def fit(self, model: keras.models.Model, X, y):
tensor_board = keras.callbacks.TensorBoard(
log_dir=self.context.tensor_board_dir, histogram_freq=1)
early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss',
mode='auto',
patience=20)
model.fit(X,
y,
batch_size=self.context.batch_size,
epochs=self.context.epochs,
validation_split=self.context.validation_split,
callbacks=[tensor_board, early_stopping])
return model
def train(self,
full_model: keras.models.Model,
input_data,
epochs=100,
validation_data=None,
validation_split=None,
batch_size=32,
**kwargs):
"""Train the given autoencoder model with the builder's configuration."""
given_input_shape = input_data.shape[1:]
if given_input_shape != self.__input_shape:
raise ValueError(
f"Input shape {given_input_shape} does not match {self.__input_shape}"
)
callbacks = []
if self.__early_stopping is not None and (
validation_data is not None or validation_split is not None):
callbacks.append(
keras.callbacks.EarlyStopping(patience=self.__early_stopping))
return full_model.fit(input_data,
input_data,
epochs=epochs,
batch_size=batch_size,
callbacks=callbacks,
validation_data=validation_data,
validation_split=validation_split,
shuffle=True,
**kwargs)
