Skip to content
Snippets Groups Projects
neural_nets_intro.ipynb 565 KiB
Newer Older
  • Learn to ignore specific revisions
  • chadhat's avatar
    chadhat committed
    {
     "cells": [
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "# Introduction to Neural Networks\n",
        "\n",
        "## TO DO: Almost all the figues and schematics will be replaced or improved slowly\n",
        "\n",
        "<img src=\"./images/neuralnets/Colored_neural_network.svg\"/>\n",
        "source: https://en.wikipedia.org/wiki/Artificial_neural_network\n",
        "\n"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "## History of Neural networks\n",
        "\n",
        "**TODO: Make it more complete and format properly**\n",
        "\n",
        "1943 - Threshold Logic\n",
        "\n",
        "1940s - Hebbian Learning\n",
        "\n",
        "1958 - Perceptron\n",
        "\n",
        "1975 - Backpropagation\n",
        "\n",
        "1980s - Neocognitron\n",
        "\n",
        "1982: Hopfield Network\n",
        "\n",
        "1986: Convolutional Neural Networks\n",
        "\n",
        "1997: Long-short term memory (LSTM) model\n",
        "\n",
    
        "2014: Gated Recurrent Units, Generative Adversarial Networks(Check)?"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "## Why the boom now?\n",
        "* Data\n",
        "* Data\n",
        "* Data\n",
        "* Availability of GPUs\n",
        "* Algorithmic developments which allow for efficient training and training for deeper networks\n",
        "* Much easier access than a decade ago"
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "## Building blocks\n",
        "### Perceptron\n",
        "\n",
        "Smallest unit of a neural network is a **perceptron** like node.\n",
        "\n",
        "**What is a Perceptron?**\n",
        "\n",
        "It is a simple function which has multiple inputs and a single output.\n",
        "\n",
        "Step 1: Weighted sum of the inputs is calculated\n",
        "\n",
        "\\begin{equation*}\n",
        "weighted\\_sum = \\sum_{k=1}^{num\\_inputs} w_{i} x_{i}\n",
        "\\end{equation*}\n",
        "\n",
        "Step 2: The following activation function is applied\n",
        "\n",
        "$$\n",
        "f(weighted\\_sum) = \\left\\{\n",
        "        \\begin{array}{ll}\n",
    
        "            0 & \\quad weighted\\_sum < threshold \\\\\n",
        "            1 & \\quad weighted\\_sum \\geq threshold\n",
    
    chadhat's avatar
    chadhat committed
        "        \\end{array}\n",
        "    \\right.\n",
        "$$\n",
    
        "\n",
        "You can see that this is also a linear classifier as we introduced in script 02."
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 17,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [],
       "source": [
        "%matplotlib inline\n",
    
        "%config IPCompleter.greedy=True\n",
        "%config InlineBackend.figure_format = 'retina'"
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 18,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [
        {
         "data": {
          "text/plain": [
           "1"
          ]
         },
    
         "execution_count": 18,
    
    chadhat's avatar
    chadhat committed
         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
        "import numpy as np\n",
        "def perceptron(X, w, threshold=1):\n",
        "    # This function computes sum(w_i*x_i) and \n",
        "    # applies a perceptron activation\n",
        "    linear_sum = np.dot(X,w)\n",
        "    output=0\n",
        "    if linear_sum >= threshold:\n",
        "        output = 1\n",
        "        # print(\"The perceptron has peaked\")\n",
        "    return output\n",
        "X = [1,0]\n",
        "w = [1,1]\n",
        "perceptron(X,w)"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "#### Boolean AND\n",
        "\n",
        "| x$_1$ | x$_2$ | output |\n",
        "| --- | --- | --- |\n",
        "| 0 | 0 | 0 |\n",
        "| 1 | 0 | 0 |\n",
        "| 0 | 1 | 0 |\n",
        "| 1 | 1 | 1 |"
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 158,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
          "Perceptron output for x1, x2 =  [0, 0]  is  0\n",
          "Perceptron output for x1, x2 =  [1, 0]  is  0\n",
          "Perceptron output for x1, x2 =  [0, 1]  is  0\n",
          "Perceptron output for x1, x2 =  [1, 1]  is  1\n"
    
    chadhat's avatar
    chadhat committed
         ]
        }
       ],
       "source": [
        "# Calculating Boolean AND using a perceptron\n",
        "import matplotlib.pyplot as plt\n",
    
        "threshold = 1.5\n",
    
    chadhat's avatar
    chadhat committed
        "w=[1,1]\n",
        "X=[[0,0],[1,0],[0,1],[1,1]]\n",
        "for i in X:\n",
    
        "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))"
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": []
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "In this simple case we can rewrite our equation to $x_2 = ...... $ which describes a line in 2D:"
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 20,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [
        {
         "data": {
    
          "image/png": "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\n",
    
    chadhat's avatar
    chadhat committed
          "text/plain": [
    
           "<matplotlib.figure.Figure at 0x7fe999f134e0>"
    
    chadhat's avatar
    chadhat committed
          ]
         },
         "metadata": {
    
          "image/png": {
           "height": 252,
           "width": 388
          },
    
    chadhat's avatar
    chadhat committed
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ],
       "source": [
        "# Plotting the decision boundary\n",
        "plt.xlim(-1,2)\n",
        "plt.ylim(-1,2)\n",
        "for i in X:\n",
        "    plt.plot(i,\"o\",color=\"b\");\n",
        "# Plotting the decision boundary\n",
        "# that is a line given by w_1*x_1+w_2*x_2-threshold=0\n",
        "plt.plot(np.arange(-3,4), 1.5-np.arange(-3,4), \"--\", color=\"black\");"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "**Exercise :Can you compute a Boolean \"OR\" using a perceptron?**\n",
        "\n",
        "Hint: copy the code from the \"AND\" example and edit the weights and/or threshold"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "#### Boolean OR\n",
        "\n",
        "| x$_1$ | x$_2$ | output |\n",
        "| --- | --- | --- |\n",
        "| 0 | 0 | 0 |\n",
        "| 1 | 0 | 1 |\n",
        "| 0 | 1 | 1 |\n",
        "| 1 | 1 | 1 |"
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 21,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [],
       "source": [
        "# Calculating Boolean OR using a perceptron\n",
        "# Edit the code below"
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 157,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
    
          "Perceptron output for x1, x2 =  [0, 0]  is  0\n",
          "Perceptron output for x1, x2 =  [1, 0]  is  1\n",
          "Perceptron output for x1, x2 =  [0, 1]  is  1\n",
          "Perceptron output for x1, x2 =  [1, 1]  is  1\n"
    
    chadhat's avatar
    chadhat committed
         ]
        },
        {
         "data": {
    
          "image/png": "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\n",
    
    chadhat's avatar
    chadhat committed
          "text/plain": [
    
           "<matplotlib.figure.Figure at 0x7fe8e711fc18>"
    
    chadhat's avatar
    chadhat committed
          ]
         },
         "metadata": {
    
          "image/png": {
           "height": 252,
           "width": 388
          },
    
    chadhat's avatar
    chadhat committed
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ],
       "source": [
        "# Solution\n",
        "# Calculating Boolean OR using a perceptron\n",
        "import matplotlib.pyplot as plt\n",
        "threshold=0.6\n",
        "w=[1,1]\n",
        "X=[[0,0],[1,0],[0,1],[1,1]]\n",
        "for i in X:\n",
    
        "    print(\"Perceptron output for x1, x2 = \" , i , \" is \" , perceptron(i,w,threshold))\n",
    
    chadhat's avatar
    chadhat committed
        "# Plotting the decision boundary\n",
        "plt.xlim(-1,2)\n",
        "plt.ylim(-1,2)\n",
        "for i in X:\n",
        "    plt.plot(i,\"o\",color=\"b\");\n",
        "# Plotting the decision boundary\n",
        "# that is a line given by w_1*x_1+w_2*x_2-threshold=0\n",
        "plt.plot(np.arange(-3,4), threshold-np.arange(-3,4), \"--\", color=\"black\");"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "**Optional exercise: Create a NAND gate with perceptrons**"
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 23,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [],
       "source": [
        "# Calculating Boolean NAND using a perceptron\n",
        "\n",
        "\n",
        "\n"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "In fact a single perceptron can compute \"AND\", \"OR\" and \"NOT\" boolean functions.\n",
        "However, it cannot compute some other boolean functions such as \"XOR\"\n",
        "\n",
        "WHAT CAN WE DO?\n",
        "Hint: What is the significance of the NAND gate we created above\n",
        "\n",
        "We said a single perceptron can't compute these functions. We didn't say that about **multiple Perceptrons**"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "**XOR function**\n",
        "\n",
        "**TO DO: INSERT IMAGE HERE!!!!!!!!!!!!!!**"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "### Google Playground\n",
        "\n",
        "UWE: move up before discussing gradient stuff etc\n",
        "\n",
        "https://playground.tensorflow.org/\n",
        "\n",
        "<img src=\"./images/neuralnets/google_playground.png\"/>"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "## Learning\n",
        "\n",
        "Now we know that we can compute complex functions if we stack together a number of perceptrons.\n",
        "\n",
        "However, we can DO NOT want to set the weights and thresholds by hand as we did in the examples above.\n",
        "\n",
        "We want some algorithm to do this for us!\n",
        "\n",
        "In order to achieve this we first need to choose a loss function for the problem at hand\n",
        "\n",
        "\n",
        "### Loss function\n",
        "As in the case of other machine learning algorithms we need to define a so-called \"Loss function\". In simple words this function measures how close are the predictions of our network to the supplied labels. Once we have this function we need an algorithm to update the weights of the network such that this loss decreases. As one can already imagine the choice of an appropriate loss function is very important to the success of the trained model. Fortunately, for classification and regression (which comprise of a large range of probelms) these loss functions are well known. Generally **crossentropy** and **mean squared error** loss functions are chosen for classification and regression problems, respectively.\n",
        "\n",
        "### Gradient based learning\n",
        "Once we have a loss function we want to solve an **optimization problem** which minimizes this loss by updating the weights of the network and this is how the learning actually happens.\n",
        "\n",
        "One of the most popular optimization method used in machine learning is **Gradient-descent**\n",
        "\n",
        "INSERT MORE EXPLAINATIONS HERE\n",
        "\n",
    
    chadhat's avatar
    chadhat committed
        "### Activation Functions\n",
        "\n",
    
        "In order to train the network we need to change Perceptron's **step** activation function as it does not allow training using the back-propagation algorithm among other drawbacks.\n",
        "\n",
        "Non-Linear functions such as:\n",
        "\n",
        "* ReLU (Rectified linear unit)\n",
        "\n",
        "\\begin{equation*}\n",
        "f(z) = \\mathrm{max}(0,z)\n",
        "\\end{equation*}\n",
        "\n",
        "* Sigmoid\n",
        "\n",
        "\\begin{equation*}\n",
        "f(z) = \\frac{1}{1+e^{-z}}\n",
        "\\end{equation*}\n",
        "\n",
        "* tanh\n",
        "\n",
        "\\begin{equation*}\n",
        "f(z) = \\frac{e^{z} - e^{-z}}{e^{z} + e^{-z}}\n",
        "\\end{equation*}\n",
        "\n",
        "\n",
        "are some of the most popular choices used as activation functions.\n",
        "\n",
        "Linear activations are **NOT** used because it can be mathematically shown that if linear activations are used then output is just a linear function of the input. So adding any number of hidden layers does not help to learn interesting functions.\n",
        "\n",
        "Non-linear activation functions allow the network to learn more complex representations."
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 135,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [
        {
         "data": {
    
          "image/png": "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\n",
    
    chadhat's avatar
    chadhat committed
          "text/plain": [
    
           "<matplotlib.figure.Figure at 0x7fe8eb5e6978>"
    
    chadhat's avatar
    chadhat committed
          ]
         },
         "metadata": {
    
          "image/png": {
           "height": 250,
           "width": 597
          },
    
    chadhat's avatar
    chadhat committed
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ],
       "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
    
        "plt.figure(figsize=(10, 4))\n",
    
    chadhat's avatar
    chadhat committed
        "\n",
        "pts=np.arange(-20,20, 0.1)\n",
        "\n",
    
        "plt.subplot(1, 3, 1)\n",
    
    chadhat's avatar
    chadhat committed
        "# Sigmoid\n",
    
        "plt.plot(pts, 1/(1+np.exp(-pts))) ;\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
    
    chadhat's avatar
    chadhat committed
        "# tanh\n",
    
        "plt.plot(pts, np.tanh(pts*np.pi)) ;\n",
    
    chadhat's avatar
    chadhat committed
        "\n",
        "# Rectified linear unit (ReLu)\n",
    
        "plt.subplot(1, 3, 3)\n",
    
    chadhat's avatar
    chadhat committed
        "pts_relu=[max(0,i) for i in pts];\n",
    
        "plt.plot(pts, pts_relu) ;"
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "Suggestion Uwe:\n",
        "\n",
        "1. more layers might improve power of single perctptron.\n",
        "\n",
        "2. regrettably math show that just \"stacking\" perceptrons only adds little improvements\n",
        "\n",
        "3. way around: look at nature how neuron works and introduce non linear activation functions.\n",
        "\n",
        "4. theoretical background: universal approximation theorem.\n",
        "\n",
        "\n",
        "\n",
    
    chadhat's avatar
    chadhat committed
        "### Multi-layer preceptron neural network\n",
        "Universal function theorem\n",
        "\n",
    
        "epochs\n"
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "# Introduction to Keras"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "What is **Keras**?\n",
        "\n",
        "* It is a high level API to create and work with neural networks\n",
    
        "* Supports multiple backends such as TensorFlow from Google, Theano (Although Theano is dead now) and CNTK (Microsoft Cognitive Toolkit)\n",
        "* Very good for creating neural nets very quickly and hides away a lot of tedious work\n",
        "* Has been incorporated into official TensorFlow (which obviously only works with tensforflow) and as of TensorFlow 2.0 this will the main api to use TensorFlow (check reference)\n"
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": 35,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [
        {
    
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "_________________________________________________________________\n",
          "Layer (type)                 Output Shape              Param #   \n",
          "=================================================================\n",
          "dense_9 (Dense)              (None, 4)                 36        \n",
          "_________________________________________________________________\n",
          "activation_7 (Activation)    (None, 4)                 0         \n",
          "_________________________________________________________________\n",
          "dense_10 (Dense)             (None, 4)                 20        \n",
          "_________________________________________________________________\n",
          "dense_11 (Dense)             (None, 1)                 5         \n",
          "_________________________________________________________________\n",
          "activation_8 (Activation)    (None, 1)                 0         \n",
          "=================================================================\n",
          "Total params: 61\n",
          "Trainable params: 61\n",
          "Non-trainable params: 0\n",
          "_________________________________________________________________\n"
    
    chadhat's avatar
    chadhat committed
         ]
        }
       ],
       "source": [
        "# Say hello to keras\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "\n",
        "# Creating a model\n",
        "model = Sequential()\n",
        "\n",
        "# Adding layers to this model\n",
        "# 1st Hidden layer\n",
    
        "# A Dense/fully-connected layer which takes as input a \n",
        "# feature array of shape (samples, num_features)\n",
        "# Here input_shape = (8,) means that the layer expects an input with num_features = 8 \n",
        "# and the sample size could be anything\n",
        "# Then we specify an activation function\n",
        "model.add(Dense(units=4, input_shape=(8,)))\n",
    
    chadhat's avatar
    chadhat committed
        "model.add(Activation(\"relu\"))\n",
        "\n",
    
        "# 2nd Hidden layer\n",
        "# This is also a fully-connected layer and we do not need to specify the\n",
        "# shape of the input anymore (We need to do that only for the first layer)\n",
        "# NOTE: Now we didn't add the activation seperately. Instead we just added it\n",
        "# while calling Dense(). This and the way used for the first layer are Equivalent!\n",
        "model.add(Dense(units=4, activation=\"relu\"))\n",
        "\n",
        "          \n",
    
    chadhat's avatar
    chadhat committed
        "# The output layer\n",
        "model.add(Dense(units=1))\n",
        "model.add(Activation(\"sigmoid\"))\n",
        "\n",
        "model.summary()"
       ]
      },
      {
       "cell_type": "code",
    
       "execution_count": null,
    
    chadhat's avatar
    chadhat committed
       "metadata": {},
       "outputs": [],
       "source": [
    
        "# Fitting the model "
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "**TO DO: Move this example after the previous dataset examples**"
    
    chadhat's avatar
    chadhat committed
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "### MNIST Dataset\n",
    
    chadhat's avatar
    chadhat committed
        "\n",
    
        "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
    
    chadhat's avatar
    chadhat committed
        "\n",
    
        "\n",
        ">The MNIST database of handwritten digits, available from this page, 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.\n",
        ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
        ">source: http://yann.lecun.com/exdb/mnist/\n",
        "\n",
        "The problem we want to solve using this dataset is: multi-class classification\n",
        "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. "
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 184,
       "metadata": {},
       "outputs": [],
       "source": [
        "# Loading the dataset in keras\n",
        "# Later you can explore and play with other datasets with come with Keras\n",
        "from keras.datasets import mnist\n",
        "\n",
        "# Loading the train and test data\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 185,
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "(60000, 28, 28)\n"
         ]
        }
       ],
       "source": [
        "# Looking at the dataset\n",
        "print(X_train.shape)"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 186,
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "This digit is:  8\n"
         ]
        },
        {
         "data": {
          "image/png": "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\n",
          "text/plain": [
           "<matplotlib.figure.Figure at 0x7fe8e68579e8>"
          ]
         },
         "metadata": {
          "image/png": {
           "height": 250,
           "width": 253
          },
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ],
       "source": [
        "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "i=np.random.randint(0,X_train.shape[0])\n",
        "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
        "print(\"This digit is: \" , y_train[i])"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 187,
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   3  18  18  18 126 136\n",
          "  175  26 166 255 247 127   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0  30  36  94 154 170 253 253 253 253 253\n",
          "  225 172 253 242 195  64   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0  49 238 253 253 253 253 253 253 253 253 251\n",
          "   93  82  82  56  39   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0  18 219 253 253 253 253 253 198 182 247 241\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0  80 156 107 253 253 205  11   0  43 154\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0  14   1 154 253  90   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0 139 253 190   2   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0  11 190 253  70   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0  35 241 225 160 108   1\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0  81 240 253 253 119\n",
          "   25   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  45 186 253 253\n",
          "  150  27   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16  93 252\n",
          "  253 187   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 249\n",
          "  253 249  64   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  46 130 183 253\n",
          "  253 207   2   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0  39 148 229 253 253 253\n",
          "  250 182   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0  24 114 221 253 253 253 253 201\n",
          "   78   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0  23  66 213 253 253 253 253 198  81   2\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0  18 171 219 253 253 253 253 195  80   9   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0  55 172 226 253 253 253 253 244 133  11   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0 136 253 253 253 212 135 132  16   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]\n",
          " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
          "    0   0   0   0   0   0   0   0   0   0]]\n"
         ]
        }
       ],
       "source": [
        "# Look at the data values for a couple of images\n",
        "print(X_train[0])"
    
    chadhat's avatar
    chadhat committed
       ]
      },
    
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
    
        "The data consists of values between 0-255 representing the **grayscale level**"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 188,
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "(60000,)\n"
         ]
        }
       ],
       "source": [
        "# The labels are the digit on the image\n",
        "print(y_train.shape)"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 190,
       "metadata": {},
       "outputs": [],
       "source": [
        "# Scaling the data\n",
        "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
        "# We could use the previously introduced function from SciKit learn. However, here it is sufficient to\n",
        "# just divide the input data by 255\n",
        "X_train_norm = X_train/255.\n",
        "X_test_norm = X_test/255.\n",
    
        "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
        "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
        "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "**IMPORTANT: One-Hot encoding**\n",
        "\n",
        "**TODO: Better frame the explaination**\n",
        "In such problems the labels are provided as something called **One-hot encodings**. What this does is to convert a categorical label to a vector.\n",
        "\n",
        "For the MNIST problem where we have **10 categories** one-hot encoding will create a vector of length 10 for each of the labels. All the entries of this vector will be zero **except** for the index which is equal to the integer value of the label.\n",
        "\n",
        "For example:\n",
        "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
        "\n",
        "Fortunately, we don't have to code this ourselves because Keras has a built-in function for this."
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 191,
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "(60000, 10)\n"
         ]
        }
       ],
       "source": [
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
        "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
        "\n",
        "print(y_train_onehot.shape)"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 194,
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "Epoch 1/20\n",
          "60000/60000 [==============================] - 2s 34us/step - loss: 0.5888 - acc: 0.8434\n",
          "Epoch 2/20\n",
          "60000/60000 [==============================] - 1s 20us/step - loss: 0.2569 - acc: 0.9267\n",
          "Epoch 3/20\n",
          "60000/60000 [==============================] - 1s 16us/step - loss: 0.2024 - acc: 0.9416\n",
          "Epoch 4/20\n",
          "60000/60000 [==============================] - 1s 17us/step - loss: 0.1706 - acc: 0.9497\n",
          "Epoch 5/20\n",
          "60000/60000 [==============================] - 1s 23us/step - loss: 0.1475 - acc: 0.9563\n",
          "Epoch 6/20\n",
          "60000/60000 [==============================] - 1s 20us/step - loss: 0.1290 - acc: 0.9627\n",
          "Epoch 7/20\n",
          "60000/60000 [==============================] - 1s 23us/step - loss: 0.1162 - acc: 0.9651\n",
          "Epoch 8/20\n",
          "60000/60000 [==============================] - 1s 19us/step - loss: 0.1035 - acc: 0.9691\n",
          "Epoch 9/20\n",
          "60000/60000 [==============================] - 2s 28us/step - loss: 0.0939 - acc: 0.9716\n",
          "Epoch 10/20\n",
          "60000/60000 [==============================] - 1s 22us/step - loss: 0.0848 - acc: 0.9743\n",
          "Epoch 11/20\n",
          "60000/60000 [==============================] - 1s 25us/step - loss: 0.0777 - acc: 0.9763\n",
          "Epoch 12/20\n",
          "60000/60000 [==============================] - 1s 20us/step - loss: 0.0720 - acc: 0.9780\n",
          "Epoch 13/20\n",
          "60000/60000 [==============================] - 1s 22us/step - loss: 0.0655 - acc: 0.9808\n",
          "Epoch 14/20\n",
          "60000/60000 [==============================] - 2s 30us/step - loss: 0.0610 - acc: 0.9817\n",
          "Epoch 15/20\n",
          "60000/60000 [==============================] - 1s 16us/step - loss: 0.0563 - acc: 0.9832\n",
          "Epoch 16/20\n",
          "60000/60000 [==============================] - 1s 20us/step - loss: 0.0527 - acc: 0.9842\n",
          "Epoch 17/20\n",
          "60000/60000 [==============================] - 1s 21us/step - loss: 0.0478 - acc: 0.9854\n",
          "Epoch 18/20\n",
          "60000/60000 [==============================] - 1s 15us/step - loss: 0.0453 - acc: 0.9864\n",
          "Epoch 19/20\n",
          "60000/60000 [==============================] - 1s 18us/step - loss: 0.0419 - acc: 0.9874\n",
          "Epoch 20/20\n",
          "60000/60000 [==============================] - 1s 20us/step - loss: 0.0387 - acc: 0.9885\n"
         ]
        },
        {
         "data": {
          "text/plain": [
           "<keras.callbacks.History at 0x7fe8e7465438>"
          ]
         },
         "execution_count": 194,
         "metadata": {},
         "output_type": "execute_result"
        }
       ],
       "source": [
        "# Building the keras model\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(64,input_shape=(28*28,), activation=\"relu\"))\n",
        "\n",
        "model.add(Dense(64, activation = \"relu\"))\n",
        "\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
        "\n",
        "model_history = model.fit(X_train_prep, y_train_cat, epochs=20, batch_size=512);"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 196,
       "metadata": {},
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": [
          "10000/10000 [==============================] - 1s 85us/step\n",
          "The [loss, accuracy] are:  [0.08737125840586377, 0.974]\n"
         ]
        }
       ],
       "source": [
        "# Evaluating the model on test dataset\n",
        "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
       ]
      },
      {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
        "# Work in Progress\n",
        "\n",
        "## Network results on dataset used in previous notebooks"
    
      {
       "cell_type": "code",
       "execution_count": 98,
       "metadata": {},
       "outputs": [
        {
         "data": {
          "image/png": "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\n",
          "text/plain": [
           "<matplotlib.figure.Figure at 0x7fe91c822048>"
          ]
         },
         "metadata": {
          "image/png": {
           "height": 318,
           "width": 332
          },
          "needs_background": "light"
         },
         "output_type": "display_data"
        }
       ],
       "source": [
        "# Creating a network to solve the XOR problem\n",
        "# Loading and plotting the data\n",
        "xor = pd.read_csv(\"xor.csv\")\n",
        "xv = xor[\"x\"]\n",
        "yv = xor[\"y\"]\n",
        "\n",
        "colors = [[\"steelblue\", \"chocolate\"][i] for i in xor[\"label\"]]\n",
        "plt.figure(figsize=(5, 5))\n",
        "plt.xlim([-2, 2])\n",
        "plt.ylim([-2, 2])\n",
        "plt.title(\"Blue points are False\")\n",
        "\n",
        "\n",
        "plt.scatter(xv, yv, color=colors, marker=\"o\");"
       ]
      },
      {
       "cell_type": "code",
       "execution_count": 126,
       "metadata": {},
       "outputs": [],
       "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "# Using x and y coordinates as featues\n",
        "features = xor.iloc[:, :-1]\n",
        "# Convert boolean to integer values (True->1 and False->0)\n",
        "labels = xor.iloc[:, -1].astype(int)\n",
        "\n",
        "# Here we split the dataset into training (80%) and validation sets (20%) \n",
        "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2)\n",
        "\n",
        "\n",
        "# Building a Keras model\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(4, input_shape = (2,), activation = \"relu\"))\n",
        "\n",
        "model.add(Dense(4, activation = \"relu\"))\n",
        "\n",
        "model.add(Dense(1, activation = \"sigmoid\"))\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
        "\n",
        "num_epochs = 200\n",
        "\n",
        "model_run = model.fit(X_train, y_train, epochs=num_epochs, validation_data = (X_test,y_test))\n"
       ]