cat_noncat_L_layer_NN.ipynb

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    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import glob\n",
    "import random\n",
    "from random import shuffle\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "number of train examples: 200\n",
      "number of test examples: 40\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
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     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Loading all the images to a numpy array\n",
    "train_types = ('catnoncat_dataset/train/*.png', 'catnoncat_dataset/train/*.jpg')\n",
    "test_types = ('catnoncat_dataset/test/*.png', 'catnoncat_dataset/test/*.jpg')\n",
    "\n",
    "train_file_paths = []\n",
    "test_file_paths = []\n",
    "\n",
    "\n",
    "for file in train_types:\n",
    "    train_file_paths.extend(glob.glob(file))\n",
    "for file in test_types:\n",
    "    test_file_paths.extend(glob.glob(file))\n",
    "\n",
    "shuffle(train_file_paths)\n",
    "shuffle(test_file_paths)\n",
    "\n",
    "train_imgs_orig = np.array([np.array(Image.open(path)) for path in train_file_paths])\n",
    "train_imgs_vec = np.reshape(train_imgs_orig, (12288, train_imgs_orig.shape[0]))\n",
    "print('number of train examples: {}'.format(train_imgs_vec.shape[1]))\n",
    "\n",
    "test_imgs_orig =  np.array([np.array(Image.open(path)) for path in test_file_paths])\n",
    "test_imgs_vec = np.reshape(test_imgs_orig, (12288, test_imgs_orig.shape[0]))\n",
    "print('number of test examples: {}'.format(test_imgs_vec.shape[1]))\n",
    "\n",
    "# Example (chnage the index)\n",
    "plt.imshow(train_imgs_orig[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y_train\n",
    "Y_train = np.zeros((1,len(train_file_paths)), int)\n",
    "for i in range(len(train_file_paths)):\n",
    "    if 'png' in train_file_paths[i]:\n",
    "        Y_train[0,i] = 1\n",
    "\n",
    "# Y_test\n",
    "Y_test = np.zeros((1,len(test_file_paths)), int)\n",
    "for i in range(len(test_file_paths)):\n",
    "    if 'png' in test_file_paths[i]:\n",
    "        Y_test[0,i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing data\n",
    "train_set_x_flatten = train_imgs_orig.reshape(train_imgs_orig.shape[0], -1).T\n",
    "test_set_x_flatten =  test_imgs_orig.reshape(test_imgs_orig.shape[0], -1).T\n",
    "X_train = train_set_x_flatten/255\n",
    "X_test = test_set_x_flatten/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(Z):\n",
    "    \"\"\"\n",
    "     A -- output of sigmoid(z), same shape as Z\n",
    "    cache -- returns Z as well, useful during backpropagation\n",
    "    \"\"\"\n",
    "    \n",
    "    A = 1/(1+np.exp(-Z))\n",
    "    cache = Z\n",
    "    \n",
    "    return A, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(Z):\n",
    "    \"\"\"\n",
    "    Z -- Output of last(linear) linear layer, of any shape\n",
    "    Returns:\n",
    "    A -- Post-activation parameter, of the same shape as Z\n",
    "    cache -- a python dictionary containing \"A\" ;for computing the backward pass\n",
    "    \"\"\"\n",
    "    \n",
    "    A = np.maximum(0,Z)\n",
    "    \n",
    "    assert(A.shape == Z.shape)\n",
    "    \n",
    "    cache = Z \n",
    "    return A, cache\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_backward(dA, cache):\n",
    "    \"\"\"\n",
    "    backward propagation for a single SIGMOID.\n",
    "    dA -- post-activation gradient, of any shape\n",
    "    cache -- 'Z' where we store for computing backward propagation efficiently\n",
    "    Returns:\n",
    "    dZ -- Gradient of the cost with respect to Z\n",
    "    \"\"\"\n",
    "    \n",
    "    Z = cache\n",
    "    \n",
    "    s = 1/(1+np.exp(-Z))\n",
    "    dZ = dA * s * (1-s)\n",
    "    \n",
    "    assert (dZ.shape == Z.shape)\n",
    "    \n",
    "    return dZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu_backward(dA, cache):\n",
    "    \"\"\"\n",
    "    backward propagation for a single RELU\n",
    "    dA -- post-activation gradient, of any shape\n",
    "    cache -- 'Z' where we store for computing backward propagation efficiently\n",
    "    Returns:\n",
    "    dZ -- Gradient of the cost with respect to Z\n",
    "    \"\"\"\n",
    "    \n",
    "    Z = cache\n",
    "    dZ = np.array(dA, copy=True) # converting dz to a np object.\n",
    "    \n",
    "    # When z <= 0, you should set dz to 0 as well. \n",
    "    dZ[Z <= 0] = 0\n",
    "    \n",
    "    assert (dZ.shape == Z.shape)\n",
    "    \n",
    "    return dZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters_deep(layer_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the dimensions of each layer in our network\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])\n",
    "                    bl -- bias vector of shape (layer_dims[l], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layer_dims)            # number of layers in the network\n",
    "\n",
    "    for l in range(1, L):\n",
    "        parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) * 0.01\n",
    "        parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))\n",
    "        \n",
    "        assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l-1]))\n",
    "        assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))\n",
    "\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_forward(A, W, b):\n",
    "    \"\"\"\n",
    "    Implement the linear part of a layer's forward propagation.\n",
    "\n",
    "    Arguments:\n",
    "    A -- activations from previous layer (or input data): (size of previous layer, number of examples)\n",
    "    W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)\n",
    "    b -- bias vector, numpy array of shape (size of the current layer, 1)\n",
    "\n",
    "    Returns:\n",
    "    Z -- the input of the activation function, also called pre-activation parameter \n",
    "    cache -- a python tuple containing \"A\", \"W\" and \"b\" ; stored for computing the backward pass efficiently\n",
    "    \"\"\"\n",
    "   \n",
    "    Z = np.dot(W, A) + b\n",
    "    \n",
    "    assert(Z.shape == (W.shape[0], A.shape[1]))\n",
    "    cache = (A, W, b)\n",
    "    \n",
    "    return Z, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_activation_forward(A_prev, W, b, activation):\n",
    "    \"\"\"\n",
    "    Implement the forward propagation for the LINEAR->ACTIVATION layer\n",
    "\n",
    "    Arguments:\n",
    "    A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)\n",
    "    W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)\n",
    "    b -- bias vector, numpy array of shape (size of the current layer, 1)\n",
    "    activation -- the activation to be used in this layer, stored as a text string: \"sigmoid\" or \"relu\"\n",
    "\n",
    "    Returns:\n",
    "    A -- the output of the activation function, also called the post-activation value \n",
    "    cache -- a python tuple containing \"linear_cache\" and \"activation_cache\";\n",
    "             stored for computing the backward pass efficiently\n",
    "    \"\"\"\n",
    "    \n",
    "    if activation == \"sigmoid\":\n",
    "        # Inputs: \"A_prev, W, b\". Outputs: \"A, activation_cache\".\n",
    "        Z, linear_cache = linear_forward(A_prev, W, b)\n",
    "        A, activation_cache = sigmoid(Z)\n",
    "    \n",
    "    elif activation == \"relu\":\n",
    "        # Inputs: \"A_prev, W, b\". Outputs: \"A, activation_cache\".\n",
    "        Z, linear_cache = linear_forward(A_prev, W, b)\n",
    "        A, activation_cache = relu(Z)\n",
    "    \n",
    "    assert (A.shape == (W.shape[0], A_prev.shape[1]))\n",
    "    cache = (linear_cache, activation_cache)\n",
    "\n",
    "    return A, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L_model_forward(X, parameters):\n",
    "    \"\"\"\n",
    "    Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation\n",
    "    \n",
    "    Arguments:\n",
    "    X -- data, numpy array of shape (input size, number of examples)\n",
    "    parameters -- output of initialize_parameters_deep()\n",
    "    \n",
    "    Returns:\n",
    "    AL -- last post-activation value\n",
    "    caches -- list of caches containing:\n",
    "                every cache of linear_activation_forward() (there are L-1 of them, indexed from 0 to L-1)\n",
    "    \"\"\"\n",
    "\n",
    "    caches = []\n",
    "    A = X\n",
    "    L = len(parameters) // 2                  # number of layers in the neural network\n",
    "    \n",
    "    # Implement [LINEAR -> RELU]*(L-1). Add \"cache\" to the \"caches\" list.\n",
    "    for l in range(1, L):\n",
    "        A_prev = A \n",
    "        A, cache = linear_activation_forward(A_prev, parameters['W' + str(l)], parameters['b' + str(l)], activation='relu')\n",
    "        caches.append(cache)\n",
    "    \n",
    "    # Implement LINEAR -> SIGMOID. Add \"cache\" to the \"caches\" list.\n",
    "    AL, cache = linear_activation_forward(A, parameters['W' + str(L)], parameters['b' + str(L)], activation='sigmoid')\n",
    "    caches.append(cache)\n",
    "    \n",
    "    assert(AL.shape == (1,X.shape[1]))\n",
    "            \n",
    "    return AL, caches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost(AL, Y):\n",
    "    \"\"\"\n",
    "    Implement the cost function defined by equation (7).\n",
    "\n",
    "    Arguments:\n",
    "    AL -- probability vector corresponding to your label predictions, shape (1, number of examples)\n",
    "    Y -- true \"label\" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)\n",
    "\n",
    "    Returns:\n",
    "    cost -- cross-entropy cost\n",
    "    \"\"\"\n",
    "    \n",
    "    m = Y.shape[1]\n",
    "\n",
    "    # Compute loss from aL and y.\n",
    "    cost = (-1/m) * np.sum(np.multiply(Y, np.log(AL)) + np.multiply((1-Y), np.log(1-AL)))\n",
    "    \n",
    "    cost = np.squeeze(cost)      # To make sure your cost's shape is what we expect (e.g. this turns [[17]] into 17).\n",
    "    assert(cost.shape == ())\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_backward(dZ, cache):\n",
    "    \"\"\"\n",
    "    Implement the linear portion of backward propagation for a single layer (layer l)\n",
    "\n",
    "    Arguments:\n",
    "    dZ -- Gradient of the cost with respect to the linear output (of current layer l)\n",
    "    cache -- tuple of values (A_prev, W, b) coming from the forward propagation in the current layer\n",
    "\n",
    "    Returns:\n",
    "    dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev\n",
    "    dW -- Gradient of the cost with respect to W (current layer l), same shape as W\n",
    "    db -- Gradient of the cost with respect to b (current layer l), same shape as b\n",
    "    \"\"\"\n",
    "    A_prev, W, b = cache\n",
    "    m = A_prev.shape[1]\n",
    "\n",
    "    dW = (1/m)*np.dot(dZ, A_prev.T)\n",
    "    db = (1/m)*np.sum(dZ, axis=1, keepdims=True)\n",
    "    dA_prev = np.dot(W.T,dZ)\n",
    "    \n",
    "    assert (dA_prev.shape == A_prev.shape)\n",
    "    assert (dW.shape == W.shape)\n",
    "    assert (db.shape == b.shape)\n",
    "    \n",
    "    return dA_prev, dW, db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_activation_backward(dA, cache, activation):\n",
    "    \"\"\"\n",
    "    Implement the backward propagation for the LINEAR->ACTIVATION layer.\n",
    "    \n",
    "    Arguments:\n",
    "    dA -- post-activation gradient for current layer l \n",
    "    cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently\n",
    "    activation -- the activation to be used in this layer, stored as a text string: \"sigmoid\" or \"relu\"\n",
    "    \n",
    "    Returns:\n",
    "    dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev\n",
    "    dW -- Gradient of the cost with respect to W (current layer l), same shape as W\n",
    "    db -- Gradient of the cost with respect to b (current layer l), same shape as b\n",
    "    \"\"\"\n",
    "    linear_cache, activation_cache = cache\n",
    "    \n",
    "    if activation == \"relu\":\n",
    "        dZ = relu_backward(dA, cache[1])\n",
    "        dA_prev, dW, db = linear_backward(dZ, linear_cache)\n",
    "        \n",
    "    elif activation == \"sigmoid\":\n",
    "        dZ = sigmoid_backward(dA, cache[1])\n",
    "        dA_prev, dW, db = linear_backward(dZ, linear_cache)\n",
    "    \n",
    "    return dA_prev, dW, db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L_model_backward(AL, Y, caches):\n",
    "    \"\"\"\n",
    "    Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group\n",
    "    \n",
    "    Arguments:\n",
    "    AL -- probability vector, output of the forward propagation (L_model_forward())\n",
    "    Y -- true \"label\" vector (containing 0 if non-cat, 1 if cat)\n",
    "    caches -- list of caches containing:\n",
    "                every cache of linear_activation_forward() with \"relu\" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)\n",
    "                the cache of linear_activation_forward() with \"sigmoid\" (it's caches[L-1])\n",
    "    \n",
    "    Returns:\n",
    "    grads -- A dictionary with the gradients\n",
    "             grads[\"dA\" + str(l)] = ... \n",
    "             grads[\"dW\" + str(l)] = ...\n",
    "             grads[\"db\" + str(l)] = ... \n",
    "    \"\"\"\n",
    "    grads = {}\n",
    "    L = len(caches) # the number of layers\n",
    "    m = AL.shape[1]\n",
    "    Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL\n",
    "    \n",
    "    # Initializing the backpropagation\n",
    "    dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))\n",
    "    \n",
    "    # Lth layer (SIGMOID -> LINEAR) gradients. Inputs: \"dAL, current_cache\". Outputs: \"grads[\"dAL-1\"], grads[\"dWL\"], grads[\"dbL\"]\n",
    "    current_cache = caches[L-1]\n",
    "    grads[\"dA\" + str(L-1)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_activation_backward(dAL, current_cache, activation = \"sigmoid\")\n",
    "    \n",
    "    # Loop from l=L-2 to l=0\n",
    "    for l in reversed(range(L-1)):\n",
    "        # lth layer: (RELU -> LINEAR) gradients.\n",
    "        # Inputs: \"grads[\"dA\" + str(l + 1)], current_cache\". Outputs: \"grads[\"dA\" + str(l)] , grads[\"dW\" + str(l + 1)] , grads[\"db\" + str(l + 1)] \n",
    "        current_cache = caches[l]\n",
    "        dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads[\"dA\" + str(l + 1)], current_cache, activation = \"relu\")\n",
    "        \n",
    "        grads[\"dA\" + str(l)] = dA_prev_temp\n",
    "        grads[\"dW\" + str(l + 1)] = dW_temp\n",
    "        grads[\"db\" + str(l + 1)] = db_temp\n",
    "\n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters(parameters, grads, learning_rate):\n",
    "    \"\"\"\n",
    "    Update parameters using gradient descent\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    grads -- python dictionary containing your gradients, output of L_model_backward\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "                  parameters[\"W\" + str(l)] = ... \n",
    "                  parameters[\"b\" + str(l)] = ...\n",
    "    \"\"\"\n",
    "    \n",
    "    L = len(parameters) // 2 # number of layers in the neural network\n",
    "\n",
    "    # Update rule for each parameter. Use a for loop.\n",
    "    for l in range(L):\n",
    "        parameters[\"W\" + str(l+1)] = parameters[\"W\" + str(l+1)] - learning_rate * grads[\"dW\" + str(l + 1)]\n",
    "        parameters[\"b\" + str(l+1)] = parameters[\"b\" + str(l+1)] - learning_rate * grads[\"db\" + str(l + 1)]\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def L_layer_model(X_train, Y_train, X_test, Y_test, 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. Can then be used to predict.\n",
    "    \"\"\"\n",
    "\n",
    "    costs = []                         # keep track of cost\n",
    "    \n",
    "    # Parameters initialization.\n",
    "    parameters = initialize_parameters_deep(layers_dims)\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "    for i in range(1, num_iterations+1):\n",
    "        # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.\n",
    "        AL, caches = L_model_forward(X_train, parameters)\n",
    "        \n",
    "        # Compute cost.\n",
    "        cost = compute_cost(AL, Y_train)\n",
    "    \n",
    "        # Backward propagation.\n",
    "        grads = L_model_backward(AL, Y_train, caches)\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",
    "    # Testset forward prop\n",
    "    AL_test, caches_test = L_model_forward(X_test, parameters)\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",
    "    # Converting outputs to 0(non-cat) and 1(cat)\n",
    "    m_train = X_train.shape[1]\n",
    "    m_test = X_test.shape[1]\n",
    "    Y_prediction_train = np.zeros((1,m_train))\n",
    "    Y_prediction_test = np.zeros((1,m_test))\n",
    "\n",
    "    for i in range(AL.shape[1]):\n",
    "        if AL[0,i] > 0.5:\n",
    "            Y_prediction_train[0,i] = 1\n",
    "\n",
    "    for i in range(AL_test.shape[1]):\n",
    "        if AL_test[0,i] > 0.5:\n",
    "            Y_prediction_test[0,i] = 1\n",
    "    \n",
    "    # Print the accuracy\n",
    "    print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))\n",
    "    print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))\n",
    "    \n",
    "    return Y_prediction_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cost after iteration 100: 0.693145\n",
      "Cost after iteration 200: 0.693143\n",
      "Cost after iteration 300: 0.693139\n",
      "Cost after iteration 400: 0.693130\n",
      "Cost after iteration 500: 0.693097\n",
      "Cost after iteration 600: 0.692891\n",
      "Cost after iteration 700: 0.683883\n",
      "Cost after iteration 800: 0.527576\n",
      "Cost after iteration 900: 0.478458\n",
      "Cost after iteration 1000: 0.396437\n",
      "Cost after iteration 1100: 0.386845\n",
      "Cost after iteration 1200: 0.305289\n",
      "Cost after iteration 1300: 0.394626\n",
      "Cost after iteration 1400: 0.195559\n",
      "Cost after iteration 1500: 0.187319\n",
      "Cost after iteration 1600: 0.134333\n",
      "Cost after iteration 1700: 0.031246\n",
      "Cost after iteration 1800: 0.009445\n",
      "Cost after iteration 1900: 0.004284\n",
      "Cost after iteration 2000: 0.002246\n",
      "Cost after iteration 2100: 0.001409\n",
      "Cost after iteration 2200: 0.000997\n",
      "Cost after iteration 2300: 0.000752\n",
      "Cost after iteration 2400: 0.000598\n",
      "Cost after iteration 2500: 0.000493\n"
     ]
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "train accuracy: 100.0 %\ntest accuracy: 72.5 %\n"
     ]
    }
   ],
   "source": [
    "layers_dims = [12288, 12, 4, 1] #  3-layer model\n",
    "prediction_test = L_layer_model(X_train, Y_train, X_test, Y_test, layers_dims,learning_rate=0.075, num_iterations = 2500, print_cost = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "This is a cat image\nYou predicted: cat\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Check Yourself :)\n",
    "index = 19\n",
    "\n",
    "plt.imshow(test_imgs_orig[index])\n",
    "if Y_test[0,index] == 1:\n",
    "    print('This is a cat image')\n",
    "else:\n",
    "    print('This is a non-cat image')\n",
    "\n",
    "if prediction_test[0,index] == 1:\n",
    "    print('You predicted: cat')\n",
    "else:\n",
    "    print('You predicted: non-cat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}