[Python] EfficientNetb0 validation loss stagnan not decreases, validation accuracy increases...

can u help to figure this out, using efficientnetb0 with my dataset train 1515, validation 326, test 327, have 6 classes, the validation on the first epoch decrease but in nearly last epochs validation loss and accuracy stuck

from tensorflow.keras.preprocessing.image import ImageDataGenerator
# Menggunakan ImageDataGenerator untuk preprocessing
train_datagen_1 = ImageDataGenerator(
preprocessing_function=keras.applications.efficientnet.preprocess_input, # Normalisasi gambar
rotation_range=30, # Rotasi gambar hingga 40 derajat
width_shift_range=0.1, # Geser horizontal
height_shift_range=0.1, # Geser vertikal
shear_range=0.1, # Distorsi gambar
zoom_range=0.1, # Zoom in/out
brightness_range=[0.8, 1.2],
fill_mode='reflect'
)

val_test_datagen_1 = ImageDataGenerator(
preprocessing_function=keras.applications.efficientnet.preprocess_input,
# Normalisasi gambar tanpa augmentasi untuk validasi dan test
)

# Memuat dataset dari folder dengan preprocessing
train_generator_1 = train_datagen_1.flow_from_directory(
os.path.join(output_dir, 'train'),
batch_size=batch_size_1,
target_size=(224, 224),
class_mode='categorical', # Klasifikasi multi-kelas
shuffle=True
)

val_generator_1 = val_test_datagen_1.flow_from_directory(
os.path.join(output_dir, 'val'),
batch_size=batch_size_1,
target_size=(224, 224),
class_mode='categorical',
shuffle=True
)

test_generator_1 = val_test_datagen_1.flow_from_directory(
os.path.join(output_dir, 'test'),
batch_size=batch_size_1,
class_mode='categorical',
target_size=(224, 224),
shuffle=False # Do not shuffle test data
)


from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, GlobalMaxPooling2D, Dense, Dropout, BatchNormalization
from tensorflow.keras import regularizers

# Jumlah kelas dalam dataset
num_classes_1 = train_generator_1.num_classes

# Load pre-trained EfficientNetB0 model with ImageNet weights (transfer learning)
base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

x=GlobalMaxPooling2D()(base_model.output)
x=Dense(512, activation='relu', bias_regularizer=regularizers.L2(1e-3))(x)
x = Dropout(0.3)(x)
x=Dense(256, activation='relu', bias_regularizer=regularizers.L2(1e-3))(x)
x = Dropout(0.4)(x)
x=Dense(128, activation='relu', bias_regularizer=regularizers.L2(1e-3))(x)
x = Dropout(0.4)(x)
predictions = Dense(num_classes_1, activation='softmax')(x)

# Define the model
model_1 = Model(inputs=base_model.input, outputs=predictions)

# Compile the model
model_1.compile(optimizer='Adamax', loss='categorical_crossentropy', metrics=['accuracy'])

# Model summary
model_1.summary()


Epoch 1/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 34s 357ms/step - accuracy: 0.2407 - loss: 1.8402 - val_accuracy: 0.4699 - val_loss: 1.4752
Epoch 2/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 34s 354ms/step - accuracy: 0.3408 - loss: 1.6115 - val_accuracy: 0.5394 - val_loss: 1.2970
Epoch 3/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 33s 347ms/step - accuracy: 0.4078 - loss: 1.4846 - val_accuracy: 0.5532 - val_loss: 1.1945
Epoch 4/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 34s 360ms/step - accuracy: 0.4714 - loss: 1.3449 - val_accuracy: 0.5718 - val_loss: 1.1228
Epoch 5/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 41s 361ms/step - accuracy: 0.5302 - loss: 1.2129 - val_accuracy: 0.6181 - val_loss: 0.9750
Epoch 6/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 33s 346ms/step - accuracy: 0.5883 - loss: 1.0862 - val_accuracy: 0.6620 - val_loss: 0.8812
Epoch 7/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 41s 345ms/step - accuracy: 0.6415 - loss: 0.9847 - val_accuracy: 0.6551 - val_loss: 0.8544
Epoch 8/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 41s 346ms/step - accuracy: 0.7070 - loss: 0.8461 - val_accuracy: 0.7199 - val_loss: 0.7719
Epoch 9/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 41s 432ms/step - accuracy: 0.7443 - loss: 0.7486 - val_accuracy: 0.7338 - val_loss: 0.7709
Epoch 10/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 34s 359ms/step - accuracy: 0.7685 - loss: 0.7007 - val_accuracy: 0.7569 - val_loss: 0.6712
Epoch 11/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 41s 361ms/step - accuracy: 0.7830 - loss: 0.6317 - val_accuracy: 0.7801 - val_loss: 0.6585
Epoch 12/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 33s 349ms/step - accuracy: 0.8102 - loss: 0.5673 - val_accuracy: 0.7731 - val_loss: 0.7297
Epoch 13/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 33s 349ms/step - accuracy: 0.8379 - loss: 0.5060 - val_accuracy: 0.7593 - val_loss: 0.6847
Epoch 14/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 41s 350ms/step - accuracy: 0.8537 - loss: 0.4300 - val_accuracy: 0.7593 - val_loss: 0.6988
Epoch 15/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 33s 350ms/step - accuracy: 0.8716 - loss: 0.3819 - val_accuracy: 0.7870 - val_loss: 0.6517
Epoch 16/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 33s 349ms/step - accuracy: 0.8931 - loss: 0.3353 - val_accuracy: 0.7824 - val_loss: 0.7737
Epoch 17/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 41s 348ms/step - accuracy: 0.8804 - loss: 0.3973 - val_accuracy: 0.8009 - val_loss: 0.7213
Epoch 18/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 82s 785ms/step - accuracy: 0.8933 - loss: 0.3527 - val_accuracy: 0.7940 - val_loss: 0.6943
Epoch 19/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 34s 360ms/step - accuracy: 0.9056 - loss: 0.3201 - val_accuracy: 0.8218 - val_loss: 0.6387
Epoch 20/20
95/95 ━━━━━━━━━━━━━━━━━━━━ 34s 359ms/step - accuracy: 0.9130 - loss: 0.2675 - val_accuracy: 0.7986 - val_loss: 0.7243


i already try to decrease validation loss but it still hard can u guys help me

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