training_model_with_contrast_and_dropout.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "Z-6bQhaGj6jY"
      ],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "##extracting the data from directories and create test and train generator\n"
      ],
      "metadata": {
        "id": "pGAoluQBjv8g"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4t7HUNuKi0DT",
        "outputId": "4702498f-c6b5-400e-e851-924e064238e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_dir = \"/content/drive/MyDrive/tumor_dataset_contrast/test\"\n",
        "train_dir = \"/content/drive/MyDrive/tumor_dataset_contrast/train\""
      ],
      "metadata": {
        "id": "MzJOM6BAjfOQ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1.0/255)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1.0/255)\n",
        "\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size = (64,64),\n",
        "    batch_size=32,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    shuffle=False,\n",
        "    target_size = (64,64),\n",
        "    batch_size=32,\n",
        "    class_mode='categorical'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItYfQkJVjWaJ",
        "outputId": "2c1f1fa5-3d7a-4153-fbbb-6c0114500f65"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1503 images belonging to 3 classes.\n",
            "Found 600 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##builing up the models"
      ],
      "metadata": {
        "id": "Z-6bQhaGj6jY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_model1 = Sequential([\n",
        "    Conv2D(64, (3, 3), activation='relu', input_shape=(64, 64, 3)),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Conv2D(64, (3, 3), activation='relu'),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Flatten(),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dense(3, activation='softmax')  # 3 classes\n",
        "])\n",
        "\n",
        "my_model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "bVLn5YlxkAPw",
        "outputId": "56e9f84d-d3b9-4d99-be84-bd9e21f0e187"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │           \u001b[38;5;34m1,792\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_6 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_7 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_3 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │         \u001b[38;5;34m802,880\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │             \u001b[38;5;34m195\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12544</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">802,880</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m841,795\u001b[0m (3.21 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">841,795</span> (3.21 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m841,795\u001b[0m (3.21 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">841,795</span> (3.21 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "my_model2 = Sequential([\n",
        "    Conv2D(64, (3, 3), activation='relu', input_shape=(64, 64, 3)),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Conv2D(64, (3, 3), activation='relu'),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Flatten(),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dropout(0.2),\n",
        "    Dense(3, activation='softmax')  # 3 classes\n",
        "])\n",
        "\n",
        "my_model2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "7puAU6tTkQuQ",
        "outputId": "4ad69d35-63e3-4048-a31f-5069e4ecdc88"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │           \u001b[38;5;34m1,792\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_8 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m31\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_9 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │         \u001b[38;5;34m802,880\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │             \u001b[38;5;34m195\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12544</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">802,880</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m841,795\u001b[0m (3.21 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">841,795</span> (3.21 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m841,795\u001b[0m (3.21 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">841,795</span> (3.21 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##training the models"
      ],
      "metadata": {
        "id": "ZUbb2xCtl3yg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oy-T09c8xiRh",
        "outputId": "5875bc01-2eec-499a-c625-4c89029ceb06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2s/step - accuracy: 0.5897 - loss: 0.8394\n",
            "Epoch 1: val_loss improved from inf to 0.50651, saving model to my_model1.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m273s\u001b[0m 5s/step - accuracy: 0.5907 - loss: 0.8371 - val_accuracy: 0.8033 - val_loss: 0.5065\n",
            "Epoch 2/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 622ms/step - accuracy: 0.7399 - loss: 0.5403\n",
            "Epoch 2: val_loss improved from 0.50651 to 0.43327, saving model to my_model1.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 794ms/step - accuracy: 0.7401 - loss: 0.5402 - val_accuracy: 0.8167 - val_loss: 0.4333\n",
            "Epoch 3/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 580ms/step - accuracy: 0.8284 - loss: 0.4182\n",
            "Epoch 3: val_loss improved from 0.43327 to 0.33555, saving model to my_model1.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 770ms/step - accuracy: 0.8284 - loss: 0.4180 - val_accuracy: 0.8533 - val_loss: 0.3355\n",
            "Epoch 4/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 597ms/step - accuracy: 0.8840 - loss: 0.2944\n",
            "Epoch 4: val_loss improved from 0.33555 to 0.26542, saving model to my_model1.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 792ms/step - accuracy: 0.8840 - loss: 0.2942 - val_accuracy: 0.9033 - val_loss: 0.2654\n",
            "Epoch 5/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 599ms/step - accuracy: 0.9161 - loss: 0.2221\n",
            "Epoch 5: val_loss improved from 0.26542 to 0.25161, saving model to my_model1.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 822ms/step - accuracy: 0.9160 - loss: 0.2222 - val_accuracy: 0.9067 - val_loss: 0.2516\n",
            "Epoch 6/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 600ms/step - accuracy: 0.9334 - loss: 0.1833\n",
            "Epoch 6: val_loss improved from 0.25161 to 0.22498, saving model to my_model1.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 776ms/step - accuracy: 0.9336 - loss: 0.1830 - val_accuracy: 0.9033 - val_loss: 0.2250\n",
            "Epoch 7/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 583ms/step - accuracy: 0.9564 - loss: 0.1260\n",
            "Epoch 7: val_loss did not improve from 0.22498\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 773ms/step - accuracy: 0.9564 - loss: 0.1260 - val_accuracy: 0.8450 - val_loss: 0.3342\n",
            "Epoch 8/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 598ms/step - accuracy: 0.9655 - loss: 0.1199\n",
            "Epoch 8: val_loss did not improve from 0.22498\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 793ms/step - accuracy: 0.9656 - loss: 0.1195 - val_accuracy: 0.9117 - val_loss: 0.2337\n",
            "Epoch 9/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 610ms/step - accuracy: 0.9865 - loss: 0.0520\n",
            "Epoch 9: val_loss improved from 0.22498 to 0.16492, saving model to my_model1.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 787ms/step - accuracy: 0.9865 - loss: 0.0521 - val_accuracy: 0.9283 - val_loss: 0.1649\n",
            "Epoch 10/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 567ms/step - accuracy: 0.9932 - loss: 0.0325\n",
            "Epoch 10: val_loss did not improve from 0.16492\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 756ms/step - accuracy: 0.9932 - loss: 0.0326 - val_accuracy: 0.9350 - val_loss: 0.1649\n",
            "Epoch 11/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 604ms/step - accuracy: 0.9987 - loss: 0.0184\n",
            "Epoch 11: val_loss improved from 0.16492 to 0.16006, saving model to my_model1.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 810ms/step - accuracy: 0.9987 - loss: 0.0184 - val_accuracy: 0.9317 - val_loss: 0.1601\n",
            "Epoch 12/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 609ms/step - accuracy: 0.9990 - loss: 0.0132\n",
            "Epoch 12: val_loss did not improve from 0.16006\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 781ms/step - accuracy: 0.9989 - loss: 0.0134 - val_accuracy: 0.9333 - val_loss: 0.1618\n",
            "Epoch 13/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 700ms/step - accuracy: 0.9980 - loss: 0.0167\n",
            "Epoch 13: val_loss did not improve from 0.16006\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 880ms/step - accuracy: 0.9980 - loss: 0.0167 - val_accuracy: 0.9283 - val_loss: 0.2089\n",
            "Epoch 14/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 598ms/step - accuracy: 0.9999 - loss: 0.0056\n",
            "Epoch 14: val_loss did not improve from 0.16006\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 792ms/step - accuracy: 0.9998 - loss: 0.0056 - val_accuracy: 0.9233 - val_loss: 0.2245\n",
            "Epoch 15/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 601ms/step - accuracy: 0.9980 - loss: 0.0088\n",
            "Epoch 15: val_loss did not improve from 0.16006\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 800ms/step - accuracy: 0.9981 - loss: 0.0087 - val_accuracy: 0.9383 - val_loss: 0.1909\n"
          ]
        }
      ],
      "source": [
        "# Compile the model\n",
        "my_model1.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Callbacks\n",
        "early_stopping1 = EarlyStopping(monitor='val_loss', patience=4, restore_best_weights=True)\n",
        "model_checkpoint1 = ModelCheckpoint('my_model1.keras', monitor='val_loss', save_best_only=True, mode=\"auto\", verbose=1)\n",
        "\n",
        "history3 = my_model1.fit(\n",
        "    train_generator,\n",
        "    validation_data=test_generator,\n",
        "    epochs=15,\n",
        "    callbacks=[early_stopping1, model_checkpoint1]\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix , ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "predictions = my_model1.predict(test_generator)\n",
        "predicted_classes = np.argmax(predictions, axis=1)\n",
        "\n",
        "true_classes = test_generator.classes\n",
        "class_labels = list(test_generator.class_indices.keys())\n",
        "\n",
        "cm = confusion_matrix(true_classes , predicted_classes)\n",
        "\n",
        "#plot---\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix = cm , display_labels = class_labels)\n",
        "disp.plot(cmap = plt.cm.Blues)\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "U_HNDCqjrFUh",
        "outputId": "e6ce9159-8590-4367-f2ea-a1de6506ad4b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 445ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot accuracy\n",
        "plt.plot(history3.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history3.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot loss\n",
        "plt.plot(history3.history['loss'], label='Training Loss')\n",
        "plt.plot(history3.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "c57q7F46rkZC",
        "outputId": "672fb104-9f3b-4540-c466-48d903bb97ee"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "my_model2.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Callbacks\n",
        "early_stopping2 = EarlyStopping(monitor='val_loss', patience=4, restore_best_weights=True)\n",
        "model_checkpoint2 = ModelCheckpoint('my_model2.keras', monitor='val_loss', save_best_only=True, mode=\"auto\", verbose=1)\n",
        "\n",
        "history2 = my_model2.fit(\n",
        "    train_generator,\n",
        "    validation_data=test_generator,\n",
        "    epochs=15,\n",
        "    callbacks=[early_stopping2, model_checkpoint2]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TlA9X_HipTni",
        "outputId": "f08e9128-13d3-4acc-cb62-ada18378794e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 703ms/step - accuracy: 0.5400 - loss: 0.8899\n",
            "Epoch 1: val_loss improved from inf to 0.53229, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 912ms/step - accuracy: 0.5416 - loss: 0.8874 - val_accuracy: 0.7833 - val_loss: 0.5323\n",
            "Epoch 2/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 592ms/step - accuracy: 0.7591 - loss: 0.5692\n",
            "Epoch 2: val_loss improved from 0.53229 to 0.43824, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 795ms/step - accuracy: 0.7589 - loss: 0.5692 - val_accuracy: 0.8117 - val_loss: 0.4382\n",
            "Epoch 3/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 608ms/step - accuracy: 0.8066 - loss: 0.4500\n",
            "Epoch 3: val_loss improved from 0.43824 to 0.43429, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 832ms/step - accuracy: 0.8065 - loss: 0.4498 - val_accuracy: 0.7700 - val_loss: 0.4343\n",
            "Epoch 4/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 618ms/step - accuracy: 0.8242 - loss: 0.3917\n",
            "Epoch 4: val_loss improved from 0.43429 to 0.34502, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 791ms/step - accuracy: 0.8247 - loss: 0.3910 - val_accuracy: 0.8617 - val_loss: 0.3450\n",
            "Epoch 5/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 590ms/step - accuracy: 0.9031 - loss: 0.2620\n",
            "Epoch 5: val_loss improved from 0.34502 to 0.26168, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 790ms/step - accuracy: 0.9032 - loss: 0.2617 - val_accuracy: 0.8983 - val_loss: 0.2617\n",
            "Epoch 6/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 611ms/step - accuracy: 0.9301 - loss: 0.1746\n",
            "Epoch 6: val_loss improved from 0.26168 to 0.19832, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 794ms/step - accuracy: 0.9302 - loss: 0.1747 - val_accuracy: 0.9233 - val_loss: 0.1983\n",
            "Epoch 7/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 588ms/step - accuracy: 0.9572 - loss: 0.1367\n",
            "Epoch 7: val_loss improved from 0.19832 to 0.18042, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 773ms/step - accuracy: 0.9572 - loss: 0.1367 - val_accuracy: 0.9350 - val_loss: 0.1804\n",
            "Epoch 8/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 610ms/step - accuracy: 0.9541 - loss: 0.1292\n",
            "Epoch 8: val_loss improved from 0.18042 to 0.17881, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 804ms/step - accuracy: 0.9540 - loss: 0.1295 - val_accuracy: 0.9350 - val_loss: 0.1788\n",
            "Epoch 9/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 605ms/step - accuracy: 0.9796 - loss: 0.0835\n",
            "Epoch 9: val_loss improved from 0.17881 to 0.16841, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 781ms/step - accuracy: 0.9796 - loss: 0.0833 - val_accuracy: 0.9350 - val_loss: 0.1684\n",
            "Epoch 10/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 558ms/step - accuracy: 0.9858 - loss: 0.0438\n",
            "Epoch 10: val_loss did not improve from 0.16841\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 755ms/step - accuracy: 0.9858 - loss: 0.0438 - val_accuracy: 0.9417 - val_loss: 0.1691\n",
            "Epoch 11/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 596ms/step - accuracy: 0.9866 - loss: 0.0431\n",
            "Epoch 11: val_loss improved from 0.16841 to 0.15265, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 794ms/step - accuracy: 0.9866 - loss: 0.0433 - val_accuracy: 0.9417 - val_loss: 0.1527\n",
            "Epoch 12/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 622ms/step - accuracy: 0.9957 - loss: 0.0332\n",
            "Epoch 12: val_loss did not improve from 0.15265\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 793ms/step - accuracy: 0.9956 - loss: 0.0332 - val_accuracy: 0.9317 - val_loss: 0.1825\n",
            "Epoch 13/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 585ms/step - accuracy: 0.9964 - loss: 0.0202\n",
            "Epoch 13: val_loss improved from 0.15265 to 0.13564, saving model to my_model2.keras\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 780ms/step - accuracy: 0.9963 - loss: 0.0202 - val_accuracy: 0.9500 - val_loss: 0.1356\n",
            "Epoch 14/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 653ms/step - accuracy: 0.9887 - loss: 0.0228\n",
            "Epoch 14: val_loss did not improve from 0.13564\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 875ms/step - accuracy: 0.9887 - loss: 0.0228 - val_accuracy: 0.9233 - val_loss: 0.2220\n",
            "Epoch 15/15\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 678ms/step - accuracy: 0.9942 - loss: 0.0191\n",
            "Epoch 15: val_loss did not improve from 0.13564\n",
            "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 901ms/step - accuracy: 0.9942 - loss: 0.0191 - val_accuracy: 0.9350 - val_loss: 0.1664\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix , ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "predictions = my_model2.predict(test_generator)\n",
        "predicted_classes = np.argmax(predictions, axis=1)\n",
        "\n",
        "true_classes = test_generator.classes\n",
        "class_labels = list(test_generator.class_indices.keys())\n",
        "\n",
        "cm = confusion_matrix(true_classes , predicted_classes)\n",
        "\n",
        "#plot---\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix = cm , display_labels = class_labels)\n",
        "disp.plot(cmap = plt.cm.Blues)\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "N1RekWC5uwJC",
        "outputId": "aeea6ab5-c6b2-4d04-ec33-51ae54ac9648"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 507ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot accuracy\n",
        "plt.plot(history2.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history2.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Plot loss\n",
        "plt.plot(history2.history['loss'], label='Training Loss')\n",
        "plt.plot(history2.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "etju_Wp-vIei",
        "outputId": "f26bb4a0-da39-4969-ad6d-ae1861323455"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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++OGHNGrUCDc3N5KSkmjSpAkjRozA29ubFStW0KtXL6pWrWpbq2rUqFF8/vnnfPTRR7Ru3Zpz586xf/9+AJ5//nkGDx7Mhx9+aGu1/PrrrylbtiwPPPBAjuMTKe4MwyA+OY2YK6nWR2IKl66kEnMlhZgrqVy6kkLs1f/GJKZeP5aYyt3OoU5Js3Dy4hVOXrySZRlHBxMBXq4E+rgR5ONOgLcbQT5uV59b/1vGyw0Xp4LXnmEYBldSzCQkp3E5OY34pDTik9O4fPW/NQO9qFs2/4ZE3MyuCdCff/6Z7ots+PDhAPTp04d58+Zx7tw5Tp48aTtfuXJlVqxYwSuvvMLHH39MuXLl+O9//0tYWJitTNeuXYmKimLcuHFERETQsGFDVq1alWFgdG5KTDVTe9zq2xfMZXvfDMPDJXf+Cd98800efPBB2/NSpUrRoEED2/O33nqLpUuXsnz58gwtEDfq27cv3bt3B2DixIl88sknbNu2jYcffjjT8qmpqcyaNYuqVasCMHjwYN58803b+WnTpjFq1Chb68v06dOzlYgsXLiQkJAQ6tSpA1iXS5g9e7YtAVqwYAFRUVH88ccftuSsWrVqtuvfeecdunXrxhtvvGE7duP7kV3Dhg3jqaeeSnfs1Vdftf08ZMgQVq9ezbfffkvz5s25fPkyH3/8MdOnT6dPnz4AVK1aldatWwPw1FNPMXjwYH744QeeeeYZwNqS1rdvXzWli9yCYRjsj7jMhkNRbDp8gTMxibYkJ+0uWlk8XBwp6eGCr4fz1YcLvu7ONxxzoeQN50p6uODt5oTZMDgfl0xEXBLnYpOIiE3kXGwSkbbnSbYWoLOxSZyNTQJiMo3BZAK/Eq4EeqdPjIJ83Aj0drc9d3PO3oKmaWYLCclmLienEn9D4hKfSRJje56cRnzS9fKXk9NISE7jVm/tkAeqFd8E6L777uNWyxBd6064+Zq//vrrlvUOHjz4ll/SklHTpk3TPY+Pj2fChAmsWLGCc+fOkZaWRmJiYrqENDP169e3/ezp6Ym3t/ctF6H08PCwJT9g3XbhWvnY2FgiIyPTreLt6OhIkyZNbC01WZkzZw49e/a0Pe/Zsyf33nsv06ZNw8vLi507d9KoUaMsW6Z27tzJgAEDbvka2XHz+2o2m5k4cSLffvstZ86cISUlheTkZNuig/v27SM5OZl27dplWp+bm5utS++ZZ55hx44d7NmzJ11Xo4hYnY9LYuPhaDYcsj6i45OzLOvi5GBNVNyticuNCYz1+Q3Jjaf1vz4ezrg63dkq6U5A+VIelC+V9aK6aWYL0fEpnItNJCI2KUOCdC4ukcjYZFLMFqIuJxN1OZm/z8RmWZ+vhzOBV1uQfD1cSEhOn9hca6XJ7R4NBxOUcHXCy82ZEq5OlHBzwtPV6Zb3nh8K1SDogsrd2ZG9b4bdvmAevG5u8fT0TPf81VdfZc2aNXzwwQdUq1YNd3d3OnfuTEpKyi3ruXmQr8lkumWykln5u12bc+/evWzZsoVt27alG/hsNptZuHAhAwYMsG31kJXbnc8szsxWTL75fZ08eTIff/wxU6dOpV69enh6ejJs2DDb+3q71wVrN1jDhg05ffo0c+fO5YEHHqBixYq3vU6kqEtMMbPt+EU2Hopiw6Fo9kdcTnfe3dmR0CqlaBPiT81AL1uiU9LDBTdnhwLXiurk6EDg1dabrFgsBhevpNgSpIi4661JEdcSpdgkElPNtm6+m9+XrLg4OeB1NWEp4ep0NYlxsiUxJVyd8XJzwtPFkRJXk5sbz1+7NjeHa+QmJUC5wGQy5VpXVEGxadMm+vbta+t6io+P5/jx4/kag4+PDwEBAfzxxx+0bdsWsCYxO3bsoGHDhlleN3v2bNq2bZthi5W5c+cye/ZsBgwYQP369fnvf//LxYsXM20Fql+/PuHh4VmOHfP39083WPvQoUNcuZJ1P/41mzZt4oknnrC1TlksFg4ePEjt2rUBCAkJwd3dnfDwcJ5//vlM66hXrx5Nmzbl888/Z8GCBbZVz0WKG4vFYF9EHBsORbPxUDTbjl8kJe36H1wmE9QN9qFNiB+tQ/xoUrHkHbfYFFQODib8SrjiV8I1y+4kwzCIS0zjXFyiLSmKTUy1JTZeV5MZT1dHvFydr7bQOBa59+pmRetbW3JNSEgIS5YsoWPHjphMJsaOHXvbbqe8MGTIECZNmkS1atWoWbMm06ZN49KlS1n+NZGamspXX33Fm2++Sd26ddOde/7555kyZQr//PMP3bt3Z+LEiXTq1IlJkyYRFBTEX3/9RXBwMC1atGD8+PG0a9eOqlWr0q1bN9LS0li5cqWtRemBBx5g+vTptGjRArPZzIgRI7I1xT0kJITFixfz+++/U7JkSaZMmUJkZKQtAXJzc2PEiBG89tpruLi40KpVK6Kiovjnn3/o379/unsZPHgwnp6e6WaniRR1kXFJV7u0oth0OJro+PSt0kE+blcTHn9aVS1N6RJFd4mT7DKZTPh4WLvsagbeenuI4kQJkGRqypQpPPfcc7Rs2RI/Pz9GjBhhly1CRowYQUREBL1798bR0ZGBAwcSFhaW5e7ky5cv58KFC5kmBbVq1aJWrVrMnj2bKVOm8L///Y//+7//45FHHiEtLY3atWvbWo3uu+8+vvvuO9566y3effddvL29ba1QAB9++CH9+vWjTZs2BAcH8/HHH7N9+/bb3s+YMWM4evQoYWFheHh4MHDgQDp16kRs7PV++7Fjx+Lk5MS4ceM4e/YsQUFBvPDCC+nq6d69O8OGDaN79+7ap0uy7XJSKpuPXGDfucuU8nQm0Mc6QDbA243Sni44OBS8borEFDNbj12wJT0HI+PTnfdwceSeKqVpE+JHmxB/qvp7FsjuFil4CsxmqAWJNkMtuCwWC7Vq1eKZZ57hrbfesnc4dnP8+HGqVq3KH3/8QePGjfPkNfS7XvilmS3sPhPLhoPW5OGvUzFZrivj7Gi6YYr11ZlDV2cVXZtR5F/CFSfHvJ1ubbEY7D0XZ0t4/jx+iRRz+m6temV9bAlP4wolC+QUcLGPnGyGqhYgKdBOnDjB//73P+69916Sk5OZPn06x44d49lnn7V3aHaRmprKhQsXGDNmDPfcc0+eJT9SeJ28cIXfDkWx8VA0m45EczkpLd35yn6eNK5QkvjkVOt4kLgkzl9OJtVscPpSIqcvJQKXMq3bwQRlvNJPtb4+9dqaNJXxds3x2JFzsYm2cTwbD0dzMSF9t1awjxttQvxpU92PVlX9KOnpkqP6RTKjBEgKNAcHB+bNm8err76KYRjUrVuXtWvXUqtWLXuHZhebNm3i/vvvp3r16ixevNje4UgBEJto7dbacCiKjYejOXEh/WB8bzcnWl9tLWldzS/TqcepV6dR26ZXX512bZ1RdH36dZrFsB6LS2LnLbZM9CvhkiE5CrxhAT9fDxd2nYqxtfIcOp++W8vTxZEWVUtbYw7xo4qfurUk9ykBkgKtfPnybNq0yd5hFBi3WztLir40s4Vdp2P47Wq31q7Tsem6tZwcTDSuUNLaRVTdn3plfXC8zdgeZ0cHgn3dCfbNehkGi8UgOiH5+nTrdAnS9XVqktOsa9dEx6ew50z2xg2aTFC/nC9tryZqDcv7qltL8pwSIBGRAu7EhQR+OxTNhoNRbD5ygcvJ6bu1qvh52sbE3FO1NCVcc/+j3cHBRBkv67YL9ctlXsYwDGKupN60YF+ibX2aa4lTfHIaZX3daVvdGnPLqqXx9VC3luQvJUAiIgWMtVsr2pr0HIri1MXEdOd9PZxpVc2PNtWs69uUK2nfFXWvMZlMlPR0oaSnC7WDsx6AmpRqxtWp4C08KMWLEiARETtLNVvYecOYmF2nYtLtoeTkYKJJxZK0rW4dx1M3G91aBVl296QSyUtKgERE8plhGBy/cIUNV7ds2HzkAvE3dWtV9fe0znwK8SO0St50a4kUZ/o/SkQknxiGwbzfjzN747Gr082vK3m1W6vt1ZlPtxqQLCJ3TwmQiEg+SDVbGLN0D4v+tM4fd3a0dmu1CfGnbYg/dYK9C+RKzCJFleYZSrbdd999DBs2zPa8UqVKTJ069ZbXmEwmli1bdtevnVv1iNhD7JVU+szZxqI/T+FggjGP1mLnuIdYOLAFg+6vRr1yPkp+RPKZEqBioGPHjjz88MOZntuwYQMmk4ndu3fnuN4//viDgQMH3m146UyYMCHTnd7PnTtHhw4dcvW1spKYmEipUqXw8/MjOTk5X15Tiq4TFxJ4auYmfj9yAU8XR/7bpynPt6mCp8b0iNiVEqBioH///qxZs4bTp09nODd37lyaNm1K/fr1c1yvv78/Hh75M/02MDAQV9f82dX5+++/p06dOtSsWdPurU6GYZCWlnb7glIg/Xn8Ik9++jtHohII8nHjuxda8kDNAHuHJSIoASoWHnvsMfz9/Zk3b1664/Hx8Xz33Xf079+fCxcu0L17d8qWLYuHhwf16tXjm2++uWW9N3eBHTp0iLZt2+Lm5kbt2rVZs2ZNhmtGjBhB9erV8fDwoEqVKowdO5bU1FQA5s2bxxtvvMGuXbswmUyYTCZbzDd3gf3999888MADuLu7U7p0aQYOHEh8/PXl9Pv27UunTp344IMPCAoKonTp0gwaNMj2Wrcye/ZsevbsSc+ePZk9e3aG8//88w+PPfYY3t7eeHl50aZNG44cOWI7P2fOHOrUqYOrqytBQUEMHjwYsG5gajKZ2Llzp61sTEwMJpOJ9evXA7B+/XpMJhM///wzTZo0wdXVlY0bN3LkyBGeeOIJAgICKFGiBM2aNWPt2rXp4kpOTmbEiBGUL18eV1dXqlWrxuzZszEMg2rVqvHBBx+kK79z505MJhOHDx++7XsiOffDzjM8+/lWLiakUK+sDz8ManXLtXFEJH+pDTY3GAakXrl9udzm7GFdQ/42nJyc6N27N/PmzWP06NG2xce+++47zGYz3bt3Jz4+niZNmjBixAi8vb1ZsWIFvXr1omrVqjRv3vy2r2GxWHjqqacICAhg69atxMbGphsvdI2Xlxfz5s0jODiYv//+mwEDBuDl5cVrr71G165d2bNnD6tWrbJ9ufv4+GSoIyEhgbCwMFq0aMEff/zB+fPnef755xk8eHC6JG/dunUEBQWxbt06Dh8+TNeuXWnYsCEDBgzI8j6OHDnC5s2bWbJkCYZh8Morr3DixAkqVqwIwJkzZ2jbti333Xcfv/zyC97e3mzatMnWSjNz5kyGDx/Ou+++S4cOHYiNjb2jrTxGjhzJBx98QJUqVShZsiSnTp3ikUce4Z133sHV1ZUvv/ySjh07cuDAASpUqABA79692bx5M5988gkNGjTg2LFjREdHYzKZeO6555g7dy6vvvqq7TXmzp1L27ZtqVatWo7jk6wZhsEn4Yf5aO1BAB6qHcDUbg3xcNHHrUhBov8jc0PqFZgYnP+v+/pZcPHMVtHnnnuOyZMn8+uvv3LfffcB1i/Ap59+Gh8fH3x8fNJ9OQ4ZMoTVq1fz7bffZisBWrt2Lfv372f16tUEB1vfi4kTJ2YYtzNmzBjbz5UqVeLVV19l4cKFvPbaa7i7u1OiRAmcnJwIDAzM8rUWLFhAUlISX375JZ6e1vufPn06HTt25L333iMgwNrFULJkSaZPn46joyM1a9bk0UcfJTw8/JYJ0Jw5c+jQoQMlS5YEICwsjLlz5zJhwgQAZsyYgY+PDwsXLsTZ2RmA6tWr265/++23+b//+z+GDh1qO9asWbPbvn83e/PNN3nwwQdtz0uVKkWDBg1sz9966y2WLl3K8uXLGTx4MAcPHuTbb79lzZo1tG/fHoAqVarYyvft25dx48axbds2mjdvTmpqKgsWLMjQKiR3JznNzMjv/2bpX2cA+FfbKox4uKYGOIsUQOoCKyZq1qxJy5YtmTNnDgCHDx9mw4YN9O/fHwCz2cxbb71FvXr1KFWqFCVKlGD16tWcPHkyW/Xv27eP8uXL25IfgBYtWmQot2jRIlq1akVgYCAlSpRgzJgx2X6NG1+rQYMGtuQHoFWrVlgsFg4cOGA7VqdOHRwdr684GxQUxPnz57Os12w288UXX9CzZ0/bsZ49ezJv3jwsFgtg7TZq06aNLfm50fnz5zl79izt2rXL0f1kpmnTpumex8fH8+qrr1KrVi18fX0pUaIE+/bts713O3fuxNHRkXvvvTfT+oKDg3n00Udt//4//vgjycnJdOnS5a5jFauLCSn0/O9Wlv51BkcHE5OeqseoR2op+REpoNQClBucPaytMfZ43Rzo378/Q4YMYcaMGcydO5eqVavavjAnT57Mxx9/zNSpU6lXrx6enp4MGzaMlJSUXAt38+bN9OjRgzfeeIOwsDBbS8qHH36Ya69xo5uTFJPJZEtkMrN69WrOnDlD165d0x03m82Eh4fz4IMP4u6e9eJ0tzoH4OBg/Xvjxt3csxqTdGNyB/Dqq6+yZs0aPvjgA6pVq4a7uzudO3e2/fvc7rUBnn/+eXr16sVHH33E3Llz6dq1a74NYi/qjkTF89y8Pzhx4Qpebk7M7NGE1iF+9g5LRG5BLUC5wWSydkXl9yOHGwk+88wzODg4sGDBAr788kuee+4523igTZs28cQTT9CzZ08aNGhAlSpVOHjwYLbrrlWrFqdOneLcuXO2Y1u2bElX5vfff6dixYqMHj2apk2bEhISwokTJ9KVcXFxwWw23/a1du3aRUJCgu3Ypk2bcHBwoEaNGtmO+WazZ8+mW7du7Ny5M92jW7dutsHQ9evXZ8OGDZkmLl5eXlSqVInw8PBM6/f39wdI9x7dOCD6VjZt2kTfvn158sknqVevHoGBgRw/ftx2vl69elgsFn799dcs63jkkUfw9PRk5syZrFq1iueeey5bry239vuRaJ6csYkTF65QvpQ7S15sqeRHpBBQAlSMlChRgq5duzJq1CjOnTtH3759bedCQkJYs2YNv//+O/v27eNf//oXkZGR2a67ffv2VK9enT59+rBr1y42bNjA6NGj05UJCQnh5MmTLFy4kCNHjvDJJ5+wdOnSdGUqVarEsWPH2LlzJ9HR0Zmuw9OjRw/c3Nzo06cPe/bsYd26dQwZMoRevXrZxv/kVFRUFD/++CN9+vShbt266R69e/dm2bJlXLx4kcGDBxMXF0e3bt34888/OXToEF999ZWt623ChAl8+OGHfPLJJxw6dIgdO3Ywbdo0wNpKc8899/Duu++yb98+fv3113Rjom4lJCSEJUuWsHPnTnbt2sWzzz6brjWrUqVK9OnTh+eee45ly5Zx7Ngx1q9fz7fffmsr4+joSN++fRk1ahQhISGZdlFKznz75yl6z95GXFIajSv4svSlVoQEeNk7LBHJBiVAxUz//v25dOkSYWFh6cbrjBkzhsaNGxMWFsZ9991HYGAgnTp1yna9Dg4OLF26lMTERJo3b87zzz/PO++8k67M448/ziuvvMLgwYNp2LAhv//+O2PHjk1X5umnn+bhhx/m/vvvx9/fP9Op+B4eHqxevZqLFy/SrFkzOnfuTLt27Zg+fXrO3owbXBtQndn4nXbt2uHu7s7XX39N6dKl+eWXX4iPj+fee++lSZMmfP7557butj59+jB16lQ+/fRT6tSpw2OPPcahQ4dsdc2ZM4e0tDSaNGnCsGHDePvtt7MV35QpUyhZsiQtW7akY8eOhIWF0bhx43RlZs6cSefOnXnppZeoWbMmAwYMSNdKBtZ//5SUFPr165fTt0huYLEYvLdqP68t3k2axaBjg2AWDLgHvxL5s1aViNw9k3HjgAQBIC4uDh8fH2JjY/H2Tr9uR1JSEseOHaNy5cq4ubnZKUKRO7NhwwbatWvHqVOnbttapt/1zCWlmhn+7U5W/h0BwMsPVGNY++oa7CxSANzq+/tmGgQtUgwkJycTFRXFhAkT6NKlyx13FRZ3UZeTef7LP9l1KgZnRxPvPlWfp5uUs3dYInIH1AUmUgx88803VKxYkZiYGN5//317h1MoHYi4TKcZm9h1KgZfD2e+7h+q5EekEFMLkEgx0Ldv33SD3iVnfj0YxaD5O4hPTqOynydz+jajsl/2FiEVkYJJCZCIyC18teUEE5b/g9liEFq5FP/p1QRfDxd7hyUid0kJ0B3S2HEp6vLidzwyLomvt5zA2dGB+2uUoU6wd4EdPGy2GExcuY/ZG48B8HTjckx6qh4uTho5IFIUKAHKoWvTna9cuZKt1XdFCqsrV6wb/Ga27UeO60pJ47PfjvKfX4+SmGpd6HLKmoP4lXDlvhr+PFCzDK1D/PB2u/vXyg0JyWkMXfgXa/dZt075d1gNXrqvqm3hUBEp/JQA5ZCjoyO+vr62PaU8PDz0oShFimEYXLlyhfPnz+Pr65tuP7WcslgMlv51hsmrDxARlwRA4wq++Hu5svFQNNHxySzefprF20/j5GCiScWSPFCzDPfXLENImRJ2+X/rXGwi/ef9yd5zcbg4OTDlmQY8Vt8Omx2LSJ6y+zpAM2bMYPLkyURERNCgQQOmTZuW5e7jqampTJo0iS+++IIzZ85Qo0YN3nvvPR5++GFbmQkTJvDGG2+ku65GjRrs378/2zHdbh0BwzCIiIggJiYm23WKFDa+vr4EBgbecRKy5egF3l6xlz1n4gAoV9KdUR1q8Ug9a50paRb+PH6RX/afZ92B8xyJSr9oY1lfd+6v6c/9NcrQomppPFzy/u+1PWdi6f/FH0TGJVPa04XP+zSlcYWSef66IpI7crIOkF0ToEWLFtG7d29mzZpFaGgoU6dO5bvvvuPAgQOUKVMmQ/kRI0bw9ddf8/nnn1OzZk1Wr17N8OHD+f3332nUqBFgTYAWL17M2rVrbdc5OTnh55f9vXmy+waazeYsN7MUKcycnZ3vuOXnWHQC7/68j9X/WLdS8XJ1YvAD1ejTshJuzlnXefLCFdYdsCZDm49cIDnt+lYfLk4O3FOlNA/U8Of+mmWoWDr3Z2Ct2RvJy9/8RWKqmZAyJZjTtxnlS2mzWJHCpNAkQKGhoTRr1sy2hYHFYqF8+fIMGTKEkSNHZigfHBzM6NGjGTRokO3Y008/bdumAKwJ0LJly7K9yWRmcvIGiohVzJUUPgk/zFdbjpNqNnB0MPFs8woMax9C6RxuEZGYYmbz0WjW7Y/il/3nOROTmO58FT9P7q9ZhvtrlKFZ5ZK4Ot15N51hGMzeeIx3Vu7DMKBNiB8zejQuMOORRCT7CsVK0CkpKWzfvp1Ro0bZjjk4ONC+fXs2b96c6TXJyckZluR3d3dn48aN6Y4dOnSI4OBg3NzcaNGiBZMmTaJChQpZxpKcnJxu0824uLg7uSWRYiklzcLXW07wcfghYhOtLaL31/Dn9Udq3fHGoO4ujjxQM4AHagbwpmFw+Hy8tXVofxR/HL/I0egEjm48xuyNx/B0caRVNT/ur1mG+2r4E+ST/ckJaWYL45f/w/ytJwF4NrQCbzxeB2dHzfSSPHI5ErZ8CuZUuG8kuOmPbHuxWwIUHR2N2WzOsCR/QEBAluN1wsLCmDJlCm3btqVq1aqEh4ezZMkSzGazrUxoaCjz5s2jRo0anDt3jjfeeIM2bdqwZ88evLwy/zCeNGlShnFDInJrhmGwZm8kk37ez7Fo6/idGgFejH60Fm2r++fa65hMJkICvAgJ8GJg26rEJaWy6VD01e6yKKIuJ/O/vZH8b6+1y61WkDf3X+0qa1TeF6cskpm4pFQGzd/BhkPRmEww+pFa9G9dWZMaJG9cjoRNH8OfsyHNOiGAAyvg6TlQrol9Yyum7NYFdvbsWcqWLcvvv/9OixYtbMdfe+01fv31V7Zu3ZrhmqioKAYMGMCPP/6IyWSiatWqtG/fnjlz5pCYmJihPEBMTAwVK1ZkypQp9O/fP9MymbUAlS9fXl1gIlnYcyaWt1fsZcvRiwD4lXDh/x6qwTNNy+OYj+v6WCwGe8/FsW7/eX45cJ6dp2K48RPNx92ZttX9ub+GP/dW97d1xZ26eIX+X/zBwch43J0dmdqtIWF1AvMtbilGMkt8yjWzHo89CQ5O8MBYaPkyOKjl8W4Vii4wPz8/HB0diYyMTHc8MjKSwMDMP4j8/f1ZtmwZSUlJXLhwgeDgYEaOHEmVKlWyfB1fX1+qV6/O4cOHsyzj6uqKq2vOxiiIFEeRcUlMXn2A73ecxjCsg5MHtKnMi/dVo4Rr/n+cODiYqFvWh7plfRjSLoSLCSn8dtA6bujXg1HEJqby466z/LjrLCYTNCjnS6tqpVn0xymi41Mo4+XK7D7NqFfOJ99jlyIuq8TnvlFQ9QFIioUfh8LeZbB2PBxdB0/+B7yUiOcXuyVALi4uNGnShPDwcDp16gRYB0GHh4czePDgW17r5uZG2bJlSU1N5fvvv+eZZ57Jsmx8fDxHjhyhV69euRm+SLGS2UKGTzQM5t9hNShXsuDMlCrl6UKnRmXp1KgsaWYLu07HWKfZ749i77k4dp6KYeepGMDaVTanb9McjRkSua3bJT7XuljdfaHLPPjrK1j5GhxdDzNbwZOzIORBOwVfvNh9GnyfPn34z3/+Q/PmzZk6dSrffvst+/fvJyAggN69e1O2bFkmTZoEwNatWzlz5gwNGzbkzJkzTJgwgWPHjrFjxw58fX0BePXVV+nYsSMVK1bk7NmzjB8/np07d7J37178/bM3LkGzwESsLBaDJX+dYfLq/UTGWbuJm1QsyZhHa9GokK2PExGbxPoD1pYhbzdnxnWsjacdWq2kiMpu4pOZqAOw+DmI3GN9fs8gaD8enNQzkVOFogsMoGvXrkRFRTFu3DgiIiJo2LAhq1atsg2MPnnyJA439IkmJSUxZswYjh49SokSJXjkkUf46quvbMkPwOnTp+nevTsXLlzA39+f1q1bs2XLlmwnPyJitfnIBd5ZmfVChoVNoI8b3ZpXoFvzrGeEiuTY3SQ+1/jXgOfDYc042PYf2DIDTmy0DpD2q5a38Rdjdl8JuiBSC5AUZ8eiE5i0cp9tVlV2FzKUQiAxBiL/gYtHwTDftnieMTlAYD0IbFB4B/7mRuKTmQM/w7KXIPEiOHvCox9Ag+53Xl8xU2gWQiyolABJcXRtIcMvNx8nzXJ3CxmKnVnM1iQncg9E7LEmPZF7IPaUvSNLz70UVLnPmjBUvR98ytk7otvLq8TnRnFnYclAOL7B+rxeF3h0itYMygYlQHdJCZAUJ3mxkKHko2utOpH/QOTf1oTn/D5Iy3xpEHzKg191+44vSU2E039CyuX0x/1qWBOhqg9AxVbgWsI+8WUmPxKfG1nMsHEKrJtkba0rWUlrBmWDEqC7pARIioP8WshQcklOW3Wc3KFMLQisCwHXHrXBvYAMXjenWpOgo+vgyC9wZjsY1/d/w8EZKtxjTYiq3A9BDe3TXZbfic/NTm6F75/XmkHZpAToLikBkqIuq4UMuzQpl+XKyZKPbK06e64nPLdr1QmoCwF1ric8paqAQyEas5V4CY79Zk2GjvwCMSfTn8/v7jJ7Jz43Soy5vmYQWN8HrRmUKSVAd0kJkBRVlxJSeGflvgwLGb5wb1W8tPln/rvWqhPx9w0Jzz+3btUJqG1NdALqXf1vHeuaMkWJYVjflyO/wJF11sQoQ3dZ9avJUC53lxWkxOdGhnF9zaC0RPDw05pBmVACdJeUAElRtOPkJQbP38HZWOuHer4vZGgY1laMg6usf+0XZ4mXrInOLVt1KtzQonM14SlVuXC16uQWc6q1i+xa61Bm3WXlQ6+PHwpqkPP3qaAmPjcr7GsGpaVYE9r9P1pnt1W4J1erVwJ0l5QASVFiGAZzNx1n0s/7SDUbVCrtwZSuDWmcHwsZWizWL6v9P8K+H61/1Ut6tladq11XgXWhTO2i16qTm7LVXXbv9RaiW3WXFZbE50apSdfXDAIIrA+d5xbcNYNSEuDwWutnwMH/QXKs9XjzgfDI5Fx9KSVAd0kJkBQVl5NSGfH9blb+HQHAI/UCee/p+nnb3WVOheMbYf9PsH8FXD53/Zyjq/Wv9NIF9IM6v7h4WgcoF+dWndySk+6yKvdDpdbW7rLCmPjc7OY1gx6ZDA2fLRixX7lobe3d96P13+baewxQIgBqPmqd3l+xZa6+rBKgu6QESIqCvWfjeGn+do5fuIKTg4nRj9aib8tKebOKc2qi9UNu34/WD+WkmOvnXLyg+kNQ8zHreAVXTa2XPJSd7rLgRhCxu/AmPje6ec2gup3hsY/ss2ZQ3FnrHz37frT+EXTjYpslK0GtjlDrcSjbNM9msSkBuktKgKSw+/aPU4z9YQ/JaRaCfdyY3qNx7nd5JcbAof9ZP+wOr4XUK9fPefhBzUegZkdrV0RhGZ8gRU/iJTi24WpCFJ6+u6wwJz43unnNIN+K0HkOlGua96994Yj1M2Dfj3Dmz/TnAupak56aj1nHseXDe6wE6C4pAZLCKjHFzNgf9rB4+2kA7q3uz0ddG1LK0yV3XuByJBxYAft+snY1WFKvn/Mpb/2gq9XROrBR3TpS0FzrLjvxO5SsCJXaFO7E52YZ1gwaAy2H5m5ri2FYW8/2/WRNeqL2pT9fPvTq58Bj1qUY8pkSoLukBEgKo6NR8bw0fwf7Iy7jYILhD1bnpfuq4eBwlx/wl45f/7A7tRW44SPDv+b1pCeoQdH6MhEpjPJizSCLGU5ts34G7P8xfSuag5M1kazV0Tqux85rEykBuktKgKSw+Wn3WUYs3k1Cihm/Ei580q0RLav53Vll16arX/uwi/g7/fngxlf78juCX8jdBy8iuSvDmkGlodMs61i87Lpxuvr+FZAQdf2ckztUa2cdz1P9oYKzujg5+/52yqeYRCQPpKRZmLhyH/N+Pw5A88qlmNa9EQHebjmr6Np09X3LrbO3bpyubnK0ztS49hdeYdiwUqQ4M5mgcW9rd9S1NYMWdLn9mkHJ8dbxfPt/goOrITnu+jk3H6jewdq1VbUduOTT+mF5SC1AmVALkBQGpy9dYdCCv9h1KgaAF+6tyqsPVc/+Vha3na7+gPXDrnoH8Cyd+zcgInnvdmsGXblonbm5/6esp6vX6mjt5nIs+KvFqwvsLikBkoLul/2RvLJoF7GJqfi4OzPlmQa0qxVw+wstFji0Gvb+kMV09TBr0lOtvaarixQlN68ZFDoQzuzIZLp6ZetnQB5PV88rSoDukhIgKajSzBamrDnIp+uPAFC/nA8znm1M+VLZaI6+dML6AXhi4/Vj16ar13ocKrfVdHWRouzmNYOuscN09byiMUAiRdD5uCSGfPMXW49Zd3Dv3aIiox+thavTbaabGwbs+BJWvw4p8da//hr31nR1keLGOxh6/wC/T4Oj6693c9thunpBoARIpBDYfOQCQ775i+j4ZDxdHHn36fp0bBB8+wvjzsGPL1sXLASo0AI6fVpsP/BEij0HR2g9zPoo5pQAiRRgFovBzF+P8OH/DmAxoEaAF5/2bExV/xK3vtAwYM/3sOL/rON8HF2h3Vi45yW1+IiIoARIpMC6lJDC8G93su6Adf2NpxuX4+1OdXF3uU0Ck3ABVgy/vhBaUEPrQmhlauZpvCIihYkSIJEC6K+Tlxi84C/OxCTi6uTAm0/U4Zmm5W+/kemBn2H5y5Bw3rpCa9vXoM3wQjF9VUQkPykBEilADMNg3u/HmbhyH6lmg0qlPfi0RxNqB99mNmJSLKwaBTvnW5/714InZ1p3vRYRkQyUAIkUEJeTUhn5/d+s+Nu6IGGHuoG817k+3m63ab05uh5+GAyxpwATtBwC948G5xyuBi0iUowoARIpAPadi+Ol+Ts4Fp2Ak4OJ1x+pRb9WlW7d5ZWSAGsnwLbPrM9LVoYnZ1mntouIyC0pARKxs2//OMXYH/aQnGYh2MeN6T0a07jCbTYXPLUNlv7r+p5dzZ6H9m+A621mh4mICKAESMRuElPMjP1hD4u3nwbg3ur+fNS1IaU8XbK+KC0Z1k+CTR+DYQHvsvD4NOvOzCIikm1KgETs4GhUPC/N38H+iMs4mGD4g9V56b5qODjcosvr3G5rq8/5vdbnDbrDw++Cu2++xCwiUpQoARLJZyt2n2PE97uJT07Dr4QLn3RrRMtqfllfYE6DjR/Br++CJQ08/eGxqdYl7EVE5I4oARLJR3M2HuPNn6wtOM0rl2Ja90YEeN9itlbUQWurz9kd1ue1OlqTH89bJEwiInJbSoBE8snx6ATeW7UfgAFtKjPi4Zo4OTpkXthiga2zIPwNSEsCNx945AOo16VQ79QsIlJQKAESyQeGYTBmmXWmV5sQP15/pFbWU9wvHYdlg+DERuvzau2tA529s7H5qYiIZIsSIJF88MPOs2w8HI2rkwNvd6qbefJjGLDjC1g9GlLiwdkTwt6BJn3V6iMiksuyaH/PPzNmzKBSpUq4ubkRGhrKtm3bsiybmprKm2++SdWqVXFzc6NBgwasWrXqruoUyWsxV1J46+q4n5fbhVCxtGfGQnHnYH4X+HGoNfmp0BJe3ARN+yn5ERHJA3ZNgBYtWsTw4cMZP348O3bsoEGDBoSFhXH+/PlMy48ZM4b//Oc/TJs2jb179/LCCy/w5JNP8tdff91xnSJ5bdLK/VxISKF6QAkGtKmS/qRhwO7v4NN74PAacHSFh96BviugVGX7BCwiUgyYDMMw7PXioaGhNGvWjOnTpwNgsVgoX748Q4YMYeTIkRnKBwcHM3r0aAYNGmQ79vTTT+Pu7s7XX399R3VmJi4uDh8fH2JjY/H2vs0mlCK3sPXoBbp+tgWAxS+0oGmlUtdPJkTDiuGw9wfr8+BG8OR/wL+GHSIVESn8cvL9bbcWoJSUFLZv30779u2vB+PgQPv27dm8eXOm1yQnJ+Pmln7KsLu7Oxs3brzjOq/VGxcXl+4hcreS08y8vvRvALo3r5A++dm/0trqs/cHcHCybl7af42SHxGRfGK3BCg6Ohqz2UxAQEC64wEBAURERGR6TVhYGFOmTOHQoUNYLBbWrFnDkiVLOHfu3B3XCTBp0iR8fHxsj/Lly9/l3YnAf349ypGoBPxKuDLy4ZrWg0lxsOwlWNgdEqLAvxY8Hw73vgaOt9n1XUREco3dB0HnxMcff0xISAg1a9bExcWFwYMH069fPxwc7u42Ro0aRWxsrO1x6tSpXIpYiqujUfFMX3cYgHEda+Pj4QyJl+CLx2DnfMAErYbCv36F4IZ2jVVEpDiy2zR4Pz8/HB0diYyMTHc8MjKSwMDATK/x9/dn2bJlJCUlceHCBYKDgxk5ciRVqlS54zoBXF1dcXV1vcs7ErEyDIPRS/eQkmahbXV/OtYPsiY/Xz4B53aBhx90mw8V7rF3qCIixZbdWoBcXFxo0qQJ4eHhtmMWi4Xw8HBatGhxy2vd3NwoW7YsaWlpfP/99zzxxBN3XadIblmy4wybj17AzdmBdzrVxZQUkz756fOjkh8RETuz60KIw4cPp0+fPjRt2pTmzZszdepUEhIS6NevHwC9e/embNmyTJo0CYCtW7dy5swZGjZsyJkzZ5gwYQIWi4XXXnst23WK5KWLCSm8vcK65s/QdtUp756cMfkJqG3nKEVExK4JUNeuXYmKimLcuHFERETQsGFDVq1aZRvEfPLkyXTje5KSkhgzZgxHjx6lRIkSPPLII3z11Vf4+vpmu06RvDRx5T4uXUmlZqAXzzctqeRHRKSAsus6QAWV1gGSO7H5yAW6f74FkwmW9qtDw3W9lfyIiOSjQrEOkEhRkpxmZvTVNX/6N/FV8iMiUsBpM1SRXPDpuiMcjU6gSolURkWNhMjdSn5ERAowJUAid+nw+Xhmrj+CN/EsLTEVx8i9Sn5ERAo4JUAid8G65s/fuJnjWO49GZ+YQ0p+REQKASVAInfhu+2n2XfsJN+4TqJSyjElPyIihYQSIJE7dCE+mWkr/mC+y0TqmI4r+RERKUQ0C0zkDk1Zvo1PzW9Qz+E4hpIfEZFCRQmQyB3Y8s8Ruu0fTD2H46S6lcak5EdEpFBRAiSSQ0lxFyj5fWfqORwn3skX534/KfkRESlklACJ5ETiJWL/8wg1LEe5iLdafkRECiklQCLZlXiJpNkdCUjYT7Thzd4Hv8azfH17RyUiIndACZBIdiRewvjyCdyi/yba8OaTch/SqmVbe0clIiJ3SAmQyO0kXoIvn8B0bhfRhjfPGWP5V5eOmEwme0cmIiJ3SAmQyK1cTX44t4uLePNsymgef7A9ZX3d7R2ZiIjcBSVAIlm5Ifm57OhLt+TROAfVoW/LSvaOTERE7pJWghbJzA3JT4prKZ6OG8FhyrPsqXo4OervBhGRwk4JkMjNbkh+DA8/BljGctDwp1+rStQv52vv6EREJBfoT1mRG92Q/ODhx7yQT/g1xp9Abzf+76Ea9o5ORERyiRIgkWtuSn6OPbaQd/6wzvR644k6lHBVg6mISFGhBEgEMiQ/lt7LefXXVNIsBg/VDiCsTqC9IxQRkVykBEjkpuSHPj+y8IQX209cwtPFkQmP17F3hCIiksuUAEnxlknyc96jCpN+3gfA/z1Ug2Ct+SMiUuQoAZLiK5Pkh4DavPXTPi4npVGvrA99tOaPiEiRpARIiqcskp/1B87z466zOJhg0lP1cHTQdhciIkWREiApfrJIfhJTzIxZtgeA51pVpm5ZHzsHKiIieUUJkBQvWSQ/AB+HH+L0pUSCfdx45cHqdg5URETykhIgKV4WP5dp8rPvXByfbzgKwJtP1MVTa/6IiBRpSoCk+Di/D478AiZH6LPclvxYLAajlvyN2WLQoW4g7WsH2DlQERHJa0qApPjY/oX1vzU6QMD1tX3mbzvJzlMxlHB1YnxHrfkjIlIcKAGS4iE1EXYtsP7cpJ/tcGRcEu//vB+Af4fVINDHzR7RiYhIPlMCJMXD3h8gKRZ8KkDV+22H3/xxL5eT02hQ3pee91S0Y4AiIpKflABJ8bB9nvW/jXuDgyMAv+yPZMXf53B0MDHxybpa80dEpBhRAiRF3/l9cHKzdfBzo54AXElJY+yyfwDo37oydYK15o+ISHFi9wRoxowZVKpUCTc3N0JDQ9m2bdsty0+dOpUaNWrg7u5O+fLleeWVV0hKSrKdnzBhAiaTKd2jZs2aeX0bUpDdOPjZOwiAqWsPcSYmkbK+7gxrH2LH4ERExB7sutjJokWLGD58OLNmzSI0NJSpU6cSFhbGgQMHKFOmTIbyCxYsYOTIkcyZM4eWLVty8OBB+vbti8lkYsqUKbZyderUYe3atbbnTk5a06XYymTw8z9nY5m98RgAb3eqi4eLfj9ERIobu7YATZkyhQEDBtCvXz9q167NrFmz8PDwYM6cOZmW//3332nVqhXPPvsslSpV4qGHHqJ79+4ZWo2cnJwIDAy0Pfz8/PLjdqQgumnws9li8PrVNX8erRfE/TUzJtoiIlL02S0BSklJYfv27bRv3/56MA4OtG/fns2bN2d6TcuWLdm+fbst4Tl69CgrV67kkUceSVfu0KFDBAcHU6VKFXr06MHJkydvGUtycjJxcXHpHlJE3DT4+avNx9l1OhYvVyfGd6xt19BERMR+7Nb2Hx0djdlsJiAg/aq7AQEB7N+/P9Nrnn32WaKjo2ndujWGYZCWlsYLL7zA66+/bisTGhrKvHnzqFGjBufOneONN96gTZs27NmzBy8vr0zrnTRpEm+88Ubu3ZwUDDcNft59OoZJV9f8ea1DTcp4a80fEZHiyu6DoHNi/fr1TJw4kU8//ZQdO3awZMkSVqxYwVtvvWUr06FDB7p06UL9+vUJCwtj5cqVxMTE8O2332ZZ76hRo4iNjbU9Tp06lR+3I3nthsHPUaZS/Our7SSnWWhXsww9mlewb2wiImJXdmsB8vPzw9HRkcjIyHTHIyMjCQwMzPSasWPH0qtXL55//nkA6tWrR0JCAgMHDmT06NE4OGTM53x9falevTqHDx/OMhZXV1dcXV3v4m6kwLlh8HNqoz68+PV2zsUmUdXfk4+6NcRBa/6IiBRrdmsBcnFxoUmTJoSHh9uOWSwWwsPDadGiRabXXLlyJUOS4+hoXdTOMIxMr4mPj+fIkSMEBQXlUuRSKNww+Hn8njL8eeISXm5OfN67Kd5uzvaOTkRE7Myu83+HDx9Onz59aNq0Kc2bN2fq1KkkJCTQr591unLv3r0pW7YskyZNAqBjx45MmTKFRo0aERoayuHDhxk7diwdO3a0JUKvvvoqHTt2pGLFipw9e5bx48fj6OhI9+7d7XafYgdXBz/vLPM4C/44g8kEn3RvRBX/EvaNS0RECgS7JkBdu3YlKiqKcePGERERQcOGDVm1apVtYPTJkyfTtfiMGTMGk8nEmDFjOHPmDP7+/nTs2JF33nnHVub06dN0796dCxcu4O/vT+vWrdmyZQv+/v75fn9iJ1cHPxsmR176pxYAr4XV5P4amvIuIiJWJiOrvqNiLC4uDh8fH2JjY/H29rZ3OJJTP4+ErTNZR3P6JQ2jY4NgPunWEJNJ435ERIqynHx/F6pZYCK3lZqIcXXw87yU+6kT7M37T9dX8iMiIuloDwApUox/lmFKiuW04cdet8Ys690UdxdHe4clIiIFjFqApEiJWDcLgEXmB5jesxllfd3tHJGIiBRESoCkyNi2bRNBsTtJMxyo2H4goVVK2zskEREpoHKcAFWqVIk333zztvtrieSno1HxHFg5HYADPq14+t6mdo5IREQKshwnQMOGDWPJkiVUqVKFBx98kIULF5KcnJwXsYlky+WkVAZ9sYnHjfUAVH/0ZQ16FhGRW7qjBGjnzp1s27aNWrVqMWTIEIKCghg8eDA7duzIixhFsmSxGLyyaCc1L67Dx3QFs3d5nEPa2TssEREp4O54DFDjxo355JNPbKst//e//6VZs2Y0bNiQOXPmZLk1hUhu+mjtQdbuO09P518AcGzaBxw060tERG7tjqfBp6amsnTpUubOncuaNWu455576N+/P6dPn+b1119n7dq1LFiwIDdjFUln5d/nmPbLYUJMp2liOgAmR2jY095hiYhIIZDjBGjHjh3MnTuXb775BgcHB3r37s1HH31EzZo1bWWefPJJmjVrlquBitxo37k4/u/bXQC8U+FPiARqdABvbXorIiK3l+MEqFmzZjz44IPMnDmTTp064eyccWftypUr061bt1wJUORmFxNSGPDlnySmmnmgqhfNLqy2nmjSz76BiYhIoZHjBOjo0aNUrFjxlmU8PT2ZO3fuHQclkpVUs4VB83dw+lIiFUt7MK3hSUwrYsGnAlS9397hiYhIIZHjQdDnz59n69atGY5v3bqVP//8M1eCEsnKOyv2sfnoBTxdHPm8d1M8d39lPdGktwY/i4hItuU4ARo0aBCnTp3KcPzMmTMMGjQoV4ISycy3f55i3u/HAZjStSHVTafh1BYNfhYRkRzLcQK0d+9eGjdunOF4o0aN2Lt3b64EJXKzHScvMWbpHgCGtQ8hrE4gbP/CelKDn0VEJIdynAC5uroSGRmZ4fi5c+dwctLm8pL7IuOSeOGr7aSYLYTVCeDlB0IgNRF2XV1mQYOfRUQkh3KcAD300EOMGjWK2NhY27GYmBhef/11HnzwwVwNTiQp1cy/vtrO+cvJVA8owYfPNMTBwQR7f4AkDX4WEZE7k+Mmmw8++IC2bdtSsWJFGjVqBMDOnTsJCAjgq6++yvUApfgyDIMxy/aw81QMPu7OfN67KSVcr/7K/nl1lqEGP4uIyB3IcQJUtmxZdu/ezfz589m1axfu7u7069eP7t27Z7omkMidmvf7cRZvP42DCWY825iKpT2tJ87v0+BnERG5K3c0aMfT05OBAwfmdiwiNr8fjubtFfsAeP2RWrQO8bt+UoOfRUTkLt3xqOW9e/dy8uRJUlJS0h1//PHH7zooKd5OXbzCSwt2YLYYPNWoLP1bV75+UoOfRUQkF9zRStBPPvkkf//9NyaTybbru8lkAsBsNuduhFKsJCSnMeDLP4m5kkqDcj5MfKqe7XcLuGnw8wP2C1RERAq1HM8CGzp0KJUrV+b8+fN4eHjwzz//8Ntvv9G0aVPWr1+fByFKcWEYBv9evIv9EZfxK+HKrF5NcHO+aYBzusHPOf71FRERAe6gBWjz5s388ssv+Pn54eDggIODA61bt2bSpEm8/PLL/PXXX3kRpxQDM9YdZuXfETg7mvhPr8YE+binL6DBzyIikkty/Ce02WzGy8sLAD8/P86ePQtAxYoVOXDgQO5GJ8XG2r2RfPC/gwC8+URdmlQslbHQ9nnW/2rws4iI3KUctwDVrVuXXbt2UblyZUJDQ3n//fdxcXHhs88+o0qVKnkRo+SmuHPgURqcXOwdic3h85cZtmgnAL3uqUj35hUyFkpNhF3fWH/W4GcREblLOW4BGjNmDBaLBYA333yTY8eO0aZNG1auXMknn3yS6wFKLrGY4Zd3YEot+Px+SIyxd0QAxCamMuDL7cQnp9G8cinGdaydeUENfhYRkVyU4xagsLAw28/VqlVj//79XLx4kZIlS6afrSMFR8IF+L4/HF1nfR65Bxb1hJ5L7NoSZLYYvPzNXxyLTqCsrzszezTG2TGLnFyDn0VEJBfl6JskNTUVJycn9uzZk+54qVKllPwUVKf/hP+0tSY/Tu7wwBhwKQHHN8APg+DqMgb28P7q/fx6MAo3Zwf+06sJpUu4Zl5Qg59FRCSX5agFyNnZmQoVKmitn8LAMOCP/8KqUWBJhdLV4JmvIKA2BDeGBc/A39+CTzloPz7fw/th5xn+8+tRACZ3bkDdsj5ZF9bgZxERyWU57ksYPXo0r7/+OhcvXsyLeCQ3pCTAkoGw8lVr8lPrcRiwzpr8AFRrBx2vjtfaOAX+nJOv4e05E8tri3cD8OJ9VenYIDjrwhr8LCIieSDHY4CmT5/O4cOHCQ4OpmLFinh6eqY7v2PHjlwLTu5A9CFY1Aui9lm7jB58E1oMgpu7KBv1gNhTsH4SrPg/8AqGGg/nfXjxyQz88k+S0yzcV8OfVx+qcesLNPhZRETyQI4ToE6dOuVBGJIr/lkGPwyGlMtQIgC6zIOKLbMuf+8IaxL019ewuB/0/QnKNsmz8MwWg5fm7+BsbBJV/Dz5uFsjHB1uM3ZMg59FRCQP5DgBGj8+d8eLzJgxg8mTJxMREUGDBg2YNm0azZs3z7L81KlTmTlzJidPnsTPz4/OnTszadIk3Nzc7rjOQs+cCmsnwObp1ucVW0HnueAVcOvrTCZ4bKp1baAj4bCgK/RfA6Uq3/q6OzR741G2HbtICVcnPuvdFB9351tfcOPg50a98iQmEREpnuz6J/WiRYsYPnw448ePZ8eOHTRo0ICwsDDOnz+fafkFCxYwcuRIxo8fz759+5g9ezaLFi3i9ddfv+M6C724c/BFx+vJT6uh0Hv57ZOfaxyd4ZkvILA+JETB109bp83nssPn420rPY97rDbVypS4/UU3Dn72Csz1mEREpPjKcQLk4OCAo6Njlo+cmDJlCgMGDKBfv37Url2bWbNm4eHhwZw5mQ/K/f3332nVqhXPPvsslSpV4qGHHqJ79+5s27btjuss1I5tsE5xP7kZXL2h69fWMT+OOWzYc/WCHt+BT3m4eAS+6WYdfJxLzBaD1xbvIiXNQtvq/nRpWu72F2nws4iI5KEcd4EtXbo03fPU1FT++usvvvjiC954441s15OSksL27dsZNWqU7ZiDgwPt27dn8+bNmV7TsmVLvv76a7Zt20bz5s05evQoK1eupFevXndcJ0BycjLJycm253Fxcdm+D7swDNj0MYS/AYYFytSBrl9B6ap3XqdXIPRYDHMegtPbYMkA6PIFOOQsqc3M3E3H2HEyhhKuTrz7VL3srRmlwc8iIpKHcpwAPfHEExmOde7cmTp16rBo0SL69++frXqio6Mxm80EBKTvqgkICGD//v2ZXvPss88SHR1N69atMQyDtLQ0XnjhBVsX2J3UCTBp0qQcJW92lRQLy16C/T9ZnzfoDo9OARePu6+7TE3o9g181Qn2/QirR0OHd++qyqNR8Uxebd0kd8yjtQj2db/NFVdp8LOIiOShXPtmueeeewgPD8+t6jK1fv16Jk6cyKeffsqOHTtYsmQJK1as4K233rqrekeNGkVsbKztcerUqVyKOJdF7IHP7rMmP44u1gHMnWbmTvJzTaVW1joBts6EzTPuuCpr19duktMstAnxo2uz8tm7UIOfRUQkj+W4BSgziYmJfPLJJ5QtWzbb1/j5+eHo6EhkZGS645GRkQQGZj7gdezYsfTq1Yvnn38egHr16pGQkMDAgQMZPXr0HdUJ4OrqiqtrFtswFBQ7F8BPwyEt0dot9MwXULZx3rxWvc4QdxbWjIXVr4N3MNR5MsfVzPv9OH+euGTt+nq6fva3S9HgZxERyWM5bgEqWbIkpUqVsj1KliyJl5cXc+bMYfLkydmux8XFhSZNmqRrNbJYLISHh9OiRYtMr7ly5QoON3WHXBt4bRjGHdVZ4KUmwY9DYdmL1uSnWnv41695l/xc03IINB9o/XnJv+BE1mOoMnMsOoHJq63djq8/Uouy2e360uBnERHJBzluAfroo4/S/SXv4OCAv78/oaGhlCxZMkd1DR8+nD59+tC0aVOaN2/O1KlTSUhIoF8/6xdf7969KVu2LJMmTQKgY8eOTJkyhUaNGhEaGsrhw4cZO3YsHTt2tCVCt6uzULl0Ar7tDed2Aia4bxS0/Xf+jIkxmeDhd60tQft/ss4M678G/Kvf9lLL1VlfSakWWlfzo3vzbHZ9gXUxRw1+FhGRPJbjBKhv37659uJdu3YlKiqKcePGERERQcOGDVm1apVtEPPJkyfTtfiMGTMGk8nEmDFjOHPmDP7+/nTs2JF33nkn23UWGgf/Z52JlRQD7qXg6c+trT/5ycERnvocvnwcTv9hXSPo+bW3XWPoi83H+eP4JTxdHJmU3Vlf11zr/tLgZxERyUMmwzCMnFwwd+5cSpQoQZcuXdId/+6777hy5Qp9+vTJ1QDtIS4uDh8fH2JjY/H29s7fF7eYYf278Nv71udlm1ino/vmoBUltyVEw+wH4eJRCGoAfVeCa+YLGR6PTuDhj38jKdXC253q0vOeitl/nfP74NN7rIOfh+/V+B8REcmRnHx/5/hP7EmTJuHn55fheJkyZZg4cWJOq5MbJVywtrJcS36aPQ/9frZv8gPg6WddI8ijNJzbBd/1BXNahmIWi8Fr3+8mKdVCy6qlebZ5hZy9jgY/i4hIPslxAnTy5EkqV864V1TFihU5efJkrgRVLJ3+07qq89F14OQOT34Gj34ITgVkdlrpqvDst9bYDq+BFa9YF2S8wVdbTrDt2EU8XBx57+n6ONxuo9Mb3Tj4uWkhHK8lIiKFSo4ToDJlyrB79+4Mx3ft2kXp0qVzJahixTBg2+cw52GIOw2lq8GAX6BBV3tHllG5ptB5DpgcYMeX8NsHtlMnL1zh3Z+ts75GdahJ+VI5XJvoxsHPVTT4WURE8laOE6Du3bvz8ssvs27dOsxmM2azmV9++YWhQ4fSrVu3vIix6EpJgCUDYeWrYEmFWo/DgHUQUNvekWWt5iPQ4WoX3bq3YeeCq11fu0hMNXNPlVL0CM3BuJ9rNPhZRETyUY5ngb311lscP36cdu3a4eRkvdxisdC7d2+NAcqJ6EOwqBdE7bMO+n3wTWgxyDr9vKBrPgBiT1n3I1s+hF9Omdhy1Bt3Z0fef7pBzrq+QCs/i4hIvstxAuTi4sKiRYt4++232blzJ+7u7tSrV4+KFe/gr/7i6p9l8MNgSLkMJQKhy1yo2NLeUeVMuwkQewb2LOaeP1+hlmkc3To8QoXSd7AthwY/i4hIPrvjrTBCQkIICQnJzViKPnMqrJ0Am6dbn1dsbR1Tc5t1dQokBwcsj89g38GD1EnZzXz3D/Ct1THn9Wjws4iI2EGOB1s8/fTTvPfeexmOv//++xnWBpKbLH/5evLTaij0/qFwJj9XLdgRSfe4IRwyylHKcgGHBV0gMSZnlWjws4iI2EGOE6DffvuNRx55JMPxDh068Ntvv+VKUEVWy8HWLq+uX1vH/Djmyl60dnHq4hUmrdxHHJ781eZz631F7YNFPSEtOfsVafCziIjYQY6/ceLj43Fxcclw3NnZmbi4uFwJqsgKqANDd8GddBUVIIZhMHLJbhJSzDSvVIrOD7SAHt+BSwk4vsE6vik7C4xr8LOIiNhJjhOgevXqsWjRogzHFy5cSO3aBXj6dkHh7GbvCO7aN9tOsenwBdycHXiv89UFD4PqwzNfgoMT/P0thL95+4o0+FlEROwkx30wY8eO5amnnuLIkSM88IB1zEZ4eDgLFixg8eLFuR6gFCynL13hnRV7Afh3WE0q+3leP1mtHXT8BH54CTZOAZ9y0Kx/5hVp8LOIiNhRjhOgjh07smzZMiZOnMjixYtxd3enQYMG/PLLL5QqVSovYpQCwjAMRi35m4QUM00rlqRvy0oZCzXqAbGnYf1E6wKP3sHWFp6bXRv87KvBzyIikv/uaNTpo48+yqZNm0hISODo0aM888wzvPrqqzRo0CC345MCZNEfp9hwKBpXJwfe71wfx6wWPLz3NeuYHsMCi5+D09szlrnW/dW4jwY/i4hIvrvjb57ffvuNPn36EBwczIcffsgDDzzAli1bcjM2KUDOxCTy9op9APw7rAZV/EtkXdhkgsc+gqrtIPUKLHgGLh69fj7d4OeeeRy5iIhIRjlKgCIiInj33XcJCQmhS5cueHt7k5yczLJly3j33Xdp1qxZXsUpdnSt6ys+OY3GFXzp16ry7S9ydIZnvoDA+nAlGr7uDAkXrOc0+FlEROws2wlQx44dqVGjBrt372bq1KmcPXuWadOm5WVsUkB89+dpfjsYhYuTA5O7NMi66+tmrl7W6fE+FeDiEfimG1y5qMHPIiJid9keBP3zzz/z8ssv8+KLL2oLjGLkXGwib/1knfX16kPVqXqrrq/MeAVCz8Uw+0E4vQ0+u0+Dn0VExO6y3QK0ceNGLl++TJMmTQgNDWX69OlER0fnZWxiZ9e6vi4np9Gogi/9W1e5s4r8a0C3b8DRBWJOWI9p8LOIiNhRtr+B7rnnHj7//HPOnTvHv/71LxYuXEhwcDAWi4U1a9Zw+fLlvIxT7GDx9tOsP3C16+tWs76yo1IreHKW9WdHFw1+FhERuzIZRnb2LMjcgQMHmD17Nl999RUxMTE8+OCDLF++PDfjs4u4uDh8fHyIjY3F29vb3uHYRURsEg9+9CuXk9IY2aEmL9xbNXcqPvabNQGqcE/u1CciInJVTr6/76oPokaNGrz//vucPn2ab7755m6qkgLEMAxeX/o3l5PSaFDel+dbZ2PWV3ZVbqvkR0RE7C5XBmE4OjrSqVOnItH6I7Bkxxl+2X8eF0cHPuhcHydHjdUREZGiRd9skk5kXBJv/PgPAMMeDCEkwMvOEYmIiOQ+JUBiYxgGry/5m7ikNOqX82Fgmzuc9SUiIlLAKQESm2U7zxB+tetrcucG6voSEZEiS99wAsD5uCQmLLcueDi0fQg1AtX1JSIiRZcSILk662sPsYmp1Cvrw7/aqutLRESKNiVAwvJdZ1m7LxJnRxOTu2jWl4iIFH36pivmzl9OYvxy66yvlx8IoWZg8Vz4UUREihclQMWYYRiMWbqHmCup1An25oX7cmm1ZxERkQJOCVAx9uPuc/xvbyRODiY+6NIAZ3V9iYhIMaFvvGIq6nIy43/YA8CQB0KoFaSuLxERKT4KRAI0Y8YMKlWqhJubG6GhoWzbti3Lsvfddx8mkynD49FHH7WV6du3b4bzDz/8cH7cSqFgGAZjl+3h0pVUagd589L96voSEZHixcneASxatIjhw4cza9YsQkNDmTp1KmFhYRw4cIAyZcpkKL9kyRJSUlJszy9cuECDBg3o0qVLunIPP/wwc+fOtT13dXXNu5soZFb8fY5V/0Tg5GCd9aWuLxERKW7s/s03ZcoUBgwYQL9+/ahduzazZs3Cw8ODOXPmZFq+VKlSBAYG2h5r1qzBw8MjQwLk6uqarlzJkiXz43YKvOj4ZMb9YJ31Nej+atQJ9rFzRCIiIvnPrglQSkoK27dvp3379rZjDg4OtG/fns2bN2erjtmzZ9OtWzc8PT3THV+/fj1lypShRo0avPjii1y4cCHLOpKTk4mLi0v3KKrG//APFxNSqBnoxaD7q9k7HBEREbuwawIUHR2N2WwmICAg3fGAgAAiIiJue/22bdvYs2cPzz//fLrjDz/8MF9++SXh4eG89957/Prrr3To0AGz2ZxpPZMmTcLHx8f2KF++/J3fVAG24+QlVvx9Dsers75cnOzeACgiImIXdh8DdDdmz55NvXr1aN68ebrj3bp1s/1cr1496tevT9WqVVm/fj3t2rXLUM+oUaMYPny47XlcXFyRTILW7T8PwCP1gqhbVl1fIiJSfNm1CcDPzw9HR0ciIyPTHY+MjCQwMPCW1yYkJLBw4UL69+9/29epUqUKfn5+HD58ONPzrq6ueHt7p3sURRsORQPQJsTPzpGIiIjYl10TIBcXF5o0aUJ4eLjtmMViITw8nBYtWtzy2u+++47k5GR69ux529c5ffo0Fy5cICgo6K5jLqxir6Sy+3QMoARIRETE7oNAhg8fzueff84XX3zBvn37ePHFF0lISKBfv34A9O7dm1GjRmW4bvbs2XTq1InSpUunOx4fH8+///1vtmzZwvHjxwkPD+eJJ56gWrVqhIWF5cs9FUSbj0ZjMaBamRIE+bjbOxwRERG7svsYoK5duxIVFcW4ceOIiIigYcOGrFq1yjYw+uTJkzg4pM/TDhw4wMaNG/nf//6XoT5HR0d2797NF198QUxMDMHBwTz00EO89dZbxXotoN+udn+1rqbWHxEREZNhGIa9gyho4uLi8PHxITY2tsiMB2r7/jpOXrzC7D5NaVcr4PYXiIiIFDI5+f62exeY5L2TF65w8uIVnBxMhFYpffsLREREijglQMXAhsNRADSuUJISrnbv9RQREbE7JUDFwMZr4380+0tERARQAlTkmS0Gmw5r/R8REZEbKQEq4nafjiEuKQ1vNyfql/O1dzgiIiIFghKgIu5a91fLqn44OpjsHI2IiEjBoASoiNtwWON/REREbqYEqAhLSE7jr5OXAI3/ERERuZESoCJs67ELpJoNypdyp2JpT3uHIyIiUmAoASrCru/+7m/nSERERAoWJUBFmC0B0v5fIiIi6SgBKqLOxSZy+Hw8DibrDDARERG5TglQEXVt+nu9cr74eDjbORoREZGCRQlQEbXxsLq/REREsqIEqAiy3LD9hdb/ERERyUgJUBG0P+Iy0fEpeLg40rhCSXuHIyIiUuAoASqCNhyKAuCeKqVxcdI/sYiIyM307VgEXRv/01rjf0RERDKlBKiISUo1s+3YRUDbX4iIiGRFCVAR8+fxSySnWQjwdqVamRL2DkdERKRAUgJUxGw4bB3/07qaPyaTyc7RiIiIFExKgIqYawsgtq2u7i8REZGsKAEqQqLjk/nnbBwArTQAWkREJEtKgIqQa4sf1gryxq+Eq52jERERKbiUABUh17q/NPtLRETk1pQAFRGGYWj9HxERkWxSAlREHIlK4FxsEi5ODjSvXMre4YiIiBRoSoCKiI1Xt79oXqkUbs6Odo5GRESkYFMCVERsOKTd30VERLJLCVARkGq2sOXoBUDjf0RERLJDCVAR8NfJGBJSzJT2dKF2kLe9wxERESnwlAAVAdfG/7Ss5oeDg7a/EBERuR0lQEXAhqvT39uo+0tERCRbCkQCNGPGDCpVqoSbmxuhoaFs27Yty7L33XcfJpMpw+PRRx+1lTEMg3HjxhEUFIS7uzvt27fn0KFD+XEr+S72Siq7TsUAGgAtIiKSXXZPgBYtWsTw4cMZP348O3bsoEGDBoSFhXH+/PlMyy9ZsoRz587ZHnv27MHR0ZEuXbrYyrz//vt88sknzJo1i61bt+Lp6UlYWBhJSUn5dVv5ZvPRaCwGVPX3JNjX3d7hiIiIFAp2T4CmTJnCgAED6NevH7Vr12bWrFl4eHgwZ86cTMuXKlWKwMBA22PNmjV4eHjYEiDDMJg6dSpjxozhiSeeoH79+nz55ZecPXuWZcuW5eOd5Y8Ntu0v/O0ciYiISOFh1wQoJSWF7du30759e9sxBwcH2rdvz+bNm7NVx+zZs+nWrRuenp4AHDt2jIiIiHR1+vj4EBoammWdycnJxMXFpXsUFtr+QkREJOfsmgBFR0djNpsJCAhIdzwgIICIiIjbXr9t2zb27NnD888/bzt27bqc1Dlp0iR8fHxsj/Lly+f0Vuzi1MUrnLhwBScHE/dULW3vcERERAoNu3eB3Y3Zs2dTr149mjdvflf1jBo1itjYWNvj1KlTuRRh3rrW/dWogi8lXJ3sHI2IiEjhYdcEyM/PD0dHRyIjI9Mdj4yMJDAw8JbXJiQksHDhQvr375/u+LXrclKnq6sr3t7e6R6FwYar6/9o/I+IiEjO2DUBcnFxoUmTJoSHh9uOWSwWwsPDadGixS2v/e6770hOTqZnz57pjleuXJnAwMB0dcbFxbF169bb1lmYmC0Gvx+5uv2Fpr+LiIjkiN37TYYPH06fPn1o2rQpzZs3Z+rUqSQkJNCvXz8AevfuTdmyZZk0aVK662bPnk2nTp0oXTr92BeTycSwYcN4++23CQkJoXLlyowdO5bg4GA6deqUX7eV5/4+E0tsYipebk7UL+tj73BEREQKFbsnQF27diUqKopx48YRERFBw4YNWbVqlW0Q88mTJ3FwSN9QdeDAATZu3Mj//ve/TOt87bXXSEhIYODAgcTExNC6dWtWrVqFm5tbnt9PfrFtf1G1NE6OhXool4iISL4zGYZh2DuIgiYuLg4fHx9iY2ML7Higrv/ZzNZjF3mrU1163VPR3uGIiIjYXU6+v9V0UAglJKex4+QlQPt/iYiI3AklQIXQ1mMXSDUblC/lTsXSHvYOR0REpNBRAlQIXVv/p3U1f0wmk52jERERKXyUABVCG237f6n7S0RE5E4oASpkImKTOHQ+HpPJOgNMREREck4JUCFzbfPT+mV98PVwsXM0IiIihZMSoELm2vo/Wv1ZRETkzikBKkQsFsPWAqT9v0RERO6cEqBCZH/EZaLjU/BwcaRxhZL2DkdERKTQUgJUiGw8bO3+Cq1cChcn/dOJiIjcKX2LFiK29X/U/SUiInJXlAAVEkmpZrYduwho/R8REZG7pQSokNh+4hLJaRYCvF0JKVPC3uGIiIgUakqAConfrk1/1/YXIiIid00JUCGh7S9ERERyjxKgQuBCfDL/nI0DoFU1JUAiIiJ3SwlQIbDpyAUAagZ64e/laudoRERECj8lQIXAte0v1P0lIiKSO5QAFXCGYdjG/2j9HxERkdyhBKiAOxKVwNnYJFwcHWheqZS9wxERESkSlAAVcNe6v5pVLom7i6OdoxERESkalAAVcNd2f29dTd1fIiIiuUUJUAGWaraw5ai2vxAREcltSoAKsJ2nYohPTqOUpwu1g7ztHY6IiEiRoQSoALu2+3vLqqVxcND2FyIiIrlFCVABtkHr/4iIiOQJJUAFVGxiKrtOxQBa/0dERCS3KQEqoDYfuYDFgCr+npT1dbd3OCIiIkWKEqACauPhq91f2vxUREQk1ykBKqC0/YWIiEjeUQJUAJ26eIXjF67g6GDinira/kJERCS3KQEqgK5Nf29U3hcvN2c7RyMiIlL0KAEqgGzjf9T9JSIikifsngDNmDGDSpUq4ebmRmhoKNu2bbtl+ZiYGAYNGkRQUBCurq5Ur16dlStX2s5PmDABk8mU7lGzZs28vo1cY7YYbDp8AYDWWv9HREQkTzjZ88UXLVrE8OHDmTVrFqGhoUydOpWwsDAOHDhAmTJlMpRPSUnhwQcfpEyZMixevJiyZcty4sQJfH1905WrU6cOa9eutT13crLrbebInjOxxCam4uXmRINyPvYOR0REpEiya2YwZcoUBgwYQL9+/QCYNWsWK1asYM6cOYwcOTJD+Tlz5nDx4kV+//13nJ2tY2MqVaqUoZyTkxOBgYF5Gnteubb7e4sqpXFytHsDnYiISJFkt2/YlJQUtm/fTvv27a8H4+BA+/bt2bx5c6bXLF++nBYtWjBo0CACAgKoW7cuEydOxGw2pyt36NAhgoODqVKlCj169ODkyZO3jCU5OZm4uLh0D3vR9hciIiJ5z24JUHR0NGazmYCAgHTHAwICiIiIyPSao0ePsnjxYsxmMytXrmTs2LF8+OGHvP3227YyoaGhzJs3j1WrVjFz5kyOHTtGmzZtuHz5cpaxTJo0CR8fH9ujfPnyuXOTOZSQnMb2E5cArf8jIiKSlwrP4BjAYrFQpkwZPvvsMxwdHWnSpAlnzpxh8uTJjB8/HoAOHTrYytevX5/Q0FAqVqzIt99+S//+/TOtd9SoUQwfPtz2PC4uzi5J0LZjF0k1G5Qr6U6l0h75/voiIiLFhd0SID8/PxwdHYmMjEx3PDIyMsvxO0FBQTg7O+Po6Gg7VqtWLSIiIkhJScHFxSXDNb6+vlSvXp3Dhw9nGYurqyuurq53eCe559r6P21C/DCZTHaORkREpOiyWxeYi4sLTZo0ITw83HbMYrEQHh5OixYtMr2mVatWHD58GIvFYjt28OBBgoKCMk1+AOLj4zly5AhBQUG5ewN54Nr6P62rqftLREQkL9l1mtHw4cP5/PPP+eKLL9i3bx8vvvgiCQkJtllhvXv3ZtSoUbbyL774IhcvXmTo0KEcPHiQFStWMHHiRAYNGmQr8+qrr/Lrr79y/Phxfv/9d5588kkcHR3p3r17vt9fTkTGJXEwMh6TCVpWLW3vcERERIo0u44B6tq1K1FRUYwbN46IiAgaNmzIqlWrbAOjT548iYPD9RytfPnyrF69mldeeYX69etTtmxZhg4dyogRI2xlTp8+Tffu3blw4QL+/v60bt2aLVu24O9fsFtVrnV/1SvrQ0nPzFuzREREJHeYDMMw7B1EQRMXF4ePjw+xsbF4e3vny2sOW/gXy3ae5aX7qvLaw4Vn5WoREZGCIiff31pprwAwDIONV7e/0P5fIiIieU8JUAGwP+Iy0fHJuDs70riir73DERERKfKUABUAG6+O/wmtUgpXJ8fblBYREZG7pQSoANhwdf+v1tW0/YWIiEh+UAJkZ0mpZrYe1fgfERGR/KQEyM62n7hEcpqFMl6uVA8oYe9wREREigUlQHZ2bf2f1tr+QkREJN8oAbKza9tftAnR+B8REZH8ogTIji4mpPDP2TgAWmkAtIiISL5RAmRHmw5HYxhQM9CLMl5u9g5HRESk2FACZEcbDl3b/V2tPyIiIvlJCZCdGIZhWwCxtcb/iIiI5CslQHZyNDqBs7FJuDg6EFq5tL3DERERKVaUANnJtdafppVK4u6i7S9ERETykxIgO9mg7i8RERG7UQJkB6lmC1uubX9RTdtfiIiI5DclQHaw81QM8clplPRwpk6wt73DERERKXaUANnBte6vltX8cHDQ9hciIiL5TQmQHWy8uv5PW43/ERERsQslQPksLimVXadjAWgdovE/IiIi9qAEKJ9tPnIBs8Wgip8nZX3d7R2OiIhIsaQEKJ9p9WcRERH7UwKUz7T/l4iIiP0pAcpHpy5e4fiFKzg6mLinqra/EBERsRclQPlo42Fr91ej8r54uznbORoREZHiSwlQPrqYkIK7s6PG/4iIiNiZyTAMw95BFDRxcXH4+PgQGxuLt3furtScnGYmJc2Cl1qAREREclVOvr+d8ikmucrVyRFXJ+3+LiIiYk/qAhMREZFiRwmQiIiIFDtKgERERKTYUQIkIiIixY7dE6AZM2ZQqVIl3NzcCA0NZdu2bbcsHxMTw6BBgwgKCsLV1ZXq1auzcuXKu6pTREREihe7JkCLFi1i+PDhjB8/nh07dtCgQQPCwsI4f/58puVTUlJ48MEHOX78OIsXL+bAgQN8/vnnlC1b9o7rFBERkeLHrusAhYaG0qxZM6ZPnw6AxWKhfPnyDBkyhJEjR2YoP2vWLCZPnsz+/ftxds58HZ2c1pmZvFwHSERERPJGTr6/7dYClJKSwvbt22nfvv31YBwcaN++PZs3b870muXLl9OiRQsGDRpEQEAAdevWZeLEiZjN5juuU0RERIofuy2EGB0djdlsJiAgIN3xgIAA9u/fn+k1R48e5ZdffqFHjx6sXLmSw4cP89JLL5Gamsr48ePvqE6A5ORkkpOTbc/j4uLu4s5ERESkoLP7IOicsFgslClThs8++4wmTZrQtWtXRo8ezaxZs+6q3kmTJuHj42N7lC9fPpciFhERkYLIbgmQn58fjo6OREZGpjseGRlJYGBgptcEBQVRvXp1HB2vbyVRq1YtIiIiSElJuaM6AUaNGkVsbKztcerUqbu4MxERESno7JYAubi40KRJE8LDw23HLBYL4eHhtGjRItNrWrVqxeHDh7FYLLZjBw8eJCgoCBcXlzuqE8DV1RVvb+90DxERESm67NoFNnz4cD7//HO++OIL9u3bx4svvkhCQgL9+vUDoHfv3owaNcpW/sUXX+TixYsMHTqUgwcPsmLFCiZOnMigQYOyXaeIiIiIXXeD79q1K1FRUYwbN46IiAgaNmzIqlWrbIOYT548iYPD9RytfPnyrF69mldeeYX69etTtmxZhg4dyogRI7JdZ3ZcWxlAg6FFREQKj2vf29lZ4ceu6wAVVKdPn9ZAaBERkULq1KlTlCtX7pZllABlwmKxcPbsWby8vDCZTLlad1xcHOXLl+fUqVPFcqyR7r943z/oPSju9w96D3T/eXf/hmFw+fJlgoOD0/UgZcauXWAFlYODw20zx7tV3Adb6/6L9/2D3oPifv+g90D3nzf37+Pjk61yhWodIBEREZHcoARIREREih0lQPnM1dWV8ePH4+rqau9Q7EL3X7zvH/QeFPf7B70Huv+Ccf8aBC0iIiLFjlqAREREpNhRAiQiIiLFjhIgERERKXaUAImIiEixowQoH82YMYNKlSrh5uZGaGgo27Zts3dI+WbSpEk0a9YMLy8vypQpQ6dOnThw4IC9w7Kbd999F5PJxLBhw+wdSr45c+YMPXv2pHTp0ri7u1OvXj3+/PNPe4eVb8xmM2PHjqVy5cq4u7tTtWpV3nrrrWztWVQY/fbbb3Ts2JHg4GBMJhPLli1Ld94wDMaNG0dQUBDu7u60b9+eQ4cO2SfYPHKr9yA1NZURI0ZQr149PD09CQ4Opnfv3pw9e9Z+Aeey2/0O3OiFF17AZDIxderUfItPCVA+WbRoEcOHD2f8+PHs2LGDBg0aEBYWxvnz5+0dWr749ddfGTRoEFu2bGHNmjWkpqby0EMPkZCQYO/Q8t0ff/zBf/7zH+rXr2/vUPLNpUuXaNWqFc7Ozvz888/s3buXDz/8kJIlS9o7tHzz3nvvMXPmTKZPn86+fft47733eP/995k2bZq9Q8sTCQkJNGjQgBkzZmR6/v333+eTTz5h1qxZbN26FU9PT8LCwkhKSsrnSPPOrd6DK1eusGPHDsaOHcuOHTtYsmQJBw4c4PHHH7dDpHnjdr8D1yxdupQtW7YQHBycT5FdZUi+aN68uTFo0CDbc7PZbAQHBxuTJk2yY1T2c/78eQMwfv31V3uHkq8uX75shISEGGvWrDHuvfdeY+jQofYOKV+MGDHCaN26tb3DsKtHH33UeO6559Ide+qpp4wePXrYKaL8AxhLly61PbdYLEZgYKAxefJk27GYmBjD1dXV+Oabb+wQYd67+T3IzLZt2wzAOHHiRP4ElY+yuv/Tp08bZcuWNfbs2WNUrFjR+Oijj/ItJrUA5YOUlBS2b99O+/btbcccHBxo3749mzdvtmNk9hMbGwtAqVKl7BxJ/ho0aBCPPvpout+F4mD58uU0bdqULl26UKZMGRo1asTnn39u77DyVcuWLQkPD+fgwYMA7Nq1i40bN9KhQwc7R5b/jh07RkRERLr/D3x8fAgNDS22n4lg/Vw0mUz4+vraO5R8YbFY6NWrF//+97+pU6dOvr++NkPNB9HR0ZjNZgICAtIdDwgIYP/+/XaKyn4sFgvDhg2jVatW1K1b197h5JuFCxeyY8cO/vjjD3uHku+OHj3KzJkzGT58OK+//jp//PEHL7/8Mi4uLvTp08fe4eWLkSNHEhcXR82aNXF0dMRsNvPOO+/Qo0cPe4eW7yIiIgAy/Uy8dq64SUpKYsSIEXTv3r3YbJD63nvv4eTkxMsvv2yX11cCJPlu0KBB7Nmzh40bN9o7lHxz6tQphg4dypo1a3Bzc7N3OPnOYrHQtGlTJk6cCECjRo3Ys2cPs2bNKjYJ0Lfffsv8+fNZsGABderUYefOnQwbNozg4OBi8x5I5lJTU3nmmWcwDIOZM2faO5x8sX37dj7++GN27NiByWSySwzqAssHfn5+ODo6EhkZme54ZGQkgYGBdorKPgYPHsxPP/3EunXrKFeunL3DyTfbt2/n/PnzNG7cGCcnJ5ycnPj111/55JNPcHJywmw22zvEPBUUFETt2rXTHatVqxYnT560U0T579///jcjR46kW7du1KtXj169evHKK68wadIke4eW76597ukz8Xryc+LECdasWVNsWn82bNjA+fPnqVChgu0z8cSJE/zf//0flSpVypcYlADlAxcXF5o0aUJ4eLjtmMViITw8nBYtWtgxsvxjGAaDBw9m6dKl/PLLL1SuXNneIeWrdu3a8ffff7Nz507bo2nTpvTo0YOdO3fi6Oho7xDzVKtWrTIse3Dw4EEqVqxop4jy35UrV3BwSP+R6+joiMVisVNE9lO5cmUCAwPTfSbGxcWxdevWYvOZCNeTn0OHDrF27VpKly5t75DyTa9evdi9e3e6z8Tg4GD+/e9/s3r16nyJQV1g+WT48OH06dOHpk2b0rx5c6ZOnUpCQgL9+vWzd2j5YtCgQSxYsIAffvgBLy8vWz+/j48P7u7udo4u73l5eWUY7+Tp6Unp0qWLxTioV155hZYtWzJx4kSeeeYZtm3bxmeffcZnn31m79DyTceOHXnnnXeoUKECderU4a+//mLKlCk899xz9g4tT8THx3P48GHb82PHjrFz505KlSpFhQoVGDZsGG+//TYhISFUrlyZsWPHEhwcTKdOnewXdC671XsQFBRE586d2bFjBz/99BNms9n2uViqVClcXFzsFXauud3vwM0Jn7OzM4GBgdSoUSN/Asy3+WZiTJs2zahQoYLh4uJiNG/e3NiyZYu9Q8o3QKaPuXPn2js0uylO0+ANwzB+/PFHo27duoarq6tRs2ZN47PPPrN3SPkqLi7OGDp0qFGhQgXDzc3NqFKlijF69GgjOTnZ3qHliXXr1mX6/3yfPn0Mw7BOhR87dqwREBBguLq6Gu3atTMOHDhg36Bz2a3eg2PHjmX5ubhu3Tp7h54rbvc7cLP8ngZvMowiugypiIiISBY0BkhERESKHSVAIiIiUuwoARIREZFiRwmQiIiIFDtKgERERKTYUQIkIiIixY4SIBERESl2lACJiGTBZDKxbNkye4chInlACZCIFEh9+/bFZDJleDz88MP2Dk1EigDtBSYiBdbDDz/M3Llz0x1zdXW1UzQiUpSoBUhECixXV1cCAwPTPUqWLAlYu6dmzpxJhw4dcHd3p0qVKixevDjd9X///TcPPPAA7u7ulC5dmoEDBxIfH5+uzJw5c6hTpw6urq4EBQUxePDgdOejo6N58skn8fDwICQkhOXLl9vOXbp0iR49euDv74+7uzshISEZEjYRKZiUAIlIoTV27Fiefvppdu3aRY8ePejWrRv79u0DICEhgbCwMEqWLMkff/zBd999x9q1a9MlODNnzmTQoEEMHDiQv//+m+XLl1OtWrV0r/HGG2/wzDPPsHv3bh555BF69OjBxYsXba+/d+9efv75Z/bt28fMmTPx8/PLvzdARO5cvm27KiKSA3369DEcHR0NT0/PdI933nnHMAzDAIwXXngh3TWhoaHGiy++aBiGYXz22WdGyZIljfj4eNv5FStWGA4ODkZERIRhGIYRHBxsjB49OssYAGPMmDG25/Hx8QZg/Pzzz4ZhGEbHjh2Nfv365c4Ni0i+0hggESmw7r//fmbOnJnuWKlSpWw/t2jRIt25Fi1asHPnTgD27dtHgwYN8PT0tJ1v1aoVFouFAwcOYDKZOHv2LO3atbtlDPXr17f97Onpibe3N+fPnwfgxRdf5Omnn2bHjh089NBDdOrUiZYtW97RvYpI/lICJCIFlqenZ4Yuqdzi7u6erXLOzs7pnptMJiwWCwAdOnTgxIkTrFy5kjVr1tCuXTsGDRrEBx98kOvxikju0hggESm0tmzZkuF5rVq1AKhVqxa7du0iISHBdn7Tpk04ODhQo0YNvLy8qFSpEuHh4XcVg7+/P3369OHrr79m6tSpfPbZZ3dVn4jkD7UAiUiBlZycTERERLpjTk5OtoHG3333HU2bNqV169bMnz+fbdu2MXv2bAB69OjB+PHj6dOnDxMmTCAqKoohQ4bQq1cvAgICAJgwYQIvvPACZcqUoUOHDly+fJlNmzYxZMiQbMU3btw4mjRpQp06dUhOTuann36yJWAiUrApARKRAmvVqlUEBQWlO1ajRg32798PWGdoLVy4kJdeeomgoCC++eYbateuDYCHhwerV69m6NChNGvWDA8PD55++mmmTJliq6tPnz4kJSXx0Ucf8eqrr+Ln50fnzp2zHZ+LiwujRo3i+PHjuLu706ZNGxYuXJgLdy4iec1kGIZh7yBERHLKZDKxdOlSOnXqZO9QRKQQ0hggERERKXaUAImIiEixozFAIlIoqfdeRO6GWoBERESk2FECJCIiIsWOEiAREREpdpQAiYiISLGjBEhERESKHSVAIiIiUuwoARIREZFiRwmQiIiIFDtKgERERKTY+X/8lsmFgmyG6gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}