{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "\n", "
\n", "
\n", "\n", "--------------------\n", "# Introduction to Convolutional Neural Networks" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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, our algorithm will say, with some associated error, what type of digit is the presented input.\n", "\n", "This lesson is not intended to be a reference for _machine learning, convolutions or TensorFlow_. The intention is to give notions to the user about these fields and awareness of Data Scientist Workbench capabilities. We recommend that the students search for further references to understand completely the mathematical and theoretical concepts involved.\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Table of contents\n", "\n", " * What is Deep Learning\n", " * Simple test: Is tensorflow working?\n", " * 1st part: classify MNIST using a simple model\n", " * Evaluating the final result\n", " * How to improve our model?\n", " * 2nd part: Deep Learning applied on MNIST\n", " * Summary of the Deep Convolutional Neural Network\n", " * Define functions and train the model\n", " * Evaluate the model" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "# What is Deep Learning?" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "**Brief Theory:** 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": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "\"HTML5\n", "
It's time for deep learning. Our brain does't work with one or three layers. Why it would be different with machines?.
" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "In this tutorial, we first classify MNIST using a simple Multi-layer percepetron and then, in the second part, we use deeplearning to improve the accuracy of our results.\n", "\n", "\n", "# 1st part: classify MNIST using a simple model." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We are going to create a simple Multi-layer percepetron, a simple type of Neural Network, to performe classification tasks on the MNIST digits dataset. If you are not familiar with the MNIST dataset, please consider to read more about it: click here " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### What is MNIST?" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "### Import the MNIST dataset using TensorFlow built-in feature" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "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": [ "import tensorflow as tf\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The One-hot = True argument only means that, in contrast to Binary representation, the labels will be presented in a way that only one bit will be on for a specific digit. For example, five and zero in a binary code would be:" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "
\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",
    "
" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Using a different notation, the same digits using one-hot vector representation can be show as: " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "
\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",
    "
" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Understanding the imported data" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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 date 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 test 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": { "deletable": true, "editable": true }, "source": [ "### Creating an interactive section" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "sess = tf.InteractiveSession()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Creating placeholders" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "It's 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", "__Placeholder 'X':__ represents the \"space\" allocated input or the images. \n", " * Each input has 784 pixels distributed by a 28 width x 28 height matrix \n", " * The 'shape' argument defines the tensor size by its dimensions. \n", " * 1st dimension = None. Indicates that the batch size, can be of any size. \n", " * 2nd dimension = 784. Indicates the number of pixels on a single flattened MNIST image. \n", " \n", "__Placeholder 'Y':___ represents the final output or the labels. \n", " * 10 possible classes (0,1,2,3,4,5,6,7,8,9) \n", " * The 'shape' argument defines the tensor size by its dimensions. \n", " * 1st dimension = None. Indicates that the batch size, can be of any size. \n", " * 2nd dimension = 10. Indicates the number of targets/outcomes \n", "\n", "__dtype for both placeholders:__ 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 here\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, 784])\n", "y_ = tf.placeholder(tf.float32, shape=[None, 10])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Assigning bias and weights to null tensors" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "### Execute the assignment operation " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# run the op initialize_all_variables using an interactive session\n", "sess.run(tf.initialize_all_variables())" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Adding Weights and Biases to input" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The only difference from 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": { "deletable": true, "editable": true }, "source": [ "\n", "\"HTML5 \n", "
Illustration showing how weights and biases are added to neurons/nodes.
\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "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": { "deletable": true, "editable": true }, "source": [ "### Softmax Regression" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Softmax is an activation function that is normally used in classification problems. It generate 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 the larger probability.\n", "\n", "For comparison, below is the one-hot vector for a nine digit label:" ] }, { "cell_type": "raw", "metadata": { "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "0 -->.0.1% \n", "1 -->...2% \n", "2 -->...3% \n", "3 -->...2% \n", "4 -->..12% \n", "5 -->..10% \n", "6 -->..57%\n", "7 -->..20%\n", "8 -->..55%\n", "9 -->..80% " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "y = tf.nn.softmax(tf.matmul(x,W) + b)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "### Cost function" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Type of optimization: Gradient Descent" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This is the part where you configure the optimizer for you Neural Network. There are several optimizers available, in our case we will use Gradient Descent that is very well stablished." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Training batches" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "batch = mnist.train.next_batch(50)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(50, 784)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "batch[0].shape" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "numpy.ndarray" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(batch[0])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(55000, 784)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mnist.train.images.shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "### Test" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The final accuracy for the simple ANN model is: 90.8500015736 % \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": 16, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sess.close() #finish the session" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "# Evaluating the final result" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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", "Reference here" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "# How to improve our model?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "#### Several options as follow:\n", "- Regularization of Neural Networks using DropConnect\n", "- Multi-column Deep Neural Networks for Image Classification \n", "- APAC: Augmented Pattern Classification with Neural Networks\n", "- Simple Deep Neural Network with Dropout\n", "\n", "#### In the next part we are going to explore the option:\n", "- Simple Deep Neural Network with Dropout (more than 1 hidden layer)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "# 2nd part: Deep Learning applied on MNIST" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "### Starting the code" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "### The MNIST data" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "### Initial parameters" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Create general parameters for the model" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "### Input and output" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Create place holders for inputs and outputs" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, flat])\n", "y_ = tf.placeholder(tf.float32, shape=[None, class_output])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Converting images of the data set to tensors" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The input image is a 28 pixels by 28 pixels and 1 channel (grayscale)\n", "\n", "In this case the first dimension is the __batch number__ of the image (position of the input on the batch) and can be of any size (due to -1)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "x_image = tf.reshape(x, [-1,28,28,1]) " ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_image" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Convolutional Layer 1" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Defining kernel weight and bias\n", "Size of the filter/kernel: 5x5; \n", "Input channels: 1 (greyscale); \n", "32 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 `[filter_height, filter_width, in_channels, out_channels]`" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "\"HTML5\n", "\n", "#### Convolve with weight tensor and add biases.\n", "\n", "Defining a function to create convolutional layers. To creat convolutional layer, we use __tf.nn.conv2d__. 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]\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 patch, right-multiplies the filter matrix and the image patch vector.\n", "\n", "Output:\n", "- A `Tensor` (a 2-D convolution) of size \n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Apply the ReLU activation Function" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "In this step, we just go through all outputs convolution layer, __covolve1__, and wherever a negative number occurs,we swap it out for a 0. It is called ReLU activation Function." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "h_conv1 = tf.nn.relu(convolve1)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "#### Apply the max pooling" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Use the max pooling operation already defined, so the output would be 14x14x32" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Defining a function to perform max pooling. The maximum pooling is an operation that finds maximum values and simplifies the inputs using the spacial correlations between them. \n", "\n", "__Kernel size:__ 2x2 (if the window is a 2x2 matrix, it would result in one output pixel) \n", "__Strides:__ dictates the sliding behaviour of the kernel. In this case it will move 2 pixels everytime, thus not overlapping.\n", "\n", "\n", "\"HTML5 \n", "\n" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "h_pool1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #max_pool_2x2" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### First layer completed" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer1= h_pool1" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Convolutional Layer 2\n", "#### Weights and Biases of kernels" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Filter/kernel: 5x5 (25 pixels) ; Input channels: 32 (from the 1st Conv layer, we had 32 feature maps); 64 output feature maps \n", "__Notice:__ here, the input is 14x14x32, the filter is 5x5x32, we use 64 filters, and the output of the convolutional layer would be 14x14x64.\n", "\n", "__Notice:__ 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": 69, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "\n", "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": { "deletable": true, "editable": true }, "source": [ "#### Convolve image with weight tensor and add biases." ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "convolve2= tf.nn.conv2d(layer1, W_conv2, strides=[1, 1, 1, 1], padding='SAME')+ b_conv2" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Apply the ReLU activation Function" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "h_conv2 = tf.nn.relu(convolve2)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "#### Apply the max pooling" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "h_pool2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #max_pool_2x2" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Second layer completed" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer2= h_pool2" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "So, what is the output of the second layer, layer2?\n", "- it is 64 matrix of [7x7]\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Fully Connected Layer 3" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Type: Fully Connected Layer. 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 matrics, and convert them to an 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", "\"HTML5 \n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Flattening Second Layer" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer2_matrix = tf.reshape(layer2, [-1, 7*7*64])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Weights and Biases between layer 2 and 3" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 75, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "#### Matrix Multiplication (applying weights and biases)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "fcl3=tf.matmul(layer2_matrix, W_fc1) + b_fc1" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Apply the ReLU activation Function" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "h_fc1 = tf.nn.relu(fcl3)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Third layer completed" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [], "source": [ "layer3= h_fc1" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer3" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Optional phase for reducing overfitting - Dropout 3" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 80, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "keep_prob = tf.placeholder(tf.float32)\n", "layer3_drop = tf.nn.dropout(layer3, keep_prob)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Layer 4- Readout Layer (Softmax Layer)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Type: Softmax, Fully Connected Layer." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Weights and Biases" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": 81, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "#### Matrix Multiplication (applying weights and biases)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "fcl4=tf.matmul(layer3_drop, W_fc2) + b_fc2" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Apply the Softmax activation Function\n", "__softmax__ allows us to interpret the outputs of __fcl4__ as probabilities. So, __y_conv__ is a tensor of probablities." ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "y_conv= tf.nn.softmax(fcl4)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer4= y_conv" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer4" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "# Summary of the Deep Convolutional Neural Network" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now is time to remember the structure of our network" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "# Define functions and train the model" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Define the loss function\n", "\n", "We need to compare our output, layer4 tensor, with ground truth for all mini_batch. we can use __cross entropy__ 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 __code__ in the toolbar) to see hoe cross entropy changes." ] }, { "cell_type": "raw", "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "__reduce_sum__ computes the sum of elements of __(y_ * tf.log(layer4)__ across second dimension of the tensor, and __reduce_mean__ computes the mean of all elements in the tensor.." ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(layer4), reduction_indices=[1]))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Define the optimizer\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": 87, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Define prediction\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": 88, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "correct_prediction = tf.equal(tf.argmax(layer4,1), tf.argmax(y_,1))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Define accuracy\n", "It makes more sense to report accuracy using average of correct cases." ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "#### Run session, train" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [], "source": [ "sess.run(tf.initialize_all_variables())" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "*If you want a fast result (**it might take sometime to train it**)*" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "step 0, loss 7.90357, training accuracy 0.04\n", "step 100, loss 0.631492, training accuracy 0.84\n", "step 200, loss 0.233621, training accuracy 0.92\n", "step 300, loss 0.373831, training accuracy 0.9\n", "step 400, loss 0.0972801, training accuracy 0.96\n", "step 500, loss 0.220456, training accuracy 0.92\n", "step 600, loss 0.0622852, training accuracy 1\n", "step 700, loss 0.131813, training accuracy 0.96\n", "step 800, loss 0.234988, training accuracy 0.9\n", "step 900, loss 0.0737939, training accuracy 1\n", "step 1000, loss 0.0693521, training accuracy 0.98\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", " loss, train_accuracy = sess.run([cross_entropy, accuracy], feed_dict={x: batch[0],y_: batch[1],keep_prob: 1.0})\n", " print(\"step %d, loss %g, training accuracy %g\"%(i, float(loss),float(train_accuracy)))\n", " train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "
\n", "### You can run this cell if you REALLY have time to wait (change the type of the cell to code)\n", "
" ] }, { "cell_type": "raw", "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": { "deletable": true, "editable": true }, "source": [ "*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.*" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "# Evaluate the model" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Print the evaluation to the user" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test accuracy 0.9644\n" ] } ], "source": [ "print(\"test accuracy %g\"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Visualization" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Do you want to look at all the filters?" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "kernels = sess.run(tf.reshape(tf.transpose(W_conv1, perm=[2, 3, 0,1]),[32,-1]))" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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cjHc+PGJsFfPcPWbOB/DmJsVBLo4rABHzvUwOM7JhsEBN+mfq1O8UdD5DcMCB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G3at1Jc3E/fXsLIurzWKg2CRtHSj3q48fEYIfxgWm3Xvm4u92Bofm63VmfyWvuBgK7t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\nHaP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GGFP62+lsRRenyx3PuzFXQTM1loqH8UCccCSuChp02jLecfyF1zHIxzqmBZfdc7Ix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Ha2fa3rXkNIgS/m7FofZZ4AWWVvpgBW9Rr3534zQDydTu8BsCMIXJmzwQ5zuFeukt1PtsQF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\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VSt3RTxn4s+U1dA8qe8yztm4fV+7WcXnGIVIgL86jnKWMbzKgrlLFD5VMdQDGTDnyAAuK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from utils 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": { "deletable": true, "editable": true }, "source": [ "Do you want to see the output of an image passing through first convolution layer?\n" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FQbRFUdHSWpeJkCXPsivq3VALHPi5oD54YPfLj8dI4B4WEraOU3JFbW2NrbU6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\nrBYiYdZ6/hSA7787/j4An/R/0FJmXse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"text/plain": [ "" ] }, "metadata": {}, "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": 100, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(1, 14, 14, 64)" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ActivatedUnits.shape" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ActivatedUnitsL1 = sess.run(convolve1,feed_dict={x:np.reshape(sampleimage,[1,784],order='F'),keep_prob:1.0})\n", "filters = ActivatedUnitsL1.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('Cov1_ ' + str(i))\n", " plt.imshow(ActivatedUnitsL1[0,:,:,i], interpolation=\"nearest\", cmap=\"gray\")" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "What about second convolution layer?" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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ffOtC0/p///d/q75bt25V7SNHjrTaXJrVylUfcenm7NmzVl8t/mp1EQDqsOCv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\nIXkIG20IIYQQQgghhBBC8hB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oQwghhBBCCCGEEEJSh8OjCCGEEEIIIYQQQvIQNtoQQggh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\n4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1Dieeqpp8T62LFjjbUzZ86IbTdu3GisDRsmzxYpPXamXc7oJTsDBpiHeZOei6FD5bH+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Ya9u2rZibSWyvk3S8AMjvkfQaA0CPHj2Mse9973tibl1dnRj/5je/aYzZ/ibp+BmQ90mZdpzT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\nWgA4C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NoyLUlZnKTsUICcjcU2Yi1lZAC87D0mbOtrdOnSxWiLzNZRG1K2DlsGmXQkMqNK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\n7VrRV+r39e3bV/S95pprjLYOHTqIv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P0kgCeSca2pneRqh7qhblx0Q+1QO67aoW6oGxfd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\n4LgAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ActivatedUnitsL2 = sess.run(convolve2,feed_dict={x:np.reshape(sampleimage,[1,784],order='F'),keep_prob:1.0})\n", "filters = ActivatedUnitsL2.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('Conv_2 ' + str(i))\n", " plt.imshow(ActivatedUnitsL2[0,:,:,i], interpolation=\"nearest\", cmap=\"gray\")" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "sess.close() #finish the session" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Thanks for completing this lesson!" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Authors\n", "\n", "
\"Saeed
\n", "#### Saeed Aghabozorgi\n", "\n", "[Saeed Aghabozorgi](https://ca.linkedin.com/in/saeedaghabozorgi), PhD is Sr. Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "
\n", "Learn more about __Deep Leaning and TensorFlow:__\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### References:\n", "\n", "https://en.wikipedia.org/wiki/Deep_learning \n", "http://sebastianruder.com/optimizing-gradient-descent/index.html#batchgradientdescent \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 " ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }