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
