2.2-Review-CNN-MNIST-Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/qo20b88v1hbjztubt06609ovs85q8fau.png\" width=\"400px\"  align=\"center\"></a>\n",
    "<h1 align=\"center\"><font size=\"5\">CONVOLUTIONAL NEURAL NETWORK APPLICATION</font></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Introduction</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will use the famous [MNIST Dataset](http://yann.lecun.com/exdb/mnist/) to build two Neural Networks capable to perform handwritten digits classification. The first Network is a simple Multi-layer Perceptron (MLP) and the second one is a Convolutional Neural Network (CNN from now on). In other words, when given an input our algorithm will say, with some associated error, what type of digit this input represents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "<font size = 3><strong>Click on the links to go to the following sections:</strong></font>\n",
    "<br>\n",
    "<h2>Table of Contents</h2>\n",
    "<ol>\n",
    "    <li><a href=\"#ref1\">What is Deep Learning</a></li>\n",
    "    <li><a href=\"#ref2\">Simple test: Is TensorFlow working?</a></li>\n",
    "    <li><a href=\"#ref3\">1st part: classify MNIST using a simple model</a></li>\n",
    "    <li><a href=\"#ref4\">Evaluating the final result</a></li>\n",
    "    <li><a href=\"#ref5\">How to improve our model?</a></li>\n",
    "    <li><a href=\"#ref6\">2nd part: Deep Learning applied on MNIST</a></li>\n",
    "    <li><a href=\"#ref7\">Summary of the Deep Convolutional Neural Network</a></li>\n",
    "    <li><a href=\"#ref8\">Define functions and train the model</a></li>\n",
    "    <li><a href=\"#ref9\">Evaluate the model</a></li>\n",
    "</ol>    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref1\"></a>\n",
    "<h2>What is Deep Learning?</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Brief Theory:</b> Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://ibm.box.com/shared/static/gcbbrh440604cj2nksu3f44be87b8ank.png\" alt=\"HTML5 Icon\" style=\"width: 600px; height: 450px;\">\n",
    "<div style=\"text-align: center\">It's time for deep learning. Our brain doesn't work with only one or three layers. Why it would be different with machines?. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>In Practice, defining the term \"Deep\":</b> in this context, deep means that we are studying a Neural Network which has several hidden layers (more than one), no matter what type (convolutional, pooling, normalization, fully-connected etc). The most interesting part is that some papers noticed that Deep Neural Networks with the right architectures/hyper-parameters achieve better results than shallow Neural Networks with the same computational power (e.g. number of neurons or connections). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>In Practice, defining \"Learning\":</b> In the context of supervised learning, digits recognition in our case, the learning part consists of a target/feature which is to be predicted using a given set of observations with the already known final prediction (label). In our case, the target will be the digit (0, 1, 2, 3, 4, 5, 6, 7, 8, 9) and the observations are the intensity and relative position of the pixels. After some training, it is possible to generate a \"function\" that map inputs (digit image) to desired outputs(type of digit). The only problem is how well this map operation occurs. While trying to generate this \"function\", the training process continues until the model achieves a desired level of accuracy on the training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Notice:</b> This notebook has been created with TensorFlow version 1.8, and might not work with other versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.14.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we first classify MNIST using a simple Multi-layer perceptron and then, in the second part, we use deeplearning to improve the accuracy of our results.\n",
    "\n",
    "<a id=\"ref3\"></a>\n",
    "<h2>1st part: classify MNIST using a simple model.</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to create a simple Multi-layer perceptron, a simple type of Neural Network, to perform classification tasks on the MNIST digits dataset. If you are not familiar with the MNIST dataset, please consider to read more about it: <a href=\"http://yann.lecun.com/exdb/mnist/\">click here</a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>What is MNIST?</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to LeCun's website, the MNIST is a: \"database of handwritten digits that has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Import the MNIST dataset using TensorFlow built-in feature</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very important to notice that MNIST is a high optimized data-set and it does not contain images. You will need to build your own code if you want to see the real digits. Another important side note is the effort that the authors invested on this data-set with normalization and centering operations.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1105 07:57:32.287674 140736006943616 deprecation.py:323] From <ipython-input-2-8bf8ae5a5303>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
      "W1105 07:57:32.289580 140736006943616 deprecation.py:323] From /anaconda3/envs/fm1/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please write your own downloading logic.\n",
      "W1105 07:57:32.290799 140736006943616 deprecation.py:323] From /anaconda3/envs/fm1/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W1105 07:57:32.511821 140736006943616 deprecation.py:323] From /anaconda3/envs/fm1/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.data to implement this functionality.\n",
      "W1105 07:57:32.514243 140736006943616 deprecation.py:323] From /anaconda3/envs/fm1/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use tf.one_hot on tensors.\n",
      "W1105 07:57:32.560175 140736006943616 deprecation.py:323] From /anaconda3/envs/fm1/lib/python3.7/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <span style=\"background-color:#dcdcdc\"> one-hot = True</span> argument only means that, in contrast to Binary representation, the labels will be presented in a way that to represent a number N, the $N^{th}$ bit is 1 while the the other bits are 0. For example, five and zero in a binary code would be:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre>\n",
    "Number representation:    0\n",
    "Binary encoding:        [2^5]  [2^4]   [2^3]   [2^2]   [2^1]   [2^0]  \n",
    "Array/vector:             0      0       0       0       0       0 \n",
    "\n",
    "Number representation:    5\n",
    "Binary encoding:        [2^5]  [2^4]   [2^3]   [2^2]   [2^1]   [2^0]  \n",
    "Array/vector:             0      0       0       1       0       1  \n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a different notation, the same digits using one-hot vector representation can be show as: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<pre>\n",
    "Number representation:    0\n",
    "One-hot encoding:        [5]   [4]    [3]    [2]    [1]   [0]  \n",
    "Array/vector:             0     0      0      0      0     1   \n",
    "\n",
    "Number representation:    5\n",
    "One-hot encoding:        [5]   [4]    [3]    [2]    [1]    [0]  \n",
    "Array/vector:             1     0      0      0      0      0   \n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Understanding the imported data</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The imported data can be divided as follow:\n",
    "\n",
    "- Training (mnist.train) >>  Use the given dataset with inputs and related outputs for training of NN. In our case, if you give an image that you know that represents a \"nine\", this set will tell the neural network that we expect a \"nine\" as the output.  \n",
    "        - 55,000 data points\n",
    "        - mnist.train.images for inputs\n",
    "        - mnist.train.labels for outputs\n",
    "  \n",
    "   \n",
    "- Validation (mnist.validation) >> The same as training, but now the data is used to generate model properties (classification error, for example) and from this, tune parameters like the optimal number of hidden units or determine a stopping point for the back-propagation algorithm  \n",
    "        - 5,000 data points\n",
    "        - mnist.validation.images for inputs\n",
    "        - mnist.validation.labels for outputs\n",
    "  \n",
    "  \n",
    "- Test (mnist.test) >> the model does not have access to this informations prior to the testing phase. It is used to evaluate the performance and accuracy of the model against \"real life situations\". No further optimization beyond this point.  \n",
    "        - 10,000 data points\n",
    "        - mnist.test.images for inputs\n",
    "        - mnist.test.labels for outputs\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Creating an interactive section</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have two basic options when using TensorFlow to run your code:\n",
    "\n",
    "- [Build graphs and run session] Do all the set-up and THEN execute a session to evaluate tensors and run operations (ops) \n",
    "- [Interactive session] create your coding and run on the fly. \n",
    "\n",
    "For this first part, we will use the interactive session that is more suitable for environments like Jupyter notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating placeholders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It iss a best practice to create placeholders before variable assignments when using TensorFlow. Here we'll create placeholders for inputs (\"Xs\") and outputs (\"Ys\").   \n",
    "\n",
    "<b>Placeholder 'X':</b> represents the \"space\" allocated input or the images. \n",
    "<ul>\n",
    "    <li>Each input has 784 pixels distributed by a 28 width x 28 height matrix</li>   \n",
    "    <li>The 'shape' argument defines the tensor size by its dimensions.</li>     \n",
    "    <li>1st dimension = None. Indicates that the batch size, can be of any size.</li>     \n",
    "    <li>2nd dimension = 784. Indicates the number of pixels on a single flattened MNIST image.</li>    \n",
    "</ul>\n",
    "    \n",
    "<b>Placeholder 'Y':</b> represents the final output or the labels.\n",
    "<ul>\n",
    "    <li>10 possible classes (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)</li>  \n",
    "    <li>The 'shape' argument defines the tensor size by its dimensions.</li>    \n",
    "    <li>1st dimension = None. Indicates that the batch size, can be of any size.</li>     \n",
    "    <li>2nd dimension = 10. Indicates the number of targets/outcomes</li>   \n",
    "</ul>\n",
    "<b>dtype for both placeholders:</b> if you not sure, use tf.float32. The limitation here is that the later presented softmax function only accepts float32 or float64 dtypes. For more dtypes, check TensorFlow's documentation <a href=\"https://www.tensorflow.org/api_docs/python/tf/DType\">here</a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x  = tf.placeholder(tf.float32, shape=[None, 784])\n",
    "y_ = tf.placeholder(tf.float32, shape=[None, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Assigning bias and weights to null tensors</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to create the weights and biases, for this purpose they will be used as arrays filled with zeros. The values that we choose here can be critical, but we'll cover a better way on the second part, instead of this type of initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Weight tensor\n",
    "W = tf.Variable(tf.zeros([784, 10],tf.float32))\n",
    "# Bias tensor\n",
    "b = tf.Variable(tf.zeros([10],tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Execute the assignment operation</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before, we assigned the weights and biases but we did not initialize them with null values. For this reason, TensorFlow need to initialize the variables that you assign.  \n",
    "Please notice that we're using this notation \"sess.run\" because we previously started an interactive session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the op initialize_all_variables using an interactive session\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Adding Weights and Biases to input</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only difference for our next operation to the picture below is that we are using the mathematical convention for what is being executed in the illustration. The tf.matmul operation performs a matrix multiplication between x (inputs) and W (weights) and after the code add biases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<img src=\"https://ibm.box.com/shared/static/88ksiymk1xkb10rgk0jwr3jw814jbfxo.png\" alt=\"HTML5 Icon\" style=\"width:400px;height:350px;\"> \n",
    "<div style=\"text-align:center\">Illustration showing how weights and biases are added to neurons/nodes. </div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add:0' shape=(?, 10) dtype=float32>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mathematical operation to add weights and biases to the inputs\n",
    "tf.matmul(x,W) + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Softmax Regression</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Softmax is an activation function that is normally used in classification problems. It generates the probabilities for the output. For example, our model will not be 100% sure that one digit is the number nine, instead, the answer will be a distribution of probabilities where, if the model is right, the nine number will have a larger probability than the other other digits.\n",
    "\n",
    "For comparison, below is the one-hot vector for a nine digit label:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "0 --> 0  \n",
    "1 --> 0 \n",
    "2 --> 0\n",
    "3 --> 0\n",
    "4 --> 0\n",
    "5 --> 0\n",
    "6 --> 0\n",
    "7 --> 0\n",
    "8 --> 0\n",
    "9 --> 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A machine does not have all this certainty, so we want to know what is the best guess, but we also want to understand how sure it was and what was the second better option. Below is an example of a hypothetical distribution for a nine digit:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "0 -->0.01  \n",
    "1 -->0.02  \n",
    "2 -->0.03  \n",
    "3 -->0.02  \n",
    "4 -->0.12  \n",
    "5 -->0.01  \n",
    "6 -->0.03\n",
    "7 -->0.06\n",
    "8 -->0.1\n",
    "9 -->0.6  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = tf.nn.softmax(tf.matmul(x,W) + b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic function output is used for the classification between two target classes 0/1. Softmax function is generalized type of logistic function. That is, Softmax can output a multiclass categorical probability distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Cost function</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a function that is used to minimize the difference between the right answers (labels) and estimated outputs by our Network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Type of optimization: Gradient Descent</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the part where you configure the optimizer for your Neural Network. There are several optimizers available, in our case we will use Gradient Descent because it is a well established optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Training batches</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train using minibatch Gradient Descent.\n",
    "\n",
    "In practice, Batch Gradient Descent is not often used because is too computationally expensive. The good part about this method is that you have the true gradient, but with the expensive computing task of using the whole dataset in one time. Due to this problem, Neural Networks usually use minibatch to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load 50 training examples for each training iteration   \n",
    "for i in range(1000):\n",
    "    batch = mnist.train.next_batch(50)\n",
    "    train_step.run(feed_dict={x: batch[0], y_: batch[1]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Test</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final accuracy for the simple ANN model is: 91.25999808311462 % \n"
     ]
    }
   ],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "acc = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}) * 100\n",
    "print(\"The final accuracy for the simple ANN model is: {} % \".format(acc) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.close() #finish the session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref4\"></a>\n",
    "<h2>Evaluating the final result</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is the final result good?\n",
    "\n",
    "Let's check the best algorithm available out there (10th June 2016):\n",
    "  \n",
    "_Result:_ 0.21% error (99.79% accuracy)  \n",
    "<a href=\"http://cs.nyu.edu/~wanli/dropc/\">Reference here</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref5\"></a>\n",
    "<h2>How to improve our model?</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Several options as follow:</h4>\n",
    "<ul>\n",
    "    <li>Regularization of Neural Networks using DropConnect</li>\n",
    "    <li>Multi-column Deep Neural Networks for Image Classification</li> \n",
    "    <li>APAC: Augmented Pattern Classification with Neural Networks</li>\n",
    "    <li>Simple Deep Neural Network with Dropout</li>\n",
    "</ul>\n",
    "<h4>In the next part we are going to explore the option:</h4>\n",
    "<ul>\n",
    "    <li>Simple Deep Neural Network with Dropout (more than 1 hidden layer)</li>\n",
    "</ul> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref6\"></a>\n",
    "<h2>2nd part: Deep Learning applied on MNIST</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first part, we learned how to use a simple ANN to classify MNIST. Now we are going to expand our knowledge using a Deep Neural Network. \n",
    "\n",
    "\n",
    "Architecture of our network is:\n",
    "    \n",
    "- (Input) -> [batch_size, 28, 28, 1]  >> Apply 32 filter of [5x5]\n",
    "- (Convolutional layer 1)  -> [batch_size, 28, 28, 32]\n",
    "- (ReLU 1)  -> [?, 28, 28, 32]\n",
    "- (Max pooling 1) -> [?, 14, 14, 32]\n",
    "- (Convolutional layer 2)  -> [?, 14, 14, 64] \n",
    "- (ReLU 2)  -> [?, 14, 14, 64] \n",
    "- (Max pooling 2)  -> [?, 7, 7, 64] \n",
    "- [fully connected layer 3] -> [1x1024]\n",
    "- [ReLU 3]  -> [1x1024]\n",
    "- [Drop out]  -> [1x1024]\n",
    "- [fully connected layer 4] -> [1x10]\n",
    "\n",
    "\n",
    "The next cells will explore this new architecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Starting the code</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# finish possible remaining session\n",
    "sess.close()\n",
    "\n",
    "#Start interactive session\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>The MNIST data</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Initial parameters</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create general parameters for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = 28 # width of the image in pixels \n",
    "height = 28 # height of the image in pixels\n",
    "flat = width * height # number of pixels in one image \n",
    "class_output = 10 # number of possible classifications for the problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Input and output</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create place holders for inputs and outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x  = tf.placeholder(tf.float32, shape=[None, flat])\n",
    "y_ = tf.placeholder(tf.float32, shape=[None, class_output])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Converting images of the data set to tensors</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The input image is 28 pixels by 28 pixels, 1 channel (grayscale). In this case, the first dimension is the <b>batch number</b> of the image, and can be of any size (so we set it to -1). The second and third dimensions are width and height, and the last one is the image channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Reshape:0' shape=(?, 28, 28, 1) dtype=float32>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_image = tf.reshape(x, [-1,28,28,1])  \n",
    "x_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Convolutional Layer 1</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Defining kernel weight and bias</h4>\n",
    "We define a kernel here. The Size of the filter/kernel is 5x5;  Input channels is 1 (grayscale);  and we need 32 different feature maps (here, 32 feature maps means 32 different filters are applied on each image. So, the output of convolution layer would be 28x28x32). In this step, we create a filter / kernel tensor of shape <code>[filter_height, filter_width, in_channels, out_channels]</code>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "W_conv1 = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1))\n",
    "b_conv1 = tf.Variable(tf.constant(0.1, shape=[32])) # need 32 biases for 32 outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://ibm.box.com/shared/static/vn26neef1nnv2oxn5cb3uueowcawhkgb.png\" style=\"width: 800px; height: 400px;\" alt=\"HTML5 Icon\" >\n",
    "\n",
    "</h4>Convolve with weight tensor and add biases.</h4>\n",
    "\n",
    "To create convolutional layer, we use <b>tf.nn.conv2d</b>. It computes a 2-D convolution given 4-D input and filter tensors.\n",
    "\n",
    "Inputs:\n",
    "- tensor of shape [batch, in_height, in_width, in_channels]. x of shape [batch_size,28 ,28, 1]\n",
    "- a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]. W is of size [5, 5, 1, 32]\n",
    "- stride which is  [1, 1, 1, 1]. The convolutional layer, slides the \"kernel window\" across the input tensor. As the input tensor has 4 dimensions:  [batch, height, width, channels], then the convolution operates on a 2D window on the height and width dimensions. __strides__ determines how much the window shifts by in each of the dimensions. As the first and last dimensions are related to batch and channels, we set the stride to 1. But for second and third dimension, we could set other values, e.g. [1, 2, 2, 1]\n",
    "    \n",
    "    \n",
    "Process:\n",
    "- Change the filter to a 2-D matrix with shape [5\\*5\\*1,32]\n",
    "- Extracts image patches from the input tensor to form a *virtual* tensor of shape `[batch, 28, 28, 5*5*1]`.\n",
    "- For each batch, right-multiplies the filter matrix and the image vector.\n",
    "\n",
    "Output:\n",
    "- A `Tensor` (a 2-D convolution) of size tf.Tensor 'add_7:0' shape=(?, 28, 28, 32)- Notice: the output of the first convolution layer is 32 [28x28] images. Here 32 is considered as volume/depth of the output image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "convolve1= tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1], padding='SAME') + b_conv1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://ibm.box.com/shared/static/iizf4ui4b2hh9wn86pplqxu27ykpqci9.png\" style=\"width: 800px; height: 400px;\" alt=\"HTML5 Icon\" >\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Apply the ReLU activation Function</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, we just go through all outputs convolution layer, <b>convolve1</b>, and wherever a negative number occurs, we swap it out for a 0. It is called ReLU activation Function.<br> Let f(x) is a ReLU activation function $f(x) = max(0,x)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_conv1 = tf.nn.relu(convolve1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Apply the max pooling</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>max pooling</b> is a form of non-linear down-sampling. It partitions the input image into a set of rectangles and, and then find the maximum value for that region. \n",
    "\n",
    "Lets use <b>tf.nn.max_pool</b> function to perform max pooling. \n",
    "<b>Kernel size:</b> 2x2 (if the window is a 2x2 matrix, it would result in one output pixel)  \n",
    "<b>Strides:</b> dictates the sliding behaviour of the kernel. In this case it will move 2 pixels everytime, thus not overlapping. The input is a matrix of size 28x28x32, and the output would be a matrix of size 14x14x32.\n",
    "\n",
    "<img src=\"https://ibm.box.com/shared/static/kmaja90mn3aud9mro9cn8pbbg1h5pejy.png\" alt=\"HTML5 Icon\" style=\"width: 800px; height: 400px;\"> \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'MaxPool:0' shape=(?, 14, 14, 32) dtype=float32>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #max_pool_2x2\n",
    "conv1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First layer completed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Convolutional Layer 2</h3>\n",
    "<h4>Weights and Biases of kernels</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply the convolution again in this layer. Lets look at the second layer kernel:  \n",
    "- Filter/kernel: 5x5 (25 pixels) \n",
    "- Input channels: 32 (from the 1st Conv layer, we had 32 feature maps) \n",
    "- 64 output feature maps  \n",
    "\n",
    "<b>Notice:</b> here, the input image is [14x14x32], the filter is [5x5x32], we use 64 filters of size [5x5x32], and the output of the convolutional layer would be 64 convolved image, [14x14x64].\n",
    "\n",
    "<b>Notice:</b> the convolution result of applying a filter of size [5x5x32] on image of size [14x14x32] is an image of size [14x14x1], that is, the convolution is functioning on volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "W_conv2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1))\n",
    "b_conv2 = tf.Variable(tf.constant(0.1, shape=[64])) #need 64 biases for 64 outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Convolve image with weight tensor and add biases.</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "convolve2= tf.nn.conv2d(conv1, W_conv2, strides=[1, 1, 1, 1], padding='SAME') + b_conv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Apply the ReLU activation Function</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_conv2 = tf.nn.relu(convolve2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Apply the max pooling</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'MaxPool_1:0' shape=(?, 7, 7, 64) dtype=float32>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #max_pool_2x2\n",
    "conv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second layer completed. So, what is the output of the second layer, layer2?\n",
    "- it is 64 matrix of [7x7]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Fully Connected Layer</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need a fully connected layer to use the Softmax and create the probabilities in the end. Fully connected layers take the high-level filtered images from previous layer, that is all 64 matrices, and convert them to a flat array.\n",
    "\n",
    "So, each matrix [7x7] will be converted to a matrix of [49x1], and then all of the 64 matrix will be connected, which make an array of size [3136x1]. We will connect it into another layer of size [1024x1]. So, the weight between these 2 layers will be [3136x1024]\n",
    "\n",
    "\n",
    "<img src=\"https://ibm.box.com/shared/static/pr9mnirmlrzm2bitf1d4jj389hyvv7ey.png\" alt=\"HTML5 Icon\" style=\"width: 800px; height: 400px;\"> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Flattening Second Layer</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer2_matrix = tf.reshape(conv2, [-1, 7 * 7 * 64])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Weights and Biases between layer 2 and 3</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Composition of the feature map from the last layer (7x7) multiplied by the number of feature maps (64); 1027 outputs to Softmax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "W_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1))\n",
    "b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024])) # need 1024 biases for 1024 outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Matrix Multiplication (applying weights and biases)</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "fcl = tf.matmul(layer2_matrix, W_fc1) + b_fc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Apply the ReLU activation Function</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Relu_2:0' shape=(?, 1024) dtype=float32>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_fc1 = tf.nn.relu(fcl)\n",
    "h_fc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Third layer completed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Dropout Layer, Optional phase for reducing overfitting</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a phase where the network \"forget\" some features. At each training step in a mini-batch, some units get switched off randomly so that it will not interact with the network. That is, it weights cannot be updated, nor affect the learning of the other network nodes.  This can be very useful for very large neural networks to prevent overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W1105 07:58:05.902701 140736006943616 deprecation.py:506] From <ipython-input-31-00684a134129>:2: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'dropout/mul_1:0' shape=(?, 1024) dtype=float32>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep_prob = tf.placeholder(tf.float32)\n",
    "layer_drop = tf.nn.dropout(h_fc1, keep_prob)\n",
    "layer_drop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Readout Layer (Softmax Layer)</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Type: Softmax, Fully Connected Layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Weights and Biases</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In last layer, CNN takes the high-level filtered images and translate them into votes using softmax.\n",
    "Input channels: 1024 (neurons from the 3rd Layer); 10 output features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "W_fc2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1)) #1024 neurons\n",
    "b_fc2 = tf.Variable(tf.constant(0.1, shape=[10])) # 10 possibilities for digits [0,1,2,3,4,5,6,7,8,9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Matrix Multiplication (applying weights and biases)</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc=tf.matmul(layer_drop, W_fc2) + b_fc2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Apply the Softmax activation Function</h4>\n",
    "<b>softmax</b> allows us to interpret the outputs of <b>fcl4</b> as probabilities. So, <b>y_conv</b> is a tensor of probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Softmax_1:0' shape=(?, 10) dtype=float32>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_CNN= tf.nn.softmax(fc)\n",
    "y_CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref7\"></a>\n",
    "<h2>Summary of the Deep Convolutional Neural Network</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now is time to remember the structure of  our network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0) Input - MNIST dataset\n",
    "#### 1) Convolutional and Max-Pooling\n",
    "#### 2) Convolutional and Max-Pooling\n",
    "#### 3) Fully Connected Layer\n",
    "#### 4) Processing - Dropout\n",
    "#### 5) Readout layer - Fully Connected\n",
    "#### 6) Outputs - Classified digits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref8\"></a>\n",
    "<h2>Define functions and train the model</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Define the loss function</h4>\n",
    "\n",
    "We need to compare our output, layer4 tensor, with ground truth for all mini_batch. we can use <b>cross entropy>/b> to see how bad our CNN is working - to measure the error at a softmax layer.\n",
    "\n",
    "The following code shows an toy sample of cross-entropy for a mini-batch of size 2 which its items have been classified. You can run it (first change the cell type to <b>code</b> in the toolbar) to see how cross entropy changes."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import numpy as np\n",
    "layer4_test =[[0.9, 0.1, 0.1],[0.9, 0.1, 0.1]]\n",
    "y_test=[[1.0, 0.0, 0.0],[1.0, 0.0, 0.0]]\n",
    "np.mean( -np.sum(y_test * np.log(layer4_test),1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>reduce_sum</b> computes the sum of elements of <b>(y_ * tf.log(layer4)</b> across second dimension of the tensor, and <b>reduce_mean</b> computes the mean of all elements in the tensor.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_CNN), reduction_indices=[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Define the optimizer</h4>\n",
    "\n",
    "It is obvious that we want minimize the error of our network which is calculated by cross_entropy metric. To solve the problem, we have to compute gradients for the loss (which is minimizing the cross-entropy) and apply gradients to variables. It will be done by an optimizer: GradientDescent or Adagrad. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Define prediction</h4>\n",
    "Do you want to know how many of the cases in a mini-batch has been classified correctly? lets count them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(y_CNN, 1), tf.argmax(y_, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Define accuracy</h4>\n",
    "It makes more sense to report accuracy using average of correct cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Run session, train</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>If you want a fast result (<b>it might take sometime to train it</b>)</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0, training accuracy 0.02\n",
      "step 100, training accuracy 0.92\n",
      "step 200, training accuracy 0.92\n",
      "step 300, training accuracy 0.9\n",
      "step 400, training accuracy 0.88\n",
      "step 500, training accuracy 0.96\n",
      "step 600, training accuracy 0.94\n",
      "step 700, training accuracy 0.98\n",
      "step 800, training accuracy 1\n",
      "step 900, training accuracy 0.92\n",
      "step 1000, training accuracy 0.92\n"
     ]
    }
   ],
   "source": [
    "for i in range(1100):\n",
    "    batch = mnist.train.next_batch(50)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})\n",
    "        print(\"step %d, training accuracy %g\"%(i, float(train_accuracy)))\n",
    "    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success alertsuccess\" style=\"margin-top: 20px\">\n",
    "<font size = 3><strong><i>You can run this cell if you REALLY have time to wait, or you are running it using PowerAI (<b>change the type of the cell to code</b>)</i></strong></font>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "for i in range(20000):\n",
    "    batch = mnist.train.next_batch(50)\n",
    "    if i%100 == 0:\n",
    "        train_accuracy = accuracy.eval(feed_dict={\n",
    "            x:batch[0], y_: batch[1], keep_prob: 1.0})\n",
    "        print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n",
    "    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>PS. If you have problems running this notebook, please shutdown all your Jupyter runnning notebooks, clear all cells outputs and run each cell only after the completion of the previous cell.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref9\"></a>\n",
    "<h2>Evaluate the model</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the evaluation to the user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test accuracy 0.9659000012278557\n"
     ]
    }
   ],
   "source": [
    "# evaluate in batches to avoid out-of-memory issues\n",
    "n_batches = mnist.test.images.shape[0] // 50\n",
    "cumulative_accuracy = 0.0\n",
    "for index in range(n_batches):\n",
    "    batch = mnist.test.next_batch(50)\n",
    "    cumulative_accuracy += accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})\n",
    "print(\"test accuracy {}\".format(cumulative_accuracy / n_batches))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Visualization</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you want to look at all the filters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernels = sess.run(tf.reshape(tf.transpose(W_conv1, perm=[2, 3, 0,1]),[32, -1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-11-05 07:59:40--  http://deeplearning.net/tutorial/code/utils.py\n",
      "Resolving deeplearning.net (deeplearning.net)... 132.204.26.28\n",
      "Connecting to deeplearning.net (deeplearning.net)|132.204.26.28|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 5097 (5.0K) [text/plain]\n",
      "Saving to: ‘utils1.py’\n",
      "\n",
      "utils1.py           100%[===================>]   4.98K  --.-KB/s    in 0s      \n",
      "\n",
      "2019-11-05 07:59:40 (67.5 MB/s) - ‘utils1.py’ saved [5097/5097]\n",
      "\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "!/usr/local/Cellar/wget/1.20.3_1/bin/wget --output-document utils1.py http://deeplearning.net/tutorial/code/utils.py\n",
    "import utils1\n",
    "from utils1 import tile_raster_images\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "%matplotlib inline\n",
    "image = Image.fromarray(tile_raster_images(kernels, img_shape=(5, 5) ,tile_shape=(4, 8), tile_spacing=(1, 1)))\n",
    "### Plot image\n",
    "plt.rcParams['figure.figsize'] = (18.0, 18.0)\n",
    "imgplot = plt.imshow(image)\n",
    "imgplot.set_cmap('gray')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you want to see the output of an image passing through first convolution layer?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a2e17d198>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "plt.rcParams['figure.figsize'] = (5.0, 5.0)\n",
    "sampleimage = mnist.test.images[1]\n",
    "plt.imshow(np.reshape(sampleimage,[28,28]), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ActivatedUnits = sess.run(convolve1,feed_dict={x:np.reshape(sampleimage,[1,784],order='F'),keep_prob:1.0})\n",
    "filters = ActivatedUnits.shape[3]\n",
    "plt.figure(1, figsize=(20,20))\n",
    "n_columns = 6\n",
    "n_rows = np.math.ceil(filters / n_columns) + 1\n",
    "for i in range(filters):\n",
    "    plt.subplot(n_rows, n_columns, i+1)\n",
    "    plt.title('Filter ' + str(i))\n",
    "    plt.imshow(ActivatedUnits[0,:,:,i], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about second convolution layer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ActivatedUnits = sess.run(convolve2,feed_dict={x:np.reshape(sampleimage,[1,784],order='F'),keep_prob:1.0})\n",
    "filters = ActivatedUnits.shape[3]\n",
    "plt.figure(1, figsize=(20,20))\n",
    "n_columns = 8\n",
    "n_rows = np.math.ceil(filters / n_columns) + 1\n",
    "for i in range(filters):\n",
    "    plt.subplot(n_rows, n_columns, i+1)\n",
    "    plt.title('Filter ' + str(i))\n",
    "    plt.imshow(ActivatedUnits[0,:,:,i], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.close() #finish the session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Want to learn more?\n",
    "\n",
    "Running deep learning programs usually needs a high performance platform. __PowerAI__ speeds up deep learning and AI. Built on IBM’s Power Systems, __PowerAI__ is a scalable software platform that accelerates deep learning and AI with blazing performance for individual users or enterprises. The __PowerAI__ platform supports popular machine learning libraries and dependencies including TensorFlow, Caffe, Torch, and Theano. You can use [PowerAI on IMB Cloud](https://cocl.us/ML0120EN_PAI).\n",
    "\n",
    "Also, you can use __Watson Studio__ to run these notebooks faster with bigger datasets.__Watson Studio__ is IBM’s leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, __Watson Studio__ enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of __Watson Studio__ users today with a free account at [Watson Studio](https://cocl.us/ML0120EN_DSX).This is the end of this lesson. Thank you for reading this notebook, and good luck on your studies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thanks for completing this lesson!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created by <a href=\"https://linkedin.com/in/saeedaghabozorgi\"> Saeed Aghabozorgi </a>, <a href=\"https://linkedin.com/in/luisotsm\">Luis Otavio Silveira Martins</a>, <a href=\"https://linkedin.com/in/erich-natsubori-sato\"> Erich Natsubori Sato </a></h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References:\n",
    "\n",
    "https://en.wikipedia.org/wiki/Deep_learning    \n",
    "http://ruder.io/optimizing-gradient-descent/<br> \n",
    "http://yann.lecun.com/exdb/mnist/  \n",
    "https://www.quora.com/Artificial-Neural-Networks-What-is-the-difference-between-activation-functions  \n",
    "https://www.tensorflow.org/versions/r0.9/tutorials/mnist/pros/index.html  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "Copyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.3"
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 },
 "nbformat": 4,
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}