DNN_Image_Classify.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from L_Layer_model import *\n",
    "import scipy\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from utils import *\n",
    "\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": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x_orig, train_y, test_x_orig, test_y = load_image()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 13\n",
      "Number of testing examples: 24\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_x_orig shape: (13, 64, 64, 3)\n",
      "train_y shape: (1, 13)\n",
      "test_x_orig shape: (24, 64, 64, 3)\n",
      "test_y shape: (1, 24)\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))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f54dbb29b10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d6f0AlccvwXTd1kwndS4Pe9etQOCZAzmkdQGNwM+4UhGT1Xve87uqHaZi27vv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f550c05f190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see a picture example\n",
    "plt.imshow(train_x_orig[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_x's shape: (12288, 13)\n",
      "test_x's shape: (12288, 24)\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))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Layer Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## mention how many layer network you want here by adding layer dimensions in layer_dims :)\n",
    "\n",
    "layers_dims = [12288, 20, 7, 5, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):\n",
    "\n",
    "    np.random.seed(1)\n",
    "    costs = []                         # keep track of cost\n",
    "    \n",
    "    # Parameters initialization.\n",
    "    parameters = init_parameters_L(layers_dims)\n",
    "    # Loop (gradient descent)\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.\n",
    "        AL, caches = L_model_forward(X, parameters)\n",
    "        \n",
    "        # Compute cost.\n",
    "        cost = compute_cost(AL, Y)\n",
    "    \n",
    "        # Backward propagation.\n",
    "        grads = L_model_backward(AL, Y, caches)\n",
    "        ### END CODE HERE ###\n",
    " \n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\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": [
    "# Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693146\n",
      "Cost after iteration 100: 0.692220\n",
      "Cost after iteration 200: 0.691584\n",
      "Cost after iteration 300: 0.691147\n",
      "Cost after iteration 400: 0.690846\n",
      "Cost after iteration 500: 0.690640\n",
      "Cost after iteration 600: 0.690498\n",
      "Cost after iteration 700: 0.690400\n",
      "Cost after iteration 800: 0.690333\n",
      "Cost after iteration 900: 0.690287\n",
      "Cost after iteration 1000: 0.690255\n",
      "Cost after iteration 1100: 0.690233\n",
      "Cost after iteration 1200: 0.690218\n",
      "Cost after iteration 1300: 0.690208\n",
      "Cost after iteration 1400: 0.690201\n",
      "Cost after iteration 1500: 0.690196\n",
      "Cost after iteration 1600: 0.690192\n",
      "Cost after iteration 1700: 0.690190\n",
      "Cost after iteration 1800: 0.690188\n",
      "Cost after iteration 1900: 0.690187\n",
      "Cost after iteration 2000: 0.690187\n",
      "Cost after iteration 2100: 0.690186\n",
      "Cost after iteration 2200: 0.690186\n",
      "Cost after iteration 2300: 0.690185\n",
      "Cost after iteration 2400: 0.690185\n"
     ]
    },
    {
     "data": {
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fze1XnsLmPQf5+sPL/UkcznUCHsI9zPRRA7hx9jjmrdzGPS9sjLs5zvV4HsI9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\nds65GHkIO+dcjDyEnXMuRv8flpHZEIBdiPAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f54dbc3aad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.538461538462\n"
     ]
    }
   ],
   "source": [
    "pred_train = predict(train_x, train_y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "pred_test = predict(test_x, test_y, parameters)"
   ]
  }
 ],
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