freshness_v2pro.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "L4",
      "authorship_tag": "ABX9TyMFS/lLb3HwMWg2Iv3avyLQ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rahulx911/grid-6.0/blob/main/freshness_v2pro.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0iYBsMiMHF_",
        "outputId": "82cee454-dfb8-43e0-e214-9e3144c75cf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Path to the zip file in Google Drive\n",
        "zip_file_path = '/content/drive/MyDrive/Fruits_Vegetables_Dataset_new.zip'\n",
        "\n",
        "# Destination directory to extract the contents\n",
        "output_directory = '/content/drive/MyDrive/dataset-fruveg-2'\n",
        "\n",
        "def unzip_to_gdrive(zip_path, extract_path):\n",
        "    \"\"\"\n",
        "    Unzips a file from Google Drive and extracts its contents to a specified directory.\n",
        "    :param zip_path: Path to the zip file\n",
        "    :param extract_path: Directory to save extracted contents\n",
        "    \"\"\"\n",
        "    # Check if the zip file exists\n",
        "    if not os.path.exists(zip_path):\n",
        "        print(f\"Zip file not found at: {zip_path}\")\n",
        "        return\n",
        "\n",
        "    # Create the output directory if it doesn't exist\n",
        "    if not os.path.exists(extract_path):\n",
        "        os.makedirs(extract_path)\n",
        "\n",
        "    # Unzip the file\n",
        "    with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "        zip_ref.extractall(extract_path)\n",
        "        print(f\"Extracted files to: {extract_path}\")\n",
        "\n",
        "# Unzip the file\n",
        "unzip_to_gdrive(zip_file_path, output_directory)\n"
      ],
      "metadata": {
        "id": "X0rpCRMUMdFo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "974c6415-540e-48fe-d722-8042a6e75347"
      },
      "execution_count": null,
      "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",
            "Extracted files to: /content/drive/MyDrive/dataset-fruveg-2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "def verify_dataset_structure(dataset_dir):\n",
        "    \"\"\"\n",
        "    Scans the dataset directory and prints the folder structure\n",
        "    along with the total count of images in each subdirectory.\n",
        "    \"\"\"\n",
        "    for root, dirs, files in os.walk(dataset_dir):\n",
        "        if files:\n",
        "            # Filter image files only (if needed)\n",
        "            image_files = [file for file in files if file.lower().endswith(('png', 'jpg', 'jpeg'))]\n",
        "            print(f\"Directory: {root}\")\n",
        "            print(f\"Number of images: {len(image_files)}\")\n",
        "            print(\"-\" * 40)\n",
        "        else:\n",
        "            print(f\"Directory: {root} is empty.\")\n",
        "            print(\"-\" * 40)\n",
        "\n",
        "# Set the dataset directory path\n",
        "dataset_dir = '/content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new'\n",
        "\n",
        "# Run the verification\n",
        "verify_dataset_structure(dataset_dir)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q07AktfGNHE-",
        "outputId": "086d110e-d2ad-451d-80dd-5e5ac6851e4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new\n",
            "Number of images: 0\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/frsshpineapple\n",
            "Number of images: 83\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/RottenCucumber\n",
            "Number of images: 175\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/rottenmango\n",
            "Number of images: 83\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/FreshTomato\n",
            "Number of images: 644\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/RottenBellpepper\n",
            "Number of images: 142\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/FreshCarrot\n",
            "Number of images: 619\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/FreshBellpepper\n",
            "Number of images: 442\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/RottenCarrot\n",
            "Number of images: 214\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/RottenTomato\n",
            "Number of images: 582\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/freshlemon\n",
            "Number of images: 83\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/FreshApple\n",
            "Number of images: 303\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/rottenbanana\n",
            "Number of images: 410\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/FreshPotato\n",
            "Number of images: 601\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/freshoranges\n",
            "Number of images: 688\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/rottenlemon\n",
            "Number of images: 83\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/rottenpineapple\n",
            "Number of images: 83\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/rottenapple\n",
            "Number of images: 458\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/rottenoranges\n",
            "Number of images: 432\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/rottenbrocoli\n",
            "Number of images: 83\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/freshbrocoli\n",
            "Number of images: 83\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/FreshCucumber\n",
            "Number of images: 673\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/freshmango\n",
            "Number of images: 190\n",
            "----------------------------------------\n",
            "Directory: /content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new/RottenPotato\n",
            "Number of images: 206\n",
            "----------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow --upgrade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "jNBYcW33Qtvc",
        "outputId": "4b0b3d3e-f28d-4a9a-86dc-7caec263ea94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Ignoring invalid distribution -ensorflow (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting tensorflow\n",
            "  Using cached tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.1 kB)\n",
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            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.6.0)\n",
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            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.19,>=2.18->tensorflow) (3.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.19,>=2.18->tensorflow) (3.0.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.5.0->tensorflow) (3.0.0)\n",
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            "Using cached tensorflow-2.18.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (615.3 MB)\n",
            "\u001b[33mWARNING: Ignoring invalid distribution -ensorflow (/usr/local/lib/python3.10/dist-packages)\u001b[0m\u001b[33m\n",
            "\u001b[0mInstalling collected packages: tensorflow\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.18.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed tensorflow-2.18.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "tensorflow"
                ]
              },
              "id": "743e0f5ceeef4dbbb09aad40c6181952"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications import EfficientNetV2M\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
        "from tensorflow.keras import mixed_precision\n",
        "\n",
        "# Disable mixed precision to avoid potential issues\n",
        "mixed_precision.set_global_policy('float32')\n",
        "\n",
        "# Limit GPU memory growth\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "if gpus:\n",
        "    try:\n",
        "        for gpu in gpus:\n",
        "            tf.config.experimental.set_memory_growth(gpu, True)\n",
        "    except RuntimeError as e:\n",
        "        print(e)\n",
        "\n",
        "# Set seeds for reproducibility\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Dataset path\n",
        "dataset_dir = '/content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new'\n",
        "\n",
        "# Image data generator\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    validation_split=0.2  # Use 20% of data for validation\n",
        ")\n",
        "\n",
        "# Model creation for Freshness Index Prediction\n",
        "def create_freshness_index_model(input_shape=(224, 224, 3)):\n",
        "    base_model = EfficientNetV2M(weights='imagenet', include_top=False, input_shape=input_shape)\n",
        "\n",
        "    x = base_model.output\n",
        "    x = GlobalAveragePooling2D()(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "\n",
        "    # Freshness index regression (1-10 scale)\n",
        "    freshness_index_output = Dense(1, activation='linear', name='freshness_index_output')(x)\n",
        "\n",
        "    # Create and compile the model\n",
        "    model = Model(inputs=base_model.input, outputs=freshness_index_output)\n",
        "    model.compile(\n",
        "        optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
        "        loss='mean_squared_error',\n",
        "        metrics=['mean_absolute_error', 'accuracy']  # Added accuracy\n",
        "    )\n",
        "\n",
        "    return model\n",
        "\n",
        "# Custom data generator for Freshness Index Prediction\n",
        "def freshness_data_generator(generator):\n",
        "    while True:\n",
        "        images = next(generator)  # Retrieve image batch\n",
        "        batch_size = images.shape[0]  # Use actual batch size from generator output\n",
        "        freshness_labels = np.zeros((batch_size, 1))  # Freshness index (1-10 scale)\n",
        "\n",
        "        # Retrieve filenames corresponding to the batch\n",
        "        filenames = [generator.filenames[idx] for idx in generator.index_array[:batch_size]]\n",
        "        for i, filename in enumerate(filenames):\n",
        "            # Extract directory name\n",
        "            dir_name = os.path.basename(os.path.dirname(filename)).lower()\n",
        "\n",
        "            # Determine freshness index\n",
        "            if 'fresh' in dir_name:\n",
        "                freshness = np.random.uniform(7, 10)  # Fresh items closer to 10\n",
        "            else:\n",
        "                freshness = np.random.uniform(1, 4)  # Rotten items closer to 1\n",
        "\n",
        "            freshness_labels[i] = freshness\n",
        "\n",
        "        yield images, freshness_labels\n",
        "\n",
        "# Training function\n",
        "def train_freshness_index_model():\n",
        "    # Initialize data generators\n",
        "    train_generator = datagen.flow_from_directory(\n",
        "        dataset_dir,\n",
        "        target_size=(224, 224),  # EfficientNetV2M default input size\n",
        "        batch_size=32,\n",
        "        class_mode=None,\n",
        "        subset='training',\n",
        "        shuffle=True\n",
        "    )\n",
        "\n",
        "    validation_generator = datagen.flow_from_directory(\n",
        "        dataset_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=32,\n",
        "        class_mode=None,\n",
        "        subset='validation',\n",
        "        shuffle=True\n",
        "    )\n",
        "\n",
        "    model = create_freshness_index_model()\n",
        "\n",
        "    # Debugging the generator\n",
        "    generator = freshness_data_generator(train_generator)\n",
        "    images, labels = next(generator)\n",
        "    print(f\"Debug: Images shape: {images.shape}\")\n",
        "    print(f\"Debug: Freshness index labels shape: {labels.shape}\")\n",
        "\n",
        "    # Callbacks\n",
        "    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-7, verbose=1)\n",
        "    model_checkpoint = ModelCheckpoint('/content/drive/MyDrive/fiv1.keras', save_best_only=True, monitor='val_loss', verbose=1)\n",
        "\n",
        "    # Train the model\n",
        "    history = model.fit(\n",
        "        freshness_data_generator(train_generator),\n",
        "        steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
        "        validation_data=freshness_data_generator(validation_generator),\n",
        "        validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
        "        epochs=200,  # Train for 100 epochs\n",
        "        callbacks=[reduce_lr, model_checkpoint]\n",
        "    )\n",
        "\n",
        "    model.save('/content/drive/MyDrive/fiv1.keras')\n",
        "    print(\"Training completed!\")\n",
        "\n",
        "    # Calculate final validation accuracy\n",
        "    val_results = model.evaluate(\n",
        "        freshness_data_generator(validation_generator),\n",
        "        steps=validation_generator.samples // validation_generator.batch_size\n",
        "    )\n",
        "# Run the training function\n",
        "train_freshness_index_model()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OV87_7JVNJcM",
        "outputId": "5d03a844-4737-4521-c45b-765e9edf94f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 5899 images belonging to 23 classes.\n",
            "Found 1461 images belonging to 23 classes.\n",
            "Debug: Images shape: (32, 224, 224, 3)\n",
            "Debug: Freshness index labels shape: (32, 1)\n",
            "Epoch 1/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 727ms/step - accuracy: 0.0000e+00 - loss: 44.6082 - mean_absolute_error: 5.7343\n",
            "Epoch 1: val_loss improved from inf to 29.28276, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m341s\u001b[0m 861ms/step - accuracy: 0.0000e+00 - loss: 44.5970 - mean_absolute_error: 5.7335 - val_accuracy: 0.0000e+00 - val_loss: 29.2828 - val_mean_absolute_error: 4.4569 - learning_rate: 1.0000e-04\n",
            "Epoch 2/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - accuracy: 0.0000e+00 - loss: 32.0270 - mean_absolute_error: 4.6488\n",
            "Epoch 2: val_loss did not improve from 29.28276\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 347ms/step - accuracy: 0.0000e+00 - loss: 32.0203 - mean_absolute_error: 4.6482 - val_accuracy: 0.0000e+00 - val_loss: 29.7522 - val_mean_absolute_error: 4.5521 - learning_rate: 1.0000e-04\n",
            "Epoch 3/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 31.7414 - mean_absolute_error: 4.7325\n",
            "Epoch 3: val_loss improved from 29.28276 to 20.39855, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 307ms/step - accuracy: 0.0000e+00 - loss: 31.7320 - mean_absolute_error: 4.7316 - val_accuracy: 0.0000e+00 - val_loss: 20.3986 - val_mean_absolute_error: 3.6023 - learning_rate: 1.0000e-04\n",
            "Epoch 4/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 26.5793 - mean_absolute_error: 4.4150\n",
            "Epoch 4: val_loss improved from 20.39855 to 16.60328, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 307ms/step - accuracy: 0.0000e+00 - loss: 26.5706 - mean_absolute_error: 4.4144 - val_accuracy: 0.0000e+00 - val_loss: 16.6033 - val_mean_absolute_error: 3.3666 - learning_rate: 1.0000e-04\n",
            "Epoch 5/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 20.1286 - mean_absolute_error: 3.8763\n",
            "Epoch 5: val_loss improved from 16.60328 to 11.69387, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 306ms/step - accuracy: 0.0000e+00 - loss: 20.1223 - mean_absolute_error: 3.8758 - val_accuracy: 0.0000e+00 - val_loss: 11.6939 - val_mean_absolute_error: 3.0242 - learning_rate: 1.0000e-04\n",
            "Epoch 6/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 14.8121 - mean_absolute_error: 3.2914\n",
            "Epoch 6: val_loss did not improve from 11.69387\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m48s\u001b[0m 259ms/step - accuracy: 0.0000e+00 - loss: 14.8084 - mean_absolute_error: 3.2910 - val_accuracy: 0.0000e+00 - val_loss: 11.7560 - val_mean_absolute_error: 3.1660 - learning_rate: 1.0000e-04\n",
            "Epoch 7/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.7228 - mean_absolute_error: 3.0995\n",
            "Epoch 7: val_loss improved from 11.69387 to 10.36523, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 287ms/step - accuracy: 0.0000e+00 - loss: 12.7221 - mean_absolute_error: 3.0995 - val_accuracy: 0.0000e+00 - val_loss: 10.3652 - val_mean_absolute_error: 3.0361 - learning_rate: 1.0000e-04\n",
            "Epoch 8/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 13.0139 - mean_absolute_error: 3.2106\n",
            "Epoch 8: val_loss improved from 10.36523 to 9.93549, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 290ms/step - accuracy: 0.0000e+00 - loss: 13.0129 - mean_absolute_error: 3.2106 - val_accuracy: 0.0000e+00 - val_loss: 9.9355 - val_mean_absolute_error: 3.0060 - learning_rate: 1.0000e-04\n",
            "Epoch 9/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 13.0843 - mean_absolute_error: 3.1952\n",
            "Epoch 9: val_loss improved from 9.93549 to 9.91168, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 289ms/step - accuracy: 0.0000e+00 - loss: 13.0841 - mean_absolute_error: 3.1952 - val_accuracy: 0.0000e+00 - val_loss: 9.9117 - val_mean_absolute_error: 2.9896 - learning_rate: 1.0000e-04\n",
            "Epoch 10/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.5707 - mean_absolute_error: 3.1472\n",
            "Epoch 10: val_loss improved from 9.91168 to 9.07564, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 287ms/step - accuracy: 0.0000e+00 - loss: 12.5670 - mean_absolute_error: 3.1469 - val_accuracy: 0.0000e+00 - val_loss: 9.0756 - val_mean_absolute_error: 2.8383 - learning_rate: 1.0000e-04\n",
            "Epoch 11/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.4399 - mean_absolute_error: 3.0193\n",
            "Epoch 11: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.4426 - mean_absolute_error: 3.0193 - val_accuracy: 0.0000e+00 - val_loss: 13.0592 - val_mean_absolute_error: 3.2405 - learning_rate: 1.0000e-04\n",
            "Epoch 12/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.4970 - mean_absolute_error: 3.0562\n",
            "Epoch 12: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.4959 - mean_absolute_error: 3.0561 - val_accuracy: 0.0000e+00 - val_loss: 10.3156 - val_mean_absolute_error: 3.0345 - learning_rate: 1.0000e-04\n",
            "Epoch 13/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.4579 - mean_absolute_error: 3.0647\n",
            "Epoch 13: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.4570 - mean_absolute_error: 3.0646 - val_accuracy: 0.0000e+00 - val_loss: 10.3921 - val_mean_absolute_error: 3.0074 - learning_rate: 1.0000e-04\n",
            "Epoch 14/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 13.0294 - mean_absolute_error: 3.2181\n",
            "Epoch 14: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 13.0264 - mean_absolute_error: 3.2178 - val_accuracy: 0.0000e+00 - val_loss: 10.0826 - val_mean_absolute_error: 3.0451 - learning_rate: 1.0000e-04\n",
            "Epoch 15/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 13.1485 - mean_absolute_error: 3.1768\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
            "\n",
            "Epoch 15: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 13.1441 - mean_absolute_error: 3.1766 - val_accuracy: 0.0000e+00 - val_loss: 9.6915 - val_mean_absolute_error: 2.9863 - learning_rate: 1.0000e-04\n",
            "Epoch 16/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9295 - mean_absolute_error: 3.1414\n",
            "Epoch 16: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.9311 - mean_absolute_error: 3.1414 - val_accuracy: 0.0000e+00 - val_loss: 9.6416 - val_mean_absolute_error: 2.9787 - learning_rate: 5.0000e-05\n",
            "Epoch 17/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.6340 - mean_absolute_error: 3.1011\n",
            "Epoch 17: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.6367 - mean_absolute_error: 3.1013 - val_accuracy: 0.0000e+00 - val_loss: 9.5444 - val_mean_absolute_error: 2.9084 - learning_rate: 5.0000e-05\n",
            "Epoch 18/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.9611 - mean_absolute_error: 3.0979\n",
            "Epoch 18: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.9608 - mean_absolute_error: 3.0977 - val_accuracy: 0.0000e+00 - val_loss: 9.5635 - val_mean_absolute_error: 2.9468 - learning_rate: 5.0000e-05\n",
            "Epoch 19/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.8649 - mean_absolute_error: 3.0532\n",
            "Epoch 19: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.8657 - mean_absolute_error: 3.0532 - val_accuracy: 0.0000e+00 - val_loss: 10.0363 - val_mean_absolute_error: 2.9549 - learning_rate: 5.0000e-05\n",
            "Epoch 20/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.7015 - mean_absolute_error: 3.0675\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
            "\n",
            "Epoch 20: val_loss did not improve from 9.07564\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.7018 - mean_absolute_error: 3.0676 - val_accuracy: 0.0000e+00 - val_loss: 10.1415 - val_mean_absolute_error: 2.9881 - learning_rate: 5.0000e-05\n",
            "Epoch 21/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.7525 - mean_absolute_error: 3.0568\n",
            "Epoch 21: val_loss improved from 9.07564 to 9.00346, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 288ms/step - accuracy: 0.0000e+00 - loss: 11.7518 - mean_absolute_error: 3.0567 - val_accuracy: 0.0000e+00 - val_loss: 9.0035 - val_mean_absolute_error: 2.8320 - learning_rate: 2.5000e-05\n",
            "Epoch 22/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.9849 - mean_absolute_error: 3.0625\n",
            "Epoch 22: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.9824 - mean_absolute_error: 3.0622 - val_accuracy: 0.0000e+00 - val_loss: 9.4224 - val_mean_absolute_error: 2.8910 - learning_rate: 2.5000e-05\n",
            "Epoch 23/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.3563 - mean_absolute_error: 3.0007\n",
            "Epoch 23: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.3562 - mean_absolute_error: 3.0006 - val_accuracy: 0.0000e+00 - val_loss: 10.1050 - val_mean_absolute_error: 2.9903 - learning_rate: 2.5000e-05\n",
            "Epoch 24/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.0081 - mean_absolute_error: 3.0747\n",
            "Epoch 24: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.0094 - mean_absolute_error: 3.0749 - val_accuracy: 0.0000e+00 - val_loss: 9.7210 - val_mean_absolute_error: 2.9579 - learning_rate: 2.5000e-05\n",
            "Epoch 25/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.3445 - mean_absolute_error: 3.1793\n",
            "Epoch 25: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.3437 - mean_absolute_error: 3.1792 - val_accuracy: 0.0000e+00 - val_loss: 9.3595 - val_mean_absolute_error: 2.9247 - learning_rate: 2.5000e-05\n",
            "Epoch 26/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.4468 - mean_absolute_error: 3.1435\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
            "\n",
            "Epoch 26: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.4460 - mean_absolute_error: 3.1434 - val_accuracy: 0.0000e+00 - val_loss: 9.5302 - val_mean_absolute_error: 2.9489 - learning_rate: 2.5000e-05\n",
            "Epoch 27/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.5586 - mean_absolute_error: 3.1417\n",
            "Epoch 27: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.5559 - mean_absolute_error: 3.1415 - val_accuracy: 0.0000e+00 - val_loss: 9.7375 - val_mean_absolute_error: 2.9829 - learning_rate: 1.2500e-05\n",
            "Epoch 28/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.2251 - mean_absolute_error: 3.0134\n",
            "Epoch 28: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.2239 - mean_absolute_error: 3.0133 - val_accuracy: 0.0000e+00 - val_loss: 9.1450 - val_mean_absolute_error: 2.8690 - learning_rate: 1.2500e-05\n",
            "Epoch 29/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 10.9076 - mean_absolute_error: 2.9427\n",
            "Epoch 29: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 10.9077 - mean_absolute_error: 2.9427 - val_accuracy: 0.0000e+00 - val_loss: 9.4073 - val_mean_absolute_error: 2.9008 - learning_rate: 1.2500e-05\n",
            "Epoch 30/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.7373 - mean_absolute_error: 3.0473\n",
            "Epoch 30: val_loss did not improve from 9.00346\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.7367 - mean_absolute_error: 3.0473 - val_accuracy: 0.0000e+00 - val_loss: 9.8027 - val_mean_absolute_error: 2.9583 - learning_rate: 1.2500e-05\n",
            "Epoch 31/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.1303 - mean_absolute_error: 3.0900\n",
            "Epoch 31: val_loss improved from 9.00346 to 8.98423, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 287ms/step - accuracy: 0.0000e+00 - loss: 12.1289 - mean_absolute_error: 3.0899 - val_accuracy: 0.0000e+00 - val_loss: 8.9842 - val_mean_absolute_error: 2.8383 - learning_rate: 1.2500e-05\n",
            "Epoch 32/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 10.9073 - mean_absolute_error: 2.9905\n",
            "Epoch 32: val_loss did not improve from 8.98423\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 10.9100 - mean_absolute_error: 2.9906 - val_accuracy: 0.0000e+00 - val_loss: 9.0103 - val_mean_absolute_error: 2.8316 - learning_rate: 1.2500e-05\n",
            "Epoch 33/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.0574 - mean_absolute_error: 3.0070\n",
            "Epoch 33: val_loss did not improve from 8.98423\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.0575 - mean_absolute_error: 3.0069 - val_accuracy: 0.0000e+00 - val_loss: 9.6825 - val_mean_absolute_error: 2.9453 - learning_rate: 1.2500e-05\n",
            "Epoch 34/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.7535 - mean_absolute_error: 3.0692\n",
            "Epoch 34: val_loss did not improve from 8.98423\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.7547 - mean_absolute_error: 3.0694 - val_accuracy: 0.0000e+00 - val_loss: 9.5268 - val_mean_absolute_error: 2.9274 - learning_rate: 1.2500e-05\n",
            "Epoch 35/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.8489 - mean_absolute_error: 3.0922\n",
            "Epoch 35: val_loss did not improve from 8.98423\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.8485 - mean_absolute_error: 3.0920 - val_accuracy: 0.0000e+00 - val_loss: 9.2382 - val_mean_absolute_error: 2.8805 - learning_rate: 1.2500e-05\n",
            "Epoch 36/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.4121 - mean_absolute_error: 3.0971\n",
            "Epoch 36: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n",
            "\n",
            "Epoch 36: val_loss did not improve from 8.98423\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.4107 - mean_absolute_error: 3.0970 - val_accuracy: 0.0000e+00 - val_loss: 9.5878 - val_mean_absolute_error: 2.9433 - learning_rate: 1.2500e-05\n",
            "Epoch 37/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.3048 - mean_absolute_error: 3.1024\n",
            "Epoch 37: val_loss did not improve from 8.98423\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.3053 - mean_absolute_error: 3.1026 - val_accuracy: 0.0000e+00 - val_loss: 9.2768 - val_mean_absolute_error: 2.8921 - learning_rate: 6.2500e-06\n",
            "Epoch 38/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.0093 - mean_absolute_error: 3.0686\n",
            "Epoch 38: val_loss did not improve from 8.98423\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.0080 - mean_absolute_error: 3.0684 - val_accuracy: 0.0000e+00 - val_loss: 9.2967 - val_mean_absolute_error: 2.8945 - learning_rate: 6.2500e-06\n",
            "Epoch 39/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.4027 - mean_absolute_error: 3.0020\n",
            "Epoch 39: val_loss did not improve from 8.98423\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.4029 - mean_absolute_error: 3.0019 - val_accuracy: 0.0000e+00 - val_loss: 9.2353 - val_mean_absolute_error: 2.8725 - learning_rate: 6.2500e-06\n",
            "Epoch 40/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.7942 - mean_absolute_error: 2.9956\n",
            "Epoch 40: val_loss improved from 8.98423 to 8.90024, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 287ms/step - accuracy: 0.0000e+00 - loss: 11.7940 - mean_absolute_error: 2.9957 - val_accuracy: 0.0000e+00 - val_loss: 8.9002 - val_mean_absolute_error: 2.8155 - learning_rate: 6.2500e-06\n",
            "Epoch 41/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - accuracy: 0.0000e+00 - loss: 11.9222 - mean_absolute_error: 3.0849\n",
            "Epoch 41: val_loss did not improve from 8.90024\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.9236 - mean_absolute_error: 3.0852 - val_accuracy: 0.0000e+00 - val_loss: 10.3365 - val_mean_absolute_error: 3.0700 - learning_rate: 6.2500e-06\n",
            "Epoch 42/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.8473 - mean_absolute_error: 3.0690\n",
            "Epoch 42: val_loss did not improve from 8.90024\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.8457 - mean_absolute_error: 3.0687 - val_accuracy: 0.0000e+00 - val_loss: 9.3529 - val_mean_absolute_error: 2.8982 - learning_rate: 6.2500e-06\n",
            "Epoch 43/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.4259 - mean_absolute_error: 3.0270\n",
            "Epoch 43: val_loss improved from 8.90024 to 8.59003, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 286ms/step - accuracy: 0.0000e+00 - loss: 11.4259 - mean_absolute_error: 3.0270 - val_accuracy: 0.0000e+00 - val_loss: 8.5900 - val_mean_absolute_error: 2.7684 - learning_rate: 6.2500e-06\n",
            "Epoch 44/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.1976 - mean_absolute_error: 3.1052\n",
            "Epoch 44: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 12.1986 - mean_absolute_error: 3.1053 - val_accuracy: 0.0000e+00 - val_loss: 9.3593 - val_mean_absolute_error: 2.9071 - learning_rate: 6.2500e-06\n",
            "Epoch 45/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.8153 - mean_absolute_error: 3.0862\n",
            "Epoch 45: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.8115 - mean_absolute_error: 3.0857 - val_accuracy: 0.0000e+00 - val_loss: 10.0094 - val_mean_absolute_error: 3.0058 - learning_rate: 6.2500e-06\n",
            "Epoch 46/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.5674 - mean_absolute_error: 3.0195\n",
            "Epoch 46: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 11.5688 - mean_absolute_error: 3.0197 - val_accuracy: 0.0000e+00 - val_loss: 9.5495 - val_mean_absolute_error: 2.9344 - learning_rate: 6.2500e-06\n",
            "Epoch 47/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.1654 - mean_absolute_error: 3.1497\n",
            "Epoch 47: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.1674 - mean_absolute_error: 3.1499 - val_accuracy: 0.0000e+00 - val_loss: 9.8290 - val_mean_absolute_error: 2.9810 - learning_rate: 6.2500e-06\n",
            "Epoch 48/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.0523 - mean_absolute_error: 3.1107\n",
            "Epoch 48: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-06.\n",
            "\n",
            "Epoch 48: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 12.0499 - mean_absolute_error: 3.1104 - val_accuracy: 0.0000e+00 - val_loss: 10.3002 - val_mean_absolute_error: 3.0614 - learning_rate: 6.2500e-06\n",
            "Epoch 49/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.3418 - mean_absolute_error: 3.0329\n",
            "Epoch 49: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.3413 - mean_absolute_error: 3.0328 - val_accuracy: 0.0000e+00 - val_loss: 9.8564 - val_mean_absolute_error: 2.9902 - learning_rate: 3.1250e-06\n",
            "Epoch 50/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.0361 - mean_absolute_error: 2.9806\n",
            "Epoch 50: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.0378 - mean_absolute_error: 2.9807 - val_accuracy: 0.0000e+00 - val_loss: 9.7895 - val_mean_absolute_error: 2.9723 - learning_rate: 3.1250e-06\n",
            "Epoch 51/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 10.8141 - mean_absolute_error: 2.9801\n",
            "Epoch 51: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 10.8154 - mean_absolute_error: 2.9801 - val_accuracy: 0.0000e+00 - val_loss: 9.8914 - val_mean_absolute_error: 2.9905 - learning_rate: 3.1250e-06\n",
            "Epoch 52/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.6446 - mean_absolute_error: 3.0677\n",
            "Epoch 52: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.6457 - mean_absolute_error: 3.0679 - val_accuracy: 0.0000e+00 - val_loss: 9.0993 - val_mean_absolute_error: 2.8519 - learning_rate: 3.1250e-06\n",
            "Epoch 53/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.0062 - mean_absolute_error: 3.1087\n",
            "Epoch 53: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-06.\n",
            "\n",
            "Epoch 53: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.0037 - mean_absolute_error: 3.1084 - val_accuracy: 0.0000e+00 - val_loss: 9.2509 - val_mean_absolute_error: 2.8828 - learning_rate: 3.1250e-06\n",
            "Epoch 54/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.5960 - mean_absolute_error: 3.0509\n",
            "Epoch 54: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.5978 - mean_absolute_error: 3.0511 - val_accuracy: 0.0000e+00 - val_loss: 9.9331 - val_mean_absolute_error: 2.9956 - learning_rate: 1.5625e-06\n",
            "Epoch 55/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.9615 - mean_absolute_error: 3.0678\n",
            "Epoch 55: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9604 - mean_absolute_error: 3.0677 - val_accuracy: 0.0000e+00 - val_loss: 9.9712 - val_mean_absolute_error: 2.9993 - learning_rate: 1.5625e-06\n",
            "Epoch 56/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.2708 - mean_absolute_error: 3.0898\n",
            "Epoch 56: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.2681 - mean_absolute_error: 3.0896 - val_accuracy: 0.0000e+00 - val_loss: 10.0437 - val_mean_absolute_error: 3.0097 - learning_rate: 1.5625e-06\n",
            "Epoch 57/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.4015 - mean_absolute_error: 3.0236\n",
            "Epoch 57: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.4011 - mean_absolute_error: 3.0234 - val_accuracy: 0.0000e+00 - val_loss: 9.4053 - val_mean_absolute_error: 2.9063 - learning_rate: 1.5625e-06\n",
            "Epoch 58/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.1484 - mean_absolute_error: 2.9700\n",
            "Epoch 58: ReduceLROnPlateau reducing learning rate to 7.81249980263965e-07.\n",
            "\n",
            "Epoch 58: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.1545 - mean_absolute_error: 2.9707 - val_accuracy: 0.0000e+00 - val_loss: 9.5306 - val_mean_absolute_error: 2.9307 - learning_rate: 1.5625e-06\n",
            "Epoch 59/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.0844 - mean_absolute_error: 3.1170\n",
            "Epoch 59: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 12.0838 - mean_absolute_error: 3.1167 - val_accuracy: 0.0000e+00 - val_loss: 9.1346 - val_mean_absolute_error: 2.8661 - learning_rate: 7.8125e-07\n",
            "Epoch 60/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.1363 - mean_absolute_error: 3.0091\n",
            "Epoch 60: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 11.1397 - mean_absolute_error: 3.0094 - val_accuracy: 0.0000e+00 - val_loss: 9.0449 - val_mean_absolute_error: 2.8432 - learning_rate: 7.8125e-07\n",
            "Epoch 61/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.6919 - mean_absolute_error: 3.1574\n",
            "Epoch 61: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - accuracy: 0.0000e+00 - loss: 12.6896 - mean_absolute_error: 3.1572 - val_accuracy: 0.0000e+00 - val_loss: 9.6112 - val_mean_absolute_error: 2.9428 - learning_rate: 7.8125e-07\n",
            "Epoch 62/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - accuracy: 0.0000e+00 - loss: 11.6724 - mean_absolute_error: 3.0819\n",
            "Epoch 62: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.6716 - mean_absolute_error: 3.0816 - val_accuracy: 0.0000e+00 - val_loss: 9.7918 - val_mean_absolute_error: 2.9753 - learning_rate: 7.8125e-07\n",
            "Epoch 63/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.5024 - mean_absolute_error: 3.0039\n",
            "Epoch 63: ReduceLROnPlateau reducing learning rate to 3.906249901319825e-07.\n",
            "\n",
            "Epoch 63: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.5025 - mean_absolute_error: 3.0042 - val_accuracy: 0.0000e+00 - val_loss: 9.2391 - val_mean_absolute_error: 2.8711 - learning_rate: 7.8125e-07\n",
            "Epoch 64/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.1612 - mean_absolute_error: 3.1018\n",
            "Epoch 64: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 12.1559 - mean_absolute_error: 3.1012 - val_accuracy: 0.0000e+00 - val_loss: 9.1098 - val_mean_absolute_error: 2.8543 - learning_rate: 3.9062e-07\n",
            "Epoch 65/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.3036 - mean_absolute_error: 2.9670\n",
            "Epoch 65: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.3047 - mean_absolute_error: 2.9673 - val_accuracy: 0.0000e+00 - val_loss: 9.1715 - val_mean_absolute_error: 2.8735 - learning_rate: 3.9062e-07\n",
            "Epoch 66/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.9041 - mean_absolute_error: 3.1109\n",
            "Epoch 66: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9035 - mean_absolute_error: 3.1107 - val_accuracy: 0.0000e+00 - val_loss: 9.1988 - val_mean_absolute_error: 2.8701 - learning_rate: 3.9062e-07\n",
            "Epoch 67/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.0397 - mean_absolute_error: 2.9926\n",
            "Epoch 67: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.0399 - mean_absolute_error: 2.9925 - val_accuracy: 0.0000e+00 - val_loss: 9.5006 - val_mean_absolute_error: 2.9291 - learning_rate: 3.9062e-07\n",
            "Epoch 68/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 10.9268 - mean_absolute_error: 2.9524\n",
            "Epoch 68: ReduceLROnPlateau reducing learning rate to 1.9531249506599124e-07.\n",
            "\n",
            "Epoch 68: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 10.9300 - mean_absolute_error: 2.9528 - val_accuracy: 0.0000e+00 - val_loss: 9.4795 - val_mean_absolute_error: 2.9226 - learning_rate: 3.9062e-07\n",
            "Epoch 69/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.1561 - mean_absolute_error: 3.1287\n",
            "Epoch 69: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.1570 - mean_absolute_error: 3.1289 - val_accuracy: 0.0000e+00 - val_loss: 9.0586 - val_mean_absolute_error: 2.8470 - learning_rate: 1.9531e-07\n",
            "Epoch 70/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - accuracy: 0.0000e+00 - loss: 12.4400 - mean_absolute_error: 3.2189\n",
            "Epoch 70: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 12.4385 - mean_absolute_error: 3.2186 - val_accuracy: 0.0000e+00 - val_loss: 9.1582 - val_mean_absolute_error: 2.8578 - learning_rate: 1.9531e-07\n",
            "Epoch 71/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.8731 - mean_absolute_error: 3.0673\n",
            "Epoch 71: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.8736 - mean_absolute_error: 3.0673 - val_accuracy: 0.0000e+00 - val_loss: 9.8865 - val_mean_absolute_error: 2.9815 - learning_rate: 1.9531e-07\n",
            "Epoch 72/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.3855 - mean_absolute_error: 3.0134\n",
            "Epoch 72: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 11.3861 - mean_absolute_error: 3.0135 - val_accuracy: 0.0000e+00 - val_loss: 9.5075 - val_mean_absolute_error: 2.9169 - learning_rate: 1.9531e-07\n",
            "Epoch 73/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.6853 - mean_absolute_error: 3.0632\n",
            "Epoch 73: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
            "\n",
            "Epoch 73: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.6851 - mean_absolute_error: 3.0630 - val_accuracy: 0.0000e+00 - val_loss: 9.1273 - val_mean_absolute_error: 2.8576 - learning_rate: 1.9531e-07\n",
            "Epoch 74/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.6359 - mean_absolute_error: 3.0254\n",
            "Epoch 74: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.6369 - mean_absolute_error: 3.0254 - val_accuracy: 0.0000e+00 - val_loss: 9.6659 - val_mean_absolute_error: 2.9475 - learning_rate: 1.0000e-07\n",
            "Epoch 75/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.1467 - mean_absolute_error: 2.9938\n",
            "Epoch 75: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.1460 - mean_absolute_error: 2.9937 - val_accuracy: 0.0000e+00 - val_loss: 9.2490 - val_mean_absolute_error: 2.8755 - learning_rate: 1.0000e-07\n",
            "Epoch 76/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 10.3726 - mean_absolute_error: 2.8945\n",
            "Epoch 76: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 10.3737 - mean_absolute_error: 2.8946 - val_accuracy: 0.0000e+00 - val_loss: 9.1717 - val_mean_absolute_error: 2.8673 - learning_rate: 1.0000e-07\n",
            "Epoch 77/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.0824 - mean_absolute_error: 2.9497\n",
            "Epoch 77: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.0827 - mean_absolute_error: 2.9498 - val_accuracy: 0.0000e+00 - val_loss: 8.9903 - val_mean_absolute_error: 2.8375 - learning_rate: 1.0000e-07\n",
            "Epoch 78/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.3623 - mean_absolute_error: 3.0066\n",
            "Epoch 78: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.3653 - mean_absolute_error: 3.0069 - val_accuracy: 0.0000e+00 - val_loss: 8.9000 - val_mean_absolute_error: 2.8156 - learning_rate: 1.0000e-07\n",
            "Epoch 79/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.0221 - mean_absolute_error: 3.0984\n",
            "Epoch 79: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.0206 - mean_absolute_error: 3.0981 - val_accuracy: 0.0000e+00 - val_loss: 9.4015 - val_mean_absolute_error: 2.8965 - learning_rate: 1.0000e-07\n",
            "Epoch 80/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 10.9265 - mean_absolute_error: 2.9718\n",
            "Epoch 80: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 10.9282 - mean_absolute_error: 2.9719 - val_accuracy: 0.0000e+00 - val_loss: 8.9609 - val_mean_absolute_error: 2.8312 - learning_rate: 1.0000e-07\n",
            "Epoch 81/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 10.8357 - mean_absolute_error: 2.9783\n",
            "Epoch 81: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 10.8381 - mean_absolute_error: 2.9784 - val_accuracy: 0.0000e+00 - val_loss: 9.1484 - val_mean_absolute_error: 2.8635 - learning_rate: 1.0000e-07\n",
            "Epoch 82/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.4207 - mean_absolute_error: 3.0221\n",
            "Epoch 82: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.4214 - mean_absolute_error: 3.0222 - val_accuracy: 0.0000e+00 - val_loss: 9.4950 - val_mean_absolute_error: 2.9192 - learning_rate: 1.0000e-07\n",
            "Epoch 83/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.9774 - mean_absolute_error: 3.0918\n",
            "Epoch 83: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.9797 - mean_absolute_error: 3.0920 - val_accuracy: 0.0000e+00 - val_loss: 9.7726 - val_mean_absolute_error: 2.9661 - learning_rate: 1.0000e-07\n",
            "Epoch 84/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9538 - mean_absolute_error: 3.1066\n",
            "Epoch 84: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.9525 - mean_absolute_error: 3.1065 - val_accuracy: 0.0000e+00 - val_loss: 9.2996 - val_mean_absolute_error: 2.8892 - learning_rate: 1.0000e-07\n",
            "Epoch 85/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.8521 - mean_absolute_error: 3.0844\n",
            "Epoch 85: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.8525 - mean_absolute_error: 3.0844 - val_accuracy: 0.0000e+00 - val_loss: 10.4094 - val_mean_absolute_error: 3.0722 - learning_rate: 1.0000e-07\n",
            "Epoch 86/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.6814 - mean_absolute_error: 3.0559\n",
            "Epoch 86: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.6824 - mean_absolute_error: 3.0560 - val_accuracy: 0.0000e+00 - val_loss: 9.7799 - val_mean_absolute_error: 2.9623 - learning_rate: 1.0000e-07\n",
            "Epoch 87/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.7788 - mean_absolute_error: 3.0964\n",
            "Epoch 87: val_loss did not improve from 8.59003\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.7776 - mean_absolute_error: 3.0961 - val_accuracy: 0.0000e+00 - val_loss: 9.7504 - val_mean_absolute_error: 2.9688 - learning_rate: 1.0000e-07\n",
            "Epoch 88/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.6611 - mean_absolute_error: 3.0241\n",
            "Epoch 88: val_loss improved from 8.59003 to 8.21994, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m53s\u001b[0m 289ms/step - accuracy: 0.0000e+00 - loss: 11.6624 - mean_absolute_error: 3.0243 - val_accuracy: 0.0000e+00 - val_loss: 8.2199 - val_mean_absolute_error: 2.7114 - learning_rate: 1.0000e-07\n",
            "Epoch 89/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.8275 - mean_absolute_error: 3.0750\n",
            "Epoch 89: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.8290 - mean_absolute_error: 3.0752 - val_accuracy: 0.0000e+00 - val_loss: 9.2953 - val_mean_absolute_error: 2.8809 - learning_rate: 1.0000e-07\n",
            "Epoch 90/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.7376 - mean_absolute_error: 3.1291\n",
            "Epoch 90: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.7323 - mean_absolute_error: 3.1286 - val_accuracy: 0.0000e+00 - val_loss: 9.6381 - val_mean_absolute_error: 2.9478 - learning_rate: 1.0000e-07\n",
            "Epoch 91/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 10.8951 - mean_absolute_error: 2.9220\n",
            "Epoch 91: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 10.8980 - mean_absolute_error: 2.9225 - val_accuracy: 0.0000e+00 - val_loss: 9.1438 - val_mean_absolute_error: 2.8704 - learning_rate: 1.0000e-07\n",
            "Epoch 92/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.7424 - mean_absolute_error: 3.0944\n",
            "Epoch 92: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 11.7423 - mean_absolute_error: 3.0943 - val_accuracy: 0.0000e+00 - val_loss: 9.6906 - val_mean_absolute_error: 2.9590 - learning_rate: 1.0000e-07\n",
            "Epoch 93/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.7942 - mean_absolute_error: 3.0639\n",
            "Epoch 93: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.7927 - mean_absolute_error: 3.0639 - val_accuracy: 0.0000e+00 - val_loss: 10.2247 - val_mean_absolute_error: 3.0471 - learning_rate: 1.0000e-07\n",
            "Epoch 94/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.5954 - mean_absolute_error: 3.1012\n",
            "Epoch 94: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.5964 - mean_absolute_error: 3.1013 - val_accuracy: 0.0000e+00 - val_loss: 9.1989 - val_mean_absolute_error: 2.8724 - learning_rate: 1.0000e-07\n",
            "Epoch 95/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.5031 - mean_absolute_error: 3.1693\n",
            "Epoch 95: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 12.5003 - mean_absolute_error: 3.1688 - val_accuracy: 0.0000e+00 - val_loss: 9.2947 - val_mean_absolute_error: 2.8897 - learning_rate: 1.0000e-07\n",
            "Epoch 96/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.2419 - mean_absolute_error: 2.9911\n",
            "Epoch 96: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.2438 - mean_absolute_error: 2.9914 - val_accuracy: 0.0000e+00 - val_loss: 8.9680 - val_mean_absolute_error: 2.8263 - learning_rate: 1.0000e-07\n",
            "Epoch 97/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9506 - mean_absolute_error: 3.0952\n",
            "Epoch 97: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.9484 - mean_absolute_error: 3.0949 - val_accuracy: 0.0000e+00 - val_loss: 9.1963 - val_mean_absolute_error: 2.8735 - learning_rate: 1.0000e-07\n",
            "Epoch 98/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - accuracy: 0.0000e+00 - loss: 10.9727 - mean_absolute_error: 2.9488\n",
            "Epoch 98: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 10.9727 - mean_absolute_error: 2.9488 - val_accuracy: 0.0000e+00 - val_loss: 9.2794 - val_mean_absolute_error: 2.8843 - learning_rate: 1.0000e-07\n",
            "Epoch 99/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.2446 - mean_absolute_error: 2.9706\n",
            "Epoch 99: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.2454 - mean_absolute_error: 2.9707 - val_accuracy: 0.0000e+00 - val_loss: 9.3748 - val_mean_absolute_error: 2.8981 - learning_rate: 1.0000e-07\n",
            "Epoch 100/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.2997 - mean_absolute_error: 3.0281\n",
            "Epoch 100: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.3014 - mean_absolute_error: 3.0282 - val_accuracy: 0.0000e+00 - val_loss: 9.4298 - val_mean_absolute_error: 2.9084 - learning_rate: 1.0000e-07\n",
            "Epoch 101/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - accuracy: 0.0000e+00 - loss: 11.6057 - mean_absolute_error: 3.0618\n",
            "Epoch 101: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.6084 - mean_absolute_error: 3.0620 - val_accuracy: 0.0000e+00 - val_loss: 9.4978 - val_mean_absolute_error: 2.9199 - learning_rate: 1.0000e-07\n",
            "Epoch 102/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - accuracy: 0.0000e+00 - loss: 12.4190 - mean_absolute_error: 3.1925\n",
            "Epoch 102: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.4186 - mean_absolute_error: 3.1924 - val_accuracy: 0.0000e+00 - val_loss: 9.3261 - val_mean_absolute_error: 2.8932 - learning_rate: 1.0000e-07\n",
            "Epoch 103/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.9055 - mean_absolute_error: 3.0965\n",
            "Epoch 103: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.9084 - mean_absolute_error: 3.0965 - val_accuracy: 0.0000e+00 - val_loss: 9.7336 - val_mean_absolute_error: 2.9640 - learning_rate: 1.0000e-07\n",
            "Epoch 104/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.6893 - mean_absolute_error: 3.0198\n",
            "Epoch 104: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.6892 - mean_absolute_error: 3.0199 - val_accuracy: 0.0000e+00 - val_loss: 9.1911 - val_mean_absolute_error: 2.8727 - learning_rate: 1.0000e-07\n",
            "Epoch 105/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.5237 - mean_absolute_error: 3.0515\n",
            "Epoch 105: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.5228 - mean_absolute_error: 3.0514 - val_accuracy: 0.0000e+00 - val_loss: 9.0465 - val_mean_absolute_error: 2.8484 - learning_rate: 1.0000e-07\n",
            "Epoch 106/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.4668 - mean_absolute_error: 3.0428\n",
            "Epoch 106: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.4663 - mean_absolute_error: 3.0425 - val_accuracy: 0.0000e+00 - val_loss: 8.8577 - val_mean_absolute_error: 2.8211 - learning_rate: 1.0000e-07\n",
            "Epoch 107/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 10.7514 - mean_absolute_error: 2.8967\n",
            "Epoch 107: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 10.7542 - mean_absolute_error: 2.8971 - val_accuracy: 0.0000e+00 - val_loss: 8.5179 - val_mean_absolute_error: 2.7450 - learning_rate: 1.0000e-07\n",
            "Epoch 108/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.0307 - mean_absolute_error: 3.0659\n",
            "Epoch 108: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.0288 - mean_absolute_error: 3.0657 - val_accuracy: 0.0000e+00 - val_loss: 9.8842 - val_mean_absolute_error: 2.9908 - learning_rate: 1.0000e-07\n",
            "Epoch 109/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.3667 - mean_absolute_error: 2.9829\n",
            "Epoch 109: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.3672 - mean_absolute_error: 2.9831 - val_accuracy: 0.0000e+00 - val_loss: 10.3262 - val_mean_absolute_error: 3.0604 - learning_rate: 1.0000e-07\n",
            "Epoch 110/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.7800 - mean_absolute_error: 3.0812\n",
            "Epoch 110: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.7788 - mean_absolute_error: 3.0809 - val_accuracy: 0.0000e+00 - val_loss: 10.3686 - val_mean_absolute_error: 3.0673 - learning_rate: 1.0000e-07\n",
            "Epoch 111/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - accuracy: 0.0000e+00 - loss: 10.6475 - mean_absolute_error: 2.9355\n",
            "Epoch 111: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 10.6491 - mean_absolute_error: 2.9358 - val_accuracy: 0.0000e+00 - val_loss: 10.3335 - val_mean_absolute_error: 3.0620 - learning_rate: 1.0000e-07\n",
            "Epoch 112/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.4369 - mean_absolute_error: 3.0311\n",
            "Epoch 112: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 11.4377 - mean_absolute_error: 3.0312 - val_accuracy: 0.0000e+00 - val_loss: 9.7762 - val_mean_absolute_error: 2.9751 - learning_rate: 1.0000e-07\n",
            "Epoch 113/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.4832 - mean_absolute_error: 3.1202\n",
            "Epoch 113: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.4833 - mean_absolute_error: 3.1203 - val_accuracy: 0.0000e+00 - val_loss: 9.4739 - val_mean_absolute_error: 2.9186 - learning_rate: 1.0000e-07\n",
            "Epoch 114/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.7931 - mean_absolute_error: 3.1059\n",
            "Epoch 114: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.7921 - mean_absolute_error: 3.1058 - val_accuracy: 0.0000e+00 - val_loss: 9.6868 - val_mean_absolute_error: 2.9570 - learning_rate: 1.0000e-07\n",
            "Epoch 115/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.5118 - mean_absolute_error: 3.0307\n",
            "Epoch 115: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.5127 - mean_absolute_error: 3.0306 - val_accuracy: 0.0000e+00 - val_loss: 10.0787 - val_mean_absolute_error: 3.0182 - learning_rate: 1.0000e-07\n",
            "Epoch 116/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 10.3974 - mean_absolute_error: 2.8838\n",
            "Epoch 116: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 10.3991 - mean_absolute_error: 2.8840 - val_accuracy: 0.0000e+00 - val_loss: 9.8746 - val_mean_absolute_error: 2.9833 - learning_rate: 1.0000e-07\n",
            "Epoch 117/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.3958 - mean_absolute_error: 2.9670\n",
            "Epoch 117: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.3968 - mean_absolute_error: 2.9672 - val_accuracy: 0.0000e+00 - val_loss: 9.6055 - val_mean_absolute_error: 2.9411 - learning_rate: 1.0000e-07\n",
            "Epoch 118/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.2657 - mean_absolute_error: 3.0023\n",
            "Epoch 118: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.2671 - mean_absolute_error: 3.0025 - val_accuracy: 0.0000e+00 - val_loss: 9.3552 - val_mean_absolute_error: 2.8965 - learning_rate: 1.0000e-07\n",
            "Epoch 119/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.5673 - mean_absolute_error: 3.1971\n",
            "Epoch 119: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.5669 - mean_absolute_error: 3.1968 - val_accuracy: 0.0000e+00 - val_loss: 9.7162 - val_mean_absolute_error: 2.9559 - learning_rate: 1.0000e-07\n",
            "Epoch 120/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.8738 - mean_absolute_error: 3.1052\n",
            "Epoch 120: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.8742 - mean_absolute_error: 3.1053 - val_accuracy: 0.0000e+00 - val_loss: 9.0257 - val_mean_absolute_error: 2.8419 - learning_rate: 1.0000e-07\n",
            "Epoch 121/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 13.1496 - mean_absolute_error: 3.2167\n",
            "Epoch 121: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 13.1489 - mean_absolute_error: 3.2165 - val_accuracy: 0.0000e+00 - val_loss: 9.0561 - val_mean_absolute_error: 2.8444 - learning_rate: 1.0000e-07\n",
            "Epoch 122/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.0377 - mean_absolute_error: 2.9957\n",
            "Epoch 122: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.0383 - mean_absolute_error: 2.9956 - val_accuracy: 0.0000e+00 - val_loss: 9.1189 - val_mean_absolute_error: 2.8579 - learning_rate: 1.0000e-07\n",
            "Epoch 123/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 10.6681 - mean_absolute_error: 2.9460\n",
            "Epoch 123: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 10.6701 - mean_absolute_error: 2.9461 - val_accuracy: 0.0000e+00 - val_loss: 9.0465 - val_mean_absolute_error: 2.8467 - learning_rate: 1.0000e-07\n",
            "Epoch 124/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - accuracy: 0.0000e+00 - loss: 12.2115 - mean_absolute_error: 3.0378\n",
            "Epoch 124: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 12.2086 - mean_absolute_error: 3.0377 - val_accuracy: 0.0000e+00 - val_loss: 8.9534 - val_mean_absolute_error: 2.8301 - learning_rate: 1.0000e-07\n",
            "Epoch 125/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.9717 - mean_absolute_error: 3.0602\n",
            "Epoch 125: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9696 - mean_absolute_error: 3.0600 - val_accuracy: 0.0000e+00 - val_loss: 9.4710 - val_mean_absolute_error: 2.9099 - learning_rate: 1.0000e-07\n",
            "Epoch 126/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 10.5606 - mean_absolute_error: 2.8982\n",
            "Epoch 126: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 10.5649 - mean_absolute_error: 2.8987 - val_accuracy: 0.0000e+00 - val_loss: 9.7386 - val_mean_absolute_error: 2.9621 - learning_rate: 1.0000e-07\n",
            "Epoch 127/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.7673 - mean_absolute_error: 3.1046\n",
            "Epoch 127: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.7672 - mean_absolute_error: 3.1045 - val_accuracy: 0.0000e+00 - val_loss: 9.5307 - val_mean_absolute_error: 2.9220 - learning_rate: 1.0000e-07\n",
            "Epoch 128/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.3473 - mean_absolute_error: 3.0806\n",
            "Epoch 128: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.3446 - mean_absolute_error: 3.0805 - val_accuracy: 0.0000e+00 - val_loss: 9.7166 - val_mean_absolute_error: 2.9495 - learning_rate: 1.0000e-07\n",
            "Epoch 129/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9100 - mean_absolute_error: 3.0725\n",
            "Epoch 129: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9097 - mean_absolute_error: 3.0726 - val_accuracy: 0.0000e+00 - val_loss: 9.4398 - val_mean_absolute_error: 2.9102 - learning_rate: 1.0000e-07\n",
            "Epoch 130/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.4219 - mean_absolute_error: 3.1942\n",
            "Epoch 130: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.4189 - mean_absolute_error: 3.1938 - val_accuracy: 0.0000e+00 - val_loss: 9.8396 - val_mean_absolute_error: 2.9813 - learning_rate: 1.0000e-07\n",
            "Epoch 131/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.7390 - mean_absolute_error: 2.9706\n",
            "Epoch 131: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.7378 - mean_absolute_error: 2.9707 - val_accuracy: 0.0000e+00 - val_loss: 9.4884 - val_mean_absolute_error: 2.9143 - learning_rate: 1.0000e-07\n",
            "Epoch 132/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.9058 - mean_absolute_error: 3.0302\n",
            "Epoch 132: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9048 - mean_absolute_error: 3.0303 - val_accuracy: 0.0000e+00 - val_loss: 9.1698 - val_mean_absolute_error: 2.8654 - learning_rate: 1.0000e-07\n",
            "Epoch 133/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.2312 - mean_absolute_error: 3.1352\n",
            "Epoch 133: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.2286 - mean_absolute_error: 3.1349 - val_accuracy: 0.0000e+00 - val_loss: 9.2683 - val_mean_absolute_error: 2.8903 - learning_rate: 1.0000e-07\n",
            "Epoch 134/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.1826 - mean_absolute_error: 3.0852\n",
            "Epoch 134: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.1801 - mean_absolute_error: 3.0851 - val_accuracy: 0.0000e+00 - val_loss: 9.4997 - val_mean_absolute_error: 2.9103 - learning_rate: 1.0000e-07\n",
            "Epoch 135/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.8754 - mean_absolute_error: 3.0892\n",
            "Epoch 135: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.8759 - mean_absolute_error: 3.0892 - val_accuracy: 0.0000e+00 - val_loss: 9.4118 - val_mean_absolute_error: 2.9061 - learning_rate: 1.0000e-07\n",
            "Epoch 136/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.3124 - mean_absolute_error: 3.0363\n",
            "Epoch 136: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.3143 - mean_absolute_error: 3.0364 - val_accuracy: 0.0000e+00 - val_loss: 9.1333 - val_mean_absolute_error: 2.8686 - learning_rate: 1.0000e-07\n",
            "Epoch 137/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.7686 - mean_absolute_error: 3.1801\n",
            "Epoch 137: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.7646 - mean_absolute_error: 3.1797 - val_accuracy: 0.0000e+00 - val_loss: 9.6103 - val_mean_absolute_error: 2.9429 - learning_rate: 1.0000e-07\n",
            "Epoch 138/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.2994 - mean_absolute_error: 2.9538\n",
            "Epoch 138: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.3016 - mean_absolute_error: 2.9541 - val_accuracy: 0.0000e+00 - val_loss: 9.2718 - val_mean_absolute_error: 2.8852 - learning_rate: 1.0000e-07\n",
            "Epoch 139/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.0633 - mean_absolute_error: 3.1348\n",
            "Epoch 139: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.0634 - mean_absolute_error: 3.1348 - val_accuracy: 0.0000e+00 - val_loss: 9.8228 - val_mean_absolute_error: 2.9850 - learning_rate: 1.0000e-07\n",
            "Epoch 140/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.2768 - mean_absolute_error: 3.1314\n",
            "Epoch 140: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.2770 - mean_absolute_error: 3.1314 - val_accuracy: 0.0000e+00 - val_loss: 8.7855 - val_mean_absolute_error: 2.8009 - learning_rate: 1.0000e-07\n",
            "Epoch 141/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.8588 - mean_absolute_error: 3.0639\n",
            "Epoch 141: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.8579 - mean_absolute_error: 3.0638 - val_accuracy: 0.0000e+00 - val_loss: 10.1522 - val_mean_absolute_error: 3.0225 - learning_rate: 1.0000e-07\n",
            "Epoch 142/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9551 - mean_absolute_error: 3.0958\n",
            "Epoch 142: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9533 - mean_absolute_error: 3.0957 - val_accuracy: 0.0000e+00 - val_loss: 9.2772 - val_mean_absolute_error: 2.8835 - learning_rate: 1.0000e-07\n",
            "Epoch 143/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.4070 - mean_absolute_error: 3.0206\n",
            "Epoch 143: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.4074 - mean_absolute_error: 3.0206 - val_accuracy: 0.0000e+00 - val_loss: 8.9690 - val_mean_absolute_error: 2.8347 - learning_rate: 1.0000e-07\n",
            "Epoch 144/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 10.9908 - mean_absolute_error: 2.9583\n",
            "Epoch 144: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 10.9914 - mean_absolute_error: 2.9584 - val_accuracy: 0.0000e+00 - val_loss: 9.6545 - val_mean_absolute_error: 2.9549 - learning_rate: 1.0000e-07\n",
            "Epoch 145/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.7896 - mean_absolute_error: 3.0619\n",
            "Epoch 145: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.7887 - mean_absolute_error: 3.0617 - val_accuracy: 0.0000e+00 - val_loss: 9.3091 - val_mean_absolute_error: 2.8985 - learning_rate: 1.0000e-07\n",
            "Epoch 146/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.0055 - mean_absolute_error: 2.9601\n",
            "Epoch 146: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.0062 - mean_absolute_error: 2.9601 - val_accuracy: 0.0000e+00 - val_loss: 9.4168 - val_mean_absolute_error: 2.9101 - learning_rate: 1.0000e-07\n",
            "Epoch 147/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 10.5346 - mean_absolute_error: 2.8874\n",
            "Epoch 147: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 10.5372 - mean_absolute_error: 2.8877 - val_accuracy: 0.0000e+00 - val_loss: 9.0164 - val_mean_absolute_error: 2.8374 - learning_rate: 1.0000e-07\n",
            "Epoch 148/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.5232 - mean_absolute_error: 3.0251\n",
            "Epoch 148: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.5226 - mean_absolute_error: 3.0251 - val_accuracy: 0.0000e+00 - val_loss: 8.9618 - val_mean_absolute_error: 2.8263 - learning_rate: 1.0000e-07\n",
            "Epoch 149/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.3901 - mean_absolute_error: 2.9878\n",
            "Epoch 149: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.3887 - mean_absolute_error: 2.9878 - val_accuracy: 0.0000e+00 - val_loss: 8.9161 - val_mean_absolute_error: 2.8206 - learning_rate: 1.0000e-07\n",
            "Epoch 150/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.4322 - mean_absolute_error: 3.1463\n",
            "Epoch 150: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.4311 - mean_absolute_error: 3.1462 - val_accuracy: 0.0000e+00 - val_loss: 9.5345 - val_mean_absolute_error: 2.9307 - learning_rate: 1.0000e-07\n",
            "Epoch 151/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.6236 - mean_absolute_error: 3.0334\n",
            "Epoch 151: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.6238 - mean_absolute_error: 3.0334 - val_accuracy: 0.0000e+00 - val_loss: 9.4957 - val_mean_absolute_error: 2.9289 - learning_rate: 1.0000e-07\n",
            "Epoch 152/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.0945 - mean_absolute_error: 2.9742\n",
            "Epoch 152: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.0941 - mean_absolute_error: 2.9742 - val_accuracy: 0.0000e+00 - val_loss: 9.1486 - val_mean_absolute_error: 2.8694 - learning_rate: 1.0000e-07\n",
            "Epoch 153/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.4183 - mean_absolute_error: 2.9870\n",
            "Epoch 153: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.4171 - mean_absolute_error: 2.9871 - val_accuracy: 0.0000e+00 - val_loss: 9.5610 - val_mean_absolute_error: 2.9328 - learning_rate: 1.0000e-07\n",
            "Epoch 154/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.4932 - mean_absolute_error: 3.1627\n",
            "Epoch 154: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.4926 - mean_absolute_error: 3.1625 - val_accuracy: 0.0000e+00 - val_loss: 8.9910 - val_mean_absolute_error: 2.8388 - learning_rate: 1.0000e-07\n",
            "Epoch 155/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.7203 - mean_absolute_error: 3.0944\n",
            "Epoch 155: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.7181 - mean_absolute_error: 3.0941 - val_accuracy: 0.0000e+00 - val_loss: 9.0028 - val_mean_absolute_error: 2.8360 - learning_rate: 1.0000e-07\n",
            "Epoch 156/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 10.5909 - mean_absolute_error: 2.8808\n",
            "Epoch 156: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 10.5932 - mean_absolute_error: 2.8810 - val_accuracy: 0.0000e+00 - val_loss: 9.8940 - val_mean_absolute_error: 2.9876 - learning_rate: 1.0000e-07\n",
            "Epoch 157/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.5185 - mean_absolute_error: 3.1733\n",
            "Epoch 157: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.5184 - mean_absolute_error: 3.1733 - val_accuracy: 0.0000e+00 - val_loss: 9.9731 - val_mean_absolute_error: 3.0018 - learning_rate: 1.0000e-07\n",
            "Epoch 158/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.7073 - mean_absolute_error: 3.2132\n",
            "Epoch 158: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.7068 - mean_absolute_error: 3.2132 - val_accuracy: 0.0000e+00 - val_loss: 9.4995 - val_mean_absolute_error: 2.9202 - learning_rate: 1.0000e-07\n",
            "Epoch 159/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.5679 - mean_absolute_error: 3.1936\n",
            "Epoch 159: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.5706 - mean_absolute_error: 3.1937 - val_accuracy: 0.0000e+00 - val_loss: 9.2681 - val_mean_absolute_error: 2.8875 - learning_rate: 1.0000e-07\n",
            "Epoch 160/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.3152 - mean_absolute_error: 3.1690\n",
            "Epoch 160: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.3142 - mean_absolute_error: 3.1688 - val_accuracy: 0.0000e+00 - val_loss: 9.2063 - val_mean_absolute_error: 2.8726 - learning_rate: 1.0000e-07\n",
            "Epoch 161/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.5988 - mean_absolute_error: 3.0769\n",
            "Epoch 161: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 11.5994 - mean_absolute_error: 3.0770 - val_accuracy: 0.0000e+00 - val_loss: 9.2502 - val_mean_absolute_error: 2.8758 - learning_rate: 1.0000e-07\n",
            "Epoch 162/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.4874 - mean_absolute_error: 3.0600\n",
            "Epoch 162: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.4877 - mean_absolute_error: 3.0599 - val_accuracy: 0.0000e+00 - val_loss: 9.2365 - val_mean_absolute_error: 2.8819 - learning_rate: 1.0000e-07\n",
            "Epoch 163/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.1364 - mean_absolute_error: 2.9927\n",
            "Epoch 163: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.1376 - mean_absolute_error: 2.9928 - val_accuracy: 0.0000e+00 - val_loss: 9.0675 - val_mean_absolute_error: 2.8505 - learning_rate: 1.0000e-07\n",
            "Epoch 164/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - accuracy: 0.0000e+00 - loss: 11.9035 - mean_absolute_error: 3.1391\n",
            "Epoch 164: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 11.9035 - mean_absolute_error: 3.1391 - val_accuracy: 0.0000e+00 - val_loss: 9.2836 - val_mean_absolute_error: 2.8870 - learning_rate: 1.0000e-07\n",
            "Epoch 165/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 12.3231 - mean_absolute_error: 3.1641\n",
            "Epoch 165: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - accuracy: 0.0000e+00 - loss: 12.3227 - mean_absolute_error: 3.1640 - val_accuracy: 0.0000e+00 - val_loss: 9.9200 - val_mean_absolute_error: 2.9994 - learning_rate: 1.0000e-07\n",
            "Epoch 166/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 11.9818 - mean_absolute_error: 3.0517\n",
            "Epoch 166: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9815 - mean_absolute_error: 3.0516 - val_accuracy: 0.0000e+00 - val_loss: 9.4792 - val_mean_absolute_error: 2.9210 - learning_rate: 1.0000e-07\n",
            "Epoch 167/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.0189 - mean_absolute_error: 3.1104\n",
            "Epoch 167: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.0165 - mean_absolute_error: 3.1102 - val_accuracy: 0.0000e+00 - val_loss: 9.3217 - val_mean_absolute_error: 2.8905 - learning_rate: 1.0000e-07\n",
            "Epoch 168/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - accuracy: 0.0000e+00 - loss: 10.8569 - mean_absolute_error: 2.9515\n",
            "Epoch 168: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 10.8577 - mean_absolute_error: 2.9515 - val_accuracy: 0.0000e+00 - val_loss: 9.5365 - val_mean_absolute_error: 2.9326 - learning_rate: 1.0000e-07\n",
            "Epoch 169/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.6855 - mean_absolute_error: 3.0159\n",
            "Epoch 169: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.6858 - mean_absolute_error: 3.0160 - val_accuracy: 0.0000e+00 - val_loss: 9.8046 - val_mean_absolute_error: 2.9772 - learning_rate: 1.0000e-07\n",
            "Epoch 170/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.4586 - mean_absolute_error: 3.0692\n",
            "Epoch 170: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.4590 - mean_absolute_error: 3.0692 - val_accuracy: 0.0000e+00 - val_loss: 9.9244 - val_mean_absolute_error: 2.9922 - learning_rate: 1.0000e-07\n",
            "Epoch 171/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.1544 - mean_absolute_error: 3.1443\n",
            "Epoch 171: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.1556 - mean_absolute_error: 3.1443 - val_accuracy: 0.0000e+00 - val_loss: 8.9977 - val_mean_absolute_error: 2.8334 - learning_rate: 1.0000e-07\n",
            "Epoch 172/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9126 - mean_absolute_error: 3.0660\n",
            "Epoch 172: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9107 - mean_absolute_error: 3.0659 - val_accuracy: 0.0000e+00 - val_loss: 9.0216 - val_mean_absolute_error: 2.8474 - learning_rate: 1.0000e-07\n",
            "Epoch 173/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 12.1764 - mean_absolute_error: 3.1342\n",
            "Epoch 173: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 12.1772 - mean_absolute_error: 3.1342 - val_accuracy: 0.0000e+00 - val_loss: 9.0030 - val_mean_absolute_error: 2.8455 - learning_rate: 1.0000e-07\n",
            "Epoch 174/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.6772 - mean_absolute_error: 3.0277\n",
            "Epoch 174: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - accuracy: 0.0000e+00 - loss: 11.6771 - mean_absolute_error: 3.0277 - val_accuracy: 0.0000e+00 - val_loss: 9.1946 - val_mean_absolute_error: 2.8738 - learning_rate: 1.0000e-07\n",
            "Epoch 175/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9161 - mean_absolute_error: 3.1085\n",
            "Epoch 175: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.9151 - mean_absolute_error: 3.1083 - val_accuracy: 0.0000e+00 - val_loss: 9.1727 - val_mean_absolute_error: 2.8687 - learning_rate: 1.0000e-07\n",
            "Epoch 176/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 10.4839 - mean_absolute_error: 2.8648\n",
            "Epoch 176: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 10.4847 - mean_absolute_error: 2.8649 - val_accuracy: 0.0000e+00 - val_loss: 9.1463 - val_mean_absolute_error: 2.8675 - learning_rate: 1.0000e-07\n",
            "Epoch 177/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.7954 - mean_absolute_error: 3.1762\n",
            "Epoch 177: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.7947 - mean_absolute_error: 3.1762 - val_accuracy: 0.0000e+00 - val_loss: 9.3539 - val_mean_absolute_error: 2.8961 - learning_rate: 1.0000e-07\n",
            "Epoch 178/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.6203 - mean_absolute_error: 3.0626\n",
            "Epoch 178: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.6201 - mean_absolute_error: 3.0626 - val_accuracy: 0.0000e+00 - val_loss: 9.2834 - val_mean_absolute_error: 2.8883 - learning_rate: 1.0000e-07\n",
            "Epoch 179/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9626 - mean_absolute_error: 3.0371\n",
            "Epoch 179: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.9616 - mean_absolute_error: 3.0372 - val_accuracy: 0.0000e+00 - val_loss: 10.1060 - val_mean_absolute_error: 3.0150 - learning_rate: 1.0000e-07\n",
            "Epoch 180/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.7059 - mean_absolute_error: 3.0507\n",
            "Epoch 180: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.7065 - mean_absolute_error: 3.0506 - val_accuracy: 0.0000e+00 - val_loss: 9.7917 - val_mean_absolute_error: 2.9737 - learning_rate: 1.0000e-07\n",
            "Epoch 181/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.3583 - mean_absolute_error: 2.9703\n",
            "Epoch 181: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.3577 - mean_absolute_error: 2.9702 - val_accuracy: 0.0000e+00 - val_loss: 9.8295 - val_mean_absolute_error: 2.9755 - learning_rate: 1.0000e-07\n",
            "Epoch 182/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 10.7934 - mean_absolute_error: 2.9495\n",
            "Epoch 182: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 10.7947 - mean_absolute_error: 2.9496 - val_accuracy: 0.0000e+00 - val_loss: 9.8911 - val_mean_absolute_error: 2.9902 - learning_rate: 1.0000e-07\n",
            "Epoch 183/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.1932 - mean_absolute_error: 2.9794\n",
            "Epoch 183: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.1955 - mean_absolute_error: 2.9796 - val_accuracy: 0.0000e+00 - val_loss: 9.4585 - val_mean_absolute_error: 2.9163 - learning_rate: 1.0000e-07\n",
            "Epoch 184/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.9910 - mean_absolute_error: 3.0788\n",
            "Epoch 184: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.9890 - mean_absolute_error: 3.0786 - val_accuracy: 0.0000e+00 - val_loss: 10.0215 - val_mean_absolute_error: 3.0021 - learning_rate: 1.0000e-07\n",
            "Epoch 185/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 12.7787 - mean_absolute_error: 3.1999\n",
            "Epoch 185: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 12.7765 - mean_absolute_error: 3.1997 - val_accuracy: 0.0000e+00 - val_loss: 9.2560 - val_mean_absolute_error: 2.8670 - learning_rate: 1.0000e-07\n",
            "Epoch 186/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 10.9215 - mean_absolute_error: 2.9342\n",
            "Epoch 186: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 10.9219 - mean_absolute_error: 2.9342 - val_accuracy: 0.0000e+00 - val_loss: 9.3120 - val_mean_absolute_error: 2.8810 - learning_rate: 1.0000e-07\n",
            "Epoch 187/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - accuracy: 0.0000e+00 - loss: 11.2689 - mean_absolute_error: 3.0114\n",
            "Epoch 187: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - accuracy: 0.0000e+00 - loss: 11.2692 - mean_absolute_error: 3.0114 - val_accuracy: 0.0000e+00 - val_loss: 10.0705 - val_mean_absolute_error: 3.0211 - learning_rate: 1.0000e-07\n",
            "Epoch 188/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - accuracy: 0.0000e+00 - loss: 12.1115 - mean_absolute_error: 3.1573\n",
            "Epoch 188: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.1116 - mean_absolute_error: 3.1573 - val_accuracy: 0.0000e+00 - val_loss: 9.4222 - val_mean_absolute_error: 2.9091 - learning_rate: 1.0000e-07\n",
            "Epoch 189/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.6369 - mean_absolute_error: 3.0480\n",
            "Epoch 189: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.6373 - mean_absolute_error: 3.0481 - val_accuracy: 0.0000e+00 - val_loss: 9.9973 - val_mean_absolute_error: 3.0034 - learning_rate: 1.0000e-07\n",
            "Epoch 190/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.8549 - mean_absolute_error: 3.0925\n",
            "Epoch 190: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.8548 - mean_absolute_error: 3.0925 - val_accuracy: 0.0000e+00 - val_loss: 9.3347 - val_mean_absolute_error: 2.9007 - learning_rate: 1.0000e-07\n",
            "Epoch 191/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.7002 - mean_absolute_error: 3.0389\n",
            "Epoch 191: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 11.6990 - mean_absolute_error: 3.0388 - val_accuracy: 0.0000e+00 - val_loss: 9.2480 - val_mean_absolute_error: 2.8708 - learning_rate: 1.0000e-07\n",
            "Epoch 192/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.6217 - mean_absolute_error: 3.0028\n",
            "Epoch 192: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.6190 - mean_absolute_error: 3.0026 - val_accuracy: 0.0000e+00 - val_loss: 9.6414 - val_mean_absolute_error: 2.9455 - learning_rate: 1.0000e-07\n",
            "Epoch 193/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.9413 - mean_absolute_error: 3.0243\n",
            "Epoch 193: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.9396 - mean_absolute_error: 3.0243 - val_accuracy: 0.0000e+00 - val_loss: 9.4237 - val_mean_absolute_error: 2.9071 - learning_rate: 1.0000e-07\n",
            "Epoch 194/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 11.1587 - mean_absolute_error: 2.9797\n",
            "Epoch 194: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.1579 - mean_absolute_error: 2.9796 - val_accuracy: 0.0000e+00 - val_loss: 9.2330 - val_mean_absolute_error: 2.8777 - learning_rate: 1.0000e-07\n",
            "Epoch 195/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - accuracy: 0.0000e+00 - loss: 11.6271 - mean_absolute_error: 3.0246\n",
            "Epoch 195: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 11.6261 - mean_absolute_error: 3.0246 - val_accuracy: 0.0000e+00 - val_loss: 9.3667 - val_mean_absolute_error: 2.8948 - learning_rate: 1.0000e-07\n",
            "Epoch 196/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - accuracy: 0.0000e+00 - loss: 11.6381 - mean_absolute_error: 3.0278\n",
            "Epoch 196: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - accuracy: 0.0000e+00 - loss: 11.6389 - mean_absolute_error: 3.0279 - val_accuracy: 0.0000e+00 - val_loss: 9.7186 - val_mean_absolute_error: 2.9561 - learning_rate: 1.0000e-07\n",
            "Epoch 197/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.1200 - mean_absolute_error: 3.1359\n",
            "Epoch 197: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.1200 - mean_absolute_error: 3.1359 - val_accuracy: 0.0000e+00 - val_loss: 9.6452 - val_mean_absolute_error: 2.9474 - learning_rate: 1.0000e-07\n",
            "Epoch 198/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.1548 - mean_absolute_error: 3.1584\n",
            "Epoch 198: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.1545 - mean_absolute_error: 3.1583 - val_accuracy: 0.0000e+00 - val_loss: 8.8797 - val_mean_absolute_error: 2.8162 - learning_rate: 1.0000e-07\n",
            "Epoch 199/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.4223 - mean_absolute_error: 3.1088\n",
            "Epoch 199: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - accuracy: 0.0000e+00 - loss: 12.4193 - mean_absolute_error: 3.1086 - val_accuracy: 0.0000e+00 - val_loss: 8.7935 - val_mean_absolute_error: 2.8027 - learning_rate: 1.0000e-07\n",
            "Epoch 200/200\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - accuracy: 0.0000e+00 - loss: 12.1149 - mean_absolute_error: 3.1159\n",
            "Epoch 200: val_loss did not improve from 8.21994\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 257ms/step - accuracy: 0.0000e+00 - loss: 12.1137 - mean_absolute_error: 3.1158 - val_accuracy: 0.0000e+00 - val_loss: 9.5709 - val_mean_absolute_error: 2.9296 - learning_rate: 1.0000e-07\n",
            "Training completed!\n",
            "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 216ms/step - accuracy: 0.0000e+00 - loss: 8.7815 - mean_absolute_error: 2.8119\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, BatchNormalization\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications import EfficientNetV2M\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
        "from tensorflow.keras import mixed_precision\n",
        "\n",
        "# Disable mixed precision to avoid potential issues\n",
        "mixed_precision.set_global_policy('float32')\n",
        "\n",
        "# Limit GPU memory growth\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "if gpus:\n",
        "    try:\n",
        "        for gpu in gpus:\n",
        "            tf.config.experimental.set_memory_growth(gpu, True)\n",
        "    except RuntimeError as e:\n",
        "        print(e)\n",
        "\n",
        "# Set seeds for reproducibility\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Dataset path\n",
        "dataset_dir = '/content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new'\n",
        "\n",
        "# Image data generator\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    validation_split=0.2  # Use 20% of data for validation\n",
        ")\n",
        "\n",
        "# Model creation for Freshness Index Prediction\n",
        "def create_freshness_index_model(input_shape=(224, 224, 3)):\n",
        "    base_model = EfficientNetV2M(weights='imagenet', include_top=False, input_shape=input_shape)\n",
        "\n",
        "    x = base_model.output\n",
        "    x = GlobalAveragePooling2D()(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "\n",
        "    # Freshness index regression (1-10 scale as integers)\n",
        "    freshness_index_output = Dense(1, activation='relu', name='freshness_index_output')(x)\n",
        "\n",
        "    # Create and compile the model\n",
        "    model = Model(inputs=base_model.input, outputs=freshness_index_output)\n",
        "    model.compile(\n",
        "        optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
        "        loss='mean_squared_error',\n",
        "        metrics=['mean_absolute_error']  # Removed accuracy as it doesn't fit regression\n",
        "    )\n",
        "\n",
        "    return model\n",
        "\n",
        "# Custom data generator for Freshness Index Prediction\n",
        "def freshness_data_generator(generator):\n",
        "    while True:\n",
        "        images = next(generator)  # Retrieve image batch\n",
        "        batch_size = images.shape[0]  # Use actual batch size from generator output\n",
        "        freshness_labels = np.zeros((batch_size, 1), dtype=int)  # Freshness index (1-10 scale)\n",
        "\n",
        "        # Retrieve filenames corresponding to the batch\n",
        "        filenames = [generator.filenames[idx] for idx in generator.index_array[:batch_size]]\n",
        "        for i, filename in enumerate(filenames):\n",
        "            # Extract directory name\n",
        "            dir_name = os.path.basename(os.path.dirname(filename)).lower()\n",
        "\n",
        "            # Determine freshness index\n",
        "            if 'fresh' in dir_name:\n",
        "                freshness = np.random.randint(7, 11)  # Fresh items closer to 10\n",
        "            else:\n",
        "                freshness = np.random.randint(1, 5)  # Rotten items closer to 1\n",
        "\n",
        "            freshness_labels[i] = freshness\n",
        "\n",
        "        yield images, freshness_labels\n",
        "\n",
        "# Training function\n",
        "def train_freshness_index_model():\n",
        "    # Initialize data generators\n",
        "    train_generator = datagen.flow_from_directory(\n",
        "        dataset_dir,\n",
        "        target_size=(224, 224),  # EfficientNetV2M default input size\n",
        "        batch_size=32,\n",
        "        class_mode=None,\n",
        "        subset='training',\n",
        "        shuffle=True\n",
        "    )\n",
        "\n",
        "    validation_generator = datagen.flow_from_directory(\n",
        "        dataset_dir,\n",
        "        target_size=(224, 224),\n",
        "        batch_size=32,\n",
        "        class_mode=None,\n",
        "        subset='validation',\n",
        "        shuffle=True\n",
        "    )\n",
        "\n",
        "    model = create_freshness_index_model()\n",
        "\n",
        "    # Debugging the generator\n",
        "    generator = freshness_data_generator(train_generator)\n",
        "    images, labels = next(generator)\n",
        "    print(f\"Debug: Images shape: {images.shape}\")\n",
        "    print(f\"Debug: Freshness index labels shape: {labels.shape}\")\n",
        "\n",
        "    # Callbacks\n",
        "    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-7, verbose=1)\n",
        "    model_checkpoint = ModelCheckpoint('/content/drive/MyDrive/fiv1.keras', save_best_only=True, monitor='val_loss', verbose=1)\n",
        "\n",
        "    # Train the model\n",
        "    history = model.fit(\n",
        "        freshness_data_generator(train_generator),\n",
        "        steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
        "        validation_data=freshness_data_generator(validation_generator),\n",
        "        validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
        "        epochs=100,  # Train for 100 epochs\n",
        "        callbacks=[reduce_lr, model_checkpoint]\n",
        "    )\n",
        "\n",
        "    model.save('/content/drive/MyDrive/fiv1.keras')\n",
        "    print(\"Training completed!\")\n",
        "\n",
        "    # Calculate final validation metrics\n",
        "    val_results = model.evaluate(\n",
        "        freshness_data_generator(validation_generator),\n",
        "        steps=validation_generator.samples // validation_generator.batch_size\n",
        "    )\n",
        "    print(f\"Validation Results: {val_results}\")\n",
        "\n",
        "# Post-processing: rounding predictions during inference\n",
        "def predict_freshness(model, image):\n",
        "    pred = model.predict(image)\n",
        "    return np.clip(np.round(pred), 1, 10).astype(int)\n",
        "\n",
        "# Run the training function\n",
        "train_freshness_index_model()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DbbR0BcEhaqr",
        "outputId": "793bda07-647c-4b40-f932-c53708d56716"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Physical devices cannot be modified after being initialized\n",
            "Found 5899 images belonging to 23 classes.\n",
            "Found 1461 images belonging to 23 classes.\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/efficientnetv2-m_notop.h5\n",
            "\u001b[1m214201816/214201816\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n",
            "Debug: Images shape: (32, 224, 224, 3)\n",
            "Debug: Freshness index labels shape: (32, 1)\n",
            "Epoch 1/100\n",
            "\u001b[1m 21/184\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m29:53\u001b[0m 11s/step - loss: 37.2080 - mean_absolute_error: 5.1719"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:1054: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12s/step - loss: 28.6297 - mean_absolute_error: 4.4743 \n",
            "Epoch 1: val_loss improved from inf to 13.56302, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3003s\u001b[0m 15s/step - loss: 28.6009 - mean_absolute_error: 4.4718 - val_loss: 13.5630 - val_mean_absolute_error: 3.1556 - learning_rate: 1.0000e-04\n",
            "Epoch 2/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 248ms/step - loss: 16.1803 - mean_absolute_error: 3.3695\n",
            "Epoch 2: val_loss did not improve from 13.56302\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 355ms/step - loss: 16.1779 - mean_absolute_error: 3.3693 - val_loss: 24.4992 - val_mean_absolute_error: 4.2011 - learning_rate: 1.0000e-04\n",
            "Epoch 3/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 14.1995 - mean_absolute_error: 3.1888\n",
            "Epoch 3: val_loss did not improve from 13.56302\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m52s\u001b[0m 282ms/step - loss: 14.1994 - mean_absolute_error: 3.1889 - val_loss: 14.8217 - val_mean_absolute_error: 3.3335 - learning_rate: 1.0000e-04\n",
            "Epoch 4/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 14.7870 - mean_absolute_error: 3.2823\n",
            "Epoch 4: val_loss improved from 13.56302 to 10.47256, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 304ms/step - loss: 14.7823 - mean_absolute_error: 3.2817 - val_loss: 10.4726 - val_mean_absolute_error: 2.6783 - learning_rate: 1.0000e-04\n",
            "Epoch 5/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - loss: 11.4651 - mean_absolute_error: 2.7981\n",
            "Epoch 5: val_loss improved from 10.47256 to 8.85663, saving model to /content/drive/MyDrive/fiv1.keras\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 300ms/step - loss: 11.4637 - mean_absolute_error: 2.7975 - val_loss: 8.8566 - val_mean_absolute_error: 2.5534 - learning_rate: 1.0000e-04\n",
            "Epoch 6/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 12.4108 - mean_absolute_error: 3.0859\n",
            "Epoch 6: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 12.4082 - mean_absolute_error: 3.0858 - val_loss: 14.6746 - val_mean_absolute_error: 3.4625 - learning_rate: 1.0000e-04\n",
            "Epoch 7/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 12.1587 - mean_absolute_error: 3.0868\n",
            "Epoch 7: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 12.1568 - mean_absolute_error: 3.0864 - val_loss: 9.1183 - val_mean_absolute_error: 2.6961 - learning_rate: 1.0000e-04\n",
            "Epoch 8/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.6638 - mean_absolute_error: 3.0217\n",
            "Epoch 8: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 256ms/step - loss: 11.6639 - mean_absolute_error: 3.0218 - val_loss: 11.3713 - val_mean_absolute_error: 3.0415 - learning_rate: 1.0000e-04\n",
            "Epoch 9/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.3808 - mean_absolute_error: 2.9798\n",
            "Epoch 9: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.3808 - mean_absolute_error: 2.9797 - val_loss: 9.0001 - val_mean_absolute_error: 2.6383 - learning_rate: 1.0000e-04\n",
            "Epoch 10/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.8077 - mean_absolute_error: 3.0605\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
            "\n",
            "Epoch 10: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.8061 - mean_absolute_error: 3.0604 - val_loss: 10.4712 - val_mean_absolute_error: 2.9757 - learning_rate: 1.0000e-04\n",
            "Epoch 11/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.2796 - mean_absolute_error: 2.9899\n",
            "Epoch 11: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.2788 - mean_absolute_error: 2.9897 - val_loss: 10.3137 - val_mean_absolute_error: 2.8660 - learning_rate: 5.0000e-05\n",
            "Epoch 12/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.0879 - mean_absolute_error: 2.9104\n",
            "Epoch 12: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.0881 - mean_absolute_error: 2.9106 - val_loss: 11.1403 - val_mean_absolute_error: 2.9643 - learning_rate: 5.0000e-05\n",
            "Epoch 13/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.4399 - mean_absolute_error: 2.9627\n",
            "Epoch 13: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.4381 - mean_absolute_error: 2.9624 - val_loss: 10.1495 - val_mean_absolute_error: 2.7846 - learning_rate: 5.0000e-05\n",
            "Epoch 14/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.8795 - mean_absolute_error: 3.0612\n",
            "Epoch 14: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.8786 - mean_absolute_error: 3.0613 - val_loss: 10.2606 - val_mean_absolute_error: 2.9548 - learning_rate: 5.0000e-05\n",
            "Epoch 15/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 10.9848 - mean_absolute_error: 3.0029\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
            "\n",
            "Epoch 15: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 10.9849 - mean_absolute_error: 3.0027 - val_loss: 12.2157 - val_mean_absolute_error: 3.0291 - learning_rate: 5.0000e-05\n",
            "Epoch 16/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.1982 - mean_absolute_error: 2.9324\n",
            "Epoch 16: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.1975 - mean_absolute_error: 2.9322 - val_loss: 10.7776 - val_mean_absolute_error: 2.9455 - learning_rate: 2.5000e-05\n",
            "Epoch 17/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.2573 - mean_absolute_error: 2.9414\n",
            "Epoch 17: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.2570 - mean_absolute_error: 2.9415 - val_loss: 10.4306 - val_mean_absolute_error: 2.9393 - learning_rate: 2.5000e-05\n",
            "Epoch 18/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.4188 - mean_absolute_error: 2.9734\n",
            "Epoch 18: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.4167 - mean_absolute_error: 2.9730 - val_loss: 9.9466 - val_mean_absolute_error: 2.7711 - learning_rate: 2.5000e-05\n",
            "Epoch 19/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.0395 - mean_absolute_error: 2.9281\n",
            "Epoch 19: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.0406 - mean_absolute_error: 2.9283 - val_loss: 10.3682 - val_mean_absolute_error: 2.9257 - learning_rate: 2.5000e-05\n",
            "Epoch 20/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.0276 - mean_absolute_error: 2.9698\n",
            "Epoch 20: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
            "\n",
            "Epoch 20: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.0283 - mean_absolute_error: 2.9700 - val_loss: 10.5574 - val_mean_absolute_error: 2.9730 - learning_rate: 2.5000e-05\n",
            "Epoch 21/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.0833 - mean_absolute_error: 2.9643\n",
            "Epoch 21: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.0834 - mean_absolute_error: 2.9642 - val_loss: 10.8111 - val_mean_absolute_error: 2.9738 - learning_rate: 1.2500e-05\n",
            "Epoch 22/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.2449 - mean_absolute_error: 2.9847\n",
            "Epoch 22: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.2451 - mean_absolute_error: 2.9849 - val_loss: 10.8031 - val_mean_absolute_error: 3.0391 - learning_rate: 1.2500e-05\n",
            "Epoch 23/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.3004 - mean_absolute_error: 3.0305\n",
            "Epoch 23: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.3008 - mean_absolute_error: 3.0306 - val_loss: 10.5122 - val_mean_absolute_error: 2.9908 - learning_rate: 1.2500e-05\n",
            "Epoch 24/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.4620 - mean_absolute_error: 3.0407\n",
            "Epoch 24: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.4602 - mean_absolute_error: 3.0406 - val_loss: 10.1866 - val_mean_absolute_error: 2.9228 - learning_rate: 1.2500e-05\n",
            "Epoch 25/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.2324 - mean_absolute_error: 3.0281\n",
            "Epoch 25: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n",
            "\n",
            "Epoch 25: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.2324 - mean_absolute_error: 3.0281 - val_loss: 9.7821 - val_mean_absolute_error: 2.8627 - learning_rate: 1.2500e-05\n",
            "Epoch 26/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.3224 - mean_absolute_error: 3.0434\n",
            "Epoch 26: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.3227 - mean_absolute_error: 3.0434 - val_loss: 10.5335 - val_mean_absolute_error: 2.9773 - learning_rate: 6.2500e-06\n",
            "Epoch 27/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 10.1536 - mean_absolute_error: 2.8297\n",
            "Epoch 27: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 10.1525 - mean_absolute_error: 2.8293 - val_loss: 11.1896 - val_mean_absolute_error: 2.9686 - learning_rate: 6.2500e-06\n",
            "Epoch 28/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 10.5007 - mean_absolute_error: 2.7872\n",
            "Epoch 28: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 10.5035 - mean_absolute_error: 2.7878 - val_loss: 11.6633 - val_mean_absolute_error: 3.0781 - learning_rate: 6.2500e-06\n",
            "Epoch 29/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.4887 - mean_absolute_error: 2.9970\n",
            "Epoch 29: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.4891 - mean_absolute_error: 2.9972 - val_loss: 10.9372 - val_mean_absolute_error: 3.0237 - learning_rate: 6.2500e-06\n",
            "Epoch 30/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.6131 - mean_absolute_error: 3.0413\n",
            "Epoch 30: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-06.\n",
            "\n",
            "Epoch 30: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.6115 - mean_absolute_error: 3.0411 - val_loss: 9.4847 - val_mean_absolute_error: 2.7758 - learning_rate: 6.2500e-06\n",
            "Epoch 31/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.8420 - mean_absolute_error: 3.0963\n",
            "Epoch 31: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.8425 - mean_absolute_error: 3.0965 - val_loss: 10.1518 - val_mean_absolute_error: 2.9355 - learning_rate: 3.1250e-06\n",
            "Epoch 32/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.3909 - mean_absolute_error: 3.0545\n",
            "Epoch 32: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.3916 - mean_absolute_error: 3.0546 - val_loss: 11.0517 - val_mean_absolute_error: 3.0646 - learning_rate: 3.1250e-06\n",
            "Epoch 33/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.1229 - mean_absolute_error: 3.0017\n",
            "Epoch 33: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.1233 - mean_absolute_error: 3.0018 - val_loss: 10.5978 - val_mean_absolute_error: 2.9701 - learning_rate: 3.1250e-06\n",
            "Epoch 34/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.4202 - mean_absolute_error: 3.0369\n",
            "Epoch 34: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.4210 - mean_absolute_error: 3.0371 - val_loss: 9.8352 - val_mean_absolute_error: 2.8829 - learning_rate: 3.1250e-06\n",
            "Epoch 35/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.6126 - mean_absolute_error: 3.0960\n",
            "Epoch 35: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-06.\n",
            "\n",
            "Epoch 35: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.6123 - mean_absolute_error: 3.0959 - val_loss: 11.2217 - val_mean_absolute_error: 3.0912 - learning_rate: 3.1250e-06\n",
            "Epoch 36/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.3283 - mean_absolute_error: 3.0338\n",
            "Epoch 36: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.3272 - mean_absolute_error: 3.0336 - val_loss: 10.3293 - val_mean_absolute_error: 2.9523 - learning_rate: 1.5625e-06\n",
            "Epoch 37/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 10.9584 - mean_absolute_error: 2.9871\n",
            "Epoch 37: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 10.9596 - mean_absolute_error: 2.9874 - val_loss: 10.3953 - val_mean_absolute_error: 2.9603 - learning_rate: 1.5625e-06\n",
            "Epoch 38/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.5739 - mean_absolute_error: 3.0810\n",
            "Epoch 38: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.5734 - mean_absolute_error: 3.0810 - val_loss: 11.3098 - val_mean_absolute_error: 3.1125 - learning_rate: 1.5625e-06\n",
            "Epoch 39/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.3205 - mean_absolute_error: 3.0507\n",
            "Epoch 39: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.3207 - mean_absolute_error: 3.0507 - val_loss: 10.7742 - val_mean_absolute_error: 3.0370 - learning_rate: 1.5625e-06\n",
            "Epoch 40/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.6552 - mean_absolute_error: 3.1053\n",
            "Epoch 40: ReduceLROnPlateau reducing learning rate to 7.81249980263965e-07.\n",
            "\n",
            "Epoch 40: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.6542 - mean_absolute_error: 3.1053 - val_loss: 10.2786 - val_mean_absolute_error: 2.9625 - learning_rate: 1.5625e-06\n",
            "Epoch 41/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.7353 - mean_absolute_error: 3.1354\n",
            "Epoch 41: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.7344 - mean_absolute_error: 3.1352 - val_loss: 10.3399 - val_mean_absolute_error: 2.9624 - learning_rate: 7.8125e-07\n",
            "Epoch 42/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.2058 - mean_absolute_error: 3.0337\n",
            "Epoch 42: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.2066 - mean_absolute_error: 3.0338 - val_loss: 10.8947 - val_mean_absolute_error: 3.0405 - learning_rate: 7.8125e-07\n",
            "Epoch 43/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.1844 - mean_absolute_error: 3.0377\n",
            "Epoch 43: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.1847 - mean_absolute_error: 3.0377 - val_loss: 10.1570 - val_mean_absolute_error: 2.9552 - learning_rate: 7.8125e-07\n",
            "Epoch 44/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.5017 - mean_absolute_error: 3.0670\n",
            "Epoch 44: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.5012 - mean_absolute_error: 3.0669 - val_loss: 9.9938 - val_mean_absolute_error: 2.9357 - learning_rate: 7.8125e-07\n",
            "Epoch 45/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.4198 - mean_absolute_error: 3.0338\n",
            "Epoch 45: ReduceLROnPlateau reducing learning rate to 3.906249901319825e-07.\n",
            "\n",
            "Epoch 45: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.4190 - mean_absolute_error: 3.0337 - val_loss: 10.5192 - val_mean_absolute_error: 3.0060 - learning_rate: 7.8125e-07\n",
            "Epoch 46/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.0606 - mean_absolute_error: 2.9945\n",
            "Epoch 46: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.0609 - mean_absolute_error: 2.9946 - val_loss: 10.4502 - val_mean_absolute_error: 2.9816 - learning_rate: 3.9062e-07\n",
            "Epoch 47/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.4807 - mean_absolute_error: 3.0760\n",
            "Epoch 47: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.4798 - mean_absolute_error: 3.0758 - val_loss: 10.2475 - val_mean_absolute_error: 2.9535 - learning_rate: 3.9062e-07\n",
            "Epoch 48/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.5413 - mean_absolute_error: 3.0842\n",
            "Epoch 48: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.5412 - mean_absolute_error: 3.0843 - val_loss: 11.1049 - val_mean_absolute_error: 3.0364 - learning_rate: 3.9062e-07\n",
            "Epoch 49/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.7384 - mean_absolute_error: 3.1193\n",
            "Epoch 49: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.7371 - mean_absolute_error: 3.1191 - val_loss: 10.3403 - val_mean_absolute_error: 2.9434 - learning_rate: 3.9062e-07\n",
            "Epoch 50/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.2100 - mean_absolute_error: 3.0342\n",
            "Epoch 50: ReduceLROnPlateau reducing learning rate to 1.9531249506599124e-07.\n",
            "\n",
            "Epoch 50: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.2102 - mean_absolute_error: 3.0342 - val_loss: 10.8739 - val_mean_absolute_error: 3.0706 - learning_rate: 3.9062e-07\n",
            "Epoch 51/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.4243 - mean_absolute_error: 3.0530\n",
            "Epoch 51: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.4236 - mean_absolute_error: 3.0529 - val_loss: 10.1746 - val_mean_absolute_error: 2.9466 - learning_rate: 1.9531e-07\n",
            "Epoch 52/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.0453 - mean_absolute_error: 3.0001\n",
            "Epoch 52: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.0455 - mean_absolute_error: 3.0001 - val_loss: 10.4416 - val_mean_absolute_error: 2.9495 - learning_rate: 1.9531e-07\n",
            "Epoch 53/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.3286 - mean_absolute_error: 3.0191\n",
            "Epoch 53: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.3283 - mean_absolute_error: 3.0191 - val_loss: 10.7432 - val_mean_absolute_error: 3.0221 - learning_rate: 1.9531e-07\n",
            "Epoch 54/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.1073 - mean_absolute_error: 2.9945\n",
            "Epoch 54: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.1074 - mean_absolute_error: 2.9945 - val_loss: 10.1869 - val_mean_absolute_error: 2.9179 - learning_rate: 1.9531e-07\n",
            "Epoch 55/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.3722 - mean_absolute_error: 3.0534\n",
            "Epoch 55: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
            "\n",
            "Epoch 55: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.3727 - mean_absolute_error: 3.0536 - val_loss: 9.8213 - val_mean_absolute_error: 2.8633 - learning_rate: 1.9531e-07\n",
            "Epoch 56/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.5402 - mean_absolute_error: 3.0907\n",
            "Epoch 56: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.5397 - mean_absolute_error: 3.0906 - val_loss: 11.0360 - val_mean_absolute_error: 3.0656 - learning_rate: 1.0000e-07\n",
            "Epoch 57/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.1841 - mean_absolute_error: 3.0064\n",
            "Epoch 57: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.1835 - mean_absolute_error: 3.0064 - val_loss: 10.9997 - val_mean_absolute_error: 3.0551 - learning_rate: 1.0000e-07\n",
            "Epoch 58/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.1768 - mean_absolute_error: 3.0182\n",
            "Epoch 58: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - loss: 11.1773 - mean_absolute_error: 3.0184 - val_loss: 10.7354 - val_mean_absolute_error: 2.9862 - learning_rate: 1.0000e-07\n",
            "Epoch 59/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 11.4538 - mean_absolute_error: 3.0680\n",
            "Epoch 59: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.4530 - mean_absolute_error: 3.0678 - val_loss: 9.5512 - val_mean_absolute_error: 2.8365 - learning_rate: 1.0000e-07\n",
            "Epoch 60/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.1170 - mean_absolute_error: 3.0114\n",
            "Epoch 60: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.1177 - mean_absolute_error: 3.0116 - val_loss: 10.3276 - val_mean_absolute_error: 2.9237 - learning_rate: 1.0000e-07\n",
            "Epoch 61/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.5007 - mean_absolute_error: 3.0775\n",
            "Epoch 61: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.4998 - mean_absolute_error: 3.0774 - val_loss: 10.4827 - val_mean_absolute_error: 2.9678 - learning_rate: 1.0000e-07\n",
            "Epoch 62/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.7108 - mean_absolute_error: 3.1004\n",
            "Epoch 62: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - loss: 11.7089 - mean_absolute_error: 3.1002 - val_loss: 10.4947 - val_mean_absolute_error: 2.9798 - learning_rate: 1.0000e-07\n",
            "Epoch 63/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.2328 - mean_absolute_error: 3.0102\n",
            "Epoch 63: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - loss: 11.2334 - mean_absolute_error: 3.0102 - val_loss: 11.0832 - val_mean_absolute_error: 3.0740 - learning_rate: 1.0000e-07\n",
            "Epoch 64/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 10.9410 - mean_absolute_error: 2.9842\n",
            "Epoch 64: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 10.9423 - mean_absolute_error: 2.9844 - val_loss: 10.3423 - val_mean_absolute_error: 2.9677 - learning_rate: 1.0000e-07\n",
            "Epoch 65/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.3014 - mean_absolute_error: 3.0604\n",
            "Epoch 65: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - loss: 11.3017 - mean_absolute_error: 3.0604 - val_loss: 10.5362 - val_mean_absolute_error: 2.9761 - learning_rate: 1.0000e-07\n",
            "Epoch 66/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.4283 - mean_absolute_error: 3.0739\n",
            "Epoch 66: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.4283 - mean_absolute_error: 3.0738 - val_loss: 10.7598 - val_mean_absolute_error: 3.0044 - learning_rate: 1.0000e-07\n",
            "Epoch 67/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 11.3797 - mean_absolute_error: 3.0602\n",
            "Epoch 67: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.3792 - mean_absolute_error: 3.0601 - val_loss: 10.3970 - val_mean_absolute_error: 2.9910 - learning_rate: 1.0000e-07\n",
            "Epoch 68/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.5091 - mean_absolute_error: 3.0774\n",
            "Epoch 68: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.5082 - mean_absolute_error: 3.0773 - val_loss: 10.4445 - val_mean_absolute_error: 2.9849 - learning_rate: 1.0000e-07\n",
            "Epoch 69/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 11.5166 - mean_absolute_error: 3.0672\n",
            "Epoch 69: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.5156 - mean_absolute_error: 3.0671 - val_loss: 10.5204 - val_mean_absolute_error: 2.9722 - learning_rate: 1.0000e-07\n",
            "Epoch 70/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.2029 - mean_absolute_error: 3.0170\n",
            "Epoch 70: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.2037 - mean_absolute_error: 3.0172 - val_loss: 10.4409 - val_mean_absolute_error: 2.9469 - learning_rate: 1.0000e-07\n",
            "Epoch 71/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.1931 - mean_absolute_error: 3.0184\n",
            "Epoch 71: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.1930 - mean_absolute_error: 3.0183 - val_loss: 10.4443 - val_mean_absolute_error: 2.9915 - learning_rate: 1.0000e-07\n",
            "Epoch 72/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.1802 - mean_absolute_error: 3.0010\n",
            "Epoch 72: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.1806 - mean_absolute_error: 3.0010 - val_loss: 10.5781 - val_mean_absolute_error: 2.9855 - learning_rate: 1.0000e-07\n",
            "Epoch 73/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.0886 - mean_absolute_error: 2.9798\n",
            "Epoch 73: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - loss: 11.0889 - mean_absolute_error: 2.9800 - val_loss: 10.4787 - val_mean_absolute_error: 2.9696 - learning_rate: 1.0000e-07\n",
            "Epoch 74/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.6486 - mean_absolute_error: 3.0867\n",
            "Epoch 74: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.6470 - mean_absolute_error: 3.0865 - val_loss: 10.0935 - val_mean_absolute_error: 2.9068 - learning_rate: 1.0000e-07\n",
            "Epoch 75/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 11.4666 - mean_absolute_error: 3.0622\n",
            "Epoch 75: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.4663 - mean_absolute_error: 3.0621 - val_loss: 10.2081 - val_mean_absolute_error: 2.9413 - learning_rate: 1.0000e-07\n",
            "Epoch 76/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.7682 - mean_absolute_error: 3.1249\n",
            "Epoch 76: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.7687 - mean_absolute_error: 3.1250 - val_loss: 10.2609 - val_mean_absolute_error: 2.9479 - learning_rate: 1.0000e-07\n",
            "Epoch 77/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.4539 - mean_absolute_error: 3.0984\n",
            "Epoch 77: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.4533 - mean_absolute_error: 3.0982 - val_loss: 10.6484 - val_mean_absolute_error: 3.0038 - learning_rate: 1.0000e-07\n",
            "Epoch 78/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 11.1093 - mean_absolute_error: 2.9922\n",
            "Epoch 78: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.1090 - mean_absolute_error: 2.9921 - val_loss: 10.3824 - val_mean_absolute_error: 2.9528 - learning_rate: 1.0000e-07\n",
            "Epoch 79/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.1571 - mean_absolute_error: 3.0118\n",
            "Epoch 79: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.1581 - mean_absolute_error: 3.0121 - val_loss: 10.3975 - val_mean_absolute_error: 2.9789 - learning_rate: 1.0000e-07\n",
            "Epoch 80/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.6218 - mean_absolute_error: 3.1119\n",
            "Epoch 80: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.6205 - mean_absolute_error: 3.1117 - val_loss: 10.5726 - val_mean_absolute_error: 3.0153 - learning_rate: 1.0000e-07\n",
            "Epoch 81/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 11.1184 - mean_absolute_error: 3.0258\n",
            "Epoch 81: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.1192 - mean_absolute_error: 3.0259 - val_loss: 10.2879 - val_mean_absolute_error: 2.9675 - learning_rate: 1.0000e-07\n",
            "Epoch 82/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.5752 - mean_absolute_error: 3.0946\n",
            "Epoch 82: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.5745 - mean_absolute_error: 3.0944 - val_loss: 10.6713 - val_mean_absolute_error: 2.9874 - learning_rate: 1.0000e-07\n",
            "Epoch 83/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.2785 - mean_absolute_error: 3.0239\n",
            "Epoch 83: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.2791 - mean_absolute_error: 3.0241 - val_loss: 9.1270 - val_mean_absolute_error: 2.7568 - learning_rate: 1.0000e-07\n",
            "Epoch 84/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.0013 - mean_absolute_error: 3.0004\n",
            "Epoch 84: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.0020 - mean_absolute_error: 3.0005 - val_loss: 9.7329 - val_mean_absolute_error: 2.8681 - learning_rate: 1.0000e-07\n",
            "Epoch 85/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.2179 - mean_absolute_error: 3.0280\n",
            "Epoch 85: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.2177 - mean_absolute_error: 3.0280 - val_loss: 10.5578 - val_mean_absolute_error: 2.9968 - learning_rate: 1.0000e-07\n",
            "Epoch 86/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 10.8405 - mean_absolute_error: 2.9696\n",
            "Epoch 86: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 10.8434 - mean_absolute_error: 2.9700 - val_loss: 10.7191 - val_mean_absolute_error: 3.0273 - learning_rate: 1.0000e-07\n",
            "Epoch 87/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 10.9770 - mean_absolute_error: 2.9908\n",
            "Epoch 87: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 10.9775 - mean_absolute_error: 2.9909 - val_loss: 10.0185 - val_mean_absolute_error: 2.8737 - learning_rate: 1.0000e-07\n",
            "Epoch 88/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.2041 - mean_absolute_error: 3.0187\n",
            "Epoch 88: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.2037 - mean_absolute_error: 3.0187 - val_loss: 10.9839 - val_mean_absolute_error: 3.0483 - learning_rate: 1.0000e-07\n",
            "Epoch 89/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 11.3259 - mean_absolute_error: 3.0553\n",
            "Epoch 89: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.3264 - mean_absolute_error: 3.0553 - val_loss: 10.9848 - val_mean_absolute_error: 3.0408 - learning_rate: 1.0000e-07\n",
            "Epoch 90/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.4593 - mean_absolute_error: 3.0816\n",
            "Epoch 90: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 11.4593 - mean_absolute_error: 3.0815 - val_loss: 10.5894 - val_mean_absolute_error: 2.9689 - learning_rate: 1.0000e-07\n",
            "Epoch 91/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 11.2138 - mean_absolute_error: 2.9987\n",
            "Epoch 91: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.2123 - mean_absolute_error: 2.9984 - val_loss: 10.4834 - val_mean_absolute_error: 2.9866 - learning_rate: 1.0000e-07\n",
            "Epoch 92/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 10.9649 - mean_absolute_error: 2.9408\n",
            "Epoch 92: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 255ms/step - loss: 10.9657 - mean_absolute_error: 2.9409 - val_loss: 9.8237 - val_mean_absolute_error: 2.8641 - learning_rate: 1.0000e-07\n",
            "Epoch 93/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.0049 - mean_absolute_error: 2.9953\n",
            "Epoch 93: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.0053 - mean_absolute_error: 2.9954 - val_loss: 10.4892 - val_mean_absolute_error: 2.9893 - learning_rate: 1.0000e-07\n",
            "Epoch 94/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.4331 - mean_absolute_error: 3.0442\n",
            "Epoch 94: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 253ms/step - loss: 11.4331 - mean_absolute_error: 3.0442 - val_loss: 10.1940 - val_mean_absolute_error: 2.9200 - learning_rate: 1.0000e-07\n",
            "Epoch 95/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.3958 - mean_absolute_error: 3.0363\n",
            "Epoch 95: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 11.3942 - mean_absolute_error: 3.0359 - val_loss: 10.0552 - val_mean_absolute_error: 2.9251 - learning_rate: 1.0000e-07\n",
            "Epoch 96/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 10.7568 - mean_absolute_error: 2.9184\n",
            "Epoch 96: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 254ms/step - loss: 10.7583 - mean_absolute_error: 2.9188 - val_loss: 10.5232 - val_mean_absolute_error: 3.0043 - learning_rate: 1.0000e-07\n",
            "Epoch 97/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.4086 - mean_absolute_error: 3.0625\n",
            "Epoch 97: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 11.4075 - mean_absolute_error: 3.0622 - val_loss: 9.7835 - val_mean_absolute_error: 2.8627 - learning_rate: 1.0000e-07\n",
            "Epoch 98/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 10.7038 - mean_absolute_error: 2.9085\n",
            "Epoch 98: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 252ms/step - loss: 10.7045 - mean_absolute_error: 2.9087 - val_loss: 9.7493 - val_mean_absolute_error: 2.8504 - learning_rate: 1.0000e-07\n",
            "Epoch 99/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 11.3284 - mean_absolute_error: 3.0254\n",
            "Epoch 99: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.3280 - mean_absolute_error: 3.0254 - val_loss: 11.0992 - val_mean_absolute_error: 3.1003 - learning_rate: 1.0000e-07\n",
            "Epoch 100/100\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 11.2862 - mean_absolute_error: 3.0451\n",
            "Epoch 100: val_loss did not improve from 8.85663\n",
            "\u001b[1m184/184\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 253ms/step - loss: 11.2865 - mean_absolute_error: 3.0451 - val_loss: 10.2871 - val_mean_absolute_error: 2.9511 - learning_rate: 1.0000e-07\n",
            "Training completed!\n",
            "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 169ms/step - loss: 10.1665 - mean_absolute_error: 2.9154\n",
            "Validation Results: [10.490424156188965, 2.9814553260803223]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Path to the trained model\n",
        "freshness_model_path = '/content/drive/MyDrive/fiv1.keras'\n",
        "sample_image_path = '/content/Screenshot 2024-09-24 at 1.49.55 PM.png'  # Replace with your test image path\n",
        "\n",
        "# Load the trained freshness model\n",
        "freshness_model = load_model(freshness_model_path)\n",
        "\n",
        "def preprocess_image(image_path, target_size=(224, 224)):\n",
        "    \"\"\"Preprocess the image for prediction.\"\"\"\n",
        "    img = load_img(image_path, target_size=target_size)  # Load and resize the image\n",
        "    img_array = img_to_array(img)  # Convert to numpy array\n",
        "    img_array = img_array / 255.0  # Normalize pixel values\n",
        "    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
        "    return img, img_array\n",
        "\n",
        "def predict_freshness(image_path):\n",
        "    \"\"\"Predict the freshness index and estimate the shelf life.\"\"\"\n",
        "    # Preprocess the image\n",
        "    original_img, img_array = preprocess_image(image_path)\n",
        "\n",
        "    # Predict freshness index\n",
        "    freshness_prediction = freshness_model.predict(img_array)\n",
        "    freshness_index = freshness_prediction[0][0]\n",
        "\n",
        "    # Determine freshness status\n",
        "    if freshness_index >= 7:\n",
        "        freshness_status = \"Fresh\"\n",
        "        shelf_life = \"5+ days\"\n",
        "    elif freshness_index >= 4:\n",
        "        freshness_status = \"Moderately Fresh\"\n",
        "        shelf_life = \"2-4 days\"\n",
        "    else:\n",
        "        freshness_status = \"Not Fresh\"\n",
        "        shelf_life = \"0-1 day\"\n",
        "\n",
        "    # Print results\n",
        "    print(f\"Freshness Index: {freshness_index:.2f}\")\n",
        "    print(f\"Freshness Status: {freshness_status}\")\n",
        "    print(f\"Estimated Shelf Life: {shelf_life}\")\n",
        "\n",
        "    # Display the image with predictions\n",
        "    original_img_array = np.array(original_img)\n",
        "    plt.imshow(original_img_array.astype(\"uint8\"))\n",
        "    plt.title(f\"Freshness: {freshness_status} ({freshness_index:.2f}), Shelf Life: {shelf_life}\")\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "# Test the model with the sample image\n",
        "predict_freshness(sample_image_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "IOEh_RCqk0O5",
        "outputId": "bf10521c-316c-4b1f-d33a-9eda7d9d9541"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 13s/step\n",
            "Freshness Index: 5.88\n",
            "Freshness Status: Moderately Fresh\n",
            "Estimated Shelf Life: 2-4 days\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from collections import Counter\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import (\n",
        "    Conv2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization\n",
        ")\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n",
        "\n",
        "# Set the seed for reproducibility\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Paths to the dataset\n",
        "dataset_dir = '/content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new'\n",
        "\n",
        "# Automatically infer the number of valid classes\n",
        "class_dirs = [d for d in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, d))]\n",
        "num_classes = len(class_dirs)\n",
        "\n",
        "# Calculate class weights to handle imbalance\n",
        "class_counts = {}\n",
        "for class_name in class_dirs:\n",
        "    class_path = os.path.join(dataset_dir, class_name)\n",
        "    class_counts[class_name] = len(os.listdir(class_path))\n",
        "\n",
        "# Generate class weights inversely proportional to the class counts\n",
        "total_images = sum(class_counts.values())\n",
        "class_weights = {\n",
        "    idx: total_images / (num_classes * count)\n",
        "    for idx, (class_name, count) in enumerate(sorted(class_counts.items()))\n",
        "}\n",
        "print(\"Class Weights:\", class_weights)\n",
        "\n",
        "# Data augmentation and preprocessing\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale=1.0 / 255,\n",
        "    validation_split=0.2,  # Split dataset into training and validation\n",
        "    rotation_range=20,     # Augmentations\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",
        "\n",
        "def build_fruiznet(input_shape=(160, 160, 3), num_classes=num_classes):\n",
        "    \"\"\"Build the FruizNet CNN model.\"\"\"\n",
        "    model = Sequential()\n",
        "\n",
        "    # First Convolutional Block\n",
        "    model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=input_shape))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Second Convolutional Block\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Third Convolutional Block\n",
        "    model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Fourth Convolutional Block\n",
        "    model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "    # Flatten and Fully Connected Layers\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(128, activation='relu'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Dropout(0.5))\n",
        "\n",
        "    # Output Layer\n",
        "    model.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "    # Compile the model\n",
        "    model.compile(\n",
        "        optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
        "        loss='categorical_crossentropy',\n",
        "        metrics=['accuracy']\n",
        "    )\n",
        "\n",
        "    return model\n",
        "\n",
        "def train_fruiznet():\n",
        "    \"\"\"Train the FruizNet model on the fruit dataset.\"\"\"\n",
        "    # Prepare the data generators\n",
        "    train_generator = datagen.flow_from_directory(\n",
        "        dataset_dir,\n",
        "        target_size=(160, 160),\n",
        "        batch_size=16,\n",
        "        class_mode='categorical',\n",
        "        subset='training',\n",
        "        shuffle=True\n",
        "    )\n",
        "\n",
        "    validation_generator = datagen.flow_from_directory(\n",
        "        dataset_dir,\n",
        "        target_size=(160, 160),\n",
        "        batch_size=16,\n",
        "        class_mode='categorical',\n",
        "        subset='validation',\n",
        "        shuffle=True\n",
        "    )\n",
        "\n",
        "    # Debugging: Print class indices\n",
        "    print(\"Training generator class indices:\", train_generator.class_indices)\n",
        "    print(\"Validation generator class indices:\", validation_generator.class_indices)\n",
        "\n",
        "    # Build the FruizNet model\n",
        "    model = build_fruiznet()\n",
        "\n",
        "    # Callbacks for learning rate reduction and model checkpointing\n",
        "    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-7, verbose=1)\n",
        "    model_checkpoint = ModelCheckpoint(\n",
        "        '/content/drive/MyDrive/fruiznet_best_model.keras',\n",
        "        save_best_only=True,\n",
        "        monitor='val_loss',\n",
        "        verbose=1\n",
        "    )\n",
        "\n",
        "    # Train the model with class weights\n",
        "    history = model.fit(\n",
        "        train_generator,\n",
        "        steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
        "        validation_data=validation_generator,\n",
        "        validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
        "        class_weight=class_weights,  # Include class weights\n",
        "        epochs=100,  # Adjust epochs as needed\n",
        "        callbacks=[reduce_lr, model_checkpoint]\n",
        "    )\n",
        "\n",
        "    # Save the final model\n",
        "    model.save('/content/drive/MyDrive/fruiznet_final_model.keras')\n",
        "    print(\"Training completed!\")\n",
        "\n",
        "# Run the training function\n",
        "train_fruiznet()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Knir-Xt60b9T",
        "outputId": "d8712150-3a84-4570-ac6a-a65b333f22d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class Weights: {0: 1.0556350114416475, 1: 0.7244086760231623, 2: 0.517601683029453, 3: 0.4761321119855503, 4: 0.5330781453127257, 5: 0.4975396022918773, 6: 2.2441471571906355, 7: 1.4857085346215781, 8: 1.8233695652173914, 9: 1.5503045578659946, 10: 0.5495086360929125, 11: 3.8664222105814563, 12: 3.8664222105814563, 13: 1.6801730025039836, 14: 0.466443377148635, 15: 3.820393374741201, 16: 0.6946169772256728, 17: 0.780810324764625, 18: 3.8664222105814563, 19: 3.820393374741201, 20: 3.8664222105814563, 21: 0.7428542673107891, 22: 3.8664222105814563}\n",
            "Found 5899 images belonging to 23 classes.\n",
            "Found 1461 images belonging to 23 classes.\n",
            "Training generator class indices: {'FreshApple': 0, 'FreshBellpepper': 1, 'FreshCarrot': 2, 'FreshCucumber': 3, 'FreshPotato': 4, 'FreshTomato': 5, 'RottenBellpepper': 6, 'RottenCarrot': 7, 'RottenCucumber': 8, 'RottenPotato': 9, 'RottenTomato': 10, 'freshbrocoli': 11, 'freshlemon': 12, 'freshmango': 13, 'freshoranges': 14, 'frsshpineapple': 15, 'rottenapple': 16, 'rottenbanana': 17, 'rottenbrocoli': 18, 'rottenlemon': 19, 'rottenmango': 20, 'rottenoranges': 21, 'rottenpineapple': 22}\n",
            "Validation generator class indices: {'FreshApple': 0, 'FreshBellpepper': 1, 'FreshCarrot': 2, 'FreshCucumber': 3, 'FreshPotato': 4, 'FreshTomato': 5, 'RottenBellpepper': 6, 'RottenCarrot': 7, 'RottenCucumber': 8, 'RottenPotato': 9, 'RottenTomato': 10, 'freshbrocoli': 11, 'freshlemon': 12, 'freshmango': 13, 'freshoranges': 14, 'frsshpineapple': 15, 'rottenapple': 16, 'rottenbanana': 17, 'rottenbrocoli': 18, 'rottenlemon': 19, 'rottenmango': 20, 'rottenoranges': 21, 'rottenpineapple': 22}\n",
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: 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[1m 17/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m53s\u001b[0m 153ms/step - accuracy: 0.0976 - loss: 4.3188"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:1054: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 186ms/step - accuracy: 0.2108 - loss: 2.8902\n",
            "Epoch 1: val_loss improved from inf to 4.61271, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 233ms/step - accuracy: 0.2113 - loss: 2.8874 - val_accuracy: 0.0570 - val_loss: 4.6127 - learning_rate: 1.0000e-04\n",
            "Epoch 2/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.5625 - loss: 2.4752"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/contextlib.py:153: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n",
            "  self.gen.throw(typ, value, traceback)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 2: val_loss did not improve from 4.61271\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.5625 - loss: 2.4752 - val_accuracy: 0.0000e+00 - val_loss: 4.7665 - learning_rate: 1.0000e-04\n",
            "Epoch 3/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.4544 - loss: 1.6754\n",
            "Epoch 3: val_loss improved from 4.61271 to 1.48249, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 220ms/step - accuracy: 0.4545 - loss: 1.6751 - val_accuracy: 0.5440 - val_loss: 1.4825 - learning_rate: 1.0000e-04\n",
            "Epoch 4/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.6875 - loss: 1.3707\n",
            "Epoch 4: val_loss improved from 1.48249 to 1.47993, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 738us/step - accuracy: 0.6875 - loss: 1.3707 - val_accuracy: 0.4000 - val_loss: 1.4799 - learning_rate: 1.0000e-04\n",
            "Epoch 5/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - accuracy: 0.5306 - loss: 1.4648\n",
            "Epoch 5: val_loss improved from 1.47993 to 1.32119, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 217ms/step - accuracy: 0.5306 - loss: 1.4647 - val_accuracy: 0.5962 - val_loss: 1.3212 - learning_rate: 1.0000e-04\n",
            "Epoch 6/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.3125 - loss: 2.0215\n",
            "Epoch 6: val_loss improved from 1.32119 to 1.02727, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 744us/step - accuracy: 0.3125 - loss: 2.0215 - val_accuracy: 0.8000 - val_loss: 1.0273 - learning_rate: 1.0000e-04\n",
            "Epoch 7/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - accuracy: 0.5555 - loss: 1.3481\n",
            "Epoch 7: val_loss did not improve from 1.02727\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 214ms/step - accuracy: 0.5555 - loss: 1.3478 - val_accuracy: 0.6731 - val_loss: 1.1575 - learning_rate: 1.0000e-04\n",
            "Epoch 8/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.6250 - loss: 0.6878\n",
            "Epoch 8: val_loss improved from 1.02727 to 0.42223, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 844us/step - accuracy: 0.6250 - loss: 0.6878 - val_accuracy: 1.0000 - val_loss: 0.4222 - learning_rate: 1.0000e-04\n",
            "Epoch 9/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - accuracy: 0.6135 - loss: 1.1215\n",
            "Epoch 9: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 215ms/step - accuracy: 0.6136 - loss: 1.1214 - val_accuracy: 0.6504 - val_loss: 1.1795 - learning_rate: 1.0000e-04\n",
            "Epoch 10/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.6875 - loss: 0.5743\n",
            "Epoch 10: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84us/step - accuracy: 0.6875 - loss: 0.5743 - val_accuracy: 0.2000 - val_loss: 3.3298 - learning_rate: 1.0000e-04\n",
            "Epoch 11/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.6359 - loss: 1.0429\n",
            "Epoch 11: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 214ms/step - accuracy: 0.6360 - loss: 1.0429 - val_accuracy: 0.6456 - val_loss: 1.1305 - learning_rate: 1.0000e-04\n",
            "Epoch 12/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.6875 - loss: 1.5669\n",
            "Epoch 12: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 646us/step - accuracy: 0.6875 - loss: 1.5669 - val_accuracy: 0.6000 - val_loss: 1.0980 - learning_rate: 1.0000e-04\n",
            "Epoch 13/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.6641 - loss: 0.9782\n",
            "Epoch 13: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
            "\n",
            "Epoch 13: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 213ms/step - accuracy: 0.6641 - loss: 0.9782 - val_accuracy: 0.7205 - val_loss: 1.0217 - learning_rate: 1.0000e-04\n",
            "Epoch 14/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.7500 - loss: 2.1149\n",
            "Epoch 14: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117us/step - accuracy: 0.7500 - loss: 2.1149 - val_accuracy: 0.2000 - val_loss: 1.0722 - learning_rate: 5.0000e-05\n",
            "Epoch 15/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - accuracy: 0.7086 - loss: 0.8147\n",
            "Epoch 15: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 213ms/step - accuracy: 0.7086 - loss: 0.8147 - val_accuracy: 0.7315 - val_loss: 0.9214 - learning_rate: 5.0000e-05\n",
            "Epoch 16/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.4957\n",
            "Epoch 16: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86us/step - accuracy: 0.8125 - loss: 0.4957 - val_accuracy: 0.8000 - val_loss: 1.1730 - learning_rate: 5.0000e-05\n",
            "Epoch 17/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.7334 - loss: 0.7546\n",
            "Epoch 17: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 210ms/step - accuracy: 0.7334 - loss: 0.7547 - val_accuracy: 0.7431 - val_loss: 0.8609 - learning_rate: 5.0000e-05\n",
            "Epoch 18/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.6031\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
            "\n",
            "Epoch 18: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96us/step - accuracy: 0.7500 - loss: 0.6031 - val_accuracy: 0.2000 - val_loss: 2.1442 - learning_rate: 5.0000e-05\n",
            "Epoch 19/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.7369 - loss: 0.7084\n",
            "Epoch 19: val_loss did not improve from 0.42223\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.7369 - loss: 0.7084 - val_accuracy: 0.7651 - val_loss: 0.7659 - learning_rate: 2.5000e-05\n",
            "Epoch 20/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.9324\n",
            "Epoch 20: val_loss improved from 0.42223 to 0.29341, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 789us/step - accuracy: 0.7500 - loss: 0.9324 - val_accuracy: 1.0000 - val_loss: 0.2934 - learning_rate: 2.5000e-05\n",
            "Epoch 21/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.7590 - loss: 0.6464\n",
            "Epoch 21: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.7590 - loss: 0.6464 - val_accuracy: 0.7837 - val_loss: 0.7697 - learning_rate: 2.5000e-05\n",
            "Epoch 22/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.3703\n",
            "Epoch 22: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98us/step - accuracy: 0.7500 - loss: 0.3703 - val_accuracy: 0.4000 - val_loss: 1.3213 - learning_rate: 2.5000e-05\n",
            "Epoch 23/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.7696 - loss: 0.6345\n",
            "Epoch 23: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.7697 - loss: 0.6346 - val_accuracy: 0.7734 - val_loss: 0.7763 - learning_rate: 2.5000e-05\n",
            "Epoch 24/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.3366\n",
            "Epoch 24: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88us/step - accuracy: 0.8125 - loss: 0.3366 - val_accuracy: 0.6000 - val_loss: 1.0649 - learning_rate: 2.5000e-05\n",
            "Epoch 25/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.7841 - loss: 0.5771\n",
            "Epoch 25: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-05.\n",
            "\n",
            "Epoch 25: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.7841 - loss: 0.5772 - val_accuracy: 0.8049 - val_loss: 0.7296 - learning_rate: 2.5000e-05\n",
            "Epoch 26/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 11ms/step - accuracy: 0.7500 - loss: 0.6878\n",
            "Epoch 26: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88us/step - accuracy: 0.7500 - loss: 0.6878 - val_accuracy: 1.0000 - val_loss: 0.3008 - learning_rate: 1.2500e-05\n",
            "Epoch 27/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.7871 - loss: 0.5757\n",
            "Epoch 27: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.7871 - loss: 0.5756 - val_accuracy: 0.7912 - val_loss: 0.7204 - learning_rate: 1.2500e-05\n",
            "Epoch 28/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8750 - loss: 0.3942\n",
            "Epoch 28: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129us/step - accuracy: 0.8750 - loss: 0.3942 - val_accuracy: 0.6000 - val_loss: 1.8125 - learning_rate: 1.2500e-05\n",
            "Epoch 29/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.7951 - loss: 0.5573\n",
            "Epoch 29: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.7951 - loss: 0.5575 - val_accuracy: 0.7960 - val_loss: 0.7135 - learning_rate: 1.2500e-05\n",
            "Epoch 30/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.6250 - loss: 1.7413\n",
            "Epoch 30: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-06.\n",
            "\n",
            "Epoch 30: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165us/step - accuracy: 0.6250 - loss: 1.7413 - val_accuracy: 0.8000 - val_loss: 0.5291 - learning_rate: 1.2500e-05\n",
            "Epoch 31/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.8012 - loss: 0.5378\n",
            "Epoch 31: val_loss did not improve from 0.29341\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 210ms/step - accuracy: 0.8012 - loss: 0.5378 - val_accuracy: 0.8036 - val_loss: 0.6972 - learning_rate: 6.2500e-06\n",
            "Epoch 32/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8750 - loss: 0.2786\n",
            "Epoch 32: val_loss improved from 0.29341 to 0.24771, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 817us/step - accuracy: 0.8750 - loss: 0.2786 - val_accuracy: 1.0000 - val_loss: 0.2477 - learning_rate: 6.2500e-06\n",
            "Epoch 33/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.7917 - loss: 0.5773\n",
            "Epoch 33: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.7917 - loss: 0.5772 - val_accuracy: 0.7837 - val_loss: 0.7255 - learning_rate: 6.2500e-06\n",
            "Epoch 34/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.7961\n",
            "Epoch 34: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76us/step - accuracy: 0.7500 - loss: 0.7961 - val_accuracy: 0.8000 - val_loss: 0.4849 - learning_rate: 6.2500e-06\n",
            "Epoch 35/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.7895 - loss: 0.5539\n",
            "Epoch 35: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.7896 - loss: 0.5539 - val_accuracy: 0.8063 - val_loss: 0.6602 - learning_rate: 6.2500e-06\n",
            "Epoch 36/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.6850\n",
            "Epoch 36: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120us/step - accuracy: 0.8125 - loss: 0.6850 - val_accuracy: 0.6000 - val_loss: 1.1590 - learning_rate: 6.2500e-06\n",
            "Epoch 37/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.7997 - loss: 0.5292\n",
            "Epoch 37: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-06.\n",
            "\n",
            "Epoch 37: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.7997 - loss: 0.5293 - val_accuracy: 0.7871 - val_loss: 0.7162 - learning_rate: 6.2500e-06\n",
            "Epoch 38/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.6875 - loss: 0.6861\n",
            "Epoch 38: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122us/step - accuracy: 0.6875 - loss: 0.6861 - val_accuracy: 1.0000 - val_loss: 0.2804 - learning_rate: 3.1250e-06\n",
            "Epoch 39/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.7965 - loss: 0.5301\n",
            "Epoch 39: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.7966 - loss: 0.5301 - val_accuracy: 0.8015 - val_loss: 0.6754 - learning_rate: 3.1250e-06\n",
            "Epoch 40/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.6875 - loss: 0.5004\n",
            "Epoch 40: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143us/step - accuracy: 0.6875 - loss: 0.5004 - val_accuracy: 1.0000 - val_loss: 0.2741 - learning_rate: 3.1250e-06\n",
            "Epoch 41/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8118 - loss: 0.5081\n",
            "Epoch 41: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.8118 - loss: 0.5082 - val_accuracy: 0.7988 - val_loss: 0.7057 - learning_rate: 3.1250e-06\n",
            "Epoch 42/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.6294\n",
            "Epoch 42: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-06.\n",
            "\n",
            "Epoch 42: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140us/step - accuracy: 0.8125 - loss: 0.6294 - val_accuracy: 0.8000 - val_loss: 0.4557 - learning_rate: 3.1250e-06\n",
            "Epoch 43/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.8023 - loss: 0.5255\n",
            "Epoch 43: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.8024 - loss: 0.5254 - val_accuracy: 0.8008 - val_loss: 0.6826 - learning_rate: 1.5625e-06\n",
            "Epoch 44/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8750 - loss: 0.2973\n",
            "Epoch 44: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91us/step - accuracy: 0.8750 - loss: 0.2973 - val_accuracy: 0.8000 - val_loss: 2.4499 - learning_rate: 1.5625e-06\n",
            "Epoch 45/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8130 - loss: 0.5159\n",
            "Epoch 45: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.8130 - loss: 0.5159 - val_accuracy: 0.7995 - val_loss: 0.6866 - learning_rate: 1.5625e-06\n",
            "Epoch 46/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.8125 - loss: 0.6999\n",
            "Epoch 46: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80us/step - accuracy: 0.8125 - loss: 0.6999 - val_accuracy: 0.8000 - val_loss: 0.4958 - learning_rate: 1.5625e-06\n",
            "Epoch 47/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.8123 - loss: 0.4976\n",
            "Epoch 47: ReduceLROnPlateau reducing learning rate to 7.81249980263965e-07.\n",
            "\n",
            "Epoch 47: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 205ms/step - accuracy: 0.8123 - loss: 0.4977 - val_accuracy: 0.8194 - val_loss: 0.6496 - learning_rate: 1.5625e-06\n",
            "Epoch 48/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0965\n",
            "Epoch 48: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84us/step - accuracy: 1.0000 - loss: 0.0965 - val_accuracy: 0.8000 - val_loss: 0.8713 - learning_rate: 7.8125e-07\n",
            "Epoch 49/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8250 - loss: 0.5065\n",
            "Epoch 49: val_loss did not improve from 0.24771\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.8250 - loss: 0.5065 - val_accuracy: 0.8077 - val_loss: 0.6733 - learning_rate: 7.8125e-07\n",
            "Epoch 50/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.1072\n",
            "Epoch 50: val_loss improved from 0.24771 to 0.17057, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 758us/step - accuracy: 1.0000 - loss: 0.1072 - val_accuracy: 1.0000 - val_loss: 0.1706 - learning_rate: 7.8125e-07\n",
            "Epoch 51/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.8129 - loss: 0.5124\n",
            "Epoch 51: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.8129 - loss: 0.5124 - val_accuracy: 0.8118 - val_loss: 0.6677 - learning_rate: 7.8125e-07\n",
            "Epoch 52/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.0789\n",
            "Epoch 52: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92us/step - accuracy: 1.0000 - loss: 0.0789 - val_accuracy: 1.0000 - val_loss: 0.2867 - learning_rate: 7.8125e-07\n",
            "Epoch 53/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8166 - loss: 0.5185\n",
            "Epoch 53: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 207ms/step - accuracy: 0.8165 - loss: 0.5184 - val_accuracy: 0.8139 - val_loss: 0.6643 - learning_rate: 7.8125e-07\n",
            "Epoch 54/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.8750 - loss: 0.5093\n",
            "Epoch 54: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353us/step - accuracy: 0.8750 - loss: 0.5093 - val_accuracy: 1.0000 - val_loss: 0.2592 - learning_rate: 7.8125e-07\n",
            "Epoch 55/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - accuracy: 0.8176 - loss: 0.4875\n",
            "Epoch 55: ReduceLROnPlateau reducing learning rate to 3.906249901319825e-07.\n",
            "\n",
            "Epoch 55: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 210ms/step - accuracy: 0.8176 - loss: 0.4876 - val_accuracy: 0.8098 - val_loss: 0.6615 - learning_rate: 7.8125e-07\n",
            "Epoch 56/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.9375 - loss: 0.2957\n",
            "Epoch 56: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154us/step - accuracy: 0.9375 - loss: 0.2957 - val_accuracy: 0.6000 - val_loss: 0.9163 - learning_rate: 3.9062e-07\n",
            "Epoch 57/100\n",
            "\u001b[1m366/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.8207 - loss: 0.4952\n",
            "Epoch 57: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 207ms/step - accuracy: 0.8207 - loss: 0.4951 - val_accuracy: 0.7981 - val_loss: 0.6416 - learning_rate: 3.9062e-07\n",
            "Epoch 58/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.8125 - loss: 0.4480\n",
            "Epoch 58: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88us/step - accuracy: 0.8125 - loss: 0.4480 - val_accuracy: 0.6000 - val_loss: 1.1636 - learning_rate: 3.9062e-07\n",
            "Epoch 59/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.8075 - loss: 0.5223\n",
            "Epoch 59: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 205ms/step - accuracy: 0.8075 - loss: 0.5223 - val_accuracy: 0.8043 - val_loss: 0.6590 - learning_rate: 3.9062e-07\n",
            "Epoch 60/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.4825\n",
            "Epoch 60: ReduceLROnPlateau reducing learning rate to 1.9531249506599124e-07.\n",
            "\n",
            "Epoch 60: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93us/step - accuracy: 0.7500 - loss: 0.4825 - val_accuracy: 0.8000 - val_loss: 0.3157 - learning_rate: 3.9062e-07\n",
            "Epoch 61/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.8095 - loss: 0.5083\n",
            "Epoch 61: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 206ms/step - accuracy: 0.8095 - loss: 0.5084 - val_accuracy: 0.8043 - val_loss: 0.6767 - learning_rate: 1.9531e-07\n",
            "Epoch 62/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.9375 - loss: 0.3674\n",
            "Epoch 62: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85us/step - accuracy: 0.9375 - loss: 0.3674 - val_accuracy: 0.8000 - val_loss: 0.7211 - learning_rate: 1.9531e-07\n",
            "Epoch 63/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8256 - loss: 0.4931\n",
            "Epoch 63: val_loss did not improve from 0.17057\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.8256 - loss: 0.4931 - val_accuracy: 0.8008 - val_loss: 0.6633 - learning_rate: 1.9531e-07\n",
            "Epoch 64/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.4952\n",
            "Epoch 64: val_loss improved from 0.17057 to 0.06951, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 847us/step - accuracy: 0.7500 - loss: 0.4952 - val_accuracy: 1.0000 - val_loss: 0.0695 - learning_rate: 1.9531e-07\n",
            "Epoch 65/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.8188 - loss: 0.4892\n",
            "Epoch 65: val_loss did not improve from 0.06951\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 207ms/step - accuracy: 0.8187 - loss: 0.4892 - val_accuracy: 0.8104 - val_loss: 0.6723 - learning_rate: 1.9531e-07\n",
            "Epoch 66/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.4602\n",
            "Epoch 66: val_loss did not improve from 0.06951\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134us/step - accuracy: 0.7500 - loss: 0.4602 - val_accuracy: 0.6000 - val_loss: 2.0139 - learning_rate: 1.9531e-07\n",
            "Epoch 67/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.8192 - loss: 0.4831\n",
            "Epoch 67: val_loss did not improve from 0.06951\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 207ms/step - accuracy: 0.8192 - loss: 0.4832 - val_accuracy: 0.8070 - val_loss: 0.6685 - learning_rate: 1.9531e-07\n",
            "Epoch 68/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.7167\n",
            "Epoch 68: val_loss improved from 0.06951 to 0.05273, saving model to /content/drive/MyDrive/fruiznet_best_model.keras\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 753us/step - accuracy: 0.7500 - loss: 0.7167 - val_accuracy: 1.0000 - val_loss: 0.0527 - learning_rate: 1.9531e-07\n",
            "Epoch 69/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8136 - loss: 0.5040\n",
            "Epoch 69: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.8136 - loss: 0.5039 - val_accuracy: 0.8084 - val_loss: 0.6662 - learning_rate: 1.9531e-07\n",
            "Epoch 70/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.2277\n",
            "Epoch 70: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259us/step - accuracy: 0.8125 - loss: 0.2277 - val_accuracy: 0.6000 - val_loss: 2.2275 - learning_rate: 1.9531e-07\n",
            "Epoch 71/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.8152 - loss: 0.4902\n",
            "Epoch 71: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 210ms/step - accuracy: 0.8151 - loss: 0.4903 - val_accuracy: 0.8015 - val_loss: 0.6716 - learning_rate: 1.9531e-07\n",
            "Epoch 72/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8750 - loss: 0.4424\n",
            "Epoch 72: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82us/step - accuracy: 0.8750 - loss: 0.4424 - val_accuracy: 0.6000 - val_loss: 0.6329 - learning_rate: 1.9531e-07\n",
            "Epoch 73/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.8197 - loss: 0.4849\n",
            "Epoch 73: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
            "\n",
            "Epoch 73: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 207ms/step - accuracy: 0.8197 - loss: 0.4850 - val_accuracy: 0.8049 - val_loss: 0.6752 - learning_rate: 1.9531e-07\n",
            "Epoch 74/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8750 - loss: 0.5447\n",
            "Epoch 74: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86us/step - accuracy: 0.8750 - loss: 0.5447 - val_accuracy: 1.0000 - val_loss: 0.1018 - learning_rate: 1.0000e-07\n",
            "Epoch 75/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8142 - loss: 0.5212\n",
            "Epoch 75: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.8142 - loss: 0.5210 - val_accuracy: 0.8132 - val_loss: 0.6607 - learning_rate: 1.0000e-07\n",
            "Epoch 76/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.6875 - loss: 0.7187\n",
            "Epoch 76: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109us/step - accuracy: 0.6875 - loss: 0.7187 - val_accuracy: 0.8000 - val_loss: 0.4402 - learning_rate: 1.0000e-07\n",
            "Epoch 77/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8054 - loss: 0.5103\n",
            "Epoch 77: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.8054 - loss: 0.5103 - val_accuracy: 0.8111 - val_loss: 0.6579 - learning_rate: 1.0000e-07\n",
            "Epoch 78/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.5091\n",
            "Epoch 78: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90us/step - accuracy: 0.8125 - loss: 0.5091 - val_accuracy: 0.6000 - val_loss: 1.3432 - learning_rate: 1.0000e-07\n",
            "Epoch 79/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8272 - loss: 0.4859\n",
            "Epoch 79: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 208ms/step - accuracy: 0.8272 - loss: 0.4859 - val_accuracy: 0.8029 - val_loss: 0.6648 - learning_rate: 1.0000e-07\n",
            "Epoch 80/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.1051\n",
            "Epoch 80: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112us/step - accuracy: 1.0000 - loss: 0.1051 - val_accuracy: 0.8000 - val_loss: 0.6735 - learning_rate: 1.0000e-07\n",
            "Epoch 81/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.8143 - loss: 0.5092\n",
            "Epoch 81: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 206ms/step - accuracy: 0.8143 - loss: 0.5092 - val_accuracy: 0.8056 - val_loss: 0.6491 - learning_rate: 1.0000e-07\n",
            "Epoch 82/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.8125 - loss: 0.3229\n",
            "Epoch 82: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95us/step - accuracy: 0.8125 - loss: 0.3229 - val_accuracy: 0.6000 - val_loss: 2.0726 - learning_rate: 1.0000e-07\n",
            "Epoch 83/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8159 - loss: 0.5094\n",
            "Epoch 83: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 207ms/step - accuracy: 0.8159 - loss: 0.5093 - val_accuracy: 0.8022 - val_loss: 0.6912 - learning_rate: 1.0000e-07\n",
            "Epoch 84/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.9375 - loss: 0.4175\n",
            "Epoch 84: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88us/step - accuracy: 0.9375 - loss: 0.4175 - val_accuracy: 0.6000 - val_loss: 0.7040 - learning_rate: 1.0000e-07\n",
            "Epoch 85/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - accuracy: 0.8136 - loss: 0.5096\n",
            "Epoch 85: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 207ms/step - accuracy: 0.8136 - loss: 0.5095 - val_accuracy: 0.8070 - val_loss: 0.6687 - learning_rate: 1.0000e-07\n",
            "Epoch 86/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7500 - loss: 0.5353\n",
            "Epoch 86: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 249us/step - accuracy: 0.7500 - loss: 0.5353 - val_accuracy: 0.2000 - val_loss: 2.7504 - learning_rate: 1.0000e-07\n",
            "Epoch 87/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8071 - loss: 0.5063\n",
            "Epoch 87: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 207ms/step - accuracy: 0.8071 - loss: 0.5063 - val_accuracy: 0.8029 - val_loss: 0.6623 - learning_rate: 1.0000e-07\n",
            "Epoch 88/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.9375 - loss: 0.1598\n",
            "Epoch 88: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97us/step - accuracy: 0.9375 - loss: 0.1598 - val_accuracy: 1.0000 - val_loss: 0.1865 - learning_rate: 1.0000e-07\n",
            "Epoch 89/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.8213 - loss: 0.4772\n",
            "Epoch 89: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 205ms/step - accuracy: 0.8212 - loss: 0.4773 - val_accuracy: 0.8029 - val_loss: 0.6734 - learning_rate: 1.0000e-07\n",
            "Epoch 90/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 1.0000 - loss: 0.1937\n",
            "Epoch 90: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99us/step - accuracy: 1.0000 - loss: 0.1937 - val_accuracy: 0.2000 - val_loss: 2.3174 - learning_rate: 1.0000e-07\n",
            "Epoch 91/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8146 - loss: 0.4819\n",
            "Epoch 91: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 208ms/step - accuracy: 0.8146 - loss: 0.4819 - val_accuracy: 0.8063 - val_loss: 0.6550 - learning_rate: 1.0000e-07\n",
            "Epoch 92/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.5000 - loss: 0.7629\n",
            "Epoch 92: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90us/step - accuracy: 0.5000 - loss: 0.7629 - val_accuracy: 1.0000 - val_loss: 0.1707 - learning_rate: 1.0000e-07\n",
            "Epoch 93/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - accuracy: 0.8216 - loss: 0.4740\n",
            "Epoch 93: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.8215 - loss: 0.4741 - val_accuracy: 0.7981 - val_loss: 0.6816 - learning_rate: 1.0000e-07\n",
            "Epoch 94/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.2833\n",
            "Epoch 94: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90us/step - accuracy: 0.8125 - loss: 0.2833 - val_accuracy: 0.8000 - val_loss: 0.7526 - learning_rate: 1.0000e-07\n",
            "Epoch 95/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - accuracy: 0.8102 - loss: 0.4874\n",
            "Epoch 95: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 205ms/step - accuracy: 0.8102 - loss: 0.4875 - val_accuracy: 0.8146 - val_loss: 0.6546 - learning_rate: 1.0000e-07\n",
            "Epoch 96/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.9091 - loss: 0.5897\n",
            "Epoch 96: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185us/step - accuracy: 0.9091 - loss: 0.5897 - val_accuracy: 0.8000 - val_loss: 0.9547 - learning_rate: 1.0000e-07\n",
            "Epoch 97/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8090 - loss: 0.5019\n",
            "Epoch 97: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.8091 - loss: 0.5019 - val_accuracy: 0.8091 - val_loss: 0.6428 - learning_rate: 1.0000e-07\n",
            "Epoch 98/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.2551\n",
            "Epoch 98: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114us/step - accuracy: 0.8125 - loss: 0.2551 - val_accuracy: 0.8000 - val_loss: 0.7582 - learning_rate: 1.0000e-07\n",
            "Epoch 99/100\n",
            "\u001b[1m367/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - accuracy: 0.8217 - loss: 0.4872\n",
            "Epoch 99: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 209ms/step - accuracy: 0.8217 - loss: 0.4872 - val_accuracy: 0.8084 - val_loss: 0.6638 - learning_rate: 1.0000e-07\n",
            "Epoch 100/100\n",
            "\u001b[1m  1/368\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.8125 - loss: 0.4187\n",
            "Epoch 100: val_loss did not improve from 0.05273\n",
            "\u001b[1m368/368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104us/step - accuracy: 0.8125 - loss: 0.4187 - val_accuracy: 0.8000 - val_loss: 0.8897 - learning_rate: 1.0000e-07\n",
            "Training completed!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Paths\n",
        "model_path = '/content/drive/MyDrive/fruiznet_final_model.keras'\n",
        "dataset_dir = '/content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new'\n",
        "sample_image_path = '/content/Screenshot 2024-09-24 at 1.49.55 PM.png'  # Replace with your sample image path\n",
        "\n",
        "# Load the trained model\n",
        "model = load_model(model_path)\n",
        "\n",
        "# Load class labels from the dataset directory\n",
        "class_labels = sorted([d for d in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, d))])\n",
        "\n",
        "def preprocess_image(image_path, target_size=(160, 160)):\n",
        "    \"\"\"Preprocess the image for prediction.\"\"\"\n",
        "    img = load_img(image_path, target_size=target_size)  # Load and resize the image\n",
        "    img_array = img_to_array(img)  # Convert to numpy array\n",
        "    img_array = img_array / 255.0  # Normalize pixel values\n",
        "    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
        "    return img, img_array\n",
        "\n",
        "def predict_image_class(image_path):\n",
        "    \"\"\"Predict the class of the input image.\"\"\"\n",
        "    # Preprocess the image\n",
        "    original_img, img_array = preprocess_image(image_path)\n",
        "\n",
        "    # Make predictions\n",
        "    predictions = model.predict(img_array)\n",
        "    predicted_class_idx = np.argmax(predictions[0])  # Get the index of the highest probability\n",
        "    predicted_label = class_labels[predicted_class_idx]  # Map index to label\n",
        "\n",
        "    # Extract the class after \"fresh\" or \"rotten\"\n",
        "    if \"fresh\" in predicted_label.lower():\n",
        "        display_label = predicted_label.lower().split(\"fresh\")[-1].strip().capitalize()\n",
        "    elif \"rotten\" in predicted_label.lower():\n",
        "        display_label = predicted_label.lower().split(\"rotten\")[-1].strip().capitalize()\n",
        "    else:\n",
        "        display_label = predicted_label  # Default to full label if no keyword\n",
        "\n",
        "    # Format the output as \"Produce: Label\"\n",
        "    formatted_label = f\"Produce: {display_label}\"\n",
        "\n",
        "    # Print results\n",
        "    print(formatted_label)\n",
        "\n",
        "    # Convert the original image (PIL) to a NumPy array for display\n",
        "    original_img_array = np.array(original_img)\n",
        "\n",
        "    # Display the image with the prediction\n",
        "    plt.imshow(original_img_array.astype(\"uint8\"))\n",
        "    plt.title(formatted_label)\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "    return formatted_label\n",
        "\n",
        "# Test the model with the sample image\n",
        "predicted_label = predict_image_class(sample_image_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "id": "3GxYFya8aEl1",
        "outputId": "b3bf47ee-7ad7-418c-9b73-010be34b6b55"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 327ms/step\n",
            "Produce: Tomato\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "import numpy as np\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Paths\n",
        "classification_model_path = '/content/drive/MyDrive/fruiznet_final_model.keras'\n",
        "freshness_model_path = '/content/drive/MyDrive/fiv1.keras'\n",
        "dataset_dir = '/content/drive/MyDrive/dataset-fruveg-2/Fruits_Vegetables_Dataset_new'\n",
        "sample_image_path = '/content/freshbanana 10.jpg'  # Replace with your test image path\n",
        "\n",
        "# Load the trained models\n",
        "classification_model = load_model(classification_model_path)\n",
        "freshness_model = load_model(freshness_model_path)\n",
        "\n",
        "# Load class labels from the dataset directory\n",
        "class_labels = sorted([d for d in os.listdir(dataset_dir) if os.path.isdir(os.path.join(dataset_dir, d))])\n",
        "\n",
        "# Define fruits for classification\n",
        "fruit_list = [\"apple\", \"orange\", \"banana\", \"lemon\"]\n",
        "\n",
        "def preprocess_image(image_path, target_size):\n",
        "    \"\"\"Preprocess the image for prediction.\"\"\"\n",
        "    img = load_img(image_path, target_size=target_size)  # Load and resize the image\n",
        "    img_array = img_to_array(img)  # Convert to numpy array\n",
        "    img_array = img_array / 255.0  # Normalize pixel values\n",
        "    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension\n",
        "    return img, img_array\n",
        "\n",
        "def predict_image_details(image_path):\n",
        "    \"\"\"Predict the class and freshness index of the input image.\"\"\"\n",
        "    # Preprocess for classification model (160x160)\n",
        "    original_img, classification_array = preprocess_image(image_path, target_size=(160, 160))\n",
        "\n",
        "    # Predict class\n",
        "    class_predictions = classification_model.predict(classification_array)\n",
        "    predicted_class_idx = np.argmax(class_predictions[0])  # Get the index of the highest probability\n",
        "    predicted_label = class_labels[predicted_class_idx]  # Map index to label\n",
        "\n",
        "    # Extract the class after \"fresh\" or \"rotten\"\n",
        "    if \"fresh\" in predicted_label.lower():\n",
        "        display_label = predicted_label.lower().split(\"fresh\")[-1].strip().capitalize()\n",
        "    elif \"rotten\" in predicted_label.lower():\n",
        "        display_label = predicted_label.lower().split(\"rotten\")[-1].strip().capitalize()\n",
        "    else:\n",
        "        display_label = predicted_label  # Default to full label if no keyword\n",
        "\n",
        "    # Classify as Fruit or Vegetable\n",
        "    category = \"Fruit\" if display_label.lower() in fruit_list else \"Vegetable\"\n",
        "\n",
        "    # Preprocess for freshness model (224x224)\n",
        "    _, freshness_array = preprocess_image(image_path, target_size=(224, 224))\n",
        "\n",
        "    # Predict freshness index\n",
        "    freshness_prediction = freshness_model.predict(freshness_array)\n",
        "    freshness_index = freshness_prediction[0][0]\n",
        "\n",
        "    # Determine freshness status and expected life span\n",
        "    if freshness_index >= 7:\n",
        "        freshness_status = \"Fresh\"\n",
        "        expected_life_span = 12  # Days for highly fresh produce\n",
        "    elif freshness_index >= 4:\n",
        "        freshness_status = \"Moderately Fresh\"\n",
        "        expected_life_span = 5  # Days for moderately fresh produce\n",
        "    else:\n",
        "        freshness_status = \"Not Fresh\"\n",
        "        expected_life_span = 2  # Days for less fresh produce\n",
        "\n",
        "    # Print results\n",
        "    print(f\"Produce: {display_label}\")\n",
        "    print(f\"Category: {category}\")\n",
        "    print(f\"Freshness: {freshness_status}\")\n",
        "    print(f\"Freshness Index: {freshness_index:.2f}\")\n",
        "    print(f\"Expected Shelf Life: {expected_life_span} days\")\n",
        "\n",
        "    # Display the image with predictions\n",
        "    original_img_array = np.array(original_img)\n",
        "    plt.imshow(original_img_array.astype(\"uint8\"))\n",
        "    plt.title(f\"{display_label} ({category}) - {freshness_status} ({freshness_index:.2f})\\nShelf Life: {expected_life_span} days\")\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "# Test the models with the sample image\n",
        "predict_image_details(sample_image_path)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "-u-zrvL6XP6H",
        "outputId": "b0a71009-7bc6-4c9b-eeb7-88213899a108"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 347ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 13s/step\n",
            "Produce: Banana\n",
            "Category: Fruit\n",
            "Freshness: Moderately Fresh\n",
            "Freshness Index: 5.92\n",
            "Expected Shelf Life: 5 days\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}