training=training_pl, zero_centered=ZERO_CENTER) # print(scores_pred.shape) # sys.exit(0) # losses print("Losses ...") sys.stdout.flush() loss = model.compute_loss(scores_pl, scores_pred) # sys.exit(0) # define trainer print("Train_op ...") sys.stdout.flush() train_op, global_step = model.train_op(loss, LR, beta1=BETA1, beta2=BETA2) # sys.exit(0) # define summaries print("Summaries ...") sys.stdout.flush() train_loss_summary = tf.summary.scalar("train_loss", loss) valid_loss_pl = tf.placeholder( dtype=tf.float32, shape=[]) # placeholder for mean validation loss valid_loss_summary = tf.summary.scalar("valid_loss", valid_loss_pl) # summaries and graph writer print("Initializing summaries writer ...") sys.stdout.flush() if CONTINUE_TRAINING: # if continuing training, no need to write the graph again to the events file