Anomaly_Sound_Detection_MobileNet_one_hold.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP/bJX31VNCwYIO9lDB1ZQH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MS-H2020/Open/blob/main/Anomaly_Sound_Detection_MobileNet_one_hold.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#[機械稼働音の異常検知](https://signate.jp/competitions/358)"
      ],
      "metadata": {
        "id": "P_hsgoGPLdjq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PS_5GXB4LUUO",
        "outputId": "49754cb2-4670-43c0-999d-92e4369cff19"
      },
      "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",
      "source": [
        "checkpoint_path = \"./cp.ckpt\""
      ],
      "metadata": {
        "id": "AZ76W4olMo1T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Unpack Zip file in which Melspectram images are."
      ],
      "metadata": {
        "id": "kjMTSKHgFo-1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "if os.path.isdir('./train_img'):\n",
        "  shutil.rmtree('./train_img')\n",
        "if os.path.isdir('./test_img'):\n",
        "  shutil.rmtree('./test_img')"
      ],
      "metadata": {
        "id": "nFGvIZzfVPT-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp \"/content/drive/MyDrive/Detect_Anormaly_Machines_by_Sound/01_input.zip\" ."
      ],
      "metadata": {
        "id": "UBi03mQpMSt5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "shutil.unpack_archive('/content/01_input.zip', '.')"
      ],
      "metadata": {
        "id": "K_Ox3kYwMvTu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import modules"
      ],
      "metadata": {
        "id": "uBH1mElMFuKX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import Optional\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import models, datasets, layers, applications, optimizers\n",
        "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "Gem97oRcc62T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generating Image"
      ],
      "metadata": {
        "id": "d3FZx_VqGG7-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "datagen = ImageDataGenerator(rescale=1./255, # 255で割ることで正規化\n",
        "                                   zoom_range=0.2, # ランダムにズーム\n",
        "                                   horizontal_flip = True, # 水平反転\n",
        "                                   height_shift_range=0.1,\n",
        "                                   validation_split = 0.2,\n",
        "                                  )"
      ],
      "metadata": {
        "id": "rwTGMoNLMyPF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IMG_WIDTH = 320\n",
        "IMG_HEIGHT = 240\n",
        "IMG_DEPTH = 3\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "train_dir = './train_img'\n",
        "train_generator = datagen.flow_from_directory(train_dir, target_size=(IMG_WIDTH, IMG_HEIGHT), batch_size=BATCH_SIZE, class_mode='binary', shuffle=True, subset='training')\n",
        "valid_generator = datagen.flow_from_directory(train_dir, target_size=(IMG_WIDTH, IMG_HEIGHT), batch_size=BATCH_SIZE, class_mode='binary', subset='validation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZIlxci56XXtR",
        "outputId": "a866139e-65ba-48b9-b23a-6666f9b30725"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 400 images belonging to 2 classes.\n",
            "Found 100 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator.class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vY1a3QzJYmm8",
        "outputId": "5aba0dc6-02cf-4cda-d5b9-81f6d29895da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'01_normal': 0, '02_anomaly': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CNN Model"
      ],
      "metadata": {
        "id": "2PiSEMd0GTAn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = applications.mobilenet.MobileNet(weights='imagenet',\n",
        "                                         include_top=False,\n",
        "                                         input_shape=(IMG_WIDTH, IMG_HEIGHT, IMG_DEPTH)\n",
        "                                        )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jiG04k8ecdB2",
        "outputId": "a585354d-53ed-4556-ff00-693542403810"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-10-7a7efc9f736e>:1: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "  base_model = applications.mobilenet.MobileNet(weights='imagenet',\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs:int = 50 # Defalut:50\n",
        "lr:float = 0.001 # Default:0.001\n",
        "\n",
        "model = models.Sequential([\n",
        "  base_model,\n",
        "  layers.Flatten(),\n",
        "  layers.Dense(1024, activation='relu'),\n",
        "  layers.Dropout(0.1),\n",
        "  layers.Dense(512, activation='relu'),\n",
        "  layers.Dropout(0.2),\n",
        "  layers.Dense(512, activation='relu'),\n",
        "  layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "adam_low_rate = optimizers.Adam(learning_rate=lr)\n",
        "\n",
        "# モデルを構築\n",
        "model.compile(optimizer=adam_low_rate,\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "lu6FBzXZdD-A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_generator,\n",
        "                    validation_data = valid_generator,\n",
        "                    verbose=True,\n",
        "                    callbacks=[tensorflow.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)],\n",
        "                    epochs = epochs,\n",
        ")\n",
        "\n",
        "\n",
        "# 評価する。\n",
        "test_loss, test_acc = model.evaluate(valid_generator)\n",
        "model.save('model.h5')\n",
        "\n",
        "plt.figure(figsize=(10, 5))  # グラフを表示するスペースを用意\n",
        "metrics = ['loss', 'accuracy']  # 使用する評価関数を指定\n",
        "\n",
        "for i in range(len(metrics)):\n",
        "  metric = metrics[i]\n",
        "\n",
        "  plt.subplot(1, 2, i+1)  # figureを1×2のスペースに分ける\n",
        "  plt.title(metric)\n",
        "\n",
        "  plt_train = history.history[metric]\n",
        "  plt_valid  = history.history['val_' + metric]\n",
        "\n",
        "  plt.plot(plt_train, label='train')\n",
        "  plt.plot(plt_valid, label='valid')\n",
        "  plt.legend()\n",
        "\n",
        "plt.savefig(\"./metrics.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pNXPdrS1dwyc",
        "outputId": "25865163-734f-42a1-fbd8-3e9e33ce0d74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m225s\u001b[0m 14s/step - accuracy: 0.6588 - loss: 11.6927 - val_accuracy: 0.9000 - val_loss: 5.3685\n",
            "Epoch 2/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 14s/step - accuracy: 0.8653 - loss: 0.9283 - val_accuracy: 0.9000 - val_loss: 8.7478\n",
            "Epoch 3/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 14s/step - accuracy: 0.9148 - loss: 0.2451 - val_accuracy: 0.9000 - val_loss: 11.2527\n",
            "Epoch 4/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 14s/step - accuracy: 0.9901 - loss: 0.0351 - val_accuracy: 0.9000 - val_loss: 8.3613\n",
            "Epoch 5/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 14s/step - accuracy: 0.9883 - loss: 0.0748 - val_accuracy: 0.9000 - val_loss: 6.9967\n",
            "Epoch 6/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 14s/step - accuracy: 0.9172 - loss: 0.1980 - val_accuracy: 0.9000 - val_loss: 1.6574\n",
            "Epoch 7/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 14s/step - accuracy: 0.9885 - loss: 0.0292 - val_accuracy: 0.9000 - val_loss: 0.8094\n",
            "Epoch 8/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 14s/step - accuracy: 0.9831 - loss: 0.0363 - val_accuracy: 0.9700 - val_loss: 0.1502\n",
            "Epoch 9/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 14s/step - accuracy: 0.9849 - loss: 0.0659 - val_accuracy: 0.9500 - val_loss: 0.2882\n",
            "Epoch 10/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m197s\u001b[0m 14s/step - accuracy: 0.9670 - loss: 0.1475 - val_accuracy: 0.9400 - val_loss: 0.1281\n",
            "Epoch 11/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 13s/step - accuracy: 0.9780 - loss: 0.0391 - val_accuracy: 0.9000 - val_loss: 0.6659\n",
            "Epoch 12/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 14s/step - accuracy: 0.9939 - loss: 0.0170 - val_accuracy: 0.9700 - val_loss: 0.1569\n",
            "Epoch 13/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 14s/step - accuracy: 0.9971 - loss: 0.0037 - val_accuracy: 0.9600 - val_loss: 0.0878\n",
            "Epoch 14/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m196s\u001b[0m 14s/step - accuracy: 1.0000 - loss: 0.0026 - val_accuracy: 0.9500 - val_loss: 0.1158\n",
            "Epoch 15/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 14s/step - accuracy: 0.9965 - loss: 0.0156 - val_accuracy: 0.9900 - val_loss: 0.1101\n",
            "Epoch 16/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m192s\u001b[0m 13s/step - accuracy: 0.9947 - loss: 0.0543 - val_accuracy: 0.9100 - val_loss: 0.3290\n",
            "Epoch 17/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 13s/step - accuracy: 0.9592 - loss: 0.0976 - val_accuracy: 0.9000 - val_loss: 0.5814\n",
            "Epoch 18/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 14s/step - accuracy: 0.9779 - loss: 0.0426 - val_accuracy: 0.9900 - val_loss: 0.0441\n",
            "Epoch 19/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 14s/step - accuracy: 0.9966 - loss: 0.0078 - val_accuracy: 1.0000 - val_loss: 0.0024\n",
            "Epoch 20/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 14s/step - accuracy: 1.0000 - loss: 9.4549e-04 - val_accuracy: 0.9800 - val_loss: 0.1056\n",
            "Epoch 21/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m197s\u001b[0m 14s/step - accuracy: 0.9990 - loss: 0.0052 - val_accuracy: 0.9700 - val_loss: 0.3875\n",
            "Epoch 22/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 14s/step - accuracy: 0.9860 - loss: 0.1227 - val_accuracy: 0.7600 - val_loss: 1.0183\n",
            "Epoch 23/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 14s/step - accuracy: 0.9968 - loss: 0.0289 - val_accuracy: 0.9100 - val_loss: 0.6869\n",
            "Epoch 24/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 15s/step - accuracy: 0.9876 - loss: 0.0193 - val_accuracy: 0.9000 - val_loss: 1.0581\n",
            "Epoch 25/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 13s/step - accuracy: 0.9876 - loss: 0.0219 - val_accuracy: 0.9300 - val_loss: 0.3535\n",
            "Epoch 26/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m210s\u001b[0m 14s/step - accuracy: 0.9716 - loss: 0.1260 - val_accuracy: 0.9200 - val_loss: 0.2643\n",
            "Epoch 27/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 14s/step - accuracy: 0.9498 - loss: 0.0977 - val_accuracy: 0.9000 - val_loss: 0.6574\n",
            "Epoch 28/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m192s\u001b[0m 13s/step - accuracy: 0.9994 - loss: 0.0047 - val_accuracy: 0.9800 - val_loss: 0.0595\n",
            "Epoch 29/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 13s/step - accuracy: 0.9926 - loss: 0.0381 - val_accuracy: 1.0000 - val_loss: 0.0054\n",
            "Epoch 29: early stopping\n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3s/step - accuracy: 0.9753 - loss: 0.0823\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Confution Matrix and Classification Report\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import numpy as np\n",
        "\n",
        "Valid = model.predict(valid_generator, valid_generator.samples)\n",
        "valid = np.argmax(Valid, axis=1)\n",
        "print('Confusion Matrix')\n",
        "print(confusion_matrix(valid_generator.classes, valid))\n",
        "print('Classification Report')\n",
        "print(classification_report(valid_generator.classes, valid, target_names=None))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPuGP1wGnuWU",
        "outputId": "2ec9d547-6d42-4c0c-86eb-b00292d631f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 3s/step\n",
            "Confusion Matrix\n",
            "[[90  0]\n",
            " [10  0]]\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      1.00      0.95        90\n",
            "           1       0.00      0.00      0.00        10\n",
            "\n",
            "    accuracy                           0.90       100\n",
            "   macro avg       0.45      0.50      0.47       100\n",
            "weighted avg       0.81      0.90      0.85       100\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## To Output Submitting CSV"
      ],
      "metadata": {
        "id": "csH87lgQGejm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ans = pd.read_csv(\"/content/drive/MyDrive/Detect_Anormaly_Machines_by_Sound/sample_submit.csv\",  header=None)"
      ],
      "metadata": {
        "id": "cCy45c55QrGw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "YP-5TStnRBXK",
        "outputId": "d4914262-f29f-4f7b-e25c-3e275b7e64a5"
      },
      "execution_count": null,
      "outputs": [
        {
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ans')\"\n",
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              "\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ans",
              "summary": "{\n  \"name\": \"ans\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          95,\n          15,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!cp './model.h5' '/content/drive/MyDrive/Detect_Anormaly_Machines_by_Sound/model.h5'\n",
        "#!cp '/content/drive/MyDrive/Detect_Anormaly_Machines_by_Sound/model.h5' './model.h5'"
      ],
      "metadata": {
        "id": "C4x2MevoUbV2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TEST_DIR = \"./test_img\"\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_generator = test_datagen.flow_from_directory(TEST_DIR, target_size=(IMG_WIDTH,IMG_HEIGHT), class_mode=None, shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4SccRReUReAk",
        "outputId": "e5993f7f-0e8f-4827-83c8-8edef78dcbad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 200 images belonging to 1 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(test_generator)\n",
        "pred = np.where(pred < 0.5, 0, 1)\n",
        "# 0:正常音、1:異常音となるように出力値を反転させる"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAbLbvfPSD9s",
        "outputId": "e24887ad-59a7-4f9e-aa38-1e9ef41768a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 3s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ans[1] = pred\n",
        "ans.to_csv(path_or_buf = \"/content/drive/MyDrive/Detect_Anormaly_Machines_by_Sound/submission_.csv\", index=False, header=None)"
      ],
      "metadata": {
        "id": "vebyCU5xVyBi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "_9bUnYXSn11F",
        "outputId": "db08ecad-5d13-4bcd-cd87-4089884bcd0b"
      },
      "execution_count": null,
      "outputs": [
        {
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        google.colab.notebook.generateWithVariable('ans');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    }
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}