010.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Neural Network for Image Classification: Application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from dnn_app_utils_v3 import *\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Dataset\n",
    "\n",
    "**Problem Statement**: You are given a dataset (\"data.h5\") containing:\n",
    "    - a training set of m_train images labelled as cat (1) or non-cat (0)\n",
    "    - a test set of m_test images labelled as cat and non-cat\n",
    "    - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x_orig, train_y, test_x_orig, test_y, classes = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 209\n",
      "Number of testing examples: 50\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_x_orig shape: (209, 64, 64, 3)\n",
      "train_y shape: (1, 209)\n",
      "test_x_orig shape: (50, 64, 64, 3)\n",
      "test_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "# Explore your dataset \n",
    "m_train = train_x_orig.shape[0]\n",
    "num_px = train_x_orig.shape[1]\n",
    "m_test = test_x_orig.shape[0]\n",
    "\n",
    "print (\"Number of training examples: \" + str(m_train))\n",
    "print (\"Number of testing examples: \" + str(m_test))\n",
    "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_x_orig shape: \" + str(train_x_orig.shape))\n",
    "print (\"train_y shape: \" + str(train_y.shape))\n",
    "print (\"test_x_orig shape: \" + str(test_x_orig.shape))\n",
    "print (\"test_y shape: \" + str(test_y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_x's shape: (12288, 209)\n",
      "test_x's shape: (12288, 50)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the training and test examples \n",
    "train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T   # The \"-1\" makes reshape flatten the remaining dimensions\n",
    "test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T\n",
    "\n",
    "# Standardize data to have feature values between 0 and 1.\n",
    "train_x = train_x_flatten/255.\n",
    "test_x = test_x_flatten/255.\n",
    "\n",
    "print (\"train_x's shape: \" + str(train_x.shape))\n",
    "print (\"test_x's shape: \" + str(test_x.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - L-layer Neural Network\n",
    "\n",
    "**Question**: Use the helper functions you have implemented previously to build an $L$-layer neural network with the following structure: *[LINEAR -> RELU]$\\times$(L-1) -> LINEAR -> SIGMOID*. The functions you may need and their inputs are:\n",
    "```python\n",
    "def initialize_parameters_deep(layers_dims):\n",
    "    ...\n",
    "    return parameters \n",
    "def L_model_forward(X, parameters):\n",
    "    ...\n",
    "    return AL, caches\n",
    "def compute_cost(AL, Y):\n",
    "    ...\n",
    "    return cost\n",
    "def L_model_backward(AL, Y, caches):\n",
    "    ...\n",
    "    return grads\n",
    "def update_parameters(parameters, grads, learning_rate):\n",
    "    ...\n",
    "    return parameters\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### CONSTANTS ###\n",
    "layers_dims = [12288, 20, 7, 5, 1] #  4-layer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: L_layer_model\n",
    "\n",
    "def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):#lr was 0.009\n",
    "    \"\"\"\n",
    "    Implements a L-layer neural network: [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- data, numpy array of shape (number of examples, num_px * num_px * 3)\n",
    "    Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)\n",
    "    layers_dims -- list containing the input size and each layer size, of length (number of layers + 1).\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    print_cost -- if True, it prints the cost every 100 steps\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "\n",
    "    np.random.seed(1)\n",
    "    costs = []                         # keep track of cost\n",
    "    \n",
    "    # Parameters initialization. (≈ 1 line of code)\n",
    "    ### START CODE HERE ###\n",
    "    parameters = initialize_parameters_deep(layers_dims)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        AL, caches = L_model_forward(X, parameters)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Compute cost.\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        cost = compute_cost(AL, Y)\n",
    "        ### END CODE HERE ###\n",
    "    \n",
    "        # Backward propagation.\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        grads =  L_model_backward(AL, Y, caches)\n",
    "        ### END CODE HERE ###\n",
    " \n",
    "        # Update parameters.\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        ### END CODE HERE ###\n",
    "                \n",
    "        # Print the cost every 100 training example\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "        if print_cost and i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "            \n",
    "    # plot the cost\n",
    "    plt.plot(np.squeeze(costs))\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per tens)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will now train the model as a 4-layer neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.771749\n",
      "Cost after iteration 100: 0.672053\n",
      "Cost after iteration 200: 0.648263\n",
      "Cost after iteration 300: 0.611507\n",
      "Cost after iteration 400: 0.567047\n",
      "Cost after iteration 500: 0.540138\n",
      "Cost after iteration 600: 0.527930\n",
      "Cost after iteration 700: 0.465477\n",
      "Cost after iteration 800: 0.369126\n",
      "Cost after iteration 900: 0.391747\n",
      "Cost after iteration 1000: 0.315187\n",
      "Cost after iteration 1100: 0.272700\n",
      "Cost after iteration 1200: 0.237419\n",
      "Cost after iteration 1300: 0.199601\n",
      "Cost after iteration 1400: 0.189263\n",
      "Cost after iteration 1500: 0.161189\n",
      "Cost after iteration 1600: 0.148214\n",
      "Cost after iteration 1700: 0.137775\n",
      "Cost after iteration 1800: 0.129740\n",
      "Cost after iteration 1900: 0.121225\n",
      "Cost after iteration 2000: 0.113821\n",
      "Cost after iteration 2100: 0.107839\n",
      "Cost after iteration 2200: 0.102855\n",
      "Cost after iteration 2300: 0.100897\n",
      "Cost after iteration 2400: 0.092878\n"
     ]
    },
    {
     "data": {
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D4eefNZhIRESqF3sCNbOWQBEwLVHm7g5MBfrW8DTDganu/mldYuncGQ47TCsTiYhI9WJP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\n6lMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf1e1824e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.985645933014\n"
     ]
    }
   ],
   "source": [
    "pred_train = predict(train_x, train_y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8\n"
     ]
    }
   ],
   "source": [
    "pred_test = predict(test_x, test_y, parameters)"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "TSPse",
   "launcher_item_id": "24mxX"
  },
  "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}