 - val_acc: 0.9000\n",
+ "Epoch 6/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.3003 - acc: 0.9000 - val_loss: 0.2985 - val_acc: 0.9000\n",
+ "Epoch 7/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2956 - acc: 0.9286 - val_loss: 0.3037 - val_acc: 0.9000\n",
+ "Epoch 8/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2891 - acc: 0.9429 - val_loss: 0.3035 - val_acc: 0.9000\n",
+ "Epoch 9/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2809 - acc: 0.9000 - val_loss: 0.2815 - val_acc: 0.9000\n",
+ "Epoch 10/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2809 - acc: 0.9286 - val_loss: 0.2907 - val_acc: 0.9000\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "plt.plot(hist.history['acc'])\n",
- "plt.plot(hist.history['val_acc'])"
+ "data": {
+ "text/plain": [
+ ""
]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s4_Atvn5K4K9"
+ },
+ "source": [
+ "You can try to experiment with different training parameters to see how they affect the training:\n",
+ "* Setting `batch_size` to be too large (or not specifying it at all) may result in less stable training, because with low-dimensional data small batch sizes provide more precise direction of the gradient for each specific case\n",
+ "* Too high `learning_rate` may result in overfitting, or in less stable results, while too low learning rate means it will take more epochs to achieve the result\n",
+ "\n",
+ "> Note that you can call `fit` function several times in a row to further train the network. If you want to start training from scratch - you need to re-run the cell with the model definition. \n",
+ "\n",
+ "To make sure our training worked, let's plot the line that separates two classes. Separation line is defined by the equation $W\\times x + b = 0.5$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283
},
+ "id": "PgRTHttLwHl9",
+ "outputId": "e4407e1b-edf5-48e5-fdc2-da28120a3c6b"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Multi-Class Classification\n",
- "\n",
- "If you need to solve a problem of multi-class classification, your network would have more that one output - corresponding to the number of classes $C$. Each output will contain the probability of a given class.\n",
- "\n",
- "> Note that you can also use a network with two outputs to perform binary classification in the same manner. That is exactly what we will demonstrate now.\n",
- "\n",
- "When you expect a network to output a set of probabilities $p_1,\\dots, p_C$, we need all of them to add up to 1. To ensure this, we use `softmax` as a final activation function on the last layer. **Softmax** takes a vector input, and makes sure that all components of that vector are transformed into probabilities.\n",
- "\n",
- "Also, since the output of the network is a $C$-dimensional vector, we need labels to have the same form. This can be achieved by using **one-hot encoding**, when the number of a class $i$ is converted to a vector of zeroes, with 1 at the $i$-th position.\n",
- "\n",
- "To compare the probability output of the neural network with expected one-hot-encoded label, we use **cross-entropy loss** function. It takes two probability distributions, and outputs a value of how different they are.\n",
- "\n",
- "So, to summarize what we need to do for multi-class classification with $C$ classes:\n",
- "* The network should have $C$ neurons in the last layer\n",
- "* Last activation function should be **softmax**\n",
- "* Loss should be **cross-entropy loss**\n",
- "* Labels should be converted to **one-hot encoding** (this can be done using `numpy`, or using Keras utils `to_categorical`)"
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n",
- "70/70 [==============================] - 1s 6ms/step - loss: 0.6524 - acc: 0.7000 - val_loss: 0.5936 - val_acc: 0.9000\n",
- "Epoch 2/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.5715 - acc: 0.8286 - val_loss: 0.5255 - val_acc: 0.8333\n",
- "Epoch 3/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.4820 - acc: 0.8714 - val_loss: 0.4213 - val_acc: 0.9000\n",
- "Epoch 4/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.4426 - acc: 0.9000 - val_loss: 0.3694 - val_acc: 0.9333\n",
- "Epoch 5/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.3602 - acc: 0.9000 - val_loss: 0.3454 - val_acc: 0.9000\n",
- "Epoch 6/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.3209 - acc: 0.8857 - val_loss: 0.2862 - val_acc: 0.9333\n",
- "Epoch 7/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2905 - acc: 0.9286 - val_loss: 0.2787 - val_acc: 0.9000\n",
- "Epoch 8/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2698 - acc: 0.9000 - val_loss: 0.2381 - val_acc: 0.9333\n",
- "Epoch 9/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2639 - acc: 0.8857 - val_loss: 0.2217 - val_acc: 0.9667\n",
- "Epoch 10/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.2592 - acc: 0.9286 - val_loss: 0.2391 - val_acc: 0.9000\n"
- ]
- }
- ],
- "source": [
- "model = keras.models.Sequential([\n",
- " keras.layers.Dense(5,input_shape=(2,),activation='relu'),\n",
- " keras.layers.Dense(2,activation='softmax')\n",
- "])\n",
- "model.compile(keras.optimizers.Adam(0.01),'categorical_crossentropy',['acc'])\n",
- "\n",
- "# Two ways to convert to one-hot encoding\n",
- "train_labels_onehot = keras.utils.to_categorical(train_labels)\n",
- "test_labels_onehot = np.eye(2)[test_labels]\n",
- "\n",
- "hist = model.fit(x=train_x_norm,y=train_labels_onehot,\n",
- " validation_data=[test_x_norm,test_labels_onehot],batch_size=1,epochs=10)"
+ "data": {
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",
+ "text/plain": [
+ "
"
]
- },
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_dataset(train_x,train_labels,model.layers[0].weights[0],model.layers[0].weights[1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dvAiaj_JndyP"
+ },
+ "source": [
+ "## Plotting the training graphs\n",
+ "\n",
+ "`fit` function returns `history` object as a result, which can be used to observe loss and metrics on each epoch. In the example below, we will re-start the training with small learning rate, and will observe how the loss and accuracy behave.\n",
+ "\n",
+ "> **Note** that we are using slightly different syntax for defining `Sequential` model. Instead of `add`-ing layers one by one, we can also specify the list of layers right when creating the model in the first place - this is a bit shorter syntax, and you may prefer to use it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Sparse Categorical Cross-Entropy\n",
- "\n",
- "Often labels in multi-class classification are represented by class numbers. Keras also supports another kind of loss function called **sparse categorical crossentropy**, which expects class number to be integers, and not one-hot vectors. Using this kind of loss function, we can simplify our training code:"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "70/70 [==============================] - 1s 5ms/step - loss: 0.6600 - acc: 0.6143 - val_loss: 0.6351 - val_acc: 0.8000\n",
+ "Epoch 2/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.6384 - acc: 0.7143 - val_loss: 0.6187 - val_acc: 0.8333\n",
+ "Epoch 3/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.6188 - acc: 0.7571 - val_loss: 0.6001 - val_acc: 0.8667\n",
+ "Epoch 4/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.6022 - acc: 0.7714 - val_loss: 0.5837 - val_acc: 0.9000\n",
+ "Epoch 5/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.5860 - acc: 0.8571 - val_loss: 0.5673 - val_acc: 0.9000\n",
+ "Epoch 6/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.5702 - acc: 0.8571 - val_loss: 0.5597 - val_acc: 0.8667\n",
+ "Epoch 7/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.5568 - acc: 0.8286 - val_loss: 0.5458 - val_acc: 0.9000\n",
+ "Epoch 8/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.5430 - acc: 0.8714 - val_loss: 0.5325 - val_acc: 0.9000\n",
+ "Epoch 9/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.5308 - acc: 0.8714 - val_loss: 0.5234 - val_acc: 0.9000\n",
+ "Epoch 10/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.5175 - acc: 0.9143 - val_loss: 0.5170 - val_acc: 0.8667\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = keras.models.Sequential([\n",
+ " keras.layers.Dense(1,input_shape=(2,),activation='sigmoid')])\n",
+ "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.05),loss='binary_crossentropy',metrics=['acc'])\n",
+ "hist = model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n",
- "70/70 [==============================] - 1s 6ms/step - loss: 0.2353 - acc: 0.9143 - val_loss: 0.2190 - val_acc: 0.9000\n",
- "Epoch 2/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2243 - acc: 0.9286 - val_loss: 0.1886 - val_acc: 0.9333\n",
- "Epoch 3/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.2366 - acc: 0.9143 - val_loss: 0.2262 - val_acc: 0.9000\n",
- "Epoch 4/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.2259 - acc: 0.9429 - val_loss: 0.2124 - val_acc: 0.9000\n",
- "Epoch 5/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.2061 - acc: 0.9429 - val_loss: 0.2691 - val_acc: 0.9000\n",
- "Epoch 6/10\n",
- "70/70 [==============================] - 0s 2ms/step - loss: 0.2200 - acc: 0.9286 - val_loss: 0.2344 - val_acc: 0.9000\n",
- "Epoch 7/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2133 - acc: 0.9286 - val_loss: 0.1973 - val_acc: 0.9000\n",
- "Epoch 8/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2062 - acc: 0.9429 - val_loss: 0.1893 - val_acc: 0.9000\n",
- "Epoch 9/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2060 - acc: 0.9571 - val_loss: 0.2719 - val_acc: 0.9000\n",
- "Epoch 10/10\n",
- "70/70 [==============================] - 0s 3ms/step - loss: 0.2021 - acc: 0.9571 - val_loss: 0.2293 - val_acc: 0.9000\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model.compile(keras.optimizers.Adam(0.01),'sparse_categorical_crossentropy',['acc'])\n",
- "model.fit(x=train_x_norm,y=train_labels,validation_data=[test_x_norm,test_labels],batch_size=1,epochs=10)"
+ "data": {
+ "text/plain": [
+ "[]"
]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Multi-Label Classification\n",
- "\n",
- "Sometime we have cases when our objects can belong to two classes at once. As an example, suppose we want to develop a classifier for cats and dogs on the picture, but we also want to allow cases when both cats and dogs are present.\n",
- "\n",
- "With multi-label classification, instead of one-hot encoded vector, we will have a vector that has 1 in position corresponding to all classes relevant to the input sample. Thus, output of the network should not have normalized probabilities for all classes, but rather for each class individually - which corresponds to using **sigmoid** activation function. Cross-entropy loss can still be used as a loss function.\n",
- "\n",
- "> **Note** that this is very similar to using **different neural networks** to do binary classification for each particular class - only the initial part of the network (up to final classification layer) is shared for all classes."
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
]
- },
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(hist.history['acc'])\n",
+ "plt.plot(hist.history['val_acc'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multi-Class Classification\n",
+ "\n",
+ "If you need to solve a problem of multi-class classification, your network would have more that one output - corresponding to the number of classes $C$. Each output will contain the probability of a given class.\n",
+ "\n",
+ "> Note that you can also use a network with two outputs to perform binary classification in the same manner. That is exactly what we will demonstrate now.\n",
+ "\n",
+ "When you expect a network to output a set of probabilities $p_1,\\dots, p_C$, we need all of them to add up to 1. To ensure this, we use `softmax` as a final activation function on the last layer. **Softmax** takes a vector input, and makes sure that all components of that vector are transformed into probabilities.\n",
+ "\n",
+ "Also, since the output of the network is a $C$-dimensional vector, we need labels to have the same form. This can be achieved by using **one-hot encoding**, when the number of a class $i$ is converted to a vector of zeroes, with 1 at the $i$-th position.\n",
+ "\n",
+ "To compare the probability output of the neural network with expected one-hot-encoded label, we use **cross-entropy loss** function. It takes two probability distributions, and outputs a value of how different they are.\n",
+ "\n",
+ "So, to summarize what we need to do for multi-class classification with $C$ classes:\n",
+ "* The network should have $C$ neurons in the last layer\n",
+ "* Last activation function should be **softmax**\n",
+ "* Loss should be **cross-entropy loss**\n",
+ "* Labels should be converted to **one-hot encoding** (this can be done using `numpy`, or using Keras utils `to_categorical`)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "BmHNhUU8bqEX"
- },
- "source": [
- "## Summary of Classification Loss Functions\n",
- "\n",
- "We have seen that binary, multi-class and multi-label classification differ by the type of loss function and activation function on the last layer of the network. It may all be a little bit confusing if you are just starting to learn, but here are a few rules to keep in mind:\n",
- "* If the network has one output (**binary classification**), we use **sigmoid** activation function, for **multiclass classification** - **softmax**\n",
- "* If the output class is represented as one-hot-encoding, the loss function will be **cross entropy loss** (categorical cross-entropy), if the output contains class number - **sparse categorical cross-entropy**. For **binary classification** - use **binary cross-entropy** (same as **log loss**)\n",
- "* **Multi-label classification** is when we can have an object belonging to several classes at the same time. In this case, we need to encode labels using one-hot encoding, and use **sigmoid** as activation function, so that each class probability is between 0 and 1.\n",
- "\n",
- "| Classification | Label Format | Activation Function | Loss |\n",
- "|---------------|-----------------------|-----------------|----------|\n",
- "| Binary | Probability of 1st class | sigmoid | binary crossentropy |\n",
- "| Binary | One-hot encoding (2 outputs) | softmax | categorical crossentropy |\n",
- "| Multiclass | One-hot encoding | softmax | categorical crossentropy |\n",
- "| Multiclass | Class Number | softmax | sparse categorical crossentropy |\n",
- "| Multilabel | One-hot encoding | sigmoid | categorical crossentropy |\n"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "70/70 [==============================] - 1s 6ms/step - loss: 0.6524 - acc: 0.7000 - val_loss: 0.5936 - val_acc: 0.9000\n",
+ "Epoch 2/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.5715 - acc: 0.8286 - val_loss: 0.5255 - val_acc: 0.8333\n",
+ "Epoch 3/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.4820 - acc: 0.8714 - val_loss: 0.4213 - val_acc: 0.9000\n",
+ "Epoch 4/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.4426 - acc: 0.9000 - val_loss: 0.3694 - val_acc: 0.9333\n",
+ "Epoch 5/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.3602 - acc: 0.9000 - val_loss: 0.3454 - val_acc: 0.9000\n",
+ "Epoch 6/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.3209 - acc: 0.8857 - val_loss: 0.2862 - val_acc: 0.9333\n",
+ "Epoch 7/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2905 - acc: 0.9286 - val_loss: 0.2787 - val_acc: 0.9000\n",
+ "Epoch 8/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2698 - acc: 0.9000 - val_loss: 0.2381 - val_acc: 0.9333\n",
+ "Epoch 9/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2639 - acc: 0.8857 - val_loss: 0.2217 - val_acc: 0.9667\n",
+ "Epoch 10/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.2592 - acc: 0.9286 - val_loss: 0.2391 - val_acc: 0.9000\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = keras.models.Sequential([\n",
+ " keras.layers.Dense(5,input_shape=(2,),activation='relu'),\n",
+ " keras.layers.Dense(2,activation='softmax')\n",
+ "])\n",
+ "model.compile(keras.optimizers.Adam(0.01),'categorical_crossentropy',['acc'])\n",
+ "\n",
+ "# Two ways to convert to one-hot encoding\n",
+ "train_labels_onehot = keras.utils.to_categorical(train_labels)\n",
+ "test_labels_onehot = np.eye(2)[test_labels]\n",
+ "\n",
+ "hist = model.fit(x=train_x_norm,y=train_labels_onehot,\n",
+ " validation_data=[test_x_norm,test_labels_onehot],batch_size=1,epochs=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sparse Categorical Cross-Entropy\n",
+ "\n",
+ "Often labels in multi-class classification are represented by class numbers. Keras also supports another kind of loss function called **sparse categorical crossentropy**, which expects class number to be integers, and not one-hot vectors. Using this kind of loss function, we can simplify our training code:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "gZ-kWx84bMDH"
- },
- "source": [
- "**Task**: \n",
- "Use Keras to train a classifier for MNIST handwritten digits:\n",
- "* Notice that Keras contains some standard datasets, including MNIST. To use MNIST from Keras, you only need a couple of lines of code (more information [here](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist))\n",
- "* Try several network configuration, with different number of layers/neurons, activation functions.\n",
- "\n",
- "What is the best accuracy you were able to achieve?"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "70/70 [==============================] - 1s 6ms/step - loss: 0.2353 - acc: 0.9143 - val_loss: 0.2190 - val_acc: 0.9000\n",
+ "Epoch 2/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2243 - acc: 0.9286 - val_loss: 0.1886 - val_acc: 0.9333\n",
+ "Epoch 3/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.2366 - acc: 0.9143 - val_loss: 0.2262 - val_acc: 0.9000\n",
+ "Epoch 4/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.2259 - acc: 0.9429 - val_loss: 0.2124 - val_acc: 0.9000\n",
+ "Epoch 5/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.2061 - acc: 0.9429 - val_loss: 0.2691 - val_acc: 0.9000\n",
+ "Epoch 6/10\n",
+ "70/70 [==============================] - 0s 2ms/step - loss: 0.2200 - acc: 0.9286 - val_loss: 0.2344 - val_acc: 0.9000\n",
+ "Epoch 7/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2133 - acc: 0.9286 - val_loss: 0.1973 - val_acc: 0.9000\n",
+ "Epoch 8/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2062 - acc: 0.9429 - val_loss: 0.1893 - val_acc: 0.9000\n",
+ "Epoch 9/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2060 - acc: 0.9571 - val_loss: 0.2719 - val_acc: 0.9000\n",
+ "Epoch 10/10\n",
+ "70/70 [==============================] - 0s 3ms/step - loss: 0.2021 - acc: 0.9571 - val_loss: 0.2293 - val_acc: 0.9000\n"
+ ]
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "yX6hqiafwHl9"
- },
- "source": [
- "## Takeaways\n",
- "\n",
- "* **Keras** is really recommended for beginners, because it allows to construct networks from layers quite easily, and then train it with just a couple of lines of code\n",
- "* If non-standard architecture is needed, you would need to learn a bit deeper into Tensorflow. Or you can ask someone to implement custom logic as a Keras layer, and then use it in Keras models\n",
- "* It is a good idea to look at PyTorch as well and compare approaches. \n",
- "\n",
- "A good sample notebook from the creator of Keras on Keras and Tensorflow 2.0 can be found [here](https://t.co/k694J95PI8)."
+ "data": {
+ "text/plain": [
+ ""
]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
}
- ],
- "metadata": {
- "celltoolbar": "Slideshow",
- "colab": {
- "collapsed_sections": [],
- "name": "IntroKerasTF.ipynb",
- "provenance": []
- },
- "interpreter": {
- "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5"
- },
- "kernelspec": {
- "display_name": "Python 3.8.12 64-bit (conda)",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.12"
- },
- "livereveal": {
- "start_slideshow_at": "selected"
- }
+ ],
+ "source": [
+ "model.compile(keras.optimizers.Adam(0.01),'sparse_categorical_crossentropy',['acc'])\n",
+ "model.fit(x=train_x_norm,y=train_labels,validation_data=[test_x_norm,test_labels],batch_size=1,epochs=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Multi-Label Classification\n",
+ "\n",
+ "Sometime we have cases when our objects can belong to two classes at once. As an example, suppose we want to develop a classifier for cats and dogs on the picture, but we also want to allow cases when both cats and dogs are present.\n",
+ "\n",
+ "With multi-label classification, instead of one-hot encoded vector, we will have a vector that has 1 in position corresponding to all classes relevant to the input sample. Thus, output of the network should not have normalized probabilities for all classes, but rather for each class individually - which corresponds to using **sigmoid** activation function. Cross-entropy loss can still be used as a loss function.\n",
+ "\n",
+ "> **Note** that this is very similar to using **different neural networks** to do binary classification for each particular class - only the initial part of the network (up to final classification layer) is shared for all classes."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BmHNhUU8bqEX"
+ },
+ "source": [
+ "## Summary of Classification Loss Functions\n",
+ "\n",
+ "We have seen that binary, multi-class and multi-label classification differ by the type of loss function and activation function on the last layer of the network. It may all be a little bit confusing if you are just starting to learn, but here are a few rules to keep in mind:\n",
+ "* If the network has one output (**binary classification**), we use **sigmoid** activation function, for **multiclass classification** - **softmax**\n",
+ "* If the output class is represented as one-hot-encoding, the loss function will be **cross entropy loss** (categorical cross-entropy), if the output contains class number - **sparse categorical cross-entropy**. For **binary classification** - use **binary cross-entropy** (same as **log loss**)\n",
+ "* **Multi-label classification** is when we can have an object belonging to several classes at the same time. In this case, we need to encode labels using one-hot encoding, and use **sigmoid** as activation function, so that each class probability is between 0 and 1.\n",
+ "\n",
+ "| Classification | Label Format | Activation Function | Loss |\n",
+ "|---------------|-----------------------|-----------------|----------|\n",
+ "| Binary | Probability of 1st class | sigmoid | binary crossentropy |\n",
+ "| Binary | One-hot encoding (2 outputs) | softmax | categorical crossentropy |\n",
+ "| Multiclass | One-hot encoding | softmax | categorical crossentropy |\n",
+ "| Multiclass | Class Number | softmax | sparse categorical crossentropy |\n",
+ "| Multilabel | One-hot encoding | sigmoid | categorical crossentropy |\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gZ-kWx84bMDH"
+ },
+ "source": [
+ "**Task**: \n",
+ "Use Keras to train a classifier for MNIST handwritten digits:\n",
+ "* Notice that Keras contains some standard datasets, including MNIST. To use MNIST from Keras, you only need a couple of lines of code (more information [here](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist))\n",
+ "* Try several network configuration, with different number of layers/neurons, activation functions.\n",
+ "\n",
+ "What is the best accuracy you were able to achieve?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yX6hqiafwHl9"
+ },
+ "source": [
+ "## Takeaways\n",
+ "\n",
+ "* **Keras** is really recommended for beginners, because it allows to construct networks from layers quite easily, and then train it with just a couple of lines of code\n",
+ "* If non-standard architecture is needed, you would need to learn a bit deeper into Tensorflow. Or you can ask someone to implement custom logic as a Keras layer, and then use it in Keras models\n",
+ "* It is a good idea to look at PyTorch as well and compare approaches. \n",
+ "\n",
+ "A good sample notebook from the creator of Keras on Keras and Tensorflow 2.0 can be found [here](https://t.co/k694J95PI8)."
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Slideshow",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "IntroKerasTF.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ },
+ "livereveal": {
+ "start_slideshow_at": "selected"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "vscode": {
+ "interpreter": {
+ "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
}
diff --git a/lessons/3-NeuralNetworks/05-Frameworks/lab/translations/LabFrameworks.tr.ipynb b/lessons/3-NeuralNetworks/05-Frameworks/lab/translations/LabFrameworks.tr.ipynb
new file mode 100644
index 00000000..141b85ee
--- /dev/null
+++ b/lessons/3-NeuralNetworks/05-Frameworks/lab/translations/LabFrameworks.tr.ipynb
@@ -0,0 +1,418 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# PyTorch/TensorFlow ile Sınıflandırma\n",
+ "\n",
+ "[Yeni Başlayanlar için YZ Müfredatı](https://github.com/microsoft/ai-for-beginners)'ndan Laboratuvar Ödevi.\n",
+ "\n",
+ "## Bölüm 1: Süsen Sınıflandırma\n",
+ "\n",
+ "Süsen (Iris) veri kümesi, 3 farklı süsen sınıfının 150 kaydını içerir. Her kayıt 4 sayısal parametre içerir: Çanak yaprağı uzunluğu/genişliği ve taç yaprağı uzunluğu/genişliği. Güçlü bir sinir ağına ihtiyacınız olmayan basit bir veri kümesi örneğidir.\n",
+ "\n",
+ "### Veri Kümesini Alma\n",
+ "\n",
+ "Süsen veri kümesi, Scikit Learn'de yerleşiktir, böylece onu kolayca alabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Öznitelikler: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'], Sınıflar: ['setosa' 'versicolor' 'virginica']\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.datasets import load_iris\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "iris = load_iris()\n",
+ "features = iris['data']\n",
+ "labels = iris['target']\n",
+ "class_names = iris['target_names']\n",
+ "feature_names = iris['feature_names']\n",
+ "\n",
+ "print(f\"Öznitelikler: {feature_names}, Sınıflar: {class_names}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Veriyi Görselleştirme\n",
+ "\n",
+ "Çoğu durumda, ayrılabilir görünüp görünmediklerini görmek için verileri görselleştirmek mantıklıdır; bu bize iyi bir sınıflandırma modeli oluşturabilmemiz gerektiğini garanti eder. Birkaç özniteliğimiz olduğundan, farklı sınıfları farklı nokta renkleriyle gösteren bir dizi ikili 2B dağılım grafiği oluşturabiliriz. Bu, **seaborn** adlı bir paket tarafından otomatik olarak yapılabilir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n",
+ "0 5.1 3.5 1.4 0.2 \n",
+ "1 4.9 3.0 1.4 0.2 \n",
+ "2 4.7 3.2 1.3 0.2 \n",
+ "3 4.6 3.1 1.5 0.2 \n",
+ "4 5.0 3.6 1.4 0.2 \n",
+ ".. ... ... ... ... \n",
+ "145 6.7 3.0 5.2 2.3 \n",
+ "146 6.3 2.5 5.0 1.9 \n",
+ "147 6.5 3.0 5.2 2.0 \n",
+ "148 6.2 3.4 5.4 2.3 \n",
+ "149 5.9 3.0 5.1 1.8 \n",
+ "\n",
+ " Label \n",
+ "0 0 \n",
+ "1 0 \n",
+ "2 0 \n",
+ "3 0 \n",
+ "4 0 \n",
+ ".. ... \n",
+ "145 2 \n",
+ "146 2 \n",
+ "147 2 \n",
+ "148 2 \n",
+ "149 2 \n",
+ "\n",
+ "[150 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import seaborn as sns\n",
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.DataFrame(features,columns=feature_names).join(pd.DataFrame(labels,columns=['Label']))\n",
+ "\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "| \n", + " | sepal length (cm) | \n", + "sepal width (cm) | \n", + "petal length (cm) | \n", + "petal width (cm) | \n", + "Label | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "5.1 | \n", + "3.5 | \n", + "1.4 | \n", + "0.2 | \n", + "0 | \n", + "
| 1 | \n", + "4.9 | \n", + "3.0 | \n", + "1.4 | \n", + "0.2 | \n", + "0 | \n", + "
| 2 | \n", + "4.7 | \n", + "3.2 | \n", + "1.3 | \n", + "0.2 | \n", + "0 | \n", + "
| 3 | \n", + "4.6 | \n", + "3.1 | \n", + "1.5 | \n", + "0.2 | \n", + "0 | \n", + "
| 4 | \n", + "5.0 | \n", + "3.6 | \n", + "1.4 | \n", + "0.2 | \n", + "0 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 145 | \n", + "6.7 | \n", + "3.0 | \n", + "5.2 | \n", + "2.3 | \n", + "2 | \n", + "
| 146 | \n", + "6.3 | \n", + "2.5 | \n", + "5.0 | \n", + "1.9 | \n", + "2 | \n", + "
| 147 | \n", + "6.5 | \n", + "3.0 | \n", + "5.2 | \n", + "2.0 | \n", + "2 | \n", + "
| 148 | \n", + "6.2 | \n", + "3.4 | \n", + "5.4 | \n", + "2.3 | \n", + "2 | \n", + "
| 149 | \n", + "5.9 | \n", + "3.0 | \n", + "5.1 | \n", + "1.8 | \n", + "2 | \n", + "
150 rows × 5 columns
\n", + ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.pairplot(df,hue='Label')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Veriyi Normalleştirme ve Kodlama\n",
+ "\n",
+ "Veriyi sinir ağı eğitimine hazırlamak için girdileri [0..1] aralığında normalleştirmemiz gerekiyor. Bu, düz `numpy` işlemleri veya [Scikit Learn yöntemleri](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html) kullanılarak yapılabilir.\n",
+ "\n",
+ "Ayrıca, hedef etiketin birebir olarak kodlanmasını isteyip istemediğinize karar vermeniz gerekir. PyTorch ve TensorFlow, sınıf numarasını bir tamsayı (0'dan N-1'e kadar) veya birebir kodlanmış vektör olarak besleme yapmanızı sağlar. Sinir ağı yapısı oluştururken, buna göre kayıp fonksiyonunu belirtmeniz gerekir (örn. sayısal gösterim için *seyrek kategorik çapraz-entropi* ve birebir kodlama için *çapraz-entropi kaybı*). Birebir kodlama [Sklearn kullanılarak](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html) veya bu kod parçası kullanılarak da yapılabilir:\n",
+ "\n",
+ "```python\n",
+ "n_values = np.max(labels) + 1\n",
+ "labels_onehot = np.eye(n_values)[labels]\n",
+ "``` "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Verileri normalleştirmek ve kodlamak için kod"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Veriyi Eğitim ve Test Olarak Bölme\n",
+ "\n",
+ "Ayrı eğitim ve test veri kümemiz olmadığı için, bunu eğitim ve test veri kümesini [Sklearn kullanarak](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) ayırmamız gerekiyor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Veriyi Bölme"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Sinir Ağını Tanımlama ve Eğitme\n",
+ "\n",
+ "Artık yola çıkmaya hazırsınız, tercih ettiğiniz çerçeveyi içe aktarın, sinir ağını tanımlayın ve eğitimin davranışını ve geçerleme doğruluğunu gözlemleyerek eğitime başlayın."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Ağı tanımla"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Ağı eğit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Eğitim/geçerleme doğruluk grafiğini görselleştirin"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Deney\n",
+ "\n",
+ "Artık sonucu nasıl etkilediğini görmek için farklı ağ mimarilerini deneyebilirsiniz. Deneyin:\n",
+ "1. 3 nöronlu tek katmanlı ağ (sınıf sayısına eşit)\n",
+ "1. Küçük/orta/büyük gizli katmana sahip iki katmanlı ağ\n",
+ "1. Daha fazla katman kullanma\n",
+ "\n",
+ "Çok sayıda nöron (parametre) içeren zengin modeli kullanırken, aşırı öğrenmeyi gözlemlediğinizden emin olun."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Deney"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Bölüm 2: MNIST Eğitimi\n",
+ "\n",
+ "Hem Keras hem de PyTorch yerleşik veri kümesi olarak MNIST'i içerir, bu nedenle onu birkaç satır kodla kolayca elde edebilirsiniz ([Keras](https://keras.io/api/datasets/mnist/), [PyTorch](https://pytorch.org/vision/stable/datasets.html)). Ayrıca hem eğitim hem de test veri kümelerini manuel olarak bölmeden yükleyebileceksiniz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Veri kümesini yükleme"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Şimdi, veri kümesinin normalleştirildiğinden emin olmak (muhtemelen zaten olacaktır), bir sinir ağı tanımlayıp eğitmek için yukarıdaki adımları uygulamanız gerekir.\n",
+ "\n",
+ "## Ana Fikirler\n",
+ "\n",
+ "1. Sinir ağları, geleneksel makine öğrenmesi görevleri için kullanılabilir. Ancak, çoğu durumda çok güçlüdürler ve aşırı öğrenmeye neden olabilirler.\n",
+ "1. Bu ödevde aşırı öğrenme davranışını gözlemlemeniz ve bundan kaçınmaya çalışmanız önemlidir.\n",
+ "1. Keras gibi çerçevelerle, bazen bir sinir ağını eğitmek oldukça basittir. Ama neler olduğunu anlamalısınız."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/3-NeuralNetworks/05-Frameworks/lab/translations/README.tr.md b/lessons/3-NeuralNetworks/05-Frameworks/lab/translations/README.tr.md
new file mode 100644
index 00000000..2c02ec1e
--- /dev/null
+++ b/lessons/3-NeuralNetworks/05-Frameworks/lab/translations/README.tr.md
@@ -0,0 +1,16 @@
+# PyTorch/TensorFlow ile Sınıflandırma
+
+[Yeni Başlayanlar için YZ Müfredatı](https://github.com/microsoft/ai-for-beginners)'dan Laboratuvar Ödevi.
+
+## Görev
+
+PyTorch veya TensorFlow kullanarak tek ve çok katmanlı tam bağlı ağları kullanarak iki sınıflandırma sorununu çözün:
+
+1. **[Süsen (iris) sınıflandırması](https://en.wikipedia.org/wiki/Iris_flower_data_set)** problemi - klasik makine öğrenmesi tarafından ele alınabilen tablo girdi verileriyle ilgili bir problem örneği. Amacınız, süsenleri 4 sayısal parametreye dayalı olarak 3 sınıfa sınıflandırmak olacaktır.
+1. Daha önce gördüğümüz **MNIST** el yazısı rakam sınıflandırma problemi.
+
+Elde edebileceğiniz en iyi doğruluğu elde etmek için farklı ağ mimarilerini deneyin.
+
+## Not Defteri Başlatma
+
+[LabFrameworks.tr.ipynb](LabFrameworks.tr.ipynb)'yi açarak laboratuvarı başlatın.
diff --git a/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroKeras.tr.ipynb b/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroKeras.tr.ipynb
new file mode 100644
index 00000000..51c93bfe
--- /dev/null
+++ b/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroKeras.tr.ipynb
@@ -0,0 +1,851 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "En2vX4FuwHlu"
+ },
+ "source": [
+ "## Keras ile Sinir Ağlarına En Basit Giriş\n",
+ "\n",
+ "> Bu not defteri, [Yeni Başlayanlar için YZ Müfredatı](http://github.com/microsoft/ai-for-beginners)'nın bir parçasıdır. Eksiksiz öğrenme materyalleri kümesi için kod deposunu ziyaret edin.\n",
+ "\n",
+ "### Sinir Çerçeveleri\n",
+ "\n",
+ "Sinir ağlarını eğitmek için çeşitli çerçeveler vardır. Ancak, hızlı bir başlangıç yapmak ve işlerin içeride nasıl çalıştığına dair fazla ayrıntıya girmek istemiyorsanız [Keras](https://keras.io/) kullanmayı düşünmelisiniz. Bu kısa eğitim başlamanıza yardımcı olacak ve işlerin nasıl yürüdüğünü daha iyi anlamak istiyorsanız - [Tensorflow ve Keras'a Giriş](IntroKerasTF.tr.ipynb) not defterine bakın."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8cACQoFMwHl3"
+ },
+ "source": [
+ "### İşleri hazırlamak\n",
+ "\n",
+ "Keras, Tensorflow 2.x çerçevesinin bir parçasıdır. Tensorflow'un 2.x.x sürümünün kurulu olduğundan emin olalım:\n",
+ "```\n",
+ "pip install tensorflow\n",
+ "```\n",
+ "veya\n",
+ "```\n",
+ "conda install tensorflow\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "xwqVx9-bwHl3",
+ "outputId": "2aa591b4-b647-441f-9c8e-4e0da2d517a0",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Tensorflow sürümü = 2.9.0\n",
+ "Keras sürümü\" = 2.9.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "from tensorflow import keras\n",
+ "import numpy as np\n",
+ "from sklearn.datasets import make_classification\n",
+ "import matplotlib.pyplot as plt\n",
+ "print(f'Tensorflow sürümü = {tf.__version__}')\n",
+ "print(f'Keras sürümü\" = {keras.__version__}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6tp2xGV7wHl4"
+ },
+ "source": [
+ "## Temel Kavramlar: Tensör\n",
+ "\n",
+ "**Tensör** çok boyutlu bir dizidir. Farklı veri türlerini temsil etmek için tensör kullanmak çok uygundur:\n",
+ "* 400x400 - siyah beyaz resim\n",
+ "* 400x400x3 - renkli resim \n",
+ "* 16x400x400x3 - 16 adet renkli resimden minigrup\n",
+ "* 25x400x400x3 - 25 fps'lik videonun bir saniyesi\n",
+ "* 8x25x400x400x3 - 8 adet 1 saniyelik videodan minigrup\n",
+ "\n",
+ "Tensörler, sinir ağı içindeki ağırlıkların yanı sıra, girdi/çıktı verilerini temsil etmek için de bize uygun bir yol sağlar."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A10prCPowHl7"
+ },
+ "source": [
+ "## Örnek Problem\n",
+ "\n",
+ "İkili sınıflandırma problemini ele alalım. Böyle bir soruna iyi bir örnek, boyutuna ve yaşına göre kötü ve iyi huylular arasında tümör sınıflandırması olabilir. Bazı örnek veriler oluşturarak başlayalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "j0OTPkGpwHl7"
+ },
+ "outputs": [],
+ "source": [
+ "np.random.seed(0) # tekrarlanabilirlik için tohumu seçin - rastgele varyasyonların etkilerini keşfetmek için değiştirin\n",
+ "\n",
+ "n = 100\n",
+ "X, Y = make_classification(n_samples = n, n_features=2,\n",
+ " n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n",
+ "X = X.astype(np.float32)\n",
+ "Y = Y.astype(np.int32)\n",
+ "\n",
+ "split = [ 70*n//100 ]\n",
+ "train_x, test_x = np.split(X, split)\n",
+ "train_labels, test_labels = np.split(Y, split)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "c-_BjSHPwHl8"
+ },
+ "outputs": [],
+ "source": [
+ "def plot_dataset(features, labels, W=None, b=None):\n",
+ " # çizimi hazırlamak\n",
+ " fig, ax = plt.subplots(1, 1)\n",
+ " ax.set_xlabel('$x_i[0]$ -- (öznitelik 1)')\n",
+ " ax.set_ylabel('$x_i[1]$ -- (öznitelik 2)')\n",
+ " colors = ['r' if l else 'b' for l in labels]\n",
+ " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
+ " if W is not None:\n",
+ " min_x = min(features[:,0])\n",
+ " max_x = max(features[:,1])\n",
+ " min_y = min(features[:,1])*(1-.1)\n",
+ " max_y = max(features[:,1])*(1+.1)\n",
+ " cx = np.array([min_x,max_x],dtype=np.float32)\n",
+ " cy = (0.5-W[0]*cx-b)/W[1]\n",
+ " ax.plot(cx,cy,'g')\n",
+ " ax.set_ylim(min_y,max_y)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283
+ },
+ "id": "tq0vFchQwHl8",
+ "outputId": "9a5aa6a0-c92f-4d72-9e78-c0f615804bff"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_l/jnklp1bj4cl95rc01tf5vx4h0000gn/T/ipykernel_36984/3412616912.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "plot_dataset(train_x, train_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Verileri Normalleştirme\n",
+ "\n",
+ "Eğitimden önce, girdi özniteliklerimizi [0,1] (veya [-1,1]) standart aralığına getirmek yaygındır. Bunun tam nedenlerini kursun ilerleyen kısımlarında tartışacağız, ancak kısaca nedeni şudur. Ağımız üzerinden akan değerlerin çok büyük veya çok küçük olmasını önlemek istiyoruz ve normalde tüm değerleri 0'a yakın küçük bir aralıktaki tutmada hemfikiriz. Böylece ağırlıkları küçük rastgele sayılarla ilkliyoruz ve sinyalleri aynı değer aralığında tutuyoruz.\n",
+ "\n",
+ "Verileri normalleştirirken en küçük değeri çıkarmamız ve aralığa bölmemiz gerekiyor. Eğitim verilerini kullanarak en küçük değeri ve değer aralığını hesaplıyoruz ve ardından eğitim kümesindeki aynı minimum/aralık değerlerini kullanarak test/geçerleme veri kümesini normalleştiriyoruz. Bunun nedeni, gerçek hayatta sadece eğitim kümesini bileceğiz ve ağın tahmin etmesi istenecek gelen tüm yeni değerleri değil. Bazen yeni değer [0,1] aralığının dışına çıkabilir, ancak bu çok önemli değildir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_x_norm = (train_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))\n",
+ "test_x_norm = (test_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SjPlpf2-wHl8"
+ },
+ "source": [
+ "## Tek Katmanlı Ağ Eğitimi (Algılayıcı)\n",
+ "\n",
+ "Çoğu durumda, bir sinir ağı bir dizi katman olacaktır. Keras'ta `Sequential` (dizili) model kullanılarak aşağıdaki şekilde tanımlanabilir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Metal device set to: Apple M1 Pro\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:09.553986: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
+ "2022-11-06 19:39:09.554333: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: )\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " dense (Dense) (None, 1) 3 \n",
+ " \n",
+ " activation (Activation) (None, 1) 0 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 3\n",
+ "Trainable params: 3\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = keras.models.Sequential()\n",
+ "model.add(keras.Input(shape=(2,)))\n",
+ "model.add(keras.layers.Dense(1))\n",
+ "model.add(keras.layers.Activation(keras.activations.sigmoid))\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Burada önce modeli oluşturuyoruz ve ardından ona katmanlar ekliyoruz:\n",
+ "* İlk `Input` (Girdi) katmanı (aslında katman diyemeyiz) ağın girdi boyutunun beyanını içerir.\n",
+ "* `Dense` (yoğun) katman, eğitilebilir ağırlıkları içeren gerçek algılayıcıdır.\n",
+ "* Son olarak, ağın sonucunu 0-1 aralığına getirmek (onu olasılık yapmak) için *sigmoid* `Activation` (etkinleştirme) işlevine sahip bir katman vardır.\n",
+ "\n",
+ "Girdi boyutu ve etkinleştirme işlevi, kısa olması için doğrudan `Dense` katmanında da belirtilebilir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_1\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " dense_1 (Dense) (None, 1) 3 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 3\n",
+ "Trainable params: 3\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = keras.models.Sequential()\n",
+ "model.add(keras.layers.Dense(1,input_shape=(2,),activation='sigmoid'))\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Modeli eğitmeden önce, onu **derlememiz** gerekir, bu da esasen şunu belirtmek anlamına gelir:\n",
+ "* **Kayıp fonksiyonu**, kaybın nasıl hesaplandığını tanımlar. İki sınıflı sınıflandırma problemimiz olduğu için *ikili çapraz entropi kaybı* kullanacağız.\n",
+ "* **Optimizer (eniyileyici)** kullanmak için. En basit seçenek, *rasgele gradyan inişi* için `sgd`'yi kullanmaktır veya `adam` gibi daha karmaşık eniyileyicileri kullanabilirsiniz.\n",
+ "* Eğitimimizin başarısını ölçmek için kullanmak istediğimiz **metrikler**. Sınıflandırma görevi olduğundan, iyi bir metrik `Accuracy` (doğruluk) (veya kısaca `acc`) olacaktır.\n",
+ "\n",
+ "Kaybı, metrikleri ve eniyileyiciyi dizgiler (string) olarak veya Keras çerçevesinden bazı nesneler sağlayarak belirtebiliriz. Örneğimizde, modelimizin öğrenme oranına ince ayar yapmak için `learning_rate` parametresini belirtmemiz gerekiyor ve bu nedenle Keras SGD eniyileyicisinin tam adını sağlıyoruz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.2),loss='binary_crossentropy',metrics=['acc'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Modeli derledikten sonra `fit` metodunu çağırarak asıl eğitimi yapabiliriz. En önemli parametreler şunlardır:\n",
+ "* `x` ve `y` sırasıyla eğitim verilerini, öznitelikleri ve etiketleri belirtir.\n",
+ "* Her dönemde geçerlemenin yapılmasını istiyorsak, bir dizi özellik ve etiket olacak olan `validation_data` parametresini belirtebiliriz.\n",
+ "* `epochs`, dönemlerin sayısını belirtti.\n",
+ "* Eğitimin minigruplarda gerçekleşmesini istiyorsak, `batch_size` parametresini belirtebiliriz. Ayrıca verileri `x`/`y`/`validation_data`'ya aktarmadan önce manuel olarak önceden toplu işleyebilirsiniz; bu durumda `batch_size` gerekmez."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:10.066835: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
+ "2022-11-06 19:39:10.202253: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "70/70 [==============================] - 2s 8ms/step - loss: 0.7261 - acc: 0.4714 - val_loss: 0.6578 - val_acc: 0.6667\n",
+ "Epoch 2/10\n",
+ "11/70 [===>..........................] - ETA: 0s - loss: 0.6590 - acc: 0.6364"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:11.498184: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.6363 - acc: 0.7000 - val_loss: 0.5965 - val_acc: 0.7667\n",
+ "Epoch 3/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.5643 - acc: 0.7857 - val_loss: 0.5456 - val_acc: 0.7667\n",
+ "Epoch 4/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.5084 - acc: 0.8143 - val_loss: 0.4868 - val_acc: 0.9333\n",
+ "Epoch 5/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.4752 - acc: 0.8857 - val_loss: 0.4628 - val_acc: 0.9000\n",
+ "Epoch 6/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.4419 - acc: 0.9286 - val_loss: 0.4313 - val_acc: 0.9000\n",
+ "Epoch 7/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.4128 - acc: 0.9143 - val_loss: 0.4014 - val_acc: 0.9000\n",
+ "Epoch 8/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.3960 - acc: 0.9143 - val_loss: 0.3805 - val_acc: 0.9333\n",
+ "Epoch 9/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.3779 - acc: 0.9143 - val_loss: 0.3646 - val_acc: 0.9333\n",
+ "Epoch 10/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.3641 - acc: 0.9286 - val_loss: 0.3525 - val_acc: 0.9000\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s4_Atvn5K4K9"
+ },
+ "source": [
+ "Eğitimi nasıl etkilediklerini görmek için farklı eğitim parametreleriyle denemeler yapabilirsiniz:\n",
+ "* `batch_size` ayarının çok büyük olması (veya hiç belirtilmemesi) daha az kararlı eğitime neden olabilir, çünkü düşük boyutlu verilerle küçük toplu iş boyutları her bir özel durum için gradyanın daha kesin yönünü sağlar.\n",
+ "* Çok yüksek `learning_rate` (öğrenme oranı), aşırı öğrenme ile veya daha az kararlı sonuçlarla sonuçlanabilirken, çok düşük öğrenme oranı, sonuca ulaşmanın daha fazla dönem alacağı anlamına gelir.\n",
+ "\n",
+ "> Ağı daha fazla eğitmek için `fit` (oturt) işlevini arka arkaya birkaç kez çağırabileceğinizi unutmayın. Eğitime sıfırdan başlamak istiyorsanız - hücreyi model tanımıyla yeniden çalıştırmanız gerekir.\n",
+ "\n",
+ "Eğitimimizin işe yaradığından emin olmak için iki sınıfı ayıran çizgiyi çizelim. Ayırma çizgisi $W\\times x + b = 0.5$ denklemiyle tanımlanır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283
+ },
+ "id": "PgRTHttLwHl9",
+ "outputId": "e4407e1b-edf5-48e5-fdc2-da28120a3c6b"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_l/jnklp1bj4cl95rc01tf5vx4h0000gn/T/ipykernel_36984/3412616912.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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qVFXlyeKMGayfGTGi/T5g337L6afISMulBDodT5rPnQM+/BD45S8d85hE+xozOJ5eg+MOIiOZvdHSntYqL2cqVQrZhA0kwBFeS1G4kGPQIOD66/kZajYzW9PZ5sINDWzPoV3XGr6+DKq++gr4f/9Pmqf2JK8pMnYH2tnDO+9wdZQ1AYvRyDOPqVMdPz7hUWSZuBDg9JTWb6yz4AZgKcD58/ystqXLfVgYp77OneveWIVtvHaKylVNnMjCt9xcy9dVVb5h+vQBxoxx+NCEZ5EARwgbmc3siGxr01tfX668ko7IPcvrioxdXWIicOONPFPIze24WZSqsso/OBj46U+l06awmUxRCWGjoCBON9XV2XY7o5FBTk/3VfN22hSVZHBcyOzZPFP46CMgM5NV/jExTXtRFRaygj8xkUsPJXsjukACHCFs5O/PFahff239lg+qyu7Jqam2tQAR3SdFxi5IUYB587jR53ff8XL2LIMeX1+ufLruOq4C6KyFuBCdkABHiC5YuJDNYquqWL9jSW0tg5zZs63bJkjYj1aDU2WsQr2pHn4+UuHtMlJTebnuupb9epKSmCoVohskwBGiC+bM4ZYPhw8zo6PXd3xdg4Fb4yQmtt8wUDhWREBE4/9L60oRGywptHY1NLC7pao2zcP2lIgI25v/CWGBBDhCdIFeD/zhD8Cjj7JOMjKSmZzmhccNDczwVFVxBdXPfuZaPde8hY/OB+H6cJQbylFaKwFOG3l5wN693COqooLfCwgApkzhFFG/ftJ/RrglCXCE6KKJE4G//Q14/nng1CnW2DTfi8pkamryt3gxF4LYsqxc2E9kYCTKDeVSaNycycSGTmvXMsUYEdE031pTwwLg9eu57cPtt8sWB8LtSIAjRDdMmQK8/jqwZQvw5Zfc9FgLbGJi2NNs1izuRC7BjfNEBUYhqyxLCo01ZjPbaq9Zw/TjqFEtszRRUYzMS0vZnbKmBrjvvp6dthKimyTAEaKbUlKAO+9k4fHZs1w+7ufH1VJJSZLddwVaobH0wvlRRgYj8thYRuLtURQGOv7+3Jm8f3/gmmt6dpxCdIMEOELYSViYtOtwVdILpxlVZcrRbO44uGkuJISXTZuAK67wnIZ7qgqcOcM+PBUVDOgiI/kmljMTjyABjhDC40UFSC+cRmfPAkePAgkJ1t8mIYHBwPffs/DY3R05wqm3o0e5CsDHhwGP2cxapNGjgfnzWWAt3JYEOEK0o6IC2L8fOHmSn38BAUDfvkBaGhAf7+zRCVtJBqeZnBy+qPv2tf42ej0P/jk57h/g7NwJvP023+RJSS1Xiakq6462buXKgQceAIYPd+54RZe5ZYCzbNkyfPrppzhx4gQCAwMxZcoU/O///i8GDx7s7KEJN2cwAF98wc+3ggIWBvv5sXB40ybgs8+ACROAm26yvCmncB3SzbgZo5EH9K5Mwdi6P4mrOXoUWLmSPRyGDWv7N9DqjiIjgRMngOXLgZ//nIGQcDtuua5j69ateOihh7B7925s3LgRDQ0NmD17Nqqrq509NOHG6uqAFSuA1asZ6AwZws/AgQP5/xEjeCK7fj3w979zuxzhHqTIuBltJVRHm1x2RFXdu/5GVYF164CyMmavOgvwFAUYPJgZq82be2qEws7cMoOzfv36Fl+vXLkSsbGx2L9/P6ZPn97ubQwGAwwGQ+PXFVpDKyF+9MEH/Czr3581la3pdECvXpyiP3oUeOMN4MknO+9iLFyDTFE1k5LCHV/Ly63vHmwwsE4lJcWhQ3OorCzW3lhbQKzTcZXZd99xKwnptOx23DKD01p5eTkAIKqTOYNly5YhPDy88ZKcnNxTwxNuIC8P2LaN9TXtBTfN+fkBgwax3vL773tmfKIbVBVRWgZHpqhYczJyJJCfb/1t8vMZ3Iwa5bhxOdrRo6y7CQ+3/jaxsUzVHj3quHEJh3H7AEdVVTzxxBOYOnUqRowY0eH1nn76aZSXlzdecnJyenCUwtXt28fawl69rLu+lqnfvt32TL/oASUlwIYNwLPPAo89hsh/vgUAKC2/yAJbb6Yo7EDp6wtcumT5+pWV/Jtdfrl7T1FVVTErY0vtkY8P/5XyB7fkllNUzT388MM4fPgwduzY0en19Ho99DKXIDqwZw+71Nvy2RcXBxw/zi731rQTET3AZGIDu6+/5pl3QAAQFIRIHzMQBpTUlQK//jVwww3cgsBbe52kpXEZ9CefAPX1TF22brWtqgwUc3PZ/+bKK50zVnvR6bp+NiJtyN2SWwc4jzzyCL744gts27YNSVLlLrrIbGbm2taT04AA1ivW1DhkWMJWZjPw3ntcBhcRwQrxH8/Ao8D6O4POjNryIgSuWMEnbu5c7wxyFAW48Ua+iP/7X9amhIVxflZR+LcpLeX3rrsOuO0299+mISKiqdeNtQGL0cjrSv2NW3LLAEdVVTzyyCNYs2YNtmzZgn7SjEl0QUUFs++qyhN/s9m226sqP/u0LLZwsq1bebBOSGizhj8U/vBRFZgUFSV949A7t5LL5Xr3du+6ku7w8QEWLGBfm4wMFqGVlfFnISHA7Nn8WZ8+nhEEjhkDREcDRUWsrbFGfj6QnMwllMLtuGWA89BDD+G9997D559/jtDQUBQUFAAAwsPDESg73opOmExsb7FzJxv5aW09fviBP9Pr+Rlozed5WRmnteTkzgXU1wPffMMK8HYWGyhQEIlAFKEGpahD7969WTi6dSsLbj3hAN5V8fHAtdcC8+YBtbWM9IOCWKPjSWJjGbB99RXf5JbOTIxGrjRbuNC9a4+8mFtOLL722msoLy/HzJkzkZCQ0Hj54IMPnD004cKqq9m364UXWH+qLfvu1Yv1NHl53KLn4EH2AeuMqrL2Zto0rrgVTnbsGLcS6N27w6tEggepUtTyG/HxfLIvXOiJEbo+Hx9mbsLCPC+40Vx9NftAnDjBM5qOGI28zqhRwMyZPTY8YV9u+SpWZdmKsJHWxG/bNq6SDQtr+fPhw1mTWlzMDu1mMzB2bMcneRcvMnMzcaLDhy6sceYMszidZHAjwZ+VaAFOVBSj2rNnOQ0hPF9iIvDggzzTOXKEr4H4eGb+APb7ycvj3PWoUcD999u2rFy4FLcMcISw1bffAjt2AKmp7Wdc/Pw4zb5vHz/jzp1jFru98q7CQi4uue0227bzEQ5UW2vxKlE/Bjil+HFeUlGYxnP37QeEbQYMYIfO7dt5xqOd0QDMXKWkADffDEydKsGNm5MAR3i8ujp2KA4N7Xw6KTERGDcOOHSIQczhw5zx8Pfn519xMTM3gYHci2r+fO8u3XApVqzw0aaoGjM4AOca3X11kLBdbCzwk59wFd3x4+yRoygMaIYOlfbkHkICHOHxDh8GsrN54mZJcjIDodOnuZP4/v1NK2cjItgK5LLLmO2R4MaFJCTwCamvb5puaKUpg/NjgFNVxQOZbA/vvYKD2RNIeCQJcITHy8tjBsbak7KICH7m+fjwBG/8eB4zExLkWOiyxo5lCq6goMN6mqYi4x+npPLyuIvq4ME9NUohRA+SAEd4vPr6rt3O35/HSikkdgPBwexM/N57XBbXzrLeFkXGFRVcKjdjRpe61JrNzPDl5HDBjb8/SzcGDZKmt0K4CglwhMcLCLC9Q7vW8FTaXziI0ci5w/37m/ZDio1l6mzEiK7Vxcybx9VUu3dzPrLVrqmNU1T1ldxZes4czjfawGQCdu1iO4ETJ1iQrih8vQQEMBk0axYwZYoEOkI4mwQ4wuP168fpqepq63vWVFTw+ChNsu1MVRmAfPYZgwyzuWlp9/ffA5s28Y9+/fW2p86Cg7ms198f2LuXGZq4ODatU1VEGuuAKKDEXMXGdrffblO/l/p6JojWrWNQ07t3yxiqspLteI4e5Sq8227z3HYyQrgDefsJjzd0KDBwIFeDDhpk3W3y8lh707+/Y8fmdb75BnjnHQY2/fq1TZHV1gLnzwP//GfTDta2CA8HHn6YPU527mTQVF4OKAqiwpkVKo0JAe66y6YUi6oCn37KnSASE4HIyLbXCQ1lBqekhNthBQRwtZ0QwjkkwBEez8eHmyGfPMml3tHRnV//4kWeec+aJSul7OrQIeDdd5lO62hz3MBARqHZ2cC//816mpEjbfs9fn4sOh47lk/4j0uAI+tzgf98iVJztc3zR7m53KA8Jqb94Ka5qCjOwK1fzxmwxETbhi+EsA+ZJRZeYepUzkpcvMiDVXsba5pMPK6WlrJFhqwetSNVZfampqbj4Ka55GQGJt9+a3sBVXPR0dwsMiUFkb1SAACltaU2d0Pfs4evC2v3aIyLYyZn715bByyEsBfJ4AivoNOxJiI0FFi7ljMYwcEszwB4LK2r44HplluAq66S7I1dZWUBmZnWpzMUhevyv/+eS5VSUro9hKhAbsJpUk2oNFYiTB9m4RZUX88u2JGR1r8mtL5J27cD110nO84L4QwS4Aiv4eMDLFjAbE5GBks0Skr4s0GD+P3x42V3cIc4eZJVuLbsbREZyXTbyZN2CXAC/QKh99HDYDKgpLbE6gCnpoYBcKtFWRYFB/Mh19QwsBZC9CwJcITXiY7mCuE5c5qmqmRJr4PV1vKPbEtaTFF4sWKfKWtFBUYhvyofpbWl6BvR16rbdHWGTDKAQjiXfKwLr6bTSXDTI7ozR2PHtdaRgawQLq0rtfo2wcGsfa6utu13aW0JOtngXAjhQJLBEcIOVJWzKYcPs4cOAISFAaNHs1+K1+vVy+JeUW0YjYw+Y2LsNozIAAY4JbUlVt/Gz4+roT74gPXR1mRmVBUoKwOuuUZ64QjhLPLWE6Kbzpxh4fLBgzyoaRkhs5n1POPGAVdf7eU9dbRILz/f+nqa/Hyupho1ym7D0AqNS2utz+AAwKRJbPBXXGxdvFVUxJY8ss2HEM4jyXkhuuH774EXXwQ2b+Z0xMiR3GlgxAj+PziYzXn/9jdmd7xWUBD3iiot5f4GltTVsUHf9Ol23S9Dm6KyJYMDcKX55Zcz5tIydB0pL+een1dcYd2KeCGEY0iAI0QXnTsHrFjBlVgjR7J4ufn0haLweyNH8jorVnC1tNe66io2FzpxggFMR2pruXJqwgRGCXYUFfBjBseGGhyAz+UttwCzZwMXLjBr17r2ubaW38/N5S70N98shcZCOJNMUQnRRRs38ox+5MjOD2SKwmXomZnsdbd0ac+N0aWEhnKvqDfe4Dp9X1/2xdE2CKuq4h/UZOJulUuX2r4224LGImMbp6gANmBesoRTjZs3M8A1Gpt+7ufHPT5nzQJmzpTaGyGcTd6CQnTBxYvAvn3sRWfNWbqiAPHx7Ig7f771HXE9TnQ08OijDHC2bWOm5sIF/iwwkLU606ezIZEDtnJvLDKus22KSuPry0TUzJncVDM7m0GOvz9Li4YPt76GWgjhWBLgCNEFR45w2mn4cOtvExPDg+KRI7bvIelRAgOBadO4NCk7m93wAC47S0526Lr9rhYZt+bnB4wZw4sQwjVJgCNEF/y4f6NNx2Ktz11VlePG5VZ0Ots6G9tBV4uMhRDuR4qMheiCrhaPas15hXM0ZnBsLDIWQrgfCXCE6ILwcP5rMll/G5OJDeC024qep9XgdHeKSgjh+iTAEaILRo9moXBhofW3uXiRtxk92nHjEp3TpqjKDeUwmW2IToUQbkdqcIRXq6lhsXBDA3vRaTsKWBIRAaSnA59+yqCl9VZLFRXsh1JczN0JfHy4N9Gdd0oGx5m0DA4AlNWVIToo2omjEUI4kgQ4wuuoKnD+PJds79jBzrOqypUxgwdzgc/YsQxKampYCxsc3DaImT2bvW2OHweGDuXPa2u5Siovj73stCXDZWVc9bxzJ693660MkkTP8vPxQ4h/CKqMVSipLZEARwgPJgGO8CpmM/Df/wKffcagIyqKGRidjjsIHDgAbNnCYCQign1PFIX/nzGDzXW1HjZxccADDwCvv86gJjiYgdOlS1zxHBzMrE1tLVc/p6WxDufrrzld9cgj/P2iZ0UFRqHKWCWFxkJ4OKnBEV5DVYHPPwfefZeZlZEjuVdQSAinp4KDuYQ7O5vZnQMHeBu9ngHJW28Bv/kNN9Y0m3mf/foBTz7JjExWFnD2LL9fVcWpL39//p4pU4DISPbCGToUOHQIePtt24qUhX1IobEQ3kEyOMJrnDjBzE1UFLMvzdXXs7ludjazNVFR3BH6wgXuJB0dzaAmLw945x12r12wgNmdXr3Y8C8hgZsyav1u/P2Z7fH3b/m7/P3Z7v/gQTbyHTasp/4CApBeOEJ4C8ngCK+xaxenjFoHNwDwww9ATg4DmYAA1smEhXFXaG33aJ2OGZ+ICBYXZ2a2vG9VBQYO5H5E/fvzuq2DG01oKGt0vvvO7g9TWCC9cITwDhLgCK9QXMxpp1692v7MYGDtTGBgy32EAgIYhOTmtrx+fDyLj7dv59d1dZxyiraxXjUmBti/v+WGjcLxZIpKCO8gAY7wCgUFXC3VXhCSn8+amdYbVysKA56ysra3iY9njU5eHouIjUbb94bU6zk1Vltr2+1E92gZHJmiEsKzSQ2O8ApGIwt629s7Sgtg2vuZTsceOa1FR3PjzJwc1tDodLYXDJvNvJ2vvAutYzYznVZdzTlErWrbRo0ZHFeboioqAs6da+ovkJDALcplbw8hukQ+WoVXCAhgIFFfz8xJc/X1HW+aaTK1X0ejHXMMBq6+io3lKipbjrelpazXCQy0/jZeqaaGc3nbtrFYymjkExAcDIwfzyVqw4e3bVTUAZcrMj51ivOdu3dz6R3Agq7QULa9njYNGDfOobusC+GJJMARXqFPH04rFRayJ01zfn5Ny76bM5sZ4LRXt6OqTUvIdTr2yDl+nNe35jhrMvFEfcYMOW51qqAAWLECOHyYT1R8PCNCVWX197ffMvC58krg9tutmid0mSJjVQW2bgX+/W+mEWNjm9KBqsrvffcdsG8fcM01wM03tywSE0J0Sj5ahVcICgKmT+cJsqq2/FlEBL/XOsiprmaSIDGx7f0VF/N2KSn8Oi2NMwrnz1s3nqws3u/48TY+EG9SXAy8/DIruAcOZJvp8HCm1PT6pvX5sbHAV18B//lP+/OJrbhMkfHu3cDKlXzhjRjB5X1atKsonIIbNozzoZ99BnzySdsXrxCiQ90KcOrr65GTk4OTJ0+ipMRF0r1CdGDyZAYVp0+3PE4kJHA2oKqq6XtGI2dG+vRpfwqpoKApqAF4LLrtNv7//PmOj0OqyuBGp+P1ZbuGTnz2GXDsGDsjtp5XbE6LNL/5Bti71+LdukSRcU0N8MEHTOX16dN5nU10NIO49eubOkkKISyyOcCpqqrC8uXLMXPmTISHh6Nv374YNmwYevXqhT59+uDee+/Fvn37HDFWIbolMRG4+25mc06eZP0MwGNnnz5Nq6Gqq1kfk5ICDBnS9n4KCngfU6e2/P6UKcCSJZxFyMxkk0CDgccwg4FfZ2YyAbFkCTfrFB24dInr+uPjO5+WUdWmwqqGBk5XWchyaDU4Tp2iOniQBdN9+lh3/ZgYTsnt2ePYcQnhQWyqwfn73/+OF154AX379sX8+fPxy1/+Er1790ZgYCBKSkpw5MgRbN++HVdddRUmT56Ml19+GQMHDnTU2IWwWVoaA4x33+XJsKqyoV9oKKejsrKYjRk0iLMGzY+t2iKemhpmX0aMaHnfisKamr59OfuwfTvvT6vLiYnhlg6TJzdNbYkO7N/PVUWt/8ia+npGmtnZjEbNZgY4588z4zNvXoddFrUpqpr6GhgaDND7dpIdchRt11Vra2oUhS+gnTuB+fPb9jQQQrRhU4Cza9cubN68GSNHjmz35xMnTsQ999yD119/HW+99Ra2bt3qsABn27Zt+Mtf/oL9+/cjPz8fa9aswcKFCx3yu4RnGTWK5RyHD7MD8fnzPDZOmMBjZkkJe+ZkZXHLBlXl1xUVnC1YtAiYO7fjWYU+fXi5+mr2yTEYmGDo3VuOS1bLy2MA0F4FdlER63JKf8zABAU17Yqal8e6nf37gaVL240kwwPCoUCBChWldaWID4l37GNpTVUZKYeG2na7sDBmtsrL5YUkhBVsCnA++ugjq66n1+vx4IMPdmlA1qqursbo0aNx99134yc/+YlDf5fwPHo9A5q0NAY627Zx1kCn4/GyuJhfh4UxEVBfz8xOQwOwbh1/PnkykJracaATGspASnSBwdD+crSiIk7T1NQw+mx9ndpa1qscOwb84x/AY4+1CXJ0ig4RAREorStFaa2TAhyTyfb+NorSdFshhEVuu0x83rx5mDdvnrOHIdyY2QysWcNa1ro6lnskJ/M4YjIB33/PmpmGBvar6duXAVBdHW+3YQM34ly82PaTcWFBaCijyuaMRkadNTWcrmkdIGjL4IKDGXkeO9a0BXyrqaCowCiU1pU6p9BYp2NhdHa2bberrWW0HRzskGEJ4Wm6tIqqtrYWO3bswLFjx9r8rK6uDu+88063B2ZvBoMBFRUVLS7Cu331FfDhh8z2Dx/O6SftmHn+PKeowsJ4LC0u5iqrmBhONY0cyet/+y3w6qssTBZ2NHgwp520SnCAe2qUljJz0172o7qa6beoKAYRAwawmvzo0TZXdXqhcXo6X1DtNWDqyMWLfOFFRTluXEJ4EJsDnB9++AFDhw7F9OnTMXLkSMycORP5+fmNPy8vL8fdd99t10Haw7JlyxAeHt54SW7d7U14lfx84IsvGMC03l28pISZG203gPBw/v/YsaZ9oxSFJ+GDBnHGZM2aHn8Inm3UKE4t5eXxa1Vl1KnTtT91parM7CQnN63rDwxkKm7nzjZX1wqNnbZUPC2NEfKlS9Zdv6aGj33qVNm6QQgr2Rzg/OIXv8DIkSNRWFiIkydPIiwsDJdddhmybU239rCnn34a5eXljZecnBxnD0k40d69zMq018QvO5vTUGFhTd8LC2OCQDveagID2W9u586mLvvCDvR6YPZsRpQlJczklJUxQ9OaqjKzExzctqg4KoqRaau6lcZuxs5q9hcXB8yaxdbalrLJRiO3cxg3jhkcIYRVbA5wdu3ahT/+8Y+IiYlBamoqvvjiC8ybNw/Tpk3DWRduQqXX6xEWFtbiIrxTfT2LisPD254M19ayX01wcMuf6XQs42iviV9sLI9T+/c7fuxe5fLLuUVBfj6jzvb2waivZ6Tq4wOMGcOUW3O+vk2NiJpxiQ03b7wRuOoqPrbz5xnINGcycVnf8ePA2LFcFdbB0nchRFs2FxnX1tbCt9X2x//85z+h0+kwY8YMvPfee3YbnBCOUFXFlbbtxbjl5U01rK3p9fyZydRyB3AfH3596hSPV8JOfHyAO+5g0PLFF01PTkgIo0yjsalgd8QIVom31tDAJ6dVYOAS3Yy1jo+9ewObNvEFZDYzkjaZ+P9evYDrrwcWLGBELoSwms0BzpAhQ5CRkYGhQ4e2+P7LL78MVVUxf/58uw2uM1VVVTh9+nTj1+fOncOhQ4cQFRWFFOmiJjphMvH42F4pgzaT0V77FUXhMae9ulBfXyk0dggfHza2mz4dePxxrqIKDuYTFBoKJCUxhdbRDqclJbxtq5MypxcZa/z8gOuuA664gr19Tp9mBB4QwHqi8eOlqFiILrI5wLn++uvx/vvvY9GiRW1+9sorr8BsNuP111+3y+A6k5GRgVmzZjV+/cQTTwAAFi9ejFWrVjn89wv3FRzMk+fa2rZ7QWnHSbO5bZBTX8/b+bbzrjGZ2i8PEXYSEQHcdRdQWcn9M9p7ElqrreUTetllbX7k9CLj1oKCuNfHlCnOHokQHsPmGpynn34aa9eu7fDnr776Ksy2LH3sopkzZ0JV1TYXCW6EJYGBbPJXVNT2Z2Fh/HlNTcvvqyoLj7U+OaWlQE4Ol5JnZ/O4O2BAjwzfe40bxxbRp05Z3lXbbAbOnGnac6MVpxcZCyEcrlu7iQvhrtLTOQvQegFLUBBLIloHOHV1zN7odFwxtWUL8N13XI21ZQuPpSdOAD/80FOPwAuFhrJmJSqKf+zWjQA1BgNXTiUlAffc025hrstMUQkhHEYCHOGVBg/mVgvnzjX1ttGkpPCYWFnJr41G1rcCPG4WFDQtD4+KYhlFQgI31/zznxnwCAcZNgx45BE+ST/8wECnqIgptUuX+ASdOcMn+NFH2X66HS5RZCyEcCi33apBiO7Q6VjSUVPDnb979WKtqq8v+68NG8atGiorOSXl78/EQEQEMz8Avy4tZcZn0iReJzsbWLmS15k82ZmP0IMNGwb84Q8sON62jUus6+pYbzNpEjBtGhsFak9UOxqXideWQlVVKNI8TwiPIwGO8FqhocDDD7OsY/t2JgMABjn19czOlJYyGKqsbFpWXlXFwMjHh7cdO5ZLyIGmEpGPPgJGj25qqivsLDiYXX0vu4zL14xGPglBQVZ1+tWmqOrN9aiur0aIv+zOLYSnkQBHeLXgYODWW4F589io7/x5TlkFBQH9+7Oo+P/+D1i7lgGNtsl1aip/Fh3ddrVVnz5c7XvoEGt9hAMpCvvi2CjYLxh+Oj/Um+tRWlsqAY4QHqjLAc5//vMf3HHHHe3+7KmnnsJf/vKXLg9KiJ4WHs7Gua2VlzPgmTGDGR2tD1urzalb8PfncXfXLglwXJWiKIgMjERhdSFK60qRHC570wnhabpcZPzwww/jv//9b5vvP/744/jPf/7TrUEJ4SrKyjgDotXeBAV1HtxoQkO5w4BwXVJoLIRn63KAs3r1atxxxx3Ytm1b4/ceeeQRfPjhh9i8ebNdBieEs5nNHXc97oyicJcA4bqaFxoLITxPlwOcuXPn4vXXX8fChQuRkZGBBx98EJ9++ik2b96MIUOG2HOMQjhNcDBrV1svJbektlY67Ls6rdBYMjhCeKZuFRnfeuutKC0txdSpU9GrVy9s3boVqamp9hqbEE7XqxdXJe/ZY33AYjJxldWkSY4dm9eqrweOHuUy8eJifi82lvs2DRnS8b5UrTR2M5Zmf0J4JJsCHG2/p9ZiY2MxduxYvPrqq43fe/HFF7s3MiFcgKKwrcrevczKWLPs++JFBkZpaY4fn1dRVT4RX3zBZn4NDU3r8+vqgPXruTXD9ddzjb4FMkUlhGezKcA5ePBgu98fMGAAKioqGn8uTbOEJxk9mr1u9uwBhg5tt/N/o7IybmB9661cQi7s6NtvgXfeYQanT5+20WZ1NTsZ5+QAS5daXMImRcZCeDabAhwpHhbeSK8H7r2XveQOHuRsSGxsy5kQgwHIy+PU1Lx5wMKFThuuZzp0CPjPf7iErX//9q8THMwI9Nw54O23GWEOGtThXTZmcGSKSgiPJI3+hLBCdDS3NvriC/a3OXaM01c+Ppwp8fXl9khXXglccQW/FnaiqsDXXzN6HDas8+sqCtCvH2t0Nm3qPMCRImMhPJrNNTjPPfccgoODO6zH0UgNjvA04eHAokXAtdey63FeHks/QkLY2Xj06KaSEGFHZ88yYOnd27rrKwoQFwdkZHBn1Pj4dq8mRcZCeDaba3Dq6+sb/y+EN4qMZKZGYzAAhw8DX37JgEevB5KSgDFjZC8quzhxghuA9etn/W1iYoAjR3jbDgIcq4qMVZU1PYcOccWW2cwXwIgRjGpb79MhhHAZXa7BkXoc4e3q64FvvuFMSHY2p6oUhcdEnY5BzsyZwJw5nW5sLSypqeEf1pbFC9r1a2o6vIrFKaqzZzkn+f333LPDx6epg6NW73Pttdy5XHiG+nqgsJBnLX5+LLaTtKzbsssy8dYURcHf/va3Lg1ICHdgMAArV3Jhj7YxZ/PPQaORWzW88w5rXn/2M15PdIEWWNhKK5LqgDZFVVZXBrNqhk5plo05cgR4/XXOQyYlscBKG4Oqcnv5gwe5q+rdd7OXgHBfJSWc0tyyBcjNZRDr48MAZ+ZMYMIETnsKt2KXZeKtyTJx4clUFfjwQ2DjRq5WDgtrex1/f/6suhrYupUZnJ/9TGY0uiQmhv9q1dzWMBj4x+5krb42RaVCRYWhAhEBEfxBTg6wYgVQVASMHNk2uFIUPunDhnH7+VWruFnZyJE2PSzhIjIzgbfeYho2OJhBja8vO3YWFXFF3tq1wB13AFOmOHu0wgayTFwIG+XmAps3s7SjveCmueBgBjo7d3K38k4W9YiOjBnDs+eLF60vNM7PB5KTOw069L56BPkFoaa+BiW1JU0BzpYtDHLaC26aUxQ+uceOscngiBFdyzQJ5zl6FHjlFU5BDh/eNuMXEsK6q6ws4I03GDRPnuyUoQrbyfmkEDbauxcoLW1KLFgSEcFMzu7dDh2W5woLA6ZO5dn0j4scOlVXB1RUcGrBQv1Em0Lj0lL2AYiNtS7dpigMuo4c4VykcB91dZxDLisDBg/ueDpTp+McdEMDezGVSFsBd9GtAGf79u244447kJ6ejtzcXADAv//9b+zYscMugxPCEVSV2eiPPgJ+8xvg8ceBX/6SWeqjR5mZ7ojZzGxMRIRtJ+sxMeyEbOumneJHc+cyo3L8OAucOlJbC5w8yZqJmTMt3m2bQuNjx4BLlxjgWCs8nDU5R49afxvhfIcOMSgdMMC6N3PfvqzJyshw9MiEnXS5Hdknn3yCRYsW4ac//SkOHjwIg8EAAKisrMQf//hHrF271m6DFMJeKip4ErZvH0/cwsJYL1NeDpw6xRVRQ4YAd97J2YfW6uqYjbG1YDgoiMfA6mpZOt4lkZHAgw+yNub771nUlJjY9ERUVfHgU1/PKYR77+X0ggVteuFUV1ssTm5DUXiWX1lp66OyXUMDA6nz5xno+fuzCHrkSK76EdbbsYPPXWd7rzTn48M55y1bON8s3TxdXpefoeeffx6vv/467rzzTqxevbrx+1OmTMGzzz5rl8EJYU/l5cDLL7NJX3Jyy4UxmqoqHj///nfgf/6n7a4A2qyFqtr2u7XrS5FxN8THM922dy8rt8+cYTExwKhx2DBg+nRg4kSro8g2U1RdfYJU1bEHPJOJj3nTJq7c0qbqFIW/t29fHnRnzZJAxxoNDWwDEBlp2+2iolgLVlHB/wuX1uV35MmTJzF9+vQ23w8LC0NZWVl3xiSE3akq8O9/M7gZMqTj0oyQEB4njx1jsuA3v2mZCNDr+ZmYm2vbLEZVFRAaalVSQXQmNJR7YcyYwQNUZSUP8uHhbARoY4DSZsPNyEjeh8Fgff8Ts5kvsIgIm3631err+eJdv57ZhpSUlinEujpmr954g0Hf3XdL4yVL6uv5vNmSqQN4fbO582lS4TK6fD6ZkJCA06dPt/n+jh070L+jzfCEcJLz5zl1npJi+bil07Hm8PRpBkTNKQqTBFVV/JyzhqqydnXqVOuz4cICX18uSRs/Hhg3jnUUXci+tNlwc/hwTvnk51t/J0VFLLIaO9bm32+VTz4BvvqKU3KpqW3nRwMCmGpMSWHvgvfftz3F6G30er4ZbQ1UjEa+9iSAdAtdDnDuu+8+PProo9izZw8URUFeXh7effddPPnkk3jwwQftOUYhum3PHmaVrT3J9vPj59+2bW0DmQkTmInZv591O6dPM6PT0QKfwkL+3okTu/MIhCO0KTIOCGBxckWFdQc/k4nB0MSJQK9e9h9gbi6DlthYyy/e0FCu6NqyRVZ0WaLTsf2ArSuiLl3i2U94uEOGJeyry1NUP//5z1FeXo5Zs2ahrq4O06dPh16vx5NPPomHH37YnmMUotsOHOBnki0rn2JjOQty6VJTE9OzZ1kGUVjIWk8/Px4Ttd5vffpwpkTLEhUX8wT/pptY9yNcS7sbbk6fzi7Fhw5xPrOjtJvJxFVdAwYAV1/tmAFqPQmsbSIYFcWgaM+etgVkoqX0dL6ZKysZHFpiMDAzNm2a9DtyE10OcLKzs/Hcc8/h17/+NY4dOwaz2Yxhw4YhODgY2dnZSElJsec4hegyVeXiGFtrL/39WYtYV8ev9+8H3nyTNYb9+vEz8eRJHucCA7lC+dAhbmA9eDBXafn4AAsWADfcIJ+JrqjdDTfDw4H77gOWL+cTGhLC6SG9npmdnBwGEVVVjFovu6zTjsldZjZzpY8tkbmicCy7dvFFJ/sodWzwYGD0aP6thg3r/APCZAJ++IHXGz2658YouqXLAU6/fv2Qn5+P2NhYpKWlNX6/uLgY/fr1g6mzZiJC9CBtJagWqFjLZGIm28+Pm1K/8QZP9rQGt7GxrEk9f55Bj7ag54cfGNzccAMwezZnL2T1lGtqU2SsiYsDHnuMTY+2bGHV+fnzXIpXX8/lwnFxjGBXr+ZBcu5crmKy15NdW8sgKjjYttsFBfF21dUS4HRGp2NBdkUFGzX26dN+MFlZyU7G/fpxvxWpv3EbXQ5w1A6K2KqqqhAgLwDhYlJTub1CUpL1tykpYcY/MpINT4uKWIOqff4pCk/sExJ43CsrY1BkNvO6kydLV3dXp9XgtJii0oSFAfPm8cD2xz/yCR4wgBmS2NimM36DgRmdN97gfObNN9snyGm+uWdXbys6FxPDQPadd5ity85mkKPtRVVezvTs+PHA4sXWbxUiXILNAY62o7iiKPjd736HoGYV/SaTCXv27MGYMWPsNkAh7GHKFBYM19Za1yJFVXk8W7CAx65jxzgb0d5xQ1FY/9m8BlRR2LZE2pK4Nm2Kqk0GR1Naym3ja2q4NL29ZcV6PetdioqAzz5jVDx7dvcHFxDAg21hoW0FzFrWx9bMj7eKjmaQc+4ca5cOH+bzHRDAepv0dK7YkzSs27E5wNF2FFdVFZmZmfBvVoDn7++P0aNH48knn7TfCIWwg+HDgYEDOdU0bJjlE9ycHB5TJk7kLEVVFU/krZWYyILkU6f4+4Rr0qaoqoxVqDfVw8+nVTS6e3fTk2ipZ0pMDF8o69fzwNjdltU6He/n7beZFrTmAKuqTD3OmSPTU7ZQFAap/fsDt93m7NEIO7E5wNF2FL/77rvx0ksvIczSdspCuAA/P26/8NJLXPjS0d56qsrgprqaJ+zHjzPAqa5mxtraZrVBQSzVKC+37+MQ9tW4gziAsroy9ApulikxGll/ExJi/ROfmMiA6NAhnvl314QJwBdfsMgrIcHy9YuKmEqcNKn7v1sIN9flnNvKlSsluBFuZeBA4OGH2Q/t2DH2r6moaNpfKjube1RlZbGsYtMm4LXXONV07Bi//uEH24qVpd+aa/PR+SBcz54mbaapzp1jtGtNYKHx92c24Pvv7TPA+HjWAZWUsOdAZ8rKuITvqqv4IhfCy9mUwXniiSfw3HPPITg4uLEWpyMvvvhitwYmhCMMGcLtFzIyeHKek8Ol4Dpd08Xfn9NTcXFNJ+41NbwcPMiu+GlpnW+7UFfH21rTXkM4V2RgJMoN5W0LjaurmcWxddGEXs9gw14WLOAL6ssvWcTcu3fLF5a20ajJBFxzDZsuSZGxELYFOAcPHkT9j+1atVqc9ijy5hIuLCICuPJKNqy9eJHHjsJC7jJeW8sgqHm5Q1wc+90EBfG4cvEiA6TJkzs+9uXn8yR60KCeeESiOyIDIpGFrLYZHB8fBgqqalvAYDbbd+NNHx/g1lu5oeaWLZw3zcpqGltAAOdcZ81iTx5b91cSwkPZ9C7U6m9a/18Id+Tr27Tqc8MGngSPHNm2ljMmhpfCwqb/X7zIKa32Apj6erbOuOUWqfN0B43djGtbZXCiorgSqbzc+l2nVZVRcmKifQep03Ep4OTJrPHJzuY8qr8/ex90VFQmhBez42mGEM5VUsLMyqlTnF0ICmL/m7Q0BiUdyc/n7Xr3bn+hiqLw+FFaymNdeDgDl6wsLrpofrLe0MAT7GHD7FNjKhyvw144SUmMePfutT7AqaxkUNSs+aldaTvBDh7smPsXwoN0K8D59ttv8e2336KwsBDmVjsSvv32290amBDWqq4GPv2UXe0vXeJJra8vg41Nm/iz9HTgxhvbr4k5dIjBS2dNAOPjuVn0oUP8HUFBLLO4dIk1qCYTMzyFhZziuu8+64+JwrmiAjroZqwoXKa9bx+r0S0tqlBVZlbGjWNkLYRwqi4HOM888wyeffZZpKWlISEhQepuhFNUVgL//Cf7c8XFsd9N80y92cwg5IsvOAX10ENtN2UuLeWxzNJLuE8fljucOcNApryc2ZriYh7bYmO5PcPcuY7ZWFo4RmMGp/UUFcBgZeZMzmFqG5C1x2zmEru4uI47Gasqi5b9/KRpnBA9oMsBzuuvv45Vq1Zh0aJF9hyPTV599VX85S9/QX5+PoYPH45//OMfmDZtmtPG480aGliIW1rKbEZQELPojuwkYDYDq1YxuBk8uP2CX52Ox5yICE5DrVwJ/M//tAyCTCbra0jj4hjIlJfz/mbO5N57UVHAmDGStXFHjd2M69rpZuzrywZKqsoCX4D1NWFhfNHU13NpdmkpW13fe2/L7I3ZzH4Eu3dzt1ajkS++/v2BqVP54pFCLSEcossBjtFoxJQpU+w5Fpt88MEHeOyxx/Dqq6/isssuw/LlyzFv3jwcO3ZMdjLvQbW1bIS3bRs/x41Gfl9RGAxMnQpMn27/mkuAv2/vXi4usbSSV+umn5HBQKx5d+GwMB6HrF0soyhcIp6QwFW5Xe2ppp3Q+/pKfagzdVhkrAkKApYuZTCyfTtw9Cj7CwCMoBMSgKuvZhFw8545JSWMqA8d4jxqVBSzN/X1jMr37GFWaPFiYOjQpttp0/2S5RGiW7oc4CxduhTvvfcefvvb39pzPFZ78cUXsWTJEixduhQA8I9//ANff/01XnvtNSxbtswpY/I2ZWXAm2/y5DQggDUs2tZkDQ1cafTBB8B33/HEdsQI+/7+3bvZm8baLFFoKAuDv/uuZYAzYgQDlooKFhBb4+JFBnC2bsNgNnOKa/duBltGI49j/fpxhe+YMd3v8C9s0+mGmxo/PwYw6elsAFhczNRfYCAzNq33fSotZdvsw4f55A4Y0PLnCQlcBXX2LPB//8cgRztbuHiR0W9EBF8UaWky5ylEF3Q5wKmrq8OKFSvwzTffYNSoUfBrtaOgIxv9GY1G7N+/H7/85S9bfH/27NnYtWtXu7cxGAwwGAyNX1dUVDhsfN6gro7Bzc6dXCrd+qCsLcFOSGDG5PXXgccfb/s531X19az9jIqyrUVJTAxnCm6/vWnM/fszyNm3r2nmoTPabuG33GJbI7+yMk6p7d/fdELv78/jZEYGs1F9+gCLFgGjRll/v6J7tAxOhxtuNtd8z6KOqCp3pz58mJmZZvv1taDXc251+3ZWpffpw0hbe1GdP88W2mvWcPPOhQvt219HCA/X5XfL4cOHG3cNP3LkiL3GY5WioiKYTCbExcW1+H5cXBwKCgravc2yZcvwzDPP9MTwvMLu3cywDxzYecZBp+OqoiNH+Dn9//6ffZqs1tXxBNjWJrMBAWz82nxXcUXhDMPJk8zw9O3b8RjNZm7Y2a8f96qyVnk5T9QPHuT99+/f8nfExzObc/Ys8MorwAMPcNWWcDytBqfDKSpbZWXxiU5J6Ti4ARgInT7NKvjKSlbIt26sZDYzo7N6Na9z550ynymElboc4LhCo7/WK7dUVe1wNdfTTz/dYnuJiooKJCcnO3R8nspk4v5Mfn5NU1KdURTWX2Zm8rPfll25O6I1mW3VncAiVWXQ1foYMWIEcPfdLJk4dozTbc2zOarKWYncXB637rvP+i2KVBV4910e8zo7off35wn9qVPcQPq3v2VBs3AsbYrKqgyONfbs4Xxnnz6dX+/iRdbz6PV8UebkMMXZ/MWp1fgEBADr1vGNdOWV9hmnEB6uy1Vsl19+ebsZkdLSUlx++eXdGpQlMTEx8PHxaZOtKSwsbJPV0ej1eoSFhbW4iK45c4YHYVsKh8PDeQJ64IB9xhAQ0LSayRZlZU0NalubOpXTaBMmsD40M5OZp8xMXurqOFPw//6fbX3WcnM5BZWU1PkJPcCAKjWVt9m3z6aHJrpIm6IymAyora/t/h1mZlqe61RVRvv19ZySCg5mUFRd3f71IyMZCH37LW8jhLCoyxmcLVu2IDMzEwcPHsS7776L4B+PGEajEVu3brXbANvj7++P8ePHY+PGjbj++usbv79x40YsWLDAob9bsH6ytrbzzSZbUxQe3AsL7TMGnY6rs44dY0bJmqy92czpqZkzOy5lGDGCMwVnzzK4qajgfUdEcMrIlo2lNXv3Nq0itoZOx2Peli08WZdVxI4V6h8KH8UHJtWE0rpSBPp1s8q7uprpzc5UVjKDo72JfHz4Am1o6Pg2iYkscM7MZH8eIUSnurUO8ZtvvkFBQQEmT56MrKwsOw3JOk888QTefPNNvP322zh+/Dgef/xxZGdn4/777+/RcXgjs7lrdTSK0vnnt63S0li7oq3YteTCBWZ9LHXRVxTOFCxYwILf229njU5XghuAx6PQUNv+ZrGx3EIiL69rv1NYT1EU+05TBQQw6u5MaSlTglohmNnc/txpc4GBzN6cO9f9MQrhBboV4CQkJGDr1q0YNWoUJkyYgC1aI6wecMstt+Af//gHnn32WYwZMwbbtm3D2rVr0cfSvLfotuBgfg43W5RmFaPRvo3wIiPZNLahgUGOqnZ83dxcjvfGG3t+xW11te2LX/z8eIy09W8susauhcaDB1ueO21oaNk+u6aGBW3tzZ02pyhMnwohLOpygKMV8+r1erz77rt49NFHMXfuXLz66qt2G5wlDz74ILKysmAwGLB//35Mnz69x363Nxs0iPUkHSxYa1dNDaeoRo6071imTwfuuov/z8xk1kM7eTaZOMbMTB5PFi0CrrjCvr/fGoGBlk/oW9Om3SzV7Aj7sGsGZ/JkZnEqKzu+jo9PU0Suqszm9OljORJWVZmzFMJKXa7BUVudLv/mN7/B0KFDsXjx4m4PSri2gAAGFv/6l/X1LxcucNpn+HD7jkVRWKfSty+waxeb+J040ZTxj4oCrr2WPdqctQHz4MEMsqztlAywz050NJsJCsdr7GbcWbM/aw0ezA6QBw7w3/beICEhTNMZDIz+Q0LYOKoz9fV8AXV1rlQIL9PlAOfcuXPo1SrX/5Of/ARDhgxBRkZGtwcmXNtll7HJ34kT7HPTWZCTl8dg45prHNenLDWVl2uv5XSVwcAT3aQkBgrONHEisH49Zy2Cgxno+Pp23IlfVVmicfXVlmcshH3YdYpKp2Nn4tJS7sY6aFDbVFx0NKPvrCwuMRw1ynLXyIICBkHSIEkIq3T5cNNRrcvw4cMx3N6n6cLlxMSwF8yrr7KVR2IiP7ObZyiqq5m58fEBbruNmXtHi4rixZVERvL4tm4dAxZFYYCTlMTjVWRky79bdjb/vhMnOm/M3sambsbWSErirq4rVrCDpE7HynGtdXVJSVOkO3iw5SV2tbUS9Qpho26dT5eVleGtt97C8ePHoSgKhg4diiVLliDc2g19hFsbMAB44gng88+5/UBmZlMDvoYGTmUNHgzMm8eDtT06GLsTVQU2bwY+/pi1QT4+zOJERHC24dgxNrJNTuYJvK8vgxuTiQkA6UPZcxozOPaYotKkpAC//jU329y2jc2jKisZ7CQmcq+PU6e4VUNJScf7jlRVsfnUlCl8MwkhrGJTgHP48GGMGDECOp0OGRkZmDNnDgIDAzFx4kSoqoq///3v+OMf/4gNGzZgnPRp8Aq9ewMPPsgD+IEDrB2pr2fWfdgwy9NXnuzrr7klkV7PtiWDB/NvdOkSfx4SwkDw+HH+/ZKSeNy77TZg2jTnjt3bWLXhZlcEBnKDzsmTmYExGFh7Ex7Of41GBjbffMOlfr168YWhKKzNKSzk9WbMAO65x7rW4UIIAICitq4W7oROp0NBQQFiY2Mxbdo0pKam4o033oDvj4UVDQ0NWLp0Kc6ePYtt27Y5bND2UFFRgfDwcJSXl0tXY2F3x48Df/5z06ajGm2X9fPnGQyazU3LwW+4gV2SZePonrfq0Crc/fndmJs6F+t+uq5nf7m2J9Xu3ayS17oZa5HxlCk8W5CNNoUAYP3x26Z3TPPC4oyMjBbBDQD4+vri5z//OdIsdVITwsPt3MkuyK2XxWsBT2Iig5r6ema4ios5eyErgJ3D7htu2kJRuGvtwIHA9dfzhWM2M5MTEdHz4xHCQ9jUB+ell15CTU0NACAsLAzZ2dltrpOTk4NQS6sBhPBgly5xv8XOlngrCmuUQkM565CQwEUy+/f33DhFE7sXGXdVSAij36QkCW6E6CabApyDBw+i/seN3m655RYsWbIEH3zwAXJycnDhwgWsXr0aS5cuxW233eaQwQrhDrKzWTMaE2P9bbRl42fOOG5comMOq8ERQjiNTVNUmzdvbvz/X//6VyiKgjvvvBMNP24w5OfnhwceeAB/+tOf7DtKIdyIwcAMTUd9bjri69vxZtLCsZpPUamq2tipXQjhvrpctebv74+XXnoJy5Ytw5kzZ6CqKlJTUxEkVf7Cy/n7s25U66ZsrYYGWSTjLNoUlUk1odJYiTC9LDwQwt3ZFOBkZ2cjJSWlxfeCgoIwsoMNhnJzc9HbUvtxITxMUhKb9xUXW78iymRiQNSvn2PHJtoX6BcIvY8eBpMBpbWl3Q9wzGbONxYX88kNCmIRcUiIfQYshLDIpgBnwoQJmD9/Pu69915M7KDNanl5OT788EO89NJLuO+++/DII4/YZaBCuIv4eCAtDfj2W+sDnMJCFiXLAkTniQqMQn5VPkpqS9Anov1O7RYZjaww37aN+5hoO3/rdHxhTJsGTJ0q+0kJ0QNsCnCOHz+OP/7xj5g7dy78/PyQlpaGxMREBAQEoLS0FMeOHcPRo0eRlpaGv/zlL5gnXTeFizKZuI1ETQ1rX7Stgexl6lS2NCko4HGtM3V1DHBuvFEWzjhTZGAk8qvyu15oXFUFrFzJzsQ+PlwNpa0ora/ni+G994AdO4B77wVGjLDf4IUQbdjU6E9TV1eHtWvXYvv27cjKykJtbS1iYmIwduxYzJkzByPc4I0rjf68U0UFsG8fsHUr9zk0GnlyHRoKTJrEprNDhnR/WwlVBb74Anj//aaVv+3V41RUAOfO8Xc/8ohsM+RMU9+eip05O/HxTR/jJ8N+YtuNjUbuO/Xtt9zDpKOpKLMZ+OEHRtSPP84dYoUQNnFIoz9NQEAAbrjhBtxwww1dHqAQPS0rC1i+nMeXgABmVvR6BiNlZcBXX3HvqGuuAX7yE3bI7ypFAa67jvfx2WfAkSNAWBgvisKsTVERxzFzJnD33RLcOFu3euHs38/MTWfBDcAod/Bg7lD7ySfAz3/ufZu0CdFDpPe38Aq5ucArrzDIGTq0bdf7uDheiop43FFV7oVo61Lv5nQ6bv48fnxT1qisjCfxej0DqfR0YNAg792vy5VY1QvHYOBafkVhRKotmdO2prGmiFhRuJPq0aMsRJYsjhAOIQGO8HiqCqxeDZw9Cwwf3nkwoTXn++orXnfUqO7//rg44NprgblzWaahLaoJDOz+fQv7iQroIIOjqnzx7NnDwiqtcDgkBLjsMs4/Hj3Kf60VFsYNyTIyJMARwkEkwBEe7/x5IDOTJ83WZEpiYlgPumuXfQIcja8vi4hzczmbkZvL0o3QUO6lOGaM9MFxpsYMTvP9qOrqWBi8dSs3C4uObppLrKzkz0wmvmBmz7b+l2l7deTn2/ERCCGakwBHeLy9e1nM28eGlb9xcTy5vnix8z2lbFFQAHz8MXDgAKeq/P0ZcBkMwNdfs3/OlVcCc+bIxtHO0NjNWJuiMhqBt98GvvmGO6T27duyXiYykk/a0aOMVjMzgdGjrZ/XVBQGR0IIh5CPUeHxsrM5HWRLLWdUFHDyZFN/mu7KyWEN0A8/8JiYnNxyPEYjT+ZXrWJQdeedEuT0tDZFxps2seq8X7+m5d6tKQpfIMHBfHKjoqyPpI1G6QsghAN1o4Syyc6dO2EwGOxxV0LYnbYU3BY6HYuBf9xbtlsqK7mC+PRptj6Jjm4bbPn787jYuzewdi3w5Zfd/73CNi2KjI1GBjdBQR0HN403jGRHx7o6VrFb03nDYOCLbPTo7g9cCNEuuwQ48+bNQ25urj3uSgi7Cw3l8coWBgOXeNujEDgjAzh2jP11LNUARUTw8s03QHl593+3sF6LDM7hwwxWrOk47OvL6NTXl8vwiost3yY3F0hJsW+RlxCiBbsEOF3oFShEjxkxgqUOP256b5WLF7kopm/f7v1uk4n1qXq99X114uP5+w8c6N7vFrZpvqM4LlzgCyYgwLobJycDsbFASQlQaqETcmEhU4NXX80XhhDCIewS4AjhysaPZ9BQUGDd9c1mZk+mT+9+BicnhyuMbdl6yNeXwdD+/d373cI22hRVuaEcJkOdbUVbgYF8oQUHszV1cXHbqaraWs5Tlpayk+TMmfYbvBCiDbuUMS5fvhxx9lpqIoSdhYUBs2Zx2wStm3BHVJW1osnJwOTJ3f/dNTWcHrM2EaAJCOBKq9by8jh7UlHBr8PCOMthSwsW0T4tgwMAZb71iLY1Mx0ZyR3D+/VjM8DcXEaqOh1fBH5+/NncuYyepYOxEA5llwCnT58+8JUlH8KFzZ8PXLoEbNzIIt+4uLb1MNXVLLuIiQGWLrXP6ikfHx7HbD1Wms0tV1GdO8fi4wMHmADQiqbNZh5Xx43jjEe/ft0fs7fy8/FDiH8IqoxVKI0NQ7S/P7Mu1qbxKiu5ikrbVOzAAaYN6+sZiQ4fzvlSf3/HPhAhBAA7BTjz5s3DoUOH0L9/f3vcnRB25+cH3HMPjz+bNrHoV69npsRsZodhf3823Lv9dm4XZA/R0Wx4W17OhTbWqqnhiiqAGZsVK5i9SUzkMnPt5F9VORvy7bfA8ePAffcBI0faZ+zeKCowClXGKpSk9GK0mJPD/aWskZfHVVEDBzICTUpy7GCFEJ2yS4AjRcbCHfj7AzffzGZ6+/fzUl7O4CclhVNSQ4fad1+omBggLY2ZI2sDnJoaZm8mT2ZGacUKLs4ZObLtrIai8HdER7Nvz/LlwJNPdr842ltFBkQiuzwbpfWVwOWX8w9aXg6Eh3d+w6IiPhkzZ3ZvAzMhhN3IvJILM5u5F9/Jk8yU+/ryYDZmjOXWHKJjUVHAVVfx0hOmTOFejCUl/N2dUVUGNUOGMNhauZKJgfaCm+YUhVmnzEwuMV+61K4PwWu06IUz80Y+GevWcb6yV6+2T4LZzGmosjLg+uuBSZN6fMxCiPZJkbELUlVmFzZu5FSKtnmx9tmakABMncpaRUsnlsL5hg/nNkVffsnnMDKy/eupKnDqFIOgW29lQLR3L1eAWVOPqii87p49rDmKjbXv4/AGLXrh+PqypXRQEKPGzEy+4YKD+WRVVTXtT3X77cB110n2RggXYpcA5/bbb7fH3Qjwc3PdOq74MRpZh9G/f9MBrqGBJ4yrVzOz88ADttV2iJ6n0wG33caT/Q0buCVDfDwDHUVhDerFi6yl6d0bWLKEtUDffssgZ/hw639XTAy3RjpyhDMswjYteuEAnNe87Tauetq7F9i5k4ENwKzOTTcBEybYb8MyIYTdyBSVi9m5E3j3XZ4ktlfb6OvL2sXYWODgQeCNN4DHHpNdqF2dvz+TASNHcifxw4c59aSqrPmJj2eW57LLmoqLq6oYANmSFNDpeJvKSsc8Dk/XZj8qgH/QpCReFizglgwAV1dJxsY5amuBQ4eaVqnp9fzAtHcRnXBrEuC4EIMB+Pxzfp5a6mvi78+ai4MHuRXA9Ok9M0bRdT4+LDgeP56NcgsLm5rlDhjA1VbNNZ+WtJUcd7umzY7irfn48OxDOEdNDdOgW7fyTaSqTT0Y9Hpg0CDgiit4piBvAq8nAY4L+f577nxt7Wr7gABmdLZtY02OvJ/dg6KwkWBycufXCw/nZ7fJZP1JqcnEfztrZig61qLIWLiWykquatu1i2+OgQNb9hSqrmaXzhMnGPzcfLNkc7ycHBJdyP79PKDZsj1NQgLf09nZjhuXcI5Ro1hfVVho/W0KCzl9KZtUd027U1TC+RoauKRw504GNn36tG2YGBzMDE5MDLBmDYsZhVezKYPzxRdf2PwLrrrqKgTaY0tmL1BUZHtL/+BgnqxYU3NhNDIQqqtj5qdXLy4AEa4pMpJLzD/9lEGLpZNRk4kBzk9+wh3Jhe3aFBkL13DkCDM3AwZY7iwdE8MPua++4lRVR8sWhcezKcBZuHChTXeuKApOnTolHY6t1JV6C2t6LBYXcwHIli0MhurrOZ0VFsa2HenprM2TrXFcz1VXcXXy8eOd10+aTLzOwIFsZCi6xuIUVV0d60B0OhZNyRY1PWPnTr7IWxeqdSQhgT029u+XN4QXs/ndWVBQgFgrG2yESjc6m8TGcmGALaqqeELTUT+ckye50ursWX429O7NKTCTiXsarV3LwOfaa3nmL5/XriU+Hrj/fuD113kSGx3N72nPk9Y2oLiYwc399/PnomvanaIymzkP/N13wL59TIUqCtNk06dzmbj80R2npIQfjLY0dvLxYTp81y4JcLyYTYezxYsX2zTddMcddyBMqh2tNmEC90myZX+/ggJg7Nj2C1bPngVeeYU9VoYPb3n27+PD1h1xcdyE8uOP+f2bb5ZMjqvp35/bL2zezCXmJ060/HlCAhsDzpolzf26S5uiqqmvgaHBAH2tEVi1it0Ta2s5/aE1+rt0CXj7beCLL4CFC4F586TS3xEqK5k5s3WqKSiIkb+qyoeal7IpwFm5cqVNd/7aa6/ZdH1vN2IE9/fLyrJus8eaGp5cTp/efgf5999nr5Xhwzv/3NUaBX71FQtbhw7t8kMQDhIbC9xyC3cMP3KkqeYqNJSvG0mW2kd4QDgUKFChorQkF/GrPmbmpm/ftmnSqCi+0fLzgX//m3O/CxbIwdTemu8sK4QN7H66kZOTg3vuucfed9vCCy+8gClTpiAoKAgRHlRN6efHaSK9Hjh/vvP3c20t2/pPngyMG9f25z/8wJqMvn2tO6ns1YurLL/7rsvDFz0gNJQ1U7Nn85KeLsGNPekUHSICIgAApevWALt3c2VOR3PAOh3nfSMjWQ2emdlzg/UWYWHMxmgdpK1VVWX9PifCI9k9wCkpKcG//vUve99tC0ajETfddBMeeOABh/4eZ0hLA+6+m5+bR49yZVXzQKeujlNPp09zhc2SJe0vK9cy6rYc/GJj+XleIitkhRdrLDQ+sJNFT9bMF8fFMaW6fbuDR+eFIiI4f3/pkvW3aWhgrdSUKQ4blnB9NpeUWloqfvbs2S4PxlrPPPMMAGDVqlUO/13OMGMGg41Nm4ADBzgloZ2E+PgwKzNjBjBzZsdbNJw/b3vD1chI4Nw5fo5Y2vVaCE8VFRiFs6VnUVJeACQNs/6G8fF8w+blWW5FLmyTns4itLIy63ogXLjA56C99LbwGjYHOAsXLoSiKFA7mT9RXDAlaDAYYDAYGr+uqKhw4mgsGzqUl9xcTjfV1HAKKyaGNRete1y1ZjTa3sRTp2NJQUND18cthLtr7IXjW2/bssLoaKZdtYOrsJ+hQ3lGt24dP9g6S03n53PfmzvvlPlbL2fzFFVCQgI++eQTmM3mdi8HDhxwxDi7bdmyZQgPD2+8JFvqk+8ievfm6phrrmHNxbhxloMbgNPWzeI5qxgMDKKkL6PwZo1Lxf1Mtt1QO7Gz9Y0nLNPpgDvuYGOonBwWIFZXN/1cVdn34tgxzs3ffjs/OIVXsznAGT9+fKdBjKXsTkf+8Ic/QFGUTi8ZGRk236/m6aefRnl5eeMlJyeny/flDsaM4eesyYbP6IICICXF8h5JQniyxgyOUmfbDc1m2/dasaS+nkV3R4+yqZU3F8gFBgJLl7LZ04ABTG8fOdJ0KS/nqovHH2djLxecSRA9y+YpqqeeegrVzSPnVlJTU7F582abB/Lwww/j1ltv7fQ6ffv2tfl+NXq9Hnp7fvC4uLQ0LuooLGSfFEtMJp4QzZjBLI4Q3qqxyNjHyPlaa6epiotZyJaS0v1BlJY2tR/PyWlqPx4ayoN4ejowZIj3HcT9/IDLL+cH1fHjnI6qr2dTv/79uUeVt/1NRIdsDnCmTZvW6c+Dg4MxY8YMmwcSExODmJgYm28n2hcVxQzthx+yg3FnU9FmM5vHpaYCEyf23BiFcEWNU1QhPkxrJiVZd8OLFzmF0t2uxj/8AKxYwcxNcDDPUPR6vlFLS9mwSms/fsMN3tl+3MeHxYgjRjh7JMKFueU7Izs7GyUlJcjOzobJZMKhH/c3SE1NRYi1e5V4geuv5zLzTZtY/xgX17bwuKKCK66SkoB775V96YRonKKKCQFOlvBsoaPlipq8PAYjU6d275dnZQH//CczE63bjwMt249/9BGnxKT9uBDtsinAOXz4MEaMGAGdle3Ijx49isGDB8PXzmcYv/vd71r02hk7diwAYPPmzZg5c6Zdf5c78/dn0NKrF1dYHjvG72kng9XV/NweP541ed2YARTCYzRmcML8gGnTgG3bOO0UEdF+y/ALF9ig6qc/BYbZsKy8NbMZeO893p+0Hxei22yKPMaOHYuCggL00t5cFqSnp+PQoUN230181apVHtsDx978/XmCd9VV3Fj3++9Zi6fXc7p64kROTckWOkJQYw2OsRy47z6eBezaxVqYqCjWe6gq05/V1WxadfvtwBVXdC+TcuoUz0JsaT9+8SLHJgGOEG3YFOCoqorf/va3CLKUrv2R0Wjs0qCE/UVGclNd2VhXeCNVZWKksJB1w1pNanu1aY1TVLWlDG5+9jOeIezZw71M6uoYgKSkcCO4tDTOAXfX3r22tx/v1YvjWrjQPmMQwoPYFOBMnz4dJ0+etPr66enpNu0+LoQQ9mQyAQcPAjt2MHup9ff08WEpy9SpwGWXtawjbpyiqi2BqqpsXNq/Py833cQgxMeHUZI9U59ZWV1vP15UJAGOEK3YFOBs2bLFQcMQQgj7MhpZ0rJhA7M28fFMuigKVxZfvAh88AFLbJYsaerqr01R1ZvrUVNfg2D/ZkGHr6/juuMajbYHTFr78fp6x4xJCDfW7dOPl156CQBw8uRJmM3mbg9ICCG6y2wGVq8GvvySszjDhrF8RiuR8fNj1mbkSG5v9Prr7KUHAMF+wfDTsRlUSW0PNtYLC2OQYwtpPy5Eh7od4Iz4sQ/B448/jsGDB2PcuHFYtGgR/vd//xdfffVVtwcohBC2OnaMmZvevTtvfaAoLLIvL2dA1NDAbuyNhcZ1pT00YnSt/fjFi4zUpP24EG10OcCprKwEAFxxxRUAgLVr1+LUqVPYsmULHnjgAURGRmLjxo32GaUQQthg1y7WAkdFWb6uonBF4enTTVmcFoXGPWX8eK7IKiy07vomE1BVxU0ordmgTggv0+UGNdOmTcP69esR36prZ1hYGKZMmYIpU6Z0e3BCCGGr4mIgIwOIDakBsouYltHpWMAbHd1unUtQEMtY9uwBRo9uWWjcY6KiGKxY23785EkWPkv7cSHa1eUMTlpaGiZNmoQTJ060+P7Bgwdx9dVXd3tgQgjRFSUZZ1F18BTCD24Bdu9mtLNnD6uJt23jaqV2poFCQriUHIBzpqgALveeNYtjzM9vf7qqspJzcPHx7ORpTZpKCC/U5QDnzTffxD333IOpU6dix44d+OGHH3DzzTcjLS3Nqza1FEK4kK1bYVrxFsz5F6H46FhhHBvLNeGhoUzv7NvHrpetVh5pq6sAJ2VwAHbg/NnPgFtuYabp2DHuTZWVxb2pMjNZdzN2LHfNHjy4Z8cnhBvp1h4Kv//97+Hv74+rrroKJpMJc+bMwb59+zBOW28phBA9Ze9eYOVKBOmioY8OQZ1eB3+l2aokf39OURkM7B2j03Ft+I9TVnV1TQXJTqnBaT7Om29mV879+9nIp6KCwU9KCncTHzjQfj14TCbutnvyJDsz+/sDMTH828jmdMKNdTnAyc/Px7Jly/Dmm29i2LBhOHHiBG699VYJboQQPc9gAD7+GDAa0XtoGPpfKsXJ4hiE6YvbXlev575SWVlcgRQfD5OJK7THj+dVGgOcnp6iai4qih2Ur7rKMfevquzMvGEDs0Rah2btZ7Gx/IMMGcL+Pz4+HFO/frK3i3ALXQ5w+vfvjyFDhuCjjz7CNddcg6+//ho333wzLly4gF/84hf2HKMQQnTu8GHg/Hmgf3/46FTM6HsemYVxqDfp4OfTTn8uvZ5rw7Ozgfh4FBRwFksLcJw2RdVTVBX47DMGhWYzA73mXZQrKrikbONGZnT69WPn5qAg7ns1fTqLm2X1lnBhXQ5wVq5ciVtvvbXx6zlz5mDz5s249tprcf78ebz66qt2GaAQQli0dy8P2j/W/6Ul5mFozCUcu9QLQ3tdgq9ObXubkBCgoABleTUoLQ3C7bczsQM4sci4p2zdCnz0ERAezsiuueJiTo2V/rgXl8EA1NRw13KDgcHkoUPcaf3uu/l3FMIFdTnP2Dy40YwbNw67du2SLR2EED0rP79FBiLE34j70vYjNaoYRy/FoqgmEGqrGKfeJwDZZWHIzTFh3jxg/vymn3l0Bqeuji2efX3bBjdlZQwWy8tZoB0VxamqixeBvDx2Wx4yhLVAmzcDK1fa3n1ZiB5i94nUvn37YufOnfa+WyGE6JjJ1LQPw4+SwirwRPpuXNnvLGrr/XC4MA4niqLxQ3EUjhT2wg+lvRDuV4PFC8qxeDGP9xqnFhk72qFDQE4O2zw3p6rA8eOcnoqJaaqz8fHh5fx5TmcBzNr07w9s386ASAgX1K1VVB2JlMp7IURPio7myqhW4kKq8dDEfcitCEVGXiIuVITBYPJBmN6AoQFZGOt/FEHXjW7zSejRU1QHDvDf1u08ysuZqQkLaxMsIjQUKCnhdbTP95AQBkHbtgFTpkjhsXA5DglwhBCiR02YwKZ+DQ0tUzE/6h1Wid5hJ1t+8/hxYOQ4IDGxzfW1KarS2lKYVTN0igcdvIuLWTDcWn4+p6/Cw9v+zNcXjUvNmktM5N/x3DlgwADrfr/RyN+lbRQaF8daHyHsTAIcIYT7GzcOSEhgnUhKiuXr19VxumXatLbZCjRNUalQUWGoQERAhJ0H7EQ+PmhTkARwXytf33b/Ho3ay+ycP8/sjqUA59IlNlncsoUBTkMDxxITw1VZkya1nTYTohs86LRECOG1QkOBq69mo7qios6vazSy78vYsby0Q++rR5AfswoeV2gcF8dVUa2Z21lOrzEamW1pPa2lBTyWdkA/dAh49lngzTc5DRYfD/TtywxQRQXwn//w59u2tR98CdEFEuAIITzDnDnA9ddzCubUqbYHcZOJGZ7jx4ExY7glQmBgh3fnsYXGEyawf03rv49e33GgUlnJ1VRhYS2/bzQyC9PZFFNmJvDqq3xeRoxgT53gYP6+oCBm3EaO5JTVm28CskhF2IlMUQkhPINOxy0OEhPZnffMGR6AtSyDqjJ7cdNNwDXXtF9r0kxkYCRyK3M9r9B42DAgNZVZrCFDmv4+sbEMDFvXMWl1NykpbaeoCgo4rZSa2v7vqq0F3nmHWZrBgzue/lIUZnTOnQPefZfjionp1sMUQgIcIVxIfT2PGXV1nBGIjZX6S5vodKznmDKFG1WeOsVpK72ef8xx4ywGNhqP7YXj6wvceCPw8svcwLN/fwYYcXHsdFhR0bRDudHI+pp+/Vjj1JzZzL45113X8Yv0++9Zo5Oa2nltj6ZPH+DIEe4AP3dudx6lEBLgCOEKSktZf7l1K1uUaPWXUVFNXfGTk509Sjfi68vOu6NGdfkuPHaKCgBGjwaWLGGjviNHGPz16sVNPPfvZ+BiNvOF2KcPp/R8fJpubzZzc85+/YDLLuv492zfzqDT2i0ddDouP9+yBbjiCkb5QnSRBDhCONmxYyw9yMpiaUJsLI8HJhPLFt59F1i/HrjtNmDWLJ4Im0wsWfD3b3dVtFu7eJG7AVRW8rGGhfF43NMzFo1LxT1tikqTns6gZutWYM8evhABBhha1+JRo5h90V5kqsrgJyeHEfe99/IF256GBr6otf0vrBUVxRVXFRXsbyREF3nYR6PzqCqQm8szcbOZB6q+fT3v4CPs6+RJ4JVXGMgMH97yJBlg5j8piceTN9/kjILRyH0QTSZef8gQnkSPGOHeex9mZQFff81MVklJy42to6OByZOB2bP59+gJWgbH46aomktN5WX+fNbk1NbyRZWVxYAnOxs4cYIvLLOZL76wML7gfvITfsh1pKGBt7G1AaBOx9vV13fnkQkhAU531dcDBw8yE3vkCKf7tT3/+vcHZszg9EJoqLNHKlxNfT3rL4uKuEFzZ/WXERHApk080R46lHWdvr48hmzfzoUnAwcC99zDWQN3k5kJLF/Ok4TERAZr2nHRbOYJ/Zdf8nr33896VUdr7GbsiVNUrfXqxUtzRiP/4JmZzNr4+XF59/jxnLayVFPj78/btLckvTNGI1/c7TUjFMIGEuB0Q1UVsGoVDzCqyhq8pCS+72tquIjj6FFmgO+7T3pYiZYyM/ka6dev82NFeTkDm7o6nlyHhLSs90xI4M+OHwf+8Q/g0UcZXLuLrCwGN0VFnBFp/bfQ6Vj/2qsXkwmvvw489VS7DYjtqrHIuM6DMzid8fdnMDN+fNdur9MBaWnA55/blnYrLGTTPyuLwYXoiAQ4XWQ0sj5v0yYeTFpnaEJCeEZdX88g55VXgCeeaHuSJHpefj6zbsXFzA6EhnJ6aODAnt1OZ9cuTjN10ooFJhO3Dior40G+vLxpUUrz6c+AAK7+1ep5fvvbzu/Xlaxfz8xNe8FNczodp+MyM4FvvwUWLXLsuNyiyNhgYMHS2bNMHwcEMPIbN65tzxpnmDwZ2LiR9TTWjMdg4L8ddJgWwhYS4HTRrl3M3AwYwGCmI35+nFI4ehT44gsuXBDOkZMD/Pe/XIFaWsoDpqJwmickhM/TvHkdNre1K1UFTp+2XH9ZWMhALDKSYw0MZJlEbW3boFqnAwYN4v1+/z2PLa6uoIDPR2KidccznY41rbt2Adde27TvoyO4dJFxQwOjvG++YZ1MQ0NT7YqicCpp2jT2++nsA8rRUlMZbG3bxjdYZ0ViJhPrgEaMYFW5EN0kAU4XmEycdvL1te6zw9eXnzd79rCWT7I4Pe/UKTZTPX+eB1NtKlFTUcFu8j/8ACxezNVKjqSqTcekzuTk8JilrZZVFH7dUVd9f39eZ/t2Zvld/ST48GEGmyNGWH+b2FhOxx05wmO4o2g1OC5XZKwVb61fzyr0/v1bbqHQ0MClaKtXcw70wQdtX8lkLzodcNddTD1+/z1XXmnRukZVuWQuK4sB0c9+1nZLCCG6QLZq6IJTp3ggtKUGoFcv1hgcOOC4cYn2XbzIuo3cXB5IY2LaHvjDwjjFo9MB//qX458nnY7HptabMzdnMrG4tnkPNW3lVGer82Ji+BqtqrLfeB1FWwpuSyCmrTSrrHTMmDQuO0W1Zg2wdi2L+vr1axsM+PryZ4MHc0na2287d0VSZCTw2GPsa1NZ2TSllpPDoCYzkx+OkyYBjz8uxYrCbiSD0wVFRZwqtiXzq9PxUljouHGJ9m3fzs/T5itzOpKSwuzA2rXsbebImpxx44APP+QJbHsHeG2VbfOl49XVlrsb+/mx6NhgcP3Ve13NMPVEZkqboqo0VqLeVA8/HxdoOldYyGmpmBjLWZmAAGZE9u7lHPmYMT0xwvZFRAAPPcSgZs+epiWngYGcV5082fpux0JYSQKcLmho6NrtdLqmGjrRM6qrGeBERbXtMdORpCSu1tG26mlNVZn5z8hgP7SGBn5+jxrFY4i1q1snTeIsQ1lZ+7UkPj5NU1IAszdms+UVuloXZHfoiRMWxr+nLe1StPefo2toIwIiGv9fVleGXsEuMLeckcEzLGvn9IKD+cLZudO5AQ7AF21KCi833eTcsQivIAFOF2irU7TpAms1NLjGwgZvcuIEV00NHGj9bUJDueffkSNtA5wzZ4CPP+YJcVUVXwuKwsD1m29YYjBvHnD55ZYP2H37chXtt98yI9N6pkHbqiE3l8ep4mI2vGu9JVBrWtNAZ9aWWmv0aD6moqKOG+K2dvEir9uNXRis4qPzQbg+HOWGcpTWlbpGgLNrF18MtqQWY2NZYFZa6tiqbCFcjNTgdMGgQaypsWW6qaaGB7CeaFAmmlRVMUNg65Y2vr6si2zu2DHg739nxj86Ghg5koFTaioDitRUHqjfeINTTx0VAmsUhUud09IYiBUXc6zNf56SwsC4oIBtQcaN6zwzU1/Py4wZPbvkvat69WIjzIICy38vgCcVRUXA1Kk9c7LgUoXG2uaWtu6+GhjICNwdirKEsCM3+Ah0PZGR3Mbl0qWWB6TO5OZySfnw4Y4dm2hJp7P+OWpOVVtm5y5eZOBy6RJnB8LD204T+fuz5rNXL+Czz4DNmy3/nvBw4JFHgKuu4nRaZiazRxcucMVXURGDLT8/Tml1dgKuTZ316dMzS93tZc4cZr5OnOg8yDGZWB81cCAzZD3BpQqNFaXrL2jttkJ4EXnFd9H06Wy8dvq05c+bwkJeZ/Zs2Zuqp0VE8G9eW2v9bVSVB9Pm+/zt3MkFH4MHW66DjIlhsLN+vXU1V2FhwAMPAM88A/z0pwxQwsI4FbVgAfDXvzIjk5fX8WKYhgbuaxURwe0a3GF6SpOczMffuzcDvPx8/v01DQ187EeOcEX0/ffzvdcTGrsZu0IGR1H4orB1+VhFBae1pDOw8DJyuO2ivn15IFmxgmeeKSn8DGnOaGTmxmjkvnSO7Nkh2jdkCLMqFy4wg2aNkhIGCloWpKaGfcoiI60/Ce7dmyu3Dh8GJkywfH1F4YE+OZmvldYGDADeeotBjK8vs0R+fjz4X7rE15j2mnTHLOGQIcCTT7Ie6bvvmKlRlKaTh9hY1qVecUXPBTdAs/2oXKXZ35QpwP79fOKtOVtSVaYBr7/evaJeIexAApxumDCB09sff8wVN0ZjU9FpXR2vk5TEZqKXXy4rIJ3Bzw+YOZOBqMFguX+Y2cygdNaspnYcp08zg5Caav3v1euZhcjMtC7AsWTQIOB3v+MWE9u2cRqrqorTaMOGMaM4bpzrLwvvTO/ewJ13Atddx7+blqgID2e9kzMSEFEBP3YzdoUpKoBPcmIiuxdbs+FYcTFfFO7Q1loIO5MAp5tGjGAH8uPH2VOroIAnV9HRzACMGeM+ewJ5qssu4+rajAxmCjoKcsxmZkiSkniQ1QLSmhr+zNZl1/7+nB2wl+BgFtdOmcL71QK2sDDPKq+IjGTA5gpcqsgYYLBy442M2C9c6HwTy9JSfiBdf71t0bkQHkICHDvw8WGgY0u7edFzQkK4mzvA7H5ICE+CtX412iql4mJO8/zsZ/xXoxUbd9SQryNdCYqsodM5r/O+t2ksMnaVKSqAc911dcD777MwKT6eZ1TanF5FBVOOisL08U03SfpYeCW3C3CysrLw3HPPYdOmTSgoKEBiYiLuuOMO/PrXv4a/O3Q2E04REwM8+iiwYwewZQsLhrWGcYrCuo5bbmHmoPUWHDExXJlbUWH9NImqcsoyPt6ej0L0NJcqMtYoClcs9O7NLpYZGWzMpAU4QUGcypo+nWvwbWnWJYQHcbsA58SJEzCbzVi+fDlSU1Nx5MgR3HvvvaiursZf//pXZw9PuLCQEGDuXNZDHT/OliJmM6d+hg7tuH6lb18W7h44YH2AU1rK66al2Wv0whlcrsi4ueHDecnPZ1FWXR1ThgkJrM+RrI3wcm4X4MydOxdz585t/Lp///44efIkXnvtNQlwHOTSJW7eWFfXtIJn0CD3PTH092cHXWspCmcFDhxg4aulQl6zmVvuzJjBVVHCfWkZHJcpMm5PQgJThRcuMNjJz2eE3a9fy14HQngZtwtw2lNeXo6oqKhOr2MwGGBo1pSkwp7Vnx7q9Glg61bujVdc3NRjTK9ngDNjBgtevWFmcMIErsbauJEZnY666JpMbBvQty+Xe8tJtHvTanBcaoqqObOZS+u2bwe+/57zqNr27FFRXD01bZoUGQuv5PYBzpkzZ/Dyyy/jb3/7W6fXW7ZsGZ555pkeGpX727kTWLWKPWFiY5kJ11bqVFVxtdHRo6xxvOce27vHuxs/P2DxYv5/yxYuJU9IYKCj7UWVn88MT2oqC5Ule+P+XHqKqqGBe4J89RU7QMbHsyGXtkNrYSHw3//yzbxoEc9InMxk4mxaeTlPlkJD2eNJGqAKR1BUtSt9v+3vD3/4g8UAZN++fUhrVtSQl5eHGTNmYMaMGXjzzTc7vW17GZzk5GSUl5cjTHbAbOHAAeCVV/hh1Ldvx1mIykp+WF11FXDvvd7xIVVfz7/P9u0M7rS9rvz9GdDMmMGTZpkZ8AwVhgqE/4mFVzW/qkGgn4v0fFBVBjcffcRIu6MXnKqyZ47ZzHbRTuqHU1PDPdy2buV0t9ZZXK9nuZD2vnHnPk6i51RUVCA8PNzi8dtlApyioiIUFRV1ep2+ffsi4Me1vXl5eZg1axYmTZqEVatWQWdjIxBr/0DexmgEfv97rjIaNMjyFEtFBTMXTz3FhRveQjtulJQwEAwO5ge1pUaCwr2oqgq/5/xgUk3IfSIXiaGJlm/UE86cAZ57jqlTa7ZhP32a13v++R5Pt5aUsG3P/v3MhCYmNnV9r63livbaWjZzvO8+xmtCdMba47fLnHPHxMQgJibGquvm5uZi1qxZGD9+PFauXGlzcCM6duQIszKdZW6aCwtjQe3OnWxs6C01J4rCPaP69HH2SIQjKYqCyMBIFNUUobS21HUCnD17eHZh7QuwTx+2Wz94kJ0ve0hVFfD662yCOnhwU+8pTVAQp3SNRm5r8uqrwOOPs3xIiO5yu8ggLy8PM2fORHJyMv7617/i0qVLKCgoQEFBgbOH5hH27GFGovUHUWfi44FDh7jjthCexuUKjauqeEYRE2P9GYWfH5c97tzp2LG1smUL2/S0F9w05+/PVg1HjnCTWiHswWUyONbasGEDTp8+jdOnTyOpVZtyF5ltc2v5+W03DbUkNJT7+ZWVSWM74XlcrtC4rIxBTq9ett0uNJTV8ba25O4ig4E1N2Fh1p0w+fnxIe3cyQbMsvm56C63y+DcddddUFW13YvoPrPZ9ttoDVTlKRCeyOW6GZvNXQtSdDqmZ3vojXrkCOvUbKmpiY1lJvjgQceNS3gPt8vgCMeKjmY9oi1qalhca2vmRwh30Lgflas0+wsO5pxOXZ1tb7raWqZIeqhmsbiY8ZQthffaSswSF4klPUJNDft6VFXxuY+M5AoSL1j26vmPUNgkLY0p4vp6poytUVDAjUY729hYCHfV2M3YVaaooqL4htu1y/p+BGYzD3A9uEzcZOr6bbV94kQ3XLrEzfe2beNSNe0J8fNj86GZM4H0dI8+M5UAR7Qwbhz38MvLs26BhsHAYGj69B47MRSiR7lckbGiAFOnAt99x6xMoBW9eS5dYjA0YYLjx/ejoCDOhpnN1n82aNf39MahDnfmDLB8OZsORUYyoNFaztfUsA/I66+zAvzeez22cZcckkQLwcHA1Vfzc/PSpc6vazQy8zl6NDB+fM+MT4ie5nJFxgAwahT7Mpw6xTdiZyoq2NV45kwgLq5HhgcAQ4bw2FpcbP1tKiq4Ke7QoY4bl8fLzeV6+3PnmOlLSWm5n462Nj81lctmV6xgds8DSYAj2rjqKuD66zkPfuoUA/7mTCautjp+nO+fn/1MzriE53K5ImOAB6ylS4ExY/hGLChou0Kgvp5VvufPA1deCdx0U48OMS6OCaOCAuvrmnNzuS1M//6OHZtH+/JLBjdDhnS+I3JAANfvZ2RwKssDyRSVaEOnA26+masfNmxgttNo5Pe1z9DYWOCGG4Brr+VZmhCeyuWKjDUxMcCjjwKffgrs3s1lS76+PKjV13MqKykJWLgQmDfP+qI6O7r8cnYwPnuWQUtnC79ycniiNGeO9zQMtbuCAgYsCQmdBzeagACm7bduBa64wimvEUeSAEe0S1FYVzNlCjfVPHWKWUx/f56ZjRsngY3wDi45RaUJDwfuvhuYP5+RRE4O55dDQoCBAzmN5cT06sCB3Iz3zTeZaEpJ4dCaq6nhsP39uSfomDFOGapnOHiQqfeRI62/TUICa3KOH+fUpweRAEd0yteXNTajRzt7JEI4h0tOUbUWHQ3Mnu3sUbRr8mQmCT79lLtF1NVx6bii8P9+fiwHmT8fmDjR2aN1c0VFTLXbkgILDGTGr9QFA/hukgBHCCE60XyKSlVVKDJ/YrORI4Fhw7goYf9+HofNZq54HzeOdTde0JbF8brSqRVgQNSddf0uSl5SQgjRCS2DY1JNqDJWIVQf6uQRuScfHwY5w4Y5eyQeLDTU9iBHazrkgf1wZBWVEEJ0ItAvEHoftuN16WkqIYYPZ81VZaX1t7l4katGPHBtvgQ4QghhgUsXGguhGTiQy8MvXLDu+mYz5wunTuWuqB5GAhwhhLDALQqNhdDp2Kk1IIBL0zqjqqz6TknhklkPJAGOEEJY4LK9cIRobdw4rrdvaODS79ZdilWVK6YyM9lL6Wc/4/48HkiKjIUQwgKX23BTiM5ceSUQEQF89RWzNLW1XI+vqgx8wsOBadOABQs8um20BDhCCGGBVoMjU1TCbaSlsdHjyZPM1pSVcSlbTAy/n5Li8S2jJcARQggLZIpKuCUvX5svNThCCGGBFBkL4X4kwBFCCAsaMzhSgyOE25AARwghLJAiYyHcjwQ4QghhgRQZC+F+JMARQggLpMhYCPcjAY4QQlggRcZCuB8JcIQQwgJtiqrcUA6T2eTk0QghrCEBjhBCWKBNUQEMcoQQrk8CHCGEsMDPxw8h/iEAZJpKCHchAY4QQlhBCo2FcC8S4AghhBWk0FgI9yIBjhBCWEErNJZmf0K4BwlwhBDCCo3djGWKSgi3ILuJC+FlzGagro7/BgZyw2FhmVaDI1NUQrgHCXCE8BLFxcC+fcDWrUBpKaCqQEAAMGUKMGkS0KcPoCjOHqXrkg03hXAvEuAI4eFUFdiwAVizBigsBIKDgbAwBjNVVcAHHwDr1gHTpgG3386sjmhLioyFcC8S4AjhwVQV+PJL4P33gaAgYMQIQNeq8i4pCSgpAb76CqiuBu67D9DrnTNeVyZFxkK4FykyFsKDZWYCH38MhIcDycltgxuAmZzoaKB/f05frVvX8+N0B1JkLIR7kQBHCA+2bRtQUwPEx1u+bkgIA6HNm3kb0ZIUGQvhXiTAEcJD5eYCBw8CCQnW3yYhAcjLAw4ccNy43JVMUQnhXiTAEcJDZWcDZWVAVJT1t/Hz4/Lx8+cdNiy3JUXGQrgXKTIWwkMZDKy5sXXpt68vi41FS9oUVU19DYwmI/x9/J08IgfIywP27+e/RiOX2w0bBowaJZXnwu1IgCOEh/L35yoqVbUtyGlo4FJy0VJ4QDgUKFChorS2FHEhcc4ekv0UFACffMK5yZISvnh0OqC+Hli7FkhJAebMAS6/vP1KdSFckAQ4QnSgvJwns+fPA7W1XGbdvz8wbhwLcl1dUhIQGsqmftZOUzU0MBhKTnbs2NyRTtEhIiACpXWlKKkt8ZwAJzsbeOUV4NQpoHdvZmuaR8QGAwu63ngDuHgRuO02CXKEW3DLAGf+/Pk4dOgQCgsLERkZiSuvvBL/+7//i8TERGcPTXiA6mrgiy+A7dv5ea7TcTsD7eCfkADMmAFce61rZ+2Tk4HRo4Fdu6wPcAoKgMREBnGircjASJTWlXpOoXF5ObB8OXD2LJsktbdvh17PyL64mG+M6Ghg7tyeH6sQNnLLMHzWrFn48MMPcfLkSXzyySc4c+YMbrzxRmcPS3iAigrg5ZeBDz9kQDNsGDB8ODBkCD//Bw1iAPTee8DrrzOz46oUBZg+nbMNhYWWr19Tw9mJadPcI0PlDB5XaLx3L3DiBF/gljYli45mGnPdOinSEm7BLTM4jz/+eOP/+/Tpg1/+8pdYuHAh6uvr4efn58SRCXdmMgFvvw3s2QMMHsx9mlrz82NmJCqKTfFCQoB77nHdPZzGjgUWLAA++ogBW0JC+2MtL+dU3JQpzEyJ9jXuR+UJzf4aGvgiDgxkZbk1EhOBH35g/4GpUx07PiG6yS0DnOZKSkrw7rvvYsqUKZ0GNwaDAQaDofHrioqKnhiecCMnTvCEtn//9oOb5oKDWa6wYwdw5ZXcqNIVKQpwww2cZfjiC3Y2Dg9nbY6iMANVXMxj3BVXAIsXy15UnWnsZuwJU1RZWbzY0ijJz48vHAlwhBtwyykqAPjFL36B4OBgREdHIzs7G59//nmn11+2bBnCw8MbL8lSRSla2bWLK2NDQ627flQUMx979zp2XN3l4wPMnw88+ywDmOhoTkdVVfHEff584Le/BR54QKamLPGobsY1NSwgthTNtxYQwAZLQrg4lwlw/vCHP0BRlE4vGRkZjdd/6qmncPDgQWzYsAE+Pj648847oapqh/f/9NNPo7y8vPGSk5PTEw9LuInqaq6Yiomx/jaKAkREMDAymx02NLtJTOR01bJlwF//Cvz5z8CLLzLoGTxYFsZYo7GbsSdMUWlNkjr53GyXqlo/pSWEE7nMq/Thhx/Grbfe2ul1+vbt2/j/mJgYxMTEYNCgQRg6dCiSk5Oxe/dupKent3tbvV4PvSsveRFOVVPD7E14uG23CwzkNI/B4D5TO35+DMyE7Txqiioqiim7igrb2l3X1to2rSWEk7hMgKMFLF2hZW6a19gIYYuunsyazbydZD+8g0dNUSUksI/Ajh3WBzhVVSzomjjRsWMTwg7c7mN57969eOWVV3Do0CGcP38emzdvxu23344BAwZ0mL0RwpLQUHalt7X2vKIC6NWLS7GF5/OoDI6isFDY15fFZJaoKpfaDR7MixAuzu0CnMDAQHz66ae44oorMHjwYNxzzz0YMWIEtm7dKlNQosv8/dkzprTU+iyOyQTU1bHpn6suExf2pdXgeEQGB2AGZ+ZMBi6dRfeqyuXhMTHAzTdb7pkjhAtwmSkqa40cORKbNm1y9jCEB5o4EfjqKyA/nwW5luTkAPHxwPjxjh+bcA0e1QcHYPbmzjsZwGzezC0ZEhOZzlQU9sopKGAHyMREYOlSNgUUwg24XYAjhKP07g1cdx27FPv6ArGxHV83N5f7EN54oxTsepPmU1SqqkLxhNRdYCCwZAmzOdu2AUePcn8qReElLg645RZOZ/Xu7ezRCmE1CXCEaObaaxm4fPYZcOkSMzRRUfycN5uBoiJuexAaCixaxOy+8B7aFJXRZERNfQ2C/T1k23U/PyA9HZg8GTh3ji/0hgYGP6mp1jeHEsKFSIAjRDM+PsBPfsLP9B07gAMHgLy8phqbyEhg9mzu1zR0qHPHKnpesF8w/HR+qDfXo7Su1HMCHI2isJV3//7OHokQ3SYBjhCtKAowZgwvubnAhQvskaPXA337dj51JTyboiiIDIxEYXUhSmpLkBSW5OwhCSE6IAGOEJ3o3VvKDkRLkQEMcDym0FgID+V2y8SFEMKZPKoXjhAeTAIcIYSwgcf1whHCQ0mAI4QQNmjM4MgUlRAuTQIcIYSwgUftRyWEB5MARwghbNDYzVhqcIRwaRLgCCGEDaTIWAj3IAGOEELYQIqMhXAPEuAIIYQNpMhYCPcgAY4QQthAioyFcA8S4AghhA20KSqpwRHCtUmAI4QQNtCmqMrqymBWzU4ejRCiIxLgCCGEDbQpKrNqRoWhwsmjEUJ0RAIcIYSwgd5XjyC/IABSaCyEK5MARwghbCSFxkK4PglwhBDCRlJoLITrkwBHCCFsJL1whHB9EuAIIYSNZIpKCNcnAY4QQthI9qMSwvVJgCOEEDaSDI4Qrk8CHCGEsFFjkbHU4AjhsiTAEUIIG8kUlRCuTwIcIYSwkUxRCeH6fJ09AGdRVRUAUFEhrdaFELbRN+iBOqCotEg+Q4ToYdp7TjuOd0RRLV3DQ124cAHJycnOHoYQQgghuiAnJwdJSUkd/txrAxyz2Yy8vDyEhoZCURQAjAqTk5ORk5ODsLAwJ4/Q/uTxuTd5fO5NHp97k8fnOlRVRWVlJRITE6HTdVxp47VTVDqdrsPILywszOWf4O6Qx+fe5PG5N3l87k0en2sIDw+3eB0pMhZCCCGEx5EARwghhBAeRwKcZvR6PX7/+99Dr9c7eygOIY/Pvcnjc2/y+NybPD7347VFxkIIIYTwXJLBEUIIIYTHkQBHCCGEEB5HAhwhhBBCeBwJcIQQQgjhcSTAscBgMGDMmDFQFAWHDh1y9nDsZv78+UhJSUFAQAASEhKwaNEi5OXlOXtYdpGVlYUlS5agX79+CAwMxIABA/D73/8eRqPR2UOzmxdeeAFTpkxBUFAQIiIinD2cbnv11VfRr18/BAQEYPz48di+fbuzh2Q327Ztw3XXXYfExEQoioLPPvvM2UOym2XLlmHChAkIDQ1FbGwsFi5ciJMnTzp7WHbz2muvYdSoUY3N79LT07Fu3TpnD8thli1bBkVR8Nhjjzl7KHYhAY4FP//5z5GYmOjsYdjdrFmz8OGHH+LkyZP45JNPcObMGdx4443OHpZdnDhxAmazGcuXL8fRo0fx97//Ha+//jp+9atfOXtodmM0GnHTTTfhgQcecPZQuu2DDz7AY489hl//+tc4ePAgpk2bhnnz5iE7O9vZQ7OL6upqjB49Gq+88oqzh2J3W7duxUMPPYTdu3dj48aNaGhowOzZs1FdXe3sodlFUlIS/vSnPyEjIwMZGRm4/PLLsWDBAhw9etTZQ7O7ffv2YcWKFRg1apSzh2I/qujQ2rVr1SFDhqhHjx5VAagHDx509pAc5vPPP1cVRVGNRqOzh+IQf/7zn9V+/fo5exh2t3LlSjU8PNzZw+iWiRMnqvfff3+L7w0ZMkT95S9/6aQROQ4Adc2aNc4ehsMUFhaqANStW7c6eygOExkZqb755pvOHoZdVVZWqgMHDlQ3btyozpgxQ3300UedPSS7kAxOBy5evIh7770X//73vxEUFOTs4ThUSUkJ3n33XUyZMgV+fn7OHo5DlJeXIyoqytnDEK0YjUbs378fs2fPbvH92bNnY9euXU4aleiq8vJyAPDI95rJZMLq1atRXV2N9PR0Zw/Hrh566CFcc801uPLKK509FLuSAKcdqqrirrvuwv3334+0tDRnD8dhfvGLXyA4OBjR0dHIzs7G559/7uwhOcSZM2fw8ssv4/7773f2UEQrRUVFMJlMiIuLa/H9uLg4FBQUOGlUoitUVcUTTzyBqVOnYsSIEc4ejt1kZmYiJCQEer0e999/P9asWYNhw4Y5e1h2s3r1ahw4cADLli1z9lDszqsCnD/84Q9QFKXTS0ZGBl5++WVUVFTg6aefdvaQbWLt49M89dRTOHjwIDZs2AAfHx/ceeedUF24sbWtjw8A8vLyMHfuXNx0001YunSpk0Zuna48Pk+hKEqLr1VVbfM94doefvhhHD58GO+//76zh2JXgwcPxqFDh7B792488MADWLx4MY4dO+bsYdlFTk4OHn30UfznP/9BQECAs4djd161VUNRURGKioo6vU7fvn1x66234ssvv2zxAWsymeDj44Of/vSn+Ne//uXooXaJtY+vvRfyhQsXkJycjF27drls+tXWx5eXl4dZs2Zh0qRJWLVqFXQ6147nu/L8rVq1Co899hjKysocPDrHMBqNCAoKwkcffYTrr7++8fuPPvooDh06hK1btzpxdPanKArWrFmDhQsXOnsodvXII4/gs88+w7Zt29CvXz9nD8ehrrzySgwYMADLly939lC67bPPPsP1118PHx+fxu+ZTCYoigKdTgeDwdDiZ+7G19kD6EkxMTGIiYmxeL3/+7//w/PPP9/4dV5eHubMmYMPPvgAkyZNcuQQu8Xax9ceLc41GAz2HJJd2fL4cnNzMWvWLIwfPx4rV650+eAG6N7z5678/f0xfvx4bNy4sUWAs3HjRixYsMCJIxPWUFUVjzzyCNasWYMtW7Z4fHAD8DG78uekLa644gpkZma2+N7dd9+NIUOG4Be/+IVbBzeAlwU41kpJSWnxdUhICABgwIABSEpKcsaQ7Grv3r3Yu3cvpk6disjISJw9exa/+93vMGDAAJfN3tgiLy8PM2fOREpKCv7617/i0qVLjT+Lj4934sjsJzs7GyUlJcjOzobJZGrs0ZSamtr4enUXTzzxBBYtWoS0tDSkp6djxYoVyM7O9piaqaqqKpw+fbrx63PnzuHQoUOIiopq81njbh566CG89957+PzzzxEaGtpYNxUeHo7AwEAnj677fvWrX2HevHlITk5GZWUlVq9ejS1btmD9+vXOHppdhIaGtqmX0uoyPaKOymnrt9zIuXPnPGqZ+OHDh9VZs2apUVFRql6vV/v27avef//96oULF5w9NLtYuXKlCqDdi6dYvHhxu49v8+bNzh5al/zzn/9U+/Tpo/r7+6vjxo3zqGXGmzdvbve5Wrx4sbOH1m0dvc9Wrlzp7KHZxT333NP4uuzVq5d6xRVXqBs2bHD2sBzKk5aJe1UNjhBCCCG8g+sXJgghhBBC2EgCHCGEEEJ4HAlwhBBCCOFxJMARQgghhMeRAEcIIYQQHkcCHCGEEEJ4HAlwhBBCCOFxJMARQgghhMeRAEcIIYQQHkcCHCGEEEJ4HAlwhPByM2fOhKIoUBSlcdNOa9x1112Nt/vss88cNr7i4mLExsYiKyur8XszZ87EY489hqysLJvHbQ3t/u15P63v016/o7Ubb7wRL774ot3vVwh3IwGOEAL33nsv8vPz2+wg/Oqrr6Jfv34ICAjA+PHjsX379safvfTSS8jPz3f42JYtW4brrrsOffv2bfzep59+iueeew7Jycntjru7tPvX2CMYaX2fXbFt2zZcd911SExM7DCw/N3vfocXXngBFRUV3fpdQrg7CXCEEAgKCkJ8fDx8fX0bv/fBBx/gsccew69//WscPHgQ06ZNw7x585CdnQ0ACA8PR3x8vEPHVVtbi7feegtLly5t8f2oqCiEhobCx8enzbjtQbt/V7vP6upqjB49Gq+88kqH1xk1ahT69u2Ld999t1u/Swh3JwGOEB7o/fffR0BAAHJzcxu/t3TpUowaNQrl5eVW3ceLL76IJUuWYOnSpRg6dCj+8Y9/IDk5Ga+99pqjht3GunXr4Ovri/T09MbvGQwG/M///A9iY2MREBCAqVOnYt++fY0/16atWl9mzpwJgNmY//mf/8HPf/5zREVFIT4+Hn/4wx9a/N7mGZu77roLW7duxUsvvdR4X1lZWVBVFX/+85/Rv39/BAYGYvTo0fj44487fCyWskDr169HeHg43nnnnQ6vM2/ePDz//PO44YYbOv6jAZg/fz7ef//9Tq8jhKeTAEcID3Trrbdi8ODBWLZsGQDgmWeewddff41169YhPDzc4u2NRiP279+P2bNnt/j+7NmzsWvXLoeMuT3btm1DWlpai+/9/Oc/xyeffIJ//etfOHDgAFJTUzFnzhyUlJQAQOO0lXY5ePAgoqOjMX369Mb7+Ne//oXg4GDs2bMHf/7zn/Hss89i48aN7Y7hpZdeQnp6euM0Xn5+PpKTk/Gb3/wGK1euxGuvvYajR4/i8ccfxx133IGtW7fa/DhXr16Nm2++Ge+88w7uvPNOm2/f2sSJE7F3714YDIZu35cQ7sq+eV0hhEtQFAUvvPACbrzxRiQmJuKll17C9u3b0bt3bwDAN998g8zMTDz++OPt3r6oqAgmkwlxcXEtvh8XF4eCggKHj1+TlZWFxMTExq+rq6vx2muvYdWqVZg3bx4A4I033sDGjRvx1ltv4amnnmqctgKAuro6LFy4EOnp6S2yNKNGjcLvf/97AMDAgQPxyiuv4Ntvv8VVV13VZgzh4eHw9/dvnMbTxvHiiy9i06ZNjdml/v37Y8eOHVi+fDlmzJhh9WN89dVX8atf/Qqff/45Zs2aZdsfqAO9e/eGwWBAQUEB+vTpY5f7FMLdSIAjhIe69tprMWzYMDzzzDPYsGEDhg8f3vizK6+8EldeeaXF+1AUpcXXqqq2+V5n3n33Xdx3332NX69btw7Z2dltvjdt2rR2b19bW4uAgIDGr8+cOYP6+npcdtlljd/z8/PDxIkTcfz48Ta3X7JkCSorK7Fx40bodE0J61GjRrW4XkJCAgoLC61+XMeOHUNdXV2bgMhoNGLs2LFW388nn3yCixcvYseOHZg4caLVt7MkMDAQAFBTU2O3+xTC3UiAI4SH+vrrr3HixIl2MzHz5s3Diy++iKFDh7Z725iYGPj4+LTJ1hQWFra5r87Mnz8fkyZNavy6d+/eGDNmTJvvdSQmJgalpaWNX6uqCsC6wOv555/H+vXrsXfv3jbFvX5+fi2+VhQFZrPZykeFxut+9dVXbcav1+utvp8xY8bgwIEDWLlyJSZMmGBT8NgZbbquV69edrk/IdyR1OAI4YEOHDiAm266CcuXL8ecOXPw29/+tsXPT506hYEDB3Z4e39/f4wfP75NXcrGjRsxZcoUq8cRGhqK1NTUxktgYGC73+vI2LFjcezYscavU1NT4e/vjx07djR+r76+HhkZGS2CtU8++QTPPvssPvzwQwwYMMDq8XbE398fJpOp8ethw4ZBr9cjOzu7xWNJTU1FcnKy1fc7YMAAbN68GZ9//jkeeeSRbo9Tc+TIESQlJSEmJsZu9ymEu5EMjhAeJisrC9dccw1++ctfYtGiRRg2bBgmTJiA/fv3Y/z48SgvL0dISIjFpdVPPPEEFi1ahLS0NKSnp2PFihXIzs7G/fff30OPBJgzZw6efvpplJaWIjIyEsHBwXjggQfw1FNPISoqCikpKfjzn/+MmpoaLFmyBAAP7nfeeSd+8YtfYPjw4Y1ZKH9/f0RFRXVpHH379sWePXuQlZWFkJAQREVF4cknn8Tjjz8Os9mMqVOnoqKiArt27UJISAgWL15s9X0PGjQImzdvxsyZM+Hr64t//OMfHV63qqoKp0+fbvz63LlzOHToUOPfQrN9+/Y2BeJCeBsJcITwICUlJZg3bx7mz5+PX/3qVwCA8ePH47rrrsOvf/1rrF+/HkeOHGlRj9ORW265BcXFxXj22Wcbm+mtXbu2R4tWR44cibS0NHz44YeNdTt/+tOfYDabsWjRIlRWViItLQ1ff/01IiMjAQAZGRmoqanB888/j+eff77xvmbMmIEtW7Z0aRxPPvkkFi9ejGHDhqG2thbnzp3Dc889h9jYWCxbtgxnz55FREQExo0b1/h3t8XgwYOxadMmzJw5Ez4+Pvjb3/7W7vUyMjJaFCI/8cQTAIDFixdj1apVAFhYvWbNGnz99de2P1AhPIiiapPaQgivsHz5cpSUlODpp58GwP4sY8aM6TRz0BlFUbBmzRosXLjQfoNsZu3atXjyySdx5MiRFoXCon3//Oc/8fnnn2PDhg3OHooQTiWfFkJ4maNHj7a7JUNISAgyMzOtvp/7778fISEh9h5eG1dffTXuu+++Fk0LRcf8/Pzw8ssvO3sYQjidZHCE8HK5ubmora0FAKSkpMDf39+q2xUWFjbud5SQkIDg4GCHjVEIIWwlAY4QQgghPI5MUQkhhBDC40iAI4QQQgiPIwGOEEIIITyOBDhCCCGE8DgS4AghhBDC40iAI4QQQgiPIwGOEEIIITyOBDhCCCGE8DgS4AghhBDC4/x/ie23ApmezmcAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_dataset(train_x,train_labels,model.layers[0].weights[0],model.layers[0].weights[1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dvAiaj_JndyP"
+ },
+ "source": [
+ "## Eğitim grafiklerini çizme\n",
+ "\n",
+ "`fit` işlevi, sonuç olarak, her dönemdeki kaybı ve metrikleri gözlemlemek için kullanılabilen `history` (tarih) nesnesini döndürür. Aşağıdaki örnekte küçük bir öğrenme oranı ile eğitime yeniden başlayacağız ve kayıp ve doğruluğun nasıl davrandığını gözlemleyeceğiz.\n",
+ "\n",
+ "> `Sequential` modeli tanımlamak için biraz farklı sözdizimi kullandığımızı **unutmayın**. Katmanları tek tek eklemek (`add`) yerine, ilk etapta modeli oluştururken katmanların listesini de belirtebiliriz - bu biraz daha kısa sözdizimidir ve onu kullanmayı tercih edebilirsiniz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:15.640677: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "70/70 [==============================] - 1s 7ms/step - loss: 0.7444 - acc: 0.3714 - val_loss: 0.7277 - val_acc: 0.3667\n",
+ "Epoch 2/10\n",
+ "11/70 [===>..........................] - ETA: 0s - loss: 0.6793 - acc: 0.6364"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:16.060911: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.7116 - acc: 0.4714 - val_loss: 0.7075 - val_acc: 0.4333\n",
+ "Epoch 3/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.6860 - acc: 0.5143 - val_loss: 0.6813 - val_acc: 0.5000\n",
+ "Epoch 4/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.6644 - acc: 0.5857 - val_loss: 0.6614 - val_acc: 0.6000\n",
+ "Epoch 5/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.6436 - acc: 0.7286 - val_loss: 0.6447 - val_acc: 0.7333\n",
+ "Epoch 6/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.6234 - acc: 0.7286 - val_loss: 0.6256 - val_acc: 0.8000\n",
+ "Epoch 7/10\n",
+ "70/70 [==============================] - 0s 5ms/step - loss: 0.6050 - acc: 0.8286 - val_loss: 0.6073 - val_acc: 0.8333\n",
+ "Epoch 8/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.5842 - acc: 0.7571 - val_loss: 0.5868 - val_acc: 0.9000\n",
+ "Epoch 9/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.5720 - acc: 0.9429 - val_loss: 0.5778 - val_acc: 0.8333\n",
+ "Epoch 10/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.5568 - acc: 0.9143 - val_loss: 0.5615 - val_acc: 0.9000\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = keras.models.Sequential([\n",
+ " keras.layers.Dense(1,input_shape=(2,),activation='sigmoid')])\n",
+ "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.05),loss='binary_crossentropy',metrics=['acc'])\n",
+ "hist = model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(hist.history['acc'])\n",
+ "plt.plot(hist.history['val_acc'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Çok Sınıflı Sınıflandırma\n",
+ "\n",
+ "Bir çok sınıflı sınıflandırma problemini çözmeniz gerekiyorsa, ağınızın birden fazla çıktısı olacaktır - ki $C$ sınıflarının sayısına karşılık gelir. Her çıktı belirli bir sınıfın olasılığını içerecektir.\n",
+ "\n",
+ "> Aynı şekilde ikili sınıflandırma gerçekleştirmek için iki çıktılı bir ağ da kullanabileceğinizi unutmayın. Şimdi tam olarak bunu göstereceğiz.\n",
+ "\n",
+ "Bir ağdan bir $p_1,\\dots, p_C$ olasılıkları kümesi çıktılamasını beklediğinizde, hepsinin toplamının 1'e eşit olmasına ihtiyacımız var. Bunu sağlamak için, son katmanda son etkinleştirme fonksiyonu olarak `softmax` kullanıyoruz. **Softmaks** bir vektör girdisi alır ve bu vektörün tüm bileşenlerinin olasılıklara dönüştürülmesini sağlar.\n",
+ "\n",
+ "Ayrıca, ağın çıktısı $C$ boyutlu bir vektör olduğundan, aynı forma sahip etiketlere ihtiyacımız var. Bu, $i$ sınıfının değeri, $i$. konumda 1 ile sıfırlardan oluşan bir vektöre dönüştürüldüğünde **bire bir kodlama** kullanılarak gerçekleştirilebilir.\n",
+ "\n",
+ "Sinir ağının olasılık çıktısını beklenen bire bir kodlanmış etiketle karşılaştırmak için **çapraz entropi kaybı** işlevini kullanırız. İki olasılık dağılımı alır ve ne kadar farklı olduklarının bir değerini verir.\n",
+ "\n",
+ "O halde $C$ sınıfı olan çok sınıflı sınıflandırma için yapmamız gerekenleri özetlemek gerekirse:\n",
+ "* Ağın son katmanında $C$ adet nöronları olmalıdır.\n",
+ "* Son etkinleştirme işlevi **softmaks** olmalıdır.\n",
+ "* Kayıp, **çapraz entropi kaybı** olmalıdır.\n",
+ "* Etiketler **bire bir kodlamaya** dönüştürülmelidir (bu, `numpy` kullanılarak veya Keras utils `to_categorical` kullanılarak yapılabilir)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:20.200324: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "70/70 [==============================] - 2s 11ms/step - loss: 0.8018 - acc: 0.5429 - val_loss: 0.7117 - val_acc: 0.4000\n",
+ "Epoch 2/10\n",
+ " 1/70 [..............................] - ETA: 0s - loss: 0.6854 - acc: 1.0000"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:21.829635: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.7058 - acc: 0.4286 - val_loss: 0.6920 - val_acc: 0.5333\n",
+ "Epoch 3/10\n",
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.6942 - acc: 0.5429 - val_loss: 0.6883 - val_acc: 0.5000\n",
+ "Epoch 4/10\n",
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.6890 - acc: 0.5286 - val_loss: 0.6836 - val_acc: 0.4667\n",
+ "Epoch 5/10\n",
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.6815 - acc: 0.5286 - val_loss: 0.6752 - val_acc: 0.5667\n",
+ "Epoch 6/10\n",
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.6754 - acc: 0.5429 - val_loss: 0.6652 - val_acc: 0.4667\n",
+ "Epoch 7/10\n",
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.6585 - acc: 0.6714 - val_loss: 0.6493 - val_acc: 0.4667\n",
+ "Epoch 8/10\n",
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.6419 - acc: 0.6286 - val_loss: 0.6240 - val_acc: 0.7667\n",
+ "Epoch 9/10\n",
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.6024 - acc: 0.7571 - val_loss: 0.5896 - val_acc: 0.8000\n",
+ "Epoch 10/10\n",
+ "70/70 [==============================] - 1s 10ms/step - loss: 0.5626 - acc: 0.8143 - val_loss: 0.5576 - val_acc: 0.8000\n"
+ ]
+ }
+ ],
+ "source": [
+ "model = keras.models.Sequential([\n",
+ " keras.layers.Dense(5,input_shape=(2,),activation='relu'),\n",
+ " keras.layers.Dense(2,activation='softmax')\n",
+ "])\n",
+ "model.compile(keras.optimizers.Adam(0.01),'categorical_crossentropy',['acc'])\n",
+ "\n",
+ "# Bire bir kodlamaya dönüştürmenin iki yolu\n",
+ "train_labels_onehot = keras.utils.to_categorical(train_labels)\n",
+ "test_labels_onehot = np.eye(2)[test_labels]\n",
+ "\n",
+ "hist = model.fit(x=train_x_norm,y=train_labels_onehot,\n",
+ " validation_data=[test_x_norm,test_labels_onehot],batch_size=1,epochs=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Seyrek Kategorik Çapraz Entropi\n",
+ "\n",
+ "Çok sınıflı sınıflandırmadaki etiketler genellikle sınıf numaralarıyla temsil edilir. Keras ayrıca, sınıf numarasının bire bir vektörler değil, tamsayılar olmasını bekleyen **seyrek kategorik çapraz entropi** adı verilen başka bir tür kayıp işlevini de destekler. Bu tür bir kayıp fonksiyonunu kullanarak eğitim kodumuzu basitleştirebiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ " 1/70 [..............................] - ETA: 18s - loss: 0.5918 - acc: 1.0000"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:28.237102: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "70/70 [==============================] - 1s 8ms/step - loss: 0.5351 - acc: 0.8286 - val_loss: 0.5325 - val_acc: 0.7667\n",
+ "Epoch 2/10\n",
+ "12/70 [====>.........................] - ETA: 0s - loss: 0.3990 - acc: 0.8333"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 19:39:28.847968: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "70/70 [==============================] - 0s 7ms/step - loss: 0.4780 - acc: 0.8143 - val_loss: 0.4772 - val_acc: 0.8333\n",
+ "Epoch 3/10\n",
+ "70/70 [==============================] - 0s 7ms/step - loss: 0.4411 - acc: 0.8571 - val_loss: 0.4366 - val_acc: 0.9000\n",
+ "Epoch 4/10\n",
+ "70/70 [==============================] - 0s 7ms/step - loss: 0.4054 - acc: 0.8571 - val_loss: 0.4014 - val_acc: 0.9000\n",
+ "Epoch 5/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.3745 - acc: 0.8714 - val_loss: 0.3893 - val_acc: 0.8667\n",
+ "Epoch 6/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.3534 - acc: 0.8571 - val_loss: 0.3512 - val_acc: 0.9000\n",
+ "Epoch 7/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.3263 - acc: 0.9000 - val_loss: 0.3430 - val_acc: 0.9333\n",
+ "Epoch 8/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.3072 - acc: 0.8857 - val_loss: 0.3165 - val_acc: 0.9333\n",
+ "Epoch 9/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.2857 - acc: 0.8714 - val_loss: 0.2885 - val_acc: 0.9333\n",
+ "Epoch 10/10\n",
+ "70/70 [==============================] - 0s 6ms/step - loss: 0.2774 - acc: 0.8857 - val_loss: 0.2887 - val_acc: 0.9333\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.compile(keras.optimizers.Adam(0.01),'sparse_categorical_crossentropy',['acc'])\n",
+ "model.fit(x=train_x_norm,y=train_labels,validation_data=[test_x_norm,test_labels],batch_size=1,epochs=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Çok Etiketli Sınıflandırma\n",
+ "\n",
+ "> **Note** that this is very similar to using **different neural networks** to do binary classification for each particular class - only the initial part of the network (up to final classification layer) is shared for all classes.\n",
+ "\n",
+ "Bazen nesnelerimizin aynı anda iki sınıfa ait olabileceği durumlar olur. Örnek olarak, resimdeki kediler ve köpekler için bir sınıflandırıcı geliştirmek istediğimizi, ancak hem kedilerin hem de köpeklerin bulunduğu durumlara da izin vermek istediğimizi varsayalım.\n",
+ "\n",
+ "Çok etiketli sınıflandırma ile, bire bir kodlanmış vektör yerine, girdi örneğiyle ilgili tüm sınıflara karşılık gelen 1 konumunda bir vektöre sahip olacağız. Bu nedenle, ağın çıktısı tüm sınıflar için normalleştirilmiş olasılıklara sahip olmamalı, bunun yerine her sınıf için ayrı ayrı olmalıdır - bu, **sigmoid** etkinleştirme fonksiyonunun kullanılmasına karşılık gelir. Çapraz entropi kaybı hala bir kayıp fonksiyonu olarak kullanılabilir.\n",
+ "\n",
+ "> Bunun her bir sınıf için ikili sınıflandırma yapmak için **farklı sinir ağları** kullanmaya çok benzer olduğuna **dikkat edin** - tüm sınıflar için ağın yalnızca ilk kısmı (son sınıflandırma katmanına kadar) paylaşılır."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BmHNhUU8bqEX"
+ },
+ "source": [
+ "## Sınıflandırma Kaybı Fonksiyonlarının Özeti\n",
+ "\n",
+ "Ağın son katmanındaki kayıp fonksiyonu ve etkinleştirme fonksiyonuna göre ikili, çok sınıflı ve çok etiketli sınıflandırmanın farklılık gösterdiğini gördük. Yeni öğrenmeye başlıyorsanız, biraz kafa karıştırıcı olabilir, ancak burada aklınızda bulundurmanız gereken birkaç kural vardır:\n",
+ "* Eğer ağda bir tane çıktılı (**ikili sınıflandırma**) varsa **sigmoid** etkinleştirme işlevini kullanırız, **çok sınıflı sınıflandırma** içinse **softmaks**.\n",
+ "* Çıktı sınıfı bire bir kodlama olarak temsil edilirse, kayıp işlevi **çapraz entropi kaybı** (kategorik çapraz entropi) eğer çıktı sınıf numarası içeriyorsa **seyrek kategorik çapraz entropi** olacaktır. **İkili sınıflandırma** için **ikili çapraz entropi** kullanın (**logaritmik kayıp** ile aynıdır).\n",
+ "* **Çok etiketli sınıflandırma**, aynı anda birkaç sınıfa ait bir nesneye sahip olabileceğimiz zamandır. Bu durumda, etiketleri bire bir kodlama kullanarak kodlamamız ve etkinleştirme fonksiyonu olarak **sigmoid** kullanmamız gerekir, böylece her sınıf olasılığı 0 ile 1 arasında olur.\n",
+ "\n",
+ "| Sınıflandırma | Etiket Formatı | Etkinleştirme Fonksiyonu | Kayıp |\n",
+ "|---------------|-----------------------|-----------------|----------|\n",
+ "| İkili | 1. sınıf olasılığı | sigmoid | ikili çapraz entropi |\n",
+ "| İkili | Bire bir kodlama (2 çıktılı) | softmax | kategorik çapraz entropi |\n",
+ "| Çok sınıflı | Bire bir kodlama | softmaks | kategorik çapraz entropi |\n",
+ "| Çok sınıflı | Sınıf sayısı | softmaks | seyrek kategorik çapraz entropi |\n",
+ "| Çok etiketli | Bire bir kodlama | sigmoid | kategorik çapraz entropi |\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gZ-kWx84bMDH"
+ },
+ "source": [
+ "**Görev**:\n",
+ "MNIST el yazısı rakamları için bir sınıflandırıcı eğitmede Keras'ı kullanın:\n",
+ "* Keras'ın MNIST dahil bazı standart veri kümeleri içerdiğine dikkat edin. MNIST'i Keras'tan kullanmak için yalnızca birkaç satır koda ihtiyacınız vardır (daha fazla bilgi [burada](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist)dır).\n",
+ "* Farklı sayıda katman/nöron, etkinleştirme işlevleriyle birkaç ağ yapılandırması deneyin.\n",
+ "\n",
+ "Ulaşabildiğiniz en iyi doğruluk nedir?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yX6hqiafwHl9"
+ },
+ "source": [
+ "## Ana Fikirler\n",
+ "\n",
+ "* **Keras**, yeni başlayanlar için gerçekten tavsiye edilir, çünkü katmanlardan ağları oldukça kolay bir şekilde oluşturmaya ve ardından sadece birkaç satır kodla eğitmeye olanak tanır.\n",
+ "* Standart olmayan bir mimari gerekiyorsa, Tensorflow'u biraz daha derinlemesine öğrenmeniz gerekir. Veya birinden özel mantığı bir Keras katmanı olarak uygulamasını isteyebilir ve ardından bunu Keras modellerinde kullanabilirsiniz.\n",
+ "* PyTorch'a da bakmak ve yaklaşımları karşılaştırmak iyi bir fikirdir.\n",
+ "\n",
+ "Keras yaratıcısından Keras ve Tensorflow 2.0'nin üzerine güzel bir örnek not defterini [burada](https://t.co/k694J95PI8) bulabilirsiniz."
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Slideshow",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "IntroKerasTF.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ },
+ "livereveal": {
+ "start_slideshow_at": "selected"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroKerasTF.tr.ipynb b/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroKerasTF.tr.ipynb
new file mode 100644
index 00000000..e2191012
--- /dev/null
+++ b/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroKerasTF.tr.ipynb
@@ -0,0 +1,1484 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "En2vX4FuwHlu"
+ },
+ "source": [
+ "## Tensorflow ve Keras'a Giriş\n",
+ "\n",
+ "> Bu not defteri, [Yeni Başlayanlar İçin YZ Müfredatı](http://github.com/microsoft/ai-for-beginners)'nın bir parçasıdır. Eksiksiz öğrenme materyalleri kümesi için kod deposunu ziyaret edin.\n",
+ "\n",
+ "### Sinirsel Çerçeveler\n",
+ "\n",
+ "Sinir ağlarını eğitmek için şunlara ihtiyacınız olduğunu öğrendik:\n",
+ "* Matrisleri hızla çarpmalısınız (tensörler).\n",
+ "* Gradyan inişi optimizasyonunu gerçekleştirmek için gradyanları hesaplamalısınız.\n",
+ "\n",
+ "Sinir ağı çerçevelerinin yapmanıza izin verdikleri şunlardır:\n",
+ "* Kullanılabilir herhangi bir hesaplamada, CPU veya GPU'da ve hatta TPU'da tensörlerle çalışırsınız.\n",
+ "* Gradyanları otomatik olarak hesaplarsınız (tüm yerleşik tensör işlevleri için açıkça programlanmıştır).\n",
+ "\n",
+ "İsteğe bağlı olarak:\n",
+ "* Sinir Ağı kurucusu / daha üst seviye APIler (ağı bir dizi katman olarak tanımlayın).\n",
+ "* Basit eğitim işlevleri (Scikit Learn'de olduğu gibi `fit`)\n",
+ "* Gradyan inişine ek olarak bir dizi optimizasyon (eniyileme) algoritması.\n",
+ "* Veri işleme soyutlamaları (bu ideal olarak GPU'da da çalışacaktır)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8cACQoFMwHl3"
+ },
+ "source": [
+ "### En Popüler Çerçeveler\n",
+ "\n",
+ "* Tensorflow 1.x - yaygın olarak kullanılabilen ilk çerçevedir (Google). Statik hesaplama çizgesini tanımlamaya, GPU'ya göndermeye ve açıkça değerlendirmeye izin verir.\n",
+ "* PyTorch - Facebook'tan popülaritesi artan bir çerçevedir.\n",
+ "* Keras - sinir ağlarını kullanarak birleştirmek ve basitleştirmek için Tensorflow/PyTorch'un üstünde daha üst seviye API'dir (Francois Chollet).\n",
+ "* Tensorflow 2.x + Keras - **dinamik hesaplama çizgesini** destekleyen ve numpy'ya (ve PyTorch'a) çok benzer tensör işlemlerini gerçekleştirmeye olanak tanıyan tümleşik Keras işlevselliğine sahip Tensorflow'un yeni sürümüdür.\n",
+ "\n",
+ "Tensorflow 2.x ve Keras'ı ele alacağız. Tensorflow'un 2.x.x sürümünün kurulu olduğundan emin olun:\n",
+ "```\n",
+ "pip install tensorflow\n",
+ "```\n",
+ "veya\n",
+ "```\n",
+ "conda install tensorflow\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "xwqVx9-bwHl3",
+ "outputId": "2aa591b4-b647-441f-9c8e-4e0da2d517a0",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2.9.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "print(tf.__version__)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6tp2xGV7wHl4"
+ },
+ "source": [
+ "## Temel Kavramlar: Tensör\n",
+ "\n",
+ "**Tensör** çok boyutlu bir dizilimdir. Farklı veri türlerini temsil etmek için tensörleri kullanmak çok uygundur:\n",
+ "* 400x400 - siyah beyaz resim\n",
+ "* 400x400x3 - renkli resim\n",
+ "* 16x400x400x3 - 16 renkli resimden oluşan minigrup\n",
+ "* 25x400x400x3 - 25 fps'lik videonun bir saniyesi\n",
+ "* 8x25x400x400x3 - 8 adet 1 saniyelik videodan oluşan minigrup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qG2bsaR7wHl4"
+ },
+ "source": [
+ "### Basit Tensörler\n",
+ "\n",
+ "Np dizilimleri listelerinden kolayca basit tensörler oluşturabilir veya rastgele üretebilirsiniz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ybpnk08HwHl4",
+ "outputId": "fad9ed4a-df82-44a0-84ea-324bc71ea46f"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tf.Tensor(\n",
+ "[[1 2]\n",
+ " [3 4]], shape=(2, 2), dtype=int32)\n",
+ "tf.Tensor(\n",
+ "[[-0.15611048 -0.6395791 0.15967956]\n",
+ " [ 1.2029059 0.6616139 1.4267689 ]\n",
+ " [-0.3284896 0.43672192 -0.30175865]\n",
+ " [ 0.0704657 -0.01082341 -1.0820643 ]\n",
+ " [-0.1884 -0.22958039 1.9114271 ]\n",
+ " [-0.02439586 -1.553597 -1.7745456 ]\n",
+ " [-0.05847295 -0.6536034 -0.19704701]\n",
+ " [ 0.12037303 -1.0853446 -0.94284475]\n",
+ " [-0.513617 -0.0538025 -0.580708 ]\n",
+ " [ 0.9643102 -1.971696 -0.7634352 ]], shape=(10, 3), dtype=float32)\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = tf.constant([[1,2],[3,4]])\n",
+ "print(a)\n",
+ "a = tf.random.normal(shape=(10,3))\n",
+ "print(a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AXFMsV3r09Ux"
+ },
+ "source": [
+ "Numpy'de olduğu gibi eleman bazında gerçekleştirilen tensörlerde aritmetik işlemleri kullanabilirsiniz. Gerekirse, tensörler otomatik olarak gerekli boyuta genişletilir. Tensörden numpy-dizilimini çıkarmak için `.numpy()` kullanın:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "e5Nu5Xgj1DnQ",
+ "outputId": "0dfc8758-4ffd-4968-c7bf-6ba8d435df2e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tf.Tensor(\n",
+ "[[ 0. 0. 0. ]\n",
+ " [ 1.3590164 1.301193 1.2670894 ]\n",
+ " [-0.17237912 1.0763011 -0.4614382 ]\n",
+ " [ 0.22657618 0.6287557 -1.2417438 ]\n",
+ " [-0.03228952 0.40999871 1.7517476 ]\n",
+ " [ 0.13171463 -0.91401786 -1.9342251 ]\n",
+ " [ 0.09763753 -0.01402426 -0.3567266 ]\n",
+ " [ 0.2764835 -0.44576544 -1.1025243 ]\n",
+ " [-0.3575065 0.5857766 -0.74038756]\n",
+ " [ 1.1204207 -1.3321168 -0.9231148 ]], shape=(10, 3), dtype=float32)\n",
+ "[0.85546464 0.52751434 1.1731348 ]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(a-a[0])\n",
+ "print(tf.exp(a)[0].numpy())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uQ5zN6cVyrG7"
+ },
+ "source": [
+ "## Değişkenler\n",
+ "\n",
+ "Değişkenler, `assign` (\"atama\") ve `assign_add` (\"atama_topla\") kullanılarak değiştirilebilen tensör değerlerini temsil etmek için kullanışlıdır. Genellikle sinir ağı ağırlıklarını temsil etmek için kullanılırlar.\n",
+ "\n",
+ "Örnek olarak, `a` tensörünün tüm satırlarının toplamını almanın aptalca bir yolunu görelim:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7pu0UZ-_yqfB",
+ "outputId": "6708c83e-02e6-4442-8757-45918eb1fbc2"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "s = tf.Variable(tf.zeros_like(a[0]))\n",
+ "for i in a:\n",
+ " s.assign_add(i)\n",
+ "\n",
+ "print(s)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rIh1EHcezlNo"
+ },
+ "source": [
+ "Bunu yapmanın çok daha iyi bir yolu:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aQIdWZ1kzn6P",
+ "outputId": "1c123d9a-ecd2-4f2e-828e-5ade85ac8f63"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tf.reduce_sum(a,axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U-auwezDwHl6"
+ },
+ "source": [
+ "## Gradyanları Hesaplama\n",
+ "\n",
+ "Geri yayma için gradyanları hesaplamanız gerekir. Bu, `tf.GradientTape()` ifadesi kullanılarak yapılır:\n",
+ " * Hesaplamalarımızın çevresine `with tf.GradientTape` bloğunu ekleyin.\n",
+ " * Gradyanları hesaplamamız gereken tensörleri `tape.watch` çağırarak işaretleyin (tüm değişkenler otomatik olarak izlenir).\n",
+ " * İhtiyacımız olan her şeyi hesaplayın (hesaplama çizgesi oluşturun).\n",
+ " * `tape.gradient` kullanarak gradyanları elde edin."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m8vFOXr7wHl6",
+ "outputId": "860ac72e-50c7-4ff2-f258-747f27194f90"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tf.Tensor(\n",
+ "[[-0.8448666 -0.09937061]\n",
+ " [-0.9634435 0.8072742 ]], shape=(2, 2), dtype=float32)\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = tf.random.normal(shape=(2, 2))\n",
+ "b = tf.random.normal(shape=(2, 2))\n",
+ "\n",
+ "with tf.GradientTape() as tape:\n",
+ " tape.watch(a) # `a` öğesine uygulanan işlemlerin geçmişini kaydetmeye başlayın\n",
+ " c = tf.sqrt(tf.square(a) + tf.square(b)) # `a`'yı kullanarak biraz matematik yapın\n",
+ " # `c`'nin `a`'ya göre gradyanı nedir?\n",
+ " dc_da = tape.gradient(c, a)\n",
+ " print(dc_da)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8sfjBMBu59B5"
+ },
+ "source": [
+ "## Örnek 1: Doğrusal Bağlanım\n",
+ "\n",
+ "Artık klasik **doğrusal bağlanım** problemini çözecek kadar bilgimiz var. Küçük bir sentetik veri kümesi oluşturalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "id": "j723455WwHl7"
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.datasets import make_classification, make_regression\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "import random"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 282
+ },
+ "id": "WJNK_J6v6I-Z",
+ "outputId": "eb4a66a6-6b9a-4c8a-bc24-d81eeb2d3f27"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "np.random.seed(13) # yeniden üretilebilirlik için tohumu seçin - rastgele değişimlerim etkilerini keşfetmek için değiştirin\n",
+ "\n",
+ "train_x = np.linspace(0, 3, 120)\n",
+ "train_labels = 2 * train_x + 0.9 + np.random.randn(*train_x.shape) * 0.5\n",
+ "\n",
+ "plt.scatter(train_x,train_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ng4rZmGc6oxk"
+ },
+ "source": [
+ "Doğrusal bağlanım, $f_{W,b}(x) = Wx+b$ doğrusuyla tanımlanır, burada $W$ ve $b$ bulmamız gereken model parametreleridir. $\\{x_i,y_u\\}_{i=1}^N$ (**kayıp işlevi** olarak da adlandırılır) veri kümemizdeki hata, ortalama kare hatası olarak tanımlanabilir:\n",
+ "$$\n",
+ "\\mathcal{L}(W,b) = {1\\over N}\\sum_{i=1}^N (f_{W,b}(x_i)-y_i)^2\n",
+ "$$\n",
+ "\n",
+ "Modelimizi ve kayıp fonksiyonumuzu tanımlayalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "id": "QxhI4GlB6aiH"
+ },
+ "outputs": [],
+ "source": [
+ "input_dim = 1\n",
+ "output_dim = 1\n",
+ "learning_rate = 0.1\n",
+ "\n",
+ "# Bu bizim ağırlık matrisimiz\n",
+ "w = tf.Variable([[100.0]])\n",
+ "# Bu bizim ek girdi vektörümüz\n",
+ "b = tf.Variable(tf.zeros(shape=(output_dim,)))\n",
+ "\n",
+ "def f(x):\n",
+ " return tf.matmul(x,w) + b\n",
+ "\n",
+ "def compute_loss(labels, predictions):\n",
+ " return tf.reduce_mean(tf.square(labels - predictions))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JUxwj3367gD2"
+ },
+ "source": [
+ "Modeli bir dizi minigrup üzerinde eğiteceğiz. Aşağıdaki formülleri kullanarak model parametrelerini ayarlayarak gradyan inişini kullanacağız:\n",
+ "$$\n",
+ "\\begin{array}{l}\n",
+ "W^{(n+1)}=W^{(n)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial W} \\\\\n",
+ "b^{(n+1)}=b^{(n)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial b} \\\\\n",
+ "\\end{array}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "id": "-991PErM7fJU"
+ },
+ "outputs": [],
+ "source": [
+ "def train_on_batch(x, y):\n",
+ " with tf.GradientTape() as tape:\n",
+ " predictions = f(x)\n",
+ " loss = compute_loss(y, predictions)\n",
+ " # `tape.gradient` öğesinin bir listeyle de çalıştığını unutmayın (w, b).\n",
+ " dloss_dw, dloss_db = tape.gradient(loss, [w, b])\n",
+ " w.assign_sub(learning_rate * dloss_dw)\n",
+ " b.assign_sub(learning_rate * dloss_db)\n",
+ " return loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "idr2VEWb9rr0"
+ },
+ "source": [
+ "Eğitimi yapalım. Veri kümesinden (**dönem** olarak adlandırılan) birkaç geçiş yapacağız, onu minigruplara ayıracağız ve yukarıda tanımlanan işlevi çağıracağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "id": "nOuu0qpx-wAp"
+ },
+ "outputs": [],
+ "source": [
+ "# Verileri karıştırın.\n",
+ "indices = np.random.permutation(len(train_x))\n",
+ "features = tf.constant(train_x[indices],dtype=tf.float32)\n",
+ "labels = tf.constant(train_labels[indices],dtype=tf.float32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3zdIf6c_85Ht",
+ "outputId": "43b04684-8b90-4c65-d5ff-20ebac61c73c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 94.5247\n",
+ "Dönem 1: son toplu iş kaybı = 9.3428\n",
+ "Dönem 2: son toplu iş kaybı = 1.4166\n",
+ "Dönem 3: son toplu iş kaybı = 0.5224\n",
+ "Dönem 4: son toplu iş kaybı = 0.3807\n",
+ "Dönem 5: son toplu iş kaybı = 0.3495\n",
+ "Dönem 6: son toplu iş kaybı = 0.3413\n",
+ "Dönem 7: son toplu iş kaybı = 0.3390\n",
+ "Dönem 8: son toplu iş kaybı = 0.3384\n",
+ "Dönem 9: son toplu iş kaybı = 0.3382\n"
+ ]
+ }
+ ],
+ "source": [
+ "batch_size = 4\n",
+ "for epoch in range(10):\n",
+ " for i in range(0,len(features),batch_size):\n",
+ " loss = train_on_batch(tf.reshape(features[i:i+batch_size],(-1,1)),tf.reshape(labels[i:i+batch_size],(-1,1)))\n",
+ " print('Dönem %d: son toplu iş kaybı = %.4f' % (epoch, float(loss)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Artık optimize edilmiş $W$ ve $b$ parametrelerini elde ettik. Değerlerinin, veri kümesi oluşturulurken kullanılan orijinal değerlere benzer olduğuna dikkat edin ($W=2, b=1$)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "US6q0nCBD-LL",
+ "outputId": "65a79620-a3eb-445b-aafb-60a60575ab0e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(,\n",
+ " )"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w,b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 282
+ },
+ "id": "_e6xRMZFDnyI",
+ "outputId": "d202b7fe-4383-4d82-b98e-a20f3180093e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(train_x,train_labels)\n",
+ "x = np.array([min(train_x),max(train_x)])\n",
+ "y = w.numpy()[0,0]*x+b.numpy()[0]\n",
+ "plt.plot(x,y,color='red')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0giuwC9GHzi8"
+ },
+ "source": [
+ "## Hesaplamalı Çizge ve GPU Hesaplamaları\n",
+ "\n",
+ "Tensör ifadesini her hesapladığımızda, Tensorflow, mevcut bilgi işlem cihazında, CPU veya GPU, hesaplanabilen bir hesaplamalı çizge oluşturur. Kodumuzda keyfi Python işlevi kullandığımız için, bunlar hesaplamalı çizgenin bir parçası olarak dahil edilemezler ve bu nedenle kodumuzu GPU'da çalıştırırken verileri CPU ve GPU arasında ileri geri iletmemiz ve CPU'da özelleştirilmiş işlevi hesaplamamız gerekir.\n",
+ "\n",
+ "Tensorflow, Python işlevimizi, bu işlevi aynı hesaplamalı çizgenin bir parçası yapacak olan `@tf.function` dekoratörünü kullanarak işaretlememizi sağlar. Bu dekoratör, standart Tensorflow tensör işlemlerini kullanan işlevlere uygulanabilir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "id": "HK7HPLz3Hyrl"
+ },
+ "outputs": [],
+ "source": [
+ "@tf.function\n",
+ "def train_on_batch(x, y):\n",
+ " with tf.GradientTape() as tape:\n",
+ " predictions = f(x)\n",
+ " loss = compute_loss(y, predictions)\n",
+ " # `tape.gradient` öğesinin bir listeyle de çalıştığını unutmayın (w, b).\n",
+ " dloss_dw, dloss_db = tape.gradient(loss, [w, b])\n",
+ " w.assign_sub(learning_rate * dloss_dw)\n",
+ " b.assign_sub(learning_rate * dloss_db)\n",
+ " return loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J7HusxWkGjLX"
+ },
+ "source": [
+ "Kod değişmedi, ancak bu kodu GPU'da ve daha büyük veri kümesinde çalıştırıyor olsaydınız, hızdaki farkı fark ederdiniz.\n",
+ "\n",
+ "## Veri Kümesi API'si\n",
+ "\n",
+ "Tensorflow, verilerle çalışmak için uygun bir API içerir. Kullanmaya çalışalım. Modelimizi de sıfırdan eğiteceğiz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "oYro9Lbr8q0M",
+ "outputId": "78c0a6de-71bd-4eef-8819-439495b28672"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 173.4585\n",
+ "Dönem 1: son toplu iş kaybı = 13.8459\n",
+ "Dönem 2: son toplu iş kaybı = 4.5407\n",
+ "Dönem 3: son toplu iş kaybı = 3.7364\n",
+ "Dönem 4: son toplu iş kaybı = 3.4334\n",
+ "Dönem 5: son toplu iş kaybı = 3.1790\n",
+ "Dönem 6: son toplu iş kaybı = 2.9458\n",
+ "Dönem 7: son toplu iş kaybı = 2.7311\n",
+ "Dönem 8: son toplu iş kaybı = 2.5332\n",
+ "Dönem 9: son toplu iş kaybı = 2.3508\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 18:27:07.717288: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ }
+ ],
+ "source": [
+ "w.assign([[10.0]])\n",
+ "b.assign([0.0])\n",
+ "\n",
+ "# Kolay toplu yineleme için bir tf.data.Dataset nesnesi oluşturun\n",
+ "dataset = tf.data.Dataset.from_tensor_slices((train_x.astype(np.float32), train_labels.astype(np.float32)))\n",
+ "dataset = dataset.shuffle(buffer_size=1024).batch(256)\n",
+ "\n",
+ "for epoch in range(10):\n",
+ " for step, (x, y) in enumerate(dataset):\n",
+ " loss = train_on_batch(tf.reshape(x,(-1,1)), tf.reshape(y,(-1,1)))\n",
+ " print('Dönem %d: son toplu iş kaybı = %.4f' % (epoch, float(loss)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A10prCPowHl7"
+ },
+ "source": [
+ "## Örnek 2: Sınıflandırma\n",
+ "\n",
+ "Şimdi ikili sınıflandırma problemini ele alacağız. Böyle bir soruna iyi bir örnek, boyutuna ve yaşına göre habis ve iyi huylu arasında bir tümör sınıflandırması olabilir.\n",
+ "\n",
+ "Çekirdek model bağlanıma benzer, ancak farklı kayıp işlevi kullanmamız gerekiyor. Örnek veriler üreterek başlayalım:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "id": "j0OTPkGpwHl7"
+ },
+ "outputs": [],
+ "source": [
+ "np.random.seed(0) # yeniden üretilebilirlik için tohumu seçin - rastgele değişimlerin etkilerini keşfetmek için değiştirin\n",
+ "\n",
+ "n = 100\n",
+ "X, Y = make_classification(n_samples = n, n_features=2,\n",
+ " n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n",
+ "X = X.astype(np.float32)\n",
+ "Y = Y.astype(np.int32)\n",
+ "\n",
+ "split = [ 70*n//100, (15+70)*n//100 ]\n",
+ "train_x, valid_x, test_x = np.split(X, split)\n",
+ "train_labels, valid_labels, test_labels = np.split(Y, split)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "id": "c-_BjSHPwHl8"
+ },
+ "outputs": [],
+ "source": [
+ "def plot_dataset(features, labels, W=None, b=None):\n",
+ " # çizimi hazırla\n",
+ " fig, ax = plt.subplots(1, 1)\n",
+ " ax.set_xlabel('$x_i[0]$ -- (öznitelik 1)')\n",
+ " ax.set_ylabel('$x_i[1]$ -- (öznitelik 2)')\n",
+ " colors = ['r' if l else 'b' for l in labels]\n",
+ " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
+ " if W is not None:\n",
+ " min_x = min(features[:,0])\n",
+ " max_x = max(features[:,1])\n",
+ " min_y = min(features[:,1])*(1-.1)\n",
+ " max_y = max(features[:,1])*(1+.1)\n",
+ " cx = np.array([min_x,max_x],dtype=np.float32)\n",
+ " cy = (0.5-W[0]*cx-b)/W[1]\n",
+ " ax.plot(cx,cy,'g')\n",
+ " ax.set_ylim(min_y,max_y)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283
+ },
+ "id": "tq0vFchQwHl8",
+ "outputId": "9a5aa6a0-c92f-4d72-9e78-c0f615804bff"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_l/jnklp1bj4cl95rc01tf5vx4h0000gn/T/ipykernel_36985/2482543244.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_dataset(train_x, train_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Verileri Normalleştirme\n",
+ "\n",
+ "Eğitimden önce, girdi özniteliklerimizi [0,1] (veya [-1,1]) standart aralığına getirmek yaygındır. Bunun kesin nedenlerini dersin ilerleyen kısımlarında tartışacağız, ancak kısaca nedeni şudur. Ağımız üzerinden akan değerlerin çok büyük veya çok küçük olmasını önlemek istiyoruz ve normalde küçük aralıktaki tüm değerleri 0'a yakın tutmayı kabul ediyoruz. Böylece ağırlıkları küçük rastgele sayılarla ilkliyoruz ve sinyalleri aynı aralıkta tutuyoruz.\n",
+ "\n",
+ "Verileri normalleştirirken en küçük değeri çıkarmamız ve aralık boyuna bölmemiz gerekiyor. Eğitim verilerini kullanarak minimum değeri ve aralığı hesaplıyoruz ve ardından eğitim kümesindeki aynı minimum/aralık değerlerini kullanarak test/geçerleme veri kümesini normalleştiriyoruz. Bunun nedeni, gerçek hayatta ağın tahmin etmesi istenecek tüm gelen yeni değerleri değil sadece eğitim kümesini bileceğiz. Bazen yeni değerler [0,1] aralığının dışına çıkabilir, ancak bu çok önemli değildir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_x_norm = (train_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))\n",
+ "valid_x_norm = (valid_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))\n",
+ "test_x_norm = (test_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SjPlpf2-wHl8"
+ },
+ "source": [
+ "## Tek Katmanlı Algılayıcı Eğitme\n",
+ "\n",
+ "Tek katmanlı algılayıcıyı eğitmek için Tensorflow gradyan hesaplama araçlarını kullanalım.\n",
+ "\n",
+ "Sinir ağımızın 2 girdisi ve 1 çıktısı olacaktır. $W$ ağırlık matrisinin boyutu $2\\times1$ ve ek girdi vektörü $b$ -- $1$ olacaktır.\n",
+ "\n",
+ "Çekirdek model önceki örnektekiyle aynı olacaktır, ancak kayıp fonksiyonu lojistik kayıp olacaktır. Lojistik kaybı uygulamak için, ağımızın çıktısı olarak **olasılık** değerini almamız gerekiyor, yani `sigmoid` etkinleştirme işlevini kullanarak $z$ çıktısını [0,1] aralığına getirmemiz gerekiyor: $ p=\\sigma(z)$.\n",
+ "\n",
+ "Gerçek $y_i\\in\\{0,1\\}$ sınıfına karşılık gelen i. girdi değeri için $p_i$ olasılığını alırsak, kaybı $\\mathcal{L_i}=-(y_i\\log p_i + (1-y_i)log(1-p_i))$ olarak hesaplarız.\n",
+ "\n",
+ "Tensorflow'da, bu adımların her ikisi de (sigmoid ve ardından lojistik kayıp uygulamak), `sigmoid_cross_entropy_with_logits` işlevine bir çağrı kullanılarak yapılabilir. Ağımızı minigruplar halinde eğittiğimiz için, `reduce_mean` kullanarak bir minigrubun tüm öğelerindeki kaybın ortalamasını almamız gerekiyor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "id": "kdDxWeCqwHl8"
+ },
+ "outputs": [],
+ "source": [
+ "W = tf.Variable(tf.random.normal(shape=(2,1)),dtype=tf.float32)\n",
+ "b = tf.Variable(tf.zeros(shape=(1,),dtype=tf.float32))\n",
+ "\n",
+ "learning_rate = 0.1\n",
+ "\n",
+ "@tf.function\n",
+ "def train_on_batch(x, y):\n",
+ " with tf.GradientTape() as tape:\n",
+ " z = tf.matmul(x, W) + b\n",
+ " loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=z))\n",
+ " dloss_dw, dloss_db = tape.gradient(loss, [W, b])\n",
+ " W.assign_sub(learning_rate * dloss_dw)\n",
+ " b.assign_sub(learning_rate * dloss_db)\n",
+ " return loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zAAgw0h6KzUd"
+ },
+ "source": [
+ "16 elemandan oluşan minigrupları kullanacağız ve birkaç dönemlik eğitim yapacağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "PfyqjVb2wHl8",
+ "outputId": "308850b8-fe17-4cda-ac27-8bcda210f113"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 18:27:07.945330: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 0.6366\n",
+ "Dönem 1: son toplu iş kaybı = 0.7524\n",
+ "Dönem 2: son toplu iş kaybı = 0.7179\n",
+ "Dönem 3: son toplu iş kaybı = 0.6107\n",
+ "Dönem 4: son toplu iş kaybı = 0.6817\n",
+ "Dönem 5: son toplu iş kaybı = 0.6261\n",
+ "Dönem 6: son toplu iş kaybı = 0.6710\n",
+ "Dönem 7: son toplu iş kaybı = 0.5960\n",
+ "Dönem 8: son toplu iş kaybı = 0.6175\n",
+ "Dönem 9: son toplu iş kaybı = 0.5421\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Kolay toplu yineleme için bir tf.data.Dataset nesnesi oluşturun\n",
+ "dataset = tf.data.Dataset.from_tensor_slices((train_x_norm.astype(np.float32), train_labels.astype(np.float32)))\n",
+ "dataset = dataset.shuffle(128).batch(2)\n",
+ "\n",
+ "for epoch in range(10):\n",
+ " for step, (x, y) in enumerate(dataset):\n",
+ " loss = train_on_batch(x, tf.expand_dims(y,1))\n",
+ " print('Dönem %d: son toplu iş kaybı = %.4f' % (epoch, float(loss)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s4_Atvn5K4K9"
+ },
+ "source": [
+ "Eğitimimizin işe yaradığından emin olmak için iki sınıfı ayıran doğruyu çizelim. Ayırma doğrusu $W\\times x + b = 0.5$ denklemiyle tanımlanır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283
+ },
+ "id": "PgRTHttLwHl9",
+ "outputId": "e4407e1b-edf5-48e5-fdc2-da28120a3c6b"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_l/jnklp1bj4cl95rc01tf5vx4h0000gn/T/ipykernel_36985/2482543244.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_dataset(train_x,train_labels,W.numpy(),b.numpy())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Modelimizin geçerleme verileri üzerinde nasıl davrandığını görelim."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 282
+ },
+ "id": "oEQswfCGrmHw",
+ "outputId": "3cf61882-60e1-4baa-8e51-0c31ea80875c"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pred = tf.matmul(test_x,W)+b\n",
+ "fig,ax = plt.subplots(1,2)\n",
+ "ax[0].scatter(test_x[:,0],test_x[:,1],c=pred[:,0]>0.5)\n",
+ "ax[1].scatter(test_x[:,0],test_x[:,1],c=valid_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Geçerleme verilerinin doğruluğunu hesaplamak için boole (boolean) türünü float (kayan virgüllü sayı) türüne çevirebilir ve ortalamayı hesaplayabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "HUjdeIefsIsg",
+ "outputId": "f267f505-8ba4-43ef-9ebe-df124c3c05a1"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tf.reduce_mean(tf.cast(((pred[0]>0.5)==test_labels),tf.float32))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Burada neler olduğunu açıklayalım:\n",
+ "* `pred`, ağ tarafından tahmin edilen değerlerdir. Tam olasılık değiller, çünkü bir etkinleştirme işlevi kullanmadık, ancak 0.5'ten büyük değerler sınıf 1'e ve daha küçük değerler sınıf 0'a karşılık gelir.\n",
+ "* `pred[0]>0.5`, sonuçların bir boole tensörünü oluşturur; burada `True` sınıf 1'e ve `False` - sınıf 0'a karşılık gelir.\n",
+ "* Bu tensörü, `True` doğru tahmine ve `False` yanlış olana karşılık gelen beklenen boole vektörünü veya doğru tahminleri elde ederek, `valid_labels` (geçerli etiketler) etiketleriyle karşılaştırırız.\n",
+ "* `tf.cast` kullanarak bu tensörü kayan virgüllü sayıya dönüştürüyoruz.\n",
+ "* Daha sonra ortalama değeri `tf.reduce_mean` kullanarak hesaplarız - bu tam olarak bizim istediğimiz doğruluktur."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_95qF9lY2kHp"
+ },
+ "source": [
+ "## TensorFlow/Keras Optimize Edicileri Kullanma\n",
+ "\n",
+ "Tensorflow, birçok kullanışlı işlevsellik içeren Keras ile yakından tümleştirilmiştir. Örneğin, farklı **eniyileme (optimizasyon) algoritmaları** kullanabiliriz. Bunu yapalım ve ayrıca eğitim sırasında elde edilen doğruluğu yazdıralım."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ups7nlV22ofp",
+ "outputId": "aa4dff06-82b9-4b2f-ca00-33970ea2b989"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 18:27:09.139136: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son topluiş kaybı = 7.6350, acc = 0.5000\n",
+ "Dönem 1: son topluiş kaybı = 7.5484, acc = 0.5000\n",
+ "Dönem 2: son topluiş kaybı = 7.4980, acc = 1.0000\n",
+ "Dönem 3: son topluiş kaybı = 7.1606, acc = 1.0000\n",
+ "Dönem 4: son topluiş kaybı = 7.3958, acc = 1.0000\n",
+ "Dönem 5: son topluiş kaybı = 7.6092, acc = 0.5000\n",
+ "Dönem 6: son topluiş kaybı = 7.5016, acc = 0.5000\n",
+ "Dönem 7: son topluiş kaybı = 6.5573, acc = 1.0000\n",
+ "Dönem 8: son topluiş kaybı = 8.2018, acc = 0.5000\n",
+ "Dönem 9: son topluiş kaybı = 6.9698, acc = 1.0000\n",
+ "Dönem 10: son topluiş kaybı = 6.9775, acc = 1.0000\n",
+ "Dönem 11: son topluiş kaybı = 7.1308, acc = 1.0000\n",
+ "Dönem 12: son topluiş kaybı = 7.2976, acc = 1.0000\n",
+ "Dönem 13: son topluiş kaybı = 7.2084, acc = 1.0000\n",
+ "Dönem 14: son topluiş kaybı = 6.7165, acc = 1.0000\n",
+ "Dönem 15: son topluiş kaybı = 6.5100, acc = 1.0000\n",
+ "Dönem 16: son topluiş kaybı = 6.8080, acc = 1.0000\n",
+ "Dönem 17: son topluiş kaybı = 6.1264, acc = 1.0000\n",
+ "Dönem 18: son topluiş kaybı = 5.5414, acc = 1.0000\n",
+ "Dönem 19: son topluiş kaybı = 4.6002, acc = 1.0000\n"
+ ]
+ }
+ ],
+ "source": [
+ "optimizer = tf.keras.optimizers.Adam(0.01)\n",
+ "\n",
+ "W = tf.Variable(tf.random.normal(shape=(2,1)))\n",
+ "b = tf.Variable(tf.zeros(shape=(1,),dtype=tf.float32))\n",
+ "\n",
+ "@tf.function\n",
+ "def train_on_batch(x, y):\n",
+ " vars = [W, b]\n",
+ " with tf.GradientTape() as tape:\n",
+ " z = tf.sigmoid(tf.matmul(x, W) + b)\n",
+ " loss = tf.reduce_mean(tf.keras.losses.binary_crossentropy(z,y))\n",
+ " correct_prediction = tf.equal(tf.round(y), tf.round(z))\n",
+ " acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
+ " grads = tape.gradient(loss, vars)\n",
+ " optimizer.apply_gradients(zip(grads,vars))\n",
+ " return loss,acc\n",
+ "\n",
+ "for epoch in range(20):\n",
+ " for step, (x, y) in enumerate(dataset):\n",
+ " loss,acc = train_on_batch(tf.reshape(x,(-1,2)), tf.reshape(y,(-1,1)))\n",
+ " print('Dönem %d: son toplu iş kaybı = %.4f, acc = %.4f' % (epoch, float(loss),acc))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dvAiaj_JndyP"
+ },
+ "source": [
+ "**Görev 1**: Eğitim sırasında kayıp fonksiyonu ve eğitim ve geçerleme verileri üzerindeki doğruluk grafiklerini çizin.\n",
+ "\n",
+ "**Görev 2**: Bu kodu kullanarak MNIST sınıflandırma problemini çözmeye çalışın. İpucu: Kayıp işlevi olarak `softmax_crossentropy_with_logits` veya `sparse_softmax_cross_entropy_with_logits` kullanın. İlk durumda, beklenen çıktı değerlerini *birebir kodlanmış* ve ikinci durumda, tamsayı sınıf numarası olarak beslemeniz gerekir."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "995iCprDrgYQ"
+ },
+ "source": [
+ "## Keras\n",
+ "### İnsanlar için Derin Öğrenme\n",
+ "\n",
+ "* Keras, başlangıçta Francois Chollet tarafından Tensorflow, CNTK ve Theano üzerinde çalışmak ve tüm alt seviye çerçeveleri birleştirmek için geliştirilmiş bir kütüphanedir. Hala Keras'ı ayrı bir kitaplık olarak kurabilirsiniz, ancak bunu yapmanız önerilmez.\n",
+ "* Artık Keras, Tensorflow kütüphanesinin bir parçası olarak dahil edilmiştir.\n",
+ "* Katmanlardan kolayca sinir ağları oluşturabilirsiniz.\n",
+ "* Tüm eğitimi yapmak için `fit` işlevinin yanı sıra tipik verilerle (resimler, metinler, vb.) çalışmak için birçok işlev içerir. \n",
+ "* Çok sayıda örnek vardır.\n",
+ "* İşlevsel API ve Ardışık API içerir.\n",
+ "\n",
+ "Keras, sinir ağları için daha yüksek düzeyde soyutlamalar sağlayarak, tensörler ve gradyanlar açısından değil, katmanlar, modeller ve optimize ediciler açısından çalışmamıza olanak tanır.\n",
+ "\n",
+ "Keras'ın yaratıcısından Klasik Derin Öğrenme kitabına bakabilirsiniz: [Python ile Derin Öğrenme](https://www.manning.com/books/deep-learning-with-python)\n",
+ "\n",
+ "### İşlevsel API\n",
+ "\n",
+ "İşlevsel API'yi kullanırken, ağa **girdi**yi `keras.Input` olarak tanımlarız ve ardından bir dizi hesaplamadan geçirerek **çıktı**yı hesaplarız. Son olarak, **model**i girdiyi çıktıya dönüştüren bir nesne olarak tanımlarız.\n",
+ "\n",
+ "**Model** nesnesini elde ettikten sonra şunları yapmamız gerekir:\n",
+ "* Kayıp işlevini ve modelimizde kullanmak istediğimiz optimize ediciyi belirterek **derlemek**, \n",
+ "* Eğitim (ve muhtemelen geçerleme) verileriyle `fit` işlevini çağırarak **eğitmek**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "QJWplVfy34Eo",
+ "outputId": "9be976f2-4f9a-495c-bddc-a7f9ec30989a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"model_1\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " input_2 (InputLayer) [(None, 2)] 0 \n",
+ " \n",
+ " dense_3 (Dense) (None, 1) 3 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 3\n",
+ "Trainable params: 3\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "Epoch 1/15\n",
+ "7/9 [======================>.......] - ETA: 0s - loss: 0.7315 - accuracy: 0.4107"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 18:27:11.824716: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "9/9 [==============================] - 0s 16ms/step - loss: 0.7176 - accuracy: 0.5000\n",
+ "Epoch 2/15\n",
+ "9/9 [==============================] - 0s 8ms/step - loss: 0.6572 - accuracy: 0.7429\n",
+ "Epoch 3/15\n",
+ "9/9 [==============================] - 0s 8ms/step - loss: 0.6184 - accuracy: 0.9143\n",
+ "Epoch 4/15\n",
+ "9/9 [==============================] - 0s 9ms/step - loss: 0.5828 - accuracy: 0.9429\n",
+ "Epoch 5/15\n",
+ "9/9 [==============================] - 0s 8ms/step - loss: 0.5538 - accuracy: 0.8571\n",
+ "Epoch 6/15\n",
+ "9/9 [==============================] - 0s 9ms/step - loss: 0.5182 - accuracy: 0.9143\n",
+ "Epoch 7/15\n",
+ "9/9 [==============================] - 0s 7ms/step - loss: 0.4968 - accuracy: 0.9429\n",
+ "Epoch 8/15\n",
+ "9/9 [==============================] - 0s 6ms/step - loss: 0.4673 - accuracy: 0.9571\n",
+ "Epoch 9/15\n",
+ "9/9 [==============================] - 0s 7ms/step - loss: 0.4494 - accuracy: 0.9286\n",
+ "Epoch 10/15\n",
+ "9/9 [==============================] - 0s 7ms/step - loss: 0.4311 - accuracy: 0.9286\n",
+ "Epoch 11/15\n",
+ "9/9 [==============================] - 0s 7ms/step - loss: 0.4169 - accuracy: 0.9429\n",
+ "Epoch 12/15\n",
+ "9/9 [==============================] - 0s 6ms/step - loss: 0.4003 - accuracy: 0.9286\n",
+ "Epoch 13/15\n",
+ "9/9 [==============================] - 0s 7ms/step - loss: 0.3950 - accuracy: 0.9000\n",
+ "Epoch 14/15\n",
+ "9/9 [==============================] - 0s 7ms/step - loss: 0.3740 - accuracy: 0.9286\n",
+ "Epoch 15/15\n",
+ "9/9 [==============================] - 0s 6ms/step - loss: 0.3659 - accuracy: 0.9429\n"
+ ]
+ }
+ ],
+ "source": [
+ "inputs = tf.keras.Input(shape=(2,))\n",
+ "z = tf.keras.layers.Dense(1,kernel_initializer='glorot_uniform',activation='sigmoid')(inputs)\n",
+ "model = tf.keras.models.Model(inputs,z)\n",
+ "\n",
+ "model.compile(tf.keras.optimizers.Adam(0.1),'binary_crossentropy',['accuracy'])\n",
+ "model.summary()\n",
+ "h = model.fit(train_x_norm,train_labels,batch_size=8,epochs=15)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 282
+ },
+ "id": "K2Kf60IrZcqs",
+ "outputId": "b60b868d-3562-4715-f5d5-1f9764e45f09"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(h.history['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iJruFXmb_dur"
+ },
+ "source": [
+ "### Ardışık API\n",
+ "\n",
+ "Alternatif olarak, bir modeli **katmanlar dizisi** olarak düşünmeye başlayabilir ve bu katmanları `model` nesnesine ekleyerek belirtebiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "iWc_kSr8_YXt",
+ "outputId": "345dbe65-629d-468f-ed75-1d412c966340"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_1\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " dense_4 (Dense) (None, 5) 15 \n",
+ " \n",
+ " dense_5 (Dense) (None, 1) 6 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 21\n",
+ "Trainable params: 21\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "Epoch 1/15\n",
+ "5/9 [===============>..............] - ETA: 0s - loss: 0.6960 - accuracy: 0.4500"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 18:27:13.289710: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "9/9 [==============================] - 1s 39ms/step - loss: 0.6682 - accuracy: 0.5143 - val_loss: 0.7002 - val_accuracy: 0.4667\n",
+ "Epoch 2/15\n",
+ "8/9 [=========================>....] - ETA: 0s - loss: 0.6642 - accuracy: 0.5312"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2022-11-06 18:27:13.634944: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "9/9 [==============================] - 0s 11ms/step - loss: 0.6553 - accuracy: 0.5571 - val_loss: 0.5634 - val_accuracy: 1.0000\n",
+ "Epoch 3/15\n",
+ "9/9 [==============================] - 0s 12ms/step - loss: 0.5889 - accuracy: 0.7571 - val_loss: 0.5148 - val_accuracy: 1.0000\n",
+ "Epoch 4/15\n",
+ "9/9 [==============================] - 0s 12ms/step - loss: 0.5161 - accuracy: 0.9286 - val_loss: 0.4654 - val_accuracy: 0.8667\n",
+ "Epoch 5/15\n",
+ "9/9 [==============================] - 0s 9ms/step - loss: 0.4707 - accuracy: 0.8429 - val_loss: 0.3745 - val_accuracy: 1.0000\n",
+ "Epoch 6/15\n",
+ "9/9 [==============================] - 0s 10ms/step - loss: 0.4001 - accuracy: 0.9143 - val_loss: 0.2882 - val_accuracy: 1.0000\n",
+ "Epoch 7/15\n",
+ "9/9 [==============================] - 0s 10ms/step - loss: 0.3562 - accuracy: 0.9143 - val_loss: 0.2326 - val_accuracy: 1.0000\n",
+ "Epoch 8/15\n",
+ "9/9 [==============================] - 0s 9ms/step - loss: 0.3034 - accuracy: 0.9286 - val_loss: 0.1751 - val_accuracy: 1.0000\n",
+ "Epoch 9/15\n",
+ "9/9 [==============================] - 0s 10ms/step - loss: 0.2728 - accuracy: 0.9286 - val_loss: 0.1492 - val_accuracy: 1.0000\n",
+ "Epoch 10/15\n",
+ "9/9 [==============================] - 0s 9ms/step - loss: 0.2558 - accuracy: 0.9286 - val_loss: 0.1248 - val_accuracy: 1.0000\n",
+ "Epoch 11/15\n",
+ "9/9 [==============================] - 0s 9ms/step - loss: 0.2340 - accuracy: 0.9286 - val_loss: 0.1160 - val_accuracy: 1.0000\n",
+ "Epoch 12/15\n",
+ "9/9 [==============================] - 0s 9ms/step - loss: 0.2329 - accuracy: 0.9286 - val_loss: 0.0965 - val_accuracy: 1.0000\n",
+ "Epoch 13/15\n",
+ "9/9 [==============================] - 0s 9ms/step - loss: 0.2105 - accuracy: 0.9571 - val_loss: 0.1220 - val_accuracy: 1.0000\n",
+ "Epoch 14/15\n",
+ "9/9 [==============================] - 0s 10ms/step - loss: 0.2151 - accuracy: 0.9429 - val_loss: 0.0964 - val_accuracy: 1.0000\n",
+ "Epoch 15/15\n",
+ "9/9 [==============================] - 0s 11ms/step - loss: 0.2091 - accuracy: 0.9429 - val_loss: 0.0808 - val_accuracy: 1.0000\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model = tf.keras.models.Sequential()\n",
+ "model.add(tf.keras.layers.Dense(5,activation='sigmoid',input_shape=(2,)))\n",
+ "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
+ "\n",
+ "model.compile(tf.keras.optimizers.Adam(0.1),'binary_crossentropy',['accuracy'])\n",
+ "model.summary()\n",
+ "model.fit(train_x_norm,train_labels,validation_data=(test_x_norm,test_labels),batch_size=8,epochs=15)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BmHNhUU8bqEX"
+ },
+ "source": [
+ "## Sınıflandırma Kaybı Fonksiyonları\n",
+ "\n",
+ "Ağın son katmanında kayıp fonksiyonunu ve etkinleştirme fonksiyonunu doğru bir şekilde belirlemek önemlidir. Ana kurallar şunlardır:\n",
+ "* Ağın bir çıktısı varsa (**ikili sınıflandırma**) **sigmoid**, **çok sınıflı sınıflandırma** içinse **softmax** etkinleştirme işlevini kullanırız.\n",
+ "* Kayıp işlevi, çıktı sınıfı birebir kodlama olarak temsil edilirse **çapraz entropi kaybı** (kategorik çapraz entropi), çıktı sınıf numarası içeriyorsa **seyrek kategorik çapraz entropi** olacaktır. **İkili sınıflandırma** için **ikili çapraz entropi** kullanın (**logaritmik kayıp** ile aynıdır).\n",
+ "* **Çok etiketli sınıflandırma**, aynı anda birkaç sınıfa ait bir nesneye sahip olabileceğimiz zamandır. Bu durumda, etiketleri birebir kodlama kullanarak kodlamamız ve etkinleştirme fonksiyonu olarak **sigmoid** kullanmamız gerekir, böylece her sınıf olasılığı 0 ile 1 arasında olur.\n",
+ "\n",
+ "| Sınıflandırma | Etiket Formatı | Etkinleştirme Fonksiyonu | Kayıp |\n",
+ "|---------------|-----------------------|-----------------|----------|\n",
+ "| İkili | 1. sınıf olasılığı | sigmoid | ikili çapraz entropi |\n",
+ "| İkili | Bire bir kodlama (2 çıktılı) | softmax | kategorik çapraz entropi |\n",
+ "| Çok sınıflı | Bire bir kodlama | softmaks | kategorik çapraz entropi |\n",
+ "| Çok sınıflı | Sınıf sayısı | softmaks | seyrek kategorik çapraz entropi |\n",
+ "| Çok etiketli | Bire bir kodlama | sigmoid | kategorik çapraz entropi |\n",
+ "\n",
+ "> İkili sınıflandırma, çok sınıflı sınıflandırmanın iki çıktılı özel bir durumu olarak da ele alınabilir. Bu durumda **softmax** kullanmamız gerekir.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gZ-kWx84bMDH"
+ },
+ "source": [
+ "**Görev 3**:\n",
+ "MNIST sınıflandırıcısını eğitmek için Keras'ı kullanın:\n",
+ "* Keras'ın MNIST dahil bazı standart veri kümesi içerdiğine dikkat edin. MNIST'i Keras'tan kullanmak için yalnızca birkaç satır koda ihtiyacınız vardır (daha fazla bilgi [burada](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist))\n",
+ "* Farklı sayıda katman/nöron, etkinleştirme işlevleriyle birkaç ağ yapılandırması deneyin.\n",
+ "\n",
+ "Ulaşabildiğiniz en iyi doğruluk nedir?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yX6hqiafwHl9"
+ },
+ "source": [
+ "## Ana Fikirler\n",
+ "\n",
+ "* Tensorflow, tensörler üzerinde düşük seviyede çalışmanıza izin verir, aşırı esnekliğe sahip olursunuz.\n",
+ "* Veri (`td.Data`) ve katmanlar (`tf.layers`) ile çalışmak için uygun araçlar vardır.\n",
+ "* Yeni başlayanlar/tipik görevler için, katmanlardan ağ oluşturmaya izin veren **Keras** kullanılması önerilir.\n",
+ "* Standart dışı mimari gerekiyorsa kendi Keras katmanınızı uygulayabilir ve ardından Keras modellerinde kullanabilirsiniz.\n",
+ "* PyTorch'a da bakmak ve yaklaşımları karşılaştırmak iyi bir fikirdir.\n",
+ "\n",
+ "Keras yaratıcısından Keras ve Tensorflow 2.0'nin üzerine güzel bir örnek not defterini [burada](https://t.co/k694J95PI8) bulabilirsiniz."
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Slideshow",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "IntroKerasTF.ipynb",
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+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ },
+ "livereveal": {
+ "start_slideshow_at": "selected"
+ },
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+ "interpreter": {
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diff --git a/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroPyTorch.tr.ipynb b/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroPyTorch.tr.ipynb
new file mode 100644
index 00000000..57b116c0
--- /dev/null
+++ b/lessons/3-NeuralNetworks/05-Frameworks/translations/IntroPyTorch.tr.ipynb
@@ -0,0 +1,1716 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "En2vX4FuwHlu"
+ },
+ "source": [
+ "## PyTorch'a Giriş\n",
+ "\n",
+ "> Bu not defteri, [Yeni Başlayanlar için YZ Müfredatı](http://github.com/microsoft/ai-for-beginners)'nın bir parçasıdır. Eksiksiz öğrenme materyalleri kümesi için kaynak deposunu ziyaret edin.\n",
+ "\n",
+ "### Sinir Çerçeveleri\n",
+ "\n",
+ "Sinir ağlarını eğitmek için şunlara ihtiyacınız olduğunu öğrendik:\n",
+ "* Matrisleri hızla çarpın (tensörler).\n",
+ "* Gradyan inişi optimizasyonunu gerçekleştirmek için gradyanları hesaplayın.\n",
+ "\n",
+ "Sinir ağı çerçeveleri şunları yapmanıza izin verir:\n",
+ "* Mevcut herhangi bir hesaplamada, CPU veya GPU'da ve hatta TPU'da, tensörlerle çalışın.\n",
+ "* Gradyanları otomatik olarak hesaplayın (tüm yerleşik tensör işlevleri için açıkça programlanmışlardır).\n",
+ "\n",
+ "İsteğe bağlı olarak:\n",
+ "* Sinir Ağı yapıcısı / daha üst seviye API (ağı bir dizi katman olarak tanımlamak)\n",
+ "* Basit eğitim işlevleri (Scikit Learn'de olduğu gibi `fit`)\n",
+ "* Gradyan inişine ek olarak bir dizi optimizasyon algoritması\n",
+ "* Veri işleme soyutlamaları (bu ideal olarak GPU'da da çalışır)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8cACQoFMwHl3"
+ },
+ "source": [
+ "### En Popüler Çerçeveler\n",
+ "\n",
+ "* Tensorflow 1.x - yaygın olarak kullanılabilen ilk çerçevedir (Google). Statik hesaplama çizgesini tanımlamaya, GPU'ya göndermeye ve açıkça değerlendirmeye izin verir.\n",
+ "* PyTorch - Facebook'tan popülaritesi artan bir çerçevedir.\n",
+ "* Keras - sinir ağlarını (Francois Chollet) kullanarak birleştirmek ve basitleştirmek için Tensorflow/PyTorch'un üzerüne daha üst seviye API'dir.\n",
+ "* Tensorflow 2.x + Keras - **dinamik hesaplama çizgesini** destekleyen ve numpy'ye (ve PyTorch) çok benzer tensör işlemleri gerçekleştirmeye olanak tanıyan tümleşik Keras işlevselliğine sahip Tensorflow'un yeni sürümüdür.\n",
+ "\n",
+ "Bu Defterde PyTorch kullanmayı öğreneceğiz. PyTorch'un en son sürümünün kurulu olduğundan emin olmanız gerekir - bunu yapmak için [sitelerindeki talimatları](https://pytorch.org/get-started/locally/) izleyin. Normalde bunu yapmak bu kadar basittir\n",
+ "```\n",
+ "pip install torch torchvision\n",
+ "```\n",
+ "veya\n",
+ "```\n",
+ "conda install pytorch -c pytorch\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "xwqVx9-bwHl3",
+ "outputId": "38564a63-0567-4406-ee1a-1d3618f27351",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'1.11.0'"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "torch.__version__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6tp2xGV7wHl4"
+ },
+ "source": [
+ "## Temel Kavramlar: Tensör\n",
+ "\n",
+ "**Tensör** çok boyutlu bir dizidir. Farklı veri türlerini temsil etmek için tensör kullanmak çok uygundur:\n",
+ "* 400x400 - siyah beyaz resim\n",
+ "* 400x400x3 - renkli resim \n",
+ "* 16x400x400x3 - 16 adet renkli resimden minigrup\n",
+ "* 25x400x400x3 - 25 fps'lik videonun bir saniyesi\n",
+ "* 8x25x400x400x3 - 8 adet 1 saniyelik videodan minigrup"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qG2bsaR7wHl4"
+ },
+ "source": [
+ "### Basit Tensörler\n",
+ "\n",
+ "Np dizilimleri listelerinden kolayca basit tensörler oluşturabilir veya rastgele üretebilirsiniz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ybpnk08HwHl4",
+ "outputId": "54e2c89b-b373-4389-b285-49b0510be931"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[1, 2],\n",
+ " [3, 4]])\n",
+ "tensor([[ 0.9740, 1.1339, 0.9433],\n",
+ " [-0.6964, -1.0682, -0.3335],\n",
+ " [-0.5078, 0.4401, 0.3818],\n",
+ " [-0.0231, 0.4968, -0.8708],\n",
+ " [-0.2964, -0.5230, 1.0721],\n",
+ " [-1.0783, -0.6082, 0.3048],\n",
+ " [-0.6351, 1.7860, 0.0811],\n",
+ " [-0.0606, 0.4921, 0.0091],\n",
+ " [-0.5768, -0.4674, -1.4946],\n",
+ " [ 0.7530, -0.7547, 0.0091]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = torch.tensor([[1,2],[3,4]])\n",
+ "print(a)\n",
+ "a = torch.randn(size=(10,3))\n",
+ "print(a)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AXFMsV3r09Ux"
+ },
+ "source": [
+ "Numpy'de olduğu gibi eleman bazında gerçekleştirilen tensörlerde aritmetik işlemleri kullanabilirsiniz. Gerekirse, tensörler otomatik olarak gerekli boyuta genişletilir. Tensörden numpy-dizilimini çıkarmak için `.numpy()` kullanın:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "e5Nu5Xgj1DnQ",
+ "outputId": "c1fbcd86-dde6-40b6-8edf-7a37f9d60901"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[ 0.0000, 0.0000, 0.0000],\n",
+ " [-1.6703, -2.2020, -1.2768],\n",
+ " [-1.4818, -0.6938, -0.5614],\n",
+ " [-0.9971, -0.6370, -1.8141],\n",
+ " [-1.2703, -1.6569, 0.1288],\n",
+ " [-2.0523, -1.7420, -0.6384],\n",
+ " [-1.6091, 0.6522, -0.8621],\n",
+ " [-1.0346, -0.6417, -0.9341],\n",
+ " [-1.5508, -1.6012, -2.4378],\n",
+ " [-0.2210, -1.8885, -0.9341]])\n",
+ "[2.6484823 3.1076026 2.5683248]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(a-a[0])\n",
+ "print(torch.exp(a)[0].numpy())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uQ5zN6cVyrG7"
+ },
+ "source": [
+ "## Yerinde ve Hizası Dışında İşlemler\n",
+ "\n",
+ "`+`/`add` gibi tensör işlemleri yeni tensörler döndürür. Ancak bazen mevcut tensörü yerinde değiştirmeniz gerekir. İşlemlerin çoğunun `_` ile biten yerinde karşılıkları vardır:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Mjkbcw3-ACKS",
+ "outputId": "ca021008-9ab6-4b09-c5a5-bbe854cd1493"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Hizası dışında toplarken sonuç: tensor(8)\n",
+ "Yerinde topladıktan sonra sonuç: tensor(8)\n"
+ ]
+ }
+ ],
+ "source": [
+ "u = torch.tensor(5)\n",
+ "print(\"Hizası dışında toplarken sonuç:\",u.add(torch.tensor(3)))\n",
+ "u.add_(torch.tensor(3))\n",
+ "print(\"Yerinde topladıktan sonra sonuç:\", u)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DLPUcVsXACKT"
+ },
+ "source": [
+ "Bir matristeki tüm satırların toplamını saf bir şekilde şöyle hesaplayabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7pu0UZ-_yqfB",
+ "outputId": "bd2e8c6a-39e1-4f29-990b-9591e866936c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([-2.1476, 0.9275, 0.1025])\n"
+ ]
+ }
+ ],
+ "source": [
+ "s = torch.zeros_like(a[0])\n",
+ "for i in a:\n",
+ " s.add_(i)\n",
+ "\n",
+ "print(s)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rIh1EHcezlNo"
+ },
+ "source": [
+ "Ama aşağıdakini kullanmak çok daha iyidir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aQIdWZ1kzn6P",
+ "outputId": "89000bb4-f45e-493b-a7b0-39fa4e7d92c1"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([-2.1476, 0.9275, 0.1025])"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "torch.sum(a,axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5UzUmEZhACKT"
+ },
+ "source": [
+ "[Resmi dokümantasyonda](https://pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html) PyTorch tensörleri hakkında daha fazla bilgi edinebilirsiniz."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U-auwezDwHl6"
+ },
+ "source": [
+ "## Gradyanları Hesaplama\n",
+ "\n",
+ "Geri yayma için gradyanları hesaplamanız gerekir. Herhangi bir PyTorch tensörünün `requires_grad` özelliğini `True` olarak ayarlayabiliriz; bu, tüm işlemlerde gradyan hesaplamaları için bu tensörün izlenmesiyle sonuçlanacaktır. Gradyanları hesaplamak için `backward()` yöntemini çağırmanız gerekir, ardından gradyan `grad` özelliği kullanılarak mevcut hale gelir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m8vFOXr7wHl6",
+ "outputId": "7054c2b1-0b61-4938-937d-813f75f0b195"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[0.2352, 0.0731],\n",
+ " [0.2213, 0.2482]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "a = torch.randn(size=(2, 2), requires_grad=True)\n",
+ "b = torch.randn(size=(2, 2))\n",
+ "\n",
+ "c = torch.mean(torch.sqrt(torch.square(a) + torch.square(b))) # `a` kullanarak biraz matematik yapın\n",
+ "c.backward() # tüm gradyanlari hesaplamak için backward() çağırın\n",
+ "# `c`'nin `a`'ya göre gradyanı nedir?\n",
+ "print(a.grad)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nPj3rtrtACKU"
+ },
+ "source": [
+ "Daha kesin olmak gerekirse, PyTorch gradyanları otomatik olarak **biriktirir**. `backward` işlevi çağrılırken `retain_graph=True` değerini belirtirseniz, hesaplamalı çizge korunur ve `grad` alanına yeni gradyan eklenir. Hesaplama gradyanlarını sıfırdan yeniden başlatmak için, `zero_()` çağırarak `grad` alanını açıkça 0'a kurmamız gerekiyor:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "z_VIw8MoACKU",
+ "outputId": "36a28b11-6919-47ab-c3f9-c7f1d8500423"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[0.7055, 0.2193],\n",
+ " [0.6638, 0.7445]])\n",
+ "tensor([[0.2352, 0.0731],\n",
+ " [0.2213, 0.2482]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "c = torch.mean(torch.sqrt(torch.square(a) + torch.square(b)))\n",
+ "c.backward(retain_graph=True)\n",
+ "c.backward(retain_graph=True)\n",
+ "print(a.grad)\n",
+ "a.grad.zero_()\n",
+ "c.backward()\n",
+ "print(a.grad)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HM9sUkVgCiG9"
+ },
+ "source": [
+ "PyTorch, gradyanları hesaplamak için **hesaplama çizgesi** oluşturur ve bakımını yapar. `requires_grad` bayrağı `True` olarak ayarlanmış her tensör için PyTorch, ifadenin türevini zincir türev alma kuralına göre hesaplayan `grad_fn` adlı özel bir işlevinin bakımını yapar:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "PcxHb-7jC7Vv",
+ "outputId": "3b3fa138-6d09-4636-8a71-f4a4051c7827"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor(0.8781, grad_fn=)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(c)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rvLfNiblACKV"
+ },
+ "source": [
+ "Burada `c`, `mean` ('ortalama') işlevi kullanılarak hesaplanır, bu nedenle `grad_fn`, `MeanBackward` adlı bir işleve işaret eder.\n",
+ "\n",
+ "Çoğu durumda, PyTorch'un bir skaler fonksiyonun (kayıp fonksiyonu gibi) gradyanını hesaplamasını isteriz. Bununla birlikte, bir tensörün gradyanını başka bir tensöre göre hesaplamak istiyorsak, PyTorch bize bir Jacobian matrisinin ve belirli bir vektörün çarpımını hesaplamamıza izin verir.\n",
+ "\n",
+ "Varsayalım $\\vec{y}=f(\\vec{x})$ vektörümüz olsun, öyleki\n",
+ "$\\vec{x}=\\langle x_1,\\dots,x_n\\rangle$ ve\n",
+ "$\\vec{y}=\\langle y_1,\\dots,y_m\\rangle$, o zaman $\\vec{y}$ gradyanı $\\vec{x}$'e göre bir **Jacobian** ile tanımlanır:\n",
+ "\n",
+ "$$\n",
+ "\\begin{align}J=\\left(\\begin{array}{ccc}\n",
+ " \\frac{\\partial y_{1}}{\\partial x_{1}} & \\cdots & \\frac{\\partial y_{1}}{\\partial x_{n}}\\\\\n",
+ " \\vdots & \\ddots & \\vdots\\\\\n",
+ " \\frac{\\partial y_{m}}{\\partial x_{1}} & \\cdots & \\frac{\\partial y_{m}}{\\partial x_{n}}\n",
+ "\\end{array}\\right)\\end{align}\n",
+ "$$\n",
+ "\n",
+ "Bize Jacobian'ın tamamına erişim vermek yerine, PyTorch Jacobian'ın $v^T\\cdot J$ çarpımını $v=(v_1 \\dots v_m)$ vektörü ile hesaplar. Bunu yapmak için ``backward``'ı çağırmamız ve argüman olarak `v`'yi geçmemiz gerekiyor. `v`'nin boyutu, gradyanı hesapladığımız orijinal tensörün boyutuyla aynı olmalıdır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "VUNYiQCOACKV",
+ "outputId": "e3127c21-fce6-420d-f347-ec40cc827e7e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[1.1758, 0.0731],\n",
+ " [0.2213, 1.2409]])\n"
+ ]
+ }
+ ],
+ "source": [
+ "c = torch.sqrt(torch.square(a) + torch.square(b))\n",
+ "c.backward(torch.eye(2)) # eye(2) 2x2 birim matris anlamına gelir\n",
+ "print(a.grad)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dGHlkVlvACKV"
+ },
+ "source": [
+ "PyTorch'ta Jacobian hesaplama hakkında daha fazla bilgiyi [resmi dokümantasyon](https://pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html)da bulabilirsiniz."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FnVvj4LkD15r"
+ },
+ "source": [
+ "# Örnek 0: Gradyan İnişi Kullanarak Eniyileme\n",
+ "\n",
+ "İki değişkenli basit bir fonksiyonun, $f(x_1,x_2)=(x_1-3)^2+(x_2+2)^2$, minimumunu bulmak için otomatik türev almayı deneyelim. Tensör `x` bir noktanın mevcut koordinatlarını tutsun. Bir başlangıç noktası $x^{(0)}=(0,0)$ ile başlıyoruz ve gradyan inişi formülünü kullanarak bir dizideki sonraki noktayı hesaplıyoruz:\n",
+ "$$\n",
+ "x^{(n+1)} = x^{(n)} - \\eta\\nabla f\n",
+ "$$\n",
+ "Burada $\\eta$ sözde **öğrenme oranı** (kodda `lr` ile göstereceğiz) ve $\\nabla f = (\\frac{\\partial f}{\\partial x_1},\\frac{\\partial f}{\\partial x_2})$, $f$'in gradyanıdır.\n",
+ "\n",
+ "Başlamak için, `x`'in başlangıç değerini ve `f` fonksiyonunu tanımlayalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "nDw5mV9KEeOa"
+ },
+ "outputs": [],
+ "source": [
+ "x = torch.zeros(2,requires_grad=True)\n",
+ "f = lambda x : (x-torch.tensor([3,-2])).pow(2).sum()\n",
+ "lr = 0.1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wt815LWdEj77"
+ },
+ "source": [
+ "Şimdi 15 gradyan inişi yinelemesi yapalım. Her yinelemede, (3,-2)'deki minimum noktaya yaklaştığımızdan emin olmak için `x` koordinatlarını güncelleyeceğiz ve yazdıracağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KfwMf555EyWJ",
+ "outputId": "67e2199c-61ff-4ad1-9c48-b4a646bf8bbd"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Adım 0: x[0]=0.6000000238418579, x[1]=-0.4000000059604645\n",
+ "Adım 1: x[0]=1.0800000429153442, x[1]=-0.7200000286102295\n",
+ "Adım 2: x[0]=1.4639999866485596, x[1]=-0.9760000705718994\n",
+ "Adım 3: x[0]=1.7711999416351318, x[1]=-1.1808000802993774\n",
+ "Adım 4: x[0]=2.0169599056243896, x[1]=-1.3446400165557861\n",
+ "Adım 5: x[0]=2.2135679721832275, x[1]=-1.4757120609283447\n",
+ "Adım 6: x[0]=2.370854377746582, x[1]=-1.5805696249008179\n",
+ "Adım 7: x[0]=2.4966835975646973, x[1]=-1.6644556522369385\n",
+ "Adım 8: x[0]=2.597346782684326, x[1]=-1.7315645217895508\n",
+ "Adım 9: x[0]=2.677877426147461, x[1]=-1.7852516174316406\n",
+ "Adım 10: x[0]=2.7423019409179688, x[1]=-1.8282012939453125\n",
+ "Adım 11: x[0]=2.793841600418091, x[1]=-1.8625609874725342\n",
+ "Adım 12: x[0]=2.835073232650757, x[1]=-1.8900487422943115\n",
+ "Adım 13: x[0]=2.868058681488037, x[1]=-1.912039041519165\n",
+ "Adım 14: x[0]=2.894446849822998, x[1]=-1.929631233215332\n"
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(15):\n",
+ " y = f(x)\n",
+ " y.backward()\n",
+ " gr = x.grad\n",
+ " x.data.add_(-lr*gr)\n",
+ " x.grad.zero_()\n",
+ " print(\"Adım {}: x[0]={}, x[1]={}\".format(i,x[0],x[1]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8sfjBMBu59B5"
+ },
+ "source": [
+ "## Örnek 1: Doğrusal Bağlanım\n",
+ "\n",
+ "Artık klasik **doğrusal bağlanım** problemini çözecek kadar bilgimiz var. Küçük sentetik veri kümesi oluşturalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "j723455WwHl7"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.datasets import make_classification, make_regression\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "import random"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 282
+ },
+ "id": "WJNK_J6v6I-Z",
+ "outputId": "09e6386e-a6d4-4b81-c8d2-153f0acf9696"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "np.random.seed(13) # yeniden üretilebilirlik için tohumu seçin - rastgele değişimlerin etkilerini keşfetmek için değiştirin\n",
+ "\n",
+ "train_x = np.linspace(0, 3, 120)\n",
+ "train_labels = 2 * train_x + 0.9 + np.random.randn(*train_x.shape) * 0.5\n",
+ "\n",
+ "plt.scatter(train_x,train_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ng4rZmGc6oxk"
+ },
+ "source": [
+ "Doğrusal bağlanım, $f_{W,b}(x) = Wx+b$ doğrusuyla tanımlanır, burada $W$ ve $b$ bulmamız gereken model parametreleridir. $\\{x_i,y_u\\}_{i=1}^N$ (**kayıp işlevi** olarak da adlandırılır) veri kümemizdeki hata, ortalama kare hatası olarak tanımlanabilir:\n",
+ "$$\n",
+ "\\mathcal{L}(W,b) = {1\\over N}\\sum_{i=1}^N (f_{W,b}(x_i)-y_i)^2\n",
+ "$$\n",
+ "Modelimizi ve kayıp fonksiyonumuzu tanımlayalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "QxhI4GlB6aiH"
+ },
+ "outputs": [],
+ "source": [
+ "input_dim = 1\n",
+ "output_dim = 1\n",
+ "learning_rate = 0.1\n",
+ "\n",
+ "# This is our weight matrix\n",
+ "w = torch.tensor([100.0],requires_grad=True,dtype=torch.float32)\n",
+ "# This is our bias vector\n",
+ "b = torch.zeros(size=(output_dim,),requires_grad=True)\n",
+ "\n",
+ "def f(x):\n",
+ " return torch.matmul(x,w) + b\n",
+ "\n",
+ "def compute_loss(labels, predictions):\n",
+ " return torch.mean(torch.square(labels - predictions))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JUxwj3367gD2"
+ },
+ "source": [
+ "Modeli bir dizi minigrup üzerinde eğiteceğiz. Aşağıdaki formülleri kullanarak model parametrelerini ayarlayarak gradyan inişini kullanacağız:\n",
+ "$$\n",
+ "\\begin{array}{l}\n",
+ "W^{(n+1)}=W^{(n)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial W} \\\\\n",
+ "b^{(n+1)}=b^{(n)}-\\eta\\frac{\\partial\\mathcal{L}}{\\partial b} \\\\\n",
+ "\\end{array}\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "id": "-991PErM7fJU"
+ },
+ "outputs": [],
+ "source": [
+ "def train_on_batch(x, y):\n",
+ " predictions = f(x)\n",
+ " loss = compute_loss(y, predictions)\n",
+ " loss.backward()\n",
+ " w.data.sub_(learning_rate * w.grad)\n",
+ " b.data.sub_(learning_rate * b.grad)\n",
+ " w.grad.zero_()\n",
+ " b.grad.zero_()\n",
+ " return loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "idr2VEWb9rr0"
+ },
+ "source": [
+ "Eğitimi yapalım. Veri kümesinden (**dönemler** olarak adlandırılır) birkaç geçiş yapacağız, onu minigruplara ayıracağız ve yukarıda tanımlanan işlevi çağıracağız:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "nOuu0qpx-wAp"
+ },
+ "outputs": [],
+ "source": [
+ "# Shuffle the data.\n",
+ "indices = np.random.permutation(len(train_x))\n",
+ "features = torch.tensor(train_x[indices],dtype=torch.float32)\n",
+ "labels = torch.tensor(train_labels[indices],dtype=torch.float32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3zdIf6c_85Ht",
+ "outputId": "6520288c-da59-4a9f-c37e-cd99779c3073"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 94.5247\n",
+ "Dönem 1: son toplu iş kaybı = 9.3428\n",
+ "Dönem 2: son toplu iş kaybı = 1.4166\n",
+ "Dönem 3: son toplu iş kaybı = 0.5224\n",
+ "Dönem 4: son toplu iş kaybı = 0.3807\n",
+ "Dönem 5: son toplu iş kaybı = 0.3495\n",
+ "Dönem 6: son toplu iş kaybı = 0.3413\n",
+ "Dönem 7: son toplu iş kaybı = 0.3390\n",
+ "Dönem 8: son toplu iş kaybı = 0.3384\n",
+ "Dönem 9: son toplu iş kaybı = 0.3382\n"
+ ]
+ }
+ ],
+ "source": [
+ "batch_size = 4\n",
+ "for epoch in range(10):\n",
+ " for i in range(0,len(features),batch_size):\n",
+ " loss = train_on_batch(features[i:i+batch_size].view(-1,1),labels[i:i+batch_size])\n",
+ " print('Dönem %d: son toplu iş kaybı = %.4f' % (epoch, float(loss)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Şimdi optimize edilmiş $W$ ve $b$ parametrelerini elde ettik. Değerlerinin, veri kümesi oluşturulurken kullanılan orijinal değerlere benzer olduğuna dikkat edin ($W=2, b=1$)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "US6q0nCBD-LL",
+ "outputId": "c804b779-3231-4f6f-c854-032d211b2853"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([1.8617], requires_grad=True), tensor([1.0711], requires_grad=True))"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "w,b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 282
+ },
+ "id": "_e6xRMZFDnyI",
+ "outputId": "79e6c360-265a-401d-ce39-8f211917a13d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(train_x,train_labels)\n",
+ "x = np.array([min(train_x),max(train_x)])\n",
+ "with torch.no_grad():\n",
+ " y = w.numpy()*x+b.numpy()\n",
+ "plt.plot(x,y,color='red')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0giuwC9GHzi8"
+ },
+ "source": [
+ "## GPU'daki Hesaplamalar\n",
+ "\n",
+ "Hesaplamalar için GPU'yu kullanmak için PyTorch, tensörleri GPU'ya taşımayı ve GPU için hesaplamalı çizgeyi oluşturmayı destekler. Geleneksel olarak, kodumuzun başında mevcut hesaplama cihazı `device` (`cpu` veya `cuda`'dır) tanımlarız ve ardından `.to(device)` çağrısını kullanarak tüm tensörleri bu cihaza taşırız. Ayrıca, `device=...` parametresini tensör oluşturma koduna ileterek, belirtilen cihaz üzerinde önceden tensörler oluşturabiliriz. Bu kod, hem CPU'da hem de GPU'da değişiklik yapmadan çalışır:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "HK7HPLz3Hyrl",
+ "outputId": "7e14cccb-d376-4e59-be66-4ab3f5c3f6f4"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Hesaplamaları şurada yapıyor cpu\n",
+ "Dönem 0: son toplu iş kaybı = 94.5247\n",
+ "Dönem 1: son toplu iş kaybı = 9.3428\n",
+ "Dönem 2: son toplu iş kaybı = 1.4166\n",
+ "Dönem 3: son toplu iş kaybı = 0.5224\n",
+ "Dönem 4: son toplu iş kaybı = 0.3807\n",
+ "Dönem 5: son toplu iş kaybı = 0.3495\n",
+ "Dönem 6: son toplu iş kaybı = 0.3413\n",
+ "Dönem 7: son toplu iş kaybı = 0.3390\n",
+ "Dönem 8: son toplu iş kaybı = 0.3384\n",
+ "Dönem 9: son toplu iş kaybı = 0.3382\n"
+ ]
+ }
+ ],
+ "source": [
+ "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+ "\n",
+ "print('Hesaplamaları şurada yapıyor '+device)\n",
+ "\n",
+ "### Changes here: indicate device\n",
+ "w = torch.tensor([100.0],requires_grad=True,dtype=torch.float32,device=device)\n",
+ "b = torch.zeros(size=(output_dim,),requires_grad=True,device=device)\n",
+ "\n",
+ "def f(x):\n",
+ " return torch.matmul(x,w) + b\n",
+ "\n",
+ "def compute_loss(labels, predictions):\n",
+ " return torch.mean(torch.square(labels - predictions))\n",
+ "\n",
+ "def train_on_batch(x, y):\n",
+ " predictions = f(x)\n",
+ " loss = compute_loss(y, predictions)\n",
+ " loss.backward()\n",
+ " w.data.sub_(learning_rate * w.grad)\n",
+ " b.data.sub_(learning_rate * b.grad)\n",
+ " w.grad.zero_()\n",
+ " b.grad.zero_()\n",
+ " return loss\n",
+ "\n",
+ "batch_size = 4\n",
+ "for epoch in range(10):\n",
+ " for i in range(0,len(features),batch_size):\n",
+ " ### Changes here: move data to required device\n",
+ " loss = train_on_batch(features[i:i+batch_size].view(-1,1).to(device),labels[i:i+batch_size].to(device))\n",
+ " print('Dönem %d: son toplu iş kaybı = %.4f' % (epoch, float(loss)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "A10prCPowHl7"
+ },
+ "source": [
+ "## Örnek 2: Sınıflandırma\n",
+ "\n",
+ "Şimdi ikili sınıflandırma problemini ele alacağız. Böyle bir soruna iyi bir örnek, boyutuna ve yaşına göre habis ve iyi huylu arasında tümör sınıflandırması olabilir.\n",
+ "\n",
+ "Çekirdek model regresyona benzer, ancak farklı kayıp fonksiyonu kullanmamız gerekiyor. Örnek veriler üreterek başlayalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "j0OTPkGpwHl7"
+ },
+ "outputs": [],
+ "source": [
+ "np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations\n",
+ "\n",
+ "n = 100\n",
+ "X, Y = make_classification(n_samples = n, n_features=2,\n",
+ " n_redundant=0, n_informative=2, flip_y=0.1,class_sep=1.5)\n",
+ "X = X.astype(np.float32)\n",
+ "Y = Y.astype(np.int32)\n",
+ "\n",
+ "split = [ 70*n//100, (15+70)*n//100 ]\n",
+ "train_x, valid_x, test_x = np.split(X, split)\n",
+ "train_labels, valid_labels, test_labels = np.split(Y, split)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "c-_BjSHPwHl8"
+ },
+ "outputs": [],
+ "source": [
+ "def plot_dataset(features, labels, W=None, b=None):\n",
+ " # prepare the plot\n",
+ " fig, ax = plt.subplots(1, 1)\n",
+ " ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n",
+ " ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n",
+ " colors = ['r' if l else 'b' for l in labels]\n",
+ " ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
+ " if W is not None:\n",
+ " min_x = min(features[:,0])\n",
+ " max_x = max(features[:,1])\n",
+ " min_y = min(features[:,1])*(1-.1)\n",
+ " max_y = max(features[:,1])*(1+.1)\n",
+ " cx = np.array([min_x,max_x],dtype=np.float32)\n",
+ " cy = (0.5-W[0]*cx-b)/W[1]\n",
+ " ax.plot(cx,cy,'g')\n",
+ " ax.set_ylim(min_y,max_y)\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283
+ },
+ "id": "tq0vFchQwHl8",
+ "outputId": "919f1922-f789-4779-cbdc-4f9e742c358b"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_l/jnklp1bj4cl95rc01tf5vx4h0000gn/T/ipykernel_37297/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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B7r9fe3sOIdyG2WzfKiNrEz0hPIRHBjjffvtts8+XLFmC+Ph47Ny5E5MnT3bRqISn+/JLfiQmtr0vlcEApKayHGHjRn7+wAPtb6UjhFsKDGSwYrHY3shP0pbCg3jFW3N5eTkAILqD3RJNJhNMJtOFz41Go9PHJTxHcTHw7bdckNHZpptBQUDv3sDmzcDUqWy4KtyQxQLk5rLfi6Lwl5uUJD1S+vcHoqL4oo+L03ZMVRWLkqUMQHgQjw9wVFXFE088gYkTJyIjI6Pd+y1YsADPPfdcN45MeJIdO1he0MFLqJmwMGbrN26UAMftVFfzF7p2LYtprRc2wcH8BU+axGpxrfsbeZuEBHb7XbGCvWO0BHxnzzIwGjjQ+eMTwkE8PsB56KGHsHfvXmzYsKHD+z311FN44oknLnxuNBqRYssqAuHVNm3i+c+WjH1CAhu9lpbygli4gcJCYPFiLiX29+d8o7WQqqIC2LqV+x5ddBFw552+W0Q1bRqwfTtw6hSQnt7xfQsL+e/06Y3FxkJ4AI8OcB5++GF8+eWXWLduHZLb28vkJwEBAQgICOimkQlPoqpASYnty7+Dg7kPYGWlBDhuoawMePllBjcDB3JKpamoKH5UVgKrV7PY9uc/Z02Kr+nfH5g7F3jzTeDwYRaXtayvqa/n3k51dcD11wMTJrhmrELYySMDHFVV8fDDD2Pp0qVYs2YNevXq5eohCQ9SUcHu89u2NV6c7t7N2pvYWO0zF6rKf329pMNtLFvG4GbQoI5/iaGhQN++nF/MyGBmwhdNmsQo/ZNPgBMnGPAFB/MFXVPDGqbkZODKK4GLL5YXuvA4HhngPPjgg3jvvffwxRdfICwsDAUFBQCAiIgIBEmVv2iHxcL+Nl9+yeXeen1j1qa8nNn6wkJe/Keldf5+XlnJ48PDnT920YnycnbajYvTFqGGhDBzs3Ytp2t8dSnc6NHA0KHcWHP7du7pZLEw0s/MlM6WwqN55F/1K6+8AgCYOnVqs9uXLFmCefPmdf+AhNtTVeCzz3ixGhTEIKbpOc1s5qbFVVWNewv27dtxkHP+PC9uJcBxA7t3MzodNEj7MUlJ7OZ48CBP8r7KYABGjeKHEF7EIwMc1To3IIRGW7YAS5dyGio+vvXXExMZqNTVsY5y/37Wn/bo0fbjlZYyUMrKcu64hUaFhYxibSmCDQ5mJCvdeYXwSjZ2eRLC81inpiyWtoMboHHTZLOZnzc0sCyhrVjaaAROn2ZZQv/+zhu3sEFDg2uOFUK4LQlwhNc7fJgfPXt2fL9evVhz2tDATE5uLhfmWFVWAkeOcGHJzJnALbfY3ghWOIk9dSIWC/+Vuj0hvJJHTlEJYYucHC4KCQ3t+H6KwoxMRASzN4cOcfPkhAR+3drIdcoUbmjsq3Wpbql/fxYNV1Z2/ou2Ki5md2M703CqytdVXR3LWIKCZKGREO5E3qKF16ut1X7iURTW3SQksJyjadPbxEQGONLrzEFMJkaRFRX8PDyc84T2dBgeMICByqFD2rYTUFUupZsxozGC1ai2Ftizh4u2cnKY8fP3Z1H6pEnA8OG+2VpHCHcjAY7wenq97ccoCmc9Ro3idJRwoLIy9qBZs4bzgNbCJz8/rs+fOpWdhiMitD+mnx+DlSNH2H2xo6BFVYGTJ9n0b9o0m4aekwO88QYXXykKEBPD10lDA1dZb9vGQOfuu4E+fWx6aCGEg0mAI7xeQgJPRtapBC1qanhVbuPFvejMmTPAokXAgQNcpta7d2PH4dpa9mF5/XVuqXD//VzKrdX48czKfPwx1/snJ7f+hdfUMKgKDATmzbNpeionB1i4kN+iX7/WDx0fz6TUkSO832OP8ekJIVxDAhzh9YYP57muoIAd6bXIy+PJacgQ547NpxQVAa+8wmmkQYNap9YCA1npXVcH7NvH+z7+eOfbu1spCnDNNayr+fprRiSKwsdVVW7Cqdfze1x3HZvcaVRTw8xNQQEweHD7U54BAfx6djbjtGefbb1jhBCie0iAI7xecDALg//7Xza67WzRTGUlT2hTp9o3vSXasXIlz/xDhnRcoW0wMADav5/r+2+8Ufv3UBROO40fz0KZnTsZWPn5MR03ZgyXytn4i/3xR05L9evXeT2XTsf75eTwuLFjbfpWQggHkQBH+ITp0zl1sHkzayRCQtq+n9HI8oypUxkUCQcxGlmVGxurbfmZXs8Clw0bgMsv174yysrahdEBnRhVFVi3joGN1ilOa9Zm/XrGVLK6SojuJwGO8AkhISzp0OtZ3mE2c7VUSAhPYJWVrE3V64FLL+VGyzK14EA//sgf8MCB2o9JSGBUunevS3eyrq5m9iY21rbjYmOBo0dZWiStdoTofhLgCJ8REQE89BAweTKvrPftA0pK+LXgYHYmvuiizmdQhB2sP2hbfrB6PaPP0lLnjEmjujoGxLYu/fb3b+yTIwGOEN1P3saFT9HrWVs6ahS3IKqo4PRBeLjtV+htKSvjbAzAx4yM7PpjegVr12BbKUrjMnIXMRhYwmPrjg4NDTzOnrY+QoiukwBH+CRF4bLe9vamsoXZzNrZTZtY01pby9sDA4HMTM6uDBrk4w0CQ0IY5Kiq9oIUVeUx7RVMdZPgYPa02bWLRepaFRUB48ZJ0z8hXEUCHCG6oKqKq7PWr+dURHx8Y++cqipgxQr2s5s8GbjjDvu2TPIKQ4YwnVVSwuJhLYqK2IzPxWv1FYUdirdv195LyWTiv5MmSYGxEK4iWwUKYSeTCXjtNa5+7tGDq4/j45lwCAnh/zMyGPB89x37olhPfD4nKYnzgnl5bW/R3pKqsulfZiZ/uC42YgRX3+XkdD58VeX9+vYFhg3rluEJIdogAY4Qdlq1iquY+/RhvU17IiJ4n3XrmM3xSYrCtfoxMcDx4x1HCarKZUvx8VzS5gaCgoC77mKwevAgMzltMZk4XdmjB7drkOkpIVxHAhwh7FBXx2DFmq3pTGgop6dWr27/5Oj1BgxglBAYyCigvLx5oKOqvO3AAf6w7r6baRA30a8f8MgjDFZzchjoFBZy1q2wkJ9bmwE+9pjsRSWEq0kNjhB22LePDQF79dJ+TFISj9m/n7M1Pmn8eO5B9dVXDHJOnWpcOt7QwEhwzBhg9mxWZruZfv2AZ55hW5/16xnQVFfzKYwZI7uJC+FOJMARwg75+bb3RgkM5Dm8oMB54/IIQ4Zww6acHDbxKynhFFZ0NKOD3r3dujI3KIhx2rhx7HNjMrEpZFCQWw9bCJ8jAY4Qdqivt/9Yn52iakpRmA7p18/VI7GbonAmzWdXxgnh5qQGRwg7WDeotoX1/jJ9IYQQzicZHCHs0Ls3r9yNxrZXUFl3GCgv51SWnx8/AgN5rBBCCOeSAEf4pPp6Fgpv3sw614YGBiqZmSwWtTbra0+/ftw3ct++5gGOqgJnz7KY+Px5TkcpCm+vqWHdbHGxbQ19hRBC2E4CHOFzDhwA3n2X7VjMZgYoisKAZO9e4MMP2cckOJi7jPv5AT17srDUukJGp+PmnNnZXCIcH89dBQ4c4AbYFgsXC0VG8rErK3lbQwPw4osMqm64wce3bxBCCCeSAEf4lD17gFde4aaY6enNd3m2WNjLZOdOfj0pCRg6lMHMyZPAxo085sYbmekZPx44cwb49FPuP2U08nhrzxvrYxqNzBgNH96YwfnsMwZK11zTzT8AIYTwEVJkLHxGQQHwxhvcQXzgwNbBzY8/MgMTFASkpnIvqYoKICWFq5r79GFA85//sIOxogDXXQfMncvMzI4dnJIymxnUFBczK+Tvz1b/gwbxmNhYZna++YbLzYUQQjieBDjCZ2zezAClX7/W9S/HjwNHj3K6KiyM2ZXgYE4lWXcHDwgA+vdnMPT222zyptMBs2YBM2cCyckMhvz9Ab2eO0+PHQtccgkb8jb9nomJ3Etyx47ue/5CCOFLZIpK+ITqau4FFRXVOrhpaGCAYzA0X8IdEsIMTF5e85VP6eksLl6/nlmdhgZOa/XrB6SlNS4H76iIWFG4R9XatcDll0stjhBCOJpkcIRPyM3lFFVbq6MKCricOzS0+e06HQOPwsLmtysKi4o3b2YTXutUlvV4RdG2Qio0lFNZ1dX2PSchhBDtkwyO8Ak1NSz01etbf62khNNO/m38Nfj5td15OC6u+eaKgO3Lvq33t7VhoN1UlUU/RiM/Dw/nXJknrFevrWWF+I8/ssGQnx+Xuo0Zw3lDnVyrCSGakwBH+AS9nudEi6X1udBkan+KqKPAB+B5NziYhcnV1dxOSavq6uYrrpymro7Bwfr13OnTWlQUGAhkZHCHyBEjOEfnblSVW7B/8w1w+jQ/t7aR3rIF+O47VozfcAN3KxdCiJ9IgCN8Qo8erHkpKeH0UlP+/m1nUVSVWZ+IiLa/pqoMnAwGICsL+Phj9svRkhCxdjqeMaPtAMphysu5dGzLFg6sRw9mbQDOrW3bxo/x44G77mr7ybqKqgKffMJ1+AEBrNRuGYRVVDCrc/YsMH8+1+ILIQSkBkf4iNhYrmhqWU8DcNWUxdI6yKmtZbKgZ8/Wx1hrdqw1PePG8XFKSrSNp7iYM0Tjxtn2PGxSXQ0sWsTMTVoa16lHRTFIMBj4/0GDuCZ+3Tpg8WL3Kghat44Ng6KjgV692s4whYVxDX9ZGfDaa1wmJ4QQkABH+JAJEzgd1DLISUri7VVVjbdZG/RZMz8t5eezCWB6Oj/v1QuYMoWJhIqKjsdRUcGVWVOmMO5wmh9+YOamf38uCWtPSAjvs2ULj3EH9fXAt99yLrBlyq0pi4UFVklJ7Ma4fn23DVEI4d5kikr4jEGDgNmzgY8+YrYmPp6zNsHB7GFz+HBj87+iIiYOMjJaTzmVlrKOZ+LExq8pCnDLLUyArFnD7E5iImdWrEwmBjZVVcD06cDPfubE+t7aWg4kPLx5R8P2BAUxG7J2LQfn6i3P9+8HTpxoPwKsrWWUefIkI1HrZl8LFzIzNXFi62VxQgifIgGO8BmKAsyZw3+/+oq9bKxdhZOTgZwc7i2l1/McmZraWM9aUcHzZ2kpP264gQt4mgoMBO67jz1zVq3iCiuzufHr/v58zIsvBi69tO0VXQ6zdy+Lcvv00X5MYiIHvXcv5/NcKTubxdFtBWcFBSyaLi/nDzU4mBGnXs8U2t/+xsLku+9mZkoI4ZM8NsBZt24d/va3v2Hnzp3Iz8/H0qVLcY1s7CM64ecHXHstFw1t3cqExbZtPGdWVfHr1q0Wtm/nud7fnwmDqiqeR5OSgEOHgBUrWEPTdOWUwcDGfZdcwtrX06eZuQkIYHAzbFjzrI7TFBZy+saWbxYQwGPOn3feuLQyGtuOAAsK+IsxmbhWv+mSuIAAZqGSkpj9+fe/gcceY3GyEMLneGyAU1VVheHDh+POO+/Edddd5+rhCA+iKExs9OjBBoC5uZyKSkzkjM65c+xsfOQIZ0EsFmZ5Ro5kzU1oKFBw1ozX/lmD1T1rcP/PjOiTGdUs0gkIYBLEZYmQhgb7j62vd9w47GUwtK76NpmYuTGZgJiY9uf3DAam0bKzgTffBJ59tpuiSuG16uuZ8j19mplFg4H7sgwd6uRUrOgKjw1wZs2ahVmzZmm+v8lkgslkuvC50drsTPgkiwV45x1mcTIymtfgJiUxURAUxCksRWH2xmAA4kKqoZw5g94nT8JcXolD2Yl4aVMJnshcj5RL+rOSecAA1zfPs86tqar2sVjvr6Vmx9l69GCQ1nT8eXmcloqLa/s5mUyNU1bWKPbYMZ6YMjO7d/zCO9TXc7pz1SrWe9XX87Vl7RHRqxcwbRrnnZ3a70HYw2dWUS1YsAAREREXPlJSUlw9JOFCOTnAxo2sYW25wMhauxoTw8xNRAQ/ig8Vou6HdcDu3UBVFfwiwzCoVy1ykYZPToyG+vU3wIIFXNrctPjGFfr3Z6qpvFz7Mda179bWzK40ahQzYkVF/FxVufOpv3/7XYsrKhiRWjNp1iBvw4buGbPwLiYTM4CvvcbgOi2NGZuMDP6blsaar9deY6+ptlqeC5fymQDnqaeeQnl5+YWP06dPu3pIwoW2bOGKp8jI5rdbz6PWhrlWMUoxUs9tR+35Si6/iooC9Hro/BQkR1bix+q+yE2dyOzBhx8CX37ZjXswtKFXL74R5+drPyYvj2/cvXo5b1xaJSYCo0dzTGYzi6CMxvbbPtfV8eedltY8uxMdzW3iuzJlJ3yPqvLveMUKTkX17t06sxkUxNtTUni/Dz5w7d+8aMVnApyAgACEh4c3+xC+qbaWU1MxMa2/ZjSyPrfpCmNFtaDHub0IVqtw3hIDFc2nRyIDa2E0BWJ7XhI7/8XEAF98wTSRqygKK50DAhgkdCYvjxHdxRe7fnrNas4cZpOys3k1bbG0PzVVUsLgJjm5+desVeNydS1sceoUp6WshXkdCQ/nlOrq1SzoE27DZwIcIayqqhr3kGqpuprnwqbZm+Cq8wiqKYEpMBJmiwKLpfkxigIE+DUgryKMN8THM1LassV5T0KLkSOBm2/mEz52rO2TfF0dv1ZVxfuOHNn942xPjx7Agw9yuu3YMaCysjFTo6r8JZ4/z591795cGtdyU7H6et4mRcbCFlu38nXV1lVQW2JjOcW7datzxyVsIlVRwucoSmOdYEstgxcACDOegU41o0FnQHu5DZ2ios7s1/gNYmOBzZvZWdBV+zspCjBrFpdOf/45M0rWzoYAozlVZYr9mmuady50F2lpwK9+xWDxz3/m8jbrZqEGA/fRSE3llXZbO6aWljKT1d5uqkK0VF8PbNrEaWitfw+Kwvtv2sQ+FFJw7BY89rdQWVmJnCZTACdOnMCePXsQHR2N1NRUF45MuLvQUJ7zy8v5ntSUXs8aVoul8ZwYZCpHg58BZnNjq5jyciYTrPcrr/VDqH/jKj1ERXFfpOJi125gqSjcLTwzk415tm1r3KsiPp7r2IcPd4+VU+2JjAQuu4xjXLCAy9wMBn4eHt7+Sai6mieaCRO6dbjCw1VX86Oj7U3aEhzceKyUQLgFjw1wduzYgWnTpl34/IknngAAzJ07F2+99ZaLRiU8gcHAc/477zB50fT8GBXF4KeysjEuUSxmqFDQ0MAA6PjxxtWiAFBv0aG4HqjJLURONGdLdNYoyR16ygAMBsaP54enGjOGU2g5Odx3o73VVADrbnJyuBpr8ODuG6PwfNY/bCkY9ngeW4MzdepUqKra6kOCG6HF2LFcYHPuXPPb9XrOitTWNr6/NfgHoKHWDJOJF2eKwos760etPgxJoeVI0hVg1y72ojNXmxqzDMIxgoOBe+5hBufgQe6d0ZaqKuDAAXYwvusumS4QtgkObrzKsUVlJdPD7a30E93OYwMcIbqiZ0+WpxQX86Op5GRmb4qKGOSc90tEQ009dIqK0FDGLdaLPGN9EGrNeoyLOYaE6AaEhzNxcHbXObY97tmz25+bV+vTB3j0UTZTPHWKgUx+Pqfd8vK4SefZs8zcPPYYgyEhbOHvzxRvWZn2LI6q8v6TJ0tA7Ua69Juor69HQUEBqqurERcXh+imm/II4cYUhXW1dXXA11/z/JiYyMAmNJTnxw0buHgnROmJcL+DiAupglnH9eMNFh2K68JQY9ZjQuwRjI4+DoCrr8ymBpw/U4uIEVMQIcWtjte7N/DMMwxmNm7kxmBmMyPP6dNZczN4sBQWC/uNHcs3hnPnuJqvM+fOMSXs6k1qRTM2BziVlZV499138f7772Pbtm3Ntj9ITk7GjBkzcN9992FMy62WhXAz/v5cGd2nD7BuHc+Xp083rrDq14+zICdOBONQ5XCkVp9EnTkEdQiEBQpiAyowJe4AxkQfg5/CKz1FtSCt9jCO6PuhVM3EpS5+jl7LYGAUOmoUg5v6et7WUV2OEFolJbGw/cMP+brq6OK9pIRp4BtvlIyhm7EpwPnnP/+JF154Aenp6Zg9ezZ+/etfo2fPnggKCkJJSQn279+P9evXY/r06Rg/fjxefPFF9HOHtu9CtEOn447gY8dyA+ozZxp3/05L43vbww8DVT0TEZ+bi7DSQzAE6ZAaVYU+4YUI8vupiFhVEWAyIqI8F2WRaVgffx+i9ofj0mtd+/x8gp+fZGuEYykKG03W1ADLlrHfUlISa3OsKioam2jOns37C7eiqKr2UvEbbrgBzzzzDIYOHdrh/UwmE9544w0YDAbcc889XR6kMxiNRkRERKC8vFy6Got2nTgBPP00S2nC9LUYePAzpJ9ci+Dq8zD7B8LsZ4BiMUNfX4V6QygK44Zg39BbcLAyBSEhwL/+5epnIISwm8XCflarV3MqtLa2McUbGMjVfFOnAllZkj3sRlrP3zYFON5EAhyhxalTwO9+x/oc6+KIoJoSJOXtQMK5H2EwVcDsF4DSyF7ISx6Lkqg+gKLgzBm2b/n73106fO9mNjcuawsKkiyOcB6LhXua5eaycM9gYIPJfv0ksHEBredvKfcWogORkVwK3nSfx5qgaBzrMwPH+sxo97iKCl7cCQdTVc4jbtvGKvCqKgY4YWFcwTJ2rLaiUCFsodNx5d6AAa4eibCBXaFnTU0NNmzYgOzs7FZfq62txX//+98uD0wIdxARwd54589rP8a6XdJFFzlvXD6prg54913g2WeB//2P7aQNBjYvKioC3nqL6bZPP2V2Rwjh02wOcI4cOYJBgwZh8uTJGDp0KKZOnYr8/PwLXy8vL8edd97p0EEK4UpZWczetOyX055Tp5i9Hj7cuePyKQ0NwNtvA0uXsvZh2DD+kGNj+ZGeDgwdymmqDz7g6pe2NhYTQvgMmwOcX/3qVxg6dCgKCwtx+PBhhIeH46KLLkKubBMvvFT//sCMGewnV1ra/v1UFTh5kgmFG26QJsYOtXo1sGIFg5r4+Lb3n1IUFkv16AF89ZXrd3MXQriUzQHOpk2b8Kc//QmxsbHo27cvvvzyS8yaNQuTJk3C8ePHnTFGIVxKUYCbbgKuuIINAQ8ebN7k1GJhn6/9+9lb5847ufRcOEh9PbBqFTM3WjYujYnhL2fNGsniCOHDbC4yrqmpgX+LVtT/+c9/oNPpMGXKFLz33nsOG5wQ7sJgAO64gw1yrU0Bz5zh11SVMyNpacz21NTw64MGycIeh9i/n+v109K0H2Pdryonh7+Urigt5U7s1qg2NJTTYVLMLIRbsznAGThwIHbs2IFBLZaIvPjii1BVFbNnz3bY4IRwJ35+jU0BT55kj689e4B9+1jjmpvLTsiqyoCob19g2jRuayPb03RBbi6zOLbM+YWF8bjcXPsDnIICTott3szUXdNdpqOigMxMzl327m3f4wshnMrmt905c+bg/fffx+23397qay+99BIsFgteffVVhwxOCHekKCwF2biRK5UBJheaNjmtquI+VocO8d877mDQI+xgMrVdc9MRReFHk61kbHL8OPDKK8wAxcc339tKVRnRrljB6Pbee4ERI+z7PkIIp7G5Buepp57CsmXL2v36yy+/DIvMewsv98UX/IiNZWuMpsENwN45/fqxA/Ly5VzU45stNR0gIMC+H56q8lhbFRYCr77KabGMDBYuN51rVBQgLo7TVKWlwOLFDISEEG5FWjAKYaNz5xi0REWxnrUjERE8P/7wA6e1hB2SkznHV1ur/ZiKChYlJyfb/v3WrGHX2s6KqBSFUWxBAV8QEsEK4VYkwBE+q6QE2LGDU03btjXWz3Rm+3b2xNFaYxoTw07IW7d2bbw+a+hQ9rmxbmyoRV4eMHCg7fU3FRXA+vX8pWmpEFcUpul27wbOnrXtewkhnEpKH4XPOXaM57DNm5s37wsLY3O+SZOAkSPb3mJGVbmKKixM+xY0igJERzOQmjPHvlkTn2YwABdfDLz2GgOQlvOBLZWW8hc1dart+wQdOMCMjC0t+aOjGdz8+KN9GSNb1dUxjWgy8WeTkOB9L6qiIgap9fV8bqmpgOwZKGwkAY7wKevWAe+8w+xNXFzjLISq8ry4aRMzNFdcAdx4Y+vVT7W1zMaEhtr2fUNDeW6urPS+c1G3uPhiFv6uXMmMSXR068JjVWX9TFERcOWVwIQJtn+figr+a8uyN0VhIGU91lmKiphqXLOGQVhDA8cZF8dgbuxYBjueSlWBw4d5JbB1K5flm82Nz/Gii/g7taVdgPBpEuAIn7F1K/Dmm/x/Rkbz86M1yxIdzazOZ58x8LnxRtsX8HREyjTspNezg2JgILB2LTMm0dGs5lZVRo6lpSyMuv564LrruneXZ1V17Aulpd27+eI9e5YZrPh4nvgbGhj4vPkm64DuuIObp3kaiwX4+mv+4VVWMqAZMIC/w4YGBq4ff8zg7o47ZKM3oUmXApz169dj0aJFOHbsGD755BP07NkT77zzDnr16oWJEyc6aoxCdFl1NVcy1dezLrQjMTG8cFy2DBg9mv1srAICeH45f57vwVpVVbGNi62ZH9FEYCAwbx6zFVu3Mt1m3U08PJxpt7FjOU1kb7BhnQapr2dQpYWq8qOzqTN7/fgj8PLLfK4ZGa0Dt9BQBggnTnBFl07Hn4Mn+fZb4L33WJWfnt78a3o9s3ZJSazUf/113uZpz1F0O7sDnE8//RS33347br31VuzevRumn/pNVFRU4E9/+lOHS8mF6G579rCIuLPgxioujrMA27Y1D3B0OmDiRK4iNpt5HlQUBi89erTd60ZVmRW6/nqeo0UXKArQqxc/rr22McAJCdEekHQkI4Mn0oICICVF2zHFxcwcOaMXTlUV8N//MqvRv3/7gZtOB/Tpw+Xq77zD+0ZGOn48znD2LDdRDQvjksP2WH/3R48C778PDBnC37sQ7bA7h/v888/j1VdfxWuvvQZ9kzeWCRMmYNeuXQ4ZnBCOsnEjzwFam+0pCnvcbNzIc4vVkSOsQz12jE3+9u7lBfaWLVwKfuAAt2poqqSE792yP5WDGQwMLCIjHRPcADxhTprEX1pDQ/v3U1VmTVSVxbCjRjEwcrTdu9mNuXdvbVmpXr04np07HT8WZ9m2jUGi1p9fejqvVnbvduqwhOezO4Nz+PBhTJ48udXt4eHhKCsr68qYhHAoVeW+UbYuwggP59R/WRlnAbZsYalDcTHPN7m5DFyCgpjNqapiY9vCQnbxDwtjQfLZs8BVV0lHf48xdSoDhIMHWYVuLTiur2dmJzcXKC9ngFNZyRPuiBGOr8OxLtnz89MewPn5NdYpTZ3q/puhmUxc0hgZqf1np9fzd7J+PdOpQrTD7gAnMTEROTk5SG8xX7phwwb0lndy4WbsOffodDzObOZ+j6+/zvfjjIzGJrk5OazvCQtjQBQSwgBnyxbOcJjNwPTpwC23OLcGVThQbCwwfz7rXg4c4OcWC5CdzcBGUfh5bS2Difp6vjiys4FbbwWCgx0zjro6Ziqio207LiYGyM9n8KVl93VXKi9ncbit02mRkbxqMZvdP4gTLmN3gHP//ffj0UcfxZtvvglFUZCXl4fNmzfjySefxDPPPOPIMQrRJYrC93lbE4s1NQxigoKA//2P78WDBzduczR8OGdITpxoPqOhqsCpUwxw7r2XG246agZFdJO0NODJJ4Hvvwc+/ZQdIc1mRrCKwiCmf39OCUVE8AWwfDmXiv/857ZtDNqe+np+T1s3MdPpGIDV13d9DM5mNvMPxtYVb9YgUwIc0QG7A5xf/vKXKC8vx7Rp01BbW4vJkycjICAATz75JB566CFHjlGILhs/nhfjFov299Jz54CsLJ67Dh1iwNI0C6PTcXYiNZUrdcvKGOT4+XFqKjWVswQS3HiouDhOgXz/PZcsWzMpAQFcpt00UxMTw6BmwwZWm99yS9e/f0AAXzx1dbYdV1fHKRxPqGgPCmIAV1tr2yo0k4lXF/LHJTrQpUYRL7zwAoqKirBt2zZs2bIF58+fxx//+EdHjU0Ih8nM5Dno/Hlt96+uZgYnNZWbRpeUtF/Do9PxfNe/PzM8AwZwGuv0aRYlCw+2aROnUMaP5y92wABGtW1NQwUHMyhav57HdJVezzRhSYltx50/zxeiJ6wwiojgaiitf5hAY1fOceNk3ld0yK4Ap76+HtOmTcORI0cQHByMzMxMjB07FqHS5EO4qYQEThUVFjK70p6GBk45ffMNi4M/+oirbg8eZIfjggJmgToTFMQZAmc3txVOZN2Xqq2uye2Jj2fqb8cOx4xhwgRmOJou5etIbW1jLwNPOPkrCseq07Veftie0lIGRrIsUXTCrgBHr9dj//79UDzhD0iIn1x/PQt+T53iQpiWJQqVldwJ4Pvv+XlmJlc+RUXx/ffECc5A7NrlGeUNootyc5lZiI/XfoyfHwOSgwcdM4ZBg4Bhw9iXoLMXndnMlOHgwdyg1FMMHcpM1ZEjnT/H6moWF2dlMb0qRAfsrsG544478MYbb+DPf/6zI8cjhNMYDMDdd7PdxurVfD9VVc4E1NRwG5yaGr7XDhvWWMIQEcGShvh4XiAfO8bjRo1qv76xpobHOKu5regGtbVM6dla56HXa8+4dMbPjy/aykoWkaWl8QXZ9OJSVZltOnmSnSzvvdf2wmRXMhiAe+7hH82+ffwDjY1t/hzNZqZfz59nVuu22zwjQyVcyu4Ap66uDq+//jpWrlyJzMxMhLSY7/3HP/7R5cEJYY/6etZZGgytz016PTB7NnDppewTdvQozw3W/jUzZjBj01RCAoMfk4lBj6LwXBIX17qrvFV+Pr/Wv78TnqDoHgYDAwzrho9amc2OWUVlFRcHPPYY50r37GFmKTycYzKbubwvJIRTNnPnssjZ01if48cfcy543z4WWfv78w+6vp5XGNddB8yZ47il+MKr2R3g7N+/H6NGjQIAHJFKSuFi9fV8T9y4kbMD1tWj/ftzin/YsOYXtcHB3K/voot4fnjqKRYGtwxuABYnx8ay/iYuju+7Oh2nulJTW6/KqqvjBffUqZ51IS1aSExkv5XiYu27dKsqMxG9ejl2LLGxDABOnOA+XPv3c7omMJA7rY8fz60aunODUUeLjgbuv59XIDt2MFVaU8MumwMGcM7Y1p5AwqfZHeCsXr3akeMQwm65ucAbbzDLYjYzINHrObuwaROweTMz93ff3fZ5Z/duZm8GDmz78RWF769lZfyIjOTUU1ERz31NN92sr+eS8mHDWCYgPFhsLLMiy5ZpD3CKi3kSdsZGkIrCojBvb6SamMjW30J0kd0Bzh/+8Id2v6YoCp5++ml7H1p4KFVl2YLFwgvL7ui/lZsL/Otf/LdPn9YzAz16cEyHDvF+jz7a+vxw7hzH3tEsREICMHIkg6Hz53lR2dDA7Rni4vj/ggKe3zIyeCHq7k1khQYTJnC7hIKCzqd+6uu59O7SS5vvq2Tt/GjNSOj1fKwhQ2yb+hJC2MTuv66lS5c2+7y+vh4nTpyAv78/+vTp0y0Bzssvv4y//e1vyM/Px5AhQ/Cvf/0LkyZNcvr3Fc2VljKjvG4dT/6qygBn/HheAPfq5Zx6wLo6dsjPzeXCkfay84GB/Hp2Nu//zDPNe6CZTNrGl5LCAOr4cdbYGI38v7WeNDERuPlmrtSSTLqXGDiQu5a//z4j98TEtl8stbWsWh8yhC8CgH8Ie/YAq1Zx/rSiovFYf39uUz9lCj9kLlMIh7M7wNndxk6uRqMR8+bNw5w5c7o0KC0+/PBDPPbYY3j55Zdx0UUXYdGiRZg1axays7ORKssHu4WqAmvWAJ98wgvcoCDWPup0POl//DHw7becqrn9dsf3Hdu3j9NSfft2Xnqg07EeJyeH55zx4xu/FhLC56JFbCw/ysq4H+Nll3GVa1QUV19pWTVVU8MxHDzIc15AAIOn0aM9sz7UqykKp0v8/IDPP+f28dYdzHU6RsfnzvF+o0ZxBVNMDF9Q33zDRkq1tQyM0tIaA5zqalaqHz7M9OKdd3LOc8+exquEyEi+uAYM8OzaGiFcRFFVrW/t2uzfvx9XXnklTp486ciHbWXcuHEYNWoUXnnllQu3DRo0CNdccw0WLFjQ6fFGoxEREREoLy9HuK3bTAsAwHffAf/9LzPuKSmtp6RUlYFAbi4LfR94wLGLSxYuZF+aIUO0H3PwIGsVn3yy8Vzz44/AggUsGNa6OOP8eZ7bnn9ee1DS0MCuyCtXspUHwAt567ZB0dHAmDFMGDSt6xFu4vRpYNs2pirLyxt7DAweDEyaxMKrgADed/Vq4LXXOJfZdLqqpcpKBk3h4XzxVVbyRaEofFEEBTGLdOWVnCMVQmg+fzt8ArisrAzl5eWOfthm6urqsHPnTvz6179udvuMGTOwadOmNo8xmUwwmUwXPjd21M5WdOrQIeCDD/ie3LNn2/dRFF7sBgRwdVNKCnDjjY75/iYTxxAba9txsbHM4lRV8dwDMEDq04dlEn37dv4YqsqL9unTbQtu3n6bGa3QUBY9N52VsFhYv/Ptt7ywf/jhjs+LwgVSUvhxxRWNG48FB/NF3nTaqrKS2R69vvNfYlUVX0wHDwKTJzNj0/Kx9u9n/c7cuWzHLYTQxO4A59///nezz1VVRX5+Pt555x1cdtllXR5YR4qKimA2m5HQYmVDQkICCgoK2jxmwYIFeO6555w6Ll+yYQNrULQ0TA0OZtZ+3TpO6TgiYWYyccWU9YJZK72eMwYmU2OA4+/PYOXVVzlL0FnQlJvLAuIpU7R/3y+/5GbTycmceWhJp2PWJiqK57rFi4Ff/MIzthPyOYGBHUe2u3cDeXmdN0EqL+c8p9nMF2N5eev6ntBQdjPOzWW6NDqac6FCiE7ZHeD885//bPa5TqdDXFwc5s6di6eeeqrLA9Oi5VYRqqq2u33EU089hSeeeOLC50ajESkpKU4dn7c6f56Zeq0rZwHe99Ahvp874iK0aQ82W1h3+25Z0zl5MguHly5leURSUusFLiYTzzP+/ryYbm9ZeUtlZZyWspZudMTfnyUXBw5wSwipmfdAmzfzRdZZB+STJ1mEFR/PF11eHgu02prHTUlhlfzy5ZwKky6+QnTK7gDnxIkTjhyHTWJjY+Hn59cqW1NYWNgqq2MVEBCAAFsv90WbcnO5wfHgwdqPsZYVHDvmmAAnIIDTStu327ZVUFERzw8t94XV6YCbbmJmZtkyBmN6feO5xrpSKi2Ne1rZss/fjh3ss6P152Uw8Oe1di0bEUp9qYc5d671C6ylmhrW9AQH8w/DYGCwU1vbdoCjKEz/ZWfzj0jLXKoQPs7ut87c3Fy0V5+cm5tr94C0MBgMGD16NFauXNns9pUrV2LChAlO/d6CmQzA9hOvvz9LDhxBUZjdUNXG8XSmvp4Zn8mT274A1umAyy8H/vQn4KGHWNMZG8vs08SJLEz+4x9t38R4xw4GZLb0BerRg9tI5OXZ9r2EG9CybqOoiFkbW+Ygw8Mb96QSQnTK7gxOr169kJ+fj/gWl8/FxcXo1asXzLbOHdjoiSeewO23347MzExkZWVh8eLFyM3Nxfz58536fQUvNlWVH7Zkys1mx24hM3w4szg5OcyOdDQWVeX90tM7X4wSHs5tFqZOdcw4y8qa993RIiiIfX4cFRCKbhQXx74JHamr47/Wq4T6el4BdJRlVpTGHgxCiE7ZHeC0V+9SWVmJQFvfze1w0003obi4GH/4wx+Qn5+PjIwMLFu2DGlpaU7/3r4uOZn1JEVF2pczm838cOQWPYGBwF13cbn4wYNcmdRW2UN9PYObmBjev7sLd/39tffZsbJYeD7rjm7QwsHGj2farqGh/U7FLdOflZWMvjvro9BZy20hxAU2/6VYC3Wt2zEEN7kkN5vN2Lp1K0aMGOGwAXbkgQcewAMPPNAt30s06tGDvWS+/157gFNYyFqZ0aMdO5Z+/YBHHuFeVEeOMCCIjW3ci6qoiP+mpzO4GTTIsd9fi5492c/NFkYjyzja2vxTuLnRo9nYLy+PzZXaYt2WvqGB0axOx0LijtKQFgsDHHlRCKGJzQGOtYOxqqrYt28fDE2WoxgMBgwfPhxPPvmk40Yo3NJFF3EjSy1b9NTWMsC57jrnvDf37w88+yxX565dy8Up1dUMdjIyWHMzcmTndZ/OMmoU8NlnXFpfU9O4V1fPnlyt1da0XWEhl9THxHT/eEUXhYezV87bb3PJYVtXAXFxrGi39tNJT++8Wt7aw0Aa/gmhic0BjnUX8TvvvBMLFy6ULsA+KiODAcv773PqKSmp7YvPigru1zRuHHDNNc4bT3Awg64JE5j9qKtjrVBYmGtXIe3axS0rzpzhLERMDMdTVcXg8OBBbv45cGDjdFRJCQMg2Y3cg82cyRf/55/zF9qzZ/MI22Lhi/bkSfYFGDmy4/lIs5l9DK64QtpcC6GR3ZO5S5YsAQBkZ2cjNzcXddaiuZ/Mnj27ayMTbs26RY9ez94x+/bxwtW6F1VNDS84AwPZEO+uu7ong6Io7rOL95Yt7NZfXc3Aa/duXqxHRHCcqtrYqLa2lue48nJuSH311a6ZThMOotMBN9zAwOaHHzhHWVvbeBWg0zGqTUhgINSRhgb2LejblwGOEEITu/eiOnHiBK655hrs27cPiqJcWDJuLTx29iqqrpK9qBynoID9aNauZcZdVZk9ycxkFmLAAN8rlj15kvtbVVczQ6MoDFx272b2JiiIAZ81GCwu5lRfaiq7Kt96q2ww7TXMZhaI5eQwyNHr+csePpwvhkWLmOoLDmYq1FpoXFfHrE1ZGedh77+fLyYhfJzW87fdAc5VV10FPz8/vPbaa+jduze2bduG4uJi/N///R/+/ve/Y5Kbt2CVAMfx6uuZkbAuB3fkknBP8+67wKeftt5aqLycjRJPn26+2reujhf7zz/PbI+vBYQ+rbKSBW1r1gAnTvAPCeCLoGdPFpFNnGj7xmtCeCmnb7a5efNmrFq1CnFxcdDpdNDpdJg4cSIWLFiARx555EIxsvAder0s8AB4wb1xI89HLeuSIiIY9PTvz9KMhgZmcQwGNsD185PgxueEhgIzZrDF95EjjWnQ0FCmPztbOi6EaJPdAY7ZbEboT0UVsbGxyMvLw4ABA5CWlobDtq6JFcKLHD/OVVAd1dAEBHAlcVPnzrHUQppx+yi9nlvbCyEcwu4AJyMjA3v37kXv3r0xbtw4/PWvf4XBYMDixYvRW+aJhQ+rreW/tmZi9HppUiuEEI5id4Dzu9/9DlU/9ZF//vnnceWVV2LSpEmIiYnBhx9+6LABCuFp9Hr7trKw9scRXqK4mB/WorTkZJl/FKIb2R3gzJw588L/e/fujezsbJSUlCAqKqrNLRyE8BUJCVwuX1oKREdrO8ZiYW1pcrJzxyaczGJhc6ONG7ldQ0VF47LC3r1ZMDxmDBs0CSGcyqGbmkRrfTcXwoulpADDhgGbN2sPcIqK2AQwM9O5YxNOVFcHfPgh8N13nKeMj2eHYp2Onx89yqZHAwZwyXd72zgIIRyiSz1e169fj9tuuw1ZWVk4e/YsAOCdd97Bhg0bHDI4ITyRogCTJnFPxJKSzu9fX892J2PGdN6tX7gpiwX44APgiy+4lDAjg7/MgADOWYaFceO0AQPY9O+ll/hLF0I4jd0BzqeffoqZM2ciKCgIu3fvhslkAgBUVFTgT3/6k8MGKIQnGjWKe0nl5TE70x6TiTMa1q0vhIfas4eZm6SkjjcQ0+u5vC4nB/joI9u3mRdCaGZ3gPP888/j1VdfxWuvvQa9Xn/h9gkTJmDXrl0OGZwQnkqnA372M+Daa9ncb98+dnyuruZsRWkpA5ucHG7R8OCDsrGmx1JV1tzU1Wmbk/TzY7HVnj3cpEwI4RR21+AcPnwYkydPbnV7eHg4ysrKujImIbyCXs8gZ9Qo7ku1ZQszOqrKGY2ePYERI/ghDRI9WF4e9+Do0UP7MVFRbGe9fTuLtoQQDmd3gJOYmIicnBykp6c3u33Dhg3SB0eInygK91QcOJC7qW/dysU12dncm+r0aeDbbxnsTJ3KXdelI7+HKSzkFva2LIFTFPYEkAyOEE5jd4Bz//3349FHH8Wbb74JRVGQl5eHzZs348knn8QzzzzjyDEK4fEaGhjIfPMNN9eMj+dFvJ8f63AKCoA33uDG0/fcAwwe7OoRC80aGmxvegQ0/vKFEE5hd4Dzy1/+EuXl5Zg2bRpqa2sxefJkBAQE4Mknn8RDDz3kyDEK4dFUlRtvfvope+T06dP860FBQK9enLY6fBh4+WXgsceAvn1dMlxhq8BALpmrr7dtC/i6OjZMcndGI1ONdXV8fj17ApGRrh6VEJ2yKcDZu3cvMjIyoNOxNvmFF17Ab3/7W2RnZ8NisWDw4MEX9qcSQtChQ8zcxMd3PP2k03Eq68AB4L33gN/+VhrfeoTevVl/c+6c9nqa+npGtO6cqjt5ks2cNmzgUkCzmS/ImBggK4ubprWM1oVwIzatoho5ciSKflrz2rt3bxQXFyM4OBiZmZkYO3asBDdCtGHzZqCqCoiL6/y+isLecIcP80N4gJAQdiguLdW+7Ds/n5mQUaOcOzZ7qCqwdi3wwgvAxx8zc9OnDzcC7duXgc7SpcCf/gSsXClL3YXbsinAiYyMxIkTJwAAJ0+ehMViccqghPAWJSVcPWVLA7/QUJZmbN3qvHEJB5swgQHLkSOdn/DLyzntc8klDI7czaZNwJtvsrZo6FA+L4OB0bdeDyQm8nYAePttBkNCuCGbpqiuu+46TJkyBYmJiVAUBZmZmfBrJ4d+/PhxhwxQCE9WVMTtiFosNuxUSAhw6lTz2yorgV27GDAVFvK2+Hhg/HgmAiSB6kKJicDddwOvvsoGR+np3GCzKbOZ1eSlpcDMmcDll7tkqB0qLwfef59BWlpa+/dTFE7HnTrF7SkyMmT5n3A7NgU4ixcvxrXXXoucnBw88sgjuPfeexEmm8YJ0a6GBpZa6GxsqanTsUwD4Llm1SruApCXx3pW64X/mTNspZKUBMyeDVx8se2LeYSDjBgBPPooA4SjR/kLDA3lL6S2llM9CQlsjjR7Nn+R7mbnTk6faa0NSknh/lo7drB1txBuxOa/sMt+ehHv3LkTjz76qAQ4QnQgKIjZfZOJ2X2tTCYusFFV4MsveZFsMHAro5aPU1/PnjpvvMEsz+zZEuS4zKBBwDPPMIuzZQsj0Pp6rjoaPZof7roCyVp7Exiovbpdp2MQt3YtMH26VMULt2JTgJObm4vUn3bAXbJkSaf3P3v2LHr27GnfyAQAXvSdOsXeKf7+zALLhoyeIyWFmf6TJ7Uv+zabGeCMGsUL448/ZrDTXqNcvZ4zIgUFvG9SEjfuFC7i788aFWudiqeoreVKMFsDsMhIzsVWVgIREc4YmRB2sSnAGTNmDGbPno17770XY8eObfM+5eXl+Oijj7Bw4ULcf//9ePjhhx0yUF9TUsIi0zVreBFYV8eLpbAwnrwmTOCiBlunPnyBqvJntm8f618Uhe+7w4fb1k3fEfz9gSlT2Lm4oUHbrERhIYPYUaOA115jAkDLuHv0YOJg1SogM1OyOMJG1vlUW1KNAF9oFgsjcyHciE0BzsGDB/GnP/0Jl112GfR6PTIzM5GUlITAwECUlpYiOzsbBw4cQGZmJv72t79h1qxZzhq3VztyBFi8GDh+nNnfnj2BgAC+h5SVAd9/D6xbxxrFG2+0/f3Imx05Aixfzn0My8sbA0CLhfsgZmby5/ZTIrJbjBnD39nhw5zB6CgorajgxfBNN7EWNTubGRmtkpLYR+fYMWkUKGwUGMh50Npa244zmXhcUJBzxiWEnRRVtb2JQW1tLZYtW4b169fj5MmTqKmpQWxsLEaOHImZM2ciIyPDGWN1KKPRiIiICJSXlyPcjbqJnjwJ/POfrPMbMKD9Ke3iYt5nzhzWLEomh/WRr70GnD/PoDAqqjGLYbE0/sxSUoD58xlsdJdjx4CXXuJ0Y1oap5yaZljMZs4OFBezUPjee5mJee01LlDRmo1RVdZ83nuv1HwKO7z1FvDVV7ZNr+3fD8yYAdx3n9OG1VJNDS9iCgqY3Q4MZKueQYOkDMgXaD1/21XGHxgYiGuvvRbXXnut3QMUrVkswAcfcHqls+mnmBj+u3w534uGDeueMbqro0cZDFRUtB0Q6HRstBcTw0zK4sXAk08yEOoOffoAjz/OBTYHDjDQCQnhuEwmTkMlJAA33wxcfXXjhbSi2DbVZL2/rRfhQgBgh+Lvv2efHi0XfpWVfLFOmOD8sQGorgZWrGBN85kzDOitl+gBAcxaXnopMHGiXPSJLuxFJRwvJ4cnv7Q0bX+cMTHMSGza5NsBjqpyI8vz5zvPduh0zIzt28cMye23d984U1OBX/6SU4/btjHIMZlYVzV8eOsFNl1ZRSzTlsIu/fpxHnftWu4bEhDQ/n3r6pianDChW9KhRiOwaBE7g0dGcqhNt/6qquJwjhzhqsIbb5Rsjq+TAMeNbNvGP9LevbUfk5DAlTbWwlRfdPYssHs360+0ZDt0Ov6sNm0Crrqqe1ftKgqzOVq28OnRo3HD6Y7OM02ZTDwmIaFr4xRNNDQw7VdWxjRrSAhP/i0b+XkDnQ64806+Ee3YwQaGsbHNr7is8715ecDIkWxw6ORIoqEBWLKEf7P9+7dd7hMSwqCnuJg7SYSG8u9b+C4JcNzI6dP8I7VlSiIykpmfoqKOA5zaWuDHH7nKxmjkCbNnT2YNEhO7PHSX2reP5x6t+xwC/FkdPMjygYkTnTa0Lhk6lFmfvDzuNq5FXh4zgJ62QtktVVXxjLp2LVMD1s6LOh2j6cmT+eLxtiuLiAjgkUfYc2DzZv6RBAQwLdjQwAKYmBhg1iymSbrhCmH/fg6lT5/Oa5ljYjjEZcv464mKcvrwhJuSAMeNWJeC20Kn4wWV9b23JbOZ++GtXMkAyroK1GLh91u6lBnpa6/13Kv+ykrba1WsF5yVlc4ZkyMEBABTp3JboOrqzhMG1dV8PjfdpD3jI9pRXMxCrR07gKAgWJJTkVObjOzzcaio9oPf7hLEbNuHkd/vRfzjtzJ14E3CwoC77gKuvJI/g0OH+AILCuIcb2Zmt/Zc2LiR72VatyNJTOQKxJ07WZMjfJMEOG4kIoJTDLawrtBs6+RnNgP/+x/wzTf8et++zeesVZXv4ytWcPXWww8Dycldegou0ZV+L+7eK+biizk7sn49py7baxxeUcHankmTgGnTuneMXqeqihXr27YBAwZgd2k6lu/qh4PnY1FVr4dOAQAVFgsQszwfY47swlV/CUHiaBvW83uK+Hj2VXDhvlklJVwxZUuizM+PK6s2bZIAx5c5JMDZuHEjMjMzESCXjV0yfDhPZFobwgFcJpmc3Pa+eMuWAV9/zWx6W2laReH0emQkp2sWLWIRrKftvmFtnmrLnk/19Y0NAN1ZUBCXfOv1fLNuaGCmzboXVVUVl5f7+zMYmjdP2pF02YYN3OBrwAD8kDcI7+wdjup6fySHG9EnoO7C3SwqUFQVhuUHQnHkV4V48LUkzVOJQruKCk6x2zrVFBzMCzhVdf8LGeEcDllIN2vWLJw9e9YRD+XTRo9m1regQNv9zWZOSUyd2jwzA7DO5rvvuNKzszcGf3/WTB48yGy0pxk+nE38ioq0H1NQwDS2J9SqhIWxb88vf8muyLW1XCJ75gz/P2UKv/bzn3tecOp26utZcxMcjB0lvfHfH4fDT7FgcFwRwpsENwCgU4D40Bpk9CzFyZwGvPr3Cpteg0Iba3Bie8c2CWx8nUMyOHb0CuySF154Ad988w327NkDg8GAsrKybv3+zhIeDlxyCfDuu/x/R20oLBZOXfTuDbS1a8auXTyJDxyo7Xvr9QyS1q5l7aQnLa+MjwfGjeNUXMsFH21paGDa+7LLGjMh7k6v54KVkSO5HL6igreHhbG/j3CQQ4eA48dhTknH19v7wWT2R6+ojqMWv5BADKw4gQO7k7FxYxiuvrqbxuojwsOZjamstO3vtbKS748S5Pguj2yFVFdXhxtuuAE///nPXT0Uh7vqKjYFPXWKV+gNDc2/rqrMzhw4wGzPvfc2Nv1raudOZmZs6aWSmMg6jtOnu/YcXOGyy7jh5KFDHW+J09DATNXgwZzS8URxcXzj7t1bghuHKy0FzGYcqkrB0eIYJIeXd36MosDfT0WkXyXWrZMmi44WGcntTgoLtR/T0MBFFN3Uf1C4KYdkcBYtWoSEblyC89xzzwEA3nrrLc3HmEwmmJpU8BqNRkcPyyH0ei5eiIsDfviBJ2x//8a9qKyraUaP5hYN7c35l5WxyM4WgYEsWq6q6vLT6HbJyZyiWbSIS0pjY1mrYg3w6uuZ0SopYXAzf37bgaHwcWYzoKrYVxiPmgZ/hBraWZ7YhsTwKuScZmZ1+HAnjtEHTZgArF7N9zUtq9LPnGHt4ahRzh6ZcGcOCXDS0tLg35W2q91gwYIFFwIjd6fXc9n2JZdwqunHH/mHHRDAXi9jx3JFVEdTMX5+ts9Zqyof05Omp5rq35/bL6xZwzrRw4cbfwaKwje8yy9nzZIEN6JNoaGAoqC00gCDn0X7caoKQ4geFot7tx7wVAMH8u92+XK+P3VUa5afz+zNnDlSk+brHBKVzJo1C3v27EFvW1rwdrOnnnoKTzzxxIXPjUYjUmzpDOcCERFc8mvPst+ePZnJsEV5Oee4o6Nt/37uIjGRma3LL+c0nrVHTng4t3HwlJob4SIDBwIJCfDLL9d+gWAy8aokPh4ocOAeSDU1bNGdnc2iK72eqcrMTP6B+1BxiU4H3HYbp55Wr+aPIimp8e9ZVXkRmJ/PTPTPfibtEoQbFRn//ve/7zTDsn37dmRmZtr1+AEBAT61jH3cOO61pKVBnNW5c8waeUNj1ogImX8XdggLAyZOROy2k6gzZ2pbYmw0AgkJqA6KgcHggNYDZjOXQK5cybkWgCsAzGYGU19+ybmX667z/DbkNggKAu65h30G16xhB/e6Ov5+LBb+6rKymOkZMcKn4j/RDreZV3rooYdw8803d3if9PT07hmMFxg8mM1VDx/mRWlnf+xlZd26KbAQ7mvyZIxccRRfrDqP8toARAZ10H2zvJyFXv36IS9fQe/enCq1W0MD8M47bGIVGtp6R0lVZSHZqlVAbi7w0EPcz8NH6PVcHDBlChcLFBQwyAkM5DYOqakS2IhGblNkHBsbi9jYWEcMR4DvuTfdBCxcyCudvn3b/8MvL+fKqSuu4DSOED4tKQnp/3cdhh4+jM0neiAiuQZKUGDzP6C6usbgZvhw1EYloqaI2YMulSMuW8aPjrpzxsQ07875q1913FPCC/n58b1K3q9ERxwS4Nxyyy2OeBjNcnNzUVJSgtzcXJjNZuzZswcA0LdvX4Rq3azEBwwZAtx/P/DGG9yQMiamcWWRqvL9OT+f75mzZgG33OK5BcZCOJIyZDCueT4MJ54uxuGzOvQ3nITO/6fiGouFf0RxcUD//qiNTsLhw5wWzsrqwjc1GrlvipbunH5+nKs5dIhdly+5pAvfWAjvpKjd3aXPAebNm4e333671e2rV6/G1KlTNT2G0WhEREQEysvLEe7lVz+nT3Ozug0bWGdj/Y2HhLCT76RJXHbu5gvhhLCbxQIcOcJeKg0NnNLo16/zPkL79qpY/I8KnD1UiVhDORLCauAXZAASElAbEoO8Ah2qqvj3c//9XVydt2YN8NJLnFPW+sd49CjnZp55Rv6Ahc/Qev72yADHEXwpwLEyGoFjx7g4Q6/nm3tamsxZC+9VX889vNatYz1aTQ1f76rKYGTsWNZzdLQZ+Jkz7PC9cWNjszlFYRIlPZ3HT5qkfafrdv3jH9zgU2v7cYDLBM+dA/7wh/abYgnhZbSevyXk9yHh4Wz1L4QvqK4G3nqLiRE/P66stgYhFgu3vFi+HNi6FZg7F5g4se3HSU4Gbr2VNWoHD/JxdTrOIg0e3HofOLvZ252zro6DEkI0IwGOEA5WVMTapvp6NmdMS3PA1b2wiXUx0vffM7HRsuGbTsd6tPh4bovyxhuMFTrqQhEZ2cUam85Yi+NsYbE0ppOEEM3YFOB8+eWXNn+D6dOnIygoyObjhPAkqsp6z02bmBEoK2usRY2PZ3ZgwgRmA4Tz7d/PzE1aWsfdbBWF9zlyBPj4Y2DYMAdmZGyVnAzs3WvbMUYjo2dP7s4phJPYFOBcc801Nj24oig4evSoW3c4FqKrLBbgq6+Azz5jSUR8PHuh+Pkxi1NYCHzwAU+48+a1vfu7cKyNG4GGehUR+hqgvJ6/jMDANgtxrUHOiRNcbTh6tAsGDPCF8f333AxOa8vtggLv6c4phIPZPEVVUFCAeI1/TGGyEYjwcqoKfPMN8P77nMJoWedpMPDCvGdPFngvWsQCb6mFcp7Ck9XYvdyIhOKzwLEzjdM4QUGMZHr2bJXWCQzk3bZudWGAM2gQI+MDB1jc01n1f2mpdOcUogM27Zoyd+5cm6abbrvtNp9ZoSR80+nTwOefs4C7R4/276coXM1bXQ289x5QW9ttQ/Qtp0+j7K+LUfVjDsJKcxkAhIQwuKmp4c61a9YAx4+3qncJCWHtlMv4+QE338zlXUeOdFyPU1bG5V3Tp0u3OyHaYVMGZ8mSJTY9+CuvvGLT/YXwNFu38kJ66NDO76sozPDk5PA8O26c88fnU/LygIULYcmpA8KmQYmxAE13BA8IYNBgNHITSwBoMn2uKNzuyaUGDgTmzwdef53zZVFRjJz1+uY7Svr5cVmXI7pzWixcQ29dR+/vD8TGcr+rLm+sJYTrOHwV1enTp/Hss8/izTffdPRDC+FWamrYPDEqSnsvIYOB992wQQIch1JVLps6cQKhg8YjoFBFTb0eBr8W+0gpCk/a5eWsRI6O5twi+Pt0i1rdESOA3/yGhUTr17OZnzXyCg/nUq6JExmAdCW4UVX23VmxghXy1iZBVgkJrLpOS+P30emYXRo8mMGiEG7O4QFOSUkJ3n77bQlwhNcrLeUFta1bqIWHc59ETTtVC21ycli7kpaGpLBq9I8pwt5zCYgIbGejzPBwVn+fOQNERqKhgUvLx4zp3mG3KykJuOEG7qFy/HhjZiUuDkhJ6foLR1VZGf/RR3ziLeuSiou5omv58sadLAMDGeikpXHTrcmTpf+BcGs2BzidLRU/fvy43YMRwpOYzTxP6GyqZOP9GxokwHGorVu5hK1XL+gUFZPTcrE7PxG1DX4I9G9j3sladJybC/Tvj7wCAxITmRRxK6GhzKI42rp1XNrXVvFYfj6waxd/nlFRLByrquI4LBZ+/Y03gD17HLA/hRDOY3OAc80110BRFHS0w4Mi79rCBwQFsTSitlb7ql4AMJk4K2JrYCQ6cPQoMxA/vfeMTszDiB4F2JGfhEGx52FoWotjFRQEGI0oOl2NqjoDbrih4545XqO2ltkbP7/WwU1REbBzJ1+k8fH8eQYHczuIvDzuTdGrFx9j+3a+iB95hPcRws3Y/BabmJiITz/9FBaLpc2PXbt2OWOcQridmBiu7LXuT6SFtcZ1/HjnjcsnmUzN6lGC9A24d/QuDIs/h4NFccivCIXZ0vzCq8ZsQE5FPIqKFMyZA8yc2d2DdpEff2TmqmXXSVVt3IsiOroxvejvz5/tyZPM4ACcrhowANixA9iypVuHL4RWNgc4o0eP7jCI6Sy7I4S3UBRusqiq2pd9Fxcze+M2tR7eIjiYXRWbiA+pwmPjt+DagQehU1Rkn4/DvnNxOFAYh73n4nGqLAJpoSWYf0c1brzRhzJqu3bxRduyULikhBt0RUS0njsNDeXXy8sbbwsM5GOsXWv78jNV5R5acq4QTmTzFNUvfvELVFVVtfv1vn37YvXq1V0alBCeYvhwtiHZs4eLS9polHtBdTWz/JdfzppO4UDDhnHpd4vCpqigWtw+fC+u7H8Eu/ITca4qBKYGf4Qa6tCvdh+G9K+H/trL7LjU82BFRZyea8m6gVpbe1Xo9Qxi6uqa356UxALvnBxmdDpiNnMp+ubNLGCur+cfzIABbFaYkcHvI4SD2BzgTJo0qcOvh4SEYMqUKXYPSAhPEhgI3Hcf8OKLXMSTnNw8uw/wfb2wkBfHkyaxdYmUqTnY2LGsKykubnNZW1RQLS7pfaLxBosF2H8AuOQe31vyrNO1nTmpruZUlC0vzuBgpi+bZnbacu4c8OabXJpvLUKzFrCtWcO+Cf36AXfe2boduBB2kt3EheiiHj2Axx/nopRdu7jyOCiocS+q+nrWa15/PTBnTtsXz6KLkpLYWGj5ck6nBAa2f19VZVFySopvzhX26NHY6LCpjqaL6uoYkLT8uSoKPyxtFHFbnTsH/POf7M7cu3frpeVJSVwGn50N/OtfwKOPNmvAKIS9bApw9u7di4yMDOg0TlYfOHAAAwYMgH9HeXshvEBsLPDgg5yC2raNGzfW1vK9fNAgIDOTK26FE/3sZ5x+2b4dSE1llqBlNqK2lr+cqCjg7rvZV8bXZGYCK1cyY9N09VNQUPu1NBUVDERabr1TW8tppvb64ZjNzNwcOdLxHG5QEL9+8CDw2mvA00/LyizRZTZFHiNHjkRBQQHiNL4pZGVlYc+ePbKbuPAJisLamjlz7DteVZn9OX6cWXy9HkhM5P6LPlMA2xVhYcBDD7Gj8fbt3CgsIoJTUGYzp1EUhdmB22/nCdUXDRnCxn3HjrH+xRoEJiQwEKmra16HY627SUtrHTDm5/P2/v3b/l6HDnHLid69Oy5QA/gi79+fY9i9G7joIvuenxA/sSnAUVUVTz/9NII1RtZ1LQvShBCtqCprLtes4Qpe63kYaFyNO2UK6zB9JRlqXZmmKIxPNJeFhIcDDzwAnDrF5n/bt3P6IyCARawXXcSNw3yt7qYpf3/Ol774Ipd+p6fzBxwXx94HTeuY6uvZsjstjdF2U/X1zOzcdFPbhckAC4rr6rR3PNbrObe7bh1f8FKsJrrAprfLyZMn4/Dhw5rvn5WVZdPu40L4GlUFli1jx/zqap5Dmnbir6hg1n7/fl7Y3nYbcPYsz9/WC+3kZKBvX+/I8uTnc4pvwwY+d4AzTZMmsVwmPl7DgygKT9rp6Tz5ms384cjJstHIkZyie+stvrgSEhjU9O/PX0BxMX9eDQ18QY4c2Xzfq4YGZmcGDeLeWG2xWBix2zo3GxfH7FJZmczrii6xKcBZs2aNk4bh2erqmIXdupU1GGYzV9KMGcPW7y2nrYWwWrMGePddXuC2tXgkLIwfZWW833ffMatTU9N4n6AgnmemTmUDQU8MdOrrgaVLue9jcTFnlkJCGABadwb44gtuoH3llTbuMdnV3ba91UUXMZhYs4aZruxs3h4Rwem9wEC+sAYObMzQmM1cDnjuXOPO5+0FIXV1DIRsXfqt1zO61dpcSoh2dDnhvXDhQjz66KM4fPgw+vXrp7kA2Vvs3Al8/DHrFi0WnqgUhZ9v3coFC7NmAZddJu+zormKCuCzz3juSEpq/34WC2tz8vL4upoyhWUU1oSE0cgprn37eOH7s5951lSW2Qz873/AN98wiTBsWOtki8UCFBQwyKuuZmLGx95qnKN/f35cfTVThNXVDDDOn2dvm8OHmamxrpZSVf6SZs9mtNlRSk2vb9x4zRZmM98s25v2EkKjLr8NZmRkAAAef/xxHD16FGFhYRgyZAgyMjKQkZGBK664osuDdFcbN/LKsqaGV98tV1A2NPDq8+23eQX+s5/Jm7I7qKlhVr60lCfOkBAGDNHR3TuOnTv5+hg4sOP7HTnCaarwcO5/2LRGB+Dt4eFsNPvFFywvueEGz5mRWbeOq7uTkzkd1RadjkGgwcB2N+np7c+M+BRV5Xxl08r0pCQWbtlyRZWQwI+Wj33sGDM7VVV8vOhoTldp2WDTz4/B08aNret3OlJczDfU9l4MQmhkd4BTUVGBsLAwXHLJJQCAZcuWAQCMRiP279+P/fv3Y+XKlV4b4Bw7xsDFbObfcFsnE39/Tl8XFwNffsm/8Ysv7v6xCiovB1avZmf5s2cb236oKt+vs7L4+0lN7Z7xbNzI81FH2ZbKSgY4gYGNUzZnznDmoGWdbHQ0p3q+/ZbPJSXFueN3hIYG4IcfGLhoOZ/FxnIl+OrVbHvjsxcMqsqVRqtXM3VXUdHYwM9g4JvS1KnAxIn2p/MUhcVdffvaP86LLgI2beJ0U0e9iazMZl6BTJkiKW/RZXYHOJMmTcK3336LHi12ow0PD8eECRMwYcKELg/Ona1dy8AlI6PzK+WYGGZwVq7k+41kXrtfQQHw6quseYyM5Ht2y7KCL7/k3oH33cdpEmdSVZYxdLZ7dV4e3++tMwEGAy+mTaa2FwLFx3O6ats2zwhwsrN5sWDLWJOSmNHKyWl/dbJXU1WmsT7+mC+ExMTmS7irqhgVZ2dzimnuXG3BhTMMG8Yl6UePcll+R2+WqspMVM+e7NUjRBfZff2TmZmJcePG4dChQ81u3717Ny6//PIuD8ydFRWxviY+Xvs0QFIS6yf273fu2ERrRiOwaBFP/IMG8VzQNMj082Ot1NChDFoXLeJJ19labJvU5tdPneJYW96vvaazisKaz/XrW28b5I5OnuQ4benpFhbGoO/UKacNy7398APbZoeEMGiIimr+AgkJYeSXnMyq9Pfe67jTsDMZDMBdd3H66+DB9utxzGZGrEFBwLx5Mj0lHMLuAOf111/HXXfdhYkTJ2LDhg04cuQIbrzxRmRmZiLAy3tM5OTwRGhLE9SgIL6RHz3qvHGJtm3YwMxN08UgbVEUnhcKCpjN6Wyj47o6BrvnznEqyRaKwiml6ur279PQwMx+0zFb9yfs6HmEhXE8HeyJ6zbsCcKs53KTybFj8Qjl5VxuFhDAqLwj4eEMclavZkbHVfr2BR55hFcWhw4xq1RayhdpWRnfULOzmer++c+59FQIB+hSkfGzzz4Lg8GA6dOnw2w2Y+bMmdi+fTtGefkL1Lp60db5f3//xt4eonvU1XE6MSxM29SgovCcsHcvkJvL9+SWrNsxrFvH843FwvPNiBHsTdbZruJWWVksn7BY2n4tWQOsphfnVVWsv+ysvZSqdh6guYOubB7tk1O9u3YxAh80SNv9o6NZcLZlS+fV7M7Uvz/w7LMc/9q1TN01NDB9mpbGeiHZz0Q4mN0BTn5+PhYsWIDXX38dgwcPxqFDh3DzzTd7fXAD8ORlz8nDeiIU3efgQU5lpKdrPyYykm1A9uxpHuBYLGzK9/nnzOBFRrJliKIw6P3hB753jx7NHmqdvVdnZvJivKCg7WXi1kyNNaCuq2Mg1Fm9SnU1A6CQEO3P2VWSk/k8tdagArzwDwhgqYbP2biRT96WwuG4OAY4113XedGXM4WGApMnsxCxpIQpOIOBfyie1NdAeAy7p6h69+6N9evX4+OPP8bOnTvx2Wef4YEHHsBf/vIXR47PLcXHs2bAaNR+jNnME2RnWWXhWOXlvFC0pcZSUXhhWVLSeJuqctrqf//j14cNY/BjDXISErjUvGdPnoP+85/OXx+xscDMmczWl5a2/ro1mKmtZXBTUsKAoKPWI6rK4CsryzOC6aFDGXzm5Wk/Ji8P6NfPtQkJl7BWpmvd9sAqNJRFS7a8YTmTTscXf8+eDL4kuBFOYneAs2TJEuzevfvCMvCZM2di9erVWLhwIR544AGHDdAd9e3LN9f8fO3HnD/Pv+XRo503LtFaV6ZpmtZlHjjApnxRUXxfbq84ODSUr42dO3n/zsyezc68+fksbG7aoRhg4NTQwCyPtWN+R1OjpaUcw/jxnX9vd2AwANOmMeukZfq2rIwXC9Om+eAqYuu8oz0NjjxlzlIIB7I7wLn55ptb3TZq1Chs2rTJ67d00OmYabVYtF0U1dfzwisrq/ubyfm6sDD+vmwpZlVVnkSbTjFt2MDgo2UvtLYEBPB+mzczm9IRf3/uLzV/PgOYU6dYl2P9yMvjRszx8QycOsrKVFayR87kyTzGU1x8MQOWEyeYpWrrPKyqvEg4fRqYMYOzHD5Hp2Mhrq3V49XVTGHamvkRwsM5PDeYnp6OjRs3Ovph3c748byqX7GCUxUREW3fz2TiooGhQ9kNXXSvgQOZcSko0N7Ar6KC9StDh/LzggL2x9G00eNP4uPZEmDHDk5DdcTPjyf5SZP4mjp+nMGUdQuHwYO5GefKlZxyS0pqPuVmMjEDVFnJx7n1Vs/pYgzwed59N5/T2rUM0mJiGmuIKisZ+ISHA3PmADfe6IPZG6sJEzquTG9LYSEwfbosvRY+xymTn1E+UAmv17N/lp8fsGoVrywTEvgmrCg8QeXnMxMwciSbx/nAj8XtBAczo/Hf/zIw6Gy6X1X5uxw1qrGB69mznBoZPFj799Xp+Bo5eVL7MXo9V2KNGNH6a3ffzdVTP/zAx6yvb/yavz+DtxtvBC691DNXFwUGsl3KlCnsMbV5c+OUVXAwn9u4cazX8aTgzeEyMzn3mZ+vrcq6ooIvLNnXQvggqe7qgqAgvimPHctu5Dt3copBVTmVMGwYT66jRtnWyEw41qRJXERy6BBX17Z39a+qDB7Cw1kXYz2R1tfza7a2BfDz67jPjS38/Tk1M3UqL+BzcxsXoaSk8LXmCUXFHWm6M8C11zbOxISGuq4Rr9uJieHOve+9xzegjua8q6uZDrz4YtuicyG8hMcFOCdPnsQf//hHrFq1CgUFBUhKSsJtt92G3/72tzC44NLVzw8YPpwf5883FkGGhjJj4LN75biR2FjWuLz8MqeNevRgwbf1d6Oq/L2dPcss/p13Nk5PAQwcFKVxk2Ot6usdv1TbYGChurcXqwcFdd7rx2fNns15u2++YZFXz57Nr6Dq6pjhKS/nXlDz5slKJeGTPO5Vf+jQIVgsFixatAh9+/bF/v37ce+996Kqqgp///vfXTq2uDjbuht7GrOZQYEnThH06gX83/+xj83Wrax1sVJVZm2ysnhxPGRI82NTU3nhfP689mX+DQ0sk+jXz3HPQQgAjLJvvZWpu1Wr2B7dWkWvKI3dKq+5hmk/iRSFj1JU1fPXDv7tb3/DK6+8guPHj2s+xmg0IiIiAuXl5QgPD3fi6DyXdS+kbdtYLFtVxffW3r15Yeip0yJFRWziZ822hYRw09Sm+xW2tGQJ9zccOlRbgHf2LLMtCxYweBLCKRoauM3B8eOcs9TrufnmiBES2AivpfX87XEZnLaUl5cjupP11yaTCaYmm9cY3aXplZuqqADeeYfBjdHIVWIBAZx22byZH+npwO23MzjwJLGxLMa1xcSJXCp+5oy2TsLFxcDNN0twI5zM359XGsOGuXok2tXX849EVTm15olV8cIjeHyAc+zYMbz44ov4f//v/3V4vwULFuC5557rplF5tspKduLdupUn85aZjaQkXiyeOAH8+9/AAw+0vfLHm/TrB9xyC/DWW7xYTk1tXdagqmy0d+YMC5tnz3bJUIVwP6rKtOa2bbxSsC6RCwri0ndZIiecwG2mqH7/+993GoBs374dmZmZFz7Py8vDlClTMGXKFLz++usdHttWBiclJUWmqNrw+uusXxwwoOPVK6rK6f+YGODpp23rE+OJVJXvzR99xBpOg6H5XlRlZczYTJjA5n2yck4IcBrts8+A777jFUBkZGPTwerqxiZHU6bwKsIT571Ft9I6ReU2AU5RURGKioo6vE96ejoCfzrj5uXlYdq0aRg3bhzeeust6GxcriQ1OG0rKAB+9zu+x2gpmLZYuGz57ru5tNoXGI1sCbBuHTtUWyy8EB0/nh+pqXIhKgQA/nG8+y7wxRd8Q4mPb/3HoaoMcvLyOHd8zz1d22ZeeD2Pq8GJjY1FbGyspvuePXsW06ZNw+jRo7FkyRKbgxvRvh07WD+idUpfp2MWY80avjf5Qr+S8HBuLTB1Ki9OzebGpeRCiCY2b+bSxaSk9nv2KArTwAYDV4X17t15+28hNPC4yCAvLw9Tp05FSkoK/v73v+P8+fMoKChAQUGBq4fmFfbu5aoiW07W8fGcsjl71nnjckeKwgvNwEAJboRoxWLhlQ+gbRO+sDCmQletsm3zOCHa4TYZHK1WrFiBnJwc5OTkIDk5udnX3GS2zaNVVdmeHTYYmMmorXXOmIQQHujoUeDgQWZvtEpK4uqFffu8v5ulcDqPC3DmzZuHefPmuXoYXisggFMutmhoYH8cWe0phLjg9Gle9YSFaT8mMJBvKKdPS4DjCFVVwO7drD0oKWFNQWIi9xfKyPD6WiePC3CEc/XrxyZ4qqp92qW4mBuJJiQ4dWhCCE/SlWmmJitehR0sFmDlStY/nT3LK9CgIL6xHzgArF7NWqcbbuBu0F7K42pwhHONG8cLLq19EFWVAc7EidLUTgjRhMHANwh7Sgdkqbj9VBX45BO2Xy8vZ7+PQYPYZ6hXL+5Fk5bGhl4vvcSdor2UBDiimV69uB3BqVPapqrOnmX2Zvx4549NCOFBUlPZDMra1E+LmhpOm6SmOm9c3m79emDpUq5MS09vexoqKIiBT309u5fm5HT3KLuFBDiiGUXhPn59+3KLm/ayzKrKafKaGuDGGxkYCSHEBX37MnOQl6f9mPx8npSHDnXasLxaQwOwYgVrbTprZKYofOMuLmZTLy8kAY5oJTEReOQRZjKPHgUOHWJ9WlUVp65OneJycp0OmDePGxYLIUQzOh2bRel0PIl2xmhkZ+OLL/b64lenyc4Gjh0DevbUdn9FYZ+PLVu4C7GXkSJj0abkZOCpp1hwvG4dcOQItyLQ6djS4uqrWYhvywpQIYSPGT8eOHmSUyZ1dUCPHm13Mi4qYhv1GTMY4Aj7HD7Mn7Mt+8TExbHw+MgR7kTsRSTAEe0KCgKysvgeVVzcOD0eGekbHYuFEF2k03EOOyAAWL6cqd+ICO5FpShMC1v3p7rmGuCmm1rvYiu0q6riz9wWOl3jhnpeRl5JolOK4nWBvRCiu/j7A9ddx11ot21jEax1mWZ4ODBrFpdvpqRIS/Cu0uu5RNweXhhYet8zEkII4X4SEzm3feWVTAerKtPEXnhidRlrMzKzmb1vtKis1L67soeRImMhhBDdx8+PU1RhYRLcONqoUSwaPn9e+zF5eUD//vzwMhLgCCGEEN4gOpqFk+fOscdNZyoquLR8yhTtGR8PIgGOEEII4S2uvJJ9hA4e7HjLC6ORG5tOnsz6KC8k+UEhhBDCW0RHAw8+CCxezFVr/v6sf7LuRVVRwSX5fn7A9OnAHXd47U7JEuAIIYQQ3iQhAfi//+Mu4uvWsWNrbS1XqYWEcPPASZOAYcO8ug7Ke5+ZEEII4auCgzn9NHEi99WpqmKAExXFAMgHluRLgCOEEEJ4K52Ou4f7ICkyFkIIIYTXkQBHCCGEEF5HAhwhhBBCeB0JcIQQQgjhdSTAEUIIIYTXkQBHCCGEEF5HAhwhhBBCeB0JcIQQQgjhdSTAEUIIIYTXkQBHCCGEEF5HtmoQwoeUlgJ79gBlZYDFwn33hgwBkpN9YmsaIYQPkQBHCB9QWAh89x2waRP/bw1mLBYgIgIYMQKYORMYONClwxRCCIeRAEcIL3fqFPDyy8CRI0B8PDB4MODnx6+pKrM669YB+/cDd98NjB/v2vEKIYQjSIAjhBcrKQFefRXIyeFUlH+Lv3hFAaKjgago4Phx4I03gLAw3lcIITyZFBkL4cXWrwcOHQIGDWod3DSlKEDv3qzN+fprZnaEEMKTSYAjhJeqqeHUU2Rkx8GNlaKw2PjAAeDYMacPTwghnEoCHCG81KFDwJkzQGKi9mPCw4HKSmDvXueNS7g5VWV0XFEB1Ne7ejRC2E1qcITwUhUVgNkMGAzaj1EUZntKS503LuGmysuBnTuZ9iso4BI7vR7IzASysoD+/QGdXBMLzyEBjhAdqKnhe319PQOFxEQgIMDVo9LOnt42qio9cXyKqgJbtgDvvQfk5/MFHhnJ4MZkYlHWDz8Ao0cDd97JrwnhASTAEaINeXnA1q28mD1/npkQf3+gRw9g6lRgzBguuXZnERFcDm4yaQ/KVJXPNTrauWMTbmT9ei6fs1jYCKllwVZSEmA08o+hqgp45BHOZQrh5jwy3zh79mykpqYiMDAQiYmJuP3225GXl+fqYQkvoKp8v3/uOeC//2XWPikJSE9ncFNYyHPBH/8I7Nrl6tF2bOBAICWFwZpW5eU8dw0f7rxxCTdy6hTwv/8xZdenT/vV6OHhfEHt3Al8+GH3jlEIO3lkgDNt2jR89NFHOHz4MD799FMcO3YM119/vauHJbzA5s3A668DtbXAsGFAaioQHMwMSEgI0KsXkJHB/jKvvOLexbgBAcw2VVQAdXWd319VgdOngaFDGdAJH7BlC1BUBKSldX7fgABG+1u3AufOOX9sQnSRRwY4jz/+OMaPH4+0tDRMmDABv/71r7FlyxbUS8W/6ILiYuDdd3mi79Wr/ToUnQ7o25eBw9tvA9XV3TtOW0ycyIDl0KGOF8SoKnD0KKfdrrpKanB8QkUF05UxMdp/4TExjO63b3fu2IRwAI8McJoqKSnBu+++iwkTJkCv17d7P5PJBKPR2OxDiKZ27GCNpZbshbUxXm4uN690VxERwPz5zDplZzND0zTQsVh4Mb5vH7sZ33cfF8sIH3DmDLM3cXHaj9HpgMBA4OBB541LCAfx2ADnV7/6FUJCQhATE4Pc3Fx88cUXHd5/wYIFiIiIuPCRkpLSTSMVnqChAVi7ltNQWlfCGgy87/r17t35NzERePxx4LbbgNBQ7kl14AA/srNZVHzFFcCTTwIjR7p6tKLb1NU1Vs/bQq9377SlED9RVNU93pp///vf47nnnuvwPtu3b0dmZiYAoKioCCUlJTh16hSee+45RERE4Ouvv4bSTqrVZDLBZDJd+NxoNCIlJQXl5eUIlxUBPq+kBPjlLxngREVpP+7cOWZzFi7k+767q6pitqa0lNmbkBDuO2XLRbzwEgcPspq+d2/bmiUdPcrt53/xC6cNTYiOGI1GREREdHr+dptl4g899BBuvvnmDu+T3mTuIDY2FrGxsejfvz8GDRqElJQUbNmyBVlZWW0eGxAQgABPamAiulVDAy9mbe1jptPxuPp6zwhwQkJkt3Dxk549WVNTVMTiYS2sXY5lHlN4ALcJcKwBiz2sSaimGRohbBEYyABFy2qjpurqeKzEzsLjhIcDF10EfPIJ5zG1FBoXFzPFOXas88cnRBd5XA3Otm3b8NJLL2HPnj04deoUVq9ejVtuuQV9+vRpN3sjRGfCwrjj9vnzth1XUgKMGsWGekJ4nKwsdnU8fbrz+9bVAWfPssulLRucCeEiHhfgBAUF4bPPPsMll1yCAQMG4K677kJGRgbWrl0rU1DCborCJdWKwh44WlRUAEFBPEcI4ZF69QJ+9jMGLydPsjCrLVVVrNkZPhy46aZuHaIQ9nKbKSqthg4dilWrVrl6GMILDRsGDB4M/PgjC287ysrU1QEnTgATJkg5gvBwF1/MlVQffMCldcHB3G9Kp+M+H0VFnL/NygLuvtu2KnwhXMjjAhwhnCUgALj3Xq6IOnCAzV3Dw5uXJqgqUFbG/jfDhgF33SUbLAsPpyjAlClslrRjB/slnD/PbI7BAEyfzkh+0CDbl5QL4UJus0y8u2ldZiZ8T0EB8M47XE5dWckgx8+PK60qKtg8b9Qo9pWRi1nhdcxmTknV1zObExTk6hEJ0YzHLRMXwl306MGmd8eOcdudw4fZ18zaM2bcOO5RJdsZCK/k5ye7hQuvIAGOEG1QFO431bevq0cihBDCHlI9IIQQQgivIwGOEEIIIbyOBDhCCCGE8DoS4AghhBDC60iAI4QQQgivIwGOEEIIIbyOBDhCCCGE8DoS4AghhBDC60iAI4QQQgivIwGOEEIIIbyOBDhCCCGE8DoS4AghhBDC60iAI4QQQgivIwGOEEIIIbyOBDhCCCGE8DoS4AghhBDC60iAI4QQQgivIwGOEEIIIbyOBDhCCCGE8DoS4AghhBDC60iAI4QQQgivIwGOEEIIIbyOBDhCCCGE8DoS4AghhBDC60iAI4QQQgivIwGOEEIIIbyOBDhCCCGE8DoS4AghhBDC60iAI4QQQgivIwGOEEIIIbyOBDhCCCGE8DoeHeCYTCaMGDECiqJgz549rh6O0EhVgbNngU2bgNWr+W9uLm8XQgghHMHf1QPoil/+8pdISkrCjz/+6OqhCA1UFdi7F1i/Hti1CzAaAUXh7WFhwPDhwKRJwKhRvF0IIYSwl8cGOMuXL8eKFSvw6aefYvny5a4ejuiExQJ89RXw6adATQ3QoweQktIY4JSVMZOzYwcwezZw3XWAn5+rRy2EEMJTeWSAc+7cOdx77734/PPPERwcrOkYk8kEk8l04XOj0eis4Yk2rFwJfPABEBEB9O7d/GuKAkRF8eP8eeCTTwCDAbjmGpcMVQghhBfwuBocVVUxb948zJ8/H5mZmZqPW7BgASIiIi58pKSkOHGUoqniYmDpUiA4mJmbjsTFMdD5+mvW6QghhBD2cJsA5/e//z0URenwY8eOHXjxxRdhNBrx1FNP2fT4Tz31FMrLyy98nD592knPRLS0YwdQWAj07Knt/j16MCjavt254xJCCOG9FFV1j7UrRUVFKCoq6vA+6enpuPnmm/HVV19BaVKFajab4efnh1tvvRVvv/22pu9nNBoRERGB8vJyhIeHd2nson0WC/Db3wJnzgB9+mg/7tQpZnL+8hdAr3fe+IQQQngWredvt6nBiY2NRWxsbKf3+/e//43nn3/+wud5eXmYOXMmPvzwQ4wbN86ZQxR2MJmA0lLA1hgyPJyrrCorGegIIYQQtnCbAEer1NTUZp+HhoYCAPr06YPk5GRXDEl0wGLhKilbl31bV1dZLM4ZlxBCCO/mNjU4wjsFBrK4uKbGtuNqaoCAAB4rhBBC2MrjA5z09HSoqooRI0a4eiiiDX5+QFYWp6lsqfYqKgLGjgWCgpw3NiGEEN7L4wMc4f7GjmVNTUmJtvuXlzOwycpy7riEEEJ4LwlwhNOlpXELhrNnWTTckepq4ORJBjf9+nXL8IQQQnghjysyFp5HUYBbb2XwsmYNuxn36MFuxVb19UBBAaeyJk4E5s4FdBJ+CyGEsJMEOKJbBAYC998PpKYyyDl6lCuk/PwAs5nBTGIiMGsWcMUVUnsjhBCiayTAEd3GYACuvhqYPh3Ys4dTUdXVDGZSU4GRI4GfVv0LIYQQXSIBjuh2wcHAhAn8EEIIIZxBqhyEEEII4XUkwBFCCCGE15EARwghhBBeRwIcIYQQQngdCXCEEEII4XUkwBFCCCGE15EARwghhBBex2f74Kg/bW1tNBpdPBIhhBBCaGU9b1vP4+3x2QCnoqICAJCSkuLikQghhBDCVhUVFYiIiGj364raWQjkpSwWC/Ly8hAWFgZFUQAwKkxJScHp06cRHh7u4hE6njw/zybPz7PJ8/Ns8vzch6qqqKioQFJSEnQd7MrssxkcnU6H5OTkNr8WHh7u9r/grpDn59nk+Xk2eX6eTZ6fe+goc2MlRcZCCCGE8DoS4AghhBDC60iA00RAQACeffZZBAQEuHooTiHPz7PJ8/Ns8vw8mzw/z+OzRcZCCCGE8F6SwRFCCCGE15EARwghhBBeRwIcIYQQQngdCXCEEEII4XUkwOmEyWTCiBEjoCgK9uzZ4+rhOMzs2bORmpqKwMBAJCYm4vbbb0deXp6rh+UQJ0+exN13341evXohKCgIffr0wbPPPou6ujpXD81hXnjhBUyYMAHBwcGIjIx09XC67OWXX0avXr0QGBiI0aNHY/369a4eksOsW7cOV111FZKSkqAoCj7//HNXD8lhFixYgDFjxiAsLAzx8fG45pprcPjwYVcPy2FeeeUVDBs27ELzu6ysLCxfvtzVw3KaBQsWQFEUPPbYY64eikNIgNOJX/7yl0hKSnL1MBxu2rRp+Oijj3D48GF8+umnOHbsGK6//npXD8shDh06BIvFgkWLFuHAgQP45z//iVdffRW/+c1vXD00h6mrq8MNN9yAn//8564eSpd9+OGHeOyxx/Db3/4Wu3fvxqRJkzBr1izk5ua6emgOUVVVheHDh+Oll15y9VAcbu3atXjwwQexZcsWrFy5Eg0NDZgxYwaqqqpcPTSHSE5Oxp///Gfs2LEDO3bswMUXX4yrr74aBw4ccPXQHG779u1YvHgxhg0b5uqhOI4q2rVs2TJ14MCB6oEDB1QA6u7du109JKf54osvVEVR1Lq6OlcPxSn++te/qr169XL1MBxuyZIlakREhKuH0SVjx45V58+f3+y2gQMHqr/+9a9dNCLnAaAuXbrU1cNwmsLCQhWAunbtWlcPxWmioqLU119/3dXDcKiKigq1X79+6sqVK9UpU6aojz76qKuH5BCSwWnHuXPncO+99+Kdd95BcHCwq4fjVCUlJXj33XcxYcIE6PV6Vw/HKcrLyxEdHe3qYYgW6urqsHPnTsyYMaPZ7TNmzMCmTZtcNCphr/LycgDwyr81s9mMDz74AFVVVcjKynL1cBzqwQcfxBVXXIFLL73U1UNxKAlw2qCqKubNm4f58+cjMzPT1cNxml/96lcICQlBTEwMcnNz8cUXX7h6SE5x7NgxvPjii5g/f76rhyJaKCoqgtlsRkJCQrPbExISUFBQ4KJRCXuoqoonnngCEydOREZGhquH4zD79u1DaGgoAgICMH/+fCxduhSDBw929bAc5oMPPsCuXbuwYMECVw/F4XwqwPn9738PRVE6/NixYwdefPFFGI1GPPXUU64esk20Pj+rX/ziF9i9ezdWrFgBPz8/3HHHHVDduLG1rc8PAPLy8nDZZZfhhhtuwD333OOikWtjz/PzFoqiNPtcVdVWtwn39tBDD2Hv3r14//33XT0UhxowYAD27NmDLVu24Oc//znmzp2L7OxsVw/LIU6fPo1HH30U//vf/xAYGOjq4TicT23VUFRUhKKiog7vk56ejptvvhlfffVVszdYs9kMPz8/3HrrrXj77bedPVS7aH1+bb2Qz5w5g5SUFGzatMlt06+2Pr+8vDxMmzYN48aNw1tvvQWdzr3jeXt+f2+99RYee+wxlJWVOXl0zlFXV4fg4GB8/PHHmDNnzoXbH330UezZswdr16514egcT1EULF26FNdcc42rh+JQDz/8MD7//HOsW7cOvXr1cvVwnOrSSy9Fnz59sGjRIlcPpcs+//xzzJkzB35+fhduM5vNUBQFOp0OJpOp2dc8jb+rB9CdYmNjERsb2+n9/v3vf+P555+/8HleXh5mzpyJDz/8EOPGjXPmELtE6/NrizXONZlMjhySQ9ny/M6ePYtp06Zh9OjRWLJkidsHN0DXfn+eymAwYPTo0Vi5cmWzAGflypW4+uqrXTgyoYWqqnj44YexdOlSrFmzxuuDG4DP2Z3fJ21xySWXYN++fc1uu/POOzFw4ED86le/8ujgBvCxAEer1NTUZp+HhoYCAPr06YPk5GRXDMmhtm3bhm3btmHixImIiorC8ePH8cwzz6BPnz5um72xRV5eHqZOnYrU1FT8/e9/x/nz5y98rUePHi4cmePk5uaipKQEubm5MJvNF3o09e3b98Lr1VM88cQTuP3225GZmYmsrCwsXrwYubm5XlMzVVlZiZycnAufnzhxAnv27EF0dHSr9xpP8+CDD+K9997DF198gbCwsAt1UxEREQgKCnLx6LruN7/5DWbNmoWUlBRUVFTggw8+wJo1a/Dtt9+6emgOERYW1qpeylqX6RV1VC5bv+VBTpw44VXLxPfu3atOmzZNjY6OVgMCAtT09HR1/vz56pkzZ1w9NIdYsmSJCqDND28xd+7cNp/f6tWrXT00u/znP/9R09LSVIPBoI4aNcqrlhmvXr26zd/V3LlzXT20Lmvv72zJkiWuHppD3HXXXRdel3Fxceoll1yirlixwtXDcipvWibuUzU4QgghhPAN7l+YIIQQQghhIwlwhBBCCOF1JMARQgghhNeRAEcIIYQQXkcCHCGEEEJ4HQlwhBBCCOF1JMARQgghhNeRAEcIIYQQXkcCHCGEEEJ4HQlwhBBCCOF1JMARQjQzdepUKIoCRVEubOKpxbx58y4c9/nnnzttfMXFxYiPj8fJkycBcHfn++67D9HR0TaP2Rtcf/31+Mc//uHqYQjhdiTAEUK0cu+99yI/P7/VjsIvv/wyevXqhcDAQIwePRrr16+/8LWFCxciPz/f6WNbsGABrrrqKqSnpwMAvv32W7z11lv4+uuv2xyzvaZOnYrHHnvMIY9lr3Xr1uGqq65CUlJSu4HjM888gxdeeAFGo7H7ByiEG5MARwjRSnBwMHr06AF/f/8Lt3344Yd47LHH8Nvf/ha7d+/GpEmTMGvWLOTm5gIAIiIi0KNHD6eOq6amBm+88QbuueeeC7cdO3YMiYmJmDBhQqsxu4O6ujq7j62qqsLw4cPx0ksvtXufYcOGIT09He+++67d30cIbyQBjhA+4P3330dgYCDOnj174bZ77rkHw4YNQ3l5uabH+Mc//oG7774b99xzDwYNGoR//etfSElJwSuvvOKsYbeyfPly+Pv7IysrCwCnxR5++GHk5uZCUZQLWR1VVfHXv/4VvXv3RlBQEIYPH45PPvmk2WN9++23mDhxIiIjIxETE4Mrr7wSx44du/C4a9euxcKFCy9Mu1mnxNLT0/Gvf/2r2WONGDECv//97wEw8/PQQw/hiSeeQGxsLKZPn655TC3NmjULzz//PK699toO7zd79my8//77nf34hPApEuAI4QNuvvlmDBgwAAsWLAAAPPfcc/juu++wfPlyREREdHp8XV0ddu7ciRkzZjS7fcaMGdi0aZNTxtyWdevWITMz88LnCxcuxB/+8AckJycjPz8f27dvBwD87ne/w5IlS/DKK6/gwIEDePzxx3Hbbbdh7dq1F46tqqrCE088ge3bt+OHH36ATqfDnDlzYLFYsHDhQmRlZV2YqsvPz0dKSormcb799tvw9/fHxo0bsWjRIs1jstfYsWOxbds2mEymLj+WEN7CvXK5QginUBQFL7zwAq6//nokJSVh4cKFWL9+PXr27AkA+P7777Fv3z48/vjjbR5fVFQEs9mMhISEZrcnJCSgoKDA6eO3OnnyJJKSki58HhERgbCwMPj5+V2YHquqqsI//vEPrFq16kKmp3fv3tiwYQMWLVqEKVOmAACuu+66Zo/9xhtvID4+HtnZ2cjIyIDBYLgwVWervn374q9//euFz7WOyV49e/aEyWRCQUEB0tLSuvRYQngLCXCE8BFXXnklBg8ejOeeew4rVqzAkCFDLnzt0ksvxaWXXtrpYyiK0uxzVVVb3daRd999F/fff/+Fz5cvX47c3NxWt02aNKnN42tqahAYGNjh98jOzkZtbe2FqSGruro6jBw58sLnx44dw9NPP40tW7agqKgIFosFAJCbm9vlQuWmWSZbxmSvoKAgAEB1dXWXH0sIbyEBjhA+4rvvvsOhQ4fazMTMmjUL//jHPzBo0KA2j42NjYWfn1+rbE1hYWGrx+rI7NmzMW7cuAuf9+zZEyNGjGh1W3tiY2NRWlra4fewBirffPNNq8cKCAi48P+rrroKKSkpeO2115CUlASLxYKMjIxOi4J1Oh1UVW12W319fbPPQ0JC7BqTvUpKSgAAcXFxXX4sIbyFBDhC+IBdu3bhhhtuwKJFi/DBBx/g6aefxscff3zh60ePHkW/fv3aPd5gMGD06NFYuXIl5syZc+H2lStX4uqrr9Y8jrCwMISFhbV5uxYjR47E//73vw7vM3jwYAQEBCA3N7fdqZ/i4mIcPHgQixYtupAt2rBhQ7P7GAwGmM3mVsfGxcU1Ww5vNBpx4sSJLo+pK/bv34/k5GTExsY6/LGF8FQS4Ajh5U6ePIkrrrgCv/71r3H77bdj8ODBGDNmDHbu3InRo0ejvLwcoaGhnS6vfuKJJ3D77bcjMzMTWVlZWLx4MXJzczF//vxueibAzJkz8dRTT6G0tBRRUVFt3icsLAxPPvkkHn/8cVgsFkycOBFGoxGbNm1CaGgo5s6di6ioKMTExGDx4sVITExEbm4ufv3rXzd7nPT0dGzduhUnT55EaGgooqOjodPpcPHFF+Ott97CVVddhaioKDz99NPw8/PrcNxaxtSWyspK5OTkXPj8xIkT2LNnD6Kjo5Gamnrh9vXr17cqABfC56lCCK9VXFysDhw4UL3vvvua3T579mx15syZqqqq6oYNG9RbbrnlwtemTJmiPvroo20+3n/+8x81LS1NNRgM6qhRo9S1a9e2ug8AdenSpQ57Di2NHz9effXVVy98/s9//lNNS0trdh+LxaIuXLhQHTBggKrX69W4uDh15syZzca7cuVKddCgQWpAQIA6bNgwdc2aNc3GfvjwYXX8+PFqUFCQCkA9ceKEqqqqWl5ert54441qeHi4mpKSor711lvq8OHD1WeffVZV1fZ/flrG1NLq1atVAK0+5s6de+E+NTU1anh4uLp582abfo5CeDtFVVtMJgshfMqiRYtQUlKCp556CgD7uIwYMaJVrxetFEXB0qVLcc011zhukE0sW7YMTz75JPbv3w+dTjpd/Oc//8EXX3yBFStWuHooQrgVeXcQwscdOHCgzS0ZQkNDsW/fPs2PM3/+fISGhjp6eK1cfvnluP/++5s1LfRler0eL774oquHIYTbkQyOEKKZs2fPoqamBgCQmpoKg8Gg6bjCwsIL+yElJia2WkkkhBDdSQIcIYQQQngdmaISQgghhNeRAEcIIYQQXkcCHCGEEEJ4HQlwhBBCCOF1JMARQgghhNeRAEcIIYQQXkcCHCGEEEJ4HQlwhBBCCOF1JMARQgghhNf5/x8uye8PqFh2AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_dataset(train_x, train_labels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "SjPlpf2-wHl8"
+ },
+ "source": [
+ "## Tek Katmanlı Algılayıcı Eğitme\n",
+ "\n",
+ "Tek katmanlı algılayıcı eğitmek için PyTorch gradyan hesaplama makinelerini kullanalım.\n",
+ "\n",
+ "Sinir ağımızın 2 girdisi ve 1 çıktısı olacaktır. $W$ ağırlık matrisinin boyutu $2\\times1$ ve ek girdi vektörünün, $b$, $1$ olacaktır.\n",
+ "\n",
+ "Kodumuzu daha yapılandırılmış hale getirmek için tüm parametreleri tek bir sınıfta gruplandıralım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "J1KaixW-cMWJ"
+ },
+ "outputs": [],
+ "source": [
+ "class Network():\n",
+ " def __init__(self):\n",
+ " self.W = torch.randn(size=(2,1),requires_grad=True)\n",
+ " self.b = torch.zeros(size=(1,),requires_grad=True)\n",
+ "\n",
+ " def forward(self,x):\n",
+ " return torch.matmul(x,self.W)+self.b\n",
+ "\n",
+ " def zero_grad(self):\n",
+ " self.W.data.zero_()\n",
+ " self.b.data.zero_()\n",
+ "\n",
+ " def update(self,lr=0.1):\n",
+ " self.W.data.sub_(lr*self.W.grad)\n",
+ " self.b.data.sub_(lr*self.b)\n",
+ "\n",
+ "net = Network()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rQ7W6TOacIAI"
+ },
+ "source": [
+ "> `W.zero_()` yerine `W.data.zero_()` kullandığımızı unutmayın. Bunu yapmamız gerekiyor çünkü *Autograd* mekanizması kullanılarak izlenen bir tensörü doğrudan değiştiremeyiz.\n",
+ "\n",
+ "Çekirdek model önceki örnektekiyle aynı olacaktır, ancak kayıp fonksiyonu bir lojistik kaybı olacaktır. Lojistik kaybı uygulamak için, ağımızın çıktısı olarak **olasılık** değerini almamız gerekiyor, yani `sigmoid` etkinleştirme fonksiyonunu kullanarak $z$ çıktısını [0,1] aralığına getirmemiz gerekiyor: $ p=\\sigma(z)$.\n",
+ "\n",
+ "Gerçek $y_i\\in\\{0,1\\}$ sınıfına karşılık gelen i. girdi değeri için $p_i$ olasılığını alırsak, kaybı $\\mathcal{L_i}=-(y_i\\log p_i + (1-y_i)\\log(1-p_i))$ olarak hesaplarız.\n",
+ "\n",
+ "PyTorch'ta, bu adımların her ikisi de (sigmoid ve ardından lojistik kayıp uygulamak) `binary_cross_entropy_with_logits` işlevine yapılan bir çağrı kullanılarak yapılabilir. Ağımızı minigruplar halinde eğittiğimiz için, bir minigrubun tüm öğelerindeki kaybın ortalamasını almamız gerekiyor ve bu aynı zamanda `binary_cross_entropy_with_logits` işlevi tarafından otomatik olarak yapılır:\n",
+ "\n",
+ "> `binary_crossentropy_with_logits` çağrısı, `sigmoid` çağrısına ve ardından `binary_crossentropy` çağrısına eşdeğerdir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "id": "kdDxWeCqwHl8"
+ },
+ "outputs": [],
+ "source": [
+ "def train_on_batch(net, x, y):\n",
+ " z = net.forward(x).flatten()\n",
+ " loss = torch.nn.functional.binary_cross_entropy_with_logits(input=z,target=y)\n",
+ " net.zero_grad()\n",
+ " loss.backward()\n",
+ " net.update()\n",
+ " return loss"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zAAgw0h6KzUd"
+ },
+ "source": [
+ "Verilerimizde dolaşırken veri kümelerini yönetmek için yerleşik PyTorch mekanizmasını kullanacağız. İki kavram üzerine kuruludur:\n",
+ "* **Veri kümesi (Dataset)** ana veri kaynağıdır, **Yinelenebilir (Iterable)** veya **Harita tarzı (Map-style)** olabilir.\n",
+ "* **Veri yükleyici (Dataloader)**, bir veri kümesindeki verilerin yüklenmesinden ve minigruplara bölünmesinden sorumludur.\n",
+ "\n",
+ "Bizim durumumuzda, bir tensöre dayalı bir veri kümesi tanımlayacağız ve onu 16 elemanlı minigruplara ayıracağız. Her minigrup iki tensör; girdi verileri (boyut=16x2) ve etiketler (sınıf numarası - 16 uzunluğunda tamsayı tipli bir vektördür) içerir."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "PfyqjVb2wHl8",
+ "outputId": "b3a685a9-304c-4e7e-adf9-2858cc47c3a5"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[tensor([[ 1.5442, 2.5290],\n",
+ " [-1.6284, 0.0772],\n",
+ " [-1.7141, 2.4770],\n",
+ " [-1.4951, 0.7320],\n",
+ " [-1.6899, 0.9243],\n",
+ " [-0.9474, -0.7681],\n",
+ " [ 3.8597, -2.2951],\n",
+ " [-1.3944, 1.4300],\n",
+ " [ 4.3627, 3.1333],\n",
+ " [-1.0973, -1.7011],\n",
+ " [-2.5532, -0.0777],\n",
+ " [-1.2661, -0.3167],\n",
+ " [ 0.3921, 1.8406],\n",
+ " [ 2.2091, -1.6045],\n",
+ " [ 1.8383, -1.4861],\n",
+ " [ 0.7173, -0.9718]]),\n",
+ " tensor([1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 1., 1., 1.])]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Create a tf.data.Dataset object for easy batched iteration\n",
+ "dataset = torch.utils.data.TensorDataset(torch.tensor(train_x),torch.tensor(train_labels,dtype=torch.float32))\n",
+ "dataloader = torch.utils.data.DataLoader(dataset,batch_size=16)\n",
+ "\n",
+ "list(dataloader)[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xrwgkbQjhkEp"
+ },
+ "source": [
+ "Artık ağımızı 15 dönem eğitmek için tüm veri kümesinde dolaşabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "QGchp9D6gVJa",
+ "outputId": "b4c4751d-cb56-4104-d5b5-f1ae9d3d858d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 0.5761\n",
+ "Dönem 1: son toplu iş kaybı = 0.4701\n",
+ "Dönem 2: son toplu iş kaybı = 0.4053\n",
+ "Dönem 3: son toplu iş kaybı = 0.3614\n",
+ "Dönem 4: son toplu iş kaybı = 0.3296\n",
+ "Dönem 5: son toplu iş kaybı = 0.3053\n",
+ "Dönem 6: son toplu iş kaybı = 0.2859\n",
+ "Dönem 7: son toplu iş kaybı = 0.2702\n",
+ "Dönem 8: son toplu iş kaybı = 0.2570\n",
+ "Dönem 9: son toplu iş kaybı = 0.2457\n",
+ "Dönem 10: son toplu iş kaybı = 0.2360\n",
+ "Dönem 11: son toplu iş kaybı = 0.2275\n",
+ "Dönem 12: son toplu iş kaybı = 0.2200\n",
+ "Dönem 13: son toplu iş kaybı = 0.2133\n",
+ "Dönem 14: son toplu iş kaybı = 0.2072\n"
+ ]
+ }
+ ],
+ "source": [
+ "for epoch in range(15):\n",
+ " for (x, y) in dataloader:\n",
+ " loss = train_on_batch(net,x,y)\n",
+ " print('Dönem %d: son toplu iş kaybı = %.4f' % (epoch, float(loss)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Elde ettiğimiz parametreler aşağıdadır:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5QaDiCQUkFOT",
+ "outputId": "45b4a66b-1222-40f4-c758-d58f1c7daf8c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[1.3857],\n",
+ " [0.3547]], requires_grad=True) tensor([0.], requires_grad=True)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(net.W,net.b)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s4_Atvn5K4K9"
+ },
+ "source": [
+ "Eğitimimizin işe yaradığından emin olmak için iki sınıfı ayıran doğruyu çizelim. Ayırma doğrusu $W\\times x + b = 0.5$ denklemiyle tanımlanır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 283
+ },
+ "id": "PgRTHttLwHl9",
+ "outputId": "d9abf92f-cb70-4c56-ccd0-5e027239da58"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/_l/jnklp1bj4cl95rc01tf5vx4h0000gn/T/ipykernel_37297/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+ " fig.show()\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_dataset(train_x,train_labels,net.W.detach().numpy(),net.b.detach().numpy())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1W4TZfXOmIlS"
+ },
+ "source": [
+ "Şimdi doğrulama veri kümesindeki doğruluğu hesaplayalım:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "HUjdeIefsIsg",
+ "outputId": "a1a363d4-a307-4769-9ccf-fe8a857b62af"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor(0.8000)"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pred = torch.sigmoid(net.forward(torch.tensor(valid_x)))\n",
+ "torch.mean(((pred.view(-1)>0.5)==(torch.tensor(valid_labels)>0.5)).type(torch.float32))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Burada neler olduğunu açıklayalım:\n",
+ "* `pred`, tüm geçerleme veri kümesi için tahmin edilen olasılıkların vektörüdür. Onu ağımız üzerinden `valid_x` orijinal geçerleme verilerini çalıştırarak ve olasılıkları elde etmek için `sigmoid` uygulayarak hesaplıyoruz.\n",
+ "* `pred.view(-1)` orijinal tensörün düzleştirilmiş bir görünümünü oluşturur. `view` (görünüm), numpy'deki `reshape` (yeniden şekillendir) işlevine benzer.\n",
+ "* `pred.view(-1)>0.5`, tahmin edilen sınıfı gösteren bir boole tensörü veya doğruluk değeri döndürür (False (Yanlış) = sınıf 0, True (Doğru) = sınıf 1).\n",
+ "* Benzer şekilde, `torch.tensor(valid_labels)>0.5)` geçerleme etiketleri için doğruluk değerlerinin boole tensörünü oluşturur.\n",
+ "* Bu iki tensörü eleman bazında karşılaştırırız ve `True` doğru tahmine ve `False` yanlışa karşılık gelen başka bir boole tensörü elde ederiz.\n",
+ "* Bu tensörü kayan virgüllü sayıya çeviririz ve `torch.mean` kullanarak ortalama değerini alırız, istenen doğruluk budur."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_95qF9lY2kHp"
+ },
+ "source": [
+ "## Sinir Ağları ve Optimize Ediciler\n",
+ "\n",
+ "PyTorch'ta, bir sinir ağını temsil etmek için özel bir `torch.nn.Module` modülü tanımlanmıştır. Kendi sinir ağınızı tanımlamanın iki yöntemi vardır:\n",
+ "* **Ardışık (Sequential)** olarak, yalnızca ağınızı oluşturan katmanların bir listesini belirtirsiniz.\n",
+ "* `torch.nn.Module`'den devralınan bir **sınıf** olarak tanımlarsınız.\n",
+ "\n",
+ "İlk yöntem, ardışık katman bileşimine sahip standart ağları belirtmenize izin verirken, ikincisi daha esnektir ve gelişigüzel karmaşık mimarilerden oluşan ağları ifade etme fırsatı verir.\n",
+ "\n",
+ "Modüllerin içinde standart **katmanları** kullanabilirsiniz, örneğin:\n",
+ "* `Linear` (Doğrusal), tek katmanlı algılayıcıya eşdeğer yoğun doğrusal katman. Ağımız için yukarıda tanımladığımızla aynı mimariye sahiptir.\n",
+ "* `Softmax`, `Sigmoid`, `ReLU`, etkinleştirme işlevlerine karşılık gelen katmanlar.\n",
+ "* Özel ağ türleri için başka katmanlar da vardır - evrişim, yinelemeli, vb. Kursun ilerleyen bölümlerinde bunların çoğuna yeniden değineceğiz.\n",
+ "\n",
+ "> PyTorch'taki etkinleştirme işlevinin ve kayıp işlevlerinin çoğu iki biçimde mevcuttur: **İşlev** (`torch.nn.functional` ad alanının içinde) ve **katman olarak** (`torch.nn` ad alanının içinde). Etkinleştirme işlevleri için, ayrı katman nesnesi oluşturmadan `torch.nn.functional` öğesinden işlevsel öğeleri kullanmak genellikle daha kolaydır.\n",
+ "\n",
+ "Tek katmanlı algılayıcıyı eğitmek istiyorsak, yalnızca bir yerleşik `Linear` katmanı kullanabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "D77pXPR6oFRs",
+ "outputId": "efa49e5c-72d4-4781-89d4-4ab6597d2b0e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Parameter containing:\n",
+ "tensor([[-0.3171, 0.3296]], requires_grad=True), Parameter containing:\n",
+ "tensor([0.6366], requires_grad=True)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "net = torch.nn.Linear(2,1) # 2 girdi, 1 çıktı\n",
+ "\n",
+ "print(list(net.parameters()))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0tbe0Et_oiNo"
+ },
+ "source": [
+ "Gördüğünüz gibi `parameters()` yöntemi, eğitim sırasında ayarlanması gereken tüm parametreleri döndürür. $W$ ağırlık matrisine ve $b$ ek girdiye karşılık gelirler. Gradyanları parametrelere göre hesaplamamız gerektiğinden, `requires_grad` öğesinin `True` olarak ayarlandığını fark edebilirsiniz.\n",
+ "\n",
+ "PyTorch ayrıca **gradyan inişi** gibi optimizasyon yöntemlerini uygulayan yerleşik **eniyileyiciler** içerir. Bir **rasgele gradyan inişi eniyileyiciyi** şu şekilde tanımlayabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "id": "B4AxyrFMozh0"
+ },
+ "outputs": [],
+ "source": [
+ "optim = torch.optim.SGD(net.parameters(),lr=0.05)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6eB8v58eo9pp"
+ },
+ "source": [
+ "Eniyileyiciyi kullanınca, eğitim döngümüz şöyle görünecektir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ups7nlV22ofp",
+ "outputId": "503d8ae9-35f3-4ecb-e2ff-4da2ec2914eb"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 0.9087961316108704, geçerleme doğruluğu = 0.4000000059604645\n",
+ "Dönem 1: son toplu iş kaybı = 0.7703850865364075, geçerleme doğruluğu = 0.46666666865348816\n",
+ "Dönem 2: son toplu iş kaybı = 0.6663016676902771, geçerleme doğruluğu = 0.6000000238418579\n",
+ "Dönem 3: son toplu iş kaybı = 0.587394654750824, geçerleme doğruluğu = 0.7333333492279053\n",
+ "Dönem 4: son toplu iş kaybı = 0.5265983939170837, geçerleme doğruluğu = 0.7333333492279053\n",
+ "Dönem 5: son toplu iş kaybı = 0.4788611829280853, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 6: son toplu iş kaybı = 0.44066122174263, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 7: son toplu iş kaybı = 0.4095434844493866, geçerleme doğruluğu = 0.8666666746139526\n",
+ "Dönem 8: son toplu iş kaybı = 0.38377830386161804, geçerleme doğruluğu = 0.8666666746139526\n",
+ "Dönem 9: son toplu iş kaybı = 0.3621285855770111, geçerleme doğruluğu = 0.800000011920929\n"
+ ]
+ }
+ ],
+ "source": [
+ "val_x = torch.tensor(valid_x)\n",
+ "val_lab = torch.tensor(valid_labels)\n",
+ "\n",
+ "for ep in range(10):\n",
+ " for (x,y) in dataloader:\n",
+ " z = net(x).flatten()\n",
+ " loss = torch.nn.functional.binary_cross_entropy_with_logits(z,y)\n",
+ " optim.zero_grad()\n",
+ " loss.backward()\n",
+ " optim.step()\n",
+ " acc = ((torch.sigmoid(net(val_x).flatten())>0.5).float()==val_lab).float().mean()\n",
+ " print(f\"Dönem {ep}: son toplu iş kaybı = {loss}, geçerleme doğruluğu = {acc}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vRLXEQ4Qrcvx"
+ },
+ "source": [
+ "> Ağımızı girdi verilerine uygulamak için `net.forward(x)` yerine `net(x)` kullanabileceğimizi fark edebilirsiniz, çünkü `nn.Module` Python '__call__()' işlevini uygular.\n",
+ "\n",
+ "Bunu dikkate alarak, genel `train` (eğitim) fonksiyonunu tanımlayabiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5c6WsBhlrlIs",
+ "outputId": "54de8404-4170-4a15-abba-039d06d5e946"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 0.9851850867271423, geçerleme doğruluğu = 0.4000000059604645\n",
+ "Dönem 1: son toplu iş kaybı = 0.8353211879730225, geçerleme doğruluğu = 0.46666666865348816\n",
+ "Dönem 2: son toplu iş kaybı = 0.709876298904419, geçerleme doğruluğu = 0.46666666865348816\n",
+ "Dönem 3: son toplu iş kaybı = 0.6093153953552246, geçerleme doğruluğu = 0.6666666865348816\n",
+ "Dönem 4: son toplu iş kaybı = 0.530561625957489, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 5: son toplu iş kaybı = 0.46913135051727295, geçerleme doğruluğu = 0.7333333492279053\n",
+ "Dönem 6: son toplu iş kaybı = 0.4209415018558502, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 7: son toplu iş kaybı = 0.382853239774704, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 8: son toplu iş kaybı = 0.3525124490261078, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 9: son toplu iş kaybı = 0.3280949890613556, geçerleme doğruluğu = 0.800000011920929\n"
+ ]
+ }
+ ],
+ "source": [
+ "def train(net, dataloader, val_x, val_lab, epochs=10, lr=0.05):\n",
+ " optim = torch.optim.Adam(net.parameters(),lr=lr)\n",
+ " for ep in range(epochs):\n",
+ " for (x,y) in dataloader:\n",
+ " z = net(x).flatten()\n",
+ " loss = torch.nn.functional.binary_cross_entropy_with_logits(z,y)\n",
+ " optim.zero_grad()\n",
+ " loss.backward()\n",
+ " optim.step()\n",
+ " acc = ((torch.sigmoid(net(val_x).flatten())>0.5).float()==val_lab).float().mean()\n",
+ " print(f\"Dönem {ep}: son toplu iş kaybı = {loss}, geçerleme doğruluğu = {acc}\")\n",
+ "\n",
+ "net = torch.nn.Linear(2,1)\n",
+ "\n",
+ "train(net,dataloader,val_x,val_lab,lr=0.03)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KzuIDqJ8sFYm"
+ },
+ "source": [
+ "## Ağı Ardışık Katmanlar Olarak Tanımlama\n",
+ "\n",
+ "Şimdi çok katmanlı algılayıcıyı eğitelim. Sadece bir katman dizisi belirterek tanımlanabilir. Ortaya çıkan nesne otomatik olarak `Module`'den devralır, ör. ayrıca tüm ağın tüm parametrelerini döndürecek `parameters` yöntemine sahip olacaktır."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "tBtytmEAsq-O",
+ "outputId": "06ad840b-c2b7-409e-e01e-a9170548151d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sequential(\n",
+ " (0): Linear(in_features=2, out_features=5, bias=True)\n",
+ " (1): Sigmoid()\n",
+ " (2): Linear(in_features=5, out_features=1, bias=True)\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "net = torch.nn.Sequential(torch.nn.Linear(2,5),torch.nn.Sigmoid(),torch.nn.Linear(5,1))\n",
+ "print(net)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5r5RbLB1s6YB"
+ },
+ "source": [
+ "Bu çok katmanlı ağı yukarıda tanımladığımız `train` fonksiyonunu kullanarak eğitebiliriz:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ogXKdcfIs_ND",
+ "outputId": "957ccd8d-0076-4e9b-89f1-edc1de75f18e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 0.6021518111228943, geçerleme doğruluğu = 0.6000000238418579\n",
+ "Dönem 1: son toplu iş kaybı = 0.505835235118866, geçerleme doğruluğu = 0.6000000238418579\n",
+ "Dönem 2: son toplu iş kaybı = 0.4291416108608246, geçerleme doğruluğu = 0.7333333492279053\n",
+ "Dönem 3: son toplu iş kaybı = 0.35532107949256897, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 4: son toplu iş kaybı = 0.2888007164001465, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 5: son toplu iş kaybı = 0.23157590627670288, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 6: son toplu iş kaybı = 0.1831870824098587, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 7: son toplu iş kaybı = 0.1437370777130127, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 8: son toplu iş kaybı = 0.11355496197938919, geçerleme doğruluğu = 0.800000011920929\n",
+ "Dönem 9: son toplu iş kaybı = 0.09217184782028198, geçerleme doğruluğu = 0.800000011920929\n"
+ ]
+ }
+ ],
+ "source": [
+ "train(net,dataloader,val_x,val_lab)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jY4R1XEGtEzJ"
+ },
+ "source": [
+ "## Ağı Sınıf Olarak Tanımlamak\n",
+ "\n",
+ "`torch.nn.Module`'den miras alınan bir sınıfı kullanmak daha esnek bir yöntemdir, çünkü onun içinde herhangi bir hesaplama tanımlayabiliriz. `Module` birçok şeyi otomatikleştirir, örn. PyTorch katmanları olan tüm dahili değişkenleri otomatik olarak anlar ve eniyileme için parametrelerini toplar. Ağın tüm katmanlarını sınıfın üyeleri olarak tanımlamanız yeterlidir:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "SlsJmGu0tMsZ",
+ "outputId": "240d5c89-096c-4392-99cd-1ade5ff3e3e1"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MyNet(\n",
+ " (fc1): Linear(in_features=2, out_features=10, bias=True)\n",
+ " (func): ReLU()\n",
+ " (fc2): Linear(in_features=10, out_features=1, bias=True)\n",
+ ")\n"
+ ]
+ }
+ ],
+ "source": [
+ "class MyNet(torch.nn.Module):\n",
+ " def __init__(self,hidden_size=10,func=torch.nn.Sigmoid()):\n",
+ " super().__init__()\n",
+ " self.fc1 = torch.nn.Linear(2,hidden_size)\n",
+ " self.func = func\n",
+ " self.fc2 = torch.nn.Linear(hidden_size,1)\n",
+ "\n",
+ " def forward(self,x):\n",
+ " x = self.fc1(x)\n",
+ " x = self.func(x)\n",
+ " x = self.fc2(x)\n",
+ " return x\n",
+ " \n",
+ "net = MyNet(func=torch.nn.ReLU())\n",
+ "print(net)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "HwdapRxft-7M",
+ "outputId": "6eb900cf-4902-4a04-c62b-497b68455406"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dönem 0: son toplu iş kaybı = 0.7364251017570496, geçerleme doğruluğu = 0.46666666865348816\n",
+ "Dönem 1: son toplu iş kaybı = 0.7092168927192688, geçerleme doğruluğu = 0.5333333611488342\n",
+ "Dönem 2: son toplu iş kaybı = 0.6822843551635742, geçerleme doğruluğu = 0.5333333611488342\n",
+ "Dönem 3: son toplu iş kaybı = 0.6552965044975281, geçerleme doğruluğu = 0.5333333611488342\n",
+ "Dönem 4: son toplu iş kaybı = 0.6281629800796509, geçerleme doğruluğu = 0.6000000238418579\n",
+ "Dönem 5: son toplu iş kaybı = 0.5996261835098267, geçerleme doğruluğu = 0.6666666865348816\n",
+ "Dönem 6: son toplu iş kaybı = 0.5690117478370667, geçerleme doğruluğu = 0.7333333492279053\n",
+ "Dönem 7: son toplu iş kaybı = 0.5358816981315613, geçerleme doğruluğu = 0.8666666746139526\n",
+ "Dönem 8: son toplu iş kaybı = 0.5006707310676575, geçerleme doğruluğu = 0.8666666746139526\n",
+ "Dönem 9: son toplu iş kaybı = 0.46446147561073303, geçerleme doğruluğu = 0.800000011920929\n"
+ ]
+ }
+ ],
+ "source": [
+ "train(net,dataloader,val_x,val_lab,lr=0.005)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dvAiaj_JndyP"
+ },
+ "source": [
+ "**Görev 1**: Eğitim sırasında kayıp fonksiyonunun ve doğruluğun, eğitim ve geçerleme verilerindeki grafiğini çizin.\n",
+ "\n",
+ "**Görev 2**: Bu kodu kullanarak MNIST sınıflandırma problemini çözmeye çalışın. İpucu: Kayıp işlevi olarak `crossentropy_with_logits` kullanın."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ana Fikirler\n",
+ "\n",
+ "* PyTorch, düşük seviyede tensörler üzerinde çalışmanıza izin verir, en fazla esnekliğe sahip olursunuz.\n",
+ "* Datasets (Veri Kümeleri) ve Dataloaders (Veri Yükleyiciler) gibi verilerle çalışmak için uygun araçlar vardır.\n",
+ "* `Sequential` (Ardışık) sözdizimini kullanarak veya `torch.nn.Module`'den bir sınıf miras alarak sinir ağı mimarilerini tanımlayabilirsiniz.\n",
+ "* Bir ağı tanımlamaya ve eğitmeye daha da basit bir yaklaşım için PyTorch Lightning'e bakın."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "celltoolbar": "Slideshow",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "IntroPyTorch.ipynb",
+ "provenance": []
+ },
+ "interpreter": {
+ "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ },
+ "livereveal": {
+ "start_slideshow_at": "selected"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/3-NeuralNetworks/05-Frameworks/translations/Overfitting.tr.md b/lessons/3-NeuralNetworks/05-Frameworks/translations/Overfitting.tr.md
new file mode 100644
index 00000000..aade8301
--- /dev/null
+++ b/lessons/3-NeuralNetworks/05-Frameworks/translations/Overfitting.tr.md
@@ -0,0 +1,75 @@
+# Aşırı Öğrenme
+
+Aşırı öğrenme, makine öğrenmesinde son derece önemli bir kavramdır ve bunu doğru yapmak çok önemlidir!
+
+Aşağıdaki 5 noktayı (aşağıdaki grafiklerde `x` ile temsil edilen) yaklaşıklama problemini göz önünde bulundurun:
+
+ | 
+-------------------------|--------------------------
+**Doğrusal model, 2 parametre** | **Doğrusal olmayan model, 7 parametre**
+Eğitim hatası = 5.3 | Eğitim hatası = 0
+Geçerleme hatası = 5.1 | Geçerleme hatası = 20
+
+* Solda iyi bir düz doğru yaklaşımı görüyoruz. Parametre sayısı yeterli olduğundan, model nokta dağılımının arkasındaki fikri doğru alır.
+* Sağdaki model çok güçlüdür. Sadece 5 noktamız ve modelin 7 parametresi olduğu için, kendisini tüm noktalardan geçecek şekilde ayarlayabilir ve hatayı 0 yapar. Ancak bu, modelin verinin arkasındaki doğru örüntüyü anlamasını engeller, dolayısıyla geçerleme hatası çok yüksektir.
+
+Modelin zenginliği (parametre sayısı) ile eğitim örneklerinin sayısı arasında doğru bir denge kurmak çok önemlidir.
+
+## Aşırı öğrenme neden olur?
+
+ * Yeterli eğitim verisi yoktur.
+ * Model çok güçlüdür.
+ * Girdi verisinde çok fazla gürültü vardır.
+
+## Aşırı öğrenme nasıl tespit edilir?
+
+Yukarıdaki şekilden de görebileceğiniz gibi, aşırı öğrenme, çok düşük bir eğitim hatası ve yüksek bir geçerleme hatası ile tespit edilebilir. Normalde eğitim sırasında hem eğitim hem de geçerleme hatalarının azalmaya başladığını görürüz ve ardından bir noktada geçerleme hatası azalmayı durdurabilir ve yükselmeye başlayabilir. Bu, aşırı öğrenmenin bir işaretidir ve muhtemelen bu noktada eğitimi durdurmamız (veya en azından modelin anlık görüntüsünü almamız) gerektiğinin göstergesi olacaktır.
+
+
+
+## Aşırı öğrenme nasıl önlenir?
+
+Aşırı öğrenmenin meydana geldiğini görüyorsanız, aşağıdakilerden birini yapabilirsiniz:
+
+ * Eğitim verisinin miktarını artırın
+ * Modelin karmaşıklığını azaltın
+ * [Hattan düşürme](../../../4-ComputerVision/08-TransferLearning/TrainingTricks.md#Dropout) gibi bazı [düzenlileştirme teknikleri](../../../4-ComputerVision/08-TransferLearning/TrainingTricks.md), ki daha sonra ele alacağız, kullanın.
+
+## Aşırı öğrenme ve yanlılık-varyans ödünleşmesi
+
+Aşırı öğrenme aslında istatistikte [yanlılık-varyans ödünleşmesi](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff) adı verilen daha genel bir sorundur. Modelimizde olası hata kaynaklarını göz önüne alırsak iki tür hata görebiliriz:
+
+* **Yanlılık hataları**, algoritmamızın eğitim verileri arasındaki ilişkiyi doğru şekilde yakalayamamasından kaynaklanır. Modelimizin yeterince güçlü olmamasından (**eksik öğrenme**) kaynaklanabilir.
+* **Varyans hataları**, modelin anlamlı ilişki yerine girdi verilerindeki gürültüyü yaklaşıklamasından (**aşırı öğrenme**) kaynaklanır.
+
+Eğitim esnasında, yanlılık hatası azalır (modelimiz verilere yaklaşmayı öğrenirken) ve varyans hatası artar. Aşırı öğrenmeyi önlemek için eğitimi - manuel olarak (aşırı öğrenmeyi tespit ettiğimizde) veya otomatik olarak (düzenlileştirmeyi katarak) durdurmak önemlidir.
+
+## Vargılar
+
+Bu derste, en popüler iki YZ çerçevesi olan TensorFlow ve PyTorch için çeşitli API'ler arasındaki farkları öğrendiniz. Ayrıca, çok önemli bir konuyu da öğrendiniz, aşırı öğrenme.
+
+## 🚀 Kendini Sınama
+
+Ekli defterlerde alt kısımda 'görevler'i bulacaksınız; defterler üzerinden çalışın ve görevleri tamamlayın.
+
+## [Ders sonrası sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/205)
+
+## Gözden Geçirme ve Bireysel Çalışma
+
+Aşağıdaki konularda biraz araştırma yapın:
+
+- TensorFlow
+- PyTorch
+- Aşırı öğrenme
+
+Kendinize şu soruları sorun:
+
+- TensorFlow ve PyTorch arasındaki fark nedir?
+- Aşırı öğrenme ile eksik öğrenme arasındaki fark nedir?
+
+## [Ödev](../lab/README.md)
+
+Bu laboratuvar çalışmasında, PyTorch veya TensorFlow kullanarak tek ve çok katmanlı tam bağlı ağları kullanarak iki tane sınıflandırma problemini çözmeniz isteniyor.
+
+* [Talimatlar](../lab/README.md)
+* [Not defteri](../lab/LabFrameworks.ipynb)
diff --git a/lessons/3-NeuralNetworks/05-Frameworks/translations/README.tr.md b/lessons/3-NeuralNetworks/05-Frameworks/translations/README.tr.md
new file mode 100644
index 00000000..c60ec198
--- /dev/null
+++ b/lessons/3-NeuralNetworks/05-Frameworks/translations/README.tr.md
@@ -0,0 +1,44 @@
+# Sinir Ağları Çerçeveleri
+
+Daha önce öğrendiğimiz gibi, sinir ağlarını verimli bir şekilde eğitebilmek için iki şey yapmamız gerekiyor:
+
+* Tensörler üzerinde çalışmak için, örn. sigmoid veya softmaks gibi bazı işlevleri çarpmak, toplamak ve hesaplamak
+* Gradyan inişi eniyileme gerçekleştirmek için tüm ifadelerin gradyanlarını hesaplamak
+
+## [Ders öncesi sınavı](https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/105)
+
+`numpy` kitaplığı ilk kısmı yapabilirken, gradyanları hesaplamak için bazı mekanizmalara ihtiyacımız var. Önceki bölümde geliştirdiğimiz [çerçevemiz](../../04-OwnFramework/translations/OwnFramework.tr.ipynb)de tüm türev fonksiyonlarını geri yayan `backward` (geri) yöntemi içinde elle programlamamız gerekiyordu. İdeal olarak, bir çerçeve bize tanımlayabileceğimiz *herhangi bir ifadenin* gradyanlarını hesaplama fırsatı vermelidir.
+
+Bir diğer önemli şey de hesaplamaları GPU'da veya [TPU](https://en.wikipedia.org/wiki/Tensor_Processing_Unit) gibi diğer özelleştirilmiş işlem birimlerinde gerçekleştirebilmektir. Derin sinir ağı eğitimi *çok* hesaplama gerektirir ve bu hesaplamaları GPU'larda paralel hale getirebilmek çok önemlidir.
+
+> ✅ 'Paralelleştirme' terimi, hesaplamaları birden fazla cihaza dağıtmak anlamına gelir.
+
+Şu anda en popüler iki sinirsel çerçeve şunlardır: [TensorFlow](http://TensorFlow.org) ve [PyTorch](https://pytorch.org/). Her ikisi de hem CPU hem de GPU üzerinde tensörlerle çalışmak için düşük düzey bir API sağlar. Düşük düzey API'nin yanı sıra, buna uygun olarak [Keras](https://keras.io/) ve [PyTorch Lightning](https://pytorchlightning.ai/) olarak adlandırılan daha üst düzey API de vardır.
+
+Düşük düzey API | [TensorFlow](http://TensorFlow.org) | [PyTorch](https://pytorch.org/)
+--------------|-------------------------------------|--------------------------------
+Üst düzey API| [Keras](https://keras.io/) | [PyTorch Lightning](https://pytorchlightning.ai/)
+
+**Düşük düzey API'ler** her iki çerçevede de **hesaplamalı çizgeler** oluşturmanıza olanak tanır. Bu çizge, verilen girdi parametreleriyle çıktının (genellikle kayıp işlevi) nasıl hesaplanacağını tanımlar ve varsa GPU'da hesaplama için gönderilebilir. Bu hesaplama çizgesinde türev almak ve daha sonra model parametrelerini optimize etmede kullanılabilecek gradyanları hesaplamak için işlevler vardır.
+
+**Üst düzey API'ler** sinir ağlarını bir **katmanlar dizisi** olarak kabul eder ve sinir ağlarının çoğunu oluşturmayı çok daha kolay hale getirir. Modeli eğitmek genellikle verilerin hazırlanmasını ve ardından işi yapmak için bir `fit` (oturt) işlevinin çağrılmasını gerektirir.
+
+Üst düzey API, çok fazla ayrıntı hakkında endişelenmeden tipik sinir ağlarını çok hızlı bir şekilde oluşturmanıza olanak tanır. Aynı zamanda, düşük seviyeli API, eğitim süreci üzerinde çok daha fazla kontrol sunar ve bu nedenle, yeni sinir ağı mimarileriyle uğraşırken araştırmalarda çok kullanılırlar.
+
+Her iki API'yi birlikte kullanabileceğinizi anlamak da önemlidir, örn. düşük seviyeli API kullanarak kendi ağ katmanı mimarinizi geliştirebilir ve ardından bunu yüksek seviyeli API ile oluşturulmuş ve eğitilmiş daha büyük ağ içinde kullanabilirsiniz. Ayrıca bir dizi katman olarak üst düzey API'yi kullanarak bir ağ tanımlayabilir ve ardından eniyilemeyi gerçekleştirmek için kendi düşük düzeyli eğitim döngünüzü kullanabilirsiniz. Her iki API de aynı basit temel kavramları kullanır ve birlikte iyi çalışacak şekilde tasarlanmışlardır.
+
+## Öğrenme
+
+Bu kursta, içeriğin çoğunu hem PyTorch hem de TensorFlow için sunuyoruz. Tercih ettiğiniz çerçeveyi seçebilir ve yalnızca ilgili not defterlerini inceleyebilirsiniz. Hangi çerçeveyi seçeceğinizden emin değilseniz, internette **PyTorch ve TensorFlow** ile ilgili bazı tartışmaları okuyun. Daha iyi anlamak için her iki çerçeveye de bakabilirsiniz.
+
+Mümkün olduğunda, basitlik için üst düzey API'leri kullanacağız. Ancak, sinir ağlarının sıfırdan nasıl çalıştığını anlamanın önemli olduğuna inanıyoruz, bu nedenle başlangıçta düşük seviye API ve tensörlerle çalışmaya başlıyoruz. Ancak, hızlı ilerlemek istiyorsanız ve bu ayrıntıları öğrenmek için çok fazla zaman harcamak istemiyorsanız, bunları atlayabilir ve doğrudan üst düzey API not defterlerine geçebilirsiniz.
+
+## ✍️ Alıştırmalar: Çerçeveler
+
+Öğrenmenize aşağıdaki not defterlerinde devam edin:
+
+Düşük düzey API | [TensorFlow+Keras Not Defteri](IntroKerasTF.tr.ipynb) | [PyTorch](IntroPyTorch.tr.ipynb)
+--------------|-------------------------------------|--------------------------------
+Üst düzey API| [Keras](IntroKeras.tr.pynb) | *PyTorch Lightning*
+
+Çerçevelere hakim olduktan sonra, [aşırı öğrenme](Overfitting.tr.md) kavramını tekrarlayalım.
\ No newline at end of file
diff --git a/lessons/3-NeuralNetworks/translations/README.tr.md b/lessons/3-NeuralNetworks/translations/README.tr.md
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+# Sinir Ağlarına Giriş
+
+
+
+Giriş bölümünde tartıştığımız gibi, zekaya ulaşmanın yollarından biri bir **bilgisayar modeli** veya bir **yapay beyin** eğitmektir. 20. yüzyılın ortalarından beri, araştırmacılar farklı matematiksel modeller denediler, son yıllara kadar bu yön oldukça başarılı oldu. Beynin bu tür matematiksel modellerine **sinir ağları** denir.
+
+> Bazen sinir ağları, gerçek nöron ağlarından değil, modellerden bahsettiğimizi vurgulamak için *Yapay Sinir Ağları*, YSA olarak adlandırılır.
+
+## Makine Öğrenmesi
+
+Sinir Ağları, amacı sorunları çözebilen bilgisayar modellerini eğitmek için verileri kullanmak olan **Makine Öğrenmesi** adlı daha büyük bir disiplinin parçasıdır. Makine Öğrenmesi, Yapay Zekanın büyük bir bölümünü oluşturur, ancak bu müfredatta klasik MÖ'yi ele almıyoruz.
+
+> Klasik Makine Öğrenmesi hakkında daha fazla bilgi edinmek için ayrı **[Yeni Başlayanlar için Makine Öğrenmesi](http://github.com/microsoft/ml-for-beginners)** müfredatımızı ziyaret edin.
+
+Makine Öğrenmesinde, **X** örneklerinden oluşan bir veri kümemiz ve bunlara karşılık gelen **Y** çıktı değerlerine sahip olduğumuzu varsayıyoruz. Örnekler genellikle **öznitelikler** içeren N boyutlu vektörlerdir ve çıktılara **etiketler** denir.
+
+En yaygın iki makine öğrenmesi sorununu ele alacağız:
+
+* **Sınıflandırma**, bir girdi nesnesini iki veya daha fazla sınıfa ayırmamızı gerektirir.
+* **Bağlanım**, girdi örneklerinin her biri için bir sayı tahmin etmemizi gerektirir.
+
+> Girdileri ve çıktıları tensörler olarak temsil ederken, girdi veri kümesi M×N boyutunda bir matristir; burada M, örnek sayısı ve N, öznitelik sayısıdır. Çıktı etiketleri Y, M boyutlu bir vektördür.
+
+Bu müfredatta sadece sinir ağı modellerine odaklanacağız.
+
+## Bir Sinir Modeli
+
+Biyolojiden, beynimizin, her biri birden fazla "girdi" (akson) ve bir çıktı (dendrit) içeren sinir hücrelerinden oluştuğunu biliyoruz. Aksonlar ve dendritler elektrik sinyallerini iletebilir ve aksonlar ve dendritler arasındaki bağlantılar farklı derecelerde iletkenlik sergileyebilir (nöromediatörler tarafından kontrol edilirler).
+
+ | 
+----|----
+Gerçek Sinir *([İmge](https://en.wikipedia.org/wiki/Synapse#/media/File:SynapseSchematic_lines.svg) Wikipedia'dan)* | Yapay Sinir *(İmge sahibi Yazar)*
+
+Böylece, bir nöronun en basit matematiksel modeli birkaç X girdisi 1, ..., XN ve bir Y çıktısı ve bir dizi W ağırlığı içerir 1, ..., WN. Çıktı şöyle hesaplanır:
+
+
+
+burada f bir doğrusal olmayan **etkinleştirme işlevi**dir.
+
+> Sinirin ilk modelleri, 1943'te Warren McCullock ve Walter Pitts tarafından yazılan [Sinir aktivitesinin özünde olan fikirlerin mantıksal bir hesabı](http://www.springerlink.com/content/61446605110620kg/fulltext.pdf) adlı klasik makalede tanımlanmıştır. Donald Hebb, "[Davranışın Organizasyonu: Bir Nöropsikolojik Teori](https://books.google.com/books?id=VNetYrB8EBoC)" adlı kitabında bu ağları eğitebilmenin yolunu önerdi.
+
+## Bu Bölümdekiler
+
+Bu bölümde şunları öğreneceğiz:
+* [Algılayıcı](03-Perceptron/translations/README.tr.md), iki sınıflı sınıflandırma için en eski sinir ağı modellerinden biridir
+* [Çok katmanlı ağlar](04-OwnFramework/translations/README.tr.md) eşleştirilmiş bir not defteri ile [kendi çerçevemizi nasıl oluştururuz](04-OwnFramework/translations/OwnFramework.tr.ipynb)
+* [Sinir ağları çerçeveleri](05-Frameworks/translations/README.tr.md), şu not defterleriyle birlikte: [PyTorch](05-Frameworks/translations/IntroPyTorch.tr.ipynb) ve [Keras/Tensorflow](05-Frameworks/translations/IntroKerasTF.tr.ipynb)
+* [Aşırı öğrenme](05-Frameworks/translations/Overfitting.tr.md)
\ No newline at end of file
diff --git a/translations/README.tr.md b/translations/README.tr.md
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+++ b/translations/README.tr.md
@@ -0,0 +1,172 @@
+[](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE)
+[](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/)
+[](https://GitHub.com/microsoft/AI-For-Beginners/issues/)
+[](https://GitHub.com/microsoft/AI-For-Beginners/pulls/)
+[](http://makeapullrequest.com)
+
+[](https://GitHub.com/microsoft/AI-For-Beginners/watchers/)
+[](https://GitHub.com/microsoft/AI-For-Beginners/network/)
+[](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/)
+[](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)
+[](https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
+# Yeni Başlayanlar İçin Yapay Zeka - Müfredat
+
+| ](../lessons/sketchnotes/ai-overview.png)|
+|:---:|
+| Yeni Başlayanlar İçin YZ - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ |
+
+Microsoft'taki Azure Bulut Savunucuları, **Yapay Zeka** ile ilgili her şeyi içeren 12 haftalık, 24 derslik bir müfredat sunmaktan memnuniyet duyar.
+
+Bu müfredatta şunları öğreneceksiniz:
+
+* "Eski bilindik" sembolik yaklaşım ile **Bilgi Temsili** ve akıl yürütme dahil olmak üzere Yapay Zekaya farklı yaklaşımlar ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).
+* Modern YZ'nin merkezinde yer alan **Sinir Ağları** ve **Derin Öğrenme**. En popüler çerçevelerden ikisinde kod kullanarak bu önemli konuların arkasındaki kavramları göstereceğiz - [TensorFlow](http://Tensorflow.org) ve [PyTorch](http://pytorch.org).
+* İmgelerle ve metinlerle çalışmak için **Sinir Mimarileri**. Yakın dönem modellerini ele alacağız, ancak en son teknoloji biraz eksik olabilir.
+* **Genetik Algoritmalar** ve **Çok Etmenli Sistemler** gibi daha az popüler olan yapay zeka yaklaşımları.
+
+Bu müfredatta neleri ele almayacağız:
+
+* **İşte YZ** kullanımına ilişkin iş vakaları. Microsoft Öğrenme yolunundaki [İş için YZ'ye Giriş](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-cacaste)'i veya [INSEAD](https://www.insead.edu/) ile işbirliğiyle geliştirilmiş [YZ İş Okulu](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste)'nu almayı düşünün.
+* **Klasik Yapay Öğrenme**, [Yeni Başlayanlar için Yapay Öğrenme Müfredatı](http://github.com/Microsoft/ML-for-Beginners)'mızda iyice açıklanmıştır.
+* **[Bilişsel Hizmetler](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-cacaste)** kullanılarak oluşturulmuş pratik YZ uygulamaları. Bunun gibi [görme](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-cacaste), [doğal dil işleme](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste) ve diğerleri için Microsoft Öğrenme modülleriyle başlamanızı öneririz.
+* [Azure Yapay Öğrenme](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) veya [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-cacaste) gibi belli YÖ **Bulut Çerçeveleri**. [Azure Yapay Öğrenme ile yapay öğrenme çözümleri oluşturma ve çalıştırma](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-cacaste) ve [Azure Databricks ile yapay öğrenme çözümleri oluşturma ve çalıştırma](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-cacaste) öğrenme yollarını kullanmayı düşünün.
+* **Konuşkan YZ** ve **Sohbet Botları**. Ayrı bir [konuşkan YZ çözümleri yaratma](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-cacaste) öğrenme yolu vardır, ayrıca daha fazla ayrıntı için [bu blog gönderisine](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) bakın.
+* Derin öğrenmenin ardındaki **Derin Matematik**. Bunun için Ian Goodfellow, Yoshua Bengio ve Aaron Courville tarafından yazılmış [Derin Öğrenme](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) adlı [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) adresinden de ulaşılabilir kitabı tavsiye ediyoruz.
+
+For a gentle introduction to *AI in the Cloud* topics you may consider taking the [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-cacaste) Learning Path.
+
+*Bulutta Yapay Zeka* konularına nazik bir giriş için [Azure'de yapay zekayı kullanmaya başlama](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-cacaste) öğrenme yolunu almayı düşünebilirsiniz.
+
+---
+# İçerik
+
+
+
+
+**[Dersin Zihinsel Haritası](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)**
+
+Her ders, bazı ön okuma materyalleri (yukarıda **Metin** olarak bağlantılandırılmıştır) ve genellikle (**PyTorch** veya **TensorFlow**) çerçevelerine özel bazı yürütülebilir Jupyter Defterleri içerir. Yürütülebilir not defteri ayrıca birçok teorik materyal içerir, bu nedenle konuyu anlamak için not defterlerinin en az bir sürümünü (PyTorch veya TensorFlow) üzerinden gitmeniz gerekir. Ayrıca bazı konular için öğrendiğiniz materyali belirli bir probleme uygulamayı deneme fırsatı veren laboratuvarlar (**Labs**) da vardır.
+
+Bazı bölümler, ilgili konuları kapsayan **MS Öğrenme** modüllerine bağlantılar da içerir. Microsoft Öğrenme, uygun bir GPU etkin öğrenme ortamı sağlar, ancak içerik açısından bu müfredatın biraz daha derine inmesini bekleyebilirsiniz.
+
+# Başlarken
+
+**Öğrenciler**, müfredatı kullanmanın birkaç yolu vardır. Her şeyden önce, metni okuyabilir ve kodu doğrudan GitHub'da inceleyebilirsiniz. Kodu not defterlerinden herhangi birinde çalıştırmak istiyorsanız [talimatlarımızı okuyun](./etc/how-to-run.md) ve bunu nasıl yapacağınızla ilgili daha fazlasını [bu blog yayınında](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) bulabilirsiniz.
+
+> **Not**: [Bu müfredattaki kodun nasıl çalıştırılacağına ilişkin talimatlar](./etc/how-to-run.md)
+
+Ancak, dersi kendi kendine çalışma projesi olarak almak istiyorsanız, tüm depoyu kendi GitHub hesabınıza çatallamanızı ve alıştırmaları kendi başınıza veya bir grupla tamamlamanızı öneririz:
+
+- Bir ders öncesi sınavı ile başlayın.
+- Dersin giriş metnini okuyun.
+- Derste ek not defterleri varsa, bunları gözden geçirin, kodu okuyun ve yürütün. Hem TensorFlow hem de PyTorch not defterleri sağlanmışsa bunlardan birine odaklanabilirsiniz - en sevdiğiniz çerçeveyi seçin.
+- Defterler genellikle, deney yapmak için kodu biraz değiştirmenizi gerektiren bazı zorluklar içerir.
+- Ders sonrası sınavını yapın.
+- Eğer modüle bağlı bir laboratuvar varsa - o zaman ödevi tamamlayın.
+- "Yüksek sesle öğrenmek" için [tartışma panosunu](https://github.com/microsoft/AI-For-Beginners/discussions) ziyaret edin.
+- Diğer öğrencilerle [Gitter](https://gitter.im/Microsoft/ai-for-beginners)'da veya [Telegram kanalı](http://t.me/ai_for_beginners)nda konuşun.
+
+> Daha fazla çalışma için bu [Microsoft Öğrenme](https://docs.microsoft.com/en-us/users/dmitrysoshnikov-9132/collections/31zgizg2p418yo/?WT.mc_id=academic-77998-cacaste) modüllerini ve öğrenme yollarını takip etmenizi öneririz.
+
+**Eğitimciler**, bu müfredatın nasıl kullanılacağına ilişkin [bazı öneriler ekledik](/etc/for-teachers.md).
+
+---
+
+## Katkıda Bulunanlar
+
+**✍️ Ana Yazar:** [Dmitry Soshnikov](http://soshnikov.com), PhD
+**🔥 Editör:** [Jen Looper](https://twitter.com/jenlooper), PhD
+**🎨 Sketchnote Ressamı:** [Tomomi Imura](https://twitter.com/girlie_mac)
+**✅ Sınav Yaratıcı:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
+**🙏 Temel Katkıda Bulunanlar:** [Evgenii Pishchik](https://github.com/Pe4enIks) + +## Ekiple Tanışın + +[](https://youtu.be/m2KrAk0cC1c "Promo video") + +> 🎥 Proje ve onu oluşturan kişiler hakkındaki video için yukarıdaki resme tıklayın! + +--- + +## Eğitbilim + +Bu müfredatı oluştururken iki eğitbilimsel ilke seçtik: Uygulamalı **proje tabanlı** olmasını ve **sıkça sınavlar** içermesini sağlamak. + +İçeriğin projelerle uyumlu olması sağlanarak süreç öğrenciler için daha ilgi çekici hale getirilecek ve kavramların akılda kalıcılığı artırılacaktır. Buna ek olarak, bir dersten önce düşük riskli bir sınav, öğrencinin bir konuyu öğrenmeye yönelik niyetini belirlerken, dersten sonra ikinci bir sınav daha fazla akılda kalmasını sağlar. Bu müfredat esnek ve eğlenceli olacak şekilde tasarlanmıştır ve kısmen veya tamamen çalışılabilir. Projeler küçük başlar ve 12 haftalık döngünün sonunda giderek daha karmaşık hale gelir. + +> [Davranış Kuralları](etc/CODE_OF_CONDUCT.md), [Katkıda Bulunma](etc/CONTRIBUTING.md) ve [Çeviri](etc/TRANSLATIONS.md) yönergelerimizi bakın. [Destek belgelerimizi burada](etc/SUPPORT.md) ve [güvenlik bilgilerini burada](etc/SECURITY.md) bulabilirsiniz. Yapıcı geri bildiriminizi memnuniyetle karşılıyoruz! + +> **Sınavlarla ilgili bir not**: Her biri üç sorudan oluşan toplam 50 sınav için tüm sınavlar [bu uygulamada](https://red-field-0a6ddfd03.1.azurestaticapps.net/) bulunur. Bunlar derslerin içerisinden bağlantılıdır ancak sınav uygulaması yerel olarak çalıştırılabilir; `etc/quiz-app` klasöründeki talimatları izleyin. + +## Çevrimdışı erişim + +[Docsify](https://docsify.js.org/#/) kullanarak bu belgeleri çevrimdışı çalıştırabilirsiniz. Bu depoyu çatallayın, yerel makinenize [Docsify'ı yükleyin](https://docsify.js.org/#/quickstart) ve ardından bu deponun `etc/docsify` klasörüne `docsify serve` yazın. Web sitesi, localhost'unuzdaki 3000 numaralı bağlantı noktasında hizmet sağlayacak: `localhost:3000`. Müfredatın bir pdf'i [bu bağlantıda](/etc/pdf/readme.pdf) mevcuttur. + +## Yardım Aranıyor! + +Bir çeviriye katkıda bulunmak ister misiniz? Lütfen [çeviri yönergelerimizi](etc/TRANSLATIONS.md) okuyun. + +## Diğer Müfredatlar + +Ekibimiz başka müfredatlar da üretiyor! Bir bakın: + +- [Yeni Başlayanlar için Web Geliştirme](https://aka.ms/webdev-beginners) +- [Yeni Başlayanlar için IoT](https://aka.ms/iot-beginners) +- [Yeni Başlayanlar için Yapay Öğrenme](https://aka.ms/ml-beginners) +- [Yeni Başlayanlar için Veri Bilimi](https://aka.ms/datascience-beginners)
+
+burada f bir doğrusal olmayan **etkinleştirme işlevi**dir.
+
+> Sinirin ilk modelleri, 1943'te Warren McCullock ve Walter Pitts tarafından yazılan [Sinir aktivitesinin özünde olan fikirlerin mantıksal bir hesabı](http://www.springerlink.com/content/61446605110620kg/fulltext.pdf) adlı klasik makalede tanımlanmıştır. Donald Hebb, "[Davranışın Organizasyonu: Bir Nöropsikolojik Teori](https://books.google.com/books?id=VNetYrB8EBoC)" adlı kitabında bu ağları eğitebilmenin yolunu önerdi.
+
+## Bu Bölümdekiler
+
+Bu bölümde şunları öğreneceğiz:
+* [Algılayıcı](03-Perceptron/translations/README.tr.md), iki sınıflı sınıflandırma için en eski sinir ağı modellerinden biridir
+* [Çok katmanlı ağlar](04-OwnFramework/translations/README.tr.md) eşleştirilmiş bir not defteri ile [kendi çerçevemizi nasıl oluştururuz](04-OwnFramework/translations/OwnFramework.tr.ipynb)
+* [Sinir ağları çerçeveleri](05-Frameworks/translations/README.tr.md), şu not defterleriyle birlikte: [PyTorch](05-Frameworks/translations/IntroPyTorch.tr.ipynb) ve [Keras/Tensorflow](05-Frameworks/translations/IntroKerasTF.tr.ipynb)
+* [Aşırı öğrenme](05-Frameworks/translations/Overfitting.tr.md)
\ No newline at end of file
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+[](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE)
+[](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/)
+[](https://GitHub.com/microsoft/AI-For-Beginners/issues/)
+[](https://GitHub.com/microsoft/AI-For-Beginners/pulls/)
+[](http://makeapullrequest.com)
+
+[](https://GitHub.com/microsoft/AI-For-Beginners/watchers/)
+[](https://GitHub.com/microsoft/AI-For-Beginners/network/)
+[](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/)
+[](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)
+[](https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
+# Yeni Başlayanlar İçin Yapay Zeka - Müfredat
+
+| ](../lessons/sketchnotes/ai-overview.png)|
+|:---:|
+| Yeni Başlayanlar İçin YZ - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ |
+
+Microsoft'taki Azure Bulut Savunucuları, **Yapay Zeka** ile ilgili her şeyi içeren 12 haftalık, 24 derslik bir müfredat sunmaktan memnuniyet duyar.
+
+Bu müfredatta şunları öğreneceksiniz:
+
+* "Eski bilindik" sembolik yaklaşım ile **Bilgi Temsili** ve akıl yürütme dahil olmak üzere Yapay Zekaya farklı yaklaşımlar ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).
+* Modern YZ'nin merkezinde yer alan **Sinir Ağları** ve **Derin Öğrenme**. En popüler çerçevelerden ikisinde kod kullanarak bu önemli konuların arkasındaki kavramları göstereceğiz - [TensorFlow](http://Tensorflow.org) ve [PyTorch](http://pytorch.org).
+* İmgelerle ve metinlerle çalışmak için **Sinir Mimarileri**. Yakın dönem modellerini ele alacağız, ancak en son teknoloji biraz eksik olabilir.
+* **Genetik Algoritmalar** ve **Çok Etmenli Sistemler** gibi daha az popüler olan yapay zeka yaklaşımları.
+
+Bu müfredatta neleri ele almayacağız:
+
+* **İşte YZ** kullanımına ilişkin iş vakaları. Microsoft Öğrenme yolunundaki [İş için YZ'ye Giriş](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-cacaste)'i veya [INSEAD](https://www.insead.edu/) ile işbirliğiyle geliştirilmiş [YZ İş Okulu](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste)'nu almayı düşünün.
+* **Klasik Yapay Öğrenme**, [Yeni Başlayanlar için Yapay Öğrenme Müfredatı](http://github.com/Microsoft/ML-for-Beginners)'mızda iyice açıklanmıştır.
+* **[Bilişsel Hizmetler](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-cacaste)** kullanılarak oluşturulmuş pratik YZ uygulamaları. Bunun gibi [görme](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-cacaste), [doğal dil işleme](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste) ve diğerleri için Microsoft Öğrenme modülleriyle başlamanızı öneririz.
+* [Azure Yapay Öğrenme](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-cacaste) veya [Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-cacaste) gibi belli YÖ **Bulut Çerçeveleri**. [Azure Yapay Öğrenme ile yapay öğrenme çözümleri oluşturma ve çalıştırma](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-cacaste) ve [Azure Databricks ile yapay öğrenme çözümleri oluşturma ve çalıştırma](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-cacaste) öğrenme yollarını kullanmayı düşünün.
+* **Konuşkan YZ** ve **Sohbet Botları**. Ayrı bir [konuşkan YZ çözümleri yaratma](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-cacaste) öğrenme yolu vardır, ayrıca daha fazla ayrıntı için [bu blog gönderisine](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) bakın.
+* Derin öğrenmenin ardındaki **Derin Matematik**. Bunun için Ian Goodfellow, Yoshua Bengio ve Aaron Courville tarafından yazılmış [Derin Öğrenme](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) adlı [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) adresinden de ulaşılabilir kitabı tavsiye ediyoruz.
+
+For a gentle introduction to *AI in the Cloud* topics you may consider taking the [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-cacaste) Learning Path.
+
+*Bulutta Yapay Zeka* konularına nazik bir giriş için [Azure'de yapay zekayı kullanmaya başlama](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-cacaste) öğrenme yolunu almayı düşünebilirsiniz.
+
+---
+# İçerik
+
+| Numara | Ders | Giriş | PyTorch | Keras/TensorFlow | Lab |
|---|---|---|---|---|---|
| I | YZ'ye Giriş | ||||
| 1 | YZ'ye Giriş ve Tarihçesi | Metin | |||
| II | Simgesel YZ | ||||
| 2 | Bilgi Temsili ve Uzman Sistemler | Metin | Uzman Sistem, Ontoloji, Kavram Çizgesi | ||
| III | Sinir Ağlarına Giriş | ||||
| 3 | Algılayıcı | +Metin + | Defter | Lab | |
| 4 | Çok Katmanlı Algılayıcı ve Kendi Çerçevemizi Oluşturma | Metin | Defter | Lab | |
| 5 | +Çerçevelere Giriş (PyTorch/TensorFlow) Aşırı Öğrenme |
+ Metin Metin |
+ PyTorch | +Keras/TensorFlow | +Lab |
| IV | Bilgisayarla Görme | +YZ Temelleri: Bilgisayarla Görmeyi Keşfedin | +|||
| Bilgisayarla Görmede Microsoft Öğrenme Modülü | +PyTorch | +TensorFlow | +|||
| 6 | Bilgisayarla Görmeye Giriş. OpenCV | Metin | Defter | Lab | |
| 7 | Evrişimli Sinir Ağları CNN Mimarileri | Metin Metin | PyTorch | TensorFlow | Lab |
| 8 | Önceden Eğitilmiş Ağlar ve Öğrenme Aktarımı Eğitim Püf Noktaları | Metin Metin | PyTorch | TensorFlow Hattan düşürme örneklemi Düşman kedi | Lab |
| 9 | Otomatik kodlayıcılar and Değişken otomatik kodlayıcılars | Metin | PyTorch | TensorFlow | |
| 10 | Üretken Çekişmeli Ağları Sanatsal Tarz Aktarımı | Metin | PyTorch | TensorFlow GAN Tarz Aktarımı | |
| 11 | Nesne Tespiti | Metin | PyTorch | TensorFlow | Lab |
| 12 | Anlamsal Bölünme. U-Net | Metin | PyTorch | TensorFlow | |
| V | Doğal Dil İşleme | +YZ Temelleri: Doğal Dil İşlemeyi Keşfedin | +|||
| Doğal Dil Üstüne Microsoft Öğrenme Modülü | +PyTorch | +TensorFlow | +|||
| 13 | Metin Temsili. Bow/TF-IDF | Metin | PyTorch | TensorFlow | |
| 14 | Anlamsal kelime gömmeleri. Word2Vec and GloVe | Metin | PyTorch | TensorFlow | |
| 15 | Dil Modelleme. Kendi gömmelerinizi eğitme | Text | TensorFlow | Lab | |
| 16 | Yinelemeli Sinir Ağları | Metin | PyTorch | TensorFlow | |
| 17 | Üretken Yinelemeli Ağlar | Metin | PyTorch | TensorFlow | Lab |
| 18 | Dönüştürücüler. BERT. | Metin | PyTorch | TensorFlow | |
| 19 | Adlandırılmış Varlık Tanıma | Metin | TensorFlow | Lab | |
| 20 | Büyük Dil Modelleri, Anlık Programlama ve Birkaç Atışlı Görevler | Metin | PyTorch | ||
| VI | Diğer YZ Teknikleri | ||||
| 21 | Genetik Algoritmalar | Metin | Defter | ||
| 22 | Derin Pekiştirmeli Öğrenme | Metin | TensorFlow | Lab | |
| 23 | Çok Etmenli Sistemler | Metin | |||
| VII | YZ Etiği | ||||
| 24 | YZ Etiği ve Sorumlu YZ | Metin | Microsoft Öğrenme Modülü: Sorumlu YZ İlkeleri | ||
| Ekstralar | |||||
| X1 | Çok Modlu Ağlar, CLIP and VQGAN | Metin | Defter | ||
+**🔥 Editör:** [Jen Looper](https://twitter.com/jenlooper), PhD
+**🎨 Sketchnote Ressamı:** [Tomomi Imura](https://twitter.com/girlie_mac)
+**✅ Sınav Yaratıcı:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
+**🙏 Temel Katkıda Bulunanlar:** [Evgenii Pishchik](https://github.com/Pe4enIks) + +## Ekiple Tanışın + +[](https://youtu.be/m2KrAk0cC1c "Promo video") + +> 🎥 Proje ve onu oluşturan kişiler hakkındaki video için yukarıdaki resme tıklayın! + +--- + +## Eğitbilim + +Bu müfredatı oluştururken iki eğitbilimsel ilke seçtik: Uygulamalı **proje tabanlı** olmasını ve **sıkça sınavlar** içermesini sağlamak. + +İçeriğin projelerle uyumlu olması sağlanarak süreç öğrenciler için daha ilgi çekici hale getirilecek ve kavramların akılda kalıcılığı artırılacaktır. Buna ek olarak, bir dersten önce düşük riskli bir sınav, öğrencinin bir konuyu öğrenmeye yönelik niyetini belirlerken, dersten sonra ikinci bir sınav daha fazla akılda kalmasını sağlar. Bu müfredat esnek ve eğlenceli olacak şekilde tasarlanmıştır ve kısmen veya tamamen çalışılabilir. Projeler küçük başlar ve 12 haftalık döngünün sonunda giderek daha karmaşık hale gelir. + +> [Davranış Kuralları](etc/CODE_OF_CONDUCT.md), [Katkıda Bulunma](etc/CONTRIBUTING.md) ve [Çeviri](etc/TRANSLATIONS.md) yönergelerimizi bakın. [Destek belgelerimizi burada](etc/SUPPORT.md) ve [güvenlik bilgilerini burada](etc/SECURITY.md) bulabilirsiniz. Yapıcı geri bildiriminizi memnuniyetle karşılıyoruz! + +> **Sınavlarla ilgili bir not**: Her biri üç sorudan oluşan toplam 50 sınav için tüm sınavlar [bu uygulamada](https://red-field-0a6ddfd03.1.azurestaticapps.net/) bulunur. Bunlar derslerin içerisinden bağlantılıdır ancak sınav uygulaması yerel olarak çalıştırılabilir; `etc/quiz-app` klasöründeki talimatları izleyin. + +## Çevrimdışı erişim + +[Docsify](https://docsify.js.org/#/) kullanarak bu belgeleri çevrimdışı çalıştırabilirsiniz. Bu depoyu çatallayın, yerel makinenize [Docsify'ı yükleyin](https://docsify.js.org/#/quickstart) ve ardından bu deponun `etc/docsify` klasörüne `docsify serve` yazın. Web sitesi, localhost'unuzdaki 3000 numaralı bağlantı noktasında hizmet sağlayacak: `localhost:3000`. Müfredatın bir pdf'i [bu bağlantıda](/etc/pdf/readme.pdf) mevcuttur. + +## Yardım Aranıyor! + +Bir çeviriye katkıda bulunmak ister misiniz? Lütfen [çeviri yönergelerimizi](etc/TRANSLATIONS.md) okuyun. + +## Diğer Müfredatlar + +Ekibimiz başka müfredatlar da üretiyor! Bir bakın: + +- [Yeni Başlayanlar için Web Geliştirme](https://aka.ms/webdev-beginners) +- [Yeni Başlayanlar için IoT](https://aka.ms/iot-beginners) +- [Yeni Başlayanlar için Yapay Öğrenme](https://aka.ms/ml-beginners) +- [Yeni Başlayanlar için Veri Bilimi](https://aka.ms/datascience-beginners)