def eval_and_save(variables_pack_for_eval_and_save, round_num, sess):
loss_sum = 0.
accuracy_sum = 0.
nums = 0
stored_arr = []
model = variables_pack_for_eval_and_save['model']
for src, tgt in variables_pack_for_eval_and_save['test_set']:
nums += 1
uids, mids, cats, mid_his, cat_his, mid_mask, target, sl = gn_fn.prepare_data(
src, tgt)
prob, loss, acc = model.calculate(
sess, [uids, mids, cats, mid_his, cat_his, mid_mask, target, sl])
loss_sum += loss
accuracy_sum += acc
prob_1 = prob[:, 0].tolist()
target_1 = target[:, 0].tolist()
for p, t in zip(prob_1, target_1):
stored_arr.append([p, t])
test_auc = calc_auc(stored_arr)
accuracy_sum = accuracy_sum / nums
loss_sum = loss_sum / nums
if variables_pack_for_eval_and_save['best_auc'] < test_auc:
variables_pack_for_eval_and_save['best_auc'] = test_auc
variables_pack_for_eval_and_save['best_round'] = round_num
variables_pack_for_eval_and_save['saver'].save(
sess, variables_pack_for_eval_and_save['CHECKPOINT_DIR'])
return test_auc, loss_sum, accuracy_sum
placeholders)
sess.run(update_local_vars_op, feed_dict=feed_dict)
print('Weights succesfully initialized')
sys.stdout.flush()
# begin training process
print('Begin training')
sys.stdout.flush()
temp_start_time = time.time()
loss_sum = 0.0
accuracy_sum = 0.0
local_iter_cnt = 0
for epoch in range(local_epoch_num):
for src, tgt in succinct_train_set:
uids, mids, cats, mid_his, cat_his, mid_mask, target, sl = gn_fn.prepare_data(
src, tgt)
loss, acc = model.train(sess, [
uids, mids, cats, mid_his, cat_his, mid_mask, target, sl,
hyperparameters['learning_rate']
])
loss_sum += loss
accuracy_sum += acc
local_iter_cnt += 1
print('%d round training over' % round_num)
if local_iter_cnt > 0:
print(
'time: %d ----> iter: %d ----> train_loss: %.4f ---- train_accuracy: %.4f'
% (time.time() - temp_start_time, local_iter_cnt,
loss_sum / local_iter_cnt, accuracy_sum / local_iter_cnt))
else: