Training Segmentation Model.py

adam = tf.keras.optimizers.Adam(lr = 0.05, epsilon = 0.1)
seg_model.compile(optimizer = adam, 
                  loss = focal_tversky, 
                  metrics = [tversky]
                 )
#callbacks
earlystopping = EarlyStopping(monitor='val_loss',
                              mode='min', 
                              verbose=1, 
                              patience=20
                             )
# save the best model with lower validation loss
checkpointer = ModelCheckpoint(filepath="ResUNet-segModel-weights.hdf5", 
                               verbose=1, 
                               save_best_only=True
                              )
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
                              mode='min',
                              verbose=1,
                              patience=10,
                              min_delta=0.0001,
                              factor=0.2
                             )
h = seg_model.fit(train_data, 
                  epochs = 60, 
                  validation_data = val_data,
                  callbacks = [checkpointer, earlystopping, reduce_lr]
                 )