)
if batch_nr % 1500 == 0:
with open(txt_log_name, 'a') as txt_file:
txt_file.write("Batch: " + str(batch_nr) + "/" +
str(num_training_batches) + " batch_loss: " +
str(batch_loss) + "\n")
batch_nr += 1
with open(txt_log_name, 'a') as txt_file:
txt_file.write("Epoch loss: " + str(epoch_loss / batch_nr) + "\n")
# ***********************************************Testing************************************************************
if epoch_nr == MAX_EPOCHS - 1:
with open(txt_log_name) as f:
f.write("**************************Testing*********************\n")
datahandler.reset_user_batch_data_test()
items, item_targets, session_lengths, session_reps, session_rep_lengths, user_list, sess_time_reps, time_targets, first_rec_targets = datahandler.get_next_test_batch(
)
model.eval_mode()
batch_nr = 0
while len(items) > int(BATCHSIZE / 2):
batch_start_time = time.time()
predictions = model.predict_on_batch(items, session_reps,
sess_time_reps, user_list,
time_targets, session_lengths,
session_rep_lengths, True)
tester.evaluate_batch(predictions[:, 1:], item_targets,
session_lengths, predictions[:, 0],
first_rec_targets)
items, item_targets, session_lengths, session_reps, session_rep_lengths, user_list, sess_time_reps, time_targets, first_rec_targets = datahandler.get_next_test_batch(
)
batch_nr += 1
