def test(sess, m_valid):
m_valid.restore(sess)
fetches = [m_valid.accuracy, m_valid.prediction]
accuracy, predictions = sess.run(fetches)
print('accuracy: %.4f' % accuracy)
base_reader.write_results(predictions, FLAGS.relations_file, FLAGS.results_file)
def evaluation(sess, test_model, data):
acc_count = 0
step = 0
predict = []
for batch in data:
step = step + 1
acc, pre, lable = test_model.run_iter(sess, batch, Training=False)
predict.extend(pre)
acc_count += acc
#print(predict)
base_reader.write_results(predict, cfg.relations_file, cfg.results_file)
return acc_count / (step * cfg.batch_size)
def evaluation(sess, test_model, data):
acc_count = 0
step = 0
predict = []
#<##>=================
target = []
#</##>================
for batch in data:
step = step + 1
acc, pre, lable = test_model.run_iter(sess, batch, Training=False)
predict.extend(pre)
#<##>=================
target.extend(lable)
#</##>=================
acc_count += acc
#print(predict)
relations_file_path = os.path.join(data_path, cfg.relations_file)
results_file_path = os.path.join(out_path, cfg.results_file)
base_reader.write_results(predict, relations_file_path, results_file_path)
#<##>=================
base_reader.write_results(target, relations_file_path, 'out/test_keys.txt')
#</##>=================
#base_reader.write_results(predict, cfg.relations_file, cfg.results_file)
return acc_count / (step * cfg.batch_size)
