Binary_Cat_vs_NonCat.ipynb

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  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Binary_Cat_vs_NonCat.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pkraison/ex.ai/blob/master/Binary_Cat_vs_NonCat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "ZZtgCnH9mBPc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Cat vs Non_cat classification**\n",
        "\n",
        "In this notebook I tried to demonstrate the neural network architecture for classifying cats from non-cat images."
      ]
    },
    {
      "metadata": {
        "id": "pmm4UPjYv-Pl",
        "colab_type": "code",
        "outputId": "4ff1ccbc-d293-4e34-d84d-fb0e997add0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "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",
        "\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)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "V34YsTsIl9wC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "k3Cj52qUdsPZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_data():\n",
        "    train_dataset = h5py.File('datasets/train_catvnoncat.h5', \"r\")\n",
        "    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n",
        "    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n",
        "\n",
        "    test_dataset = h5py.File('datasets/test_catvnoncat.h5', \"r\")\n",
        "    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n",
        "    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n",
        "\n",
        "    classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n",
        "    \n",
        "    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n",
        "    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n",
        "    \n",
        "    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gyOwBZs6c1Wr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def sigmoid(Z):\n",
        "    \"\"\"\n",
        "    Implementation of the sigmoid activation in numpy\n",
        "    \n",
        "    Arguments:\n",
        "    Z -- numpy array of any shape\n",
        "    \n",
        "    Returns:\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\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "U2kc_VQWc1ug",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def relu(Z):\n",
        "    \"\"\"\n",
        "    Implementation of the RELU function.\n",
        "    Arguments:\n",
        "    Z -- Output of the 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\" ; stored for computing the backward pass efficiently\n",
        "    \"\"\"\n",
        "    \n",
        "    A = np.maximum(0,Z)\n",
        "    \n",
        "    assert(A.shape == Z.shape)\n",
        "    \n",
        "    cache = Z \n",
        "    return A, cache\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Bg-whOnbc2Dk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def relu_backward(dA, cache):\n",
        "    \"\"\"\n",
        "    Implementation of the backward propagation for a single RELU unit.\n",
        "    Arguments:\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) # just converting dz to a correct 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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8ERoe-fyc2cf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def sigmoid_backward(dA, cache):\n",
        "    \"\"\"\n",
        "    Implementation of the backward propagation for a single SIGMOID unit.\n",
        "    Arguments:\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\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xTEn22hBwHIT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_x_orig, train_y, test_x_orig, test_y, classes = load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KzlZoIVBwHPu",
        "colab_type": "code",
        "outputId": "adf8993a-3a5a-4b37-e42a-87ef40074af6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "cell_type": "code",
      "source": [
        "# Example of a picture\n",
        "index = 17\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.\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y = 0. It's a non-cat picture.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9cb0489be0>"
            ],
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ARMeJ6DtE9AIRPUdEnx78Pk9E3yai04P/527ozAaDYc8wiqrfA/Cbzrn7ATwE4NeJ6G0A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\nRK5CYII=\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "pOyOF5y8wHNX",
        "colab_type": "code",
        "outputId": "bcf7d5ea-7b87-4666-9ec3-ae193fba463f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "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))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2klIEkCFwHFl",
        "colab_type": "code",
        "outputId": "1ccafef4-9393-463e-d790-4b9154fd6f5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "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"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train_x's shape: (12288, 209)\n",
            "test_x's shape: (12288, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OMZxvHfvCUjK",
        "colab_type": "code",
        "outputId": "c44efd85-5d4b-46a0-df35-1d84fdc52f7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "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)\n",
        "print(layers_dims)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12288, 7, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "F1BoZm8td9a-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def initialize_parameters(n_x, n_h, n_y):\n",
        "    \"\"\"\n",
        "    Argument:\n",
        "    n_x -- size of the input layer\n",
        "    n_h -- size of the hidden layer\n",
        "    n_y -- size of the output layer\n",
        "    \n",
        "    Returns:\n",
        "    parameters -- python dictionary containing your parameters:\n",
        "                    W1 -- weight matrix of shape (n_h, n_x)\n",
        "                    b1 -- bias vector of shape (n_h, 1)\n",
        "                    W2 -- weight matrix of shape (n_y, n_h)\n",
        "                    b2 -- bias vector of shape (n_y, 1)\n",
        "    \"\"\"\n",
        "    \n",
        "    np.random.seed(1)\n",
        "    \n",
        "    W1 = np.random.randn(n_h, n_x)*0.01\n",
        "    b1 = np.zeros((n_h, 1))\n",
        "    W2 = np.random.randn(n_y, n_h)*0.01\n",
        "    b2 = np.zeros((n_y, 1))\n",
        "    \n",
        "    assert(W1.shape == (n_h, n_x))\n",
        "    assert(b1.shape == (n_h, 1))\n",
        "    assert(W2.shape == (n_y, n_h))\n",
        "    assert(b2.shape == (n_y, 1))\n",
        "    \n",
        "    parameters = {\"W1\": W1,\n",
        "                  \"b1\": b1,\n",
        "                  \"W2\": W2,\n",
        "                  \"b2\": b2}\n",
        "    \n",
        "    return parameters     \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vXZUl8mhd_ro",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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(1)\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]) / np.sqrt(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\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OFyxA2DMd__e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def linear_forward(A, W, b):\n",
        "    \"\"\"\n",
        "    Implement the linear part of a layer's forward propagation.\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",
        "    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",
        "    Z = W.dot(A) + b\n",
        "    \n",
        "    assert(Z.shape == (W.shape[0], A.shape[1]))\n",
        "    cache = (A, W, b)\n",
        "    \n",
        "    return Z, cache\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Gs5_Vg7teAQR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def linear_activation_forward(A_prev, W, b, activation):\n",
        "    \"\"\"\n",
        "    Implement the forward propagation for the LINEAR->ACTIVATION layer\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",
        "    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",
        "        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\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C8AwiLIAeAtw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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_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",
        "        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\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CmVaMnh4eQgP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def compute_cost(AL, Y):\n",
        "    \"\"\"\n",
        "    Implement the cost function defined by equation (7).\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",
        "    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.dot(Y,np.log(AL).T) - np.dot(1-Y, np.log(1-AL).T))\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\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MiaFiOlHeRT-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def linear_backward(dZ, cache):\n",
        "    \"\"\"\n",
        "    Implement the linear portion of backward propagation for a single layer (layer l)\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",
        "    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\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RyVBgVT9e6oI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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, activation_cache)\n",
        "        dA_prev, dW, db = linear_backward(dZ, linear_cache)\n",
        "        \n",
        "    elif activation == \"sigmoid\":\n",
        "        dZ = sigmoid_backward(dA, activation_cache)\n",
        "        dA_prev, dW, db = linear_backward(dZ, linear_cache)\n",
        "    \n",
        "    return dA_prev, dW, db"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MjU6559jeQbu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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\" (there are (L-1) or them, indexes from 0 to L-2)\n",
        "                the cache of linear_activation_forward() with \"sigmoid\" (there is one, index 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: \"AL, Y, caches\". Outputs: \"grads[\"dAL\"], grads[\"dWL\"], grads[\"dbL\"]\n",
        "    current_cache = caches[L-1]\n",
        "    grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = linear_activation_backward(dAL, current_cache, activation = \"sigmoid\")\n",
        "    \n",
        "    for l in reversed(range(L-1)):\n",
        "        # lth layer: (RELU -> LINEAR) gradients.\n",
        "        current_cache = caches[l]\n",
        "        dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads[\"dA\" + str(l + 2)], current_cache, activation = \"relu\")\n",
        "        grads[\"dA\" + str(l + 1)] = dA_prev_temp\n",
        "        grads[\"dW\" + str(l + 1)] = dW_temp\n",
        "        grads[\"db\" + str(l + 1)] = db_temp\n",
        "\n",
        "    return grads"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TuAllYcpfLYU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "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",
        "        \n",
        "    return parameters\n",
        "\n",
        "def predict(X, y, parameters):\n",
        "    \"\"\"\n",
        "    This function is used to predict the results of a  L-layer neural network.\n",
        "    \n",
        "    Arguments:\n",
        "    X -- data set of examples you would like to label\n",
        "    parameters -- parameters of the trained model\n",
        "    \n",
        "    Returns:\n",
        "    p -- predictions for the given dataset X\n",
        "    \"\"\"\n",
        "    \n",
        "    m = X.shape[1]\n",
        "    n = len(parameters) // 2 # number of layers in the neural network\n",
        "    p = np.zeros((1,m))\n",
        "    \n",
        "    # Forward propagation\n",
        "    probas, caches = L_model_forward(X, parameters)\n",
        "\n",
        "    \n",
        "    # convert probas to 0/1 predictions\n",
        "    for i in range(0, probas.shape[1]):\n",
        "        if probas[0,i] > 0.5:\n",
        "            p[0,i] = 1\n",
        "        else:\n",
        "            p[0,i] = 0\n",
        "    \n",
        "    #print results\n",
        "    #print (\"predictions: \" + str(p))\n",
        "    #print (\"true labels: \" + str(y))\n",
        "    print(\"Accuracy: \"  + str(np.sum((p == y)/m)))\n",
        "        \n",
        "    return p"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dvVsQSNVfMVs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def print_mislabeled_images(classes, X, y, p):\n",
        "    \"\"\"\n",
        "    Plots images where predictions and truth were different.\n",
        "    X -- dataset\n",
        "    y -- true labels\n",
        "    p -- predictions\n",
        "    \"\"\"\n",
        "    a = p + y\n",
        "    mislabeled_indices = np.asarray(np.where(a == 1))\n",
        "    plt.rcParams['figure.figsize'] = (40.0, 40.0) # set default size of plots\n",
        "    num_images = len(mislabeled_indices[0])\n",
        "    for i in range(num_images):\n",
        "        index = mislabeled_indices[1][i]\n",
        "        \n",
        "        plt.subplot(2, num_images, i + 1)\n",
        "        plt.imshow(X[:,index].reshape(64,64,3), interpolation='nearest')\n",
        "        plt.axis('off')\n",
        "        plt.title(\"Prediction: \" + classes[int(p[0,index])].decode(\"utf-8\") + \" \\n Class: \" + classes[y[0,index]].decode(\"utf-8\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e_nejUzOCsKc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "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 0 if cat, 1 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",
        "    parameters = initialize_parameters(n_x, n_h, n_y)\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",
        "    for i in range(0, num_iterations):\n",
        "\n",
        "        # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: \"X, W1, b1\". Output: \"A1, cache1, A2, cache2\".\n",
        "        A1, cache1 = linear_activation_forward(X, W1, b1, 'relu')\n",
        "        A2, cache2 = linear_activation_forward(A1, W2, b2, 'sigmoid')\n",
        "        #computing cost\n",
        "        cost = compute_cost(A2, Y)\n",
        "        # Initializing backward propagation\n",
        "        dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))\n",
        "        \n",
        "        dA1, dW2, db2 = linear_activation_backward(dA2, cache2, 'sigmoid')\n",
        "        dA0, dW1, db1 = linear_activation_backward(dA1, cache1, 'relu')\n",
        "        \n",
        "        grads['dW1'] = dW1\n",
        "        grads['db1'] = db1\n",
        "        grads['dW2'] = dW2\n",
        "        grads['db2'] = db2\n",
        "        \n",
        "        parameters = update_parameters(parameters, grads, learning_rate)\n",
        "        \n",
        "        W1 = parameters[\"W1\"]\n",
        "        b1 = parameters[\"b1\"]\n",
        "        W2 = parameters[\"W2\"]\n",
        "        b2 = parameters[\"b2\"]\n",
        "       \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",
        "    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",
        "    return parameters"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yhxlyAW7C93I",
        "colab_type": "code",
        "outputId": "ea2a16b8-4b93-4f10-bf0d-bf044f7d70b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        }
      },
      "cell_type": "code",
      "source": [
        "parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cost after iteration 0: 0.693049735659989\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: 0.515830477276473\n",
            "Cost after iteration 600: 0.47549013139433255\n",
            "Cost after iteration 700: 0.4339163151225749\n",
            "Cost after iteration 800: 0.4007977536203887\n",
            "Cost after iteration 900: 0.3580705011323798\n",
            "Cost after iteration 1000: 0.33942815383664127\n",
            "Cost after iteration 1100: 0.3052753636196265\n",
            "Cost after iteration 1200: 0.2749137728213016\n",
            "Cost after iteration 1300: 0.2468176821061485\n",
            "Cost after iteration 1400: 0.19850735037466102\n",
            "Cost after iteration 1500: 0.17448318112556624\n",
            "Cost after iteration 1600: 0.17080762978096653\n",
            "Cost after iteration 1700: 0.11306524562164727\n",
            "Cost after iteration 1800: 0.09629426845937152\n",
            "Cost after iteration 1900: 0.08342617959726864\n",
            "Cost after iteration 2000: 0.07439078704319083\n",
            "Cost after iteration 2100: 0.06630748132267929\n",
            "Cost after iteration 2200: 0.05919329501038171\n",
            "Cost after iteration 2300: 0.05336140348560557\n",
            "Cost after iteration 2400: 0.04855478562877019\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9cb04131d0>"
            ],
            "image/png": 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xrMe5fZJXL5u/XNCdhas3c9dwP0XJxTc4WwGLol6XBdOq8hPgg1gzJF0lqVRS\naXl5eS2W6Fz19G7blOtO6MDrY8v466dzqPCDRWktnsGpGNNi/muTdDFQAjwQa76ZDTGzEjMrKSgo\nqMUSnau+n5/ckVMPa8H/fPgNF//ta5b4aUppK57BWQa0jnpdCHznbGJJJwO/Bc40s21xrMe5/ZKd\nmcETF/fkvnO6MmHRWk4dPIJ3JiwOuywXgngG5xigo6S2kuoAA4Dh0Q0k9QCeJBKaftNrl/AkMaB3\nER/8/Ht0bN6Qnw+dwA2vjGfd5h1hl+YOoLgFp5ntBAYBHwHTgdfMbKqkuyWdGTR7AGgIvC5pgqTh\nVSzOuYTSJr8Br119NL/8YSc+mLyUUweP4ItZK8Muyx0gMkuundwlJSVWWloadhnO/cfksnXc9Op4\n5pRv4opji7mtb2dysjPDLsvVkKSxZlZSnbZ+5ZBz+6lrYR7v3fA9Lj+mmGdHzueMR75gyuJ1YZfl\n4sh7nM7VohEzy/nVsIms3rSdnxzXjsNbNaJlXg4tGuXQPDeHOlneV0lUNelxZsW7GOfSyfGdCvjo\npuP57dtTYl7f3qxhXQ7Kq8tBjXI4KC8n+FmPU7q0IK9edggVu33hPU7n4mTd5h0sXb+Fpeu2snzd\nVpat38ry9VtZum4ry9ZFnq8JjsaXtGnC69ccjRTr9Gd3IHiP07kEkFc/m7z62XQ+qFGVbbbu2MWr\nYxZx5/CpvDluMf17+lXHycB3uDgXopzsTC45qg3dWzfmTx98w/qtfj5oMvDgdC5kGRni7n6HsWrT\nNgb/a1bY5bhq8OB0LgF0K2zMwN5FPP/VfL5Ztj7sctxeeHA6lyB+9cNDyM3J4nfvTCXZDtqmGw9O\n5xJEkwZ1+NWphzB63mqG+901E5oHp3MJZECvIrq2yuOP709no482n7A8OJ1LIJnBgaLl67fx8L/9\nQFGi8uB0LsH0KGrCBSWteeaLecxaviHsclwMHpzOJaBb+x5C/TqZ3PWuHyhKRB6cziWg/IZ1+eWp\nhzBy9iren7ws7HJcJR6cziWoi/q0oUvLRvzhH9P8tsQJxoPTuQSVmSHuOeswlq7byqOfzA67HBfF\ng9O5BNazTVP6H1nI05/PZW75xrDLcQEPTucS3O2ndSYnK5O73p3mB4oShAencwmuILcuN5/SiREz\ny/lo6vKwy3F4cDqXFC49ug2dD8rlnvemMW2JDwISNg9O55JAVmYG957dlfVbdvCjhz/np8+XMnHR\n2rDLSltxDU5JfSXNkDRb0u0x5h8vaZyknZLOjWctziW7nm2a8MVtJ3HzyZ0YM381/R4byaXPjKZ0\n/uqwS0s7cbvnkKRMYCZwClANbhymAAALqElEQVQGjAEGmtm0qDbFQCPgl8BwMxu2t+X6PYecgw1b\nd/D3UQt4+vN5rN60naPaNeXGkzpydPt8v2/RPkqU+6r3Bmab2Vwz2w4MBfpFNzCz+WY2CaiIYx3O\npZzcnGyuO6EDX9x2Inecfihzyzdx4dNfc+4TX/HpjBV+9D3O4hmcrYBFUa/Lgmk1JukqSaWSSsvL\ny2ulOOdSQf06Wfz0e+0YceuJ3NPvMJau3cLlz46h32MjeXfiErbu2BV2iSkpnne5jLW9sE9/Bs1s\nCDAEIpvq+1OUc6koJzuTS44u5oJeRbw1vozHP53DDa+Mp1FOFmd2P5j+RxbSvXVj34yvJfEMzjKg\nddTrQsCHtXYujupkZXBBryLO7dmar+asYtjYRQwbW8aLoxbSvqAB/XsWck6PQg7Kywm71KQWz+Ac\nA3SU1BZYDAwALozj5znnApkZ4riOzTiuYzM2bN3B+5OXMmxsGfd/OIM/fzSDYzs049yehZx62EHk\nZGeGXW7SidtRdQBJPwIGA5nAM2Z2r6S7gVIzGy6pF/AW0ATYCiwzs8P2tEw/qu7cvpu/chNvjivj\njXGLWbx2C7l1szi9W0tO6tyco9rn0ygnO+wSQ1OTo+pxDc548OB0bv9VVBij5q3ijbGL+WDKUjZv\n30WGoGthY45tn89xHZpxZJsmadUb9eB0zlXbtp27mLBwLSNnr2TknFVMWLSWXRVG3awMSoqbcEz7\nZhzboRldW+WRmZG6B5c8OJ1z+2zjtp2MnreKkbNXMXL2Sr5ZFrnvUW5OFn3a5tOruAklxU3p2iqP\nOlmpc9V2TYIzngeHnHNJqGHdLE7q3IKTOrcAYOXGbXw1ZxVfzlnJ13NX8/H0yAhNdbMyOKJ1Y3oX\nN6WkuAlHtmmSNvtIvcfpnKuR8g3bGLtgDaXzVzNm/mqmLFnPrgpDgkNa5NIrCNIerZvQumm9pDl3\n1DfVnXMHzObtO5mwcC1j5q+hdMFqxi1Yw6btkSuWmtTPpmthY44ozKNb8LN5o8Q8h9Q31Z1zB0z9\nOlkc06EZx3RoBsDOXRV8s2wDE8vWMmnROiaWreXxT1eyqyLSSTuoUQ7dCvM4onVjuhXm0bVVHo3r\n1wlzFWrMg9M5V6uyMjM4vFUeh7fK46I+kWlbtu9i6pJ1TCxbx6SytUwqW8c/p/13NPvmuXXp1CI3\neDSkY/AzN0H3mXpwOufirl6dTEqKm1JS3PQ/09Zt2cGUxeuYsngdM5dvZNaKDbwyeiFbogYmOTgv\n5z8h2rFFLu0LGtImvz75DeqEuu/Ug9M5F4q8etkc2yFyjuhuFRVG2ZotzFy+gZkrNjBz2QZmLt/I\nV3NXsX3nf0efbFg3izb59SnOb0BRfn2K8+vTJr8BxfkNaJ5bl4w4n2/qwemcSxgZGaIovz5F+fU5\nuUuL/0zfVWEsWLWJ+as2MX/lZhau3sz8VZuYtnQ9H01dxs6K/x7krpuVQZv8+gy5pITiZg3iUqcH\np3Mu4WVmiHYFDWlX0PA783buqmDpuq3MX7WJBas2BwG7mSZxPODkwemcS2pZmRm0blqf1k3r872O\nB+YzU+d6KeecO0A8OJ1zroY8OJ1zroY8OJ1zroY8OJ1zroY8OJ1zroY8OJ1zroY8OJ1zroaSbjxO\nSeXAghq+rRmwMg7lhCnV1inV1gd8nZLF7nVqY2YF1XlD0gXnvpBUWt0BSpNFqq1Tqq0P+Doli31Z\nJ99Ud865GvLgdM65GkqX4BwSdgFxkGrrlGrrA75OyaLG65QW+zidc642pUuP0znnao0Hp3PO1VBK\nB6ekvpJmSJot6faw66kNkuZLmixpgqSkvMG8pGckrZA0JWpaU0n/kjQr+NkkzBprqop1ukvS4uC7\nmiDpR2HWWFOSWkv6RNJ0SVMl/TyYnpTf1R7Wp8bfU8ru45SUCcwETgHKgDHAQDObFmph+0nSfKDE\nzJL2JGRJxwMbgRfM7PBg2v3AajO7L/gj18TMbguzzpqoYp3uAjaa2Z/DrG1fSWoJtDSzcZJygbHA\nWcDlJOF3tYf1OZ8afk+p3OPsDcw2s7lmth0YCvQLuSYHmNkIYHWlyf2A54PnzxP5B500qlinpGZm\nS81sXPB8AzAdaEWSfld7WJ8aS+XgbAUsinpdxj7+khKMAf+UNFbSVWEXU4tamNlSiPwDB5qHXE9t\nGSRpUrApnxSbtLFIKgZ6AF+TAt9VpfWBGn5PqRycsW6snAr7JY41syOB04Drg01El5j+CrQHugNL\ngQfDLWffSGoIvAHcZGbrw65nf8VYnxp/T6kcnGVA66jXhcCSkGqpNWa2JPi5AniLyC6JVLA82Ae1\ne1/UipDr2W9mttzMdplZBfAUSfhdScomEjIvmdmbweSk/a5irc++fE+pHJxjgI6S2kqqAwwAhodc\n036R1CDYqY2kBsAPgSl7flfSGA5cFjy/DHgnxFpqxe5wCZxNkn1XkgT8DZhuZv8bNSspv6uq1mdf\nvqeUPaoOEJxWMBjIBJ4xs3tDLmm/SGpHpJcJkAW8nIzrJOkV4AQiw3ktB+4E3gZeA4qAhcB5ZpY0\nB1uqWKcTiGz+GTAfuHr3vsFkIOk44HNgMlARTP4Nkf2CSfdd7WF9BlLD7ymlg9M55+IhlTfVnXMu\nLjw4nXOuhjw4nXOuhjw4nXOuhjw4nXOuhjw4XZUkfRn8LJZ0YS0v+zexPiteJJ0l6XdxWvZv9t6q\nxsvsKum52l6uqx1+OpLbK0knAL80sx/X4D2ZZrZrD/M3mlnD2qivmvV8CZy5v6NKxVqveK2LpI+B\nK81sYW0v2+0f73G6KknaGDy9D/heMFbhzZIyJT0gaUwwMMLVQfsTgvEOXyZykjGS3g4GJJm6e1AS\nSfcB9YLlvRT9WYp4QNIURcYdvSBq2Z9KGibpG0kvBVeCIOk+SdOCWr4zNJikTsC23aEp6TlJT0j6\nXNJMST8Opld7vaKWHWtdLpY0Opj2ZDDEIZI2SrpX0kRJoyS1CKafF6zvREkjohb/LpEr3lyiMTN/\n+CPmg8gYhRC5Aua9qOlXAXcEz+sCpUDboN0moG1U26bBz3pELmXLj152jM/qD/yLyNVeLYhcmdIy\nWPY6ImMOZABfAccBTYEZ/HfrqXGM9bgCeDDq9XPAh8FyOhIZ1yCnJusVq/bg+aFEAi87eP04cGnw\n3IAzguf3R33WZKBV5fqBY4F3w/534I/vPrKqG7DORfkh0E3SucHrPCIBtB0YbWbzotreKOns4Hnr\noN2qPSz7OOAVi2wOL5f0GdALWB8suwxA0gSgGBgFbAWelvQP4L0Yy2wJlFea9ppFBnWYJWku0LmG\n61WVHwA9gTFBh7ge/x0EY3tUfWOJDLINMBJ4TtJrwJv/XRQrgIOr8ZnuAPPgdPtCwA1m9tG3Jkb2\nhW6q9Ppk4Ggz2yzpUyI9u70tuyrbop7vArLMbKek3kQCawAwCDip0vu2EAnBaJV37hvVXK+9EPC8\nmf06xrwdFnQld9cPYGbXSOoDnA5MkNTdzFYR+V1tqebnugPI93G66tgA5Ea9/gi4NhiiC0mdgtGa\nKssD1gSh2Rk4Kmrejt3vr2QEcEGwv7EAOB4YXVVhioytmGdm7wM3ERmsobLpQIdK086TlCGpPdCO\nyOZ+ddersuh1+TdwrqTmwTKaSmqzpzdLam9mX5vZ74CV/Hc4xE4k2YhK6cJ7nK46JgE7JU0ksn/w\nISKbyeOCAzTlxL59wofANZImEQmmUVHzhgCTJI0zs4uipr8FHA1MJNILvNXMlgXBG0su8I6kHCK9\nvZtjtBkBPChJUT2+GcBnRPajXmNmWyU9Xc31quxb6yLpDiKj9GcAO4DrgQV7eP8DkjoG9f87WHeA\nE4F/VOPz3QHmpyO5tCDpISIHWj4Ozo98z8yGhVxWlSTVJRLsx5nZzrDrcd/mm+ouXfwRqB92ETVQ\nBNzuoZmYvMfpnHM15D1O55yrIQ9O55yrIQ9O55yrIQ9O55yrIQ9O55yrof8PhmY5+sp7TuUAAAAA\nSUVORK5CYII=\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oWu2F4rdRsk_",
        "colab_type": "code",
        "outputId": "172f6e9f-becd-40e0-b762-7ed5e17a9c53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "predictions_train = predict(train_x, train_y, parameters)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9999999999999998\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YT69Azvih7Lv",
        "colab_type": "code",
        "outputId": "85332511-8978-416b-db76-2465f7a7fb5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "predictions_test = predict(test_x, test_y, parameters)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.72\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YsHC2tmviA3F",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "layers_dims = [12288, 20, 7, 5, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tp31aMnViIG0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):#lr was 0.009\n",
        "    \"\"\"\n",
        "    Implementation of 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.\n",
        "    parameters = initialize_parameters_deep(layers_dims)\n",
        "    \n",
        "    # Loop (gradient descent)\n",
        "    for i in range(0, num_iterations):\n",
        "\n",
        "        # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.\n",
        "        \n",
        "        AL, caches = L_model_forward(X, parameters)\n",
        "        \n",
        "        \n",
        "        \n",
        "        # Compute cost.\n",
        "        \n",
        "        cost = compute_cost(AL, Y)\n",
        "        \n",
        "    \n",
        "        # Backward propagation.\n",
        "        \n",
        "        grads = L_model_backward(AL, Y, caches)\n",
        "         \n",
        "        # Update parameters.\n",
        "        \n",
        "        parameters = update_parameters(parameters, grads, learning_rate)\n",
        "        \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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aCeITRM4iNaX",
        "colab_type": "code",
        "outputId": "0d24371e-1566-459c-a1f4-408fbeaf2647",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        }
      },
      "cell_type": "code",
      "source": [
        "parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9cb032c748>"
            ],
            "image/png": 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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "xgkopoAwilCD",
        "colab_type": "code",
        "outputId": "cb597df5-f12e-4a74-d2bd-f8fb941f9ec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "pred_train = predict(train_x, train_y, parameters)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9856459330143539\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_XOH3nOPjcu5",
        "colab_type": "code",
        "outputId": "59bc9800-a21b-492f-e5af-36a8379377b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "pred_test = predict(test_x, test_y, parameters)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GKpyH6tujhtv",
        "colab_type": "code",
        "outputId": "0d94e34f-1ba3-448f-a504-7acd5d480d1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "cell_type": "code",
      "source": [
        "print_mislabeled_images(classes, test_x, test_y, pred_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9cb02f8240>"
            ],
            "image/png": 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dM/saM/vcNvIuxB8Wkx6/32Fmv5skyfvG/18a/1mSJB/Bfk+GEN5nZl9nI7k7IW4kkx5X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Fl3RqwCb4cCSk3R6dPNmVaHMfa9POUNoLvBOtz4h5icPmN4c5129qTlzfVB0m4v16A+EkYidO\nC0ViiFpNvXmtqfEfp6UrXnzxyvWwfGdJto+ZDBh6GIBuHLgyFx50sBqNsFBGUNMLsmJu+6qvtcWr\nYblSU7vsPyIMSwk4vvK2xuiRU0Ly3bwsm9oyLD7jWBc9zKsd4I44xWayGl+der8tu7ApdIHaIdPR\n9WVd7NI6ExN4t02khoMy1mXMCxyvtOwkboTWnJm8nr3ZGoy86PUwPjCXc/zxmTmGXMQMtC/vkD3Q\nUt2nPdh1O+jbbZ3Thv3z8irwe93++fNAwe05KI/NGWLGYJ2aH9Pxaxdl27kKS9wf/74PhOV9+9XP\nIogo2imi2IvgQGnXyPl8tG1Rk7CqdxEbOR5wUZynEf+5sHpOAJ/iZdW+ATzyGVN0sI42YKtb2VC5\nVYW9Nyxus4V+325sKKbJjmudTSDmjWOtirREjHExrHw3ZVPd9dVPYlnFlsSjOZF+goAc1yeqK5ES\nrob2/ulpIS+dFOJYWN7HE6rL/Jz6P7tYMqN7cX8wBuZJ0NHze2ldn/NZqijr4uLhx3T9sb4V9B0b\nwrU1NoVriwN71anLjnn5pS/p/Gvf0vGbqmPuSVJp9BXw97BcOxNTQO0A7VCBPW0yruOdweHDyIj4\nHQ/cFiKF8mmivvRu1YYqZmVNa+Qm/HmLebXN+Jj6SRqIsWaSTBmsD0QDRaLEfucjumsCe7UN7G86\n4InmM7Cbh6V5vUW8BhFnun4WFtsp9F/GXrt4UMdxnA7iNlpiL0xrziDehpjdqXHtj6YnNT9ubGL9\nS3A/xZgM6KgGMXSqn5yaIYJPbLb0DB6szDtAHKd2jqfQJ5JAheUQA1SB2btyU+OVtudEE3tJoPLc\nwXNopBcw/kG82gGKLwY0nPN3CC/lEuc0DBcVG3J8CGrKHXIdokkj9xqKiRpwTmrAsb987iAs1f/H\n87KxY9iCgPr4N2YAX6prbP3fn9TxekcP9N/8C71Acd/35q/6/+BVX8fT4fy9R7U2/6tfVCUW/zdV\n1hc+o3Pq9RFfXP4O6znga6dwfALxU5IYO8RhxFERac9unowgyVQ+gD5zkYir+OByHPhPv9tfdyst\nrUkXt/WML2+qL8+P6T77iGZoqDOv+VqrTt4jRsDWkubyzXWhjwIM8DryZerSjwAAIABJREFUQW0g\nmYIIYkvnV4DU8JBjqI8Le5NDDjOL/VcVdbaCMLnS0jWvXL4alpuonyNzWq+yyEsu1YDjvqZ8742X\nnw3L/8f/8s/C8rFc/10OF/WMxZzGbRLxb7esWMkD9jFRUJ/rbCk+uWdD8czTQEMyCI8Dr+uMAfv9\nHsTst4CPvCoclfsHyik6Ih+PjJJJ5rEUT46NqS4mx4WxSXPfhP1IGt8r6siTNdE2BSDGPHAsxoHE\n9BHnlRFjNZA/yuwggwox7TWm1bzOmUW16bWm8uftLexFrymnE8f3hFZPv81hvbwvrfVg87rw6Mm0\n3mnmuFDpyQXhc9YuCJOVz+n8uw8rx7U399GwfHBG9bEn0Dh7cU2zXCvQXsyJc2G8vbw9Y8RrwHll\nEJN3OrpXLtM///ChPeGxckJt00EwkUYsz+8uG0CDlyr69nPh/Bldc0Nznw+k7j4g66Zntfe+WtO4\n37vnUFgmAr6tbuk4zn5n1DQ9dygsx4HgIyZ6eVG50Kmi6mJrWXmOL/7eL4fldlrjtQc0Yqehcem3\nNeYqDX3j9Gs6J4dckgs+dGcHo1bZUt67u6aKThV0/0YGe7Kyzn+hp/7177bV1mlgEdcRBK/31N/c\nTfWT7RVdx0UOKEp25jcxXXNsfDYs11E3m8hJB9i0esinXTwrTFzH/5Gw7LfUJyt4zsS2rpPOaj7o\nxjQPlda0brQaGi+TOfFwkjvPMDuh362MaSJcWwPGGHFQAFxwD2tbj3gpl99dnCFC7jCSUw0GFR3G\n7JHc2ggqNrTP4HAw+Jz1NbV1KqGx8J7jOvu+fTq/1tRF/+OLOv/Ta0BQgWv0s/cA2Z1S+fRyv06/\nclX3OYnPlR8/rntm8SLcFo5NAiePv1NgazXa+m3VRx9L6LdL27rXS9dVXl3XOT9wP1CB7EtgLLMZ\nPOT+iBRN5vWPh7QsORWg7j8puqHzp4t6rwdBCMXQcTJAfNdwHRC7nQ4Gxql8/9mm+CkEuC2kx5z5\nvK792F7Vx2ev6XqTiEP2ZbUn+C8NxdSlji7K2H/ocHWH7JmHcdKGaLS/RppMJpPJZDKZTCaTyWQy\nmUwmk8lkMplMJpPJZDKZTCMo+6Mbk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPpNvWGehon\nYEdWrsiSzYEtqpeGpTOQS8QSJD0ipWBnH8jqi9b9CVj691ydU4P9ogMb0w4wAalkaud3un8T1v60\n527DGtSFZWF6TPfPTcFKe0IWa8mk6oZoqv+XvTeNsSw97/vOufeeu6+1V1dVd/XePT37yhGHi0jK\n4iKKsigxchQpUQLbcgBBMODkQ2AEsZEgCBIECBAEygdLkYQkUixHJi2Zm8JVJGeGs8/09L5Wd9de\ndff93pMPt/r8f0eqoqbpeHwZPP8vfOf0uWd5l+d93vcU/78IbL6nerpvcUf1d3Wg6wwmVN5tyLIz\nPSvfpq++Ijv7K3XZ4VVffSMoN309TzIjG6YuUAIFYGle+NBTQXm4JZvF6xW9C23Ep4q65vwxWRUW\nj8lOL7Mwur7rwbYYlukpoAYSAPicv/JSUN5p61kq6GcLj+jZz57R/bN9WVN++9sXg3Krqt8O0SY9\nIsX4N2zR8cZouFH4uMPzKzWrfvvEU48E5TfP3wzK5YqQYQNYBl66rr5056b8yAqHZcPrErcCr2/f\nle06x9Q2EEe9PSt5ImSyaV0j5enaSYyhm+u6xkYN1op4khiuSbwUzyqkFZuSsNCPeLRsVt8oN9Vn\nSkkgIeA1d5AJdx/3jcG2t028FNhUxRSs1bKKNy3gCXz4RGaAE4onNS4LBVloekArdNqj9iHi5866\n7B/bCOe0mCYWqdFRfaSAAMjBO668IzvK+rasU1NTGpdOAp5/7McuvOkgd9je9/i46LkXhCu5CATQ\nay/+ZVAOoQsRx9ZWNc7q6G8nHzoblMvbstrcWJdF8MS06rFV1zk+8CWRSdW7jz7f3uOahBz3YLmY\ndNUhfMRmH+0VQX8k6sofxvY9J+xZub9NOm1As7hmGniSDqxThz5HIOdgWKEiSxpG97cSTMZVNx4w\nJx3aFgJp40WAb4zp/F0gR3qwlu4gJlb3sIuPP3YkOLZ0VDie29dkOT6PmNioq00YF5ZOyp59cXFZ\nL0W3cAdlIpWI80L7R0L1hIA35nipRBasCFoCI+4OweVxiRiDBTQROFG0gYu66DU1LzE3iiFXIwqm\ntql4SFRS2x1ZCg87at9OXbmfh/yX6BfihYjLieGdmrtARpY1v8cSsuSNJojkRA5E9Gof3tRg70Ti\nuo4bU3nYU92wz3S7Gowe5sUIUEzRBBFU7G+6bw94mfqW6jWSU/v7uOawI9vm3CIQfXt1VTwkLGdx\n4VxQ7ncVb7sN5ZnxSfmy3npZ/ax540u6Tgq4Mg8xooo8BDkJc7GdHVizYs3D+ojADnYcVURbEGXp\nR9UH4mD/9WH13Adeaxs5xa3bsppOY1ykkIOkgINrJPfPKaJ4Bvb5+yheDLOQvfT8tPr4yir6Buak\nDhYVLbxHAwi6AdEECTJzkDd6xE5p/PVge84oHcKEAMvEPHCipL4ainGwcM9m9Y495rqYp7sYu7sV\njXUXmKViSWvBGnC15R3FJOIeu91RnjdVBGpxShb6MaAqV4EeqdXUP9JAV2WBpkulgM/y9u9nw5Bd\nMeYCxPwB+mgMqNtYfMz//09A+fmMqSEslH9A+YBzgHHyiZQ6CEeFbs7zD8RBefv8+3tBVIWe3d3/\nHJLBUjo/k/q3m98QNfU//a7utVHWy/z2P1UlTB/VuHf/DfYkmIYvn1Qf/if/s+71ws+qgv7gd0bH\n33pV9+dYMf34coHa20YM3kQ8SRNhgd/2sH6PI6uPolzEXug0kY3Yj51HvCrlOV7UN4iZ6O9dn/ln\nFP3RG+p4uQFr+oGSlNlF7UedOaM1S6kkT/zJorAbV9/4Gp5F+xDlrmqk1eeeBPePojiuutwFYqeC\nfdc+5q3NO3oGfwgcVWpZ5zeFOdlZVTmV1vu+1VI7xzIa962K5r/1a+eD8iOf+ZSuk1LQKx0aoVGL\n2Oc7fgSoTOBn/V3Nsz5yZG9e82jnELCnjys/mqhoL4K4DA/7FU4d8ej3L+v4gq4TeUnrDLc53jFj\n5YYwzU3st2dzyh0effjhoNxoqh9euqp2b9TUpru7Wmsw9+r11R88jPVTpzUucuC/P3NaSNw3L6hP\nThZGdZom9huYiwQpKRgrXaBZu67y6JVrwoHlJvRcCdCZ3ec/F5Svvg5cVF1I5DI2WvJN1SWocs7t\nr38lKB89rRjwCz//q0GZ6IxjwMctx/Vd4MtVXN9/sNyLeVsaa81BVfWQx1jb3ha25/DCCMPz2DG1\nzdKy2vi1CzeD8tSk9ueK+NZSK+t6N29+Lyi//qLwUtxkPrqksTsoC6P0yptvBuUOJviP/S3V6+wh\nrWXLVSZS46eVa+8EZUxhjgt2abMOfHNbfWD2wz8TlKcOPxaUzz7zwaD8+nn1nz//f/40KNcHGpep\nWcVvr6H6mslpf66Adc3W2gi1FsW3vfNXrgXlzXuKhSeOqM/MpNVely/rO9VqQ2N6iCR1iPYdAC/V\naCrGv/umvv+VJvEdADlwBHs3pQntDafAVQ1h0RC/+F2HmsD3k/bta/gXPefssWNB2e1pnNWxz3Xp\n3e8H5R18f5opaD166yri2d57xYGCOzSjtqkCtVjF2G5hXiTemDhwH2vd0JYn/oN720N331Mcn/vc\nxDSNuV9GaB/wwHNC/xWUPMTUDx9X2zy1pLq+DOrkFy+rjv74lgb+LCaC3zir55nBFu/XV/Tba+uj\n8qdP6NypNPczgTVDn41h/76KT0347OJ00B8aoG3G49hLx/ryTRGRnPO3gAAvYZ7GOryqzwlOHB0o\nhfVwaPka0X3TyP9m8yp/aEnlJvbfvoz3uq6UwHkc6KgY+zP68DF8upuK6r1O7iFlD5dQr7gG49cU\nPiGfxmfBh7exhkH7FAcax4dTiv9v1/QwUezd829NQp+CD0Ayuwdhpw7QeI9ck8lkMplMJpPJZDKZ\nTCaTyWQymUwmk8lkMplMJpNpDGV/dGMymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMj2g3le8\n1OFF2dy1YWvWgvUn0Q/tnrya4rBN9GETTVt8oqZisAxLwha1BivrIezOYrBNo93Y/ev0Yf/WD9mk\nwaI1qfLS45O6f1FWk34fltwureF1zU5b91oGtiIC9MTFNmzVYeHW7eu3EzFZqbXj8mT6Zln1euUd\nWXyuXZCV3Y178qtKwmJ9Iq93eeGpJ4LykSm9+9v//MtB+TtX4XsFn/WjR4XG+OTf/kJQfqv7clBu\nDke/re3q2ok4+kFb3lY3yq8E5bIvy8rsLNrkFCzworKRcyKwaQZOodODFSr+Po2YHiJV6NXeD3un\njaHgrQ3smwtc08SS+snTT5wMym+cB9KmoToivuTdd2S7eurRM0E5Hoe/GCx5aQfZa8oCbHtbtn6F\nvXFcyOkZ4+hTRNb1+7ogrf7ZKpmE7t8Fa4OoqbinHy9MwjYftoKpEALgr1sqO47jgObidGCtP8AT\nESVCV/MecANtXMdDv53PaFxM5dXPW/htA/GD6A9aEc/MCHvheaqfSnk01jY2ZNVGakw2ScyK2qSO\nF6+0gGjD+IgDBVZvAPuxI6vMKbSnCyyE4+pdfTYu8SRt2OjmnbHTz33280H5hRc+EpT/WVyxtrK1\nEpRvXJGdaBm+go8/93xQ7sM2O4n5LJ0jugb4FLRlB3WdQvnuqjAsg86ozdwoUC4YN56v+u+6slx1\n4f8fAhDRjjAUU2HT6XCuxykO7QBh2Ynr12BDCkKiE0EMctAPmQ8McU4iAhQmYgPRQkn8FrRMp4+6\nSgBr1QP+LEPLSNQ932W4Z9e9sYXxMSUv56m52aC8dgeW357a/ue/8GtB+ZEnPhyUixPKW1zUfSSE\nkYIV4wG2qBH8h4+WpgXrOGoQF5okjpyJfZsYpwHyVRdt6nd1DuOhl1IbxHB9H9gRWvimi5ovu7Cb\nr5fRn/fGuoux7cHiN4rrdTEHpDFndJFzcr7EkHB6VVlT+9Ow+40AtxXZf7L1gSnwh7TkRc+Oom4w\nLBljhj1Za3c7fGZgpzCvR4AgZX8eEBELLGwd6IYIxotTw/HrsvGObo/aITOpfDaW0O+qG8qvOxuK\nn/2GclQvq1x0YkLPm4/pubo9PXunC3RUBvixuuq+XlMdo5mdARFw4+3c70xPqV6aHaCD0WeG8H9t\n14EoxTktYJlu3xVWiHjcQh5lVFiuqJyP67J+jx78aJu941GAQ7tYR3C9MFkECriieBGBr261pfm9\nDZwo7ZtTsLLvIRcl1or5cMgMl0sZf/9zDs3PBOWJouYZWm4nMM5mZ2SDvrmjOBXzlAcMsPWwtSNk\nR72m3H9iQvsFHq5fKcP2u6I14KFDo9z1yLJwtt2u7n/jruZCf6C27GEev7MmbEI2o/wrBoRXHGXW\ncQRIriHiOXObUL4RQnOO+XoRiMQQwil2QPlAvBTKWL/wtwfiq+LvAVkFW2l/73w3fsD1DsRkOQcc\n5/nE9Knt8ojHzEv/bUCnmh2N1z/8E/W3ck0P/Y/+G73M0kNY48Z+/CdiV52Y0DU//2t6nmc+PHqG\n3/nvFEv/+R9gb6815pPPGIvdcxNtsYbc+pSvOWcCDbaD+LPhqW12MV92sL86A+v0Mu418DVfbWCp\nHYvsH8fu57Q97AdEuFbr67kG2B85fFxr2iee1Np4Yxu5MLYYp6c0x9zDP3Tr2GNGLhpFglvMK99I\nesBvI6etAy3RBv434nL/FnskCfnfJ3K6TrqkdUZ382pQ3r4m5Es2o9yDWMJ6g3uzOufc0cN6HuxX\n5rOj3OYdcPbOJjQvOsAWDcua033siXGv2sV+0fYZYVPqL2mOuH1beXpjCzhIbGDFLmjed87jHIca\n73kxitFY2VG757HOK6a1hqtVlV/cvCkMdAd5HtdEcSCXfIzpXpSsRewzdtTur11SLlXGGB3cb8uu\n+nhhWejbo2k9C/drd7EG6gO70bigd7p1V2Nis6T+cOycrnlvRXtZiaxyLNaBW9b1t1dvBuVsQ/le\n7OYFnbP7QlCOIDe+HlUdLJb1nC/4yhG/hfX8e1EccdPHXtLKLa3pHn14MSiX+sqHE7Oj8To1oza7\n+rr2ce7eEF7nEBBwiaj6hBcRKuxoUQ/QOqO9291dHY9hv7SCtcgW1jAdrC3efEP4r7PIn7/3lurp\n43/rk87YCd8CE0Dc9rHnkQcWu4NveguHhYB77iOfCMqTi5oTdlr4NoS9U34LiGDeiESA6a4prl59\n97WgXCyMNqR3ETte3NQ+y00giGdLmtt+87T618YVPdcc8DDNtsbTZlnjoN/VOD56RuNg5YpQhafi\ny0EZ20fOcKD+44f28lROp8XvyWRUrgIRzDo7fEzfh7qo1+u3tHdSmtcYisV1TTeNbxRdtecPr9zQ\nuzzx0aDcuaR5KZ8a/TblqD7yOfWPU0eErWzzG3UHcRhz5IDM5/DiDkWsD1jGWsjHOpzfHTkT+sPx\nzp8jXBhDRPewzL25x/Ctsb2mOPZH17R3863L6tsXdtSX4qilXz+pZzitLQzni6I6Ot8CXuo390h6\nCbTL5XvAHQEFtaPu4Nyo656vaAoIoaZAlArN189P67dLOZVfv61+tYu9v9P4fHpFn+ud6xiKE1jv\nFhL67QLQTVmg5Yl5TSWAZUqp/j65rOPvIJd4RVuazllt14RiRgILFmx5OUexVr6PqaqiooiR6uHb\naBLvh8+OoW+mt3aBo5rEP2TxnRJ5gh/CvnGFxW9L+49v/wFzVHO6MZlMJpPJZDKZTCaTyWQymUwm\nk8lkMplMJpPJZDKZHlD2Rzcmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJtMD6n3FSyU92KIX\nZL+YTsi2pzeEJVtf5RbsNWMe/lZooHLUly0Q3OadHmzk2rAGc2GFOoC9UAxYFW8P71SrylPJS+qe\ng6Q8pI58RPZ+MSCtiJTqV/WuXdie9+sqJ4i6Qp1dr8smsDOEJX5b1nXkUMCl3HkoLjuuy11d/50t\nWCzNCksRScqObmdd717dlhVivKh3v/iV/zso//FXZO22otOdGLgFxVn5ZN2ATWRzCYig9v3f6b1p\nJ/72y7Ji9Q7La6t0VP2mVJSPVuSsrrNxXc/+5qu6ZrUin64WyVi0eYOPVSRkLYX+FB1vW9QwtgWW\nW8TF5OUXdvK0bAgPzQrldWtF9odEUdyDjX95TZZ+MxPqY05cKBPWXWtH/Xl7W/aWyb3+U8zId6wG\nHNE6rOwbsLGtw3J/Kqs+QCdkH5gCtuk8LFJzxHG0cD7iV9bTfcsYjOW2ytUufgsnM8Yg2gdGMZDr\niCUJWBKWMmrDQk7PGYUFeQt2yAPYO7q4bxK2uLmSrJF3d0d124BlM23j4oiZEbBB6mWdH8OYCGG1\n8K5ejJgQ9UUHMcB31CYh+8WuxrHTUP/zd+GBB5vBcRFt6KenNT5+67f+s6C8tXk3KP+X/1jHva6s\nRXttxexGTXVRgy3wQ4/JRvX6RcXdKhArRLKsruv65SoxGaP+FkUcSbtqax/W4ZzPfHLk8Nt+n3//\nS9tJzO+MtcQdwcmTUbeP40NYf9LK0oUlLVkRPvoVcTtwPg+hTSKwVwXl0nFhwerzvYCicIBOiYH5\nglDlNGExeT+HuXxFNs33LcQdx3Fmp4UAWV3XBPzw0+eC8k99WPbARHe0GprrI8BueECDRBG0/APA\nCbR4JsbTPcB2dFwE91xn0FW7RGHRnMiLUUfsFPNSF/1tAEv6GCbJKPBxtO3tNjSnDmEFnAamtAvr\n4HptNHbb6FM54MaY5Q9wn+qu4gVt9mmjGotziQA8IPLxaFyWvCHGmIsceAhbYqLVWopTfl/vFInp\n+u2mzuk2gGLLEGmIuILYM4DlsItFQQidBvxeCxi6qA9EDNqzceENnd8Y5UKFxRPBsVha9bFzS162\nvV3lNZkS8ywgPUuYR3tqE/w0hN6ijS/IAE4aeQhJSAn00c4AMWgMNQNM0RYqoFJRv20D79YE/olx\niflcEzbRK8hRJzC20gvKe9NZWVnHUPE15EE9zgN77dEGvqzZwjMCsZlFPhlFrG211O84nFgm/jAH\nTFajoXtl08xdVb62ojmdjrl13JeYuCHxWKjXQkn5e6WsmAX6hJNLq5/fWVMeFsG8m4Old7ure9U5\nF7mci9Rvjx7TuuTM6VOO4zhOGni5JrAitD1vNIBkAIpzB+vLa7dk9c/8Ng10nxvRs/uurkO8ItFb\nEeSx1JhTFx3Hox81jofwS0iOQnip/bFQXEf7vA7DEn5L7JR7EA7KQ553HzUF5BQRVT7u48YwEHj/\n2P7PHtpoSanx8nmOlfevUbtY437xX2sc37qjivr8r6r8iV8EmvgIMesPhp1ivk3c8fLR0TP8w/+a\nCHX1/T/+XaxFB+NtlT9uehmxv4Y4w1rcoaM68RDI4XpAiqRwzRQ6+ibGaAV7BoPu/v2E68EQbniv\nnMVYTGNsbTZ0/76v8mMF4YscF/lvR3PJoRnNf8OhcCA+kHiTc8rZvZpyvD7m3TRwgtksMIqbyhOa\nyMmGqHvup3Q6CBRdzYtuVPuiDF/JqpgE/Zru1cDknM1p7vqpJ58Lyk8+98Gg/PEPf0AXRQ787mB0\nt3eRD7RjWpNk4ojth7RBMgT20UeeGUdPWzoMXDVQBm+8oj3CnYj2/7hcdEPJjfMTqU9++NNB+fyF\nt4LyE8dPB+Uzy6eCcjaiNvhe6tWg7MXRhwdgFgHFOxiEKk/H8Tx15KW3b2NPHP3n2eXRWuXICT2X\nh3EQQRzf3dQ4W5oXgmp1Qzlkdg5IemLNuE6O6v4LM0LjfPWVKzof+LW776j/NMvqP88/8aiO39H+\nxzvf+aaeB+vCm6/+ud7rTeFFj2Kf8/K06uEgyicPT5QUG5aPHQ/KzOF9YCwGKe2jLh8a1e29NeWl\nt69hLYH1Qa+uuq+va6+uu/a9oMw1Tw2YlZ2KnqVZ1prZw16yBxQS+9aNG8q718vXcf6YrxfntQaf\nnBG+b2tT48DH97+tNfUHL67cJF5Uv3WAEkuk9M0qPlTdVXeATapg/wBrlj72ErqzirHLTz7lOI7j\nNBpCO70BTBmWJk4O30y/DnwfNzqPTOie97DPssEFBo73e+obtS1d89tf11zVwQaCC6zdEHlCD/ss\nyYRiXKmg9RHXzB3sa62Cw7Pjai66fkc4uGtf+kZQXttU/UzlgSle0x75blMxtIbcuA4EdXkPueX1\ntbdwYkFj+9Cc9h96WJe2sV/bw9q4i+MhbDr3PLngJl6KEyDm7sgBAck/IE6Ni9z3gEvmN4I4Ymcm\nq3p8DZi8pKdYu5VWbOzuqN2PFHWdPFBMf3pL9fvPLqoNPon0srb3DeL3z+vcW02Vd/DtrYYxR1Iu\n94JCNYD/ALXJeQyp4pV1XegG7pvP6qQ3NO06X8J+3y6wTHOI8TmE7F84DoQX8IYTwDh5yM+JmvKw\nDvjorI6/BpIq/oQh9I4kavcPWGa/VB394PymYs0HF3ECCW24djauf+iiP91pKW7PtpR7bGNvll2U\nSDdipELfivA4oeH6gMnreH8BMZlMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZxlD2Rzcmk8lk\nMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJtMD6n3FS8EJz6FVfROWb7Q/peVbLA1bZqILYPcZj8uG\nyY0C7bIjO/IYLOZpKeTD6isKG73ofbxTEtb3BVleNfp6qRh4Ky5wGVEcj5aAoWjIj6kAa9NcT9fv\nEd+B9/Px22Sb6KOg6HRh03oDf1+1mJGF2/k7stMe0pI9D4u4vGztHLTVqxcvBeW3bsoK7tvvAm3S\n0TWPHpet3uQ8EBie7P8aKeAU9uxhez1ZoV4HuuruedmfTjwP1M6c3jWdlLVU5JjavtqUDeAwqjbs\nwyJrSOQJMRIJ9A+wR4jOiMRCXtTjpwMstBxX48z15M82MS+7xhPLst3b3dbYmiyqrh3YrJVhUTrd\n0flOXLabbkR9zKelF1EU/cHe/yJG0CIMNtVD2ClOZ2UFmYT12lpF46wFq+WpvOogCbyGCzRHHmiA\nCdg+uuir6xvyf9u6o/hR6cG2bUD7Mh3vMzahT3Yjepe1pp4Z9ConjVjZhlVtAzi7qSJjnM5PZWW/\n1oPlqLfX50uhNtb9aU/bhYdcbqj3y8DiNhFH3I7RElD3T2ZgBQd72vtoI8dxHKcva0+/DqTUqqxD\nm7dVzoqwMzbygIWhUV0qpTi9dFjWiv/gt/7zoPzma7Ka/fbX/iwo12qql9nFI0GZSKSJCcXgRFrz\nzz0giXaqipO0oL0f6vKe/j0Dq79mT+/k4TjtF/2QBafGVj8Um2Cj7Id80gNFgayKhs4H4gf9MIyX\n0nNGooh9sNn3XPXzDlKmzhD9FlbpxOckcR3arbsOYxzmeMwtqRRsIgcc66PjHM9Xb2j+zWRkj/v4\n8x8Lyp/53N/ROVnFdqJzBs7fbAsasnrEs9NmcYDY0G4rBjFOjKMSwKG46J/9jvISF/0qAuwUGRnE\nUUUwh7R2NbYGA3lzunHlW0RNDdq6L1FPcVj4xjujum42gbQBDjWZ0Tu1YbtLjA3zlT6sUyMJ3T9Z\nJ/ZK87ibFmYmhKUZ7G+3OwRqigHPg33zsKu66dZ1L8YgPmcXfcyPqO6JXBj2dJ0+rtPBwgQExhD2\nkOd3gb3pNEfXHGqKcQZRxe3bPxACNYZYM3lCVu0ZPHsqhjgI1O4AOU8uo34w8NHP8PBRYGySxKtg\n7RTz3sNY/3eoUkn9IZtRjrWxqb5RqyvfGiDvCGFPQog05H8b6s+FrCyKM8CrTeTVlsQhcB7tAM2Y\nSIzqnXMSn6tS1fP2+kBpAl3VhVU30VjhyIm8Kqln3AUyroZzjsweMS8QAAAgAElEQVQJuzGAXfht\noINrwGBFgBtpgbNbrQLt1VEsyyO3m5hQu0ViWGfhDSIJtSfxhpWKnqfZUuyZhSX79LTwX6m03j25\ntz6OuIwvwJkAGeIldf8eUH3ERDaABbx0XTb7Q+QhSwvypya+Kk6MIP2QQ9hKrBcj7+tWzIMrRtQr\nMVLOj132eZ33gJfi3kbo/y7mHVC+j5ICUor3cTHOHVK/eO0QSotxFJb7CZULBfV3F3tZD2pB/W+i\nPmLTK29oDn7rvF7sT/5Pvdh//Fsqf/wXVSf5PBF9B+lHv9cAuXa9zRz5R/7M9CO0O+Q8xz6puu6g\ngrshyhks/RGXOlhf1IHqDLXTARxfUuWY3hM/ndwbl3msBTNZrVMKSZVryEuT2MuIuEDPc98V47Ld\n1G/nZoW9mZzW/BfFevvWihA1vot9zp6uv4U5qe+Tj6d66iFn5z4gl7it7dtB2XOwGY6TGCdY9ZWq\n9pLKr7wYlG+s6vnffFfY00cfeVz3feqjo3se0R4CG3CInMHfAC8AKM6hg72VWdVlH1ikUhKoj5pQ\nJZE09vz2JyQdqHEPE88uCWnzyLx4CNu3tE8dqShPmi8eCsqL0/rt9VXtgQ/Rt+NRYMDy2g+bnNSa\nK4W8Y5hWjS3MKzeZmdKe7amnP+w4juOkS8qpuL4dVIBTqwmPe2Plpt4Ja5zcpK7z3MPqd0SvTM8I\nmx57SntN37utc1avCim1tqL7zh87G5RLU7pOEmjPN/7494JyYVJ5XveK8FUD4F8W8A0nVcSi74AO\nxxz7heeEuHr0maeD8jtXtAdTBpr6A89o73w2Pxrr3/mKcELFkt4ps6E8s9tWv6msfCso97cUR7iH\nVkBIOb+inLZQUH0UctpzT3UUB7exT1zzVS5gjbJeJzpz/HT93deD8q1LQr2FYjP225PYX21hD2CI\nPS3uHxA5Pz2pNo3F1A87XY3XeEJzV7Gg8Xf4hMb9qXNPOI7jOE0waq5eVJ+tA4+bzWicb0TxzTSm\ncwpVfXu7taL+AzqSE8P810Wcjs4qx98Ezpk5QBLfZF2f+w3AOjo6PjOtvk188c6OODmXzr8UlD/6\n0eeD8mc+/cmg/NI7Glvn7xGpjTUU1m45IIpiPcVWp7uq59+bAzfxfWoyo/l9Dm22NK9ys6k2Js6v\njD23DsY/8wHSpXzkD5wLiZTyMU+HvleP+cwYpkvtv88UiXAPg6gy1e8d7NEcP3kyKC+f0TyanVQf\nK6YUr34P34DWNjQWGr4Gw8WanuFbr4+O3wF62kWO7AEx3fe5x4D9fiyU5rn3jzqYyOmkGZzzagV7\nSqjAUlrla1X1gZuI9x3ErDV862CaefOyjp9a1TOcSuhdXljUOSeAoOI6g1vet1vAz2I/Fls6IRxU\nE/H3Dv5uoTwYtf9f3NE1plJ616MTeBZcD58unSb+YaOv/fE7ieWg3MJ+qetgjIbGmdrTP2Dlyx5t\neCmTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk+nfsuyPbkwmk8lkMplMJpPJZDKZTCaTyWQy\nmUwmk8lkMplMpgfU++ppXKnI+myyKEu2KHAh/S5QBLDQB/nBGUZk59MLGcABb9GUXWIfdtphT0uV\nYx5spNK6ZjM5sh7rxGUj1uvJlsgf7m8FRouwIazhBy3YEuNvnojRKGR0vAF78xJs+TP4bXMAu3ng\nnLJz+m0c1ljLsGB2N+UDVYaNf7MKrE8F5bqu/05ddmpd+Gr1j8oGrwBL/WFez/BOUzaKE2dlR+7B\nSmu4Z+F19/uyllt7Q1ZwRGFsXdB7JGAnXcB7F5b0XNMn9bxzV/R+rU2gBlBP0bSuk0zpXtEocAoh\n+3n2yzGUf8CY4PCIwLo9L/vAY0dlo/r6G7K6TKNeFhdkORpHH/BhORrh3/xFZDmagXXqoRn1jVtX\nRzaBtbpiRNZTPRdTaotcAvbusO6PAzUQA0Zupg9UEjF1sMBD2HGmYKF/9MThoNzYEqagBdxHpSvb\ncZDhHNfZ3yqa90rAhj3mqf566G99eK9HYAGZyalNlpfUntm87JznF9SerJNIyP5+9L+sy1YbCBlg\nS/p0bY8DYwWbxWxax108+9SMbBzjnuK/D8t0t6e69FuyQ/bvqi82rt8MytsbsrJULxsfRRBDut39\nEQ9D4E2effYDQfnJx2Xne++O4uTLP/huUF46LJRJvay62AAW48Sp00F5ZfXloDwYsH+qH6b2xl0+\nifGB8RxHn3VhXUu7Rgy5kKVrCA9x0N8Fhyy8YeMPC84E5vRBj5axNAfEdVCmRT9RdX0XGJAh3hc5\nQdaDPSFsKGm33kV7cqZIIN4k0ur/8wuKia09pM1uXb988rlng/Lnv/AbQfnhc48E5SSQGqEqCGWB\nihc+4kskZJGKPoHj9KElrjMH9Mi4z4spWHjz/XvAtXWAXPN7wFHl+f66Zr+D/tNFYB/Q3hJzVFbt\nNOwq7xx0YDuOuTaxx8QcYGJpAmPTh1V+HcfjScXgGOa87W3dsz+UvSvRW7kl2Q8nCujvHDcD5smO\njiMf5tjy45qTBg3FqS4wau02rKKJR2joHQdYTwyRn/dhId1hm/iwmcbc3Ovqt40K8hagEfMzI3Rf\nLKV+M8B9upy3kKsMXa1Poh3YHzuMlUALEVeKfKoB5/4M8l6uJ0Csc4ZoCMa4cVSxqBxrCpbxd1c3\ng3J3hzbYwL5h7mTO10bbcG67dVfrCg94jcNzeoYkcplaA+tLVON0YtSHE7AWp3W5B+wQ759AIxWB\n9h36uk8HHuEtWOVX+Sy4Zhc22C+9pTxpBjjUKNfSWCdnkOflsorfJSApf/iaLNw7t/Xbxx9RLkFs\nFtcEWawnFudkeV/MaSxW63qvQ8A4ra7KIjwGnklqL57du6vx1AYOuVTSfQbAqfRTqo92U3G+DaxW\nC/l7dyh0RA05/uyksFczyGPTWawnELN6SID6gxB7e/wUhV80eTIh/BLKByKaDkBKxd7DOSGOzf7I\nqDBqyv3rxw5ATRFjFXreyAHnI0Y4SbVjtoT8HTiccQi13Z6e+dXXgDP4bTXW51/Uy//9/0LlhSXi\n0nhVzGko7zZH5T/4Hf3ua/9if9yk6cEU4T4Bjg+4jXMA9pW2/3Ec74QQeNyDw5yK30YwLmL4B+Kl\nkCY5yb1OE0ee1EsKC5AC3600qXicB15qG8iLVlcX30YOUN7V3JAtaF9jCFTLRz72XFB2gfW7e0dY\nnfKmcozdNgcv8irkFck4cm/ktM0+15FAh5JSRYQ5EcTEIKN9KsCllG9qH/XyzZtB+Wt/8dWg/Pxv\nj/KJnysJt+VlyXXVw0SPCJviIFchAquN53rJVW5QRX50ekHrhgngUx1No2GFkBo/OSpfvxCU80va\nZ5k+JpTX5VdeDcq/922VaxijHaDiWzWs+foqnzyjtpmf0/Wj2OdITWq8fOKjHw3KM8h9CnuIpha2\nWS7fEqasv3YtKO8CKVyva5y1se/WAwa0OivsR2ZSOVAyofyhNKUcLxJXub6rNZ+HtWMM6LmJKfXh\nwqT625WvfSkoJ5rYn8fk7w70nG2ge/tZ1c1f2QAPSicWjwXlhxaByurp/OquOncPc23lglBZrdxo\nLPbuaL1x6iHhqirIwaMtfS8bAp2TmkAuNtB7zLnKIX/l46ozD+vFG5sqv/WO6pvrotyEdklTKa1F\nfvas6n4clUhr3kgA1x0Dg8RDOV/UGmQKfTUe536s+iHXa5PzR4Ly9CLWekCz3VnRfnQZfbvb0bx0\n/zuhz11A4J+SGbXFFNol0ldfvrqh9c70hN77qRN6xoGncbY4pzhy6uiyrtnTNS+uYV89FJDxnNgL\njQEzOADmOoPnPzSr9VG5orgSGQK5vKO11coWvnXU8H0miT06rC8TUZ2zfFjtOb+gsdBt6769PXR7\nB+uwVewtLM/gmxT2vg7NqN/cWdP+wE3kDz2irpE3Dd395/fhAd/gfKxTByG81HjLw3ck7gkO+N0R\ne3MZYL3u3QOaMqr+HE9g3kDeNjOveZd1nQUCbqasa77z1ttB+a2y4l5vL1eL4iJEk5cyuv80kEzT\nUfX36aHixaN57D0S94q/ZbhxV/XRQa44mVN9/PQi0E5K95w8huJ5pFiNA/btV3u65vousFrIA76u\nMOX83bM65wlMka8olXZWOjpnHVsYpSTyXrRJGffawfeT+w332o76Tfya6uZX0ipjSeDcG6p9ygW1\n95kTj+n++P7QLmvDNPTtIrQc3X908fsXixHf/esn/wiZ043JZDKZTCaTyWQymUwmk8lkMplMJpPJ\nZDKZTCaTyfSAsj+6MZlMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZHlDvK17q6s3LQbk5C5vA\nImzlYcHZg7VWDFZmGdg89eU+5exuy46vD0t6F9bBfgQoiqyshjo52Tg2Y/BJcke+Qy5QKrRc9eKy\nHYtFZI00gP13qyXvp6iv96Bdaxx2iv000VGyfCOzwE2pnIzFcQpsrLp6D+K5BtOyWzo+ofIqbEbj\nEVjydfSOm1uq4xb8KV2iaNBWUdiixmAhG00ChxNR/XQbspq7+tWbo+f6gazuhrRPhw2Uv67r3X4p\nKDqJrtpt/ozuk5qBzSAsLuPoH120Qzy5f7t1O3peYhCI+RpL+fAj8zGIXPps4Z1TGqNHjsrqPQGk\n09qm2qmU028PL8iSfq6xrN+WcK+o7Am9rGwIJ2GpeGuvvTsYWzlY4NJ2zI0Sk6RnHMBKcLag93aB\nb6gAVdHu0QJez9uCrWimIO+1COzlYhvqG82BfNtS6Leg8zhpjKE8aRL408g6bJ29lN7x0KyeITcN\nXBRwHNlZvW82L1vE4oR+6xJ1hLpqNEbxkZb4rFfiHLJJ9icg9ODh1ocXdhQxNJmWdWQfcdOvyo7V\ngaWksyU7yN49WUtXdhWn2phHxlEhLAYs76Ih+ztaBqI/w2bxMz//haBcbwiTsHL9Kq6jupiYFu7h\nxnVZ4JYrsOBj/MbjZPe8wyOwMuxgrMT4HrBL5V/59hiC6Ok/3B8vNfTZlwb7nhOh7x/xa/A4pK0g\nrdd9YM6ILuyhzmJRxkdgF4HNiwIv1Yctt4exO6RtKO+F44dhY/rMs08G5e9+d4T/mi7qPR5/7FxQ\nfvhhIaViyA1o6c8y+xOtFWkryz4aNlOkfTp/S0v7wb7Hx1IkZ7Df5jUPER/UqoHvg8QgOwmUHxCJ\n3Zrqun5PKMJBS9bUxDT0gEHqNZSjxhJ/3fo9CxQpUWkt/K6D+SA/qTmdeCnmWCngXvod5aI92I6H\nWFq0iR+w3WGt39O8OAB6jv2nDWxLp4HfDnT9XVjDZsp6tnRE88kANr+9Hq+vZ+sONEa6QJu0mrrv\n1q7iaRTWubU9G/F2HRbiyGFrsBlPuWoH5/bFoJid17MkMhxPOr0FPJ4LS9cIrJyjKcRc4Fd8xC9a\npEZCcXb8lM1qzJVKioWTUyqzXfpNxjTiBJF3kH2JblsHV+zmPeWxfcT1TApoQVwHwyWwgC5hLLbR\nBzhr1WvqJ5MTwDll1H+bGP/rO4oRbaxrVlaVGyWBtUoCWXXzrvJP4paTsE/uhXIMVU4eKNI0UEzH\nlrUO+Mb3XgvKgzd1neUlnbO4qHIaqLUI0L3FvNajybTavwaUMecQ2n5v7+WCDaBOS0DVJpCXtlqq\n+3ZbsWMH+L0E7KyPANHpAVncwnS2sql2qOH6nGsnkGungXuMAQk7lsLeRmgBTBQUuUNM9A5CR3kH\nnBM94Poej6PiybEhdur++dF9jv3Va7yXZwwReDDogfxNpZUPRF3hOPpjnPdUqnqXP/xdPefaPb3w\nP/rv1T9PnIJ1PPK/jbJi6B/+zmgM/v7/qLHYbjJ3Nv3YYm6Nwwe6peMs2u+3XK6DVPZCF1UxxnGJ\nfUYejnP5RfynM4qlXkzxL4/8ug5E4mRRaIilBe0FXb6lOaCMOL04peOf/MRDQTnlax6/eF752aAs\nlM6hpMbuRlfxu4tx38e6M4X9riiXTY76fgrY1jYQzgPOr8RLoVEiqDMvuv8+KjH2XaBa2f5TE5r3\nFvdi9zz2J/vEb+aAHQ6FKfwH1s8RJDxPR3TNr/oqexHlunEmSP7+a8SQeJwddgzl51XPm2vqV9PH\nhdjsZpTTpFzVUbeuPapOR+XuUH0mC0TFjUvvBGVsnzt+U/PM0WNCFc0ABXp4SUikZH7U3uWy7jmb\nV1+r3FSe2akhfseUa6XjWLMgYFw4/3pQzs8tB+XtLeWfb7+r/aitFvI9vZIzmVOcaGP8XV3Tu55I\na12dQB27QJhMYO+hhP2X7ITiilvSPlgM73jmpPA8EzHtS751XvuMzz2i34Iu7aTmzgbl6qaw77Ub\nozX/samjwbGjU8otL2eVazMp6aWFt2ptC4+S5Bgl0h1Y2m4dca0OTG9Va/j1LbX5PNb5x+b13ouT\nTIbGT4888XxQ7uC7TKdF1HYXZcTDpsbC6z/8blCuNcVS2dxQu/Pb2jNPPxGUl6f0DH/+518Mym+c\nf1e/7WB87e37hGL3jGKKO9ComMG3qeaWnquJNepUSb89CxRbDH08V1AfmCxoDN3H1o8eCOtn7n/i\nG2Eyje+2wF1FYviugu8wyQRQQVhD3V0Vlikb11xx4Yq+F9/dBpa5L7Qec8pBXZitZlVxYjCtmMh3\n2d4erReb6B93a+oHO4cVgxbnFC+yab1HASh4rgvdA757vBeFYJZESo3xGuKvKg+kD7/X9ZCvcF8q\nmVD5xm2hoPI51XsUfckJ4VOdfcsRYuWQk50r6Bnu4BcnsqM2Iwp4YVb9PZ/Ue3z8jPpAEt+pYlXg\nRzFHt5FvvaHXc95Q1wt9l/jMCR1/fkl9YB7bBHeV3jq+Ug/n3Ra+J/DjId6rw3ZAXV5u6qQ/vK4X\nWMUa8TtAUDV9nfP1Vf32Xhd7JGj/b+I5G/gbg6m9v0NoYa1y1dPY+lob/amttjz1tOLtL/2scv9M\nUXHwnQtCgNbfVRwetLAbh/EawrgdlKJyq8ON7n/SATKnG5PJZDKZTCaTyWQymUwmk8lkMplMJpPJ\nZDKZTCaT6QFlf3RjMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJpPJZDI9oN5XvFQGlttrO/JYqrZk\nD3ocdtRx2EF7sCLNwOK6Cks0WgFF4XM6jMnDqZHRfXsJWcSB+uTEgLrw92zEiUNJpmRBGAEyyenD\n9rMpSyV6EYWQWfAYzUd0vELrSPxdVCyrh4zj/Qaw+HTSsPQf6PxBVxZYnYHeewHXb+NdNmHv32zA\nJjkuW6p0QZZPCbRPzNV1iByJo5IHDmxXy7rmu38kG9jN8yP7Q1SNM4AlWRMWfxGP+Cf1sxuvy/Zu\n44b6ypEj6kOtHdiD0Qq+r3ptw34eBCFnCCsvpwf7s+F426I6QyDUgPdy2J/5N3mw+pqel23h3Iys\nCm/ckE0gLdUHA7XvADbxTg+IiqisCiNxjfvJKR2P7lke0q6sA3t84niIdaFDGPECnRYsjaf1Hvxt\npQ7/N/jO8To+Y0NO6IPZBb33sRnZQe5sajylYGWWgv0p6HFOBTFjE832kbPzQXnxqOxHM7CSzKJO\nIp7iVjRGNBTiHS2nUQ/RPZvfFGJfFNZ1RG6ECD/oQyEbZVj/ZYCRSCK216qy4UwBIxWnbfWa4nm9\nqr7VpI3oYLwxGhwf3a7KiYTqut5QXaSBXYgh7j7zzAeC8vwhWXT/D//tfxWUL7wla9pjp+RhuLYu\nC9oe8RNgSoVc/O+PRcRFIsZi6AQebCHhSu50h+p3fdgURmEnzLE7pI0fLIQjLi28ga0gnwV9JmQH\niDnYC2VDtIlUeeAgluHyqZiewYtw3PCSmMiIneJYxPm0MS0D3RfbQ+y48K9cPCzLVQ9jK2RzyjJx\nUWwUl3aU+9vYh2xOQzaLKtNQtQv71rHHaODdYrTmBLIoBdvu1o68Nn2gknyM6X5LeQfjXjSt/Km2\nqviWQA4ST2vsRCPKa3ot2bfftyXlPb2MbKHLwK4So5grKtb6QA1NTCm+uBgU6YKuSWTYEO8dwTPS\nGr7fhd017JWZl/Y6wDpi3q2U9a514LHabdVTekt13EOnHNKiu888XOU6cDRXrwv5tb6qObtSAdoL\nYye2N3Zi6DdRzON4XKcE//S4XHOdyCH9g+8QYYTzke93OvqHXAxIU199xcMIJCqo08PcOeY5ah7W\n1zPTQE5UNM/XamqXO/eUf3a6++fuIVvYEGZQ/9AASniron7bbO0fSxvIR8v10flLc8pbO2hI5opD\nPEwVCLUsUbZ6XKfbJZxKasLemBbeMbR7BvbKfF72jQR+O1XS8xeBIqU9+zKQS0+UldNevQX/ZKiK\ndiOmdOWOxtzmrmID56IyUFzJlJ5ze1vx9/68Pj+vvDiFGLu7qzm0XNHYbjfVxsQxLixoL2J6SnbF\nQ8RZ5rT1mt5vq6L6qDWvBOUTy8IKzM8K+ZAe9///E1kqnBeZLzCcEAsSPSBJoCvzgWWeT9bNAdgp\nbx/cgqbuH4GrOuC5mK6E3g/3gVX30BlvBMPfJCIYv/qvNdZb2Of4e/9YlbK2qfP/5R8qrvzwm6Pf\nNmvjjfb9yVTIRF8loIZpVc+uyl+mkAtOAOtHzOBuVbkjl1NETXHZlPKAQfKUL1b7o3sl8Yy0uCeu\ng+vbhTmVq8B+37ml+HrurGzlT86fCcr5ouawwyX52r97V5iQM0nNFa2u3vXW28ol4kCvcn7oco+B\nSJsS9hYx93ex9dXH/Ochtuaw501Usot6a7ZUDwOgRbJAwbaBklqojc6PDbnmw7WZL+OdfDx7BGtR\nj+s/IF7X8Vz3XhMKqT5zWOcDJX5QvwxrvJEaEzNK5G/eUE5R31QfO7V0KCj/7MPqqy9/9y+C8p2B\n2iuTVTsWs3r/WzXlRrVt5TLdNtBAWE9Fu1oT1baAlt67V7epnOrJ49ozfO1FYVqutjGeE/i+gcbb\n3lS+d3dDz3XC1zjgXsnq3etBuRdVjDhW0HvHgFK9Fdd1Hjqh+k64ytuieaFgBusa62nElckUsIiL\nQkd1sLb/9Gc/G5R/6rnHgvLmFbXn9S3tW7cHquMbd24G5YmucuNMV7EkOhzts0UPK+54Be0Zv76u\n9ntkATFlUXt77qz2etff/DNdB/sslZZiSk/V5OSwX/i5T6kvXruh/YfNTQWqG3fVtstLmiPGUXdv\nCVvWAyKLgSbGOZL71B3120vndZ23zr8alNtYzPsJzS3n335D59exBsU8ms5zPYh9jvZo/UpMZzqr\neo5jL7R6RUhqB989e329E7Hic5Pap2ongV+Lqw8Qc99DvCdyiXnvBLCFyQT3LVGOMSPAnj9wiaBU\nORXs2//ly8LT/fynfiYol1Y05upX9MyrFZVTGcWJeUfrQhfxketBb++bhY92qtS1n1DF3oI/h/0d\nvF8CdRlDPuWGsIhc2/3NqKnQrkyokwLN+Dde5d+tnn7yyaC8tq4xsVPWHOn0OyjeDsrbu+rbJ08q\nn+N+DVH0B+1iMR9OF7R/VHUV73/tlPr8Cwuja0aBrM4VsY+Lfdkh9ioHmNyI/uSSkp+JV3Z1Tgv7\nkMuT6j8fOQIUYlzlRw+rfHJBv108pvL/9rauc7GufLWCb4c+/j6BKzSWLynFdra6uk4ESLXZrObv\nV7BYrpWU83GfdrencfnRs0JwPvn0s47jhL+9TmDPpVTC/I7On8kBx8hvS0AHcp9qfR0IuhXsNYUQ\nvNwA+P8eiTzmOz0mk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJtP4yf7oxmQymUwmk8lkMplM\nJpPJZDKZTCaTyWQymUwmk8lkekC9r3ipbFoWPrfXZSnd2YRFGOz0z52WJVTXlV0QsSZxYE0SQM10\nXdivxWUd53jAUhB9gj8/ykVhydQf2dRFErRxhd0oLNqrsMEf9olpoR217IoKHfkhdYqwgCcuCoZP\nREJ4HtBOsG1zUzo/l4GdIqzqOxVZGF7cUd3EfdilLqoO0gVZi9I2LQ5cjQPcTiwKPEIU58BO3W/r\n2S5+XxaG5ZUGzh/9D5EhXdip9jp61wStxGDzVq+oLhu7wIntqq1olzqoAyEEO64hvKvcISzlYKnl\nEkvmHmR6NiaCjawTgRUj24t/kxdRH8jAYnBpUbakV67JRo4OtByjEdra9mHf56scSahvH3/4ZFB+\n94cje8U+LBFp/hXD+HdgoT+AjTEtbR2fcURtl4G9XBf2b23Y+0/2YKW7qvdeXJBl/MSULNGef2Q5\nKH/5B5eC8s0tWZwto+rbeM57GLsLy7r+E4+fC8rJrO7l+/vbEhOJELLKAx7EhXUi8Sr5PZTcYAAU\nBlFUesSQdTIxXC5slJMJ2NVhqHSAYqmVdX0ie2JAzO3ck11cO9TmiPNj/qelPuJ6HPgd4prSGSCl\nEO8HxBUi7i0tCR31+V/5T4LyH7T+16C8syurv0RaNn3Hj8jm1+3J+rMFDMt9DFYL/SjFGBljv9M5\n/S7Hojq8hznPD2GhFDtS4Ft1cN8IMSm4bx/zLscE+xvDNC0ro4gsWRADvBAdCfgO/EPMVWwgEaTd\noc3t/teZmBDKo9VWTLx9Q/ab96un1QA6cV3W6ESOhVBQ70EhW1T0yyjxEgfYgrMf07I8AWRcf8Dc\nZvzUB3IwgvgWRbyKpfU+CdhY9oBO7ADJORhoPEWRM0VgtR6DF2ljV7/tdRVvh5hzum2VvT10zICY\nQ7huxlIYz8QOofUGGE8ZWCFXK4rHboRjSPbDA9iYE9fmw3J/gGdv1dRXe7Ax7jX13i0gX1pdPVu9\nDmvmlq6/vqY8FpRXp40ccaeqttoo6/orG8jbYXUcgW1sAnXlAa2Q2As3tJLlfFMAJzKX0jWmiphn\nEdcYm7pdXAjjL+Mjjz3ACnUQwjoiT45j3h+ONwolDSvomTnlmVWMs1Zb7dgDDnZ1TXMb1w9eCEO7\nP2IhikZgfGMs53pxt6Zn6O/1mSH6Thz3JC0nmVZMIW6pWu1S06EAACAASURBVNd7dIF6TOA67OO0\nXU4DvTRZUHypVTVGtyoac3w/D5MS8VIRzN/tjn7rAht59IgQCo0G8gTEFfbVtQ21z8qq0Ja0IG/D\nwrmHdd+gr+sXCrJQv//uxbyODdCWFcSdPq5XwnqmWJTVf6mgnIvouxZiFtGgu2X1yxJQfBHU070t\nvXcX8frQjPr3WArtEsI8MWCxfFBAPBAdddBx3gvnRA54Bl7n/jNwl+tA1BWxUwecz+swB0LuWgeC\nbvCAude4ifnEt7+h/n/+Hc21jYbGd7MOtPlP9quPtaLIsRhbBkzd0QDDUFvopKmc5tezh7SvsNZQ\nfGsAa8w9sCjmChfrjT7G6wuPaH+itrc2f+u21uuNJmIw5q35KWH9bt8FWhTz1pljis2g0jjf/q6w\nRkmgDJJD9dnCiYeD8hLQVHc3hQFstpBPYi6P9oGXQkLAXILzMceQ4zI3BuIRv13E+o/XaQ7020Rc\nY7GUUlDKpFUR1abOye3tETyC/d0kgpyPPYT+HbWPi/7hACseRpBj3xo5TBt5WQjzDBH16YbQGeyw\n472P2q6qn8wdEeJ545KQMxN55RTPPKG+V0qqHr/7hrAqO22106//wi8H5S9+9ctB+XuXLwdlF3Pt\n2uq9oLx681pQfvisMB2zM6M8JVPSc924JLzQazeUj+0wR20Cu4p10r01ne/HtDZ+JKu8Kj+j/njk\nsHLR67vCUMSA5U0gd83klM+dO6bY8OV/9adB+S5yr2nkdmeJU85ofFxI6NkSBV3zyHGds7Gt95pA\nPvzoJDaBasrnWl1izjW+a8hXc4lRW6VLGufll88H5Rng6xbnFJObWPMcOaKYlR4qPpcvfikoLy0J\nQfXuReG8EvhuNAd038NHdfzOTb3T2j21+a1b+GYwhopg/ZvAmprfBaLIY7PAGBKVNIwJYXbitMZr\nFQjd7SrmSBxPYuNlEtiwVEHrozRQfb2974RTmPO6ajpna0Vtt1ERYqk0jf3+TT3LNtZeOQ/7rtjj\nv31Ze4lb3NfCt4JKQ2OU+3pE3kdxvhfHWgkocYrr6mRC47vX0xz/nReF83r6UfXz2JDrdj3zsKd3\nL8yovovzQCIjPibaev5EKrP37BpD9QHXuhrD3BsmktnDdyPuv1Mu98VDx/cvOwce53XGe1786Ec+\nFpS3dxRPNoEi3LinfOvLX/1eUPYwh8zMaNwwj+2gbbjW8A9IHY4c1zi+8uZrQfnFTewz7v1gYRJt\nva34t1HR8Wl8j4pwXwhJ+GRRMaieUb87v64+OwS6FDR1x8X1mQ/xu0EGuMczSNX+wfM6fqep63wX\nWLbqts7BtO5caOp5HinqtzVX4+/hj3woKC88JIzY6obmy2odSDfkhaeBdZwCXttJjtp8YW4uOJQD\nuioOjJsbyqOxh429GH4DmZ5WPD9yeDkoEzdex5qHHN2hv3/uGsJRPeBYHPPPkSaTyWQymUwmk8lk\nMplMJpPJZDKZTCaTyWQymUwm0/jJ/ujGZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWR6QL2v\neKnMgmymbv2lrA/rLSCDYLsZTclGbOYQLKCjshskaqMHm8wEcE0TSfk2Nfs6vwp8QzLktK7rxPf8\n4/2eLI3SMdkeNWqyloMjlOPDKpFohiHQKI0MvLCysA7r6NljOf1dVAEWrKUiPFVhdxbtA3e1K4un\nxlD2SSlYhXrL8qXqr+Idb8uOLnIGeCnYLUdg85SCrWwU1n6eCyu9ptr2zmvEjMiabv5J2Ym1N0YW\nd817slYcNNR+J5ZkG3XysGyplueFJfsXL8m288YdtVW3psbKwbOrT1t+h1gkB8cxbGhzTSdZd8z/\nnq2pvh+yDo8QHwS7PJS9tPr/0pLq3Xdk7dtoq637QHD4sO8jv8sdAlEBvNvcw7KFe/aFkdXi+R/I\njpN29An0626MFv2wMYbNWwZ2o5mcLO3SeNfilp5rZUP9h3b9u9uyMZ6fle1jApZop46pnlqwqv/T\n78ti7y5wMXTkbeAdP3Z8Hs+PsYt3JNLJx71iwJlEYA0ZI3IB/d+HXWc6NxqjAyB7ekAFNYAD6bK9\nibrCS9XwXLSIayRUZ82a7N+6HcX8JBBMa7D2bHVpL6f3yCSITBtH0Y4aiB7YTveASPOAUKLVJUU0\nwwsf+khQPn1aNsNr67KSvnNL8/G3vvrnQbnTlGVgB/amL75xa+/+6C+If8TlRIE3IfYrZLEN1FsM\n5/t4vz7mtkQUuBfMtT3UXw/PE8Xk7GE+JiGRzwMyo1MAFnMAj8v4AdNAPKH5rF1XnMWwDFmKExvy\n6GPC6TWbijHb60CBZUcxbH5hKTgW9YAhGu7vt8ncI4TcQ5wiUopOneyXoXmOKJYDsF20Yoy54420\noZVnDwhGH32GxIsEbKKjsJcdNJWzDB1dJxZHLoVx7Ed1vAVrX+YjHmLjcKB422mN+m0fDRBHPE5i\nXmw31B/bmMOYoyZSuk/GBxoM7tL9pnLLfltxOpoA14pzCZ6t01QuWt8SmrHbIkpAN9upqtzEoK4B\nT1Brq2+vbeO3wIiy4zZ9oA4xLopAQDHeeGzzBOp5b33A4RGDtTjpAok0rGSLnJOAsOwR74gzaJ2K\neOclkY+jP8XxjMMe5hHwV7zseI/FKN6nCCv8xYXFoEyUZRN9vtkCDggoNk6XRDNyHp3IKS9MYWFI\nVFID1yd29P7zrKPfpZPKV4hA5nj1EFQ6XfWfZFLnT5X0XFsV4OXQUbLIbycnlDNtbmnu7oJNRQRO\no6Nxs12BbX1Z43uipLmtAzv9BOLaAlAlrqf44eI5qzXFnqHDNeX+aK8BckovqbFTyOr699GczHNr\nFWBC8LylvOL29IyeN0RIwn90MC/SRnyNlspALmSBqB0Ae9brqY/W6qqDKmL+WCq0RjwAy0Tva+Kl\nYgecfxCaKnTOARipB7kmyZgYZ+6B78FrkOO2f67tA/eDUBPqvz/pYk65sTbeiND/v2tpRvtbIHo5\nFSBIOGyIR2IettMCTh6Lkzi7P+bgAVBCLmLsAEjyGOLeL//0B3X9zii+/ZM//kpwrIFcZ35K7zQ9\nJdReDbj39U3NSTMl9UFidpOe9qaI7v7gMV2zOKk9GmJM63XNYUiTnXhc13Qjygd6A2BJgX/a2FYQ\nYA7J/dIo7O+LQEd3BsgD2n2cr+Mzk5q7vvD88zqeU55x9ZqwJMXICMu+u4V98w0xTHLT2puK5JS/\nDyvaW/GxVxbBXB9DHptFrnL6sHK0NJA5oYVhCFMcYk05Pyl66W3tLz///AtBeXJhOShXL18IysU5\n4RUWj2kv5lRL/fmhM48E5UPIdf/Ov6f97uYf/e9B+e27wsX0EKfrVbXf2r07Qfna5dFeTxTfE1pY\nV7WR89aBuH3nvPZda1ircW/48FGN44ir/htJAoN+RHFn2lH+1C2rn/RnheqKJ7XPyX2tR86d1bN9\n+1tBeQu55ZMT+NYxpXHmADnBfYtuW/d693XV2Ul3NSiXTiHnBG7n8ScfC8ovvrQSlCsJ7Q//5q9+\nynEcx7mzqVh9bV2osOc+8nRQ/vCHPhCUC8e0L7R2T2N7WFOM89Ia35OzGtPnsG+ALS5n4Gu8btfU\n/vfayofzyJMns1zbj58yobWA3i2dVOzKAvk0PaexFc3oeL+q9qoiBm6sCd02t6j4eQcYt66jCm4S\ntY399nRCddrd2wohUnGmpLb79pr60U3EztK0xllsV32z3seeS0997NbVi0H5rRs6P4m16TTnRYwz\nrpOZTHvY6wmh5ZFj+/hewP3sAuaZTlf32tgQ5u7r33kxKD/86DNBuYL9oNqO3iW1oNiayGnOJpad\nzxDfm8f4vPzuEsJLHbAvH8daMIFc6b0AZzjL+e+Fx8r5csznyBmsqScmFWsPLwkn+GJL89bNFX2L\neOjcE0H5w/h2cfmqvpXduqVx0Uf/DNeKWiFb1N8PHANS9N1XhZx+5eIoF4wR7YTxRMz9NNaLDWzy\nMXdeLug/6jH99kYF3yKwprx1T32pBCz9YxrqziQwUiDDhf6SYz6r+jiUw3yZxN4mthu2MZV/UU3i\n+PhuFpvUXPvLv/H3g3Iyr4fb2FQbrq6pfPee4ub62vq+x++tjsZxEbjweaCmDs3ru2eppLZMpRTz\nIxjHXP9kgKlfWlTMv3vvblBudzTX97E/5mOfOIw9/fERqGP+lwEmk8lkMplMJpPJZDKZTCaTyWQy\nmUwmk8lkMplMJtP4yf7oxmQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkekC9r3ipw0/LD+lj\nK88F5dfflFXUdFb2UxFHtkDNBqy+gH8aOrIvc+Iqz0zJdqgD/8BhHRafWdknpRP6bQUW0Jm9R+7h\nd5Gh7M06wOjQdto/wI6aNs2RFM5Jw74sDetR2Be6sDpqw1K11lKZFs9uRvWULshij5blEU/3HeZh\n1VRX2dcjOElPbZKCdXguBhvxISxSd/TMm2/IOq61KdurOHAxWdoZ7llruss6NwW7rxLqJufpGpMF\nWcs9vCTbvuvXhTJow3LfR51FiNqBRaEfwm7Ag442U7ATfDDDqfdfQ1ihRzxgBtiOsNJ1iAWJqy/N\nL8hGLgEc3HZVbbZblX9ZF7aFKdgKuj1ZOjrAt8WLsi87+dyTjuM4zibsETtAZMzM6HfEqrQ6GtsJ\n+I5l00A5wFKu2dF4OgSLuMm46sYDkiWDMdSCL3Emq7GSBuLj1GFZHT+5Jtv/r70hi7MqkAXzk/rt\nfBFWx7QkhOVhCDlEzNkB+LsucAa9vtAffeBV4ntWc2gyp9xQHyJuq9WGbfUBWBrikjrAQsVh0VjI\naXx32nquiUlZ0LUxLqt4j04Nzx4db+t+WrdHMT/wuIfj3S6tP/X+CdgBeoiHMdjuTU5qPE1MyKbv\n9rV3g/L588LELc7pnCzGd3wvZg6AhXJ99Vk35PKv9wjZ/nmwHI7C3hwdtYv27aArZVKw9US/asPO\nu94Gmor4qhCaCvelhXdO47uUAsKkq9/yOkPYx/7Mz/9KUH75+98NylcuqY7ZbrOTGtMTJZXrdcUh\n2m/PLS07juM4X/gP/qPg2KkzjwdlDn/OVG4I3YFyyJOTOCr9OoK5jdf3/f3P5wzIvtDHuB9HsV56\nTdrHwxIU9tvRqMohXBT6Q6sCPCbqyMtoPslOKe+pAQXTAZKTGMA2kKyRvfzSg104/32IQdesA7UD\nZEthEhb3QKJ1G4q7MWAlB8iRW1u3gnI0KYtiN6Z4xHrtIF/duCe70d0NoWDW11TevKfcwAdGsYU5\nknNhIaYyhrEzuaTYlzqkvNCtAqEHe+M2Ao7n6flTGcXBzPRoLm/gWbpbslZNRFTHCXjPFpBX5ICQ\n3d6CBewA5yQRp2AXnowjf/c5voFJczGnwOq/kBnv/8+FT9Qr8phcXv1wbk75Z7mMfAR9eHsH4wmI\noT4CExFQE0WsffA8nT6RUuqHjOX35+xOd3+MIvGEHdhXxz3gzhJqrwz6WgHYq90acmeMLSK5OrA9\nhjOykwMqooncmFRCIqi2q4oBJ5OKE8WiLMIbFY1R5r2JnOJBtSIrZ2IpU8hbiOrpAB0aI2osr/sm\n4bF8PxbXgHzeWJWFcdTXu+Zzeo9+F1gyIE1bWJf2gHfzsW1CG/YJ2LNnYW9e3tE1E8CycE65u6a1\n6VgqRrwUjx+ElIocUOZvD0A6hc7Z/zo+7L1DmKj9cFQ8l9cjUoplLt4j+x9nLuXinmmM1yjOGbwX\n+3iT6T3o733uE0H5j/7ytaD88LLmwh7i0tV7ymnKREox1wVeysVEEIkpp4xi4A+RUzSBteI6/U++\nKTzEQ0dGVvFxT3MPiEzOFvA6nJe5gNkoY98V73fmiOL3c3Oae2Z6yvdKwH7nC9gbdomQAHYR5YmS\n9hu49q4M9Fvmnz3MqR7m9airuokiZkzm9cwdMFxjWPM3gXNNR3X904fV5kdPCkFz6szpoLx2fbSe\nX734zeBYq6p5cfmUkDaJGpDPJw+pnFa7kZk6g72vWcyvH8Rzpdb03jXgRmLsZ2hzxk3mJOMICf/G\nm9orWV4U7nnx8NGg7B9aDsrcH8yXtA/4wceeDMpxIBNc5DfT88It/MPf/E+D8vmbN4LyV158KShf\nj6tfvXP5ku67N6fd2lI+NrOg9dAu9m4ryMF2ahrnaaDt5+b122PHTwXlQzM6xx3otzev6b4LMWLi\nNM4iMe07RbMaK1EXiI8ZjelddJQCxrf/lLAUm2eUN9a3tdd656ae5851oZ6KEeXwHnBaiZ7aLQ3k\n0skJ1cM3viYEVDwv/NCLb42uvwSE3mefEkbq3AsP63cK204Da++Tp4Sx2t1RHazcUE77w2ta87Rb\nOj7A/ljc055DLqvnOXNO14wCcTcEFnoc9bd/+deDMteLaYwn7ou2W3qf3TL3HoQdIaKNCN0ivpX1\nZoXPIYU1hnUN59HTD2mMTE/s/dZX3Od6sou1SQ/zdRN73dxfrbT02zZwyIvTatMNxPgBVrgNIG57\nwDYTp57CHpMX19zWxZ4/Y3l/SDSVROTLFOaEXl/vm+U6Fc/WKKvdXDx/JqXzE0CtNSsaSPWG5tFE\navQMKeDH+A2vBdwyv1fwe66HdVGC3w45hxGzHtqRdXD8QTXeXxi5X5xA2UGOcO3aW0G5Vlef/Myn\nfzEof/hDPx2U2ffWN7RvWK8hLiEPYw25eIZd7OHPHVWelNsc7WOurGs+6KBvVjGeKsD0+Qd4l2yU\nuWDEdwxiwjA8oh094/91Ref/2Q1d53gOORPuNaNQ7pyYxb4hvlFEcOMqvo28s6vyWw1838fezblH\nFLNml4TWYzvnsKeyuKC/4zh7WmOO7Xb7tvaNV+6M/gZke1v7SFtb2pe9evVqUCaubHZW89Y0ELX5\nvMZ/Kql+UzoAU7+5tR2UG4gRXIpwkLrOAf/wHjTeu64mk8lkMplMJpPJZDKZTCaTyWQymUwmk8lk\nMplMJtMYyv7oxmQymUwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkekC9r3ipXky2hR//OVladuu0\nl4YtUEK2XxH4MLV6svqjm/DEhKzjikBXNAeyPWrFYVOeln1Sx5e9EE30ont2YMkk7ENhM5XN69nb\nNdiMw3IoDrvwRB5IG7hu0eK6DRu/tbqsjpykLI1iCVijA0flw+4zlgYKI4X70vaO1ssuLO5gw5f2\nVE9eRFZNQyBBcq7aqnZLdbxxQbZwbkftMD0pG6taQ1aImaie4VA0u/ce+9vYtYApWN2QLVXj4pWg\nXN6GtTcQIy1a+DkS2wRkgpBdYRidATRVyItqvO3f+nW1i5eEcWsSlqCwV+ff57lRnT85LRvQqQm1\n7/Wbsga7uSJ7v1On1B65GdmBRTOwb4d9rhMBemx2ZFl79MyR4NjKJaHpNndhTejBKhhIihpsE/2+\nWjibk13kAOd4IetylWvAdPR7elfSU5aO6TmjUdjtwYbw4WXVwdsrRGrIjvXckuo4CRv2XucAu8+Q\nrTkeH7gPmhzSzrI/UB0OgJq6b4u7DfRJpaY4TJwF8VYRvPdwQKtJ2ADi/C7O2S4r9hFLQ+wSqIPO\nxq76dBvohlaXI3z8RERPFPEkZDWNwOTC2juG/hCyvAuhnqI4n1aPulcN6IcW2jVKpMVA/aFUGPWl\nTkv36fRCnQ3vRGSK2jGd0Fziw8bRgdV5AhamIKI5SdIOYJkbRV9KAQNUaQJlBUtE0gbisGJMRnV+\nAfbfsR7eF1bqxII99tSzQTmdkf3w6oosh9lU2YzG5Z2bsrbt1WhvqvOXT551HMdxzj3yjJ7d0zVC\n6AP9LGTVzX4Q6hMQESC85rC/v2VsLLr/dfgUIZThGMrLyMa5W9OYCAXSITE+sOVHfRGT0kd99XcV\n02JgvviwXXVRRw0gc6KYN1xY4vp7WKEIcjbmqG6UeamesUfbW4ybHtA5LWBskog7PeSlnfrtoOx4\nygEy08d1fcS1ellzyJ0Lsg3dXVMO3kZcmcFYROrhNBAD0sA/gaTqxDDAo8ASHj17QseHsoWv3tW7\ndFp69xhyoWhM7dPZ6wuxjuojiswxT+dixMG8qikUHz0+L1BkGeQzzP05F9Bat4+lXRRzhJciZs8Z\na0UwhzB2pdKqvIlJvdvCAvKVttpuCIvrXWBVvb6un82ozTyMP2LR4lhThnxn90n7h8RNMF5yfgK+\nlvbHCeDDYkC8ljAXp5OYr2GXjK7krG9qPMUZO/C4feBGQvWNc7IFjZuZOdn1xzAh1yoa0ynE0Baw\nGLs7WqPx2RJYf7Rg7dsA3q+IAZNNK1dP8rd7a8PtHeXjrJAp2BLH0cbNpvqED2RIH7kln6UL7NuA\nOZqrdq6W9a4erKVjQBYMhkCEuWP+/3/iRst7wkgdgI5izIkedB0c9/a/vnsQgorpxf3ckfc58D32\nX+eFf4t5nzlWUi91+CSQDUlgA5v750wm04Mq6SgwzWa0hvq7n3ohKEd9xZYvfeeHQflfva58q4Y1\n8i5wjKDYOxHgKImsGmBN3UO5CzT3N94QTvc+PrFBlDX2HRrY8zxySGPryk3NBxs7itPJGeareq7b\nmzp/+qjyuu9+80tB+dHymaB8/HGtoZjiE6kxM6k578aK9hb9od6bCEkH6+1SQWtH39FaMDVUfT+1\npP3Vv7wk7E3f5Vyke80XZZFfyKr945gL5yeV3/p7a+4rr/0gONYdaC1aOvpQUJ7NKX75qIQBsEhD\nrDO2kWc2UX8e1jP/S1m41VXk1MvYkz42p7n5BJBc37mifPyfOuOn1TvCdH37+6rfzwLDmZ8QAmEw\n0F6oCzRxAqh7F/mND+wMETUp5ENPnFN/fvXCxaDMbxA14E7XyqPccWtXOdvqLpAabfU7D3kjsa4z\nM8KHHTmifc5HzgqrNV1S37+zo3xv5YrGcf64+tsGMMxuWxiK6TkhQNLY04l5qqcPnlF/T+IbQmRa\nSK7IV78XlOd83fftWeGaZmaV69Yvqi4j8xqjLYyzXErv2AMipZDTOf/+L306KD+9F0sWris/HLym\nPZ/hOa2Z221k4cgPiZA9dEjvPTuleNfF3lQPcZ7oQA/5fgfHd7F2amC91PkxIDjvp/LAdXfailet\nmvreIA4cOHLBGeC9jxwWVi+f/5mgnMD3yEwGC/gD8L6Or/YbYF+7D6x3pz2aL5sN/fvmttYvu8Ct\nHFkSsmUX6PkuNgfLwIF1sI96+qje6eq6kFlcD1exFuTcnAcuOIm4RvxhF+9K1CLXlJ2e5uZ2T30s\ng++XXeQHvsM60/seP6Ixvd5STJzAWM8B/7WxpfFVw/vOzo1wcIfn9Lu7txXPW8BE9tBm4b1T9SEP\n63l+A/H3J0qFFNpfRf1xxIVH33iPxdDXUiyh6lXF9XfOvxmUpyeF5vupD3woKM/Oap5ZQv/PABkX\nwkuF6oX7ZDraRd8++8TzQbnfGOVByUtCRtbWbwble+uaL6fTmE/xiT6HdWQxqzxpS+HIGTg6noqq\nX21U9VzXgX/iG13Evbg3ldkGnlyPHNqTSCE2TWFuXsVHtF5Kz7ZSVhzaeuX7QfmX1jVGlhY193su\n+z+eB3inYlHzK/FOW3sx7+5dfc+9fVvl1TVgqVZwzorywzQQqBMTQFAhVyFeKptFjgZkeRN7Pa67\n/wgchnDR+DjzHjTmOz0mk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMJtP4yf7oxmQymUwmk8lk\nMplMJpPJZDKZTCaTyWQymUwmk8lkekC9r3ipclOWYkdnZWP0M594Lih/72XZBfkubNOGsP3all1Y\n2pOl0FxG1kHFuKyDIkNZ/VVh6Q/nRCfmyK5qAvaW/p7V/mAgy6E6bMbdlqyZSindZ6IkK0YiFVbK\nsrrbgmVnD/akbkrPmCqqniKwK/ZgFTUgMSQm26MurK8HTdVldAiLPdg300I/HoN9HvxmW30hXPK+\nrrN7RVZXu6u6F63ac7DEjMPmr9FUG3ZastNLDkbPk4np35surMQGRDvo/lVYC8aAAmB52JZdnQ9r\nRReWfB6xL6gnP4RxOcBmfszt32gJHEM/jGThg4Z692ETSFxNGpaji3OyaLx0RZayF6/JGuzo4VtB\neWpWtqu0LnVcPIOv/nbf6Xv+iGzJNm7Iauz61Y2gnE2ovSaLslLLZvVO0QSQEcB1JGGf20DM6sIy\nt94CDgR9LwF7uahLi0GgRNA30niGRw8LI7XbUPtMAj9TA/qHSuWBlhjSlhRWiAkch/1br6937LRV\n37tAs91bHdniEhFFe07is2qwEB7gWaIhC0U9ChFCSQRl2jgOMbiqsBOMhBBFumgLlqpNlMdRQ8Qx\nH0Q3IgeiofZCLKddPt3yMT8QX0U0F63z3rlwKSjHafc53N8O/JlnRlaMt2D/fP0uMIFpzFt9YpJg\nd5giVot9VsUe0CA+Ym0KmMhWn5geXIaYM5fvrfPjEZWTQAmwzqqwXgdZItSfk1nZE84vyAZzYlq2\nrq/+4C9wX42RDaAR11aVH0QSsJlGTOr29nBCiMlExIWmJ7Y9fU5DfWLfnzpD/EOIshcJmYfue74b\nQi0iNvXHeyzGU5oHXMS6bl2WvF1YAXuw44wi1/HSmnOIo2oBO9IBHjOK/kz8p+/rvkTsZSc01/b3\nfPFdxMsIEAQ9oEubHYwVxJdMG5bAsLXv9XVOwmF9yOfUy+n9GneEFIhGVTfDuGxF/brmlXRPMaMV\n07MdluOvUwASqTMg8gz4JcTNBPBLW239Q5s5dkf1GoMt90RBVqRs815D9ut9MFLaa6uO4ziOh7w1\njbjmpdQnIqg/D8jZhqZcB/RUJx/T+XHkMz6QQ7St9oELHOAcF/N+AqgxfzjefKlen77MtFfHOgXv\nNjml/KkC3FGlinYE3nAIngRzEBe22V6Eay71n3j8Ry+dD8bRAouBmB0NzWE6hxa4UbTdLPC8dzfU\ngVroQIzZU1Oyx6/Bxt/xgTUGVi5fUHx5/AnZ7xPnxNc92wAAIABJREFUtL2pvN6J6HgX9vTrsAVu\nNnWviQmt1UnwaQIvlU4zDgIpinMSsC7u7q1phmjjOVgLE/9brmo8x7nejxIdp+dqA2HZRNymlTqx\nsSlgB1yuBTFnMwkmAncsxf4cPaDMDsfzmS8A4RnGTvF83DfyHu4bwlHtg6wKnRvZ/1zvgPtH93+n\nEF4KsePEk7K1PntEfeB7FxSDxntnwDTu+r2vvxSUb20rjvnouJ995uGgnAfitog9ykYH6Hf0+Q7X\noxFaxitGDZDHexhf3AfYwbppszq616eeEQrnay8JL7Be07Nwfe8MVS7van/HczS20rlTQfkQUDfV\nHe0Tv1VTXn/7Hc1JX8gpX+0B5ZMHGiCGfKPbBYqQSCnsDSWwz5gCEqi6o5xkZl7z6+eeEt7p6l3h\nh65vqW1LmHefACrEQ333gJQh1nvQHbVDFDnLa2+8FZSjM2qTuWeFW/DvrAblPlDmneOq4+9ENY9v\nohNt41kSwEgN57TP9/aK2mduejkov3hbiK2/wJw6jnip//CXvhCU/48v/sugPFFQvXz0uZ8KyhHk\nlpzxuU/O/JPYUQd5jYO1VWifA2MxHlefyWJ/4j7uuIC8tAMsHPfdEliLFoFmOHRIe7CPP3Y2KE9P\nql83kCfVq0C/VvUeK2tCWGxs6HjukPrYAjCmacQvJmjPnNMztFrKD4ee3ru2o+tk0nqecwtam754\nSX1yt654UPGVh58+rnvl57XmuPyq0H1PP6Q48ezxR4Ny5Pwo9lRbinedU8oZuhhnDvYiClzbgVTJ\n3ZQI1qVJ5J8J4pmRT/WRc3aBz4kAZexif9XJYY9+DPXEk8K6M9cfYG01AD62iYV3s4n2QOza2VA8\nrlS0N1ctKzbXaorrXL80cP0+9mC6wDQGqCnsT+7s6NoXLgsDmMKe6qF5zQEtfMtJ9HSdbfTfJ0vq\n4wWgVIiyagArN4f5Lw9USyyp33J9HsLvdlRuNDQOdnZ1L+5bHDuiNVq7s3+fZBz64FNPBOVvvqmc\noIg1K7ZLnT72kGtV4CenRvXjD1RP3LXkXjUR8aE9ZvQzD3GTx93Q3vP+v6VCtGp3/72Dcdf+OzeO\nc+3Ka0H54iX17aee+VxQPnRIqCnmk2zfTAbcduqA+nJDe9YS1/LP7s3TUaDj3n0Dc+hA3xoTeR2f\nrCteRNvqX0989CM6Z0bfOt+9IERp9abyMM9XHGlj37UCzGASeUISa+kG/gigmNU5/L5R7imW78aA\nX+toDLV6ehcXqObOuubFH7z8raC8tPjrOj9U9w7K2A/FXgv3tlJ7c93M9HRw7PgxoRNX1/Q9+fZt\nfUO+t6ocdXdX+8o3b97E+fpG9f+y955Bst3neec5p3OePHdmbgZuAC4uAIIAKTGLBpOYFMpKpKy1\n12Xvqna3astbW1JtrT/YtR+2Vlu1WyqXZZVsqcpaU7Ili7IoipIgCSBNMAFEDhe4OU3unPt0n/0w\nM+f5HaHbvNeSoJb9Pl/wou+ZE/7h/YfT/fwywJJlgPHkWBgZO5xIZ1dIBNxdLujN6cZkMplMJpPJ\nZDKZTCaTyWQymUwmk8lkMplMJpPJZLpLva1ONxV8y7A1r2+Nrp3ULxWKb+j4akvfrorN6xuTyaS+\nobQ50Le0/LK+pbU2q18PjPBNxyK+AbxU4Lfl9A2se2I6/6XdS47jOE4NvwIs4Vekizn9gpC/AG92\n9MumjTp+icivKed0fAbfavaS+EYVvujOX82m8I0x3Loz8vCtZlRvGufsj1QPhZi+/ebFVTYJuJw4\n+JZ1ChcbXdXxnV1da6aobyS6Ab4t6ugbpX6gb/vG8AvnVBG/Yt3/vmS8oW+85vCrMi8PJ4BlfeN8\nBs4gr76ubydGHC8i31rTx3Qk4S/34/iFXOT4yNdeeR7U8zQKbXWEb0p7cAByMvgGMNo8yy6Bdnto\nSd+mTuLXh5Wavvn86hv6FcvhwythfCqPtod6ddDvRq29/p0t6NhDx3WONn4hxeJPsM3g28gxODs5\n+IVrEr+uKSFHxNAekvjVejqNX7Pj1ygd/MJ6iG/s1pAPWDZZ3NupeX0LlI4LXbjI8FvAPhyg+MsX\nPmPH17V8/Mp2MMAvUvAL8Z0dxb39euC3vzs9tQ86b/H6dFzh8b0BHE98HOPRJUvPl6EDDp47n0Wc\nUy5jX+ezTqPoQMKxashfNuH5PfyizPP4ayX2V7QB9HV3gjPJcXyr2Buq3ZavyAFns6b7eXR27xcP\nVXzjO7YBp5ghHXLwTKiLHnINfgznwNDDCZBH4njuBL7l3UVCLuR1P5Wm+koS+TsHl4nZgvpHlm4Z\nuG7AXzHiJxS9vp5rHt/Q5je+lw/pVxwf/ls/EMYvPfP1MN7Zxa8vVnWe0qK+Hf/o978vjO8984jj\nOH/OcQZNfDjS/fIXGknkgsi30p3x4uf8tVD0R+x0HplwJv7QP/a2TjnvWj04uDgBn9kb/zn/GP0p\nhV8XJ+FmFvjK062y+lkPlx1hvpXJK6c163DGSWuMLC7v/RIpUdAcqNfCryZuyyUymdJY5cNRo1ZX\nfx3iV1lp/BqAjodd/Ipq0NO1hjhnLKXc4eT0yyyvr+NTyN/3zupvFwpwWeKvCvATGrpd0eipj0lz\n+YbixUBlv4tfS3RuyaUmWdB8fumwHKtSKY3HFfztqL+XY4pp/CqL4x8cFPBj5YjLSr2B3BTnLw5V\nVwEcF4ZwTqIRA8c5/poig+SahFtKqzftTjeYxKEN8FfcI7Q3OlAmUXalCb/Q7MKRJYZfBMUx72D+\njPNXRvgVsePAOWY/B/KXfHTa8Olog8prtvkre7gu4pc5sZGOuWdVfb2K9VEHz7SyrF8oLs5rbt5u\nYa6InJ1JK089+o7zYXz8sPpuo66+G+CnUAFcp7bx61C6D2bg/jWHXznxl6W1hspyhFY8xKSgjeNj\nVf266cBVcR6/yPYw+Gzhvqq4Tj6LX/Ni/tXD2iOFfYOVFa05+KvRXE511cTcn65LCdQ5f90ei7E9\nTaHoEMNf3zMB3YkDTuR4nD/iaDPhc16X7jbY24i41xzkCW/CPU66L85jIk6S4+fRvN/Fsxo/PvVp\nuQG8eOX1MK53+TtQk+nu9NIN/eqT+09Pvaz51qV15bp5OP9lsQYoJDVWVJvKh62IY56OL8HVMYAL\nmQ9n6RLmlzeqmtRWOns5+yc/IicVB047f/LipTDe3ta9Z1LKuzEP4xbGhvklje+drn5N//XvPhvG\nI0/j3w5+fX/p9iY+x7jCfQuMN70+XE/p7Ik1VxyT1EFDrgXNPvc/df+NXY0PmYzGorUF1dsnz58J\n40fOygVkiPqno6U/wF7L/v0nUPcNzBPWt1VmsWWNZ95AiS0RaE+Xudf3df021sNfuQo3oVXlxOKS\n7uFr13T8oKZ7v1hTu/j+49qXmEbdhNPUGtxZvvaKXE/KHcwtMW7QaTQJRxEvMo7CsRhzEw/tk/s7\nxRnNfR5//PEwHvThPj186/gTwJuAa/oE9jvScBWcn1edZtFmuTfVbKvfrB6WdWkOc+fBSPe1u6bj\n0wXNsY4e0bNev3Y1jDNwvenM6n7icLyMJXVvm9+v/Re6Xy6tYj63q/dJZz4rB6hH4aKytKhc4sI9\nqrRwPIwdvOtaOqQ+lXjw4f2b1KEDuk+j7De2lRc2Lyq3F3K6fgrve0b42y7qm672I+QFH04i8YTK\nslxTm+b+bbcDF54p1G/86j8L41ZzvItNpwO6BMqILsKRPViWEdadcezH8hXQTEl13UOO5d5bCvv8\nhf01A8kP1arut4/6ygS65km4/V+8dFXPhH2FW2W1wXYPTi1D3Vc+qX7/4FHljhW8T7vcVNv4+nW9\nk3Vd5G/MAfpsb9gHHg64lgYtIa+xpU0HKLi2cq86hz2pblv9OJWWYzHXozG49c0taC3b23eVSnCN\nTScyjG0+3f8iSwzk8IjTjY7hXvxEdxtSESbszUZcyKfcL5OO+v5IY/t3vv0nYdxq65gPfkBjVYr7\npXjOPBxoCgWQbCYQFCIFhs/p7N5oqP1s7+x9J2EA15P5FfWz5cPHw5hzwkVcdK6ktvT5z/9gGJ87\nL3emNy+8Fsa/8H/9om4xLuefAM7ZcyO8+8K+7862jslg3j2AC165oz5UwZ7HYFvfvwiw9+VmMTCh\noQ+QB//oz34/jD/+uByKSkV+F2L8GnfEfbzIMXvXSoM4wrlHqaQx78hh9fNqVWPk+rrWRTdvK09t\nbNANR2Pbzo7mvaMIUWHsrUd6XNTJJ/GWY/9TMqcbk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplM\nJpPpLmVfujGZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWQyme5Sb6vXfx92YVd2XwrjmQXZ+60d\nlT1b/4YskIpFWYiu5BXHO7Ig6jdkEdToy/6p78kYKAE7wJUkbAIHskbaachisDjasw7yPNke3erI\ntu1aXZZN9QbsPYHISGVhNw8na9qU0RrQgz1UCnZPC8AmubC668Iy3nF1/Ai2R4W0Pu/D0pH2tAHs\n9+EE58y5QmT0r6gcGhuwu4TNoRdxW4LNGKzyGgPYiMOCDi6HTmf/PDlaPLdpLY97B0IoPqPyq3R1\nHZ++YkRB4R6JJSKmI8LUcMbb8rtADvn9wdhjpkUu7MKJtwl6asMubAgdtEmmDQ82i7Oz6rspoB8G\naEwXr6pvrbwiS+GFeVkbzh8SViVAJxnu277FYVO/du+pMO4B1bS1rtwRgy25C7tM4ouIDxvSHhHf\nS+z1iFZSmfVhU9nt6boO+qgDm+RWW8f30E5iaM8zpJYBG9QnEgF2hridiG1tLK76HBBBE9BuUvdQ\nBfJkt9LE8Xt/HEG0EYsEBE8X9Z0AXoP2nKnk+H7cAT7Lb+r8eVhbD9FJW7A09mBbS3tDYpemUbRw\nZY6M4IBgUTkCoiLu0aIYmDOPFtAqI9pYJmAd/rnP/90wvvCq0BL/7//5T8M4GKp/VTb22nkPVoxZ\njE9x2PV30fACsvkQx9GWaVPd7wHxwDYDfAcxa0RyddFfiX9696MPh3H5lmz/Z9K6n1pLz0qc5VxJ\ntpY76B9ra7KhnJ0X1oO20dmCLJa3rsva8L0/IBvK+x5+VPcDC+cjR06GcWrfgpFtnJbUnD/E47Au\nRt0Tf8K2RW9FHh9BJE3wX6RtdQRfxfOPphutwL7C9E0s4bCrHNhvqp0kYHtLRBPzq5fiHE51FgBl\nQmyZC7vbWEKf15Gn4/u4tEJaY0x2URac7RZQUL6s+zuYjzfatAFW/aaLGGM6QCoCe9XGHHg4VF3n\ngIQdDa6EcQIYm0JJ51mZ1f2kU7qHVgM5BnPdGGzuB7Drv7GtPDiX1T0jPTl1PEu1q7Ic7KhP92BB\nPjMnC+RRTTamS9k9e9MZnNuHbW4mrfvNFHSP1brKNQmkYi4Om2vOSRxdIOGhrkbo90CrBGivRNqM\nkPP7PhYjUynmEOR+fN5uyY66CUt3WnvnYIvPvFT3dDwRgrSObXWAQISdfRI4MQ/3djA34ZqCdv3E\nitGamvbVHfT/GpCpswVdP417mS1qTVZD28sXtL7NwEI7HgcmC8yzlUMan7je7sNaOIrbUUE1auor\neeBh+5irDDGXZj2kMG/LoK46KPs8ME4p5NA6cIAH+APWXxeIgwbaCi2kW8CKpDDnyuRUBsePKp+y\nzOrIfTeAndvd0ToggTyVA5al09bY0e1P97jowKI9gmKKYKEQE0c1ESl1B9inied3xx/Pyj8YwCci\npca35Ug8yeCdmzdo1wmgjx//vNam3/mO5nu/91WNwf3hBC9rk2mCiFrgXLzXAzKoItv1M/edCOM0\nxqUu1lZl5FoHOTg21Jzs04/KIn+7rpz56kXZ4nvYM8thr3V5cW+P99VXhEmJY612z7LWZ6cWlSOf\nfObFMG7UtF80m1sL48Ul7Rc99eWnwvjL335O118+rmutaq1WAnrzyhXl70pZuTkItGdFdIbD/cEA\neNGY4h72YweYq/VdlfE3gAc5t6p55v2nhJRaxNo+m0ISBXKyBZQV97B2N/fwslcuvhl+duWW8E85\n4Lx87g/ktP50iQVAzkoi9xFdNspqnVwH+qdS0bVWsY/nYx1VBAJkCBzMNOq//Yc/+7ZdK4IgQX0U\ngE/9+Z//+TA+fFjtnH97kD84L+UeymRMNPbpuIfJ9TDaXQM4i5V5zVGXcmrLm1hvbdy6HsaJpNpn\nt6bPX/yG8lQElYw9R6IwiOTaRDuvVdVXVje0nttpKm8+8uADYXz2vFBTa8t6H1LZ1bi+UEIODZTP\niBg+KMNEfPxeVg/7q1evXA3jm0CJFAtYi3Y1p01mVcb1lsr19q4QrKmR5qWDodpNpazyaCCPLC+o\n79bx+ec+91POtOml54UT5N5pEpgSYkSIL8kA7zTEvnYfe8dEFqexH8Y2n80pp3G/u9tTHnMjiLm9\nsW6nqvqtYN9hhDFyCxjAJ/70z/BMWF9ifK+09bc722rjZ49pXfjYg0JoHz52PIydpNrSL3/51TDu\ndDQGp4GtdPHOYQQMFve4iJfiNuAIi4IUxg3ifZtoz+2m2nBv+5Uwvn1V97P2zkfC+OTJe8L4xEmN\nqa+9stdeYo7q0os9H8YD5DvubRJnxDrm+6TInueQe6rOfxXiXLReUf7+7nPqo4sLwjE+9NA79cfc\nO8byKJ1RmywV1YY97m/549dT7Pesg3ZXOfva9ZuO40QRdAXk2plZXZOYQ+4ZHzuqeekKxgnuuQwx\npz55r46vtzT23Lip9lZrKR+0NjUu9vHukPcQ3Z6fsK6dtMbl3+JE7K+vvqE+9+zz3wrjxz/0ybHn\n5P0Q9z1CzuD7qnHn4L/H49oLImZsaVHlffKk3pfs7CpfXLmifejnnv9uGFeqyq2ex3fdzHHj723i\nfGmCzOnGZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZTCaTyWS6S9mXbkwmk8lkMplMJpPJZDKZTCaT\nyWQymUwmk8lkMplMprvU24uX8mWHtF2R5d1GRpZJJ48cD+NrN14LYy8jm6cMbEtT+N5Qpyo7w+s7\nV8N4bkHomrWYLKL8lizdqj1YzMOe8MLuniXhJmwHaaE/pOUjLKRo10h0VA82ZcGIVsewK0oDDQIr\n+T4s9A8BZbDZgg0bECAJoAmqA30+dHTOuKv7nEuqnBZ9WVMONmWZN5LLm5NK6jxtYIlqHVk1xYAD\n6KJcu8DIDNpAOsBGarhfzyNY948CWXNV8RzbA52v2dZN3qjKfnECFSOiCG4rgsLAH6OuAqAYfDB+\nBp3pxkvRMj5GW1jYKcaAugkS4xE1HtpYaUZ2lUlgp3xYvuVhzb67JbvKl158I4yPVNXXCrC5z+xj\nVYrzsu5M5mFnXFQ7nYdl5whW8gHs7mnrOYTNHK0pPdiXBbCdJHpkNAGr0oG1vTNUebRhzUp8jot4\nBji4VpPYN10rBWyID6s2F9aleTA1iGIajcZbwhKLQOTWQblFXOBwzUZH56g0FWeABMoiHgwUZyLW\nycDj4VLsl7tALvSQi1MZ3DsqiPiHaRRtTunjR+REzCOWQs9D/FoHbTgHq1k3YuMHG1BYLuZyaufH\nTshK13c1ziwuox/t2572gEV46LxsPDNxff7sC7L0Iw6LFrsx4ghcYsvGt8d4Ss935tTxMH79TSDr\nDska98OPfzSMP/Np4Zy++Ku/EMb5GFBsqIdzjz0exq3Kehi3+1d1D2dlW+q4rB99vLyq++zDvvX+\nd7w7jB96p2IP9RZBIB5gpSL2j+AiRuwi6e1Iu8bx37eOWCWSCjVhXHQjFtU85/jBNjblfTGZIYpG\nn/vkhZKQBv/TAOPJoIv5Fua9A9iuurA0jgMVFnSV34gSSGV1fBf5tlHfm+PEMhpP55eFFMguCt/a\nruuYNvBorNNiSeMo7a6Hvq6fKmA86wAhg3JqlzUPTGZ0zplFzTPjvtAAiaFsQLvEHdCqFE2MmKF6\nT+2q09dBpw9jLp3StVxf99DbFD7A8VE/tZs6pnMjjNcW9Ixzpb3ygfuxk1bXdjIZ5AIgWmp11fdi\nltawwAUmxttG9wY6D63LfZwfBJ4IGmCENUfgTvdvLmJ4HiI8B2hjPSDSdmAfPyC2kxbQQDkm42wz\nqoM21yaYu6Zgg50EB5fnPBizo3luvP0sUVPMx40W1jJAH83kNU/gHDIL9GYdeYFz/FvrauPEEi4v\nCYV4/r57w3hzXWtyliWKzMkRE4d5ehvrr8qG1tgJdIwk8LPs6y5mfbRwn4W1c6T+iQ7dL+a4w7kS\n6hudIg8kQw621URH8fNMSve7C6v26zduhXEN8/Qs5l/FknINx5FyXXsgXYwLUym250nIpwhGahIK\niuf0vvfn8QnxpGuNQ19NwmEx/03Apk1ESkVy53gL6kNntU79735OSNNK7Tth/LUXtNYdGGrKdAeK\n4l2JLgDKAfO5Kxuahx3LA2/Kph1HbsRcLQtE/QdPCz94fVPttrauY5pd3U87qXuY28/3ecyvl4Hq\n3e0qTxd7OkduBPQf8A0Dn7lW/SyXlvU8EaKZmuZv1azGjPWUjveJixpoDNvc0vp1CJwT94OY13uw\np89mMc7gWZJprau/dlVjbdK/GsazOR2zclrz+cqG5qWVHdV5ujgfxh7mPM3a3jhTBQKyAfTy01/9\nuu4xrzr5xGeEC7jnpK7vYc3zfY6e+ylgE9YRX7utNfO1LY2dy3OqtxuYt+RKaosXbgj5NY2KrAv/\nCnQn6ALeQxV7p8wTWbwvODh+Z0d5gevMyP4nxkViPfl5AtidyF4iUdXz6vdsP7msPl+YBe6orvXz\n9evqu0t55aZLb9wOYwd7iNtllUERqNM6EGalkvo991kczI3Lu1orX3xDWDwHc84e3nuMkD+4ng6c\nt75HCPgeKLI+G78/nc1r/st3Tm5GZcbXSUxl+bjKuLmLtWZf+aC8qbKcA1IqiiSZ7vViFnuYfI9E\npAnXMi4/73EPf3wdYFvBaaKNcR8zUJNxSrPKb9s3hfZbBH6+OLeXV2+uC8HTiyD1MNZj/tlEvkyj\nvzoYk65U1QievKC18fK8ymkW+6jdkcb351/Q/X73qvKEizEsFmCPHWXGNkN01IhbkcQVxtR3PZe5\nBJhGrI/mZ9V3P/8TPxzGKawXl5c0Fq6tqLy5X57efx+xibXx0Gf+UtkMcb+xCXvYyTifY9JeKBHL\nfxGk8HRzqioYh15/WfOLqzdU1h/4wI+H8dKS+oo7Ye+YGGrmb+7vDIAwI5IoFnkfhHfMGDu3t/bm\nYay60iHl3fk55cX121r3c+/98IqOyRcV+3gHWSlrT6RcUzldX1cOPn2/1otPPPHHYdzH3hQ7VBCM\nbw/uhLUs9+2DCS/G+THrpNNRDvjjJ78cxu9/z0fCmJjuAHmC4yLr5ADf5k5YqgfsKh7X20DT4XsT\n8YT2XIhWd9H/1jc07263NY4PJ+B7WQbcO73bnjjdo6jJZDKZTCaTyWQymUwmk8lkMplMJpPJZDKZ\nTCaTyTSFsi/dmEwmk8lkMplMJpPJZDKZTCaTyWQymUwmk8lkMplMd6m3FS8VwKq725IlE7EY+RlZ\neqWKsutKw0ppsyyM1DbsTGeysDIb6pxLCdnr5WC7V+vIC+5KV9b21Zripr9n3Z/EuRuwkIu5Oncq\nAxRAGpZXxNIAjTUMYIWFmnBTOp4WS5W2rM9qsHiiwRFxVEFSZZOADdPh3GoYzztHdT8VWUK1dvW3\nQ9TbYAjrdVhmdduyxOtWdMwIz+5GsEx6sH5X5dBFW6jsW+3vpGiJCwtdWDQ2W4rrZcXlTeGlJriW\n/TkbLSkWsPz0OQzNHR82rX5H5THyJ1xsStQBwiLBRgYkkkfEQxpPPcFevFiS1WWhoDqbgU1uHrg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Uwmk8lkMplMJpPJZDKZTCaTyWQyme5SbyteKgHvogFsnWp1WQCWi8LCtGuw/xkthHFjXXZn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\nSZIkSZIkaUb/DRR+6xuxJSPZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "duieZT-8jp3S",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "my_image = \"lion.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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zuugc3mrj7Qh",
        "colab_type": "code",
        "outputId": "f1a63e75-05d8-4c73-c5fe-46f48ebead47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "cell_type": "code",
      "source": [
        "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_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.\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/home/pacific/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n",
            "`imread` is deprecated in SciPy 1.0.0.\n",
            "Use ``matplotlib.pyplot.imread`` instead.\n",
            "  \n",
            "/home/pacific/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imresize` is deprecated!\n",
            "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
            "Use ``skimage.transform.resize`` instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 1.0\n",
            "y = 1.0, your L-layer model predicts a \"cat\" picture.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "IOPub data rate exceeded.\n",
            "The notebook server will temporarily stop sending output\n",
            "to the client in order to avoid crashing it.\n",
            "To change this limit, set the config variable\n",
            "`--NotebookApp.iopub_data_rate_limit`.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "qNTRVRd5kASy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}