05_redes_neuronales_en_keras.ipynb

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  "metadata": {
    "colab": {
      "name": "05_redes_neuronales_en_keras.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKNAOn5Ia7pG",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# 05. Redes neuronales en Keras\n",
        "[**Python Deep Learning** Introducción práctica con Keras y TensorFlow 2. Jordi Torres. Editorial Marcombo ISBN: 9788426728289 ](https://www.marcombo.com/python-deep-learning-9788426728289/)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcFrOCQOYU1R",
        "colab_type": "code",
        "outputId": "6afeb0d3-69ab-4a2b-a671-a593854d1d8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "2.1.0-rc1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eo_CA0gPbU0v",
        "colab_type": "text"
      },
      "source": [
        "## 5.1. Precarga de los datos en Keras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbWbSbpgbR2P",
        "colab_type": "code",
        "outputId": "469dc349-df62-44d6-b8ee-666a33b55899",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZwZjJzAbR5M",
        "colab_type": "code",
        "outputId": "ff90edef-316c-4884-b279-ddcfc717c60b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(x_train[8], cmap=plt.cm.binary)\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc078392518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0h7M-9gbR70",
        "colab_type": "code",
        "outputId": "125bab01-808c-4575-b783-d53b992d23fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(y_train[8])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOmFJbrLbR-S",
        "colab_type": "code",
        "outputId": "3f20bfd1-6f95-4f46-a1cf-21054eed124b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(x_train.ndim)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjlArRbNbSAv",
        "colab_type": "code",
        "outputId": "043d4f88-bc25-4ba5-8c8a-90a116e08ba6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(x_train.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQt2DrbxbSDw",
        "colab_type": "code",
        "outputId": "1238576b-cc6f-49be-c510-2f1ba1f772e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(x_train.dtype) "
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "uint8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W80JMvrJcCSv",
        "colab_type": "code",
        "outputId": "57628d41-f506-487c-a283-90f301eb2b66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "my_slice = x_train [1:100:,:]\n",
        "print(my_slice.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(99, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PCuRzlxcCV1",
        "colab_type": "code",
        "outputId": "b9effc53-5bf0-4ba8-c2ea-f24588403e2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "my_slice = x_train [1:100,0:28, 0:28]\n",
        "print(my_slice.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(99, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bybhrOQfci2T",
        "colab_type": "code",
        "outputId": "83c5b126-707b-4df3-9933-4364dc9be9d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "my_slice = x_train [:, 14:, 14:]\n",
        "print(my_slice.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 14, 14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-TbwVWKci5Z",
        "colab_type": "code",
        "outputId": "ae01f1ae-ecca-4081-b7e0-398fe07b73d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "my_slice = x_train [:, 7:-7, 7:-7]\n",
        "print(my_slice.shape)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 14, 14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H4SaP6qOctI8",
        "colab_type": "text"
      },
      "source": [
        "## 5.2. Preprocesado de datos de entrada en una red neuronal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lt3GCRmjcjDW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "\n",
        "x_train /= 255\n",
        "x_test /= 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z79MRq-Fc92R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = x_train.reshape(60000, 784)\n",
        "x_test = x_test.reshape(10000, 784)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5C8xn-PNc95v",
        "colab_type": "code",
        "outputId": "f6c51fb4-232d-44e4-c21c-f7673bd72b89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(x_train.shape)\n",
        "print(x_test.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 784)\n",
            "(10000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JbSPFawdEdq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.utils import to_categorical\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mZpRqCFdEgT",
        "colab_type": "code",
        "outputId": "8e01b462-4a7f-4780-c8e7-7f764c811a51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(y_test[0])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfI2eoMDdEi3",
        "colab_type": "code",
        "outputId": "7b42abe2-b762-4608-ff4b-edeee213d347",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(y_train[0])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Orh9esT_dXGD",
        "colab_type": "code",
        "outputId": "45f426ce-b056-4503-e44e-a9db46339fca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(y_train.shape)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqE6m6LTdXJE",
        "colab_type": "code",
        "outputId": "61224164-fd3b-431f-fac7-0fc90f1dcce3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(x_test.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHZW1B1_dXLz",
        "colab_type": "code",
        "outputId": "25ae1c60-6c51-4a39-c3eb-fdc7608b58f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_train = to_categorical(y_train, num_classes=10)\n",
        "y_test = to_categorical(y_test, num_classes=10)\n",
        "\n",
        "\n",
        "print(y_test[0])\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxFv8Kyjdmbz",
        "colab_type": "code",
        "outputId": "ef849978-7003-4c20-affe-17401d1edb21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(y_train[0])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15w4fdrXdmkA",
        "colab_type": "code",
        "outputId": "31d88a1a-8f80-48dc-93eb-ac33fb9f1648",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(y_train.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aw4XzCxZdmnR",
        "colab_type": "code",
        "outputId": "26162b29-cf60-41fc-d620-8aa4365505ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(y_test.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ELTiOpK5eMNJ",
        "colab_type": "text"
      },
      "source": [
        "## 5.3. Definición del modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjwjDIuXeS_s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(10, activation='sigmoid', input_shape=(784,)))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJLHWHNueTZH",
        "colab_type": "code",
        "outputId": "a556d0eb-5996-4549-b553-dd2acb8456e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                7850      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 7,960\n",
            "Trainable params: 7,960\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFnV4guqtTr1",
        "colab_type": "text"
      },
      "source": [
        "## 5.4. Configuración del proceso de aprendizaje"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "putRyHXReTb5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss=\"categorical_crossentropy\",\n",
        "              optimizer=\"sgd\",\n",
        "              metrics = ['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fJpk-fczuQ5n",
        "colab_type": "text"
      },
      "source": [
        "## 5.5. Entrenamiento del modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKXNyMOBuVIA",
        "colab_type": "code",
        "outputId": "461bcba7-c173-41a8-edf2-65bb3e62535b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "model.fit(x_train, y_train, epochs=5)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 6s 100us/sample - loss: 1.8652 - accuracy: 0.5412\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 1.2450 - accuracy: 0.7417\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 67us/sample - loss: 0.9301 - accuracy: 0.8060\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 66us/sample - loss: 0.7560 - accuracy: 0.8388\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 67us/sample - loss: 0.6505 - accuracy: 0.8551\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc0525407b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRY-V06iuWpl",
        "colab_type": "text"
      },
      "source": [
        "## 5.6. Evaluación del modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ur_3LWCuc5K",
        "colab_type": "code",
        "outputId": "3eff0a44-58a5-4ce1-fb65-620363bdfc42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, y_test)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 71us/sample - loss: 0.5946 - accuracy: 0.8725\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDviVL2Guc8g",
        "colab_type": "code",
        "outputId": "0132e8a0-ddaf-45c0-aaee-20aec4c0c4d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print('Test accuracy:', test_acc)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.8725\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCn4nyM0uivj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Look at confusion matrix \n",
        "#Note, this code is taken straight from the SKLEARN website, an nice way of viewing confusion matrix.\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('Observación')\n",
        "    plt.xlabel('Predicción')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cnz952tguiyC",
        "colab_type": "code",
        "outputId": "b71ee69a-bd08-41ab-e1b6-08aa278ffe55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "from collections import Counter\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "\n",
        "# Predict the values from the validation dataset\n",
        "Y_pred = model.predict(x_test)\n",
        "# Convert predictions classes to one hot vectors \n",
        "Y_pred_classes = np.argmax(Y_pred, axis = 1) \n",
        "# Convert validation observations to one hot vectors\n",
        "Y_true = np.argmax(y_test, axis = 1) \n",
        "# compute the confusion matrix\n",
        "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
        "# plot the confusion matrix\n",
        "plot_confusion_matrix(confusion_mtx, classes = range(10))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wu07CLl+mTYum+NSsiq9nNebMmgnA+DGjqOVTkzp+XnRsq4/cc8aRTEZltk8PbJV8/XnN\nfjoOmf3UsQ8HNGf7gdNU6ziO7QdO8+GAFgB8NKglR0+H4dd9EoNG/sy04V0AqFy2GAM616F+36n4\ndZ9E6wZVKVPCuuzrRkjsOjk5MXnKdI4cO8mO3fuZ991sXWVwjZLvBWN9d3FxIXDTVg4cPkpQcAgb\nNwQStH9/2jdagN514uTkxMQvpxIccpytO/cy/7s5/HPqJO8N+5D9wSHsPXCYVm3aMXnieLvKyVCS\nySJrZLbPuJ6lgK2Sr3sOn+PW3YdPHWvXqDpL1gQBsGRNEO0bVwegYpmi7Dh4BoAzF69RqnhBXAvm\noWLpohw8fpFHj2MwmeLYdSiUTk1qWuy7URK7xYoVw9NLW/qbJ08eKlasxJUr+qVuNUq+F4z1XQhB\n7ty5AYiJiSE2Jka3BNd610nRYsWo6flfPXhUrMiV8PCn5J6jouyXe85QksmgWqLpgZ6Sr64v5eFq\npCaAdzXyHq4vaRpOf58Jp2OTGgD4VClFyWIFcSuSnxPnrlDXsxwF8+UiR/ZstKpXBfeiqS6rfYrn\nIbF76eJFQkKO4Ov3jBJLhscI300mE/7eNSlZ3JUmzZrj55/x6+XSxYscCwnBx1wPY0eNoGLZUqxY\n9gufjxprl+2MJZms/aGzdMuoGKn2+UxGlYxOvMrHtIWbyJcnJ/uXfcLbPRpy9HQYJlMcpy9cY/pP\nm1gzZwirZw9JOG4Jz0Ni98GDB/Ts9ipTp3/zVAsmM2CU746OjgQdCiH0YhjBBw9w4njGfhwfPHhA\nn55dmTztq4R6GD3uC/45d4luPXoxf+7sNCxkHjSJJRVEU+Mn0li4bwt6Sr5ev3mfooW0B7Voobzc\nuKWpcd6PesybY5ZQq8dkBo1cTKECubkQfhOARX/to27vKTQf9A137j3k7KXrKdpPjNESuzExMfTs\n9irde/am0yuddbP7PHgevufPn5+GjRqzcaMxfbt6EBMTQ58eXejWoxcdOz1bD9179GLVX/bJPWco\nyWQhEA6WbxkVw4JoChlV7EZPyde1O/6mT3vtlalPe38Cth8DIF/uHGRz0lZIDHilDrsPh3I/6jEA\nhQtofWwlihagY5MaLF8fnIzlZzFSYldKyVuvD8KjYiXee3+YLjafF0b6fuPGDe7cuQPAo0eP2LJ5\nEx46yCUbgZSSIW8OxqNiJf7vvfcTjoeG/if3vDZgNRU8POwqJ2NJJmeNlmi6zxNNLJlcomTJNK+3\nVfJ10aT+1PcuT6H8uQkNHM/479YxbeEmlnw5kH6davNvxC36fKTNr61Ypijfj+uLlJJT5yJ4a+zS\nBDu/ThtMwfy5iIk1MXTyCu4+eGTjN9ePvXv28MvSn6latRr+3tpA19gvJtKqdRtd7Bsl3wvG+n41\nIoLXB/bDZDIRJ+N4tUs32rTVZ2qZ3nWyb+8efv1lCVWqVqOOnzbANHrcFyz+aQFnz5zBwcGBEiVL\nMuNb++SeM5RkMplXyTYxhkkmAwghXgYCpJQWTabz9vaRe4Isa9lZi0pAosgoZMYEJEZIJjsWLC1z\ntxxn8fX3lr32wkkmKxQKRcoI85bJUUFUoVCkC4KM3ddpKUZOcfoV2Ad4CCHCzFlUFAqFIgE1sJQK\nqWRUUSgUCiBrDCyp13mFQpFuqCCqUCgUtqIGlhQKhcJ2BMLQPBLPCxVEFQpFuqFe5xUKhcIeMn8M\nzVhBVAJxccasoLqxf6YhdgFKvrnCMNsA5+d0MdS+URgp32vkqh+jMXCRYOZCqJaoQqFQ2IUKogqF\nQmEHKogqFAqFjWSVZZ8qiCoUivQj88fQzKGx9NYbAynlXgQfz2oJxz77ZDie1Srh512DHl07JyTf\ntRYjpGrfbF6BneNasmNcS757oxYuTg7Ur+TK5lHN2Tq6OWs+aUxpVy25c/e6L3Pymw5sHa2d612/\ntF1+//n7Snw9q5E3hxOHD9meVvDtNwZRukRR/LyqJxw7djSExg3qUMfPiwZ1/Ag+eMBm+/HoLT2c\nnN9/HztKk4Z18feuQdfOHbh3755Nto2UNQ67fJm2LZvi61kVP6//bB87GkKTBnWo6+9Fw7r217nR\n8tpWIbLG2nlD84lai5e3j9y97+Azx3fv2kmu3Ll5fWA/go/8DcDmTRtp1LgJTk5OjPjsYwC+mPhl\nirbjUvieVyMiuHo1gpqeXty/f5/6tX1ZtvIPiru5J2jczJ39Lf+cOsmMWcknxC3zv98SPhfNn4M1\nnzSm/sgNPI4x8f1btdn8dwRD21TitVm7ORtxnwGNy+JZuiDvLjhI97ovU7NUAT795UiKvic3Op+S\n3whtAvN7Q95mwuQpeHnbln5x966d5M6dmzcG9efAYS3jf8e2LRny7lBatGzNhsB1fDN9Gus3bU3R\nhiWj87t37SRXrtwMHvgah0Is1z9KaXQ+Ob8b1vVnwqQp1GvQkMU/LeDSxYuMHGN5Hst49HhWIPnR\n+aS2G9Tx5dcVf/Dx8PcZ8n//1fmMr6axbmPKdZ7NKfU6j4iI4GpEBJ5eWjl1/L1Z8dtfVKpcOdX7\njMgn6uxaTrp2mWbx9eFzX8mQ+UQzRUu0Xv0GFCzwtDxts+YtcHLSeiP8/GvZrFhohFStk6MD2Z0d\ncXQQ5HB25NqdR0gkebJn08rJkY2rdx7b5G9aflesWIkKFeyTkACtzgskqXMhBPfNrbh7d+9SrFgx\nXcrRU3o4Ob9Dz56hbv0GADRp2txmnSIjZY2TtX0l/Jk6L2pnnRstr20tWUFjKUv0iS7+aSGvdu1m\nt53kpGp/XfozefPlY+2GLRbZuHrnEXM2nObIlLY8ijGx48Q1tp+4xvs/BfPL0Po8jjZx/3EMrSf8\nZ6+dtzu1KxTm3LX7jFwWwpXb1kmOJPXbKCZP+5pX2rXm808+Ik7GsXnbbkPL04uKlasQsGYV7Tt0\n4s8/fiM8kVCbrejxrKRo+5LZtq8/X079mlfat2bEpx8RFxfHJh3rPCPIa2fk13RLMTKfaAkhxDYh\nxEkhxAkhxHtGlDNl8gScnJzo0bO3XXb0kqrNlzMbrWoWx+fjdVT/YA05XZzoUqskbzWvQK9vdlFz\neADLdl9kXHdNT2hjyBW8P15LozEb2XHyGt8O8rPbb6P4cf53TJ46nX/OXWLylOkMeet1Q8vTiznz\nfuCHeXOpX9uXB/fvk83Z2S57RsoaP3jwgL49uzJ5qmb7h/nfMWnKdE6FXmLSlOm887Y+dZ4R5LWt\n6Q/NyMHWyNf5WOADKWVloBYwRAiReseLlfy8+CfWr1vLgkVL7KpkPaVqG1Quwr+RUdx88IRYk2Tt\noTD8yhWiSon8HL6giZ/+dfAyvuVeAuB2VDTRsVr/3pKdF6hRqoBufuvNL0sW08FcziuvduVQsP0D\nS88DD4+KrFq7gV37DtKlew/KlClrsy0jZY1jYmLo07ML3br3SqjnX5fqX+cZSV5bBdFUkFJGSCkP\nmz/fB04Buglcb9wQyDfTp7Li91XkzJnTZjt6S9WG33yId5mXyOGsSS7Xr1SE01fukSdHNsoU0Ubk\nG1YuwtkITePeNV/2hHtb1SzOGfNxW/02kqLFirN75w4AdmzbStly5Z9LufZy4/p1AOLi4pg6aQID\nB79hkx0jZY2llAx5azAeHpV4J5HtosWKs3uXuc6321/nGU1eOysE0efSJ2pW/fQEgpI5l6Zkcr++\nvdi1czs3IyMpX6YEI0aOYdqUyTyJfkL7Ni0A8PPzZ+bs76z2TW+p2sMXbhFwKIzNo5oTGyc5/u9t\nft55nojbj1jwvzpICXeiohm6UJuF8HrT8rSsWRxTnOR2VDTvLrCspZGS30+ePGH4sPeIvHGDLq+0\np3r1GvwVEGh1vQzo24tdu3ZwMzISj7Il+WzEaL6dM4+PP3yf2NhYsmfPblN9J0Vv6eHk/I6KimL+\nd3MA6NDpFfr2G2CTbSNljffv3cMys+26/prtUWO/4NvZ8/h4uFbnLi7ZmTHLvjo3Wl7bajJubLQY\nw6c4CSFyAzuACVLKVN9zUpripAcpTXHSg8RTnIxAJSB5FpWAJHnSmuJkK0ZMcXIpUl669bZ8nuqF\nr9u+eFOchBDZgN+BpWkFUIVC8YKh82R7IcT75kHs40KIX4UQ2YUQpYUQQUKIUCHEciGEs/laF/N+\nqPn8y7Z+DSNH5wXwI3BKSvmVUeUoFIrMiQCEsHxL1ZYQbsC7gI+UsirgCPQAvgS+llKWA24D8X1F\ng4Db5uNfm6+zCSNbonWBvkATIUSIeUunjheFQpHxEDg4WL5ZgBOQQwjhBOQEIoAmQHx/2yKgk/lz\nR/M+5vNNhY2jV0ZKJu8mS3QbKxQKo7AybhUSQiROCDFfSjkfQEoZLoSYBvwLPAI2AoeAO1LKWPP1\nYfw3Q8gNuGy+N1YIcRd4CYi09jtkiRVLCoUiE2LBa3oSIlMaWBJCFEBrXZYG7gArgVb2umgJKogq\nFIp0QYClr+mW0Ay4IKW8ASCE+AOtSzG/EMLJ3Bp1B+ITBYQDJYAw8+t/PuCmLQVnigQkCoUia6LX\nwBLaa3wtIUROc99mU+AksA2InyPYD1hl/rzavI/5/FZp43xP1RJVKBTphl4rkaSUQUKI34DDaEvO\njwDzgbXAMiHEF+ZjP5pv+RH4WQgRCtxCG8m3CRVEFQpF+mB9n2iqSClHA6OTHD4PPJPVR0r5GOiq\nR7kqiCoUinRBmyea+SfwvDBB1MgliOdmG7sss8J7fxlm+/ws47L4PIo2GWY7PsGLEcTEGruk1Mi4\nYdQybmOsZuzEIpbywgRRhUKR8cgCMVQFUYVCkU4IXac4pRsqiCoUinQhq/SJZop5oslJJt+6dYt2\nrVtQvXIF2rVuwe3bt+0uR2852ZRkcPv36UFdfy/q+ntR1aNMQv5IS3i9aTm2jWrG1pFNmTPIFxdz\n6rOPO1Zm19jm7BjdjEGNn87cXqNUAf6d3Ym2XsVt+h56yxoDmEwmGtb2ocerHQDYsW0Ljer40qCW\nN62bNeD8uVBdy6rl40nnju3sspPS7wnw3ZxZeNeojJ9XNUaa1WettW2kBHY8Z06fxt/HM2Er8lI+\nZs38xm67tqLjPNF0I1O0RPv07c+bb7/D6wP7JRybPnUyjZo04cPhnzBt6mSmT52cqmSyJTg5OTF5\nyvSn5GSbNmueppxsavYmTJ76lAxuk6bN+GnJsoRrPvv4Q/Lmy2eRvaL5szOocVkajd3E45g4vnvd\nj46+7ggExQvkoMGYTUgJL+VxSbjHQcDnr1Rhx6nrNn0HgL79+vPW/95h8MDXbLaRlO9mz6SCR0Xu\n39eULD8c+g5Llv+BR8VK/Dh/LtO/nMjs+Qt0KWvWzBl4VKqUoJppKyn9ntevX2NdwGr2HjiCi4tL\nQiZ9a21P/HLqU3LMTZo2o1KVqixd/hvvDXnbLt/jqeDhQVCwJs9tMpko+7I7HTq+oottW1At0edE\ncpLJa9espncfLaj27tOPgNWrkrvVKvSWk01JBjceKSV//r6SLt0sn+fr5CDIns0sx5zNkWt3HvNa\nw9J8vfafhGS/N+8/Sbh+YOOyrDtyhchEx6xFb1nj8PAwNgWuo2//gQnHhBAJAVUPaeB4wsLCCFy/\nlgEDB9ttK6Xf88f53/H+hx/h4qL98Srs6qqPbR0lsJNj29YtlClTlpKlShli3xKyQks0UwTR5Lh+\n/VqC7nnRokW5fv2arvb1lpNNLIMbz949u3AtUoRyFurmXL3zmLmbz3JwYmtCvmzD/ccx7Dh1nVKF\nctHBx531nzZmyTt1KO2aC9Barq1rFmfRzvO6fAe9+OyjYYyZMBkHh/8evxmz59G9c3uqlC/F8mVL\nee8D61+Jk2P4B0OZMGnKU2XpQeLfMzT0LHv37KZx/dq0bt6YQ8H2qTM8LwnslSuW0bW7zQt17Efn\npMzphZFJmbMLIQ4IIY6as02PNbAsXStZbznZpDK48fy2Yhldulr+EOfLmY2W1YvhPyIQz4/XkdPZ\nic5+JXBxcuRJjInWk7axdPdFvurrDcDYrtWZ8OdxQ+UorGXD+gAKF3alpqf3U8fnzprB8j/WcOLs\nJXr16ceITz60u6x1awNwLeyKl7d32hdbQdLfMzY2ltu3brF1517GT/yS/n162Dxf83lJYEdHR7Mu\nYA2dX9Vl0Y5N6JmUOT0xsk/0CdBESvnALBOyWwixXkq5Xw/jrq5FiIiIoFixYkRERFC4sPWvUMmh\nt5xscjK4ALGxsaxe9Sc794MfsQcAACAASURBVFjeaqlf0ZXLNx9y60E0AOuOXMGn7EtE3HnEuiNX\nAFgfcoWv+2lBo0apAswdrK14K5jLhaZVimAySQKPRtj9vWwlaN9e1q9dw6YN63ny+DH379+je+f2\nnD1zOqGV3rlLN7p0amt3Wfv27iEgYDWBget48vgx9+7dY8BrfVi4eInNNpP7PYu7udGh0ysIIfDx\n9UM4OHAzMpJChQtbb/s5SWBvCFxPTU8vihQpYmg5qZOxW5iWYqRkspRSPjDvZjNvurWJ2rRrz9Il\nWmLqpUsW0bZ9B7tt6i0nm5IMLsC2rZupUKEibu7uFtsLv/UQr9IFyZFNW61Tr2JhQiPuERhyhboe\n2v+wtSsU4vw1rdprjdiA/+faFnAknE+XhaRrAAUYNW4iJ85e4uipc/ywaCn1GzZm6Yo/uXfvLqFn\nzwDmuvGoaHdZ4ydM4tzFME6HXmTx0mU0atzErgCa0u/Zrn1Hdu7YDsDZs2eIiY7mpUKFrLf9HCWw\nVy5P51d5M6olmgZCCEe07NLlgNlSymckky0hOcnkD4Z/Qt9e3Vm8cAElSpbi51+W2+2v3nKyKcng\ntmzVht9XLqdLt+5W2Tty8TZrD4ez4fMmxJriOH75Lkt2XyR7NkdmDfTl9abliHoSy4c/H7bJ35TQ\nW9Y4KU5OTnwzax79enXDwcGB/AXy8+3cH3Szrxcp/Z59+w3kf28Owt+7Os7Oznz3w0KrW1hGS2An\nJioqiq1bNvHtHPslr+0ii0y2N1wyGUAIkR/4E/g/KeXxJOcS6857/3P2oiE+GPljGb3W2mOoWjuf\nFLV2PnkcDXrO69by5bDOksl5SlSUNYda/sdy94f1XzzJ5HiklHfQkqM+k65fSjlfSukjpfQpVMi6\nPiSFQpG5UaPzqSCEKGxugSKEyAE0B/4xqjyFQpH5UH2iqVMMWGTuF3UAVkgpAwwsT6FQZDIycgvT\nUoyUTD4GeBplX6FQZHIyeAvTUjLF2nmFQpH1EFlknqjFQVQIUQd4OfE9UsrFBvikUCheELJADLUs\niAohfgbKAiFA/LwVCaggqlAobMYhC0RRS1uiPkBlW3WZFQqFIjmyQAy1OIgeB4oC6btmUKFQZBmE\nMG5xwPPE0iBaCDgphDiAllgEACml/QvWFQrFC8uLNLA0xkgn4hFkzrW02ZyMXfhl5NJM99eXpX2R\njYR9b1yCi1iTcUszjf49jewVM2qpbZxBPmeBGGpZEJVS7hBCFAF8zYcOSClt15tQKBQvPAJtmlNm\nJ8U/uUKIkok+dwMOAF2BbkCQEKKL8e4pFIqsjIOwfMuopNYS9RdCdJVSTgc+B3zjW59CiMLAZuC3\n5+CjQqHIimTwxCKWkmJLVEq5Ergaf12S1/ebqd2rUCgUlpAVEpCkGgillEvNHwOFEBuEEP2FEP2B\ntcA6o51LiY0bAqlexYMqFcsxdcpk3ew+fvyYerX98POqgVeNKowfO1o323pr2idFD234t1pUYPcX\nrdk1vhXz36yNi5MD9Su5snVMC3aNb8Wswf4JU1Jae7qxY1wrto1tyeZRLfAvb10m98To+XumpN8e\nz8xvviJPdkciIyPtKgeMew4B7ty5Q6/uXalZtRKe1SoTtH+fXfZqVi5HPb+aNKztTZP6mgzLxHGj\nqe/vScPa3rzaoTUREVf0cN1iBNpke0u3jIpFrUkp5XBgPlDdvM2XUuojx2glJpOJoe8OYdWa9Rw5\ndpKVy37l1MmTuth2cXEhcNNWDhw+SlBwCBs3BBK0XxdJqARN+yPHTrJj937mfTdbN79B04ZfZUfG\n86L5c/B6swo0G7uR+iMDcXAQvFq7FLMG1+L1ufuoPzKQsMgoetQtDcDOk9doOCqQxqM38O6CIL4Z\n4GdTuXr/nvH67cEhx9m6cy/zv5vDP6c0e2GXL7N180ZKlCiZhpXn73dShg8bSvOWLQk5foqgQyF4\nVKxkt81V6zazY98htu7SBCbeGfoBu4KOsGPfIVq0asO0SV/YXYa1ZPmWaGKklL9LKYeZtz+NdCo1\nDh44QNmy5ShdpgzOzs507d6DgDX2a86DNmctd+7cgCYaFhsTo1ufjd6a9knRQxveydGB7M6apn1O\nZ0cePoklOjaOc9fuA7D9xDXa+WiaUFFPYhPuy+niZPO0Hb1/z5T02wE++WgY4yd+qctvauRzePfu\nXXbv3kn/AZoEi7OzM/nz59fFdmISq4k+fPgwXSJVlk/KLITYbf73vhDiXqLtvhDi3vNx8WmuXAnH\n3b1Ewr6bmzvh4foFI5PJhL93TUoWd6VJs+b4+euv/a23pr0eXL3ziNmB/xAyrT0nvunIvUcx/HXg\nMk4OgpovFwCgva87bgVzJtzTxsuNfRPb8OvQBry74IBN5Rr5eybWbw9Ys4rixd2oVr2GLraN9Pvi\nhQsUKlSYNwcPpJavF2+/OZioqCi7bAoh6NKxNU3q+bFowfcJx78YM5JqHqX5bfmvfDpijJ2eW+uT\ntmLJ0i2jklafaD3zv3mklHkTbXmklBaJYgshHIUQR4QQmSIhs6OjI0GHQgi9GEbwwQOcOH487Zus\nQG9Ne73IlzMbrT3d8P4ogKrvryKnixNda5fi9e/2Mr6nFxtHNufB41hMcf+1ONcdDqf2Z+t47dvd\nfPpKtXT0/lkS67c7OTkxfcpkPh81Nr3dsohYUywhRw4z+M232H/wMLly5WKanX2uazdtZ9uegyz/\nI4Af589l7+5dAIwYM56/T1+gS/ee/DBvjh7uW4WwYkvTlhD5hRC/CSH+EUKcEkLUFkIUFEJsEkKc\nNf9bwHytEELMFEKECiGOCSG8bP0OFr3OCyFqCSHyJNrPI4SwtBn1HnDKFueSo3hxN8LCLifsh4eH\n4ebmppf5BPLnz0/DRo3ZuNE+ZcXE6K1prycNKxfl0o0obt5/QqxJEnAoDN9yhQg+d5P2k7bQYvwm\n9p2+wbmr95+5d9+ZG5QqnJuCuZ2tLteI3zOpfvuF8+e4ePECdXw9qVKhDOHhYdSv5cO1q1fTNvYc\n/Y7Hzc0dN3d3/MxvKq907kJIyBG7bBYvrvlW2NWVtu07cfjQwafOd+3ekzWrnn8vnc6v8zOAQCll\nRaAGWtz5BNgipSwPbDHvA7QGypu3N4C5tn4HS/tE5wIPEu1HWVKoEMIdaAvopn/r4+tLaOhZLl64\nQHR0NCuXL6NtO32W8N+4cYM7d+4A8OjRI7Zs3oSHDvrnoL+mvd6E3YrCp+xLCSqaDSoX4cyVexTK\n4wKAs5MD77apxKLtoQCUds2dcG/1UgVwyebArQfRVper9++ZnH57larVuHD5KifOnOfEmfO4ubmz\na38wRYoWtbkcI5/DokWL4u5egjOnTwOwbesWKlWyfWApKiqK+/fvJ3zetnUTlSpX4Vzo2YRr1gWs\npnwFD/sctxJtdF6fyfZCiHxAA+BHAClltFkgsyOwyHzZIqCT+XNHYLHU2A/kF0IUs+V7WLp2XiRO\ngyeljBNCWHLvN8BHQJ6ULkgimZymQScnJ76eMYv2bVtiMpno138glatUscCVtLkaEcHrA/thMpmI\nk3G82qUbbdq208W23pr2SbFXG/7w+VusCb7M1jEtiTXF8fe/d1i84xyfda5GixrFcRCChdtC2XVK\nmy7czsed7nVKE2OK43G0icFz99rkt96/Z0r67S1b6VPP8Rj5HAJM/3omA/r1ISY6mpdLl2HeDwts\ntnXj+jVe66ktMIyNNfFqtx40bd6Sfr26EXr2DA4OghIlSzFtxmy93LcMfQeMSgM3gIVCiBrAIbS3\n4CJSyvjsc1eBIubPbsDlRPeHmY9ZnanOIt15IcQfwHb+a33+D2gspeyUyj3tgDZSyv8JIRoBH0op\nU41I3t4+ck9QsIWuK/RAJSB5FidHlYAkKU3q+xNy+JCuozsvlaki24z/xeLrl/SpeQlIPMF3vpRy\nPoAQwgfYD9SVUgYJIWYA94D/k1ImTG0QQtyWUhYwj9FMllLGD55vAT6WUlodgCxtib4FzARGoGW0\n34K59ZgKdYEOQog2QHYgrxBiiZSyj7VOKhSKrImVLdFIKaVPCufCgDApZZB5/ze0/s9rQohiUsoI\n8+t6/MrLcKBEovvdzcesxtLJ9tellD2klK5SyiJSyl5pZXGSUn4qpXSXUr4M9AC2qgCqUCji0bNP\nVEp5FbgshIjv2G0KnARWA/3Mx/oB8ZN5VwOvmUfpawF3E732W4WlGkvZgUFAFbRWZbzjA20pVKFQ\nKED3pMz/BywVQjgD54EBaA3FFUKIQcAltCx0oC1bbwOEAg/N19qEpa/zPwP/AC2BcUBvrJi2JKXc\njtanqlAoFIB5sr2OQVRKGYKmB5eUpslcK4EhepRraQ96OSnlSCBKSrkIbdpSxlluo1AoMiVZYe28\npS3RGPO/d4QQVdGmCrga45JCoXhRyMhr4i3F0iA637xcaiRah2xu82eFQqGwmSwQQy0OogullCZg\nB1DGQH8UCsULgiBj5wm1FEv7RC8IIeYLIZqKrND+VigU6Y8V/aEZOepY2hKtCLRDG81aIIRYAyyL\nn+2vFxJjV6IYhdF/TRPn7tSbS/O6G2a7+8KDaV9kIz/1tjnpTpoIYdyKIjD2Gc+ezdEQu0Y941mh\nTWbpZPuHUsoVUsrOQE0gL9qrvUKhUNiMgxVbRsVi34QQDYUQc9AW9mfnv0mrCoVCYTWCrJHZ3tIV\nSxeBI8AKYLiU0r402wqFQkHG1pO3lDSDqBDCEVggpRz3HPxRKBQvCPHyIJmdNF/nzVOb9EmqaSMp\nyeCOHzOKWj41qePnRce2LYm4Yr3k69tvDKJ0iaL4eVVPOPb3saM0aVgXf+8adO3cgXv3bJOTeuuN\ngZRyL4KP53/SGbdu3aJd6xZUr1yBdq1bcPv2bZtsh549TeO6PglbGbeXmDd7Jrdv3aJLx9b416xM\nl46tuWOj/bffGMjL7kXw9XxW9mPm19PJ7eJglexwLmdHPm5altldqzKrS1U8XHMlnOtYrQirXvcl\nj4v2Nz1nNkc+b1GebzpX4dsuVWlawTo5ZpPJRMPaPvR4VUuSvGPbFhrV8aVBLW9aN2vA+XOhVtmL\nJ7k6GTdmJP7eNajt60mHNrY9g6DJdTepX4u6/l7U8q7OxPFjEsqsXqkc9fy9qefvzbGjITbZT+5Z\n/OyT4XhWq4Sfdw16dO2ckJD8eaJXApL0xNI+0T1CiFlCiPpCCK/4zVDPEpGSDO57wz5kf3AIew8c\nplWbdkyeON5q27379uPP1eueOvbO228wbvxEgg4dpX2HTsz4appNfvfp25+/1qx/6tj0qZNp1KQJ\nx06eoVGTJkyfapt2TrnyHmzbE8y2PcFs3hlEjhw5adO+IzO/nkKDho0JCjlJg4aNmfn1FJvs907G\nd9D+oG3ZvMmiBNqJGVy7JIfD7jJk5XGG/nGCsDuPASiUyxlP93xcv/8k4do2VVy5fOcRQ/84wecB\n/zDAvwROVvxf9N3smVRIpEjw4dB3mLdgMTv3H6JLt55M/3KiVb7Hk1ydDB02nKBDR9l38Ait2rRl\n0gTbXthcXFxYvX4ze4IOs2v/IbZs2sDBA5pc9/iJX7I76BC7gw5RvUZNm+wn9yw2adqcg0f+5sCh\no5QrX55pUybZZNsessIUJ0uDaE20DE7jgOnmzbbIYgMpyeAmFnqLioqyqfO5Xv0GFCjwtNRw6Nkz\n1K3fANAetFV//WGT3/XqN6BgEttr16ymdx8tM1fvPv0IWG2/zO7O7Vt5uXQZSpQsReDaNXTv1ReA\n7r36sj5gtU02k6sXgI+HD+OLSdbJDufM5kiVYnnYdFprucbGSaLMyYMH1SrBT0GXSTypSErIYZ6q\nkz2bAw+ePC2Qlxrh4WFsClxH3/7/JRgTQnD/vvY2ce/uXYoWs0kFItk6eVp22LZnMN7HxHLdMTGx\nCIvk2SwjuWexWfMWODlprX8//1q6quZagpYKT1i8ZVQsGliSUjY22hFLSSyDCzB21Ah+XfozefPl\nY+2GLbqUUbFyFQLWrKJ9h078+cdvhIddTvsmC7l+/RrFzP8TFy1alOvXr9lt86/fV9C5izbf88aN\n6xQpqtl3LVKUGzdSTftqFQGrV1G8eHGrZYeL5HHm7qMY3m1YmtIFc3Au8iHf7/uXGm55ufkwhou3\nHj11/bqT1/i8RXkW9q5BjmyOTN1yDktnbn720TDGTJjMg/v/CerNmD2P7p3bkz17DvLkzcvGbXus\n8j8txoz6XHsG8+Zj3catNtsxmUw0rOPHhfOhDH7zbXz8/Pnxh3mMHzOSLyd9QcNGjRkzfhIuLi46\neq+x+KeFvNr1+U+4ychTlyzFUrXPIkKIH4UQ6837lc35+dK676IQ4m8hRIgQwm7dj8QyuPEtgNHj\nvuCfc5fo1qMX8+fqoxEzZ94P/DBvLvVr+/Lg/n2yOVuvYmkJekzdiI6OZsO6ANq/8qoh9uN5+PAh\n06ZMYsRo619XHR0EZQvlIvDkdd7/8ySPY+Po6VWcrjWL8Uvws60fT/d8XLj5kAFLjzL0jxO8WbcU\nObKl/ahuWB9A4cKu1PT0fur43FkzWP7HGk6cvUSvPv0Y8cmHVn+H1BgzbgKnz/1L9569mDd3ls12\nHB0d2R10iBNnL3Eo+CAnTxxn9NgJHAw5wbZd+7l9+zbfTLeteyY1pkyegJOTEz169tbddlq8SK/z\nPwEbgOLm/TPAUAvvbSylrJlKWn+LSCqDm5TuPXrZ/NqdFA+Piqxau4Fd+w7SpXsPypQpq4tdAFfX\nIkREaAm0IyIiKFzYvmRYWzYFUq2GJ66umv5W4cKuXLuq2b92NYJChQrb57CZ82bZ4dq+NalcoTTh\nYWHUq+VtkexwZFQ0kVHRnLmhzYzbe+EWZQrlwjWPC9+8WoX5PapTKJczX3euTP4cTjStUIh9F7UB\nsav3nnDt/hPc8+dIs5ygfXtZv3YNNSqVZXC/3uzasY3undtz/O9j+Phqby6du3TjQNA+O2oiZbr3\n6M2qP+1/BvPnz0/9Bo3YsmkDRYsVQwiBi4sLvfv243CwvqvAfl78E+vXrWXBoiXPfS6msOJVPiO/\nzlsaRAtJKVcAcQBSyljAGEWsZEhOBhcgNJHk69qA1VTw0Efy9cZ17RU4Li6OqZMmMHBwWnJSltOm\nXXuWLtEUXJcuWUTb9vbJ7P65cjmdu/63dLNlm/Ys/+VnAJb/8jOt2ra3y348VatW42LYNU6eucDJ\nMxdwc3dn9/5DFskO33kUS2RUNG75NFGE6sXzcj4yin5LQnhj2THeWHaMyKho3v/jJHcexXLjQTTV\ni2tvGvlyOOGWLztX7z1JrQgARo2byImzlzh66hw/LFpK/YaNWbriT+7du0vo2TMAbNu6+alBJ3sJ\nPfvfMxiwZpXNtiOTyHVv37qZ8hU8uGr+gyulZO2a1VTSUVF044ZAvpk+lRW/ryJnzpy62bWGrNAS\ntXTtfJQQ4iW05e3Ea5JYcJ8ENgptMfK8eGW+xDwlmVwi+RHflGRwF/+0gLNnzuDg4ECJkiWZ8e3c\nZO9PjQF9e7Fr1w5uRkbiUbYkn40YTVRUFPO/mwNAh06v0LefbcoB/fr2YtfO7dyMjKR8mRKMGDmG\nD4Z/Qt9e3Vm8cAElSpbi51+W22QbtMG0Hdu2MG3GnIRj774/nNf792Lp4p9wL1mSH36yXE0xMf0T\n+V6hTAk+HzmGfgMsl2BOyvd7LjGscRmcHARX7z9h5o4LKV674sgV3m1YmhmvVkEAiw6Ecd/G/AFO\nTk58M2se/Xp1w8HBgfwF8vPt3B9sspVcnWwIXM/ZM6dxcHCgZMlSzJhl/TMIcPVqBG+/PhBTnAkZ\nF0enzl1o1aYd7Vs342ZkJFJKqlWvwVcz56RtLBmSexanTZnMk+gntG/TAgA/P39mzv7OJvu2kpGn\nLlmKpZLJXsC3QFXgOFAY6CKlPJbGfW5SynAhhCuwCU2+dGdK13t5+8idew9Y43+GIDMnIMnpYunf\nUevptcg4+WsjE5A4Oxk73GFkApJsBsk916vty+FDwbo+6G4Vqsm35vxp8fWjmpc/ZG+3oBFYOjp/\nWAjREPBAm5lwWkoZk8ZtSCnDzf9eF0L8CfgBKQZRhULxApHBJ9FbiqWj812BHFLKE0AnYHlak+2F\nELmEEHniPwMt0FqxCoVCAWiJmS39L6Niadt/pJTyvhCiHppy3o9AWp0/RYDdQoijwAFgrZQy0HZX\nFQpFVkJP3fn0xNIOsfiR+LbA91LKtUKIL1K7QUp5HrBuVrZCoXihyMjB0VIsDaLhQoh5QHPgSyGE\nC1ljsYFCoUhHMnKeUEuxNBB2Q5ts31JKeQcoCAw3zCuFQpHleaFe56WUD82JmVsLIVoBe6SUGw31\nTKFQZG0y+CR6S7F0dH4UsAh4CSgELBRCjDDSMYVCkfXJCss+Le0T7Q3UkFI+BhBCTAZCgFQHlxQK\nhSIl4l/nMzuWBtEraOJ0j837LsDzTT5oJ04GreQAiLMw16Wt5MmRzTDbRq6e+bWfcYtLSr21wjDb\nl+cbJyMN4OhgjKyxkRgkmIxjBm5hWkqqQVQI8S3a+ve7wAkhxCbzqWZocz8VCoXCJjS1z/T2wn7S\naonGL34+CWxBC6ixwDYjnVIoFC8AGXzU3VLSCqK/ABOAgcAltD8eJYGFwGfGuqZQKLI6GXnAyFLS\n6iicAhQASkspvaWUXkAZIB8w1WjnFApF1iX+dT6z5xNNK4i2A96QUiYI1kgp7wFvoy0BfS4YKWuc\nmMePH1Ovth9+XjXwqlGF8WNH220zMbO/nYGPZzV8alZl1sxvdLX95uCBlCzuinfNqnbbSkmi+vNP\nP8KremVq+dSkZzfbJXaTk+/94/eV+NSsSu7sjhw+ZF0KvTebV2DX+FbsHNeKeW/WwsXJgfqVXNky\nugXbxrQg4NMmlHbVRODcX8rJ7x82YvvYlvz1UWOKFUg7Y35KbNwQSPUqHlSpWI6pU2xTbU0Nk8lE\nLR9POnfUV7HcaL+tIStMcUoriEqZTMJRsxa9sUPSiTBS1jgxLi4uBG7ayoHDRwkKDmHjhkCC9u+3\n2y7AiRPHWbjgB3buCWJ/cAjr163lXKht+ufJ0bdff1YF6JPfJSWJ6iZNmnHg8DH2B4dQrnwFm+We\nk5PvrVy5Kr8s/516ZpVVSymaPwevNytP83GbaDAqEEcHwSv+JZna14e35u+j8ZiN/L7/X4a1qwzA\n2G41WbH3Io1Gb2Da6hOMeLV6GiUkj8lkYui7Q1i1Zj1Hjp1k5bJfOXXypE22UmLWzBl4VKqkq83n\n4bc16N0SFUI4CiGOCCECzPulhRBBQohQIcRyIYSz+biLeT/UfP5lW79DWkH0pBDitWQc7QP8Y2uh\n1mKkrHFiksrWxsbE6La29/Q/p/D18yNnzpw4OTlRv0ED3TShwCyJW/BZiWNbSEmiumkiiV1fP3+u\nhIXZ7muS37NipUo2y7s4OTqQ3dkRRwdBDmcnrt55hEQmTA3LmyMbV+9oiqIViudl1ylNYXX3P9dp\n7elmU5kHDxygbNlylC5TBmdnZ7p270HAGvvlr+MJCwsjcP1aBgwcrJtNMN5vaxBoAcjSzULeA04l\n2v8S+FpKWQ64DcTLMwwCbpuPf22+zibS8m0IMEQIsV0IMd287QDeRXulTzfiZY0BXWWNTSYT/t41\nKVnclSbNmuPn76+L3cqVq7J3925u3rzJw4cP2RC4XlcpZqNIKlEdz8+LFtK8Zat08uo/rt55xJzA\nfwiZ2o7jX3fg3sMYtp+4xvsLD/Lr0AYcndaernVKMWOd9v/Vict3aOftDkBbLzfy5MhGgVzWq7le\nuRKOu3uJhH03N3dddduHfzCUCZOm4OCg7/xmo/22CvGfIq0lW5rmhHBH62b8wbwvgCbAb+ZLFqHl\nQwboaN7HfL6psLHFlOovJKUMl1L6A+OAi+ZtnJTSLz5rfWoIIfILIX4TQvwjhDglhKhti5PJYZSs\nsaOjI0GHQgi9GEbwwQOcOK5PHumKlSox7MOP6NC2JZ3at6Z69Ro4OGbsSdfJSVQDTJ08EScnJ7qn\ng8RuUvLlzEYrTze8P15LtWGryeniSJdapXizhQc9v9lJjQ/X8OvuC4zv4QnA6BUh1PZwZevoFtTx\ncOXKrYeYDF4sYS3r1gbgWtgVL2/vtC/O5AgrNqCQECI40ZZUQfIb4CPMgppoy9TvmIU1AcKA+FcP\nN+AyJAhv3jVfbzWWJiDZCmy1wf4MIFBK2cXcF6GbpGC8rDHA2bNn2BC4Lo07rCN//vw0bNSYjRsD\nqVLV/sEagH4DBiWIvY0e+Rlubu662DWClCSqlyz+ifXr1xKwflOGSGPWsHIR/o2M4uZ9TQ107eFw\n/MoXokqJ/Bw+fwuAvw5cZvkwrevn2p3HDJi9B4BcLk6083bn3qM0lW6eoXhxN8ISvUmEh4fh5mZb\n10BS9u3dQ0DAagID1/Hk8WPu3bvHgNf6sHDxErttG+m3tQiwdsVSZEoaS0KIdsB1KeUhIUQjHdyz\nGMPWQgoh8gEN0LLgI6WMNqfR0wUjZI1vJJGt3bJ5Ex46yuteN/t8+d9/Wf3Xn3Tr0Us323qSkkT1\npo2BfPPVNJb/9le6SewmJezWQ7zLvEQOZ61V36CSK2eu3CNvjmyUKaL1bzeqUoSzV7TZGwVzOycM\nUrzXthK/7E5ZdTQ1fHx9CQ09y8ULF4iOjmbl8mW0bWef/HU84ydM4tzFME6HXmTx0mU0atxElwAK\nxvptCzoOLNUFOpizzS1De42fAeQXQsQ3Ft35b7l6OFBC80E4oU3bvGnLdzBO6hFKAzfQMj7VAA4B\n70kpoxJfZIlkspGyxom5GhHB6wP7YTKZiJNxvNqlG23a6je9pHePLty6eROnbNn4asYs8ufPr5vt\n1/r0ZNeO7URGRlL2ZXdGjhpL/4G2SRynJFH90bChPHnyhI5tWwLa4JItEsHJyfcWKFiQD95/l8gb\nN+jcqR3Vq9dk9dq0p7lGfgAAIABJREFUZxscPn+LNcGX2TK6BbEmyd//3mbxjnNcuf2QhUPqEifh\nblQ07y3UVinX9XBlRJfqSAn7ztzg4yWHrPYftBkMX8+YRfu2LTGZTPTrP5DKOmrCG0XG8tuyvk5L\nkFJ+CnwKYG6Jfiil7C2EWAl0QQus/YD4UbTV5v195vNbk5uJZAkWSSbbZFgIH2A/UFdKGSSEmAHc\nk1KOTOkeIyWTM3MCEgcD18YZmYDEyLl9mTkBSWakrr8Ph3SWTC5buYacuNTybrgeXu4WSSYnCqLt\nhBBl0AJoQeAI0EdK+UQIkR34GfAEbgE9zJJGVmNkSzQMCJNSBpn3fwM+MbA8hUKRyTCiX11KuR3Y\nbv58Hk2qPek1j4GuepRnWPNMSnkVuCyEiJ/81xQtkYlCoVAAVo/OZ0iMbIkC/B+w1Dwyfx6wv+NS\noVBkDUTWEKozNIhKKUMA4zLzKhSKTEv8iqXMjtEtUYVCoUgR1RJVKBQKO3gRkjIrFAqFIWiv85k/\niqogqlAo0o0s8DavgqhCoUgvBEK1RPVFYNzKophY41bmGJ0DyNnAjiNHA20bOWhg5Koit0G/GmYb\nIPzHnobZfhxtMsSuUYvyVEtUoVAobET1iSoUCoU9ZHABOktRQVShUKQbKogqFAqFHWSFgaVMt+pK\nb1njsMuXaduyKb6eVfHz+k8e+O9jR2nasC61fGrQ7VXbJJkfP35M43q1qOvnib9XNSaOHwPA4P59\n8K5eiVre1Rny5iBiYqzPrJ60HCOlnr+d8TXeNariU7Ma/fr04vHjx7rZ1lPqOTn0kAd+o3kFdk9o\nzZ6JbXizhZZPJ38uZ34f3pgDX7bj9+GNyZdTE8XrUrsUO79oza4vWrN+RDOqlLA+Z6xRv6fJZKJB\nbR+6v6olYW7dvCH1a3lTv5Y3lcqWoHf3zmlY0BeBNtne0i2jkumCqN6yxk5OTkyYPJWDR46zZcde\nvp+nyQO/8/YbjP1iIvuDzZLMX1svyezi4sKawM3sOXCE3UGH2bxxAweD9tOtRy+Cj55kX/BRHj16\nxKKFP9jsf3w5Rkk9h4eHM2f2t+zef5DgkL8xmUysXLFMF9ugr9RzUvSQB67olo/XGpWl+diNNBix\nnpY1i1PaNTfvta3MzpNX8fs4gJ0nrzLULMl86UYU7Sdupv6I9UxbfYKvB/ha7bdRv+d3s2dSIZFS\nw/pNO9i1/xC79h/C178W7Tu8YncZ1vIi6M5nOPSWNU5WHvhKOOdCz1C3nqbL07hJc1bbIG+c1NeY\nWM3XFq3aJCgYevv4ccVOtUUjpZ4BYmNjefToEbGxsTx89JBixYrrZltPqeek6CEPXKF4Xg6du8mj\naBOmOMmef67TzqcEbbzcWGaWFlm2+wJtvDS9rIOhkdx9qL1ZBIdGUryg9TIqRvye4eFhbAxcx2v9\nBz5z7t69e+zcsY027TvaVYYtCCv+y6hkuiAKxskaX7pklgf29adipSqsNf8P95cdkswmk4l6/l6U\nK1mUxk2aPSU9HBMTw7Jfl9CseUu7fTeqTtzc3Bj6/gd4lC1FmZLFyZc3H82at9DFttHoIQ/8T9hd\nankUpkAuZ3I4O9K8RnHcCuakcN7sXLurdWtcu/uYwnmzP3Nvn4Zl2Xwswibf9f49P/toGGMnTE5W\ngnndmlU0bNTkKUXX54F6nU8DIYSHECIk0XZPCDFUD9tGyBo/ePCAvj27MnmqJg88Z94PfD9/Lg3q\n+HL/ge2SzI6OjuwOOszJ0H85HHyQkyf+83XYe0OoW7c+derVt9t/o6Seb9++TcCa1Zw8c55zl8KJ\nivr/9s47vIpq68PvglAECyA9ofdOSAhIk94EQUB6kSLgxfuJvV/QK4gFL6ioWFBQRBRFBKSD9IQW\nRCK9VxGkSGhJWN8fM4kHTCTnzAxJYL/Pc57M2Zn5zT77zFmzZ8/s9YtlymR3TNMyAtuPnOHt2VuY\n9lQjvn6iIZv3n0zWYvnqknrl89OzQUlemroxoP26+X3OnTOLvPnyUz00eQvmad98Rcf7uwasHzj+\n9EPTbxT1MrP9NlWtrqrVgTDgHDDdzX342ho7IS4ujp7dOtG5S3fute2By5Yrz4xZ81i2ai2dOnel\nRIlSjuta/+6GLJxv2TyPGvEyJ37/nZGvj3akm9x+3GiTRJYsWkix4sXJly8fWbJkoV37+4iMXOWK\ntte4ZQ88edlumgybR9uRizgVe4ldR8/w+5kLFLjD6n0WuCM7x8/8dbOtYpFcjOkfQc8xyzkZe8nR\nZ3Dj+4xavYq5s2dStUIp+vfpwfKlSxjYrzcAJ44fZ8P6tTRv2dpRPQPCD6fPdDwket0u55sAu1R1\nn1Mht22NVZUhgwdQrlwFHvaxB77CknnUCPo/6L8l8/Gr6rpk0ULKlivHxE8/ZtGC+Xwy6ctkL6/8\nxUur55CiRVkbFcW5c+dQVX5aspjy5Su4ou01btkD570tGwDBeXLQJqwI0yL3MSf6EF3rlQCga70S\n/LjhUNI6E/9dj4fGR7Lrtz8Dqrfb3+ewl0cSs2Mfm7bs4pOJk6l/dyM+nDAJgBnff0uLlveQPfvf\nhyOuB8YeJPV0BZKdkHyFZXLR5C2TfXHb1jhy1Uq+su2B69aybjD956VX2LVzJx+Nty2Z291Hz97+\nO5scPXqEwQ/25XJCApcvX+a+jvfTsnUb8tyalSJFi9GsYV0A2ra7j6efS9EE9dr78dDqOSKiFu07\ndKRORBhBQUFUqx5KvwH+n1BSwk2r56txyx74s3/XI8+t2YhLuMxTn6/jzLk4xs76lQlD6tKjQSkO\nnoil37iVADzZvjJ5bs3GG70tQ4eEy5dpMny+X/vz2rrbl++mTWXoY095on0trDHR9BweU4dnlslJ\nO7D8lQ4DlVT1t39aNywsXFdGrfOkHhk6AUmQdxcMXn7/GTVruUlA8nca1atF9AZ3LZMrVAnVT6cv\nSfX6d5XJnSrL5OvN9eiJtgI2XCuAGgyGm5CMeZ69gusRRLuRwqW8wWC4ubkRLuc9vbEkIjmBZoD/\nT6obDIYbHnNj6Rqoaixwp5f7MBgMGZj0HB1TicniZDAY0gSrh5nxo6gJogaDIW1I5w/RpxYTRA0G\nQ5pxA8RQE0QNBkMacgNEURNEDQZDGpG+E4ukFhNEDQZDmmHGRF1G8W4aopdfVmaPj4TYC/Gead+S\nNbNn2pfivZmC6DX7P/I2LVydkYs90172dENPdL04xNP785+pJUMmZTYYDDcGiQ4PqXldQ6eIiCwR\nkV9FJEZEHrHL84jIAhHZYf/NbZeLiLwtIjtFZJOI1Aj0M5ggajAY0gwX84nGA4+rakWgNjBERCoC\nzwCLVLUMsMh+D1ZOjzL2ayDwfqCfwQRRg8GQZrg17VNVj6jqBnv5T2ALEAy0Aybaq00E2tvL7YBJ\nahEJ5BKRQoF8hgwZRE+dOkX3LvdTvXIFQqtUJCpydcBaBw8coHXzJoRXr0zN0L8skxN5e8xb3JY9\nM8ePH3dabca9M5bw0CqEV6/Mu2+Pcax3+tQp+vbsQu0albkrrApro/5qh3Fv/4+8t2XhRID1Hjyw\nH8VCChAeWiWp7I8//qBNq+ZUrViWNq2ac/LkSb91L1y4QOP6talbqwa1w6om2UirKv8d9gJhVSsQ\nEVqZD957J6B6p6Tfqund1KsVRr1aYZQvWYTunf23B35oYD+KhxSgpk+bvDz8RWqFVeOumqHc27oF\nRw4fTrVesTtzMGVgzaTXsqcb0L1WCIPuLsHcoXWTyuuWtmZOVyp8W1LZVwNr0qhc3lTtJ6Vj/Pln\nn6JG1YrUDq9Ot84dkhJBXzf8iaBWFM0rIut8XskmthWR4kAoEAUUUNVEo6ujQAF7ORjwNU47aJf5\nTYYMok8+NpRmLVqwcfMWotZvpJyDTOtBQUGMfO0N1m3czOJlq/jwA8syGayDb/HC+RQpcu1k0dci\nJmYzn074mGUro4hct5E5P85m186djjSfe+pRGjdtTuSGzSxdvZ6y5ax2OHTwAD8tXkCIg3r37PUA\n38+cc0XZ6DdG0bBxYzb9up2GjRsz+g3/fdyzZcvGD3MWsjJqA8sj17NowTzWrolk8ucTOXjoIGs3\nxrAmejMdO3UJqN4p6c9ZuJQVUetZEWXbA7fz3x64RzJtMvSxJ4la/zOr10bTsvU9vDri5VTr7Ttx\njm4frqXbh2vp8dFaLsQlsGSrddKbHLU/6X8rd54AYNexWHp+tI5uH67l4S9/5vk25VN1UzOlY7xx\n46as2bCJyHUbKV2mbEDfp1P89Fg6rqrhPq8P/6YncivwLTBUVc/4/k+tu9au37nOcEH09OnTrFix\njAf6WtnPs2bNSq5cuQLWS9Yy2XaEfOapx/jvyNdcSS68besWakZEkCNHDoKCgqjfoAEzArBhTuTM\n6dOsXrWCnn0sC9ysWbNyh90OLzzzBMP++6qjeter34A8ua+0Mp498wd69OwDQI+efZj1g3/2w5CM\njXRcPIIw4aMPePrZF5LsUvLlzx9QvVPSTyTRHvieAOyB69VvQO6r2sTXIfPcudiA2zyiRB4OnjzP\nkdMXUlznQvxlEuynV7IGZSK1D7KkdIw3adacoCDrAZ2aEbU4fPBgQHUPFMFdjyURyYIVQCerauKP\n67fEy3T77zG7/BBQxGfzELvMbzJcEN27Zw958+Zj0IB+1K5Zg4cGDSA2NtYV7X17bcvkiFrMmjmD\nwoWDqVK1mivaFStWZtWKFZw4cYJz584xb+6cgG2YAfbt28OdefPy78H9aVQ3nEeGDCQ2NpYfZ/1A\nocKFqVzFnXr7cuzYbxQqZA0bFSxYkGPHAsuzbdlIh1GmWCEaNWlCeEQt9uzZzXfTvqZh3Vp0ancP\nu3buCLieyeknMtsDe+Dh/3mecqWKMnXKl7wwLPU9UV9aVMrPvM1/tWeXmiFMHRTBsLbluS37X08i\nVg6+nW8GR/D14AhGzt6aFFRTi+8x7svnEz+lWYuWAdXdCW6NiYp19voE2KKqb/n86wegj73cB5jh\nU97bvktfGzjtc9nvF17nE33Uftxgs4hMERHHbljxCfFsjN7AgEGDiVy7gZw5c/Lm684vQ86ePUvP\nbvcz6s23CAoKYvTro3j+Py851k2kfIUKPPbEU9x7Twvat21F1arVyJQ58Gc04+Pj2bQxmr4DBrFk\n5Tpy5szJ6yNfZszoUTzz/HDX6p0SqXnsJCUsG+n1xOzYx3rbRvrSxYtky56dn1ZG0bvvAB4ePCDg\nuiWnn8i3X39Fx87uPgc6/OURbNu1ny7dujP+/Xf93j4ok9CgXF4W/Gp1kr5Zd5B731lN1/FrOH72\nEo81K5207uZDZ7j/gzX0+ngdfesVJ2vm1P+EfY9x35PIG6NGEhQURJduPfyuu2PcSyhaF+gFNPax\naW8NjAKaicgOoKn9HuBHYDewE/gI+FegH8FL3/lg4P+AcFWtDGTGMqxzRHBwCMEhIUTYZ9L7OnRi\n48ZoR5pxcXH07NqJzl270659B/bs3sXevXuoUzOUSmVLcujQQerXDue3o0cd7adP3/6sjFzH/EVL\nyZU7N2XKlA1Yq3BwCIWDQwirabVD23Yd2fRzNPv37uXuOmGEVirN4UMHaVw/gt9+c1bvRPLnL8CR\nI9bJ+siRI+TLF9gldyK5cuWifoOGLFowj8LBIUnjlG3btSdm8y+O6+urD5Y98Pr1a2nhkT1wl649\nmDHd/yGauqXvZOuRs/wRGwfAH7FxXFZr8O67DYepFPz3XvOe4+c4fymBUvlzpmofVx/jiXwx6TPm\nzJnNJ599kSaeWG75zqvqClUVVa2aaNWuqj+q6glVbaKqZVS1qar+Ya+vqjpEVUupahVVDdjczevL\n+SDgFhEJAnJgGdY5omDBgoSEFGH7tm0ALFm8iAoVAr+xpKoMGTSAcuUr8G/bMrlS5SrsOXCUmO27\nidm+m+DgEJZHrqNAwYKO6n7MtmE+sH8/P3w/nc5duwesVaBAQYKDQ9ix3WqHZUsXU7VaKFv3HCY6\nZifRMTspHBzC4uVrKFDAWb0Tad2mLZO/sJ4WmfzFRO5p67/98NU20j8tXkiZsuW4p+29LF/6EwAr\nli+lVOnATjAp6QPMmP4tLVq5aw+8c8dfww6zZs6gbADWxi0rF7jiUj7vrVmTlhuXz8euY9ZwVeFc\n2ZNuJBW6IzvF8+bgyKmUx1ATSe4YB1gwfy5j3nqTqdO+J0eOHH7X2w0ySepf6RXPpn2q6iEReRPY\nD5wH5qvq37xj/bVMBhj9v7fp26cncZcuUbxEScZ/PCHgeq5etZIptmVynQhr8H3Yy6940lvp0bUT\nf5w4QVCWLLw19l1HN8QAXn1zDIMH9Cbu0iWKFS/JO+9/7FJNoU+v7ixf9hMnjh+nTMkivPDicB5/\n8hl6de/CpE8nUKRoMT7/cqrfukePHuGhB/uRcDkBvXyZ9h060bJ1G2rXqcfAvr14/92x5MyZk7ff\nGx9QvVPSB/h22lQefTxwe+AHfNqkbMkiPP/icObNncOO7dvIlCkTRYsWY+y7/j2znT1LJmqVzMOI\n2VuTyh5pWpqyBaybY4dPnWfEbOtEGVokFw90LUr8ZeWywqs/buPU+bhr7iOlY/ypx4Zy8eJF2t3T\nArBuLvlbf8ek4+CYWjyzTLanV30LdAFOAd8A01T1i5S2qREWrisj13pSn4TL3lkDe222dd4jG1zw\ndu58XIJ3NtVeEuTHOGMg1B+Veptgf/Fq7nyDOhFsWO+uZXKVajX0u/krU71+2YI50qVlspdHS1Ng\nj6r+rqpxWGZ1dTzcn8FgyEj48XhTes725GUQ3Q/UFpEc9uMHTbCmYhkMBgNg3D7/EVWNEpFpwAas\n5ADRwN9mGBgMhpuY9BwdU4nXlsnDgGFe7sNgMGRUTGZ7g8FgcER6HutMLSaIGgyGNCG9j3WmFhNE\nDQZD2nEDRFETRA0GQ5rh9TPW1wMTRA0GQ5qR8UOoCaIGgyGtSOcP0aeWdBdEPZqFSmYPMxh4VedE\nvJyaeTHeu6mZWYO8m8vh5fd5wcNptgArn23kmXbpR773RPf3A15Zh2T8KJrugqjBYLg5SMxsn9Ex\nQdRgMKQZN0AMNUHUYDCkHTdCTzTDeSyB+9bDvrwz9n+EVatMePUq9OnZnQsXrp30NiW8sh1OSfu5\nZ54ktEoFIsKq0fV+5xa4CQkJ3H1XOF07WsmXly5ZRMM6NWlQO4xWTRuwe1dgbqXJWQ/37tGVu2qG\nclfNUCqWLcFdNUMd1R3gwIEDtGjaiNCqFalRrRLvvj3WsSZY7dLgrnC62O3y74cepF6tGtSNCKVP\nj86cPXvW8T7KlylBzdCq1AoPpW7tmgFpPNi4FItfaMKiF5owrm842YIy8b9eNVj9cnPmP9uI+c82\nolLIHQDcVSYvW0a3SSof2qqc48+QGtzKbJ+WZLgg6oX1cCKHDh3ivXHvsCJyLes2/kJCQgLffP1V\nwHpe2Q6npN24STPWRv/CmvU/U7pMGd58/dWA6w7wwbi3r8jU/sTQhxk/YRLLItfTqXM3Rr82MiDd\n5KyHJ03+itVro1m9Npp27Ttwb3v/LY2vJigoiFGvjyZ6068sXRHJ+A/GseXXXx3rXt0uI14bzYqo\nDaxcE01ISBE++mCc430AzFmwmKh10QSSY7fgHdnp17AUrV9bQpNXFpE5k9AuPASAV6ZvpvmrS2j+\n6hJiDp5O2mbNzhNJ5WPmbHPlM1yTGyCNU4YLom5bD19NfHw858+fJz4+nnPnz1GoUOGAtbyyHU5J\nu6mPBW5ErdocOhSQAywAhw4dZMHcH+n1QL+kMhHhzz8tK+8zp09T0Hb+9JfkrIcTUVW++/Yb7u/c\nLSBtXwoVKkRojb+sgsuXr8Dhw4G3CVjtMn/uj/T2aZdE0zdV5fyFC2niVZQcQZmF7FkykzmTcEvW\nII7+gx1zWnEDxNCMF0Tdth72JTg4mKGPPk65UsUoWbQwd9x+B02bNXdFOxG3bIevxaTPPqW5Awvc\n5556jOEjRiX5wAOMHTeeLh3aUqlMMaZ+NZlHHn/ajapewcoVy8mfvwCly5RxVXff3r1s3BhNzaus\ngv3luace46Wr2gVgyKD+lCsRzI7tWxn40MOO9gHWCatt6xbUqRXOJx/7n0Hy6OkLfLBwJ2teaUn0\nq604cz6OZVssj6+n763IgucbM7xjlSseQwsrkYcFzzXm8yF3UbbQbY4/w7UQsWYspfaVXvHaMvkR\n2y45RkSGuqHptvWwLydPnmTWzB/4dftudu07RGxsLFMmp+hm4hgntsP/xOujRhAUFETXAC1w582Z\nRb58+akeGnZF+fvvjmXqdzOJ2bGP7j378MIzT7hR3Sv4ZuoU7nfZ0vjs2bN069yRN0aPceQ3P3fO\nLPIm0y4A48Z/wpZdByhbrgLTp33tpLoALFyynNVr1vP9zB/58P33WLF8mV/b33FLFlpULUTt/8yj\nxrNzyJEtMx0iivDqjBgavLSQe177iVw5s/KvZtbJ6pcDp4h4cS7NRi7m0592M2FQbcefIVXcAF1R\nLy2TKwMPAhFANaCNiJT+561Sh5vWw74sWbSQYsWLky9fPrJkyUK79vcRGbnKFe1E3LYdvprPJ33G\nnB9nM2Fi4Ba4UatXMWf2TKpVKMWAPj1YvnQJXTq0ZfMvmwi3LZo7dOrMmqjVblad+Ph4fpgxnY73\nd3FNMy4ujm6dO9KlWw/a39fh2hv8A1GrVzF39kyqVihFf7tdBvbrnfT/zJkz06FTZ36Y4Xx4KTg4\nGID8+fPTtl171q1d49f29cvnY/+JWP44e4n4y8qcjYcJL5mHY2cuAnAp/jJTV+8jtLg1rHL2Qjzn\nLlqTDBbH/EZQZiF3zqwp6rvFDRBDPe2JVgCiVPWcqsYDSwFnR7GNm9bDvoQULcraqCjOnTuHqvLT\nksWULx+4HXNyuGE7nBLz581lzOg3+PrbGY4scP/z8khiduzj5y27+HjiZOrf3YjJX0/nzJnT7Nyx\nHYAlixcGZA/8TyxZZGkGh4S4oqeqDH6wP+XKV+CRRx9zrDfMbpdNW3bxid0u4z+ZmPSUgqoyd/ZM\nypZ1dmc7NjaWP//8M2l50cIFVKxU2S+NQyfPU6N4HrJnsa7S6pXLz46jf5L/9mxJ67SsVoith60x\n7nw+5dWL5SaTCCdjLzn6HKnhRvBY8vI50c3ACBG5E8syuTWwzg1ht62HE4mIqEX7Dh2pExFGUFAQ\n1aqH0m/AwID1vLIdTkn7zddHcfHSRdq2bp70ed4e90HA9fclKCiIMe+Op0/3zmTKlIlcuXMFbNGc\nnPVwn779mfbNVFcv5VetXMmXkz+ncuUq1AqrDsBLr4ykZSv37LBVlYcG9uXPM3+iqlSuUpXRY53d\nnT/22290vd/qb8THx9O5aze/x7ej955kdvQh5j3biPjLSsyBU0xesZcvhtQhz61ZERFiDp7imSkb\nAbgnNJje9UuQcFm5EJfAvyZ447p7Jen70aXU4pllMoCI9Af+BcQCMcBFVR161Tq+vvNhW3fs9agu\nnsgC3s+d9xIzd/7veD13PlsW79rFs7nz057k0rGdrjZ6aI1wXbwiKtXr58kZdNNZJqOqn6hqmKo2\nAE4C25NZ50NVDVfV8Lx583lZHYPBYHAdT6d9ikh+VT0mIkWxxkOv0y0/g8GQEUjPY52pxeu589/a\nY6JxwBBV9SqflsFgyIDcCGOiXlsm1/dS32AwZFysh+3TuhbOMVmcDAZD2mGCqMFgMASOuZw3GAwG\nB9wIN5YyXAISg8Fw4+DmtE8RaSki20Rkp4g841GV/4YJogaDIe1wKYqKSGZgHNAKqAh0E5GKXlXb\nFxNEDQZDmuFiZvsIYKeq7lbVS8BXQDvPPwDpbEw0esP64zmzZdqXytXzAsc9qoqX2l7rG+0bR9tr\nfX+0i7m98+gN6+flyCp5/dgku4j45t/4UFUTk60GA76JhQ8CzpLHppJ0FURVNdXzPkVknVfzaL3U\n9lrfaN842l7re133a6GqgWcNT0eYy3mDwXAjcAgo4vM+xC7zHBNEDQbDjcBaoIyIlBCRrEBX4Ifr\nseN0dTnvJ/4bz6QPba/1jfaNo+21vtd1v26oaryIPAzMAzIDE1Q15nrs29N8ogaDwXCjYy7nDQaD\nwQEmiBoMBoMDTBA1pArxwtvZY0Qkp4faBTNimxjcJ0MFUREpJyJ3iUgWe5qX2/qua9q6pUUkXESy\nXXttv7UricjddvJrt7XriUgvAFVVt4OGiLQVkUfc1PTRbge8JiLuelJb2i2A6Vz5SI1b2rVFpJf9\n11XPYhEpYx+Hmbw61m9GMkwQFZEOwAzgFeATYIiI3O6SdlkAVU1w++ASkTbAd8AbwGeJ+3JJuxUw\nBXgUmCQiBV3SzSQitwLjgWdFZDAkBVJXjhkRaQ78F/jVDb2rtO8GXgNmqOoxl7Wb29qFgMdd1r4X\n6455U+AJXJwlJCLtgWnAs8BbwCAve+o3ExkiiIpIFqAL0F9Vm2AF0yLA004DqR3kNorIl+BuIBWR\nOljBs4+qNsIy63Mlu4yINATGAgNUtT1wCfDPnDwFVPWyqp4FJmKdsOqIyKOJ/3Oqb7fL58BAVV0g\nIneISDERyeFU2yYM+NjWLiwizUSklojc4URURJoC7wE9gDJABRFp4EJ9sa8khgDdVbUPcAaoLiL5\nRSS7C9qDgG6q2hHYBPQFHhOR2xxW/aYnQwRRm9uxDlywLqVmAVmA7oFeZtpn4oeBocAlEfkCXO+R\nvqaq0fbyMCCPS5f1vwGDVHWN3QOtBTwsIuNFpJNLl97xWCeriUCEiLwlIq+KhZNj5wSW71Yh+wf+\nPfA+Vk/djbrH+yxPA/phfc/jRCS3A93MQG/7+cOcwDagErgyZhwP3AKUtzsGDYHewBjgBYe9xnjg\nVqAggKpOAPZizZ1v40DXAKCqGeIFNMOagVDffp8Z6A58gf28a4C6hbEOsLxYP7gvXKxzZuB2n+UQ\nIBrIZ5fd6dLonb6bAAAFHUlEQVR+ngdesJcfwMpgk88F3VLAM/by48A5YJxLda4G7MZKFPEg1gm9\nH9bwRB6H2lWwAtxXQF+7rCTwAdDChbpnsv+2BI4CVVxqk07AeiASeNEuawx8BlRzqD3Y/q30AkbY\ny4OAT9yo+838ykg90eXAfKCXiDRQ1QRV/RIrCFYLVFRVD6vqWVU9jnVQ3ZLYIxWRGiJS3oF2gqqe\nsd8KcAr4Q1V/F5EewCsickug+j77GaGqr9jLn2H12t246XEeKCciD2L9CEcBRUVkkFNhVf0Zqxc0\nSlU/UmsIYQKQGyjqUPsXrDHFWkAJu2w31oks1Ulu/kH/sv13LtYYZhsXeueo6jSs8dDlWCdbVHUx\ncBvOx0enAHOARsAtqtpTVccDBdy6t3CzkmGmfarqBRGZDCjWzY7ywEWgAHDEpX2csAPEGyKyFetH\n18gl7XjgrIgcEJFXgebAA6p63omuiIjaXQ37fUesNjnsqMJYJxgROQC8iGV5PVNEGgE7nWrb+r/i\nc2PJrns+3Pk+52ANnwwXkcT0iqFYJwI3+Rnrxt7rqprgVExVT4rIYqCziFwCsmOdCDY51D0NTBaR\nKYknARHpDeQBHNf7piatu8L+voCsWIHtK6zLnFAP9vEoLl6m2Zpi130XsB8o43KdswH9gRigsou6\nRYAwn/eZPGhvwbqU/xWo5LJ2DWAkMNrN7/OqfXwNFHdRLxfwf8BSrLngji7lU9hHYnt70iY30yvD\nzp23b/younC3+Crd3Fg/isdV1dHZPwX9B4C16nJyBPsJhmbALlXd5qa2rX9Fj9dtbeBu4KiqbvVi\nH17gZZvY+rdhjfefuebK/msXA7KoqitXFTczGTaIeomIZFfVCx5pe/rDMxgM1xcTRA0Gg8EBGenu\nvMFgMKQ7TBA1GAwGB5ggajAYDA4wQdSQbhGRAiLSJ63rYTD8EyaI3qSISIKIbBSRzSLyjZPkHyLS\nUERm2cv3iojfSVZEZLD98Hfi+9uw5o0vDrReBsP1wNydv0kRkbOqequ9PBlYr6pv+fxfsI6Paz6H\na2eUekJVTTILw02H6YkawJqrXVpEiovINhGZBGwGiohIcxFZLSIb7B5rYuBtKSJbRWQD0CFRSEQe\nEJF37eUCIjJdRH62X3Xs8t4issku+9wuGy4iT9jL1UUk0l5nemLmJRH5SUReE5E1IrJdROpfz0Yy\nGJLDBNGbHBEJAloBv9hFZYD3VLUSEAu8ADRV1RrAOqwclNmBj4C2WLk7U0oG/TawVFWrYU2/jBGR\nSrZmY7s8ucz2k4CnVbWqXa9hPv8LUtUIrPSFw5LZ1mC4rpggevNyi4hsxAqM+7GSLwPsU9VIe7k2\nUBFYaa/bByubUHlgj6rusGdffZHCPhpj5QlFrYxWp+2yb9TKmoWq/uG7gViJk3Op6lK7aCLgm/j4\nO/vveqC435/aYHCZDJPFyeA651W1um+BnVc41rcIWKCq3a5a74rtrjMX7b8JmOPXkA4wPVHDPxEJ\n1BWR0mA5AYjlEbUVKC4ipez1uqWw/SLgIXvbzHYvczFwv53RHhHJ47uB3Vs96TPe2Qsrm5HBkC4x\nQdSQIqr6O1am/CkisglYDZS3k7MMBGbbN5ZSMoN7BGgkIr9gXX5XtLNXjQCWisjPWKZpV9MHK6fr\nJqA68LKLH8tgcBXziJPBYDA4wPREDQaDwQEmiBoMBoMDTBA1GAwGB5ggajAYDA4wQdRgMBgcYIKo\nwWAwOMAEUYPBYHDA/wPE8JLyodO3/AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IhG3ZzBqudrv",
        "colab_type": "text"
      },
      "source": [
        "## 5.7. Generación de predicciones"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yu-36-qGv7CO",
        "colab_type": "code",
        "outputId": "80cc9293-798b-45f4-a752-fb2e1135f795",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "x_test_old = x_test.reshape(10000, 28,28)\n",
        "plt.imshow(x_test_old[11], cmap=plt.cm.binary)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc03158e780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA0EQdiAIwg4EQdiBIP4fvWhjxHFTX+IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjg6ZTy8v7UB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LF_OPtE9v7W9",
        "colab_type": "code",
        "outputId": "2f7f817d-ef16-4756-d495-39bb8588a56d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "np.argmax(predictions[11])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FnQ1qfHgwTcT",
        "colab_type": "code",
        "outputId": "5f90cc18-9a2b-4cd3-f3d9-c24edf24f7b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "print(predictions[11])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.08871808 0.03533775 0.14763168 0.0292788  0.05417016 0.0893229\n",
            " 0.46790007 0.00275134 0.07861366 0.00627557]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWfXn_M8wdj3",
        "colab_type": "code",
        "outputId": "ee26fc47-7be9-440e-9e90-08dd8c174b57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "np.sum(predictions[11])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_6zyoOJ1lHu",
        "colab_type": "text"
      },
      "source": [
        "## 5.8. Datos Fashion-MNIST"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybltm0ul14JF",
        "colab_type": "code",
        "outputId": "fa394445-dca0-4ebd-d5b9-af8908a487d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6FGwIXd14WJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6I04A_S14ZE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images.astype('float32')\n",
        "test_images = test_images.astype('float32')\n",
        "\n",
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBUKuOIM14bo",
        "colab_type": "code",
        "outputId": "28b6b6fb-bdbc-4e8f-96ef-32f6234ad20f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "print(\"train_images.shape:\",train_images.shape)\n",
        "print(\"len(train_labels:\",len(train_labels))\n",
        "print(\"test_images.shape:\",test_images.shape)\n",
        "print(\"len(test_labels):\",len(test_labels))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train_images.shape: (60000, 28, 28)\n",
            "len(train_labels: 60000\n",
            "test_images.shape: (10000, 28, 28)\n",
            "len(test_labels): 10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7f1nHfq14eT",
        "colab_type": "code",
        "outputId": "6e37553a-aa7c-46aa-c49e-5d2b33022b6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train_labels"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tk3V6Qf22sLb",
        "colab_type": "code",
        "outputId": "c8ef594f-bc9e-41b1-b202-f9693850337f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "plt.figure(figsize=(12,12))\n",
        "for i in range(50):\n",
        "    plt.subplot(10,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[train_labels[i]])\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pprF0ZFupr09x4URjjDHGmCqmIkUSZ8yYAcDuu+8OwKqrrgpAt26FhAwpbZpW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VX47w9Uah1nx8VYR9V+VROzn72HZAXNe1Suaamdef21114baFUdO4z0st9i8smx1qqp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\nhgrHEseCGBPX37gfa1C6HMcYjxvr06kK2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kmSJEmSpIcpA4kxKaU8Cdw/dKczoqxQVdXi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\n",
            "text/plain": [
              "<Figure size 864x864 with 50 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0Nj5zs-2sQ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Flatten\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Flatten(input_shape=(28, 28)))\n",
        "model.add(Dense(10, activation='sigmoid'))\n",
        "model.add(Dense(10, activation='softmax'))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRiC2Hnq2sTr",
        "colab_type": "code",
        "outputId": "e94db498-952e-4806-d31d-f7cf6cd35916",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                7850      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 7,960\n",
            "Trainable params: 7,960\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBDazX0d2sYp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='sgd',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZLXEa0b2sOv",
        "colab_type": "code",
        "outputId": "6dca79ea-1593-4603-d0a6-eecef63f6f7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=5)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 67us/sample - loss: 1.7256 - accuracy: 0.5267\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 66us/sample - loss: 1.2283 - accuracy: 0.6718\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 65us/sample - loss: 0.9901 - accuracy: 0.7079\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 63us/sample - loss: 0.8594 - accuracy: 0.7303\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 64us/sample - loss: 0.7791 - accuracy: 0.7480\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc0303328d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXwTTQTU14g8",
        "colab_type": "code",
        "outputId": "a889ca30-2d37-4a73-bb91-a3fe2db5a034",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,  test_labels)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 64us/sample - loss: 0.7647 - accuracy: 0.7466\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PehDI_R75Mdj",
        "colab_type": "code",
        "outputId": "072a4a5f-840a-46cb-9b6e-f44fdf2a121a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print('Test accuracy:', test_acc)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.7466\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUkrr-s53uD1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4ATe8YW5UEH",
        "colab_type": "code",
        "outputId": "d409feeb-cceb-4136-e4dc-2a5d770d388b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "predictions[5]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.5469452e-02, 7.8378147e-01, 1.2995119e-02, 9.1524251e-02,\n",
              "       4.5162175e-02, 5.4480811e-03, 3.2963190e-02, 4.8885582e-04,\n",
              "       1.9168286e-03, 2.5058913e-04], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aF9t8ORY5UHn",
        "colab_type": "code",
        "outputId": "1db207f7-be81-4aaf-e541-00ad95ef10fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.argmax(predictions[5])"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGBpHwk25UKh",
        "colab_type": "code",
        "outputId": "c1de83d2-07b3-44f1-c491-1ff9f790bd4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test_labels[5]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTPAeEl45iNx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)    \n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array, true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#00FF00\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('black')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zz2K3GKj5iek",
        "colab_type": "code",
        "outputId": "caed0fbc-0efe-4777-b718-9591f5502dba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "source": [
        "i = 5\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADCCAYAAAB3whgdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4b8KjBs5iMw",
        "colab_type": "code",
        "outputId": "0986da05-201f-4357-f332-d8888cb0db6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        }
      },
      "source": [
        "i = 8\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TKXv/u9Tfl/KuZT5lP/uMiPjtfu0y827gbyg3Rn2FstC+qWs/x2IAS909wMhRz9ymbEJF\n+dyRq4DLM/Pz/eqP1rylH9qz3s8rgJObBbXPACdExKc6nueB5vtDwBcoUzb9rAZWDxudX0kJ5K5O\nBJZn5tqO9X8JuD8zf5hlS+jngZd3aZiZSzNzYWYeC/yYMh+/RQxgqbtvAIdExPxm5PUWyudjTJgo\ne6+XAndn5t8P0G7/ZncBMXrP+jgy832ZOTcz51Ge3/WZ2XeEGBEzImLPoZ8pd5qO3gvfdr41wPeb\nXQ1Q5nP/p1+7YX6DjtMPjVXAMRExvbm2r6bMq/cVEQc0359Pmf+9YoDztqq9D1jaYWTmxoh4N+Um\nnknAJzLzW13aRsSnKXu7Z0XEauCDmbm0Q9NXUG4s+mYznwvwF5l5TZ92c4DLYtie9Xx2z/pEOBD4\nQnNf02Tgisz8Sse27wEub17U7gPe1qVRE/SLgTO6djIzb4mIK4HllH31t9P9zraronwOylPAuzZj\nsbCH+4AlqRKnICSpEgNYkioxgCWpEgNYkioxgCWpEgNYkioxgCWpEgNYkir5fwdB1m9Ya7XJAAAA\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STCV3LC-57tg",
        "colab_type": "code",
        "outputId": "7e34f86c-6caf-48e4-e0df-6043db233c14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "num_rows = 7\n",
        "num_cols = 2\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions[i], test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)     \n",
        "  plot_value_array(i, predictions[i], test_labels)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NA7Jr7xDlC0doiIiIKHjs0BAREVHwOOVEFDhB+l2YFelL6hMRAeG3JezQEBE57P3ebM6I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\n",
            "text/plain": [
              "<Figure size 576x1008 with 28 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5RXQxH7p3Hx",
        "colab_type": "code",
        "outputId": "fe93a2cd-837d-4de5-be6d-3e4c4ddc0f1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Flatten(input_shape=(28, 28)))\n",
        "model.add(Dense(10, activation='sigmoid'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='Adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(train_images, train_labels, epochs=5)\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels)\n",
        "print('\\nTest accuracy:', test_acc)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 1.0564 - accuracy: 0.7105\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 64us/sample - loss: 0.5611 - accuracy: 0.8222\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 65us/sample - loss: 0.4749 - accuracy: 0.8392\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 64us/sample - loss: 0.4380 - accuracy: 0.8479\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 64us/sample - loss: 0.4178 - accuracy: 0.8536\n",
            "10000/10000 [==============================] - 1s 65us/sample - loss: 0.4558 - accuracy: 0.8376\n",
            "\n",
            "Test accuracy: 0.8376\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}