A1_Predict_Freeze.ipynb

{
  "cells": [
    {
      "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/A1_Predict_Freeze.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": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WlXPl93BpDM",
        "outputId": "8d80a6ef-b2b6-425c-aa60-6f8eb3d15be2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "vCxtnct6BtlL"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwAfx2vRCdxm",
        "outputId": "353dd6d0-fa5b-4f9b-ec28-2f2ee91c26d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\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": 9,
      "metadata": {
        "id": "Sa_dq25vCUfN"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "sys.path.append('/content/drive/MyDrive/cut_panoramic/Model/Regression/All/1Re_All_1G_1e-4_16_0.2_250.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": 10,
      "metadata": {
        "id": "dDq_RhxuB3US",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8386519e-8f4e-4b70-f423-8c50d8eb18f5"
      },
      "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"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "model = load_model('/content/drive/MyDrive/cut_panoramic/Model/Regression/All/1Re_All_1G_1e-4_16_0.2_250.h5')\n",
        "height = width = model.input_shape[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1RiTwdWRBvgU",
        "outputId": "6088ae8e-c80c-46db-c909-c2d38dcef28f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fig_Age  Fig_Person_Sex  Age(year) Class  Class_Re       Filename  \\\n",
              "0           1               1          7  Y07F         1         V1.jpg   \n",
              "1           2               1          7  Y07F         1    Flip_V1.jpg   \n",
              "2           3               2          7  Y07F         1         V2.jpg   \n",
              "3           4               2          7  Y07F         1    Flip_V2.jpg   \n",
              "4           5               3          7  Y07F         1         V3.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "4745      121              77         25  Y25M        19  Flip_J463.jpg   \n",
              "4746      122              78         25  Y25M        19       J464.jpg   \n",
              "4747      123              78         25  Y25M        19  Flip_J464.jpg   \n",
              "4748      124              79         25  Y25M        19       J465.jpg   \n",
              "4749      125              79         25  Y25M        19  Flip_J465.jpg   \n",
              "\n",
              "                                          Path_filename     Sex Floder  \n",
              "0     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "1     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "2     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "3     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "4     /content/drive/My Drive/TVT_Gender/train/Femal...  Female   Both  \n",
              "...                                                 ...     ...    ...  \n",
              "4745  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "4746  /content/drive/My Drive/TVT_Gender/test/Male/J...    Male   Both  \n",
              "4747  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "4748  /content/drive/My Drive/TVT_Gender/test/Male/J...    Male   Both  \n",
              "4749  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "\n",
              "[4750 rows x 9 columns]"
            ],
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              "      <th>Fig_Age</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df = pd.read_csv (f'/content/drive/MyDrive/cut_panoramic/Data/All_Re.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = df[df['Fig_Age'].between(1,75)]\n",
        "test = df[df['Fig_Age'].between(101,125)]"
      ],
      "metadata": {
        "id": "-8k5KWBRxwf8"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DATA_PATH = \"/content/drive/My Drive/TVT_Gender\"\n",
        "os.chdir(DATA_PATH)\n",
        "train_dir = os.path.join(DATA_PATH, 'train')\n",
        "print(train_dir)\n",
        "test_dir = os.path.join(DATA_PATH, 'test')\n",
        "print(test_dir)"
      ],
      "metadata": {
        "id": "3jWpRK9HyW1q",
        "outputId": "9ee64c58-078c-4031-9cf6-8f61a61e0340",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/TVT_Gender/train\n",
            "/content/drive/My Drive/TVT_Gender/test\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "IPwibaNFDfcS"
      },
      "outputs": [],
      "source": [
        "# base_dir = '/content/drive/MyDrive/TVT_Gender'\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": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xi1ozW49B_cT",
        "outputId": "ae29e73b-a63d-4fd1-b152-183f9c8bbe1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2850 validated image filenames.\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_dataframe(\n",
        "        dataframe = train,\n",
        "        directory = train_dir,\n",
        "        x_col = 'Path_filename',\n",
        "        y_col = 'Class_Re',\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 = 'other')\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": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "c1_BK5epD6r5",
        "outputId": "9bacda23-8a83-4146-e55f-1534bc481a83"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fig_Age  Fig_Person_Sex  Age(year) Class  Class_Re       Filename  \\\n",
              "100       101              51          7  Y07F         1        V67.jpg   \n",
              "101       102              51          7  Y07F         1   Flip_V67.jpg   \n",
              "102       103              52          7  Y07F         1        V68.jpg   \n",
              "103       104              52          7  Y07F         1   Flip_V68.jpg   \n",
              "104       105              53          7  Y07F         1        V69.jpg   \n",
              "...       ...             ...        ...   ...       ...            ...   \n",
              "4745      121              77         25  Y25M        19  Flip_J463.jpg   \n",
              "4746      122              78         25  Y25M        19       J464.jpg   \n",
              "4747      123              78         25  Y25M        19  Flip_J464.jpg   \n",
              "4748      124              79         25  Y25M        19       J465.jpg   \n",
              "4749      125              79         25  Y25M        19  Flip_J465.jpg   \n",
              "\n",
              "                                          Path_filename     Sex Floder  \n",
              "100   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "101   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "102   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "103   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "104   /content/drive/My Drive/TVT_Gender/test/Female...  Female   Both  \n",
              "...                                                 ...     ...    ...  \n",
              "4745  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "4746  /content/drive/My Drive/TVT_Gender/test/Male/J...    Male   Both  \n",
              "4747  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "4748  /content/drive/My Drive/TVT_Gender/test/Male/J...    Male   Both  \n",
              "4749  /content/drive/My Drive/TVT_Gender/test/Male/F...    Male   Both  \n",
              "\n",
              "[950 rows x 9 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Fig_Age</th>\n",
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              "      <th>Class</th>\n",
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              "      <td>/content/drive/My Drive/TVT_Gender/test/Female...</td>\n",
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              "      <th>103</th>\n",
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              "      <td>Flip_V68.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Female...</td>\n",
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              "      <th>104</th>\n",
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              "      <td>7</td>\n",
              "      <td>Y07F</td>\n",
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              "      <td>V69.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Female...</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",
              "      <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>Y25M</td>\n",
              "      <td>19</td>\n",
              "      <td>Flip_J463.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/F...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "    <tr>\n",
              "      <th>4746</th>\n",
              "      <td>122</td>\n",
              "      <td>78</td>\n",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>19</td>\n",
              "      <td>J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/J...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4747</th>\n",
              "      <td>123</td>\n",
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              "      <td>19</td>\n",
              "      <td>Flip_J464.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/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",
              "      <td>25</td>\n",
              "      <td>Y25M</td>\n",
              "      <td>19</td>\n",
              "      <td>J465.jpg</td>\n",
              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/J...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
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              "      <td>/content/drive/My Drive/TVT_Gender/test/Male/F...</td>\n",
              "      <td>Male</td>\n",
              "      <td>Both</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>950 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de20fad8-5207-4143-b10f-ec3d4f27ee2b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\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",
              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-de20fad8-5207-4143-b10f-ec3d4f27ee2b 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-de20fad8-5207-4143-b10f-ec3d4f27ee2b');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "test = df[df['Fig_Age'].between(101,125)]\n",
        "test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "8TRhj_ncDbqq"
      },
      "outputs": [],
      "source": [
        "act = test['Class_Re'].tolist() #ค่าจริง\n",
        "path = test['Path_filename'].tolist() #path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "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": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_xWno8PFb5K",
        "outputId": "2e1081f4-83b9-4707-a55e-a5d688867087"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 10s 10s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.604023"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "predict_image(path[0])[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7qierSwCnFC8",
        "outputId": "3a20375e-7595-42aa-a540-838fd32e896f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
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            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n"
          ]
        }
      ],
      "source": [
        "#Predict\n",
        "pred_list = list()\n",
        "prob_list = list()\n",
        "img_path= path\n",
        "for i in range(0,len(img_path)):\n",
        "    predict = predict_image(img_path[i])\n",
        "    result = predict[0]\n",
        "    pred_list.append(result)\n",
        "    # prob_list.append(predict[result])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQqw0uH8-T4O",
        "outputId": "1ad89356-ee8f-40be-f536-2e0abbce9a6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 8.604023,\n",
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              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023,\n",
              " 8.604023]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "pred_list"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(pred_list))"
      ],
      "metadata": {
        "id": "UcNyZKXzHEED",
        "outputId": "43ce520a-4aa8-410f-ad00-3046e02e4423",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_int = np.around(pred_list)"
      ],
      "metadata": {
        "id": "tWblsHwTEpaV"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "ezbChl5EKQA7"
      },
      "outputs": [],
      "source": [
        "pred = np.array(pred_int) #แปลงเป็น array\n",
        "act = np.array(act)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred"
      ],
      "metadata": {
        "id": "ODV31M8k8sK0",
        "outputId": "ca47109d-aec1-4a0a-a4cf-9069dade5152",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9., 9., 9.,\n",
              "       9., 9., 9., 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., 9., 9., 9., 9.],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#confusion_matrix"
      ],
      "metadata": {
        "id": "g1_R7T6hZLgR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pzpwmMAJwz-",
        "outputId": "b29e7552-a88f-4fad-e4cd-646d68add218"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classifier accuracy = 5.2631578947368425%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        50\n",
            "           2       0.00      0.00      0.00        50\n",
            "           3       0.00      0.00      0.00        50\n",
            "           4       0.00      0.00      0.00        50\n",
            "           5       0.00      0.00      0.00        50\n",
            "           6       0.00      0.00      0.00        50\n",
            "           7       0.00      0.00      0.00        50\n",
            "           8       0.00      0.00      0.00        50\n",
            "           9       0.05      1.00      0.10        50\n",
            "          10       0.00      0.00      0.00        50\n",
            "          11       0.00      0.00      0.00        50\n",
            "          12       0.00      0.00      0.00        50\n",
            "          13       0.00      0.00      0.00        50\n",
            "          14       0.00      0.00      0.00        50\n",
            "          15       0.00      0.00      0.00        50\n",
            "          16       0.00      0.00      0.00        50\n",
            "          17       0.00      0.00      0.00        50\n",
            "          18       0.00      0.00      0.00        50\n",
            "          19       0.00      0.00      0.00        50\n",
            "\n",
            "    accuracy                           0.05       950\n",
            "   macro avg       0.00      0.05      0.01       950\n",
            "weighted avg       0.00      0.05      0.01       950\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\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": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "qCeaPS3pJzwt",
        "outputId": "aefccabc-7c7b-4ccc-cc4b-4e2e387c6990"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 66.5, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "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": "markdown",
      "source": [
        "#MSE and MAE"
      ],
      "metadata": {
        "id": "URHSebmuZH2o"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "wv3kRyGxArop",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ce4a4ff-753e-437e-ef8f-d70804f6c6da"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 4.7894736842105265\n",
            "MSE: 31.0\n",
            "RMSE: 5.5677643628300215\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_absolute_error,mean_squared_error\n",
        "  \n",
        "mae = mean_absolute_error(act,pred)\n",
        "mse = mean_squared_error(act,pred)\n",
        "rmse = mean_squared_error(act,pred,squared=False)\n",
        "\n",
        "print(\"MAE:\",mae)\n",
        "print(\"MSE:\",mse)\n",
        "print(\"RMSE:\",rmse)"
      ]
    }
  ],
  "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
}