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