week2_io_"group"_project_flower_recognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "week2 io \"group\" project flower recognition.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cybraia/Flower-recognition/blob/colab/week2_io_%22group%22_project_flower_recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1aUraxNPYPg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a189a04f-e1f3-4903-9dc4-5c067bc2dec7"
      },
      "source": [
        "! pip install --upgrade --force-reinstall --no-deps kaggle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kaggle\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/99/33/365c0d13f07a2a54744d027fe20b60dacdfdfb33bc04746db6ad0b79340b/kaggle-1.5.10.tar.gz (59kB)\n",
            "\r\u001b[K     |█████▌                          | 10kB 20.4MB/s eta 0:00:01\r\u001b[K     |███████████                     | 20kB 15.5MB/s eta 0:00:01\r\u001b[K     |████████████████▋               | 30kB 13.3MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 40kB 12.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▊    | 51kB 8.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61kB 5.0MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: kaggle\n",
            "  Building wheel for kaggle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaggle: filename=kaggle-1.5.10-cp37-none-any.whl size=73269 sha256=e865083e080bf1dd8e9f0811d802d8a00ce66edf5f304ab559481bdb772251ca\n",
            "  Stored in directory: /root/.cache/pip/wheels/3a/d1/7e/6ce09b72b770149802c653a02783821629146983ee5a360f10\n",
            "Successfully built kaggle\n",
            "Installing collected packages: kaggle\n",
            "  Found existing installation: kaggle 1.5.10\n",
            "    Uninstalling kaggle-1.5.10:\n",
            "      Successfully uninstalled kaggle-1.5.10\n",
            "Successfully installed kaggle-1.5.10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oaLktArbPfYN",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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0TW9kaWZpZWREYXRlLnRvTG9jYWxlRGF0ZVN0cmluZygpIDoKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgJ24vYSd9IC0gYCkpOwogICAgY29uc3QgcGVyY2VudCA9IHNwYW4oJzAlIGRvbmUnKTsKICAgIGxpLmFwcGVuZENoaWxkKHBlcmNlbnQpOwoKICAgIG91dHB1dEVsZW1lbnQuYXBwZW5kQ2hpbGQobGkpOwoKICAgIGNvbnN0IGZpbGVEYXRhUHJvbWlzZSA9IG5ldyBQcm9taXNlKChyZXNvbHZlKSA9PiB7CiAgICAgIGNvbnN0IHJlYWRlciA9IG5ldyBGaWxlUmVhZGVyKCk7CiAgICAgIHJlYWRlci5vbmxvYWQgPSAoZSkgPT4gewogICAgICAgIHJlc29sdmUoZS50YXJnZXQucmVzdWx0KTsKICAgICAgfTsKICAgICAgcmVhZGVyLnJlYWRBc0FycmF5QnVmZmVyKGZpbGUpOwogICAgfSk7CiAgICAvLyBXYWl0IGZvciB0aGUgZGF0YSB0byBiZSByZWFkeS4KICAgIGxldCBmaWxlRGF0YSA9IHlpZWxkIHsKICAgICAgcHJvbWlzZTogZmlsZURhdGFQcm9taXNlLAogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbnRpbnVlJywKICAgICAgfQogICAgfTsKCiAgICAvLyBVc2UgYSBjaHVua2VkIHNlbmRpbmcgdG8gYXZvaWQgbWVzc2FnZSBzaXplIGxpbWl0cy4gU2VlIGIvNjIxMTU2NjAuCiAgICBsZXQgcG9zaXRpb24gPSAwOwogICAgd2hpbGUgKHBvc2l0aW9uIDwgZmlsZURhdGEuYnl0ZUxlbmd0aCkgewogICAgICBjb25zdCBsZW5ndGggPSBNYXRoLm1pbihmaWxlRGF0YS5ieXRlTGVuZ3RoIC0gcG9zaXRpb24sIE1BWF9QQVlMT0FEX1NJWkUpOwogICAgICBjb25zdCBjaHVuayA9IG5ldyBVaW50OEFycmF5KGZpbGVEYXRhLCBwb3NpdGlvbiwgbGVuZ3RoKTsKICAgICAgcG9zaXRpb24gKz0gbGVuZ3RoOwoKICAgICAgY29uc3QgYmFzZTY0ID0gYnRvYShTdHJpbmcuZnJvbUNoYXJDb2RlLmFwcGx5KG51bGwsIGNodW5rKSk7CiAgICAgIHlpZWxkIHsKICAgICAgICByZXNwb25zZTogewogICAgICAgICAgYWN0aW9uOiAnYXBwZW5kJywKICAgICAgICAgIGZpbGU6IGZpbGUubmFtZSwKICAgICAgICAgIGRhdGE6IGJhc2U2NCwKICAgICAgICB9LAogICAgICB9OwogICAgICBwZXJjZW50LnRleHRDb250ZW50ID0KICAgICAgICAgIGAke01hdGgucm91bmQoKHBvc2l0aW9uIC8gZmlsZURhdGEuYnl0ZUxlbmd0aCkgKiAxMDApfSUgZG9uZWA7CiAgICB9CiAgfQoKICAvLyBBbGwgZG9uZS4KICB5aWVsZCB7CiAgICByZXNwb25zZTogewogICAgICBhY3Rpb246ICdjb21wbGV0ZScsCiAgICB9CiAgfTsKfQoKc2NvcGUuZ29vZ2xlID0gc2NvcGUuZ29vZ2xlIHx8IHt9OwpzY29wZS5nb29nbGUuY29sYWIgPSBzY29wZS5nb29nbGUuY29sYWIgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYi5fZmlsZXMgPSB7CiAgX3VwbG9hZEZpbGVzLAogIF91cGxvYWRGaWxlc0NvbnRpbnVlLAp9Owp9KShzZWxmKTsK",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "ac56f30a-05eb-4cd9-86d0-6415865dfbff"
      },
      "source": [
        "from google.colab import files\r\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d67ee24a-a40c-4863-a466-549d5ecc8a64\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d67ee24a-a40c-4863-a466-549d5ecc8a64\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"pallavis301\",\"key\":\"328d8c90dfdcc6517a29c85b49fb5805\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CfcY951P2bN"
      },
      "source": [
        "! mkdir ~/.kaggle\r\n",
        "! cp kaggle.json ~/.kaggle/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCJP035wP_m9"
      },
      "source": [
        " ! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfKbIiNXQIP-",
        "outputId": "b9f2bf7b-f77a-4c7d-ba89-dafc3a996321"
      },
      "source": [
        "! kaggle datasets download -d ravishranjan/flower-dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading flower-dataset.zip to /content\n",
            " 99% 706M/714M [00:09<00:00, 60.7MB/s]\n",
            "100% 714M/714M [00:09<00:00, 79.5MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3vNPaknQdf2",
        "outputId": "f4a82ea8-1cf1-4ecf-ab42-ddd9b0e0e838"
      },
      "source": [
        "! unzip flower-dataset.zip -d dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: dataset/flowers/flowers/train/38/image_05827.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05828.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05831.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05832.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05834.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05835.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05836.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05837.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05838.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05839.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05840.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05841.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05842.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05843.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05844.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05846.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05847.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/38/image_05848.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/39/image_07007.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/39/image_07008.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/39/image_07009.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/39/image_07011.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/39/image_07012.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/39/image_07014.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/39/image_07015.jpg  \n",
            "  inflating: dataset/flowers/flowers/train/39/image_07017.jpg  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYvZ_6zER0Kd"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZnVO5VrR62k"
      },
      "source": [
        "training_path = \"/content/dataset/flowers/flowers/train_actual\"\r\n",
        "test_path = \"/content/dataset/flowers/flowers/test_actual\" "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsL7wkszSbVX",
        "outputId": "d31d607c-7669-40e2-d7f9-f738ea19e221"
      },
      "source": [
        "train_dataset = tf.keras.preprocessing.image_dataset_from_directory(\r\n",
        "    training_path,\r\n",
        "    image_size = (200,200),\r\n",
        "    batch_size = 32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 345 files belonging to 5 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGqVe2G6T4xi",
        "outputId": "760d9c7b-8d16-4f79-8b76-00d1a845314d"
      },
      "source": [
        "test_dataset = tf.keras.preprocessing.image_dataset_from_directory(\r\n",
        "    test_path,\r\n",
        "    image_size = (200,200),\r\n",
        "    batch_size = 32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 39 files belonging to 5 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXSzuTawUEAx"
      },
      "source": [
        "CNNmodel = tf.keras.models.Sequential()\r\n",
        "\r\n",
        "\r\n",
        "CNNmodel.add(tf.keras.layers.Conv2D(32, (3,3), activation ='relu', input_shape=(200,200,3)))\r\n",
        "CNNmodel.add(tf.keras.layers.MaxPooling2D((2,2)))\r\n",
        "CNNmodel.add(tf.keras.layers.Conv2D(64, (3,3), activation ='relu'))\r\n",
        "CNNmodel.add(tf.keras.layers.MaxPooling2D((2,2)))\r\n",
        "CNNmodel.add(tf.keras.layers.Conv2D(64,(3,3), activation ='relu'))\r\n",
        "\r\n",
        "\r\n",
        "CNNmodel.add(tf.keras.layers.Flatten())\r\n",
        "CNNmodel.add(tf.keras.layers.Dense(32, activation='relu'))\r\n",
        "CNNmodel.add(tf.keras.layers.Dense(64, activation='relu'))\r\n",
        "CNNmodel.add(tf.keras.layers.Dense(5, activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1o83YL4Uf1x",
        "outputId": "02a080a7-986e-49de-cbf8-9dec25a4afd1"
      },
      "source": [
        "CNNmodel.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 198, 198, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 99, 99, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 97, 97, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 48, 48, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 46, 46, 64)        36928     \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 135424)            0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 32)                4333600   \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 5)                 325       \n",
            "=================================================================\n",
            "Total params: 4,392,357\n",
            "Trainable params: 4,392,357\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qI6l-NqfUowp"
      },
      "source": [
        "CNNmodel.compile(optimizer = 'adam',\r\n",
        "                loss='sparse_categorical_crossentropy',metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUoRIYY2U5GH",
        "outputId": "6d66bbb7-ba3c-41f9-e192-7481ec8392dd"
      },
      "source": [
        "history = CNNmodel.fit(train_dataset, validation_data=test_dataset, epochs = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "11/11 [==============================] - 2s 75ms/step - loss: 125.0491 - accuracy: 0.1738 - val_loss: 1.5281 - val_accuracy: 0.1795\n",
            "Epoch 2/15\n",
            "11/11 [==============================] - 1s 57ms/step - loss: 1.4079 - accuracy: 0.4138 - val_loss: 1.4530 - val_accuracy: 0.3590\n",
            "Epoch 3/15\n",
            "11/11 [==============================] - 1s 59ms/step - loss: 1.0051 - accuracy: 0.7028 - val_loss: 1.4378 - val_accuracy: 0.5385\n",
            "Epoch 4/15\n",
            "11/11 [==============================] - 1s 58ms/step - loss: 0.7130 - accuracy: 0.8179 - val_loss: 2.0625 - val_accuracy: 0.4359\n",
            "Epoch 5/15\n",
            "11/11 [==============================] - 1s 58ms/step - loss: 0.3215 - accuracy: 0.9109 - val_loss: 7.6875 - val_accuracy: 0.3846\n",
            "Epoch 6/15\n",
            "11/11 [==============================] - 1s 61ms/step - loss: 0.9735 - accuracy: 0.8700 - val_loss: 1.8205 - val_accuracy: 0.2564\n",
            "Epoch 7/15\n",
            "11/11 [==============================] - 1s 60ms/step - loss: 0.5790 - accuracy: 0.8351 - val_loss: 2.5080 - val_accuracy: 0.2564\n",
            "Epoch 8/15\n",
            "11/11 [==============================] - 1s 59ms/step - loss: 0.1588 - accuracy: 0.9865 - val_loss: 3.5328 - val_accuracy: 0.3590\n",
            "Epoch 9/15\n",
            "11/11 [==============================] - 1s 58ms/step - loss: 0.0528 - accuracy: 0.9914 - val_loss: 4.1667 - val_accuracy: 0.3077\n",
            "Epoch 10/15\n",
            "11/11 [==============================] - 1s 59ms/step - loss: 0.0110 - accuracy: 1.0000 - val_loss: 5.0733 - val_accuracy: 0.3333\n",
            "Epoch 11/15\n",
            "11/11 [==============================] - 1s 61ms/step - loss: 0.0185 - accuracy: 0.9963 - val_loss: 3.9787 - val_accuracy: 0.4359\n",
            "Epoch 12/15\n",
            "11/11 [==============================] - 1s 60ms/step - loss: 0.0310 - accuracy: 0.9890 - val_loss: 4.8021 - val_accuracy: 0.3590\n",
            "Epoch 13/15\n",
            "11/11 [==============================] - 1s 59ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 4.4385 - val_accuracy: 0.3590\n",
            "Epoch 14/15\n",
            "11/11 [==============================] - 1s 60ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 5.1940 - val_accuracy: 0.3846\n",
            "Epoch 15/15\n",
            "11/11 [==============================] - 1s 59ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 5.1413 - val_accuracy: 0.3333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ysg_FugbWVn0",
        "outputId": "aff3d740-24f9-4d1f-dc7b-01c332fb9300"
      },
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\r\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\r\n",
        "plt.xlabel('Epoch')\r\n",
        "plt.ylabel('Accuracy')\r\n",
        "plt.ylim([0.25, 1])\r\n",
        "plt.legend(loc='lower right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ffa1c1eb990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eXmQRtgZ0Uv0"
      },
      "source": [
        "data aug"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tgUD1mQ6KDc"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZvXFBb1d972"
      },
      "source": [
        "dataGen = tf.keras.preprocessing.image.ImageDataGenerator(\r\n",
        "    rescale = 1./255,\r\n",
        "    rotation_range =5,\r\n",
        "    horizontal_flip =True,\r\n",
        "    vertical_flip =True,)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqHaT1gYeKDY",
        "outputId": "f3c16fb5-da5d-4494-c02c-7e9fbf28bcf0"
      },
      "source": [
        "train_dataset_aug = dataGen.flow_from_directory(\r\n",
        "    training_path,\r\n",
        "    target_size = (200,200),\r\n",
        "    batch_size = 32,\r\n",
        "    shuffle = True,\r\n",
        "    seed = 865)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 345 images belonging to 5 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJD3joTmeRRB",
        "outputId": "27135e79-e444-4736-913d-ea413f84906c"
      },
      "source": [
        "test_dataset_aug = dataGen.flow_from_directory(\r\n",
        "    test_path,\r\n",
        "    target_size = (200,200),\r\n",
        "    batch_size = 32,\r\n",
        "    shuffle=True,\r\n",
        "    seed = 865)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 39 images belonging to 5 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NTporoTJeX3q"
      },
      "source": [
        "CNNmodel_aug = tf.keras.models.Sequential()\r\n",
        "\r\n",
        "CNNmodel_aug.add(tf.keras.layers.Conv2D(32, (3,3), activation ='relu', input_shape=(200,200,3)))\r\n",
        "CNNmodel_aug.add(tf.keras.layers.MaxPooling2D((2,2)))\r\n",
        "CNNmodel_aug.add(tf.keras.layers.Conv2D(64, (3,3), activation ='relu'))\r\n",
        "CNNmodel_aug.add(tf.keras.layers.MaxPooling2D((2,2)))\r\n",
        "CNNmodel_aug.add(tf.keras.layers.Conv2D(64,(3,3), activation ='relu'))\r\n",
        "\r\n",
        "CNNmodel_aug.add(tf.keras.layers.Flatten())\r\n",
        "CNNmodel_aug.add(tf.keras.layers.Dense(32, activation ='relu'))\r\n",
        "CNNmodel_aug.add(tf.keras.layers.Dense(64, activation ='relu'))\r\n",
        "CNNmodel_aug.add(tf.keras.layers.Dense(5, activation ='softmax'))\r\n",
        "\r\n",
        "CNNmodel_aug.compile(optimizer = 'adam',\r\n",
        "                loss='categorical_crossentropy',metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjeAEzQKehyQ",
        "outputId": "d3f1a786-827f-4b3d-a1de-7effb236e83f"
      },
      "source": [
        "history_2 = CNNmodel_aug.fit(train_dataset_aug, validation_data=test_dataset_aug, epochs = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "11/11 [==============================] - 5s 461ms/step - loss: 1.6974 - accuracy: 0.2306 - val_loss: 1.3886 - val_accuracy: 0.4872\n",
            "Epoch 2/15\n",
            "11/11 [==============================] - 5s 415ms/step - loss: 1.2866 - accuracy: 0.4269 - val_loss: 1.1683 - val_accuracy: 0.5128\n",
            "Epoch 3/15\n",
            "11/11 [==============================] - 5s 420ms/step - loss: 1.1385 - accuracy: 0.5143 - val_loss: 0.9618 - val_accuracy: 0.5641\n",
            "Epoch 4/15\n",
            "11/11 [==============================] - 5s 426ms/step - loss: 0.8459 - accuracy: 0.6608 - val_loss: 1.1040 - val_accuracy: 0.5128\n",
            "Epoch 5/15\n",
            "11/11 [==============================] - 5s 416ms/step - loss: 0.7732 - accuracy: 0.6986 - val_loss: 1.1820 - val_accuracy: 0.4359\n",
            "Epoch 6/15\n",
            "11/11 [==============================] - 5s 415ms/step - loss: 0.7474 - accuracy: 0.7335 - val_loss: 0.8002 - val_accuracy: 0.7179\n",
            "Epoch 7/15\n",
            "11/11 [==============================] - 5s 425ms/step - loss: 0.7469 - accuracy: 0.7133 - val_loss: 1.1389 - val_accuracy: 0.5385\n",
            "Epoch 8/15\n",
            "11/11 [==============================] - 5s 412ms/step - loss: 0.7188 - accuracy: 0.7424 - val_loss: 0.9093 - val_accuracy: 0.5897\n",
            "Epoch 9/15\n",
            "11/11 [==============================] - 5s 413ms/step - loss: 0.6548 - accuracy: 0.7821 - val_loss: 1.2897 - val_accuracy: 0.5385\n",
            "Epoch 10/15\n",
            "11/11 [==============================] - 5s 417ms/step - loss: 0.6115 - accuracy: 0.8220 - val_loss: 0.9162 - val_accuracy: 0.5897\n",
            "Epoch 11/15\n",
            "11/11 [==============================] - 5s 414ms/step - loss: 0.5103 - accuracy: 0.8043 - val_loss: 0.8813 - val_accuracy: 0.5897\n",
            "Epoch 12/15\n",
            "11/11 [==============================] - 5s 420ms/step - loss: 0.4506 - accuracy: 0.8327 - val_loss: 0.9719 - val_accuracy: 0.5897\n",
            "Epoch 13/15\n",
            "11/11 [==============================] - 5s 413ms/step - loss: 0.3711 - accuracy: 0.8724 - val_loss: 1.0048 - val_accuracy: 0.5897\n",
            "Epoch 14/15\n",
            "11/11 [==============================] - 5s 415ms/step - loss: 0.3631 - accuracy: 0.8604 - val_loss: 0.8240 - val_accuracy: 0.7179\n",
            "Epoch 15/15\n",
            "11/11 [==============================] - 4s 409ms/step - loss: 0.3218 - accuracy: 0.8979 - val_loss: 0.8293 - val_accuracy: 0.6923\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "g5_R3zO71MrA",
        "outputId": "44ef5a72-999c-4830-93f1-316dc25dc987"
      },
      "source": [
        "plt.plot(history_2.history['accuracy'], label='accuracy')\r\n",
        "plt.plot(history_2.history['val_accuracy'], label = 'val_accuracy')\r\n",
        "plt.xlabel('Epoch')\r\n",
        "plt.ylabel('Accuracy')\r\n",
        "plt.ylim([0.25, 1])\r\n",
        "plt.legend(loc='lower right')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ffa10e1b050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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APwxPJ7vzRLZlHCYxLYuktCwS0w6RmJbF9KUpZB3NO36siLBgWnkSQ+Ek0Soq4oS6Pyn7DvOPb9fz9apdNKkdzouX9+DCLuVbNN4EOFX49j4IrwNXfQ6Jc2HNZzDrAZj1ILQ4y7lLiB0DkdH+jvYEri5eLyIjgOeBYOANVX3qpOf/A5zreVgDaKCqJRZpscXrTx+Zh3N575etvP3zVtKzcujWrA43D2rNsNhGBMe/Ad/8BSIbQs3GMDkOiviQVlVSDx4l8VhySM0iKd35vmP/keP7iUDTOtVpHR1JdGQ1vlq1ExG4edAZTB7YmuphVhbClNOaT2H6tTDyOeg96bft6ZuchLDmU0jfABIErQY6dwodRkKNipmQWNLi9a4lAhEJBjYCw4AUYAkwUVUTitn/NqCHql5b0nEtEVR9O/cf4c0ft/DR4u0cyslncPtobhrUhn6t6v12RT71Cti1Cgb82UkI18+BmCLfw8U6kpPPlvRDnrsIz/f0LLZnHGZgu2geuKAjTetYWQjjAzmH4MU+UKO+c9ESVMSFhSqkJvyWFPZtgaBQaHOekxTanw/htVwLsaRE4GbTUF9gs6omeYKYCowGikwEwETgERfjMX62ac9BXp2fxBcrdqDARV0bc+OgNnRsfNKbvyAftiyETqOh2wT44VFY/HqpE0H1sGBim9Qitol7f1zGAPDjc3BgB1z2ZtFJAJzb0oadnK/zHoJdK5yEsGYGbJoFwdWg3XDodAm0GwFhNSosfDcTQVMgudDjFKBfUTuKSAugFTC3mOcnA5MBmjdv7tsojeuWbN3La/MT+WFdKtVDg/njmS24/pxWxNQt5o2+cwUczYTWg6FaTScZLHsX/vB/EGFLPZpKZt9W+Ol56DIWmp/p3WtEoEkP52voY5CyxEkKCZ/Dui8hNALaj3DuFM4YCiHujmCrLJ3FE4Dpqppf1JOqOgWYAk7TUEUGZsqmoECZsz6VV+cnsnTbPurWCOXOoe24qn8L6kaElfzipHnO91aDnO99roclbzjJ4Jy73A3cmNKa9VcICoFhj5Xt9UFB0Lyf8zXiH7DtJ6f5KOELJzlUq+X0JXS+xLk4CvZ9eZNTJgIRuQj4WlULSnnsHUCzQo9jPNuKMgH4UymPbyqhnLwCPl+xgykLkticmkVM3er8fVQnxvVu5n2HbFIcNOry29V/g47Q8hyIfwsG3F78rbcxFS1xHqz/CoY87JvhoUHBTkdyq4FwwTOQNB/WfgbrvoKVHzrJZsDt5T/PSby5IxgPPCcinwJvqup6L4+9BGgrIq1wEsAE4PKTdxKRDkBdYJGXxzWV0MHsXKYuTuZ/P25h94FsOjauxfMTunNhl8aElGZmbs5hSP4V+t144vY+18MnV8PGWdDhAt8Gb0xZ5OfCd/dD3ZZwpgvXscGh0Hao8zXyP7B5DjTu5vvz4EUiUNU/ikgtnM7ct0VEgbeAj1T1YAmvyxORW4FZOMNH31TVtSLyGBCvqjM9u04Apqqb41iNa9IOHuWtn7bw3i/bOJidR//W9fnnZV0Z2DaqbGPyty+C/BznFriwDhc6w0iXvG6JwFQOS96AtPUw4SMIDXf3XCHVXH3fe9VHoKoHRGQ6UB24A7gYuEdEXlDV/5bwum+Ab07a9vBJjx8tbdDG/7akH2LKgiQ+XZZCbn4B53duxI0D29CtvGv1JsVBcBg073/i9uBQ6DUJ4p6E9M0QdUb5zmNMeWSlwbx/QJshzrDPKs6bPoJRwCTgDOBdoK+qpopIDZyhoMUmAnP6WZWyn1fnJ/Ltmt2EBgdxWa8Ybjinte/KMyfFQbN+EFbE8XpdDQuehvg3YcSTvjmfMWUx9zHIPQQjnipyomNV480dwaXAf1R1QeGNqnpYRK5zJyxTmagqCzal82pcIouSMqgZHsLNg9pwzYCWNKjpw1viQxmwe5UzxrooNRtBx1Gw4n1nnwocZ23McTuXw7L3oP+fILqdv6PxCW8SwaPArmMPRKQ60FBVt6rqHLcCM/6Xl1/A16udInAJuw7QsFY1HrygAxP7Nndnha4t853vrc8tfp++NzijKFZ/4twhGFORVOGbe50RbYPu9Xc0PuNNIvgEOKvQ43zPtj6uRGT87khOPh/HJ/P6wiRS9h2hTXQET1/WldHdm1AtxMWhm0nzoFptaNy9+H2a94cGnZxO455XnRa35aYKWfUxpCyG0S9B+OlTpdabRBCiqjnHHqhqjoicYkaQqYr2Hcrh3UXbeGfRVvYeyqFn8zo8PDKWoR0bur9IuyokxkGrcyC4hLelCPS9Hr66E5IXO5NwjKkIRw/C7IehSU/o9ruR8FWaN4kgTURGHRvuKSKjgXR3wzIVKWXfYd5YuIVpS5I5kpvPkA4NuGlwG/q0rJiqiIBTgCtzu1Nk7lS6jIPZjzh3BZYITEVZ+Cxk7YYJHzizgU8j3iSCm4APRORFQHDqB13lalSmQqzffYDX5icxc+VOBBjVvQk3DmxD+0Y1Kz6YpDjne+vBp963WiR0vxyW/A/+8CRE2nrCxmUZibDoJedOoJTFD6sCbyaUJQJnikik53GW61EZ16gqv25xisDN25BGjbBgrjmrJdee3cq/JZmT4qBWU6jv5fyAPtfDr6/Csndg4D2uhmYMsx50qoMOPT0LJHs1oUxELgQ6AeHHZouqahkrLBl/STt4lJveX8rSbfuoHxHG3cPacWX/FtSp4ecun4J82LIA2l/gfedvVFvn7iH+LRhwZ8n9CsaUx6bZsPE7p85PzUb+jsYV3kwoexVn9bBzgTeAy4DFLsdlXPDYVwmsTsnk8dGdGNu72QlLN/rV7lVwZJ93zUKF9bkBpl0BG7+Fjhe5EZkJdHk5Tj2h+mdAv5v9HY1rvOnxOEtVrwL2qerfgf7A6TGLIoAs3JTGlyt3csu5bbiyf8vKkwTgt/6BY2WnvdVuBNSKcRatMcYNv74KGZudGcQhp+9gSW8SQbbn+2ERaQLkAo3dC8n4WnZuPn/7fA0t69fgpkFt/B3O7yXFOXMDajYs3euCQ5y1YbfMh7SNroRmAtjBPTD/aeeCo+0wf0fjKm8SwZciUgd4BlgGbAU+dDMo41uvxCWyNeMwj4/pXLnuBAByj8C2RaVvFjqm59VOkbolb/gyKmNgzt8hL9sZmXaaKzERiEgQMEdV96vqp0ALoMPJFURN5ZWUlsUrcYmM6taEc9pG+zuc30v+FfKPlj0RREZD7BhY+REctQFtxkdS4mHFB049ofqV8C7ax0pMBJ5VyV4q9Pioqma6HpXxCVXl4S/WUi0kiIdGdvR3OEVLinOW+Wtx1il3LVbfG+DoAVg1zWdhmQBWUADf3AORjWDgX/wdTYXwpmlojohcKmVaZcT408yVO/lxczr3jGjv2yqhvpQUBzF9nUliZRXTBxp1dZqHbH0jU14rP4Sdy2DY36GaHyZX+oE3ieBGnCJzR0XkgIgcFJEDLsdlyinzSC6Pf7WOrjG1uaJfC3+HU7TDe2HnirI3Cx0j4twVpCbAtp99EZkJVNmZ8MPfnYuTLuP8HU2FOWUiUNWaqhqkqmGqWsvzuFZFBGfK7tnvN7D30FH+b0wXgt0uGFdWWxcCWv5EAND5MgivY53GpnzmPw2H0uD8f5529YRK4s2EsoFFbT95oRpTeaxM3s97v2zj6v4t6RJTiUvlJs6DsJrQtGf5jxVWA3r80Rn3fXD3aTsD1LgobaPz/ul5pW/ek1WIN/PyCxdyCQf6AkuB81yJyJRLfoHy189XEx1ZjbuGV/J5f0lx0PJsZz1iX+h9LSx6EZa+A4Pv880xTWBQdWYQh0bAeYE3KNKbpqGLCn0NAzoD+7w5uIiMEJENIrJZRO4vZp9xIpIgImtFxOYnlNN7i7ayZscBHr4ollpurCLmK/u2OqWnWw/23THrt3EWE1/6FuTn+u645vS38TtInAOD73eGJAeYsjSCpQCnHIsoIsE4Q0/PB2KBiSISe9I+bYEHgAGq2gm4owzxGI89B7L51/cbOadtFBd2qeSTv5OOLUs52LfH7XsDHNwF67/27XHN6Ss327kbiGrvvH8CkDd9BP8Fjo3JCwK648wwPpW+wGZVTfIcZyowGkgotM8NwEuqug9AVVO9D92c7LGvEsjJL+Dx0Z2p9KN9k+KccdrR7X173LbDoXZzp9O40xjfHtucnn55yblDvXKG75opqxhv7gjicfoElgKLgPtU9Y9evK4pziI2x6R4thXWDmgnIj+JyC8iMqKoA4nIZBGJF5H4tLQ0L04deOZvTOPrVbu49dwzaBkV4e9wSlZQ4NQHaj3Y92sOBwVDn2udEUmp63x7bHP6ObATFjwLHUZCm8Dt9vQmEUwH3lfVd1T1A+AXEanho/OHAG2BwcBE4HVPXaMTqOoUVe2tqr2jowOv/e5UsnPzefiLNbSOiuDGQa39Hc6p7VkDhzN83yx0TI+rnEVEbCipOZXZj0BBHgx/wt+R+JU3o4bmAEOBY4VcqgPfA6eqCbADaFbocYxnW2EpwK+qmgtsEZGNOIlhiRdxGY+X521mW8ZhPri+H9VCKllRuaIcX5aylGWnvRVRHzpfAiunwpBHILwCpr0c2Q8bvoGOo8o3S7qiJM131oEIZNkHYPXHzgp39Vr5Oxq/8iYRhBdenlJVs7y8I1gCtBWRVjgJYAJw+Un7fI5zJ/CWiEThNBUleRW5ASAxLYtX5ycxpnsTBpwR5e9wvJMU53TM1Wri3jn63OAUols1zf0OwMR58MWf4MAOmP9PGPMqtOjv7jnLKvsAfPcArHjf35FUDtEd4ew7/R2F33mTCA6JSE9VXQYgIr2AI6d6karmicitwCwgGHhTVdeKyGNAvKrO9Dw3XEQSgHzgHlXNKOs/JtCoKn/7fA3VQoP464Wxp35BZZB31CkD0fMqd88T0wua9HAWrelzve/7IgByDjlNC0teh/ptYdSLsOAZeOt8OOs2OPevEFqJajxtWQCf3+IkrHPuhrP+7PSpBLLQGvY7wLtEcAfwiYjsBARoBIz35uCq+g3wzUnbHi70swJ3eb5MKX2xYic/J2bw+JjORNes5u9wvJO8GPKOuNc/UFifG+CLW5yO41ZFTpAvu+TFMONG2JvkLGE49BEIre6MVPr+Ifj5BWet20teg8bdfHvu0so57NTW//VVqNcGrv0emvXxb0ymUvFmQtkSoANwM3AT0FFVl7odmClZ5uFcnvg6gW7N6nB53+b+Dsd7SXEgwc6MYrd1vgSq1/XtUpZ5R52iZG/+AfLz4Oov4fynnCQATrXKi56HK6Y76zC/fp5TvyY/z3cxlEbKUnhtoJME+t4IN/1oScD8zikTgYj8CYhQ1TWqugaIFJFb3A/NlOSZ79ez91AO/zemc+UtKleUpDiI6V0xHbih1aHHlc7kssyTxymUwe7Vzgf7j/+G7pfDzT8Vf6fRdhjcsshZNGfe/8H/hlXscpp5OTD3Cee8uUfgqi/ggqedmkzGnMSb4aM3qOr+Yw88k78Cc/pdJbF8+z4++HU7V5/Vks5NK3FRuZMd2e/UeW89uOLO2ec60AJY+nbZj5GfBwufhSnnQlYqTJwKo186dTKrUQ8u+x+MfduZsPTaObDoZWcehZv2JMAb5zn9Fd0mwC0/V+zv3FQ53iSC4MKL0nhKR4S5F5IBil1gJS+/gL/OWEODmtW4a1glLyp3sq0LnQ/l1oMr7px1WzqzjZe+7Vwll1ZGIrw1AuY8Bh0uhFt+gfbnl+4YnS52Xtd6MMx6AN4dBfu2lT6WUynIhx+fgymDnAqsEz6EMS9DeBW6WDB+4U0i+A6YJiJDRGQI8BHwrbthBbj138C/Y+Hjq+BQ+glPvbtoGwm7DvDwyE7UrMxF5YqSFOdUd2zau2LP2/cGOJQK62Z6/5qCAvh1CrwyANI3waWeK/uI+mWLoWZD505i1IvOYjyvDIBl7/puRbWMRHjrAvjhEWj3ByfxdLjQN8c2pz1vEsF9wFycjuKbgNU4k8qMr2VnOsP7pk6EsAjY8C28fObxAmq7M7N59vsNDGoXzQVdqmC9/aQ4aDkAQir4hrLNEKjbCpb8z7v9M1PgvTHw7T1OvLf8Al0uK/8QVBGn1v3NP0GT7jDzNvhoAhzcU/ZjqrrxLlEAAB5nSURBVDozqF892ympcfEUGPceRFSROSWmUvBm1FAB8CuwFaeQ3HmAFXHxtaT5zlXiyo+cmY43/wyT50PNxjD1cphxM09/8St5BcpjoztV/qJyJ9ufDBmb/dNWHRTk9BVs/xn2rC1+P1VY8SG83B9S4mHkc87on1o+ruRatwVcNRNGPOUkx5fPhLUzSn+czB3w/iXw9d3QrJ/TOd1tvDtzJsxprdhEICLtROQREVkP/BfYDqCq56rqixUV4Gkv5zB8c6/TbhxSDa6bDec95Fw1N4yF6+fAwHsoWDWNuxMn8c8ee2lRv5IXlSvKFpfKTnur+xUQEl78UNKsNJj2R/j8ZmjY2blq7z3JvQ/VoCA482a4caHTj/HJNTD9Omcd51NRhZXT4JX+sP0XuPBZp3Jm7ZNrOhrjnZLuCNbjXP2PVNWzVfW/OLN/ja8kL3FGkix+Dfrd5HwoxJzUfh4SRvY5D3BT2JPkB4czZvUt8M09TgKpSpLiICIaGvhpBnSNek7zzqqPnSa4whJmwsv9nAlgw5+Aa76quNoz0e2c5H/uQ5DwuXM3sml28fsfSoePr4QZkyG6gzMvwK2Z0yZglJQILgF2AfNE5HVPR7G923whL8cZhfLmcGeC0lUzncWyixnj/eLczXyf2Yyd4793ZrEunuK0CSdXkdp8qk4iaD3Yvx9YfW6A3EOw4iPn8ZH98NmNzgdr7Ri4cb5TGqKiSw4Eh8Cge5y7v+p14YPL4Mvb4WjWifut/8ZpRto4C4b+HSZ966zKZkw5FZsIVPVzVZ2AM6t4Hk6piQYi8oqIDK+oAE87u9c4k5IWPgvdPJOSSqjCuTk1i9cWJHJJj6ac2T7GmcV69ZeQn+Mkkh/+XrZhkRUpNQEOpfl/LHuT7hDTx+lc3TwHXjkLVn8Cg+53PoQbnHLhPffjmxzn1ABa+o4T37afTxxEULORs8/Zd1iNHOMzoqUYviYidYGxwHhVHeJaVCXo3bu3xsfH++PU5VOQDz89D/OedK76LnoeOlxQ4ktUlYmv/0LCzgPM/ctgoiIL1RPKPuCMSV/+vtOmffFr0Kizy/+IMlr0Esx6EO5YA3WanXp/N62c5jSrgFMB9eJXoWlP/8ZUlG2L4PObnPkGEVHO+g3n3A0D7634UVfmtCAiS1W1yLHbpVqzWFX3eRaJ8UsSqLIyEuHNEU7hrw4XeMZ4l5wEAGYs38EvSXu57/wOJyYBcGa1jn7JGZuelQpTBjt3Gf6qaVOSpDiof4b/kwA4ReFangP9b3WagipjEgCnjPVNPzmjnWrHnDiIwBgfK9UdQWVQpe4Ijo3xnv2wsxbqBc96PR4983Au5z0bR/P6Nfj0prMIKqme0KEM+PpOSPgCYvo6V7mVpe04Lwf+2RK6T3RGtxhj/MJndwSmFDJT4L2L4Zu/QPP+zl1A17FeJQFV5clv1rHvcA5PjOlcchIAZ7br2Hec2a/pG52O5MWvu1/Txhs74p0O2taD/R2JMaYYlgh8TdVZIvHls5ya9SP/A3/81OvVuNIOHuWGd+OZFp/MdWe3olMTL+vEiDh3G7f8Ai3OchLQ+xc7CcmfkuJAgiqm7LQxpkwsEfjSsUlJM250RqDc/CP0vtbrIZOzE/Yw4rkFLNiUzt9GxvLA+WUYxVKrsTMbduR/nOGlL5/lDJf0VxNgUpyzUlj1uv45vzHmlLxZocx4Y92X8OUdcPQADHvM6Yz0cnhf1tE8Hv8ygWnxycQ2rsVHE7rTrmHNssci4iSg1oOdYYef3wTrv3JKJkRGl/24pZV9wCnVcPYdFXdOY0ypWSIoryP7nMXAV34EjbrCxV86pSG8FL91L3d9vJLkfYe5eXAb7hzajrAQH92o1WsN13ztDN+c+7gzGemi56DjRb45/qls/RE0H1qfWzHnM8aUiSWCssg94szuXPuZ8z0/1xnfPfAer4f35eQV8PycjbwSl0iTOtX5+Mb+9GlZz/exBgXDgD87K2bNuNFpuhr6KJx9p+/PdbKkOAipDs36un8uY0yZuZoIRGQE8DwQDLyhqk+d9Pw1wDPAsXUEX1TVN9yMqczyjkLiXFjzqVMeOifLqZ3T40roeRU07ur1oTbtOcgd01awducBxvWO4W8jY91fW6BBR2f27Iwb4YdHITgM+v/J3XMmxTkd1yHVTrmrMcZ/XEsEnpXMXgKGASnAEhGZqaoJJ+06TVVvdSuOcsnPc6pmrvkM1n/pTPWvXhc6X+osjN7ibKdOjJcKCpR3Fm3lqW/XE1EthNeu7MUfOlXgugLBoc4M5PxcZ6ZvcJizaIsbDuyE9A3Q44/uHN8Y4zNu3hH0BTarahKAiEwFRgMnJ4LKpSDfqe+y9jNngtbhDKhWy1ntqfOlTgdscOmv3ndlHuGeT1bx4+Z0zuvQgH9e2pXomn64Ug4OdeYbfHyVM8Q0OBR6XeP78yT5uey0McZrbiaCpkByoccpQL8i9rtURAYCG4E7VTX55B1EZDIwGaB58+a+j1QVUpY4zT5rP4es3RBaw1mbttMlcMZQCA0v8+FnrtzJQzNWk5uvPHlxFyb2bebfhWVCwmDcO86CN1/e4dwZdL/ct+dIioMa9Z06SMaYSs3fncVfAh+p6lERuRF4B2cNhBOo6hRgCjglJnxyZlXYtcJp9lk7AzKTIbia06na+RJoN8JZLrIcMg/n8rcv1jBz5U56NK/Df8Z1p2VUJVlUJqQajH/fWSrxiz85yaDLZb459rGy060GOQuwGGMqNTcTwQ6gcJWxGH7rFAZAVTMKPXwDeNrFeBx7EjxX/p/B3iQICoE25zkFvdpf4BRz84EfN6Xzl09Wkp51lLuHtePmwW0ICa5kH4qh1WHCR/DBWPhssvO76DSm/MdN2+DcVbUeXP5jGWNc52YiWAK0FZFWOAlgAnBC+4OINFbVXZ6Ho3BzLeS1n0PcU5C2zil50GogDLjDGVNfw3fDNrNz8/nnd+t566ettI6O4LOrzqJrTB2fHd/nwmrA5dPg/Uvh0+ucPoMOF5bvmElxzvfWg8sZnDGmIriWCFQ1T0RuBWbhDB99U1XXishjQLyqzgT+LCKjgDxgL3CNW/GgBc6Inwv+BbGjIbKBz0+xZkcmd0xbwebULK45qyX3jehA9bAqsHhItUi44hN4bwx8fDVM+BDalWPtoaQ4qNvKWaTdGFPpWRlqH8gvUF6dn8h/Zm+kfmQYz1zWjYHtKrCUg68c2QfvjobU9XD5VKfJrLTyc+GfrZz+houe832MxpgysTLULkree5hxry3imVkb+EOnRsy6Y2DVTALg3DFd+TlEtYWPLoctC0t/jB3LIOegNQsZU4VYIiin2z5azsbdB3lufHdevLwHdWpU8RWkatRzkkHdFvDheNj+S+lenxQHiNMHY4ypEiwRlEPCzgOsSN7PXcPbMaZHU//ODfClyGi4aqZT0vr9y5wKot5KioPG3XzaAW+McZclgnKYtmQ7YSFBXNyjqb9D8b2aDeHqL52F09+7BHYuP/Vrjh6ElMXWLGRMFWOJoIyyc/OZsXwHIzo1qvrNQcWp1cRJBtVrw7tjYPfqkvff9jMU5FkiMKaKsURQRt+t2c2B7Dwm9G126p2rsjrNnGQQFuEZUVTCVI+kOGd2dvMzKyw8Y0z5WSIoo48Wb6dF/Rqc2aq+v0NxX92WTjIICoV3RkH6pqL3S4pzkkBo9YqMzhhTTpYIyiApLYtft+xlXO9mBAWdJh3Ep1K/jZMMUHjnIshIPPH5g3sgNQHa2GpkxlQ1lgjK4OP4FIKDhLG9YvwdSsWKbueMJso76twZ7Nv223NbrOy0MVWVJYJSys0vYPrSFM7r0IAGtcpemrrKahgLV33hrND2zkjITHG2J8U5E9Iaeb9SmzGmcrBEUEpz1qWSnnWUCX1O807ikjTuClfOgCP74e2RzmpkSXHOJLKgKlBbyRhzAksEpTR1yXYa1QpnUFUtI+ErTXvCHz+DQ2nwxlA4sMOahYypoiwRlMLO/UeYvzGNsb1jKt/aAv7QrA9cMd0pVgeWCIypovy9QlmV8kl8CqowrncANwudrEV/p88g+Ven9LQxpsqxROCl/ALl4/hkzmkbRbN6NfwdTuXSrK/zZYypkqx9w0s/bk5nx/4jjA/kTmJjzGnJEoGXpi3ZTt0aoQyLbejvUIwxxqcsEXghPesosxP2cGnPGKqF2PBIY8zpxRKBFz5blkJuvlqzkDHmtGSJ4BRUlalLkunVoi5tG9b0dzjGGONzriYCERkhIhtEZLOI3F/CfpeKiIpIkQsr+9OSrftISjsU2DOJjTGnNdcSgYgEAy8B5wOxwEQRiS1iv5rA7cCvbsVSHlOXbCeyWggXdm3s71CMMcYVbt4R9AU2q2qSquYAU4HRRez3OPBPINvFWMok80gu36zexajuTagRZlMujDGnJzcTQVMgudDjFM+240SkJ9BMVb8u6UAiMllE4kUkPi0tzfeRFmPmih1k5xYwsU/zCjunMcZUNL91FotIEPBv4O5T7auqU1S1t6r2jo6uuGJvU5ckE9u4Fp2b1qqwcxpjTEVzMxHsAAr3sMZ4th1TE+gMxInIVuBMYGZl6TBesyOTtTsPMKFvM0QCZBUyY0xAcjMRLAHaikgrEQkDJgAzjz2pqpmqGqWqLVW1JfALMEpV412MyWsfLd5OtZAgRndveuqdjTGmCnMtEahqHnArMAtYB3ysqmtF5DERGeXWeX3hcE4eM1fs5MIujaldPdTf4RhjjKtcHQqjqt8A35y07eFi9h3sZiyl8c3q3Rw8mmcziY3xQm5uLikpKWRnV7qBfwEpPDycmJgYQkO9v4i1MZFFmLp4O62jIujbqp6/QzGm0ktJSaFmzZq0bNnS+tP8TFXJyMggJSWFVq28Xx/ESkycZHPqQeK37WN8H+skNsYb2dnZ1K9f3/5eKgERoX79+qW+O7NEcJJpS5IJCRIu6Rnj71CMqTIsCVQeZfm/sERQyNG8fD5dtoNhsQ2JrlnN3+EYY0yFsERQyA8Jqew9lGOdxMaYgGKJoJCpS7bTtE51zmlbcbOXjTFVR15enr9DcIWNGvJI3nuYhZvSuWNoW4KDrL3TmLL4+5drSdh5wKfHjG1Si0cu6nTK/caMGUNycjLZ2dncfvvtTJ48me+++44HH3yQ/Px8oqKimDNnDllZWdx2223Ex8cjIjzyyCNceumlREZGkpWVBcD06dP56quvePvtt7nmmmsIDw9n+fLlDBgwgAkTJnD77beTnZ1N9erVeeutt2jfvj35+fncd999fPfddwQFBXHDDTfQqVMnXnjhBT7//HMAZs+ezcsvv8yMGTN8+jsqL0sEHp/EJyMCY3tbs5AxVdGbb75JvXr1OHLkCH369GH06NHccMMNLFiwgFatWrF3714AHn/8cWrXrs3q1asB2Ldv3ymPnZKSws8//0xwcDAHDhxg4cKFhISE8MMPP/Dggw/y6aefMmXKFLZu3cqKFSsICQlh79691K1bl1tuuYW0tDSio6N56623uPbaa139PZSFJQIgL7+Aj+NTGNQumqZ1qvs7HGOqLG+u3N3ywgsvHL/STk5OZsqUKQwcOPD4ePp69Zx5QT/88ANTp049/rq6deue8thjx44lONhZrzwzM5Orr76aTZs2ISLk5uYeP+5NN91ESEjICee78soref/995k0aRKLFi3i3Xff9dG/2HcsEQALNqWx+0A2j4763bo5xpgqIC4ujh9++IFFixZRo0YNBg8eTPfu3Vm/fr3Xxyg87PLkcfgRERHHf/7b3/7Gueeey4wZM9i6dSuDBw8u8biTJk3ioosuIjw8nLFjxx5PFJWJdRYDUxcnExUZxnkdGvo7FGNMGWRmZlK3bl1q1KjB+vXr+eWXX8jOzmbBggVs2bIF4HjT0LBhw3jppZeOv/ZY01DDhg1Zt24dBQUFJbbhZ2Zm0rSpU4zy7bffPr592LBhvPbaa8c7lI+dr0mTJjRp0oQnnniCSZMm+e4f7UMBnwhSD2QzZ30ql/aKISwk4H8dxlRJI0aMIC8vj44dO3L//fdz5plnEh0dzZQpU7jkkkvo1q0b48ePB+Chhx5i3759dO7cmW7dujFv3jwAnnrqKUaOHMlZZ51F48bFL01777338sADD9CjR48TRhFdf/31NG/enK5du9KtWzc+/PDD489dccUVNGvWjI4dO7r0GygfUVV/x1AqvXv31vh431WqfjluM09/t4G5dw+idXSkz45rTKBYt25dpf2AqyxuvfVWevTowXXXXVch5yvq/0RElqpqkeu9VL7GqgqkqkxbkkzfVvUsCRhjXNGrVy8iIiJ49tln/R1KsQI6ESxKymBbxmHuGNrW36EYY05TS5cu9XcIpxTQjeLTliRTMzyE8zsX3x5ojDGnu4BNBPsP5/Dtmt1c3KMp4aHB/g7HGGP8JmATwYzlO8jJK2BCn+b+DsUYY/wqIBOBqjJ1cTJdY2oT26SWv8Mxxhi/CshEsDIlkw17Dlq5aWOMweVEICIjRGSDiGwWkfuLeP4mEVktIitE5EcRqZAaD1MXb6d6aDCjujWpiNMZYyqRyEgbKn4y14aPikgw8BIwDEgBlojITFVNKLTbh6r6qmf/UcC/gRFuxQSQdTSPmSt3MrJrY2qGh7p5KmMCz7f3w+7Vvj1moy5w/lO+PWYlkJeXV2nqDrl5R9AX2KyqSaqaA0wFRhfeQVULFy6PAFyf5vz1qp0czslnQl9rFjLmdHD//fefUDvo0Ucf5YknnmDIkCH07NmTLl268MUXX3h1rKysrGJf9+677x4vH3HllVcCsGfPHi6++GK6detGt27d+Pnnn9m6dSudO3c+/rp//etfPProowAMHjyYO+64g969e/P888/z5Zdf0q9fP3r06MHQoUPZs2fP8TgmTZpEly5d6Nq1K59++ilvvvkmd9xxx/Hjvv7669x5551l/r2dQFVd+QIuA94o9PhK4MUi9vsTkAgkA22LOdZkIB6Ib968uZbH6Bd/1KHPxmlBQUG5jmOMcSQkJPj1/MuWLdOBAwcef9yxY0fdvn27ZmZmqqpqWlqatmnT5vjffERERLHHys3NLfJ1a9as0bZt22paWpqqqmZkZKiq6rhx4/Q///mPqqrm5eXp/v37dcuWLdqpU6fjx3zmmWf0kUceUVXVQYMG6c0333z8ub179x6P6/XXX9e77rpLVVXvvfdevf3220/Y7+DBg9q6dWvNyclRVdX+/fvrqlWrivx3FPV/AsRrMZ/Xfr8vUdWXgJdE5HLgIeDqIvaZAkwBp9ZQWc+1fvcBViTv56ELO55QctYYU3X16NGD1NRUdu7cSVpaGnXr1qVRo0bceeedLFiwgKCgIHbs2MGePXto1KhRicdSVR588MHfvW7u3LmMHTuWqKgo4Le1BubOnXt8fYHg4GBq1659yoVujhW/A2fBm/Hjx7Nr1y5ycnKOr51Q3JoJ5513Hl999RUdO3YkNzeXLl26lPK3VTQ3E8EOoHD7S4xnW3GmAq+4GA9TFycTFhzEJT1j3DyNMaaCjR07lunTp7N7927Gjx/PBx98QFpaGkuXLiU0NJSWLVv+bo2BopT1dYWFhIRQUFBw/HFJaxvcdttt3HXXXYwaNYq4uLjjTUjFuf7663nyySfp0KGDT0tau9lHsARoKyKtRCQMmADMLLyDiBQu8nMhsMmtYLJz85mxfAfDOzWkXkSYW6cxxvjB+PHjmTp1KtOnT2fs2LFkZmbSoEEDQkNDmTdvHtu2bfPqOMW97rzzzuOTTz4hIyMD+G2tgSFDhvDKK871a35+PpmZmTRs2JDU1FQyMjI4evQoX331VYnnO7a2wTvvvHN8e3FrJvTr14/k5GQ+/PBDJk6c6O2v55RcSwSqmgfcCswC1gEfq+paEXnMM0II4FYRWSsiK4C7KKJZyFdmrd1N5pFcm0lszGmoU6dOHDx4kKZNm9K4cWOuuOIK4uPj6dKlC++++y4dOnTw6jjFva5Tp0789a9/ZdCgQXTr1o277roLgOeff5558+bRpUsXevXqRUJCAqGhoTz88MP07duXYcOGlXjuRx99lLFjx9KrV6/jzU5Q/JoJAOPGjWPAgAFeLbHprYBZj+D7tbuZtiSZ16/qTVCQ9Q8Y4yu2HkHFGjlyJHfeeSdDhgwpdp/SrkcQMDOLh3dqxP+u6WNJwBhTJe3fv5927dpRvXr1EpNAWfh91JAxxlS01atXH58LcEy1atX49ddf/RTRqdWpU4eNGze6cmxLBMaYclPVKjUku0uXLqxYscLfYbiiLM39AdM0ZIxxR3h4OBkZGWX6ADK+papkZGQQHh5eqtfZHYExplxiYmJISUkhLS3N36EYnMQcE1O6uVKWCIwx5RIaGnp8RqypmqxpyBhjApwlAmOMCXCWCIwxJsBVuZnFIpIGeFc45PeigHQfhuO2qhRvVYoVqla8VSlWqFrxVqVYoXzxtlDV6KKeqHKJoDxEJL64KdaVUVWKtyrFClUr3qoUK1SteKtSrOBevNY0ZIwxAc4SgTHGBLhASwRT/B1AKVWleKtSrFC14q1KsULVircqxQouxRtQfQTGGGN+L9DuCIwxxpzEEoExxgS4gEkEIjJCRDaIyGYRud/f8RRHRJqJyDwRSfAs43m7v2PyhogEi8hyESl+gdZKQETqiMh0EVkvIutEpL+/YyqJiNzpeR+sEZGPRKR0ZSVdJiJvikiqiKwptK2eiMwWkU2e775bU7Ecion1Gc97YZWIzBCROv6M8ZiiYi303N0ioiISVdRryyIgEoGIBAMvAecDscBEEYn1b1TFygPuVtVY4EzgT5U41sJux1mburJ7HvhOVTsA3ajEMYtIU+DPQG9V7QwEAxP8G9XvvA2MOGnb/cAcVW0LzPE8rgze5vexzgY6q2pXYCPwQEUHVYy3+X2siEgzYDiw3ZcnC4hEAPQFNqtqkqrmAFOB0X6OqUiquktVl3l+PojzQdXUv1GVTERigAuBN/wdS0lEpDYwEPgfgKrmqOp+/0Z1SiFAdREJAWoAO/0czwlUdQGw96TNo4F3PD+/A4yp0KCKUVSsqvq9quZ5Hv4ClK5+s0uK+b0C/Ae4F/DpKJ9ASQRNgeRCj1Oo5B+uACLSEugBVN718xzP4bw5C/wdyCm0AtKAtzzNWG+ISIS/gyqOqu4A/oVz9bcLyFTV7/0blVcaquouz8+7gYb+DKYUrgW+9XcQxRGR0cAOVV3p62MHSiKockQkEvgUuENVD/g7nuKIyEggVVWX+jsWL4QAPYFXVLUHcIjK02zxO5629dE4CawJECEif/RvVKWjzvj0Sj9GXUT+itMs+4G/YymKiNQAHgQeduP4gZIIdgDNCj2O8WyrlEQkFCcJfKCqn/k7nlMYAIwSka04TW7nicj7/g2pWClAiqoeu8OajpMYKquhwBZVTVPVXOAz4Cw/x+SNPSLSGMDzPdXP8ZRIRK4BRgJXaOWdWNUG54JgpedvLQZYJiKNfHHwQEkES4C2ItJKRMJwOtxm+jmmIomzAvj/gHWq+m9/x3MqqvqAqsaoakuc3+tcVa2UV62quhtIFpH2nk1DgAQ/hnQq24EzRaSG530xhErcuV3ITOBqz89XA1/4MZYSicgInGbNUap62N/xFEdVV6tqA1Vt6flbSwF6et7T5RYQicDTGXQrMAvnD+ljVV3r36iKNQC4EufKeoXn6wJ/B3UauQ34QERWAd2BJ/0cT7E8dy7TgWXAapy/10pVEkFEPgIWAe1FJEVErgOeAoaJyCacu5qn/BnjMcXE+iJQE5jt+Vt71a9BehQTq3vnq7x3QsYYYypCQNwRGGOMKZ4lAmOMCXCWCIwxJsBZIjDGmABnicAYYwKcJQJjTiIi+YWG7q7wZbVaEWlZVEVJY/wpxN8BGFMJHVHV7v4OwpiKYncExnhJRLaKyNMislpEFovIGZ7tLUVkrqem/RwRae7Z3tBT436l5+tYeYhgEXnds87A9yJS3W//KGOwRGBMUaqf1DQ0vtBzmaraBWdG6nOebf8F3vHUtP8AeMGz/QVgvqp2w6lpdGw2e1vgJVXtBOwHLnX532NMiWxmsTEnEZEsVY0sYvtW4DxVTfIUBtytqvVFJB1orKq5nu27VDVKRNKAGFU9WugYLYHZnkVbEJH7gFBVfcL9f5kxRbM7AmNKR4v5uTSOFvo5H+urM35micCY0hlf6Psiz88/89sSklcACz0/zwFuhuNrOteuqCCNKQ27EjHm96qLyIpCj79T1WNDSOt6KpceBSZ6tt2Gs+rZPTgroE3ybL8dmOKpHJmPkxR2YUwlY30ExnjJ00fQW1XT/R2LMb5kTUPGGBPg7I7AGGMCnN0RGGNMgLNEYIwxAc4SgTHGBDhLBMYYE+AsERhjTID7f8wiUG7cn1ZaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}