Handwritten_Digit_Classification.ipynb

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    "colab": {
      "name": "Handwritten Digit Classification.ipynb",
      "provenance": [],
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      "authorship_tag": "ABX9TyOleH/0lQvRRtx8pPgZP3G8",
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Mhmdaris15/Handwritten-Digit-Classification/blob/main/Handwritten_Digit_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zs-SoQ08j6cZ"
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import load_model\n",
        "from google.colab import files\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import np_utils\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras import backend as K\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "82393400-16d9-43c7-d718-e5da46334b67"
      },
      "source": [
        "files = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      Upload widget is only available when the cell has been executed in the\n",
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          "text": [
            "Saving mnist_simple_cnn.h5 to mnist_simple_cnn.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Np3IZcC0jsBs"
      },
      "source": [
        "classifier = load_model('./mnist_simple_cnn.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fkWreLplj3PA",
        "outputId": "89f1e8b9-c4d4-406b-ba5e-bd79e6e8858b"
      },
      "source": [
        "(x_train, x_test), (y_train, y_test) = mnist.load_data()"
      ],
      "execution_count": null,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sonG7NBZj45r",
        "outputId": "10f46b22-cac2-4c28-b9db-d765bbfc5536"
      },
      "source": [
        "from keras.datasets import mnist\n",
        "from keras.utils import np_utils\n",
        "import keras\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras import backend as K\n",
        "\n",
        "# loads the MNIST dataset\n",
        "(x_train, y_train), (x_test, y_test)  = mnist.load_data()\n",
        "\n",
        "# Lets store the number of rows and columns\n",
        "img_rows = x_train[0].shape[0]\n",
        "img_cols = x_train[1].shape[0]\n",
        "\n",
        "# Getting our date in the right 'shape' needed for Keras\n",
        "# We need to add a 4th dimenion to our date thereby changing our\n",
        "# Our original image shape of (60000,28,28) to (60000,28,28,1)\n",
        "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
        "\n",
        "# store the shape of a single image \n",
        "input_shape = (img_rows, img_cols, 1)\n",
        "\n",
        "# change our image type to float32 data type\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "\n",
        "# Normalize our data by changing the range from (0 to 255) to (0 to 1)\n",
        "x_train /= 255\n",
        "x_test /= 255\n",
        "\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')\n",
        "\n",
        "# Now we one hot encode outputs\n",
        "y_train = np_utils.to_categorical(y_train)\n",
        "y_test = np_utils.to_categorical(y_test)\n",
        "\n",
        "# Let's count the number columns in our hot encoded matrix \n",
        "print (\"Number of Classes: \" + str(y_test.shape[1]))\n",
        "\n",
        "num_classes = y_test.shape[1]\n",
        "num_pixels = x_train.shape[1] * x_train.shape[2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28, 1)\n",
            "60000 train samples\n",
            "10000 test samples\n",
            "Number of Classes: 10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPHzFGDYVvY0",
        "outputId": "95632ec2-5eea-4a80-d0f0-5876c58b7cf5"
      },
      "source": [
        "# Training Parameters\n",
        "batch_size = 128\n",
        "epochs = 3\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(5,5), \n",
        "                 strides=2, activation='relu', \n",
        "                 input_shape=input_shape))\n",
        "model.add(Conv2D(64, (3,3), 1, activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "model.compile(optimizer=keras.optimizers.Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "print(model.summary())\n",
        "\n",
        "history = model.fit(x_train, y_train,\n",
        "                    batch_size=batch_size,\n",
        "                    epochs=epochs,\n",
        "                    verbose=1,\n",
        "                    validation_data=(x_test, y_test))\n",
        "score = model.evaluate(x_test, y_test, verbose=0)\n",
        "\n",
        "print('Accuracy Score :', score[1])\n",
        "print('Loss Score :', score[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 12, 12, 32)        832       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 10, 10, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1600)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               204928    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 225,546\n",
            "Trainable params: 225,546\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/3\n",
            "469/469 [==============================] - 25s 52ms/step - loss: 0.6348 - accuracy: 0.8002 - val_loss: 0.0636 - val_accuracy: 0.9799\n",
            "Epoch 2/3\n",
            "469/469 [==============================] - 24s 52ms/step - loss: 0.1066 - accuracy: 0.9684 - val_loss: 0.0396 - val_accuracy: 0.9867\n",
            "Epoch 3/3\n",
            "469/469 [==============================] - 24s 52ms/step - loss: 0.0796 - accuracy: 0.9762 - val_loss: 0.0349 - val_accuracy: 0.9884\n",
            "Accuracy Score : 0.9883999824523926\n",
            "Loss Score : 0.034912943840026855\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "qKGMhh0Ij31m",
        "outputId": "d0567efa-f103-426e-a994-8742345487b5"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "history_dict = history.history\n",
        "\n",
        "loss_values = history_dict['loss']\n",
        "val_loss_values = history_dict['val_loss']\n",
        "epochs = range(1, len(loss_values) + 1)\n",
        "print(loss_values)\n",
        "print(val_loss_values)\n",
        "print(epochs)\n",
        "\n",
        "line1 = plt.plot(epochs, val_loss_values, label='Validation Loss Values')\n",
        "line2 = plt.plot(epochs, loss_values, label='Train Loss Values')\n",
        "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
        "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
        "plt.xlabel('Epochs') \n",
        "plt.ylabel('Loss')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.3070535361766815, 0.10032198578119278, 0.07587806135416031]\n",
            "[0.06364186108112335, 0.0395541712641716, 0.034912966191768646]\n",
            "range(1, 4)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "7RFMXx-2blfB",
        "outputId": "6fb6a3e7-bfce-4058-fc19-aa991d111835"
      },
      "source": [
        "acc_values = history_dict['accuracy']\n",
        "val_acc_values = history_dict['val_accuracy']\n",
        "line1 = plt.plot(epochs, acc_values, label='Accuracy Values')\n",
        "line2 = plt.plot(epochs, val_acc_values, label='Validation Accuracy Values')\n",
        "plt.setp(line1, linewidth=2, marker='o', markersize=10)\n",
        "plt.setp(line2, linewidth=2, marker='o', markersize=10)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FYI1Q4Tf2FX"
      },
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.datasets import mnist\n",
        "from keras.utils import np_utils\n",
        "from keras.models import load_model, Sequential\n",
        "from keras.layers import Dense, MaxPooling2D, Dropout, Flatten, Conv2D"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSpImyvcKF81"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tDNm8IEKTu5",
        "outputId": "e6d4a95b-9fdd-48f5-8f4a-9c262ac796d1"
      },
      "source": [
        "print(x_train.size)\n",
        "print(x_test.size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "47040000\n",
            "7840000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdGh3IEYKV61",
        "outputId": "8509aa9a-abfa-4e8c-e34b-7df246c9a9c4"
      },
      "source": [
        "# (Number of data, Rows, Columns)\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXQZDwzhLBn9"
      },
      "source": [
        "img_rows = x_train.shape[1]\n",
        "img_cols = x_train.shape[2]\n",
        "\n",
        "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1).astype('float32') / 255\n",
        "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1).astype('float32') / 255\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yX_SXTrsL15b"
      },
      "source": [
        "# Just Run Once. Don't more !\n",
        "y_train = np_utils.to_categorical(y_train)\n",
        "y_test = np_utils.to_categorical(y_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gdag8gRxMYqg",
        "outputId": "e148389c-293c-4462-cb02-f1a64857e2c3"
      },
      "source": [
        "num_classes = y_train.shape[1]\n",
        "num_pixels = x_train.shape[1] * x_train.shape[2]\n",
        "input_shape = (x_train.shape[1], x_train.shape[2], 1)\n",
        "print(num_classes)\n",
        "print(num_pixels)\n",
        "print(input_shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n",
            "784\n",
            "(28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOvw_YpXNYl1",
        "outputId": "4ad9eb3b-0631-456c-b4a5-d12508e5d400"
      },
      "source": [
        "Epoch = 3\n",
        "Batch_size = 128\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3,3), strides=2, activation='relu', input_shape=input_shape))\n",
        "model.add(Conv2D(64, kernel_size=(5,5), strides=1, activation=keras.activations.relu))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=2))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.35))\n",
        "model.add(Dense(num_classes, activation=keras.activations.softmax))\n",
        "model.compile(optimizer=keras.optimizers.SGD(0.05), loss=keras.losses.CategoricalCrossentropy(), metrics=keras.metrics.Accuracy())\n",
        "print(model.summary())\n",
        "trained = model.fit(x_train, y_train, batch_size=Batch_size, epochs=Epoch, verbose=1, validation_data=(x_test, y_test))\n",
        "scores = model.evaluate(x_test, y_test, verbose=0) \n",
        "print('Loss Score :', scores[0], '\\nAccuracy Score :', scores[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_2 (Conv2D)            (None, 13, 13, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 9, 9, 64)          51264     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 4, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 4, 4, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               131200    \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 184,074\n",
            "Trainable params: 184,074\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/3\n",
            "469/469 [==============================] - 39s 81ms/step - loss: 1.1094 - accuracy: 0.0000e+00 - val_loss: 0.1402 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/3\n",
            "469/469 [==============================] - 38s 81ms/step - loss: 0.2020 - accuracy: 1.7322e-05 - val_loss: 0.0912 - val_accuracy: 0.0000e+00\n",
            "Epoch 3/3\n",
            "469/469 [==============================] - 38s 82ms/step - loss: 0.1432 - accuracy: 1.1655e-04 - val_loss: 0.0766 - val_accuracy: 2.0000e-05\n",
            "Loss Score : 0.07655508071184158 \n",
            "Accuracy Score : 1.9999999494757503e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "8VKDvaWOP26G"
      },
      "source": [
        "epochs = range(1, Epoch + 1)\n",
        "trained_dict = trained.history\n",
        "loss_value = trained_dict['loss']\n",
        "val_loss = trained_dict['val_loss']\n",
        "acc_value = trained_dict['accuracy']\n",
        "val_acc = trained_dict['val_accuracy']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ZknVaGsgSx_U",
        "outputId": "ad3611ad-9196-4b82-ba0c-f1d481710ada"
      },
      "source": [
        "line1 = plt.plot(epochs, loss_value, label = 'Training Loss Value')\n",
        "line2 = plt.plot(epochs, val_loss, label = 'Validating Loss Value')\n",
        "plt.setp(line1, linewidth=2.0, marker='o')\n",
        "plt.setp(line2, linewidth=2.0, marker='*')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ccEWP1rxxZwL+Fh/e+u4wT368V48MVLNnf5rD/vTGsmXLGDBgAHFxcezevfu80zhV/ec//+FXv/oVLVq0ICwsjJEjR1Yu27VrF1deeSX9+vVj8eLFTg+4duuttwIQHx9fOSDdt99+W3lDmREjRhAZGen05/zuu+8YP348AGPHjuXbb78FYOjQoUyePJl58+ZV/sEfPHgwf/7zn3nuuec4fPgwQUFB1bZX0/e2Zs0akpKS6NevH1999ZVLA8x99dVXPPvss8TGxpKcnExhYWHlkUtd6BGBi66+rA2vTRrAPW9vZuF/f8LPIjx8Qy89MlDud4Ff7pX+9SBsWQQWfygrhvi74aYXycnJqfUw1KNGjeLBBx9ky5Yt5OfnEx8fz08//cQLL7zAxo0biYyMZPLkydWGXXbW5MmTWbFiBTExMSxatIi1a9c69bqKIavdOVw1WH/9p6am8sknnxAfH8/mzZsZP348SUlJfPLJJ9x44428/vrrXHPNNee9bsiQIRw/fpzt27ezbt06UlJSKCws5De/+Q2bNm2iU6dOPPHEEw6/N/shq+2XG2P44IMP6NmzfkeU1SOCWrimVzteGT8AXx/h9W8O8n+ff69HBqpxyDtl/eM/7Qvrv7l1bzAOCQlh2LBhTJkypfJXbXZ2NsHBwYSHh3Py5Ek+/fTTC27jqquuYsWKFRQUFJCTk8O//vWvymU5OTl06NCBkpISFi9eXDk/NDSUnJwcl2odOnQoy5YtA+Dzzz/n559/dvq1Q4YMqfwFv2zZMq688koAfvzxR5KSkpg7dy5t2rTh6NGjHDx4kO7duzNr1ixGjRrFjh07qm1PRLjjjju46667uOGGGwgMDKz8o966dWtyc3NrvEqoa9eubN68GeC8u71de+21/OMf/6j8e7N161anP9+FaBDU0vV92vOPcXFYfISX1xzg71/+cPEXKeVuYxfDTS9C+37Wf8cuvvhrnDBu3Di2b99eGQQxMTHExcXRq1cvxo8fz9ChQy/4+gEDBnDHHXcQExPDDTfcwMCBAyuXPfnkkyQlJTF06NDzhm8eO3Yszz//PHFxcfz4449O1fn444/z+eef07dvX9577z3at29f45FQ//79K4er/t3vfsc//vEP3nzzTfr3709KSgp///vfAZg9e3blPY+HDBlCTEwMy5Yto2/fvsTGxrJr1y7uvPNOp763iIgIpk+fTt++fRk+fPh530PVz/HAAw+QkJBQeSc4sA75XVJSQv/+/enTpw+PPvqoU9/LRdU0LGljndwxDHVdfLQt3XR7+GPT5aGPzctf/VCrbXjTsMr1wZvq0mGoXVNYWGhKSkqMMcasW7fOxMTE1Go7Tf37cnUYam0jqKORMR0pKy/nd8u28/zq/fj6CPdcfYmny1LKKx05coQxY8ZQXl6Ov78/8+bN83RJTYIGQT34VVw0JWWGP7y/g2c+3YevxYepV3TzdFlKeZ0ePXrU23lzb6JtBPVkTEIn/vyrfgA8+fEe3vrukEfrUc2H0QsRlAtqs79oENSj8UmdmTuqDwCPfbSbJal1v75XebfAwEAyMjI0DJRTjDFkZGQQGBjo0uv01FA9u3NwV0rLDHM/3sMjy3fi6yOMGdjJ02WpJio6Opq0tDROnz5dp+0UFha6/MehIWhdrnGmrsDAQKKjo13argaBG0y5ohul5eX8edU+HvpwB74W4dYBrv2HUQrAz8+Pbt3q3t60du1a4uLi6qGi+qV1ucZddempITeZcdUlzB7eE2Pg9+9t56Nt6Z4uSSmlHNIgcKP7hl3Kb6/rQbmB3y3bzic7jnu6JKWUqsatQSAiI0Rkv4gcEJGHHSyfLCKnRWSbbZrmzno84YFrezBz2KWUlRseSNnK6t0nPF2SUkqdx21BICIW4BXgBqA3ME5EejtY9V1jTKxtmu+uejxFRPif6y/jnqu7U1pumLlkC1/urfv4L0opVV/ceUSQCBwwxhw0xhQDKcAoN75foyUiPDyiF1Ov6EZJmeHed7awdv8pT5ellFIAiLuuTxaR0cAIY8w02/NJQJIxZqbdOpOBZ4DTwPfAg8aYow62NQOYAdCuXbv4qreAc1Zubi4hISG1em19MMbwzt5ivjxSiq8PPDggkD6tLR6vqyZal2saa13QeGvTulxTl7qGDRu22RiT4HBhTYMQ1XUCRgPz7Z5PAl6usk4rIMD2+B7gq4ttt7ENOueq8vJyM+fDHabLQx+bnn9cZdYdONMo6nJE63JNY63LmMZbm9blmrrUxQUGnXPnqaF0wL4nVbRtnn0IZRhjimxP5wPxbqynURARnhrVlzsSOlFYUs6URRvZf7bs4i9USik3cWcQbAR6iEg3EfEHxgIr7VcQkQ52T0cCe91YT6Ph4yM8c2s/bh0QRUFJGX/dXMjmw87fQEMppeqT24LAGFMKzARWY/0Dv8wYs1tE5opIxQ1LZ4nIbhHZDswCJrurnsbGx0d4fnQMo2I7UlgGkxduYNvRTE+XpZTyQm4dYsIYswpYVWXeY3aP5wBz3FlDY2bxEf7v9hiOnTjJxhOl3LkglSXTB9E3KtzTpSmlvIj2LPYwX4sP9/QPYHifdmQXljJxQSp7jmV7uiyllBfRIGgEfH2Ef4wbwHWXtyUzv4SJC1LZf8K1m3YrpVRtaRA0Ev6+PrwyYQBXX9aGs3nFTJi/ngOnNAyUUu6nQdCIBPhaeH1SPFdc2pozucWMm5fKwdO5ni5LKdXMaRA0MoF+FubdmcDg7q04nVPE+HmpHM7I83RZSqlmTIOgEQryt7BgcgKJXVtyIruQ8fNSOXo239NlKaWaKQ2CRqqFvy8L7x7IgM4RpGcWMG7eetIzCzxdllKqGdIgaMRCAnxZNCWRmE4RpP1cwPh56zmRVejpspRSzYwGQSMXFujHW1MS6RsVxuGMfMbNW8+pbA0DpVT90SBoAsKD/HhnahKXdwjjpzN5jJu3ntM5RRd/oVJKOUGDoImIaOHP4mlJ9GwXyo+n85g4P5WzecWeLksp1QxoEDQhLYP9WTw9iUvbhrD/ZA4T5qeSma9hoJSqGw2CJqZ1SABLpiXRvXUwe49nM3FBKln5JZ4uSynVhGkQNEFtwwJZMn0QXVq1YFd6NncuTCW7UMNAKVU7GgRNVPtwaxhERwaxPS2LyQs3kFtU6umylFJNkAZBExYVEcTS6YPoGB7IliOZTHlzI/nFGgZKKddoEDRxnVq2YOmMQbQPC2TDobNMWbSRgmK9B7JSynkaBM1Al1bBLJmeRNvQANYfPMv0tzZRWKJhoJRyjgZBM9G9TQhLpg+idYg/3x44wz1vb6aoVMNAKXVxGgTNyKVtQ1g8bRAtg/35+vvT/OadLRSXlnu6LKVUI6dB0Mz0bB/KO1OTiGjhx5f7TnH/0i2UlGkYKKVqpkHQDPXuGMY7U5MIC/Rl9e6T/DZlG6UaBkqpGmgQNFN9o8J5e2oSoQG+fLLzOP/z3nbKyo2ny1JKNUIaBM1YTKcIFk1JJNjfwkfbjjH7fQ0DpVR1GgTNXHyXSN68O5EgPwsfbklnzoc7KNcwUErZ0SDwAondWrJw8kAC/XxYtimNP360C2M0DJRSVhoEXmLwJa2Yf+dAAnx9WJJ6hCdW7tYwUEoBGgRe5YoerXnjzgT8LT7887vDPPXJXg0DpZQGgbe5+rI2/L+JA/CzCAu+/YlnP9unYaCUl9Mg8ELXXt6Ol8cPwNdHeP3rg/zf599rGCjlxdwaBCIyQkT2i8gBEXn4AuvdJiJGRBLcWY86Z3if9rw0Lg6Lj/DymgO89OUBT5eklPIQtwWBiFiAV4AbgN7AOBHp7WC9UOABINVdtSjHbuzXgb/eEYuPwF+/+J5X1mgYKOWN3HlEkAgcMMYcNMYUAynAKAfrPQk8BxS6sRZVg5ExHXnh9hhE4PnV+3njmx89XZJSqoGJu84Ni8hoYIQxZprt+SQgyRgz026dAcD/GmNuE5G1wO+NMZscbGsGMAOgXbt28SkpKbWqKTc3l5CQkFq91p0aQ13fpJWwcFcxAON6+TO8q1+jqMsRrct1jbU2rcs1dalr2LBhm40xDk+/+9apqjoQER/gRWDyxdY1xrwBvAGQkJBgkpOTa/Wea9eupbavdafGUFcycGnqER5ZvpOl+4q5vGcPOnPI43U50hi+L0caa13QeGvTulzjrrrceWooHehk9zzaNq9CKNAXWCsih4BBwEptMPac8UmdmTuqDwCPfbSbtUdLPFyRUqohuDMINgI9RKSbiPgDY4GVFQuNMVnGmNbGmK7GmK7AemCko1NDquHcObgrj95kbdNftLuYZZuOergipZS7uS0IjDGlwExgNbAXWGaM2S0ic0VkpLveV9Xd1Cu6MeeGXgA89MEOPtyS5uGKlFLu5NY2AmPMKmBVlXmP1bBusjtrUa655+pL+P7Aj3zwQwm/f287vhYfRsZ09HRZSik30J7FqkY3X+LPb6/rQbmBB9/dxqqdxz1dklLKDTQI1AU9cG0PZg67lLJyw6ylW/l89wlPl6SUqmcaBOqCRIT/uf4y7rmqO6XlhvuWbOHLvSc9XZZSqh5pEKiLEhEevqEXU4Z2o6TMcO87W/j6+9OeLkspVU80CJRTRIRHb7qcuwZ3obisnBlvbeK/B854uiylVD3QIFBOExGeGNmH8UmdKSotZ+o/N/LdjxmeLkspVUcaBMolIsJTo/oyJiGawhJrGGw8dNbTZSml6kCDQLnMx0d45tb+3BoXRX5xGZMXbmDz4Z89XZZSqpY0CFStWHyE52+PYWRMR/JsYbD9aKany1JK1YIGgao1i4/w4pgYftmvAzlFpUxakMqu9CxPl6WUcpEGgaoTX4sPfxsby/W925FdWMrEBansOZbt6bKUUi5wKghEJNh2/wBE5DIRGSkifu4tTTUVfhYfXh4/gGt7tSUzv4SJC1LZfyLH02UppZzk7BHBN0CgiEQBnwOTgEXuKko1Pf6+Prw6cQBXX9aGs3nFTJi/ngOncj1dllLKCc4GgRhj8oFbgVeNMbcDfdxXlmqKAnwtvD4pnisubc2Z3GLGz1vPT2fyPF2WUuoinA4CERkMTAA+sc2zuKck1ZQF+lmYd2cCg7q35FROEePeWM/hDA0DpRozZ4Pgt8AcYLnt5jLdgTXuK0s1ZUH+FhbcNZCBXSM5kV3I+HmpHD2b7+mylFI1cCoIjDFfG2NGGmOeszUanzHGzHJzbaoJCw7w5c27ExnQOYL0zALGzVvPscwCT5ellHLA2auGlohImIgEA7uAPSIy272lqaYuJMCXRVMSiekUQdrP1jA4kVXo6bKUUlU4e2qotzEmG7gF+BTohvXKIaUuKCzQj7fuTqRvVBiHM/IZP289p7I1DJRqTJwNAj9bv4FbgJXGmBLAuK8s1ZyEt/Dj7SlJXN4hjINn8hg/P5XTOUWeLkspZeNsELwOHAKCgW9EpAug3UeV0yKD/XlnaiKXtQvhwKlcJs5P5WxesafLUkrhfGPxS8aYKGPMjcbqMDDMzbWpZqZVSACLpw3ikjbB7D+Zw8T5qWTmaxgo5WnONhaHi8iLIrLJNv0f1qMDpVzSJjSApdMH0b11MHuOZzNpwQayCko8XZZSXs3ZU0MLgRxgjG3KBt50V1GqeWsbFsiS6YPo0qoFO9OzuHPhBrILNQyU8hRng+ASY8zjxpiDtulPQHd3Fqaat/bh1jCIjgxi+9FMJi/cQG5RqafLUsorORsEBSJyRcUTERkKaO8gVSdREUEsnT6IjuGBbDmSyZQ3N5JfrGGgVENzNgh+DbwiIodE5BDwMnCP26pSXqNTyxYsnTGI9mGBbDh0lqmLNlFQXObpspTyKs5eNbTdGBMD9Af6G2PigGvcWpnyGl1aBbNkehJtQgP47mAGM97eRGGJhoFSDcWlO5QZY7JtPYwBfueGepSX6t4mhKXTk2gd4s9/fjjDPW9vpqhUw0CphlCXW1XKRVcQGSEi+0XkgIg87GD5r0Vkp4hsE5FvRaR3HepRTdylbUNZPG0QLYP9+fr709y3eAvFpeWeLkupZq8uQXDBISZExAK8AtwA9AbGOfhDv8QY088YEwv8BXixDvWoZqBn+1DemZpERAs/vth7ivuXbqGkTMNAKXe6YFIhWrUAABrTSURBVBCISI6IZDuYcoCOF9l2InDAdrlpMZACjLJfwe40E1g7qOn4RYreHcN4Z2oSYYG+rN59kt+mbKNUw0AptxFj3PO3V0RGAyOMMdNszycBScaYmVXWuw9re4M/cI0x5gcH25oBzABo165dfEpKSq1qys3NJSQkpFavdSety7GDmWU8v6mQglIY1MHCjP4B+Ih4vK6aNNa6oPHWpnW5pi51DRs2bLMxJsHhQmOMWyZgNDDf7vkk4OULrD8e+OfFthsfH29qa82aNbV+rTtpXTXbdOis6f3op6bLQx+bB9/dasrKyhtFXY401rqMaby1aV2uqUtdwCZTw9/VurQRXEw60MnuebRtXk1SsA5zrVSl+C6RvHl3IkF+Fj7cks6cD3dS7qajWKW8lTuDYCPQQ0S6iYg/MBZYab+CiPSwe/pLoNppIaUSu7Vk4eSBBPr58O6mo7y9p7jiKFIpVQ/cFgTGmFJgJrAa2AssM9Yb388VkZG21WaKyG4R2Ya1neAud9WjmrbBl7Ri/p0D8ff1Yc3RUp5YuVvDQKl64uvOjRtjVgGrqsx7zO7xA+58f9W8XNGjNW9Mimfaoo3887vD+Fp8+OMvL0fkol1alFIX4M5TQ0rVu+SebZkZF4CfRVjw7U8899l+PTJQqo40CFSTE9vWl5fHD8DXR3jt6x958d/fe7okpZo0DQLVJA3v056XxsVh8RH+8dUB/v6FXmegVG1pEKgm68Z+HXhxTAw+An/94nteWXPA0yUp1SRpEKgmbVRsFC/cHoMIPL96P/O+OejpkpRqcjQIVJN364Bonru1PwBPr9rLwm9/8nBFSjUtGgSqWRgzsBN//lU/AOZ+vIe3vzvk0XqUako0CFSzMT6pM3NH9QHg0Y92syT1iIcrUqpp0CBQzcqdg7vy6E3W2148snwnyzYd9XBFSjV+GgSq2Zl6RTfm3NALgIc+2MHyrWkerkipxk2DQDVL91x9CbOH98QY+J9l21m5/ZinS1Kq0dIgUM3WfcMu5YFre1Bu4MF3t7Fq53FPl6RUo6RBoJq1317Xg/uGXUJZuWHW0q18vvuEp0tSqtHRIFDNmojw++t7cs9V3SktN9y3ZAtf7Tvp6bKUalQ0CFSzJyI8fEMvpgztRkmZ4ddvb+Hr7097uiylGg0NAuUVRIRHb7qcOwd3obisnBlvbeK/B854uiylGgUNAuU1RIQnbu7DuMTOFJWWM/WfG1l/MMPTZSnlcRoEyqv4+AhP39KXMQnRFJaUM2XRRjYeOuvpspTyKA0C5XV8fIRnbu3PrXFR5BeXMXnhBrYc+dnTZSnlMRoEyitZfITnb49hZExH8orLuGvBBrYfzfR0WUp5hAaB8loWH+HFMTHc2K89OUWlTFqQyq70LE+XpVSD0yBQXs3X4sPfx8Zxfe92ZBeWMnFBKnuPZ3u6LKUalAaB8np+Fh9eHj+Aa3u1JTO/hAnzU/n+ZI6ny1KqwWgQKAX4+/rw6sQBXH1ZG87mFTN+XioHTuV6uiylGoQGgVI2Ab4WXp8UzxWXtuZMbhHj563npzN5ni5LKbfTIFDKTqCfhXl3JjCoe0tO5RQx7o31HM7QMFDNmwaBUlUE+VtYcNdABnaN5ER2IePnpXL0bL6ny1LKbTQIlHIgOMCXN+9OZEDnCNIzCxg/fz3HMgs8XZZSbqFBoFQNQgJ8WTQlkZjocI6eLWDcvPWcyCr0dFlK1Tu3BoGIjBCR/SJyQEQedrD8dyKyR0R2iMiXItLFnfUo5aqwQD/empJE36gwDmfkM37eek7laBio5sVtQSAiFuAV4AagNzBORHpXWW0rkGCM6Q+8D/zFXfUoVVvhLfx4e0oSl3cI4+CZPMbPS+VMbpGny1Kq3rjziCAROGCMOWiMKQZSgFH2Kxhj1hhjKlrh1gPRbqxHqVqLDPbnnamJXNYuhAOncpk4P5WzecWeLkupeuHOIIgCjto9T7PNq8lU4FM31qNUnbQKCWDxtEFc0iaYfSdymDg/lcx8DQPV9Ikxxj0bFhkNjDDGTLM9nwQkGWNmOlh3IjATuNoYU+2YW0RmADMA2rVrF5+SklKrmnJzcwkJCanVa91J63KNp+vKLCznmQ2FnMw3dA3zYfbAQIL9xON1XUhjrU3rck1d6ho2bNhmY0yCw4XGGLdMwGBgtd3zOcAcB+tdB+wF2jqz3fj4eFNba9asqfVr3Unrck1jqOtYZr658rmvTJeHPjajXv7WZBcUN4q6atJYa9O6XFOXuoBNpoa/q+48NbQR6CEi3UTEHxgLrLRfQUTigNeBkcaYU26sRal61SE8iKUzBhEdGcS2o5nEP/kFkz/LY+izX7Fia7qny1PKJW4LAmNMKdbTPaux/uJfZozZLSJzRWSkbbXngRDgPRHZJiIra9icUo1OVEQQdw/tigDFZeUApGcW8NAHO1i28Yhni1PKBb7u3LgxZhWwqsq8x+weX+fO91fK3RZ+e4iqrWxFpeX84YOdPPvZfqIigqxTpPXf6Ejr4+iIFoQF+SIiHqlbKXtuDQKlmrsLDTtxNq+Ys3nF7KzhrmchAb4OQ6JiXpuQAA0K1SA0CJSqg44RQaQ7CIOOEYEs/81Q0n4uID2zgPSfC0j7Ob/ycXpmAblFpew/mcP+Gm6C4+/rcy4gqhxZREUG0T4sEF+LjhKj6k6DQKk6mD28J3M+3ElBSVnlvCA/C38Y3ot2YYG0CwskvktktdcZY8jMLyE9s4A0ByGRnllAZn4JP53Jq/GeCBYfoX1YoO1U0/khERURRMeIIAL9LG777Kr50CBQqg5uibP2kXx+9X7SMwuIighi9vCelfNrIiJEBvsTGexP36hwh+vkFpVyLNMWEj8XkGYfFD8XcCqnqDI0NtTwPm1CA4iKCMK/pJDv8vfanYZqQVRkECEB+idAaRAoVWe3xEVxS1wUa9euJTk5ud62GxLgy2XtQrmsXajD5YUlZRzPKrSFQ/WwOJ5VyOmcIk7nWPtobjhxsNo2woP8qrVTWE9FWYMisoWftlN4AQ0CpZqoQD8L3VoH0611sMPlpWXlnMwpIv3nAr74bgth7btUnoqqCIusghKyCkrYczzb4TaC/CznnXKKtm/YjmhB29AAfHw0KJo6DQKlmilfi09lI3P+YV+Sk3uct9wYw5ncYru2CdtRhd3pp5yiUg6cyuXAqVyH7+FnETqEO77qKTqiBR0iAvHTBu1GT4NAKS8lIrQJDaBNaACxnSIcrpNVUGLXLpF/LiRsQZGRV8yRs/kcqeFWniJYG7QdNGZHRwZRVOaesc6UazQIlFI1Cg/yIzzIj94dwxwuLyguqwyGikZt+6ufTmQXcjzLOm06/LPDbbRa9+9zIXFeUFjbKcKD/Nz5ERUaBEqpOgjyt3Bp2xAubet4RMySsnJOZBVy1EFIpGcWkHY2n4y8YjLyitmR5rjjXWiAr4OjiRaVj1uH+GuDdh1pECil3MbP4kOnli3o1LKFw+VfrVlD7wGDSc/MP69t4lwnPGs7xb4TOew74bjjXYCt4519Y3aU3ZVP7UIDtOPdRWgQKKU8xkeE9uGBtA8PJN7BHcuNMfycX1LZmJ1WJSQqrnw6eCaPgxfpeHdunKfzg6JjRCABvt7d8U6DQCnVaIkILYP9aRnsT7/omjveOepLUREUp+063vGT4/dpGxpw3umn/NMllO87WRkWzb3jXfP+dEqpZi8kwJee7UPp2d6Fjnd2gXEiu5BTOUWcyili65HMyte9vWdT5eOIFn4OG7MrTkVFNPGOd94TBDkniN36CMQvh9B2nq5GKdVAXOl4l56ZT9rZAjbuPYgJiqhsr8jMLyEzv4Tdxxx3vGvhb6l2iWx0ZIvKy2TbhDTujnfeEwRf/4XwrD3w9XNw04uerkYp1UjYd7yDlgCstaSTnJwEnN/xztElshUd7344lcsPNXS887f40CEisPJ97K96io4Mon34hTverdiafm48q/VfOTWelUvfQb1tqbF6qi2UWsdaEYBNC6yTxR8eOQ6W5v8VKKVqz9mOdw5Dwq7j3eGMfA5nOO545yPQrqJBu0pj9r7j2fz1i+8pLDl3F7w5H+4EqLcwaP5/BR/YAav/CLs/AFN+bn5ZsTUkwqMgvDNEdIaITrZ/O0N4JwiPBot2ZlFKXZi14104fTo6btDOL64YSdbxJbInc851vNuI44539gpKynh+9X4NAqeFtoeAUDCGcnzwoRyC24KPBXKOQ+YR63TYwWvFB0I7nguIcLugiOhsDQrfgAb/SEqppqWFvy+Xtg3l0raOG7SLS60d79Iy86uFxHcHMxy+5kJ3x3NV8w8CgLxTkDCFzfRjIDsh9ySMXQwlhZCdfi4Mso6ee5x5FHKOQXaadTryneNth7S3Cwf7Iwrbc7+ghv2sSqkmx9/Xh86tWtC5VfWOd0Of/aqGu+DV398W7wiCsYsByFu7FpLvPjffLxBaXWKdHCkttgZFZUDYBUXWEchKh9wT1imthluDBLc5/3RTRGeI6GINifBOEOC4a75SSkHNd8GbPbxnvb2HdwRBbfn6Q8tu1smRstLzTy9lHYXMw+dCIysN8k5bp/TNjrcR1JJ4SySc7OMgMDpDoOPBvpRS3qG2d8FzhQZBXVh8baeDOgFDqy8vL7Oehqo83VT1FNRRKDhLKGdh74+O3yMw/NxRRGVA2AdFhHWsX6VUs+Wuu+BV0CBwJx8LhHW0Tp0HVV9eXg55p9ny1QoGXNL6/PaJiseFWXBip3VyxD+0ehuF/SmoFi01KJRSF6RB4Ek+PhDajuzwntA3ufpyYyA/w3a66ajjRu3iHDi12zo54tfCwSmnTrZ2is7WNgwNCqW8mgZBYyYCwa2tU1R89eXGQMHPjq94qmjQLsyC0/uskyO+gbaAqHrFU2f8izKsRy0+OoSvUs2ZBkFTJmI99dOiJXSMdbxOQaYtJKpc8WTXRkHGD9apiiEAG35t7S9R9YqnitAI7WA9BaaUarI0CJq7oAjr1L6f4+VFObYrnI5Wa9QuPv0j/iVZcPagdXLExxfCoqp0tLM7BRUWpb2zlWrkNAi8XUAotOttnapYt3YtyUMS7Y4oDlc/BZV7wtaG4ahrNtbe2WFRVXpl2zVqa+9spTxOg0BdmH8LaNPTOjlS2Tv7MNU73R2F7GPWf7OOwpF1DjYg1tNLVcd5sh/GQ3tnK+VWbg0CERkB/B2wAPONMc9WWX4V8DegPzDWGPO+O+tRbuBs72xHfSgyj1iX5RyzTkdTHW8jpN15p5s6niqCH0rONXL7Ox5nXinlHLcFgYhYgFeAXwBpwEYRWWmM2WO32hFgMvB7d9WhPMyp3tnHHF/xlHnE2js796R1SrfeMeoygB9eO7eNFq2r9KHocv4pqADHA30ppazceUSQCBwwxhwEEJEUYBRQGQTGmEO2ZeWONqC8gMX33GkgR8rLIOfEeUN4HNubSseg0nPz8s9Yp2NbHW8jKLL6yLH2p6CCHI8xr5S3EGOMezYsMhoYYYyZZns+CUgyxsx0sO4i4OOaTg2JyAxgBkC7du3iU1JSalVTbm4uISGNb5A3rcs159VlyvEvziSw8NR5U0DR6crHlvLiC26v1NKCwsC2tqkNhYHt7B63pdQ39KKd7vyLztJz53Ps7/cwxQGR9fVR602T+G/ZiDTHuoYNG7bZGJPgaFmTaCw2xrwBvAGQkJBgajvWhrvG6agrrcs1LtVlDOSdqdJ/4vxGbd+SPELyDhGSd8jxNvyCLzDUeGdrh79P/geTu58hJf+B4Y3vVqjN4r9lA/K2utwZBOlAJ7vn0bZ5SjUcEQhpY52iL9Q7224Yj6p9Koqy4fRe63Sht4Jzt0IVCyTOsDam+wZZr3yqmHwDrUN/+Nn+dfg8SDvqqQbjziDYCPQQkW5YA2AsMN6N76eU687rnR3neJ2CTAdXPFVcLnvYOoxHVaYMUv9f3Wqz+NuFiKPQCHIQMlXXtc6PPPsDHA5wsG6Qho5yXxAYY0pFZCawGuvlowuNMbtFZC6wyRizUkQGAsuBSOBmEfmTMaaPu2pSqlYqemd36O94+Uf3wdbFlIsFH1MG3a6EPrdCSQGUFlj/rZhKC+0eO1qWb+2bUZJvva92WTEUOQgaF8UA7LjACj5+FwmYGo5anF63xbmjI0uTOCPtVdz6X8QYswpYVWXeY3aPN2I9ZaRU01WQWf1WqAl3X/x1F2KMNQTsg+G8ECl0sKyGdUsKOHv6GC1DAmsOpPISa+DUQ+hclI9fZWgklQK7WzoOGIdHODWEUdVAqlhXQ8cp+i0pVVc13Qq1LkSsQ2/4BkA9dKzecaFGxsrQqXqk4kTIuLLueaFTAkXZ1o9WeLLuH7AmPr4XD43K4Di3rNPR45C6v8q6NRzhVLzO3WNq5ZwgdusjEL8cQtvV66Y1CJTydueFjpv7VNiHTmkh679dw6ABMTWcKqvpuQuBVF5qbewvynapzEsAahhnsUY+vhdp06l4fqFlNZye8w2Er58jPGsPfP0c3FS/V6ZpECilGo596ACFQR0cDnhYL4yBshIHRyoVwVHzUcyRg/vp3L7NxQOp8rEtdIpzrJObnHdlmm8A/PFUvWxXg0Ap1TyJWIc48fV3+aUHZS2dXblevyJ0nDqKcfE0W1Eu5By3PgbrkcLlN8H1T7v8uWqiQaCUUnVlHzqB4fW//X89CFsWUSYWLGVFEBBWr+0EGgRKKdXY5Z2C+LvZYn9lWj3SIFBKqcbOHVem2dG7kiullJfTIFBKKS+nQaCUUl5Og0AppbycBoFSSnk5DQKllPJybrtVpbuIyGngcC1f3ho4U4/l1BetyzVal+saa21al2vqUlcXY0wbRwuaXBDUhYhsqumenZ6kdblG63JdY61N63KNu+rSU0NKKeXlNAiUUsrLeVsQvOHpAmqgdblG63JdY61N63KNW+ryqjYCpZRS1XnbEYFSSqkqNAiUUsrLNYsgEJGFInJKRHbVsFxE5CUROSAiO0RkgN2yu0TkB9t0VwPXNcFWz04RWSciMXbLDtnmbxORTQ1cV7KIZNnee5uIPGa3bISI7Ld9lw83cF2z7WraJSJlItLStsyd31cnEVkjIntEZLeIPOBgnQbfx5ysq8H3MSfravB9zMm6GnwfE5FAEdkgItttdf3JwToBIvKu7TtJFZGudsvm2ObvF5HhtSrCGNPkJ+AqYACwq4blNwKfYr3l5yAg1Ta/JdZbVLcEIm2PIxuwriEV7wfcUFGX7fkhoLWHvq9k4GMH8y3Aj0B3wB/YDvRuqLqqrHsz8FUDfV8dgAG2x6HA91U/tyf2MSfravB9zMm6Gnwfc6YuT+xjtn0mxPbYD0gFBlVZ5zfAa7bHY4F3bY97276jAKCb7buzuFpDszgiMMZ8A5y9wCqjgLeM1XogQkQ6AMOBfxtjzhpjfgb+DYxoqLqMMets7wuwHoiur/euS10XkAgcMMYcNMYUAylYv1tP1DUOWFpf730hxpjjxpgttsc5wF4gqspqDb6POVOXJ/YxJ7+vmrhtH6tFXQ2yj9n2mVzbUz/bVPUqnlHAP22P3weuFRGxzU8xxhQZY34CDmD9Dl3SLILACVHAUbvnabZ5Nc33hKlYf1FWMMDnIrJZRGZ4oJ7BtkPVT0Wkj21eo/i+RKQF1j+mH9jNbpDvy3ZIHof1V5s9j+5jF6jLXoPvYxepy2P72MW+r4bex0TEIiLbgFNYfzjUuH8ZY0qBLKAV9fR96a0qGwERGYb1f9Ir7GZfYYxJF5G2wL9FZJ/tF3ND2IJ1XJJcEbkRWAH0aKD3dsbNwH+NMfZHD27/vkQkBOsfht8aY7Lrc9t14UxdntjHLlKXx/YxJ/87Nug+ZowpA2JFJAJYLiJ9jTEO28rcwVuOCNKBTnbPo23zaprfYESkPzAfGGWMyaiYb4xJt/17ClhOLQ73assYk11xqGqMWQX4iUhrGsH3ZTOWKofs7v6+RMQP6x+PxcaYDx2s4pF9zIm6PLKPXawuT+1jznxfNg2+j9m2nQmsofrpw8rvRUR8gXAgg/r6vuq74cNTE9CVmhs/f8n5DXkbbPNbAj9hbcSLtD1u2YB1dcZ6Tm9IlfnBQKjd43XAiAasqz3nOhsmAkds350v1sbObpxryOvTUHXZlodjbUcIbqjvy/bZ3wL+doF1Gnwfc7KuBt/HnKyrwfcxZ+ryxD4GtAEibI+DgP8AN1VZ5z7ObyxeZnvch/Mbiw9Si8biZnFqSESWYr0KobWIpAGPY21wwRjzGrAK61UdB4B84G7bsrMi8iSw0bapueb8Q0F31/UY1vN8r1rbfSg11pEF22E9PATr/xhLjDGfNWBdo4F7RaQUKADGGuteVyoiM4HVWK/uWGiM2d2AdQH8CvjcGJNn91K3fl/AUGASsNN2HhfgEax/ZD25jzlTlyf2MWfq8sQ+5kxd0PD7WAfgnyJiwXqWZpkx5mMRmQtsMsasBBYAb4vIAawhNdZW824RWQbsAUqB+4z1NJNLdIgJpZTyct7SRqCUUqoGGgRKKeXlNAiUUsrLaRAopZSX0yBQSikvp0GglI1tpMltdlN9jnzZVWoYVVUpT2sW/QiUqicFxphYTxehVEPTIwKlLsI2Dv1fbGPRbxCRS23zu4rIV2Id7/9LEelsm99ORJbbBlTbLiJDbJuyiMg825jzn4tIkG39WWIdI3+HiKR46GMqL6ZBoNQ5QVVODd1htyzLGNMPeBn4m23eP4B/GmP6A4uBl2zzXwK+NsbEYL2/QkXP2B7AK8aYPkAmcJtt/sNAnG07v3bXh1OqJtqzWCkbEck1xoQ4mH8IuMYYc9A2aNkJY0wrETkDdDDGlNjmHzfGtBaR00C0MabIbhtdsQ4v3MP2/CHAzxjzlIh8BuRiHYFzhTk3Nr1SDUKPCJRyjqnhsSuK7B6Xca6N7pfAK1iPHjbaRpdUqsFoECjlnDvs/v3O9ngdtsG/gAlYR40E+BK4FypvOBJe00ZFxAfoZIxZAzyEdeTLakclSrmT/vJQ6pwgu1EpAT4zxlRcQhopIjuw/qofZ5t3P/CmiMwGTmMbcRR4AHhDRKZi/eV/L3C8hve0AO/YwkKAl4x1THqlGoy2ESh1EbY2ggRjzBlP16KUO+ipIaWU8nJ6RKCUUl5OjwiUUsrLaRAopZSX0yBQSikvp0GglFJeToNAKaW83P8HCvjuQoWvTvAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CEkYhPf-T4-g",
        "outputId": "b31e545e-39bb-440c-ad61-2f1712c5760b"
      },
      "source": [
        "line1 = plt.plot(epochs, acc_value, label = 'Training Accuracy Value')\n",
        "line2 = plt.plot(epochs, val_acc, label = 'Validating Accuracy Value')\n",
        "plt.setp(line1, linewidth=2.0, marker='o')\n",
        "plt.setp(line2, linewidth=2.0, marker='*')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "gZHfBGcaVHKk"
      },
      "source": [
        "model.save('my_model.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "mEFJWkKAgy3N",
        "outputId": "edfddf7a-5f9a-462f-815e-8d6ab53ae578"
      },
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(model, to_file='My Mnist Model.png', show_shapes=True, show_dtype=True, show_layer_names=True, rankdir='LR')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "hnHncHZKi0CM",
        "outputId": "ff6d0c65-00f5-428c-e679-b9208f319479"
      },
      "source": [
        "plot_model(model, to_file='My Mnist Model(2).png', show_shapes=True, show_dtype=True, show_layer_names=True, dpi=70)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEziFyzFjWhF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}