Deep+Neural+Network+-+Application+v8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Neural Network for Image Classification: Application\n",
    "\n",
    "When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! \n",
    "\n",
    "You will use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. Hopefully, you will see an improvement in accuracy relative to your previous logistic regression implementation.  \n",
    "\n",
    "**After this assignment you will be able to:**\n",
    "- Build and apply a deep neural network to supervised learning. \n",
    "\n",
    "Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first import all the packages that you will need during this assignment. \n",
    "- [numpy](https://www.numpy.org/) is the fundamental package for scientific computing with Python.\n",
    "- [matplotlib](http://matplotlib.org) is a library to plot graphs in Python.\n",
    "- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.\n",
    "- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end.\n",
    "- dnn_app_utils provides the functions implemented in the \"Building your Deep Neural Network: Step by Step\" assignment to this notebook.\n",
    "- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "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",
    "You will use the same \"Cat vs non-Cat\" dataset as in \"Logistic Regression as a Neural Network\" (Assignment 2). The model you had built had 70% test accuracy on classifying cats vs non-cats images. Hopefully, your new model will perform a better!\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).\n",
    "\n",
    "Let's get more familiar with the dataset. Load the data by running the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x_orig, train_y, test_x_orig, test_y, classes = load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will show you an image in the dataset. Feel free to change the index and re-run the cell multiple times to see other images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0. It's a non-cat picture.\n"
     ]
    },
    {
     "data": {
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NMAeVWwOzop8F8tmFenW7XAXZcEhaCk1p2zJIYZdrOtJroiGlnyKQGr8dWXkbSQbJMD0t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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb17af4abe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture\n",
    "index = 10\n",
    "plt.imshow(train_x_orig[index])\n",
    "print (\"y = \" + str(train_y[0,index]) + \". It's a \" + classes[train_y[0,index]].decode(\"utf-8\") +  \" picture.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": "markdown",
   "metadata": {},
   "source": [
    "As usual, you reshape and standardize the images before feeding them to the network. The code is given in the cell below.\n",
    "\n",
    "<img src=\"images/imvectorkiank.png\" style=\"width:450px;height:300px;\">\n",
    "\n",
    "<caption><center> <u>Figure 1</u>: Image to vector conversion. <br> </center></caption>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "$12,288$ equals $64 \\times 64 \\times 3$ which is the size of one reshaped image vector."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Architecture of your model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you are familiar with the dataset, it is time to build a deep neural network to distinguish cat images from non-cat images.\n",
    "\n",
    "You will build two different models:\n",
    "- A 2-layer neural network\n",
    "- An L-layer deep neural network\n",
    "\n",
    "You will then compare the performance of these models, and also try out different values for $L$. \n",
    "\n",
    "Let's look at the two architectures.\n",
    "\n",
    "### 3.1 - 2-layer neural network\n",
    "\n",
    "<img src=\"images/2layerNN_kiank.png\" style=\"width:650px;height:400px;\">\n",
    "<caption><center> <u>Figure 2</u>: 2-layer neural network. <br> The model can be summarized as: ***INPUT -> LINEAR -> RELU -> LINEAR -> SIGMOID -> OUTPUT***. </center></caption>\n",
    "\n",
    "<u>Detailed Architecture of figure 2</u>:\n",
    "- The input is a (64,64,3) image which is flattened to a vector of size $(12288,1)$. \n",
    "- The corresponding vector: $[x_0,x_1,...,x_{12287}]^T$ is then multiplied by the weight matrix $W^{[1]}$ of size $(n^{[1]}, 12288)$.\n",
    "- You then add a bias term and take its relu to get the following vector: $[a_0^{[1]}, a_1^{[1]},..., a_{n^{[1]}-1}^{[1]}]^T$.\n",
    "- You then repeat the same process.\n",
    "- You multiply the resulting vector by $W^{[2]}$ and add your intercept (bias). \n",
    "- Finally, you take the sigmoid of the result. If it is greater than 0.5, you classify it to be a cat.\n",
    "\n",
    "### 3.2 - L-layer deep neural network\n",
    "\n",
    "It is hard to represent an L-layer deep neural network with the above representation. However, here is a simplified network representation:\n",
    "\n",
    "<img src=\"images/LlayerNN_kiank.png\" style=\"width:650px;height:400px;\">\n",
    "<caption><center> <u>Figure 3</u>: L-layer neural network. <br> The model can be summarized as: ***[LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID***</center></caption>\n",
    "\n",
    "<u>Detailed Architecture of figure 3</u>:\n",
    "- The input is a (64,64,3) image which is flattened to a vector of size (12288,1).\n",
    "- The corresponding vector: $[x_0,x_1,...,x_{12287}]^T$ is then multiplied by the weight matrix $W^{[1]}$ and then you add the intercept $b^{[1]}$. The result is called the linear unit.\n",
    "- Next, you take the relu of the linear unit. This process could be repeated several times for each $(W^{[l]}, b^{[l]})$ depending on the model architecture.\n",
    "- Finally, you take the sigmoid of the final linear unit. If it is greater than 0.5, you classify it to be a cat.\n",
    "\n",
    "### 3.3 - General methodology\n",
    "\n",
    "As usual you will follow the Deep Learning methodology to build the model:\n",
    "    1. Initialize parameters / Define hyperparameters\n",
    "    2. Loop for num_iterations:\n",
    "        a. Forward propagation\n",
    "        b. Compute cost function\n",
    "        c. Backward propagation\n",
    "        d. Update parameters (using parameters, and grads from backprop) \n",
    "    4. Use trained parameters to predict labels\n",
    "\n",
    "Let's now implement those two models!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Two-layer neural network\n",
    "\n",
    "**Question**:  Use the helper functions you have implemented in the previous assignment to build a 2-layer neural network with the following structure: *LINEAR -> RELU -> LINEAR -> SIGMOID*. The functions you may need and their inputs are:\n",
    "```python\n",
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    ...\n",
    "    return parameters \n",
    "def linear_activation_forward(A_prev, W, b, activation):\n",
    "    ...\n",
    "    return A, cache\n",
    "def compute_cost(AL, Y):\n",
    "    ...\n",
    "    return cost\n",
    "def linear_activation_backward(dA, cache, activation):\n",
    "    ...\n",
    "    return dA_prev, dW, db\n",
    "def update_parameters(parameters, grads, learning_rate):\n",
    "    ...\n",
    "    return parameters\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### CONSTANTS DEFINING THE MODEL ####\n",
    "n_x = 12288     # num_px * num_px * 3\n",
    "n_h = 7\n",
    "n_y = 1\n",
    "layers_dims = (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: two_layer_model\n",
    "\n",
    "def two_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Implements a two-layer neural network: LINEAR->RELU->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (n_x, number of examples)\n",
    "    Y -- true \"label\" vector (containing 1 if cat, 0 if non-cat), of shape (1, number of examples)\n",
    "    layers_dims -- dimensions of the layers (n_x, n_h, n_y)\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    print_cost -- If set to True, this will print the cost every 100 iterations \n",
    "    \n",
    "    Returns:\n",
    "    parameters -- a dictionary containing W1, W2, b1, and b2\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(1)\n",
    "    grads = {}\n",
    "    costs = []                              # to keep track of the cost\n",
    "    m = X.shape[1]                           # number of examples\n",
    "    (n_x, n_h, n_y) = layers_dims\n",
    "    \n",
    "    # Initialize parameters dictionary, by calling one of the functions you'd previously implemented\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Get W1, b1, W2 and b2 from the dictionary parameters.\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: \"X, W1, b1, W2, b2\". Output: \"A1, cache1, A2, cache2\".\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        A1, cache1 = linear_activation_forward(X, W1, b1, activation=\"relu\")\n",
    "        A2, cache2 = linear_activation_forward(A1, W2, b2, activation=\"sigmoid\")\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Compute cost\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        cost = compute_cost(A2, Y)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Initializing backward propagation\n",
    "        dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))\n",
    "        \n",
    "        # Backward propagation. Inputs: \"dA2, cache2, cache1\". Outputs: \"dA1, dW2, db2; also dA0 (not used), dW1, db1\".\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        dA1, dW2, db2 = linear_activation_backward(dA2, cache2, activation=\"sigmoid\")\n",
    "        dA0, dW1, db1 = linear_activation_backward(dA1, cache1, activation=\"relu\")\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Set grads['dWl'] to dW1, grads['db1'] to db1, grads['dW2'] to dW2, grads['db2'] to db2\n",
    "        grads['dW1'] = dW1\n",
    "        grads['db1'] = db1\n",
    "        grads['dW2'] = dW2\n",
    "        grads['db2'] = db2\n",
    "        \n",
    "        # Update parameters.\n",
    "        ### START CODE HERE ### (approx. 1 line of code)\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        # Retrieve W1, b1, W2, b2 from parameters\n",
    "        W1 = parameters[\"W1\"]\n",
    "        b1 = parameters[\"b1\"]\n",
    "        W2 = parameters[\"W2\"]\n",
    "        b2 = parameters[\"b2\"]\n",
    "        \n",
    "        # Print the cost every 100 training example\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, np.squeeze(cost)))\n",
    "        if print_cost and i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "       \n",
    "    # plot the cost\n",
    "\n",
    "    plt.plot(np.squeeze(costs))\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per hundreds)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to train your parameters. See if your model runs. The cost should be decreasing. It may take up to 5 minutes to run 2500 iterations. Check if the \"Cost after iteration 0\" matches the expected output below, if not click on the square (⬛) on the upper bar of the notebook to stop the cell and try to find your error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6930497356599888\n",
      "Cost after iteration 100: 0.6464320953428849\n",
      "Cost after iteration 200: 0.6325140647912677\n",
      "Cost after iteration 300: 0.6015024920354665\n",
      "Cost after iteration 400: 0.5601966311605747\n",
      "Cost after iteration 500: 0.5158304772764729\n",
      "Cost after iteration 600: 0.47549013139433255\n",
      "Cost after iteration 700: 0.43391631512257495\n",
      "Cost after iteration 800: 0.400797753620389\n",
      "Cost after iteration 900: 0.3580705011323798\n",
      "Cost after iteration 1000: 0.3394281538366411\n",
      "Cost after iteration 1100: 0.3052753636196264\n",
      "Cost after iteration 1200: 0.2749137728213018\n",
      "Cost after iteration 1300: 0.24681768210614854\n",
      "Cost after iteration 1400: 0.19850735037466094\n",
      "Cost after iteration 1500: 0.17448318112556666\n",
      "Cost after iteration 1600: 0.17080762978096128\n",
      "Cost after iteration 1700: 0.11306524562164724\n",
      "Cost after iteration 1800: 0.09629426845937152\n",
      "Cost after iteration 1900: 0.08342617959726856\n",
      "Cost after iteration 2000: 0.07439078704319078\n",
      "Cost after iteration 2100: 0.06630748132267927\n",
      "Cost after iteration 2200: 0.05919329501038164\n",
      "Cost after iteration 2300: 0.05336140348560553\n",
      "Cost after iteration 2400: 0.048554785628770115\n"
     ]
    },
    {
     "data": {
      "image/png": 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B7xSxbutuhvz5XR56d7mvuqegWAbnDKCLpA6S6gEXAVOiG0hqCzwDjDKzJTGs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\nuVry4HTOuVry4HTOuVry4HTOuVr6fyvKkJvl3CS0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb17af4a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Cost after iteration 0**</td>\n",
    "        <td> 0.6930497356599888 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 100**</td>\n",
    "        <td> 0.6464320953428849 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **...**</td>\n",
    "        <td> ... </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 2400**</td>\n",
    "        <td> 0.048554785628770226 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good thing you built a vectorized implementation! Otherwise it might have taken 10 times longer to train this.\n",
    "\n",
    "Now, you can use the trained parameters to classify images from the dataset. To see your predictions on the training and test sets, run the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "predictions_train = predict(train_x, train_y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Accuracy**</td>\n",
    "        <td> 1.0 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.72\n"
     ]
    }
   ],
   "source": [
    "predictions_test = predict(test_x, test_y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Accuracy**</td>\n",
    "        <td> 0.72 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: You may notice that running the model on fewer iterations (say 1500) gives better accuracy on the test set. This is called \"early stopping\" and we will talk about it in the next course. Early stopping is a way to prevent overfitting. \n",
    "\n",
    "Congratulations! It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). Let's see if you can do even better with an $L$-layer model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - 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": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### CONSTANTS ###\n",
    "layers_dims = [12288, 20, 7, 5, 1] #  4-layer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "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 (num_px * num_px * 3, number of examples)\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 hundreds)')\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. \n",
    "\n",
    "Run the cell below to train your model. The cost should decrease on every iteration. It may take up to 5 minutes to run 2500 iterations. Check if the \"Cost after iteration 0\" matches the expected output below, if not click on the square (⬛) on the upper bar of the notebook to stop the cell and try to find your error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": {
      "image/png": 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kk5WRzh2XDePMQZ35weS5vtrump0Hp0tKmRlp/O7SoZzWv4ibnpnD8zPrvfiA\nc03mwemSVlZGOn+4fDijehXyrSdm+XGertl4cLqklt0mnT9eOYLjSttz3WPTeWP+uqhLcknAg9Ml\nvdysDO67eiRHdclj4kPT/LLD7oh5cLqU0L5tGx766ih6dmzH+AcrqPAh6dwR8OB0KaNDu0weGj+S\nLvnZXH3fVGZXbo66JJegPDhdSinOy+bh8aNon9OGK+79gI/XbI26JJeA4hqcksZImi9pkaSb6nj+\nMkmzJc2R9J6k4+JZj3MA3Qra8uj448nOSOcrf/wHn1Rtj7okl2DiFpyS0gmulT4WGARcImlQrWZL\ngNPM7Bjgx8Bd8arHuVhlHXN45OujALjs7n+wYuPOiCtyiSSePc6RwCIzW2xme4HHgXGxDczsPTM7\ncNWtvwN+3VfXYvoU5fLQ10axu3o/597+Lv/97Bxe+2gtO/ZUR12aa+XieXngEmBFzONKYFQD7b8G\nvFzXE5ImABMAysrKmqs+5xjYNZ9Hxx/Pr15fwHMzVvLIP5bTJl2M6FnI6KOKGH1UMf2Kc5EUdamu\nFZGZxWfG0oXAGDMbHz6+HBhlZpPqaHs6cAdwspltaGi+5eXlVlFREY+SXYrbW11DxdKNvLmgijfn\nr2PB2mDbZ0lBW07tX8Too4o4qW8ncrPi2d9wUZE0zczKG9M2nr8BK4HuMY9Lw2mfIulY4I/A2EOF\npnPxlJmRxol9O3Fi30589/MDWbV5F2+FIfrCrFU89sFyMtLEZaPK+O7ZA8nKSI+6ZBeReAbnVKCf\npF4EgXkxcGlsA0llwDPA5Wa2II61ONdk3QracsnIMi4ZWcbe6hqmLdvE5FmreOD9ZcxcsZk7vjKc\nkoK2UZfpIhC3nUNmVg1MAl4F5gFPmNlcSRMlTQybfR/oCNwhaaYkXwd3rVJmRhon9OnI/zv/GO78\nynAWV+3gnN++zVt+aeKUFLdtnPHi2zhda7Bk/Q6ueXga89du49/O6M91n+lLWprvQEpkTdnG6WcO\nOXcYenVqx7PXnsR5Q0r41esL+OoDU9m0Y2/UZbkW4sHp3GFqm5nOrV86jp+cezTvLdrAOb97x89/\nTxEenM4dAUl85fgePDHxBAAu/P37PPqP5STaJjDXNB6czjWDId0LeOG6kxnVu5DvPjuH/3hyNrv2\n7o+6LBcnHpzONZPCdpncf/VIbjijH8/MqOTc29/liakr2Lp7X9SluWbme9Wdi4M35q/jRy98xJL1\nO8jMSOMa/c18AAAMdklEQVSzA4s5d0gJo48qJjPD+yutUWs5c8i5lHX6UcWM7l/ErMotPDdjJS/M\nWsVLc9bQvm0bzj62K+cNLWF4WQc/hClBeY/TuRawb38N7yxaz3MzVvKXuWvZtW8/JQVtOXdoN84d\nUkK/znlRl5jymtLj9OB0roXt2FPNXz5aw7MzVvHOwipqDPp3zg16qUcVU96zA23SfXW+pXlwOpcg\n1m3bzYuzVvP6vLVMXbqRffuN3KwMTu7bidMHFHFa/2K6tM+OusyU4MHpXALavqeadxet5835wYhM\nq7fsBoIxQ08PxwYdVlZAhvdG48KD07kEZ2bMX7uNN+dX8cbH65i2bBPVNUZedgbDyjowrKwDQ8sK\nGFJWQH52m6jLTQoenM4lma279/HuwvVMWbie6cs2sWDdNsxAgn7FuQeDdFhZB/oU5fre+sPgwelc\nktu6ex+zV2xh+vJNzFi+iRkrNrN5Z3CgfV52BkO6FzC8RwdG9CxkSPcC2vmo9Yfkx3E6l+Tys9tw\ncr9OnNyvExCs2i9ev4MZyzczffkmpi/bxG/+uhAzSE8Tg7vlU96jkBE9OzC8ZweK83yH05HwHqdz\nSWrr7n3MWL6ZiqUbmbp0IzOWb2ZPdQ0APTvmUN4zCNLynoX07tQu5S9I56vqzrl/sbe6hrmrtlCx\ndBMfLN1IxdKNbApX7/OzMzimtD3HlBRwXGl7jiltT0lB25QKUw9O59whmRmfVO2gYulGZq/cwpzK\nLXy8Ziv79geZUNguk2NK2odBWsCxpe3pnJ+8q/i+jdM5d0iS6FucS9/iXC4Op+2p3s/Hq7eFQbqZ\n2ZVbuP3N9eyvCcK0KC+L/p1z6VecR9/iXPoV59Kvcx6F7TKjW5AIeHA65w7KykjnuO4FHNe9AOgB\nwK69+/lo9RZmV25h7qqtLFq3naemVbJ9T/XB13XKzQyDNI9+nYMw7t0pl+K8rKQ8NCquwSlpDPAb\nIB34o5n9rNbzA4D7gGHAf5vZ/8WzHudc07XNTGd4j0KG9yg8OM3MWL1lNwvXbWfh2m0sWredBWu3\n8dzMlWzb/c9AzcpIo0fHHMoK29GjY054P4ceHdtR2qFtwp6TH7fglJQO3A58DqgEpkqabGYfxTTb\nCFwPnBuvOpxzzU8S3Qra0q2gLaf1Lzo43cxYt20PC9duZ8mGHSzfsINlG3aybMNO3l20nl37/jkq\nfnqa6FaQTWlBDvltM8jNakNuVjq52eH97IzgcVYbcrMyyMvOoKSgLR1awWaBePY4RwKLzGwxgKTH\ngXHAweA0s3XAOklnx7EO51wLkUTn/Gw652cfPMb0ADOjatselm0MgnT5hh0s3bCTVZt3sWzDTrbt\nrmb7nuB2YJtqXXp3aseQ8CypYWUd6N85t8XP349ncJYAK2IeVwKjDmdGkiYAEwDKysqOvDLnXIuT\nRHF+NsX52YzoWVhvOzNj976agyG6fXc12/bsY9vuahat286M5Zt5a34Vz0xfCUBOZjrHlRYwrEcB\nQ7sHp552zM2K67IkxM4hM7sLuAuCw5EiLsc5F0eSaJuZTtvMdIryPh2AZw0OfpoZKzbuOnjK6fTl\nm/nDW4upDnuqPTvm8PMLjmVU745xqTGewbkS6B7zuDSc5pxzR0QSZR1zKOuYw7lDS4Bg7/+clVuC\nc/eXb6Y4jsecxjM4pwL9JPUiCMyLgUvj+H7OuRTWNjOdkb0KGdmr/s0AzSVuwWlm1ZImAa8SHI50\nr5nNlTQxfP5OSV2ACiAfqJH0b8AgM9sar7qcc+5IxXUbp5m9BLxUa9qdMffXEKzCO+dcwkjMo0+d\ncy5CHpzOOddEHpzOOddEHpzOOddEHpzOOddEHpzOOddECTcCvKQqYFkTX9YJWB+HcqLky9T6Jdvy\nQHIvUw8zKzpUY0jA4DwckioaOyR+ovBlav2SbXnAl+kAX1V3zrkm8uB0zrkmSpXgvCvqAuLAl6n1\nS7blAV8mIEW2cTrnXHNKlR6nc841Gw9O55xroqQOTkljJM2XtEjSTVHX0xwkLZU0R9JMSRVR13M4\nJN0raZ2kD2OmFUp6TdLC8GeHKGtsqnqW6RZJK8Pvaqakz0dZY1NJ6i7pDUkfSZor6YZwekJ+Vw0s\nT5O/p6TdxhlenngBMZcnBi6pdXnihCNpKVBuZgl7ELKkU4HtwINmdnQ47RfARjP7WfhProOZfSfK\nOpuinmW6BdhuZv8XZW2HS1JXoKuZTZeUB0wjuJT3VSTgd9XA8nyJJn5PydzjPHh5YjPbCxy4PLGL\nmJlNATbWmjwOeCC8/wDBL3TCqGeZEpqZrTaz6eH9bcA8gqvXJuR31cDyNFkyB2ddlyc+rA+plTHg\ndUnTwssmJ4vOZrY6vL8G6BxlMc3oOkmzw1X5hFilrYuknsBQ4B8kwXdVa3mgid9TMgdnsjrZzIYA\nY4FvhquIScWC7UfJsA3p90BvYAiwGrg12nIOj6Rc4Gng32pfDywRv6s6lqfJ31MyB2dSXp7YzFaG\nP9cBzxJskkgGa8NtUAe2Ra2LuJ4jZmZrzWy/mdUAd5OA35WkNgQh84iZPRNOTtjvqq7lOZzvKZmD\n8+DliSVlElyeeHLENR0RSe3CjdpIagecCXzY8KsSxmTgyvD+lcDzEdbSLA6ES+g8Euy7kiTgHmCe\nmf0y5qmE/K7qW57D+Z6Sdq86QHhYwa/55+WJfxpxSUdEUm+CXiYEVyh9NBGXSdJjwGiC4bzWAj8A\nngOeAMoIhg38kpklzM6WepZpNMHqnwFLgW/EbBts9SSdDLwNzAFqwsnfJdgumHDfVQPLcwlN/J6S\nOjidcy4eknlV3Tnn4sKD0znnmsiD0znnmsiD0znnmsiD0znnmsiDM0VIei/82VPSpc087+/W9V7x\nIulcSd+P07y3x2m+oyW9eITzuF/ShQ08P0nSV4/kPVzjeHCmCDM7MbzbE2hScErKOESTTwVnzHvF\ny7eBO450Jo1Yrrhr5hruBa5rxvm5enhwpoiYntTPgFPCcQf/XVK6pP+VNDUc5OAbYfvRkt6WNBn4\nKJz2XDi4yNwDA4xI+hnQNpzfI7HvpcD/SvpQwRiiX46Z95uSnpL0saRHwrM6kPSzcLzE2ZL+ZZgv\nSf2BPQeG1Qt7YXdKqpC0QNI54fRGL1cd7/FTSbMk/V1S55j3uTCmzfaY+dW3LGPCadOB82Nee4uk\nhyS9CzzUQK2SdJuCMWVfB4pj5vEvn5OZ7QSWSkq4UzsTjpn5LQVuBOMNQnA2y4sx0ycA3wvvZwEV\nQK+w3Q6gV0zbwvBnW4LT0jrGzruO97oAeI3gzK3OwHKgazjvLQTjB6QB7wMnAx2B+fzzxIyCOpbj\nauDWmMf3A6+E8+lHMApWdlOWq9b8DfhCeP8XMfO4H7iwns+zrmXJJhidqx8ggjNtXgxfcwvBWJBt\nD/EdnB/z+XUDNgMXNvQ5Af8N3Bj171uy37zH6c4ErpA0k+BUuo4Ef+wAH5jZkpi210uaBfydYACV\nfjTsZOAxCwZQWAu8BYyImXelBQMrzCTYhLAF2A3cI+l8YGcd8+wKVNWa9oSZ1ZjZQmAxMKCJyxVr\nL3BgW+S0sK5DqWtZBgBLzGyhBYn2cK3XTDazXeH9+mo9lX9+fquAv4XtG/qc1hGErIujyLfxuMgJ\nuM7MXv3URGk0Qc8s9vFngRPMbKekNwl6VYdrT8z9/UCGmVWHq5lnEPSsJgGfqfW6XUD7WtNqnzds\nNHK56rAvDLqDdYX3qwk3bUlKAzIbWpYG5n9AbA311VrnJRwO8TllE3xGLo68x5l6tgF5MY9fBa5R\nMNwWkvorGHmptvbApjA0BwDHxzy378Dra3kb+HK4Da+IoAf1QX2FKRgnsb2ZvQT8O3BcHc3mAX1r\nTbtIUpqkPgTjKs5vwnI11lJgeHj/i0BdyxvrY6BnWBMEA0nUp75ap/DPz68rcHr4fEOfU38SbBSm\nROQ9ztQzG9gfrnLfD/yGYNVyerhTo4q6L4XwCjBR0jyCYPp7zHN3AbMlTTezy2KmPwucAMwi6AV+\n28zWhMFblzzgeUnZBL2wb9XRZgpwqyTF9AyXEwRyPjDRzHZL+mMjl6ux7g5rm0XwWTTUayWsYQLw\nZ0k7Cf6J5NXTvL5anyXoSX4ULuP7YfuGPqeTCLahujjy0ZFcwpH0G+AFM3td0v0EO12eirisyEka\nCnzLzC6PupZk56vqLhH9D5ATdRGtUCfg5qiLSAXe43TOuSbyHqdzzjWRB6dzzjWRB6dzzjWRB6dz\nzjWRB6dzzjXR/wcWGroGraFWCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb15cfe7c18>"
      ]
     },
     "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": [
    "**Expected Output**:\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Cost after iteration 0**</td>\n",
    "        <td> 0.771749 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 100**</td>\n",
    "        <td> 0.672053 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **...**</td>\n",
    "        <td> ... </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 2400**</td>\n",
    "        <td> 0.092878 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **Train Accuracy**\n",
    "    </td>\n",
    "    <td>\n",
    "    0.985645933014\n",
    "    </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8\n"
     ]
    }
   ],
   "source": [
    "pred_test = predict(test_x, test_y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Test Accuracy**</td>\n",
    "        <td> 0.8 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats! It seems that your 4-layer neural network has better performance (80%) than your 2-layer neural network (72%) on the same test set. \n",
    "\n",
    "This is good performance for this task. Nice job! \n",
    "\n",
    "Though in the next course on \"Improving deep neural networks\" you will learn how to obtain even higher accuracy by systematically searching for better hyperparameters (learning_rate, layers_dims, num_iterations, and others you'll also learn in the next course). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  6) Results Analysis\n",
    "\n",
    "First, let's take a look at some images the L-layer model labeled incorrectly. This will show a few mislabeled images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WKaAcijnxvclhEfbYOiSH8pgGRL+SLuPCp/DL3gq/i2PzXvHnEZs4ZgUXW6Hi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\nLV3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2eWpJdX0Z1MkvXlYd/fEtDfxZTAS/cVbPM4Mt3q+v6LfX1kflT5/QuVNp7mcCa4Y+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q2vd2VBVK3rPLT3PjA+a/iwmRro3gtJWgjDRhr0hj6si6qwmvborkzsByyTiLShXZYH+7KF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\nrOMnoaMm4KWgoa9+dvPyrTD+5pPaG/rCvxS+4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0z5FEpWVxEBd1xGeIS+YzBiCeo4T/qI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\nFxtEq1UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1382b9940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_mislabeled_images(classes, test_x, test_y, pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A few types of images the model tends to do poorly on include:** \n",
    "- Cat body in an unusual position\n",
    "- Cat appears against a background of a similar color\n",
    "- Unusual cat color and species\n",
    "- Camera Angle\n",
    "- Brightness of the picture\n",
    "- Scale variation (cat is very large or small in image) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7) Test with your own image (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Change your image's name in the following code\n",
    "    4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "y = 1.0, your L-layer model predicts a \"cat\" picture.\n"
     ]
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb1383011d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## START CODE HERE ##\n",
    "my_image = \"my_cat.jpg\" # change this to the name of your image file \n",
    "my_label_y = [1] # the true class of your image (1 -> cat, 0 -> non-cat)\n",
    "## END CODE HERE ##\n",
    "\n",
    "fname = \"images/\" + my_image\n",
    "image = np.array(ndimage.imread(fname, flatten=False))\n",
    "my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((num_px*num_px*3,1))\n",
    "my_image = my_image/255.\n",
    "my_predicted_image = predict(my_image, my_label_y, parameters)\n",
    "\n",
    "plt.imshow(image)\n",
    "print (\"y = \" + str(np.squeeze(my_predicted_image)) + \", your L-layer model predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**:\n",
    "\n",
    "- for auto-reloading external module: http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}