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# To save the best model | |
checkpointer = ModelCheckpoint(filepath='weights.best.model.hdf5', verbose=2, save_best_only=True) | |
# To reduce learning rate dynamically | |
lr_reduction = ReduceLROnPlateau(monitor='val_loss', patience=5, verbose=2, factor=0.2) |
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