L-model forward.ipynb

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      "C:\\Users\\MANDY\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: linear_forward\n",
    "\n",
    "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 dictionary containing \"A\", \"W\" and \"b\" ; stored for computing the backward pass efficiently\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    Z = np.dot(W, A) + b\n",
    "    ### END CODE HERE ###\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: linear_activation_forward\n",
    "\n",
    "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 dictionary 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",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        Z, linear_cache = linear_forward(A_prev, W, b)\n",
    "        A, activation_cache = sigmoid(Z)\n",
    "        ### END CODE HERE ###\n",
    "    \n",
    "    elif activation == \"relu\":\n",
    "        # Inputs: \"A_prev, W, b\". Outputs: \"A, activation_cache\".\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        Z, linear_cache = linear_forward(A_prev, W, b)\n",
    "        A, activation_cache = relu(Z)\n",
    "        ### END CODE HERE ###\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_prev, W, b = linear_activation_forward_test_case()\n",
    "\n",
    "A, linear_activation_cache = linear_activation_forward(A_prev, W, b, activation = \"sigmoid\")\n",
    "print(\"With sigmoid: A = \" + str(A))\n",
    "\n",
    "A, linear_activation_cache = linear_activation_forward(A_prev, W, b, activation = \"relu\")\n",
    "print(\"With ReLU: A = \" + str(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: L_model_forward\n",
    "\n",
    "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_relu_forward() (there are L-1 of them, indexed from 0 to L-2)\n",
    "                the cache of linear_sigmoid_forward() (there is one, indexed 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",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        A, cache = linear_activation_forward(A_prev, \n",
    "                                             parameters['W' + str(l)], \n",
    "                                             parameters['b' + str(l)], \n",
    "                                             activation='relu')\n",
    "        caches.append(cache)\n",
    "        \n",
    "        ### END CODE HERE ###\n",
    "    \n",
    "    # Implement LINEAR -> SIGMOID. Add \"cache\" to the \"caches\" list.\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    AL, cache = linear_activation_forward(A, \n",
    "                                          parameters['W' + str(L)], \n",
    "                                          parameters['b' + str(L)], \n",
    "                                          activation='sigmoid')\n",
    "    caches.append(cache)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert(AL.shape == (1, X.shape[1]))\n",
    "            \n",
    "    return AL, caches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'L_model_forward_test_case' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-916b5820bff6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mL_model_forward_test_case\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mAL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcaches\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mL_model_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"AL = \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Length of caches list = \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcaches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'L_model_forward_test_case' is not defined"
     ]
    }
   ],
   "source": [
    "X, parameters = L_model_forward_test_case()\n",
    "AL, caches = L_model_forward(X, parameters)\n",
    "print(\"AL = \" + str(AL))\n",
    "print(\"Length of caches list = \" + str(len(caches)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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