def train(self,
x_train,
batch_size,
epochs,
run_folder,
print_every_n_batches=100,
initial_epoch=0,
lr_decay=1):
custom_callback = CustomCallback(run_folder, print_every_n_batches,
initial_epoch, self)
lr_sched = step_decay_schedule(initial_lr=self.learning_rate,
decay_factor=lr_decay,
step_size=1)
checkpoint2 = ModelCheckpoint(os.path.join(run_folder,
'weights/weights.h5'),
save_weights_only=True,
verbose=1)
callbacks_list = [checkpoint2, custom_callback, lr_sched]
self.model.fit(x_train,
x_train,
batch_size=batch_size,
shuffle=True,
epochs=epochs,
initial_epoch=initial_epoch,
callbacks=callbacks_list)
def train(self,
x_train,
batch_size,
epochs,
run_folder,
print_every_n_batches=100,
initial_epoch=0,
lr_decay=1):
## Custom callback to print every n batches and squeeze images to a particular marker
custom_callback = CustomCallback(run_folder, print_every_n_batches,
initial_epoch, self)
## Custom Step delay scheduler for reducing the learning rate
lr_sched = step_decay_schedule(initial_lr=self.learning_rate,
decay_factor=lr_decay,
step_size=1)
## For saving the weight parameters every epoc
checkpoint2 = ModelCheckpoint(filepath=os.path.join(
run_folder, 'weights/weights.h5'),
save_weights_only=True,
verbose=1)
callbacks_list = [checkpoint2, custom_callback, lr_sched]
self.model.fit(x=x_train,
y=x_train,
batch_size=batch_size,
shuffle=True,
epochs=epochs,
initial_epoch=initial_epoch,
callbacks=callbacks_list)
def train(self,
batch_size,
epochs,
run_folder,
directory,
print_every_n_batches=100,
initial_epoch=0,
lr_decay=1):
x_train = keras.preprocessing.image_dataset_from_directory(
directory,
labels="inferred",
label_mode="int",
class_names=None,
color_mode="rgb",
batch_size=32,
image_size=(128, 128),
shuffle=True,
seed=None,
validation_split=None,
subset=None,
interpolation="bilinear",
follow_links=False,
)
custom_callback = CustomCallback(run_folder, print_every_n_batches,
initial_epoch, self)
lr_sched = step_decay_schedule(initial_lr=self.learning_rate,
decay_factor=lr_decay,
step_size=1)
checkpoint_filepath = os.path.join(
run_folder, "weights/weights-{epoch:03d}-{loss:.2f}.h5")
checkpoint1 = ModelCheckpoint(checkpoint_filepath,
save_weights_only=True,
verbose=1)
checkpoint2 = ModelCheckpoint(os.path.join(run_folder,
'weights/weights.h5'),
save_weights_only=True,
verbose=1)
callbacks_list = [checkpoint1, checkpoint2, custom_callback, lr_sched]
self.model.fit(x_train,
x_train,
batch_size=batch_size,
shuffle=True,
epochs=epochs,
initial_epoch=initial_epoch,
callbacks=callbacks_list)
def train_with_generator(self, data_flow, epochs, run_folder, print_every_n_batches = 100, initial_epoch = 0, lr_decay = 1):
custom_callback = CustomCallback(run_folder, print_every_n_batches, initial_epoch, self)
lr_sched = step_decay_schedule(initial_lr=self.learning_rate, decay_factor=lr_decay, step_size=1)
checkpoint_filepath=os.path.join(run_folder, "weights/weights-{epoch:03d}-{loss:.2f}.h5")
checkpoint1 = ModelCheckpoint(checkpoint_filepath, save_weights_only = True, verbose=1)
checkpoint2 = ModelCheckpoint(os.path.join(run_folder, 'weights/weights.h5'), save_weights_only = True, verbose=1)
callbacks_list = [checkpoint1, checkpoint2, custom_callback, lr_sched]
self.model.fit_generator(
data_flow
, shuffle = True
, epochs = epochs
, initial_epoch = initial_epoch
, callbacks = callbacks_list
)
def train(self,
x_train,
batch_size,
epochs,
run_folder,
print_every_n_batches=100,
initial_epoch=0,
lr_decay=1):
custom_callback = CustomCallback(run_folder, print_every_n_batches,
initial_epoch, self)
lr_sched = step_decay_schedule(initial_lr=self.learning_rate,
decay_factor=lr_decay,
step_size=1)
checkpoint_filepath = os.path.join(
run_folder, "weights/weights.h5"
) # https://github.com/davidADSP/GDL_code/issues/73#issuecomment-725401490
# checkpoint_filepath=os.path.join(run_folder, "weights/weights-{epoch:03d}-{loss:.2f}.h5")
checkpoint1 = ModelCheckpoint(checkpoint_filepath,
save_weights_only=True,
verbose=1)
checkpoint2 = ModelCheckpoint(os.path.join(run_folder,
'weights/weights.h5'),
save_weights_only=True,
verbose=1)
callbacks_list = [checkpoint1, checkpoint2, custom_callback, lr_sched]
self.model.fit(x_train,
x_train,
batch_size=batch_size,
shuffle=True,
epochs=epochs,
initial_epoch=initial_epoch,
callbacks=callbacks_list)