{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":13464365,"sourceType":"datasetVersion","datasetId":8546820}],"dockerImageVersionId":31193,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install protobuf==3.20.3 --quiet","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:19:59.993060Z","iopub.execute_input":"2025-11-21T19:19:59.993999Z","iopub.status.idle":"2025-11-21T19:20:03.260329Z","shell.execute_reply.started":"2025-11-21T19:19:59.993971Z","shell.execute_reply":"2025-11-21T19:20:03.259389Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:20:03.261934Z","iopub.execute_input":"2025-11-21T19:20:03.262180Z","iopub.status.idle":"2025-11-21T19:20:03.266644Z","shell.execute_reply.started":"2025-11-21T19:20:03.262157Z","shell.execute_reply":"2025-11-21T19:20:03.265999Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"import os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.applications import ResNet50, VGG16, InceptionV3\nfrom tensorflow.keras.applications.resnet50 import preprocess_input as resnet_pre\nfrom tensorflow.keras.applications.vgg16 import preprocess_input as vgg_pre\nfrom tensorflow.keras.applications.inception_v3 import preprocess_input as inception_pre\nfrom tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Dropout, Input\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\nfrom tensorflow.keras import regularizers\nfrom tensorflow.keras.utils import to_categorical\nfrom sklearn.metrics import classification_report, confusion_matrix","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:20:03.267449Z","iopub.execute_input":"2025-11-21T19:20:03.267662Z","iopub.status.idle":"2025-11-21T19:20:03.281273Z","shell.execute_reply.started":"2025-11-21T19:20:03.267645Z","shell.execute_reply":"2025-11-21T19:20:03.280577Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"TRAIN_DIR_SOURCE = '/kaggle/input/cloiud-dataset/clouds_train'\nTEST_DIR_SOURCE = '/kaggle/input/cloiud-dataset/clouds_test'\nBATCH_SIZE = 64\nEPOCHS = 75\nLEARNING_RATE = 0.0001\n\ndata = []","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:20:03.282918Z","iopub.execute_input":"2025-11-21T19:20:03.283364Z","iopub.status.idle":"2025-11-21T19:20:03.296134Z","shell.execute_reply.started":"2025-11-21T19:20:03.283340Z","shell.execute_reply":"2025-11-21T19:20:03.295283Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"if os.path.exists(TRAIN_DIR_SOURCE):\n    for class_name in os.listdir(TRAIN_DIR_SOURCE):\n        class_path = os.path.join(TRAIN_DIR_SOURCE, class_name)\n        if os.path.isdir(class_path):\n            for img_name in os.listdir(class_path):\n                data.append({\n                    'filepath': os.path.join(class_path, img_name),\n                    'label': class_name\n                })\n\nif os.path.exists(TEST_DIR_SOURCE):\n    for class_name in os.listdir(TEST_DIR_SOURCE):\n        class_path = os.path.join(TEST_DIR_SOURCE, class_name)\n        if os.path.isdir(class_path):\n            for img_name in os.listdir(class_path):\n                data.append({\n                    'filepath': os.path.join(class_path, img_name),\n                    'label': class_name\n                })\n\ndf = pd.DataFrame(data)\nprint(f\"{len(df)}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:20:03.297102Z","iopub.execute_input":"2025-11-21T19:20:03.297393Z","iopub.status.idle":"2025-11-21T19:20:03.365575Z","shell.execute_reply.started":"2025-11-21T19:20:03.297366Z","shell.execute_reply":"2025-11-21T19:20:03.364912Z"}},"outputs":[{"name":"stdout","text":"960\n","output_type":"stream"}],"execution_count":9},{"cell_type":"code","source":"class_names = sorted(df['label'].unique())\nNUM_CLASSES = len(class_names)\nprint(f\"{class_names}\")\n\ntrain_df, temp_df = train_test_split(\n    df, \n    train_size=0.7, \n    stratify=df['label'],\n    random_state=42\n)\n\nval_df, test_df = train_test_split(\n    temp_df, \n    test_size=0.5, \n    stratify=temp_df['label'], \n    random_state=42\n)\n\nprint(f\"Train: {len(train_df)}\")\nprint(f\"Val: {len(val_df)}\")\nprint(f\"Test: {len(test_df)}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:20:03.366333Z","iopub.execute_input":"2025-11-21T19:20:03.366564Z","iopub.status.idle":"2025-11-21T19:20:03.379351Z","shell.execute_reply.started":"2025-11-21T19:20:03.366522Z","shell.execute_reply":"2025-11-21T19:20:03.378586Z"}},"outputs":[{"name":"stdout","text":"['cirriform clouds', 'clear sky', 'cumulonimbus clouds', 'cumulus clouds', 'high cumuliform clouds', 'stratiform clouds', 'stratocumulus clouds']\nTrain: 672\nVal: 144\nTest: 144\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"class_weights_arr = compute_class_weight(\n    class_weight='balanced', \n    classes=np.unique(train_df['label']), \n    y=train_df['label']\n)\nclass_weights_dict = {i: weight for i, weight in enumerate(class_weights_arr)}\n\nfor idx, name in enumerate(class_names):\n    print(f\"{name}: {class_weights_dict[idx]:.2f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:20:03.380459Z","iopub.execute_input":"2025-11-21T19:20:03.380789Z","iopub.status.idle":"2025-11-21T19:20:03.386975Z","shell.execute_reply.started":"2025-11-21T19:20:03.380771Z","shell.execute_reply":"2025-11-21T19:20:03.386164Z"}},"outputs":[{"name":"stdout","text":"cirriform clouds: 0.96\nclear sky: 1.10\ncumulonimbus clouds: 5.33\ncumulus clouds: 0.65\nhigh cumuliform clouds: 0.58\nstratiform clouds: 1.55\nstratocumulus clouds: 1.04\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"def models(architecture, input_shape, num_classes):\n    if architecture == \"ResNet50\":\n        base_model = ResNet50(weights='imagenet', include_top=False, input_shape=input_shape)\n    elif architecture == \"VGG16\":\n        base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)\n    elif architecture == \"InceptionV3\":\n        base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=input_shape)\n    base_model.trainable = False \n    x = base_model.output\n    x = GlobalAveragePooling2D()(x)\n    x = Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.01))(x)\n    x = Dropout(0.6)(x)\n    output = Dense(num_classes, activation='softmax')(x)\n    model = Model(inputs=base_model.input, outputs=output)\n    model.compile(optimizer=Adam(learning_rate=LEARNING_RATE),\n                  loss='categorical_crossentropy',\n                  metrics=['accuracy'])\n    return model\n\n\nmodel_configs = [\n    (\"ResNet50\", \"ResNet50\", (224, 224, 3), resnet_pre),\n    (\"VGG16\", \"VGG16\", (224, 224, 3), vgg_pre),\n    (\"InceptionV3\", \"InceptionV3\", (299, 299, 3), inception_pre)\n]\n\ntrained_models_list = []\nhistories = {}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:20:03.387956Z","iopub.execute_input":"2025-11-21T19:20:03.388195Z","iopub.status.idle":"2025-11-21T19:20:03.402272Z","shell.execute_reply.started":"2025-11-21T19:20:03.388167Z","shell.execute_reply":"2025-11-21T19:20:03.401437Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"import tensorflow as tf\n\nprint(\"Available GPUs:\", tf.config.list_physical_devices('GPU'))\n\nfor name, arch, shape, pre_func in model_configs:\n    print(f\"\\n>>> : {name}\")\n\n    train_datagen = ImageDataGenerator(\n        preprocessing_function=pre_func,\n        rotation_range=40,\n        width_shift_range=0.25,\n        height_shift_range=0.25,\n        shear_range=0.25,\n        zoom_range=0.3,\n        horizontal_flip=True,\n        vertical_flip=True,\n        brightness_range=[0.7, 1.3],\n        fill_mode='nearest'\n    )\n\n    val_datagen = ImageDataGenerator(preprocessing_function=pre_func)\n\n    train_gen = train_datagen.flow_from_dataframe(\n        dataframe=train_df,\n        x_col='filepath', y_col='label',\n        target_size=shape[:2],\n        batch_size=BATCH_SIZE,\n        class_mode='categorical',\n        shuffle=True\n    )\n\n    val_gen = val_datagen.flow_from_dataframe(\n        dataframe=val_df,\n        x_col='filepath', y_col='label',\n        target_size=shape[:2],\n        batch_size=BATCH_SIZE,\n        class_mode='categorical',\n        shuffle=False\n    )\n\n    with tf.device('/GPU:0'):\n        model = models(arch, shape, NUM_CLASSES)\n\n        reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, verbose=1)\n        early_stop = EarlyStopping(monitor='val_loss', patience=7, restore_best_weights=True)\n\n        history = model.fit(\n            train_gen,\n            epochs=EPOCHS,\n            validation_data=val_gen,\n            callbacks=[reduce_lr, early_stop],\n            class_weight=class_weights_dict,\n            verbose=1\n        )\n\n    trained_models_list.append((name, model))\n    histories[name] = history.history","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T19:20:03.403405Z","iopub.execute_input":"2025-11-21T19:20:03.403674Z","iopub.status.idle":"2025-11-21T20:13:43.230017Z","shell.execute_reply.started":"2025-11-21T19:20:03.403650Z","shell.execute_reply":"2025-11-21T20:13:43.229342Z"}},"outputs":[{"name":"stdout","text":"Available GPUs: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n\n>>> : ResNet50\nFound 672 validated image filenames belonging to 7 classes.\nFound 144 validated image filenames belonging to 7 classes.\n","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1763752804.393043    1826 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15513 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/75\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1763752820.065894    1885 service.cc:148] XLA service 0x7fda00003480 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1763752820.065928    1885 service.cc:156]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\nI0000 00:00:1763752821.666057    1885 cuda_dnn.cc:529] Loaded cuDNN version 90300\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m 2/11\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - accuracy: 0.1094 - loss: 7.5216 ","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1763752826.473997    1885 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 2s/step - accuracy: 0.1420 - loss: 7.1636 - val_accuracy: 0.2778 - val_loss: 6.2649 - learning_rate: 1.0000e-04\nEpoch 2/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.2999 - loss: 6.5830 - val_accuracy: 0.3611 - val_loss: 5.9631 - learning_rate: 1.0000e-04\nEpoch 3/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.3574 - loss: 6.1673 - val_accuracy: 0.5139 - val_loss: 5.7587 - learning_rate: 1.0000e-04\nEpoch 4/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.4477 - loss: 5.6984 - val_accuracy: 0.5903 - val_loss: 5.5356 - learning_rate: 1.0000e-04\nEpoch 5/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.5455 - loss: 5.4445 - val_accuracy: 0.6528 - val_loss: 5.3464 - learning_rate: 1.0000e-04\nEpoch 6/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.5295 - loss: 5.3733 - val_accuracy: 0.7014 - val_loss: 5.1758 - learning_rate: 1.0000e-04\nEpoch 7/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.6028 - loss: 5.1497 - val_accuracy: 0.7431 - val_loss: 5.0211 - learning_rate: 1.0000e-04\nEpoch 8/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6227 - loss: 4.9550 - val_accuracy: 0.7431 - val_loss: 4.8792 - learning_rate: 1.0000e-04\nEpoch 9/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6605 - loss: 4.8398 - val_accuracy: 0.7431 - val_loss: 4.7485 - learning_rate: 1.0000e-04\nEpoch 10/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6615 - loss: 4.7079 - val_accuracy: 0.7708 - val_loss: 4.6354 - learning_rate: 1.0000e-04\nEpoch 11/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6907 - loss: 4.5986 - val_accuracy: 0.7917 - val_loss: 4.5123 - learning_rate: 1.0000e-04\nEpoch 12/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.6609 - loss: 4.5443 - val_accuracy: 0.7917 - val_loss: 4.4153 - learning_rate: 1.0000e-04\nEpoch 13/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7411 - loss: 4.3526 - val_accuracy: 0.7986 - val_loss: 4.3100 - learning_rate: 1.0000e-04\nEpoch 14/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.7375 - loss: 4.1873 - val_accuracy: 0.8194 - val_loss: 4.2200 - learning_rate: 1.0000e-04\nEpoch 15/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7590 - loss: 4.1649 - val_accuracy: 0.7986 - val_loss: 4.1320 - learning_rate: 1.0000e-04\nEpoch 16/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7303 - loss: 4.1636 - val_accuracy: 0.8194 - val_loss: 4.0429 - learning_rate: 1.0000e-04\nEpoch 17/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7697 - loss: 3.9785 - val_accuracy: 0.8264 - val_loss: 3.9628 - learning_rate: 1.0000e-04\nEpoch 18/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.7846 - loss: 3.9674 - val_accuracy: 0.8264 - val_loss: 3.8666 - learning_rate: 1.0000e-04\nEpoch 19/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.7739 - loss: 3.8619 - val_accuracy: 0.8194 - val_loss: 3.7867 - learning_rate: 1.0000e-04\nEpoch 20/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7082 - loss: 3.8744 - val_accuracy: 0.8264 - val_loss: 3.7222 - learning_rate: 1.0000e-04\nEpoch 21/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.7989 - loss: 3.6547 - val_accuracy: 0.8264 - val_loss: 3.6603 - learning_rate: 1.0000e-04\nEpoch 22/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7751 - loss: 3.6582 - val_accuracy: 0.8264 - val_loss: 3.6099 - learning_rate: 1.0000e-04\nEpoch 23/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8337 - loss: 3.4930 - val_accuracy: 0.8264 - val_loss: 3.5186 - learning_rate: 1.0000e-04\nEpoch 24/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7797 - loss: 3.5765 - val_accuracy: 0.8264 - val_loss: 3.4575 - learning_rate: 1.0000e-04\nEpoch 25/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7861 - loss: 3.4740 - val_accuracy: 0.8194 - val_loss: 3.3974 - learning_rate: 1.0000e-04\nEpoch 26/75\n\u001b[1m11/11\u001b[0m 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\u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8074 - loss: 3.1938 - val_accuracy: 0.8403 - val_loss: 3.1030 - learning_rate: 1.0000e-04\nEpoch 31/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8502 - loss: 3.0782 - val_accuracy: 0.8403 - val_loss: 3.0555 - learning_rate: 1.0000e-04\nEpoch 32/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8488 - loss: 3.0148 - val_accuracy: 0.8472 - val_loss: 3.0061 - learning_rate: 1.0000e-04\nEpoch 33/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8067 - loss: 3.0508 - val_accuracy: 0.8333 - val_loss: 2.9616 - learning_rate: 1.0000e-04\nEpoch 34/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8135 - loss: 2.9589 - val_accuracy: 0.8472 - val_loss: 2.9050 - learning_rate: 1.0000e-04\nEpoch 35/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8292 - loss: 2.8664 - val_accuracy: 0.8472 - val_loss: 2.8532 - learning_rate: 1.0000e-04\nEpoch 36/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8276 - loss: 2.8434 - val_accuracy: 0.8472 - val_loss: 2.7962 - learning_rate: 1.0000e-04\nEpoch 37/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8215 - loss: 2.8021 - val_accuracy: 0.8472 - val_loss: 2.7567 - learning_rate: 1.0000e-04\nEpoch 38/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8531 - loss: 2.7851 - val_accuracy: 0.8472 - val_loss: 2.7199 - learning_rate: 1.0000e-04\nEpoch 39/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8275 - loss: 2.7282 - val_accuracy: 0.8333 - val_loss: 2.6916 - learning_rate: 1.0000e-04\nEpoch 40/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8162 - loss: 2.7478 - val_accuracy: 0.8472 - val_loss: 2.6529 - learning_rate: 1.0000e-04\nEpoch 41/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8474 - loss: 2.6396 - val_accuracy: 0.8472 - val_loss: 2.6039 - learning_rate: 1.0000e-04\nEpoch 42/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8257 - loss: 2.6381 - val_accuracy: 0.8472 - val_loss: 2.5737 - learning_rate: 1.0000e-04\nEpoch 43/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8263 - loss: 2.6269 - val_accuracy: 0.8403 - val_loss: 2.5417 - learning_rate: 1.0000e-04\nEpoch 44/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8384 - loss: 2.5219 - val_accuracy: 0.8403 - val_loss: 2.4898 - learning_rate: 1.0000e-04\nEpoch 45/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8232 - loss: 2.5285 - val_accuracy: 0.8542 - val_loss: 2.4407 - learning_rate: 1.0000e-04\nEpoch 46/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8384 - loss: 2.4726 - val_accuracy: 0.8611 - val_loss: 2.4185 - learning_rate: 1.0000e-04\nEpoch 47/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8544 - loss: 2.4431 - val_accuracy: 0.8472 - val_loss: 2.3980 - learning_rate: 1.0000e-04\nEpoch 48/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8219 - loss: 2.4022 - val_accuracy: 0.8542 - val_loss: 2.3683 - learning_rate: 1.0000e-04\nEpoch 49/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8634 - loss: 2.3269 - val_accuracy: 0.8403 - val_loss: 2.3413 - learning_rate: 1.0000e-04\nEpoch 50/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8416 - loss: 2.2949 - val_accuracy: 0.8472 - val_loss: 2.3108 - learning_rate: 1.0000e-04\nEpoch 51/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8677 - loss: 2.3002 - val_accuracy: 0.8472 - val_loss: 2.2711 - learning_rate: 1.0000e-04\nEpoch 52/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8338 - loss: 2.2510 - val_accuracy: 0.8472 - val_loss: 2.2380 - learning_rate: 1.0000e-04\nEpoch 53/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8990 - loss: 2.1983 - val_accuracy: 0.8611 - val_loss: 2.2086 - learning_rate: 1.0000e-04\nEpoch 54/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8548 - loss: 2.1668 - val_accuracy: 0.8611 - val_loss: 2.1681 - learning_rate: 1.0000e-04\nEpoch 55/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8767 - loss: 2.1604 - val_accuracy: 0.8611 - val_loss: 2.1472 - learning_rate: 1.0000e-04\nEpoch 56/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8654 - loss: 2.1429 - val_accuracy: 0.8472 - val_loss: 2.1288 - learning_rate: 1.0000e-04\nEpoch 57/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8879 - loss: 2.0660 - val_accuracy: 0.8472 - val_loss: 2.1001 - learning_rate: 1.0000e-04\nEpoch 58/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8469 - loss: 2.0944 - val_accuracy: 0.8542 - val_loss: 2.0609 - learning_rate: 1.0000e-04\nEpoch 59/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8388 - loss: 2.0726 - val_accuracy: 0.8611 - val_loss: 2.0508 - learning_rate: 1.0000e-04\nEpoch 60/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8784 - loss: 2.0256 - val_accuracy: 0.8472 - val_loss: 2.0212 - learning_rate: 1.0000e-04\nEpoch 61/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.8821 - loss: 1.9809 - val_accuracy: 0.8472 - val_loss: 1.9999 - learning_rate: 1.0000e-04\nEpoch 62/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8724 - loss: 2.0193 - val_accuracy: 0.8472 - val_loss: 1.9775 - learning_rate: 1.0000e-04\nEpoch 63/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8614 - loss: 1.9924 - val_accuracy: 0.8681 - val_loss: 1.9593 - learning_rate: 1.0000e-04\nEpoch 64/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8518 - loss: 1.9775 - val_accuracy: 0.8681 - val_loss: 1.9474 - learning_rate: 1.0000e-04\nEpoch 65/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8933 - loss: 1.8659 - val_accuracy: 0.8542 - val_loss: 1.9263 - learning_rate: 1.0000e-04\nEpoch 66/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8845 - loss: 1.8709 - val_accuracy: 0.8542 - val_loss: 1.8901 - learning_rate: 1.0000e-04\nEpoch 67/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8805 - loss: 1.8711 - val_accuracy: 0.8750 - val_loss: 1.8618 - learning_rate: 1.0000e-04\nEpoch 68/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8544 - loss: 1.8846 - val_accuracy: 0.8750 - val_loss: 1.8549 - learning_rate: 1.0000e-04\nEpoch 69/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8600 - loss: 1.8559 - val_accuracy: 0.8472 - val_loss: 1.8354 - learning_rate: 1.0000e-04\nEpoch 70/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8698 - loss: 1.7766 - val_accuracy: 0.8542 - val_loss: 1.8061 - learning_rate: 1.0000e-04\nEpoch 71/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8825 - loss: 1.8143 - val_accuracy: 0.8611 - val_loss: 1.7903 - learning_rate: 1.0000e-04\nEpoch 72/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8787 - loss: 1.7578 - val_accuracy: 0.8542 - val_loss: 1.7787 - learning_rate: 1.0000e-04\nEpoch 73/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.8904 - loss: 1.7357 - val_accuracy: 0.8542 - val_loss: 1.7554 - learning_rate: 1.0000e-04\nEpoch 74/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1000ms/step - accuracy: 0.8981 - loss: 1.6936 - val_accuracy: 0.8542 - val_loss: 1.7306 - learning_rate: 1.0000e-04\nEpoch 75/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 987ms/step - accuracy: 0.8686 - loss: 1.7316 - val_accuracy: 0.8542 - val_loss: 1.7069 - learning_rate: 1.0000e-04\n\n>>> : VGG16\nFound 672 validated image filenames belonging to 7 classes.\nFound 144 validated image filenames belonging to 7 classes.\nEpoch 1/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 2s/step - accuracy: 0.1662 - loss: 6.8952 - val_accuracy: 0.2083 - val_loss: 5.7590 - learning_rate: 1.0000e-04\nEpoch 2/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 970ms/step - accuracy: 0.1961 - loss: 6.5430 - val_accuracy: 0.2083 - val_loss: 5.5372 - learning_rate: 1.0000e-04\nEpoch 3/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 983ms/step - accuracy: 0.2288 - loss: 6.2888 - val_accuracy: 0.2014 - val_loss: 5.3766 - learning_rate: 1.0000e-04\nEpoch 4/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.2172 - loss: 6.1125 - val_accuracy: 0.2431 - val_loss: 5.2641 - learning_rate: 1.0000e-04\nEpoch 5/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 991ms/step - accuracy: 0.2334 - loss: 6.2091 - val_accuracy: 0.2847 - val_loss: 5.1495 - learning_rate: 1.0000e-04\nEpoch 6/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.2883 - loss: 5.7775 - val_accuracy: 0.2847 - val_loss: 5.0613 - learning_rate: 1.0000e-04\nEpoch 7/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.3030 - loss: 5.3909 - val_accuracy: 0.3333 - val_loss: 4.9452 - learning_rate: 1.0000e-04\nEpoch 8/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 979ms/step - accuracy: 0.3258 - loss: 5.4105 - val_accuracy: 0.3750 - val_loss: 4.8621 - learning_rate: 1.0000e-04\nEpoch 9/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 979ms/step - accuracy: 0.3224 - loss: 5.1886 - val_accuracy: 0.3958 - val_loss: 4.7904 - learning_rate: 1.0000e-04\nEpoch 10/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.3596 - loss: 5.0366 - val_accuracy: 0.4097 - val_loss: 4.7200 - learning_rate: 1.0000e-04\nEpoch 11/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.3720 - loss: 5.0640 - val_accuracy: 0.4167 - val_loss: 4.6322 - learning_rate: 1.0000e-04\nEpoch 12/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.3876 - loss: 4.7199 - val_accuracy: 0.4514 - val_loss: 4.5555 - learning_rate: 1.0000e-04\nEpoch 13/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.3753 - loss: 4.6206 - val_accuracy: 0.4931 - val_loss: 4.4656 - learning_rate: 1.0000e-04\nEpoch 14/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.4274 - loss: 4.5618 - val_accuracy: 0.5069 - val_loss: 4.4105 - learning_rate: 1.0000e-04\nEpoch 15/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.4184 - loss: 4.5032 - val_accuracy: 0.5625 - val_loss: 4.3266 - learning_rate: 1.0000e-04\nEpoch 16/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.4440 - loss: 4.5800 - val_accuracy: 0.5833 - val_loss: 4.2486 - learning_rate: 1.0000e-04\nEpoch 17/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.4228 - loss: 4.4354 - val_accuracy: 0.6042 - val_loss: 4.1878 - learning_rate: 1.0000e-04\nEpoch 18/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.4899 - loss: 4.3335 - val_accuracy: 0.6250 - val_loss: 4.1331 - learning_rate: 1.0000e-04\nEpoch 19/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.4640 - loss: 4.1584 - val_accuracy: 0.6458 - val_loss: 4.0770 - learning_rate: 1.0000e-04\nEpoch 20/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.5242 - loss: 4.1014 - val_accuracy: 0.6458 - val_loss: 4.0244 - learning_rate: 1.0000e-04\nEpoch 21/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.5476 - loss: 4.0538 - val_accuracy: 0.6528 - val_loss: 3.9639 - learning_rate: 1.0000e-04\nEpoch 22/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.5272 - loss: 4.1776 - val_accuracy: 0.6458 - val_loss: 3.9137 - learning_rate: 1.0000e-04\nEpoch 23/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.5304 - loss: 3.9360 - val_accuracy: 0.6597 - val_loss: 3.8585 - learning_rate: 1.0000e-04\nEpoch 24/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.4823 - loss: 3.9562 - val_accuracy: 0.6667 - val_loss: 3.8064 - learning_rate: 1.0000e-04\nEpoch 25/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.5576 - loss: 3.9369 - val_accuracy: 0.6667 - val_loss: 3.7574 - learning_rate: 1.0000e-04\nEpoch 26/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.5225 - loss: 3.9405 - val_accuracy: 0.6667 - val_loss: 3.7063 - learning_rate: 1.0000e-04\nEpoch 27/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.5727 - loss: 3.7645 - val_accuracy: 0.6667 - val_loss: 3.6653 - learning_rate: 1.0000e-04\nEpoch 28/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.5487 - loss: 3.8553 - val_accuracy: 0.6806 - val_loss: 3.6291 - learning_rate: 1.0000e-04\nEpoch 29/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.5741 - loss: 3.6438 - val_accuracy: 0.6736 - val_loss: 3.5815 - learning_rate: 1.0000e-04\nEpoch 30/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.5650 - loss: 3.6330 - val_accuracy: 0.6736 - val_loss: 3.5349 - learning_rate: 1.0000e-04\nEpoch 31/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.6106 - loss: 3.5821 - val_accuracy: 0.6667 - val_loss: 3.4935 - learning_rate: 1.0000e-04\nEpoch 32/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.6270 - loss: 3.5240 - val_accuracy: 0.6667 - val_loss: 3.4574 - learning_rate: 1.0000e-04\nEpoch 33/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6236 - loss: 3.5753 - val_accuracy: 0.6736 - val_loss: 3.4284 - learning_rate: 1.0000e-04\nEpoch 34/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6505 - loss: 3.3800 - val_accuracy: 0.6806 - val_loss: 3.4071 - learning_rate: 1.0000e-04\nEpoch 35/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.5951 - loss: 3.4694 - val_accuracy: 0.6875 - val_loss: 3.3717 - learning_rate: 1.0000e-04\nEpoch 36/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.5978 - loss: 3.3858 - val_accuracy: 0.6875 - val_loss: 3.3266 - learning_rate: 1.0000e-04\nEpoch 37/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.6191 - loss: 3.4061 - val_accuracy: 0.6944 - val_loss: 3.2920 - learning_rate: 1.0000e-04\nEpoch 38/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.6584 - loss: 3.3530 - val_accuracy: 0.6944 - val_loss: 3.2543 - learning_rate: 1.0000e-04\nEpoch 39/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6365 - loss: 3.2369 - val_accuracy: 0.6944 - val_loss: 3.2152 - learning_rate: 1.0000e-04\nEpoch 40/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6580 - loss: 3.2818 - val_accuracy: 0.6944 - val_loss: 3.1829 - learning_rate: 1.0000e-04\nEpoch 41/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6697 - loss: 3.2250 - val_accuracy: 0.7014 - val_loss: 3.1575 - learning_rate: 1.0000e-04\nEpoch 42/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6409 - loss: 3.1966 - val_accuracy: 0.7153 - val_loss: 3.1254 - learning_rate: 1.0000e-04\nEpoch 43/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6394 - loss: 3.1553 - val_accuracy: 0.7083 - val_loss: 3.0952 - learning_rate: 1.0000e-04\nEpoch 44/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.6862 - loss: 3.0731 - val_accuracy: 0.7153 - val_loss: 3.0697 - learning_rate: 1.0000e-04\nEpoch 45/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6703 - loss: 3.0910 - val_accuracy: 0.7222 - val_loss: 3.0335 - learning_rate: 1.0000e-04\nEpoch 46/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6851 - loss: 2.9704 - val_accuracy: 0.7222 - val_loss: 3.0003 - learning_rate: 1.0000e-04\nEpoch 47/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6299 - loss: 3.1188 - val_accuracy: 0.7222 - val_loss: 2.9736 - learning_rate: 1.0000e-04\nEpoch 48/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6585 - loss: 2.9804 - val_accuracy: 0.7222 - val_loss: 2.9460 - learning_rate: 1.0000e-04\nEpoch 49/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6886 - loss: 2.8990 - val_accuracy: 0.7500 - val_loss: 2.9097 - learning_rate: 1.0000e-04\nEpoch 50/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7093 - loss: 2.9333 - val_accuracy: 0.7569 - val_loss: 2.8835 - learning_rate: 1.0000e-04\nEpoch 51/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6868 - loss: 2.9293 - val_accuracy: 0.7569 - val_loss: 2.8665 - learning_rate: 1.0000e-04\nEpoch 52/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6407 - loss: 2.9608 - val_accuracy: 0.7361 - val_loss: 2.8563 - learning_rate: 1.0000e-04\nEpoch 53/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6682 - loss: 2.8503 - val_accuracy: 0.7431 - val_loss: 2.8299 - learning_rate: 1.0000e-04\nEpoch 54/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7169 - loss: 2.8227 - val_accuracy: 0.7639 - val_loss: 2.7976 - learning_rate: 1.0000e-04\nEpoch 55/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 1s/step - accuracy: 0.6989 - loss: 2.7600 - val_accuracy: 0.7708 - val_loss: 2.7616 - learning_rate: 1.0000e-04\nEpoch 56/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7077 - loss: 2.8242 - val_accuracy: 0.7639 - val_loss: 2.7381 - learning_rate: 1.0000e-04\nEpoch 57/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.6867 - loss: 2.8132 - val_accuracy: 0.7569 - val_loss: 2.7209 - learning_rate: 1.0000e-04\nEpoch 58/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7198 - loss: 2.7154 - val_accuracy: 0.7639 - val_loss: 2.6966 - learning_rate: 1.0000e-04\nEpoch 59/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 989ms/step - accuracy: 0.6999 - loss: 2.7123 - val_accuracy: 0.7708 - val_loss: 2.6747 - learning_rate: 1.0000e-04\nEpoch 60/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7077 - loss: 2.6780 - val_accuracy: 0.7708 - val_loss: 2.6516 - learning_rate: 1.0000e-04\nEpoch 61/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7131 - loss: 2.6615 - val_accuracy: 0.7917 - val_loss: 2.6197 - learning_rate: 1.0000e-04\nEpoch 62/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 993ms/step - accuracy: 0.7007 - loss: 2.6823 - val_accuracy: 0.7986 - val_loss: 2.5925 - learning_rate: 1.0000e-04\nEpoch 63/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 984ms/step - accuracy: 0.7378 - loss: 2.6043 - val_accuracy: 0.7986 - val_loss: 2.5708 - learning_rate: 1.0000e-04\nEpoch 64/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7038 - loss: 2.6191 - val_accuracy: 0.7917 - val_loss: 2.5512 - learning_rate: 1.0000e-04\nEpoch 65/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7193 - loss: 2.5907 - val_accuracy: 0.7917 - val_loss: 2.5288 - learning_rate: 1.0000e-04\nEpoch 66/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7116 - loss: 2.5791 - val_accuracy: 0.7917 - val_loss: 2.5094 - learning_rate: 1.0000e-04\nEpoch 67/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7277 - loss: 2.5679 - val_accuracy: 0.8056 - val_loss: 2.4828 - learning_rate: 1.0000e-04\nEpoch 68/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7144 - loss: 2.5577 - val_accuracy: 0.8056 - val_loss: 2.4617 - learning_rate: 1.0000e-04\nEpoch 69/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7292 - loss: 2.5352 - val_accuracy: 0.8056 - val_loss: 2.4448 - learning_rate: 1.0000e-04\nEpoch 70/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7251 - loss: 2.4671 - val_accuracy: 0.8125 - val_loss: 2.4261 - learning_rate: 1.0000e-04\nEpoch 71/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7584 - loss: 2.5007 - val_accuracy: 0.8125 - val_loss: 2.4107 - learning_rate: 1.0000e-04\nEpoch 72/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7313 - loss: 2.3736 - val_accuracy: 0.8194 - val_loss: 2.3887 - learning_rate: 1.0000e-04\nEpoch 73/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1s/step - accuracy: 0.7204 - loss: 2.5199 - val_accuracy: 0.8194 - val_loss: 2.3728 - learning_rate: 1.0000e-04\nEpoch 74/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 990ms/step - accuracy: 0.7029 - loss: 2.5513 - val_accuracy: 0.8125 - val_loss: 2.3669 - learning_rate: 1.0000e-04\nEpoch 75/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 1000ms/step - accuracy: 0.7647 - loss: 2.4016 - val_accuracy: 0.8125 - val_loss: 2.3480 - learning_rate: 1.0000e-04\n\n>>> : InceptionV3\nFound 672 validated image filenames belonging to 7 classes.\nFound 144 validated image filenames belonging to 7 classes.\nDownloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n\u001b[1m87910968/87910968\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 0us/step\nEpoch 1/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 4s/step - accuracy: 0.1546 - loss: 6.8995 - val_accuracy: 0.1944 - val_loss: 6.3567 - learning_rate: 1.0000e-04\nEpoch 2/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.2603 - loss: 6.2899 - val_accuracy: 0.4514 - val_loss: 6.1614 - learning_rate: 1.0000e-04\nEpoch 3/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.4150 - loss: 5.9794 - val_accuracy: 0.5069 - val_loss: 6.0082 - learning_rate: 1.0000e-04\nEpoch 4/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.4494 - loss: 5.8924 - val_accuracy: 0.5069 - val_loss: 5.9013 - learning_rate: 1.0000e-04\nEpoch 5/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.4820 - loss: 5.6553 - val_accuracy: 0.5139 - val_loss: 5.7964 - learning_rate: 1.0000e-04\nEpoch 6/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.5075 - loss: 5.5523 - val_accuracy: 0.5069 - val_loss: 5.7018 - learning_rate: 1.0000e-04\nEpoch 7/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.5730 - loss: 5.3675 - val_accuracy: 0.5764 - val_loss: 5.5742 - learning_rate: 1.0000e-04\nEpoch 8/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6188 - loss: 5.2216 - val_accuracy: 0.5764 - val_loss: 5.4687 - learning_rate: 1.0000e-04\nEpoch 9/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.5872 - loss: 5.0998 - val_accuracy: 0.5625 - val_loss: 5.4039 - learning_rate: 1.0000e-04\nEpoch 10/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.5901 - loss: 4.9969 - val_accuracy: 0.5903 - val_loss: 5.2918 - learning_rate: 1.0000e-04\nEpoch 11/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6791 - loss: 4.8680 - val_accuracy: 0.5903 - val_loss: 5.2082 - learning_rate: 1.0000e-04\nEpoch 12/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6295 - loss: 4.7912 - val_accuracy: 0.5903 - val_loss: 5.0945 - learning_rate: 1.0000e-04\nEpoch 13/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6306 - loss: 4.7566 - val_accuracy: 0.5764 - val_loss: 4.9988 - learning_rate: 1.0000e-04\nEpoch 14/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.6294 - loss: 4.6410 - val_accuracy: 0.6319 - val_loss: 4.8637 - learning_rate: 1.0000e-04\nEpoch 15/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.6976 - loss: 4.4913 - val_accuracy: 0.5833 - val_loss: 4.8278 - learning_rate: 1.0000e-04\nEpoch 16/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.7174 - loss: 4.4271 - val_accuracy: 0.6111 - val_loss: 4.7433 - learning_rate: 1.0000e-04\nEpoch 17/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6826 - loss: 4.3409 - val_accuracy: 0.6111 - val_loss: 4.6588 - learning_rate: 1.0000e-04\nEpoch 18/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6693 - loss: 4.2986 - val_accuracy: 0.6250 - val_loss: 4.5850 - learning_rate: 1.0000e-04\nEpoch 19/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6838 - loss: 4.3089 - val_accuracy: 0.6111 - val_loss: 4.5520 - learning_rate: 1.0000e-04\nEpoch 20/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6857 - loss: 4.2107 - val_accuracy: 0.6458 - val_loss: 4.4547 - learning_rate: 1.0000e-04\nEpoch 21/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.6953 - loss: 4.0954 - val_accuracy: 0.6736 - val_loss: 4.3701 - learning_rate: 1.0000e-04\nEpoch 22/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7460 - loss: 4.0105 - val_accuracy: 0.6667 - val_loss: 4.2872 - learning_rate: 1.0000e-04\nEpoch 23/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7385 - loss: 3.9766 - val_accuracy: 0.6597 - val_loss: 4.2212 - learning_rate: 1.0000e-04\nEpoch 24/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7570 - loss: 3.8853 - val_accuracy: 0.6528 - val_loss: 4.1903 - learning_rate: 1.0000e-04\nEpoch 25/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.6991 - loss: 3.9031 - val_accuracy: 0.6597 - val_loss: 4.1372 - learning_rate: 1.0000e-04\nEpoch 26/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.6920 - loss: 3.9098 - val_accuracy: 0.6875 - val_loss: 4.0322 - learning_rate: 1.0000e-04\nEpoch 27/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7135 - loss: 3.7498 - val_accuracy: 0.6944 - val_loss: 3.9401 - learning_rate: 1.0000e-04\nEpoch 28/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7607 - loss: 3.7765 - val_accuracy: 0.6667 - val_loss: 3.9217 - learning_rate: 1.0000e-04\nEpoch 29/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7478 - loss: 3.6649 - val_accuracy: 0.7014 - val_loss: 3.8450 - learning_rate: 1.0000e-04\nEpoch 30/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7354 - loss: 3.6638 - val_accuracy: 0.7153 - val_loss: 3.7870 - learning_rate: 1.0000e-04\nEpoch 31/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7737 - loss: 3.5218 - val_accuracy: 0.7361 - val_loss: 3.7296 - learning_rate: 1.0000e-04\nEpoch 32/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7737 - loss: 3.5164 - val_accuracy: 0.7222 - val_loss: 3.6578 - learning_rate: 1.0000e-04\nEpoch 33/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7756 - loss: 3.4978 - val_accuracy: 0.7569 - val_loss: 3.5765 - learning_rate: 1.0000e-04\nEpoch 34/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7639 - loss: 3.4591 - val_accuracy: 0.7639 - val_loss: 3.5481 - learning_rate: 1.0000e-04\nEpoch 35/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7282 - loss: 3.4266 - val_accuracy: 0.7431 - val_loss: 3.5042 - learning_rate: 1.0000e-04\nEpoch 36/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7860 - loss: 3.2960 - val_accuracy: 0.7500 - val_loss: 3.4559 - learning_rate: 1.0000e-04\nEpoch 37/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7739 - loss: 3.2371 - val_accuracy: 0.7778 - val_loss: 3.3781 - learning_rate: 1.0000e-04\nEpoch 38/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7520 - loss: 3.2480 - val_accuracy: 0.7847 - val_loss: 3.3519 - learning_rate: 1.0000e-04\nEpoch 39/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7708 - loss: 3.1984 - val_accuracy: 0.7500 - val_loss: 3.3308 - learning_rate: 1.0000e-04\nEpoch 40/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8108 - loss: 3.1460 - val_accuracy: 0.7639 - val_loss: 3.2567 - learning_rate: 1.0000e-04\nEpoch 41/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7631 - loss: 3.1000 - val_accuracy: 0.7917 - val_loss: 3.2023 - learning_rate: 1.0000e-04\nEpoch 42/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8096 - loss: 3.0152 - val_accuracy: 0.7778 - val_loss: 3.1645 - learning_rate: 1.0000e-04\nEpoch 43/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7677 - loss: 3.0441 - val_accuracy: 0.7778 - val_loss: 3.1424 - learning_rate: 1.0000e-04\nEpoch 44/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7977 - loss: 2.9438 - val_accuracy: 0.7639 - val_loss: 3.1070 - learning_rate: 1.0000e-04\nEpoch 45/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7719 - loss: 3.0169 - val_accuracy: 0.7847 - val_loss: 3.0895 - learning_rate: 1.0000e-04\nEpoch 46/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8176 - loss: 2.9077 - val_accuracy: 0.7847 - val_loss: 3.0216 - learning_rate: 1.0000e-04\nEpoch 47/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7625 - loss: 3.0024 - val_accuracy: 0.7708 - val_loss: 3.0035 - learning_rate: 1.0000e-04\nEpoch 48/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8350 - loss: 2.8044 - val_accuracy: 0.7708 - val_loss: 2.9587 - learning_rate: 1.0000e-04\nEpoch 49/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.7855 - loss: 2.8212 - val_accuracy: 0.7986 - val_loss: 2.8913 - learning_rate: 1.0000e-04\nEpoch 50/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8074 - loss: 2.7711 - val_accuracy: 0.7778 - val_loss: 2.8941 - learning_rate: 1.0000e-04\nEpoch 51/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8163 - loss: 2.7258 - val_accuracy: 0.7708 - val_loss: 2.8380 - learning_rate: 1.0000e-04\nEpoch 52/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8199 - loss: 2.6926 - val_accuracy: 0.8056 - val_loss: 2.7948 - learning_rate: 1.0000e-04\nEpoch 53/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8128 - loss: 2.6694 - val_accuracy: 0.8056 - val_loss: 2.7687 - learning_rate: 1.0000e-04\nEpoch 54/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8222 - loss: 2.6311 - val_accuracy: 0.7986 - val_loss: 2.7447 - learning_rate: 1.0000e-04\nEpoch 55/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8054 - loss: 2.6615 - val_accuracy: 0.7917 - val_loss: 2.7415 - learning_rate: 1.0000e-04\nEpoch 56/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7978 - loss: 2.6004 - val_accuracy: 0.7986 - val_loss: 2.6883 - learning_rate: 1.0000e-04\nEpoch 57/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8115 - loss: 2.5579 - val_accuracy: 0.7847 - val_loss: 2.6645 - learning_rate: 1.0000e-04\nEpoch 58/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8372 - loss: 2.4904 - val_accuracy: 0.7986 - val_loss: 2.6251 - learning_rate: 1.0000e-04\nEpoch 59/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8126 - loss: 2.5512 - val_accuracy: 0.7986 - val_loss: 2.6011 - learning_rate: 1.0000e-04\nEpoch 60/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8313 - loss: 2.4481 - val_accuracy: 0.8125 - val_loss: 2.5738 - learning_rate: 1.0000e-04\nEpoch 61/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.7947 - loss: 2.4623 - val_accuracy: 0.8056 - val_loss: 2.5346 - learning_rate: 1.0000e-04\nEpoch 62/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.7827 - loss: 2.5024 - val_accuracy: 0.8125 - val_loss: 2.4797 - learning_rate: 1.0000e-04\nEpoch 63/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8089 - loss: 2.4973 - val_accuracy: 0.8056 - val_loss: 2.4738 - learning_rate: 1.0000e-04\nEpoch 64/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8530 - loss: 2.2927 - val_accuracy: 0.8194 - val_loss: 2.4375 - learning_rate: 1.0000e-04\nEpoch 65/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8211 - loss: 2.3354 - val_accuracy: 0.8125 - val_loss: 2.4167 - learning_rate: 1.0000e-04\nEpoch 66/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8109 - loss: 2.3360 - val_accuracy: 0.8194 - val_loss: 2.4012 - learning_rate: 1.0000e-04\nEpoch 67/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8185 - loss: 2.3542 - val_accuracy: 0.8125 - val_loss: 2.3811 - learning_rate: 1.0000e-04\nEpoch 68/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8118 - loss: 2.2502 - val_accuracy: 0.8194 - val_loss: 2.3293 - learning_rate: 1.0000e-04\nEpoch 69/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8080 - loss: 2.3092 - val_accuracy: 0.8125 - val_loss: 2.3262 - learning_rate: 1.0000e-04\nEpoch 70/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8326 - loss: 2.1981 - val_accuracy: 0.8125 - val_loss: 2.3076 - learning_rate: 1.0000e-04\nEpoch 71/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8215 - loss: 2.2592 - val_accuracy: 0.8056 - val_loss: 2.2687 - learning_rate: 1.0000e-04\nEpoch 72/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8597 - loss: 2.1512 - val_accuracy: 0.8125 - val_loss: 2.2483 - learning_rate: 1.0000e-04\nEpoch 73/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8552 - loss: 2.1148 - val_accuracy: 0.8056 - val_loss: 2.2345 - learning_rate: 1.0000e-04\nEpoch 74/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 2s/step - accuracy: 0.8273 - loss: 2.1232 - val_accuracy: 0.8125 - val_loss: 2.2290 - learning_rate: 1.0000e-04\nEpoch 75/75\n\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 2s/step - accuracy: 0.8322 - loss: 2.1073 - val_accuracy: 0.8194 - val_loss: 2.1717 - learning_rate: 1.0000e-04\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"val_predictions = []\ny_val_true = None\n\nfor name, model in trained_models_list:\n    if name == \"MetaModel\":\n        continue\n    print(f\"{name}\")\n\n    config = next(item for item in model_configs if item[0] == name)\n    pre_func = config[3]\n    input_shape = config[2]\n\n    val_gen = ImageDataGenerator(preprocessing_function=pre_func).flow_from_dataframe(\n        dataframe=val_df,\n        x_col='filepath', y_col='label',\n        target_size=input_shape[:2],\n        batch_size=BATCH_SIZE,\n        class_mode='categorical',\n        shuffle=False\n    )\n    \n    if y_val_true is None:\n        y_val_true = val_gen.classes\n        y_val_encoded = to_categorical(y_val_true, NUM_CLASSES)\n    \n    preds = model.predict(val_gen, verbose=1)\n    val_predictions.append(preds)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T20:40:31.886979Z","iopub.execute_input":"2025-11-21T20:40:31.887282Z","iopub.status.idle":"2025-11-21T20:40:35.369373Z","shell.execute_reply.started":"2025-11-21T20:40:31.887260Z","shell.execute_reply":"2025-11-21T20:40:35.368742Z"}},"outputs":[{"name":"stdout","text":"ResNet50\nFound 144 validated image filenames belonging to 7 classes.\n\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 133ms/step\nVGG16\nFound 144 validated image filenames belonging to 7 classes.\n\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 123ms/step\nInceptionV3\nFound 144 validated image filenames belonging to 7 classes.\n\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 193ms/step\n","output_type":"stream"}],"execution_count":21},{"cell_type":"code","source":"meta_input_val = np.concatenate(val_predictions, axis=1)\nprint(f\"meta input shape {meta_input_val.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T20:40:37.582364Z","iopub.execute_input":"2025-11-21T20:40:37.582675Z","iopub.status.idle":"2025-11-21T20:40:37.587264Z","shell.execute_reply.started":"2025-11-21T20:40:37.582656Z","shell.execute_reply":"2025-11-21T20:40:37.586582Z"}},"outputs":[{"name":"stdout","text":"meta input shape (144, 21)\n","output_type":"stream"}],"execution_count":22},{"cell_type":"code","source":"def create_meta_model(input_dim, num_classes):\n    inp = Input(shape=(input_dim,))\n    x = Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01))(inp)\n    x = Dropout(0.4)(x)\n    out = Dense(num_classes, activation='softmax')(x)\n    return Model(inp, out)\n\nmeta_model = create_meta_model(meta_input_val.shape[1], NUM_CLASSES)\nmeta_model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T20:40:40.344414Z","iopub.execute_input":"2025-11-21T20:40:40.345253Z","iopub.status.idle":"2025-11-21T20:40:40.376223Z","shell.execute_reply.started":"2025-11-21T20:40:40.345224Z","shell.execute_reply":"2025-11-21T20:40:40.375587Z"}},"outputs":[],"execution_count":23},{"cell_type":"code","source":"h_meta = meta_model.fit(\n    meta_input_val, y_val_encoded,\n    epochs=75, batch_size=8,\n    verbose=1\n)\n\nhistories['MetaModel'] = h_meta.history\ntrained_models_list.append((\"MetaModel\", meta_model))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T20:41:47.354525Z","iopub.execute_input":"2025-11-21T20:41:47.355135Z","iopub.status.idle":"2025-11-21T20:41:53.110415Z","shell.execute_reply.started":"2025-11-21T20:41:47.355111Z","shell.execute_reply":"2025-11-21T20:41:53.109673Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9247 - loss: 0.4254 \nEpoch 2/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9291 - loss: 0.4004 \nEpoch 3/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9032 - loss: 0.3995 \nEpoch 4/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9272 - loss: 0.3767 \nEpoch 5/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8787 - loss: 0.4565 \nEpoch 6/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8806 - loss: 0.4018 \nEpoch 7/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8790 - loss: 0.4206 \nEpoch 8/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8920 - loss: 0.4876 \nEpoch 9/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9078 - loss: 0.4259 \nEpoch 10/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9068 - loss: 0.4059 \nEpoch 11/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8932 - loss: 0.4194 \nEpoch 12/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8627 - loss: 0.4867 \nEpoch 13/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8617 - loss: 0.4604 \nEpoch 14/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9074 - loss: 0.3870 \nEpoch 15/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8962 - loss: 0.4112 \nEpoch 16/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8745 - loss: 0.4542 \nEpoch 17/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9250 - loss: 0.4469 \nEpoch 18/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8180 - loss: 0.5243 \nEpoch 19/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8855 - loss: 0.4420 \nEpoch 20/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8680 - loss: 0.4545 \nEpoch 21/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8798 - loss: 0.5317 \nEpoch 22/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9026 - loss: 0.4212 \nEpoch 23/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8882 - loss: 0.4503 \nEpoch 24/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8735 - loss: 0.4650 \nEpoch 25/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8938 - loss: 0.4594 \nEpoch 26/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8459 - loss: 0.4694 \nEpoch 27/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9031 - loss: 0.4477 \nEpoch 28/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8979 - loss: 0.4781 \nEpoch 29/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9054 - loss: 0.4297 \nEpoch 30/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9086 - loss: 0.3701 \nEpoch 31/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8593 - loss: 0.4716 \nEpoch 32/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.4844 \nEpoch 33/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8699 - loss: 0.4629 \nEpoch 34/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8971 - loss: 0.4292 \nEpoch 35/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9276 - loss: 0.3668 \nEpoch 36/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8944 - loss: 0.4479 \nEpoch 37/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8609 - loss: 0.5255 \nEpoch 38/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9225 - loss: 0.4000 \nEpoch 39/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8452 - loss: 0.4483 \nEpoch 40/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8846 - loss: 0.4333 \nEpoch 41/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9201 - loss: 0.4844 \nEpoch 42/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8493 - loss: 0.4359 \nEpoch 43/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8999 - loss: 0.4171 \nEpoch 44/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9002 - loss: 0.4109 \nEpoch 45/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8590 - loss: 0.5238 \nEpoch 46/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9230 - loss: 0.4115 \nEpoch 47/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8954 - loss: 0.4850 \nEpoch 48/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8777 - loss: 0.4656 \nEpoch 49/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8822 - loss: 0.4345 \nEpoch 50/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8632 - loss: 0.4819 \nEpoch 51/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8945 - loss: 0.4369 \nEpoch 52/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9003 - loss: 0.4415 \nEpoch 53/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9043 - loss: 0.4374 \nEpoch 54/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9310 - loss: 0.3857 \nEpoch 55/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8983 - loss: 0.4088 \nEpoch 56/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9287 - loss: 0.4060 \nEpoch 57/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9167 - loss: 0.4333 \nEpoch 58/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9176 - loss: 0.3601 \nEpoch 59/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8537 - loss: 0.4667 \nEpoch 60/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9176 - loss: 0.3777 \nEpoch 61/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8748 - loss: 0.4220 \nEpoch 62/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9330 - loss: 0.3356 \nEpoch 63/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8876 - loss: 0.4436 \nEpoch 64/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9108 - loss: 0.4007 \nEpoch 65/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8895 - loss: 0.4276 \nEpoch 66/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8873 - loss: 0.4784 \nEpoch 67/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8776 - loss: 0.4863 \nEpoch 68/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8698 - loss: 0.3854 \nEpoch 69/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9330 - loss: 0.3299 \nEpoch 70/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8249 - loss: 0.4622 \nEpoch 71/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8371 - loss: 0.4403 \nEpoch 72/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8718 - loss: 0.4624 \nEpoch 73/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9162 - loss: 0.3798 \nEpoch 74/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8290 - loss: 0.4874 \nEpoch 75/75\n\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9092 - loss: 0.4165 \n","output_type":"stream"}],"execution_count":27},{"cell_type":"code","source":"def plot_all_comparison(hist_dict):\n    plt.figure(figsize=(20, 8))\n    plt.suptitle(\"Model Performans Karşılaştırması (Augmented & Weighted)\", fontsize=18)\n    colors = ['#e74c3c', '#3498db', '#2ecc71', '#8e44ad', '#f1c40f'] \n    plt.subplot(1, 2, 1)\n    idx = 0\n    for name, h in hist_dict.items():\n        val_acc = h.get('val_accuracy')\n        if val_acc:\n             plt.plot(val_acc, label=f'{name}', linewidth=2.5, color=colors[idx % 5])\n        elif name == 'MetaModel':\n             plt.plot(h['accuracy'], label=f'{name} (Meta)', linewidth=2.5, color='black', linestyle='--')\n        idx += 1\n    plt.title('Validation Accuracy')\n    plt.legend()\n    plt.grid(True, alpha=0.3)\n\n    plt.subplot(1, 2, 2)\n    idx = 0\n    for name, h in hist_dict.items():\n        if name != 'MetaModel':\n            plt.plot(h['accuracy'], label=f'{name}', linewidth=2.5, color=colors[idx % 5])\n        idx += 1\n    plt.title('Training Accuracy (Base Models)')\n    plt.legend()\n    plt.grid(True, alpha=0.3)\n    plt.show()\n\nplot_all_comparison(histories)\n\ntest_preds_all = []\ny_test_true = None\n\nbase_models_only = [m for m in trained_models_list if m[0] != \"MetaModel\"]\n\nfor name, model in base_models_only:\n    config = next(item for item in model_configs if item[0] == name)\n    pre_func = config[3]\n    input_shape = config[2]\n    \n    test_gen = ImageDataGenerator(preprocessing_function=pre_func).flow_from_dataframe(\n        dataframe=test_df,\n        x_col='filepath', y_col='label',\n        target_size=input_shape[:2],\n        batch_size=BATCH_SIZE,\n        class_mode='categorical',\n        shuffle=False\n    )\n    \n    if y_test_true is None:\n        y_test_true = test_gen.classes\n        \n    p = model.predict(test_gen, verbose=1)\n    test_preds_all.append(p)\n\nmeta_test_input = np.concatenate(test_preds_all, axis=1)\nfinal_probs = meta_model.predict(meta_test_input)\nfinal_preds = np.argmax(final_probs, axis=1)\n\nprint(classification_report(y_test_true, final_preds, target_names=class_names))\n\nplt.figure(figsize=(10, 8))\ncm = confusion_matrix(y_test_true, final_preds)\nsns.heatmap(cm, annot=True, fmt='d', cmap='Greens', xticklabels=class_names, yticklabels=class_names)\nplt.title('Meta-Model Confusion Matrix (Stratified Test Set)')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T20:41:59.883173Z","iopub.execute_input":"2025-11-21T20:41:59.883490Z","iopub.status.idle":"2025-11-21T20:42:04.604417Z","shell.execute_reply.started":"2025-11-21T20:41:59.883469Z","shell.execute_reply":"2025-11-21T20:42:04.603681Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x800 with 2 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"Found 144 validated image filenames belonging to 7 classes.\n\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 136ms/step\nFound 144 validated image filenames belonging to 7 classes.\n\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 148ms/step\nFound 144 validated image filenames belonging to 7 classes.\n\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 182ms/step\n\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n                        precision    recall  f1-score   support\n\n      cirriform clouds       0.87      0.95      0.91        21\n             clear sky       1.00      1.00      1.00        18\n   cumulonimbus clouds       0.00      0.00      0.00         4\n        cumulus clouds       0.81      0.94      0.87        32\nhigh cumuliform clouds       0.89      0.86      0.87        36\n     stratiform clouds       1.00      0.85      0.92        13\n  stratocumulus clouds       0.70      0.70      0.70        20\n\n              accuracy                           0.86       144\n             macro avg       0.75      0.76      0.75       144\n          weighted avg       0.84      0.86      0.85       144\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 2 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\n"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.preprocessing import image\nimport requests\nfrom io import BytesIO\nfrom PIL import Image\n\ndef predict_cloud(img_path, trained_models_list, model_configs, meta_model, class_names):\n    \"\"\"\n    Tek resimden tahmin yapar (path veya URL olabilir) ve görselleştirir.\n    \"\"\"\n    if img_path.startswith(\"http\"):\n        response = requests.get(img_path)\n        img = Image.open(BytesIO(response.content)).convert(\"RGB\")\n    else:\n        img = Image.open(img_path).convert(\"RGB\")\n\n    single_preds = []\n\n    for name, model in trained_models_list:\n        if name == \"MetaModel\":\n            continue\n\n        config = next(item for item in model_configs if item[0] == name)\n        input_shape = config[2]\n        pre_func = config[3]\n        resized_img = img.resize(input_shape[:2])\n        img_array = image.img_to_array(resized_img)\n        img_array = np.expand_dims(img_array, axis=0)\n        img_model = pre_func(img_array.copy())\n        pred = model.predict(img_model, verbose=0)\n        single_preds.append(pred)\n\n    stack_input = np.hstack(single_preds)\n\n    final_pred = meta_model.predict(stack_input)\n    pred_label = np.argmax(final_pred)\n    pred_prob = np.max(final_pred)\n    \n    print(f\"Tahmin: {class_names[pred_label]} ({pred_prob:.2f})\")\n\n    plt.figure(figsize=(6,6))\n    plt.imshow(img)\n    plt.title(f\"{class_names[pred_label]} ({pred_prob:.2f})\")\n    plt.axis(\"off\")\n    plt.show()\n\n    return class_names[pred_label], pred_prob","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T20:52:03.985479Z","iopub.execute_input":"2025-11-21T20:52:03.986320Z","iopub.status.idle":"2025-11-21T20:52:03.994455Z","shell.execute_reply.started":"2025-11-21T20:52:03.986296Z","shell.execute_reply":"2025-11-21T20:52:03.993581Z"}},"outputs":[],"execution_count":37},{"cell_type":"code","source":"print(image_list[1])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T20:59:21.572562Z","iopub.execute_input":"2025-11-21T20:59:21.573349Z","iopub.status.idle":"2025-11-21T20:59:21.577588Z","shell.execute_reply.started":"2025-11-21T20:59:21.573325Z","shell.execute_reply":"2025-11-21T20:59:21.576775Z"}},"outputs":[{"name":"stdout","text":"https://www.eoas.ubc.ca/courses/atsc113/flying/met_concepts/01-met_concepts/01a-clouds/images-01a/images-st/Ac-stull.jpg\n","output_type":"stream"}],"execution_count":49},{"cell_type":"code","source":"image_list = [\n    \"https://www.metoffice.gov.uk/binaries/content/gallery/metofficegovuk/hero-images/weather/cloud/cirrus-hooks.jpg\",\n]\n\ni = 0\n\nwhile i<len(image_list):\n    predict_cloud(\n        image_list[i],\n        trained_models_list,\n        model_configs,\n        meta_model,\n        class_names)\n    i+=1","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T21:00:48.007501Z","iopub.execute_input":"2025-11-21T21:00:48.008350Z","iopub.status.idle":"2025-11-21T21:00:48.785684Z","shell.execute_reply.started":"2025-11-21T21:00:48.008325Z","shell.execute_reply":"2025-11-21T21:00:48.785036Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\nTahmin: cirriform (1.00)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 600x600 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":54},{"cell_type":"code","source":"import os\n\nsave_dir = \"weights\"\nos.makedirs(save_dir, exist_ok=True)\n\nfor name, model in trained_models_list:\n    file_path = os.path.join(save_dir, f\"{name}.keras\")\n    model.save(file_path)\n    print(f\"{name} saved to {file_path}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T20:47:08.124128Z","iopub.execute_input":"2025-11-21T20:47:08.124720Z","iopub.status.idle":"2025-11-21T20:47:10.285992Z","shell.execute_reply.started":"2025-11-21T20:47:08.124680Z","shell.execute_reply":"2025-11-21T20:47:10.285236Z"}},"outputs":[{"name":"stdout","text":"ResNet50 saved to weights/ResNet50.keras\nVGG16 saved to weights/VGG16.keras\nInceptionV3 saved to weights/InceptionV3.keras\nMetaModel saved to weights/MetaModel.keras\nMetaModel saved to weights/MetaModel.keras\nMetaModel saved to weights/MetaModel.keras\nMetaModel saved to weights/MetaModel.keras\nMetaModel saved to weights/MetaModel.keras\n","output_type":"stream"}],"execution_count":33}]}