H4_OptimizedModel.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "H4_OptimizedModel.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8e20MIwRvC8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "88d67e51-11ec-4a07-f2f1-0aa8c55a09b3"
      },
      "source": [
        "#Import essential libraries\n",
        "\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "from keras import optimizers\n",
        "from keras.utils import np_utils\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "import gzip\n",
        "import os\n",
        "import sys\n",
        "import struct\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sys, time"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIq-1Z40SXzh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the Dataset\n",
        "\n",
        "def read_image(fi):\n",
        "    channel = 1\n",
        "    magic, n, rows, columns = struct.unpack(\">IIII\", fi.read(16))\n",
        "    assert magic == 0x00000803\n",
        "    assert rows == 28\n",
        "    assert columns == 28\n",
        "    rawbuffer = fi.read()\n",
        "    assert len(rawbuffer) == n * rows * columns * channel\n",
        "    rawdata = np.frombuffer(rawbuffer, dtype='>u1', count=n*rows*columns*channel)\n",
        "    return rawdata.reshape(n, rows, columns, channel).astype(np.float32) / 255.0\n",
        "\n",
        "def read_label(fi):\n",
        "    magic, n = struct.unpack(\">II\", fi.read(8))\n",
        "    assert magic == 0x00000801\n",
        "    rawbuffer = fi.read()\n",
        "    assert len(rawbuffer) == n\n",
        "    return np.frombuffer(rawbuffer, dtype='>u1', count=n)\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    os.system('wget -N http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz')\n",
        "    os.system('wget -N http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz')\n",
        "    os.system('wget -N http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz')\n",
        "    os.system('wget -N http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz')\n",
        "    \n",
        "    np.savez_compressed(\n",
        "        'mnist',\n",
        "        train_x=read_image(gzip.open('train-images-idx3-ubyte.gz', 'rb')),\n",
        "        train_y=read_label(gzip.open('train-labels-idx1-ubyte.gz', 'rb')),\n",
        "        test_x=read_image(gzip.open('t10k-images-idx3-ubyte.gz', 'rb')),\n",
        "        test_y=read_label(gzip.open('t10k-labels-idx1-ubyte.gz', 'rb'))\n",
        "    )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzxlN1JCSeJg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        },
        "outputId": "868de9d6-5f60-4920-edaa-3601a61ecc48"
      },
      "source": [
        "data = np.load('mnist.npz')\n",
        "\n",
        "print(data['train_x'].shape, data['train_x'].dtype)\n",
        "print(data['train_y'].shape, data['train_y'].dtype)\n",
        "print(data['test_x'].shape, data['test_x'].dtype)\n",
        "print(data['test_y'].shape, data['test_y'].dtype)\n",
        "\n",
        "X_train = data['train_x']\n",
        "y_train = data['train_y']\n",
        "\n",
        "X_test = data['test_x']\n",
        "y_test = data['test_y']\n",
        "\n",
        "y_train = np_utils.to_categorical(y_train, 10)\n",
        "y_test = np_utils.to_categorical(y_test, 10)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28, 1) float32\n",
            "(60000,) uint8\n",
            "(10000, 28, 28, 1) float32\n",
            "(10000,) uint8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1nVMZtdUdiC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Parameters for the optimized model\n",
        "learning_rate = 0.000122\n",
        "Number_of_Dense_Layer\t= 2\t\n",
        "Number_of_Input_Neurons = 125\t\n",
        "Number_of_Neurons_Dense = 86\t\n",
        "Activation = 'relu'\n",
        "batch_size=80\t\n",
        "adam_decay=0.000491"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "estuQoP1SfPd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_model_optimized(learning_rate,Number_of_Input_Neurons,Number_of_Neurons_Dense,Activation,adam_decay):\n",
        "  model = Sequential()\n",
        "  # add Convolutional layers\n",
        "  model.add(Conv2D(filters=Number_of_Input_Neurons, kernel_size=(3,3), activation=Activation, padding='same',\n",
        "                     input_shape=(28,28,1)))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Conv2D(filters=64, kernel_size=(3,3), activation=Activation, padding='same'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Conv2D(filters=64, kernel_size=(3,3), activation=Activation, padding='same'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Flatten())\n",
        "  # Densely connected layers\n",
        "  model.add(Dense(Number_of_Neurons_Dense,activation=Activation,name=\"hidden_1\"))\n",
        "  model.add(Dense(64,activation=Activation,name=\"hidden_2\"))\n",
        "  model.add(Dense(10,activation='softmax',name=\"output_layer\"))\n",
        "  #setup our optimizer and compile\n",
        "  adam = Adam(lr=learning_rate, decay= adam_decay)\n",
        "  model.compile(optimizer = adam, loss='categorical_crossentropy', metrics=[\"accuracy\"])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tH9p-F2Sk87",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "88b7cfaf-351a-4a2e-e266-c1acb4b50280"
      },
      "source": [
        "result = create_model_optimized(learning_rate,Number_of_Input_Neurons,Number_of_Neurons_Dense,Activation,adam_decay)\n",
        "result.summary()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_21 (Conv2D)           (None, 28, 28, 125)       1250      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_21 (MaxPooling (None, 14, 14, 125)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 14, 14, 64)        72064     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_22 (MaxPooling (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 7, 7, 64)          36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_23 (MaxPooling (None, 3, 3, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_8 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "hidden_1 (Dense)             (None, 86)                49622     \n",
            "_________________________________________________________________\n",
            "hidden_2 (Dense)             (None, 64)                5568      \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 166,082\n",
            "Trainable params: 166,082\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8m2oakqxSscf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "outputId": "c433ac30-0199-4732-d5be-71efc5865058"
      },
      "source": [
        "optimized_model = result.fit(X_train, y_train, batch_size=batch_size, epochs =4, validation_split=.15)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/4\n",
            "51000/51000 [==============================] - 14s 268us/step - loss: 0.5536 - acc: 0.8414 - val_loss: 0.1474 - val_acc: 0.9574\n",
            "Epoch 2/4\n",
            "51000/51000 [==============================] - 12s 241us/step - loss: 0.1256 - acc: 0.9627 - val_loss: 0.0962 - val_acc: 0.9729\n",
            "Epoch 3/4\n",
            "51000/51000 [==============================] - 12s 239us/step - loss: 0.0947 - acc: 0.9717 - val_loss: 0.0842 - val_acc: 0.9766\n",
            "Epoch 4/4\n",
            "51000/51000 [==============================] - 12s 241us/step - loss: 0.0787 - acc: 0.9762 - val_loss: 0.0756 - val_acc: 0.9798\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HY4U2jFUSyOH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "outputId": "bc57674c-9582-4a56-a9e0-f0af260a619f"
      },
      "source": [
        "loss, accuracy = result.evaluate(X_test,y_test)\n",
        "print('Accuracy = ', accuracy)\n",
        "print('Loss = ', loss)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 136us/step\n",
            "Accuracy =  0.9803\n",
            "Loss =  0.06795461275689303\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsviSwnaS1Zy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predicted Labels on the optimized model\n",
        "y_Predict = result.predict(X_test)\n",
        "y_numeric = np.argmax(y_Predict, axis=1)\n",
        "y_numeric_onehotencoded = np_utils.to_categorical(y_numeric, 10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzDQTGe_Yv3W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_df = pd.DataFrame(y_numeric_onehotencoded)\n",
        "new_df.to_csv('mnist_final_4.csv') \n",
        "files.download('mnist_final_4.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vibe0d3bTMW2",
        "colab_type": "text"
      },
      "source": [
        "## Training and Validation Loss for Various Activation Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVNuxAkpTRml",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_model_optimized(activations,batch_size):\n",
        "  for active in activations:\n",
        "    model = Sequential()\n",
        "    # add Convolutional layers\n",
        "    model.add(Conv2D(filters=125, kernel_size=(3,3), activation=active, padding='same',\n",
        "                     input_shape=(28,28,1)))\n",
        "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "    model.add(Conv2D(filters=64, kernel_size=(3,3), activation=active, padding='same'))\n",
        "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "    model.add(Conv2D(filters=64, kernel_size=(3,3), activation=active, padding='same'))\n",
        "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "    model.add(Flatten())\n",
        "    # Densely connected layers\n",
        "    model.add(Dense(86,activation=active,name=\"hidden_1\"))\n",
        "    model.add(Dense(50,activation=active,name=\"hidden_2\"))\n",
        "    model.add(Dense(10,activation='softmax',name=\"output_layer\"))\n",
        "    #setup our optimizer and compile\n",
        "    adam = Adam(lr=0.000122, decay= 0.000491)\n",
        "    model.compile(optimizer = adam, loss='categorical_crossentropy', metrics=[\"accuracy\"])\n",
        "\n",
        "    optimized_model = result.fit(X_train, y_train, batch_size=batch_size, epochs =15, validation_split=.15)\n",
        "    show_plots(optimized_model.history)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "geKsM2w7TkLf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sys, time\n",
        "\n",
        "def show_plots(history):\n",
        "    \"\"\" Useful function to view plot of loss values & accuracies across the various epochs \"\"\"\n",
        "    loss_vals = history['loss']\n",
        "    val_loss_vals = history['val_loss']\n",
        "    epochs = range(1, len(history['acc'])+1)\n",
        "    \n",
        "    f, ax = plt.subplots(nrows=1,ncols=2,figsize=(16,4))\n",
        "    \n",
        "    # plot losses on ax[0]\n",
        "    ax[0].plot(epochs, loss_vals, color='navy',marker='o', label='Training Loss')\n",
        "    ax[0].plot(epochs, val_loss_vals, color='firebrick', marker='*', label='Validation Loss')\n",
        "    ax[0].set_title('Training & Validation Loss')\n",
        "    ax[0].set_xlabel('Epochs')\n",
        "    ax[0].set_ylabel('Loss')\n",
        "    ax[0].legend(loc='best')\n",
        "    ax[0].grid(True)\n",
        "    \n",
        "    # plot accuracies\n",
        "    acc_vals = history['acc']\n",
        "    val_acc_vals = history['val_acc']\n",
        "\n",
        "    ax[1].plot(epochs, acc_vals, color='navy', marker='o', label='Training Accuracy')\n",
        "    ax[1].plot(epochs, val_acc_vals, color='firebrick', marker='*', label='Validation Accuracy')\n",
        "    ax[1].set_title('Training & Validation Accuracy')\n",
        "    ax[1].set_xlabel('Epochs')\n",
        "    ax[1].set_ylabel('Accuracy')\n",
        "    ax[1].legend(loc='best')\n",
        "    ax[1].grid(True)\n",
        "    \n",
        "    plt.show()\n",
        "    plt.close()\n",
        "    \n",
        "    # delete locals from heap before exiting\n",
        "    del loss_vals, val_loss_vals, epochs, acc_vals, val_acc_vals"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52gwUEtKTbA9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "066cdc32-1f51-40c1-8996-619b2ce1243e"
      },
      "source": [
        "activations = ['tanh','sigmoid','relu', 'softmax']\n",
        "\n",
        "optimized_activation_plot = create_model_optimized(activations,batch_size)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/15\n",
            "51000/51000 [==============================] - 14s 278us/step - loss: 0.0214 - acc: 0.9936 - val_loss: 0.0396 - val_acc: 0.9887\n",
            "Epoch 2/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0207 - acc: 0.9939 - val_loss: 0.0395 - val_acc: 0.9888\n",
            "Epoch 3/15\n",
            "51000/51000 [==============================] - 12s 242us/step - loss: 0.0205 - acc: 0.9942 - val_loss: 0.0392 - val_acc: 0.9890\n",
            "Epoch 4/15\n",
            "51000/51000 [==============================] - 13s 245us/step - loss: 0.0204 - acc: 0.9940 - val_loss: 0.0404 - val_acc: 0.9886\n",
            "Epoch 5/15\n",
            "51000/51000 [==============================] - 13s 249us/step - loss: 0.0200 - acc: 0.9942 - val_loss: 0.0387 - val_acc: 0.9882\n",
            "Epoch 6/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0197 - acc: 0.9940 - val_loss: 0.0406 - val_acc: 0.9888\n",
            "Epoch 7/15\n",
            "51000/51000 [==============================] - 12s 243us/step - loss: 0.0196 - acc: 0.9943 - val_loss: 0.0393 - val_acc: 0.9892\n",
            "Epoch 8/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0193 - acc: 0.9945 - val_loss: 0.0386 - val_acc: 0.9884\n",
            "Epoch 9/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0190 - acc: 0.9944 - val_loss: 0.0397 - val_acc: 0.9891\n",
            "Epoch 10/15\n",
            "51000/51000 [==============================] - 12s 245us/step - loss: 0.0189 - acc: 0.9946 - val_loss: 0.0391 - val_acc: 0.9887\n",
            "Epoch 11/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0185 - acc: 0.9948 - val_loss: 0.0386 - val_acc: 0.9892\n",
            "Epoch 12/15\n",
            "51000/51000 [==============================] - 12s 244us/step - loss: 0.0183 - acc: 0.9947 - val_loss: 0.0395 - val_acc: 0.9886\n",
            "Epoch 13/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0182 - acc: 0.9947 - val_loss: 0.0390 - val_acc: 0.9890\n",
            "Epoch 14/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0181 - acc: 0.9947 - val_loss: 0.0392 - val_acc: 0.9888\n",
            "Epoch 15/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0177 - acc: 0.9949 - val_loss: 0.0384 - val_acc: 0.9890\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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agWAFf9caanfhs0ePkrF8OcdSUshcuRLH2bN4+vsT2r8/4YMGETZoEP4RERf8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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/15\n",
            "51000/51000 [==============================] - 13s 261us/step - loss: 0.0176 - acc: 0.9951 - val_loss: 0.0378 - val_acc: 0.9892\n",
            "Epoch 2/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0174 - acc: 0.9951 - val_loss: 0.0394 - val_acc: 0.9890\n",
            "Epoch 3/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0172 - acc: 0.9951 - val_loss: 0.0394 - val_acc: 0.9882\n",
            "Epoch 4/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0170 - acc: 0.9950 - val_loss: 0.0388 - val_acc: 0.9897\n",
            "Epoch 5/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0169 - acc: 0.9953 - val_loss: 0.0390 - val_acc: 0.9884\n",
            "Epoch 6/15\n",
            "51000/51000 [==============================] - 13s 249us/step - loss: 0.0168 - acc: 0.9951 - val_loss: 0.0382 - val_acc: 0.9896\n",
            "Epoch 7/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0165 - acc: 0.9952 - val_loss: 0.0408 - val_acc: 0.9884\n",
            "Epoch 8/15\n",
            "51000/51000 [==============================] - 13s 245us/step - loss: 0.0165 - acc: 0.9955 - val_loss: 0.0381 - val_acc: 0.9896\n",
            "Epoch 9/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0161 - acc: 0.9958 - val_loss: 0.0406 - val_acc: 0.9884\n",
            "Epoch 10/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0160 - acc: 0.9956 - val_loss: 0.0382 - val_acc: 0.9892\n",
            "Epoch 11/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0159 - acc: 0.9956 - val_loss: 0.0390 - val_acc: 0.9894\n",
            "Epoch 12/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0157 - acc: 0.9958 - val_loss: 0.0388 - val_acc: 0.9892\n",
            "Epoch 13/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0156 - acc: 0.9956 - val_loss: 0.0381 - val_acc: 0.9889\n",
            "Epoch 14/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0156 - acc: 0.9958 - val_loss: 0.0392 - val_acc: 0.9892\n",
            "Epoch 15/15\n",
            "51000/51000 [==============================] - 13s 250us/step - loss: 0.0153 - acc: 0.9959 - val_loss: 0.0384 - val_acc: 0.9891\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cwy5dKiX/vfPns/See8g/coQ+TzxB2xGV92D042l53nlE9u7NsrFjWX7//ez9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\n",
            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/15\n",
            "51000/51000 [==============================] - 13s 262us/step - loss: 0.0152 - acc: 0.9960 - val_loss: 0.0394 - val_acc: 0.9888\n",
            "Epoch 2/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0151 - acc: 0.9960 - val_loss: 0.0385 - val_acc: 0.9893\n",
            "Epoch 3/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0151 - acc: 0.9960 - val_loss: 0.0391 - val_acc: 0.9894\n",
            "Epoch 4/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0149 - acc: 0.9960 - val_loss: 0.0397 - val_acc: 0.9893\n",
            "Epoch 5/15\n",
            "51000/51000 [==============================] - 12s 245us/step - loss: 0.0148 - acc: 0.9961 - val_loss: 0.0389 - val_acc: 0.9894\n",
            "Epoch 6/15\n",
            "51000/51000 [==============================] - 12s 245us/step - loss: 0.0145 - acc: 0.9964 - val_loss: 0.0398 - val_acc: 0.9889\n",
            "Epoch 7/15\n",
            "51000/51000 [==============================] - 12s 245us/step - loss: 0.0144 - acc: 0.9962 - val_loss: 0.0376 - val_acc: 0.9897\n",
            "Epoch 8/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0143 - acc: 0.9964 - val_loss: 0.0389 - val_acc: 0.9889\n",
            "Epoch 9/15\n",
            "51000/51000 [==============================] - 12s 244us/step - loss: 0.0142 - acc: 0.9962 - val_loss: 0.0387 - val_acc: 0.9892\n",
            "Epoch 10/15\n",
            "51000/51000 [==============================] - 12s 244us/step - loss: 0.0141 - acc: 0.9962 - val_loss: 0.0381 - val_acc: 0.9894\n",
            "Epoch 11/15\n",
            "51000/51000 [==============================] - 12s 243us/step - loss: 0.0141 - acc: 0.9964 - val_loss: 0.0382 - val_acc: 0.9896\n",
            "Epoch 12/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0140 - acc: 0.9965 - val_loss: 0.0388 - val_acc: 0.9889\n",
            "Epoch 13/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0138 - acc: 0.9965 - val_loss: 0.0405 - val_acc: 0.9888\n",
            "Epoch 14/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0136 - acc: 0.9967 - val_loss: 0.0393 - val_acc: 0.9887\n",
            "Epoch 15/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0136 - acc: 0.9965 - val_loss: 0.0386 - val_acc: 0.9899\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5rbjHgtzMzDwOHz5W/sjIUJKS2pCS0pbRo3uTnJzgfaZthw7HZkGueq2K966u\nA9OGDuKauoZs1Q7YY5Aak/p41IKWl1OQmekNiPNWreLI9u0AhERGOhM+VbQSDxlCRHx8tec6mRlR\ntbycozt3OoHuli3eINezZQslBw5404VERRHbqxdxvXoR26sXuStWsP+//yUkIoLy4mK633BDQIOe\nuprltby4mINr13pbiPNWrfJOUFWdxIsvpq0b9Mb17l1nEwCdqExlRUVkvfgim2bORETofffdnPGj\nHzXIjZATKSsqYtHAgWipn/EvIt6x2GFxcSd89V0ObdHihDcV1j76KNlvvBHw3zkIvtmFK9672ORk\njmzbRnlpKZ1Hjyb59tuJ6927obN30lbccQeR7dpVGiJR0WukYv6Bg+vWkf/NNxxct46D33zj/b8m\n4eFOUOwGxK3696dlSkq1f2N1feOlto9aMNWzxyA1vMZUpvJyZfnyHbz77nfMn7+R777bD8CQIR0Z\nMyaFsWP7MGBA+xpvWp9umcrKysnOzmf9+r2VWow3bNh33LjSqjp2jKVnz9ZucBvvBrfOepcuLQkP\nDz2pvJxsYFpSUnZca/f69XvZuDG32tbuQ4eKeP31td6gHpxg8bnnRnH11X0pKSmntLSckpKyEy6X\nlJS56/6XH3zwY7/dut35Ub1CQ50WbSe4bUNycoL3mbZdurSs9BzZunzvTldj+luqK02xTLWtmy0A\nDqDCffvIW7WKA25AnL9+vTfQiE1OPtZKPHQoMe7zMgv37uXTW27hotdeq/Qlrby4mMPZ2ccFuYez\nsig7euwfSnjr1t4gNzYpyVlOSnJadH0Cv4ovnr6t2hVfPAMhUH9E5SUl5K9fz+5PPuH7efMo2rsX\ncCbmanvOOQx8/HFiOnas8+tC7cp0JCeH9b/7Hbs/+ojYXr0YMG0abc85JyD5qS1V5cDXX5Mzfz47\n33+fkvx8QiIj0dJStKwMCQ0lqmNHWvTsiRYXU+LxUOrxeF99H23lV0gI4bGxxwXJezIyKnV19SYP\n4IRyTfGft6/Sw4cp2LKFL665xu9NimAZL1vbz0lVObpzpxMUuwFx/rp1lOQ7Y7BCIiKIS0mp3FKc\nnExIeHid33ixAPj0WQDc8Bq6TEVFpXz66VbefXcj7767kd27CwgNFUaO7MHYsX246qqUkx5PGbDv\nG+VKWNh0vzPXisCRI1MDNhb3dMvkr7V7/fp9fPfd/kqt3Q3lj3+8zBvk9ugRf9I3ChqDhv5bCoSm\nWKam/hzgoBDVrh2d0tPplJ4OOONbD65dS97KlRxYtYqdCxey/c03AYhs25bWQ4ZQtH8/ZZs28fUv\nfkHrQYOc7stbtnBk+/ZKgUdGWhWBAAAgAElEQVR0p07E9upFwvDh3iA39owziExIqFXefIPdpjzz\nakh4OK0HD6b14MGUHDxI9pw53lbtmK5dAxb81lZMly4M+9vf2PPZZ3wzfTpLb7qJTldeSepDDxHd\noUO95uVwdjY58+eTM38+R7ZvJzQ6mg6XXkqXsWPZ9eGHzu9ieDhaWkr7H/zA7++FqlJ29Kg3IC45\ndKhScHzca0EBpR4PR3fvJrpDBwr37fPOHl4hNDqa/06cSFxyMnFJSc5P795EJCQ06rH0danE46Eg\nMxPP5s143NeCzEyO7tx5LFHFe6FKSGQkHdPTSX3ooYbJcAMREWI6dyamc2fv/1VV5cj333tbifPX\nrWPHe++R/cYbfs8RTDP5G9MUVG2Jmzr1fFq0iGD+/O9YtCiTgoJiYmMjSE9PYuzYFC6/PJnWraMb\nOtvHCQmRE85c25gnogoLC6F3b6e78Nixfbzby8rKCQ//rd+gHuCZZy4lPDyE8PBQwsJCTmv5nHNe\nrPZZtj//+VmBKroxfjXev9YgFBYdTdsRI2g7YgTgdF/2bN5M3qpVrPvNb9i9eLE3be7SpeQuXQoi\ndLj0UjqNGuUNcmPPOKPRPDO3MfE3VruxSLzwQtqefTaZ7mOf9nz2GSn33kvPm28mJDxwk1QUHzzI\nzoULyXnnHQ58/TWI0Pbss+l91110vOwyZ4I3IHvOnONm6/ZHRAiLiSEsJoaoxMSTzs/aX//auUkR\nHk55SQmtzzyTuKQkPJmZ7FiwoNKM4+Hx8cQlJTk9GZKTvQFyZPuau8DVt9p2ry0+eNC5qVUR6GZm\nUpCZ6Z2UDZwW8dikJNoMG1ap/FteeMF7k6K8uJiw2FgbQ43zO9miWzdadOtGp8svB5z/rUe2b+fg\nunXkLl/Org8/9M70HhoVRYfLLmt2Nw98iUg68Cecpym8oKpPVtnfHXgJaIfzhIYbVTXH3fcUcIWb\n9Leq+qa7XYDf4UxsWQY8r6p/rofiBJX66rZ5spMRnap//nMtkye/533MjDPu838BSExswcSJ/Rk7\ntg8XXdSzUQeQFYJt0qPQ0JBqg/ru3Vtx331n19m1mvKzbE3wafz/bYKYhITQ0n2UUIeLL2bDE0+w\n68MPKS8qIiQykg6XXkq/qVPtS24tNfZW7dCoKFLuuYcuY8bwzfTpbHj8cb6fN48Bjz1GwvDhdXad\n8uJi9mRkkDN/Pns/+4zy4mLikpPp++CDdL7qKqL9tIrXNFt3XfF3k2LQE08ATkte0d693hbQilbQ\nXYsWsX3uXO85wlu2dILCpCRifQLjqA4dKgXG9TmrddVHihXl5nqD24pyeDZvpmj/fu8xoTExxPbq\n5YxNd8sRm5RETOfOSOjx3b+K8/JqdZPCOP9bW/ToQYsePeg8ejSA98ZLWVFRs755ICKhwEzgEiAH\nWCEiC1TVtzn8aeA1VX1VRC4CngBuEpErgCHAYCASyBCRRap6CLgV6Ar0UdVyETn+GXJ1LNiCxeon\nCeK0rqeqlJerdzzmnDnf8POff1ApKL3ttgVs3LiP887rzpEjJRw9WuK+lla7XJv9vuNOfSUmtmDn\nzl9WmvSpKQjGSY/qK6gPxvfONF0WADcSFY9bKi8u9rbwhLds2Wy/pAWzFt27M/yFF9jzySd8M306\nX06cSOexY0mdMuWUP29V5eDq1eTMn8+O99+n5MABIhIS6D5pEl3HjaNlamqjaDU90U0KESEqMdF5\nNJU7Czi4gfH+/cdaTd3XXR99RIk78zq4j+5yg8i4pCT2L1tG2ebNrJ8xg94/+xlaXo6WlTk/Fcvu\nK1W3+SxTzTFaXs43jz1WaWxutY8US06m/YUXVmrRrTouv7bvXaBvUgSjxtw7pAEMBzJVNQtAROYC\nYwDfADgVuM9d/gyY77P9c1UtBUpFZC2QDrwF3AHcoKrlAKq6N5CF8Bcs3nbbAnbv9jB6dEqdXee9\n9zbyyCOfecdJVlwnKyuPH/7wjBon/qlYrs3kQa+8sua4SZaOHCnh//2/Bbz22ppaTURU3XJNCgtL\n+e1vlwBLqk0TERFKdHQYMTHhREeHu69hREeHk5AQ412OiXFeo6PD+P3vv/R7rr17Dze54LdCsD2P\ntT4D04r3rimOLTXBxQLgRqTiS5q18AQ/EaHDxRfT9txzyXz+ebb84x/s+fhj+tx3H90nTSIkrHZ/\nmke+/947rvfwtm1Oz4FLLqHLuHG0O++8Wp+nMRMRotq1I6pdu+MmECvKzT3WwpqZiWfTJr7/178q\npdn53nvsfO+9+sosMV270uXqq2k9eLDflmlT/xp775B61hn43mc9BxhRJc0a4GqcbtLjgDgRSXC3\n/0ZEngFigAs5Fjj3Aq4TkXHAPuAeVd0cqEJMnfrJccFiYWEp99//Efff/1GgLuu9zqOPZvDooxmn\ndZ7QUCE8PJTw8BDCwkIoKCj2m+7o0VLy8wu9YymjosJOMN4yxF0OrXb5gQf8vz8isGTJj/wGuNHR\nYbWefdfXm2+ur3bMrGk8gi2oN6YmTf/bcRCxFp7mJyw6mj733UeXceP45rHH+Gb6dLb/618MeOwx\n2gwd6ndsacmhQ8643vnzyVuxAoCEESNIuv12OqanEx4X15BFqleRCQlEJiTQ9qxjE2gU7t3LumnT\nvN2/JTyc+IED6Tp+PBHx8UhoqPMTEuJ9xc82f+kkNNRJ67Ntw1NPkfP2297J19qdfz4pd9/dgO+K\nMaftfuA5EbkV+BzYAZSp6mIRGQZ8iRPkLsUZ7wtOl+hCVU0TkatxxhCfX/XEIjIZmAyQmJhIRkbG\nKWVw+/bjg6oKU6f2qXbfyZox47tq9z311ADCwoTQUCEsTAgLCyE01He9Ytn/9qo3xq6/fhl79hQd\nd53ExEiefDLpNEuiOB9VGYmJkX6v0759JCUlWeTnQ371b+9JufHGjjz9tIeiomOt0JGRIdx4Y8dT\n/uxrUlBQELBzN5RgK1OwlQesTE2NBcDGNAKxPXsy4uWX2fXhh6z/7W/577XX0nXCBLS8nLwVK9j4\n7LMkXnQROfPns+fjjykvLia2Vy/6/PKXdB4zhpjOnRu6CI1GVPv2RCYkUF5S4p3VumWfPnS/7rqA\nXK/U47HutaYp2YEzVrdCF3ebl6ruxGkBRkRigfGqetDdNwOY4e57A9jkHpYDvO0uvwO87O/iqjoL\nmAXOY5BOtRtkt26rq52453e/q7u/9X/+89lqr/Pgg1fX2XUAnnkmwe9YzGeeuYKRI+uuda6+rgMw\nciT07WvPYz1dwVamYCsPWJmaGguAjWkkRIRO6em0v+ACPhg8mO/nzfPu2z53rnciqJ4330yXceNo\nNWCAda2tRn0OJ7DutaaJWQEki0hPnMD3euAG3wQi0hbIc8fzPoTTmlsxgVa8quaKyEBgIFDx+IL5\nOF2itwI/4FhgHBD1NXFPfc76W19jMet7MiLrXmuMaWwsADamkQmLieHiL75gzcMPs/c//4HycggJ\nIWHYMAY//TQxnTo1dBYbPRtOYIx/qloqIncBH+I8BuklVV0vItOBlaq6ABgJPCEiitMF+mfu4eHA\nEvfG2yGcxyNVzAL3JDBbRH4BFAC3BbIcFizWzXWCuYXHGGOqYwGwMY1QVPv2zuOKVAmJjHS6PCcl\nWfBrjDltqroQWFhl26M+y/OAeX6OK8SZCdrfOQ9y7PnA9cKCRWOMMafCAmBjGil7dIsxxhhjjDF1\nywJgYxopG1tqjDHGGGNM3Tr5h7oZY4wxxhhjjDFNkAXAxhhjjDHGGGOaBQuAjTHGGGOMMcY0CxYA\nG2OMMcYYY4xpFiwANsYYY4wxxhjTLFgAbIwxxhhjjDGmWbAA2BhjjDHGGGNMs2ABsDHGGGOMMcaY\nZsECYGOMMcYYY4wxzYIFwMYYY4wxxhhjmgULgI0xxhhjjDHGNAsWABtjjDHGGGOMaRYCGgCLSLqI\nbBSRTBGZ4md/pIi86e7/SkR6uNuHi8hq92eNiIzzOWabiKxz960MZP6NMcYYY4wxxgSPsECdWERC\ngZnAJUAOsEJEFqjqBp9kPwEOqGqSiFwPPAVcB3wDpKlqqYh0BNaIyHuqWuoed6Gq7g9U3o0xxhhj\njDHGBJ9AtgAPBzJVNUtVi4G5wJgqacYAr7rL84Afioio6hGfYDcK0ADm0xhjjGk2atE7q7uIfCIi\na0UkQ0S6+Ox7SkS+cX+u89n+iohs9em9Nbi+ymOMMcacjEAGwJ2B733Wc9xtftO4AW8+kAAgIiNE\nZD2wDrjdJyBWYLGIrBKRyQHMvzHGGBNUfHpnjQJSgYkiklol2dPAa6o6EJgOPOEeewUwBBgMjADu\nF5GWPsc9oKqD3Z/VAS6KMcYYc0oC1gX6dKnqV0A/EekLvCoii1S1EDhPVXeISHvgIxH5TlU/r3q8\nGxxPBkhMTCQjI6M+s39aCgoKmlR+a8PK1DQEW5mCrTxgZTKnzds7C0BEKnpn+Q5PSgXuc5c/A+b7\nbP/cvSFdKiJrgXTgrfrIuDHGGFMXAhkA7wC6+qx3cbf5S5MjImFAKyDXN4GqfisiBUB/YKWq7nC3\n7xWRd3Aq8+MCYFWdBcwCSEtL05EjR9ZFmepFRkYGTSm/tWFlahqCrUzBVh6wMpnT5q931ogqadYA\nVwN/AsYBcSKS4G7/jYg8A8QAF1I5cJ4hIo8CnwBTVLWo6sXt5nTjYmVqGoKtTMFWHrAyNTWBDIBX\nAMki0hMn0L0euKFKmgXALcBSYALwqaqqe8z37iRY3YE+wDYRaQGEqKrHXb4Up3uWMcYYY+rG/cBz\nInIrzg3mHUCZqi4WkWHAl8A+nLq7zD3mIWA3EIFz8/lX+Kmf7eZ042JlahqCrUzBVh6wMjU1AQuA\n3eD1LuBDIBR4SVXXi8h0nJbcBcCLwOsikgnk4QTJAOcBU0SkBCgH7lTV/SJyBvCOiFTk/Q1V/SBQ\nZTDGGGOCTI29s1R1J04LMCISC4xX1YPuvhnADHffG8Amd/su9/AiEXkZJ4g2xhhjGp2AjgFW1YXA\nwirbHvVZLgSu8XPc68DrfrZnAYPqPqfGGGNMs1Bj7ywRaQvkqWo5TsvuS+72UCBeVXNFZCAwEFjs\n7uuoqrvEuUM9FudxhsYYY0yj02gnwTLGGGNM3apl76yRwBMiojhdoH/mHh4OLHF7YR0CbvR5QsNs\nEWkHCLAauL2+ymSMMcacDAuAjTHGmGakFr2z5gHz/BxXiDMTtL9zXlTH2TTGGGMCIpDPATbGGGOM\nMcYYYxoNC4CNMcYYY4wxxjQLFgAbY4wxxhhjjGkWLAA2xhhjjDHGGNMsWABsjDHGGGOMMaZZsADY\nGGOMMcYYY0yzYAGwMcYYY4wxxphmwQJgY4wxxhhjjDHNggXAxhhjjDHGGGOaBQuAjTHGGGOMMcY0\nCxYAG2OMMcYYY4xpFiwANsYYY4wxxhjTLFgAbIwxxhhjjDGmWbAA2BhjjDHGGGNMs2ABsDHGGGOM\nMcaYZsECYGOMMcYYY4wxzYIFwMYYY0wzIiLpIrJRRDJFZIqf/d1F5BMRWSsiGSLSxWffUyLyjftz\nnZ9j/ywiBYEugzHGGHOqLAA2xhhjmgkRCQVmAqOAVGCiiKRWSfY08JqqDgSmA0+4x14BDAEGAyOA\n+0Wkpc+504DWAS+EMcYYcxosADbGGGOaj+FApqpmqWoxMBcYUyVNKvCpu/yZz/5U4HNVLVXVw8Ba\nIB28gfX/AA8GOP/GGGPMaQlr6AwYY4wxpt50Br73Wc/Bac31tQa4GvgTMA6IE5EEd/tvROQZIAa4\nENjgHnMXsEBVd4lItRcXkcnAZIDExEQyMjJOtzz1pqCgoEnltzasTE1DsJUp2MoDVqamxgJgY4wx\nxvi6H3hORG4FPgd2AGWqulhEhgFfAvuApUCZiHQCrgFG1nRiVZ0FzAJIS0vTkSNrPKTRyMjIoCnl\ntzasTE1DsJUp2MoDVqamxrpAG2OMMc3HDqCrz3oXd5uXqu5U1atV9UxgqrvtoPs6Q1UHq+olgACb\ngDOBJCBTRLYBMSKSGfCSGGOMMafAWoCNMcaY5mMFkCwiPXEC3+uBG3wTiEhbIE9Vy4GHgJfc7aFA\nvKrmishAYCCwWFVLgQ4+xxeoalK9lMYYY4w5SRYAG2OMMc2EqpaKyF3Ah0Ao8JKqrheR6cBKVV2A\n05X5CRFRnC7QP3MPDweWuGN8DwE3usGvMcYY02RYAGyMMcY0I6q6EFhYZdujPsvzgHl+jivEmQm6\npvPH1kE2jTHGmICwMcDGGGOMMcYYY5qFgAbAIpIuIhtFJFNEpvjZHykib7r7vxKRHu724SKy2v1Z\nIyLjantOY4wxxhhjjDHGn4AFwO5kGTOBUThdpiaKSNWuUz8BDriTZfwReMrd/g2QpqqDgXTg7yIS\nVstzGmOMMcYYY4wxxwlkC/BwIFNVs1S1GJgLjKmSZgzwqrs8D/ihiIiqHvGZWCMK0JM4pzHGGGOM\nMcYYc5xAToLVGfjeZz0HGFFdGndmynwgAdgvIiNwHr3QHbjJ3V+bcwIgIpOByQCJiYlkZGScdoHq\nS0FBQZPKb21YmZqGYCtTsJUHrEzGGGOMMaej0c4CrapfAf1EpC/wqogsOsnjZwGzANLS0nTkyJF1\nn8kAycjIoCnltzasTE1DsJUp2MoDViZjjDHGmNMRyC7QO4CuPutd3G1+04hIGNAKyPVNoKrfAgVA\n/1qe0xhjjAlqInK3iLRu6HwYY4wxTU0gA+AVQLKI9BSRCOB6YEGVNAuAW9zlCcCnqqruMWEAItId\n6ANsq+U5jTHGmGCXCKwQkbfcpyNIQ2fIGGOMaQoCFgC7k1jdBXwIfAu8parrRWS6iFzlJnsRSBCR\nTOA+oOKxRucBa0RkNfAOcKeq7q/unIEqgzHGGNMYqeojQDJOPXorsFlEHheRXg2aMWOMMaaRC+gY\nYFVdCCyssu1Rn+VC4Bo/x70OvF7bcxpjjDHNjdtjajewGygFWgPzROQjVX2wYXNnjDHGNE6NdhIs\nY4wJFiUlJeTk5FBYWHja52rVqhXffvttHeSq8WjMZYqKiqJLly6Eh4c3dFYqEZF7gZuB/cALwAOq\nWiIiIcBmwAJgY0yTVpd1Z6A15nrsVDXmMp1u3WwBsDHGBFhOTg5xcXH06NGD0x2q6fF4iIuLq6Oc\nNQ6NtUyqSm5uLjk5OfTs2bOhs1NVG+BqVc323aiq5SJyZQPlyRhj6kxd1p2B1ljrsdPRWMtUF3Vz\nICfBMsYYAxQWFpKQkNDoK3BTmYiQkJDQWFsfFgF5FSsi0lJERoD36QnGGNOkWd1p/KmLutkCYGOM\nqQdWgTdNjfhzex7nEYEVCtxtxhgTNBrx/2DTgE7398ICYGOMCXK5ubkMHjyYwYMH06FDBzp37uxd\nLy4urtU5fvSjH7Fx48YTppk5cyazZ8+uiyxz3nnnsXr16jo5V5ASVdWKFVUtx4Y1GWNMnWmKdSfA\nnj17CAsL44UXXqizcwYbqyyNMaaRmT17HVOnfsL27fl069aKGTN+yKRJA075fAkJCd5gctq0acTG\nxnL//fdXSqOqqCohIf7vi7788ss1XudnP/vZKefRnLQsEbmHY62+dwJZtTlQRNKBPwGhwAuq+mSV\n/d2Bl4B2ON2sb1TVHHffU8AVbtLfquqb7vYXgTRAgE3Ararq20JtjDEB1ZB1Z3Uaou586623OPvs\ns5kzZw633XZbnZ7bV2lpKWFhTTOUtBZgY4xpRGbPXsfkye+RnZ2PKmRn5zN58nvMnr2uzq+VmZlJ\namoqkyZNol+/fuzatYvJkyeTlpZGv379mD59ujdtRYtsaWkp8fHxTJkyhUGDBnH22Wezd+9eAB55\n5BGeffZZb/opU6YwfPhwUlJS+PLLLwE4fPgw48ePJzU1lQkTJpCWlsbatWtrld+jR49yyy23MGDA\nAIYMGcLnn38OwLp16xg2bBiDBw9m4MCBZGVl4fF4GDVqFIMGDaJ///7MmzevLt+6xuB24BxgB5AD\njAAm13SQiIQCM4FRQCowUURSqyR7GnhNVQcC04En3GOvAIYAg93r3S8iLd1jfqGqg9xjtgN3nV7x\njDGm9hq67rznnnvqve6srpfUnDlzePbZZ8nKymLXrl3e7e+//z5Dhgxh0KBBXHrppYAz0dUtt9zC\nwIEDGThwIPPnz/fmtcLcuXO9gfSNN97IHXfcwfDhw3n44YdZtmwZZ599NmeeeSbnnnsumzdvBpzg\n+Be/+AX9+/dn4MCB/PWvf2Xx4sVMmDDBe95FixZxzTXHPQ23XjTNsN0YY5qon//8A1av3l3t/mXL\ncigqKqu07ciREn7yk3f5xz9WUVZWRmhoaKX9gwd34Nln008pP9999x2vvfYaaWlpADz55JO0adOG\n0tJSLrzwQiZMmEBqauX4KD8/nx/84Ac8+eST3Hfffbz00ktMmTLluHOrKsuXL2fBggVMnz6dDz74\ngL/85S906NCBf//736xZs4YhQ4bUOq9//vOfiYyMZN26daxfv57LL7+czZs389e//pX777+f6667\njqKiIlSVd999lx49erBo0SJvnoOJqu4Frj+FQ4cDmaqaBSAic4ExwAafNKnAfe7yZ8B8n+2fq2op\nUCoia4F04C1VPeSeT4BooPomEWOMOUmnW3f6U5d157Rp0+jevXuD153btm0jLy+PoUOHcs011/DW\nW29x7733snv3bu644w6WLFlC9+7dycvL8+a7Xbt2rF27FlXl4MGDNZZ9165dLFu2jJCQEPLz81my\nZAlhYWF88MEHPPLII7z55ps8//zz7Ny5kzVr1hAaGkpeXh7x8fHcdddd5ObmkpCQwMsvv8yPf/zj\nk33r60StAmAR6QXkqGqRiIwEBuLcHa75XTLGGFNrVSvwmrafrl69enkrcHDuHL/44ouUlpayc+dO\nNmzYcFwlHh0dzahRowAYOnQoS5Ys8Xvuq6++2ptm27ZtAHzxxRf86le/AmDQoEH069ev1nn94osv\neOCBBwDo168fnTp1IjMzk3POOYff/e53ZGdnc/XVV5OUlMTAgQOZMmUKU6ZMYfTo0Zx77rm1vk5T\nICJRwE+AfkBUxXZVrenbRGfge5/1itZjX2uAq3G6SY8D4kQkwd3+GxF5BogBLsQncBaRl4HL3W2/\nrCbfk3FbqhMTE8nIyKghu41HQUFBk8pvbViZmoZgK1Nty9OqVSs8Hg8AxcXFlJVVXw+eqO6s7rji\n4mLv+WtSVFREeHg4Ho+HgoICevbsSUpKivf4t956i9mzZ1NaWsquXbtYtWoVXbt2paysjMOHD+Px\neIiOjua8887D4/GQmprK0qVL8Xg8FBUVUVhYiMfjoaysjMsuuwyPx0NKSoq3R1NGRga/+MUv8Hg8\nnHHGGfTt29d7Xl+vvvoqY8eOxePxMHr0aO677z5+/OMf8+mnn3LeeefRpk0bPB6PtyyLFy/mjTfe\n8J4nLCzMu1xWVobH4+Ho0aOUlJTg8XgoKSnhyiuv5PDhw4DzqKoHH3yQrVu3evPg8Xj44IMPuPPO\nOzly5AgA4eHhHD58mAkTJvDSSy9x7bXXsmLFCmbNmlXrz6CqwsLCU/67qG0L8L+BNBFJAmYB7wJv\n4FR0xhhjaqmmu809ejxLdvbxrZXdu7ciI+PWOn8uX4sWLbzLmzdv5k9/+hPLly8nPj6eG2+80e9j\nBiIiIrzLoaGhlJaW+j13ZGRkjWnqwk033cTZZ5/N+++/T3p6Oi+99BIXXHABK1euZOHChUyZMoVR\no0bx8MMPBywPDeB14DvgMpxuypOAunr80f3AcyJyK/A5TjfrMlVdLCLDgC+BfcBSwPvNUlV/5Hax\n/gtwHXDc4DdVnYXzPYK0tDQdOXJkHWU58DIyMmhK+a0NK1PTEGxlqm15vv32W29999e/XnXCtCeq\nO5cs+ckp5dNXZGQkkZGRxMXFERsbS1xcnDdvmzdv5u9//zsrV6701p0iQlxcHKGhobRo0YK4uDgi\nIiK8x8TGxnrTREZGEhUV5U3fpk0b4uLiaNWqFeXl5cTFxREWFkZMTIz3+JCQEO95fb399tvs37+f\nuXPnArBz50727dtHdHQ04eHhx6UPCQnxlqdCeXk5qkpoaChxcXGIiPfY8PBw2rZt603/xBNPcMUV\nV3DnnXeSmZlJenq63/xWuOOOOxg/fjxRUVFMnDixUlfrkxUVFcWZZ555SsfWdgxwudvlaRzwF1V9\nAOh4Slc0xhhTrRkzfkhMTHilbTEx4cyY8cOAX/vQoUPExcXRsmVLdu3axYcffljn1zj33HN56623\nAGfs7oYNG2o44pjzzz/fO1Pmt99+y65du0hKSiIrK4ukpCTuvfderrzyStauXcuOHTuIjY3lpptu\n4pe//CVff/11nZelgSWp6q+Bw6r6Ks7EVFVbcv3ZAXT1We/ibvNS1Z2qerWqnglMdbcddF9nqOpg\nVb2EYxNe+R5bBswFxp9asYwx5uRZ3QkbNmygtLSUHTt2sG3bNrZt28YDDzzA3LlzOeecc/jss8/I\nzs4G8HaBvuSSS5g5cybgdL0+cOAAISEhtG7dmszMTMrLy3nnnXeqzVd+fj6dO3cG4JVXXvFuv+SS\nS/jb3/7mbX2vuF7Xrl1p27YtTz75JLfeeuvpvSmnobYBcImITARuAf7X3RZ+gvTGGGNOwaRJA5g1\nazTdu7dCxLl7PWvW6NOaybK2hgwZQmpqKn369OHmm28OSLfhu+++mx07dpCamspjjz1GamoqLVu2\n9Jv2sssuo0uXLnTp0oWJEydy9913c/ToUQYMGMCkSZN47bXXiIiI4I033qBfv34MHjyYTZs2ceON\nN7JmzRrvxFiPP/54sLX+ApS4rwdFpD/QCmhfi+NWAMki0lNEInDGES/wTSAibUWk4vvBQzgzQiMi\noW5XaERkIM5wqMXiSHK3C3AVTuu0McbUi4auO1NSUuq97mzVqlWlNHPmzGHcuHGVto0fP545c+aQ\nmJjI888/z5gxYxg0aGKHpiEAACAASURBVBCTJk3i/7d33/FVl+f/x19XNmGEEQgCMlSQGaa4qrJU\nFAWligy1uLD2p4LWgUXxK0UrrSIq1hZrRSuCFmcVVFSCuBkiyJChLEESQEYI2ffvj3MSThYk5CRn\n5P18PM7jfPbnvsghd65zjw/Agw8+yK5du+jcuTPdunUrHNI0ZcoUhg4dyllnnUWLFi3KLNe9997L\n3XffTY8ePYrMhn3zzTfTtGlTkpOT6dq1a2HyDjBy5EjatGlDu3btKv3vctwKpu8+2gvPxBdPASO8\n622Ae8tzbjC8evbs6ULJwoULA10Ev1NMoSHcYgqWeNasWeO3ax04cMBv1wqEnJwcd/jwYeecc+vX\nr3etW7d2e/fuDXCpjq60nx+w1AWwXgNuBBoA5+J5/FEqcHM5z70YT8vtJmCCd9skYLB3+Qpgg/eY\nfwGx3u1xeMb3rgG+Arp5t0cAnwOrgO+BWUC9Y5VDdXPgKabQEG4xlTcef9adVa2q6+bS6s6cnJwq\nvWdVxXTzzTe7mTNnVvo6lambyzUG2Dm3BrgdwMwaAHWdc1MqnG2LiEiNlp6eTv/+/cnNzcU5xz//\n+c+QfY5goHhbZw84537FM0b3pIqc75ybB8wrtm2iz/JcoMRzo5xzmXi+EC++PR8Ir1nGRESCSLjU\nnd26daNBgwY89dRTAS1HeWeBTsHTpSkKWAakmtnnzrk7j3qiiIiIj/r167NsWdFHUhzvDJA1lXMu\n38zuAV475sEiIhLySqs7Q1FZzy6ubuUdA5zgPM/4G4rn8UenAwOqrlgiIiJyFB+Z2V1mdqKZNSx4\nBbpQIiIiwa68bedRZnYCMAzvjJAiIiISMFd53/+fzzZHBbtDi4iI1DTlTYAnAR8AnzvnlpjZSXgm\nyBAREZFq5pxrE+gyiIiIhKLyToL1X+C/Pus/omf8iYiIBISZXVvadufcS9VdFhERkVBSrjHAZtbC\nzN40s1Tv63UzK/uhUCIiEjT69u3LBx98UGTbtGnTuOWWW456Xp06dQDYsWMHV1xxRanH9OnTh6VL\nlx71OtOmTSMjI6Nw/eKLL2bfvn3lKfpR/d///R+PPfZYpa8Tok7zeZ0D/B+eySpFRMQPwrXuLNCt\nWzeGDx/ut+uFkvJOgvUC8A7QzPv6n3ebiIhUgczUVD4fMYLMtLRKX2vEiBHMmTOnyLY5c+YwYsSI\ncp3frFkz5s4t8VScciteic+bN4/69esf9/UEnHO3+bxuAnoAdQJdLhGRQFLdWT5r164lLy+PxYsX\nc+jQIb9cszS5ublVdu3KKG8C3Ng594JzLtf7mgk0rsJyiYjUaOunT2fvkiWsf/rpSl/riiuu4L33\n3iM7OxuAzZs3s2PHDs4555zCZwv26NGDLl268Pbbb5c4f/PmzXTu3BmAw4cPM3z4cDp06MDll1/O\n4cOHC4+75ZZb6NWrF506deLBBx8E4KmnnmLHjh307duXvn37AtC6dWt2794NwNSpUzn99NPp3Lkz\n06ZNK7xfhw4duOmmm+jUqRMXXHBBkfscy9SpU+ncuXORax46dIhBgwbRtWtXOnfuzKuvvgrA+PHj\n6dixI8nJydx1110V+ncNMocAjQsWkRotUHXne++9V+L8qq47i9dzFak7Z8+ezTXXXMMFF1xQpN7f\nuHEjAwYMoGvXrpxzzjls2rQJgClTptClSxe6du3K+PHjgaKt2Lt376Z169YAzJw5k8GDB9OvXz/6\n9+9/1L8zXnrpJZKTk+natSvXXHMNBw8epE2bNuTk5ABw4MCBIuv+Ut5JsPaY2dXAbO/6CGCPX0si\nIlIDfP/nP3Ng7doy9+/55htwrnB9y6xZbJk1C8xo1Ls3ubm5REUV/dVdr0MHOj/wQJnXbNiwIb17\n92b+/PkMGTKEOXPmMGzYMMyMuLg43nzzTerVq8fu3bs544wzGDx4MGZW6rWeffZZ4uPjWbt2LStX\nrqRHjx6F+x5++GEaNmxIXl4e/fv3Z+XKldx+++1MnTqVhQsXkpiYWORay5Yt44UXXuCTTz6hTp06\nnH766Zx33nk0aNCADRs2MHv2bJ577jmGDRvG66+/ztVXX33Uf1vfa3799dc45wqv+eOPP9KsWbPC\nP1L279/Pnj17ePPNN1m3bh1m5teuZVXNzP6HZ9Zn8HyZ3RE9F1hEwlRl687S+LPu7N27N1dddVW1\n1p3F67mK1J2vvvoqCxYsYN26dTz99NOMHDkSgFGjRjF+/Hguv/xy0tLSqF27NvPnz+ftt9/m66+/\nJj4+nr1795b5b1Zg+fLlrFy5koYNG5Kbm1vq3xlr1qxh8uTJfPHFFyQmJrJ3717q1q1Lnz59eO+9\n97jsssuYM2cOQ4cOJTo6+pj3rIjytgBfj+cRSL8AO4ErgNF+LYmIiNCgWzdiGjWCCO+v54gIYho1\nokG3bpW6rm9XLt8uXM45/vSnP5GcnMyAAQP4+eef2bVrV5nX+fTTTwsr0+TkZJKTkwv3vfbaa/To\n0YPu3buzevVq1qxZc9QyffbZZ1x++eXUrl2bOnXqMHToUBYvXgxAmzZt6OaNuWfPnmzevLlccZZ1\nzS5durBgwQLuvfdeFi9eTEJCAgkJCcTFxXHDDTfwxhtvEB8fX657BInHgMe9r78A5zrnxge2SCIi\ngRHounPnzp0hU3cuXbqUxMREWrZsSf/+/fn222/Zu3cvBw8e5Oeff+byyy8HIC4ujvj4eD766COu\nu+66wjqyYcNjP3L+/PPPLzyurL8zPvnkE6688srCBL/g+BtvvJEXXvCMtH3hhRe47rrrjnm/iirv\nLNBbKDa5hpmNA6b5vUQiImHsaN82F1j5wANsmT2biNhY8rOzOWHgQJInTQLg4MGD1K1bt8L3HTJk\nCHfccQfLly8nIyODnj17AjBr1izS0tJYtmwZ0dHRtG7dmszMzApf/6effuKxxx5jyZIlNGjQgNGj\nRx/XdQrExsYWLkdGRlaoC3Rp2rVrx/Lly5k3bx73338//fv3Z+LEiXzzzTd8/PHHzJ07l+nTp/PJ\nJ59U6j7VaCuw0zmXCWBmtcystXNuc2CLJSLif5WtO49XeevOVq1ahUzdOXv2bNatW1fYZfnAgQO8\n/vrrFZ4QKyoqivz8fIASZa5du3bhckX/zjj77LPZvHkzKSkp5OXlFXYj96fytgCX5k6/lUJERApl\n7d5Nq5Ej+c3rr9Nq5Eiy/DCZR506dejbty/XX399kQk89u/fT5MmTYiOjmbhwoVs2bLlqNc599xz\neeWVVwD4/vvvWblyJeCpQGvXrk1CQgK7du1i/vz5hefUrVuXgwcPlrjWOeecw1tvvUVGRgaHDh3i\nzTff5JxzzqlUnGVdc8eOHcTHx3P11Vdz9913s3z5ctLT09m/fz8XX3wxTzzxBN99912l7l3N/gvk\n+6zn4fO4QhGRmiaQdefWrVuPep1gqTvz8/N57bXXWLVqFZs3b2bz5s28/fbbzJ49m7p169KiRQve\neustALKyssjIyOD888/nhRdeKJyQq6ALdOvWrVm2bBnAUSf7KuvvjH79+vHf//6XPXv2FLkuwLXX\nXsvIkSOrpPUXyj8GuDSld3IXEZFKOe3ZZwuXK/vtta8RI0Zw+eWXF5nVctSoUVx66aV06dKFXr16\n0b59+6Ne45ZbbuG6666jQ4cOdOjQofDb8K5du9K9e3fat2/PiSeeyNlnn114zpgxYxg4cCDNmjVj\n4cKFhdt79OjB6NGj6du3LxEREdx4441079693N2dASZPnlw4AQjA9u3bGT16NL29Y74KrvnBBx9w\n9913ExERQXR0NM8++ywHDx5kyJAhZGZm4pxj6tSp5b5vEIhyzmUXrDjnss0sJpAFEhEJpEDWne3a\ntTvqNaqi7ixez5Wn7ly8eDHNmzenWbNmhdvOPfdc1qxZw86dO/nPf/7DzTffzMSJE4mMjOT1119n\n4MCBrFixgl69ehETE8PFF1/MI488wl133cWwYcOYMWMGgwYNKvOeZf2d0alTJyZMmMB5551HZGQk\n3bt3Z+bMmYXn3H///eWecbuizPkMGK/QiWZbnXMt/VyeKtGrVy93rGdtBZOUlBT69OkT6GL4lWIK\nDeEWU7DEs3btWjp06OCXax1vF+hgFuwxlfbzM7NlzrleASoSZrYAeNo59453fQhwu3Ouf6DKVFGq\nmwNPMYWGcIupvPH4s+6sasFejx2PQMY0d+5c3n77bf7zn/+UeUxl6uajtgCb2UGOzDJZZBdQ61gX\nN7OBwJNAJPAv59yjxfbHAi8BPfHMKn2Vc26zmZ0PPArEANnA3c65T7znpAAnAAWd2i9wzqUeqywi\nIiJh5PfALDOb7l3fDlwbwPKIiIhU2m233cb8+fOZN29eld3jqAmwc+64034ziwSeAc7HUzEvMbN3\nnHO+U5vdAPzqnDvFzIYDU4CrgN3Apc65HWbWGfgAaO5z3ijnXOh8bSwiIuJHzrlNwBlmVse7nh7g\nIomIiFTa0354hvOxVGYSrGPpDWx0zv3oHac0BxhS7JghwIve5blAfzMz59y3zrkd3u2rgVre1mIR\nEZEaz8weMbP6zrl051y6mTUws8nlPHegmf1gZhvNrMSjk8yslZl9bGYrzSzFzFr47JtiZt97X1f5\nbJ/lveb3ZvZvM/PvQxtFRET8pDKTYB1Lc2Cbz/p24PSyjnHO5ZrZfqARnhbgAr8Fljvnsny2vWBm\necDrwGRXykBmMxsDjAFISkoiJSWlctFUo/T09JAqb3koptAQbjEFSzwJCQkcOHAAs8rPHZiXl1fq\nrJChLJhjcs6RmZkZFJ+jYi5yzv2pYMU596uZXQzcf7STytk76zHgJefci2bWD89zhq8xs0FAD6Ab\nEAukmNl859wBYBZwtff8V4AbgWcREakE55xf6k4JL8c7h1WBqkyAK83MOuHpFn2Bz+ZRzrmfzawu\nngT4GjzjiItwzs0AZoBnoo1Qmjwg3CY7AMUUKsItpmCJ56effiI7O5tGjRpVuiLXRBvVxznHnj17\nqF+/Pt27dw90cYqLNLPYgi+HzawWnqT0WAp7Z3nPK+id5ZsAd+TIow4XAm/5bP/UOZcL5JrZSmAg\n8JpzrnCwlpl9A7RARKQS4uLi2LNnj1/qTgkfBXVzXFzccV+jKhPgn4ETfdZbeLeVdsx2M4sCEvBM\nhoW3y9WbwLXesU4AOOd+9r4fNLNX8FTmJRJgEZFg0aJFC7Zv306aH55JmJmZWalf+sEomGOKi4uj\nRYugzOVmAR+b2Qt4JqYczZEhRUdTnt5Z3wFD8UxieTlQ18waebc/aGaPA/FAX4omzni7Pl8DjC3t\n5uqdFVwUU2gIt5jKG4+ZUbt2bbZt23bMYwMtHFuqgzmmvLw8Dh06VPhM4YqqygR4CdDWzNrgSXSH\nAyOLHfMO8DvgS+AK4BPnnDOz+sB7wHjn3OcFB3uT5PrOud3eSvYS4KMqjEFEpNKio6Np06aNX66V\nkpISjK2RlRKOMVU159wUM/sOGIDnaQ0fAK38dPm7gOlmNhr4FE8dnuec+9DMTgO+ANLw1N15xc79\nO55W4sVllFu9s4KIYgoN4RZTuMUDiinUVNkkWN4uUrfiqZTX4ukitdrMJpnZYO9hzwONzGwjnu5W\nBZNx3AqcAkw0sxXeVxM83bs+8Ha7WoGnUn6uqmIQEREJYrvwJL9XAv3w1LXHcszeWc65Hc65oc65\n7sAE77Z93veHnXPdnHPn42l5Xl9wnpk9CDTmSPdpERGRoFOlY4C9Y4LmFds20Wc5E0/FXfy8yUBZ\ns1n29GcZRUREQoWZtQNGeF+7gVcBc871Lecljtk7y8wSgb3OuXzgPuDf3u2ReHph7TGzZCAZ+NC7\n70bgQqC/9zwREZGgFNSTYImIiEgR64DFwCXOuY0AZnZHeU/2PnGhoHdWJPDvgt5ZwFLn3DtAH+Av\nZubwdIH+f97To4HF3jFhB4Crvb29AP4BbAG+9O5/wzk3qVKRioiIVAElwCIiIqFjKJ5W24Vm9j4w\nB09X5HIrR++sucDcUs7LxDMTdGnX1N8TIiISEqpsDLCIiIj4l3PuLefccKA9nkcUjQOamNmzZnbB\n0c8WERERJcAiIiIhxjl3yDn3inPuUjwTWX0L3BvgYomIiAQ9JcAiIiIhzDn3q3NuhnOuf6DLIiIi\nEuyUAIuIiIiIiEiNoARYREREREREagQlwCIiIiIiIlIjKAEWERERERGRGkEJsIiIiIiIiNQISoBF\nRERERESkRlACLCIiIiIiIjWCEmARERERERGpEZQAi4iIiIiISI2gBFhERERERERqBCXAIiIiIiIi\nUiMoARYREREREZEaQQmwiIhIDWJmA83sBzPbaGbjS9nfysw+NrOVZpZiZi189k0xs++9r6t8tt/q\nvZ4zs8TqikVERKSilACLiIjUEGYWCTwDXAR0BEaYWcdihz0GvOScSwYmAX/xnjsI6AF0A04H7jKz\net5zPgcGAFuqPAgREZFKUAIsIiJSc/QGNjrnfnTOZQNzgCHFjukIfOJdXuizvyPwqXMu1zl3CFgJ\nDARwzn3rnNtc1YUXERGpLCXAIiIiNUdzYJvP+nbvNl/fAUO9y5cDdc2skXf7QDOL93Zz7gucWMXl\nFRER8auoQBdAREREgspdwHQzGw18CvwM5DnnPjSz04AvgDTgSyCvIhc2szHAGICkpCRSUlL8WOyq\nlZ6eHlLlLQ/FFBrCLaZwiwcUU6hRAiwiIlJz/EzRVtsW3m2FnHM78LYAm1kd4LfOuX3efQ8DD3v3\nvQKsr8jNnXMzgBkAvXr1cn369DmuIAIhJSWFUCpveSim0BBuMYVbPKCYQo26QIuIiNQcS4C2ZtbG\nzGKA4cA7vgeYWaKZFfx9cB/wb+/2SG9XaMwsGUgGPqy2kouIiPiBEmAREZEawjmXC9wKfACsBV5z\nzq02s0lmNth7WB/gBzNbDyThbfEFooHFZrYGTyvu1d7rYWa3m9l2PC3KK83sX9UWlIiISAWoC7SI\niEgN4pybB8wrtm2iz/JcYG4p52XimQm6tGs+BTzl35KKiIj4n1qARUREREREpEZQAiwiIiIiIiI1\nQpUmwGY20Mx+MLONZja+lP2xZvaqd//XZtbau/18M1tmZqu87/18zunp3b7RzJ4yM6vKGERERERE\nRCQ8VFkCbGaRwDPARXjGDI0ws+Jjh24AfnXOnQI8AUzxbt8NXOqc6wL8DviPzznPAjcBbb2vgVUV\ng4iIiIiIiISPqmwB7g1sdM796JzLBuYAQ4odMwR40bs8F+hvZuac+9b7HEKA1UAtb2vxCUA959xX\nzjkHvARcVoUxiIiIiIiISJioylmgmwPbfNa3A6eXdYxzLtfM9gON8LQAF/gtsNw5l2Vmzb3X8b1m\n89JubmZjgDEASUlJpKSkHH8k1Sw9PT2kylseiik0hFtM4RYPKCYRERGRygjqxyCZWSc83aIvqOi5\nzrkZeJ5TSK9evVyfPn38W7gqlJKSQiiVtzwUU2gIt5jCLR5QTCIiIiKVUZVdoH8GTvRZb+HdVuox\nZhYFJAB7vOstgDeBa51zm3yOb3GMa4qIiIiIiIiUUJUJ8BKgrZm1MbMYYDjwTrFj3sEzyRXAFcAn\nzjlnZvWB94DxzrnPCw52zu0EDpjZGd7Zn68F3q7CGERERERERCRMVFkC7JzLBW4FPgDWAq8551ab\n2SQzG+w97HmgkZltBO4ECh6VdCtwCjDRzFZ4X028+/4A/AvYCGwC5ldVDCIiIiIiIhI+qnQMsHNu\nHjCv2LaJPsuZwJWlnDcZmFzGNZcCnf1bUhEREREREQl3VdkFWkRERERERCRoKAEWERERERGRGkEJ\nsIiIiIiIiNQISoBFRERERESkRlACLCIiIiIiIjWCEmAREZEaxMwGmtkPZrbRzMaXsr+VmX1sZivN\nLMXMWvjsm2Jm33tfV/lsb2NmX3uv+aqZxVRXPCIiIhWhBFhERKSGMLNI4BngIqAjMMLMOhY77DHg\nJedcMjAJ+Iv33EFAD6AbcDpwl5nV854zBXjCOXcK8CtwQ1XHIiIicjyUAIuIiNQcvYGNzrkfnXPZ\nwBxgSLFjOgKfeJcX+uzvCHzqnMt1zh0CVgIDzcyAfsBc73EvApdVYQwiIiLHLSrQBRAREZFq0xzY\n5rO+HU9rrq/vgKHAk8DlQF0za+Td/qCZPQ7EA32BNUAjYJ9zLtfnms1Lu7mZjQHGACQlJZGSkuKH\nkKpHenp6SJW3PBRTaAi3mMItHlBMxyt/3z4y//lP4n7/eyISEqr0Xr6UAIuIiIivu4DpZjYa+BT4\nGchzzn1oZqcBXwBpwJdAXkUu7JybAcwA6NWrl+vTp48fi121UlJSCKXylodiCg3hFlO4xQOK6Xh9\nN2ECWzdsIHHZMpInTarSe/lSAiwiIlJz/Ayc6LPewrutkHNuB54WYMysDvBb59w+776HgYe9+14B\n1gN7gPpmFuVtBS5xTRERkQLvdexIflZW4fqWWbPYMmsWEbGxDFqzpsrvrzHAIiIiNccSoK131uYY\nYDjwju8BZpZoZgV/H9wH/Nu7PdLbFRozSwaSgQ+dcw7PWOErvOf8Dni7yiMREZGQ1D8lhbrt2hWu\nR8bF0XzIEPovWlQt91cCLCIiUkN4W2hvBT4A1gKvOedWm9kkMxvsPawP8IOZrQeS8Lb4AtHAYjNb\ng6cb89U+437vBe40s414xgQ/Xy0BiYhIyElNSeHg+vUARMTGkpeVRVSdOsQ1blwt91cXaBERkRrE\nOTcPmFds20Sf5bkcmdHZ95hMPDNBl3bNH/HMMC0S9jJTU1k2diw9n3qq2v5gFwkXqZ9+ysr77yem\nUSNOuOACWo0axZbZs8lKS6u2MigBFhEREREpp/XTp7N3yRLWP/10tU7cIxLq9q9ezdJbb6Vuu3ac\nNXs20XXrAlT7/yMlwCIiIiIixxDoiXtEQlnGjh18feONRNerx+nPP1+Y/AaCxgCLiIiIiBxD/5QU\nGvTseWRDRAQnXHRRtU3cIxWTmZrK5yNGkFmNXWuldDkHDvDN9deTd/gwpz//PHFJSQEtjxJgERER\nEZGjyM3IYN3Uqfy6bBkAFhkJ+fmkpqSQ/uOPAS6dlMa3q7oETl5WFkt+/3vSN2/mtL//nXqnnhro\nIqkLtIiIiIhIWfavXs2ysWM5tHkztU86icQzzqDVyJFseOYZUlNS+PLqq2n7hz/Q7rbbiIjSn9bV\nyTlH7sGDZO7aVfj67r77cHl5hceoq3rgOOf47r772PP113R//HESzzor0EUClACLiIiIBI1wnGE4\nVGNyzvHTzJms/etfiWnQgDP/8x8SzzyzcH+v6dPJPXSI7x96iA3Tp7P7iy/oMW0a8c2bB7DUwS0z\nNZWMKVPI7NTpmJ+FvKwsslJTjyS3qalk/vKL590n4c07fLjEuRYdjcvJ8axERNB0wAC6aMKyarfu\n8cf5+e23af/HP9LisssCXZxCSoBFREREgkQ4zjAcijFl7d7NinvvJTUlhaQBA+j6l78Q27BhieOi\natem21//SuJvfsOqBx5g0aBBdH3kEZpdfHEASh381k+fTt6GDaz96185afRoMnft4vAvvxRNdL3J\nbvbevSXOj4iJIa5pU+KaNCGhUyeS+vUjLimJuCZNCrfHJiWx5pFH2DJ7NhYRgcvLY9cnn9Do9NNp\nc801nu7rUuW2zJ7NxmefpeVVV3HKLbcEujhFKAEWERERqQYuL4+s3btLbdHa9vrr4FzhsQXdNi0i\ngnbjxlErKYnYpKTC9+h69TCzAEbjkZeVVbJ1zpvQ/Pzuu6XGFOxdUdM++4xv77qLnP376fLQQ7Qa\nNeqY/9YtBg+mQbduLL/jDpbddhtpn31Gp/vvJyo+vppKHZzyc3I4uH49i4cOxeXmFm7f/sYbbH/j\njSMHmhGbmEhcUhK1mjenQY8eRZLagvfo+vXL9bnP2r2bViNH0mrECDY99xxpn33G6j//me1vvkny\n5MnU79KlKsIVr10LF7Jy4kSa9OlDl0mTguJ3lS8lwCIiIiJHcaxum845cg4cIGvXLg7v2nXkPTW1\naOtWWhrk5xc51yIjiW3cmHrt25P9669kpaV5xi9GRBBVuzaY8cPUqSXuGREXVzRBSEoq9RUZG3t8\nMeXlkbV3b9Eupz4JbsG2nH37SpYtNpa4pCTqJyeTlZbG4V9+KYy7Ttu29HrmmXL9u1e3/Oxs1j3x\nBJtmzKBO27ac8eKLFZqwp3bLlpw9Zw4/PPkkG//xD/YuXUqPJ58koUOHKix18HD5+RzavJl9K1ey\n77vv2LdqFfvXrCl8dJRFR3s+2/n5WFQUDbp355Sbb6Ze+/bEJiYSER3tt7Kc9uyzhcs9pk7FOceO\n995j9eTJLB46lDbXXkv7O+4gqk4dv91TPPatWsWy228noUMHej71VFCOiw++EomIiIgEkfVPP03e\nhg18+8c/0qRPn8IWzsM+SWF+ZmaJ86Lr1y9MTuu2a1c0OfUmr7GNGhV2yVz5wANsmT2biNhY8rOz\naT54MMmTJpGXmVli3KNvK/K+lSvJ3LWryDNqi5ShWBfRuKZN+WXBAvLWr2f5uHE0/s1vSly3MBH3\nFRFR2EoXf+KJNOzVq9SYfFunC2OKiSE/O5v0DRv4/IorOOnGGzlp9GhPkh8EDm3ZwvJx49i3ciWt\nRoyg44QJRNWqVeHrRERH0+Guu0g880y+vesuPhs6lI7jx9P62muDrhWsMpxzHN65k30rV7J/5UpP\n0rtqFbnp6QBE1qpFQqdOtB41ivrJydRPTmbjc8+xdc4ciI7G5eZSt107kvr2rZbymhnNL7mEJuee\ny9rHHuOnF19k5/vv03niRJpecEFY/WwCKWP7dr658UZiGjSg9/PPB83/7+KUAIuIiIiU4r2OHYsk\nlbs//5zdn38OQHzLlsQ1bUr95OTSW1+bNCEyLq5C9/Pttrll9myyvM8vjYyLo3bLltRu2bLMc4u3\nQpeWLB9cv57M7vqOygAAHxtJREFUXbuKnLfnq6/Y89VXANRt25bYpCQan3IKsU2aUKtp0yLvsYmJ\nFW7NKR5T+o8/EhUfzw9Tp/LTzJmc8vvf03rUqAr/W/nT9rffZtXEiVhEBL3+/ndOuPDCSl+z8dln\nc96777Li3nv5ftIk0j7/nK6PPlrqOOJgcKyJyrL27ClMcvd5E97sPXsAT8tuvVNPpfngwYXJbp2T\nTy7xWcnes4dWI0eS1rYtjTdsKPx8V6foevVInjSJE4cOZeWECSz9wx9I6t+fzg8+qMnLKil7/36+\nvv568rOzOXPWrKCe8E4JsIiIiEgxaZ9/Tt22bdn//fdgBs4RERtLUr9+dJo4kVpNmvj9nr7dNis6\nWZSZEZOQQExCAnXbtSvzuIwdO1g9aRKpixaRn53tiWnAADo/8ECV/MFaVky/rljBuqlTWfPII/z4\n/PO0vfVWWl5xBRExMX4vQ1ly09NZ9eCDbH/rLRqedhrdp04lvlkzv10/tlEjej/3HJtfeok1jz7K\nokGD6BFEj4Lx5TtRWcd77mHf998XSXgPb9/uOdCMOqecQpPzzitMduu1b19mV3tfBZ+FlJQUkq+5\npirDOaYG3bpxzltv8dPMmfzw5JOkXHghp44dS5vRo/3aFbumKHjWb8a2bZzx4ovUPeWUQBfpqJQA\nl8OsWauYMOFjtm7dT8uWCTz8cH9GjQrtwfPhGJOIiEhl7V22jHVTp7Lnq6+IO+EEGvbuzd4lSyA6\nmvzsbGIaNqyS5Le6xDdrRmzjxuTn5ByJqX79am+tadCtG2e+9BK7v/qKdY8/zqoHHmDTjBm0u+02\nWlx2WZXP1Ltv5UqWjxvHoW3baDd2LG3/8IcqGatoZrT53e9o2Ls3y8eO5ctrr+WU3/+eU8eODYpE\nq3gvh4KJygrUatGC+snJR7oyd+4cNuNmI6KjOfmmmzjh4ov5/qGHWPPoo2x/6y2SJ0+mQffugS5e\nyHD5+ay45x72fvMNPaZNo1Hv3oEu0jFFVOXFzWygmf1gZhvNbHwp+2PN7FXv/q/NrLV3eyMzW2hm\n6WY2vdg5Kd5rrvC+qrQWmjVrFWPG/I8tW/bjHGzZsp8xY/7HrFmrquRerVtPo1+/RbRuPa1K7lFw\nn+qKSUREJBTsX72ar2+4gc+HDePghg10euAB+n38MTH169Nq5EjiJ0yg1ciRAem26W8F3ZKDIabE\nM87g7Ndeo/fzzxNdrx4r7rmHlIsuYse8ebhiE4b5g8vPZ9Nzz/HZlVeSl53NWa+8wqm3317lE/Uk\ndOjAOW+9Rcthw9j47LN8Pnw4Gdu2Vek9jyb9xx/Z+M9/Urdt26I7IiKoe+qpdJs6lQu++YYBixbR\n6+mnOWXMGBLPOCNskl9f8c2bc9o//0mvv/+d7H37+OzKK1k5cSI5Bw4EumghYe3f/saOd9+lwz33\n0PzSSwNdnHKpsv/tZhYJPAOcD2wHlpjZO84533nvbwB+dc6dYmbDgSnAVUAm8ADQ2fsqbpRzbmlV\nld3XhAkfk5GRU2RbRkYOY8b8j4ULf6JWrShq1YqmVq0o4uOjC5fL8x4fH01cXBQREVaYlBbcqyAp\nBUptmXXOkZfnyMnJIycnn5ycPHJz8wuXfd89249su/PO90uN6b77PmLkyM5+nQigaEvzCrU0i4hI\nUDm4YQM/PPkkO+fPJzohgfZ3302ba68tfHxNMHXb9Jdgi8nMSOrThybnnccvH37IuieeYNltt1Gv\nY0fa33EHTfr29cvfJplpaay4+27SFi/mhIEDSX7kEWISEvwQQflExcfT9ZFHaPyb3/Ddn/7Eoksu\nIfnPf6b54MFVfm+Xn8++777jlwUL+OWjj0jftAmAhE6dSOjShf3ff184UVnDXr04cciQKi9TMDEz\nTrjwQhqffTbrnniCn156iV8+/JBOEybQ7JJLNElWGTa//DKbZsyg1ahRnDxmTKCLU25V+XVXb2Cj\nc+5HADObAwwBfBPgIcD/eZfnAtPNzJxzh4DPzCzgHci3bt1f6vaMjBzmz99IRkYOhw/nkJWVV+px\n5REbG0l2dp7vo/IK7/G7373JXXd9WGqi62/bth0gNnYyDRvWolGjeO97wSu+xPuR/fHExJTsqlTR\npL6y1K1bRETK69DWrax/8km2v/MOkbVq0e622zjphhuIrls30EWrsQqSkKYDBvDz//7HD08+yTc3\n3USD7t059Y47aHz22cd97V0pKay45x5y09NJnjyZlsOHByypaXbxxdTv2pXl48ax/I47SPvsMzo/\n+KDfZ8zNy8pi95df8suCBez6+GOy0tKwqCga9e5N66uvJmnAAOKbNWPJLbdQPzm5xORrNVFUnTp0\nfuABWlx2GSvvv5/l48ax7fXX6fLQQ9Ru1SrQxQsqv3z0EaseesgzidjEiSH1JUFVJsDNAd++HduB\n08s6xjmXa2b7gUbA7mNc+wUzywNeByY7Vzx1BDMbA4wBSEpKIiUl5XhioEmTWHbtKvlYgaSkWGbN\n6lG4np/vyM7OJysr3/ueR1ZW2euZmXmFx2dl5TNnTundYPLyHD171iUqygpfkZFGVFSE9730bUf2\nRRSuF2x76KE1/PprTol71akTxeDBJ3DgQC4HDuRw4MB+duzYw4EDOezfn0NOTol/5kK1akVSr14U\n9epFk5AQTd26UXz11V4OHy76xUBGRg633vo/du/eRGxsBDExR16xsZGFy5GRFftP9NFHu3jssfVk\nZXm+GNiyZT833PAWa9euYcCApApdq7zS09OP+3MVrMItpnCLBxSTVJ6ZDQSeBCKBfznnHi22vxXw\nb6AxsBe42jm33bvvr8AgPEOoFgBjnXPOzK4CJniv+a5z7t7qiqeiDu/cyfrp09k2dy4WFcXJN9zA\nyWPGBO3svDWRRUbS4rLLaDZoENtef531Tz/NV9deS6MzzqD9H/9Iwx49jn0Rr7ysLNY9/jg/Pv88\ndU89lbNefvmok4RVl/jmzTlr9mw2TJ/O+meeYe+yZfR48knqdy6t42P55Rw4wK6UFH5ZsIDURYvI\nO3SIyNq1aXLeeTQdMIAmffqUaPWuzORr4ap+ly6c88YbbH75ZdZNnUrKRRfR7tZbOfnGG6t1orZg\ntW/lSpaNHUv9zp3pMW1aUD7r92hCq7Qeo5xzP5tZXTwJ8DXAS8UPcs7NAGYA9OrVy/Xp0+e4bvb4\n442KtGICxMdH8/jjg+jTx3+ti19+OY0tW0q2NrdqlcC77/q3S0Fi4smlxvSPf1x61BbTjIwc9uzJ\nYM+ewyXe9+49XGR9+/aMEslvgX37chg37rujljEy0qhVy9NFvOBVq1ZUkXXPNs8xb7yxqTD5LZCV\nlc+MGVvp37839erFUq9eLAkJcdSrF0tsbORxf1MVzi3NKSkpHO//lWAUbvGAYpLKKefwpMeAl5xz\nL5pZP+AvwDVmdhZwNpDsPe4z4DwzWwX8DejpnEszsxfNrL9z7uPqiqs8snbvZsM//sGWWbNwztFq\nxAja/uEPxIXwhFbhLiI6mlbDh9Pi8svZMns2G/7+dz6/8kqa9OlD+zvvJKFTp6Oen/7TTywfO5b9\nq1fT+ppr6Dh+fEAft1RcRFQUp44bR6Mzz+TbO+/ksyuuoMPdd3PSdddhEeWfpufwjh388vHH/LJg\nAXu+/hqXm0tsYiLNL72UpgMGkHjWWeWapVmKsshI2vzudzS98EJW//nPrHv8cba/8w7Jf/4zjU47\nLdDFC5hDW7fy9Y03Ete4Mb2fe65wuEgoqcoE+GfgRJ/1Ft5tpR2z3cyigARgz9Eu6pz72ft+0Mxe\nwdPVukQC7C8FiU1VJzwPP9y/1KT04Yf7+/U+cPwxxcdHEx+fwIknlm+8TOvWpSf1SUm1mTVrKJmZ\nuYWvw4dzi63nHGVfLgcPZpOWllF4bHp6yRZtgLS0DPr1K/nxiI6OKEyGjyTHZS0fOe7zz7cyefJi\nMjNzgart1l2dibbGaovUGOUZntQRuNO7vBB4y7vsgDggBjAgGtgFnARscM4V9Jv8CPgtEBQJcPb+\n/Wx67jl+mjmTvKwsTvztb2l322165mcIiYyN5aTRo2k5bBg/vfQSm2bM4NPBgznhoos4ddy4wkeu\nZKamkjFlCoc7dmT3p5+y6qGHiIiJ4bR//IOm558f4CjKlnj66Zz37rt8d999rHnkEdI++4zuf/sb\nsYmJhTFldupUOFO3c46D69d7xvMuWOB5VBdQ+6STOPmGG0gaMIAG3bpVKImWstVq2pRezzzDrk8+\nYdWDD/LF8OG0HDaMDvfcQ0yDBqX+jMJV9q+/8vX11+Py8jj93/8mNjEx0EU6LlZK72H/XNiT0K4H\n+uNJdJcAI51zq32O+X9AF+fc772TYA11zg3z2T8a6OWcu9XnmvWdc7vNLBqYDXzknPvH0crSq1cv\nt3RptcyZVSnh1rJYfAwweJLoGTOO3tJ8PMpKtps2rcPs2b/lwIEs9u/P5MCBrMLX/v1lLWeyf38W\nubnlH2dtBklJdQpbqgtapksuH2m5Lms5Li6KL77YytSpXxUZW16rVhSPPXYBI0Z0JiYmkpiYSKKi\nIio95qI6f07V/RkPx5ZFxRQczGyZc65XoMtRUWZ2BTDQOXejd/0a4PSCeta77RXga+fck2Y2FE9v\nq0Tn3B4zewy4EU8CPN05N8HMGgCrgN/gaVV+FYhxzpWYDrTY8KSec+bMqbJY3eHDZH/0EdkffACH\nDxPVuzexQ4YQ0bTpcV0vPT2dOmE2A26oxuQyMsj+8EOyP/wQsrOJOvNMYgcPJvv998lZtAhr3BiX\nmkrkqacSd9NNRDRoEOgil4tzjpyUFLJefRWrVYu4G24g99tvyVm0iKhzzyX69NPJXbGC3G+/xXnH\n6UacfDJR3boR1b07kSecEOAIyidUP3cALiuLrHfeIefDD7H4eGKvuorcTZvIXbSI6PPOIy4IJpXz\nl+I/J5edzeHHHydv82Zq3XUXUcVnEA8Cffv2LVfdXGUJMICZXQxMwzMm6N/OuYfNbBKw1Dn3jpnF\nAf8BuuMZZzTc51vpzUA9PN807wMuALYAn+L51jkSz7fMdzrnjjoDVagkwAVC8Y/BslRXwuPvJM45\nR1ZWXomkuX//l0pMVlbgppt6FLZWF7RgHz5c9nJ29vFPnOYrOjqiMCE+ntfrr68ptQW9QYM4pkwZ\nUCQxL94Vvfj22FjPrOalCddEW0l95YViTGGeADcDpgNt8NS5v8XzRIZEPGOHr/IeugC4xzm32Mwu\nBe4H8oEvgJOdc5cdrSxVVTfnZWay+eWX2fjPf5K9dy9JAwbQ/o47qNe+faWuG4qf02MJ9Ziy9u5l\n04wZbHruuVL3R8TGMmjNmlL3BbMDP/zAokGDKOsPjiZ9+tB0wACS+vcPyS78of65Aziwbh2LLrmk\n1J9RqH7uivP9Obn8fJbdfjs733+fnk89RbOLLw5s4cpQ3rq5SscAO+fmAfOKbZvos5wJXFnGua3L\nuGxPf5VPqt6oUV0YNapLlf+y83dXdTPzJnZ1SEo68u1Xy5YJZY7VnjGjYs8+y8vzTI52+HBOkcT5\n8OFczjjjX2Um2tOmXUh2dt5RXvmFyzk5RfdlZOSUOL6s7uO//prJmDHvVigm8MxqXlqCvGZNWomk\nPyMjh1tueZc1a1K9XezL/6pVK5qoqJLdu6pz9vHqvpe6qYsfHHN4knNuBzAUwMzqAL91zu0zs5uA\nr5xz6d5984EzgcXOuf8B//NuHwP45xu+Y8hMTWXZ2LH0fOopYhIS2Praa6x/5hmyUlNpfM45nHrH\nHTTo2rU6iiIBENuwIR3Hj6f54MEsve02MjZvBiAiJoYTLrqIjvfdF9gCHqd6p55Kv4UL+Wr06MKY\niIykQffudJ8yhdqtWweyeALUa9+eAZ99xpJbbmH/ypVF9llkJAsvuIC4pCTimjQhrmnTou9JScQ2\nbkxEdHSF7un7+666u1qvefRRds6fT8c//Slok9+KCMVJsERKVZBsVyV/jtWOjIw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            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 51000 samples, validate on 9000 samples\n",
            "Epoch 1/15\n",
            "51000/51000 [==============================] - 14s 266us/step - loss: 0.0136 - acc: 0.9964 - val_loss: 0.0384 - val_acc: 0.9892\n",
            "Epoch 2/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0134 - acc: 0.9966 - val_loss: 0.0396 - val_acc: 0.9890\n",
            "Epoch 3/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0133 - acc: 0.9966 - val_loss: 0.0387 - val_acc: 0.9892\n",
            "Epoch 4/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0133 - acc: 0.9965 - val_loss: 0.0392 - val_acc: 0.9893\n",
            "Epoch 5/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0131 - acc: 0.9968 - val_loss: 0.0387 - val_acc: 0.9894\n",
            "Epoch 6/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0131 - acc: 0.9966 - val_loss: 0.0384 - val_acc: 0.9893\n",
            "Epoch 7/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0129 - acc: 0.9968 - val_loss: 0.0386 - val_acc: 0.9898\n",
            "Epoch 8/15\n",
            "51000/51000 [==============================] - 13s 248us/step - loss: 0.0128 - acc: 0.9968 - val_loss: 0.0399 - val_acc: 0.9891\n",
            "Epoch 9/15\n",
            "51000/51000 [==============================] - 13s 251us/step - loss: 0.0127 - acc: 0.9969 - val_loss: 0.0375 - val_acc: 0.9896\n",
            "Epoch 10/15\n",
            "51000/51000 [==============================] - 13s 249us/step - loss: 0.0127 - acc: 0.9967 - val_loss: 0.0401 - val_acc: 0.9890\n",
            "Epoch 11/15\n",
            "51000/51000 [==============================] - 13s 245us/step - loss: 0.0126 - acc: 0.9968 - val_loss: 0.0393 - val_acc: 0.9890\n",
            "Epoch 12/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0125 - acc: 0.9968 - val_loss: 0.0378 - val_acc: 0.9898\n",
            "Epoch 13/15\n",
            "51000/51000 [==============================] - 13s 246us/step - loss: 0.0124 - acc: 0.9969 - val_loss: 0.0375 - val_acc: 0.9894\n",
            "Epoch 14/15\n",
            "51000/51000 [==============================] - 12s 244us/step - loss: 0.0123 - acc: 0.9970 - val_loss: 0.0394 - val_acc: 0.9891\n",
            "Epoch 15/15\n",
            "51000/51000 [==============================] - 13s 247us/step - loss: 0.0122 - acc: 0.9971 - val_loss: 0.0392 - val_acc: 0.9889\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jOPT55+x8+WVy7cB3wF130eO661zOV+ANZapORDYYYxpn4Hkb1Rh1s6dV7/IK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\n",
            "text/plain": [
              "<Figure size 1152x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4cU_lFJkcE2-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}