3_AC3_Predict_AllAge_Unfreeze_(2e-5)_500.ipynb

{
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Wanita-8943/Main_Project/blob/main/3_AC3_Predict_AllAge_Unfreeze_(2e-5)_500.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vYOj7qs68CC"
      },
      "source": [
        "##เรียกใช้ CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WlXPl93BpDM",
        "outputId": "82cdec47-8814-4794-a4ab-858594508da7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "vCxtnct6BtlL"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwAfx2vRCdxm",
        "outputId": "f942117c-277c-446f-fa6c-da06af10b828"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'efficientnet_keras_transfer_learning'...\n",
            "remote: Enumerating objects: 1082, done.\u001b[K\n",
            "remote: Counting objects: 100% (245/245), done.\u001b[K\n",
            "remote: Compressing objects: 100% (124/124), done.\u001b[K\n",
            "remote: Total 1082 (delta 122), reused 241 (delta 121), pack-reused 837\u001b[K\n",
            "Receiving objects: 100% (1082/1082), 14.09 MiB | 14.41 MiB/s, done.\n",
            "Resolving deltas: 100% (619/619), done.\n",
            "/content/efficientnet_keras_transfer_learning\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "%cd /content\n",
        "if not os.path.isdir(\"efficientnet_keras_transfer_learning\"):\n",
        " !git clone https://github.com/Wanita-8943/efficientnet_keras_transfer_learning\n",
        "%cd efficientnet_keras_transfer_learning/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1RiTwdWRBvgU",
        "outputId": "eff8105d-3f1a-4e6f-8dc9-2d98e1c4dada"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fig_Age  Fig_Person_Sex  Age(year) Class  Class_Re       Filename  \\\n",
              "0           1               1          7   Y07         0         V1.jpg   \n",
              "1           2               1          7   Y07         0    Flip_V1.jpg   \n",
              "2           3               2          7   Y07         0         V2.jpg   \n",
              "3           4               2          7   Y07         0    Flip_V2.jpg   \n",
              "4           5               3          7   Y07         0         V3.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "4745      121              77         25   Y25        18  Flip_J463.jpg   \n",
              "4746      122              78         25   Y25        18       J464.jpg   \n",
              "4747      123              78         25   Y25        18  Flip_J464.jpg   \n",
              "4748      124              79         25   Y25        18       J465.jpg   \n",
              "4749      125              79         25   Y25        18  Flip_J465.jpg   \n",
              "\n",
              "                                          Path_filename     Sex Floder  \n",
              "0     /content/drive/My Drive/TVT_All_Age/train/Y07/...  Female   Both  \n",
              "1     /content/drive/My Drive/TVT_All_Age/train/Y07/...  Female   Both  \n",
              "2     /content/drive/My Drive/TVT_All_Age/train/Y07/...  Female   Both  \n",
              "3     /content/drive/My Drive/TVT_All_Age/train/Y07/...  Female   Both  \n",
              "4     /content/drive/My Drive/TVT_All_Age/train/Y07/...  Female   Both  \n",
              "...                                                 ...     ...    ...  \n",
              "4745  /content/drive/My Drive/TVT_All_Age/test/Y25/F...    Male   Both  \n",
              "4746  /content/drive/My Drive/TVT_All_Age/test/Y25/J...    Male   Both  \n",
              "4747  /content/drive/My Drive/TVT_All_Age/test/Y25/F...    Male   Both  \n",
              "4748  /content/drive/My Drive/TVT_All_Age/test/Y25/J...    Male   Both  \n",
              "4749  /content/drive/My Drive/TVT_All_Age/test/Y25/F...    Male   Both  \n",
              "\n",
              "[4750 rows x 9 columns]"
            ],
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              "\n",
              "  <div id=\"df-970f8f18-20a6-4952-bedc-b9827b3c221a\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Fig_Age</th>\n",
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              "      <th>Age(year)</th>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>7</td>\n",
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              "      <td>25</td>\n",
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              "      <td>J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/J...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "      <th>4747</th>\n",
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              "      <td>Y25</td>\n",
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              "      <td>Flip_J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/F...</td>\n",
              "      <td>Male</td>\n",
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              "    <tr>\n",
              "      <th>4748</th>\n",
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              "      <td>Y25</td>\n",
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              "      <td>J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/J...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/F...</td>\n",
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              "<p>4750 rows × 9 columns</p>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = pd.read_csv (r'/content/drive/MyDrive/cut_panoramic/Data/New_All_Age_0.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Sa_dq25vCUfN"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/cut_panoramic/Model/Classification/All_Age/(2e-5)3_AC3_AllAge_Unfreeze_500.h5')\n",
        "\n",
        "from efficientnet.layers import Swish, DropConnect\n",
        "from efficientnet.model import ConvKernalInitializer\n",
        "from tensorflow.keras.utils import get_custom_objects\n",
        "\n",
        "get_custom_objects().update({\n",
        "    'ConvKernalInitializer': ConvKernalInitializer,\n",
        "    'Swish': Swish,\n",
        "    'DropConnect':DropConnect\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "dDq_RhxuB3US",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89c9fe4e-eed5-4d63-e217-af4f6527dba2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.9/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
            "Instructions for updating:\n",
            "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n",
            "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model = load_model('/content/drive/MyDrive/cut_panoramic/Model/Classification/All_Age/(2e-5)3_AC3_AllAge_Unfreeze_500.h5')\n",
        "height = width = model.input_shape[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "IPwibaNFDfcS"
      },
      "outputs": [],
      "source": [
        "base_dir = '/content/drive/MyDrive/TVT_All_Age'\n",
        "os.makedirs(base_dir, exist_ok=True)\n",
        "\n",
        "# Directories for our training,\n",
        "# validation and test splits\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "os.makedirs(train_dir, exist_ok=True)\n",
        "validation_dir = os.path.join(base_dir, 'validation')\n",
        "os.makedirs(validation_dir, exist_ok=True)\n",
        "test_dir = os.path.join(base_dir, 'test')\n",
        "os.makedirs(test_dir, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xi1ozW49B_cT",
        "outputId": "38c8ce8b-3329-420a-891a-a3ce5db70fc4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2850 images belonging to 19 classes.\n",
            "{0: 'Y07', 1: 'Y08', 2: 'Y09', 3: 'Y10', 4: 'Y11', 5: 'Y12', 6: 'Y13', 7: 'Y14', 8: 'Y15', 9: 'Y16', 10: 'Y17', 11: 'Y18', 12: 'Y19', 13: 'Y20', 14: 'Y21', 15: 'Y22', 16: 'Y23', 17: 'Y24', 18: 'Y25'}\n"
          ]
        }
      ],
      "source": [
        "batch_size = 16\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255, #โมเดลส่วนใหญ่ต้องใช้ RGB ในช่วง 0–1\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "\n",
        "# Note that the validation data should not be augmented!\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        # This is the target directory #ไดเรกเป้าหมาย\n",
        "        train_dir,\n",
        "        # รูปภาพทั้งหมดจะถูกปรับขนาดตามความสูงและความกว้างของเป้าหมาย\n",
        "        target_size=(height, width),\n",
        "        batch_size=batch_size,\n",
        "        # Since we use categorical_crossentropy loss, we need categorical labels\n",
        "        #เนื่องจากเราใช้ categorical_crossentropy loss เราจึงต้องมีป้ายกำกับตามหมวดหมู่\n",
        "        class_mode='categorical')\n",
        "\n",
        "#label\n",
        "labels = (train_generator.class_indices)\n",
        "labels = dict((v,k) for k,v in labels.items())\n",
        "print(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "c1_BK5epD6r5",
        "outputId": "7ace3683-5b15-4c6a-e01d-14e37d6da062"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fig_Age  Fig_Person_Sex  Age(year) Class  Class_Re       Filename  \\\n",
              "100       101              51          7   Y07         0        V67.jpg   \n",
              "101       102              51          7   Y07         0   Flip_V67.jpg   \n",
              "102       103              52          7   Y07         0        V68.jpg   \n",
              "103       104              52          7   Y07         0   Flip_V68.jpg   \n",
              "104       105              53          7   Y07         0        V69.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "4745      121              77         25   Y25        18  Flip_J463.jpg   \n",
              "4746      122              78         25   Y25        18       J464.jpg   \n",
              "4747      123              78         25   Y25        18  Flip_J464.jpg   \n",
              "4748      124              79         25   Y25        18       J465.jpg   \n",
              "4749      125              79         25   Y25        18  Flip_J465.jpg   \n",
              "\n",
              "                                          Path_filename     Sex Floder  \n",
              "100   /content/drive/My Drive/TVT_All_Age/test/Y07/V...  Female   Both  \n",
              "101   /content/drive/My Drive/TVT_All_Age/test/Y07/F...  Female   Both  \n",
              "102   /content/drive/My Drive/TVT_All_Age/test/Y07/V...  Female   Both  \n",
              "103   /content/drive/My Drive/TVT_All_Age/test/Y07/F...  Female   Both  \n",
              "104   /content/drive/My Drive/TVT_All_Age/test/Y07/V...  Female   Both  \n",
              "...                                                 ...     ...    ...  \n",
              "4745  /content/drive/My Drive/TVT_All_Age/test/Y25/F...    Male   Both  \n",
              "4746  /content/drive/My Drive/TVT_All_Age/test/Y25/J...    Male   Both  \n",
              "4747  /content/drive/My Drive/TVT_All_Age/test/Y25/F...    Male   Both  \n",
              "4748  /content/drive/My Drive/TVT_All_Age/test/Y25/J...    Male   Both  \n",
              "4749  /content/drive/My Drive/TVT_All_Age/test/Y25/F...    Male   Both  \n",
              "\n",
              "[950 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-19fcda4c-ace9-4800-aabd-ce0982eafd99\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fig_Age</th>\n",
              "      <th>Fig_Person_Sex</th>\n",
              "      <th>Age(year)</th>\n",
              "      <th>Class</th>\n",
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              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y07/V...</td>\n",
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              "      <td>V68.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y07/V...</td>\n",
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              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>104</td>\n",
              "      <td>52</td>\n",
              "      <td>7</td>\n",
              "      <td>Y07</td>\n",
              "      <td>0</td>\n",
              "      <td>Flip_V68.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y07/F...</td>\n",
              "      <td>Female</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>105</td>\n",
              "      <td>53</td>\n",
              "      <td>7</td>\n",
              "      <td>Y07</td>\n",
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              "      <td>V69.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y07/V...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4745</th>\n",
              "      <td>121</td>\n",
              "      <td>77</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25</td>\n",
              "      <td>18</td>\n",
              "      <td>Flip_J463.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/F...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4746</th>\n",
              "      <td>122</td>\n",
              "      <td>78</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25</td>\n",
              "      <td>18</td>\n",
              "      <td>J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/J...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "    <tr>\n",
              "      <th>4747</th>\n",
              "      <td>123</td>\n",
              "      <td>78</td>\n",
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              "      <td>Y25</td>\n",
              "      <td>18</td>\n",
              "      <td>Flip_J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/F...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "    <tr>\n",
              "      <th>4748</th>\n",
              "      <td>124</td>\n",
              "      <td>79</td>\n",
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              "      <td>18</td>\n",
              "      <td>J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/J...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "      <td>Flip_J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_All_Age/test/Y25/F...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>950 rows × 9 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19fcda4c-ace9-4800-aabd-ce0982eafd99')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-19fcda4c-ace9-4800-aabd-ce0982eafd99 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-19fcda4c-ace9-4800-aabd-ce0982eafd99');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "test = df[df['Fig_Age'].between(101,125)]\n",
        "test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "8TRhj_ncDbqq"
      },
      "outputs": [],
      "source": [
        "act = test['Class'].tolist() #ค่าจริง\n",
        "path = test['Path_filename'].tolist() #path\n",
        "act_Re = test['Class_Re'].tolist() #ค่าจริง"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "2eBCfOnOD8aX"
      },
      "outputs": [],
      "source": [
        "# act"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "tBFLv9UJD9m0"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "def predict_image(img_path):\n",
        "    # Read the image and resize it\n",
        "    img = image.load_img(img_path, target_size=(height, width)) #รูปทุกรูปมีขนาดไม่เท่ากันจึงตั้งไว้ว่าเวลาจะให้ model ดึงรูปมาทำนายให้ดึงรูปเข้ามาตามขนาดที่ตั้งไว้ตามพารามิตเตอร์\n",
        "    # Convert it to a Numpy array with target shape.\n",
        "    x = image.img_to_array(img)  #model ไม่สามารถทำนายรูปภาพโดยตรงได้ จึงเเปลงรูปภาพให้เป็น array เเล้วให้โมเดลทำนาย\n",
        "    # Reshape\n",
        "    x = x.reshape((1,) + x.shape) # เพิ่ม ไดเมนชั่นของโมเดล ให้เป็น 4 ได้ เมนชั่น โดยที่ 1, คือบอกให้โมเดลนำเข้าทีละ 1 รูปเเล้วค่อยทำนาย เเละ + ไดเมนชั่น คือ 150 ,150 ,3(สีของ RGB)\n",
        "    x /= 255.\n",
        "    result = model.predict([x])\n",
        "    return result[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_xWno8PFb5K",
        "outputId": "91c5c157-fe98-4531-b1bf-cb629e4a94f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.9695444e-01, 2.7957999e-03, 2.4959954e-04, 2.0570586e-07,\n",
              "       9.3042955e-11, 6.2115020e-14, 6.1048407e-18, 1.6762244e-19,\n",
              "       1.3784880e-19, 6.3984462e-20, 5.4732332e-21, 9.0774203e-21,\n",
              "       2.0122616e-21, 6.0183096e-20, 1.5408730e-20, 1.9167762e-21,\n",
              "       8.8239214e-20, 5.9661927e-20, 2.5238475e-19], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "predict_image(path[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Vjx_4HEsEqsq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "960f5c8c-e390-4a12-f082-f63055249796"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 57ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Y07'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "no = np.argmax(predict_image(path[0]))\n",
        "labels[no]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "FX6FmOMzEjDk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06d98701-9b3f-482c-b007-cb5a3b693b15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 54ms/step\n",
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      ],
      "source": [
        "#Predict\n",
        "pred_list = list()\n",
        "prob_list = list()\n",
        "pred_Re = list()\n",
        "\n",
        "img_path= path\n",
        "for i in range(0,len(img_path)):\n",
        "    predict = predict_image(img_path[i])\n",
        "    result = np.argmax(predict)\n",
        "    pred_list.append(labels[result])\n",
        "    pred_Re.append(result)\n",
        "    prob_list.append(predict[result])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "vQqw0uH8-T4O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d358f442-1e95-49c8-f34e-98d0d08cfece"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "pred_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "X1VKsxTELIIh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e42b3c0-a3cc-47c8-ee6d-4aecc0988f9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.99695444"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "prob_list[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "u_MQmuk3JiRj"
      },
      "outputs": [],
      "source": [
        "# data_train = valframe\n",
        "# #เช็คคลาสใน Predicted\n",
        "# pred_class = set(data_train['category_ov'])\n",
        "# print('Predicted : ',len(pred_class))\n",
        "# print(pred_class)\n",
        "# print('-'*50)\n",
        "# #เช็คคลาสใน Actual\n",
        "# classe = set(data_train['class'])\n",
        "# print('Actual : ',len(classe))\n",
        "# print(classe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ezbChl5EKQA7"
      },
      "outputs": [],
      "source": [
        "pred = np.array(pred_list) #แปลงเป็น array\n",
        "act = np.array(act)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_pzpwmMAJwz-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65c036b1-7f30-4a33-bd4b-88faff3ead38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classifier accuracy = 20.42105263157895%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         Y07       0.63      0.88      0.73        50\n",
            "         Y08       0.28      0.14      0.19        50\n",
            "         Y09       0.25      0.48      0.33        50\n",
            "         Y10       0.16      0.26      0.20        50\n",
            "         Y11       0.14      0.54      0.23        50\n",
            "         Y12       0.17      0.34      0.23        50\n",
            "         Y13       0.23      0.36      0.28        50\n",
            "         Y14       0.05      0.02      0.03        50\n",
            "         Y15       0.06      0.02      0.03        50\n",
            "         Y16       0.10      0.10      0.10        50\n",
            "         Y17       0.29      0.04      0.07        50\n",
            "         Y18       0.67      0.04      0.08        50\n",
            "         Y19       0.18      0.14      0.16        50\n",
            "         Y20       0.12      0.04      0.06        50\n",
            "         Y21       0.12      0.06      0.08        50\n",
            "         Y22       0.21      0.06      0.09        50\n",
            "         Y23       0.11      0.18      0.14        50\n",
            "         Y24       0.23      0.06      0.10        50\n",
            "         Y25       0.22      0.12      0.16        50\n",
            "\n",
            "    accuracy                           0.20       950\n",
            "   macro avg       0.22      0.20      0.17       950\n",
            "weighted avg       0.22      0.20      0.17       950\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# act = data_train['class'].array\n",
        "# pred = data_train['category_ov'].array\n",
        "\n",
        "cmat = confusion_matrix(act, pred)\n",
        "print('classifier accuracy = {}%'.format((100.*np.trace(cmat))/(np.sum(cmat))))\n",
        "\n",
        "#Marking the Confusion Matrix\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(classification_report(act, pred))#performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "qCeaPS3pJzwt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "outputId": "1880cbd7-05e5-442c-f7a4-f9a435683fad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 66.5, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#create CF \n",
        "data = {'Actual': act,'Predicted' : pred,}\n",
        "df = pd.DataFrame(data, columns=['Actual','Predicted'])\n",
        "conf_mat = pd.crosstab(df['Actual'],df['Predicted'],rownames=['Actual'],colnames=['Predicted'])\n",
        "\n",
        "#Confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "cm = confusion_matrix(act, pred)\n",
        "\n",
        "#plot Confusion matrix\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "fig, ax = plt.subplots(figsize=(10, 10))\n",
        "\n",
        "ax = sns.heatmap(conf_mat, annot=True, fmt=\"d\", cmap=\"YlGnBu\") #Blues,Oranges,Reds\n",
        "ax.set_title('Confusion matrix',fontsize=20)\n",
        "ax.set_ylabel('True label',fontsize=18)\n",
        "ax.set_xlabel('Predicted label',fontsize=18)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "wv3kRyGxArop"
      },
      "outputs": [],
      "source": [
        "act_Re = np.array(act_Re)\n",
        "pred_int = np.around(pred_Re)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "act_Re"
      ],
      "metadata": {
        "id": "IOAm41XXT4rT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce097710-879c-4199-a72c-e4f27f71fb3e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
              "        0,  0,  0,  0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  2,\n",
              "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
              "        2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,\n",
              "        3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  4,  4,\n",
              "        4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,\n",
              "        4,  4,  4,  4,  4,  4,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,\n",
              "        5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  6,  6,  6,\n",
              "        6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,\n",
              "        6,  6,  6,  6,  6,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,\n",
              "        7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  8,  8,  8,  8,\n",
              "        8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,\n",
              "        8,  8,  8,  8,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,\n",
              "        9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10, 10,\n",
              "       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
              "       10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n",
              "       11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12,\n",
              "       12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
              "       12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
              "       13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14,\n",
              "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
              "       14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,\n",
              "       15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16,\n",
              "       16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,\n",
              "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
              "       17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18,\n",
              "       18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,  0,\n",
              "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
              "        0,  0,  0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
              "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  2,  2,\n",
              "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
              "        2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,\n",
              "        3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  3,  4,  4,  4,\n",
              "        4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,\n",
              "        4,  4,  4,  4,  4,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,\n",
              "        5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5,  6,  6,  6,  6,\n",
              "        6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,\n",
              "        6,  6,  6,  6,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,\n",
              "        7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  7,  8,  8,  8,  8,  8,\n",
              "        8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,\n",
              "        8,  8,  8,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,\n",
              "        9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10, 10, 10,\n",
              "       10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
              "       10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n",
              "       11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12,\n",
              "       12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n",
              "       12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,\n",
              "       13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14,\n",
              "       14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,\n",
              "       15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,\n",
              "       15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16,\n",
              "       16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17,\n",
              "       17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
              "       17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,\n",
              "       18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_int"
      ],
      "metadata": {
        "id": "RsQSHOXhT6vI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87a38b75-6924-4fcb-f22b-07c6d7c0291f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  0,  0,  0,  0,  0,  2,  3,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
              "        0,  0,  0,  0,  0,  0,  0,  0,  2,  2,  0,  0,  0,  0,  2,  2,  2,\n",
              "        2,  0,  0,  2,  2,  1,  0,  0,  1,  2,  2,  2,  2,  1,  0,  2,  3,\n",
              "        3,  2,  2,  4,  4,  2,  2,  2,  2,  2,  2,  1,  1,  2,  3,  2,  1,\n",
              "        1,  0,  0,  2,  0,  2,  2,  2,  2,  2,  3,  3,  3,  3,  2,  2,  4,\n",
              "        4,  3,  3,  2,  2,  2,  2,  0,  1,  2,  2,  0,  1,  0,  0,  3,  2,\n",
              "        4,  3,  4,  2,  3,  3,  3,  4,  4,  4,  4,  3,  3,  1,  4,  4,  4,\n",
              "        4,  4,  4,  4,  4,  4,  4,  6,  4,  3,  6,  5,  5,  5,  5,  3,  5,\n",
              "        5,  4,  4,  5,  5,  4,  4,  4,  4,  5,  5,  4,  4,  4,  6,  6,  6,\n",
              "        9,  8,  9,  3,  2,  5,  5,  3,  3,  5,  6,  3,  4,  4,  6,  6,  3,\n",
              "        3,  4,  6,  5,  6,  7,  4,  5,  4,  4,  6,  6,  8,  4,  5,  8,  5,\n",
              "        4,  3,  6,  4,  5, 17, 13,  4,  4,  4,  9,  4,  4,  4,  4, 16, 16,\n",
              "        4,  4,  4,  4, 16, 12,  3,  5,  4,  4,  4,  5,  3,  9,  4,  3,  9,\n",
              "        9,  7,  5,  7, 12, 17,  4,  4,  5,  4,  8,  6,  7,  5,  9,  6, 10,\n",
              "       10,  7,  6,  9,  6,  0,  6,  5,  5, 15, 15, 16,  8,  3,  8,  8,  3,\n",
              "        0, 15, 18,  4, 12, 16,  5, 12,  7, 12,  4,  5,  4,  5,  8,  8,  6,\n",
              "        6,  3,  6,  6,  9,  9,  3, 16, 14,  6,  4, 12, 17,  9, 16,  7, 16,\n",
              "        5, 18,  9,  9,  3,  4,  4,  4,  4,  4,  9, 12, 12,  4,  5, 12,  4,\n",
              "        4,  5,  4,  5,  3, 16, 12, 15,  4, 18, 18,  9,  4,  5,  4, 18, 14,\n",
              "       14,  4,  4,  4,  4,  4, 17,  4,  4,  4, 16, 14,  9,  5,  3,  5, 16,\n",
              "       12, 14, 16,  4,  6, 18, 16, 18, 16,  9,  4, 13, 16,  5, 16, 18,  8,\n",
              "        6,  5,  5, 18,  5, 18, 12, 14, 14,  9,  5,  4,  9, 12, 12, 16, 12,\n",
              "        4, 16,  5,  4,  4, 15,  6,  5, 12, 12, 14, 12, 18,  9,  4, 15, 16,\n",
              "        4,  5, 16, 12,  5, 18,  9, 15,  3,  5, 18, 18, 18,  4,  4, 17,  6,\n",
              "       18,  6,  4,  4,  4,  6,  6,  6, 12,  9, 14,  4, 12, 14,  4, 14, 15,\n",
              "        4,  4,  4,  4, 12, 12, 16, 18,  5,  5, 16, 18,  3, 14, 16, 13,  4,\n",
              "        5, 17, 12,  4,  5,  4, 14,  4,  3, 16, 16,  7,  4, 18, 17, 17, 13,\n",
              "       13, 15,  5, 18,  5,  4,  5, 18,  6,  4, 18, 13,  4,  5,  6,  4,  0,\n",
              "        0,  2,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0,\n",
              "        0,  0,  0,  0,  0,  0,  3,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,\n",
              "        1,  0,  2,  1,  2,  1,  2,  2,  2,  2,  1,  2,  0,  2,  0,  4,  2,\n",
              "        1,  2,  2,  2,  2,  2,  3,  2,  2,  1,  1,  3,  3,  3,  3,  1,  2,\n",
              "        2,  3,  2,  1,  1,  0,  3,  3,  2,  2,  2,  2,  1,  1,  3,  3,  2,\n",
              "        2,  2,  2,  3,  3,  0,  2,  2,  3,  2,  2,  2,  2,  4,  4,  5,  4,\n",
              "        4,  3,  3,  1,  1,  2,  4,  4,  5,  2,  2,  2,  2,  4,  5,  4,  4,\n",
              "        4,  4,  4,  4,  3,  6,  6,  3,  4,  5,  5,  3,  5,  2,  3,  3,  3,\n",
              "        3,  5,  5,  4,  4,  6,  6,  6,  4,  5,  4,  4,  5,  7,  6,  6,  4,\n",
              "        5,  7,  5,  7,  6,  7,  6,  9,  6,  5,  5,  8,  6,  7,  6,  8,  8,\n",
              "        6,  6,  8,  6,  6,  4,  5,  4,  4,  2,  6,  4,  4,  5, 16,  5,  4,\n",
              "        4,  3,  2,  4,  3,  4,  3,  4,  4,  4,  4,  4,  4,  4,  3,  4,  4,\n",
              "        4, 10,  6,  7,  4,  6,  9,  4,  9,  5,  4,  5, 16,  3,  3,  4,  2,\n",
              "        2,  2,  8,  6,  7,  6,  6,  6,  7,  7,  7,  5,  8,  8,  9,  0,  9,\n",
              "        0,  4,  5,  6,  4,  4,  6,  3,  9,  7,  6,  3,  6, 16, 15,  4,  6,\n",
              "        6,  2, 16, 16, 15,  3, 16, 12, 16, 16, 15,  4, 10,  7, 16, 16, 10,\n",
              "        6,  4,  7,  6,  3,  5, 16,  6, 13,  5, 10,  9, 16, 16,  9,  5, 11,\n",
              "       11, 16,  5, 16, 12,  4, 15, 16, 16, 12, 16,  4, 12,  4, 12, 10,  6,\n",
              "       15,  9, 16, 16,  3,  4,  6,  5,  4,  5,  5, 16, 13,  4, 12,  5,  6,\n",
              "        6,  6,  4,  4,  6, 13, 13,  8,  9,  4,  4, 16, 17,  9,  6,  4,  4,\n",
              "       12, 12, 16,  6,  7,  5, 16,  4,  5,  4,  5, 12,  9, 16,  6,  9,  6,\n",
              "        5,  5,  5,  6,  4, 14,  0, 16,  9,  5,  5, 16, 16, 13, 11,  4, 16,\n",
              "       16, 12,  3,  9,  9, 12,  9, 13,  6,  9, 14,  4, 17,  4,  5, 16,  9,\n",
              "       16, 16, 16, 14,  9,  9,  4, 16,  5, 16, 12, 13,  6, 16, 18,  3, 16,\n",
              "       16, 16, 18, 16, 13,  3,  4, 14,  4,  6, 16, 16, 14,  9,  9, 16, 14,\n",
              "       14, 16, 14,  9, 16, 14,  4,  5,  5, 16, 14,  5, 16, 18,  4,  9,  5,\n",
              "        5,  9, 13, 17,  4, 17, 16, 16, 16, 18, 16, 17, 16,  5,  4, 18,  5,\n",
              "        9,  4, 12, 12,  4, 16,  4, 13,  3, 14, 14,  9,  9, 16,  3])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "  \n",
        "mae = mean_absolute_error(act_Re, pred_int)\n",
        "#squared True returns MSE value, False returns RMSE value.\n",
        "mse = mean_squared_error(act_Re, pred_int) #default=True\n",
        "rmse = mean_squared_error(act_Re, pred_int, squared=False)\n",
        "  \n",
        "print(\"MAE:\",mae)\n",
        "print(\"MSE:\",mse)\n",
        "print(\"RMSE:\",rmse)"
      ],
      "metadata": {
        "id": "GvtyqoqJQCRY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bee7fa5-97b6-4cad-fa28-a191f7055ba4"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 3.5231578947368423\n",
            "MSE: 25.954736842105262\n",
            "RMSE: 5.094579162414228\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m8N6zvorT0Sk"
      },
      "execution_count": 25,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}