Anomaly_Sound_Detection_MobileNet_Stratified_One_Hold.ipynb

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      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/MS-H2020/Open/blob/main/Anomaly_Sound_Detection_MobileNet_Stratified_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": {
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        },
        "id": "PS_5GXB4LUUO",
        "outputId": "bf4c7137-ce47-4089-97b7-a22b9232e3a4"
      },
      "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 as tf\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": [
        "## Reading CSV"
      ],
      "metadata": {
        "id": "TLeSVdkAhsYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"./train.csv\")\n",
        "df[\"status\"] = df[\"status\"].astype(str)\n",
        "display(df)"
      ],
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        "colab": {
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          "height": 424
        },
        "id": "8xDMi_aahxah",
        "outputId": "8ca0a7eb-c62d-416f-b1f2-5a62bb85e67c"
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "                     image status\n",
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              "2     valid_normal_030.png      0\n",
              "3     train_normal_210.png      0\n",
              "4     train_normal_204.png      0\n",
              "..                     ...    ...\n",
              "495  valid_anomaly_030.png      1\n",
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              "497  valid_anomaly_032.png      1\n",
              "498  valid_anomaly_033.png      1\n",
              "499  valid_anomaly_027.png      1\n",
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    {
      "cell_type": "markdown",
      "source": [
        "## CNN Model"
      ],
      "metadata": {
        "id": "2PiSEMd0GTAn"
      }
    },
    {
      "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/'"
      ],
      "metadata": {
        "id": "ZIlxci56XXtR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr:float = 0.001 # Default:0.001\n",
        "\n",
        "base_model = applications.mobilenet.MobileNet(weights='imagenet',\n",
        "                                        include_top=False,\n",
        "                                        input_shape=(IMG_WIDTH, IMG_HEIGHT, IMG_DEPTH)\n",
        "                                      )\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']\n",
        "            )"
      ],
      "metadata": {
        "id": "lu6FBzXZdD-A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "287eeba9-2c4c-42a7-ae09-f599d36abc78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-6b4673f09ced>:6: 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": "markdown",
      "source": [
        "## Generating Image"
      ],
      "metadata": {
        "id": "d3FZx_VqGG7-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(\n",
        "                              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",
        "                            )\n",
        "valid_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "metadata": {
        "id": "rwTGMoNLMyPF"
      },
      "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": {
        "id": "60Qdyqr0umhV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42705bfe-f6a2-4fd5-a5f5-cf55e7c254e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 200 images belonging to 1 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs:int = 50 # Defalut:50\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_df, valid_df = train_test_split(df, test_size=0.2, stratify=df['status'], random_state=42)\n",
        "\n",
        "train_generator = train_datagen.flow_from_dataframe(dataframe = train_df,\n",
        "                                                    x_col = 'image',\n",
        "                                                    y_col = 'status',\n",
        "                                                    directory = train_dir,\n",
        "                                                    target_size = (IMG_WIDTH, IMG_HEIGHT),\n",
        "                                                    batch_size = BATCH_SIZE,\n",
        "                                                    class_mode = 'binary',\n",
        "                                                    shuffle = True,\n",
        "                                                  )\n",
        "\n",
        "valid_generator = valid_datagen.flow_from_dataframe(dataframe = valid_df,\n",
        "                                                    x_col = 'image',\n",
        "                                                    y_col = 'status',\n",
        "                                                    directory = train_dir,\n",
        "                                                    target_size = (IMG_WIDTH, IMG_HEIGHT),\n",
        "                                                    batch_size = BATCH_SIZE,\n",
        "                                                    class_mode = 'binary',\n",
        "                                                    shuffle = True,\n",
        "                                                  )\n",
        "\n",
        "history = model.fit(train_generator,\n",
        "                    validation_data = valid_generator,\n",
        "                    verbose=True,\n",
        "                    callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)],\n",
        "                    epochs = epochs,\n",
        ")\n",
        "\n",
        "# 評価する。\n",
        "test_loss, test_acc = model.evaluate(valid_generator)\n",
        "\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": "uqyxw6DAq3Xd",
        "outputId": "560ac442-53e0-4c81-f58e-6cf789c2df30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 400 validated image filenames belonging to 2 classes.\n",
            "Found 100 validated image filenames belonging to 2 classes.\n",
            "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[1m96s\u001b[0m 3s/step - accuracy: 0.8508 - loss: 13.1412 - val_accuracy: 0.9000 - val_loss: 9.6028\n",
            "Epoch 2/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - accuracy: 0.8661 - loss: 1.1819 - val_accuracy: 0.9000 - val_loss: 4.3279\n",
            "Epoch 3/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 1s/step - accuracy: 0.8965 - loss: 0.2867 - val_accuracy: 0.9000 - val_loss: 4.5628\n",
            "Epoch 4/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.9005 - loss: 0.2656 - val_accuracy: 0.9000 - val_loss: 8.1725\n",
            "Epoch 5/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.9379 - loss: 0.2158 - val_accuracy: 0.9000 - val_loss: 4.4462\n",
            "Epoch 6/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 1s/step - accuracy: 0.9417 - loss: 0.1389 - val_accuracy: 0.9000 - val_loss: 4.5475\n",
            "Epoch 7/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 2s/step - accuracy: 0.9913 - loss: 0.0394 - val_accuracy: 0.9000 - val_loss: 11.3213\n",
            "Epoch 8/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.9494 - loss: 0.0959 - val_accuracy: 0.9000 - val_loss: 4.0572\n",
            "Epoch 9/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9989 - loss: 0.0083 - val_accuracy: 0.9000 - val_loss: 2.7031\n",
            "Epoch 10/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9992 - loss: 0.0013 - val_accuracy: 0.9300 - val_loss: 2.0623\n",
            "Epoch 11/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9753 - loss: 0.1161 - val_accuracy: 0.9600 - val_loss: 0.6271\n",
            "Epoch 12/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.9917 - loss: 0.0222 - val_accuracy: 0.9600 - val_loss: 1.0322\n",
            "Epoch 13/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.9962 - loss: 0.0181 - val_accuracy: 0.9700 - val_loss: 0.6341\n",
            "Epoch 14/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 1s/step - accuracy: 0.9729 - loss: 0.0774 - val_accuracy: 0.9700 - val_loss: 1.6788\n",
            "Epoch 15/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 1s/step - accuracy: 0.9968 - loss: 0.1067 - val_accuracy: 0.9700 - val_loss: 0.1756\n",
            "Epoch 16/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.9917 - loss: 0.0330 - val_accuracy: 0.9700 - val_loss: 0.6395\n",
            "Epoch 17/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.9801 - loss: 0.0967 - val_accuracy: 0.9700 - val_loss: 0.6065\n",
            "Epoch 18/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.9974 - loss: 0.0106 - val_accuracy: 0.9700 - val_loss: 0.6232\n",
            "Epoch 19/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 1s/step - accuracy: 1.0000 - loss: 0.0016 - val_accuracy: 0.9700 - val_loss: 0.9097\n",
            "Epoch 20/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.9974 - loss: 0.0091 - val_accuracy: 0.9700 - val_loss: 0.3127\n",
            "Epoch 21/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.9918 - loss: 0.0276 - val_accuracy: 0.9700 - val_loss: 0.1751\n",
            "Epoch 22/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.9956 - loss: 0.0199 - val_accuracy: 0.9700 - val_loss: 0.0766\n",
            "Epoch 23/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 1s/step - accuracy: 0.9878 - loss: 0.0504 - val_accuracy: 0.9500 - val_loss: 0.3073\n",
            "Epoch 24/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9907 - loss: 0.0287 - val_accuracy: 1.0000 - val_loss: 0.0023\n",
            "Epoch 25/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 1s/step - accuracy: 0.9981 - loss: 0.0100 - val_accuracy: 0.9700 - val_loss: 0.1271\n",
            "Epoch 26/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 1s/step - accuracy: 0.9995 - loss: 0.0074 - val_accuracy: 0.9700 - val_loss: 0.6130\n",
            "Epoch 27/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 1s/step - accuracy: 0.9815 - loss: 0.0632 - val_accuracy: 0.9700 - val_loss: 0.2934\n",
            "Epoch 28/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.9977 - loss: 0.0272 - val_accuracy: 0.9700 - val_loss: 0.4871\n",
            "Epoch 29/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 1s/step - accuracy: 0.9919 - loss: 0.0246 - val_accuracy: 0.9800 - val_loss: 0.5403\n",
            "Epoch 30/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 1s/step - accuracy: 0.9905 - loss: 0.0474 - val_accuracy: 0.9800 - val_loss: 0.2100\n",
            "Epoch 31/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9714 - loss: 0.0791 - val_accuracy: 0.9900 - val_loss: 0.0360\n",
            "Epoch 32/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1s/step - accuracy: 0.9949 - loss: 0.0211 - val_accuracy: 0.9700 - val_loss: 0.3419\n",
            "Epoch 33/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9987 - loss: 0.0090 - val_accuracy: 0.9700 - val_loss: 0.5003\n",
            "Epoch 34/50\n",
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 1s/step - accuracy: 0.9993 - loss: 0.0074 - val_accuracy: 0.9800 - val_loss: 0.2629\n",
            "Epoch 34: early stopping\n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 1s/step - accuracy: 0.9816 - loss: 0.2409\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": "70128fbf-c63f-4017-89e1-e690344a1e5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 2s/step\n",
            "Confusion Matrix\n",
            "[[360   0]\n",
            " [ 40   0]]\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      1.00      0.95       360\n",
            "           1       0.00      0.00      0.00        40\n",
            "\n",
            "    accuracy                           0.90       400\n",
            "   macro avg       0.45      0.50      0.47       400\n",
            "weighted avg       0.81      0.90      0.85       400\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": [
        "pred = model.predict(test_generator)\n",
        "pred = np.where(pred < 0.5, 0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s12__aZgp2Ti",
        "outputId": "264e1934-fbff-48c2-a448-bdd31382df7a"
      },
      "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[1m13s\u001b[0m 2s/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/",
          "height": 424
        },
        "id": "_9bUnYXSn11F",
        "outputId": "a72e01c0-81d4-407e-83ca-0f27581afa62"
      },
      "execution_count": null,
      "outputs": [
        {
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ans')\"\n",
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              "\n",
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              "\n",
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            "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\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    }
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}