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精选文章，第一时间送达

很久以来，我们都想让机器自己创作诗歌，当无数作家、编辑还没有抬起笔时，AI已经完成了数千篇文章。现在，这里是第一步....

这诗做的很有感觉啊，这都是勤奋的结果啊，基本上学习了全唐诗的所有精华才有了这么牛逼的能力，这一般人能做到？

甚至还可以模仿周杰伦创作歌词 ！！怎么说，目前由于缺乏训练文本，导致我们的AI做的歌词有点....额，还好啦，有那么一点忧郁之风。

1.下载代码和数据集

Github地址： https://github.com/jinfagang/tensorflow_poems

数据集： 存放于项目的data文件夹内

2.环境导入

import os
import tensorflow as tf
from poems.model import rnn_model
from poems.poems import process_poems, generate_batch
import argparse
from pathlib import Path

3.参数设置

parser = argparse.ArgumentParser()
#type是要传入的参数的数据类型  help是该参数的提示信息
parser.add_argument('--batch_size', type=int, help='batch_size',default=64)
parser.add_argument('--learning_rate', type=float, help='learning_rate',default=0.0001)
parser.add_argument('--model_dir', type=Path, help='model save path.',default='./model')
parser.add_argument('--file_path', type=Path, help='file name of poems.',default='./data/poems.txt')
parser.add_argument('--model_prefix', type=str, help='model save prefix.',default='poems')
parser.add_argument('--epochs', type=int, help='train how many epochs.',default=126)args = parser.parse_args(args=[])

4.训练

下载的代码中的./model/中包含最新的训练模型，再次训练会接着训练。如果训练路径报错，需要删除./model的模型，重新开始训练。

def run_training():if not os.path.exists(args.model_dir):os.makedirs(args.model_dir)poems_vector, word_to_int, vocabularies = process_poems(args.file_path)batches_inputs, batches_outputs = generate_batch(args.batch_size, poems_vector, word_to_int)input_data = tf.placeholder(tf.int32, [args.batch_size, None])output_targets = tf.placeholder(tf.int32, [args.batch_size, None])end_points = rnn_model(model='lstm', input_data=input_data, output_data=output_targets, vocab_size=len(vocabularies), rnn_size=128, num_layers=2, batch_size=64, learning_rate=args.learning_rate)saver = tf.train.Saver(tf.global_variables())init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())with tf.Session() as sess:# sess = tf_debug.LocalCLIDebugWrapperSession(sess=sess)# sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)sess.run(init_op)start_epoch = 0checkpoint = tf.train.latest_checkpoint(args.model_dir)if checkpoint:saver.restore(sess, checkpoint)print("## restore from the checkpoint {0}".format(checkpoint))start_epoch += int(checkpoint.split('-')[-1])print('## start training...')try:n_chunk = len(poems_vector) // args.batch_sizefor epoch in range(start_epoch, args.epochs):n = 0for batch in range(n_chunk):loss, _, _ = sess.run([end_points['total_loss'],end_points['last_state'],end_points['train_op']], feed_dict={input_data: batches_inputs[n], output_targets: batches_outputs[n]})n += 1print('Epoch: %d, batch: %d, training loss: %.6f' % (epoch, batch, loss))#if epoch % 5 == 0:saver.save(sess, os.path.join(args.model_dir, args.model_prefix), global_step=epoch)except KeyboardInterrupt:print('## Interrupt manually, try saving checkpoint for now...')saver.save(sess, os.path.join(args.model_dir, args.model_prefix), global_step=epoch)print('## Last epoch were saved, next time will start from epoch {}.'.format(epoch))run_training()

5.诗词生成

import tensorflow as tf
from poems.model import rnn_model
from poems.poems import process_poems
import numpy as npstart_token = 'B'
end_token = 'E'
model_dir = './model/'
corpus_file = './data/poems.txt'lr = 0.0002def to_word(predict, vocabs):predict = predict[0]       predict /= np.sum(predict)sample = np.random.choice(np.arange(len(predict)), p=predict)if sample > len(vocabs):return vocabs[-1]else:return vocabs[sample]def gen_poem(begin_word):batch_size = 1print('## loading corpus from %s' % model_dir)tf.reset_default_graph()poems_vector, word_int_map, vocabularies = process_poems(corpus_file)input_data = tf.placeholder(tf.int32, [batch_size, None])end_points = rnn_model(model='lstm', input_data=input_data, output_data=None, vocab_size=len(vocabularies), rnn_size=128, num_layers=2, batch_size=64, learning_rate=lr)saver = tf.train.Saver(tf.global_variables())init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())with tf.Session() as sess:sess.run(init_op)checkpoint = tf.train.latest_checkpoint(model_dir)saver.restore(sess, checkpoint)x = np.array([list(map(word_int_map.get, start_token))])[predict, last_state] = sess.run([end_points['prediction'], end_points['last_state']],feed_dict={input_data: x})word = begin_word or to_word(predict, vocabularies)poem_ = ''i = 0while word != end_token:poem_ += wordi += 1if i > 24:breakx = np.array([[word_int_map[word]]])[predict, last_state] = sess.run([end_points['prediction'], end_points['last_state']],feed_dict={input_data: x, end_points['initial_state']: last_state})word = to_word(predict, vocabularies)return poem_def pretty_print_poem(poem_):poem_sentences = poem_.split('。')for s in poem_sentences:if s != '' and len(s) > 10:print(s + '。')

6.测试运行

7. 运行环境

本次使用框架TensorFlow1.13.1，本项目可以在华为提供的JupyterLab环境中运行。参考华为的实践案例：《AI作诗》：http://su.modelarts.club/dqTT https://developer.huaweicloud.com/signup/e4240e984d1c4d20bfcc83e7f7648b6c?

后台回复关键字：项目实战，可下载完整代码。

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