cur_test_loss = sess.run(loss, feed_dict={ \
x_input:test_ft_inst, \
y_target:test_label_inst, keep_prob:1.0})
test_loss_all.append(cur_test_loss)
except tf.errors.OutOfRangeError:
func.print_time()
print('Done testing -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
# calculate auc
test_pred_score_re = func.list_flatten(test_pred_score_all)
test_label_re = func.list_flatten(test_label_all)
test_auc, _, _ = func.cal_auc(test_pred_score_re, test_label_re)
test_rmse = func.cal_rmse(test_pred_score_re, test_label_re)
test_loss = np.mean(test_loss_all)
# rounding
test_auc = np.round(test_auc, 4)
test_rmse = np.round(test_rmse, 4)
test_loss = np.round(test_loss, 5)
train_loss_list = [np.round(xx, 4) for xx in train_loss_list]
print('test_auc = ', test_auc)
print('test_rmse =', test_rmse)
print('test_loss =', test_loss)
print('train_loss_list =', train_loss_list)
# append to result_list
for iii in range(n_val_batch):
# get batch
start_idx = iii * batch_size
end_idx = (iii + 1) * batch_size
cur_val_ft = val_ft_inst[start_idx:end_idx]
cur_val_label = val_label_inst[start_idx:end_idx]
# pred score
cur_val_pred_score = sess.run(pred_score, feed_dict={ \
x_input:cur_val_ft, keep_prob:1.0})
val_pred_score_all.append(cur_val_pred_score.flatten())
val_label_all.append(cur_val_label)
# calculate auc
val_pred_score_re = func.list_flatten(val_pred_score_all)
val_label_re = func.list_flatten(val_label_all)
val_auc_temp, _, _ = func.cal_auc(val_pred_score_re,
val_label_re)
# record all val results
val_auc_list.append(val_auc_temp)
# record best and save models
if val_auc_temp > best_val_auc:
best_val_auc = val_auc_temp
best_n_round = epoch
# Save the variables to disk
save_path = saver_val.save(sess, model_saving_addr)
print("Model saved in: %s" % save_path)
# count of consecutive lower
if val_auc_temp < best_val_auc:
lower_ct += 1
# once higher or equal, set to 0
else:
x_input_2:test_ft_inst_2, \
y_target_2: test_label_inst_2, keep_prob:1.0})
test_loss_all_2.append(cur_test_loss_2)
except tf.errors.OutOfRangeError:
func.print_time()
print('Done testing 2 -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
#################################
# calculate auc
test_pred_score_re_1 = func.list_flatten(test_pred_score_all_1)
test_label_re_1 = func.list_flatten(test_label_all_1)
test_auc_1, _, _ = func.cal_auc(test_pred_score_re_1, test_label_re_1)
test_rmse_1 = func.cal_rmse(test_pred_score_re_1, test_label_re_1)
test_loss_1 = np.mean(test_loss_all_1)
test_pred_score_re_2 = func.list_flatten(test_pred_score_all_2)
test_label_re_2 = func.list_flatten(test_label_all_2)
test_auc_2, _, _ = func.cal_auc(test_pred_score_re_2, test_label_re_2)
test_rmse_2 = func.cal_rmse(test_pred_score_re_2, test_label_re_2)
test_loss_2 = np.mean(test_loss_all_2)
# rounding
test_auc_1 = np.round(test_auc_1, 4)
test_rmse_1 = np.round(test_rmse_1, 4)
test_loss_1 = np.round(test_loss_1, 5)
test_auc_2 = np.round(test_auc_2, 4)
