myExercise/Copy_of_Horse_or_Human_WithAugmentation.ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of Horse-or-Human-WithAugmentation.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DayuanTan/AITensorFlowSpecialization/blob/master/myExercise/Copy_of_Horse_or_Human_WithAugmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RXZT2UsyIVe_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "f10c97f1-bbce-453f-a29a-0c87f68f197a"
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip \\\n",
        "    -O /tmp/horse-or-human.zip\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip \\\n",
        "    -O /tmp/validation-horse-or-human.zip\n",
        "  \n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/tmp/horse-or-human.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/horse-or-human')\n",
        "local_zip = '/tmp/validation-horse-or-human.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp/validation-horse-or-human')\n",
        "zip_ref.close()\n",
        "# Directory with our training horse pictures\n",
        "train_horse_dir = os.path.join('/tmp/horse-or-human/horses')\n",
        "\n",
        "# Directory with our training human pictures\n",
        "train_human_dir = os.path.join('/tmp/horse-or-human/humans')\n",
        "\n",
        "# Directory with our training horse pictures\n",
        "validation_horse_dir = os.path.join('/tmp/validation-horse-or-human/horses')\n",
        "\n",
        "# Directory with our training human pictures\n",
        "validation_human_dir = os.path.join('/tmp/validation-horse-or-human/humans')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-09-03 03:33:09--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/horse-or-human.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.24.128, 2404:6800:4003:c04::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.24.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 149574867 (143M) [application/zip]\n",
            "Saving to: ‘/tmp/horse-or-human.zip’\n",
            "\n",
            "/tmp/horse-or-human 100%[===================>] 142.65M  71.9MB/s    in 2.0s    \n",
            "\n",
            "2019-09-03 03:33:12 (71.9 MB/s) - ‘/tmp/horse-or-human.zip’ saved [149574867/149574867]\n",
            "\n",
            "--2019-09-03 03:33:14--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.200.128, 2404:6800:4003:c02::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.200.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11480187 (11M) [application/zip]\n",
            "Saving to: ‘/tmp/validation-horse-or-human.zip’\n",
            "\n",
            "/tmp/validation-hor 100%[===================>]  10.95M  31.9MB/s    in 0.3s    \n",
            "\n",
            "2019-09-03 03:33:15 (31.9 MB/s) - ‘/tmp/validation-horse-or-human.zip’ saved [11480187/11480187]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5oqBkNBJmtUv"
      },
      "source": [
        "## Building a Small Model from Scratch\n",
        "\n",
        "But before we continue, let's start defining the model:\n",
        "\n",
        "Step 1 will be to import tensorflow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvfZg3LQbD-5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BnhYCP4tdqjC"
      },
      "source": [
        "We then add convolutional layers as in the previous example, and flatten the final result to feed into the densely connected layers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gokG5HKpdtzm",
        "colab_type": "text"
      },
      "source": [
        "Finally we add the densely connected layers. \n",
        "\n",
        "Note that because we are facing a two-class classification problem, i.e. a *binary classification problem*, we will end our network with a [*sigmoid* activation](https://wikipedia.org/wiki/Sigmoid_function), so that the output of our network will be a single scalar between 0 and 1, encoding the probability that the current image is class 1 (as opposed to class 0)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PixZ2s5QbYQ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "49ee86fe-4b13-4bf3-d552-d16d57349523"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    # Note the input shape is the desired size of the image 300x300 with 3 bytes color\n",
        "    # This is the first convolution\n",
        "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(300, 300, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    # The second convolution\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The third convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The fourth convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # The fifth convolution\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    # Flatten the results to feed into a DNN\n",
        "    tf.keras.layers.Flatten(),\n",
        "    # 512 neuron hidden layer\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans')\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0903 03:34:24.921269 140223105640320 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8DHWhFP_uhq3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "29fd640a-b53e-4a49-c8c2-ff8f64a89b20"
      },
      "source": [
        "from tensorflow.keras.optimizers import RMSprop\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=RMSprop(lr=1e-4),\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0903 03:34:31.642444 140223105640320 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ClebU9NJg99G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7c814428-fa59-4f6a-8759-502acc603bb6"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# All images will be rescaled by 1./255\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255,\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "\n",
        "validation_datagen = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "# Flow training images in batches of 128 using train_datagen generator\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        '/tmp/horse-or-human/',  # This is the source directory for training images\n",
        "        target_size=(300, 300),  # All images will be resized to 150x150\n",
        "        batch_size=128,\n",
        "        # Since we use binary_crossentropy loss, we need binary labels\n",
        "        class_mode='binary')\n",
        "\n",
        "# Flow training images in batches of 128 using train_datagen generator\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "        '/tmp/validation-horse-or-human/',  # This is the source directory for training images\n",
        "        target_size=(300, 300),  # All images will be resized to 150x150\n",
        "        batch_size=32,\n",
        "        # Since we use binary_crossentropy loss, we need binary labels\n",
        "        class_mode='binary')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1027 images belonging to 2 classes.\n",
            "Found 256 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Fb1_lgobv81m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "03dddff3-6a0a-4ca6-86bd-de1aae20499d"
      },
      "source": [
        "history = model.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=8,  \n",
        "      epochs=100,\n",
        "      verbose=1,\n",
        "      validation_data = validation_generator,\n",
        "      validation_steps=8)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "8/8 [==============================] - 36s 5s/step - loss: 0.7199 - acc: 0.4994 - val_loss: 0.6606 - val_acc: 0.5000\n",
            "Epoch 2/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.6772 - acc: 0.6162 - val_loss: 0.6559 - val_acc: 0.5391\n",
            "Epoch 3/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.6429 - acc: 0.6563 - val_loss: 0.6237 - val_acc: 0.6172\n",
            "Epoch 4/100\n",
            "8/8 [==============================] - 30s 4s/step - loss: 0.6211 - acc: 0.6685 - val_loss: 0.5998 - val_acc: 0.7461\n",
            "Epoch 5/100\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.5912 - acc: 0.7266 - val_loss: 0.7479 - val_acc: 0.5117\n",
            "Epoch 6/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5689 - acc: 0.6874 - val_loss: 0.7325 - val_acc: 0.5234\n",
            "Epoch 7/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.6178 - acc: 0.7003 - val_loss: 0.5591 - val_acc: 0.6719\n",
            "Epoch 8/100\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.5569 - acc: 0.7207 - val_loss: 0.6042 - val_acc: 0.6133\n",
            "Epoch 9/100\n",
            "8/8 [==============================] - 19s 2s/step - loss: 0.4899 - acc: 0.7564 - val_loss: 0.4680 - val_acc: 0.8086\n",
            "Epoch 10/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5640 - acc: 0.7297 - val_loss: 0.8922 - val_acc: 0.5195\n",
            "Epoch 11/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5441 - acc: 0.7642 - val_loss: 0.7519 - val_acc: 0.5469\n",
            "Epoch 12/100\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.5299 - acc: 0.7383 - val_loss: 0.4858 - val_acc: 0.7461\n",
            "Epoch 13/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.5324 - acc: 0.7545 - val_loss: 0.4007 - val_acc: 0.8945\n",
            "Epoch 14/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5025 - acc: 0.7453 - val_loss: 0.9908 - val_acc: 0.5312\n",
            "Epoch 15/100\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.4870 - acc: 0.7646 - val_loss: 0.5704 - val_acc: 0.6758\n",
            "Epoch 16/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.6260 - acc: 0.6986 - val_loss: 0.5876 - val_acc: 0.6289\n",
            "Epoch 17/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5261 - acc: 0.7408 - val_loss: 0.8134 - val_acc: 0.5703\n",
            "Epoch 18/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5277 - acc: 0.7842 - val_loss: 0.8805 - val_acc: 0.5625\n",
            "Epoch 19/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4564 - acc: 0.7964 - val_loss: 1.4146 - val_acc: 0.5156\n",
            "Epoch 20/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4918 - acc: 0.7508 - val_loss: 1.0490 - val_acc: 0.5547\n",
            "Epoch 21/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.5176 - acc: 0.7597 - val_loss: 0.9786 - val_acc: 0.5586\n",
            "Epoch 22/100\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.4408 - acc: 0.7979 - val_loss: 1.2774 - val_acc: 0.5312\n",
            "Epoch 23/100\n",
            "8/8 [==============================] - 16s 2s/step - loss: 0.4343 - acc: 0.7494 - val_loss: 1.1198 - val_acc: 0.5469\n",
            "Epoch 24/100\n",
            "8/8 [==============================] - 21s 3s/step - loss: 0.4642 - acc: 0.7842 - val_loss: 1.2075 - val_acc: 0.5430\n",
            "Epoch 25/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4497 - acc: 0.7887 - val_loss: 1.0046 - val_acc: 0.5938\n",
            "Epoch 26/100\n",
            "8/8 [==============================] - 18s 2s/step - loss: 0.4148 - acc: 0.8120 - val_loss: 0.7425 - val_acc: 0.6328\n",
            "Epoch 27/100\n",
            "8/8 [==============================] - 24s 3s/step - loss: 0.3943 - acc: 0.7998 - val_loss: 1.6854 - val_acc: 0.5234\n",
            "Epoch 28/100\n",
            "8/8 [==============================] - 34s 4s/step - loss: 0.3912 - acc: 0.8311 - val_loss: 1.0774 - val_acc: 0.6055\n",
            "Epoch 29/100\n",
            "8/8 [==============================] - 29s 4s/step - loss: 0.3425 - acc: 0.8101 - val_loss: 1.0619 - val_acc: 0.6211\n",
            "Epoch 30/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.4730 - acc: 0.7931 - val_loss: 1.2071 - val_acc: 0.5859\n",
            "Epoch 31/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.3348 - acc: 0.8662 - val_loss: 1.6050 - val_acc: 0.5664\n",
            "Epoch 32/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.3531 - acc: 0.8343 - val_loss: 1.1587 - val_acc: 0.6289\n",
            "Epoch 33/100\n",
            "8/8 [==============================] - 27s 3s/step - loss: 0.4828 - acc: 0.8217 - val_loss: 2.0773 - val_acc: 0.5234\n",
            "Epoch 34/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.3461 - acc: 0.8604 - val_loss: 1.0860 - val_acc: 0.6406\n",
            "Epoch 35/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.3735 - acc: 0.8343 - val_loss: 1.5379 - val_acc: 0.5781\n",
            "Epoch 36/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.2879 - acc: 0.8810 - val_loss: 1.5098 - val_acc: 0.5898\n",
            "Epoch 37/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.3708 - acc: 0.8176 - val_loss: 1.1526 - val_acc: 0.6055\n",
            "Epoch 38/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.5530 - acc: 0.8142 - val_loss: 1.5056 - val_acc: 0.5898\n",
            "Epoch 39/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.3282 - acc: 0.8574 - val_loss: 1.3300 - val_acc: 0.5977\n",
            "Epoch 40/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.3101 - acc: 0.8665 - val_loss: 1.2195 - val_acc: 0.6289\n",
            "Epoch 41/100\n",
            "8/8 [==============================] - 27s 3s/step - loss: 0.2970 - acc: 0.8501 - val_loss: 0.7942 - val_acc: 0.7383\n",
            "Epoch 42/100\n",
            "8/8 [==============================] - 36s 5s/step - loss: 0.4080 - acc: 0.8281 - val_loss: 1.3100 - val_acc: 0.6172\n",
            "Epoch 43/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.3773 - acc: 0.8454 - val_loss: 1.1625 - val_acc: 0.6211\n",
            "Epoch 44/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.2635 - acc: 0.8888 - val_loss: 1.7561 - val_acc: 0.5703\n",
            "Epoch 45/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.3088 - acc: 0.8843 - val_loss: 1.9674 - val_acc: 0.5508\n",
            "Epoch 46/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.3220 - acc: 0.8487 - val_loss: 1.1926 - val_acc: 0.6211\n",
            "Epoch 47/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2808 - acc: 0.8977 - val_loss: 2.5960 - val_acc: 0.5352\n",
            "Epoch 48/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2740 - acc: 0.8994 - val_loss: 3.0223 - val_acc: 0.5156\n",
            "Epoch 49/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.2825 - acc: 0.8665 - val_loss: 2.0512 - val_acc: 0.5586\n",
            "Epoch 50/100\n",
            "8/8 [==============================] - 27s 3s/step - loss: 0.2715 - acc: 0.8566 - val_loss: 2.2444 - val_acc: 0.5430\n",
            "Epoch 51/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.3052 - acc: 0.8665 - val_loss: 0.8554 - val_acc: 0.6914\n",
            "Epoch 52/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2467 - acc: 0.9004 - val_loss: 1.9286 - val_acc: 0.5703\n",
            "Epoch 53/100\n",
            "8/8 [==============================] - 27s 3s/step - loss: 0.2641 - acc: 0.9005 - val_loss: 2.9378 - val_acc: 0.5195\n",
            "Epoch 54/100\n",
            "8/8 [==============================] - 36s 5s/step - loss: 0.2898 - acc: 0.8730 - val_loss: 1.4430 - val_acc: 0.6328\n",
            "Epoch 55/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.2858 - acc: 0.8632 - val_loss: 2.7037 - val_acc: 0.5234\n",
            "Epoch 56/100\n",
            "8/8 [==============================] - 33s 4s/step - loss: 0.2353 - acc: 0.9010 - val_loss: 3.2694 - val_acc: 0.5156\n",
            "Epoch 57/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.2883 - acc: 0.8854 - val_loss: 3.4983 - val_acc: 0.5117\n",
            "Epoch 58/100\n",
            "8/8 [==============================] - 36s 5s/step - loss: 0.2738 - acc: 0.8906 - val_loss: 1.3082 - val_acc: 0.6523\n",
            "Epoch 59/100\n",
            "8/8 [==============================] - 27s 3s/step - loss: 0.2758 - acc: 0.8618 - val_loss: 1.8902 - val_acc: 0.5742\n",
            "Epoch 60/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2024 - acc: 0.9088 - val_loss: 1.5136 - val_acc: 0.6406\n",
            "Epoch 61/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2456 - acc: 0.9004 - val_loss: 2.6707 - val_acc: 0.5273\n",
            "Epoch 62/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2784 - acc: 0.8843 - val_loss: 2.7575 - val_acc: 0.5781\n",
            "Epoch 63/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.2414 - acc: 0.8943 - val_loss: 2.0731 - val_acc: 0.6016\n",
            "Epoch 64/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2015 - acc: 0.9055 - val_loss: 2.0766 - val_acc: 0.5977\n",
            "Epoch 65/100\n",
            "8/8 [==============================] - 36s 5s/step - loss: 0.2333 - acc: 0.9092 - val_loss: 2.4023 - val_acc: 0.5625\n",
            "Epoch 66/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.2070 - acc: 0.9121 - val_loss: 1.6372 - val_acc: 0.6406\n",
            "Epoch 67/100\n",
            "8/8 [==============================] - 27s 3s/step - loss: 0.1949 - acc: 0.8979 - val_loss: 1.3418 - val_acc: 0.6641\n",
            "Epoch 68/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2110 - acc: 0.9248 - val_loss: 2.9831 - val_acc: 0.5273\n",
            "Epoch 69/100\n",
            "8/8 [==============================] - 28s 3s/step - loss: 0.1878 - acc: 0.9083 - val_loss: 2.6589 - val_acc: 0.5586\n",
            "Epoch 70/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2917 - acc: 0.8701 - val_loss: 1.3880 - val_acc: 0.6602\n",
            "Epoch 71/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.1670 - acc: 0.9310 - val_loss: 2.2040 - val_acc: 0.5898\n",
            "Epoch 72/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2296 - acc: 0.8988 - val_loss: 1.9557 - val_acc: 0.6055\n",
            "Epoch 73/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.2028 - acc: 0.9143 - val_loss: 1.7058 - val_acc: 0.6328\n",
            "Epoch 74/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.1587 - acc: 0.9321 - val_loss: 3.3892 - val_acc: 0.5312\n",
            "Epoch 75/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.3242 - acc: 0.9210 - val_loss: 0.1081 - val_acc: 0.9570\n",
            "Epoch 76/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.4762 - acc: 0.8509 - val_loss: 1.8461 - val_acc: 0.6289\n",
            "Epoch 77/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.1608 - acc: 0.9321 - val_loss: 2.0127 - val_acc: 0.6172\n",
            "Epoch 78/100\n",
            "8/8 [==============================] - 37s 5s/step - loss: 0.1564 - acc: 0.9473 - val_loss: 2.1221 - val_acc: 0.6094\n",
            "Epoch 79/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2535 - acc: 0.8966 - val_loss: 1.6955 - val_acc: 0.6406\n",
            "Epoch 80/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.1533 - acc: 0.9321 - val_loss: 2.0412 - val_acc: 0.6094\n",
            "Epoch 81/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2057 - acc: 0.9177 - val_loss: 1.4743 - val_acc: 0.6641\n",
            "Epoch 82/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.2036 - acc: 0.9066 - val_loss: 1.9773 - val_acc: 0.6211\n",
            "Epoch 83/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.1764 - acc: 0.9365 - val_loss: 2.9313 - val_acc: 0.5625\n",
            "Epoch 84/100\n",
            "8/8 [==============================] - 28s 4s/step - loss: 0.2671 - acc: 0.8592 - val_loss: 2.1424 - val_acc: 0.6016\n",
            "Epoch 85/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.1753 - acc: 0.9307 - val_loss: 1.6954 - val_acc: 0.6602\n",
            "Epoch 86/100\n",
            "8/8 [==============================] - 27s 3s/step - loss: 0.1504 - acc: 0.9367 - val_loss: 1.3527 - val_acc: 0.6875\n",
            "Epoch 87/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.1610 - acc: 0.9399 - val_loss: 2.8578 - val_acc: 0.5820\n",
            "Epoch 88/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2134 - acc: 0.9166 - val_loss: 1.2094 - val_acc: 0.7031\n",
            "Epoch 89/100\n",
            "8/8 [==============================] - 36s 4s/step - loss: 0.1974 - acc: 0.9248 - val_loss: 2.3035 - val_acc: 0.5977\n",
            "Epoch 90/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.1352 - acc: 0.9477 - val_loss: 2.0386 - val_acc: 0.6172\n",
            "Epoch 91/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.2055 - acc: 0.9288 - val_loss: 0.8595 - val_acc: 0.7305\n",
            "Epoch 92/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.3591 - acc: 0.8821 - val_loss: 2.6754 - val_acc: 0.5859\n",
            "Epoch 93/100\n",
            "8/8 [==============================] - 37s 5s/step - loss: 0.1540 - acc: 0.9434 - val_loss: 2.3491 - val_acc: 0.5977\n",
            "Epoch 94/100\n",
            "8/8 [==============================] - 26s 3s/step - loss: 0.1352 - acc: 0.9419 - val_loss: 2.9263 - val_acc: 0.5625\n",
            "Epoch 95/100\n",
            "8/8 [==============================] - 35s 4s/step - loss: 0.2122 - acc: 0.9102 - val_loss: 2.3497 - val_acc: 0.6016\n",
            "Epoch 96/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.1348 - acc: 0.9477 - val_loss: 3.5778 - val_acc: 0.5234\n",
            "Epoch 97/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.1695 - acc: 0.9199 - val_loss: 1.3090 - val_acc: 0.7070\n",
            "Epoch 98/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.1859 - acc: 0.9232 - val_loss: 2.3190 - val_acc: 0.6016\n",
            "Epoch 99/100\n",
            "8/8 [==============================] - 31s 4s/step - loss: 0.1975 - acc: 0.9266 - val_loss: 1.3907 - val_acc: 0.6914\n",
            "Epoch 100/100\n",
            "8/8 [==============================] - 32s 4s/step - loss: 0.1460 - acc: 0.9488 - val_loss: 2.5943 - val_acc: 0.5977\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zNPRWOVJdOH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "705b8f48-eace-4f1e-ad48-bc349d3beb3c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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osFqvXu6lFJIBGaavk+ShpxpGuSeITm/LCLnv32+n/DkhnXfeecce+9NJ7n7K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\nNPcQQsjQP0doACAeEQ06AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gwQcfjLsdjXvmzAH69AEKC+uXuKuJ2NXVqe2HX/Bz+QEt7iYuuugivPbaa0GPvfbaa7jo\nootcvb5du3ZxVVW0ivuHH36Ipk2bxtyeJjXU1koa5PDhcj872587fywocdeRu8xg0LZMmjB+/Hh8\n8MEHhxfmKC0txebNmzF8+PDDeecDBw5E37598e6774a8vrS0FH36SGWGyspKXHjhhejZsyfGjRuH\nSlPB52uvvfZwueC7774bgFRy3Lx5M0aOHImRI0cCAIqKirBz504AwEMPPYQ+ffqgT58+h8sFl5aW\nomfPnvjFL36B3r1744wzzgh6n0jYtXngwAGcddZZh0sAv/766wCAqVOnolevXujXr19IjXtNMEuW\nAOXlgJq4XJ88dy3uBocOGSsw+fH39e2smFRU/G3WrBkGDx6MmTNnYuzYsXjttdcwYcIEEBFyc3Px\nzjvvoEmTJti5cyeGDBmCc88913Ht0McffxyNGjXC8uXLsXjx4qCSvffddx+aNWuG2tpanHrqqVi8\neDFuuOEGPPTQQ5g1axZatGgR1Nb8+fPx/PPPY+7cuWBmHH/88RgxYgQKCwuxatUqvPrqq3j66acx\nYcIEvPXWW4crQobDqc21a9eiXbt2+OCDDwBICeBdu3bhnXfewYoVK0BEnlhF9Zl16+S6Z0+5ro+2\njBZ3I2oH/Pn76sjdgtmaMVsyzIzbb78d/fr1w2mnnYZNmzZh27Ztju18+eWXh0W2X79+6Nev3+Hn\n3njjDQwcOBDHHnssli5dGrEo2Jw5czBu3Djk5eUhPz8fP//5zzE7ULSkS5cuGDBgAIDwZYXdttm3\nb1988skn+L//+z/Mnj0bBQUFKCgoQG5uLq688kq8/fbbaNSokav3OFLZskWu27SR6/pky6gTQy3u\nxmAq4E9x923knqqKv2PHjsWvf/1rLFiwABUVFRg0aBAA4JVXXsGOHTswf/58ZGVloaioyLbMbyTW\nrVuHBx98EN999x0KCwsxefLkmNpRqHLBgJQMjsaWseOYY47BggUL8OGHH+LOO+/Eqaeeirvuugvz\n5s3D//73P7z55pv4+9//js8++yyu96nPbN0KEAGtWsn9+mjL6AFVHbmnHfn5+Rg5ciSuuOKKoIHU\nsrIytGrVCllZWZg1axbWr18ftp2TTjoJ06dPBwAsWbIEixcvBiDlgvPy8lBQUIBt27YdXgEJABo3\nbozy8vKQtoYPH44ZM2agoqICBw4cwDvvvIPharQuRpza3Lx5Mxo1aoSJEyfi1ltvxYIFC7B//36U\nlZXhzDPPxMMPP4xFeuWJsGzdCrRsKZOXAG3L1Ff8Lu6+jdxTyUUXXYRx48YFZc5ccsklOOecc9C3\nb18UFxejR48eYdu49tprcfnll6Nnz57o2bPn4TOA/v3749hjj0WPHj3QsWPHoHLBU6ZMwejRo9Gu\nXTvMmjXr8OMDBw7E5MmTMXjwYADAVVddhWOPPda1BQMA99577+FBUwDYuHGjbZsff/wxbr31VmRk\nZCArKwuPP/44ysvLMXbsWFRVVYGZ8dBDD7l+3yORLVuAtm2N+9nZ/lxAORa0uBsocSfyp7iTVJBM\nPsXFxWzN+16+fDl6qlEoTdqhfz9h8GCgWTPgo4/k/llnAdu3A999l9p+ecF77wFjxwIDB0p5hSOZ\nDz4Azj5b5jIcfTQw1259ugRARPOZuTjSdtqW0Wg8ZssWYzAVqF+2jDoD0Z67Ebk3a+bP31eLu0bj\nIXV1wLZtobZMfcmW0baMgRL3wkIt7q5IlU2kiQ/9uwl79khUa47c62O2jBb3YHH348HbV+Kem5uL\nXbt2aaFIM5gZu3btQm5ubqq7knJUjrs1ctfiXv9Qee5+tWV8lS3ToUMHbNy4ETt27Eh1VzRRkpub\niw4dOqS6Gyln61a5tnrufozsYkFPYjI4cEAyZQoKtLhHJCsrC126dEl1NzSamDlSInc9oCrinpfn\nX9stoi1DRLlENI+IFhHRUiL6vc02OUT0OhGtJqK5RFSUiM5qNH7HLnL3684fC9qWMVDi7teDtxvP\n/SCAU5i5P4ABAEYT0RDLNlcC2MPM3QA8DODP3nZTo/GOqVOBp59OTNtbtwKNGgGBEv8ADFumPgwl\naXE32L8/zcWdBVUiJytwsf5NxwJ4MXD7TQCnklO5RI0mxTzzjEzGSQRqdqr535+dLcKuysOmM1rc\nDQ4ckIN4drbYVH47eLvKliGiTCJaCGA7gE+Y2ToXqz2AnwCAmWsAlAFobtPOFCIqIaISPWiqSQXl\n5cCuXcC+fYlpf+vWYEsGEFsGSG10t3Mn8Ic/xC/KekDVwGzLAP4bh3Al7sxcy8wDAHQAMJiI+sTy\nZsz8FDMXM3Nxy5YtY2lCo4kLVe8tUeJurSsDGDt/KsX9o4+Au+6KvwSCHlA1sIq736yZqPLcmXkv\ngFkARlue2gSgIwAQUQMABQB2edFBjcZL1EIaZWWJad8uclc7fyrTIVXEvXRpfO0oca+t9Z8NkWzM\nnjuQhuJORC2JqGngdkMApwNYYdnsPQCXBW6PB/AZ65lIGh+iCmnGGrm/+SZwzDH2tkRlJbB3rz9t\nGSXuEdaFiYh56YFkjCFUVAA33yx2WiqpqAAC6+Mcxuy5A2ko7gDaAphFRIsBfAfx3P9DRNOI6NzA\nNs8CaE5EqwHcDGBqYrqr0cSHitz37Yst8ly0CFi1KngVHoVamMuPtowSZa8idyA5vvs33wAPPwx8\n/nni3yscL74IjBhh/MZAqC3jt4lqEScxMfNiAMfaPH6X6XYVgPO97ZpG4z0qcq+ulp0x2ooJKuKv\nqACaNg1+zi7HHfDHzu+VLWOuS58McVff9549iX+vcGzYIMHApk1A69byWL3y3DWadEdF7kBs1oyy\nB+wW37CbnQr4Y+dXEfemTfGNN5gj92QMqvpF3FXErg7gzMEzVAEt7hpNSiktlVogQGwip8TGTtyd\nInc/7Pzm/sbjuyfbllG/0e7d7l/z6adGVpRXqN9WiXxlpQh8unvuGk29YO9eufTrJ/djidzDifuW\nLUBGhrEwtsIPtkxVlTGxKp3EPZbIfcIEwLSipCcocVfXqtyvtmU0Gh+g/Pb+/eU6HlumoiL0ObUw\ndmZm8ON+2PmrqoAOHYCGDePz3auqjM/j18h93z77Ae940OKu0fgYJe6JitztctwB/9gyjRoBPXvG\nJ+6VlUbdHD967tXVkqLp5YLktbWyBi5giLs6eGhx12h8gBpMjSdyj2TLWAdTAf/YMg0bAr17x2/L\nNG4st/0YuavfxWwfxcuuXUZOvzVy1567RuMDSktFmIqK5H4iBlTtInc/7PyVlZL22asXsHFjbJ+9\npkYuKnL3o+eeCHFXgp6To20ZjcaXrFsHdOliZMtEG7kzO3vudXWy49tF7vHYMvfeCzz/fPSvs2KO\n3AFg+fLo21BnHukQuXtpyyhB79NHi7tG40tKSyVqz8mRHTJacT9wwJjVahWP3btF7MJF7tHaMp99\nBvzud8BLL0X3OjuqqiRyV+Iei++uouFURe5uZhQnMnLv31/6U1GhPXeNxjcwS+SuLJkmTaIXd/P2\nVnF3ynEHYtv5q6qAa6+V215Ux1a2TFFR7Bkz6jMnc0BVRe41Ne4yYNQZVSLEfcAAud62zd5z91v5\nAS3umiOC3btFHNQSvbGIu7l4lVXcnWanArHZMvffD/z4I9Cjh9Rijxdly2RkSMZMLIOqSjCTacvs\n22ccTNz47omyZfLygG7djPvaltFofILKlDFH7tEOKpoPBlbPXXnCzZqFvi7anX/lSuBPfwIuuggY\nN07EPd4aqypyB8SaiceWSZa419XJd65+Mze+uxe2TF1d8H01UK7OyrS4azQ+QuW4q8i9oMBbW8a8\ns1uJ9rT95pslyn7oIZkUVVsbf/15FbkDRsZMtJ8/2Z67GuPo3FnuJyNy/+tfZeDUjJO4Z2bKb+uH\neQx2aHHXHBEocY/Hcw9ny6hI3k7cGwRqr7rd+b//HjjvPBGTFi3ksXitGXPk3rOnXK9cGV0byRZ3\ndUBLZuS+erVkEqlJS4Ah7i1bSgmHrVvF4svPl/s6ctdoUsi6dUBhoZEGGc+AakZGdJE7kUR3bnf+\n/fuNfnoh7szBkbuyjqI9G0j2gKr6vpW4u4nc4x1QVa8zj0kocW/QQAReRe7qt87Kkmst7hpNClBp\nkIp4PPcWLUI99wMHRMSVgFrJznZnyzAbUaF6LyC+jJmaGvGRVeSuREkdkNySbM893sg9lnEKq7gf\nPCgHFWXJtGkTKu5EIvBa3DWaFGBOgwQMzz0aAVC2TOvW9pF7o0ZG5UUr2dnudn5VSlYJhxeRu+pr\nuom7Opi2bSviGY3nDsSWmqheowacVYnfcOIOuP99AeDPfwZmzYq+b9GixV1zRLBtG9CunXG/SRNj\nNSa37NsnIlNYaC/udpaMwq0to3K5rZF7POKuRFmdVcQr7sn23AsKxEqKJnIHYrNmrJG7df6CEnfz\n2RXgXtyZgTvvBD75JPq+RYsWd80RQUVFsPg2aSLX0fju+/bJ6xo2jF7c3doyVnHPz5cDg58i92SJ\nu/ptCgrkgBpt5B5LxozbyH3//tgi90OHguvzJJKI4k5EHYloFhEtI6KlRHSjzTYnE1EZES0MXO6y\na0ujSQV1dSJMjRoZj8Ui7uXlYkk0amTvuUcS91gidyKJ3r2M3NX3YFeTPhzJHlBVkXuTJsmP3Hfs\nkItd5H7okCxXGIu4hxt495qIC2QDqAFwCzMvIKLGAOYT0SfMbJ3jNpuZz/a+ixpNfKgd3jzYqcQ9\nmkHVeCJ3t7aMeVq7okWL+AZUlWCpyD07W3K008Vzb9xYInc1Czgc5gNWLJG7qsFTVSUpkUrc1epa\nSuQ3bw4V91jOzBJJxMidmbcw84LA7XIAywG0T3THNBqvUDu8OXKPpTJkKmwZIP7I3WrLEElf/W7L\nlJWJsGdkJC9yP3jQWMxl6VIR9+bNjVx2c+2gWDz3ZEbuUXnuRFQE4FgAc22eHkpEi4hoJhH19qBv\nGo0n2Il7rJ5748axi3sstgzgvS0DxC7uWVnGjMxkRO7qIFxYmDxbpmtX+Z2XLQut0W++HYstY64m\nmWhcizsR5QN4C8BNzGzdJRYA6MzM/QE8BmCGQxtTiKiEiEp2eFHqTqNxgdrhvfDcmzSJzXOPNVsG\nkIkzXkbuQGzirma5qhm3yfDc1e/UrJn8VpEOKF4MqDZsKCUalLi3bm087yTu8dhuicKVuBNRFkTY\nX2Hmt63PM/M+Zt4fuP0hgCwiamGz3VPMXMzMxS1btoyz6xqNO7yM3JUtc/BgcIGpRNsye/bEHil7\nGbmbxT3ZkTsA7N0b/jWVlcaM0Vgjd7VilbJlzILetKlh0aR95E5EBOBZAMuZ+SGHbdoEtgMRDQ60\nu8vLjmo0sRLOc492QFXZMkCweCTalgHcr0ZkxavIXZUwSJa4WyN3IHI6ZEWFsW2s4p6TI+K+bRuw\nYUOwuBMZ9+Px3P0SuZ8A4FIAp5hSHc8komuI6JrANuMBLCGiRQAeBXAhc7xFSjUab7AT92hXY6qt\nlXZU5A4En/Z7Ke7WMgbxliCwZssA8l3EGrmryDgVkXukA1xlpbFtrLaMecUqu9W11H2/R+4RUyGZ\neQ4Ah0nVh7f5O4C/e9UpjcZLlLhb675EUzxMlR4wi3tFhWRSqIkpXnnueXmSIaKINEt15UpJ2/vZ\nz+yfd7JlzJUP3aDEPSNDDkB+jNwrK+U3AaKP3OvqZBxBRe4KL8Xdb5G7RpPW2EXuQHTibs65Vu2o\nyNBNels0nrt1x1fDU07i/sc/ApMmObfp9YAqINZMMqpCxhO5Ryvu6vfJzQU6djR+z3SN3LW4a+o9\ndtkygAiHW8/dLnKPVtzd7vxWcY8UuS9ZIv1zat/rAVVAxD2RkXt1tWGDAdFF7mrbaG0Zs32VkWFE\n707iHqvnnplppJMmEi3umnqPl5F7rOIejS1jFXdlM9iJe12dWDKAs/BVVopYNTCZsPEMqALiuydS\n3NXBNJmRu9peCW8kcY81cs/Lc64e6iVa3DX1Hq9tGbPnDiTelsnJkfe1E/f1642DjJPwKVE2C4oS\n92jSHpIZuZvrygByMMnPDx+5M8tv0rix9C/ayN1sywDAmDHAoEHGwVUxbJgsxaeWbATc/74HDiTH\nbwe0uGuOAJQImz1nIPYB1Vg991gjd8C5vox5oetw4m797Hl5EvVHU/I4meJurgipiDRLtbpaPlPD\nhkZ9mGiwRu4XXACUlAQPbgPJfDKUAAAgAElEQVRA//7ADz9IzrvC+vseOgRccknoUobWapKJRIu7\npt5TUREauQLJt2WqqyNHyuHE3S5yNy8H5yR85oFQhTpARWPNJHNA1fx9K5o1Cx+5mwvENWwYKu5T\npwL33uv8emvkHg1WcV+3Dpg+Hfj00+DtdOSu0XhIRUWoJQMYA6purAk7WybayB2IHL07RXZOJQiW\nLpUBOiCyLWMmlpruqbBloonczeKemxtqy3zwAfD8886vt5sP4BaruKszPeuAvY7cNRoPqay0F/do\nVmNSO2s8njvgTtyjjdyPPVZuRxO5q75GU9M9mQOqdpF7pAU7IkXu+/cDa9eGPwgCsWWyWM/MVP+t\nZ4Y6ctdoPMQpcrfWl5k7N/Q0WrFvnwhGVlao5+40YGsmEeKuMmWGDpXovb5H7pHK/pp/BzvPXR2g\n58+3f328tgxgWFXqP6Ujd40mgbgV91/+Erj5Zvs2VNEwIHbPHQh/llBdLeLvJO4HDgRbDRs2yGN9\n+oS3LMJF7m7Fva5O+pbsAdVYI3c7W0aJe0mJ/evjidytB28nW0ZH7hqNh4Tz3AERkm3bgAULnMVO\nFQ0DZOcn8t5zDzc13W4ikxpM7dUrfFTrlC1jfk9Faan9GITVj070gGpZmbyH+YyjWTPph1OKYzhb\n5tAh47uPJO7xRO7qPXTkrtEkAZUtY8W81J5ajd7Jg1a13AER9tzcYM89J8cY2LTDjbiHW4LNrgSB\nSoNU4r7LoQ6rW1tm61agWzfJ8rBrA0hu5N6kSXCGk5qcFG6yFmAfuavvFgC++87+9V7YMpHEXUfu\nGo2HhBtQBWRH/Ogjue0k7mZbBpD2zJF7pGjMjS0TTtydIvc2bUTYw0XublMhN22S6pezZoW2YS1h\nkOgB1bKyYL8dMMoKhPucgH2eu/pue/UCfvpJztSsJNqWqamR319H7hqNR0Ty3PfuBf77X2NbO8y2\nDBC81J4bcY83cncSd1WaNpIt4yZyVwthzLVZRDNVkbuZSJG7eUDVassosR05Uq7tBlW9iNxVG3aR\nezIrQgJa3DVHAJHE/YsvZPbnMceIYNl5yWZbBki9uDOLuKv6J9FG7napkEo0ly4NTeFLheceb+Ru\ntmWUuI8YIVaPne/uZeRulwqZzMWxAS3umiOASAOqMwIr/qp66HYDdtZIsmHDYM890bZMYaGIkipB\n8NNPsr1Z3MvK7KNpuwFVO1tGiTtzqPhZywZHE7mXlACvv+5uW4Vd5K7WMt2yxf414QZU1Xfbti3Q\no0d4cffCc1cHk337jOUYw/2+iUCLu6be4yTuajWmPXtkIpAqBGVnzaTalsnMFAFXkbsaTDXbMoD9\nGqN2tkxmpoiYnbgDwLffhrYBRC/utbXAxInAlCnRFSmzi9zbtpX3Xb/e/jXhPHcltvn5QHGxvbgf\nPBhaPdMtTpE7s/G76shdo/GQujp7cVOo6HD0aOMAYBX3gwdlp41nQDVecQeCSxCY0yABZ8uitjY4\nP92Mtezvnj0ibN27O4t7tAOqb7whxbP27Ytci92MXeSemSmLaLgVdztbpnFjEfctW4DNm4Nfr9ZP\njaUcr5O4A4bvriN3Tdpw4onAM8+kuhfhUaLkNHtUCcioUc7ibq4IqYg2cle2TDzi3qIF8OGHQKdO\nwB13iE2hytE6ibuygewObnbiXlgIDBkig6rmSDuWyL2uDvjDH4xIuLQ0/PYKZvvIHZDPHk7cs7KM\n/PiaGqOP5u/2uOPktjUlUq2fGgvW37e83EiNVeKuI3dNWlBXB3z9tfNUbr8QqTRAQYFEc0OHOou7\nuWiYIlrP3ZpNYUekJdiuuQY47TTg1FOBa68Fnn7aeM5J3O2W2FM4ifvxx8v6qmYxjmVA9a23pDzC\nLbfIfbfifvCgtG2N3AGgc2dncTfPZ1D9VN+3OXLv31+E12rNqMg9Fuwi93bt5LZvI3ci6khEs4ho\nGREtJaIbbbYhInqUiFYT0WIiGpiY7mr8Qnm5RFhuS+amikjifvLJwJVXys6ZyMjdrS2TkyPRpx2X\nXCKDv88/Dzz8MHDOOcZzTuJut8SeolEj58gdCLZmoh1QrasDpk2Twctbb5XH1q0L3uaDD4DBg0MP\nEtaFOsx07ix2it2BpbLS+JzWEhHl5eKnN2won7t3b3txjzVytxP3jh2DP48fI/caALcwcy8AQwBc\nR0S9LNuMAXB04DIFwOOe9lLjO9QfNt3F/aGHRCjN2zhF7snw3GON6iKJu9vIvWlToG9fEUFzvru1\nnUie+4wZsrbrnXeKdVRQEBq5z5wp1sjWrcGP2y3UoejcWYKKjRtDnzOLu+qn6rf6bpWf3qVLqOce\njy1j/n1ra+V7tYq77yJ3Zt7CzAsCt8sBLAfQ3rLZWAAvsfAtgKZE1Nbz3mp8Q30RdzPR2jKVlbIj\nV1V5lwoZ646vVgWK1pax5rkXFkpUXlwcHLlbzwAiRe6PPQZ07SqrGQFAUVGouK9YIddWcY8UuQP2\n1oyduJsjd/Pvl58fWlfHK1tGiXiHDnKt/j9+jNwPQ0RFAI4FYJ3D1h7AT6b7GxF6AAARTSGiEiIq\n2WG3ZpgmbUg3cXfKljETTeSuPHe1baoj98xMEfhobBknzx0Qa+b7742DUTQDqtu3A19+CVx8sTGY\nWlQUassocbfmrUeK3IHI4q6uzZG7Wdzz8oLrzQDeRe6q/06Ru5v/ohe4FnciygfwFoCbmDmmXZqZ\nn2LmYmYubqkqIWnSEj+Ke02NDDouWmQ8piK3eCJ3J8+9psb4HrwS93iiOrtZqm4HVJklR16J+/HH\nS18XLrRvJ9yA6owZ4rmfd57xWJcuwRUny8ullg0QGrmrlEnzGqUKJZh24m6ez2C1ZcrLgw+c+fmh\n4u5F5H7woPF/ad1aDrpmzz0vL3RN1kTh6m2IKAsi7K8w89s2m2wC0NF0v0PgMU09Rf1h1R/ZD8yf\nDzz5pAzUKby0ZayeO2BUYvQqWyYeP9auMqTbyL28XATZHLkDhjVTVSVCpSLxcJH7W2+JJdOvn/FY\nUZG8l+qfeeFoq7gr0VfZJmZyc6VYmle2jJo9qj6jl5F7QYH8Z8yRe7IsGcBdtgwBeBbAcmZ+yGGz\n9wBMCmTNDAFQxswOk4Q19QE1E9JPkftnn8m1ebJMNOJuXT5PoT6jecdU2yp3MdJOm5kpl0TZMkB8\nkbv6zpS4t28vFzWoahU+pwHVPXvkdzjvvODJQEVFcq2sGWXJAKG2zKZNIpaqno4Vp3TISLaM+btV\nv5d5opPXtkyTJsY6vUByy/0C7iL3EwBcCuAUIloYuJxJRNcQ0TWBbT4EsBbAagBPA/hlYrqr8Qvq\nD3vwoLs1SJOBKlVrFrhoxF1NgLGzZfLzg0+nlXioGaNuIjLrIspWEiHu4bJlGjWS52trQ8UdEGvG\nHLmbo3+nyP299+RxsyUDGKUd1KDqihVysOvePTRy37hRDixOM0XdiLudLWON3IFga8arAVVzTr1Z\n3JMduUesosDMcwCEnZDLzAzgOq86pfE/5lKm5eWx7xRecegQMGeO3I5V3NV2duJuFgYgNnHPyQk+\nEJaUiLipthMp7k62DCCf107chwwB3n5bzk6skbuTuL/1lvjiahaoQkXuZnHv2lVmnNpF7u1D0jEM\nOnUyfH3zATdSnruTuKuCZPFE7mpuQrpF7hpNCGZx94M1M3eu7MjWhaKjyZYB3Iu7OljEGrlXVgLD\nhgGPPGI8H+/O36yZiLTZR45ky6j3dYrcAeO7tYp7bW1wiYLycqmL//Ofh0bdTZpI/8y2TI8e4p/b\nRe4qjdCOzp3le7QuuGFelMUpz12hbpuzheKJ3FXBMXPkrsRd7R++89w1Gjv8Ju6zZomgDB8eLO7h\nxM2OREbuZnHfulWyTVQBsLq6+MW9eXOjLosinsh90CA5WM6dax+5A8HR+wcfSPRrtWQUKte9pgZY\ntSpY3NVBgjly5O6UDmlXfkDNRaioCE2FBEJtmVgjd0AODObI3WrL6Mhdkxb4UdyPPVZO9a2Re8OG\n7tPPEi3uypZRUefq1XJdWSnCFm/kDrg/uEWK3PPyZLaqnbgrG8Is7jNmiMUxbJh9/1Sue2mpiGCP\nHlLG9+BBY4B+9255r0iROwBs2GA8xuw8oKoEPJLnHo8tAxgH7337pJ2srNR67lrcNTFRVmbsIKkW\n98pKKWI2cmSo7+xUy92JRHvu5sgdkAjWXPPba3EPt7qQVdwzM0M/5/HHi7hbFxm3i9w3bJCDgdNC\n4SrXfflyua8id8D4PlQaZLSR+6FD8j3aDajafbdWW4Y5PlsGMMTdvGqXSoVk1pG7Jk0oK5OBLSD1\n4v7NN7JTjRwpkWdVlRGxeiHu1tmNajvAEHc372G1ZQD5HnfuTKy45+TYn7lYxb1p01Cv/Pjj5fdd\nvDiyLROubj4gkXtVlcxeBWQwWYm7GlRVNWPCRe4FBXIxi7u5ljtgiHRlZXD2isJqy9TUiDXmVeSu\n3qugwLCFdOSuSQvKyozZgqkW988+k2hx+HBD4JTNEIu4W5fZs85uBILz3DMzjVS4cNiJOyDWTKLE\n3W79VIV5qT1z6QEzalB1zx57cTfPUo3kWat0yJkzgVatpL9tAxWoooncgdB0SOtMZCJjNSbzKkwK\nqy2j7DIvInfzQiOqhMLevfJf1JG7xvf4SdxnzZJCVyojAzAEzmonRCIWWyYvz93qPeZUSHOmx6pV\niY3cnT6/OXI3lx4w06OHIVRuIvdw4q7SIZculXYBe1uGyBB9J6zibpcVpcTdjecez/qpCjWmYrZl\nlLirMxMduWt8TV2d/IHbtpXT/VSK+4EDwLx5YskAoQJnTo9zg1Xca2qkDSdxP3TI/Q5rjdyPOUa+\nP68idyXObiN3qy1jJ+4ZGVJzHYg8oBpJ3JVXDhjiXlAgrzHbMq1bO9e0N7cVzpZRt51sGesC4eqg\nmwhbBjDOSHTkrvE1aqGOpk0lQkmluK9ZIwIzMLA8jF3kHo+420V9QLCIxCrunTqJSHkVuWdlST+j\njdxVKqSduAOGNRNpQDWSuOfnyzqwgCHuRMG57ps2hffbFZ07y/9OZdnYibs1cjd/txkZ8ltbI/dE\n2TKqdryO3DW+RqV2qcJIqRR3JQrq9N5rcbeL+gARUpUV4naHtdoybdoARx/tnbgDocXD4o3cAUPc\n4/XcAcOaUeIOyPdgjtwj+e2AMZivovdw4u70G5orQ3ply1izZaziriN3ja/xk7gr71qJu9WaiFXc\nzaVpgVBhIDKEJNrInVkOSq1bA926iS1jN+gXC9ZU0HCCq7Jo4hF3FbmrVMJI4qgGVc3i3rZtbJE7\nEF7cw9kyQPCCHYm2ZXTkrkk4paWypmVtbext+FHcVX2Qxo0loo4nW6auzrBPnGwZIHZxLy8X0VGR\ne1mZUXMlEeLuZMsQSd+3bxeRdhL3Vq2kTMLEicZjVs+9uloEPpI49u0rM2lV5A0Ytoyyh9xE7tYq\nk3Z1+622jPV3Mi/Y4ZUtc+CAtGXOcwe0565JAu+9Bzz4ILB2bextWMU9lTXdt26VnViJL1GwwMWS\nLaNeBzhHfUD04q5sGfPZxtFHy+3vvzfS9+LBKu7hbBlAPq/KLXcSdwC48UYRZoU1cndra9x6q2TL\nmCc6tWkjVpISajeRe6tW8v9TdeHtsmXMkbvdIhlmW8aLyD0nx7DElKg3biy/q47cNQlHCfP27fG3\n4ZfIvU2b4FREs8DFki0DhIq7XcSlto02cjePE3TrJrcXLgxewDlWooncAem7Ene7lY+ciFXcc3KM\nsyyFSnucP1+u3UTuRGLtqLrwkTx3u4Ozneceb+SuJrWp98vIkNs6ctckHCXE1op60eA3cbeKhRK4\nujrvxN1LW0aJe+vW4kFnZEh/vdjxzZ8diBy5m8U9XORuxTqgGs+ApBovKSmRazeROwD07OlO3J1K\nKZs9d68GVNX/xrxqV0GBccDVkbsmYShhjkbc1SCgtQ2/inthoVGACohO3K2rMXkt7gcPBkfu2dmG\nf+yFuHfrJuMpa9bI/UiDnHl59kXDIhFr5G6HVdzdRO6ARO5btsj/MdKAqt3vZ/bcvRpQVVjFXaEj\nd03CiNaW2bNHljt7//3gNrKyZEdo0kR2kHgGaONBZZ2YUdFrtAt1mLdNlOeu6pBnZsrAImBYM17s\n+GrdUrVIuBtbRhGNuFsHVOMRd2XLLFwo1pDb71Nl3KxYEdmWcYrcvbZlFOb/i1ncdeSuSRjR2jLL\nl8vOoaIqQCaOFBSI76kiFOtK8smgtlY8ThX5KbwWdzXhxWnbaCL3ujrxX1u1Mgb41KCqF+Leu7e0\nq8TdjS2j8CJyj0UcW7WS68pK91E7ILYMIOJeUSHfr3mg1mzLOHnuXqdCKuwi99xc54qZiUCL+xFG\ntLaMOr03T/UuKzP+sOpPnAprZudOEUu7yH3fPiPqjidbRvm1dgOdsdgygJTGNR+QvBT3hg2l2uLi\nxUbuuZvI3XygdoOXtox5MWy3fjsg4xVZWUbkbv2cbmyZigoJEryO3O3EPZlRO+BC3InoOSLaTkRL\nHJ4/mYjKTItn3+V9NzVekQxx37zZSP1KJOaBSTNqlqrqQ7yRu50wALHZMkCouHtpywBA//4Suaux\nEjeRe9Om7hc0AbwdUAWM7yOayD0rS7675cvtxd2NLQPIb+3VgKrC/J9R+0gy/XbAXeT+AoDREbaZ\nzcwDApdp8XdLkyiUCLv13FU+vJO4qz+xWdzPO08GCa+7LnTxYy+xzk5VKHFXWSB+EXe18//0U/AB\nycvIHRDfff164+AXKc8diM6SAbyN3AHjN4wmcgeMjBmnyJ1ZLDonWwYQa8arkr/WtgEfR+7M/CWA\n3ZG206QHsUbuGzcag6aRIvcVK2SQ7KmnZNm7Z54JbXfDBuCLL6Lvvxnr7FSFEiqvxN1JdGPx3AER\nEvMBqUsX8WK9jNwBqZYJuLNlohV3LwdUAWNQNZrIHZBB1TVr5P9nF7kD8n2HE/f9+6X/DRrE54mr\n3zc/P7gdta/4MXJ3w1AiWkREM4mot0dtajxGlerNzpadQe2Q4VizRravqTFsjnDirir1XX+9scL9\ngw+GtjttGjBqVHwDsU7i7tfI3RwVmsU9Kwt44QXgmmvc9zMcStznzpVrN7ZMvJF7vAOSsUbuPXpI\nH5YsCf2dzX2xE1bzakzxrp8KGOJu/b/4NnJ3wQIAnZm5P4DHAMxw2pCIphBRCRGV7Nixw4O31kSD\nGmBUHm+k6H3/ftlmyBC5r6yZcOKutuncWaL20aPlAGGuHggAy5bJDvXxx7F/HmvpAUU84m6X5+61\nLQOEWkkTJwK9ernvZzjatZM0SyXuiYjcE2XLRBu5q4yZtWvtbRmFm8g9HksGMH5f68B02kbuzLyP\nmfcHbn8IIIuIWjhs+xQzFzNzcUtV2FmTNJQlozzeSL67qvVxyilyvX69Ef07ibtakV4VhureXQRA\ntQWID6pmFr73XmyfBbAvPQAY4q6mfEeTLZOZKTt5osXderbhJUTiu6vp/ImM3L0aUB01Chg7Vv4v\n0WDe3smWAcIPqKpiX/FG7urg4CTuaRe5E1EbItm9iGhwoM1d4V+lSQVKgJW4R4rcld+uVjlav95Y\nqMNpQNUcuQOy2hAA/Pij0e7OnTI5Kjsb+M9/ghd8iAa72amAUSNFiXs0kbvaXk2KccqRNrcbry2T\nCPr3t5/YY8Uvnnvv3sCMGdG/vnFjI9qPNnI/4m0ZInoVwDcAuhPRRiK6koiuISLlEI4HsISIFgF4\nFMCFzObJ6ppoKC1154XHgjVydyvufftKHvL69cGlBwCJdPPygiP37GxDdFVkpar3mW9PnCiZDF99\nFdvnsZudCkhUWVBgFHGKRdzdRO7jxgF//nPw8nHhCGfLeI3y3YHkeO5e5InHirJmwkXu2paxgZkv\nYua2zJzFzB2Y+VlmfoKZnwg8/3dm7s3M/Zl5CDN/nfhu109qamSn/OtfE9N+tOK+dq1EwYWFxpqV\nVnEHguvLrF8vC2ernOlmzeTAYCfuN9wgO8S778b2eZQtY4dZrKKNyJS419TITu8k7q1bA7/9rftK\njmrnz84O/v4SgVtx9zIVskED4/FkosoQRDug6rUt4yTu6r7vIndN8ti+XURSTR33GiXAbdqIYEXy\n3NeskUFRwL24b9gQvBADINaMVdyzs4E+fYBTTxXfPdpzPVV6wMm7Vr57w4bRTc4BDHEPV1cmFtTO\nbzdO4DU9exrpeIkcUDV77vGKY6wocY9nQPXgQe8id+t7KZvQd5G7JnmoVEOzP+0lZmFu3dqdLeNW\n3JUQrl8falN07x78mVaulLOHzEwZRFuzRrJnosGp9IBCiXu0lox6jVncvdoplXgk2pIBRGiV6IUT\n3QEDgMmTgREjomvfLnJPlbjHass0bCgHWWXLJCpyLywEHn88eCWrZKDF3UcocV+1yqjH7SVKmJs0\nkWJN4cS9pkb8/6OOkvudO8sAnfLh7SL3Q4fkM1gj9+7dxR9X0f3KlYYXf845ch2tNeNUekBhjtyj\nJRmRezJQ1ky476BRI+D5543iXW7xk7g7Re6RbBm1zKDXA6p2NXquuUbsymSixd1HKHGvqEhMbZZ9\n+4wB0EiR+8aNsuOaI3dAClIB9uK+aZPYK3aROyCiXl0tBwj1WLt2wHHHRS/uTqUHFPFE7g0bJlbc\nE5kGaWbQILGkEmEHZGTIxQ/i3ratLAN49tnBj5vF3uk7UJUhvRxQ9er/Ei9a3H2EuQ7LqlXet19W\nJkJMJAITznNXEbqTuJuXZFPibk2DVJjTIdetE0Ew5yefd55MlQ831sAs64wqnGanKpSHHI8tE25x\n7FhIpi0DANdeKyUeovXT3dKggT/EnUgW8B48OPhx1Z+cHCN104qK3BNpy6QKLe4+YvNm40+YCN9d\niTsgorhrl3OOuRJ3sy0DyDRvtVCHQom7dQKTomtXifJWrjQmL6lTaQCYMkUE9L77nPs+axYwcKAx\no9WtLeOF5+6VuDdpIoKovtNE07AhcOKJiWu/QQN/DKg6oSL3cL+fWrDDiwFVNS8z2faLE1rcfcTm\nzZJBkpsbXtwXLgR++CH69vftM+yUVq0kGnaqArF2rYi4qvVRWCg7QmWlsVCHwhq5W//cOTlSHGvl\nSiNrxhy5FxYCv/oV8OabUr7VjtWr5fqFF+R62zbZeZ12XD+Ke2Gh/G7JHlhLFH6J3J1Q/Ykk7l6l\nQnbtKvtNtIPTiUKLu4/YvFlm2x19dHhxnzwZOP/86NMHzTVhVMTr5LuvWWNUKwREzFX0bs3RbtJE\nUhNXrJB27XYSlQ65cqUcWMy2DgD8+tciqk7Ru5ptOmOGHEjU7FSnlEI/ijsgZyypyAVPBFlZ/hZ3\nVeUx3JiDOXL3ov9duiQ+zdUtWtx9xObNMsB4zDHO4n7oELB0qYik8r/dYrVlAGfffc2aUPsgnLgD\nEpU6zdbs3l3GEVassK8f0qKFeMSvvmo/3rBpk1g7VVUS4TuVHlDEmy1TWWlk9/hlgMxvWCP3VMxO\njUS4szsg2HP3Y//jQYu7T6iuFqFt104i97Vr7f3wFSuMx197Lbr3MNsy4SJ35uAcd0UkcV+xItRv\nV3TvLtHwvHnOxaFuuUUGpf70p9DnNm0Cjj1WvpuXXnIuPaCIN3IH5PfIyPBfROoX/G7LAPZVQ814\nHbn7CS3uPkENEKrIXeWZW1EZJUcdBbz+erA1s2IF8Nlnzu9hjtxVXrOduO/eLQcCt+Kudp7qaufI\nXWXMVFc7i3ubNsAvfgH861+ha7Ju2iSW1aRJkgGyenX4rJN4s2UA+W4aN/bPabbf8PuAKiB9imTL\n7A4sRaQjd01CUHntStwBe2tm8WKJbm+7TdIKv/tOHq+slDzfn/0stHa6why5N2kif2Y7W+brQHUg\na33xSJG7eRsrZkE3Z8pYGTFCDmxqeT+FGo9Qg5GVlcmJ3LUl40w6RO5nnSUlLpzIyzPGVvzY/3jQ\n4u4TVI67G3Hv3RsYP15EXlkz998vVkp5uSH4ZqqqxK9Xwqxy3e0i9xdflLQuVcdd4UbcnWyZdu2M\nCCpcze6iIrk213+vqpK0zfbt5fmTTpLHw4l7w4bAhAnAySc7b+OENXLX2OP3AVUAeOIJGctxwhzV\n+7H/8aDF3SeoyL1tWxlcbNrUWdz795fnR48G3nhD7Jj775fInQj49NPQ15lLDyjsxH3XLuD994FL\nLgmd+BFP5E4kB62sLMkocEI9Z7akzGc1gFgzQOTJQK+/Dpx5Zvht7NDi7g5z5J6unrVZ3LUto0kI\nmzdL2lbLliKERx8dmjWyfbt48/36yf0LLhAv+swzJVJ95hmZcv7JJ6HtKw/bLMx29WVee00i/Msu\nC22jTRtJwxw9OvhxN5E7ABx/vPQvXCpgYaEIqjlyV2mQalGGiy8G7r1XVu9JBErcd+7U4h6OdLBl\nImEuw5uO/Q9HPcm4TX82bxbxVHnlxxwDzJ4dvI1KfVTifu65Iurr1gH//KdE4qedJgtSWxeZsKvm\n2Lp18JR+QCyZfv2kWqCVjAwpMmVFiXt+fvip7o88EnnVJSKJ3s2Ru1XcGzYE7rgjfDvxoMSdWYt7\nONSAak2NzHNIR3HUkbsm4agcd8Uxx8h0frVUGhAq7vn5YlGcfLJM4QdE3GtqgC+/DG7fyZbZvt2o\nQLl8ufj1kydH1/ecHPH/O3UKn1mSne1ugLOoKDhyV7ZMtIsnx4q5j1rcnVGee7xL7KUS7blrEo6d\nuAPGtHtAxL1tW6OGBSADRrNmGRH/CSfIn9Tqu9vZMq1by865Z4/cf/FFaefii6Pvf5Mm7pebi0RR\nkUTuKs1z0yaJ1hO9epHCLO7JXmAhnVC2jBZ3f6LF3Sc4ibvZd1+0yIjancjNlWJRVnG3s2XatpXr\nUaOA3/9e8svHjImtJO2oUfJaL+jSRSaWqPxjleOerHxzHbm7oz6Iu9lz17aMxnMOHpQsFSW2gLHO\nqcqYqa6W1YoiiTsg1nKM7E8AAB0bSURBVMySJcbEKMDeljnrLGDaNNlJf/97OcBcfnlsn+Hll6X4\nlxdY0yGVuCcLLe7uUJ57Oov7ER25E9FzRLSdiJY4PE9E9CgRrSaixUQ00Ptu1m/MOe6Kxo0len/6\naWDvXhH5Q4fcizsA/O9/xmPKljGLe14e8LvfAd9+K977558D48bF9VE8wZoOmWxxj7T2pkaoD5H7\nkT6g+gKA0WGeHwPg6MBlCoDH4+/WkYWduAPAc8/JoOrllxtlB8yr2jsxYIDM0DRbM2VlIlpOixa0\naCGzQ/0w1d4cuTOHWlaJJtLamxqhPgyoHtGpkMz8JREVhdlkLICXmJkBfEtETYmoLTNvCfOaek20\nhf+tk3QUJ5wA/OUvwM03S8piVlb42Z2KzEyZXWoV92QNSMZLQYGkVJaWiu9+8GByI/eMDDkQVlZq\ncQ9HfYvc07H/4fDCc28P4CfT/Y2Bx0IgoilEVEJEJTucVolIc/77X4maw61PasVJ3AHgppuk1MD6\n9bLKu1rKKxLDh8s6qKptc12ZdEClQ1pz3JOF8t21uDtTH8Q9N1cO5sCRact4BjM/xczFzFzc0pzP\nV49YvFhK2y5b5v41anm95s1DnyMCnn1W6smMHOm+zeJiuVZ1ZswVIdMBNZFJi7t/qQ8DqkRG9J6O\n/Q+HF+K+CYB5YbUOgceOSFTEbp6EEwk1OzXD4ddo0kQ894cfdt/mgAFiz5jFPd0i99JSOfsAUifu\nOs/dmfoQuQOG764j91DeAzApkDUzBEDZkey3q/TDaMU90oBhZmZ0g52NGsl6rErc082W6dJFPG9V\nHsGcJpoMdOQeGeuAarqKozqAp2v/nYg4oEpErwI4GUALItoI4G4AWQDAzE8A+BDAmQBWA6gAEGOm\ndP1ARe52C204sXmzu4HSaDnuOODttyXjJN1sGZUx89VXMiPX7ViDV2hxj0x9idzz80XY/ZAp5iVu\nsmUuivA8A7jOsx75jMWLgR07whf8NxOrLRONn+6W4mKpFLluXXpG7oCsy+om/dNrtLhHpr6Ie15e\n/YvaAT1DNSJ33CGrGx044G77aG2Z7dtlklIibIfjjpPruXOlSmQ6ibuqU8OcfL8dEHFv0KB+7vRe\nUR8GVAGJ3NO17+HQ4h6BtWulzsm//x1529paqQGenS3RuPrTO1FTA1x4oQjIued6018zfftK22pd\n1XSyZfLzjQJpyZzApGjUSK+fGon6ErlrcT8CYTa88+eei7z9jh1SPvfYY+X++vXht7/1Vqno+NRT\nIsRek5UlWTOqDEE6Re6A4bunInLv0SN0DVlNMOYB1YyM8Iuw+JlzzpGFb+obWtzDsGOH5Kx37iwL\nZ9gte2dG+e1Dh8p1OGvmpZdk8YobbzSWjUsExcVGP9IpcgcM3z0V4n7XXcCcOcl/33RCRe5qib10\nPcuZNElmgtc3tLiHQUXtt98uqYh2qxCZUeI+ZIhcO4n71q2yuMbIkcADD3jSVUeU7w7oyF3jLQ0a\nyJlqRUX9tDXSHS3uYVDiPHSo1Cp/8cXwy8SpwdQBA8R3dxL3zz6TaOeBB5wLeXlFOot7KiN3TWSU\nDbN/vxZ3P6LFPQxKnIuKgCuvlOqNH33kvL2K3Nu2FSvHKdd99mwZrLNbp9Rrunc3ZuClmy1z4YXA\nQw/JZCyN/1CBiRZ3f3JEiPuyZUYBrWgoLZV6L40by8IWrVpJnRcntm2TaoKNG0vU6RS5z54tFR/V\n0niJJDMTGDRIbqdb5N60KfDrXzuXZdCkFh25+5t6v9ts3CiTYNq3lyyW22+XVY/csG6dYQ1kZQET\nJkjkXl1tv/3WrbJEHZGzuO/aBSxdKlUbk8XgwXKdbuKu8Tda3P1N+ok7M7B8uevNp08Xn/yOO8SW\n+POfgVtucffa0lJjUA8Q772qyrni47ZtUgAMEHHftUsmD5n56iu5Tqa433wz8NprwQsTaDTxosS9\nvFyLux9JP3F/5RUxYa+7DtizJ+ymzJJyOGwYcO+9wBdfABdfDLz/fviBUUCyAEpLjcgdMMrolpTY\nv0ZF7oDxOmv0Pnu2DLaaBzoTTdu29TOPV5NadOTub9JP3M86C/jlL4EnnpBFRp99VpTYhoULxQK5\n9FLjsZ/9TFb3URG0E1u3ypqlZnHv1k2ifydxt0buQKi4f/ml2CR6Z9CkO3pA1d+knbhvqijEu6c9\nhm+eW441nUbiwFU3yIiheTVoZqCiAv/6l0TJEyYYT40aJVPyZ8wI/z7mTBlFRoa8lZ2419RI6QEV\nuZvXAVUcOAAsWJBcS0ajSRQ6cvc3aSfus2dL9D1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GyKvxgQsvDE75U4L8xz/K2UlWljz2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\nOZegfwAK24odHwn7qgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}