fcn32s_optimizer = keras.optimizers.AdamW(learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY)

fcn32s_loss = keras.losses.SparseCategoricalCrossentropy()

fcn32s_model.compile(
    optimizer=fcn32s_optimizer,loss=fcn32s_loss,metrics=[keras.metrics.
MeanIoU(num_classes=NUM_CLASSES, sparse_y_pred=False),keras.metrics.
SparseCategoricalAccuracy(),],)
fcn32s_history = fcn32s_model.fit(train_ds, epochs=EPOCHS, validation_data=valid_ds)

fcn16s_optimizer = keras.optimizers.AdamW(learning_rate=LEARNING_RATE, weight_decay=WEIGHT_DECAY)