def init_callbacks(self):
self.callbacks.append(
ModelCheckpoint(
filepath=os.path.join(
self.config.callbacks.checkpoint_dir,
'%s-{epoch:02d}-{val_loss:.2f}.hdf5' %
self.config.exp.name),
monitor=self.config.callbacks.checkpoint_monitor,
mode=self.config.callbacks.checkpoint_mode,
save_best_only=self.config.callbacks.checkpoint_save_best_only,
save_weights_only=self.config.callbacks.
checkpoint_save_weights_only,
verbose=self.config.callbacks.checkpoint_verbose,
))
self.callbacks.append(
TensorBoard(
log_dir=self.config.callbacks.tensorboard_log_dir,
write_graph=self.config.callbacks.tensorboard_write_graph,
))
# if hasattr(self.config,"comet_api_key"):
if ("comet_api_key" in self.config):
from comet_ml import Experiment
experiment = Experiment(api_key=self.config.comet_api_key,
project_name=self.config.exp_name)
experiment.disable_mp()
experiment.log_parameters(self.config["trainer"])
self.callbacks.append(experiment.get_callback('keras'))
def init_callbacks(self):
if (self.config.model.name == "encoder"):
import keras
from keras.callbacks import ModelCheckpoint, TensorBoard, ReduceLROnPlateau, EarlyStopping
else:
import tensorflow.keras as keras
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard, ReduceLROnPlateau, EarlyStopping
self.callbacks.append(
ModelCheckpoint(
filepath=os.path.join(
self.config.callbacks.checkpoint_dir,
'%s-{epoch:02d}-{val_loss:.2f}.hdf5' %
self.config.exp.name),
monitor=self.config.callbacks.checkpoint_monitor,
mode=self.config.callbacks.checkpoint_mode,
save_best_only=self.config.callbacks.checkpoint_save_best_only,
save_weights_only=self.config.callbacks.
checkpoint_save_weights_only,
verbose=self.config.callbacks.checkpoint_verbose,
))
self.callbacks.append(
ModelCheckpoint(
filepath=os.path.join(
self.config.callbacks.checkpoint_dir,
'best_model-%s.hdf5' %
self.config.callbacks.checkpoint_monitor),
monitor=self.config.callbacks.checkpoint_monitor,
mode=self.config.callbacks.checkpoint_mode,
save_best_only=self.config.callbacks.checkpoint_save_best_only,
))
self.callbacks.append(
ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=10,
min_lr=0.0001))
self.callbacks.append(
EarlyStopping(monitor='val_loss', patience=10, verbose=1), )
# 在TCN中使用了tensorflow_addson中的WeightNormalization层，与tensorboard不兼容
# if (self.config.model.name != "tcn"):
# self.callbacks.append(
# TensorBoard(
# log_dir=self.config.callbacks.tensorboard_log_dir,
# write_graph=self.config.callbacks.tensorboard_write_graph,
# histogram_freq=1,
# )
# )
# if self.config.dataset.name == "ptbdb":
# self.callbacks.append(
# AdvancedLearnignRateScheduler(monitor='val_main_output_loss', patience=6, verbose=1, mode='auto',
# decayRatio=0.1),
# )
if ("comet_api_key" in self.config):
from comet_ml import Experiment
experiment = Experiment(api_key=self.config.comet_api_key,
project_name=self.config.exp_name)
experiment.disable_mp()
experiment.log_parameters(self.config["trainer"])
self.callbacks.append(experiment.get_callback('keras'))
def init_callbacks(self):
if (self.config.model.name == "encoder"):
import keras
else:
import tensorflow.keras as keras
from keras.callbacks import ModelCheckpoint, TensorBoard, ReduceLROnPlateau
self.callbacks.append(
ModelCheckpoint(
filepath=os.path.join(
self.config.callbacks.checkpoint_dir,
'%s-{epoch:02d}-{val_loss:.2f}.hdf5' %
self.config.exp.name),
monitor=self.config.callbacks.checkpoint_monitor,
mode=self.config.callbacks.checkpoint_mode,
save_best_only=self.config.callbacks.checkpoint_save_best_only,
save_weights_only=self.config.callbacks.
checkpoint_save_weights_only,
verbose=self.config.callbacks.checkpoint_verbose,
))
self.callbacks.append(
ModelCheckpoint(
filepath=os.path.join(
self.config.callbacks.checkpoint_dir,
'best_model-%s.hdf5' %
self.config.callbacks.checkpoint_monitor),
monitor=self.config.callbacks.checkpoint_monitor,
mode=self.config.callbacks.checkpoint_mode,
save_best_only=self.config.callbacks.checkpoint_save_best_only,
))
self.callbacks.append(
ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=50,
min_lr=0.0001))
self.callbacks.append(
TensorBoard(
log_dir=self.config.callbacks.tensorboard_log_dir,
write_graph=self.config.callbacks.tensorboard_write_graph,
histogram_freq=1,
))
# if hasattr(self.config,"comet_api_key"):
if ("comet_api_key" in self.config):
from comet_ml import Experiment
experiment = Experiment(api_key=self.config.comet_api_key,
project_name=self.config.exp_name)
experiment.disable_mp()
experiment.log_parameters(self.config["trainer"])
self.callbacks.append(experiment.get_callback('keras'))
def init_callbacks(self):
self.callbacks.append(
ModelCheckpoint(
filepath=os.path.join(self.config.callbacks.checkpoint_dir,
'best_model.hdf5'),
monitor=self.config.callbacks.checkpoint_monitor,
mode=self.config.callbacks.checkpoint_mode,
save_best_only=self.config.callbacks.checkpoint_save_best_only,
save_weights_only=self.config.callbacks.
checkpoint_save_weights_only,
verbose=self.config.callbacks.checkpoint_verbose,
))
self.callbacks.append(
EarlyStopping(monitor='val_loss', patience=10, verbose=1))
self.callbacks.append(
AdvancedLearnignRateScheduler(monitor='val_loss',
patience=5,
verbose=1,
mode='auto',
warmup_batches=10,
decayRatio=0.1))
self.callbacks.append(
TensorBoard(
log_dir=self.config.callbacks.tensorboard_log_dir,
write_graph=self.config.callbacks.tensorboard_write_graph,
))
# if hasattr(self.config,"comet_api_key"):
if ("comet_api_key" in self.config):
from comet_ml import Experiment
experiment = Experiment(api_key=self.config.comet_api_key,
project_name=self.config.exp_name)
experiment.disable_mp()
experiment.log_parameters(self.config["args"])
self.callbacks.append(experiment.get_callback('keras'))
def init_callbacks(self):
# Stops training if accuracy does not change at least 0.005 over 10 epochs
# self.callbacks.append(
# EarlyStopping(monitor='acc', min_delta=.005, patience=10, verbose=1, mode='auto')
# )
self.callbacks.append(
TensorBoard(
log_dir=self.config.callbacks.tensorboard_log_dir,
write_graph=self.config.callbacks.tensorboard_write_graph,
))
self.callbacks.append(
ModelCheckpoint(
filepath=os.path.join(
self.config.callbacks.checkpoint_dir,
'%s-{epoch:02d}-{val_loss:.2f}.hdf5' %
self.config.exp.name),
monitor=self.config.callbacks.checkpoint_monitor,
mode=self.config.callbacks.checkpoint_mode,
save_best_only=self.config.callbacks.checkpoint_save_best_only,
save_weights_only=self.config.callbacks.
checkpoint_save_weights_only,
verbose=self.config.callbacks.checkpoint_verbose,
))
# log experiments to comet.ml
if hasattr(self.config.api, "comet"):
from comet_ml import Experiment
experiment = Experiment(
api_key=self.config.api.comet.api_key,
project_name=self.config.api.comet.exp_name)
experiment.disable_mp()
experiment.log_parameters(self.config.toDict())
self.experiment_id = experiment.id
self.callbacks.append(experiment.get_callback('keras'))