KTH_TIPS_2B_AlexNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KTH-TIPS-2B_AlexNet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kU8AvQNduNjW",
        "outputId": "dfbd1299-2153-4c8f-f549-067915660eaf"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fej0fKLrAvYj"
      },
      "source": [
        "#Importing Libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ratza6akub7g"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2 as cv\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kFyi1oTA0tS"
      },
      "source": [
        "#Parsing and Reading The Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMaXqdDUu-r1"
      },
      "source": [
        "DataDir = '/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b'\n",
        "Categories = ['aluminium_foil', 'brown_bread', 'corduroy', 'cork', 'cotton', 'cracker', 'lettuce_leaf', 'linen', 'white_bread', 'wood','wool']\n",
        "Samples = ['sample_a','sample_b','sample_c','sample_d']\n",
        "images = []\n",
        "y = np.array([])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C57_z4hDvVwD",
        "outputId": "e5ea158f-43d4-47ad-c4a2-9bc7a2053952"
      },
      "source": [
        "for category in Categories:\n",
        "  for Sample in Samples:\n",
        "    path = os.path.join(DataDir,category)\n",
        "    path = os.path.join(path,Sample)\n",
        "    class_num = Categories.index(category)\n",
        "    print(path)\n",
        "    for imgname in os.listdir(path):\n",
        "        img = cv.imread(os.path.join(path,imgname))\n",
        "        img = cv.resize(img, (300, 300))\n",
        "        images.append(img)\n",
        "        y=np.append(y,[class_num])\n",
        "      \n",
        "        \n",
        "  \n",
        "x = np.array(images)\n",
        "print(x.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/aluminium_foil/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/aluminium_foil/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/aluminium_foil/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/aluminium_foil/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/brown_bread/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/brown_bread/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/brown_bread/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/brown_bread/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/corduroy/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/corduroy/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/corduroy/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/corduroy/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cork/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cork/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cork/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cork/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cotton/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cotton/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cotton/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cotton/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cracker/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cracker/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cracker/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/cracker/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/lettuce_leaf/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/lettuce_leaf/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/lettuce_leaf/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/lettuce_leaf/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/linen/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/linen/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/linen/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/linen/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/white_bread/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/white_bread/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/white_bread/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/white_bread/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/wood/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/wood/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/wood/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/wood/sample_d\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/wool/sample_a\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/wool/sample_b\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/wool/sample_c\n",
            "/content/drive/MyDrive/PS 1/Datasets/KTH-TIPS-2B/KTH-TIPS2-b/wool/sample_d\n",
            "(4752, 300, 300, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gV3Uhd6mwsnW"
      },
      "source": [
        "y =np.array([])\n",
        "for category in Categories:\n",
        "  for Sample in Samples:\n",
        "    path = os.path.join(DataDir,category)\n",
        "    path = os.path.join(path,Sample)\n",
        "    class_num = Categories.index(category)\n",
        "    for imgname in os.listdir(path):\n",
        "      y = np.append(y,class_num)\n",
        "      print(class_num)\n",
        "         \n",
        "      \n",
        "      \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_CDkZdiz4-KE",
        "outputId": "0592e40b-f4de-4558-b2ee-550355a9c408"
      },
      "source": [
        "y = np.reshape(y,(-1,1))\n",
        "print(y.shape)\n",
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4752, 1)\n",
            "[[ 0.]\n",
            " [ 0.]\n",
            " [ 0.]\n",
            " ...\n",
            " [10.]\n",
            " [10.]\n",
            " [10.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JzeqMvDHA69B"
      },
      "source": [
        "#Test - Train Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atrvZhPNw0Hu"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size = 0.25)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oh6-suZJyr67",
        "outputId": "59a9b1b7-069d-4eb6-bd9e-480e64b6232e"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(X_val.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2850, 300, 300, 3)\n",
            "(951, 300, 300, 3)\n",
            "(951, 300, 300, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPNu4OglytJ4",
        "outputId": "443b6166-3368-4a27-81f7-4f4975740267"
      },
      "source": [
        "print(y_train.shape)\n",
        "print(y_test.shape)\n",
        "print(y_val.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2850, 1)\n",
            "(951, 1)\n",
            "(951, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SaCvJWcA_3S"
      },
      "source": [
        "#Converting numpy arrays into tensorflow datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2eXaIW1ywpB"
      },
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test))\n",
        "val_ds = tf.data.Dataset.from_tensor_slices((X_val, y_val))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quVwuNVsy3Ed",
        "outputId": "0ba10572-fe14-4f78-d8a9-ab5479ed6889"
      },
      "source": [
        "print(train_ds)\n",
        "print(train_ds)\n",
        "print(val_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<TensorSliceDataset shapes: ((300, 300, 3), (1,)), types: (tf.uint8, tf.float64)>\n",
            "<TensorSliceDataset shapes: ((300, 300, 3), (1,)), types: (tf.uint8, tf.float64)>\n",
            "<TensorSliceDataset shapes: ((300, 300, 3), (1,)), types: (tf.uint8, tf.float64)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hz2GEdMLy7wC",
        "outputId": "3d903a7c-55f9-4d74-c4ab-93d20d54b1e6"
      },
      "source": [
        "train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()\n",
        "test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()\n",
        "val_ds_size = tf.data.experimental.cardinality(val_ds).numpy()\n",
        "\n",
        "print('Train size:', train_ds_size)\n",
        "print('Test size:', test_ds_size)\n",
        "print('Val size:', val_ds_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train size: 2850\n",
            "Test size: 951\n",
            "Val size: 951\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqWhVmYeBOGy"
      },
      "source": [
        "#Building AlexNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bEQ0c4XbzAT7"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten,\\\n",
        " Conv2D, MaxPooling2D,BatchNormalization"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcUa7n-uzEM2"
      },
      "source": [
        "model = keras.models.Sequential([\n",
        "    keras.layers.Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), activation='relu', input_shape=(227,227,3)),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\n",
        "    keras.layers.Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), activation='relu', padding=\"same\"),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\n",
        "    keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\n",
        "    keras.layers.Flatten(),\n",
        "    keras.layers.Dense(4096, activation='relu'),\n",
        "    keras.layers.Dropout(0.5),\n",
        "    keras.layers.Dense(4096, activation='relu'),\n",
        "    keras.layers.Dropout(0.5),\n",
        "    keras.layers.Dense(11, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZUn1YI6zIcR",
        "outputId": "2851f133-02a6-4899-a4d9-5d6d08626950"
      },
      "source": [
        "model.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer=tf.optimizers.SGD(lr=0.0001),\n",
        "    metrics=['accuracy']    \n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oaa4ctcgzNyV",
        "outputId": "154fa433-9572-448f-c7f6-0731e536a591"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 55, 55, 96)        34944     \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 55, 55, 96)        384       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 27, 27, 96)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 27, 27, 256)       614656    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 27, 27, 256)       1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 13, 13, 384)       885120    \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 13, 13, 384)       1536      \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 13, 13, 384)       1327488   \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 13, 13, 384)       1536      \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 256)       884992    \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 13, 13, 256)       1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 4096)              37752832  \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 11)                45067     \n",
            "=================================================================\n",
            "Total params: 58,331,915\n",
            "Trainable params: 58,329,163\n",
            "Non-trainable params: 2,752\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVGhMEis5yzV"
      },
      "source": [
        "def process_image(image,label):\n",
        "    image=tf.image.per_image_standardization(image)\n",
        "    image=tf.image.resize(image,(227,227))\n",
        "    \n",
        "    return image,label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJJLq2WUzSYV"
      },
      "source": [
        "train_ds = (train_ds\n",
        "          .map(process_image)\n",
        "          .shuffle(buffer_size=train_ds_size)\n",
        "          .batch(batch_size=30,drop_remainder=True)\n",
        "         )\n",
        "\n",
        "test_ds = (test_ds\n",
        "           .map(process_image)\n",
        "          .shuffle(buffer_size=test_ds_size)\n",
        "          .batch(batch_size=30,drop_remainder=True)\n",
        "         )\n",
        "\n",
        "val_ds = (val_ds\n",
        "          .map(process_image)\n",
        "          .shuffle(buffer_size=val_ds_size)\n",
        "          .batch(batch_size=30,drop_remainder=True)\n",
        "         )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdwUKKZIzcsT",
        "outputId": "1566c8a2-db13-4aa8-9e51-48be4cf8b925"
      },
      "source": [
        "print(train_ds)\n",
        "print(test_ds)\n",
        "print(val_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<BatchDataset shapes: ((30, 227, 227, 3), (30, 1)), types: (tf.float32, tf.float64)>\n",
            "<BatchDataset shapes: ((30, 227, 227, 3), (30, 1)), types: (tf.float32, tf.float64)>\n",
            "<BatchDataset shapes: ((30, 227, 227, 3), (30, 1)), types: (tf.float32, tf.float64)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngrHUQW7BUpr"
      },
      "source": [
        "#Training AlexNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NzYMdlXzhH-",
        "outputId": "0a15e8dd-5a74-4574-ea88-b51712ae8abb"
      },
      "source": [
        "history = model.fit(train_ds,\n",
        "          epochs=50,\n",
        "          validation_data=val_ds,\n",
        "          validation_freq=1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "95/95 [==============================] - 10s 62ms/step - loss: 0.0224 - accuracy: 0.9947 - val_loss: 0.0599 - val_accuracy: 0.9860\n",
            "Epoch 2/50\n",
            "95/95 [==============================] - 9s 60ms/step - loss: 0.0213 - accuracy: 0.9947 - val_loss: 0.0388 - val_accuracy: 0.9903\n",
            "Epoch 3/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0121 - accuracy: 0.9965 - val_loss: 0.0479 - val_accuracy: 0.9892\n",
            "Epoch 4/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0095 - accuracy: 0.9979 - val_loss: 0.0445 - val_accuracy: 0.9914\n",
            "Epoch 5/50\n",
            "95/95 [==============================] - 11s 75ms/step - loss: 0.0068 - accuracy: 0.9975 - val_loss: 0.0436 - val_accuracy: 0.9914\n",
            "Epoch 6/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0086 - accuracy: 0.9986 - val_loss: 0.0401 - val_accuracy: 0.9925\n",
            "Epoch 7/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0089 - accuracy: 0.9979 - val_loss: 0.0406 - val_accuracy: 0.9914\n",
            "Epoch 8/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0116 - accuracy: 0.9968 - val_loss: 0.0398 - val_accuracy: 0.9914\n",
            "Epoch 9/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0122 - accuracy: 0.9965 - val_loss: 0.0379 - val_accuracy: 0.9925\n",
            "Epoch 10/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0082 - accuracy: 0.9989 - val_loss: 0.0276 - val_accuracy: 0.9925\n",
            "Epoch 11/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0075 - accuracy: 0.9982 - val_loss: 0.0376 - val_accuracy: 0.9925\n",
            "Epoch 12/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0121 - accuracy: 0.9961 - val_loss: 0.0372 - val_accuracy: 0.9903\n",
            "Epoch 13/50\n",
            "95/95 [==============================] - 10s 62ms/step - loss: 0.0135 - accuracy: 0.9972 - val_loss: 0.0354 - val_accuracy: 0.9925\n",
            "Epoch 14/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0053 - accuracy: 0.9993 - val_loss: 0.0361 - val_accuracy: 0.9914\n",
            "Epoch 15/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0100 - accuracy: 0.9961 - val_loss: 0.0345 - val_accuracy: 0.9935\n",
            "Epoch 16/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0049 - accuracy: 0.9996 - val_loss: 0.0339 - val_accuracy: 0.9935\n",
            "Epoch 17/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0071 - accuracy: 0.9982 - val_loss: 0.0332 - val_accuracy: 0.9935\n",
            "Epoch 18/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0058 - accuracy: 0.9986 - val_loss: 0.0350 - val_accuracy: 0.9925\n",
            "Epoch 19/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0079 - accuracy: 0.9958 - val_loss: 0.0342 - val_accuracy: 0.9925\n",
            "Epoch 20/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0058 - accuracy: 0.9989 - val_loss: 0.0344 - val_accuracy: 0.9925\n",
            "Epoch 21/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 0.0344 - val_accuracy: 0.9925\n",
            "Epoch 22/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0049 - accuracy: 0.9989 - val_loss: 0.0342 - val_accuracy: 0.9925\n",
            "Epoch 23/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0080 - accuracy: 0.9982 - val_loss: 0.0332 - val_accuracy: 0.9925\n",
            "Epoch 24/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0080 - accuracy: 0.9975 - val_loss: 0.0336 - val_accuracy: 0.9925\n",
            "Epoch 25/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0067 - accuracy: 0.9982 - val_loss: 0.0316 - val_accuracy: 0.9935\n",
            "Epoch 26/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0077 - accuracy: 0.9975 - val_loss: 0.0320 - val_accuracy: 0.9935\n",
            "Epoch 27/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0065 - accuracy: 0.9986 - val_loss: 0.0319 - val_accuracy: 0.9946\n",
            "Epoch 28/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0048 - accuracy: 0.9989 - val_loss: 0.0318 - val_accuracy: 0.9946\n",
            "Epoch 29/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0057 - accuracy: 0.9986 - val_loss: 0.0309 - val_accuracy: 0.9946\n",
            "Epoch 30/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0075 - accuracy: 0.9979 - val_loss: 0.0319 - val_accuracy: 0.9925\n",
            "Epoch 31/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0068 - accuracy: 0.9975 - val_loss: 0.0314 - val_accuracy: 0.9935\n",
            "Epoch 32/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0055 - accuracy: 0.9989 - val_loss: 0.0318 - val_accuracy: 0.9914\n",
            "Epoch 33/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0064 - accuracy: 0.9989 - val_loss: 0.0322 - val_accuracy: 0.9935\n",
            "Epoch 34/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0057 - accuracy: 0.9989 - val_loss: 0.0318 - val_accuracy: 0.9925\n",
            "Epoch 35/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0068 - accuracy: 0.9982 - val_loss: 0.0304 - val_accuracy: 0.9935\n",
            "Epoch 36/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0083 - accuracy: 0.9972 - val_loss: 0.0322 - val_accuracy: 0.9925\n",
            "Epoch 37/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.0316 - val_accuracy: 0.9925\n",
            "Epoch 38/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 0.0311 - val_accuracy: 0.9914\n",
            "Epoch 39/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0099 - accuracy: 0.9979 - val_loss: 0.0309 - val_accuracy: 0.9935\n",
            "Epoch 40/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0072 - accuracy: 0.9979 - val_loss: 0.0300 - val_accuracy: 0.9946\n",
            "Epoch 41/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0062 - accuracy: 0.9989 - val_loss: 0.0316 - val_accuracy: 0.9935\n",
            "Epoch 42/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0078 - accuracy: 0.9979 - val_loss: 0.0306 - val_accuracy: 0.9935\n",
            "Epoch 43/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0072 - accuracy: 0.9979 - val_loss: 0.0315 - val_accuracy: 0.9935\n",
            "Epoch 44/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0053 - accuracy: 0.9986 - val_loss: 0.0313 - val_accuracy: 0.9935\n",
            "Epoch 45/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0061 - accuracy: 0.9989 - val_loss: 0.0313 - val_accuracy: 0.9914\n",
            "Epoch 46/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0045 - accuracy: 0.9993 - val_loss: 0.0319 - val_accuracy: 0.9925\n",
            "Epoch 47/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0066 - accuracy: 0.9979 - val_loss: 0.0312 - val_accuracy: 0.9946\n",
            "Epoch 48/50\n",
            "95/95 [==============================] - 9s 60ms/step - loss: 0.0065 - accuracy: 0.9982 - val_loss: 0.0314 - val_accuracy: 0.9925\n",
            "Epoch 49/50\n",
            "95/95 [==============================] - 9s 62ms/step - loss: 0.0081 - accuracy: 0.9979 - val_loss: 0.0281 - val_accuracy: 0.9925\n",
            "Epoch 50/50\n",
            "95/95 [==============================] - 9s 61ms/step - loss: 0.0068 - accuracy: 0.9979 - val_loss: 0.0310 - val_accuracy: 0.9925\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQXCWgnYBZcy"
      },
      "source": [
        "#Model Evaluation with learning rate = 0.003"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ql7O1-ghzirh",
        "outputId": "e453438b-4b0f-4702-ae53-6e763255cbcb"
      },
      "source": [
        "plt.plot(history.history['accuracy'], label='Training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'Testing accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Hfuwr8uYzorK",
        "outputId": "081a62ae-a234-4691-c689-385325da4964"
      },
      "source": [
        "plt.plot(history.history['loss'], label = 'Training Loss')\n",
        "plt.plot(history.history['val_loss'], label = 'Testing Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujL1vKnzBo2v"
      },
      "source": [
        "#Model Evaluation with learning rate = 0.0001"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "llzD-3lk-LH3",
        "outputId": "4a1f9529-5856-4fe3-c993-abef10cedbae"
      },
      "source": [
        "plt.plot(history.history['accuracy'], label='Training accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'Testing accuracy')\n",
        "plt.title('Model Accuracy with learning rate = 0.0001')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "SRtWannU-b6J",
        "outputId": "36301186-9bce-4e03-ca29-d81ea04d307b"
      },
      "source": [
        "plt.plot(history.history['loss'], label = 'Training Loss')\n",
        "plt.plot(history.history['val_loss'], label = 'Testing Loss')\n",
        "plt.title('Model Loss with learning rate= 0.0001')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+n/2YztuklDpqaYCoLDT2wHxM7uxcaX8OuAq2/Q4bf2m8vCmlVCPSAFFZTfMxpa8A30A483GIag+z/6mlCKXUUUkDRGU1zceUvgJa9ISAUDjlXkhfDutmNl7+lFKqkWiAqKy6+ZiMsVVMrfvZ130vhuiuMPtx7faqlDrqaICorLr5mLK3Q+FeaN3Xvvb1gxH3QeZaWN004/qUUspbNEBUVt18TOmuBurWFdYxSjgPWvaBOU+Co9T7+VNKqUaiAaKy6uZjSl8B4gMtEg5s8/GBUx+APSmw/KPGy6dSSnmZBgh3qhost3MlxHSHgJCDt3cbBW0SYe4zUFbcOHlUSikv0wDhTlXzMaWvOND+UJEInPYg5KRC0rvez59SSjUCDRDuuJuPKS8DctMP9GCqrOMpED8MfnnE9moqyvF+PpVSyos0QLjjroqpvIG6lZsSBNhSxHmvQ9eRMO8ZeKEf/PYylBZ5N69KKeUlGiDccTcf084V9merPlW/L7INXPQ+XDcH4vrD/x6AlwbA0g/BUebNHCulVIPTAOGOu/mY0ldCs3gIjqr5/XHHweVfwRUzILwlzLgZXh9mx1AopdQRQgOEO+7mY0pfUXX1UlU6nQLX/AIXvgsZyfD7Kw2XR6WU8jINEO5Uno+pKBv2bq66gbo6ItD7fEg4F/54DQq8v7S2Uko1BA0Q7lSej2nnKvuzLgGi3PB7oSQPfnupfnlTSqlGogHCncrzMe2fYqMeAaJFT+h1Hvz5Rs1Lmiql1GFAA4Q7ledjSl8BYa0grEX9jnvKPVCSD7+9WL/jKKVUI9AA4Y5fIASEH7jTrzjFd3206AG9L4A/34S8ata9Vkqpw4AGiKqExtgqptJCyFzvfoqNujjlHigrhN9eaJjjKaWUl2iAqEr5fEy7ksE4at/FtSqx3aDPePjzLTt9h1JKHaY0QFQlJMZWMaUvt68booqp3Ml3g6MYFmopQil1+PJqgBCRUSKyXkQ2isi9bvYHisinrv2LRCS+0v72IpInInd5M59ulVcx7VwJQVEQ1b7hjh3TxS5XuvgtyN3pPk3uLp06XCnVpLwWIETEF3gFGA0kABNFJKFSssnAXmNMF+B54OlK+58DvvdWHqtVPh9T+RTfIg17/JP/YVegW/Af+7pwH6ydCd/93c7f9Gw3eL4X/PqkVkUppZqEnxePPQjYaIxJARCRT4BxQHKFNOOAKa7nnwMvi4gYY4yInAtsBvK9mMeqhcTY+ZjSV8IJNzb88aM7Q78JkPQOpCVB2hIwTvAPhfihcNzlsPU3mPsULHjOtluc8Ddo1bvh86KUUm54M0C0AbZXeJ0KDK4qjTGmTESygWgRKQLuAc4AGr96CQ6MpjaOg9egbkgn/wM2/myfD/s7dBoBbQeCX4DddtLtkLURFv3XLme6fBp0PBmG3w8dTvROnpRSysWbAaI+pgDPG2PypJqqHRG5DrgOoH37BmwjgAPzMUHDdXGtrHlHuOuv6tPEdIExz8KIB2DpB7DodfhgHFz+JcSf5J18KaUU3m2kTgPaVXjd1rXNbRoR8QMigd3YksYzIrIFuB24X0RurnwCY8wbxphEY0xibGxsw+a+fLoN/xCI7tKwx66LkOa2RHHjQjvt+McTbfuIUkp5iTcDxGKgq4h0FJEAYAIwo1KaGcAk1/MLgdnGGmaMiTfGxAP/AZ4wxrzsxbweqryKqWVv8PFt1FNXK6S5LT0ERsDUC2D3pqbOkVLqKOW1AGGMKQNuBn4E1gKfGWPWiMijInKOK9nb2DaHjcCdwCFdYZtM+XxM3qpeqo/ItnZBIqcDPjyv6q6ySilVD2IqLqt5BEtMTDRJSUkNe9DlH0H7E21bweEodQm8P9ZWOV31HQQ3a+ocKaWOMCKyxBiT6G6fjqSuTv9LDt/gANB2AEyYBll/wUcToKSgqXOklDqKaIA40nUeARe8CdsXwZfXNnVulFJHkcO1m6uqjV7n2R5NC56Hwr2eVTUZY9fIDgiBNonQIgF89c9BKXWAXhGOFu2HAM9DxjrPBtFlrof/PXDgtX8IxB1vq63aDoTOp0JAqNeyq5Q6/GmAOFq06Gl/ZiR7FiAy1tifEz6ybRdpSZC6GH5/1U4x0rIPXPmtNnwrdQzTAHG0iGxrV8HLXOdZ+l3JIL7Q5XS7gl7f8XZ7aRGsnwVfXQ9TL4QrvobAcO/lWyl12NJG6qOFiF3SNGOtZ+kzku0Icb/Ag7f7B0Hv8+HCd2HHMu0dpdQxTAPE0aRFT9i1xjZA12TXGmhZefb1CnqeDee9DlsXwmeXV782xc7VsOpzz86rlDpiaIA4mrRIgMI9dqGj6hTnwr6t0KJX9en6joexL9gZZz+/GhxlB/Y5nbD+BztQ77Wh8MVkmHmHHd2tlDoqaBvE0SS2h/2ZsRbCWlSdrrwaqroSRLkBk6C0AH64F76+Ac5+HlZ+Cn/8F3ZvhPA4OH0KFOyB3160wee818DXv76fRinVxDRAHE1auC74GWuh0ylVp9u15uD0NTnhRijJh9n/hOQZdj3tuOPggrchYdyBYBDcDH55xKYd/55tz1BKHbE0QBxNwlpAcHPbAF2djGS7cl1UB8+PffJddlbbnatg4LXQ/oRDl2Eddqft8TTrLvhoPEz4GALDav853Nm2CNKX2+lPtFeVUo1CA8TRRMSWCmrq6ror2VYv+dSyCeqkO2pOM+haewH/+m/w4blw6fT6j6XY9oedtba0AOY8CSfeDIOug6CI+h1XKVUtDRBHmxY9YOVntkeRu9X4jLGD5Hqec+i+htJvgh2F/fnV8O4YOP1hu5xq+VKqtZG+AqZdBOGtYfTT8Oebtqrrt5fgxJtsoAiOOpDeUQZ7t9gJDPdutut8+/i5Hr527IePn53OPbKtfQRFNthHV+poogHiaNOiJxTnQE6avfhVlrvTztfUsoYeTPXVcyxc8il8cQ18dJEtRSSMg94XQochni3ClPmXLTkEhsMV30BUO+h6BqQthXn/gl8fh99etuM28jMhawPsSbEjwWsjINwVLNpAx1Ng8PWHjg9R6hjkUYAQkVCg0BjjFJFuQA/ge2NMLf8TlddVbKh2FyAyatlAXR+dT4U718Gm2bD6c1g5HZa8Z0sDvc6H/hOhVR/379271a69Lb4waYYNDuXaHA8TP7ali7nPwIqPIao9xHSDHmfZnzHdoHknW1pwltmShLPMPhwlkJ8F2an2kZNmf+7dAj89CEnvwJmPQ/ez3JfClDpGeFqCmAcME5FmwP+wy4leDFzqrYypOqrY1bXrGYfu3+VqwPZ2CaKcXwB0H2UfJfnw1w+w6gtY/Cb88Qq0GQADrrQBo7xBO3enDQ6lBXDldxDd2f2xW/ez62FUVZ1WneadoN2gQ7dvmg0/3AefXGKrxUY9ZavtKnOU2sb+ohyIP0kDiToqeRogxBhTICKTgVeNMc+IyHJvZkzVUUhzCGtV9ZQbGcl2f0jzxs0X2HaJ3hfYR8EeO55iyfsw4xZ7Ue5zod03627Iy7Alh1a9az5uQ16cO58KNyywpYhfH4f/DrEN78dPst9d2lJIW2JLL2WF9j0n3w2nPlD9cZU6AnkcIETkRGyJYbJrmweVyKpJtOhZdVfXmqbYaCwhze34isE3wPY/Yen7sOJTWwXlGwiXfQ5t3a6C6H2+/rYdoveFNkj8+QYses3u8wuC1v0h8Wpb1bXxF5j3jK0CO/6KpsmvUl7iaYC4HbgP+MoYs0ZEOgG/ei9bql5aJNg7YKfz4K6sjjK7DkTHk5sub5WJQPvB9jHqSVjzFUR3hfihTZ0zCI2Gs5+DgZNtyaF1X9fCShVGiSeMg7xd8O3tdlR519ObLr9KNTCPAoQxZi4wF0BEfIAsY8yt3syYqocWPWz1x74ttq693J4UOwq6sdofaiso0rZHHG5a9qr6O/P1h4veh3dHw/RJcNUs2zai1FHAo5FSIvKRiES4ejOtBpJF5B/ezZqqs4o9mSpqzB5Mx5LAcLhkOgRFwbTxsG9bU+eofhxldqr3Ra/DTw/B2m9tm1F9bFkI62bZ9UbUEcPTKqYEY0yOiFwKfA/cCywB/uW1nKm6i+1uf2YkQ48xB7bvSgbxObBfNZyI1rbd5O0zbZC4+ocDI8hLC23D9tbfYfsiW4oLirIlpqDICs8jIDDCBpzA8AOvg6I8Xy88dxf8+brt4tx9DIS3rPk9RdmwfTFs/8OOWk9bYnuQge1mbFwz9LbsbXtsxZ8EHYZ63tFhzdd20KRxQEAYdDvTVs11OcOuiX60yk6zVabGAf0vtYMzjzCeBgh/EfEHzgVeNsaUiohO/n+4Cgy34wIyKk25Ub5IkH9w0+TraNeiJ0yYCh+eDx9fYttVtv4OO5basRcAsT1tMMjaYC/MRdlQml/9cQMjYegtMPjGque2cjphyTvw86NQnG23zbzTzpnVcyz0OBuauebeKtgD2363d/VbF9j5tYzT3jy06gPHXQbtBttHWEub/y3zYcsC2+ts0Ws2cIy4H066s/opW5K/scGh7UA7V9e672DdTFj9hV0HvcvpNn+dhlc/A3F9bFlog3SX0zzv8Va5/c5TBXsg+WvblXvrQsB1mZz9OPQZbzs/tO5b++M2ETEeLPIiIrcC9wArgDFAe2CqMWaYd7PnucTERJOUlNTU2Th8TLvIDv76228Htr3Qz/bAuej9psvXsWDldPjyGjtIr3V/O3K8wxB7wXV3111WYgNFcY6dLn3/z1w7ziLlV7sMbGgsDLsLEq86eKR3+kq7Fkdaku2AMOZ5W0pZ+y2snQm7Vtl0rfraQLBrDWBsb7G2A22HgA5DoE1izZMrlpXYgLHodVjzJXQbbad3rzjdSbnkGfD5VXasy2VfHJhk0VEG236zwSN5BuRn2O0te9tA0Wm4zU9AaK2+dreWfmA7EBiHPe6ZT1TfBpex1g6+TP7Gnj80BkJi7M/y536BrilbfOzvWFwdOrfMt2unOMtsR4s+423XbUepLdWt+MSWzDoMtYGi+xgo2merJLO3w77t9mdxHoz8Z6N1RReRJcYYt10GPQoQVRzUzxhTVnPKxqEBopKfHobfX4EH0m1DanEePNkGRjwAp9zd1Lk7+u3ZbO+IG+IiB7YK6JdH7EUosj0Mv9eu+jf3Gbs2R3Aze/Hre9Ghd8l7UmygWP+9/VsoryJqM6DuU7IbY7v//ng/RLaDi6cePGZl7UzbaB93vA0OVU2s6HTYMSUpv0LKHFvF5SgBH3+bvxY97MU2pqst/UZ18Ky6zRiY92/49THofJodNDr3aRuIB1xp/w8qVvmUB4Y1X9nfWb+JdjqY/Cw7jUvBbvszP+tAlVtlEW3stC99xttgXPn3ULgXlk2139u+bQdX35ULCLMDSk+40fbq88TK6RDbrc6dI+odIEQkEngYKO8fORd41BiTXacceYEGiEpWfApfXQc3/WnbHFKT4K3T4OJp9sKijjzG2AvpL4/aRmQffzvv1PGT7KJNTTH4cdsfMP1KKNxnVx/sd7GtRvrsCrtmyGVf1m7W3ZIC2xay6VfbXpO1wa6SWM7H3waKfhdD4mT3x3Y64Pu7YfFb0HcCjHvZBsaCPTZI/PmmvRCfcrctcS14/kBgGHSdnS04NLrqPDqd9sLudLh+ltnnQVGeVUs5HTZYb18EEXE2wEa1sz+Dm8E3N8Oqz+CWpQdPMeNOdhq8nGgHeE6YVvO53WiIAPEFtvdSed3E5UA/Y8z5NbxvFPACdlDdW8aYpyrtDwQ+AAYAu4GLjTFbRGQQ8EZ5MmCKMear6s6lAaKS9BXw+sl24Z5e59m6429vhVuXHdz1VR15jLHVR+u/tyv+tT+hafOTu8u2M2xdYNsT1v9g72Yv/7JhZsot2GMDxe4N9mdqkj1XYCQMusa2zYTF2rSlRbZ6b+23MPQ2OP2RQ+/kM9fDjw/Axp/s64AwzwJDY9m3HV463pYGx71SfdrpV9nqx5sWQbP4Op2uugCBMabGB7Dck22V9vsCm4BOQAC2/SKhUpq/Aa+5nk8APnU9DwH8XM9bAxnlr6t6DBgwwKgKSgqMmRJlzOzH7etZdxvzWGtjHI6mzZc6OpWVGvPjA8Y8HGHMGyOMKdzn3fOlLjHmk8uMeTjSmH+2NOa7u3QQE1wAACAASURBVIxJX2XMO6NtHn57peZjbPjJmIUvGZOX5d281sWse+z/b8b6qtOkzLOftfx/vI6AJFPFddXTZvpCETmpQsQZChTW8J5BwEZjTIoxpgT4BBhXKc04DpRKPgdOExExxhSYA+0bQezvCqA85h9sSwrlU27sWmPrc+vSM0Opmvj6wcjH7DxWk771/hobbY6Hiz+0Vai9L7AzB7w21E7bcsHbcOLfaj5Gl9NhyGFSaqhs2N/BL9i2objjKIPv77HtUUNv91o2PO3megPwgastAmAvMKmG97QBtld4nQoMriqNMaZMRLKBaCBLRAYD7wAdgMuNmwZxEbkOuA6gffv2Hn6UY0iLnrarqzE2UHQ/q6lzpI52VU3f7i2x3eDcV2DEfZD0LnQeYRvhj3RhsXZBrHnP2PamuOMO3p/0th34etGHXh1L4tHtpDFmhTGmH9AX6GuMOQ441Wu5sudcZIzpBQwE7hORQ7pbGGPeMMYkGmMSY2NjvZmdI1NsT9izyfaYKNh9+E6xoVR9RbaF0x48OoJDuSE320brXx49eHt+lp1EstNw2+bjRbWqbzDG5Bhjclwv76wheRpQsQm+rWub2zQi4gdEYhurK55zLZAHeDDvszpIi56233vy167XOsWGUkeMoEg7EHHTbNg8/8D2Xx6xXWFHP+P1dUjqUyFdU84WA11FpKOIBGAboWdUSjODA1VVFwKzjTHG9R4/ABHpgF3Bbks98npsKg8Iq7+wP7UEodSRZdC1dpbgXx6xVcVpS2Dph3aa/EaYMqc+AaLahmNXm8HNwI/AWuAzY6cKf1REznElexuIFpGN2BLJva7tJwErXIsSfQX8zRiTVY+8HpuiO9t+4+krILTFETkXjFLHNP9gO14jdbHtzjrrH3YA5in3NMrpqx0HISK5uA8EAgQbYzxt5PY6HQdRhVdPtA3UnUbAFV83dW6UUrXlKIVXBkFeJpTkwrmv2fXcG0h14yCqLUEYY8KNMRFuHuGHU3BQ1WjR0/7U6iWljky+/nZqkJJcaDsI+l7caKfWi/zRrjxAaAO1UkeuXufbddq7j27UsUwaII52bQcCYic+U0odmXx8PBv818A0QBztOg2HO5PtpGBKKVULOu/CsUCDg1KqDjRAKKWUcksDhFJKKbc0QCillHJLA4RSSim3NEAopZRySwOEUkoptzRAKKWUcksDhFJKKbc0QCillHJLA4RSSim3NEAopZRySwOEUkoptzRAKKWUcksDhFJKKbc0QAC784qbOgtKKXXYOeYDxOq0bIY8NZtnflhHYYmjqbOjlFKHjWM+QMRFBTO2XxyvztnEyP/MZc76jKbOklJKHRaO+QDRPDSAf4/vx8fXnoC/rw9XvruYmz5aSkZOUVNnTSmlmtQxHyDKndg5mu9vG8adZ3Tjp+RdnPbsXD74fQsOp2nqrCmlVJPQAFFBoJ8vt57WlR9vP5m+7SJ56Js1vPfblqbOllJKNQkNEG50jAll6uTBdIwJZVHK7qbOjlJKNQmvBggRGSUi60Vko4jc62Z/oIh86tq/SETiXdvPEJElIrLK9fNUb+aziryTEBdBcnpOY59aKaUOC14LECLiC7wCjAYSgIkiklAp2WRgrzGmC/A88LRrexYw1hjTB5gEfOitfFanV1wEqXsLyS4obYrTK6VUk/JmCWIQsNEYk2KMKQE+AcZVSjMOeN/1/HPgNBERY8wyY8wO1/Y1QLCIBHoxr24ltI4A0FKEUuqY5M0A0QbYXuF1qmub2zTGmDIgG4iulOYCYKkx5pDhziJynYgkiUhSZmZmg2W8XK+4SEADhFLq2HRYN1KLSC9stdP17vYbY94wxiQaYxJjY2Mb/Pyx4YHEhgeyZkd2gx9bKaUOd94MEGlAuwqv27q2uU0jIn5AJLDb9bot8BVwhTFmkxfzWa1ecREk79AShFLq2OPNALEY6CoiHUUkAJgAzKiUZga2ERrgQmC2McaISBTwHXCvMWahF/NYo4TWEWzMyKO4TOdpUkodW7wWIFxtCjcDPwJrgc+MMWtE5FEROceV7G0gWkQ2AncC5V1hbwa6AA+JyHLXo4W38lqdhLgIypyGDbvymuL0SinVZPy8eXBjzCxgVqVtD1V4XgSMd/O+x4DHvJk3T5U3VK/ZkU3vNpFNnBullGo8h3Uj9eGgQ/MQQgN8tR1CKXXM0QBRAx8foWdrHVGtlDr2aIDwQIKrJ5NTZ3ZVSh1DNEB4oFdcBPklDrbtKWjqrCilVKPRAOGBhNblDdVazaSUOnZogPBA15Zh+PkIyek6olopdezQAOGBIH9furQI0xKEUuqYogHCQwmtdcoNpdSxRQOEhxLiIsjILSYz95BJZZVS6qikAcJDCXG6NoRS6tiiAcJDvVw9mbSaSSl1rNAA4aHIEH/aRAXr2hBKqWOGBoha6BWnU24opY4dGiBqISEugs1Z+eQXlzV1VpRSyus0QNRCQusIjIF1O3ObOitKKeV1GiBqoZdrPQitZlJKHQs0QNRCXGQQkcH+JB+jDdXrd+ayOu3Y/OxKHYs0QNSCiNiG6mOsq6sxhmmLtnL2S/O59oOkps6OUqqRaICopYTWEazbmUuZw9nUWWkURaUO7vliJQ98tZrI4ADSs4vYmV3U1NlSSjUCDRC11KtNBMVlTlKy8ps6K16Xtq+Qi17/nc+SUrn1tK68dtnxACzfvq+Jc6aUagwaIGrpwNoQR3dd/MKNWYx9aQGbM/N564pE7jyjG73bROLnIxoglDpGaICopc6xoQT4+Rw27RDGGFanZfPCzxt49n/rMab+y6K+/9sWLn97ETFhAcy45SROT2gJ2GnPe7aOYIUGCKWOCX5NnYEjjZ+vD73jIvhhzU7uOKMbIQGN/xUWlTpYuDGLX9ZlMHttBjtzDrQJDO0Swwmdout87D35JTz2XTInd4vllUuOJzTw4M/Xv10UXy5NxeE0+PpInc+jlDr8aYCog3+c2YOJb/7BU9+v49FxvRv13C/8vIH/zt1IUamT0ABfTu4Wy6k9WjCkSwxjX1rAW/M31ytAfLM8jVKH4Z5RPQ4JDmADxId/bGVTZh7dWobX56OoY0BpaSmpqakUFWnHhqYWFBRE27Zt8ff39/g9GiDq4MTO0Vw9tCPvLNzMyIRWnNQ1plHO+0fKbp7/+S9O79mSK07swOBOzQn0892//7ITOvDS7A2kZObRKTasTueYnpRKnzaR9Gwd4XZ/v3ZRACzftk8DhKpRamoq4eHhxMfHI6IlzqZijGH37t2kpqbSsWNHj9+nbRB1dPeo7nSKDeUfn68gu7DU6+crKnVw35eraNc8mBcn9ufkbrEHBQeAy0/ogL+vD+8s3Fync6xOyyY5PYfxiW2rTNMpJpTwID+WaTuE8kBRURHR0dEaHJqYiBAdHV3rkpwGiDoK8vfluYv6k5FbzCPfrvH6+V74ZQObs/J58ry+VbZ7xIYHcl7/Nny+JJW9+SW1PsfnS1IJ8PPhnH5xVabx8RH6tY3ShmrlMQ0Oh4e6/B68GiBEZJSIrBeRjSJyr5v9gSLyqWv/IhGJd22PFpFfRSRPRF72Zh7ro3+7KP42vDNfLk3jxzU7vXae1WnZvDEvhfED2tZYnXXNsI4UlTqZ+sfWWp2jqNTBV8vSGJnQkqiQgGrT9m8XxfpduRSWOGp1DqXUkcVrAUJEfIFXgNFAAjBRRBIqJZsM7DXGdAGeB552bS8CHgTu8lb+Gsotp3alV1wED3y1it15Db9edZnDyb1frqRZSAD/N6by13eori3DGd49lvd/30pxmecX8J/X7iK7sJSLEtvVmLZ/uygcTsPqo3wsiDqy7d69m/79+9O/f39atWpFmzZt9r8uKam+hJ2UlMStt95a4zmGDBnSIHmdM2cOZ599doMcqyF5swQxCNhojEkxxpQAnwDjKqUZB7zvev45cJqIiDEm3xizABsoDmsBfj48d1F/cgrLeOCr1Q0yDqGitxZsZnVaDo+O60VkiGe9D645qRNZecV8s3yHx+eZnpRKXGQQQ7vU3OBesaG6Oht25TL0qdls2KXTo6uG4XAatu8poKi05puf6Oholi9fzvLly7nhhhu444479r8OCAigrKzqdV0SExN58cUXazzHb7/9Vqv8H2m82YupDbC9wutUYHBVaYwxZSKSDUQDWZ6cQESuA64DaN++fX3zW2fdW4Vz58huPPX9Or5ensZ5x1XdyFsbm7Pyef6nvxiZ0JLRvVt5/L6hXaLp0Sqct+dvZvyAtjXWPaZnFzJvQyY3j+ji0diG2PBA2kQFszy1+gAxbdE20vYV8lnSdh7woPSjjm6PfLum3gNMy5yG4lIHPj5CsL8vCXERPDy2l8fvv/LKKwkKCmLZsmUMHTqUCRMmcNttt1FUVERwcDDvvvsu3bt3Z86cOfz73/9m5syZTJkyhW3btpGSksK2bdu4/fbb95cuwsLCyMvLY86cOUyZMoWYmBhWr17NgAEDmDp1KiLCrFmzuPPOOwkNDWXo0KGkpKQwc+ZMj/L78ccf88QTT2CMYcyYMTz99NM4HA4mT55MUlISIsLVV1/NHXfcwYsvvshrr72Gn58fCQkJfPLJJ3X6jis6oru5GmPeAN4ASExMbNhb91q6dlgnfkrexcPfrGFI5xhaRgTV63jGGO77ciUBfj7889zetWpgEhGuGdaJu6avYP6GLE7uFltt+i+XpmEMXDjA88DWv11UtSWIkjIn3yxPA+C7lencN7onPjqwTtWTw2n/zZ1OQ2kdJ8xMTU3lt99+w9fXl5ycHObPn4+fnx8///wz999/P1988cUh71m3bh2//vorubm5dO/enRtvvPGQ8QTLli1jzZo1xMXFMXToUBYuXEhiYiLXX3898+bNo2PHjkycONHjfO7YsYN77rmHJUuW0KxZM0aOHMnXX39Nu3btSEtLY/Xq1QDs22f/D5966ik2b95MYGDg/m315c0AkQZUrNBu69rmLk2qiPgBkcBuL+bJa3x9hH9d2JfRL8znga9W8+YVA+rce6PM4WTaom38kbKHJ8/vU6dgc06/OJ75YR1vzk+pNkAYY5ietJ3BHZvTITrU4+P3bxfFd6vSycwtJjY88JD9s9dlsLeglHH94/hm+Q6WbttLYnzzWn8OdfSozZ2+O8YYktNziAjyx+E05BWX0a1l7cf7jB8/Hl9f20U8OzubSZMmsWHDBkSE0lL3XdbHjBlDYGAggYGBtGjRgl27dtG27cE3VIMGDdq/rX///mzZsoWwsDA6deq0f+zBxIkTeeONNzzK5+LFixk+fDixsfb/99JLL2XevHk8+OCDpKSkcMsttzBmzBhGjhwJQN++fbn00ks599xzOffcc2v9vbjjzTaIxUBXEekoIgHABGBGpTQzgEmu5xcCs01DV+I3ok6xYfx9ZDd+XruLb1eme/SejRl5fL0sjWf/t56/TVvCyOfnkvDQjzw8Yw0ndGrOhIE1Nxq7E+Dnw6Qh8czfkMX6apZIXbxlL1t2F3jUOF1R//a2HaKq7q6fL0klNjyQR8f1JtDPh29XeN4eopQ7BSUOHE5DRJAfcVHBCJC6t7DW7X6hoQduhB588EFGjBjB6tWr+fbbb6scJxAYeOAmyNfX1237hSdpGkKzZs1YsWIFw4cP57XXXuOaa64B4LvvvuOmm25i6dKlDBw4sEHO77UAYYwpA24GfgTWAp8ZY9aIyKMico4r2dtAtIhsBO4E9neFFZEtwHPAlSKS6qYH1GFp8kmd6Ncuiikz1tTYq2naoq2c/txcbv90Oa/O2cTa9FzaNw/lqpPieebCvrx5RWK9+pBfOrg9wf6+vDU/pco0nyVtJyzQj9F9PG/jAOgdF4lvFTO7ZuUVM2d9Bucd14bIYH9O69mC71alHzNraKhDGWPq3YEjp6gUESEsyI8APx9aRQaRV1zG3oK6D1TNzs6mTZs2ALz33nv1yp873bt3JyUlhS1btgDw6aefevzeQYMGMXfuXLKysnA4HHz88ceccsopZGVl4XQ6ueCCC3jsscdYunQpTqeT7du3M2LECJ5++mmys7PJy8urd/692gZhjJkFzKq07aEKz4uA8VW8N96befOW8qqmMS/O5+EZa3j5kuPdpvtyaSr/9/VqRnSP5f6zetI+OuSQkdH1FRUSwIUD2vLp4u0kxEVw/vFtiQw+UG+aV1zGrFXpnNMvrtaTDgYH+NK9ZTgr3DRUf7N8B2VOwwXH2+L22L5xzFq1k0Wb93jUS+pIYC949rm2rVSvsNTB1qx8ggN8a1WNWVluYRmhAb74+tj72uahAewrLCU9u5DwID/8fWt/v3v33XczadIkHnvsMcaMGVPnvFUlODiYV199lVGjRhEaGsrAgQOrTPvLL78cVG01ffp0nnrqKUaMGIExhpGjRnP22LGsXrWKq666CqfT3nA9+eSTOBwOLrvsMrKzszHGcOuttxIVFVXv/MsRXKNzkMTERJOUdPgsh/nSLxt49qe/eO2yAYyq1APpu5Xp3PLxUk7oFM07Vw4kyL9hA0NFO7OLuHHaEpZt20ewvy/nHhfHpYM70LtNJJ8t3s7dX6zkixtPZECH2rcP3P/VKr5dsYMVD4086CJ51gvz8fURvr3lJMAOwhvwz584p38cT57ft8E+G8Cq1Gw+XryN+0b3IDzI80nIauOdBZt56od1OJz2LthZ4V8mLNCPR87pxQUeNvCXOZz4+sgxM7p42crVBES3w2kMBujeMpzAOvy9F5c5WL8zl7jIYGIqtHkVlzr4KyOPiCC/OgUfp9N4PcDn5eURFhaGMYabbrqJrl27cscdd3j8fmMMO3OKyMwtJsDPh/bNQ+o8i/TatWvp2bPnQdtEZIkxJtFd+iO6F9Ph7Ibhnfl+9U4e/GY1J3Rqvn908i9rd3HbJ8s4vn0z3rwi0avBAaBVZBBf/W0oq1KzmfrHVr5alsbHf26nf7socotK6RQbyvHtm9Xp2P3bRvHRom1s3p1PZ9fkgMk7ckhOz+GRcw40SAb5+3JGQku+X72TR87pTYBfw9RsZuQUMfn9xWTkFrMzu4g3r0hs8CnId+wr5Okf1tG3TSQndIpGxPYS8xEQhIUbs/j79BUs3baXh8YmVFkKNMbwxdI0npi1li4twnh2fD/aNQ9p0LyWy8gponloAH51uKNuSB/+sZXWzhLat/ShbbNgNmXmk5VfQpuo4FofK7fI1qeHBx98yQr096VlRCA7s4vILighsoZZAMo5nIZtewrIKyolNNCPyBB/IoP8vfKdvfnmm7z//vuUlJRw3HHHcf3113v8XqcxpO4tZF9BCVEhAeQXl7EpM5+4yCCahwZ4/UZD52LyEn9fH/41vi9780v458y1ACzYkMWN05aSEBfBO1cNdDudtrf0aRvJ0xf2ZdH9p/PQ2QnkFJWyKTOfCQPb1fmPrLyhumJ31y+WpuLvK4fM5zS2Xxz7CkpZuNGjIS41Ki5zcMPUJeQWlXHtsI7MXpfBMz+sa5BjV/SvH9djgP9M6M9dZ3bn7yO7c+cZ3bj99G7cdnpXPrp2MNef0olpi7Zx0et/kLav8JBjbMrMY+Kbf3DX9BW0iQomeUcOo1+Yz/Sk7Q0+sPK7lemc+NRsTnr6V178ZQMZOY0/1tThNDw2M5kHv15NkL8PnWPDCAnwIyrYn735JZQ5a98WlVNYSqCfr9sAHBsWSLC/L2n7ijxq5yp1OEnJzCOvqIxmIQGUOpyk7S1kbXoum7Py2ZNf0qDtZeUD9JKTk5k2bRohIZ7dGDicTrZk5bOvoIRWEUG0axZM1xZhhAX6kbavkO17Cvd3+/UWLUF4Ua+4SG4c3pmXZm+kffMQXpu7iU4xoXxw9SAivFQdUpPIYH+uPqkjVw2NZ/2uXLq2qPuU3Z1jwwgN8GX59n1cMKAtpQ4nXy9L47QeLWkWevCd3LCusUQE+fHtih2M6NGiymO+MW8Twf6+XHZCh2oD15QZa1i6bR+vXno8Z/VpTVGpk9fnpdCtZbjH1T01WbF9H18tS+NvwzvTtpn7f2o/Xx/uG92T49o1467pKzj7xfm8MOE4Tu4WS3GZg//O2cSrv24iyN+Hx8/rzcSB7UnbV8jfp6/gH5+v5KfkXTx5fh+iww7tKlxb369K59ZPltG3bSRhgX4899NfvPjLBkb1bsXlJ3RgUMfmXr/jLCgp47ZPlvNT8i6uHBJP81DZX6qLCQtgb0EJe/NL3XaNrorDacgvcRAT5r50ICK0bRbMxox8NmXm0zoqqMr/r6JSB1uy8ilzGjrEhBAR5I8xhqJSB/sKS8kuKCW1qBQfETpEh3it2rImpQ4nm7PyKS510rZZCM1d/09+vkJ8dAiZucXsyimisNRB++gQgr1UE6EBwstuPrULP6zeyfM//0Wn2FA+nDy4xsnwGoOI0KOV+zUfPOXrI/RtG7W/oXru+kx255e4vUAH+PkwundrvluVTlGpw23V2qeLt/HELFsKmPtXFs+O7+d2epFpi7by8Z/buWlEZ87q0xqAh8YmsDEjj/u+XEV8TCgDOtSt2qycMYbHvksmJiyAG4d3rjH9qN6t6NYyjBunLmXSu39y5ZB45q7PJCUrn3P6xfF/Z/ekRbgdz9KueQifXHsCby/YzL9+XM+Z/5nHU+f33b+0a138sHont3y8jP7tonj/6kGEBfqxOSufaX9s5bOk7cxcmU73luHcfnpXRru+s4a2Oi2buz9fybqdOTw8NoGrhnZk7dq1+/cHB/gRGujH7rxiYsI8rx7JKy7FGFPtxTo4wI/4mBB27CtiS1Y+4UH+tI4MOujvLL+4jK278wGhU2zo/np8ESE4wI/gAD9aRQRRWOogdW8hW3cXEB8TSlgjlvTh4CAWH3NokBIRWkQEERLox7Y9BWzKyCMuKnh/EGlIWsXkZYF+vvxnQn/O7tuaj645oVZ3TkeC/u2jWJueQ1Gpg8+XpBIdGsDw7u4H5o3tF0decRlz1mcesm/59n08+PUahnWN4cGzE5j7VwZjXpp/yDiLxVv2MGXGGoZ3j+XOM7rv3+7v68Orlx5P66ggrv9wCTvcVPXUxg+rd7J4y17+PrK7x3eRnWLD+OqmIZzTL453F26hzGn44OpBvDjxuP3BoZyPj3DtyZ2YcctQYsODuOaDJB7+ZnWdqgz+t2YnN3+0lL5tI3nvqoH7L2gdY0L5v7MTWHT/6TxzQV9E4MZpS7n3i5UezcSbkVvEr+syakybW1TKI9+u4ZyXF7Arp4i3JiVy1VD3i9LEhAVS4nCSU4s1VHIKy/D1EUIDqr9LDg/yp2vLMFpHBlNQUsaGXXns2FdImet8m7Py8fEROrcIrbKRV0QICfCjY0wo/r4+bMnKp6Ck4cczZOQWsSotm9Vp2azZkU3yjhzWpeewfmcuGzPycBroFBta7d9eWKAfXVuEERLg69HcVHWhvZhUvfyweic3TF3CO1cmcv2HS7j8hHgeGut+yEqZw8ngJ37hhM7RvFKh+29WXjFjX1pgez7dfBLNQgNYvn0fN01bSkZuEQ+c1ZNJQ+LZmVPE2JcWEh7kx9c3DT2oy265DbtyOe/V3+gQHcL0G06sU2+P4jIHZzw3j5AAX767dVitG76NMSzdto9ecREedUIoLnPwzA/reXvBZs7tH8e/x/fzuLH05+Rd3DhtCb3iIvlgcvVVl6UOJ8//9Bf/nbuJzrFhvHzJcW5LkTlFpbw+dxPvLNhCYamD8EA/xvaP46LEdvRrG7n/zt8Yw8yV6fxzZjKZecVcMqg9d5/Z46BSX+VeM8YY1u/Kxd/Hh84tah4FbYxhbXouYYF+tI/2vFG/zOFkZ04Re/NL8PERnE5DUIAv8dGhHneHLS1zsikrD4fT0CkmjOAqApQxhoISBwF+Ph4de19BCdv2FBAe5E+Qvw/G2MZoY+yxbAkh0ONu78bVQ8zHgxKZ9mJSjeo4V0P1E7PWUeow1c7n5Ofrw1l9WjN9yXbyi8sIDfSj1OHkpmlL2ZNfwhc3DtnfdtG/XRTf3XoSd01fwZRvk/lzyx7S9hZSWFLGx9cOdhscwE53/tLE47j6/cX8Y/pKXr7kuFrXu3/w21a27Sngw8mD6tQrSkRqVcUV6OfLg2cn0Dw0gH/9uJ7iMicvTDiuxt5ev6y1wSGhdUSNwQFsKevuUT0Y0jmGOz5bzjkvL+TBMT33t/cUlTr44PctvDpnE/sKShnbL45z+sXx/ep0vlyaykeLttG9ZTjjE9uSGN+cZ/+3nvkbsujdJoI3rkikf7ua+92LCNGhgaRnF1JQUlZjAC8sdVDmdBIRXLtLlZ+vD8HOQs4acxplDieZmbsI8PPbP23Fn3/+SUBA9VUyCxfMQ3x8adG1H5uz8vnlq6lEhodxxRVXAPainl1YSmZuMUWlDvx9fegYE1rtTUFBSRmpewu59uKxvPSf5xhUzbgIT4kI3mpZ0gCh6qVlRBCtIoLYmJFHz9YRJMRV364xtl8cH/6xlZ/X7mJc/zY8OWsdizbv4fmL+9G7TeRBaaNCAnjzikTenJ/C0z+sx+E0vH75ALrWsBb2iB4tuG90D56YtQ75GJ65sOpV+Crbk1/Ci7M3MKJ7LMO6Vj/JYUO7aUQXgvx9+efMZIqnLuHVS493e7HZV1DCm/NTeHPeZnq0iuCDyYNr1enhpK4xfH/bMO6avoIHv1nDgo1ZnNKtBS/N3kB6dhEnd4vl7jO77/99nJHQkinn9GLminQ+TdrOY9/ZdoXwQD+mjE3g8hPjaxVIm4f6syuniN15JYQ0r/73klNYhkCd2gGio6NZuWI5AFOmTCEsLIy77vJ8iZk5c+YQFhbGLScOYVNmPqeffxmdYkNxOJ3syS8lK6+YUoeTID9fWkcGk5lXTEpmHvEx7quwSsucbN1dgJ+PEOTn69Edf1PTAKHqrX+7KH5Ys9Oj2WATOzSjVUQQ365Ix2kM7yzczFVD46ucoEc9gAAADJ9JREFUIl1EuO7kzgzuGM2unCJG9vJsSpBrh3XC4YRnflxHSmY+r18+wKNxBy/8/BcFJQ7uP6tnjWm9YfJJHQny9+GBr1ZzzftJvHHFgP0Xm+yCUt5akMK7C7eQX1LGmD6teezc3lWWpqoTExbIO5MG8s7CzTz9wzp+XLOLfu2iePaifgzpfOho94ggfy4Z3J5LBrdn/c5c/kjZzejerWhRm4kkv78Xdq7CF+ha5qDUaXAG+OJTzf1vRGkZkQh+Vd2Vt+oDo5/yOAtLlizhzjvvJC8vj5iYGN577z1at259yFTZTz31FK+99hq+vr5MnTqVfz//H76a+QMhoWFcef0tXHnhGI5PHEjS7wvIzcnm7bffZtCJQ0jelsl5F1zJ1o3r6dmjOzt27OCVV17h+OMHsGV3Pg6noUuLMNzFhj179nD11VeTkpJCSEgIb7zxBn379mXu3LncdtttgP1/mDdvHnl5eVx88cXk5ORQVlbGf//7X4YNG+bx9+ApDRCq3oZ0iWbehkzG9a96LetyPj7C2X1b8/7vW1iwMZPBHZt7dDHu50H1RUUiwo3DO9OjdTi3fryMca8s5JVLjufEztFVvmdjRh5TF23jkkHtayyleNOlgzsQ6OfL3Z+v4Mp3FvP8hP58ung77y7YTG6xDQy3ntaV7q3ql0cfHzst/NAuMezMLmJ491iPquO6twqv97n9fX0odTgocxgCfN2f02kMTif8f3v3HxxVdQVw/Ht2s8kGEn6YID/cyo8RBGIiiCi4YA0DKo0oQxmtxYri4CiKUPqLljKUTplpK9NSEWekSosObaC0ECmZKhiQUjOKIAoqUII6QlFCyg9DIGyW0z/eC6bwQJLsZs3u+cxk9r27meWe5JHz3n3vnpueFpszbVVl6tSplJSU0KlTJ5YvX86sWbNYsmTJeaWyO3TowCOPPPJ/Vx0bysqIANnBNDLT/WSl+9j61hZKS0uZO3cu69ev55WVL9KxQ0fmry/nxMF9DBsyGFXlkyM1nIxE6ZFz4SGoOXPmMHDgQFavXk1ZWRn3338/27dvZ/78+SxatIhwOEx1dTXBYJDFixdz2223MWvWLKLRKDU1NTH5GZ3LEoRptgk3dufOa7td8uO7Y67txnObPySnbQZPf/u6JtXQuVSFV19OyWNhHn5xK/c9/wazi5wb3vV/CGvromz58Aiv7TlE6Y5PaZPuZ/rI3nHrz6UaPyhEMOBjevF2wr8sA2D0NV2YNrJ3sx9PPle/ru3o1zW2n+mpwZm+Dzh8+AQ1p6P07ZLtWe7iSHUtB46epE/nbIjBc/61tbXs3LmTUaNGARCNRuna1Xnk91JKZQf8PjpmBbkypy0+EcaNGwfAoEGDzhbjK3/9Xzz++FQyAz7o0ou8a/KpOnGajJMRurYP0u4iV3ubN28+uxbFiBEjqKqq4vjx44TDYWbMmMGECRMYN24coVCIwYMHM2nSJCKRCGPHjmXAgAHN/vl4sQRhms3vk0bN7SgIteenRf0Y3rtTizz226tTFqum3MR3l2/nZ2ve573/HCc/1J6Nuyspr6jiZCRKut/Hjb0uY/LwXjGZtBYLdxR0o216GqU7DvJguOeX3t9pbXKz0tl3+ARHT0Y8n+H//FQd6Wk+MmJUmkVVycvLo7y8/Lz31q5dy6ZNm1izZg3z5s1jx44dX/p59eW9zy3t7ff76JmbxcdVJzgdPcORE6fJa5NObhOPq5kzZ1JUVERpaSnhcJiXX36Zm2++mU2bNrF27VoeeOABZsyYcfbmeSxZgjAtrn7Fu5aUHQyw+DvXs2D9Hp4q28tftu6ne04b7r4+xNev7sSQXjlNLoAWT4V9L7/ozPPWrG1GGsGAn4PHTvL5KaeURjDgIxjwE/AL1bV1Ma03lJGRQWVlJeXl5QwdOpRIJMKePXvo16/f2VLZw4YNo7i4mOrqarKzszl+vHFLpIbDYVasWEFhYSE1n33M3l3v0zYjjSs6Zn5pHMOHD2fZsmXMnj2bjRs3kpubS7t27aioqCA/P5/8/Hy2bNnCrl27yMzMJBQKMXnyZGpra9m2bZslCGOaw+cTZtx6NUUF3UhPcx5JNIlTXyLDeUzUmcxWPytLAMUZ748Vn8/HypUreeKJJzh27Bh1dXVMnz6dPn36eJbKHjNmDOPHj6ekpISFCxde0r8xZcoUJk6cSP/+/enbty95eXn0697F84mloqKis8uWDh06lGeffZZJkyZRUFBAmzZtWLp0KQALFixgw4YN+Hw+8vLyGD16NMXFxTz55JMEAgGysrJ44YUXYvZzasgmyhlj4sZrYtaFnDmj1NZFOVV3hlORKKpONeLW8DhovWg0SiQSIRgMUlFRwciRI9m9e/eXzrloKTZRzhjTKvl89TWREt2TpqupqaGwsJBIxKkf9cwzz3xlkkNTWIIwxpgYyc7OJplGMqxYnzEmrpJlGLu1a8rvwRKEMSZugsEgVVVVliQSTFWpqqoiGGzEzHdsiMkYE0ehUIj9+/dTWXl+iXfTsoLBIKFQ4xbTsgRhjImbQCBAz57ea0OYrz4bYjLGGOPJEoQxxhhPliCMMcZ4SpqZ1CJSCXzcjI/IBQ7HqDuticWdWizu1HIpcXdXVc/VsZImQTSXiLx1oenmycziTi0Wd2ppbtw2xGSMMcaTJQhjjDGeLEF8YXGiO5AgFndqsbhTS7PitnsQxhhjPNkVhDHGGE+WIIwxxnhK+QQhIreLyG4R2SsiMxPdn3gRkSUickhEdjZou0xE1onIv93XjonsYzyIyNdEZIOIvC8i74nINLc9qWMXkaCIvCki77hxz3Xbe4rIG+7xvlxEWu9qNhchIn4ReVtE/u7up0rcH4nIDhHZLiJvuW1NPtZTOkGIiB9YBIwG+gP3ikj/xPYqbv4I3H5O20zgVVXtDbzq7iebOuB7qtofGAI85v6Okz32WmCEql4LDABuF5EhwK+A36rqVcAR4KEE9jGepgEfNNhPlbgBClV1QIP5D00+1lM6QQA3AHtVdZ+qngaKgbsS3Ke4UNVNwH/Pab4LWOpuLwXGtminWoCqHlTVbe725zh/NK4gyWNXR7W7G3C/FBgBrHTbky5uABEJAUXAc+6+kAJxX0STj/VUTxBXAJ802N/vtqWKzqp60N3+FOicyM7Em4j0AAYCb5ACsbvDLNuBQ8A6oAI4qqp17rck6/G+APghcMbdzyE14gbnJOAVEdkqIg+7bU0+1m09CAM4Z5wikrTPPItIFvBXYLqqHndOKh3JGruqRoEBItIBWAX0TXCX4k5E7gAOqepWEbkl0f1JgGGqekBELgfWiciuhm829lhP9SuIA8DXGuyH3LZU8ZmIdAVwXw8luD9xISIBnOSwTFX/5janROwAqnoU2AAMBTqISP2JYTIe72HgThH5CGfIeATwO5I/bgBU9YD7egjnpOAGmnGsp3qC2AL0dp9wSAe+BbyU4D61pJeAie72RKAkgX2JC3f8+XngA1X9TYO3kjp2EenkXjkgIpnAKJz7LxuA8e63JV3cqvpjVQ2pag+c/89lqjqBJI8bQETaikh2/TZwK7CTZhzrKT+TWkS+gTNm6QeWqOq8BHcpLkTkz8AtOOV/PwPmAKuBFcCVOKXS71bVc29kt2oiMgz4J7CDL8akf4JzHyJpYxeRApwbkn6cE8EVqvpzEemFc2Z9GfA2cJ+q1iaup/HjDjF9X1XvSIW43RhXubtpwJ9UdZ6I5NDEYz3lE4QxxhhvqT7EZIwx5gIsQRhjjPFkCcIYY4wnSxDGGGM8WYIwxhjjyRKEMY0gIlG3Umb9V8yK/IlIj4bVdo1JNCu1YUzjnFTVAYnuhDEtwa4gjIkBtw7/r91a/G+KyFVuew8RKRORd0XkVRG50m3vLCKr3PUa3hGRm9yP8ovI7901HF5xZ0EbkxCWIIxpnMxzhpjuafDeMVXNB57GmZ0PsBBYqqoFwDLgKbf9KeA1d72G64D33PbewCJVzQOOAt+MczzGXJDNpDamEUSkWlWzPNo/wlmgZ59bHPBTVc0RkcNAV1WNuO0HVTVXRCqBUMNyD2458nXuwi6IyI+AgKr+Iv6RGXM+u4IwJnb0AtuN0bA+UBS7T2gSyBKEMbFzT4PXcnf7dZyqogATcAoHgrP046NwdmGf9i3VSWMulZ2dGNM4me4qbfX+oar1j7p2FJF3ca4C7nXbpgJ/EJEfAJXAg277NGCxiDyEc6XwKHAQY75C7B6EMTHg3oO4XlUPJ7ovxsSKDTEZY4zxZFcQxhhjPNkVhDHGGE+WIIwxxniyBGGMMcaTJQhjjDGeLEEYY4zx9D/f62oYafkpdwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRHgEWxb0VQ9",
        "outputId": "797aba97-3262-4663-b088-84d5ebc6006b"
      },
      "source": [
        "model.evaluate(test_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "31/31 [==============================] - 2s 22ms/step - loss: 0.0319 - accuracy: 0.9903\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.03186298906803131, 0.9903225898742676]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5tN0aA-8ERg"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}