M4_Predict_Unfreeze_500.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Wanita-8943/Main_Project/blob/main/M4_Predict_Unfreeze_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": "53d38e23-a900-453a-9bb8-74c3a778a859"
      },
      "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": "54b8626d-17f5-4a0a-cc7a-ab67d201c7aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'efficientnet_keras_transfer_learning'...\n",
            "remote: Enumerating objects: 1079, done.\u001b[K\n",
            "remote: Counting objects: 100% (242/242), done.\u001b[K\n",
            "remote: Compressing objects: 100% (121/121), done.\u001b[K\n",
            "remote: Total 1079 (delta 121), reused 241 (delta 121), pack-reused 837\u001b[K\n",
            "Receiving objects: 100% (1079/1079), 13.94 MiB | 21.25 MiB/s, done.\n",
            "Resolving deltas: 100% (618/618), 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": "416563e1-1e57-4389-8daa-5ff173e0364e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fig_Age  Fig_Person_Sex  Age(year) Class  Class_Re       Filename  \\\n",
              "0           1               1          7  Y07M         0       VV03.jpg   \n",
              "1           2               1          7  Y07M         0  Flip_VV03.jpg   \n",
              "2           3               2          7  Y07M         0       VV04.jpg   \n",
              "3           4               2          7  Y07M         0  Flip_VV04.jpg   \n",
              "4           5               3          7  Y07M         0       VV05.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "2370      121              77         25  Y25M        18  Flip_J463.jpg   \n",
              "2371      122              78         25  Y25M        18       J464.jpg   \n",
              "2372      123              78         25  Y25M        18  Flip_J464.jpg   \n",
              "2373      124              79         25  Y25M        18       J465.jpg   \n",
              "2374      125              79         25  Y25M        18  Flip_J465.jpg   \n",
              "\n",
              "                                          Path_filename     Sex Floder  \n",
              "0     /content/drive/My Drive/TVT_Male125/train/Y07M...  เพศชาย   Both  \n",
              "1     /content/drive/My Drive/TVT_Male125/train/Y07M...  เพศชาย   Both  \n",
              "2     /content/drive/My Drive/TVT_Male125/train/Y07M...  เพศชาย   Both  \n",
              "3     /content/drive/My Drive/TVT_Male125/train/Y07M...  เพศชาย   Both  \n",
              "4     /content/drive/My Drive/TVT_Male125/train/Y07M...  เพศชาย   Both  \n",
              "...                                                 ...     ...    ...  \n",
              "2370  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "2371  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "2372  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "2373  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "2374  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "\n",
              "[2375 rows x 9 columns]"
            ],
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              "\n",
              "  <div id=\"df-dbc96957-2ca1-4151-9120-ef5ae0a22821\">\n",
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              "      <td>/content/drive/My Drive/TVT_Male125/train/Y07M...</td>\n",
              "      <td>เพศชาย</td>\n",
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              "      <th>...</th>\n",
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              "      <td>77</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>Flip_J463.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
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              "      <th>2371</th>\n",
              "      <td>122</td>\n",
              "      <td>78</td>\n",
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              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
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              "    <tr>\n",
              "      <th>2372</th>\n",
              "      <td>123</td>\n",
              "      <td>78</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>Flip_J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
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              "    <tr>\n",
              "      <th>2373</th>\n",
              "      <td>124</td>\n",
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              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
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              "<p>2375 rows × 9 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = pd.read_csv (r'/content/drive/MyDrive/cut_panoramic/Data/New_Data_Male125.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/Male/M4_Train_500_Unfreeze.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",
        "outputId": "5a05ac36-9a8d-4e56-e1af-7f8336ae5cc8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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/Male/M4_Train_500_Unfreeze.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_Male125'\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": "89f183ff-5592-4e84-a491-2075aa8819d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1425 images belonging to 19 classes.\n",
            "{0: 'Y07M', 1: 'Y08M', 2: 'Y09M', 3: 'Y10M', 4: 'Y11M', 5: 'Y12M', 6: 'Y13M', 7: 'Y14M', 8: 'Y15M', 9: 'Y16M', 10: 'Y17M', 11: 'Y18M', 12: 'Y19M', 13: 'Y20M', 14: 'Y21M', 15: 'Y22M', 16: 'Y23M', 17: 'Y24M', 18: 'Y25M'}\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": "a81f9ac4-f12e-41c9-ff51-caf678dece2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fig_Age  Fig_Person_Sex  Age(year) Class  Class_Re       Filename  \\\n",
              "100       101              51          7  Y07M         0       VV83.jpg   \n",
              "101       102              51          7  Y07M         0  Flip_VV83.jpg   \n",
              "102       103              52          7  Y07M         0       VV84.jpg   \n",
              "103       104              52          7  Y07M         0  Flip_VV84.jpg   \n",
              "104       105              53          7  Y07M         0       VV85.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "2370      121              77         25  Y25M        18  Flip_J463.jpg   \n",
              "2371      122              78         25  Y25M        18       J464.jpg   \n",
              "2372      123              78         25  Y25M        18  Flip_J464.jpg   \n",
              "2373      124              79         25  Y25M        18       J465.jpg   \n",
              "2374      125              79         25  Y25M        18  Flip_J465.jpg   \n",
              "\n",
              "                                          Path_filename     Sex Floder  \n",
              "100   /content/drive/My Drive/TVT_Male125/test/Y07M/...  เพศชาย   Both  \n",
              "101   /content/drive/My Drive/TVT_Male125/test/Y07M/...  เพศชาย   Both  \n",
              "102   /content/drive/My Drive/TVT_Male125/test/Y07M/...  เพศชาย   Both  \n",
              "103   /content/drive/My Drive/TVT_Male125/test/Y07M/...  เพศชาย   Both  \n",
              "104   /content/drive/My Drive/TVT_Male125/test/Y07M/...  เพศชาย   Both  \n",
              "...                                                 ...     ...    ...  \n",
              "2370  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "2371  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "2372  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "2373  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "2374  /content/drive/My Drive/TVT_Male125/test/Y25M/...  เพศชาย   Both  \n",
              "\n",
              "[475 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-319df0a9-fb92-4c96-b840-95ff97dfde2a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <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",
              "      <th>Class_Re</th>\n",
              "      <th>Filename</th>\n",
              "      <th>Path_filename</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Floder</th>\n",
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              "      <th>100</th>\n",
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              "      <td>/content/drive/My Drive/TVT_Male125/test/Y07M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
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              "      <th>101</th>\n",
              "      <td>102</td>\n",
              "      <td>51</td>\n",
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              "      <td>Y07M</td>\n",
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              "      <td>เพศชาย</td>\n",
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              "      <th>102</th>\n",
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              "      <td>52</td>\n",
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              "      <td>Y07M</td>\n",
              "      <td>0</td>\n",
              "      <td>VV84.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y07M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>104</td>\n",
              "      <td>52</td>\n",
              "      <td>7</td>\n",
              "      <td>Y07M</td>\n",
              "      <td>0</td>\n",
              "      <td>Flip_VV84.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y07M/...</td>\n",
              "      <td>เพศชาย</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>Y07M</td>\n",
              "      <td>0</td>\n",
              "      <td>VV85.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y07M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2370</th>\n",
              "      <td>121</td>\n",
              "      <td>77</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>Flip_J463.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2371</th>\n",
              "      <td>122</td>\n",
              "      <td>78</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2372</th>\n",
              "      <td>123</td>\n",
              "      <td>78</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>Flip_J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2373</th>\n",
              "      <td>124</td>\n",
              "      <td>79</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2374</th>\n",
              "      <td>125</td>\n",
              "      <td>79</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>18</td>\n",
              "      <td>Flip_J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Male125/test/Y25M/...</td>\n",
              "      <td>เพศชาย</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>475 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-319df0a9-fb92-4c96-b840-95ff97dfde2a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-319df0a9-fb92-4c96-b840-95ff97dfde2a 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-319df0a9-fb92-4c96-b840-95ff97dfde2a');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </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": "0a14c168-dbc4-4dee-e00e-0cf22a5f9d20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 9s 9s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9.1049933e-01, 2.3655664e-02, 5.2871414e-02, 1.0696175e-02,\n",
              "       2.1337620e-03, 1.4270686e-04, 8.8836737e-07, 1.7459773e-09,\n",
              "       1.0428695e-08, 1.0180898e-08, 9.4500796e-10, 1.1927987e-09,\n",
              "       2.7665026e-10, 5.8224697e-10, 2.5101424e-10, 2.7039840e-10,\n",
              "       8.1600388e-10, 2.5122118e-10, 4.5789031e-10], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "predict_image(path[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "Vjx_4HEsEqsq",
        "outputId": "3e2918ea-7f37-48cb-f57e-0b07383a751f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 35ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Y07M'"
            ],
            "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FX6FmOMzEjDk",
        "outputId": "cd638a9a-6125-46d8-a301-865de52b7eba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 34ms/step\n",
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            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 49ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n"
          ]
        }
      ],
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQqw0uH8-T4O",
        "outputId": "f63c1c42-a3b0-438e-bb09-7a1417c45ccf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 'Y23M',\n",
              " 'Y23M']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "pred_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1VKsxTELIIh",
        "outputId": "d5152013-628f-4a50-c5ac-374cde8ddbac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.91049933"
            ]
          },
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pzpwmMAJwz-",
        "outputId": "e5018f4b-e3f0-41e1-c1b2-e851ddbea2d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classifier accuracy = 16.63157894736842%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Y07M       0.61      0.56      0.58        25\n",
            "        Y08M       0.29      0.20      0.24        25\n",
            "        Y09M       0.16      0.28      0.20        25\n",
            "        Y10M       0.12      0.20      0.15        25\n",
            "        Y11M       0.20      0.36      0.25        25\n",
            "        Y12M       0.09      0.32      0.14        25\n",
            "        Y13M       0.26      0.32      0.29        25\n",
            "        Y14M       0.04      0.04      0.04        25\n",
            "        Y15M       0.17      0.04      0.06        25\n",
            "        Y16M       0.08      0.04      0.05        25\n",
            "        Y17M       0.18      0.12      0.14        25\n",
            "        Y18M       0.44      0.16      0.24        25\n",
            "        Y19M       0.15      0.16      0.15        25\n",
            "        Y20M       0.12      0.08      0.10        25\n",
            "        Y21M       0.03      0.04      0.04        25\n",
            "        Y22M       0.00      0.00      0.00        25\n",
            "        Y23M       0.10      0.08      0.09        25\n",
            "        Y24M       0.18      0.08      0.11        25\n",
            "        Y25M       0.22      0.08      0.12        25\n",
            "\n",
            "    accuracy                           0.17       475\n",
            "   macro avg       0.18      0.17      0.16       475\n",
            "weighted avg       0.18      0.17      0.16       475\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "qCeaPS3pJzwt",
        "outputId": "6c5db8c4-252b-4d83-afab-cbdc2da6a087"
      },
      "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": 21,
      "metadata": {
        "id": "wv3kRyGxArop"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#MSE MAE RMSE\n"
      ],
      "metadata": {
        "id": "w-8u2XPbnpQG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "uEL0iimxsjzi"
      },
      "outputs": [],
      "source": [
        "act_Re = np.array(act_Re)\n",
        "pred_int = np.around(pred_Re)"
      ]
    },
    {
      "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": "SDcuQTeKoAA9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e68f414-eeff-4d8e-a979-55755b4ee3b0"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 3.082105263157895\n",
            "MSE: 18.854736842105265\n",
            "RMSE: 4.3422041456045415\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}