AritmaPlay_Model_Dev.ipynb

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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# AritmaPlay : Handwriting Recognizer Model"
      ],
      "metadata": {
        "id": "zf-9svjgW9-U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Project Preparation"
      ],
      "metadata": {
        "id": "F7R_ggXIXJha"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import Library"
      ],
      "metadata": {
        "id": "44TduCPVXQB4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UVHU9WwN58yX"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pathlib\n",
        "import zipfile,os,shutil\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install Kaggle"
      ],
      "metadata": {
        "id": "9iVTm1PMXYqE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle --quiet\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "\n",
        "!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "XdHmc8gW6MqD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "6ccce62a-3053-440a-d7e1-0d4ae537434a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "\n",
              "     <input type=\"file\" id=\"files-1471d525-51d3-415e-b1c8-acfd57765c54\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1471d525-51d3-415e-b1c8-acfd57765c54\">\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>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "User uploaded file \"kaggle.json\" with length 63 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset"
      ],
      "metadata": {
        "id": "8P11sicmXkzR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download Datasets"
      ],
      "metadata": {
        "id": "_RxgMPo8XrHm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download \"pradheeprio/handwritten-digit-classification\"\n",
        "local_zip = '/content/handwritten-digit-classification.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "\n",
        "!kaggle datasets download \"clarencezhao/handwritten-math-symbol-dataset\"\n",
        "local_zip = '/content/handwritten-math-symbol-dataset.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "\n",
        "!kaggle datasets download \"riyaldi/handwriting-digit-0-9\"\n",
        "local_zip = '/content/handwriting-digit-0-9.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()"
      ],
      "metadata": {
        "id": "RcmVxfTW6NNS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6305c0d5-d1ff-4088-f276-dfb44c4257e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/pradheeprio/handwritten-digit-classification\n",
            "License(s): copyright-authors\n",
            "Downloading handwritten-digit-classification.zip to /content\n",
            " 53% 10.0M/19.0M [00:00<00:00, 23.7MB/s]\n",
            "100% 19.0M/19.0M [00:00<00:00, 42.5MB/s]\n",
            "Dataset URL: https://www.kaggle.com/datasets/clarencezhao/handwritten-math-symbol-dataset\n",
            "License(s): copyright-authors\n",
            "Downloading handwritten-math-symbol-dataset.zip to /content\n",
            " 85% 49.0M/57.8M [00:00<00:00, 77.3MB/s]\n",
            "100% 57.8M/57.8M [00:00<00:00, 74.0MB/s]\n",
            "Dataset URL: https://www.kaggle.com/datasets/riyaldi/handwriting-digit-0-9\n",
            "License(s): apache-2.0\n",
            "Downloading handwriting-digit-0-9.zip to /content\n",
            " 89% 65.0M/73.2M [00:01<00:00, 42.4MB/s]\n",
            "100% 73.2M/73.2M [00:02<00:00, 38.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Delete unused class folder"
      ],
      "metadata": {
        "id": "kba6QSYl_1Ws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#delete a folder in eval folder\n",
        "folder_to_delete_eval = ['decimal val', 'div val', 'equal val', 'minus val', 'number', 'plus val', 'sign', 'times val']\n",
        "folder_to_delete_train = ['decimal' ,'div', 'equal', 'minus', 'original number', 'plus cleaned', 'times', 'original sign', 'other number', 'other sign']\n",
        "\n",
        "for folder in folder_to_delete_eval:\n",
        "  shutil.rmtree('/tmp/eval/' + folder)\n",
        "\n",
        "for folder in folder_to_delete_train:\n",
        "  shutil.rmtree('/tmp/train/' + folder)"
      ],
      "metadata": {
        "id": "AZvT4rJFVKYU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merge Train & Validation folder"
      ],
      "metadata": {
        "id": "Do9dWK8-AS3G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir = '/tmp/eval/'\n",
        "val_dir = '/tmp/train/'\n",
        "merged_dir = '/tmp/merged_folder'\n",
        "\n",
        "if not os.path.exists(merged_dir):\n",
        "    os.makedirs(merged_dir)\n",
        "\n",
        "def merge_folders(source_dir, dest_dir):\n",
        "    subfolders = os.listdir(source_dir)\n",
        "\n",
        "    for subfolder in subfolders:\n",
        "        source_subfolder = os.path.join(source_dir, subfolder)\n",
        "        dest_subfolder = os.path.join(dest_dir, subfolder)\n",
        "\n",
        "        if not os.path.exists(dest_subfolder):\n",
        "            os.makedirs(dest_subfolder)\n",
        "\n",
        "        for file in os.listdir(source_subfolder):\n",
        "            source_file = os.path.join(source_subfolder, file)\n",
        "            dest_file = os.path.join(dest_subfolder, file)\n",
        "\n",
        "            if not os.path.exists(dest_file):\n",
        "                shutil.copy2(source_file, dest_file)\n",
        "\n",
        "print(\"Merging train folder...\")\n",
        "merge_folders(train_dir, merged_dir)\n",
        "\n",
        "print(\"Merging val folder...\")\n",
        "merge_folders(val_dir, merged_dir)\n",
        "\n",
        "print(\"Merging complete!\")\n",
        "\n",
        "for folder in os.listdir(merged_dir):\n",
        "    folder_path = os.path.join(merged_dir, folder)\n",
        "    file_count = len(os.listdir(folder_path))\n",
        "    print(f\"Folder '{folder}' contains {file_count} files\")\n",
        "\n",
        "shutil.rmtree('/tmp/eval')\n",
        "shutil.rmtree('/tmp/train')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQHqXzYaWpA-",
        "outputId": "2aaba9fa-6c57-4de0-cced-a0c8e452e299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Merging train folder...\n",
            "Merging val folder...\n",
            "Merging complete!\n",
            "Folder 'three' contains 483 files\n",
            "Folder 'two' contains 484 files\n",
            "Folder 'six' contains 482 files\n",
            "Folder 'four' contains 484 files\n",
            "Folder 'nine' contains 484 files\n",
            "Folder 'five' contains 485 files\n",
            "Folder 'zero' contains 478 files\n",
            "Folder 'seven' contains 484 files\n",
            "Folder 'one' contains 487 files\n",
            "Folder 'eight' contains 483 files\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Change dataset class folder naming"
      ],
      "metadata": {
        "id": "RD1cFzzDAiQr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "folder_mapping = {\n",
        "    'one': '1',\n",
        "    'two': '2',\n",
        "    'three': '3',\n",
        "    'four': '4',\n",
        "    'five': '5',\n",
        "    'six': '6',\n",
        "    'seven': '7',\n",
        "    'eight': '8',\n",
        "    'nine': '9',\n",
        "    'zero': '0',\n",
        "}\n",
        "\n",
        "merged_dir = '/tmp/merged_folder'\n",
        "\n",
        "for old_name in list(os.listdir(merged_dir)):\n",
        "    if old_name in folder_mapping:\n",
        "        old_path = os.path.join(merged_dir, old_name)\n",
        "        new_path = os.path.join(merged_dir, folder_mapping[old_name])\n",
        "\n",
        "        try:\n",
        "            os.rename(old_path, new_path)\n",
        "            print(f\"Renamed '{old_name}' to '{folder_mapping[old_name]}'\")\n",
        "        except Exception as e:\n",
        "            print(f\"Error renaming '{old_name}': {str(e)}\")\n",
        "\n",
        "print(\"\\nRenaming complete!\")\n",
        "\n",
        "print(\"\\nFinal folder structure:\")\n",
        "for folder in sorted(os.listdir(merged_dir)):\n",
        "    folder_path = os.path.join(merged_dir, folder)\n",
        "    file_count = len(os.listdir(folder_path))\n",
        "    print(f\"Folder '{folder}' contains {file_count} files\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKxqR6atYE_a",
        "outputId": "f900fb97-a036-416d-a0a3-728c4fbb3405"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Renamed 'three' to '3'\n",
            "Renamed 'two' to '2'\n",
            "Renamed 'six' to '6'\n",
            "Renamed 'four' to '4'\n",
            "Renamed 'nine' to '9'\n",
            "Renamed 'five' to '5'\n",
            "Renamed 'zero' to '0'\n",
            "Renamed 'seven' to '7'\n",
            "Renamed 'one' to '1'\n",
            "Renamed 'eight' to '8'\n",
            "\n",
            "Renaming complete!\n",
            "\n",
            "Final folder structure:\n",
            "Folder '0' contains 478 files\n",
            "Folder '1' contains 487 files\n",
            "Folder '2' contains 484 files\n",
            "Folder '3' contains 483 files\n",
            "Folder '4' contains 484 files\n",
            "Folder '5' contains 485 files\n",
            "Folder '6' contains 482 files\n",
            "Folder '7' contains 484 files\n",
            "Folder '8' contains 483 files\n",
            "Folder '9' contains 484 files\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Merge Datasets"
      ],
      "metadata": {
        "id": "oSjzLLOvAst0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset1 = '/tmp/database/'\n",
        "dataset2 = '/tmp/merged_folder/'\n",
        "dataset3 = '/tmp/handDigitDataset/'\n",
        "merged_dir = '/tmp/merged_datasets'\n",
        "\n",
        "if not os.path.exists(merged_dir):\n",
        "    os.makedirs(merged_dir)\n",
        "\n",
        "def merge_folders(source_dir, dest_dir):\n",
        "    subfolders = os.listdir(source_dir)\n",
        "\n",
        "    for subfolder in subfolders:\n",
        "        source_subfolder = os.path.join(source_dir, subfolder)\n",
        "        dest_subfolder = os.path.join(dest_dir, subfolder)\n",
        "\n",
        "        if not os.path.exists(dest_subfolder):\n",
        "            os.makedirs(dest_subfolder)\n",
        "\n",
        "        for file in os.listdir(source_subfolder):\n",
        "            source_file = os.path.join(source_subfolder, file)\n",
        "            dest_file = os.path.join(dest_subfolder, file)\n",
        "\n",
        "            if not os.path.exists(dest_file):\n",
        "                shutil.copy2(source_file, dest_file)\n",
        "\n",
        "print(\"Merging dataset1 folder...\")\n",
        "merge_folders(dataset1, merged_dir)\n",
        "\n",
        "print(\"Merging dataset2 folder...\")\n",
        "merge_folders(dataset2, merged_dir)\n",
        "\n",
        "print(\"Merging dataset3 folder...\")\n",
        "merge_folders(dataset3, merged_dir)\n",
        "\n",
        "\n",
        "print(\"Merging complete!\")\n",
        "\n",
        "for folder in os.listdir(merged_dir):\n",
        "    folder_path = os.path.join(merged_dir, folder)\n",
        "    file_count = len(os.listdir(folder_path))\n",
        "    print(f\"Folder '{folder}' contains {file_count} files\")\n",
        "\n",
        "shutil.rmtree(dataset1)\n",
        "shutil.rmtree(dataset2)\n",
        "shutil.rmtree(dataset3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dK94C_JYY8CA",
        "outputId": "f3396534-9c5c-43e0-88f2-642bfd0e5c75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Merging dataset1 folder...\n",
            "Merging dataset2 folder...\n",
            "Merging dataset3 folder...\n",
            "Merging complete!\n",
            "Folder '7' contains 4906 files\n",
            "Folder '8' contains 4872 files\n",
            "Folder '3' contains 4886 files\n",
            "Folder '6' contains 4871 files\n",
            "Folder '1' contains 4900 files\n",
            "Folder '2' contains 4865 files\n",
            "Folder '4' contains 4891 files\n",
            "Folder '5' contains 4881 files\n",
            "Folder '9' contains 4891 files\n",
            "Folder '0' contains 4877 files\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Split Dataset"
      ],
      "metadata": {
        "id": "TlL99nADXyx9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "\n",
        "train_set, temp_test = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    \"/tmp/merged_datasets\",\n",
        "    label_mode=\"categorical\",\n",
        "    color_mode=\"grayscale\",\n",
        "    image_size=(48, 48),\n",
        "    batch_size=BATCH_SIZE,\n",
        "    shuffle=True,\n",
        "    seed=123,\n",
        "    validation_split=0.2,\n",
        "    subset=\"both\",\n",
        "    verbose=False\n",
        ")\n",
        "\n",
        "class_names = train_set.class_names\n",
        "\n",
        "val_test_split = 0.5\n",
        "\n",
        "total_batches = int(tf.data.experimental.cardinality(temp_test))\n",
        "val_batches = total_batches // 2\n",
        "\n",
        "val_set = temp_test.take(val_batches)\n",
        "test_set = temp_test.skip(val_batches)\n",
        "\n",
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_set = train_set.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
        "val_set = val_set.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "test_set = test_set.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "\n",
        "\n",
        "train_batches = tf.data.experimental.cardinality(train_set).numpy()\n",
        "val_batches = tf.data.experimental.cardinality(val_set).numpy()\n",
        "test_batches = tf.data.experimental.cardinality(test_set).numpy()\n",
        "\n",
        "print(\"\\nDataset Information:\")\n",
        "print(f\"Training:   {train_batches} batches, {train_batches * BATCH_SIZE} images\")\n",
        "print(f\"Validation: {val_batches} batches, {val_batches * BATCH_SIZE} images\")\n",
        "print(f\"Testing:    {test_batches} batches, {test_batches * BATCH_SIZE} images\")\n",
        "print(f\"Total:      {train_batches + val_batches + test_batches} batches, \"\n",
        "      f\"{(train_batches + val_batches + test_batches) * BATCH_SIZE} images\")"
      ],
      "metadata": {
        "id": "_CXaLSfG67qP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e5b7b6e-e969-49b6-a601-2ff7dedbc32f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using 39072 files for training.\n",
            "Using 9768 files for validation.\n",
            "\n",
            "Dataset Information:\n",
            "Training:   1221 batches, 39072 images\n",
            "Validation: 153 batches, 4896 images\n",
            "Testing:    153 batches, 4896 images\n",
            "Total:      1527 batches, 48864 images\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "# Training samples\n",
        "for i in range(5):\n",
        "    plt.subplot(3, 5, i+1)\n",
        "    for images, labels in train_set.take(1):\n",
        "        plt.imshow(tf.squeeze(images[i]).numpy(), cmap='gray')\n",
        "        predicted_class_index = tf.argmax(labels[i]).numpy()\n",
        "        plt.title(f'Train: {class_names[predicted_class_index]}')\n",
        "        plt.axis(\"off\")\n",
        "\n",
        "# Validation samples\n",
        "for i in range(5):\n",
        "    plt.subplot(3, 5, i+6)\n",
        "    for images, labels in val_set.take(1):\n",
        "        plt.imshow(tf.squeeze(images[i]).numpy(), cmap='gray')\n",
        "        predicted_class_index = tf.argmax(labels[i]).numpy()\n",
        "        plt.title(f'Val: {class_names[predicted_class_index]}')\n",
        "        plt.axis(\"off\")\n",
        "\n",
        "# Test samples\n",
        "for i in range(5):\n",
        "    plt.subplot(3, 5, i+11)\n",
        "    for images, labels in test_set.take(1):\n",
        "        plt.imshow(tf.squeeze(images[i]).numpy(), cmap='gray')\n",
        "        predicted_class_index = tf.argmax(labels[i]).numpy()\n",
        "        plt.title(f'Test: {class_names[predicted_class_index]}')\n",
        "        plt.axis(\"off\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 797
        },
        "id": "soLpD22-LD-J",
        "outputId": "0a5c2e41-51d0-4758-9c58-dae80c12bc3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Models"
      ],
      "metadata": {
        "id": "KXi5bSWSX_To"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 1"
      ],
      "metadata": {
        "id": "FWzOeB0aYKt3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Input((48, 48, 1)),\n",
        "    tf.keras.layers.Rescaling(1./255),\n",
        "    tf.keras.layers.RandomRotation(0.1),\n",
        "    tf.keras.layers.RandomZoom(height_factor=(-0.1, 0.1), width_factor=(-0.1, 0.1)),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
        "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "h-hSQcfm92Mc",
        "outputId": "3033e228-ea0f-47f8-d2bf-864d9f3de67b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\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\"</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",
              "│ rescaling (\u001b[38;5;33mRescaling\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ random_rotation (\u001b[38;5;33mRandomRotation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ random_zoom (\u001b[38;5;33mRandomZoom\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m4,624\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │          \u001b[38;5;34m16,010\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",
              "│ rescaling (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ random_rotation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RandomRotation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ random_zoom (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">RandomZoom</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d (<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\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<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\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<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\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<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\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<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\">1600</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<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\">10</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,010</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m20,954\u001b[0m (81.85 KB)\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\">20,954</span> (81.85 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m20,954\u001b[0m (81.85 KB)\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\">20,954</span> (81.85 KB)\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": [
        "model1.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "BroLlJU7_xwN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "es = EarlyStopping(monitor='loss', mode='min', patience=3,  restore_best_weights=True)\n",
        "\n",
        "history1 = model1.fit(\n",
        "                  train_set,\n",
        "                  epochs=20,\n",
        "                  validation_data=val_set,\n",
        "                  callbacks=[es])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYASsbrJAJBH",
        "outputId": "0e644010-d146-42fe-ebd9-b817d316f904"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 6ms/step - accuracy: 0.5612 - loss: 1.3568 - val_accuracy: 0.8125 - val_loss: 0.5008\n",
            "Epoch 2/20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/contextlib.py:153: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
            "  self.gen.throw(typ, value, traceback)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.8521 - loss: 0.5384 - val_accuracy: 0.8750 - val_loss: 0.4030\n",
            "Epoch 3/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.8819 - loss: 0.4299 - val_accuracy: 0.8750 - val_loss: 0.4364\n",
            "Epoch 4/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.8990 - loss: 0.3621 - val_accuracy: 0.9375 - val_loss: 0.2455\n",
            "Epoch 5/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9152 - loss: 0.3030 - val_accuracy: 0.8438 - val_loss: 0.5298\n",
            "Epoch 6/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.9241 - loss: 0.2720 - val_accuracy: 0.9375 - val_loss: 0.3945\n",
            "Epoch 7/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.9274 - loss: 0.2485 - val_accuracy: 0.9062 - val_loss: 0.3549\n",
            "Epoch 8/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9370 - loss: 0.2202 - val_accuracy: 0.9062 - val_loss: 0.4140\n",
            "Epoch 9/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9432 - loss: 0.2000 - val_accuracy: 0.9062 - val_loss: 0.3747\n",
            "Epoch 10/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.9450 - loss: 0.1930 - val_accuracy: 0.9062 - val_loss: 0.3772\n",
            "Epoch 11/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9480 - loss: 0.1778 - val_accuracy: 0.9375 - val_loss: 0.3453\n",
            "Epoch 12/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9521 - loss: 0.1678 - val_accuracy: 0.9062 - val_loss: 0.3308\n",
            "Epoch 13/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9494 - loss: 0.1706 - val_accuracy: 0.9062 - val_loss: 0.5670\n",
            "Epoch 14/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.9534 - loss: 0.1590 - val_accuracy: 0.9062 - val_loss: 0.3947\n",
            "Epoch 15/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.9519 - loss: 0.1537 - val_accuracy: 0.9375 - val_loss: 0.4271\n",
            "Epoch 16/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9584 - loss: 0.1377 - val_accuracy: 0.9375 - val_loss: 0.4181\n",
            "Epoch 17/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - accuracy: 0.9570 - loss: 0.1430 - val_accuracy: 0.8125 - val_loss: 0.5729\n",
            "Epoch 18/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.9605 - loss: 0.1348 - val_accuracy: 0.9375 - val_loss: 0.4638\n",
            "Epoch 19/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9612 - loss: 0.1337 - val_accuracy: 0.9062 - val_loss: 0.4122\n",
            "Epoch 20/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 5ms/step - accuracy: 0.9622 - loss: 0.1272 - val_accuracy: 0.9062 - val_loss: 0.4751\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "plt.plot(history1.history['accuracy'])\n",
        "plt.plot(history1.history['val_accuracy'])\n",
        "\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend(['Train', 'Test'])\n",
        "\n",
        "plt.figure(figsize=(8,4))\n",
        "plt.plot(history1.history['loss'])\n",
        "plt.plot(history1.history['val_loss'])\n",
        "\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend(['Train', 'Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "id": "hQIkQn-rAOoK",
        "outputId": "16f10838-b8ef-47fd-c8d1-fd592625019b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd0d69355d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Model 2"
      ],
      "metadata": {
        "id": "wRshUgRrvlKQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Input((48, 48, 1)),\n",
        "    tf.keras.layers.Rescaling(1./255),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
        "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "eLepVWg5gqMf",
        "outputId": "fbf9a382-7708-4d54-99ad-ec13ceee0c87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\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_1\"</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",
              "│ rescaling_1 (\u001b[38;5;33mRescaling\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m4,624\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │          \u001b[38;5;34m16,010\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",
              "│ rescaling_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<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\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (<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\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_3 (<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\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_3 (<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\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_1 (<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\">1600</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<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\">1600</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<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\">10</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,010</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m20,954\u001b[0m (81.85 KB)\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\">20,954</span> (81.85 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m20,954\u001b[0m (81.85 KB)\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\">20,954</span> (81.85 KB)\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": [
        "model2.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "vZKPLchsjT-o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "es = EarlyStopping(monitor='loss', mode='min', patience=3,  restore_best_weights=True)\n",
        "\n",
        "history2 = model2.fit(\n",
        "                  train_set,\n",
        "                  epochs=20,\n",
        "                  validation_data=val_set,\n",
        "                  callbacks=[es])"
      ],
      "metadata": {
        "id": "V4qRrKt3LYpC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "914eac0d-c8b7-461c-9732-2d5be02b9849"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.6319 - loss: 1.1531 - val_accuracy: 0.8750 - val_loss: 0.3977\n",
            "Epoch 2/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 2ms/step - accuracy: 0.9056 - loss: 0.3453 - val_accuracy: 0.9062 - val_loss: 0.2452\n",
            "Epoch 3/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9273 - loss: 0.2550 - val_accuracy: 0.9062 - val_loss: 0.3071\n",
            "Epoch 4/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.9355 - loss: 0.2214 - val_accuracy: 0.9688 - val_loss: 0.2084\n",
            "Epoch 5/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9464 - loss: 0.1787 - val_accuracy: 0.9688 - val_loss: 0.1592\n",
            "Epoch 6/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9550 - loss: 0.1568 - val_accuracy: 0.9688 - val_loss: 0.1249\n",
            "Epoch 7/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9603 - loss: 0.1353 - val_accuracy: 0.9688 - val_loss: 0.1554\n",
            "Epoch 8/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9633 - loss: 0.1264 - val_accuracy: 0.9688 - val_loss: 0.1575\n",
            "Epoch 9/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9670 - loss: 0.1161 - val_accuracy: 0.9688 - val_loss: 0.1944\n",
            "Epoch 10/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9672 - loss: 0.1079 - val_accuracy: 0.9688 - val_loss: 0.1484\n",
            "Epoch 11/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9689 - loss: 0.1006 - val_accuracy: 0.9688 - val_loss: 0.1497\n",
            "Epoch 12/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9703 - loss: 0.0991 - val_accuracy: 0.9688 - val_loss: 0.2092\n",
            "Epoch 13/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.9729 - loss: 0.0895 - val_accuracy: 0.9688 - val_loss: 0.2346\n",
            "Epoch 14/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9752 - loss: 0.0831 - val_accuracy: 0.9375 - val_loss: 0.2961\n",
            "Epoch 15/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9752 - loss: 0.0857 - val_accuracy: 0.9688 - val_loss: 0.2399\n",
            "Epoch 16/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9751 - loss: 0.0784 - val_accuracy: 0.9688 - val_loss: 0.2208\n",
            "Epoch 17/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9758 - loss: 0.0771 - val_accuracy: 0.9688 - val_loss: 0.2604\n",
            "Epoch 18/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9762 - loss: 0.0715 - val_accuracy: 0.9375 - val_loss: 0.2834\n",
            "Epoch 19/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9777 - loss: 0.0736 - val_accuracy: 0.9375 - val_loss: 0.3377\n",
            "Epoch 20/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9778 - loss: 0.0702 - val_accuracy: 0.9688 - val_loss: 0.3241\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "plt.plot(history2.history['accuracy'])\n",
        "plt.plot(history2.history['val_accuracy'])\n",
        "\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend(['Train', 'Test'])\n",
        "\n",
        "plt.figure(figsize=(8,4))\n",
        "plt.plot(history2.history['loss'])\n",
        "plt.plot(history2.history['val_loss'])\n",
        "\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend(['Train', 'Test'])"
      ],
      "metadata": {
        "id": "A8QzlKAOjXWH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "outputId": "8c22347e-ffd4-4162-b65a-b0f25ef3ee17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd0b8203700>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Model 3"
      ],
      "metadata": {
        "id": "qxt098Xjvr67"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Input((48, 48, 1)),\n",
        "    tf.keras.layers.Rescaling(1./255),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
        "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dropout(0.1),\n",
        "    tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "0PAKrRfxwfyc",
        "outputId": "ac90eb69-0e28-4d5b-a251-48e1331f07d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "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",
              "│ rescaling_2 (\u001b[38;5;33mRescaling\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m48\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m46\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m23\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m21\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │           \u001b[38;5;34m4,624\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_5 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m16\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │          \u001b[38;5;34m16,010\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",
              "│ rescaling_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">48</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_4 (<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\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">46</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_4 (<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\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">23</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_5 (<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\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">21</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_5 (<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\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten_2 (<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\">1600</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">1600</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<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\">10</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,010</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m20,954\u001b[0m (81.85 KB)\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\">20,954</span> (81.85 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m20,954\u001b[0m (81.85 KB)\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\">20,954</span> (81.85 KB)\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": [
        "model3.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "4HRUBxz40HVp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "es = EarlyStopping(monitor='loss', mode='min', patience=3,  restore_best_weights=True)\n",
        "\n",
        "history3 = model3.fit(\n",
        "                  train_set,\n",
        "                  epochs=20,\n",
        "                  validation_data=val_set,\n",
        "                  callbacks=[es])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mA1xZa-20LEI",
        "outputId": "3ca88252-4325-4b9a-8623-d00db63700e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.6546 - loss: 1.1016 - val_accuracy: 0.8438 - val_loss: 0.3795\n",
            "Epoch 2/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9198 - loss: 0.2887 - val_accuracy: 0.8750 - val_loss: 0.3047\n",
            "Epoch 3/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9446 - loss: 0.1962 - val_accuracy: 0.8750 - val_loss: 0.2328\n",
            "Epoch 4/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9562 - loss: 0.1486 - val_accuracy: 0.9062 - val_loss: 0.1483\n",
            "Epoch 5/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9646 - loss: 0.1219 - val_accuracy: 0.9375 - val_loss: 0.1720\n",
            "Epoch 6/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9714 - loss: 0.0977 - val_accuracy: 0.9062 - val_loss: 0.2277\n",
            "Epoch 7/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9742 - loss: 0.0894 - val_accuracy: 0.9375 - val_loss: 0.1732\n",
            "Epoch 8/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9784 - loss: 0.0721 - val_accuracy: 0.9688 - val_loss: 0.1509\n",
            "Epoch 9/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9790 - loss: 0.0686 - val_accuracy: 0.9062 - val_loss: 0.3253\n",
            "Epoch 10/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9817 - loss: 0.0595 - val_accuracy: 0.9688 - val_loss: 0.1915\n",
            "Epoch 11/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9832 - loss: 0.0539 - val_accuracy: 0.9062 - val_loss: 0.1699\n",
            "Epoch 12/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9840 - loss: 0.0506 - val_accuracy: 0.9375 - val_loss: 0.2071\n",
            "Epoch 13/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9845 - loss: 0.0499 - val_accuracy: 0.9375 - val_loss: 0.1621\n",
            "Epoch 14/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.9869 - loss: 0.0432 - val_accuracy: 0.9688 - val_loss: 0.1321\n",
            "Epoch 15/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9865 - loss: 0.0421 - val_accuracy: 0.9062 - val_loss: 0.2161\n",
            "Epoch 16/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9875 - loss: 0.0399 - val_accuracy: 0.9688 - val_loss: 0.0864\n",
            "Epoch 17/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.9882 - loss: 0.0363 - val_accuracy: 0.9375 - val_loss: 0.2119\n",
            "Epoch 18/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.9897 - loss: 0.0339 - val_accuracy: 0.9375 - val_loss: 0.0902\n",
            "Epoch 19/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9888 - loss: 0.0355 - val_accuracy: 0.9375 - val_loss: 0.2067\n",
            "Epoch 20/20\n",
            "\u001b[1m1221/1221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9892 - loss: 0.0333 - val_accuracy: 0.9688 - val_loss: 0.0951\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "plt.plot(history3.history['accuracy'])\n",
        "plt.plot(history3.history['val_accuracy'])\n",
        "\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend(['Train', 'Test'])\n",
        "\n",
        "plt.figure(figsize=(8,4))\n",
        "plt.plot(history3.history['loss'])\n",
        "plt.plot(history3.history['val_loss'])\n",
        "\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend(['Train', 'Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "id": "GklJAdnB0PEW",
        "outputId": "b6158d91-bed7-49dd-bf64-bcf9f64abc31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd0d69af6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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OPXbsGOvWrbtor69ZQkICAGlpafUmxN7e3nh7e1/yXHXSNKi48D9+u/D0UxlGM3z88cfMnTuXN998k379+rF7926mTZuGv78/kydP5vXXX+frr7/m888/p23bthw/fpzjx48DapJjREQES5YsYfTo0bi7S49eS1BWaeRgRgF7T+Sy53gee0/kkna6kLpmLLRr7UfvNiH0jlVDH3rEBEntXSGEEHalW0Ls5eVF//79SU5OtowtNZlMJCcnM2PGjIs+18fHh9jYWCoqKvjyyy+5+eabLzhmyZIlREREMG7cuEvGsmfPHgCio6Mb/ToapKIYXoi59HG28OQp8PJv1inmzZvHq6++yo033ghA+/btOXDgAO+99x6TJ08mPT2dzp07M3ToUAwGA+3atbM8Nzw8HICQkJB6P+gI52Y0aRw+XcgvJ/L45Xgue0/k8ltGAeXGCxeliA3xVb2+cWrMb6/YYIL9JPkVQgihL12/g0xKSmLy5MkMGDCAQYMGsWjRIoqKipg6dSoAd955J7GxsSxYsACAbdu2cfLkSfr27cvJkyeZP38+JpOJxx9/vNZ5TSYTS5YsYfLkyXh41H6Jhw4d4pNPPmHs2LG0bt2avXv38sgjjzBs2DB69+5tnxfuRIqKijh06BB3330306ZNs+yvrKwkODgYgClTpnDdddfRtWtXRo8ezR/+8AdGjRqlV8jChjRN42SuqvX7y/FcfjmRy/6T+RSWXVjrt5WfJ73bhNAnLoQ+VRUfwgOb+C2LEEIIYUO6JsS33HILp0+fZu7cuWRmZtK3b1/WrFljmWiXnp6Om1t1uaTS0lJmz57N4cOHCQgIYOzYsXz00UeEhITUOu+6detIT0/nrrvuuuCaXl5erFu3zpJ8x8XFMXHiRGbPnm27F+rpp3pq9eDZvPrKhYWFALz//vuWoSVm5uEPl19+OUeOHGH16tWsW7eOm2++mZEjR/Kf//ynWdcW+jtbVM4vJ9SEt19OqN7fusqd+Xq60ys2mD5xKvHt0yaEuFDfS1YvEUIIIRyB7rNUZsyYUe8QiQ0bNtS6f/XVV3PgwIFLnnPUqFHUV145Li7uglXqbM5gaPawBb1ERkYSExPD4cOHmTRpUr3HBQUFccstt3DLLbfwpz/9idGjR3P27FlCQ0Px9PSstxKIcBxFZZXsP6lKne2pSn6Pn72w4oOHm4Fu0YH0bhNC3zYh9I4LplN4AB5S61cIIYST0j0hFo7v6aef5sEHHyQ4OJjRo0dTVlbGzz//zLlz50hKSmLhwoVER0fTr18/3Nzc+OKLL4iKirL03MfHx5OcnMyQIUPw9vamVatW+r4gQaXRxG8ZBfxyIrdq3G8eqdkFmOr4HNkh3J8+baqGPcSFcJmUOxNCCOFiJCEWl3TPPffg5+fHyy+/zN/+9jf8/f3p1asXDz/8MACBgYG89NJLpKam4u7uzsCBA1m1apVluMurr75KUlIS77//PrGxsVJ2TScVRhNbD59h1b4Mvv01i7NFFw59iA72sazw1jcuhJ6xwQT7usCkt+Kz4BMCbi2gF7u8GAxu4CkrO7qcFtWOi6rasa/ekQhrMpngt/9C9+sdrh3runSzM5Olm+1D3svmKa80sflQDqv2ZfDdgSxyiyssjwX6eNA3To33NU98iwhywfd433/gy3tg2N9gxFN6R2NbZQXw1hXgGwJ/+dHh/sMRzbD3c1h+L1wzU91cWWkevDkQvIPgLxuddsihqMPOD+F/D0H7q+HO/za7LGxDOPzSzUII2yirNPJTWg4r92ay9kAm+aXVFSBa+3sxqkcU43pFk9AhFE9XH/dbcg5WPw5osOdjGP6kXf4A6yZtHeSfULeTOyFuoN4RCWsoPgurnwA02L0Urn7Ctdtx6loozFK3H16GkfP1jkhYQ9EZWDdfbXcZ7XBtWBJiIVxAaYWRH34/zer9maw7kEVBjTJoYQHejO4Zydhe0QyKD21Zk9+Sn4XiM2o7/yRk7oXoPvrGZEspa6q3f18tCbGrSH4GSs6q7bzjkPUrRPXUNyZb+r1GO978JvS5HcK76BePsI7k+aqTIrInDLr3kofbmyTEQjip0gojG1KyWbUvk+Tfsigqr67kERnkzZie0YzpGcWA+FDc3Rzrk7hdnNwFP1ct6x7aEc4egpTVrpsQGysh9dvq+ymr4dq5+sUjrOPkTvU1M0BoBzh7WP3bumpCbKyA1O/Utvn3dtWjcOfXDtejKBrh+A7Y9W+1PfYVcHe89NPxIhJC1Ku4vJL1B0+zan8G6w9mU1wjCY4O9mFMz2jG9ori8ratcGuJSbCZyQQrHwU06HUTdLgG/jtdJRKuOv7yxHbV++IdDOWFkH0Azh2FVvF6RyaaymSEb5IADXrfCu2uhP89qHr/r/6b3tHZRvpWNYbYrzVM+gLeuRKO/AC/LoeeE/WOTjSFyQgrk9R2n9uh3WB946mHJMRCOLjCskq+P5jN6n0ZrE/JprSieknk2BBfxvaKYkyvaPq2CWnZSXBNu/4Fp3aBVyCMeg4M7oABMvZA/ikI0mkpdVtKWaV+dh0NeSfh2CY1hOKK+/SNSzTdzg9Vm/UOglHPglb1u39yJxRkQmCUntHZRspq9bNzIrTuCEOTYMML8O1T0HkUeAfqG59ovJ8Xq+FqPsFw3TN6R1MvSYhtSAp4NF9LfQ/zSyv4/rdsVu3LYOPvpymrrE6C40J9GdsrmrE9o+ndJlhWgztf0RlIflptD3+yOmloMwBO7FDjEwdcuIql0zOPH+4yWo2XPrZJ9SRKQuycinLU2GGAEbMhIEJtx1yuPuz9/i30n6xffLagaarNgvpgBzDkIfjlUzh3BDa8CInP6xefaLzCbDWXA2DEHAgI1zeei5CE2AY8PVXd1uLiYnx9pYZicxQXFwPV76kryyupYN2BLFbty+DH1BzKjdVJcHxrP5UE94qmR0yQJMEXs25e3RM3uo5RCXGKCybEOWlwJhXcPKHTtSqZ+m42HP1Jff3sE6x3hKKx1s6D0lyI6gUD7q7e33VsVUK8xvUS4pxUNUba3Qs6jlD7PH1g7Mvw8Z9g6zvQ93aI7KFvnKLh1s6Dsjw1d8PB/+5KQmwD7u7uhISEkJ2dDYCfn58kMI2kaRrFxcVkZ2cTEhKCu7trroyWV1LBt79msmpfBj+l5VBhrO4R7xDuz7he0YzpGU336EBpQw1xfAfs/khtj3u19sSNLmNUj9vhDarovyvVNjX3qsUPUcmvTzC07qyS5LRk6HmjvvGJxknfBnuWqu1xC2u3466jYf1zcGg9VJS41sIV5mE/8VfVHhrR+Tro9gc4+A2sfAymrpIJds7g2Bb45RPAoNqxm2P/Py4JsY1ERamvac1JsWiakJAQy3vpKsorTWxIyWbFnpOs+y2b8hrDIbpEBjCmZzTjekfTOSJAkuDGqDlxo+8kaHtF7ccjukNIW8hNV0lxt3F2D9FmLMMlxlTv6zoaNqeqnkRJiJ2HsbJqQijQ788QN6j245E9IaiNqjV9eGP10AJXYC631nXMhY+NflF9uEvfDHuXQZ9b7RubaJya7fjyO9WQNQcnCbGNGAwGoqOjiYiIoKKi4tJPEBfw9PR0mZ5hTdPYeewcX+0+ycp9GbVWjOscEcD4PjGM7RVFpwiZMNJkOz6onrgx8ukLHzcY1NfN295VE3dcJSEuPgvpW9R2zeSo61jY/IYqYWWsdMgyR6IOO/4JWfvUEs31tuMxsON99c2AqyTERWfg+Da13aWO1xQSpyprJD+jhgN1Ga1WZBSOaft7kP0r+LZymoVV5C+kjbm7u7tMUicaLy27kP/uOcmKPSc5frbEsj8i0Jvr+8ZwQ79YLouWMcHNVpgN3z+nti82caPLaJUQ//6tKs3mCksbp60DzQgRl9UusdZmkPrPqOScSjTih+gWomiggkxYXzVpbOQ88A+r+7iuo1VCnLIGxrlIO079TlXRiOylkt+6DH4A9nyqhgKtfwHGvmTfGEXD5GfA+gVqe+R88AvVNZyGkoRYCCs7XVDG/345xYo9J9l7Is+y39/LndE9o5nQL5bBHVu3zMUybGXt3KqJG30vPnGj3RBVwqooW01McoKv8S7JXKbq/K+Z3T1U6aq9n6meREmIHd/auVCWrypJXH6RCXPxV4FXABRmqrJssZfbLUSbOb+6RF08vGDcK/Dv69UHgn6TXHehHWf23WwoL4DYAdDvTr2jaTBJiIWwguLySr77NYvlu0+yKfU0pqq5ce5uBq7uEs4N/WK5rnskvl7ybYHVHdusyjI1ZOKGh5eqwvDrVyqRdPaEuLJcjauE2uOHzbqOVglxyhpVj1k4rqOb1NhYDGpC6EXbsbeqwvDb12rcrbMnxJVlkPa92q5r/HBNHa6BHjeqhTpWPgp3fecaPeSu4vBG2P8fqtux8/zbSEIsRBNVGk1sSsthxe6TfHcgq9aqcX3jQpjQL5Y/9I6mdYC3jlG6OGPFeRM3+l/6OV3GVCfE186xbXy2lr5Z9Yz7h0NsHa+947WqFNuZVFWaLayT/WMUl2asUNUTAAZMbViC23WMSohTVql6287s6CbVoxgQCdH9Ln184vNqiMWJHaoax+XO0wvp0irLYVXVCooD74aYvrqG01iSEAvRCJqmse9kHl/tPsn/fjlFTmG55bH41n7c0C+W6/vG0j7MhUp6ObLt/6eWKPYNbfjEjc7XgcFNTfjITVeVJ5yVpbpEYt09MT5BED8UDq9XX0mHPWDf+ETDbH0HTv+mlise0cAPaZ1HqXacuQ/yTkBwG9vGaEu/X6Idny8oRi3B/t1sVee22x+cZpyqS9v6NuSkgF+YWkzGyUhCLEQDHD9bzIrdJ/lqz0kOny6y7A/192J872hu6BdL37gQmRxnT02duOEXCm0Hw7GfVEKZcO+ln+OINK26bmtdwyXMuo5RCXHKarhSEmKHk3dSrcAGqqpEQ9uxf5iaOHl8q0ooB95juxhtSdOqP9h1Hdvw5yXcB7s/Vh8kkp+B8YtsEp5ooLwTsPHvanvUs2pCr5ORhFiIepwrKmflvgxW7D7Jz8fOWfZ7e7gxqkcUE/rFcFXncDzdnWeMlEv57qkaEzfuaNxzu4yuSohXOW9CfPog5B4Dd2/oOLz+47qMhtWPQ/pWVaJNetIcy3dPQUWRSm77Tmrcc7uOVglxymrnTYizfoW8dPDwgfZXN/x57p5qgt2H42Dnh3D5HXUPGxL2sWYWVBRD3BXQ2zlrREtCLEQNpRVGvj+YzVe7T7IhJduycpzBAEM6hnFDv1gSe0QS6OP6S0k7tMMbYf+X6ivjpkzc6DoG1s5RYxdL89XQAmdjri7R4eqLr7rXqh1E9FBDRNLWQe+b7ROfuLRD69V49ia347Gwbj4c+QHKCsE7wCZh2pS5ukSHa8DLr3HPjR8KvW9RkxFXPgr3JDv8amguKW2dGs9ucHe6iXQ1SUIsBJCRV8Jb69P47+5TFJRVWvb3iAliQr9YxveJITLIR8cIhUVlOawyT0Bq4sSNsM7QuhOcSYNDydBjglVDtAtzQlzXIgbn6zpaJcQpqyQhdhSVZdXteNC9EN278ecI6wKt2sO5I3Doe7jsj9aN0R7qKxvYUNc9q85xarfqKR54t9VCEw1QWQarHlfbCX+BqJ76xtMMzpnGC2El54rKeX7lAa5+eQNLt6ZTUFZJbIgvf72mI989MoyVD17FPVd1kGTYkWx9C3J+b/7EDXMiaR6/6EwKT6sZ9tCwhNg8xjgtWX2gEPrb8qb6QOYf0fQqEeZV66B6YpozKciCkzvVdkPacV0CI6v/DiQ/A0U51olNNMxPr8PZQ6pCyDWz9I6mWSQhFi1SUVklbySnMuyl9bz/4xHKK00Mig/lk3sS+PHx4Tw+uhtdImUZZYeTexw2Vq1ONerZ5i3dak4kzEsbO5PU7wBNLUoQHHvp42P7q9JsZfmqVJvQV246bHxZbY96Ti033lSWhPhbMBkvfqyjSf1W/YzpB4FRTT/PgLshqheU5sK6eVYJTTTAuWPw4ytqe9Tzzjn0rAZJiEWLUl5p4t9bjnL1yxt4de3vFJRV0j06iCVTB7LsL1dwZacw3GQFOcf17ZNq4kbbwdDntuadK+4K8AmBkrNwYrtVwrObhlSXqMnNTZW0guqvqIV+1syCyhK1cmJzh7C0HQzewVCcAyd+tk589mIZLtGI6hJ1cfdQi/IA7F4K6duadz7RMGtmQmWpWjmx15/0jqbZJCEWLYLJpLFi90muXbiBuf/9lZzCMtqG+vGPW/uy8oGhDO8aISXTHN35Ezea++/l7qFquYJzJYkVpWoyFlx8mdvzmZPnlNWq1JXQx+/fwcFvVDse+4oV2rEndB5ZdW5nascl1e24qcMlaoobBP3+rLZXPup83/o4m5Q16oO5m4d12rEDkIRYuDRN0/j+YBZjX/+Rh5ft4fjZEsIDvXn2hp6sS7qa6/vGSo+wM6gorV4BKeE+iOxhnfM64/jLo5tUma7AaIju2/DndRyuSrTlHlMl24T9VZSqEngAV9wPkZdZ57zmHlZnGg9/5AfVSx7URg13sIaRT6tvfbL2wY5/Wuec4kIVJTXa8V8hopu+8ViJJMTCZe04epab39vCXR/+zMHMAgJ9PPhbYlc2/u0a7riiHV4e0vydxuY34OxhCIhSK1RZS6drVQ9Hzu9w5pD1zmtLluESoxvXK+Plr0q01TyHsK+fFqmKEIHR1m/HBne1SMXZI9Y7ry2Z22DXRrbji/EPg2vnqu31z6tJe8L6Nr2mPlgHxcLVT+gdjdXonhG89dZbxMfH4+PjQ0JCAtu31z+Wr6KigmeeeYaOHTvi4+NDnz59WLOm9ifi+fPnYzAYat26dav96aW0tJTp06fTunVrAgICmDhxIllZ8ovjKn7LyOfuD3dw07tb2HH0HN4ebvzl6g78+Phwpg/vhJ+XVBt0KueOVk/cSLTyxA2fYDWOE5xj2ISmqclT0LRxl+YecWfqSXQVZw/Dj1XjXBNfAG8rTtr1bQXtrlTbzvBtR8123NBx8A3Vf4qapFeWr2qNC+s6cwg2LVLbiS84Z+3reuiaEC9btoykpCTmzZvHrl276NOnD4mJiWRnZ9d5/OzZs3nvvfd44403OHDgAPfddx8TJkxg9+7dtY7r0aMHGRkZltumTZtqPf7II4/wv//9jy+++IKNGzdy6tQpbrzxRpu9TmEf6WeKeWTZHsa+/iPJB7NxdzNwe0JbNv5tOLPGdCfEz0vvEEVTrK4xcaPnROuf35xYOkMikbkP8k+Apx+0H9b455vHap7YoUq3CfvQNFj9BBjL1AIUtqh73bXGGHFHl7EHCjLAKwDaX2Xdc7tVzTHAoBbsOLrpkk8RDaRpaqiEsQw6DIfLrtc7IqvSNSFeuHAh06ZNY+rUqVx22WW8++67+Pn5sXjx4jqP/+ijj3jyyScZO3YsHTp04P7772fs2LG8+uqrtY7z8PAgKirKcgsLC7M8lpeXxwcffMDChQsZMWIE/fv3Z8mSJWzevJmtW7fa9PUK2zhdUMa8/+7n2oUb+Gr3STQNxvWOZu0jw3hhQi+igqWGsNNKWa0mCtly4oZ5YtqxzVBy7uLH6s2yOt1w8GxCuw6KUaXa0KpLXgnbS1mlSuW5edquHZs/7Bz7CUrzrH9+azK3447DwcPb+ueP7a96igFWPgbGCutfoyU6+I2a3Ozu5TIT6WrSLSEuLy9n586djBw5sjoYNzdGjhzJli1b6nxOWVkZPj61/xPw9fW9oAc4NTWVmJgYOnTowKRJk0hPT7c8tnPnTioqKmpdt1u3brRt27be65qvnZ+fX+sm9JVfWsGr36Vw9cvr+deWY1QYNa7qHMb/Zgzlrdsvp0O463yV0yJVlKheNYDB0203caNVPIR3B80Iqetscw1r+b2Zq3pBjQlYTtCT6ArKi9W3HABXPqBWSbSF1h0hrCuYKlXS4sgsqyxaebhETdfOBb/Walz1tndtd52WoryoRjt+EMI66RuPDeiWEOfk5GA0GomMjKy1PzIykszMzDqfk5iYyMKFC0lNTcVkMrF27VqWL19ORkaG5ZiEhAQ+/PBD1qxZwzvvvMORI0e46qqrKCgoACAzMxMvLy9CQkIafF2ABQsWEBwcbLnFxcU18ZWL5iqtMPL+D4cZ9tJ63vg+jeJyI33iQvjkngQ+ujuBXm2aUeReOI4fF1ZP3Bj2uG2vZfm62YEnm+VnqOVpMVTXFG4Kc0/ioe9V1QNhWz++CnnpEBwHwx6z7bXM33Y48oedvBOQuZdmt+NL8QtVVScANrwI+adsd62W4IeX1XCt4LZw1aN6R2MTuk+qa4x//OMfdO7cmW7duuHl5cWMGTOYOnUqbm7VL2PMmDHcdNNN9O7dm8TERFatWkVubi6ff/55s649a9Ys8vLyLLfjx4839+WIRqo0mli2I53hr2zg+VW/kVtcQaeIAN79c39W/PVKruwUdumTCOdw5pCakQ8weoHtJ26YE+K0ZMf9etU8xjm2PwRENP080X0gMEYtcHL0R+vEJuqWkwabX1fboxeoSh+2ZO5xTV3ruHV4ze04bpCqCmFLfSdBm0FQXqgW9RFNc/p32Pym2h7zInj56RuPjeiWEIeFheHu7n5BdYesrCyioupewjE8PJwVK1ZQVFTEsWPHOHjwIAEBAXTo0KHe64SEhNClSxfS0tIAiIqKory8nNzc3AZfF8Db25ugoKBaN2Efmqaxel8Goxb9wBNf7iMjr5SYYB9e+lNv1jx0FaN7RsmiGq5E01TNYWM5dBwB3f9o+2vG9ge/MCjLU2OJHZE5kWjOcAlQ4/6coSfR2WkarHpMteNO10G3P9j+mnGDwDdULWF83EHnxJgrnFhjMY5LcXOrWsTHDX79qnohENFw5nZsqoDOic1fVdCB6ZYQe3l50b9/f5KTky37TCYTycnJDB48+KLP9fHxITY2lsrKSr788kuuv77+mY6FhYUcOnSI6OhoAPr374+np2et66akpJCenn7J6wr7+ykthxve+on7P97F4dNFtPLzZPa47nz/2DXcPCAOD3en+pJDNMRv/4NDyfaduOHmXv0ftCMmieXFcHiD2m5uQgzVPYm/r5FV62zlwAo4vF4thjLm73Zsxw68RHdZIRzZqLbtlVhF94aB09T2qr9BZZl9rusqfl2u/s3s2Y51oms2kZSUxPvvv8+//vUvfvvtN+6//36KioqYOnUqAHfeeSezZs2yHL9t2zaWL1/O4cOH+fHHHxk9ejQmk4nHH68eX/jYY4+xceNGjh49yubNm5kwYQLu7u7cdtttAAQHB3P33XeTlJTE+vXr2blzJ1OnTmXw4MFcccUV9n0DRL32nsjlz//cxqR/buOXE3n4ebnz4LWd+eHx4dxzVQd8PN31DlHYQnkRrKn6nR/ykJooZC/mXtPfHXBp48MbVOm54LYQYYXVzdoPU6Xb8k9WjecUVlVWCGuqvqIf+rB927Ejf7A7vF71mLeKh/Cu9rvuiKfAPwLOpMKWN+13XWdXVgDfPqW2r0qC0Pb6xmNjuq5QcMstt3D69Gnmzp1LZmYmffv2Zc2aNZaJdunp6bXGB5eWljJ79mwOHz5MQEAAY8eO5aOPPqo1Qe7EiRPcdtttnDlzhvDwcIYOHcrWrVsJDw+3HPPaa6/h5ubGxIkTKSsrIzExkbfffttur1vUT9M0nv3mNxb/pFZb8nQ3MCmhHTNGdCIswAbleYRjMU/cCGkLQ5Pse+0Ow1Wv9LmjcDrFsZYjrVldwho9NJ4+ajjKwW/UV9jRfZp/TlFt49+h4BSEtIOhj9j32p2uVe347CHISbVdVYumsAyXsFI7biifYBj1HHx1L2x8GXrdDCEyMf6SNryo6kW3ag9DHtY7GpszaJqjdYU4h/z8fIKDg8nLy5PxxFaiaRpP/+8AH24+isEAE/rF8sjILsSFuuYAfnGe07/DO1eqsWq3fgrddBirtvRPkLYWrp2nekQcgckEr3aFomy44yuVyFrDro/g6xkQ3Rf+stE65xSQ/Ru8O1SVP7v9c9tWUqjPRxNUFZHrnlHftDgCkxFe6QLFOXDn19XLiNuLpsGH41Sd5m5/gFs/tu/1nU3WAdWONSNM+g90vk7viJqsofmaDMAUDkHTNF5Y9Rsfbj4KwN8n9mbhzX0lGW4pNA1WPaqS4S6j9UmGocawCQdate7UbpUMewVCu6HWO2+XRMCgVg3Lz7jU0aIhNE0tBGGqVGNk9UiGoXqMuCMt0X1yp0qGvYOrl5m2J4Ohak6Cu/pmJHWt/WNwFuaJdJpRfXhw4mS4MSQhFrrTNI2Xv03h/R/VMIkXJvTi5gHydVaLsv9LOPIDePioiRt6MY+/PL4dinL0i6Mm83CJTteChxWXHw+IgDYDqq7hQImTM9v3Hzi2CTx8YfSL+sVh/mB3fCsUn9UvjppSarRjd099Yoi8DK64X22v+pvU4a7P3s9VT7qHryoX2EJIQix0t2hdKm9vOATAM9f34PaEtjpHJOyq1sSNR9WEG70Et4Go3oAGvzvI0sYpNcYPW5sjT8ByNqV58F1VOx72KLRqp18sIW0hsidoJrVktCOwtGOdy3ZdMxMCo+HcEfjpH/rG4ohKcuG72Wr76r+pttRCSEIsdPXm96n8IzkVgNnjunPn4Hh9AxL2t+FFKMxUEzeufFDvaKoTz98dIEnMTYes/aqOaudR1j+/OTk5slGVdhNNt34BFGZBaEfHaMeO9GHn3FG1hLLBHTqP1DcW70BIfF5tb1oIZ4/oG4+jWf+CGqLVujMMfkDvaOxKEmKhm/c2HuKV734HYOaYbtxzVf0LrAgXlfUrbH1HbY99RVU/0Js5kUhzgKWNzb3UcVeopWitLaK76gGqLK2ucywaL3MfbH9PbY99GTwcoCKO+cNOWjJUlusbi3ksc9vB4NtK31gAetwIHa5R7X71E45XZlEvGb/AjvfV9tiXrTtEywlIQix0sXjTERasPgjAo9d14b6r7VinUzgG8wQkzQjdx+vfc2QW3Vd9pVpRBEc36RtLyir10zwm1NoMhhoTsFbZ5hquzmSqascmuOx6NUbWEcT0g4BIKC9Q45r1ZGnHNhj20xQGA4x5Gdw8IfVbx+hF11vNdtxjAnQcrndEdicJsbC7j7Yc5ZlvDgDw4IhOPHCtA9XJFPazdxmkb1YLRCQ60MQNN7fq6gB6DpsozYcjP6ptW467tAwR+Vb9pyga55dP1eQ1T3/Hbcd6VpsozVMTtMBxEmKA8C5w5Qy1vfoJGTK052M4sb2qHb+gdzS6kIRY2NWn29OZ899fAbj/mo48cl0XnSMSuqg5cWPY3xyvSH7NslV6fZ166HtVhi60o20XV2g3BLyD1LjBU7tsdx1XVHIO1s5V21c/DsGx+sZzPks71nH1xbR1qgxd6872XbGvIYb9DYLjIC8dfnxV72j0U3wW1s1T29fMhKAYfePRiSTEwm6++Pk4T361D4B7hrbn8cSuGFx4XXRxEeufh6LTENYFBs/QO5oLdbhalRzKP6HGh+rBXArN1r1qHl7Vi33IV8eN8/1zqrZuWFe44q96R3OhDteoUoZ56ZB9QJ8YUuzUjpvCy7+6rNjm1yEnTd949PL9s1B8BsK7V5ela4EkIRZ2sWL3SR7/ci+aBpMHt+Opcd0lGW6pMn6BHf9U2446ccPTt3oMnR41ek3G6gl19kgkzEMypB5xw53aDTs+UNvjXnHMduzlp5Ji0OfDjrGyuuybIybEoBae6HQdGMth9d9a3gS7kzvh5yVqe9wr+tWIdgCSEAubW7k3g6TP96BpcHtCW+b/sYckwy2VyQQrH62auHFj9X/WjqirjpPNjm+HkrPgE6IqTNha5+tUabes/arUm7g4cztGg55/gvbD9I6ofnqWXzu+FUpzwTcU2gyy//UbwmBQiwG5e6thSgf+q3dE9mMyVrfjXjdDvBVXwnRCHnoHIFzbmv2ZPPjZbkwa3DygDc9d31OS4fMZK8G9hfwq7lkKJ3aAV0B1LVBH1blqQtKp3Wpp46Bo+13bPJmv83X2aRt+oSrxTt+svuJOuNf217QGTVPjeO3dq7f/S9Wz5hXo+O3YnBCf3AmF2WqFQnsxJ+GdRzn237jWHWHow7Dx77BmFrQZqIaauLp9X6i/b95BMOo5vaPRnQO3UOHskn/L4oFPd2E0adzYL5YFN/bGzU2S4Vp+/w4+ux2uSoLhT+odjW0Vn4W15okbsxx/4kZgJMT2V4lE6rfQf4r9rq3HuMuuY1RC/Ptq50mI/ztdzY7Xy/AnITBKv+s3RFC0KsF2arcahnP5Hfa7tmUcvI3KBlrT0Efgl88g9xi8dpne0djX8KfU37sWToZMCJvYkJLN/Ut3UWHUGN8nhpdv6oO7JMO1lRfBN4+oSgI/vKIWqXBlyc+oYQDh3SHhL3pH0zBda8zSt5czhyAnBdw8oJMdazObX+uRH1XJN0eXlqxvMhx/FQxykg8OXXRoxzmpcCZN1frt6CC1mS/G0xfG/0OVHWtJ2g+DgffoHYVDkB5iYXWbUnO496OdlBtNjOkZxcKbJRmu08aXVBUDUItTrHwUpq5WY9pczcmdsPNDtT3uVeeZuNFljKokcHiDqlPq5Wf7a5p71dpdCT7Btr+eWVhnVeLt7CE1lrLHDfa7dmNVlsGqv6nthPurKwXYkzP9nnYdDRtegMPr1eqL9lgR0px8xw8FnyDbX88aOg6HJ0/qHYV9OVM7tjHpIRZWtfXwGe759w7KK02M7B7JP27th6e7NLMLnE6BLW+q7bGvqF6J9C3qKztXU3PiRu9bIX6I3hE1XGQPVae0shSObLTPNc2JhC0X46iPZZEOB682sfkNlbgHRMLwWeo/dXvfnElUbwiKhYpiOPKDfa5pr7KB1qZHW9LzJiwkUxFW8/PRs9z14Q5KK0wM7xrOW5P64eUhTewCmgarHlPF6ruMhkHTVFF/gLVz1KIVrmTnhzUmbjyrdzSNYzDYt9pEyTk4tlltd9Fh3GWtVeuM9r9+Q5w7poYYgZoIZM9edGdlMNSoNmGHdlx8Vn3AB33asRBNINmKsIpd6eeYsmQHxeVGruocxjt/7o+3h7veYTmm/V+qXhoPH1XuB1RR/7CuarGK711otm9Rjho7DDBitn1nuFuL+T90eyxtnJashs+Ed4fQ9ra9Vl3irlCl3krOqtJvjmjNLKgsUWN4e92kdzTOw1Jr+lvbV+VIXatKK0b0gFbtbHstIaxEEmLRbHtP5DJ58XYKyyoZ3KE1/3fHAHw8JRmuU2k+fPuU2h6aBK3i1baHlyqKDvDzB3Bqjx7RWd/aeaoOaVQvGHC33tE0TfxQVSauMAsydtv2WpbhEjr1qrl7qBJZoE/95Uv5/VtIWakmHI59Rb7ybYz4oWpoVsEptTiOLf2uczsWogkkIRbN8uupPO74YDsFpZUMig/lgykD8PWSZLheG/8OhZnQqj0Meaj2Y+2HqSL/WlXRf1v3Rtpa+jZVdxhg3ELHrkN6MR7e0KlqlrwtZ+kbK1TPGlRXBdCDOYlxtHHEFSXVE+mu+CtEdNM3Hmfj6VO9+qIt23FlOaSuU9t6jIMXookkIRZNlpJZwJ//uY28kgoubxvC4qkD8fNy0qTHHrJ+ha3vqO2xr9Q903vUc6rY/8mfYfdH9o3PmoyVVRPpgH53QJyDrlLVUJayVTZMEtO3QFke+IVBmwG2u86ldBqpemBzflcl4BzFpkWqRmxgDFz9hN7ROCfLsAkbJsTHfoLyAvCPgJjLbXcdIaxMEmLRJGnZBUz651bOFVfQp00wH941iABvSYbrpWmw8jE1PrTbH6BzPfVlg6LVrHmAdfPV5BRntOOfkLVPjUcdOV/vaJqv86iqpY332W5pY3OvXZdEcNPxWxafYGg3pHZMejt7GDa9prZHvwDeAfrG46w6jwIMashEno3Ki1na8ShwkxRDOA9praLRDp8u5Lb3t5FTWE6PmCD+fVcCQT5OUldWL3uXqVXAPP1g9IsXP3bQX9RklJKzKil2NgWZsL5qOduR88A/TN94rMG/NcQlqO3fv7X++TWtRiLhAOMuHan8mqbBqsfBWAYdhsNlN+gdkfMKCFfLEoNt/m01rcb4YRkuIZyLJMSiUY6dKeL297dxuqCMblGBLL07gWA/SYYvqiQXvputtof9DULiLn68u4davAJg17/hxM82Dc/q1s6Fsnz1denlk/WOxnosZats0Gua8zucOwLuXtBxhPXP31jm13pssyoFp6eDKyFtrVrxbOzLMpGuuWz5YSf7N/UNirs3dLjG+ucXwoYkIRYNdvxsMbe/v43M/FI6RwSw9J4EWvl76R2W41v/vCqn1rozDJ7RsOe0Gwx9bgc0WJnkuDVhz3d0k+oNx6CSej2/+rc2cyJx9EcoK7Duuc0VHdoPc4zhAKHtVek3zVg9QUoP5UWwZqbaHvKgWk1PNI+5HR/eqN5fazK34w5Xg1cLWwJZOD1JiEWDnMot4fZ/buVkbgkdwvz5eFoCYQHeeofl+E7tUeNpQZVV82jEB4jrngbvYDXe7+fFNgnPqowVapw0wICpEOtiE2rCukBoBzCWq6WNrck8Wc8RhkuYWapN6DiO+IdXIO84BLeFqx7TLw5XEt4NQtqpISiH1lv33M66Op0QSEIsGiArv5Tb39/K8bMltGvtxyfTriAisI4KCaI2k0mtSKeZoMeNjf8KMSACrp2jtr9/FgpPWz1Eq9r2Lpz+Dfxaw4g5ekdjfQaDbapNFOXAiapFMBwpkTCPAU1dpz7s2FtOqlqiGWDMi+DlZ/8YXJHBYJtqE4XZ1cO7HOmDnRANJAmxuKjsglJue38rR88UExfqy6fTriAqWJLhBtmzFE7sUIs6JD7ftHMMuAuiekNpHqybZ934rCn/FGyomix43TPgF6pvPLZi7jVNteLSxqnfqQ9NUb0guI11zmkNsf1VCbiyvOrlpO3Fsrx5haqMIBO0rMvS+2/F1Rd//xbQILoPBMVY55xC2JHuCfFbb71FfHw8Pj4+JCQksH17/cuFVlRU8Mwzz9CxY0d8fHzo06cPa9bU7qlZsGABAwcOJDAwkIiICG644QZSUlJqHXPNNddgMBhq3e677z6bvD5nlldSwaT3t3H4dBExwT58cs8VxIT46h2Wcyg+q1ZpA7hmZtP/g3BzV4taAOz5GNK3Wic+a/v2SSgvhDaDqsY+u6i2g1VZsuIz6sOONaQ46Kx8N/cay1bbudrEr1/B4Q1qctaYv8tEOmtreyV4B6m5DSd3WuecluESDtaOhWggXRPiZcuWkZSUxLx589i1axd9+vQhMTGR7OzsOo+fPXs27733Hm+88QYHDhzgvvvuY8KECezeXb2c6saNG5k+fTpbt25l7dq1VFRUMGrUKIqKak8emDZtGhkZGZbbSy+9ZNPX6oy+2nWC1OxCIoO8+fTeK4gLla8sGyz5GVU2Lbw7JDTzw1bcQLj8TrW98lG16IUjObReJTAGt6qJdLp/zrYdd0/odJ3atka1icqy6vHIjvg1c9calTU0zT7XLCtQH7AArkpS47aFdXl4qQVYwDrDJipKHbsdC9EAuv7PtXDhQqZNm8bUqVO57LLLePfdd/Hz82Px4ronEH300Uc8+eSTjB07lg4dOnD//fczduxYXn31Vcsxa9asYcqUKfTo0YM+ffrw4Ycfkp6ezs6dtT8F+/n5ERUVZbkFBQXZ9LU6o5QsNZP+lgFxtGstM4Yb7ORO2Pmh2h73qkqimuva+eDbCrL2w473m38+a6ksU19tAwy6F6J76xuPPZjH+VojIT76o+pZD4iC6L7NP5+1dRiuSsGdOwKnUy59vDVs/DsUZECreBjysH2u2RJZsx0f+QEqitUqgtF9mn8+IXSgW0JcXl7Ozp07GTmyesUuNzc3Ro4cyZYtW+p8TllZGT4+tcev+vr6smnTpnqvk5eXB0BoaO0xjR9//DFhYWH07NmTWbNmUVxc3NSX4rJSswoB6BjhAGWgnIXJCN8kARr0vhXih1jnvP6tq1d8+/55tfiFI9jyJpxJU8u0Dn9S72jsw7K0cYpaQa05zJPzuo52zJ517wBof7Xatke1iezfqpc3H/Ny3cubC+voNBIM7pB9AM4da965LItxjJbhLcJp6fYXOCcnB6PRSGRkZK39kZGRZGbW/Z99YmIiCxcuJDU1FZPJxNq1a1m+fDkZGRl1Hm8ymXj44YcZMmQIPXv2tOy//fbbWbp0KevXr2fWrFl89NFH/PnPf75ovGVlZeTn59e6uTJN00jNVglxJ0mIG27nEsjYo8bnXfeMdc/d70410am8oHqhDz3lpsPGl9X2qOfU2NqWwDdEjSWG5lWb0LTqcZddHKi6xPlqDpuwJU1TQ4JMlWp58y6jbHu9ls4vtLodN2eMuKbVKBvowO1YiEtwwC6J+v3jH/+gc+fOdOvWDS8vL2bMmMHUqVNxq6dnZfr06ezfv5/PPvus1v57772XxMREevXqxaRJk/j3v//NV199xaFDh+q99oIFCwgODrbc4uIusdqYk8spLCevpAKDATqGS0LcIEU5auwwwIjZEBh58eMby61qjC4G2PcFHPnRuudvrDWzoLIE2g2F3jfrG4u9mScOmRciaIqs/arGroevWsjAUZnHhB7frtq4rez7Ao79pN6P0Qtsdx1RzfJhpxntOOMXKDillqVvP8w6cQmhA90S4rCwMNzd3cnKyqq1Pysri6ioqDqfEx4ezooVKygqKuLYsWMcPHiQgIAAOnS4cNLFjBkz+Oabb1i/fj1t2ly8lFFCQgIAaWlp9R4za9Ys8vLyLLfjx49f6iU6tbSq3uG4Vn74eLrQamO2tHaeKo8W1QsG3G2ba8T0g4FV5171mD71YQFS18LBb9RXruNeaXlfk5oTieYsbWzuVes4HDwduHpLcBtV+g9NlYizhdI8+PYptT3sMQhpa5vriNrMH+yO/qT+DZrC3LvccYQMcRFOTbeE2MvLi/79+5OcnGzZZzKZSE5OZvDgwRd9ro+PD7GxsVRWVvLll19y/fXXWx7TNI0ZM2bw1Vdf8f3339O+fftLxrJnzx4AoqOj6z3G29uboKCgWjdXlnZaJcSdZbhEw6RvVXWHQZVJc/ew3bVGzFb1YU8fhK1v2+469akohVV/U9tX3A8R3e0fg95CO0BYV7W0cVrypY+vi7lXzhlm5VsmYDWjJ/Fi1r8ARdnQuhNc+YBtriEu1LqjWlLeVNEy2rEQF6HrkImkpCTef/99/vWvf/Hbb79x//33U1RUxNSpUwG48847mTVrluX4bdu2sXz5cg4fPsyPP/7I6NGjMZlMPP7445Zjpk+fztKlS/nkk08IDAwkMzOTzMxMSkpKADh06BDPPvssO3fu5OjRo3z99dfceeedDBs2jN69W8AM+QZKq6owIeOHG8BYWb1kcb8/Q9wg217Pt1X1+OQNf4e8k7a93vl+WqSqDgRGqxrLLVVzZukXZMKpXWq7S6L1YrIVc7JzaL2qLGJNmftg+/+p7bGvgIcsCW9XlkU6mjCOOP+UGjKBwTnasRAXoWtCfMstt/DKK68wd+5c+vbty549e1izZo1lol16enqtCXOlpaXMnj2byy67jAkTJhAbG8umTZsICQmxHPPOO++Ql5fHNddcQ3R0tOW2bNkyQPVMr1u3jlGjRtGtWzceffRRJk6cyP/+9z+7vnZHZ+4hloS4AXb8E7L2gU8IjHzaPtfscxvEXQEVRdU1W+3h7BH4sWqhkMQXwDvQftd2NOaEOG1t44eu/P6t+hnbHwLrHiLmUKL7qg9A5YWqVJy1mExqIp1mgh4T1PARYV+WJbq/a3yNc3MS3WaAWmpeCCdmw+91G2bGjBnMmDGjzsc2bNhQ6/7VV1/NgQMHLno+7RLF4+Pi4ti4cWOjYmyJzCXXJCG+hIJMWF+1LPPIeeAfZp/rmifYvTcMDqxQX3d2uta219Q0WP0EGMugwzUqgWnJ2gwEv9Zq1br0LY2bUGTuVXaWWflubqoHcOeHKvZOIy/5lAb55RM4vg08/WFUE5c3F83TZpD61qnknPq3aEypSEs7luESwvk5VZUJYR95JRVkF6ivRaUG8SWsnQtl+Wqy2+WT7XvtqJ5qMQxQY3qt/VX2+VJWQeq34OapvtpuaRPpzufmDp2rviZuTPm1ihK1LDFUf13tDMzJe8oa66xaV3JO/f6AGnoTHNv8c4rGc/eAzlUl7hpTa7q8CA5XdS7Jcs3CBUhCLC5grjARFeRDkI8VVllzVUc3wd5lgEFNpHPToRrH8FkQEAlnD8HmN2x3nfJiWF01XvjKByCss+2u5Uxqlq1qaJJ4eKMqVxccB5E9L328o+hwtSqJln9ClYxrruRnVe96eHc1OVPop2uNDzsNdXiD+rYopG3LnFgrXI4kxOICh2RBjkszVlRPpBswFWIv1ycOn+Dqr5p/eKX5K07V58dXIS9dJXHDHrPNNZxRxxHVSxvn/N6w59Scle9MveyevtVjfJu7SMfJXfDzYrU97hXrLG8umq7jteqbnzOpkFN/+dFaLO14jHO1YyHqIQmxuEBqtlSYuKSt78Dp39QY0hFz9I2l158g/irV67hm1qWPb6ycNNj8utoevQC8/K1/DWflHajee2hYkmgyVU+o6+ok44drak5lDTOTUU2kQ4NeN0P8UKuEJprBJ6h67HBDhk2YTPB7VU1qZ2zHQtRBEmJxgTTpIb64vJOw4UW1PfJptQSqngwGNabXzQNSVlYnXNagaVULgJRDp+vUkrqiNnNC0JCyVRl7oDATvAKcMxE0j5k+tUtNKG2KXf9Wz/cOUkt+C8dgWX2xAe341C5VN9o7CNo1YhKeEA6s0QlxfHw8zzzzDOnp6baIRzgAWZTjEr57SpU7azMI+k7SOxolohtc8Ve1vepvauKWNRz4LxxeD+7eMPYl+Wq0Lub6q8e3QdGZix9r7lntOMI56+0GRqpScdC0urVFZyC5qjTh8Kesv7y5aDpzpYj0LVB89uLHmodLdBwBHl62jUsIO2l0Qvzwww+zfPlyOnTowHXXXcdnn31GWZmNZ7cLuykpN3LinEqmpIe4DofWw69fgaGq7JmbA33JcvUTEBgDucdg06Lmn6+ssHoIxtBH1Ops4kIhbSGyl6qle6mljc1fRzvzrPymTMAyWzdPVZeI7AUD77FuXKJ5WrWDiB5Vqy+uu/ix5n97Z27HQpynSQnxnj172L59O927d+eBBx4gOjqaGTNmsGvXLlvEKOzo0OlCNA1C/b1oHeCEPVi2VFmmhg8ADJwG0Q62sqF3AIx+QW1veg3OHm7e+Tb+HQpOQUg7GPpws8NzaZbVvi4y/jLvhFqVzeBWXebKGZnLrx1er6qPNNTxHbD7I7U97lXbLm8umsZSNeUi7fjcMcj+taodX2efuISwgyZ3b11++eW8/vrrnDp1innz5vHPf/6TgQMH0rdvXxYvXnzJBTKEY7KMHw6X3uELbHkTzqSBfwSMeErvaOp22Q3QYbgqh7Tq8abXi80+CFvfVttjX1YVBkT9zEliWnL99aDNSUabQeDf2j5x2UJkD1VtpLIUjjRwkSOTEVYmqe2+f4a2CbaLTzSdpR2vg8ryuo8xD5WJu0L/+RNCWFGTE+KKigo+//xz/vjHP/Loo48yYMAA/vnPfzJx4kSefPJJJk1ykLGVolHMCbEsyHGe3HTY+LLaHvWcKnfmiMwT7Ny91JLCB1c2/hzmiXSmSug6rnqMrKhfTD9VD7q8UNWnros5kXD2WfkGQ+OrTez4ADL3qt+b6+y0vLlovNj+4B+uFhtK31z3MeZ/c2dvx0Kcp9EJ8a5du2oNk+jRowf79+9n06ZNTJ06lTlz5rBu3Tq++uorW8QrbMxcck0m1J1nzSxV1qzdEOh9s97RXFxYJ7jyQbW9ZqZaUaox9v0Hjv6oFmEYvcD68bki89LGUHeSWFYAR35Q266QSJgnYP2+RpXgupjCbPi+qprEtXPtt7y5aLxLtePS/OoPfK7QjoWoodEJ8cCBA0lNTeWdd97h5MmTvPLKK3Tr1q3WMe3bt+fWW2+1WpDCfqTkWh1+/w4OfgMGd+dZsviqRyG4LeQdVwt2NFRpnqqiATDsUTXRRjRMlxrl184fqnJovSpd16o9hHWxf2zWFj9UlY4rzIKM3Rc/du1cKMtTvej9p9onPtF0XWr0/l/QjpPBVAGhHWW1SuFyGp0QHz58mDVr1nDTTTfh6Vn36kL+/v4sWbKk2cEJ+yqvNHHsjJok0zlSEmIAKkph9eNq+4r7IfIyfeNpKC8/GFNVK3nzG5CT2rDnbXhRJTmta/Qyi4bpcA14+KgPIVm/1n7s9xqz8p3hA9WleHhDp2vV9sWqTRzbDL98ilre/FV9ljcXjdNxuCqzmHsMTh+s/ViKiwz7EaIOjU6Is7Oz2bZt2wX7t23bxs8//2yVoIQ+jp0potKkEeDtQVSQj97hOIafFqlleQOj4ZqZekfTOF3HqoUUTBVqTPClJthl7odt76ntMS85Z51cPXn5qaQYan/dbDLWSIhH2z0sm+lyiXHExoqqFemA/lOq6xcLx+blDx2uVtvmesMAxkpIdeJVFoW4hEYnxNOnT+f48eMX7D958iTTp0+3SlBCHzUn1BlcoReruc4egR8Xqu3E59Uyvc7EYFC9xO7ecHiDqp9cH5NJJS+aES67vrr3TzSOZdW6GkniiZ+h+Ax4B0PbwfrEZQudR6nSW1n7IPfC/xPY/n+QfQB8Q9XYYeE8zGPEa/b+n9iuakj7hKgKE0K4mEYnxAcOHODyyy+/YH+/fv04cOCAVYIS+kiVkmvVNA1WP6HKl3W4BnrcqHdETRPaAa6qKnf17ZNqcldd9n4Gx7eCpz8kykS6JjMnEid3QkGW2jYnx52vA/e6h5k5Jf/WEFdVPu38VevyM2B9VTu6zgGWNxeNY27HJ3ZA4Wm1bf4moPMoqSEtXFKjE2Jvb2+ysrIu2J+RkYGHh/ySODNzD7GMH0Z9VZj6Lbh5Os9EuvoMeVhN5irIUIttnK/kHHw3R21f8wQEx9o1PJcSGKUmj0F1kujKZaosPYnnDZv47ikoL4A2A1XdYeFcgmMhug+gVQ+TsLRjFxr2I0QNjU6IR40axaxZs8jLy7Psy83N5cknn+S662TVGmcmPcRVyothddV44SsfcP7Z1J4+anENgK3vQPZvtR///jkozoHwbnDFX+0fn6sxL2f7+xo17Ob0QXDzcM1hKObXevTH6m8fDm+A/V865vLmouHM/7Ypq+HMITiTWtWOR+oblxA20ui/VK+88grHjx+nXbt2DB8+nOHDh9O+fXsyMzN59dVXbRGjsAOjSePwaSm5BsCPr0JeulqNa9hjekdjHZ2vg25/UIttrHy0eoLdqd1q0QSoWtDDhb7S14u51/TQ+upx220Hg28r/WKylbDOaliOsVy93spyWPU39djAe6p6GYVTqqsdtxviuIsSCdFMjU6IY2Nj2bt3Ly+99BKXXXYZ/fv35x//+Af79u0jLi7OFjEKOzhxrpiyShNeHm7EhfrpHY5+ctJg8+tqe/QCNePaVYxeoBbbOPYT7PuieiIdGvS6CdpfpXeEriGqFwS1UQu5bHpN7XPF4RKghhLVrDax9S3I+V0tbz7cQZc3Fw0T3QcCY6CiCH76h9rnqu1YCKBJg379/f259957rR2L0JF5/HCHMH/c3Zx4vGxzmJcsNpZDp6oeVVcS0hau/hskPwPfPgUFmWryl1egWo5aWIfBoMZZ7vinWgIXXDuR6DpGJcIpq+DACrVv1LPgG6JnVKK5zO3458XV7biLjB8WrqvJs+AOHDhAeno65eXltfb/8Y9/bHZQwv6qJ9Q5WWkxazrwXzi8XpUpG/N3555IV5/BM2DPJ3AmDdZWTaQb/qSaDCasp8sYlRADhHVVwwpcVdsr1NfopblV96+E3rfoGpKwki5jVEIMEN4dQtvrG48QNtTohPjw4cNMmDCBffv2YTAY0KrGIprr1hqNRutGKOyixU+oKytUZckAhj4MrTvqGo7NeHirscIf3aDuR/aEQfJtj9XFD1Ul7CqKXH9Wvrun+kZl/3/U8ubjnLwqi6jWfhh4+kFFseu3Y9HiNXoM8UMPPUT79u3Jzs7Gz8+PX3/9lR9++IEBAwawYcMGG4Qo7KHFl1zb+HfIPwkh7WDoI3pHY1sdh0O/O9R/dH9YJDVFbcHTR63O5hUAfSfpHY3t9Z+ikuGrn4DIHnpHI6zF0wcun6yGVfW5Xe9ohLApg6Zdaj3X2sLCwvj+++/p3bs3wcHBbN++na5du/L999/z6KOPsnv3blvF6lDy8/MJDg4mLy+PoKAgvcNpFk3T6DX/OwrLKvnukWF0aWnDJrIPwrtDVAWG2z+HLol6R2R7mqaW1vXw0jsS4SqMlfLhSgjhcBqarzW6h9hoNBIYqBKmsLAwTp06BUC7du1ISUlpYrhCT1n5ZRSWVeLuZiC+tQtVVWgI80Q6U6Wqu9kSkmFQX2lLMiysSZJhIYQTa/RfsJ49e/LLL7/Qvn17EhISeOmll/Dy8uL//u//6NDBhSeOuLDUbFVQv11rP7w8WlgR/X3/UYsKePjC6Bf1jkYIIYQQOmh0Qjx79myKiooAeOaZZ/jDH/7AVVddRevWrVm2bJnVAxS2l9ZSJ9SV5qklZgGGPQqt2ukbjxBCCCF00eiEODGx+ivlTp06cfDgQc6ePUurVq0slSaEc2mxE+o2vAiFWRDaEa58UO9ohBBCCKGTRn0/XlFRgYeHB/v376+1PzQ0tMnJ8FtvvUV8fDw+Pj4kJCSwffv2i17/mWeeoWPHjvj4+NCnTx/WrFnT6HOWlpYyffp0WrduTUBAABMnTiQrK6tJ8bsCS8m1lrRkc+Z+2Pae2h77sipHJoQQQogWqVEJsaenJ23btrVareFly5aRlJTEvHnz2LVrF3369CExMZHs7Ow6j589ezbvvfceb7zxBgcOHOC+++5jwoQJtSpbNOScjzzyCP/73//44osv2LhxI6dOneLGG2+0ymtyRofMPcQRLaS6hHnJYs0Il10Pna7VOyIhhBBC6KjRZdc++OADli9fzkcffURoaGizLp6QkMDAgQN58803ATCZTMTFxfHAAw8wc+bMC46PiYnhqaeeYvr06ZZ9EydOxNfXl6VLlzbonHl5eYSHh/PJJ5/wpz/9CYCDBw/SvXt3tmzZwhVXXNGg2F2l7NrZonIuf3YtAAeeScTPqwXMFN/zCay4Xy2cMGMHBMfqHZEQQgghbKCh+Vqjs58333yTtLQ0YmJiaNeuHf7+tct07dq1q0HnKS8vZ+fOncyaNcuyz83NjZEjR7Jly5Y6n1NWVoaPj0+tfb6+vmzatKnB59y5cycVFRWMHDnScky3bt1o27btRRPisrIyysrKLPfz8/Mb9DodnXn8cJtWvi0jGS45B99VLVl89eOSDAshhBCi8QnxDTfcYJUL5+TkYDQaiYyMrLU/MjKSgwcP1vmcxMREFi5cyLBhw+jYsSPJycksX77cMoSjIefMzMzEy8uLkJCQC47JzMysN94FCxbw9NNPN/ZlOjxzybUWM374++egOAfCusIVf9U7GiGEEEI4gEYnxPPmzbNFHA3yj3/8g2nTptGtWzcMBgMdO3Zk6tSpLF682ObXnjVrFklJSZb7+fn5xMXF2fy6ttaiSq6d2g07PlDb416VhSmEEEIIATRhpTprCQsLw93d/YLqDllZWURFRdX5nPDwcFasWEFRURHHjh3j4MGDBAQEWBYEacg5o6KiKC8vJzc3t8HXBfD29iYoKKjWzRW0mJJr5ol0aNDrJmh/ld4RCSGEEMJBNDohdnNzw93dvd5bQ3l5edG/f3+Sk5Mt+0wmE8nJyQwePPiiz/Xx8SE2NpbKykq+/PJLrr/++gafs3///nh6etY6JiUlhfT09Ete1xWltZSSa7v/DSd3glcgjHpO72iEEEII4UAaPWTiq6++qnW/oqKC3bt3869//avRY2yTkpKYPHkyAwYMYNCgQSxatIiioiKmTp0KwJ133klsbCwLFiwAYNu2bZw8eZK+ffty8uRJ5s+fj8lk4vHHH2/wOYODg7n77rtJSkoiNDSUoKAgHnjgAQYPHtzgChOuoqC0goy8UgA6hbtwybWiM7Buvtoe/iQE1v9NgBBCCCFankYnxObe2Jr+9Kc/0aNHD5YtW8bdd9/d4HPdcsstnD59mrlz55KZmUnfvn1Zs2aNZVJceno6bm7VndilpaXMnj2bw4cPExAQwNixY/noo49qTZC71DkBXnvtNdzc3Jg4cSJlZWUkJiby9ttvN/atcHqHTqsluMMDvQn289Q5GhtKflpVl4joAYPu1TsaIYQQQjiYRtchrs/hw4fp3bs3hYWF1jidw3OFOsT/2XmCx774hSs7tuaTaS7aO37iZ/jnSECDqWugXcsbFiOEEEK0VA3N16wyqa6kpITXX3+d2Fip6epMXL7kmskIK5MADfrcLsmwEEIIIerU6CETrVq1wmAwWO5rmkZBQQF+fn6W1eKEczjk6hPqfl4MGb+ATzBc94ze0QghhBDCQTU6IX7ttddqJcRubm6Eh4eTkJBAq1atrBqcsK1UV06IC0/D98+q7RFzICBc33iEEEII4bAanRBPmTLFBmEIeyutMHL8bDHgognx2rlQmgfRfWDAXXpHI4QQQggH1ugxxEuWLOGLL764YP8XX3zBv/71L6sEJWzvSE4RJg2CfT0JD/DWOxzrOrYFfvkEMMC4heDW8PrYQgghhGh5Gp0QL1iwgLCwsAv2R0RE8MILL1glKGF7NYdL1BwC4/SMlVUr0gGX3wltBugbjxBCCCEcXqMT4vT0dNq3b3/B/nbt2pGenm6VoITtWZZsdrXhEtv/D7J/Bd9WMHK+3tEIIYQQwgk0OiGOiIhg7969F+z/5ZdfaN26tVWCEraX5ool1/IzYH3VtxQj54NfqK7hCCGEEMI5NDohvu2223jwwQdZv349RqMRo9HI999/z0MPPcStt95qixiFDaS5YoWJtXOgvABiB0C/O/WORgghhBBOotFVJp599lmOHj3Ktddei4eHerrJZOLOO++UMcROotJo4kiOWrbZZRLiIz/Avi/A4AbjXgU3q6w5I4QQQogWoNEJsZeXF8uWLeO5555jz549+Pr60qtXL9q1a2eL+IQNHDtbTIVRw9fTnZhgX73Dab7Kclj5mNoecDfE9NU1HCGEEEI4l0YnxGadO3emc+fO1oxF2EnN4RJubi5QYWLr25CTAn5hMOIpvaMRQgghhJNp9PfKEydO5O9///sF+1966SVuuukmqwQlbMulxg/nnYCNL6ntUc+q6hJCCCGEEI3Q6IT4hx9+YOzYsRfsHzNmDD/88INVghK25VIJ8bdPQkURtB0MfW7TOxohhBBCOKFGJ8SFhYV4eXldsN/T05P8/HyrBCVsK9VVSq6lJcOB/4LBHca+Aq60wIgQQggh7KbRCXGvXr1YtmzZBfs/++wzLrvsMqsEJWzHZNI4lK0qTDj1ohyVZbDqb2o74S8Q1VPfeIQQQgjhtBo9qW7OnDnceOONHDp0iBEjRgCQnJzMJ598wn/+8x+rByis62RuCSUVRrzc3Wgb6qd3OE23+XU4ewgCouCaWXpHI4QQQggn1uiEePz48axYsYIXXniB//znP/j6+tKnTx++//57QkNlZTBHl3ZajR9uH+aPh7uT1uo9dwx+eFVtJz4PPkH6xiOEEEIIp9aksmvjxo1j3LhxAOTn5/Ppp5/y2GOPsXPnToxGo1UDFNaVluUCE+rWzITKEoi/CnpO1DsaIYQQQji5JncR/vDDD0yePJmYmBheffVVRowYwdatW60Zm7ABc4WJjs6aEKesgZRV4OYhE+mEEEIIYRWN6iHOzMzkww8/5IMPPiA/P5+bb76ZsrIyVqxYIRPqnIR5yIRTTqirKIHVj6vtwdMhopu+8QghhBDCJTS4h3j8+PF07dqVvXv3smjRIk6dOsUbb7xhy9iElWmaRmqWE5dc2/Qa5B6DoFgY9rje0QghhBDCRTS4h3j16tU8+OCD3H///bJks5M6XVhGfmklbgY1qc6pnDkEmxap7cQXwNsJE3ohhBBCOKQG9xBv2rSJgoIC+vfvT0JCAm+++SY5OTm2jE1YmXlCXdtQP3w83XWOphE0DVY/AcYy6DgCLrte74iEEEII4UIanBBfccUVvP/++2RkZPCXv/yFzz77jJiYGEwmE2vXrqWgoMCWcQorMI8f7hQRqHMkjXTwG0hbC+5eMpFOCCGEEFbX6CoT/v7+3HXXXWzatIl9+/bx6KOP8uKLLxIREcEf//hHW8QorCTVGUuulRfBmqqFN658EFp31DceIYQQQricZq3M0LVrV1566SVOnDjBp59+aq2YhI2YS645VUL8wyuQdxyC28JVj+odjRBCCCFckFWWKnN3d+eGG27g66+/tsbphI04Xcm107/D5qpKJmP+Dl5OvNS0EEIIIRyW7mv3vvXWW8THx+Pj40NCQgLbt2+/6PGLFi2ia9eu+Pr6EhcXxyOPPEJpaanl8fj4eAwGwwW36dOnW4655pprLnj8vvvus9lrdAR5xRWcLigDnGRRDk2DVY+BqQK6jIZuY/WOSAghhBAuqklLN1vLsmXLSEpK4t133yUhIYFFixaRmJhISkoKERERFxz/ySefMHPmTBYvXsyVV17J77//zpQpUzAYDCxcuBCAHTt21Fo+ev/+/Vx33XXcdNNNtc41bdo0nnnmGct9Pz/X7n1MO60mPcYE+xDgres/e8P8+hUc2QgePjD6Rb2jEUIIIYQL0zUzWrhwIdOmTWPq1KkAvPvuu6xcuZLFixczc+bMC47fvHkzQ4YM4fbbbwdUb/Btt93Gtm3bLMeEh4fXes6LL75Ix44dufrqq2vt9/PzIyoqytovyWGZJ9Q5Re9wWQF8+6TaHpoEoe31jUcIIYQQLk23hLi8vJydO3cya9Ysyz43NzdGjhzJli1b6nzOlVdeydKlS9m+fTuDBg3i8OHDrFq1ijvuuKPeayxdupSkpCQM55Xq+vjjj1m6dClRUVGMHz+eOXPmXLSXuKysjLKyMsv9/Pz8xrxc3Zkn1HVuTMm1U3vgP3epBNWejGVQmget2sOQh+x7bSGEEEK0OLolxDk5ORiNRiIjI2vtj4yM5ODBg3U+5/bbbycnJ4ehQ4eiaRqVlZXcd999PPnkk3Uev2LFCnJzc5kyZcoF52nXrh0xMTHs3buXJ554gpSUFJYvX15vvAsWLODpp59u3It0IKmNrTBhMsLXD8DZQzaM6iIMbjDuVfD00ef6QgghhGgxnGAwabUNGzbwwgsv8Pbbb5OQkEBaWhoPPfQQzz77LHPmzLng+A8++IAxY8YQExNTa/+9995r2e7VqxfR0dFce+21HDp0iI4d665zO2vWLJKSkiz38/PziYuLs9Irsz1LD3FkAxPiHR9A5l7wCYY/LwdPXxtGVwefEAiOte81hRBCCNEi6ZYQh4WF4e7uTlZWVq39WVlZ9Y7tnTNnDnfccQf33HMPoJLZoqIi7r33Xp566inc3KqLZhw7dox169ZdtNfXLCEhAYC0tLR6E2Jvb2+8vb0b9NocTVFZJSdzSwDoFN6AhLgwG75/Tm1fOxfaDLBhdEIIIYQQ+tKt7JqXlxf9+/cnOTnZss9kMpGcnMzgwYPrfE5xcXGtpBdUDWQATdNq7V+yZAkRERGMGzfukrHs2bMHgOjo6Ma8BKdx+HQRAK39vWjl73XpJ6ydC2V5EN0X+k+1bXBCCCGEEDrTdchEUlISkydPZsCAAQwaNIhFixZRVFRkqTpx5513Ehsby4IFCwAYP348CxcupF+/fpYhE3PmzGH8+PGWxBhUYr1kyRImT56Mh0ftl3jo0CE++eQTxo4dS+vWrdm7dy+PPPIIw4YNo3fv3vZ78XZkLrnWoPHDxzbDL58CBhi3ENzcL/kUIYQQQghnpmtCfMstt3D69Gnmzp1LZmYmffv2Zc2aNZaJdunp6bV6hGfPno3BYGD27NmcPHmS8PBwxo8fz/PPP1/rvOvWrSM9PZ277rrrgmt6eXmxbt06S/IdFxfHxIkTmT17tm1frI7MJdcumRAbK2Bl1fLI/SdDm/42jkwIIYQQQn8G7fyxBqJB8vPzCQ4OJi8vj6CgIL3Duah7//0z3x3IYv74y5gy5CI1fbe8per/+obCAzvBL9R+QQohhBBCWFlD8zXdl24WtpdmKbl2kRrE+RmwXg1N4bqnJRkWQgghRIshCbGLK6s0cuxsMXCJkmvfPQXlBdBmIPT9s52iE0IIIYTQnyTELu5oTjFGk0agtwcRgfWUjTu8EfZ/Wb0Yhps0CyGEEEK0HJL5uDjzcImOEQEXLF8NQGU5rHpMbQ+8B6L72DE6IYQQQgj9SULs4iwr1NVXYWLrW5DzO/iHw/Cn7BiZEEIIIYRjkITYxaVmX6QGcd4J2PiS2r7uWfANsV9gQgghhBAOQhJiF2fpIa5rQt2aWVBRDG2vhD632jkyIYQQQgjHIAmxCzOaNA7nqGWbO4WfV3ItbR389jUY3GHcK1DX+GIhhBBCiBZAEmIXdvxsMeWVJnw83Yht5Vv9QEUprPqb2k64DyJ76BOgEEIIIYQDkITYhaVWDZfoEBaAu1uNHuDNb8DZwxAQBdfM1Ck6IYQQQgjHIAmxC6tz/PC5o/DjK2o78Xnwcexlp4UQQgghbE0SYhdmqTARXiMhXj0TKksh/iroOVGnyIQQQgghHIckxC7sUFUPsaXkWspq+H01uHmqFelkIp0QQgghhCTErkrTtNpDJipKYPUT6sHB0yG8q47RCSGEEEI4DkmIXVRGXilF5UY83Ay0a+0PPy6E3GMQ1Aauflzv8IQQQgghHIYkxC7K3DscH+aPZ+4R+GmRemD0C+Dlr19gQgghhBAORhJiF2UuudYpzB9WPw7Gcuh4LXT/o86RCSGEEEI4FkmIXZS5h3iM589qVTp3Lxj7skykE0IIIYQ4jyTELiotuwBfSrku/TW1Y8hD0LqjvkEJIYQQQjggSYhdVFp2IQ94rMCvJBNC2sLQJL1DEkIIIYRwSJIQu6AzhWWElhzlHveVaseYl8DLT9+ghBBCCCEclCTELig1q4BnPD7Ey2CELmOg6xi9QxJCCCGEcFiSELugir1fMsT9V8oNXjDmRb3DEUIIIYRwaJIQu5qyAvoceAmALTFToFW8ruEIIYQQQjg6SYhdzYYXCarI4YgpktO9/6J3NEIIIYQQDk8SYleSdQC2vgPA/MopdIhurXNAQgghhBCOTxJiV6FpsOox0IysNg5ko6kPnSIC9I5KCCGEEMLhSULsKvYug2M/YfTw5ZmKO4kM8ibIx1PvqIQQQgghHJ7uCfFbb71FfHw8Pj4+JCQksH379osev2jRIrp27Yqvry9xcXE88sgjlJaWWh6fP38+BoOh1q1bt261zlFaWsr06dNp3bo1AQEBTJw4kaysLJu8PrsoyYXvZgPwa6f7yKC19A4LIYQQQjSQrgnxsmXLSEpKYt68eezatYs+ffqQmJhIdnZ2ncd/8sknzJw5k3nz5vHbb7/xwQcfsGzZMp588slax/Xo0YOMjAzLbdOmTbUef+SRR/jf//7HF198wcaNGzl16hQ33nijzV6nza1/AYpOQ1gXVgeo19E5IlDnoIQQQgghnIOHnhdfuHAh06ZNY+rUqQC8++67rFy5ksWLFzNz5swLjt+8eTNDhgzh9ttvByA+Pp7bbruNbdu21TrOw8ODqKioOq+Zl5fHBx98wCeffMKIESMAWLJkCd27d2fr1q1cccUV1nyJtpfxC+x4X22PfYWUH8oA6Cg9xEIIIYQQDaJbD3F5eTk7d+5k5MiR1cG4uTFy5Ei2bNlS53OuvPJKdu7caRlWcfjwYVatWsXYsWNrHZeamkpMTAwdOnRg0qRJpKenWx7buXMnFRUVta7brVs32rZtW+91AcrKysjPz691053JBCsfBc0EPSdCh6tJyy4EoLMkxEIIIYQQDaJbD3FOTg5Go5HIyMha+yMjIzl48GCdz7n99tvJyclh6NChaJpGZWUl9913X60hEwkJCXz44Yd07dqVjIwMnn76aa666ir2799PYGAgmZmZeHl5ERIScsF1MzMz6413wYIFPP30001/wbaw52M4sQO8AmDUc5RWGDl+rhhAxhALIYQQQjSQ7pPqGmPDhg288MILvP322+zatYvly5ezcuVKnn32WcsxY8aM4aabbqJ3794kJiayatUqcnNz+fzzz5t17VmzZpGXl2e5HT9+vLkvp3mKz8K6eWr7mlkQFMOh04VoGrTy86S1v5e+8QkhhBBCOAndeojDwsJwd3e/oLpDVlZWveN/58yZwx133ME999wDQK9evSgqKuLee+/lqaeews3twvw+JCSELl26kJaWBkBUVBTl5eXk5ubW6iW+2HUBvL298fb2buzLtJ3kZ6D4DERcBglqRTrzcIlOEQEYDAY9oxNCCCGEcBq69RB7eXnRv39/kpOTLftMJhPJyckMHjy4zucUFxdfkPS6u7sDoGlanc8pLCzk0KFDREdHA9C/f388PT1rXTclJYX09PR6r+twTu6EnR+q7bGvgLuqN1ydEEuFCSGEEEKIhtK1ykRSUhKTJ09mwIABDBo0iEWLFlFUVGSpOnHnnXcSGxvLggULABg/fjwLFy6kX79+JCQkkJaWxpw5cxg/frwlMX7ssccYP3487dq149SpU8ybNw93d3duu+02AIKDg7n77rtJSkoiNDSUoKAgHnjgAQYPHuwcFSZMRjWRDg163wrxQywP1ewhFkIIIYQQDaNrQnzLLbdw+vRp5s6dS2ZmJn379mXNmjWWiXbp6em1eoRnz56NwWBg9uzZnDx5kvDwcMaPH8/zzz9vOebEiRPcdtttnDlzhvDwcIYOHcrWrVsJDw+3HPPaa6/h5ubGxIkTKSsrIzExkbffftt+L7w5dn4Ip3aDdzCMerbWQ6mSEAshhBBCNJpBq2+sgbio/Px8goODycvLIygoyD4XLcqBN/pDaS6MeRkS7rU8VGE00X3OGipNGptnjiAmxNc+MQkhhBBCOKiG5mtOVWWixVs3TyXDUb1gwF21Hjp2pohKk4a/lzvRwT76xCeEEEII4YQkIXYW6dtg91K1PW4huNce7SIVJoQQQgghmkYSYmdgrKyaSAf0uwPiBl1wSGqWSohlyWYhhBBCiMaRhNgZnNgO2QfAJwRGzq/zkLTT5iWbpeSaEEIIIURj6FplQjRQuyvhvh8hNx38w+o8xNxDLBUmhBBCCCEaRxJiZxHZQ93qYDJpHM4x9xBLQiyEEEII0RgyZMIFnMwtobTChJeHG3GhfnqHI4QQQgjhVCQhdgGp2QUAdAjzx91NKkwIIYQQQjSGJMQuQJZsFkIIIYRoOkmIXYBMqBNCCCGEaDpJiF2AlFwTQgghhGg6SYidnKZppEkPsRBCCCFEk0lC7OSyC8ooKKvE3c1AfJhUmBBCCCGEaCxJiJ2ceUJdu1A/vD3cdY5GCCGEEML5SELs5FKzVMm1jjJcQgghhBCiSSQhdnLVE+okIRZCCCGEaApJiJ2clFwTQgghhGgeSYid3CEpuSaEEEII0SySEDuxc0Xl5BSWA9Axwl/naIQQQgghnJMkxE7MPH44NsQXPy8PnaMRQgghhHBOkhA7MXPJNRk/LIQQQgjRdJIQOzHzhDqpMCGEEEII0XSSEDsx85AJ6SEWQgghhGg6SYidWFrVohySEAshhBBCNJ0kxE6qsKySU3mlgCTEQgghhBDNIQmxkzpUNaEuLMCbED8vnaMRQgghhHBekhA7KXOFCZlQJ4QQQgjRPJIQO6lUKbkmhBBCCGEVuifEb731FvHx8fj4+JCQkMD27dsvevyiRYvo2rUrvr6+xMXF8cgjj1BaWmp5fMGCBQwcOJDAwEAiIiK44YYbSElJqXWOa665BoPBUOt233332eT12YqlhzhSEmIhhBBCiObQNSFetmwZSUlJzJs3j127dtGnTx8SExPJzs6u8/hPPvmEmTNnMm/ePH777Tc++OADli1bxpNPPmk5ZuPGjUyfPp2tW7eydu1aKioqGDVqFEVFRbXONW3aNDIyMiy3l156yaav1doOmUuuhUtCLIQQQgjRHLqu97tw4UKmTZvG1KlTAXj33XdZuXIlixcvZubMmRccv3nzZoYMGcLtt98OQHx8PLfddhvbtm2zHLNmzZpaz/nwww+JiIhg586dDBs2zLLfz8+PqKgoW7wsmyutMHLsjErwZciEEEIIIUTz6NZDXF5ezs6dOxk5cmR1MG5ujBw5ki1bttT5nCuvvJKdO3dahlUcPnyYVatWMXbs2Hqvk5eXB0BoaGit/R9//DFhYWH07NmTWbNmUVxc3NyXZDdHzxRh0iDIx4PwQG+9wxFCCCGEcGq69RDn5ORgNBqJjIystT8yMpKDBw/W+Zzbb7+dnJwchg4diqZpVFZWct9999UaMlGTyWTi4YcfZsiQIfTs2bPWedq1a0dMTAx79+7liSeeICUlheXLl9cbb1lZGWVlZZb7+fn5jXm5VmVesrlTRAAGg0G3OIQQQgghXIGuQyYaa8OGDbzwwgu8/fbbJCQkkJaWxkMPPcSzzz7LnDlzLjh++vTp7N+/n02bNtXaf++991q2e/XqRXR0NNdeey2HDh2iY8eOdV57wYIFPP3009Z9QU1UXXItUOdIhBBCCCGcn25DJsLCwnB3dycrK6vW/qysrHrH9s6ZM4c77riDe+65h169ejFhwgReeOEFFixYgMlkqnXsjBkz+Oabb1i/fj1t2rS5aCwJCQkApKWl1XvMrFmzyMvLs9yOHz/ekJdpE2lSck0IIYQQwmp0S4i9vLzo378/ycnJln0mk4nk5GQGDx5c53OKi4txc6sdsru7OwCapll+zpgxg6+++orvv/+e9u3bXzKWPXv2ABAdHV3vMd7e3gQFBdW66cWSEEvJNSGEEEKIZtN1yERSUhKTJ09mwIABDBo0iEWLFlFUVGSpOnHnnXcSGxvLggULABg/fjwLFy6kX79+liETc+bMYfz48ZbEePr06XzyySf897//JTAwkMzMTACCg4Px9fXl0KFDfPLJJ4wdO5bWrVuzd+9eHnnkEYYNG0bv3r31eSMaodJo4khOVYUJKbkmhBBCCNFsuibEt9xyC6dPn2bu3LlkZmbSt29f1qxZY5lol56eXqtHePbs2RgMBmbPns3JkycJDw9n/PjxPP/885Zj3nnnHUAtvlHTkiVLmDJlCl5eXqxbt86SfMfFxTFx4kRmz55t+xdsBelniyk3mvD1dCc2xFfvcIQQQgghnJ5BM481EI2Sn59PcHAweXl5dh0+8d2vmdz70U56xgbxzQNX2e26QgghhBDOpqH5mu5LN4vGSc2WFeqEEEIIIaxJEmInc8hcci1SSq4JIYQQQliDJMROxtxD3FF6iIUQQgghrEISYidiMmkcOm3uIZaEWAghhBDCGiQhdiKn8kooLjfi6W6gXaif3uEIIYQQQrgESYidiHlBjvZh/ni4yz+dEEIIIYQ1SFblRGTJZiGEEEII65OE2IlUJ8RSYUIIIYQQwlokIXYi0kMshBBCCGF9khA7CU3TZFEOIYQQQggbkITYSeQUlpNXUoGbATqE++sdjhBCCCGEy5CE2EmkZhcAEBfqh4+nu87RCCGEEEK4DkmInYRlyWYZPyyEEEIIYVWSEDsJ84S6jpIQCyGEEEJYlSTETiLV0kMsJdeEEEIIIaxJEmInISXXhBBCCCFsQxJiJ5BXUkF2QRkAHaXChBBCCCGEVXnoHYC4tMKySkZ0iyCvpIJAH0+9wxFCCCGEcCmSEDuB2BBfFk8ZqHcYQgghhBAuSYZMCCGEEEKIFk0SYiGEEEII0aJJQiyEEEIIIVo0SYiFEEIIIUSLJgmxEEIIIYRo0SQhFkIIIYQQLZokxEIIIYQQokWThFgIIYQQQrRokhALIYQQQogWTRJiIYQQQgjRoklCLIQQQgghWjQPvQNwVpqmAZCfn69zJEIIIYQQoi7mPM2ct9VHEuImKigoACAuLk7nSIQQQgghxMUUFBQQHBxc7+MG7VIps6iTyWTi1KlTBAYGYjAYbH69/Px84uLiOH78OEFBQTa/nrOS96lh5H1qGHmfGk7eq4aR96lh5H1qGHmfLk3TNAoKCoiJicHNrf6RwtJD3ERubm60adPG7tcNCgqSRt8A8j41jLxPDSPvU8PJe9Uw8j41jLxPDSPv08VdrGfYTCbVCSGEEEKIFk0SYiGEEEII0aJJQuwkvL29mTdvHt7e3nqH4tDkfWoYeZ8aRt6nhpP3qmHkfWoYeZ8aRt4n65FJdUIIIYQQokWTHmIhhBBCCNGiSUIshBBCCCFaNEmIhRBCCCFEiyYJsRBCCCGEaNEkIXYgb731FvHx8fj4+JCQkMD27dsvevwXX3xBt27d8PHxoVevXqxatcpOkepjwYIFDBw4kMDAQCIiIrjhhhtISUm56HM+/PBDDAZDrZuPj4+dItbH/PnzL3jN3bp1u+hzWlpbMouPj7/gvTIYDEyfPr3O41tKe/rhhx8YP348MTExGAwGVqxYUetxTdOYO3cu0dHR+Pr6MnLkSFJTUy953sb+jXN0F3ufKioqeOKJJ+jVqxf+/v7ExMRw5513curUqYuesym/v47uUu1pypQpF7zm0aNHX/K8Lak9AXX+rTIYDLz88sv1ntMV25OtSELsIJYtW0ZSUhLz5s1j165d9OnTh8TERLKzs+s8fvPmzdx2223cfffd7N69mxtuuIEbbriB/fv32zly+9m4cSPTp09n69atrF27loqKCkaNGkVRUdFFnxcUFERGRoblduzYMTtFrJ8ePXrUes2bNm2q99iW2JbMduzYUet9Wrt2LQA33XRTvc9pCe2pqKiIPn368NZbb9X5+EsvvcTrr7/Ou+++y7Zt2/D39ycxMZHS0tJ6z9nYv3HO4GLvU3FxMbt27WLOnDns2rWL5cuXk5KSwh//+MdLnrcxv7/O4FLtCWD06NG1XvOnn3560XO2tPYE1Hp/MjIyWLx4MQaDgYkTJ170vK7WnmxGEw5h0KBB2vTp0y33jUajFhMToy1YsKDO42+++WZt3LhxtfYlJCRof/nLX2wapyPJzs7WAG3jxo31HrNkyRItODjYfkE5gHnz5ml9+vRp8PHSlqo99NBDWseOHTWTyVTn4y2xPQHaV199ZblvMpm0qKgo7eWXX7bsy83N1by9vbVPP/203vM09m+cszn/farL9u3bNUA7duxYvcc09vfX2dT1Pk2ePFm7/vrrG3UeaU+adv3112sjRoy46DGu3p6sSXqIHUB5eTk7d+5k5MiRln1ubm6MHDmSLVu21PmcLVu21DoeIDExsd7jXVFeXh4AoaGhFz2usLCQdu3aERcXx/XXX8+vv/5qj/B0lZqaSkxMDB06dGDSpEmkp6fXe6y0JaW8vJylS5dy1113YTAY6j2uJbanmo4cOUJmZmatNhMcHExCQkK9baYpf+NcUV5eHgaDgZCQkIse15jfX1exYcMGIiIi6Nq1K/fffz9nzpyp91hpT5CVlcXKlSu5++67L3lsS2xPTSEJsQPIycnBaDQSGRlZa39kZCSZmZl1PiczM7NRx7sak8nEww8/zJAhQ+jZs2e9x3Xt2pXFixfz3//+l6VLl2Iymbjyyis5ceKEHaO1r4SEBD788EPWrFnDO++8w5EjR7jqqqsoKCio8/iW3pbMVqxYQW5uLlOmTKn3mJbYns5nbheNaTNN+RvnakpLS3niiSe47bbbCAoKqve4xv7+uoLRo0fz73//m+TkZP7+97+zceNGxowZg9ForPN4aU/wr3/9i8DAQG688caLHtcS21NTeegdgBBNMX36dPbv33/JsVCDBw9m8ODBlvtXXnkl3bt357333uPZZ5+1dZi6GDNmjGW7d+/eJCQk0K5dOz7//PMG9Sa0VB988AFjxowhJiam3mNaYnsSzVdRUcHNN9+Mpmm88847Fz22Jf7+3nrrrZbtXr160bt3bzp27MiGDRu49tprdYzMcS1evJhJkyZdclJvS2xPTSU9xA4gLCwMd3d3srKyau3PysoiKiqqzudERUU16nhXMmPGDL755hvWr19PmzZtGvVcT09P+vXrR1pamo2iczwhISF06dKl3tfcktuS2bFjx1i3bh333HNPo57XEtuTuV00ps005W+cqzAnw8eOHWPt2rUX7R2uy6V+f11Rhw4dCAsLq/c1t+T2BPDjjz+SkpLS6L9X0DLbU0NJQuwAvLy86N+/P8nJyZZ9JpOJ5OTkWr1RNQ0ePLjW8QBr166t93hXoGkaM2bM4KuvvuL777+nffv2jT6H0Whk3759REdH2yBCx1RYWMihQ4fqfc0tsS2db8mSJURERDBu3LhGPa8ltqf27dsTFRVVq83k5+ezbdu2ettMU/7GuQJzMpyamsq6deto3bp1o89xqd9fV3TixAnOnDlT72tuqe3J7IMPPqB///706dOn0c9tie2pwfSe1SeUzz77TPP29tY+/PBD7cCBA9q9996rhYSEaJmZmZqmadodd9yhzZw503L8Tz/9pHl4eGivvPKK9ttvv2nz5s3TPD09tX379un1Emzu/vvv14KDg7UNGzZoGRkZlltxcbHlmPPfp6efflr79ttvtUOHDmk7d+7Ubr31Vs3Hx0f79ddf9XgJdvHoo49qGzZs0I4cOaL99NNP2siRI7WwsDAtOztb0zRpS+czGo1a27ZttSeeeOKCx1pqeyooKNB2796t7d69WwO0hQsXart377ZUR3jxxRe1kJAQ7b///a+2d+9e7frrr9fat2+vlZSUWM4xYsQI7Y033rDcv9TfOGd0sfepvLxc++Mf/6i1adNG27NnT62/WWVlZZZznP8+Xer31xld7H0qKCjQHnvsMW3Lli3akSNHtHXr1mmXX3651rlzZ620tNRyjpbenszy8vI0Pz8/7Z133qnzHC2hPdmKJMQO5I033tDatm2reXl5aYMGDdK2bt1qeezqq6/WJk+eXOv4zz//XOvSpYvm5eWl9ejRQ1u5cqWdI7YvoM7bkiVLLMec/z49/PDDlvc0MjJSGzt2rLZr1y77B29Ht9xyixYdHa15eXlpsbGx2i233KKlpaVZHpe2VNu3336rAVpKSsoFj7XU9rR+/fo6f9fM74XJZNLmzJmjRUZGat7e3tq11157wfvXrl07bd68ebX2XexvnDO62Pt05MiRev9mrV+/3nKO89+nS/3+OqOLvU/FxcXaqFGjtPDwcM3T01Nr166dNm3atAsS25benszee+89zdfXV8vNza3zHC2hPdmKQdM0zaZd0EIIIYQQQjgwGUMshBBCCCFaNEmIhRBCCCFEiyYJsRBCCCGEaNEkIRZCCCGEEC2aJMRCCCGEEKJFk4RYCCGEEEK0aJIQCyGEEEKIFk0SYiGEEM1iMBhYsWKF3mEIIUSTSUIshBBObMqUKRgMhgtuo0eP1js0IYRwGh56ByCEEKJ5Ro8ezZIlS2rt8/b21ikaIYRwPtJDLIQQTs7b25uoqKhat1atWgFqOMM777zDmDFj8PX1pUOHDvznP/+p9fx9+/YxYsQIfH19ad26Nffeey+FhYW1jlm8eDE9evTA29ub6OhoZsyYUevxnJwcJkyYgJ+fH507d+brr7+27YsWQggrkoRYCCFc3Jw5c5g4cSK//PILkyZN4tZbb+W3334DoKioiMTERFq1asWOHTv44osvWLduXa2E95133mH69Once++97Nu3j6+//ppOnTrVusbTTz/NzTffzN69exk7diyTJk3i7Nmzdn2dQgjRVAZN0zS9gxBCCNE0U6ZMYenSpfj4+NTa/+STT/Lkk09iMBi47777eOeddyyPXXHFFVx++eW8/fbbvP/++zzxxBMcP34cf39/AFatWsX48eM5deoUkZGRxMbGMnXqVJ577rk6YzAYDMyePZtnn30WUEl2QEAAq1evlrHMQginIGOIhRDCyQ0fPrxWwgsQGhpq2R48eHCtxwYPHsyePXsA+O233+jTp48lGQYYMmQIJpOJlJQUDAYDp06d4tprr71oDL1797Zs+/v7ExQURHZ2dlNfkhBC2JUkxEII4eT8/f0vGMJgLb6+vg06ztPTs9Z9g8GAyWSyRUhCCGF1MoZYCCFc3NatWy+43717dwC6d+/OL7/8QlFRkeXxn376CTc3N7p27UpgYCDx8fEkJyfbNWYhhLAn6SEWQggnV1ZWRmZmZq19Hh4ehIWFAfDFF18wYMAAhg4dyscff8z27dv54IMPAJg0aRLz5s1j8uTJzJ8/n9OnT/PAAw9wxx13EBkZCcD8+fO57777iIiIYMyYMRQUFPDTTz/xwAMP2PeFCiGEjUhCLIQQTm7NmjVER0fX2te1a1cOHjwIqAoQn332GX/961+Jjo7m008/5bLLLgPAz8+Pb7/9loceeoiBAwfi5+fHxIkTWbhwoeVckydPprS0lNdee43HHnuMsLAw/vSnP9nvBQohhI1JlQkhhHBhBoOBr776ihtuuEHvUIQQwmHJGGIhhBBCCNGiSUIshBBCCCFaNBlDLIQQLkxGxQkhxKVJD7EQQgghhGjRJCEWQgghhBAtmiTEQgghhBCiRZOEWAghhBBCtGiSEAshhBBCiBZNEmIhhBBCCNGiSUIshBBCCCFaNEmIhRBCCCFEiyYJsRBCCCGEaNH+H73KafPGIkRlAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation\n"
      ],
      "metadata": {
        "id": "Osyk6OyhH-FE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compare Model"
      ],
      "metadata": {
        "id": "nv-unJKn0L4q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def compare_multiple_models(models, test_set, class_names):\n",
        "    results = {}\n",
        "\n",
        "    for model, model_name in models:\n",
        "        print(f\"\\n{model_name} Evaluation:\")\n",
        "\n",
        "        loss, accuracy = model.evaluate(test_set, verbose=0)\n",
        "\n",
        "        y_pred = model.predict(test_set, verbose=0)\n",
        "        y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "\n",
        "        y_true = np.concatenate([y for x, y in test_set], axis=0)\n",
        "        y_true_classes = np.argmax(y_true, axis=1)\n",
        "\n",
        "        results[model_name] = {\n",
        "            'loss': loss,\n",
        "            'accuracy': accuracy,\n",
        "            'predictions': y_pred_classes,\n",
        "            'confusion_matrix': confusion_matrix(y_true_classes, y_pred_classes),\n",
        "            'classification_report': classification_report(y_true_classes, y_pred_classes)\n",
        "        }\n",
        "\n",
        "        print(f\"Test Loss: {loss:.4f}\")\n",
        "        print(f\"Test Accuracy: {accuracy:.4f}\")\n",
        "        print(\"\\nClassification Report:\")\n",
        "        print(results[model_name]['classification_report'])\n",
        "\n",
        "    # Accuracy Comparison\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    accuracies = [results[model_name]['accuracy'] for model_name in results.keys()]\n",
        "    plt.bar(results.keys(), accuracies)\n",
        "    plt.title('Model Accuracy Comparison')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Confusion Matrices\n",
        "    n_models = len(models)\n",
        "    fig, axes = plt.subplots(1, n_models, figsize=(5*n_models, 5))\n",
        "\n",
        "    for idx, model_name in enumerate(results.keys()):\n",
        "        sns.heatmap(results[model_name]['confusion_matrix'],\n",
        "                   annot=True,\n",
        "                   fmt='d',\n",
        "                   ax=axes[idx] if n_models > 1 else axes)\n",
        "        axes[idx].set_title(f'{model_name} Confusion Matrix') if n_models > 1 else axes.set_title(f'{model_name} Confusion Matrix')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    return results\n",
        "\n",
        "models_to_compare = [\n",
        "    (model1, \"Model 1\"),\n",
        "    (model2, \"Model 2\"),\n",
        "    (model3, \"Model 3\"),\n",
        "]\n",
        "\n",
        "results = compare_multiple_models(models_to_compare, test_set, class_names)\n",
        "\n",
        "for model_name in results:\n",
        "    print(f\"\\n{model_name} Accuracy: {results[model_name]['accuracy']:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8iFZbkQk0T6o",
        "outputId": "cfa5a0c5-6b4a-43df-c716-999df89b04a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model 1 Evaluation:\n",
            "Test Loss: 0.1788\n",
            "Test Accuracy: 0.9538\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.93      0.96       508\n",
            "           1       0.98      0.98      0.98       493\n",
            "           2       0.93      0.98      0.95       520\n",
            "           3       0.96      0.97      0.96       491\n",
            "           4       0.89      0.98      0.93       486\n",
            "           5       0.91      0.98      0.94       473\n",
            "           6       0.95      0.96      0.96       475\n",
            "           7       0.99      0.94      0.97       496\n",
            "           8       0.96      0.93      0.95       457\n",
            "           9       0.97      0.88      0.92       473\n",
            "\n",
            "    accuracy                           0.95      4872\n",
            "   macro avg       0.96      0.95      0.95      4872\n",
            "weighted avg       0.96      0.95      0.95      4872\n",
            "\n",
            "\n",
            "Model 2 Evaluation:\n",
            "Test Loss: 0.0812\n",
            "Test Accuracy: 0.9789\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.97      0.98       508\n",
            "           1       0.98      0.99      0.98       493\n",
            "           2       0.98      0.98      0.98       520\n",
            "           3       0.99      0.98      0.99       491\n",
            "           4       0.98      0.98      0.98       486\n",
            "           5       0.99      0.99      0.99       473\n",
            "           6       0.97      0.97      0.97       475\n",
            "           7       0.99      0.98      0.98       496\n",
            "           8       0.96      0.98      0.97       457\n",
            "           9       0.96      0.97      0.97       473\n",
            "\n",
            "    accuracy                           0.98      4872\n",
            "   macro avg       0.98      0.98      0.98      4872\n",
            "weighted avg       0.98      0.98      0.98      4872\n",
            "\n",
            "\n",
            "Model 3 Evaluation:\n",
            "Test Loss: 0.0852\n",
            "Test Accuracy: 0.9778\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98       508\n",
            "           1       0.97      1.00      0.99       493\n",
            "           2       0.98      0.97      0.97       520\n",
            "           3       0.99      0.97      0.98       491\n",
            "           4       0.96      0.99      0.97       486\n",
            "           5       0.98      0.98      0.98       473\n",
            "           6       0.97      0.97      0.97       475\n",
            "           7       0.98      0.98      0.98       496\n",
            "           8       0.97      0.97      0.97       457\n",
            "           9       0.98      0.97      0.97       473\n",
            "\n",
            "    accuracy                           0.98      4872\n",
            "   macro avg       0.98      0.98      0.98      4872\n",
            "weighted avg       0.98      0.98      0.98      4872\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model 1 Accuracy: 0.9538\n",
            "\n",
            "Model 2 Accuracy: 0.9789\n",
            "\n",
            "Model 3 Accuracy: 0.9778\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Deployment"
      ],
      "metadata": {
        "id": "6ZTl34mBYQKp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "FINAL_MODEL = model2"
      ],
      "metadata": {
        "id": "FtKI8OvuWJx8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Export to H5\n"
      ],
      "metadata": {
        "id": "cWuv4VZxWCgv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "FINAL_MODEL.save(\"model.h5\")"
      ],
      "metadata": {
        "id": "myQoxcX1Ai46",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f9c40a9-4b00-452b-d846-c2eeddcbcad2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save model to GCS"
      ],
      "metadata": {
        "id": "YEWyLaYdkkel"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "# auth.authenticate_user()\n",
        "\n",
        "project_id = 'capstone-c242-ps102'\n",
        "!gcloud config set project {project_id}\n",
        "\n",
        "bucket_name = 'capstone-c242-ps102_model'\n",
        "\n",
        "!gsutil mb gs://{bucket_name}\n",
        "\n",
        "!gsutil cp /content/model.h5 gs://{bucket_name}/\n",
        "\n",
        "# !gsutil cat gs://{bucket_name}/model.h5"
      ],
      "metadata": {
        "id": "meEqPbVPWdZQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84e9b539-a1d2-47fc-ee9b-edf6de66acb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;33mWARNING:\u001b[0m [m312b4ky3893@bangkit.academy] does not have permission to access projects instance [capstone-c242-ps102] (or it may not exist): The caller does not have permission. This command is authenticated as m312b4ky3893@bangkit.academy which is the active account specified by the [core/account] property\n",
            "Are you sure you wish to set property [core/project] to capstone-c242-ps102?\n",
            "\n",
            "Do you want to continue (Y/n)?  y\n",
            "\n",
            "Updated property [core/project].\n",
            "Creating gs://capstone-c242-ps102_model/...\n",
            "You are attempting to access protected data with no configured\n",
            "credentials. Please visit https://cloud.google.com/console#/project\n",
            "and sign up for an account, and then run the \"gcloud auth login\"\n",
            "command to configure gsutil to use these credentials.\n",
            "Copying file:///content/model.h5 [Content-Type=application/x-hdf5]...\n",
            "- [1 files][281.6 KiB/281.6 KiB]                                                \n",
            "Operation completed over 1 objects/281.6 KiB.                                    \n"
          ]
        }
      ]
    }
  ]
}