def search(self,
epochs=None,
callbacks=None,
fit_on_val_data=False,
**fit_kwargs):
"""Search for the best HyperParameters.
If there is not early-stopping in the callbacks, the early-stopping callback
is injected to accelerate the search process. At the end of the search, the
best model will be fully trained with the specified number of epochs.
# Arguments
callbacks: A list of callback functions. Defaults to None.
fit_on_val_data: Boolean. Use the training set and validation set for the
final fit of the best model.
"""
if self._finished:
return
if callbacks is None:
callbacks = []
# Insert early-stopping for adaptive number of epochs.
if epochs is None:
epochs = 1000
if not utils.contain_instance(callbacks, tf_callbacks.EarlyStopping):
callbacks.append(tf_callbacks.EarlyStopping(patience=10))
# Insert early-stopping for acceleration.
acceleration = False
new_callbacks = self._deepcopy_callbacks(callbacks)
if not utils.contain_instance(callbacks, tf_callbacks.EarlyStopping):
acceleration = True
new_callbacks.append(tf_callbacks.EarlyStopping(patience=10))
super().search(epochs=epochs, callbacks=new_callbacks, **fit_kwargs)
# Fully train the best model with original callbacks.
if acceleration or fit_on_val_data:
copied_fit_kwargs = copy.copy(fit_kwargs)
if fit_on_val_data:
# Concatenate training and validation data.
copied_fit_kwargs['x'] = copied_fit_kwargs['x'].concatenate(
fit_kwargs['validation_data'])
copied_fit_kwargs.pop('validation_data')
# Remove early-stopping since no validation data.
if utils.contain_instance(callbacks, tf_callbacks.EarlyStopping):
copied_fit_kwargs['callbacks'] = [
copy.deepcopy(callbacks)
for callback in callbacks
if not isinstance(callback, tf_callbacks.EarlyStopping)]
# Use best trial number of epochs.
copied_fit_kwargs['epochs'] = self._get_best_trial_epochs()
model = self.final_fit(**copied_fit_kwargs)
else:
model = self.get_best_models()[0]
model.save_weights(self.best_model_path)
self._finished = True
def search(self,
epochs=None,
callbacks=None,
validation_split=0,
verbose=1,
**fit_kwargs):
"""Search for the best HyperParameters.
If there is not early-stopping in the callbacks, the early-stopping callback
is injected to accelerate the search process. At the end of the search, the
best model will be fully trained with the specified number of epochs.
# Arguments
callbacks: A list of callback functions. Defaults to None.
validation_split: Float.
"""
if self._finished:
return
if callbacks is None:
callbacks = []
self.hypermodel.hypermodel.set_fit_args(validation_split,
epochs=epochs)
# Insert early-stopping for adaptive number of epochs.
epochs_provided = True
if epochs is None:
epochs_provided = False
epochs = 1000
if not utils.contain_instance(callbacks,
tf_callbacks.EarlyStopping):
callbacks.append(
tf_callbacks.EarlyStopping(patience=10, min_delta=1e-4))
# Insert early-stopping for acceleration.
early_stopping_inserted = False
new_callbacks = self._deepcopy_callbacks(callbacks)
if not utils.contain_instance(callbacks, tf_callbacks.EarlyStopping):
early_stopping_inserted = True
new_callbacks.append(
tf_callbacks.EarlyStopping(patience=10, min_delta=1e-4))
# Populate initial search space.
hp = self.oracle.get_space()
self._prepare_model_build(hp, **fit_kwargs)
self.hypermodel.build(hp)
self.oracle.update_space(hp)
super().search(epochs=epochs,
callbacks=new_callbacks,
verbose=verbose,
**fit_kwargs)
# Train the best model use validation data.
# Train the best model with enough number of epochs.
if validation_split > 0 or early_stopping_inserted:
copied_fit_kwargs = copy.copy(fit_kwargs)
# Remove early-stopping since no validation data.
# Remove early-stopping since it is inserted.
copied_fit_kwargs["callbacks"] = self._remove_early_stopping(
callbacks)
# Decide the number of epochs.
copied_fit_kwargs["epochs"] = epochs
if not epochs_provided:
copied_fit_kwargs["epochs"] = self._get_best_trial_epochs()
# Concatenate training and validation data.
if validation_split > 0:
copied_fit_kwargs["x"] = copied_fit_kwargs["x"].concatenate(
fit_kwargs["validation_data"])
copied_fit_kwargs.pop("validation_data")
self.hypermodel.hypermodel.set_fit_args(
0, epochs=copied_fit_kwargs["epochs"])
pipeline, model = self.final_fit(**copied_fit_kwargs)
else:
model = self.get_best_models()[0]
pipeline = pipeline_module.load_pipeline(
self._pipeline_path(
self.oracle.get_best_trials(1)[0].trial_id))
model.save(self.best_model_path)
pipeline.save(self.best_pipeline_path)
self._finished = True
def search(self,
epochs=None,
callbacks=None,
fit_on_val_data=False,
**fit_kwargs):
"""Search for the best HyperParameters.
If there is not early-stopping in the callbacks, the early-stopping callback
is injected to accelerate the search process. At the end of the search, the
best model will be fully trained with the specified number of epochs.
# Arguments
callbacks: A list of callback functions. Defaults to None.
fit_on_val_data: Boolean. Use the training set and validation set for the
final fit of the best model.
"""
if self._finished:
return
if callbacks is None:
callbacks = []
# Insert early-stopping for adaptive number of epochs.
epochs_provided = True
if epochs is None:
epochs_provided = False
epochs = 1000
if not utils.contain_instance(callbacks, tf_callbacks.EarlyStopping):
callbacks.append(tf_callbacks.EarlyStopping(patience=10))
# Insert early-stopping for acceleration.
early_stopping_inserted = False
new_callbacks = self._deepcopy_callbacks(callbacks)
if not utils.contain_instance(callbacks, tf_callbacks.EarlyStopping):
early_stopping_inserted = True
new_callbacks.append(tf_callbacks.EarlyStopping(patience=10))
# Populate initial search space.
hp = self.oracle.get_space()
self.hypermodel.build(hp)
self.oracle.update_space(hp)
super().search(epochs=epochs, callbacks=new_callbacks, **fit_kwargs)
# Train the best model use validation data.
# Train the best model with enought number of epochs.
if fit_on_val_data or early_stopping_inserted:
copied_fit_kwargs = copy.copy(fit_kwargs)
# Remove early-stopping since no validation data.
# Remove early-stopping since it is inserted.
copied_fit_kwargs['callbacks'] = self._remove_early_stopping(callbacks)
# Decide the number of epochs.
copied_fit_kwargs['epochs'] = epochs
if not epochs_provided:
copied_fit_kwargs['epochs'] = self._get_best_trial_epochs()
# Concatenate training and validation data.
if fit_on_val_data:
copied_fit_kwargs['x'] = copied_fit_kwargs['x'].concatenate(
fit_kwargs['validation_data'])
copied_fit_kwargs.pop('validation_data')
model = self.final_fit(**copied_fit_kwargs)
else:
model = self.get_best_models()[0]
model.save_weights(self.best_model_path)
self._finished = True