def _create_callbacks(self, cur_log_dir, params):
"""Creates a list of callbacks."""
callbacks = misc.get_callbacks()
if params["enable_checkpointing"]:
ckpt_full_path = os.path.join(cur_log_dir, "cp-{epoch:04d}.ckpt")
callbacks.append(
tf.keras.callbacks.ModelCheckpoint(
ckpt_full_path, save_weights_only=params["save_weights_only"]))
return callbacks
def _create_callbacks(self, cur_log_dir, init_steps, params):
"""Creates a list of callbacks."""
sfunc = optimizer.LearningRateFn(params["learning_rate"],
params["hidden_size"],
params["learning_rate_warmup_steps"])
scheduler_callback = optimizer.LearningRateScheduler(sfunc, init_steps)
callbacks = misc.get_callbacks(params["steps_between_evals"])
callbacks.append(scheduler_callback)
ckpt_full_path = os.path.join(cur_log_dir, "cp-{epoch:04d}.ckpt")
callbacks.append(
tf.keras.callbacks.ModelCheckpoint(
ckpt_full_path, save_weights_only=True))
return callbacks
