def train(self,
training_data_collection,
validation_data_collection=None,
output_model_filepath=None,
input_groups=None,
training_batch_size=32,
validation_batch_size=32,
training_steps_per_epoch=None,
validation_steps_per_epoch=None,
initial_learning_rate=.0001,
learning_rate_drop=None,
learning_rate_epochs=None,
num_epochs=None,
callbacks=['save_model', 'log'],
**kwargs):
"""
input_groups : list of strings, optional
Specifies which named data groups (e.g. "ground_truth") enter which input
data slot in your model.
"""
self.create_data_generators(training_data_collection,
validation_data_collection, input_groups,
training_batch_size, validation_batch_size,
training_steps_per_epoch,
validation_steps_per_epoch)
self.callbacks = get_callbacks(
callbacks,
output_model_filepath=output_model_filepath,
data_collection=training_data_collection,
model=self,
batch_size=training_batch_size,
backend='keras',
**kwargs)
try:
if validation_data_collection is None:
self.model.fit_generator(
generator=self.training_data_generator,
steps_per_epoch=self.training_steps_per_epoch,
epochs=num_epochs,
callbacks=self.callbacks)
else:
self.model.fit_generator(
generator=self.training_data_generator,
steps_per_epoch=self.training_steps_per_epoch,
epochs=num_epochs,
validation_data=self.validation_data_generator,
validation_steps=self.validation_steps_per_epoch,
callbacks=self.callbacks,
workers=0)
except KeyboardInterrupt:
for callback in self.callbacks:
callback.on_train_end()
except:
raise
return
def fit_one_batch(self,
training_data_collection,
output_model_filepath=None,
input_groups=None,
output_directory=None,
callbacks=['save_model', 'log'],
training_batch_size=16,
training_steps_per_epoch=None,
num_epochs=None,
show_results=False,
**kwargs):
one_batch_generator = self.keras_generator(
training_data_collection.data_generator(
perpetual=True,
data_group_labels=input_groups,
verbose=False,
just_one_batch=True,
batch_size=training_batch_size))
self.callbacks = get_callbacks(
callbacks,
output_model_filepath=output_model_filepath,
data_collection=training_data_collection,
model=self,
batch_size=training_batch_size,
backend='keras',
**kwargs)
if training_steps_per_epoch is None:
training_steps_per_epoch = training_data_collection.total_cases // training_batch_size + 1
try:
self.model.fit_generator(generator=one_batch_generator,
steps_per_epoch=training_steps_per_epoch,
epochs=num_epochs,
callbacks=self.callbacks)
except KeyboardInterrupt:
for callback in self.callbacks:
callback.on_train_end()
except:
raise
one_batch = next(one_batch_generator)
prediction = self.predict(one_batch[0])
if show_results:
check_data(output_data={
self.input_data: one_batch[0],
self.targets: one_batch[1],
'prediction': prediction
},
batch_size=training_batch_size)
return
def init_training(self, training_data_collection, kwargs):
# Outputs
add_parameter(self, kwargs, 'output_model_filepath')
# Training Parameters
add_parameter(self, kwargs, 'num_epochs', 100)
add_parameter(self, kwargs, 'training_steps_per_epoch', 10)
add_parameter(self, kwargs, 'training_batch_size', 16)
add_parameter(self, kwargs, 'callbacks')
self.callbacks = get_callbacks(backend='tensorflow',
model=self,
batch_size=self.training_batch_size,
**kwargs)
self.init_sess()
self.build_tensorflow_model(self.training_batch_size)
self.create_data_generators(
training_data_collection,
training_batch_size=self.training_batch_size,
training_steps_per_epoch=self.training_steps_per_epoch)
return
def train(self,
training_data_collection,
validation_data_collection=None,
output_model_filepath=None,
input_groups=None,
training_batch_size=32,
validation_batch_size=32,
training_steps_per_epoch=None,
validation_steps_per_epoch=None,
initial_learning_rate=.0001,
learning_rate_drop=None,
learning_rate_epochs=None,
num_epochs=None,
callbacks=['save_model', 'log'],
**kwargs):
"""
input_groups : list of strings, optional
Specifies which named data groups (e.g. "ground_truth") enter which input
data slot in your model.
"""
# Todo: investigate call-backs more thoroughly.
# Also, maybe something more general for the difference between training and validation.
# Todo: list-checking for callbacks
self.create_data_generators(training_data_collection,
validation_data_collection, input_groups,
training_batch_size, validation_batch_size,
training_steps_per_epoch,
validation_steps_per_epoch)
if validation_data_collection is None:
self.model.fit_generator(
generator=self.training_data_generator,
steps_per_epoch=self.training_steps_per_epoch,
epochs=num_epochs,
pickle_safe=True,
callbacks=get_callbacks(
callbacks=callbacks,
output_model_filepath=output_model_filepath,
data_collection=training_data_collection,
batch_size=training_batch_size,
model=self,
backend='keras',
**kwargs))
else:
self.model.fit_generator(
generator=self.training_data_generator,
steps_per_epoch=self.training_steps_per_epoch,
epochs=num_epochs,
pickle_safe=True,
validation_data=self.validation_data_generator,
validation_steps=self.validation_steps_per_epoch,
callbacks=get_callbacks(
callbacks,
output_model_filepath=output_model_filepath,
data_collection=training_data_collection,
model=self,
batch_size=training_batch_size,
backend='keras',
**kwargs))
return