test2_NN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network From Scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import time\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import misc\n",
    "from MyDenceNN import MyDenceNN\n",
    "from IPython.display import Image\n",
    "\n",
    "#random.seed(5)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pokemon_list = pd.read_csv(\"./archive/pokemon.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type1</th>\n",
       "      <th>Type2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  Type1   Type2\n",
       "0   bulbasaur  Grass  Poison\n",
       "1     ivysaur  Grass  Poison\n",
       "2    venusaur  Grass  Poison\n",
       "3  charmander   Fire     NaN\n",
       "4  charmeleon   Fire     NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHgAAAB4CAMAAAAOusbgAAAABGdBTUEAALGPC/xhBQAAAAFzUkdCAK7OHOkAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAK4UExURQAAAPDh8PDw8P/w8P/w//////DZ2dzc3PDc8LvKu7vKysq7ysrKyt3K3czMzKq7u7u7u83czZiYmJipmKmYmKmpqam6qbqpqbq6urrLy8vLy5mZmZmqmZmqqpm7qqqZqqqqqruqu7u7u7vMzIiZiJmZmZmpmampqam6qVRmVGZmZmZ2ZmZ2dmaIZmaIdoiYiIiqiJiqmKqqqqq6qnZ2doiYmIiqiIiqmJiYmJiqmFVlZTNUVFRUVFR2ZWV2ZXaIdnaZiHaqiIiZiIiqmREzIhFEIhFEMyJEIiJEMyJVRCJmMyJmRCJ3VTNEMzNVMzNVRDNmRDNmVTN3MzN3RDN3VTN3ZjOIRDOIVTOIZkQzM0RVVURmRERmVUR3RER3VUR3ZkR3d0SIVUSIZkSId0SZVUSZd1VEVVVVVVVmVVVmZlV3VVV3ZlV3d1WIVVWIZlWId1WZVVWZZlWZd1WZiFWqiGYRImZmVWZmZmZ3ZmZ3d2aIZmaId2aZZmaZd2aZiGaqZmaqd2aqiGaqmWa7d2a7mXciIndEM3dmVXd3Znd3d3eIiHeZd3eZiHeZmXeqZneqd3eqiHeqmXe7d3e7iHe7mXe7qnfMmXfMqnfdqnfdu4gREYhVVYiZiIiqiIiqmYi7d4i7iIi7mYi7qojMiIjMmYjMqojMu4jdiIjdu5kiM5kzRJlEVZl3d5mId5mZmZmqqpnMiJnMmZnMu5ndmZndqpndu5ndzJnumZnuzJn/zKoiM6ozRKpERKpEVapVVaqqqqrdqqrdzKrumaruqqruzKru3ar/qqr/3bszRLtEVbtmZrv/u7v/3bv/7swiM8wzVcxVVcxVZsx3d8yIiMzMzMz/7t0iRN0zRN1EVd1VZt1md92Imd3M3d3d3e5VZu5md+53iP+Zqv+qu/+7u//d7v/u7v/u/////0baCdQAAABEdFJOUwARERERESIzM0REREREVWZmZnd3d3d3d3d3d4iIiIiIiIiIiJmZmZmZu7u7u7u7u7u7u7vMzMzMzMzd7u7u7u7u7u7uxyNipQAABRdJREFUGBntwWljE0UYAOCxoqgEbxFskVoRvMD7QgUPPFGR00oL2GpECLCkVK1Ns0sQ8Wg03bK1KkKzGwfiTXZ8hwCKifMu67GLN3iBt/wN04L40S+79cs8D5EkSZIkSZIkSZIkSZL+0/GRY2rJ/6Cqsz3TMel4MuhOzG7O7Jp5DhlsR1zNsjmmTSaDKlJ7yQYmMkywZecPIYOlqu6mbDfb9VqOCSGYNnlMFRkUtRuyW4UQfdn3hBAl8RnraBtTRUI3/OLuraJUErmsGFBiYhfTJleTkA3tyAnGmBAZJkqHCMa2LrtgOAnVlTnWT2zpFiXGSiVWUSplC6K7YRgJUV2GDRDZbsEqRD9WKqxjItcQIeF5dXOhUGCMie5uwZgQW/oy7VsYY9lcgeUeIaEZ2skOEn0FwVhfR2eaPt/+UYFlsqwgVlSTsFyTy73Q2VHRuWRJJtPRtcnxfZ5hBZZZxwqFXBsJ3mX6xo03VDd0dKYtDhXcTHaB77no2u2swDJZViiwThK89LYiFg2VOr7vDfB933MQ0XESfYx1Z1khl1sSIUEbbhY5out7ruP5nuc6hyCib2RYTsvmsuvWzRpLgnYu5RzRQfS8Yq9BPdcZgIguJrVV8aZVqzRNm1lDgna6igN8LxWNRuup7wxARNduiGtNTVo8HtdmjiWBew4rHA9ao62t0Sh4ruP5vu8iopuYr82P99NmHUuCdrTlIqJHm6OtrfUt6LvoF3sUBV1EtBvmx7V4xYoFJHBX9GLZcTEabY02r0HPpcU1a195uPFFH8vgxmY9Ga/Q7qwhQTsqcb/J0f22d3Z9lHqeZ+kvP7p376cHNuYRAM17VmjxuDZ3IgncqLbdT68BuhINw0bXQfOltXu+37/jz7+SCACo3Na0Sps7gQTv7Lf2vLFyE6jc8xTVc2Bb2+d//PDdjn0HuqCfbU1veGgCCcHNO3/9uq2IDkdrdo+Dbu9T+w/8uHv7Twc+TCEAUHRuP4+E4Z3Xf//qMRcGcOBu7OP9v/2y98193yQoVNjgwKQICV7Vux/8vP/tPAcAjhz4tkXbv3x/5/p5C5KUgw22DeDkp5PgjUqs/+KTZ/LwDzQTaSPfuynPAQCBQoWrnEwCV5cyulQLBiBUIBaLnCNypLoaUzn0i0VI0Oos5EUAoBRAt6GCc8SitVpVlKSqW9APU6NJ0E6bkUcA4KbFbbPMgXNLVxNKTFFNQJPCQcZFJHDV1yuWodvIgdPVKUWJxVRFN2x0uJ7ogoNsSJDgHXnh1JiyVFUqVEW1dBuxzNFOKyrl0M+2bbiRhOCkZDKm6Ba1wEEERADL0pWEhQBlAEDdBEwMI8Eb34NqIqam06aum5aeUE3d1CmCzTkFAFsFm64eR4IXUYuI1DRUNZ3WTV23qA0Atg22cm8Kud7FVTDGkRBsdMpQUebA4SDbhorm2XOSyK2ESmHGcSQEU1ooAgAFtA8DAHtOc72K3FrYAwvvIGGovrXZ4ABU0ZED2P1At6BMm2dT9FQVlPtmkDCMnFbfjBw4bVR0ClhGAFQNDkgNtLuU1aBPm0jCMKTxLgOBg5u+e/nCGUk1mTKMmMUBylhuuUXJg5uqJaG4vMVFBCheVb00Nm9e47U1o08YTzkAh3Jq6gMPLk+1REgohiaMLiOht44gh41UIF+kvKjWjL6uevQIEpJLFz3+xLNTziT/qlq0ePHCmJo6hQy2M2rPOrWuJkIkSZIkSZIkSZIkSZIkifwNUWhWwPxwjjMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_dir = \"./archive/images/images/\"\n",
    "\n",
    "Image(filename=image_dir+\"bulbasaur.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type1</th>\n",
       "      <th>Type2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  Type1   Type2\n",
       "0   bulbasaur  Grass  Poison\n",
       "1     ivysaur  Grass  Poison\n",
       "2    venusaur  Grass  Poison\n",
       "3  charmander   Fire     NaN\n",
       "4  charmeleon   Fire     NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemons = pokemon_list\n",
    "pokemons.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def append_image(x, image_dir):\n",
    "    try:\n",
    "        image = Image(filename =  image_dir + x + \".png\")\n",
    "    except:\n",
    "        image = Image(filename =  image_dir + x + \".jpg\")\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "pokemons[\"Image\"] = pokemons.Name.apply(lambda x: append_image(x, image_dir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Type1</th>\n",
       "      <th>Type2</th>\n",
       "      <th>Image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>&lt;IPython.core.display.Image object&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ivysaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>&lt;IPython.core.display.Image object&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>venusaur</td>\n",
       "      <td>Grass</td>\n",
       "      <td>Poison</td>\n",
       "      <td>&lt;IPython.core.display.Image object&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;IPython.core.display.Image object&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>charmeleon</td>\n",
       "      <td>Fire</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;IPython.core.display.Image object&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  Type1   Type2                                Image\n",
       "0   bulbasaur  Grass  Poison  <IPython.core.display.Image object>\n",
       "1     ivysaur  Grass  Poison  <IPython.core.display.Image object>\n",
       "2    venusaur  Grass  Poison  <IPython.core.display.Image object>\n",
       "3  charmander   Fire     NaN  <IPython.core.display.Image object>\n",
       "4  charmeleon   Fire     NaN  <IPython.core.display.Image object>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemons.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Water       114\n",
       "Normal      105\n",
       "Grass        78\n",
       "Bug          72\n",
       "Psychic      53\n",
       "Fire         53\n",
       "Rock         46\n",
       "Electric     40\n",
       "Poison       34\n",
       "Ground       32\n",
       "Fighting     29\n",
       "Dark         29\n",
       "Ghost        27\n",
       "Dragon       27\n",
       "Steel        26\n",
       "Ice          23\n",
       "Fairy        18\n",
       "Flying        3\n",
       "Name: Type1, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemons[\"Type1\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemons[\"Image\"][]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    train_dataset = h5py.File('datasets/train_catvnoncat.h5', \"r\")\n",
    "    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n",
    "    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n",
    "\n",
    "    test_dataset = h5py.File('datasets/test_catvnoncat.h5', \"r\")\n",
    "    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n",
    "    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n",
    "\n",
    "    classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n",
    "    \n",
    "    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n",
    "    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n",
    "    \n",
    "    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0. It's a non-cat picture.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_x_orig, train_y, test_x_orig, test_y, classes = load_data()\n",
    "\n",
    "# Example of a picture\n",
    "index = 10\n",
    "plt.imshow(train_x_orig[index])\n",
    "print(\"y = \" + str(train_y[0,index]) + \". It's a \" + classes[train_y[0,index]].decode(\"utf-8\") +  \" picture.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 209\n",
      "Number of testing examples: 50\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_x_orig shape: (209, 64, 64, 3)\n",
      "train_y shape: (1, 209)\n",
      "test_x_orig shape: (50, 64, 64, 3)\n",
      "test_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "# Explore your dataset \n",
    "m_train = train_x_orig.shape[0]\n",
    "num_px = train_x_orig.shape[1]\n",
    "m_test = test_x_orig.shape[0]\n",
    "\n",
    "print (\"Number of training examples: \" + str(m_train))\n",
    "print (\"Number of testing examples: \" + str(m_test))\n",
    "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_x_orig shape: \" + str(train_x_orig.shape))\n",
    "print (\"train_y shape: \" + str(train_y.shape))\n",
    "print (\"test_x_orig shape: \" + str(test_x_orig.shape))\n",
    "print (\"test_y shape: \" + str(test_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialise Network Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_x's shape: (12288, 209)\n",
      "test_x's shape: (12288, 50)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the training and test examples \n",
    "train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T   # The \"-1\" makes reshape flatten the remaining dimensions\n",
    "test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T\n",
    "\n",
    "# Standardize data to have feature values between 0 and 1.\n",
    "train_x = train_x_flatten/255.\n",
    "test_x = test_x_flatten/255.\n",
    "\n",
    "print (\"train_x's shape: \" + str(train_x.shape))\n",
    "print (\"test_x's shape: \" + str(test_x.shape))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#################\n",
      "Units per layer\n",
      "----------------\n",
      "n[1] = 64\n",
      "----------------\n",
      "n[2] = 8\n",
      "----------------\n",
      "----------------\n",
      "\n",
      "\n",
      "######################################\n",
      "######################################\n",
      "\t  Optimising Model\n",
      "######################################\n",
      "######################################\n",
      "\n",
      "\n",
      "Epoch 100: Cost = 0.6737924117750922\n",
      "--------------------------\n",
      "Epoch 200: Cost = 0.6613790332133027\n",
      "--------------------------\n",
      "Epoch 300: Cost = 0.652211261859648\n",
      "--------------------------\n",
      "Epoch 400: Cost = 0.6400953213112373\n",
      "--------------------------\n",
      "Epoch 500: Cost = 0.6079350997453762\n",
      "--------------------------\n",
      "Epoch 600: Cost = 0.5447099925675205\n",
      "--------------------------\n",
      "Epoch 700: Cost = 0.4433332566229946\n",
      "--------------------------\n",
      "Epoch 800: Cost = 0.34239737178670276\n",
      "--------------------------\n",
      "Epoch 900: Cost = 0.275170065797247\n",
      "--------------------------\n",
      "Epoch 1000: Cost = 0.2363627206136584\n",
      "--------------------------\n",
      "Epoch 1100: Cost = 0.20722549763253734\n",
      "--------------------------\n",
      "Epoch 1200: Cost = 0.1809721616957367\n",
      "--------------------------\n",
      "Epoch 1300: Cost = 0.14862745624314078\n",
      "--------------------------\n",
      "Epoch 1400: Cost = 0.09388997639549505\n",
      "--------------------------\n",
      "Epoch 1500: Cost = 0.045434103063350245\n",
      "--------------------------\n",
      "Epoch 1600: Cost = 0.025502194756936762\n",
      "--------------------------\n",
      "Epoch 1700: Cost = 0.016364625592413785\n",
      "--------------------------\n",
      "Epoch 1800: Cost = 0.01149853698434282\n",
      "--------------------------\n",
      "Epoch 1900: Cost = 0.008602846801666696\n",
      "--------------------------\n",
      "Epoch 2000: Cost = 0.006735119262366261\n",
      "--------------------------\n"
     ]
    }
   ],
   "source": [
    "new_dence = MyDenceNN()\n",
    "\n",
    "hidden_units = {\n",
    "    \"1\" : 64,\n",
    "    \"2\" : 8\n",
    "}\n",
    "\n",
    "activations = {\n",
    "    \"1\" : \"relu\",\n",
    "    \"2\" : \"relu\",\n",
    "    \"3\" : \"sigmoid\"    \n",
    "}\n",
    "\n",
    "cost_fun = \"logistic_regression\"\n",
    "alpha = 0.01\n",
    "epochs = 2000\n",
    "display=False\n",
    "print_cost = True\n",
    "## L2 Reg Needs Fixing\n",
    "L2_reg = False\n",
    "lambd = 0.01\n",
    "keep_prob = 1\n",
    "normalise = True\n",
    "# Experiment with weighted initiation\n",
    "weighted_init = False\n",
    "\n",
    "new_dence.solve(train_x, train_y, hidden_units, activations, \n",
    "                cost_fun, alpha, epochs, display, print_cost, \n",
    "                L2_reg, lambd, keep_prob, normalise, weighted_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "costs = []\n",
    "\n",
    "for cost in new_dence.costs:\n",
    "    J = np.squeeze(new_dence.costs[cost])\n",
    "    costs.append(J)\n",
    "    \n",
    "plt.plot(costs)\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations')\n",
    "plt.title(\"Learning rate =\" + str(alpha))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}