def test_case(sess, model, data, onset='VALIDATION'):
"""pred must be list"""
def pad_list(lst, pad=-1):
inner_max_len = max(map(len, lst))
map(lambda x: x.extend([pad] * (inner_max_len - len(x))), lst)
return np.array(lst)
print '#' * 20, 'ON ' + onset + ' SET START ', '#' * 20
loss = model.fit(sess, data)
pred, true_label, lengths = model.predict(sess, data)
true_label = pad_list(true_label)
true_label = np.array(true_label)
pred = pad_list(pred, pad=0)
pred = np.array(pred)
true_label = true_label.tolist()
pred = pred.tolist()
accuracy = helper.calculate_accuracy_seq(pred, true_label, lengths)
helper.print_pred_seq(pred[:10], true_label[:10])
print 'Overall ' + onset + ' loss is: {}'.format(loss)
print 'Overall ' + onset + ' accuracy is: {}'.format(accuracy)
logging.info('Overall ' + onset + ' loss is: {}'.format(loss))
logging.info('Overall ' + onset + ' accuracy is: {}'.format(accuracy))
print '#' * 20, 'ON ' + onset + ' SET END ', '#' * 20
# return loss, pred, true_label, accuracy
return loss, pred, true_label, lengths
def test_run():
with tf.Graph().as_default():
with tf.device("/gpu:" + str(args.gpu_num)):
model = rnn_autoEncoder(test='test')
saver = tf.train.Saver()
config=tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
sess.run(tf.initialize_all_variables())
saver.restore(sess, model.weight_Path+'/parameter.weight')
_, pred = test_case(sess, model, model.test_data, onset='TEST')
decode_max_len = np.max([len(i) for i in model.test_data[2]])
_, decode_label_batch = helper.encodeNpad(model.test_data[2], model.vocab, trunLen=decode_max_len)
helper.print_pred_seq(pred, decode_label_batch, vocab=model.vocab)