vit/vision_transformer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "vit.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Tensorflow implementation\n",
        "\n",
        "[Paper](https://arxiv.org/pdf/2010.11929v2.pdf) : An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale"
      ],
      "metadata": {
        "id": "LWZt1R9W3PWD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import dependencies**"
      ],
      "metadata": {
        "id": "lhIXXxsQ2Yxx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_addons"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yk3wn4Vt4RpL",
        "outputId": "a1f1cd0b-ffac-46e7-a716-8132b4677579"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.15.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
            "\u001b[?25l\r\u001b[K     |▎                               | 10 kB 10.9 MB/s eta 0:00:01\r\u001b[K     |▋                               | 20 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |▉                               | 30 kB 17.8 MB/s eta 0:00:01\r\u001b[K     |█▏                              | 40 kB 19.7 MB/s eta 0:00:01\r\u001b[K     |█▌                              | 51 kB 20.7 MB/s eta 0:00:01\r\u001b[K     |█▊                              | 61 kB 21.8 MB/s eta 0:00:01\r\u001b[K     |██                              | 71 kB 15.2 MB/s eta 0:00:01\r\u001b[K     |██▍                             | 81 kB 16.2 MB/s eta 0:00:01\r\u001b[K     |██▋                             | 92 kB 12.4 MB/s eta 0:00:01\r\u001b[K     |███                             | 102 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███▎                            | 112 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 122 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███▉                            | 133 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████▏                           | 143 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████▍                           | 153 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████▊                           | 163 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████                           | 174 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████▎                          | 184 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████▋                          | 194 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████▉                          | 204 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████▏                         | 215 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████▌                         | 225 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████▊                         | 235 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████                         | 245 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████▍                        | 256 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 266 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████                        | 276 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 286 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████▌                       | 296 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████▉                       | 307 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 317 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████▍                      | 327 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████▊                      | 337 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████                      | 348 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████▎                     | 358 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 368 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████▉                     | 378 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████▏                    | 389 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 399 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 409 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 419 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 430 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████▋                   | 440 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 450 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▎                  | 460 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▌                  | 471 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▉                  | 481 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 491 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 501 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████▊                 | 512 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 522 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 532 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 542 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████▉                | 552 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 563 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 573 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 583 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 593 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 604 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 614 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 624 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▏             | 634 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 645 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████▉             | 655 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 665 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 675 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 686 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 696 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 706 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▋           | 716 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 727 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 737 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▌          | 747 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 757 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 768 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▎         | 778 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 788 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 798 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 808 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 819 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 829 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 839 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▍       | 849 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 860 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 870 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▎      | 880 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▋      | 890 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▉      | 901 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 911 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▌     | 921 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 931 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 942 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▎    | 952 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▋    | 962 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 972 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▏   | 983 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 993 kB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 1.0 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 1.0 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▍  | 1.0 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▊  | 1.0 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████  | 1.0 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▎ | 1.1 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▋ | 1.1 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▉ | 1.1 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 1.1 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▍| 1.1 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 1.1 MB 13.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 1.1 MB 13.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CYCRcFks22K5"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras import layers\n",
        "from tensorflow import keras\n",
        "import tensorflow_addons as tfa\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load CIFAR10 dataset**"
      ],
      "metadata": {
        "id": "S3bfzo9G2eIn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 10\n",
        "input_shape = (32, 32, 3)\n",
        "drop_rate=0.2\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
        "\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
        "\n",
        "print(f\"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}\")\n",
        "print(f\"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7DMaN-K26YB",
        "outputId": "fde145d9-38a2-42c4-a70e-74bec8d18189"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (50000, 32, 32, 3) - y_train shape: (50000, 10)\n",
            "x_test shape: (10000, 32, 32, 3) - y_test shape: (10000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Custom Layers"
      ],
      "metadata": {
        "id": "-DMR74HG2v7e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Layer to extract patches from input image**\n",
        "\n"
      ],
      "metadata": {
        "id": "va7zvYfn2zjz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Patches(layers.Layer):\n",
        "    def __init__(self, patch_size, num_patches):\n",
        "      super(Patches, self).__init__()\n",
        "      self.patch_size = patch_size\n",
        "      self.num_patches = num_patches\n",
        "\n",
        "    def call(self, images):\n",
        "      batch_size = tf.shape(images)[0]\n",
        "      patches = tf.image.extract_patches(\n",
        "          images=images,\n",
        "          sizes=[1, self.patch_size, self.patch_size, 1],\n",
        "          strides=[1, self.patch_size, self.patch_size, 1],\n",
        "          rates=[1, 1, 1, 1],\n",
        "          padding=\"VALID\",\n",
        "      )\n",
        "      patch_dims = patches.shape[-1]\n",
        "      patches = tf.reshape(patches, [batch_size, self.num_patches, patch_dims])\n",
        "      return patches\n"
      ],
      "metadata": {
        "id": "ux5zg7bo3J8g"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Positional Encoding for patches**"
      ],
      "metadata": {
        "id": "ptCgR4zh3GXI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class PositionalEncoding(layers.Layer):\n",
        "  def __init__(self, num_patches, embedding_dim):\n",
        "    super(PositionalEncoding, self).__init__()\n",
        "    self.num_patches = num_patches\n",
        "    self.embedding_dim = embedding_dim  # Number of hidden units.\n",
        "    self.positions = tf.range(start=0, limit=self.num_patches, delta=1)\n",
        "    self.emb = layers.Embedding(input_dim=self.num_patches, output_dim=self.embedding_dim)\n",
        "\n",
        "  def call(self, x):\n",
        "    position_embedding = self.emb(self.positions)\n",
        "    x = x + position_embedding\n",
        "    return x"
      ],
      "metadata": {
        "id": "Y-BJgJPIL6gb"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The transformer block**"
      ],
      "metadata": {
        "id": "t9xwBIBG3Kds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Transformer(layers.Layer):\n",
        "  def __init__(self, embedding_dim):\n",
        "    super(Transformer, self).__init__()\n",
        "    self.hidden_units=[embedding_dim, embedding_dim]\n",
        "    self.dropout_rate=drop_rate\n",
        "    self.norm1 = layers.LayerNormalization(epsilon=1e-5)\n",
        "    self.norm2 = layers.LayerNormalization(epsilon=1e-5)\n",
        "    self.attention = layers.MultiHeadAttention(num_heads=2, key_dim=128, dropout=self.dropout_rate)\n",
        "    l = []\n",
        "    for units in self.hidden_units:\n",
        "      l.append(layers.Dense(units, activation=tf.nn.gelu))\n",
        "      l.append(layers.Dropout(self.dropout_rate))\n",
        "    self.mlp = keras.models.Sequential(l)\n",
        "\n",
        "  def call(self, x):\n",
        "    x = self.norm1(x)\n",
        "    attention_output = self.attention(x, x)\n",
        "    x1 = x + attention_output\n",
        "\n",
        "    x = self.norm2(x1)\n",
        "    x = self.mlp(x)\n",
        "    return x + x1"
      ],
      "metadata": {
        "id": "uC31ooMLN_vG"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Vision Transformer Model\n",
        "![Screenshot from 2022-01-02 18-29-28.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "lm1rAPic3vfG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ViT(keras.models.Model):\n",
        "  def __init__(self, image_size=32, patch_size=8):\n",
        "    super(ViT, self).__init__()\n",
        "    self.num_patches = (image_size // patch_size) ** 2\n",
        "    self.embedding_dim = 256\n",
        "    self.blocks=2\n",
        "\n",
        "    self.data_augmentation = keras.Sequential([layers.Rescaling(scale=1.0 / 255),layers.RandomCrop(image_size, image_size),layers.RandomFlip(\"horizontal\"),],name=\"data_augmentation\",)\n",
        "    self.encoder = keras.models.Sequential([\n",
        "                                            Patches(patch_size, self.num_patches),\n",
        "                                            layers.Dense(self.embedding_dim),\n",
        "                                            PositionalEncoding(self.num_patches, self.embedding_dim)], name=\"encoder\")\n",
        "\n",
        "    self.transformers = keras.models.Sequential([Transformer(self.embedding_dim) for i in range(self.blocks)], name=\"transformers\")\n",
        "\n",
        "    self.head = keras.models.Sequential([layers.GlobalAveragePooling1D(),\n",
        "                                          layers.Dropout(rate=drop_rate),\n",
        "                                          layers.Dense(num_classes)], name=\"classifier\")\n",
        "    \n",
        "\n",
        "  def call(self, x):\n",
        "    x = self.data_augmentation(x)\n",
        "    encodings = self.encoder(x)\n",
        "    features = self.transformers(encodings)\n",
        "    logits = self.head(features)\n",
        "    return logits"
      ],
      "metadata": {
        "id": "154Il678S-DW"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Build Model and print summary**"
      ],
      "metadata": {
        "id": "M8ssHcC04I1E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = ViT(patch_size=8)\n",
        "model.build((1, 32, 32, 3))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsBjC08lXoXv",
        "outputId": "e3abb7a4-d2bb-456d-d7a8-39ce325d79e4"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vi_t_8\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " data_augmentation (Sequenti  (1, 32, 32, 3)           0         \n",
            " al)                                                             \n",
            "                                                                 \n",
            " encoder (Sequential)        (1, 16, 256)              53504     \n",
            "                                                                 \n",
            " transformers (Sequential)   (1, 16, 256)              791552    \n",
            "                                                                 \n",
            " classifier (Sequential)     (1, 10)                   2570      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 847,626\n",
            "Trainable params: 847,626\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model"
      ],
      "metadata": {
        "id": "kcmTld6_4chz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def run_experiment(model):\n",
        "    optimizer = tfa.optimizers.AdamW(learning_rate=0.001, weight_decay=0.0001)\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=optimizer,\n",
        "        loss=keras.losses.CategoricalCrossentropy(\n",
        "            from_logits=True, label_smoothing=0.1\n",
        "        ),\n",
        "        metrics=[\n",
        "            keras.metrics.CategoricalAccuracy(name=\"accuracy\"),\n",
        "            keras.metrics.TopKCategoricalAccuracy(5, name=\"top-5-accuracy\"),\n",
        "        ],\n",
        "    )\n",
        "\n",
        "    checkpoint_filepath = \"/tmp/checkpoint\"\n",
        "    checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
        "        checkpoint_filepath,\n",
        "        monitor=\"val_accuracy\",\n",
        "        save_best_only=True,\n",
        "        save_weights_only=True,\n",
        "    )\n",
        "\n",
        "    history = model.fit(\n",
        "        x=x_train,\n",
        "        y=y_train,\n",
        "        batch_size=128,\n",
        "        epochs=30,\n",
        "        validation_split=0.1,\n",
        "        callbacks=[checkpoint_callback],\n",
        "    )\n",
        "\n",
        "    model.load_weights(checkpoint_filepath)\n",
        "    _, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)\n",
        "    print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n",
        "    print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")\n",
        "\n",
        "    return history\n",
        "\n",
        "\n",
        "history = run_experiment(model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OAhhWB8fbUdm",
        "outputId": "8afb1375-8ddf-4a9d-d267-29955536b28b"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "352/352 [==============================] - 31s 51ms/step - loss: 2.0980 - accuracy: 0.2626 - top-5-accuracy: 0.7813 - val_loss: 1.8578 - val_accuracy: 0.3382 - val_top-5-accuracy: 0.8756\n",
            "Epoch 2/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.7961 - accuracy: 0.3937 - top-5-accuracy: 0.8830 - val_loss: 1.6875 - val_accuracy: 0.4460 - val_top-5-accuracy: 0.9094\n",
            "Epoch 3/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.6835 - accuracy: 0.4528 - top-5-accuracy: 0.9104 - val_loss: 1.6312 - val_accuracy: 0.4742 - val_top-5-accuracy: 0.9200\n",
            "Epoch 4/30\n",
            "352/352 [==============================] - 17s 49ms/step - loss: 1.6220 - accuracy: 0.4872 - top-5-accuracy: 0.9220 - val_loss: 1.5539 - val_accuracy: 0.5172 - val_top-5-accuracy: 0.9424\n",
            "Epoch 5/30\n",
            "352/352 [==============================] - 17s 49ms/step - loss: 1.5729 - accuracy: 0.5078 - top-5-accuracy: 0.9318 - val_loss: 1.5184 - val_accuracy: 0.5272 - val_top-5-accuracy: 0.9398\n",
            "Epoch 6/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.5375 - accuracy: 0.5259 - top-5-accuracy: 0.9376 - val_loss: 1.4948 - val_accuracy: 0.5460 - val_top-5-accuracy: 0.9494\n",
            "Epoch 7/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.5041 - accuracy: 0.5423 - top-5-accuracy: 0.9440 - val_loss: 1.4844 - val_accuracy: 0.5432 - val_top-5-accuracy: 0.9472\n",
            "Epoch 8/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.4712 - accuracy: 0.5597 - top-5-accuracy: 0.9481 - val_loss: 1.4326 - val_accuracy: 0.5808 - val_top-5-accuracy: 0.9480\n",
            "Epoch 9/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.4495 - accuracy: 0.5714 - top-5-accuracy: 0.9516 - val_loss: 1.4478 - val_accuracy: 0.5728 - val_top-5-accuracy: 0.9516\n",
            "Epoch 10/30\n",
            "352/352 [==============================] - 17s 49ms/step - loss: 1.4241 - accuracy: 0.5829 - top-5-accuracy: 0.9541 - val_loss: 1.3972 - val_accuracy: 0.6030 - val_top-5-accuracy: 0.9604\n",
            "Epoch 11/30\n",
            "352/352 [==============================] - 17s 49ms/step - loss: 1.4021 - accuracy: 0.5925 - top-5-accuracy: 0.9554 - val_loss: 1.3550 - val_accuracy: 0.6172 - val_top-5-accuracy: 0.9652\n",
            "Epoch 12/30\n",
            "352/352 [==============================] - 17s 49ms/step - loss: 1.3764 - accuracy: 0.6045 - top-5-accuracy: 0.9596 - val_loss: 1.3675 - val_accuracy: 0.6112 - val_top-5-accuracy: 0.9628\n",
            "Epoch 13/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.3576 - accuracy: 0.6179 - top-5-accuracy: 0.9612 - val_loss: 1.3618 - val_accuracy: 0.6100 - val_top-5-accuracy: 0.9596\n",
            "Epoch 14/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.3400 - accuracy: 0.6250 - top-5-accuracy: 0.9635 - val_loss: 1.3270 - val_accuracy: 0.6358 - val_top-5-accuracy: 0.9680\n",
            "Epoch 15/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.3240 - accuracy: 0.6338 - top-5-accuracy: 0.9645 - val_loss: 1.3031 - val_accuracy: 0.6384 - val_top-5-accuracy: 0.9682\n",
            "Epoch 16/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.3078 - accuracy: 0.6404 - top-5-accuracy: 0.9664 - val_loss: 1.3159 - val_accuracy: 0.6372 - val_top-5-accuracy: 0.9648\n",
            "Epoch 17/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2980 - accuracy: 0.6466 - top-5-accuracy: 0.9678 - val_loss: 1.3099 - val_accuracy: 0.6396 - val_top-5-accuracy: 0.9684\n",
            "Epoch 18/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2856 - accuracy: 0.6526 - top-5-accuracy: 0.9686 - val_loss: 1.2926 - val_accuracy: 0.6428 - val_top-5-accuracy: 0.9666\n",
            "Epoch 19/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2706 - accuracy: 0.6592 - top-5-accuracy: 0.9694 - val_loss: 1.3158 - val_accuracy: 0.6490 - val_top-5-accuracy: 0.9670\n",
            "Epoch 20/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2592 - accuracy: 0.6652 - top-5-accuracy: 0.9714 - val_loss: 1.2943 - val_accuracy: 0.6472 - val_top-5-accuracy: 0.9690\n",
            "Epoch 21/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2492 - accuracy: 0.6678 - top-5-accuracy: 0.9721 - val_loss: 1.2698 - val_accuracy: 0.6610 - val_top-5-accuracy: 0.9690\n",
            "Epoch 22/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2464 - accuracy: 0.6674 - top-5-accuracy: 0.9735 - val_loss: 1.2582 - val_accuracy: 0.6624 - val_top-5-accuracy: 0.9676\n",
            "Epoch 23/30\n",
            "352/352 [==============================] - 17s 47ms/step - loss: 1.2345 - accuracy: 0.6727 - top-5-accuracy: 0.9733 - val_loss: 1.2586 - val_accuracy: 0.6620 - val_top-5-accuracy: 0.9726\n",
            "Epoch 24/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2277 - accuracy: 0.6773 - top-5-accuracy: 0.9755 - val_loss: 1.2385 - val_accuracy: 0.6798 - val_top-5-accuracy: 0.9728\n",
            "Epoch 25/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2190 - accuracy: 0.6823 - top-5-accuracy: 0.9745 - val_loss: 1.2518 - val_accuracy: 0.6640 - val_top-5-accuracy: 0.9724\n",
            "Epoch 26/30\n",
            "352/352 [==============================] - 17s 49ms/step - loss: 1.2080 - accuracy: 0.6882 - top-5-accuracy: 0.9766 - val_loss: 1.2647 - val_accuracy: 0.6708 - val_top-5-accuracy: 0.9678\n",
            "Epoch 27/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.2029 - accuracy: 0.6891 - top-5-accuracy: 0.9771 - val_loss: 1.2706 - val_accuracy: 0.6582 - val_top-5-accuracy: 0.9708\n",
            "Epoch 28/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.1956 - accuracy: 0.6929 - top-5-accuracy: 0.9766 - val_loss: 1.2573 - val_accuracy: 0.6652 - val_top-5-accuracy: 0.9714\n",
            "Epoch 29/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.1854 - accuracy: 0.6969 - top-5-accuracy: 0.9780 - val_loss: 1.2577 - val_accuracy: 0.6700 - val_top-5-accuracy: 0.9656\n",
            "Epoch 30/30\n",
            "352/352 [==============================] - 17s 48ms/step - loss: 1.1830 - accuracy: 0.6984 - top-5-accuracy: 0.9787 - val_loss: 1.2386 - val_accuracy: 0.6710 - val_top-5-accuracy: 0.9718\n",
            "313/313 [==============================] - 4s 13ms/step - loss: 1.2752 - accuracy: 0.6560 - top-5-accuracy: 0.9692\n",
            "Test accuracy: 65.6%\n",
            "Test top 5 accuracy: 96.92%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train and Val top-5 accuracy"
      ],
      "metadata": {
        "id": "suTR6f8Q4fZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"top-5-accuracy\"], label=\"top-5-accuracy\")\n",
        "plt.plot(history.history[\"val_top-5-accuracy\"], label=\"val_top-5-accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Train and Validation Accuracy Over Epochs\", fontsize=14)\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "8NWgyHcUck1Y",
        "outputId": "1bf176a3-9ed3-41fe-837b-5866aa359752"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEXCAYAAACDChKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd3hW5dnAf3f2DpBA2ARliUyJgBusULTWgZNaldZq+SxaW21LP/38rLWt9rO1to6K1jopVXFQBTdRUWQjsrdAGIEA2etN7u+P5wReXjLehLxJSO7fdZ3rnPPM+z7rPs8WVcUwDMMw6ktYcwtgGIZhnJiYATEMwzAahBkQwzAMo0GYATEMwzAahBkQwzAMo0GYATEMwzAahBmQZkZEnhORt5tbjpoQkVUicl+I8xgjIioiqdWd1xDnShE57j7oweRlGE3NifJcmgEJEu9m1rY918Ckfwp8vxFFbTJE5OcikicicdX4hYtIloj8vgFJfwF0AXKOW8ijZdomInc1RV61yBAlIvtEpEBEkpsiz5aMiHxbRD7ynqNiEflKRH4qIs32bfKek+re8QebS6aWihmQ4Onit91cjdtP/QOLSGQwiapqrqoeakQ5m5IXgWjgqmr8LsRdl3/UN1FVLVPVPdoEo1ybMi+Py4CtwALge02UZ42ISFQz5n0rMAdYCpwJDASeAH4DvNwE+df2jt7P0e93F+CBUMt0wqGqttVzA650l+7weTqgwCTgY6AYmAqkAP8Cdnpuq4EfBKT1HPC233km7iX6PbAfyAYeBsJqkSeYfOpMF+gEvOWl8Q3wQ2AVcF8teb8KfFKN+xvAx97xz4GVQCGQBTwDtPMLO8a7fqnVnXtuN3gyFQFvAz8JuAcne7Lv8fJZBlwcoL/6b7XkNRH4GigFdgB3A+Lnvw24B3gKyPOu+y+CfHbeA24DrgeWVOPfFffxzPF0XQGM9fO/CFjo3aMc4D9AjJ9cd1Vz3x8LkP0+4FngEPCq5/4gsN5Ldxvwx6p068obuBdYVY0unwN/reE6dPeu71+q8bvMuydXeedfAH8KCJPkyTHRO48CHvLuRRGwGPh2Nc/YRcAioMz/+QhI+5jrGOBfldbF3v0pwRnBEQHh6nqOonDv4zdemC3A7QF5fMu75kXAEuA0v/jJuJ+4bE+GLcAdjfWdC+p5bsrMWstGzQZkm+fX23tBugG/AIYBJwG3eA/ut/ziPsexBiQX9wfUD7ga8AGTapEnmHzqTBf3N7gaOAsY7sUpoHYDMgGoBPr4uaUB5cB13vkdwPnedToPZ0xe9Atf9bJUa0CAUV4ed3uy/xj3AfO/B0OBKcBgoI8XtgwY4Pl38F7i3wCdgc415DUCqPDC9QOu867BbX55bfPyn+rldZuXxhl1PDe9cB+KjkC8l+5QP/94YCPuw3sOzihOxDMg3rX24f6EBwJDgLuAOD+5gjEgecAvPdn7eu7/4933dNxHdjvw24D7XG3euGfdB4z0C9/fuyZDa7gWP/P8u9bgvwF4wzu+Fffj4f+z8wPgIBDtnb8MfAmci3sHpnr3f2jAff4aGO+F6VhD3sdcxwD/qrTWAd8GBuF+pHb73YtgnqOqn74rPHnGAjcE5LHIcx+A+/lYi2eEgL/hDNhI3LM1Bs/oNtm3sCkzay0bNRuQO4OIOxN4xu/8OY41IAsC4nzgHydIGQPzqTVd7yFX4Cw//17eS3BfLfmE4f6gfu/n9gvv5Y6pIc4E3Ic0zDuvellqMiAzgA8C0njG/x7UkM+XwD1+58d8GKrJ62W8kpNfmPuAnQHp/CsgzEb/vGqQ576Ae/0CR3/cbwby8SsNBcT/HJhZS/rV6ZfJsQbkP0E8P1OATfXI+23g737nD1FNCcvP/0kgtxb/t4A13nEKx/4QfQhM945Pxv1g9AxI403giYD7fEUQum/zns+CgO3igLSu84uTgCvR/SiY5wjo66UxoQYZqvLwL0Wd5bl1985nA8/WpU8oN2sDaVyW+J94Dcl3i8hKEckRkQLcH2XPOtJZGXC+C1e9VC31yKe2dE/BvYSLqjxV9RsvTI2oaiXwT+AGEQn3nH8IvKyqJZ5854vIByKyU0TygddxxffOtaXtxym4NgN/jjoXkXgR+aOIrBGRg941yKDua11dXp8HuM0HuolIkp9bfe9RGO6v+UU/5xeB60QkxjsfDqxU1f01JDMc+Kh28YNiSaCD16ttvojs8a7dIxx97erK+2ngWhGJ9Z6D66m7/UuDEVZVc4B3cX/xiEhX3F/5S16Q0wAB1nidEwo8Hb6DMy7+HKN7DfwZV6L33+YFhDn8DKpqAa50M9Bzqus5Go573wLTDMT/Oat6F6uesyeBa7yOBw+LyHl1KdXYmAFpXAoDzu8C7gT+D1eXOQz3V1RXw2V5wLlS+70KNp9g0g3qpQ7gn7hGxm+LyJm44vYzACLSC3gHV/S+Cle0/6EXrzEbcB/20v8fXDXZMJwxbMw8/K9Nfe/ReNwH+WUR8YmID5gLtMNVYTQGlbgPqT/VNRQf9ZyKyGhcifU94Lu4j9s9NcStiXdw9fRX4KrA2uFKjjWxAUgWkW41+A/0wlTxEnCFZ2yvxVVHfub5heGu/+kc/cE/hSPPWhWB72hN5KjqpoAt2Lh1UZ93zP85q4oXBqCqc3G1BA8DqcA7IvLPRpEwSMyAhJazcdUFL6rqCmAzrqqoJeazDvc8jKxyEJGeuEbdWvFKKh8CN3nbUk8OcKWAKOBnqrpAVTcEk2YAa4HRAW6B52cDL6jqLFVdiatbDvz7LAPCqZ21uKqCwLR3qmp+8CIfw024klfgX+3Tnh/AcmBILX3/l+N+EGpiH86QA+B9bAcEIdtZQJaq/lZVF6vqRtyHKei8VdWHq479obe9rqq5teT5Gu7j+ItADxG5HNc+498Ta7a3vxhXEpmhXj2OJ5vg2rUCP/pZtchwvBx+BkUkHtcWstZzqus5WoF738YejwCqut977yfjnqMbRST6eNKsDxFNlVEbZQOuiHk2rufTbbgG9uUtLR9VXS8i7wJPicgtuB4uf/b2wfAP3F9iGUd/FDbiXpQ7ROR13Et3R7ByefwV+EJEfo378IwBLg8IswG4XETewn2Y/hfXQ8ifbcA5IvISUFpDVdGfgMXe4MkZuL/aO4H/rqfMhxGRjsAluAbOVQF+/wAWiMjJXn7TgLdEZBqu4XgQkK+q84DfAf8RkU1eWMGVbJ5S1SJcD8AfishsnDG5m+De8Q24qpXrcNUy38b1KPSnrrzBlTp/hSsJja8tQ1XdISJ3Ao+KSBnwPK4EMw7XA+zfqvqqX/gSEZmFKxkNxVWRVfltEJGXgee8NJfhOk2MAbao6utBXINAEkUksIq1OMAo3iMi+3BVS/finv2qUletz5En8yvAMyLyU0/m7kC6qvpXc9aIiNzvxVuNu88TcfqW1lfZBtOcDTAn6kbNjegZAeHa4/4683Fd7f6I60qb6RfmOY5tRH8sIJ2jwlQjTzD51JkurvfUbJzR2AH8iDq68frFjcJ9tIqA5AC/23Efw2JcPfrV3vVK9/zHUHc33h/gegYV46p+pgbcg164UlAhrvRxF65h9zm/MKOBr3BdHrWWvKq6X5ZRczfeWhurA/x+juv5FF2Nn+DXCQH3Efk3rkG2CPcTMMYv/CW4LqOluJ+F2RzpxpuE69mT613vWwPlqk52z/0P3v0r8J6l//K/vnXl7RfmY1wJWKq7FtXkexGuHSDfuy8rcWOqjum2juvJp8CyavwicY3UW7z7tseTb0RN97kWmbYR0OXb214KSOsST95S3If89IB06nqOonHvapaXxmZgai3PZTp+3xkvvdXec3IA14vylPp8y453q+oOZhiGcdyIyBpcB4rfNbcsoUJExuCMXketucNDm8CqsAzDOG68arorcX/JTzWvNEZTYQbEMIzGIBtXrfXjtv5X3pawKizDMAyjQVg3XsMwDKNBtIkqrNTUVE1PT29Q3MLCQuLj4xtXoGamtelk+rR8WptOrU0fqF6npUuX7lfVjjXFaRMGJD09nSVLgp3B4GgyMzMZM2ZM4wrUzLQ2nUyflk9r06m16QPV6yQi39QWx6qwDMMwjAZhBsQwDMNoEGZADMMwjAZhBsQwDMNoEGZADMMwjAZhBsQwDMNoEGZADMMwjAbRJsaBGIZhtDZUleLyCvJLfOSXlJNX4iOvuNw7r3Ir5+ZzTqJdXGMuzHkEMyCGYRgtkIpKZdehYr7JKWJbTiHb9heyLaeIb3IK2V9QSn6JD19l7XMZhglcNqzbiWlARGQC8ChuGdFnVPXBAP9ewLNAR9yCKN9X1Z0iMhZ4xC/oAOBaVX1TRJ7DrXldtTLYZD2yfKphGEbIyCspZ82uPD7aXs6azE0UlVZQWOajuKyCwrIKikp9FJb5KCqroLDU7cNEiI8OJy4qgvjocOKjIoiPPvo4Liqc8DBh58Fiz1AUsuNAMWUVlYfzjo4IIz0lnt6p8Yw+KYXEmAgSYyJJjIkgKdbb+7klxkQSHxWOiITseoTMgIhIOPA4bonKnbjlHWer6hq/YA/j1rF+XkTOx62Kdr265TuHeel0ADYB7/vF+4WqvhYq2Q3DOPFRVXYeLGbL/kKSYyPpmBhNakIU0RHhQcU/VFTG6l15fJ2Vyypv25ZTdCTAmvWECcRHRRDnGYM4z1CkxEfRo0MccZHhVCoUeoalsNRHTkEZBZ5xKSj1UeY7YiRiIp2R6NspkQsGptE7JZ5eKfGkp8aRlhhDWFjojEFDCGUJZCSwSVW3AIjITOBSwN+ADMQt9wluha83q0nnSmCuHll32TAM4xj25ZeycuchvtqZy8qdh1i5M5cDhWXHhEuMifCMSTQdE5xRqTrPKSxzxmJXLjsOFB+O0719LIO6JnNVRg9O7ZrEwa2ruPBb5xEdEXbcf/jlFZUUlVZQXllJSnxUSEsMjU3I1gMRkSuBCar6I+/8emCUqk71CzMDWKiqj4rIRGAWbg3gHL8wHwN/VtW3vfPngDNwawh/BEzTahaRF5FbgFsA0tLSRsycObNBehQUFJCQkNCguC2V1qaT6dPyqUunonJlX3Elh0oVAcJFCBOICHP1+OEC4WFy+DhMYH+xsjW3gq25lWzNrSSnxH3LBOiWIPRODqd3chhdE8Io9il5pUpumZJbquSVeeeeW7HviCyd4oT0pDB6JYWRnhROr6QwEqKO/qi3lXs0duzYpaqaUVOc5m5Evwt4TEQmA5/iFpevqPIUkS7AYOA9vzi/BvYAUcB04FfA/YEJq+p0z5+MjAxt6MyZbWXWzRMZ06fl8/5H8+hxagY7DhSx42AxOw8UseNgEdsPFLHjQDG5xeVBpyVUcl7YSk6WLFKooF9cON06RNAlMYLOCeGkxoURSQVUlkOlDyorIS4GkuMgIgYiYyEyzm8fQ5lEk1sRSXSXU0lK6VynDK3xHjVEp1AakCygh995d8/tMKq6C5gIICIJwBWqesgvyNXAG6pa7hdnt3dYKiL/xBkhwzCakMJSH5v3FXCgsIzc4nIOFXlbcRm5ReUcKi7nUFEZh4rLyS0qJ6ewDD745HD8qPAwurePpXuHOIZ2b0ePDnH0aB9H5+QYRFwPpPKKSioqFV+l4qtQKn1ldNzxLievn05y3oYjwpTjuuAcDIfwSAiLhPAIbx8JEga+EigvhvIi0Mpj9InC9eQhPBqGXA2j/wvSTg3tRaysgNI8KM2HkjwnZ6dToLGqsHYsgs8fhYlPQ1Rc46QZQCgNyGKgr4j0xhmOa4Hv+QcQkVTggKpW4koWzwakMclz94/TRVV3i6sovAxYFSL5DcMA9heUsnpXHqt35bJ6Vx5rd+WxNaeQ6mq/E6MjSI6LpF1cJMmxkXRJjiU5LpKinN2cM3ygMxQdYuvXIOwrha/+BfP/Age3QscBcPl06PdtCI/yjEZEcB9eVagod4akyqCUFzsDU5oHa/8DK/4Fy1+Ek8bA6FuhzzgIq+eYa18Z7PgSNn0EB7cdbSiqjssKjo3X+zwY/1voMrR++fmTvxc+vA++mgGJXeDAFug8qOHp1ULIDIiq+kRkKq76KRx4VlVXi8j9wBJVnQ2MAf4gIoqrwvpJVXwRSceVYD4JSPplEemIq+pcAUwJlQ6G0VYo81VyqKiMg0XlbN1f4BkMZzT25h1pYuzePpZTuyZx2fBu9O+cSGpCNO3iImkXG0lSbCSR4dV/aDMzcxgzons9hSqEpc/BF3+D/N3QdTiMfxn6X1T/D3oVIhAR5bbYdsf6n3w+nP8/Lt9FT8OMqyGlD4yaAkMnQXQt7R65WbDpA9j4AWz5BMryXSmofTrEJEF0EiR1hehEiE723BKde3Qi5O6Ez/4ET50HQ6+F8++B5Hpcs4pyWDQdMh90RvHsn8E5d9Uu83ES0jYQVZ0DzAlwu9fv+DWg2u64qroN6FaN+/mNK6VhhBhVVxcf7F9ysOTtdn/Q7dMh7EjX1OKyCg4UlXGwsIwDhWUcLPL2hc5AHCwqO1zddLDQVTUVllUclXR4mNCnYwJnnpzKqV2TGNg1iUFJZSTlroE9n8HulbBhI2gFwTCsFDg4zMnaPh069Hb7+I7HXpPig7DoGfjyCSg+AOnnwGVPwEljG/f61URcBzjn53DmbbDmLVjwOMy5Cz7+LZx2I4y8xYWrKIftXx4xGtleB9Ok7jD4Cug7Hnqf64xDsAz/Psz/M3z5d1j9hisBnf0zZ2xqY0smzP0V7FsHfS6ACQ9Bap8GqV8fmrsR3TBOXDbPcx+VkjzXYFvh8/Ze421F+ZGGXICkbnDKd+GUS6Dn6KM++sFQ5qtk967tlK18naTNs0k7uNy5SxTbw3qwUXvwta8ba3zdWF/Zg910wBXUHSKQHBtJ+7go2sVF0jEhmn6dEmkXF0X7uEjaxbt9j3axDIg9SPS+VbDnU9i+EhatdKWAKtr1hI6nuD/5ulCF3VvdRy5/19F+kXFHjEr7dBd2+Uvu773fBDj759BzVL2uU6MRHgmDr4RBV7j2hC+fgAWPwYLHGZbUH77Y4ZUyIqDnGTDufmc0Og5ouKGLbefSOf1H8NFvnTFZ9gKMmQYjJjuZ/Dm0A96/2xm6dr3g2n9B/wubxtBiBsQw6o+q+5B8cC+07+3qq6vq4cMijm3IrXLf/ZWrGln4d/fnPeBiGHiJ+8P2PgxFZT625FaQ99UudhwoYntOETn793Dy/kzOKfuEM2Q14aKsq+zBy3oNZbEdOSU8i5N1O2f6VnFhWKZrEQbKIxIo7dCfitQBRHbsS0yEEObz6v19JUfq/wuK4WDxkTaBg99AqTfRg4RDx/7uT7rzEOgyBDoPhtj29bpkK6p6+JSXwKHtrl3g4FZv721bMp0Mp17uSgCdBzfG3Tp+RJwR6znKyb7oaSK+mg2DJkLfca7doq4SQn1p1xOueNo15r//P64EtPApGPcbV4XnK3VVe5/9yYUfezeceTtExjSuHHVgBsQw6kNZEfzndvj6VVeSuOzJ+tUxlxbAxvfRNbPRlf8mbOk/KY5IZnnsaP5TlsHref0o1UjiF3zBuLClXBG9kNH6FZH4OBjXgw09bsF3yuV06jOcOxKij22ILjrgqjGy1xCZvZbI7LWw5R1Y49e50b8ra0SMX5fWWPcH3D3jiLHoNNC5NxaRMdCxn9sCUXWGrTHza2za9YTxv2VJ1Leaphtvt9Ng8tuw4V1nSGZ+D3qe6UpyB7fBwEth/ANOrmbADIhxYlJeDCtehi+f5My8bNh9NvQYCT1Gu8bWUPyJHdruXuA9q1xD6zl31lpVoKrkFpeTnV9Kdl4pW3MKWbs7j3W701i/51p8ZZdybthKLqxYxHjffM7kPX4TF8+e6N50L91IWEUpJHaHQf8Fg66gfZdhtK+raiKuA/Q6021HBIGSXFfKiYhteAN0qBFp2cajuRBx1VJ9LoBlz0PmQxCXAje85XqKNSNmQIwTi8IcWPy0621SlAPdRpAT2ZMu+zfCeq+/RlgkdB0GPUa5toYeoyCh0/Hlu+UTeHWy67v/vVeg33j2F5SyKiuXvXklZOeVOkORX3LYYOwrKD1qniNwbRADOidyVUYPBnRO5JQuY+mblkBcWCVs/ZSotW/RYf0nhI2Y7Oreu59+/B98kep7HBknFuGRrm3k9B81tySHMQNinBgc2Op6wyx/CXzFroH1zNuh15ms/+QTuowZ44zLjoWu//2ORa4b5oLHXPz2vZ0x6TvO/cnFJAeXryp8+ST6/j2UJp/EnFP/xCfLklj+5jy2Hzh6erZ2cZF0SoymU2IMo3rH0zHJzbXUKSmGTonR9OwQR5fkmJrnOup7AfS9gCWtcJSz0ToxA2K0bLKWwRd/db1MJByGXgNn3AadBhwbNj4FBlzkNnANjbu/ckZl+5ew4T03IC0sEtLPdo2R/SdUW3+cnVfCiq276frpNAblvMsHlafzsz1TKNxTQFpSOaf1bM/3R/dkaPd2dGsfS8fE6KBneTWM1oIZEKPloQqbPnTTMGz7zA26OvN2N5grqUvw6UREe+0iI12f/soK2LnYVXWtnwtzfwFzf0Fe8gA2tjuHLyJH8VlBN7bsLyKqIIunov7MQPmGlxOuZ8uAKTzUqwOn9WxfeynCMNoQZkCMlkVFObxzp2ssTOrmepicduNxd5Pcl1/Ke6v3sCk7ka37L2Zr0VgiyzYzVpYx7uBSMg49wwh5mu+FpbA5eTSD5Qui8FExcQbXnXJRIylnGK0LMyBGy6HoALxygyt1nP1zGPPr4Aaq1YCqsmBLDi8v3M57q/bgq1Tio8Lp3TGeoT3a0Xv4uZyUeiExqfEUxpeStP1jUtbPIWXTR9CuB1zzEuGpfRtRQcNoXZgBMVoG+ze5eYdyd8DlT7m5gBrIwcIyZi3byYyF29myv5B2cZFMPjOda0f24OSOCTVXP3WYBMMmuRHlYeFNNprXME5UzIAYzc+WTFfyCIuEG//jekvVE1Vl6TcHeXnhdt75ejdlvkoyerXntm/14cJBXYiJrEcDd7i9FoYRDPamGM3LkmfhnbsgtR9879/QvlfQUSsqlez8Ej78ppw//OUz1u/NJzE6gmtP78H3RvVkQOdGnl7CMIyjMANiNA8VPnj/Hlj4pJuA7op/HNNQnl9Szq5DJew6VMyu3GK3P1RC1iF3vCe3BF+lW5RiSPdYHrpiMN8d2pW4KHusDaMpsDfNaHpKcuG1m9w02KNvhfEPUFIBK7ceYPG2AyzaeoAVOw4ds8xpRJjQOTmGrsmxZPRqT9d2sXRtF4tv7yYmX3p2MyljGG0XMyBG03JwG8y4Bs3ZxMaRDzBbxrNo+iJW7Dx0eNqPfmkJXDS4M+kp8YeNRLd2brBeeDWr2GVmbm1iJQzDgBAbEBGZADyKW5HwGVV9MMC/F24Z2464VY2/r6o7Pb8K4Gsv6HZVvcRz7w3MBFKApcD1qloWSj2M46esrIx182dx8hfT0Ipyflz6Kz7/9CTCwzYzqFsyN57Ri9PTO3B6egfaxze8665hGE1HyAyIiIQDjwPjgJ3AYhGZrapr/II9DLygqs+LyPnAH4DrPb9iVR1WTdIPAY+o6kwR+TtwE/BkqPQwGo7P52P1wg8oXPoK/Q98xBBy2aqd+Wvaw4zoO5T/Su/A8J7tiI+2grBhnIiE8s0dCWxS1S0AIjITuBTwNyADgZ97x/OAN2tLUFwH/vOB73lOzwP3YQYkePJ2I5XldYdrIBUVlaxamkne4n/Td98HDCWHEo1kXdJZZA25gv7nXMEjMfEhy98wjKZDVDU0CYtcCUxQ1R9559cDo1R1ql+YGcBCVX1URCYCs4BUVc0RER+wAvABD6rqmyKSCnypqn28+D2Auao6qJr8bwFuAUhLSxsxc+bMBulRUFBAQkLoFqVvMlTpvfVFem2fRaWEU5BwEvmJfchL6kd+Yl+K4rqBNGza8EpVcnZvJXbnZwwp/JyespcyDWdl5DD2djqHmF6jCI+Oa2SFjtBq7pFHa9MHWp9OrU0fqF6nsWPHLlXVjJriNHfdwV3AYyIyGfgUyAIqPL9eqpolIicBH4vI10BusAmr6nRgOkBGRoY2dHrszNYwtXaFD96+A7bPgiHXsvNgGT3D95G06zO67ZrrwkQlujU0uo1wq6B1GwFxqVB8wE0xUpTjHedA0UEoyqHgUDbZe3YRnvcN5+suKlRYH3caX51yO/3Om0RGckqTqNcq7pEfrU0faH06tTZ9oGE6hdKAZAE9/M67e26HUdVdwEQAEUkArlDVQ55flrffIiKZwHBcCaWdiESoqq+6NI0Ayktg1k2w7m0495cw9r/Z8skn9Bwzxs1Ou38jZC2FXcvcfsHjEEQVV5HEcaAingISqYzvwaG+N9FnzHUM7FCP2XINwzihCaUBWQz09XpNZQHXcqTtAgCvSuqAqlYCv8b1yEJE2gNFqlrqhTkL+KOqqojMA67E9cS6EXgrhDqc2JTkwr++B9/MhwkPwegpR/uHhbt1NToNgOHXObfyEti7yq3DUZoLcSlobAc2FkQxZ3MZb64vIas0lu6pyVyV0Z0rTutOWlIIlo81DKPFEzIDoqo+EZkKvIfrxvusqq4WkfuBJao6GxgD/EFEFFeF9RMv+inAUyJSCYTh2kCqGt9/BcwUkQeA5cA/QqXDCU1BNrw0EbLXwsRnYMhVwcWLjIHuGdA9g/0FpbyxLItXPt3BxuwCYiNj+M6Q3vwxowenp7e3NTEMo40T0jYQVZ0DzAlwu9fv+DXgtWrifQEMriHNLbgeXkZNHNgKL14OBXth0r/dUqn1YF9+KQ/OXcdbK7LwVSqn9WzHgxMH850hXUiMiQyR0IZhnGg0dyO6URu5WZCQVr/ZYfesciUPXync8JZbjS9IKiuVfy3ezkNz11FcXsENZ6QzaWQP+qYlNkB4wzBaO2ZAWipbP4XnvwtRCW568/SzodfZrqdUeA2lgG++gBnXQlQ8/PBd6HRK0Nmt2ZXH3W9+zfLthxh9UgceuGwwfTq1rm6KhmE0LmZAWiqfPuxKHwMuhm3z4cP7nHtkPPQc5WdQhrtV+9bPhVcnQ3J3uP4NaNczqGwKS3385cMNPPv5NpJjI/nz1UO5fHg3a98wDNu9jR4AACAASURBVKNOzIC0RLKWwdZPYNxv4azbnVtBNnzzOWz73BmUj+537pFx0PU02L4AugyB616D+NSgsnl/9R7um72aXbklTBrZg19NGEC7OJuHyjCM4DAD0hL5/C8QkwwjJh9xS+gEp17uNoDC/UcMyjefwykXw6WPQ3Td7RU5xZXc/MISPlizl/5pibw2aTgZ6R1Co4thGK0WMyAtjf2bYM1sOOfOYxZYOor4VBh4qduCpMxXyfNfbOPh+cVIWCnTLhzATWf3JjK8YVOYGIbRtjED0tL44lGIiIZRU+oOGySqypyv9/DH99bxTU4RQzuG89gPzqVHh9DNT2UYRuvHDEhLIm83fDUTTrsBEjo2SpILt+Tw+7nr+GrHIfqlJfDs5Axk9xozHoZhHDdmQFoSXz7h5qc687bjTmrj3nweencdH67NpnNSDH+8cghXnNad8DAhc8/aRhDWMIy2jhmQlkLxQVjyrGskb5/e4GT25pXwyAcbeGXJDuKjIvjlhP784MzexEaFN56shmEYmAFpOSz+B5QVwNl3NCh6fkk5T32yhWfmb6GiUpl8Zm+mnt+HDrY8rGEYIcIMSEugvBi+fBL6jIPO1U4BVitvLN/Jb99ey4HCMi4Z2pW7xvenZ4q1cRiGEVrMgLQEVrwMRfsbVPqY/ulmfj9nHRm92vPcD05nSPd2IRDQMAzjWMyANDcVPvj8r9D9dOh1VtDRVJU/vb+Bx+Zt4uIhXfjz1cOIirDxHIZhNB1mQJqbNW/CoW9gwh8gyPmnKiuV3/xnNc8v+IZrT+/B7y4fTHiYzV1lGEbTYgakOVGF+X+B1P7Q78KgovgqKvnlrJW8viyLm8/pzX9fdIpNfGgYRrMQ0joPEZkgIutFZJOITKvGv5eIfCQiK0UkU0S6e+7DRGSBiKz2/K7xi/OciGwVkRXeNiyUOoSUTR/B3q/hrJ9CWN23otRXwa0vL+P1ZVncOa6fGQ/DMJqVkJVARCQceBwYB+wEFovIbL+laQEeBl5Q1edF5HzgD8D1QBFwg6puFJGuwFIReU9VD3nxfuGtZnhiM/8RSOoGg+tebraozMctLyxl/qb93PfdgUw+q3cTCGgYhlEzoSyBjAQ2qeoWVS0DZgKBM/8NBD72judV+avqBlXd6B3vArKBxpnbo6WwYxF8Mx/OmOrW86iF3KJyvv/MQr7YvJ+HrxpqxsMwjBaBqGpoEha5Epigqj/yzq8HRqnqVL8wM4CFqvqoiEwEZgGpqprjF2Yk8DxwqqpWishzwBlAKfARME1VS6vJ/xbgFoC0tLQRM2fObJAeBQUFJCQ0/sp8g77+Pcm5q/ly9DNURMTWGC63VHl4SQm7CyqZMjSajM7HX2gMlU7NhenT8mltOrU2faB6ncaOHbtUVTNqjKSqIdmAK4Fn/M6vBx4LCNMVeB1YDjyKq+pq5+ffBVgPjA5wEyAaZ1jurUuWESNGaEOZN29eg+PWSPY61f9NUv34d7UG23mwSMf83zwdcM9c/XRDdqNlHxKdmhHTp+XT2nRqbfqoVq8TsERr+baGshdWFtDD77y753YYddVTEwFEJAG4Qr12DhFJAt4B7lbVL/3i7PYOS0Xkn8BdIdMgVHz+KETEwsgf1xhkx4EirnlqAfmlPl760UhG9LIFnwzDaFmEsg1kMdBXRHqLSBRwLTDbP4CIpIpIlQy/Bp713KOAN3AN7K8FxOni7QW4DFgVQh0an9ydsPLfMOJGiE+pNkhOQSk3PLuIwrIKZt4y2oyHYRgtkpAZEFX1AVOB94C1wCuqulpE7heRS7xgY4D1IrIBSAN+57lfDZwLTK6mu+7LIvI18DWQCjwQKh0aHVX45CG3P+Mn1QYpKvPxw+eXsOtQMc9OzuDUrslNLKRhGEZwhHQgoarOAeYEuN3rd/wacEx3XFV9CXiphjTPb2Qxm455v4dlL7ieV+16HuNdXlHJrS8v4+udh3jq+gwreRiG0aKxkehNReaD8OkfYfj1MO63x3irKr+atZLM9ft4cOJgxg1MawYhDcMwgsdm32sKPvk/yPwDDLsOvvvXakedP/Tuel5flsXPx/Xj2pHHlk4MwzBaGmZAQs1nf4J5D8CQa+GSv1VrPJ6dv5W/f7KZ60f34rbz+zSDkIZhGPXHDEgomf8IfHQ/DL4aLnsCwo5dVnb2V7u4/+01XDioM/ddcqrNbWUYxgmDGZBQ8flf4cP7YNAVcNmT1RqP+Rv3c+crKxjZuwOPXDPMpmQ3DOOEwgxIKFjwOHzwP3Dq5XD5dAg/tq/CqqxcfvziEk7umMDTN2QQE3msgTEMw2jJmAFpbL58Et77bzjlEpj4dLXG45ucQib/cxHt4qJ4/ocjSY6NbAZBDcMwjg8zII3Jwunw7jQYcDFc+SyEH2sY9heUcuOzi6ioVJ7/4UjSkmKaQVDDMIzjx8aBNBaLn4G5v4D+34Er/1mt8QCYNmsle/JKmHHzaPp0al2zeRqG0bawEkhjsPsreOdOtyztVc/VuL7HrkPFfLQum5vPOYnTerZvWhkNwzAaGTMgjcHqN0HCXVfdWhaHen3ZTlThqhE9agxjGIZxomAGpDFYPwd6nQlxNc9dVVmpvLJkJ2eclELPlLgmFM4wDCM0mAE5XnI2w751MOA7tQZbtO0A2w8UcfXp3ZtIMMMwjNBiBuR4WfeO2/e/qNZgryzeQWJ0BBNO7dIEQhmGYYQeMyDHy/o5kDYY2veqMUheSTlzVu3mkmFdiY2yAYOGYbQOzIAcDwX7YMdCGFB76ePtr3ZTUl7J1RnWeG4YRushpAZERCaIyHoR2SQi06rx7yUiH4nIShHJFJHufn43ishGb7vRz32EiHztpflXac7ZBze8C1pZZ/vHK0t20D8tkSHdbXVBwzBaDyEzICISDjwOXAgMBCaJyMCAYA/j1j0fAtwP/MGL2wH4X2AUMBL4XxGpGjjxJHAz0NfbJoRKhzpZPweSe0DnITUG2bA3nxU7DnFVRnebadcwjFZFKEsgI4FNqrpFVcuAmcClAWEGAh97x/P8/L8NfKCqB1T1IPABMEFEugBJqvqlqirwAnBZCHWombIi2DwP+l8ItRiGV5fsICJMuHx4tyYUzjAMI/TUOZWJiHwXeEdVK+uZdjdgh9/5TlyJwp+vgInAo8DlQKKIpNQQt5u37azGvTq5bwFuAUhLSyMzM7Oe4jsKCgqqjZu670sG+YpZUdqdQzWk7atUZi4sYmjHcL5esqBB+YeCmnQ6UTF9Wj6tTafWpg80TKdg5sK6BviLiMwCnlXVdQ2QrSbuAh4TkcnAp0AWUNEYCavqdGA6QEZGho4ZM6ZB6WRmZlJt3DdfgZhkhl1ya43zXr23eg/5ZUv5yYXDGTOg5axxXqNOJyimT8untenU2vSBhulUZxWWqn4fGA5sBp4TkQUicouIJNYRNQvw73bU3XPzT3uXqk5U1eHA3Z7boVriZnnHNabZJFT4YP1c6Du+RuMBrvqqU2I05/bt2ITCGYZhNA1BtYGoah7wGq4dowuuummZiNxWS7TFQF8R6S0iUcC1wGz/ACKSKiJVMvwaeNY7fg8YLyLtvcbz8cB7qrobyBOR0V7vqxuAt4LRoVHZsRCKD9Ta+yo7r4R56/dxxYjuRIRbb2nDMFofdX7ZROQSEXkDyAQigZGqeiEwFLizpniq6gOm4ozBWuAVVV0tIveLyCVesDHAehHZAKQBv/PiHgB+izNCi4H7PTeAW4FngE24UtHc+ijcKKyfA+FR0OeCGoPMWpZFRaXa2A/DMFotwbSBXAE8oqqf+juqapGI3FRbRFWdA8wJcLvX7/g1XMmmurjPcqRE4u++BBgUhNyhQdVNX9L7XIiuvhZPVXl1yQ5Gpnegd2p8EwtoGIbRNARTt3IfsKjqRERiRSQdQFU/ColULZnstXBwa63VV0u/OciW/YVclWETJxqG0XoJxoC8Cvh34a3w3Nom673JE/tdWGOQV5bsID4qnIsG28SJhmG0XoIxIBHeQEAAvOOaV01q7aybA91GQFL1xqGw1MfbK3dz8ZCuxEfbisGGYbRegjEg+/wavRGRS4H9oROpBZO3C3Ytq7X66p2vd1NUVmHrfhiG0eoJ5hd5CvCyiDwGCG6E+A0hlaqlst7rD9C/ZgPy6pIdnNQx3tY8Nwyj1VOnAVHVzcBoEUnwzgtCLlVLZd0c6HASdOxfrfeWfQUs3naQaRcOsIkTDcNo9QRVSS8i3wFOBWKqPoyqen8I5Wp5lOTC1k9h9JQaJ098delOwsOEiafZxImGYbR+ghlI+HfcfFi34aqwrgJqXn6vtbLpQ6gsr7H6yldRyaylOxnbvxOdEmOaWDjDMIymJ5hG9DNV9QbgoKr+BjgD6BdasVog6+ZAXCr0GFmt9ycb9pGdX8rVNvbDMIw2QjAGpMTbF4lIV6AcNx9W28FXBhvfh/4TIKz6Nc1fWbKD1IQoxg7o1MTCGYZhNA/BGJD/iEg74P+AZcA2YEYohWpxfDMfSvNqrL7KKSjlo7XZTDytO5E2caJhGG2EWhvRvZlyP/KmWJ8lIm8DMaqa2yTStRTWzYGIWDhpTLXea3fn46tUxva30odhGG2HWn+XvVUIH/c7L21zxkPVjf/o8y2Iiqs2yN48V8vXJdkazw3DaDsEU9/ykYhcIW10YENCwWbIy4L+F9UYZo9nQNKSzIAYhtF2CMaA/Bg3eWKpiOSJSL6I5IVYrhZD6v6FIGHQb0KNYbLzSkiKiSA2qvoGdsMwjNZIMCPR61q6tlWTun8h9DwD4lNqDLMnr4TOVn1lGEYbI5iBhOdWtwWTuIhMEJH1IrJJRKZV499TROaJyHIRWSkiF3nu14nICr+tUkSGeX6ZXppVfqFruT64jYTCb2qtvgLYm1dq1VeGYbQ5gpnK5Bd+xzHASGApcH5tkUQkHNcAPw7YCSwWkdmqusYv2D24pW6fFJGBuNUL01X1ZeBlL53BwJuqusIv3nXeyoShZZ03eeKA2g1Idl4JJ3dMDbk4hmEYLYlgqrC+638uIj2AvwSR9khgk6pu8eLNBC4F/A2IAknecTKwq5p0JgEzg8iv8Vn3DgXxvUjocFKNQSorlez8UjonRzehYIZhGM2PqGr9IrjeWKtVdWAd4a4EJqjqj7zz64FRqjrVL0wX4H2gPRAPXKCqSwPS2QxcqqqrvPNMIAW3MuIs4AGtRgkRuQW4BSAtLW3EzJn1t0HRJfupOLQTX+dhNYbJLVV+Oq+I758SxQW9IuudR3NQUFBAQkJCc4vRaJg+LZ/WplNr0weq12ns2LFLVTWjpjh1lkBE5G+4kgK4NpNhuBHpjcEk4DlV/ZOInAG8KCKDvPEniMgooKjKeHhcp6pZIpKIMyDXAy8EJqyq04HpABkZGTpmzJgGCZiZmUltcVdl5cK8+Zw9YjBjBnVuUB5NTV06nWiYPi2f1qZTa9MHGqZTMG0g/m0NPuBfqvp5EPGygB5+5909N39uAiYAqOoCEYkBUoFsz/9a4F/+EVQ1y9vni8gMXFXZMQakqagaRGi9sAzDaGsEY0BeA0pUtQJc47iIxKlqUR3xFgN9RaQ3znBcC3wvIMx24FvAcyJyCq6Rfp+XTxhwNXBOVWARiQDaqep+EYkELgY+DEKHkLE3rxSAtCRrAzEMo20R1Eh0INbvPJYgPtqq6gOmAu8Ba3G9rVaLyP1+a6zfCdwsIl/hShqT/dozzgV2VDXCe0QD74nISmAFzjA9HYQOIWNPXgki0DHBDIhhGG2LYEogMf7L2KpqgYhUPylUAKo6B9c119/tXr/jNcBZNcTNBEYHuBUCI4LJu6nIzishNSGaCJuF1zCMNkYwX71CETmt6kRERgDFoRPpxGJvXolVXxmG0SYJpgRyB/CqiOzCLWnbGbfErQHsySulWztrQDcMo+0RzEDCxSIyAOjvOa1X1fLQinXikJ1XwvCe7ZpbDMMwjCYnmLmwfgLEq+oqbzxGgojcGnrRWj5lvkpyCstIS7QSiGEYbY9g2kBu9lYkBEBVDwI3h06kE4fs/KoxINYGYhhG2yMYAxLuv5iUN0liVOhEOnGoGgPSyWbiNQyjDRJMI/q7wL9F5Cnv/MfA3NCJdOJQNQrdqrAMw2iLBGNAfoWblHCKd74S1xOrzWPTmBiG0ZapswrLm9hwIbANN+/U+biR5W2evXmlRIWH0T7uxJiF1zAMozGpsQQiIv1ws+VOAvYD/wZQ1bFNI1rLZ29eCZ2SovFrIjIMw2gz1FaFtQ74DLhYVTcBiMjPmkSqEwQ3Ct2qrwzDaJvUVoU1EdgNzBORp0XkW7iR6IbHHpvGxDCMNkyNBkRV31TVa4EBwDzclCadRORJERnfVAK2ZLLzSq0EYhhGmyWYRvRCVZ3hrY3eHViO65nVpiko9VFQ6jMDYhhGm6Vec5Cr6kFVna6q3wqVQCcKh8eAWBWWYRhtFFvEooEcMSBWAjEMo20SUgMiIhNEZL2IbBKRadX49xSReSKyXERWishFnnu6iBSLyApv+7tfnBEi8rWX5l+lmfrQZh9eytYMiGEYbZOQGRBvzqzHgQuBgcAkERkYEOwe3FK3w3Frpj/h57dZVYd52xQ/9ydxkzn29bYJodKhNvZYCcQwjDZOKEsgI4FNqrpFVcuAmcClAWEUSPKOk4FdtSUoIl2AJFX90ls7/QXgssYVOzj25pWQEB1BQnQws8EYhmG0PkL59esG7PA73wmMCghzH/C+iNwGxAMX+Pn1FpHlQB5wj6p+5qW5MyDNbtVlLiK34ObwIi0tjczMzAYpUVBQUG3crzeVkBBR2eB0m5OadDpRMX1aPq1Np9amDzRMp+b+fZ4EPKeqfxKRM4AXRWQQbgBjT1XN8dZgf1NETq1Pwqo6HZgOkJGRoWPGjGmQgJmZmVQX929rv+CkhDDGjBndoHSbk5p0OlExfVo+rU2n1qYPNEynUFZhZQE9/M67e27+3AS8AqCqC4AYIFVVS1U1x3NfCmwG+nnxu9eRZpNg05gYhtHWCaUBWQz0FZHeIhKFaySfHRBmO/AtABE5BWdA9olIR68RHhE5CddYvkVVdwN5IjLa6311A/BWCHWoFlUlO6+UTjYGxDCMNkzIqrBU1SciU4H3gHDgWVVdLSL3A0tUdTZwJ/C0N0mjApNVVUXkXOB+ESkHKoEpqnrAS/pW4DkgFrewVZMvbnWwqJyyiko6WwnEMIw2TEjbQFR1DjAnwO1ev+M1wFnVxJsFzKohzSXAoMaVtH7YIELDMAwbid4gbAyIYRiGGZAGkW3zYBmGYZgBaQh7ct00Jp0SrQRiGEbbxQxIA9ibX0JKfBRREXb5DMNou9gXsAFk55XQydo/DMNo45gBaQC2lK1hGIYZkAaxN6/UxoAYhtHmMQNST3wVlewvKLUqLMMw2jxmQOrJvoJSVLESiGEYbR4zIPVk7+GVCK0NxDCMto0ZkHqyJ9dGoRuGYYAZkHqTnW8GxDAMA8yA1Ju9eSWEhwkp8VHNLYphGEazYgaknuzJLaVTYjRhYdLcohiGYTQrZkDqSXa+rURoGIYBZkDqzV4bhW4YhgGE2ICIyAQRWS8im0RkWjX+PUVknogsF5GVInKR5z5ORJaKyNfe/ny/OJlemiu8rVModQhkT26JjQExDMMghCsSemuaPw6MA3YCi0VktrcKYRX3AK+o6pMiMhC3emE6sB/4rqruEpFBuGVxu/nFu85bmbBJKS6rIK/EZ6PQDcMwCG0JZCSwSVW3qGoZMBO4NCCMAknecTKwC0BVl6vqLs99NRArIs1eb2RL2RqGYRxBVDU0CYtcCUxQ1R9559cDo1R1ql+YLsD7QHsgHrhAVZdWk84UVb3AO88EUoAK3LrpD2g1SojILcAtAGlpaSNmzpzZID0KCgpISEgAYP2BCv6wqIRfZMRwamp4g9JrCfjr1BowfVo+rU2n1qYPVK/T2LFjl6pqRo2RVDUkG3Al8Izf+fXAYwFhfg7c6R2fAawBwvz8TwU2Ayf7uXXz9ok443NDXbKMGDFCG8q8efMOH7+1Ikt7/ept3bAnr8HptQT8dWoNmD4tn9amU2vTR7V6nYAlWsu3NZRVWFlAD7/z7p6bPzcBrwCo6gIgBkgFEJHuwBuegdhcFUFVs7x9PjADV1XWJOz1pjGxNhDDMIzQtoEsBvqKSG8RiQKuBWYHhNkOfAtARE7BGZB9ItIOeAeYpqqfVwUWkQgRqTIwkcDFwKoQ6nAUe/NKiI0MJykmZH0PDMMwThhCZkBU1QdMxfWgWovrbbVaRO4XkUu8YHcCN4vIV8C/gMlesWkq0Ae4N6C7bjTwnoisBFbgSjRPh0qHQPbml5KWFI2IjUI3DMMI6a+0qs7Bdc31d7vX73gNcFY18R4AHqgh2RGNKWN92Jtro9ANwzCqsJHo9WCvTWNiGIZxGDMgQaKq7Mm1aUwMwzCqMAMSJHnFPkp9lVYCMQzD8DADEiR7bSEpwzCMozADEiS2lK1hGMbRmAEJkqp5sGwmXsMwDIcZkCDJzi8FoJM1ohuGYQBmQIJmT24JybGRxESeuJMoGoZhNCZmQIJkb54tJGUYhuGPTeoUJHvzSqz6ymh1lJeXs3PnTkpKSkKaT3JyMmvXrg1pHk1Ja9MnJiamQVM0mQEJkr15pfRLS2xuMQyjUdm5cyeJiYmkp6eHdI63/Px8EhNbz/vTmvRRVXJycoiPj693XKvCCoKKSmVfQal14TVaHSUlJaSkpNgEoW0YESElJYXw8Pq375oBCYKcglIqKtWmMTFaJWY8jIY+A2ZAgmBvnuvCayUQwzCMI5gBCYKqQYRmQAyjcTl06BBPPPFEo6S1bds2YmNjGTZsGMOGDWPKlCmNkq5RM9aIHgR7zIAYRkioMiC33npro6R38skns2LFikZJqzGoqKhoUNvCiUJIDYiITAAeBcKBZ1T1wQD/nsDzQDsvzDRvESpE5Ne4NdMrgNtV9b1g0gwF2XklhAmkJkSFOivDaDZ+85/VrNmV16hpDuyaxP9+99Qa/adNm8bmzZsZNmwY48aNA2Du3LmICPfccw/XXHMNmZmZ3HvvvSQmJrJp0ybGjh3LE088QVhYwypQtm3bxvXXX09hYSEAjz32GGeeeSYADz30EC+99BJhYWFceOGFPPjgg2zatIkpU6awb98+wsPDefXVV1m/fj1PPPEEb7/9NgBTp04lIyODyZMnk56ezjXXXMMHH3zAL3/5S/Lz85k+fTplZWX06dOHF198kbi4OPbu3cuUKVPYsmULAE8++STvvvsuHTp04I477gDg7rvvplOnTvz0pz9tkK6hJmQGRETCgceBccBOYLGIzPZWIaziHtxSt0+KyEDc6oXp3vG1wKlAV+BDEennxakrzUZnT14JqQnRRIRbjZ9hNCYPPvggq1atYsWKFcyaNYu///3vfPXVV+zfv5/TTz+dc889F4BFixaxZs0aevXqxYQJE3j99de58sorj0lv69atDB8+nKSkJB544AHOOeecY8J06tSJDz74gJiYGDZu3MikSZNYsmQJc+fO5a233mLhwoXExcVx4MABAK677jqmTZvG5ZdfTklJCZWVlaxfv75WvVJSUli2bBkAOTk53HzzzQDcc889/OMf/+C2227j9ttv57zzzuONN96goqKCgoICunbtysSJE7njjjuorKxk5syZLFq06LiucSgJZQlkJLBJVbcAiMhM4FLA/2OvQJJ3nAzs8o4vBWaqaimwVUQ2eekRRJqNzt68UjonW/WV0bqpraTQFMyfP59JkyYRHh5OWloa5513HosXLyYpKYmRI0dy0kknATBp0iTmz59/jAHp0qUL27dvJyUlhaVLl3LZZZexevVqkpKSjgpXXl7O1KlTWbFiBeHh4WzYsAGADz/8kB/84AfExcUB0KFDB/Lz88nKyuLyyy8H3IC7YLjmmmsOH69atYp77rmHQ4cOUVBQwLe//W0APv74Y1544QUAwsPDSU5OJjk5mZSUFJYvX87evXsZPnw4KSkp9b2UTUYoDUg3YIff+U5gVECY+4D3ReQ2IB64wC/ulwFxu3nHdaUJgIjcAtwCkJaWRmZmZr0VACgoKGDL7mJSY6XBabQ0CgoKWo0uYPocD8nJyeTn54c8n4qKimrzKSgooLKykvz8fMrKyigpKTkcrry8nOLiYiIiIg6HATd2pby8nBkzZvDgg64G+29/+xunnXYaUVFR5Ofn069fP9LT01m+fDlZWVlHhXv33Xdp37498+fPp7Kyko4dO1abP7gBg6p6jOwiQllZ2WH3/Pz8w3FV9ag4N954IzNmzGDw4MG8/PLLfPbZZ0elW1ZWdlTa1113HdOnTyc7O5tJkyY1yf0BN6Cw3s9dlbKNvQFX4tooqs6vBx4LCPNz4E7v+AxcSSIMeAz4vl+4f3jp1ZlmdduIESO0ocybN0+H/eY9/e/XVzY4jZbGvHnzmluERsX0aThr1qxpknzy8vKqdd+/f7/27NlTVVVnzZql48ePV5/Pp9nZ2dqzZ0/dvXu3zps3T2NiYnTLli1aUVGh48eP19dee+2YtLKzs9Xn86mq6ubNm7Vr166ak5NzTLg77rhDH374YVVVffbZZ9V9BlXnzp2rZ5xxhhYWFqqqHo47atQofeONN1RVtaSkRAsLC3XNmjXaq1cvLSkp0YMHD2p6err+85//VFXVXr166b59+w7nl5KSonv37tWysjK94IIL9MYbb1RV1WuuuUYfeeQRVVX1+Xx66NAhVVUtLS3Vfv36ae/evQ/r0xQsW7bsGDdgidbybQ1lpX4W0MPvvLvn5s9NwCu4O7gAiAFSa4kbTJqNSnmlcrCo3CZSNIwQkJKSwllnncWgQYNYsGABQ4YMYejQoZx//vn88Y9/pPP/t3f/wVFVWQLHv8fID4cgEBwDEoSoFASSNImAJRk0GWBlxBKXQKKiGFxxnQRwXR3BrXJGEG07vQAAD9ZJREFUUaumLGZQNI7OzE7iDzCxoiK6/kINgziuibjBILiAFqD8DCCRX8mG5Owf76VtQncSmjTpbs6nqivdt9+7fQ8v9M19971z+/UDYPTo0cyZM4ekpCQSExO9p5R8rV69mtTUVEaOHMm0adN49tlniYuLO2m7/Px8nn/+eTweD19//bU3hcekSZO4/vrrGTVqFCNHjmTRokUAvPjiiyxZsoTU1FTGjh3L7t27SUhIICcnh+TkZHJyckhLSwsY4yOPPMIVV1xBRkYGw4YN85Y/+eSTlJeXk5KSwuWXX86GDc6Z+K5du5KVlUVOTk74X8HVWu9yOg+c02PfAolAV2AdMKLFNu8Aee7zJJw5EMGZPF8HdHP3/xbnqqs26/T3OJ0RyCv/9aEOmv+WllZsD7qOcGN/sYe3s2kE0h7l5eU6efLkDmzN6TudeNrS2NioHo9HN23aFLLP8CesRiCqehyYA7wHbMS52uorEVkoIte7m90LzBaRdcDLbmeiqvoVzshkA/AuUKCqjYHqDFUMAAfrFbCFpIwxobdhwwYuu+wyxo8fz5AhQzq7OW0K6X0g6tzT8XaLst/6PN8AZATY9zHgsfbUGUo/uB2IXYVlTOfIzMwkMzOzs5txRgwfPtx7X0gksBsb2vBDndOBxPe0DsQYY3xZB9KGg/VK13PPoffPunR2U4wxJqxYB9KGg3VNxJ/fzVJeG2NMC9aBtOGHerXTV8YY44d1IG04WKfE2wS6McacxDqQNhy0EYgxYSM2Njbge1u3bmXZsmUd9lmrVq2iV69e3vVFFi5c2GF1RwtbD6QVh+oaqGvElrI1Z4d3FsDu6o6ts18K/CrkKy4AP3UgN998c4fVOW7cOG/K9nAQbuuL2AikFc1L2do9IMaExoIFCygsLPS+fuihh3j00UcZP3486enppKSk8MYbb7S7ro8//piRI0eyePFi6urqmDVrFikpKaSlpVFeXg5AcXExU6ZMITMzkyFDhvDwww+fVgwVFRVceeWVpKWlMXbsWG+q98bGRu677z6Sk5NJTU3lqaeeAqCyspKxY8fi8XgYM2YMhw4dori4mDlz5njrvO6667yJDWNjY7n33nvxeDx8+umnLFy4kNGjR5OcnMydd97ZnNWDLVu2MGHCBDweD+np6XzzzTfMnDmT5cuXe+udMWNGu/8926W129Sj5RFsKpM1m2t00Py39B9b9gW1f7iy1B/h7WxKZfLFF1/oVVdd5X2dlJSk27dv19raWlVVramp0UsvvVSbmppUVbVHjx4BP6NlypNFixbprFmzVFV148aNOnDgQD127JgWFRVpv379dN++fXr06FEdMWKEVlZW+q0vLi5OU1NTddKkSbp+/Xq/8dTW1mpDQ4Oqqq5cuVKnTp2qqqrPPPOMZmdne9/bv3+/1tfXa2JiolZUVJywb1FRkRYUFHjrnDx5svf3ANDS0lLve74JIm+55RZdsWKFqqqOGTNGX3vtNVVVPXbsmB45ckRXrVqlU6ZMUVXVgwcP6uDBg73taSmYVCZ2CqsVzWuh2wjEmNBIS0tj79697Ny5k5qaGvr06UO/fv245557WL16Neeccw47duxgz5493sSK7bVmzRrmzp0LwLBhwxg0aJB37Y+JEyd619mYOnUqa9asYdSoUSfsn56ezrZt24iNjeXtt9/mhhtuYPPmzSd9Tm1tLbfddhubN29GRGhoaACc9UXuuusuzj3X+ZqNi4ujurqa/v37M3r0aICT1irxJyYmhuzsbO/r8vJyHn/8cY4ePcqBAwcYMWIEmZmZftctufrqq8nPz6empoZXX32V7Oxsb3s6gp3CakXzKawLe9ociDGhMn36dMrKyigtLSU3N5elS5dSU1PD2rVrqaqqIj4+nrq6ug79zJb3dYkIhYWF3gnznTt3cv7553sn7a+99loaGhrYt28fhYWFZGRkeLd78MEHycrKYv369bz55ptBtbV5zZNmvnV0797dO+9RV1dHfn4+ZWVlVFdXM3v27DY/b+bMmbz00ksUFRVx++23n3LbWmMdSCv2/FjHeedCj242UDMmVHJzcykpKaGsrIzp06dTW1vLhRdeSJcuXSgvL2fbtm3tqqdnz54nLL40btw4li5dCsCmTZvYvn07Q4cOBWDlypUcOHCAY8eOsXz5cjIyMigoKKCqqoqqqiouuugidu/e7Z1fqKiooKmpib59+1JQUMAnn3zi3a62tpYBA5z17oqLi72fP3HiRJ577jmOHz8OwIEDBxg6dCi7du2isrIScBaiOn78OIMHD6aqqoqmpia+++67gMvYNncWF1xwAYcPH6asrMwbe0JCgne+o76+nqNHjwKQl5fHE088ATi5tjqSdSCt2PNjHX262R3oxoTSiBEjOHToEAMGDKB///7MmDGDzz//nJSUFF544YUT1tBoTWpqKjExMXg8HhYvXkx+fj5NTU2kpKSQm5tLcXEx3bo5ZxPGjBlDdnY2qampZGdnn3T6CqCsrIzk5GQ8Hg/z5s2jpKTEb0aK+++/nwceeIC0tDRvZwFwxx13cPHFF3vXOFm2bBldu3altLSUuXPn4vF4mDhxInV1dWRkZJCYmMjw4cOZN28e6enpfmPs3bs3s2fPJjk5mWuuucZ7Kgz8r1sCzoqsSUlJzJo1q13/jqektQmSaHkEO4n+9EebNf/Z94LaN5zZpHN4O5sm0TtDywnrYIRTPG05cuSIXnLJJd4VDwMJq/VAokFB1mVMH9q1s5thjDFB+eCDD0hKSmLu3Ln06tWrw+u3k/vGmIhSXV3NrbfeekJZt27d+Oyzz9q1f15eHnl5eSFoWfiZMGFCu+eQghHSDkREJgFP4ixH+1dV/X2L9xcDWe7LnwEXqmpvEckCFvtsOgy4UVWXi0gxcDVQ676Xp6pVIQzDmKimqhGVbTolJYWqKvsv35HUvVjgVIWsAxGRGKAQmAh8D1SKyAp1ViEEQFXv8dl+LpDmlpcDI93yOGAL8L5P9b9R1bJQtd2Ys0X37t3Zv38/ffv2jahOxHQcVWX//v00Njae8r6hHIGMAbao6rcAIlICTMFZ59yfm4Df+SmfBryjqkdD0kpjzmIJCQl8//331NTUhPRz6urqvDe3RYNoi6d79+4cOXLklPeTYIcubVYsMg2YpKp3uK9vBa5Q1Tl+th0E/DeQoKqNLd77CPijqr7lvi4GrgTqgQ+BBapa76fOO4E7AeLj4y8vKSkJKo7Dhw+3mgE0EkVbTBZP+Iu2mKItHvAfU1ZW1lpVPfka52atXaJ1Og+ckcNffV7fCjwdYNv5wFN+yvsDNUCXFmUCdAOeB37bVluCvYxXNfouEVWNvpgsnvAXbTFFWzyq/mOiEy/j3QEM9Hmd4Jb5cyPwsp/yHOB1VW1oLlDVXW5s9UARzqkyY4wxZ1goO5BKYIiIJIpIV5xOYkXLjURkGNAH+NRPHTfRomMRkf7uTwFuANZ3cLuNMca0Q8gm0VX1uIjMAd7DuYz3b6r6lYgsxBkWNXcmNwIl7nDJS0QG44xg/t6i6qUi8nOc01hVwF1ttWXt2rX7RCTYi6EvAPYFuW+4iraYLJ7wF20xRVs84D+mQa3tELJJ9GghIp9ra5NIESjaYrJ4wl+0xRRt8UBwMVkqE2OMMUGxDsQYY0xQrANp2587uwEhEG0xWTzhL9piirZ4IIiYbA7EGGNMUGwEYowxJijWgRhjjAmKdSCtEJFJIvK/IrJFRBZ0dntOl4hsFZFqEakSkc87uz3BEJG/icheEVnvUxYnIitFZLP7s09ntvFUBIjnIRHZ4R6nKhG5tjPbeCpEZKCIlIvIBhH5SkTudssj+RgFiikij5OIdBeRChFZ58bzsFueKCKfud93pe4N4K3XZXMg/rnp6Dfhk44euEl90tFHGhHZCoxS1Yi9AUpErgIOAy+oarJb9jhwQFV/73b0fVR1fme2s70CxPMQcFhVF3Vm24LhZoror6pfiEhPYC1Oxog8IvcYBYophwg8Tm4Wjx6qelhEugBrgLuBfwdeU9USEXkWWKeqf2qtLhuBBOZNR6+q/wc0p6M3nUhVVwMHWhRPwUmsifvzhjPaqNMQIJ6I5eaq+8J9fgjYCAwgso9RoJgikptL8LD7sov7UOCXQPM6S+06RtaBBDYA+M7n9fdE8C+NS4H3RWStm+4+WsSr6i73+W4gvjMb00HmiMiX7imuiDnd48tNR5QGfEaUHKMWMUGEHicRiRGRKmAvsBL4BjioqsfdTdr1fWcdyNnlF6qaDvwKKHBPn0QVN6dapJ+X/RNwKc6qnLuAP3Ruc06diMQCrwL/pqo/+r4XqcfIT0wRe5xUtVFVR+JkSR+Ds2z4KbMOJLBTSUcfEVR1h/tzL/A60ZMKf49Plub+OH9VRSxV3eP+B28C/kKEHSf3vPqrwFJVfc0tjuhj5C+mSD9OAKp6ECjHWaSvt4g0J9ht1/eddSCBtSsdfaQQkR7uBCAi0gP4J6InFf4K4Db3+W3AG53YltPW/EXr+mci6Di5E7T/CWxU1T/6vBWxxyhQTJF6nETk5yLS231+Hs6FQhtxOpJp7mbtOkZ2FVYr3MvynuCndPSPdXKTgiYil+CMOsBJ478sEuMRkZeBTJzU03uA3wHLgVeAi4FtQI6qRsTEdIB4MnFOiyiwFfhXn/mDsCYivwA+BqqBJrf4P3DmDCL1GAWK6SYi8DiJSCrOJHkMziDiFVVd6H5HlABxwP8At6if5cJPqMs6EGOMMcGwU1jGGGOCYh2IMcaYoFgHYowxJijWgRhjjAmKdSDGGGOCYh2IMUESkUafTKxVHZmxWUQG+2boNSYcndv2JsaYAI656SCMOSvZCMSYDuauu/K4u/ZKhYhc5pYPFpGP3OR7H4rIxW55vIi87q7PsE5ExrpVxYjIX9w1G9537xpGROa5a1N8KSIlnRSmMdaBGHMazmtxCivX571aVU0BnsbJZgDwFPC8qqYCS4ElbvkS4O+q6gHSga/c8iFAoaqOAA4C2W75AiDNreeuUAVnTFvsTnRjgiQih1U11k/5VuCXqvqtm4Rvt6r2FZF9OAsTNbjlu1T1AhGpARJ800a4acNXquoQ9/V8oIuqPioi7+IsQrUcWO6ztoMxZ5SNQIwJDQ3w/FT45iFq5Kc5y8lAIc5opdIng6oxZ5R1IMaERq7Pz0/d5//AyeoMMAMnQR/Ah8CvwbvQT69AlYrIOcBAVS0H5gO9gJNGQcacCfaXizHBO89d1a3Zu6rafClvHxH5EmcUcZNbNhcoEpHfADXALLf8buDPIvIvOCONX+MsUORPDPCS28kIsMRd08GYM87mQIzpYO4cyChV3dfZbTEmlOwUljHGmKDYCMQYY0xQbARijDEmKNaBGGOMCYp1IMYYY4JiHYgxxpigWAdijDEmKP8PrXTv++T+2K8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "d5UzQFEMhNTo"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "ZSddrOG6xwBF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}