val_x, val_y = process_predict(pm.val_filename, wordid, cat_to_id, max_length=pm.seq_length)
batch_val = batch_iter_predict(val_x, val_y, batch_size=64)
for x_batch, y_batch in batch_val:
real_seq_len = seq_length(x_batch)
feed_dict = model.feed_data(x_batch, y_batch, real_seq_len, 1.0)
pre_lab = session.run(model.predict, feed_dict=feed_dict)
pre_label.extend(pre_lab)
return pre_label
if __name__ == '__main__':
start = time.time()
pm = pm
categories, cat_to_id = read_category()
wordid = get_wordid(pm.vocab_filename)
pm.vocab_size = len(wordid)
pm.pre_training = get_word2vec(pm.vector_word_npz)
model = Lstm_CNN()
pre_label = val()
contents_list = list(zip(pre_label, [num for item in pm.contents_finally for num in item]))
# 在当前目录下创建文件
i, j = 1, 1
finally_data_dir = './data/Finally_Data'
if os.path.exists(os.path.join(finally_data_dir, 'No_Value.txt')):
os.remove(os.path.join(finally_data_dir, 'No_Value.txt'))
if os.path.exists(os.path.join(finally_data_dir, 'Value.txt')):
os.remove(os.path.join(finally_data_dir, 'Value.txt'))
test_loss, test_accuracy = model.test(session, x_test, y_test)
print('global_step:', global_step, 'train_loss:', train_loss, 'train_accuracy:', train_accuracy,
'test_loss:', test_loss, 'test_accuracy:', test_accuracy)
summary = tf.Summary(value=[tf.Summary.Value(tag="accuracy", simple_value=test_accuracy)])
writer_test.add_summary(summary, global_step)
summary = tf.Summary(value=[tf.Summary.Value(tag="loss", simple_value=test_loss)])
writer_test.add_summary(summary, global_step)
if global_step % num_batchs == 0:
print('Saving Model...')
saver.save(session, save_path, global_step=global_step)
pm.learning_rate *= pm.lr_decay
if __name__ == '__main__':
# pm = pm
start = time.time()
filenames = [pm.train_filename, pm.test_filename, pm.val_filename]
categories, cat_to_id = read_category() # 给10个类别编号
wordid = get_wordid(pm.vocab_filename) # 给10000个常用词词典单词编号
pm.vocab_size = len(wordid)
pm.pre_training = get_word2vec(pm.vector_word_npz)
model = Lstm_CNN()
train()
stop = time.time()
print("程序运行时间： ", (stop - start))