def train(self, input_data, labels):
"""
Train a Convolutional neural network
:param input_data: Data to be trained with neural network
:type input_data: ndarray
:param labels: Labels to be trained with neural network
:type labels: ndarray
:return: None
:rtype: NoneType
:History: 2017-Dec-06 - Written - Henry Leung (University of Toronto)
"""
# Call the checklist to create astroNN folder and save parameters
self.pre_training_checklist_child(input_data, labels)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
min_delta=self.reduce_lr_epsilon,
patience=self.reduce_lr_patience,
min_lr=self.reduce_lr_min,
mode='min',
verbose=2)
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=self.early_stopping_min_delta,
patience=self.early_stopping_patience,
verbose=2,
mode='min')
self.virtual_cvslogger = VirutalCSVLogger()
self.__callbacks = [reduce_lr, self.virtual_cvslogger
] # default must have unchangeable callbacks
if self.callbacks is not None:
if isinstance(self.callbacks, list):
self.__callbacks.extend(self.callbacks)
else:
self.__callbacks.append(self.callbacks)
start_time = time.time()
self.history = self.keras_model.fit(
x=self.training_generator,
validation_data=self.validation_generator,
epochs=self.max_epochs,
verbose=self.verbose,
workers=os.cpu_count(),
callbacks=self.__callbacks,
use_multiprocessing=MULTIPROCESS_FLAG)
print(
f'Completed Training, {(time.time() - start_time):.{2}f}s in total'
)
if self.autosave is True:
# Call the post training checklist to save parameters
self.save()
return None
def train(self, input_data, input_recon_target):
# Call the checklist to create astroNN folder and save parameters
self.pre_training_checklist_child(input_data, input_recon_target)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
min_delta=self.reduce_lr_epsilon,
patience=self.reduce_lr_patience,
min_lr=self.reduce_lr_min,
mode='min',
verbose=2)
self.virtual_cvslogger = VirutalCSVLogger()
self.keras_model.fit_generator(
generator=self.training_generator,
steps_per_epoch=self.num_train // self.batch_size,
validation_data=self.validation_generator,
validation_steps=self.val_num // self.batch_size,
epochs=self.max_epochs,
verbose=self.verbose,
workers=os.cpu_count(),
callbacks=[reduce_lr, self.virtual_cvslogger],
use_multiprocessing=MULTIPROCESS_FLAG)
if self.autosave is True:
# Call the post training checklist to save parameters
self.save()
return None
def train(self, input_data, labels, inputs_err=None, labels_err=None):
"""
Train a Bayesian neural network
:param input_data: Data to be trained with neural network
:type input_data: ndarray
:param labels: Labels to be trained with neural network
:type labels: ndarray
:param inputs_err: Error for input_data (if any), same shape with input_data.
:type inputs_err: Union([NoneType, ndarray])
:param labels_err: Labels error (if any)
:type labels_err: Union([NoneType, ndarray])
:return: None
:rtype: NoneType
:History:
| 2018-Jan-06 - Written - Henry Leung (University of Toronto)
| 2018-Apr-12 - Updated - Henry Leung (University of Toronto)
"""
if inputs_err is None:
inputs_err = np.zeros_like(input_data)
if labels_err is None:
labels_err = np.zeros_like(labels)
# Call the checklist to create astroNN folder and save parameters
self.pre_training_checklist_child(input_data, labels, inputs_err, labels_err)
reduce_lr = ReduceLROnPlateau(monitor='val_output_loss', factor=0.5, min_delta=self.reduce_lr_epsilon,
patience=self.reduce_lr_patience, min_lr=self.reduce_lr_min, mode='min',
verbose=2)
self.virtual_cvslogger = VirutalCSVLogger()
self.__callbacks = [reduce_lr, self.virtual_cvslogger] # default must have unchangeable callbacks
if self.callbacks is not None:
if isinstance(self.callbacks, list):
self.__callbacks.extend(self.callbacks)
else:
self.__callbacks.append(self.callbacks)
start_time = time.time()
self.history = self.keras_model.fit_generator(generator=self.training_generator,
steps_per_epoch=self.num_train // self.batch_size,
validation_data=self.validation_generator,
validation_steps=max(self.val_num // self.batch_size, 1),
epochs=self.max_epochs, verbose=self.verbose,
workers=os.cpu_count(),
callbacks=self.__callbacks,
use_multiprocessing=MULTIPROCESS_FLAG)
print(f'Completed Training, {(time.time() - start_time):.{2}f}s in total')
if self.autosave is True:
# Call the post training checklist to save parameters
self.save()
return None
def train(self, input_data, labels):
# Call the checklist to create astroNN folder and save parameters
self.pre_training_checklist_child(input_data, labels)
reduce_lr = ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
min_delta=self.reduce_lr_epsilon,
patience=self.reduce_lr_patience,
min_lr=self.reduce_lr_min,
mode='min',
verbose=2)
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=self.early_stopping_min_delta,
patience=self.early_stopping_patience,
verbose=2,
mode='min')
self.virtual_cvslogger = VirutalCSVLogger()
self.__callbacks = [reduce_lr, self.virtual_cvslogger
] # default must have unchangeable callbacks
if self.callbacks is not None:
if isinstance(self.callbacks, list):
self.__callbacks.extend(self.callbacks)
else:
self.__callbacks.append(self.callbacks)
start_time = time.time()
self.history = self.keras_model.fit_generator(
generator=self.training_generator,
steps_per_epoch=self.num_train // self.batch_size,
validation_data=self.validation_generator,
validation_steps=self.num_train // self.batch_size,
epochs=self.max_epochs,
verbose=self.verbose,
workers=os.cpu_count(),
callbacks=self.__callbacks,
use_multiprocessing=MULTIPROCESS_FLAG)
print(
f'Completed Training, {(time.time() - start_time):.{2}f}s in total'
)
if self.autosave is True:
# Call the post training checklist to save parameters
self.save()
return None
def fit(self,
input_data,
labels,
inputs_err=None,
labels_err=None,
sample_weights=None,
experimental=False):
"""
Train a Bayesian neural network
:param input_data: Data to be trained with neural network
:type input_data: ndarray
:param labels: Labels to be trained with neural network
:type labels: ndarray
:param inputs_err: Error for input_data (if any), same shape with input_data.
:type inputs_err: Union([NoneType, ndarray])
:param labels_err: Labels error (if any)
:type labels_err: Union([NoneType, ndarray])
:param sample_weights: Sample weights (if any)
:type sample_weights: Union([NoneType, ndarray])
:return: None
:rtype: NoneType
:History:
| 2018-Jan-06 - Written - Henry Leung (University of Toronto)
| 2018-Apr-12 - Updated - Henry Leung (University of Toronto)
"""
if inputs_err is None:
inputs_err = np.zeros_like(input_data)
if labels_err is None:
labels_err = np.zeros_like(labels)
# TODO: allow named inputs too??
input_data = {
"input": input_data,
"input_err": inputs_err,
"labels_err": labels_err
}
labels = {"output": labels, "variance_output": labels}
# Call the checklist to create astroNN folder and save parameters
norm_data_training, norm_data_val, norm_labels_training, norm_labels_val, sample_weights_training, sample_weights_val = self.pre_training_checklist_child(
input_data, labels, sample_weights)
# norm_data_training['labels_err'] = norm_data_training['labels_err'].filled(MAGIC_NUMBER).astype(np.float32)
# TODO: fix the monitor name
reduce_lr = ReduceLROnPlateau(monitor='val_output_mean_absolute_error',
factor=0.5,
min_delta=self.reduce_lr_epsilon,
patience=self.reduce_lr_patience,
min_lr=self.reduce_lr_min,
mode='min',
verbose=2)
self.virtual_cvslogger = VirutalCSVLogger()
self.__callbacks = [reduce_lr, self.virtual_cvslogger
] # default must have unchangeable callbacks
if self.callbacks is not None:
if isinstance(self.callbacks, list):
self.__callbacks.extend(self.callbacks)
else:
self.__callbacks.append(self.callbacks)
start_time = time.time()
if experimental:
dataset = tf.data.Dataset.from_tensor_slices(
(norm_data_training, norm_labels_training,
sample_weights_training)).batch(self.batch_size).shuffle(
5000,
reshuffle_each_iteration=True).prefetch(tf.data.AUTOTUNE)
val_dataset = tf.data.Dataset.from_tensor_slices(
(norm_data_val, norm_labels_val, sample_weights_val)).batch(
self.batch_size).prefetch(tf.data.AUTOTUNE)
self.history = self.keras_model.fit(
dataset,
validation_data=val_dataset,
epochs=self.max_epochs,
verbose=self.verbose,
workers=os.cpu_count() // 2,
callbacks=self.__callbacks,
use_multiprocessing=MULTIPROCESS_FLAG)
else:
self.history = self.keras_model.fit(
self.training_generator,
validation_data=self.validation_generator,
epochs=self.max_epochs,
verbose=self.verbose,
workers=os.cpu_count() // 2,
callbacks=self.__callbacks,
use_multiprocessing=MULTIPROCESS_FLAG)
print(
f'Completed Training, {(time.time() - start_time):.{2}f}s in total'
)
if self.autosave is True:
# Call the post training checklist to save parameters
self.save()
return None