C2_W1_Initialization.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "17yD0ubaHADXGI51K2_2AJkf0S30j0hII",
      "authorship_tag": "ABX9TyPHn+v5CZL2JNJzhZN1txCF"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U--1feULH2hD",
        "outputId": "8e04097b-9f7a-48c6-d0f4-2af05b505011"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The autoreload extension is already loaded. To reload it, use:\n",
            "  %reload_ext autoreload\n"
          ]
        }
      ],
      "source": [
        "# Package imports\n",
        "import numpy as np\n",
        "import copy\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "import sklearn.datasets\n",
        "import sklearn.linear_model\n",
        "%matplotlib inline\n",
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def load_planar_dataset():\n",
        "    np.random.seed(1)\n",
        "    m = 400 # number of examples\n",
        "    N = int(m/2) # number of points per class\n",
        "    D = 2 # dimensionality\n",
        "    X = np.zeros((m,D)) # data matrix where each row is a single example\n",
        "    Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue)\n",
        "    a = 4 # maximum ray of the flower\n",
        "\n",
        "    for j in range(2):\n",
        "        ix = range(N*j,N*(j+1))\n",
        "        t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta\n",
        "        r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius\n",
        "        X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]\n",
        "        Y[ix] = j\n",
        "        \n",
        "    X = X.T\n",
        "    Y = Y.T\n",
        "\n",
        "    return X, Y"
      ],
      "metadata": {
        "id": "OZxQaeS1aGIH"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X, Y = load_planar_dataset()"
      ],
      "metadata": {
        "id": "-_ajpUdwZh2C"
      },
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_dataset():\n",
        "    np.random.seed(1)\n",
        "    train_X, train_Y = sklearn.datasets.make_circles(n_samples=300, noise=.05)\n",
        "    np.random.seed(2)\n",
        "    test_X, test_Y = sklearn.datasets.make_circles(n_samples=100, noise=.05)\n",
        "    # Visualize the data\n",
        "    plt.scatter(train_X[:, 0], train_X[:, 1], c=train_Y, s=40, cmap=plt.cm.Spectral);\n",
        "    train_X = train_X.T\n",
        "    train_Y = train_Y.reshape((1, train_Y.shape[0]))\n",
        "    test_X = test_X.T\n",
        "    test_Y = test_Y.reshape((1, test_Y.shape[0]))\n",
        "    return train_X, train_Y, test_X, test_Y"
      ],
      "metadata": {
        "id": "jXr57iW8abNy"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X, train_Y, test_X, test_Y = load_dataset()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "hhn1pUfkKATz",
        "outputId": "16ee6a5e-3399-42dc-e805-2feb7e9815da"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sigmoid(x):\n",
        "    \"\"\"\n",
        "    Compute the sigmoid of x\n",
        "\n",
        "    Arguments:\n",
        "    x -- A scalar or numpy array of any size.\n",
        "\n",
        "    Return:\n",
        "    s -- sigmoid(x)\n",
        "    \"\"\"\n",
        "    s = 1/(1+np.exp(-x))\n",
        "    return s\n",
        "\n",
        "def relu(x):\n",
        "    \"\"\"\n",
        "    Compute the relu of x\n",
        "\n",
        "    Arguments:\n",
        "    x -- A scalar or numpy array of any size.\n",
        "\n",
        "    Return:\n",
        "    s -- relu(x)\n",
        "    \"\"\"\n",
        "    s = np.maximum(0,x)\n",
        "    \n",
        "    return s\n",
        "\n",
        "def forward_propagation(X, parameters):\n",
        "    \"\"\"\n",
        "    Implements the forward propagation (and computes the loss) presented in Figure 2.\n",
        "    \n",
        "    Arguments:\n",
        "    X -- input dataset, of shape (input size, number of examples)\n",
        "    Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat)\n",
        "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\":\n",
        "                    W1 -- weight matrix of shape ()\n",
        "                    b1 -- bias vector of shape ()\n",
        "                    W2 -- weight matrix of shape ()\n",
        "                    b2 -- bias vector of shape ()\n",
        "                    W3 -- weight matrix of shape ()\n",
        "                    b3 -- bias vector of shape ()\n",
        "    \n",
        "    Returns:\n",
        "    loss -- the loss function (vanilla logistic loss)\n",
        "    \"\"\"\n",
        "        \n",
        "    # retrieve parameters\n",
        "    W1 = parameters[\"W1\"]\n",
        "    b1 = parameters[\"b1\"]\n",
        "    W2 = parameters[\"W2\"]\n",
        "    b2 = parameters[\"b2\"]\n",
        "    W3 = parameters[\"W3\"]\n",
        "    b3 = parameters[\"b3\"]\n",
        "    \n",
        "    # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
        "    z1 = np.dot(W1, X) + b1\n",
        "    a1 = relu(z1)\n",
        "    z2 = np.dot(W2, a1) + b2\n",
        "    a2 = relu(z2)\n",
        "    z3 = np.dot(W3, a2) + b3\n",
        "    a3 = sigmoid(z3)\n",
        "    \n",
        "    cache = (z1, a1, W1, b1, z2, a2, W2, b2, z3, a3, W3, b3)\n",
        "    \n",
        "    return a3, cache\n",
        "\n",
        "def backward_propagation(X, Y, cache):\n",
        "    \"\"\"\n",
        "    Implement the backward propagation presented in figure 2.\n",
        "    \n",
        "    Arguments:\n",
        "    X -- input dataset, of shape (input size, number of examples)\n",
        "    Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat)\n",
        "    cache -- cache output from forward_propagation()\n",
        "    \n",
        "    Returns:\n",
        "    gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables\n",
        "    \"\"\"\n",
        "    m = X.shape[1]\n",
        "    (z1, a1, W1, b1, z2, a2, W2, b2, z3, a3, W3, b3) = cache\n",
        "    \n",
        "    dz3 = 1./m * (a3 - Y)\n",
        "    dW3 = np.dot(dz3, a2.T)\n",
        "    db3 = np.sum(dz3, axis=1, keepdims = True)\n",
        "    \n",
        "    da2 = np.dot(W3.T, dz3)\n",
        "    dz2 = np.multiply(da2, np.int64(a2 > 0))\n",
        "    dW2 = np.dot(dz2, a1.T)\n",
        "    db2 = np.sum(dz2, axis=1, keepdims = True)\n",
        "    \n",
        "    da1 = np.dot(W2.T, dz2)\n",
        "    dz1 = np.multiply(da1, np.int64(a1 > 0))\n",
        "    dW1 = np.dot(dz1, X.T)\n",
        "    db1 = np.sum(dz1, axis=1, keepdims = True)\n",
        "    \n",
        "    gradients = {\"dz3\": dz3, \"dW3\": dW3, \"db3\": db3,\n",
        "                 \"da2\": da2, \"dz2\": dz2, \"dW2\": dW2, \"db2\": db2,\n",
        "                 \"da1\": da1, \"dz1\": dz1, \"dW1\": dW1, \"db1\": db1}\n",
        "    \n",
        "    return gradients\n",
        "\n",
        "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 n_model_backward\n",
        "    \n",
        "    Returns:\n",
        "    parameters -- python dictionary containing your updated parameters \n",
        "                  parameters['W' + str(i)] = ... \n",
        "                  parameters['b' + str(i)] = ...\n",
        "    \"\"\"\n",
        "    \n",
        "    L = len(parameters) // 2 # number of layers in the neural networks\n",
        "\n",
        "    # Update rule for each parameter\n",
        "    for k in range(L):\n",
        "        parameters[\"W\" + str(k+1)] = parameters[\"W\" + str(k+1)] - learning_rate * grads[\"dW\" + str(k+1)]\n",
        "        parameters[\"b\" + str(k+1)] = parameters[\"b\" + str(k+1)] - learning_rate * grads[\"db\" + str(k+1)]\n",
        "        \n",
        "    return parameters\n",
        "\n",
        "def compute_loss(a3, Y):\n",
        "    \n",
        "    \"\"\"\n",
        "    Implement the loss function\n",
        "    \n",
        "    Arguments:\n",
        "    a3 -- post-activation, output of forward propagation\n",
        "    Y -- \"true\" labels vector, same shape as a3\n",
        "    \n",
        "    Returns:\n",
        "    loss - value of the loss function\n",
        "    \"\"\"\n",
        "    \n",
        "    m = Y.shape[1]\n",
        "    logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
        "    loss = 1./m * np.nansum(logprobs)\n",
        "    \n",
        "    return loss\n",
        "\n",
        "def predict(X, y, parameters):\n",
        "    \"\"\"\n",
        "    This function is used to predict the results of a  n-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",
        "    p = np.zeros((1,m), dtype = np.int32)\n",
        "    \n",
        "    # Forward propagation\n",
        "    a3, caches = forward_propagation(X, parameters)\n",
        "    \n",
        "    # convert probas to 0/1 predictions\n",
        "    for i in range(0, a3.shape[1]):\n",
        "        if a3[0,i] > 0.5:\n",
        "            p[0,i] = 1\n",
        "        else:\n",
        "            p[0,i] = 0\n",
        "\n",
        "    # print results\n",
        "    print(\"Accuracy: \"  + str(np.mean((p[0,:] == y[0,:]))))\n",
        "    \n",
        "    return p\n",
        "\n",
        "def plot_decision_boundary(model, X, y):\n",
        "    # Set min and max values and give it some padding\n",
        "    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1\n",
        "    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1\n",
        "    h = 0.01\n",
        "    # Generate a grid of points with distance h between them\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
        "    # Predict the function value for the whole grid\n",
        "    Z = model(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "    # Plot the contour and training examples\n",
        "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
        "    plt.ylabel('x2')\n",
        "    plt.xlabel('x1')\n",
        "    plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)\n",
        "    plt.show()\n",
        "    \n",
        "def predict_dec(parameters, X):\n",
        "    \"\"\"\n",
        "    Used for plotting decision boundary.\n",
        "    \n",
        "    Arguments:\n",
        "    parameters -- python dictionary containing your parameters \n",
        "    X -- input data of size (m, K)\n",
        "    \n",
        "    Returns\n",
        "    predictions -- vector of predictions of our model (red: 0 / blue: 1)\n",
        "    \"\"\"\n",
        "    \n",
        "    # Predict using forward propagation and a classification threshold of 0.5\n",
        "    a3, cache = forward_propagation(X, parameters)\n",
        "    predictions = (a3>0.5)\n",
        "    return predictions"
      ],
      "metadata": {
        "id": "kYKdCXlySIAF"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def model(X, Y, learning_rate = 0.01, num_iterations = 15000, print_cost = True, initialization = \"he\"):\n",
        "    \"\"\"\n",
        "    Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
        "    \n",
        "    Arguments:\n",
        "    X -- input data, of shape (2, number of examples)\n",
        "    Y -- true \"label\" vector (containing 0 for red dots; 1 for blue dots), of shape (1, number of examples)\n",
        "    learning_rate -- learning rate for gradient descent \n",
        "    num_iterations -- number of iterations to run gradient descent\n",
        "    print_cost -- if True, print the cost every 1000 iterations\n",
        "    initialization -- flag to choose which initialization to use (\"zeros\",\"random\" or \"he\")\n",
        "    \n",
        "    Returns:\n",
        "    parameters -- parameters learnt by the model\n",
        "    \"\"\"\n",
        "        \n",
        "    grads = {}\n",
        "    costs = [] # to keep track of the loss\n",
        "    m = X.shape[1] # number of examples\n",
        "    layers_dims = [X.shape[0], 10, 5, 1]\n",
        "    \n",
        "    # Initialize parameters dictionary.\n",
        "    if initialization == \"zeros\":\n",
        "        parameters = initialize_parameters_zeros(layers_dims)\n",
        "    elif initialization == \"random\":\n",
        "        parameters = initialize_parameters_random(layers_dims)\n",
        "    elif initialization == \"he\":\n",
        "        parameters = initialize_parameters_he(layers_dims)\n",
        "\n",
        "    # Loop (gradient descent)\n",
        "\n",
        "    for i in range(num_iterations):\n",
        "\n",
        "        # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
        "        a3, cache = forward_propagation(X, parameters)\n",
        "        \n",
        "        # Loss\n",
        "        cost = compute_loss(a3, Y)\n",
        "\n",
        "        # Backward propagation.\n",
        "        grads = backward_propagation(X, Y, cache)\n",
        "        \n",
        "        # Update parameters.\n",
        "        parameters = update_parameters(parameters, grads, learning_rate)\n",
        "        \n",
        "        # Print the loss every 1000 iterations\n",
        "        if print_cost and i % 1000 == 0:\n",
        "            print(\"Cost after iteration {}: {}\".format(i, cost))\n",
        "            costs.append(cost)\n",
        "            \n",
        "    # plot the loss\n",
        "    plt.plot(costs)\n",
        "    plt.ylabel('cost')\n",
        "    plt.xlabel('iterations (per hundreds)')\n",
        "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
        "    plt.show()\n",
        "    \n",
        "    return parameters"
      ],
      "metadata": {
        "id": "YUePEzVeTKKa"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# GRADED FUNCTION: initialize_parameters_zeros \n",
        "\n",
        "def initialize_parameters_zeros(layers_dims):\n",
        "    \"\"\"\n",
        "    Arguments:\n",
        "    layer_dims -- python array (list) containing the size of each layer.\n",
        "    \n",
        "    Returns:\n",
        "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
        "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
        "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
        "                    ...\n",
        "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
        "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
        "    \"\"\"\n",
        "    \n",
        "    parameters = {}\n",
        "    L = len(layers_dims)            # number of layers in the network\n",
        "    \n",
        "    for l in range(1, L):\n",
        "        #(≈ 2 lines of code)\n",
        "        # parameters['W' + str(l)] = \n",
        "        # parameters['b' + str(l)] = \n",
        "        # YOUR CODE STARTS HERE\n",
        "        parameters['W' + str(l)] = np.zeros((layers_dims[l], layers_dims[l-1]))\n",
        "        parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n",
        "        \n",
        "        # YOUR CODE ENDS HERE\n",
        "    return parameters"
      ],
      "metadata": {
        "id": "DV0QgoU2JpOD"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = initialize_parameters_zeros([3, 2, 1])\n",
        "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
        "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
        "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
        "print(\"b2 = \" + str(parameters[\"b2\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwCHJLc8Jt6v",
        "outputId": "fb4ed23a-ae01-454c-cd9d-2a165ddca4d6"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W1 = [[0. 0. 0.]\n",
            " [0. 0. 0.]]\n",
            "b1 = [[0.]\n",
            " [0.]]\n",
            "W2 = [[0. 0.]]\n",
            "b2 = [[0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = model(train_X, train_Y, initialization = \"zeros\")\n",
        "print (\"On the train set:\")\n",
        "predictions_train = predict(train_X, train_Y, parameters)\n",
        "print (\"On the test set:\")\n",
        "predictions_test = predict(test_X, test_Y, parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "DGUNUXCJTao2",
        "outputId": "097f252a-a9a3-4ad4-b2b9-ac5a4efce0de"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost after iteration 0: 0.6931471805599453\n",
            "Cost after iteration 1000: 0.6931471805599453\n",
            "Cost after iteration 2000: 0.6931471805599453\n",
            "Cost after iteration 3000: 0.6931471805599453\n",
            "Cost after iteration 4000: 0.6931471805599453\n",
            "Cost after iteration 5000: 0.6931471805599453\n",
            "Cost after iteration 6000: 0.6931471805599453\n",
            "Cost after iteration 7000: 0.6931471805599453\n",
            "Cost after iteration 8000: 0.6931471805599453\n",
            "Cost after iteration 9000: 0.6931471805599453\n",
            "Cost after iteration 10000: 0.6931471805599455\n",
            "Cost after iteration 11000: 0.6931471805599453\n",
            "Cost after iteration 12000: 0.6931471805599453\n",
            "Cost after iteration 13000: 0.6931471805599453\n",
            "Cost after iteration 14000: 0.6931471805599453\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "On the train set:\n",
            "Accuracy: 0.5\n",
            "On the test set:\n",
            "Accuracy: 0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (\"predictions_train = \" + str(predictions_train))\n",
        "print (\"predictions_test = \" + str(predictions_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLjXPvPmbMyh",
        "outputId": "fdb59678-a3d8-4340-f6c1-c6542707ce67"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predictions_train = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0]]\n",
            "predictions_test = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Model with Zeros initialization\")\n",
        "axes = plt.gca()\n",
        "axes.set_xlim([-1.5,1.5])\n",
        "axes.set_ylim([-1.5,1.5])\n",
        "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zKrVP1cFbKHT",
        "outputId": "4b188962-504b-401c-9f68-a7336a1124ec"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# GRADED FUNCTION: initialize_parameters_random\n",
        "\n",
        "def initialize_parameters_random(layers_dims):\n",
        "    \"\"\"\n",
        "    Arguments:\n",
        "    layer_dims -- python array (list) containing the size of each layer.\n",
        "    \n",
        "    Returns:\n",
        "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
        "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
        "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
        "                    ...\n",
        "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
        "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
        "    \"\"\"\n",
        "    \n",
        "    np.random.seed(3)               # This seed makes sure your \"random\" numbers will be the as ours\n",
        "    parameters = {}\n",
        "    L = len(layers_dims)            # integer representing the number of layers\n",
        "    \n",
        "    for l in range(1, L):\n",
        "        #(≈ 2 lines of code)\n",
        "        # parameters['W' + str(l)] = \n",
        "        # parameters['b' + str(l)] =\n",
        "        # YOUR CODE STARTS HERE\n",
        "        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1])*10\n",
        "        parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n",
        "        # YOUR CODE ENDS HERE\n",
        "\n",
        "    return parameters"
      ],
      "metadata": {
        "id": "Vqs0Gb3YJt4S"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = initialize_parameters_random([3, 2, 1])\n",
        "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
        "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
        "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
        "print(\"b2 = \" + str(parameters[\"b2\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89aW-xYWJt07",
        "outputId": "d03bbde0-ce6b-4154-fd8c-f310eea42eb6"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W1 = [[ 17.88628473   4.36509851   0.96497468]\n",
            " [-18.63492703  -2.77388203  -3.54758979]]\n",
            "b1 = [[0.]\n",
            " [0.]]\n",
            "W2 = [[-0.82741481 -6.27000677]]\n",
            "b2 = [[0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = model(train_X, train_Y, initialization = \"random\")\n",
        "print (\"On the train set:\")\n",
        "predictions_train = predict(train_X, train_Y, parameters)\n",
        "print (\"On the test set:\")\n",
        "predictions_test = predict(test_X, test_Y, parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "hXWAs0iYb1ni",
        "outputId": "6dd1cf3e-36c3-4c01-b96b-7396c28bce4d"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-136-86a47c7085ad>:139: RuntimeWarning: divide by zero encountered in log\n",
            "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
            "<ipython-input-136-86a47c7085ad>:139: RuntimeWarning: invalid value encountered in multiply\n",
            "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost after iteration 0: inf\n",
            "Cost after iteration 1000: 0.6247924745506072\n",
            "Cost after iteration 2000: 0.5980258056061102\n",
            "Cost after iteration 3000: 0.5637539062842213\n",
            "Cost after iteration 4000: 0.5501256393526495\n",
            "Cost after iteration 5000: 0.5443826306793814\n",
            "Cost after iteration 6000: 0.5373895855049121\n",
            "Cost after iteration 7000: 0.47157999220550006\n",
            "Cost after iteration 8000: 0.39770475516243037\n",
            "Cost after iteration 9000: 0.3934560146692851\n",
            "Cost after iteration 10000: 0.3920227137490125\n",
            "Cost after iteration 11000: 0.38913700035966736\n",
            "Cost after iteration 12000: 0.3861358766546214\n",
            "Cost after iteration 13000: 0.38497629552893475\n",
            "Cost after iteration 14000: 0.38276694641706693\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "On the train set:\n",
            "Accuracy: 0.83\n",
            "On the test set:\n",
            "Accuracy: 0.86\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (predictions_train)\n",
        "print (predictions_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpGJPXVrc8xw",
        "outputId": "3dc06def-45bd-4acf-f1fc-1f17ab88e80a"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 0 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1\n",
            "  1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0\n",
            "  0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0\n",
            "  1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0\n",
            "  0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1\n",
            "  1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1\n",
            "  0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1\n",
            "  1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1\n",
            "  1 1 1 1 0 0 0 1 1 1 1 0]]\n",
            "[[1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1\n",
            "  0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0\n",
            "  1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Model with large random initialization\")\n",
        "axes = plt.gca()\n",
        "axes.set_xlim([-1.5,1.5])\n",
        "axes.set_ylim([-1.5,1.5])\n",
        "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hiWXHo5TdAhX",
        "outputId": "8fcb3bcb-119f-46c7-a50a-a33139e1503d"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted = (predictions_test).reshape(100)\n",
        "actual = test_Y.reshape(100)\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "cm = tf.math.confusion_matrix(labels=actual,predictions=predicted)\n",
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "gGJ6qQkVlM8W",
        "outputId": "9f3a63e9-4174-4728-aaee-c12968d8e7d2"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# GRADED FUNCTION: initialize_parameters_he\n",
        "import math\n",
        "def initialize_parameters_he(layers_dims):\n",
        "    \"\"\"\n",
        "    Arguments:\n",
        "    layer_dims -- python array (list) containing the size of each layer.\n",
        "    \n",
        "    Returns:\n",
        "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
        "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
        "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
        "                    ...\n",
        "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
        "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
        "    \"\"\"\n",
        "    \n",
        "    np.random.seed(3)\n",
        "    parameters = {}\n",
        "    L = len(layers_dims) - 1 # integer representing the number of layers\n",
        "     \n",
        "    for l in range(1, L + 1):\n",
        "        #(≈ 2 lines of code)\n",
        "        # parameters['W' + str(l)] = \n",
        "        # parameters['b' + str(l)] =\n",
        "        # YOUR CODE STARTS HERE\n",
        "        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1])*math.sqrt(2.0/layers_dims[l-1])\n",
        "        parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))*math.sqrt(2.0/layers_dims[l-1])\n",
        "        \n",
        "        # YOUR CODE ENDS HERE\n",
        "        \n",
        "    return parameters"
      ],
      "metadata": {
        "id": "4CwNDnB2JtwS"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = initialize_parameters_he([2, 4, 1])\n",
        "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
        "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
        "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
        "print(\"b2 = \" + str(parameters[\"b2\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-UltIYIJtaz",
        "outputId": "a061d0b3-586b-4144-d571-5451f712c241"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "W1 = [[ 1.78862847  0.43650985]\n",
            " [ 0.09649747 -1.8634927 ]\n",
            " [-0.2773882  -0.35475898]\n",
            " [-0.08274148 -0.62700068]]\n",
            "b1 = [[0.]\n",
            " [0.]\n",
            " [0.]\n",
            " [0.]]\n",
            "W2 = [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
            "b2 = [[0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parameters = model(train_X, train_Y, initialization = \"he\")\n",
        "print (\"On the train set:\")\n",
        "predictions_train = predict(train_X, train_Y, parameters)\n",
        "print (\"On the test set:\")\n",
        "predictions_test = predict(test_X, test_Y, parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "NjAkbx-vJ8bq",
        "outputId": "047e6998-014b-4258-9025-df0201e9bda5"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost after iteration 0: 0.8830537463419761\n",
            "Cost after iteration 1000: 0.6879825919728063\n",
            "Cost after iteration 2000: 0.6751286264523371\n",
            "Cost after iteration 3000: 0.6526117768893805\n",
            "Cost after iteration 4000: 0.6082958970572938\n",
            "Cost after iteration 5000: 0.5304944491717495\n",
            "Cost after iteration 6000: 0.4138645817071794\n",
            "Cost after iteration 7000: 0.3117803464844441\n",
            "Cost after iteration 8000: 0.23696215330322562\n",
            "Cost after iteration 9000: 0.1859728720920684\n",
            "Cost after iteration 10000: 0.15015556280371808\n",
            "Cost after iteration 11000: 0.12325079292273551\n",
            "Cost after iteration 12000: 0.09917746546525937\n",
            "Cost after iteration 13000: 0.08457055954024283\n",
            "Cost after iteration 14000: 0.07357895962677366\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "On the train set:\n",
            "Accuracy: 0.9933333333333333\n",
            "On the test set:\n",
            "Accuracy: 0.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title(\"Model with He initialization\")\n",
        "axes = plt.gca()\n",
        "axes.set_xlim([-1.5,1.5])\n",
        "axes.set_ylim([-1.5,1.5])\n",
        "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "LdKj3Ps1dRk4",
        "outputId": "0017978c-0609-4561-f88e-93480d57120b"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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cw12GahxyWt2JJ9NyJc7bGNZSRISlk+4gP0QEqrvJQmU4Wz6Xtzl7Pnnid11h+eReNFK9GocVk3DMVFo4eTpFOsHMNH5+IZcfFTjFSUJQ4WW/8kK8ZsDHf32d5qrHyReX+UD0PLofyxJG8XWElk2IzfsWXsyrq781cZwjh+4FJqyt+Jx5IH3ouAFKZYdiyabTUSzr4AAIw72HsWAa7kpUlWuXPVrNaDDJbm3EeQz7ExRFhHzBHuRjROpRq4ZjE2qxdGNO4YP8APm8NZUQ6fPClwe89ctfv7dAlTPf/4dUPvAUVjdAbQHb4oOf/4X8/O89L95muffPg9Of3MZO8AvVsiWiTBroTj2WVjPi4sfbRGEsEBeX3UO1quwkU5zhnsYIEsNdSaetI0IE4rdoz1Ma9ehAobC47NJtR3i+xsJEIOUKiycOdtwn8cKXB6gqv/mzQqumI8LJduClXyFc+6Gv5Dvetjz9QR8d/fjY87+MpdlrnP7kRYKUy5PPeTa12dnEXVWSVY4gsvhv/+m1vOxrf5GN9eTkziTCXm5Lt6NcveRx+lzqQGFiuD8xgsRwV+F5EY1anJORGNYbQbsVHihIbFs4dyFNuxVx+l9+IeWH51h+5BwigufFwiCVPvzN+pG3tJEwYuF6g9JLdnnu/3ofhdoOKkLgunzwZV/CO+YegrdNd22WH1Lc6ZBpB4S2ELoW2bqHEyihk+fvXvQZNCt7jnO3E5Bp+4S2RauQAkuoV9KUt9pYwwIWaOddLv92idUvfA2vfPe7UW+f32VKk9fmejDR5Ge4fzGCxHDXsLXps7Ue0EueTkSEsVyIX/3pf8Bjj1aSd7gU/7N/s8ncSpNMK3YYd7MOWycKBKmDJ835lQbplo+fyfO/P/dVpNtNrMCnVSijCLlqh1Y5c+AxAEqbLSqb7fgaest06G8niJhdi+uvNMtp5q81yDY9+jvMirB2tkRtLku6HQyuAyBwbbZOFAD41Pf9EVvFBbKtOpn2Xj0Xq1fssV++ZZLG0u3e2irIh9GvyNztKG4qjm4zDvw7DyNIDLed/uQQRXFC4XBhwlYzZHPdx/eVXM5iftHFTVl0u1EsRA55axaBcsXhhS8PyH7Vi/j893z2mKkoYUAsX6phB9Fg4k63A5YvVbl2YQadEGJshRHZpo8MjambzcfjAERhbrVJu5ieeAyAdMunvNlm/xb7P1sKlc0Wokq26e1pHQqKsnCtzvXzFTbOlHC7AalOSOBadLPxYz53vc7fvugLsMIQtW0qGyt8yl+8HzuMnflnz6fxuhFuSrjy1HhOC0Aqdfsm8ShSrl7qDhI1RcCy4ez5DO4RqgMYnn6MIDHcVrqdiKuX9qr4qsLCosPMvEt1N2D12t6bdK0aUat2OXchRaOWbMrqIwK2E0dI/Z+v7gX6vWe6MeXqHlYYjUzcAkik5GrdEXPSMFaoI1pDEipCuuXTKUyuOVXc6Rx4jGHsQCnsdkdMV/3x2l5EZaNFN+fSzrv46b3Hu7jdJl/zUNsh7EUH7C6c4JPPfQkPP/antLI5Kn/2Ffy9Jy/ywW/8K2bnnbEqxSIkJoAGgbKx6tOox+ayUsVmYdEdKYypkbK1GbC7E6AR5As2C0sOjhvXHkvqL7O14dNp75Ve6Ze4Wb3mTR1NZrg9GEFieFoJA2V91afem2RIMJtsrAekszJICNzP1UselZnkW1UEZucdFn7zFbz13Q8dnBU4AcePRrSKPpaC44/ncDheiB1EeGk7dm4fIOEEPVjSEAusaUcd2pI41vhcUNruoDsdAtdm9VwJteOIsSSNJ7Id1k5f4MGPfZiPfeqLeNfb4vCvF/z0c3ndG38Zkbi+WBTF2esLS85YJeIoUi490SUYqqnVrys2HD13/arXy9OJt6nXQpqNEMuKi1X282IWltzBPpPCtFvNiCjUG+7dYrj1GEFieNpQ3asKe/B2cXVdnWB+DwPIZq09+z1Qr8zTKpQpdnb5tdd8Jc33lA6dsCfhZWxUGJugI2Hkrd4KIhav1nG7wcCZ0Cq45BqxeSu5pqTQyR0cDdYqpsg0/UPLTEQCuws57DDC2WwnaiUQX4fjhcyst9ju+UasSaViRHji2Q/z0Zd8+mDRY49WeOxV38r7XvPHqCof/Ia/QiaY5hq1cKz4ZT96rtWMyBdsvG40IkQG1xMxopnubodEEXs5OgfcNqYG0p2FESSGmyIMlUY97JkrLNzU3nTYqEdTl9qIDqiJBXE/kNl5h/UdeOwzvoRGaSbez7Yp7Citgh7ohziITs7FT9m4XjiYnBUIHYtWcc8ktXCtTqoTxBN2b1bM132aRRcriEh3Q6wIVBjM6huni4dqSc1ymsJul1QnwGJ0kgwtsKN4LLvzWZqVTGxyq3u43Xi8SeY1C8jXPbZPHHztCvz5y74ocYyf/57Pjv/40s/hB//H/5O4f6cTJb4AaATdrpIvxKHD00aF1XZDFpYU2xYKJZvqzrhGmM7ILe2Kabh5jCAx3DDNRsi1y97IJDE77wzs6F43eZJJolR28D0ff0JZ9Fppjhd8qccV+6XUHp+NJUuPVCegst5kZ7kw8fgSKemWH1ekzbkDjSJf7VLa7mAFIYFjYfferlvFFLuLuaHtOqTaQaJDPF/3qc5l2TiXw/FCMk2fyBbahdR0wk2EtXMlcjWPXL0DKnTyLs1KJt6/70TooZaweq5MtuGTaXoUdyclGe7N3KEtOAll80PHnsoc+N2v/Bbe/8NZ/vR5PzqyPJWyEBk3QYkFlijrqx6d9vT3ARL7XGxbWFh0aTXjlxGN+n4UOHHK9Di50zCCxHAkVJXd7YDtrWCvF8bQJLK9GZAvWGRzNm5K4s6Ch0wiqVRcCiSTs3jq4r5JUeDqA8/mDx58Cb9+OTbb7J/3LIVCtTtRkGTrXeavN4Ze24X1U0WyDY/SkKNbo4jIElbOlwnd2BfgeCFLl6pYB/gxBChttanNZghSNo1DQoaTDyK0ymla5QQn8vAF73mmaRdTtIspXC8i0/JHxqcQ55b02F3IMbvaHDGfRcDOYm7qIT7yljb0BErrX/17PvJbDqWyzea6P1LeH+IKxGurwdFtUMogIst2hAcupOO8oXZc86xUcUDj9sK2Lb0+NUY7OW6MIDEcibUVn9oEJyj0mjjthmRzNoWijW35BAcJEoHlU3EV33RaOP+MNOurPu1mhJwu8jczz2Xt7DOx+rb0SYdRcLvBiE8DwPZD5q83YpPVYGdl8UqNIQtUfyhYkVLc7rC7FIfxLlyrY4eHO8MFKG+02D1AK7phVClttWPNKVL8tM32Up5uz/eydSLP8lOxsLM09qWEjsXOYn5wiH7kWWWzhR0ooSPszucmRqQdxCNvaYP1Jl7w07u89p+8k9lnzFC/XKXTin+kfMHC86JBVnwSrhs72fdHhc3Oj9ZKs6xYeJR6aUBbGz5bG0GsKAKOLZx+IEUqZWp7HSdGkBimxvf1QCHSp6+BWJZw9sEMa9e9iVVnUykhm7Np2Wn+YuZTuJw7SekFHn89cxq3G1Dc7Y5N9pNCbovbnYFzuU++1k2UPvuFyPDyfjKf7Yc4XjiVD1+AUrVLdSE3iJTqYwcRth/hp+y4VtYRmVlrUqjuhfymuiGLV2qsnSvjZRxC1+b6hRlydQ/Hi4Vpq5gaU92alUwsODRBrTsiEkY88Y4Ub7/wVWTdiOg58Kz1T/AZW48hqnzy7zqJ+1kWXHhmBssWOu2I9VWfTjvCdoTZeXssOk9V8TwlCiEIokH3yv496Edx6ZYb6aNiuHUYQWKYmm4n4pBoV0RiM1Uf1xVOn0vHlXq3fDbXw8ExUinh1Lk0XcvlPadfRiedIQqFhpdnZr1JaFtTB2IJyaG61hTaxDAKg2z2SWG2E/cVSHdCOvlYkEikzF+vk236sQNeoTaXpTqXnXoilzAaESKD5Rpnw2+eLsXntoRmOQ2Mm8b6x8g0fQLXoj6TJUjfWJmTdMtndrWB6/WaggFdP77ev5p9GEuUl2w+NtG5blkMwnYzWYuz5yfng3hexLXL3iDqb2Kve1/pdnRy7xbD044RJIaJqPYziuMH1O0lj02i37I1Xxg3M4gIs/MpKjNKpxNh2zKoirv9jZ9F4/1ZrCE5YCkQRETC2CSaRNR3ou+jXUjFCX9HEAq1ubhVc+BahI6F5Y9qU/1DjU1bGju1+8ytNsj0Mt/75y9ttQlcuzfpH47jRyRJbyHWTA7DCiJOPFXFCqNBhFeh2mXjVPHAJMkk3G7A4pXaxN/DAj4y+2y+/Q++iNW5Hxp0oByMWZiYD7QfVeXqU95UPd9FDo/6Mzy9GEFiGMPzItZWfFqNWAMplmwWT7ikMxbpTHJvcdeFSMH34pyBfhOo/Vi2DKrHfvcrvwWAxT+vktVxg7oKsZmoNwlCL7cjZeF60UiorlpCI8He3806tAspso29kiIRk3I+4uKGXqb3WIiwebLA0uVaPPYh/4MdjCYxKnFNK7/3pi9hHKK7X4BZCqXt9tSCJHCtxFdxBbwptIrSVhsriAZO9kHplpUG1x6aOZKJq7TVPlQgi8IXvaWF84Zv5p//r99g+8PX0Ci+hHzRYnZhuimn044IEqLMklCNtRvD8WEEiWGEMFQuP9EdROGoQq0W0u3Gmcqnz6ZZubbXB6T/stwP2w0D5foVb1D2JInM+/bKqkukWEGU6PcQYP1kgUzbJ1f3iCyhPpOlnXcoVLsUd7pYYUS7kKI6nyVyEiaTnjDI1T1ytS4INMoZSlttMkPhvEosuDZOjfpYvKzLtQsz5KsdnEDpZmP/Q2G3y8x6c/AFBCmb9aGcEStKviaItYRpUduiXklT3FcWRQWq84dHXOUaXmKioxUqpy7uYCl0cg47i/lDC1S63cP9RaEdC3U/k+Ftn/s1zDxrnTc9cgn5gT+fqttinyCYKvUEBBZPOIe2XDY8vRhBYhihthsMso0HKHjduNBi3IY2zfqqx+52suNdNS57Up7de8Bf+o7n8/m/9lmkugHRD1rQs6osXK3h+tHYBKWAn7Lxci5ezqU2NzppNmayNGay012UCK1SmlZpTwvo5F2KOx1K220kglbBZWcpHxvx9xE5FvWx82doltOkOgGhbY35HELHinNA9md9k2yCO4jdxTyRbVHa6WCFipe22VnK42cOf3wn5bEIDPJKsg2fdKs6EvachJd1SB0gTBTiSLEhLWdnaZHv+9tF+MpP573RTwDwkd/aG3cYKlvrPvVaiIhQnrGZnXPI5qypeqacPO1SLJlp7Lgxv4BhhG5HJz7AXlfJ5ePorUlCpI9IvH3lz17DL3wiww+8I8OZte14pcZCYmcxR7odjNncFQgcYf1M6ZZc06QB1mez1GenFEYJqCWDENyk428v5ZlbaQzKp/RNcNWF6XM3+seqzeeoTaGB7Kc2k2F2rTnWnyQx7Hmnw+5QyPDYsWaz5KtxFFyi4HetA0vmv8J6EwAv+Oldvvqf/MqgTpffbzCGsrURxH1izqWZmXPY2Uqu+CwCpbJthMgdgvkVDCOkM5IcmSV7zZ5azck9ygebp2z+75e9gfbbCqTaAUur1ZHJLNUNmVtpDKKZ9p2KIGUnm6ruIlqlNKFjUdpq4/gRnZxDbS574Fv/raZZTpPuBBSq3fi7jiaHPafbByR+EP8mq2dKnOj5jPazdXK6HJrHHq3w2Cu/hZ99zie5+A2Pjvz+qnFRxk47YmHJJZuz2NkK8LxoLwEWcFPC/JKZvu4UzC9xjxJFys5WQK0ah9vOzDqUKvahsfalisPWRjCaqSz9fI94YrcsOVCOBLbN1RNnaBfjiaW4M+6kFeKS6EnDiQS62aO3vb0T6eZcNo5oyrqliLC9XKA6lyXViTPN53ta0jB9U+Jh+DmXtbMlFq/WezvG4dXbCzm8I/5mP/djT/LMCTdRpx2RyVpxUqsNV57yRtZ7nrJy1Tfl5O8QjCC5hwh8pdUKsQQ21n18b0+zWFvxaTYjTp5ODvn0vbjRVCotnHswHW/fi9oqlW0WlvfKeyeF90I8GUWWxcq5s/zRq16BhBFqyUjDqJHtLcFLWaS6o8USEaExc/SMa8NkQtem3dOEurtd0m1/zHlfm9LM1825XHloptfUS+nk3BvSHuuVCoFj4wTjbaEWRIsAACAASURBVH+HG1ft74sSDxjarQjfj3Ddu1tzvRcwguQeYXPdZ3tzL9Rl/4OnGpf87nYj0kPRM54Xcf2Kh9eNDd+WwInTKU6fm/ymZ1lxkuHadki93dNSoojHXvoSLj73eeQayvKlJqJNAteinXNJy7gvxFJl41SR0k6n16wpnpS2F/OEd7lZ605m43SR2dUG+boHCkHKYmu5cLQkRSuu9ZVIrxhmnL+jtIpparOZsYz/i897Ls/7sz8H9gRJJEKhoOSGXlYmtSEQgcCPQ88Nx4sRJPcAzUa499Z2gN9Ce29xfUGiqlx5skvQN41r/EhfuxyXnHAPqF/0Be98IV/wrpeydPUadhCwdvo0QTrF/NUa2ebe267rR9i1LqEFhAxCURXoZB0ix2J3MX+gk9dwa1FL2DpZZEsViXRsgj/6AeOcmcJuL/FTdUTLdLbb5OtdVh6ojESRdfI5fu81r+Gzf+u3yddrgLJx4gTvedUraJVKvODLYqd8Lm/RTUi+VN3z201DtxP1/C1KNmcxM+fgOCZs+FZgBMk9wO7O4b3M+wz7JFrNaDzUl16ToZ2AhaXkN86XvuP5ca8KG1bPnR0st/1wRIgMzqkQ2RZOuHeyvnO3sNuZPozXcGsRuaHaX/uZXWuSHyrjsj8qzFLAj8baFpc2W2RaaT78ea8m1WnFZtEHF+jk4/vusUcr/M2X/FNe51zkw49lsX2fQnWL6twSfjpDqV0lX32MU+31wTFVNdZgeuaxvjm2WQ+5dmWvD32nHVHdCTh3wfR/vxUYQXIPEB1eKWPAsCAJAp2owEzqCwLwv88/lLjc8aPE1rMCpLxxP4mlUN4yguRuxvHCESECE5IwFbJNfyBIUm2f8tZel8cgHYc2L1ytc/UZs6glWGHEiad2+ZNwDskAGWgXSoObuJqf5bezn8MrVz7AcmeTei1k9Zo38nKUL1gsn3JZve6N3JaqEIawte6zbPqb3DTGEH0PUCzbU1e66LQjnrzY4eLH29Srye1tRSCfT741hrPS9+OnbGRCOY9J2EFcP8Pq/W+4u+hXSj6MuITM3j2Vr3aTy60IZJpxhFZhpzPez37fjR5YDh+aeS6dTsTKVW9Mw242Iq5e6o71S+nTaBzhLcwwEaOR3AOUyjbVneDAZMI+w4mEzUY0lg8iEsfoD1fw7TMsRDJNn/JGC9cP8dI2u73wz0Y5PVKttp+EF1rgJrTdjWzhzCfiREW1hJ2FG+uRYTgewgn+lf3mLRVGaqFZOqEq81CBy0xr3EyaxHaqPDFxEaCbXNEewLTsvUUcq0YiIn9fRD4uIhdF5C0J698gIhsi8pHev390HOO807Es4ez5NMun3MmltHsNOJLCKHM5IZ0RUilhdt7h7Pn0gbWLsrUuC1drZDoBdqhkWwFLl2ukWz47S3l2F3IEjkVkCe28y8q5EjvLBaJ9h1Ti4oaWxqYPO1Rm15pk617ieQ13Hu18HDKVkL8ah4NLXBF543RxpJZXs5geux/6dHrH9FP2VA0WZx5OTYzs6pNOcMqLQKly+5JD72WOTSMRERv4z8AXA1eBD4nIo6r6sX2b/qqqftttH+BdhohQKjuUyg6tZsjmRoDXjXBcoVy2SWUsrl/2SKq2HYbwwEMHawGf+ddvjjvjqTKz3koI5YWZ9SarD1QSS48EaVg/XaKy2cL1QryUHZdHYfw45c3W5NBSw52FJXRyLrnmuIlLgepcNi7Lv88k1cm7tAopco29CskqsL0U1xUDqM9kKewzge3XdCKBj3QrNF/0Uj71g3+CTrBUpdIgttBtx474fkXizbWA2m7IidMpMpm9uzEM4yZuvh/1un1apnHWARynaevFwEVVfQJARN4JvBrYL0gMRySXtzmbH33T8v3JZi83dfgD8shb2vEfkeJMqF57WH+Mbt5lLV8G4gq4px7fSXSgOP701XENx08n7yaboaSnXSRMwI4f0cm7eGkHJwiJbKFZyozksgRpm43TJeZWGlhhXLbfd+MS/pbGPpedpTydvMvH/94LePHH/4JgI9mO1WopDz0rS6cTcuVJb+S287pxGPyDz8hgO3HnxitPdQedGHe3Q1Jp4ewD6UFTLsMoxylITgFXhj5fBV6SsN1rRORzgU8A/0JVryRsg4i8EXgjwJJrooD247pCLm8Nyr/3iftkH5zR1e8bAjC/2py43SR7eRKRLYkRXgBexpgb7iYa5TTlrTY61I0yEvDSzl5vlz6qzK424xbI/W1tYe1sObGMfSfvcu1CBTuIiCzZy3nZ1y7Yy2Z555f/Q177sz+X+HLSv80CPzn4QxWq1YCZWYfr+5z22qt+vbUZsLBksh+TuNOjtv478ICqPh/4XeAXJm2oqm9X1U9T1U+r2MYsksTJ0ykKxTjCSwRsG5ZPuYMaWkn86k//g8Hfth+SbXgTm0Ltzh9BgIuws5gbs5NHArtHrY5rOFbUtlg9V6addwd+kUY5zfrZ0pg2kq91yde6A7+YpXHNtYV+7a4kRAhdezRxMkHLac5UyL5gNvEQxV6jNd/TiYLG95QgUIKEroyqUK+aCK9JHKdGcg04M/T5dG/ZAFXdGvr4s8CP3IZx3bNYtnDyTIowVKJIcRw51O772KOVwd+ONx7l1UeBVCeg64WHNkjq06xkiGyhstnG9iO8zF70l+HuIkjZbExR9r+4k9B/HnC9kEzDO3L73/2880Vfyisv/lfcpjdQWmxHmO9pEpM6KYoF2ZyFHNS6y1i1JnKcGsmHgGeIyHkRSQFfAzw6vIGInBj6+GXA397G8d1xqCpbGz4X/67Nxz/a5qnHO3FJ9yNi24LrHu48zLzvK0c+BykrMfZfiW+k4m6XE0/ukt+N4/+noV1Ms3K+wtVnzrJ+tmyEyD3OQffFwrU6bufgUvZEitsNJnaZ3J2f52/f9TrmlxzKMzZLJ13OP5QelELJZIVM1hpVaAQcRygWbRxXEsuuiEB5xphcJ3FsgkRVA+DbgN8hFhDvUtWPisi/E5Ev6232JhH5qIg8BrwJeMPxjPbOYGPNHynx3u0oVy95dNq33jn9wpcHY4mHoWvTLKbGzFEy9L+lMLfa5PQntplZa5gkQ8MIzVKKpLtViPNHZtZbE/ctbLc588ltli9VOf34DvNXa4mC6S/fv8TvvvvrWT6ZolzZ69LZb60Qhoplx1qIbcPMjM25B9NIb7uTp1PYvfUQC5FszmJ21qTdTeJYvxlVfS/w3n3Lvmfo77cCb73d47oTicLkroSqsLXhc+rs4X0ZvG7EznaA7yv5vEV5JrnX9aCWVgJbJwrYXpVMZ3LL1V7KCoXdLipiCjIaBtRns+RrXdyEkjkCpDvJmfLZusfMRi/svPcMZJs+c9frbJ4eN6k99miFrx76rKpceao7krQrAtm8xeKJUXNaKm3x4DMzNOsRvq9kc30txti2JnGnO9sNPXw/uQkUxJrJYTTqIU893mV3O6RZj9hYC3jq8S7hvp7iL3x5MFGIACCCE07ISt6HpVDc6RitxDBALWHlXHni+kmRf6Whulx9LIVc08cKkzXy4UCRZiOi2x0NgVeNlydp9JYVV3eYnXfI5g5vCHe/YwTJXYLrysT5+LBS2qrKyrXxonWBr3EPkyGyX/WiQ8dyFLEgQyUvDAYAbItGZTyzPRKozSYnxtoTfCIKWGHyDTYcKNJqJteVU+LWCoabwwiSuwTLFsoz48UZRWB+8WAHtddNTkbsN7sa5kBtpEejkp5amISOFfcKNxiG2F7M0yqmUIHIioVIfSYzUo9rmG7OSbznRKGw056olfQDRuIIxYT9BWzj+rhpjCC5i1hcdpmdd7B6wSPptHD6XGpiSGMfy2KiGmENBaJ85l+/eapx1GezdLPxg530r08ksL2YS4z5N9zn9JprXb0ww+rZMlcfmol9aUn3iip+Op7tk2p6FXe6LD9VTXS8f8fblnnhywNKlWRpIQKFoonGulmMLL6LEBHmF13mF11UdaLdVlWpV0Nq1RDLijWZVFrGfCkiMNOLRMm87yv3yqAcPhDWzpZYvFwj0w5Gq7wCgQ1ByqE6nxsU4DMYkogc69B+77MrcVvg/n021jgLIIjI17qJGk3uR74L57d+lNPnUnHWek8Jdxzh1NnUgQVKDdNhBMldykFC5Oolj3ZrrxRKox5SqthEUUTQL+WucVx8sWzHUVoTeoygSqbl43ZDAtemXYhrJ1mhku4EiU73btZNjKQxGI6K44UUaqPVFCY1zkq3/ERB8shb2rzvHc/ng9/4V1x4Zgavq4N2CcaJfmswguQeo9mIRoQI9OoI7YRkssLMvINjWWRy1qDF6Lf7z008loQRS5druF6IKKgVR9WsnS3Hzs+ELPc4S9k4Lw23hmxjupYCERC4Qyaq4RjfIUTilgmGW4vxkdzF+F7E+orHpSe6rF738LyIRm0816RPp61srATYDgMh8sKXByPRLcNUNuKS75b2kg2juGrr3GoDP2VNLJXSzZr3E8OtYVIG+34EsMIIpxMwu9rgzCe2OfvxbZaf2iXVjkPak3yA/d7trWaIJjw4vhfRqId43XgcqnEtrv1h8/c75om/S+l2Ii4/2R1UKe20obYbUigd/G6gChurPucuxG9vb/3y1+8rTLNHvuYl1kXKNH1UhHolQ3G3s68bInH/CYPhFtDJu5S3x0vDJzrdd7sUd7uDkj0A6U7I0uUqK+dHX5aiaLwqRColnHkgje1IHDJ/1adRD+kXqU6lhTBUwl7EfC5vceJUCtsxGo7RSO5S1lb8sf7UqnFy4mFm3243fgy/+5XfkqiNSBiRaXiJ/deHT7a7mGNnMYfvxt0QO3mX1XPJ5cANhhuhm3MJbRkTHArUy6kRx3u/osL+SU16ibGPvKU90Eo2133a7WjQc0Q1fi7WVmJT2tZGQKMea/dRtPdsBf7e9v1+8Aajkdy1TEqi8rrK0kmHteuTi9/ZjsTx9W8bX1fYaTOz3kJFYr8IjEVlAZx5fJedhSyNmfjfJBwvJFeLu9y1iin8/f0pDIaDEGHtXJnFKzVsPxrcjFvLeUrbnanehAXGikFWd8NE02y9FqGq7G5P7gE/TLerdDrRSHfF+xHzVN+lWDaDMMZhRKBccSiVHFavezTq442sXviI8M8SorRSbX+vjW5vp35uiDAqVCSKW+6Gjj2xLW5fKPUz20vbbeozGVN7y3AkgpTN9Qcrsb8uVLoZByyhuDudNqCA1/PbPfKWNu//6zfzCef7J2/f00KmQSTuY5I5uFP1Pc/9LUbvYiozTmKWe5z9Lli2cOJ0ipnZvUZWYsHcgsNzfzu5DmZxpzNWzqQvQPZrJtDrr76VXK3VDqKBUBqYHHomhlT7kFLhBsN+RPDTDt2cC728j/pMZqzMyv6k2NhvJ9Rn9mb6v9x8kkIh2fyayQrNejR1Dq0qZEwUmBEkdyvzi86g26FlxYIiX7BGWoGKCAvLKR56OMP5h9I89KwMX/h64fPfmtzX2ppQjPGgx2RSDaRJYZuikKsbu7Lh5mkVU4OujMPCI7CF0BJUoJNzWTlXInRt3E5ArtblX/3AAi//m2+NS8X3bu7+c+SmhJVr3tQaSTZn4abMNGpMW3c4qkqrGbG7E6ARFMs2pXKsdZw8k8L3Fa8bkUrJxBvasgQrJQeWhwdoF1NkWn5ipFbi2IBu5uiZ66b2luFWsb+yggB2pOws5GjMxr47iZSlS1VSnWCgYr/px+DrnpmjuePRaSupjJDLW1x50pvKN9In7NX46nbiMGHLEoqluEHW/YQRJHc46yseuzt7r0fNRsT6is/pcymyORvXFVx3uiipscRDVfI1j1yti/b6bPspm1R3cq+Rwa7Eob6T+qu3Cilm1prj+wm0iof3TjEYDsPthon1tSyFQq07ECQza01SnWCkl4n3JKx+3Rdw9pd/f7BfdXdP0EyL78Paikd1Jxy09t1Y81k+5VIq3z/Tq9HJ7mC2N0eFSJ8ogitPeXS702eQf+Zfv3k01FeVxSs1Zlcb5Jo++YbPwvUGnaxNN32wYFJis8LKuQrBhG0jx2JrOU8kjPyrzmVN5JbhaUf7NitVCrXxPvGWwm9/ZH5kmW1P7tg+yWfiOjIQIr3ToQqr1/z7KmnRCJI7lMCP2Fib3I9dFbbWp3da/+XmkyOfM02fdDsYecAshWLVo5uzE9uhQlyKYmchy+ap4kQh0qdVznDtwgw7i3l2F3KsnK9Qm0/WYAyGQ1ElW/dYuFJj8XKVbCPZ16aA7Ufkqt3ezJ58uKgTV3boky9YiQIjLm6a3MLBSU3oEyTQbEx+fu81jCC5Q1lbSW45OkynM51G8pl//eax/uvZxnjWep981U/U8BXw0/aBeSP7iRyLxkyG+mzWJCoaborKepP563VyTZ9sK6CyORplOBxd6AZxKZ/KZptuJrmXCcBrGv9k8LeIcOZ8GteVQZSjZcHJMynml1wWlx2c3rp0Jm7h4JisdsD4SO5IVJVm43AhkUodfhNPKg8f2VZiSK8o2KpjSYi+I1Tnc7TKadNfxHDbcbyQ4u6oiSqp5/sw/XDz9dNFlq7Ux44pwOx6a+Q5SKctzj8jjddVoigOB+5XCK7MulRmx4NLarsJ9e0U8hNCjO9FjCC5A+nbWQ9CBOYWbrzXR7OcprTdTmyDu/+BVIH6fI7WhO51BsPTTbp1uIaehIqMJdMOYwcRvjikdM/EdVCF4ChSqjsBjXqE7QiVGZvyjD3ibAdYPuVi2/fPC5cRJHcg/faf4QQXiOsKiydcsrkbt0wGKZvNEwXmVxp7T1iU/LCJTs4XMRhuB5HdS5Y6SmwuIKqEjkXoWlj++D2sAo5O58uIQuXSE118f691daMWsrjscPbBNE0T/mu4kxARFpZc1q77Y8/NyTNuLxFxuhv1Fz4RaxFWEFHabpNteES2RW02Q7uU5mohRbrtYwURs+stJCHSRCVuVmUwHBf9xMODSKoL56dt/LRDdS7L7FpzxDQWCdQrGawp4313d4IRIQKxXFtfDXjoYYfMTVgI7naMs/0OpVxxOHE6RSo96tzLZCyajQhviuZRv/rT/4DHHq0gYcSJp6oUtzukvIhMO2D+eoPyRgu1hE7OZWajnZjZHgFexqGTM+8chtuHFUbYfringVhxe+fAESILIkuIhLgysPRedjIOYW95nNXusN7r1Nksp6nOZeMwdCsWIpkXwJs//ItTjyku6Di+XISRcvT3I2Z2uIMplmyKpdhhp5Fy/apHsxENNPxc3uLkmcN7Thd3OlhhNPLWYCmUt9vUZzM4XogVRuO+EeI3urUzJeNgN9wWrCBi/nqdTDtAiU1aWyfydPJx5ehrF2ZIdQJEY8GBxKV91BLUih8Mx4uIbBnrBR9ZQmQJdqj4aRv3YcU+QvahPcF3rgrWfeQPScJoJHcJm+s+zUY00h+h1YzYWDvcCZltjpc9AYhE4ozfUPcSuIYQILJlUCTPYHhaUWXpSo1MKxYUloITRCxcreN4PT+GCF7W3SveKLHA0P49KkKQtseESHmrzcxGC6endae6Ic13R6yl50a2a9RDLj3R4eLftblyqTuiaczMjRdKhdhnmU7f38+IESR3Cbs74yGG/V7sSS1ChwncZCuwqBLaFt2sk9jEKpI4g91guB2kOiGON16eRxSKCV0SpyZSSlvt8ZepAD48u1c2qLoTcP1KXHsrDKHViLuQ9oVJvmAzt+DsFUq1YiFy6lxqap/lvcqBgkRESiJyIWH582/FyUXk74vIx0Xkooi8JWF9WkR+tbf+z0XkgVtx3ruRSdVIpwliqc9kxwolKuCnbPyMg9oWO4u52LbcP5/E65tlE/JruD3YQZgYNiiA4994lnhixKFGzGxcx1rfptuNm1ltrI0Ht6gyovXPLbhceGaGE6dTnDmX5vwz0qRM9d/JPhIReR3wH4F1EXGBN6jqh3qrfx540c2cWERs4D8DXwxcBT4kIo+q6seGNvsmYEdVHxKRrwH+PfDVN3Peu40oUnxPSaXBS6gIMZwwNQkv67C1nGd2rYUQl4zwMg4bp4qDbRozWbyM0/OnKO2CS6OcMWYtw23DyziJ5UyinuP8IKwgorzZItfwiSyhPpOmUckMTF/DpFsNXvgnv43rdRCUS1FIrmARTpBV+x3ptiMUivdPsuE0HPTrfDfwqaq6IiIvBn5JRN6qqr/OwS0qpuXFwEVVfQJARN4JvBoYFiSvBr639/e7gZ8UEdHDbDn3AKrK5nrAztbklp9iwdKJZNNT5n1fyWNv2yvS2CpnaJXSuN2QyBbChIrBXtZly4T5Go6J0LVpltLkh4osxg53iYXCBOKoxF3sYC/qcGa9RaoTsn2iMGhsVdzpYCk85y8+QLrTxBrqAto6oJLE/ZYTciMcJEhsVV0BUNX/JSKfD/ymiJzhSIWWJ3IKuDL0+SrwkknbqGogIlVgDtjcfzAReSPwRoAld/paUHcqu9sHC5FsTjhxKjWxB8n+2lpA3GUu44AqqXasrnsZZywiS3oRXJFtVHbD7WV7OY+XsSnudLEipVVwqc7n0APuxcJudyx03VLI17pU57OErs3uQo7IEuavb1Oobg2ESJ848go0GjUXi8D8vvyQKNJe11EjYPocJEjqInJBVR8H6GkmjwC/AXzK7RjcUVDVtwNvB3g4W7nrNZbtzclCBCAIuKHObOmWz8K1+sC5riJsLeXp5lwkUuZXGqR7rXC9tM3WyQJ+2kSJG24TIjRmskcqDJrUjC0+FqQ6AW03boVYm88ROT5qWYlOR8uCQjkud9IbCvNLDsVyrL23WxGr1z28bixIiiWbpRPufR/6CwcLkm8GLBF5Tt9voap1Efn7wNfcgnNfA84MfT7dW5a0zVURcYAysHULzn1HEkVKsx4RhDrRXju87UG8N/oJXmG9aWSZFUYsXqnte+iUheuNke36j0WqG7J0qca1C5UD3wgNhuMkcG0Uf9zerhA6oybcRrlEJ5elUBst4tgXDIvLKRaW4ufPcfa0Ds+LuPJUd6TvSL0WEgTKmQfiRm2ddhyO32lHOK4wv+gO8sDudSbODqr6mKp+EniXiHyXxGSBHwO+5Rac+0PAM0TkvIikiIXTo/u2eRT4+t7frwX+4G73j8SVfUN2tgIa9b3Q3W4n4vFPdFi57rGxOh49sp/CIZVFP/JbDi/4st2RZbn6hD7q7AmP/W1LpddFsY8VRKR6JVUMhuPGDiJaxfHyKf3Pur8qqQh/9MpXEPaSGXuLcFwZFEG1LOmVkt97GpLMzKqxluJ1IzrtOFS41YyIIvC6yspVj93tGys2ebcxjc3iJcTRUn8KFIFfBj7rZk/c83l8G/A7gA28Q1U/KiL/Dviwqj4K/Byxk/8isM2t0YSOjTBUrjzVxfN0UCnUsYUz51Ncu+wRTRnhaNswt3h0p7gVamK13z5JCrqlvdBLVeZWGuTrXlxRVZVGOc32Ut5kvRtuO7YfMn+9QboT9No+S+y7YOjFSGH5co3r5ysjwSXrZ07zd7/yWj73je/G95Vc3qJYtg+sENHtTIp4gZ3tYJAsPEwcOhxQnnHueX/KNILEB9pAFsgAT6rqLXkdVdX3Au/dt+x7hv7uAF91K851J7Cx5uN1dUQ99iNl5apPECTfqFavuQ7shR1WZp0baqjTyTmocKAw2U8ksUO+st4iV/cQZeBfyVe7BK5Fbc50PTTcRnoZ8I4XDQkNHSvaKABRXCJodzE/cgh/Mc/80vQvY9mc0G4zFmakEexuH9zJNAzAuceDIacxfH+IWJB8OvA5wNeKyK89raO6R6lXExrgEKvHk+Z2NyVceFaWC8/K8sCFDPOL7tRC5Mc/88TIZy/j0C6kiA7YfXgcEbH9uVVwKe52Evtel24m49hguAHS7QDbH68Nl4QAqRvsZTLMzKx7wylV1n3gJplGkHyTqn6PqvqquqKqr2bcl2GYgoMUgcQ+IAKlyo3fhX/6vB/lx75zdeSAmycLbC0XaGcntx8NBQJbaMxkWD0XF2ycpMVYhzj9DYZbzaTeOEnPkAL2LbhHHVc492A67us+ZCU4CBGozB5sMrtXONS0paofTlj2S0/PcO5tikWbWnU6R4hYkE4LlZlbHHorQqucplVOk6l1WbjeGDyACtQraXYT/B5+yibljY+9mzGhwYbby1HuOQHsCWbjMFQ0ipvITePDSKUtTp+LI7SqOwFrK5ODYvpCZOEI5rO7GTML3EYWll1arZAwGK+RNfzZTcHCUopC0XpanXSdUprVlM38tTpuz1SQ6QQ4XkSQHtWEtpfzLF6pxT4SYqGjQuxsNxhuE5mmx8x6C9HJ7XP3o/s0gnSrxYPf+Ttc/HgnruPlCMunXHL56bX/fNGGlckmM8uCmTn3nney9zHJAbcRxxHOP5Rh6aTLzKw9sb9B4BOr0E/3TajK4tXaQIgIcQXW5UvVsU6J3ZzL6gNlmqUUXtqmUU6zcr4SZ8obDLeBdNNn4WqdVDcc3K+HGa3iLojpvQWqfPG73kPhw9dBewEvvnL1kjdVs7g+jiMsLCeXlQcIQ1i9lhxufy9iZoHbjGUJ5YoDFahV2xO3i6Lp7LCH8aL588SxEuNkG95YaYlB7ki9O1bfyE87bJ0sYjAcBzMbrbGAj74w2d9iV3srO3mX2txelvzc6hqlnZ2xPCjVuCzR4vL0bRNmZl3yeZsnLyZUU4VeToneFz4So5EcI5NUaduRidrKUfnT5/0o7//h5HITjh8lOtEtBacb3JoBGAy3CDfBR9cnEgZtdGuVNFvLeVYeKLO9lKey0WL+ao3idpt8tZbYxA3iJMKjkkpbt+SF727HaCTHyPySQ7MRjpX9WVyePoGpX2becQX7iDV/vHRyXokCpZ0uubrPzmKOdimduL/BcDvxXYt0d1yYqCVcuVDBjpTQsQaBIumWz+JT1YFfL9v0iaw5Ascl5Y2anUTi1tU3QrFkU90dH1cub90X2ggYQXKs11aAhgAAIABJREFUpFIWxbJFdWdUkmxvBhSK9qHCZGvDZ2sjGOj3hZLN8kl36pu3m3Pi3tXdcKRsd39vN4hYuN7A32jhZR1qM5n4gWzE2e3NcjqxHL3BcCuRUCnutpGEpMNIoDqbAdsiHL4Ve5UYhk1hVs8nsvElL+Xcb/ze6DluItR+Ycml1YoIgjgKTKy4jc/yyfsjYguMIDlWfC+itjvu4Ot2lEYtGlQdTaJWDdja6NX/6T0sjVrImsCJU1PaeUVYO1umvNkiv9vBjsajYARI+RGu75GveSMPcnmrzdZynpbpomh4mpBQe71GolgQMNTF0xKqc1nqs+P3nx1EifkmAmxmFzlvM1KSSBV2NgMWjuAjGZzLEc4/lKZRj+i0Q1Jpi2Lp/sgf6WOse8dIq5UwcxPf1I16XFm0uhNQ3QnGSqgMhMi+/erV8NDKwCP7WMLuYp6VB2fG2vEO04+SsYb/VphbbSKhKeBoeHoo7HYGQgT27j0VuPZgmfpcNrHWm1oyMTR46amL7C/ypAo72yGBf2PJiyJCsWSzsJSiXHHuKyECRpAcKwf5NPwg4olPdFhb8Vlb9XniEx2qu3sO8HBCkhUwVvzxIIf7YJ8b7KmgEtueDYang1zDS+w1Igrp9gHOd9uik1C9IRI41bqamEgoAu22eSm6EYwgOUbyeWti/Z5OKy7uqLrXtW3tuj94Y8rmkn86y4ozdY/MDVaRiN8O76+3L8PtI3SsibdmtnlwnsbmySJ+yt6L6BJoFVPMPT+5yKj+/+29eZRkWX3f+fm9JV7skftWe3UDjaAF4gAWAntACI+Nz2kkIRlb9oBH0gGNj8bH48FzGMv2zOhoMLIMtrHQCKxhBo+EJNtoadHdwgIaezTdkmjs7qYRgm6qq2vNrFxjj3jbnT9uRGRExovMqMqsysyq+zmnTsXy4sV9Ge+9372/5fuDWxJDNRhDcqiIJZw86+kGOh39HrGgULJGSi9UK3oWNjPnIjt+PRGYW7j5aloriplaru2pCpz4ttK5+gbD7aBRSI5Z6Cys3Q1J7FhcP1di5XSR9cU8189NsL5U4M9/8PsTCwkdR0hnjCG5FUyw/ZBJpy3OvzxNq6VQsSKdsdhYC9Hau4MotqVUvLTF2fMea6shrUaMkxJmZh2yOZs4UkTxYIe3UVhhzOLFLaxQjfQpxwACkW3pAGbfhqsnCkMSFAbDQdHuuKcSBRlFII4RRMf3ks51EfyMi9/x7H7lIxmeePCXWDjhsnIt0JMjBSlPOHE6dc9Imhw0xpAcAUSETN9MKF+0E3u2C5AvbC9DUp7F0sntGVscK65d9qlV9arFsmBu0aVYSviZlcLxI4obTaxQjVyaxgLl6Uwv1ddpR2TqPsoSGoUUsWnBa7iNRK5N2ElR31m97voxp7+92XtensnoKvYRxkAbkY8CUCw5FIo27ZZCRBEEup2D5ATHEfx2zNZmSBjoa65QtBEzYRqJMSRHkHTaYmLKZmtju3+JCEzNOKS80Tfua5d9GvXtTm1a7yfAdS2eePCjPP7p7+Ztn3sLqWbA7NUaVhT3irWSiAVaWXfg4gw9m6q3e+DeYBiJUrqfSBjTzjhj1SGtnigwf3ELu7NI7zWz6kOA0loTAcozyTGQxv/08/Tf8kQEpRSXL/rbrXnjgHQWWo3tz9WqERvrIafPefdcNta4GENyRJlbSFEoxVTLOlOrWHJIZyzCUFEtR0SRIpu3yGS0uGMYqAEj0kUp2FgLOHFaV6dbUcz85QrWHskpCq3sWy95ppWu4UCwg4j5S5Vt96iC+hjtmsOUzfpSgdmr1cQMri4WUNxoUt6xKumuRJ7ecbtTseLKS+0hZYl+IwL6Gmq3FMvXfJZOGpWHJIwhOcJkMhaZzLbrql6LuHpJBxi1gdAqwUunUgSBQmRYnh7A97dfzFb8PTO0FNo3XZ8whYaGg2P2alXru0HvHMyV27Qzrp6w7MK4DdREgRUp4r7sq/rf/wiNhkUc6WzHbtr9xno4ZER2o1qOac/EeGnjzt2JMSTHBKUU1674A4ZCKajXYqqViHzeHpnp1Z8qbIdRYnZWt78I6CZWqyeMyq/h4LCDaCjOAbqotbDZ3NOQtDPuWL1HYpGBmqj/5XO/yG+9FBJ1F0EKZhccMlmbtRs3L0y6uRGysHTz1e93O8a0HhOajThxJaEUlDcjLFuYmhnuj2BZMD2j5wtP/viz/NxP5xMr2JXA1myG5bMlls9NEDu7nxqOH5HfbJHfamGZynbDHsguK4pxVhuRa1GeyhDvYk1i9DncvQg+H/5LrrzUJgx1LVbcqcdaXQ5Z3aUp1W7cauX73Y5ZkRwXxpiOTc86uClhYy3UMZSczcycg5vaNgqV934C/9V/g1Qr7Pmbu0H16uTojJd+imsNSuvbPU4mV+qsLeaNSrBhJGHK1mniOxqmxQL1/Hgz/PJslnbGobDZwvFD7Ej1Yn2RLWzNZnvu2I99cJk/eqNF0hxHKWi1bm3yk8ubuXcSxpAcEzIZK9GYiEBp0u481k2zShOjf1YBfvq5X6f+cz/JZz5bA6Ba8qhNpscyIqlWSGm9ORT0nLlWo17z8TMO9WIadYuSK4a7FBHWFvPMXq32MgVj0ZXr1enxswBb+RStPQzPax7aovW23xqSCurHshgZH0mKNYqA48qu19a9jPmrHBNEhBOnUly55PdahIpAvmBTKI5OoYwixdpKQLUS9aSyp2ddiv/wU/z3wD/4K3/7psaRK7cTYywCFCo+cdVnYq3J9TMlopSRmDds08qnuH5ugsJmEyeIaWZd6hPpAy1ofc1DW7znA58FOrHBEZpaE1MO62vhgHijiFaV8DyLdjvGEqHVilGxru2anHawzAQpEWNIjhHZnM19L09vp//m7JGaW6DTG1+60Cboy9raXI9o1mNOnfMQEb7ykQxv/dDolr/DO032EXcvL0uBihQLL5WpTGeolTyUKVo0ALmtFhNrTZwwJrSFyBbc1QaBa1FPOk+UQmKFsgQ7jClstkg1Q4K0Q3UyTZgwUfkX7nM82XlsO8LMnMPajXCgHivlCZPTDrmCzepyQLMRY9swOe0wOT1+UznDNqJGpfocYx7ITKhP3/+Wwx7GbUUpRbMZU+uuNErOUFpipRyyfC0YkswWC06eTg20+h13ZeI1AuYuV3bN5+8Si5bzvn62ZBpg3ePkyi2mlusD501X+iQWLXeyfKZE6OnzJFtuMXmjgR0pnRyi+iTk0ckhK2dK+Gk9F+5fieykUY/Y2tDtFfJFm2Lp3uoVMi5vfu6RrymlXn8rnzVTxWOIUoqV6wFXLvpsrkdsrEW8dKHN5tpgJkqrEQ8ZEf15XWDVz4cf+aU9peahU19S8vTFz+4lKVYnp3/iRmOXrQz3AhOrw3G1/lWsFSuml3XMLl3zmV6u40Sq1/emv5q9+9pUZ3vQK5FRZHM2S6dSnDzjMTF57/UKuRMciiERkSkR+QMReb7z/+SI7SIRebrz7+E7Pc6jSrMRU9mKhmpKVm+EA+mJrmeN0rHDdYff6OoQ7YoIGwt51hdyxJ2zZzdjIkDW9Cu550nqVtiPAF4zRGLFxFpjpNHpJ9WKQCkef/cf8uSPP3tgYzXcPIe1IvkQ8CWl1MuAL3WeJ9FUSr228++hOze8o02tEo0sPqzVtlNViiU70ZDYFuQKyT/9OCsTiWKmVhpY8Q53w4jtd+u8aLg3CN3xbjUKcILxUnOVwOMfyRgjcgQ4LEPyLuAzncefAX7wkMZxPNmtJW7fe7YtnD7n4aW3X8xkLU6f94hjXVyVFCN74sGP8mj88ZHfkav6iBqUne8+TupIV9ujatlw97M1s3sxoaLT18YSfG/veFr3vHryuz82/F6k2FgLuHShzdVLbRr1XfKADQfCYWVtzSulrnceLwPzI7ZLi8hTQAh8RCn1O6N2KCLvB94PMO/e3eq0pQlnQBm4i1JQq4SdlYi+ar20xdn70kSdQjClYPmqT72uNY8cR5hfcrEsrRaczlg4jvD0Yw5f+XpyRpcdxiObYEWOYHW+SxT4aYet6QzpaptsPcD2I6KUTa2Uxs+YpMG7BTuImFhtkKkHxJZQmUpTm9iuTWqUdKHgxJpO/d25So0ci/XFPABbs1m8S5WhwLwCsHSRh58S/teVT3NxRWtfFQpa5j2OFBcvtDuTJP3Zes1nZs5hama7AVsYam263dpdG8bntmVticgXgYWEt34G+IxSaqJv202l1FCcREROKKWuish54MvA25VS39nru++FrK31tYC1lWGtIBGdxjg77xLHivJmSK0W49jCxJTNyvVgKNCuP9i7RpmacZiZ277odmZ0pWt+ohJrLFCZ9ChutLu7HMi46b3WeX1zNkttag+jrxRWpFNATQOto4kVxixd2MKKt1epsUBtwmNzPp/8oY6cfKoVEqRsvRrpW057jYCJ1QapdkjoWJRndFW7247wmjXe/W/+L+JOG2oRPSE6fd6jshUOpPt2EYH7XpEm8BXXr/o9IdNs1mLxRAonIWZ4r7GfrK3bNiVUSv3AqPdEZEVEFpVS10VkEbgxYh9XO/9fEJGvAN8D7GlI7gWmpp1EQ6IUbG2GTM84vPSiriHpXlTVXWIrKOhKHm2shWSyFrm8djHsrDVp5VwC1yblRwMurdCxKG60B/ylohjqcCed1ydXGzRK3sjmWF4jYPp6DacTqG3kXNYX86Yu5YhR3Ggi8aCr01JQ2GpTns4m6rY5foTX1OdvmLKHVBXaWZeVM6Whz0WuzU889yh9oUCUgiBQrK4EA+d7PyJQr0asXA8GKtob9ZhLF9ucu98z9SP74LCuyIeB93Uevw/43Z0biMikiHidxzPAm4E/vWMjPMaoWBuTnRfVuItPpWBzfdtIPfHgR3nNQ1sD2zg7RIwEcEcESUdenqKNRRKOHzF3uYIbdJpvKcjUAuauVMc7CMMdw2uEiTcSJZBqD8cnimsNFi+WmVhtMLHaYPHFLfIb4xXF/u+f/wS1/7SS+F6tEmE7yWebUjrbMekaCEOlRVENt8xhGZKPAO8QkeeBH+g8R0ReLyK/0tnmlcBTIvIM8Dg6RmIMSQcRGQii95PJWruvPsYgihRxrGg2ItqtmL/6/l/rZXNlasHQDPRWGeWuKmw0h+IwFjpFdOZKFTswAdSjQpiykzP21HC2ltve1mrrZvxZndXpXr/pxz64vOdYJqeSMxUdV4ii5NUKnRWN4dY5lGinUmodeHvC608BP9l5/ATw4B0e2rFiYSnFpYvtgaLDbp/21eWAPTtYjaBbZ/Kdb7UAPZtzXCF4+S/wsSd+hH/8DyeSP3eT36NEaGXdxPdcf7h3Rfc7sjWfdDPg2hhy94bbT2UqTbY6qMEWo4tXw5TdWwqnWhH5reEJQpds1ac6Imb2sQ8u03rbbyEi5PIW9dpOuQYolGyyOZvZBYfV5bAnvui6wskzKWq1iFo1eVWSNs2q9oVJmznGpDMWZ+/z2FoPabUU6YwwOe3iulpLqFH3hy4aNyWUJi3KGzFxrEh5QrOxvZEOXDJ0wQW+4vLFNuff+jn+h994N//3Pxo2ALFoJeFCuRNsV9q90Y2TWGybttgSbpwsjlQcbmVdvGaYKMUigESKwkaT8lxu/D+Y4bYQpB1WTxSYXq5jRTobsJlzaeZcTrywiRPG243TbmFu0zUiXQpFe8CQiOjzenZen5OTUy6lkkOrFWPZgudJRxlbt1gI+7ypIpDNWabr4T4xhuSYk0pZzC0Oy2rn8jZTsw4bq52ZGeDYemaWSllMz2xvW6tEbG2GKKXIF2zabUV5c9jNEMU6OJn767/F2979F3n8mcmBniaBZ7M1n6M6nSFb9ZFY0cyniByLfLmF24qIbYtmztFS4H1GRGJFrtLGawQEKZtGPkVxs4WKkl1oFpBpBJT7X1SKVCvspaDWi55ZsewHpchWfbxmSNgRVhyVGNHKp7h6n9vLsMvUfKav13rnRzfBYjcaheHz+NH44zz9NqczHB1Q39rYcW4KnDidGkjltWwZ0JIDsCzhzPk0azeCjkadzmScmja3wf1i/oJ3MTOzLpOTDs1mjG0L6YwMZKaEoeLaZZ9WM+65AfIFiMLRV7wWwAvJ/LPf4y0/+V/x+PI5QN+0q526gci1h1wUlensyH1aYczCxTJ2FGMpbZRKG01WF/Pky22ytWDImCgg7BeCVIqZazUyNb93w5q80WBjPktt8u6uK7odSBSz8FIFJ4h6v8nEWpPl00WC9IjbhkivV/rE6rDMST/dt7r1JJtzuQFhz3/y7udZf+hRvgzkizHtluL6leEVNujkko218VrgOo7o7Zb23NRwExhDcpdjO0K+kFwpfO1yu+fW6l6gqyshE52A5VDBYwwba9uzwfQnHud7S0/z+ff+TcLUrfexLq01cMJ4QMQPBZOrTa6fn2D+pbLWYeofi0ClryFStuqTqflDN6+plQa+Z+NnTZ/tm6G03uwZEei0B+gY6+vnk2NkXSSKx5I5aWUdmvkUjUJqwIj88z/55zzzC/HAObkXzbrJujpMzLr/HiXwY1rN4emdVgaOSaVkz4aJcQSljU3+/swTtzQGtxUyd6lMYaud6L5ygwgrjFk9WaCZc1HS6apn6257ft/MOFduj4ynTC/Xb2l89ypWFCf+PQVwgkgrG0QxqWaItVOMUSnmL1f2/A49EchSncoMrUSe+Y/JAfHdMAWFh4tZkdyjhFFyS1GAMIQz5z22NsJeGnFiNXyHzQ//F/J/87XkyxW2ZmZoZTPaVy4ysuWu2w5ZeKnca7uaSCdYr2yL1VNFrCjGipROKb2J4jHHv8nZarf95D2EHUTktlrkKv7IeiDQcY7CepPCVqsnU9DMuawvFVCWkGpHuO3kjLsuseiCw1Z28PbzaPxxvvzQiBTdXRDRagyGw8P89e9RPE9GXrC5vIVlCVMzLlMzLpvrITeWR0vB++2YH/nX/ydScFn1ZvjGG95K5GqhxmanGn1nkLa01tzViCi066O/ij22LeIRen61kkemPhxL0Z8bzyi4rZCplTpeM0R1RAG35nLHTprFCmNcPyJ0rbEainUlb3Y16nTVC4TCVqvnfgTI1AOmluusL+Vx/NG1IJHoDK9ayaNe8nrG+isfyfDEgx/laRzg5loOWBbMLjg9FQbD4WAMyT2KZUkv375nUARsmwFxOwDb0V0Vk5pkgRZ7BGiEab7xlu8ndtxewDtT0x0Vl88O+tVTrTDxptVNE40ci/WlwtjH0yykCFIWrh8P7DcGytN7B9vtIGLhUhmJtzOM8uU2ThCzeqo49jgOFaWYWq6Tr7SJ0ccQpizWF/L4I+p1ukkKewXGleiUbUSwdsxALAW5apuNODdSuTcWqMxkaOQdvutrX+Psn32L0HV50Frn//v8tshovmgnamUB20JtHQoli8WlFHLMDP3diDEk9zCTUy6plMXGWkgUKrJ5i6kZF2eHzES+YCMS7FkCcOXcA8TW4MpDALcd4bbCgWyfMGXjBHGiMVlbKtDMuzfnXhLh+rkJZq5UyNS3g7OV6Qy1yfSeHy9stnpGpIulIN0IcPwosT/4gaMUrh9hRQo/7QyshBxfxyZ8zx7SGrODiMmVBpm631tVdEfr+jELlyrUiymtriuD+5y40cCKd/9lY0tYX8zRzKc4+cLmyO0kVoSeQyvnkq4HPeOk0AoGjbzDX/nVXyO/VcbpzD5WRAfKF07oZIhUykrssz41Y6OUUKtGWBa9GF65HJnWuUcAY0jucXJ5e0+3gGUJp896XL3s9zowdivol68GvQu+mS+BlbAv0c2Kgr77eXk6g9cIBquhBepFj2ZCPcE4pFoR6WY0YA3saLz4yMgVkugq+9ttSGw/Yu5KRWc7dYJXG/M5moUUs1erpJph7/XKdIbyjE6ntqKYxYtlrBH1Nt3XslWfRiHo/W29esDclcpY7qxm3qVZ8MhU2xCrIRFO0AkQXRfi6okCpTUdR+nWEm3OZfkL6a9RqFewo233l1JQKUdMzcakUtpATs245Is2tYreLl+0e++VJm0uXWjTbqnOZ2PWbgScOZ9O7PppuDMYQ2IYCy9tce5+TwtBomeEoFMzu8altL7M1swCsb3jtFLgpwdvxO2sy9pSgamVOnao+1PUJjw2b7VSXSnmrlSwd8yuc+U2zVxqT+Pkpx3SjWFjIgqC270a6WQ69VZoHcs8tVLH32rhtaKB14vrTV20WfTIb7bG0j2zFOTKLf13ULo/+m7uLNheSZRnsuS2Wkyt1Ic+03V7bcz3rXZEKM9mKc9u1w49/u4/5HPveIpqaziGooBmPeoZC9Ark6mZ4aTSlWsB0Q7l3yiEG8s+J06ZBmqHhTEkhrEREVLe4C1rYcnl6iVdKLb00re5cv5VxGLpJQvgxCHna5f57/7Dnwz1NWkWUlzNu0isq6H3kymVaoVIgmPdUjCxWu+pDDeKKfzMcLygOpmmsKVn3N1RxKIl860oZnKlheqsmAYK8mJFYatFrtJGiVCbSFMvpm7qWFKtUKfU7nhdFNtGZMcxFTdaNIreSBmZRDo7siI1sod6f6FgI5+iPJsldC0WRhQYxpZw41Qh8W8K8JqHtnjPBz7Lk4+AMyJMg4KV6yGOa+26OlZK0RhRL1KvmjqSw8QYEsO+yOVtzpz32FgL8f2At3/9Eb7zsu/hcv4EqTjgVeXneXXlBUD3g+/y+n/1Kr6WX+JnfvM8sb3/Gb92kSWLObl+jOtrAcrCVotqSRsDJ4hpZ7RPP3Jtls8UmVypk26EKEuoTnhIFDN/qdJzwRU2W2zNZKhOZ/VK4lKFVGtbRj3VrJGup24qUcCKVOLQd5Omsjouu8CzUSOy1fqJBeqdLoVd/bPE7Sxh5UyRwNu+NVhhvGscJcmIPBp/nKcfc+CR7dcmppI7e4JeWVy95HPfK9IDUidKqfH6hBiv1qFiDIlh33hpi8WTXddRxJn1p2D9qcRtVaxYuR7w2bfr998n8Nq3Wrzmi/9guK2vUpz8zgVe8V+exgkCXnzlA7zw4KuJneHTtp12GHXb7b/HiILiVhsl7Z6oZODZrJwuEXgON05vN1NKNQPmd7R8FaWlQhpFj1QrGjAioCt8cxWfXGWd0LXYnMvSLIxwuShFrtwmX24jNzGhVui0auispDZau6ZRd1dS3c8o26KZd3U7gL5tY4HyTGbAiIBOn06KiwBEO2TiB1N5u4epeiq8J06luHo5WeoEtO5boWSzthKwtamNTjojzC+mSGcsCkWbamVYa6tYMum/h8lta7V7mNwLrXaPK8vXfCpbg7NSEVg8maJQ1DeD3/zkjwHg/t2nefkzz+IG2i0VOA5bs7M89mPvQSWsYjJVn5lr49VD7LyBVqYyAz59gIkbdYoJN+lYtDaU2w4pbrV3Pd5YYH0xT6O4w5goxeyVKunGYHbT0HfR8ZJ1jikGlC1cP1vq1YikGgELlyqJxxw4FqsnC0P6WFYU68Zh7ai3QmkUPNYXc4luudJqg+JGc8CoxgJrS3le/jeavOcDn008/s2NgPUbIVGk8zBmZh2CIGZzPcFyCszOuzTrEfXaYHW7WHD2Pg/bEi5dbOv+IZ0/WColnDrrmf7r++RItto1GECvQKJY16coxZARAf36+mrQMyTv+cBnCfyYixfaxH2TTzcMmVhb48y3n+fiKx8Y+q5mIcXaUp6Zq7VdDUlSzCFXaQ8ZErWbS0W0S2rUTL1/3xM3GkOGJN0IB4xI0ri6L25NZ0i1QpwgppVzqUxlBlSN/azLxnyOyRv1oRv9+lI+UWQxti2Wz07gdvbrp+1dixfLM7oWp7jR1AbNEt7/UyX+/MSTPPnjzyZ/ZjMcqFOKI52cURqh5SaA58HayrBEiop11875xRRn7/No1GP8tsJLC5msZdrkHjLGkBhuC0op1lYCNjuS32Kxq1x3uKNDXWNEW1Q3CDhx4cVEQwLQzKeIHcHaRcF4XOpFT984E3bVKKSwxhAmBHTP+R2yK17D31NWvUt1KrNndX1tMk1sixbADGJ8z2FrLkt7VCFihyDtDKRl78SKIn6PX+SZ39f7iRACy8WLfeTvwZO77HttdbiwsDuZyBdsatXtSYWITu0FGSnd0+5ow+nmVja5/K6HZriDGENiuC2s3QjZ7AusqgjWV8ORweN0ZkeRnS0jg81vWP4W7/7g9wDw9/7ZwuCbIqyeLDJ3qaKzuPrcXP1B5p2urVppOI4Rejabc1kmbzQGXl+fy2q3UEIqaxKRPZyRFttWYtC7pzzS2Xz1RGFsiZZG0Rt2oXWYWl7hz33xS8xcXyZMuWz+6AO84+dfhfTFOB74p/9WB8jR/c1Xrvm024p/J1CcUExM6Ru958VjrQB2Tg66xBEsnHCo12wq5RBBKE3aZHMWYThaa8vLmFXHUcUYEsOBo5RiY21Y+nu3cNzOYGkubyVm0IrAxKTT65j36F8Oyfzo6wD4u8GreebhCfy0w9X7J7WsfKTwvW3pFD9lM3e1iiiFxNq4+J4zssVrbTJDo+CRrfkoEZp5l9krVVI70nL7D22nkdqaHe7FUi96Qwaq+9nQFspzORp5d6iK/VZ4w+su8d3/7ecIm3oFlWr7LPzas7z48DdYOrVdX9MNkPvtmMsX29uTAAXlzYjyZoRYeowLSy6F0u63j1RK8P2ELDpXsCyLQpGeO7P/vZ2rFdh7RWs4XMwvYzhwKuW9+0fspFaNKfbJcYnoAOqli4NxkkxOcFPbt+qnH3PgMe2jfw/P8h7gtX855J3W3xmYnft99/Kr902SrfpaciTtaBXaBKuVqfmUVhu4QUyQstmazSKRSqyC7xqAetEjX25hxTrbaWsmQ31i2HcUO9bI+IoTqZuuRYHtmo2dLP9rn3Jz0A2nFNSqEWGghiTYN9ZHaF2hYxUKuH41IOXt3qJ2dsHl2o4MLRGYmd/9trN4wmVtFbY2IuIYMlmLuUUXN2W6XhxVjCGyW+K8AAASIUlEQVQxHChBoFi+evOGJEyIaQS+GjAiAM26brc6tzC6Uv3pxxw+jK5Z+b6v/4/857UXB1xgyhKtPrsL2Up7oFWs1wqZvVJhcy47ssAjti225nNszWW3U7B2MQbKFogSdrTjIx/74PLA89fNnOOJBz86/LlHhl8CaDeTYzki4Psxzo4ge7u1d+xHKbh6uc3UtEuhZCdmTOULNkunUqytBPi+0n3V57T8yW6IJczOp5id33MYhiOCMSSGA6W8efNGRES7snaytjosKa6UnqnOzKmeUF8cK7Y2AqrlGMsRZmYdMll9s+recD+MXqn8zz/4Xp55ePcOf6Db9O6s5LaULkhMNCJoTareAaE496d/xqv/5Kt4zSbXz5zm6be8mXppW0m4VvIobLYGvsdx4Q2ZF/hl9cegtFFs7TAQN9tGzMtYtJKkSRQDsiRdMhmLVnPv+E/gw43lgNWVgNPnvIHVid+O2VgLabdj0lmLpdNO4ncZ7g6MITEcKEGCT7yf0qQ9kAIsotsBT0wNn4rBiGAt6ICtZUEcKV58oUXYs1+KSzWfySmbucXBVcvTjzm857HP9txfI42KGi0h4gYx6/M5plbqvXqVWLQbq9IXZ3ntHz7Bdz31FG6gB3bfN7/J2Usv8Gf/zw8TzuT4l9+3yP/74C/zxfk3cTm7iK0iYrGYq6zzym89w9Pq4C7NqWknsXYnX7Dw/RjfZyCFdnLaobyl3Up7oZT+d+2Kz7n7tQuv2RiMsbSaEZVyxOlzHuldXGGG44spSDQcKFsbATeWk33s0zM2M/MpatWIzfWQKFLkCzaT006ia+TyxXaitpJlwf0PpBER1m74rK8mz57P3u/heYM3rjDQd/9+qfw3ffq7kTe8Y7uyXilOPr85JAAJELgW1+6bxGsEFDaaOGFMM5eiOpUmti2+8pEMfrnFbyz+C6LW8OpsctqmOOFw43pAsxFjWeAsTcDiNKWgxlSwd5vaLrVKxPpaQBhoQzAz55Dykm/UrWbMynWfVlNhWZDNW9Sr8YDnbelUqqd11W7F3FjWYxznFiEC51+exnGEi99pJXbUzGQtTp8zwopHFVOQaDgyFCccNtYiglANuIByeWFmXq8Q8gWbfGFvSYuJKTvRkEzPOr3Zc2VrtAtmYy1g8YS+cbWaMdeubMvge2lh8WSKVMrqFNQ9y4c7n7N+9b/mX/2sh1IeIn0dGmW7SVY76/Lb6f+jly7b5YlHoNmIUH6yi69eiylvtnuz/TiG4OoW+UqVqVPjy+dvrgesrmwb7Golol6LOHPeSzQm6YzFmfN6xRCFiu98u9VbTXS58pLP/a9IYzuCl7Y4ddbr7Dvk+pVgV4PSDQkppUa2ZW42jLDi3YoxJIYDxbKEM+c91td0zEIsmJjUq46bZXM92Uj0p5SKjIh8Qy9QH4WKyxfbA66aVlNx+cU251+e7hmlMFBcvdym/brf4R0CsWXz7Te8hRuzp7HDkNetPstrXrjQ28fTIy4fxxndxljFashl1M2gCoIY193b9RPHKrGLYBzrIsClk7sbpEolWTgRYG0tYH5HIkO+YOO44a5uy3RasB3piCwmp3ofgDan4YhiDInhwLEdYW4hxdzC3tuOIo7VyBlstRyxsKQfT0xrN1ESxQl9epe3kl1tUazlx/NFG6UUV15q025vC19JHPGKJ/8j97sOdqhTflcmbeYW3F0L8tyURSZrDbmFROgkCAwPRgT8tsLdvRAd0AZv1C19nFl/nJQp1qG8ETE3P6i4KyKcOeexshxQLQ8ad8vSNR5d0U4RXVxY3hyOySTFwQx3B4cS+RKRHxWRb4hILCIjfXIi8pdE5Fsi8oKIfOhOjtFwhOm7h09OOXjp4Zu6lxbyBX16B8GIamm1HdBvt1Vi8RyAHYSgtgvzxul9oeMNuqhSRM/GF064pEdUZ+sMqvHqRmxHRurLj9MlMJvbfWmQ5JqyHWHpZIpXvCrDy78rzamzKWbnHRaWUtz3svRARtbsvEuuYHUMpz7+QtFmetYYkruVw/plnwN+GPjkqA1ExAY+AbwDuAJ8VUQeVkr96Z0ZouEwsSwhm7OGYyQCxR11CGfOe5Q3IzY3dFyiNGEzMbUdR8lkraEZcpeuNEsUjnbJ9KMUbG2GibUQ7XbM5nqI31ZkshbzSylE9ApAobhxPaReGzZC3fTncQvubHtE9bfA9Mzel3Q6I1g2QzU62zva/fMiQjZnjzRIliWcOOURBIrA1y10dxY9Gu4uDsWQKKW+CezqHgDeCLyglLrQ2fY3gHcBxpDcIywsuVx6sU0U64pqsfSMe2Z+0P8jotOHR7lOCgWb9ZT28fenHWeyVm+F4KWtsbKTgMS02Hot6nWKBB3cL2+GnDnvYTvChefbRCNKbEqTNrPzyT6tblfAaln3oi9N2GSyNgsnXJav0etrLgKzCw65viSGuJN1Zu3Q6hIRZucdVq6HQysb2wbPO5ibvusK7i6Kwoa7h6O81jwBXO57fgX4c4c0FsMh4KYszr8sTa0a4/sxXtrquItu7kYnlnD6nMf6aicBoKM0OzW9vWpxHGFy2mFzF3kQ0Dfs4sTgzVEpxfK1wawmpSCKtHhlNm8lGh8R7QIblcHW3W+1vL3yqGxFTE47zM67LJ1MEUWKKFK4rvSORasL+L3VXCZrsXDCHXA/lSYc6tW41/ej64JbOu0ZSXbDTXPbDImIfBFICrf+jFLqd2/D970feD/AvJsswGc4foglFEo2sL+ZrW0L2axNvRYT+IpKOcJLWwM38Zk5h3TaYmM9II7A9aBe7csQsyCdtijtEJiMIu0aS6Jei3BcvaLaiVK6ApwRhqTVjAeMSPczm+shpUmbVMrCtmWoNe2lC/0FmjoAf+mCzlDrrk5EhBOnPZrNmGY9wnaEQsHGMs2hDLfAbTMkSqkf2OcurgKn+p6f7Lw26vs+BXwKdEHiPr/bcJdRKYcsX91eNfhtxbXLPgsnXIodFVvd58LC93X8ASUsLDn4fkwUQq5gky8Mr4h2U3m3bMHzLMSKhoyJWIwsIASGYiD91KsxqWn92bAT37FtoVaNiRKMVhzrbLfS5OAln8lYZDKm2tywP46ya+urwMtE5BzagPw14McOd0iG48raSnKTpbUbYc+QxJHi4oW2Tq9VADo+MT3njIxhgDYWubxFrRYPxBxEYHLKJl+0sW8E7FRdcV1J1Bjb/vwIC9XJhmo2Y5av+PidzLNsVhuFkasf3xQEGm4Ph5X++0MicgV4E/CIiHyh8/qSiDwKoJQKgZ8GvgB8E/i3SqlvHMZ4DcefUbpd/UV2W5thnxHRKEWn5/jui9yFEykyGRlIeS12sscsSzhzLk2haPdiEcUJm9Pndo9HFCfsZPFgpbPNrlxs65TlTjesRj1maytEEq5qEUinTeDbcHs4rKyt3wZ+O+H1a8A7+54/Cjx6B4dmuEuxHRKzppy+K6BWTdaVEtHxiq4OVeL+beH0uTTtdkzoK7z0YMqr48pAE6lxSKUs5hddVq4HfW0edYB+lNsrisB1IQwGU5kdd7uuxmA4aI6ya8tgODBmZp0hMUkRmJ7bvgT6hRz7UYpEUckkPM/CO0BdwtKkQ75oU69FOoaTs7BsGQrCdxF0kabfSShA6WLA2XkXGbNlr8FwsxhDYrgnmJhyUXTdVLpeYnrOYWJyO/YxOT1c5Ac6lpFUPX+nsG3pxXG6pLNCtZJcQJnN2UxOW8wv3qEBGu55jCEx3DNMTrlMTDp9dRODxiGTtZldcFhdDntV7m5KOHk6deRqK0oTDhtrIWGfzJgIZHO7t781GG4HxpAY7ilEZNdW6JNTLqUJh1YzxraFlCdHzohAV2U5zdqNgFolQixhYtJmagyJFIPhoDFnncGwA63zdfQznBxHWFhKwdJhj8Rwr2PWwAaDwWDYF8aQGAwGg2FfGENiMBgMhn1hDInBYDAY9oUxJAaDwWDYF8aQGAwGg2FfGENiMBgMhn1hDInBYDAY9oUxJAaDwWDYF8aQGAwGg2FfGENiMBgMhn1hDInBYDAY9oUxJAaDwWDYF8aQGAwGg2FfGENiMBgMhn1hDInBYDAY9oUxJAaDwWDYF8aQGAwGg2FfGENiMBgMhn1hDInBYDAY9oUxJAaDwWDYF8aQGAwGg2FfGENiMBgMhn1xKIZERH5URL4hIrGIvH6X7S6KyNdF5GkReepOjtFgMBgM4+Ec0vc+B/ww8Mkxtn2bUmrtNo/HYDAYDLfIoRgSpdQ3AUTkML7eYDAYDAfIYa1IxkUB/0FEFPBJpdSnRm0oIu8H3t952n7zc488dycGeAjMAHfzCs0c3/HGHN/x5RW3+sHbZkhE5IvAQsJbP6OU+t0xd/MWpdRVEZkD/kBE/kwp9Z+SNuwYmU91vvsppdTI2Mtx5m4+NjDHd9wxx3d82U8c+rYZEqXUDxzAPq52/r8hIr8NvBFINCQGg8FgOByObPqviOREpNB9DPxFdJDeYDAYDEeIw0r//SERuQK8CXhERL7QeX1JRB7tbDYP/KGIPAP8CfCIUur3x/yKkbGUu4C7+djAHN9xxxzf8eWWj02UUgc5EIPBYDDcYxxZ15bBYDAYjgfGkBgMBoNhXxx7Q3K3y63cxPH9JRH5loi8ICIfupNj3A8iMiUifyAiz3f+nxyxXdT57Z4WkYfv9Dhvlr1+DxHxROQ3O+//sYicvfOjvDXGOLa/JSKrfb/XTx7GOG8VEfm0iNwQkcTkHtF8vHP8z4rI6+70GG+VMY7trSJS7vvt/vFYO1ZKHet/wCvRhTRfAV6/y3YXgZnDHu/tOD7ABr4DnAdSwDPAdx322Mc8vn8KfKjz+EPAz4/YrnbYY72JY9rz9wD+NvDLncd/DfjNwx73AR7b3wJ+8bDHuo9j/AvA64DnRrz/TuAxQIDvBf74sMd8gMf2VuDzN7vfY78iUUp9Uyn1rcMex+1izON7I/CCUuqCUsoHfgN41+0f3YHwLuAzncefAX7wEMdyUIzze/Qf978H3i7HQzPoOJ9rY6F00fPGLpu8C/g3SvNHwISILN6Z0e2PMY7tljj2huQm6MqtfK0jp3I3cQK43Pf8Sue148C8Uup65/EyOu07ibSIPCUifyQiR93YjPN79LZRSoVAGZi+I6PbH+Oea+/uuH3+vYicujNDu2Mc5+ttHN4kIs+IyGMi8qpxPnDUtbaAOy+3cqc5oOM7sux2fP1PlFKqo6uWxJnO73ce+LKIfF0p9Z2DHqvhQPg94NeVUm0R+QB65fX9hzwmw3j8Z/S1VhORdwK/A7xsrw8dC0Oi7nK5lQM4vqtA/6zvZOe1I8FuxyciKyKyqJS63nEP3Bixj+7vd0FEvgJ8D9pXfxQZ5/fobnNFRBygBKzfmeHtiz2PTSnVfxy/go6D3U0c6ettPyilKn2PHxWRXxKRGbVHK497wrV1D8itfBV4mYicE5EUOnh75DObOjwMvK/z+H3A0ApMRCZFxOs8ngHeDPzpHRvhzTPO79F/3D8CfFl1op1HnD2PbUe84CHgm3dwfHeCh4H3drK3vhco97lnjzUistCN1YnIG9E2Yu8JzmFnERxAFsIPoX2UbWAF+ELn9SXg0c7j8+jskmeAb6BdRoc+9oM6vs7zdwLfRs/Sj9PxTQNfAp4HvghMdV5/PfArncffB3y98/t9HfiJwx73GMc19HsAPws81HmcBv4d8AJaAuj8YY/5AI/tn3Sus2eAx4EHDnvMN3l8vw5cB4LOtfcTwE8BP9V5X4BPdI7/6+ySLXrU/o1xbD/d99v9EfB94+zXSKQYDAaDYV/cE64tg8FgMNw+jCExGAwGw74whsRgMBgM+8IYEoPBYDDsC2NIDAaDwbAvjCExGO4gIvL7IrIlIp8/7LEYDAeFMSQGw53lF4D/5rAHYTAcJMaQGAy3ARF5Q0e0MN1RVviGiLxaKfUloHrY4zMYDpJjobVlMBw3lFJf7TTg+jkgA/yqUupukuUxGHoYQ2Iw3D5+Fq1N1QL+ziGPxWC4bRjXlsFw+5gG8kABra1lMNyVGENiMNw+Pgn8I+DXgJ8/5LEYDLcN49oyGG4DIvJeIFBKfVZEbOAJEfl+4H8DHgDyInIFrWT8hcMcq8GwX4z6r8FgMBj2hXFtGQwGg2FfGENiMBgMhn1hDInBYDAY9oUxJAaDwWDYF8aQGAwGg2FfGENiMBgMhn1hDInBYDAY9sX/D9rvvc/FzTZxAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted = (predictions_test).reshape(100)\n",
        "actual = test_Y.reshape(100)\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "cm = tf.math.confusion_matrix(labels=actual,predictions=predicted)\n",
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "fhLDXhomlcLU",
        "outputId": "e3f5f679-a0b9-4ff8-e1db-3093aa980121"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}