在中国文化传统中，诗有着极为独特而崇高的地位。诗歌开拓了人类的精神世界，给人们带来了无限的美感。本文将介绍如何使用一站式AI开发平台，自动生成属于你的藏头诗。
环境准备
基于华为云一站式AI开发平台ModelArts
ModelArts: https://www.huaweicloud.com/product/modelarts.html
AI开发平台ModelArts是面向开发者的一站式AI开发平台，为机器学习与深度学习提供海量数据预处理及半自动化标注、大规模分布式Training、自动化模型生成，及端-边-云模型按需部署能力，帮助用户快速创建和部署模型，管理全周期AI工作流。
对象存储服务OBS
OBS: https://www.huaweicloud.com/product/obs.html
对象存储服务（Object Storage Service，OBS）提供海量、安全、高可靠、低成本的数据存储能力，可供用户存储任意类型和大小的数据。适合企业备份/归档、视频点播、视频监控等多种数据存储场景。
模型和素材准备
文件已经上传至obs共享桶，在notebook中使用代码可以直接读取。
源文件地址：https://github.com/jinfagang/tensorflow_poems
实际操作
首先，在ModelArts中创建开发环境：在“开发环境”选项下选择notebook。
创建一个notebook，打开JupyterLab，选择tensorflow环境，开始体验。
使用华为云提供的接口，使用代码将公有桶poems中的文件拷贝到本地work路径下：
import moxing as mox
import os
obspath = 'obs://poems/poems/' #目标文件夹
localpath = os.path.join(os.environ['HOME'],'work/test/') #本地文件夹
mox.file.copy_parallel(obspath ,localpath) #批量拷贝obs://poems
安装指定版本numpy：
!pip install --upgrade pip
!pip install numpy==1.16.0
导入包，定义train函数：
import tensorflow as tf
from poems.model import rnn_model
from poems.poems import process_poems, generate_batch
tf.app.flags.DEFINE_integer('batch_size', 64, 'batch size.')
tf.app.flags.DEFINE_float('learning_rate', 0.01, 'learning rate.')
tf.app.flags.DEFINE_string('model_dir', os.path.abspath('./model'), 'model save path.')
tf.app.flags.DEFINE_string('file_path', os.path.abspath('./data/poems.txt'), 'file name of poems.')
tf.app.flags.DEFINE_string('model_prefix', 'poems', 'model save prefix.')
tf.app.flags.DEFINE_integer('epochs', 50, 'train how many epochs.')
FLAGS = tf.app.flags.FLAGS
def run_training():
if not os.path.exists(FLAGS.model_dir):
os.makedirs(FLAGS.model_dir)
poems_vector, word_to_int, vocabularies = process_poems(FLAGS.file_path)
batches_inputs, batches_outputs = generate_batch(FLAGS.batch_size, poems_vector, word_to_int)
input_data = tf.placeholder(tf.int32, [FLAGS.batch_size, None])
output_targets = tf.placeholder(tf.int32, [FLAGS.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=FLAGS.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.run(init_op)
start_epoch = 0
checkpoint = ("/home/ma-user/work/model/poems-42")
if checkpoint:
saver.restore(sess, "/home/ma-user/work/model/poems-42")
print("## restore from the checkpoint {0}".format(checkpoint))
start_epoch += int(checkpoint.split('-')[-1])
print('## start training...')
try:
n_chunk = len(poems_vector) // FLAGS.batch_size
for epoch in range(start_epoch, FLAGS.epochs):
n = 0
for 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 += 1
if batch%50==0:
print('Epoch: %d, batch: %d, training loss: %.6f' % (epoch, batch, loss))
if epoch % 6 == 0:
saver.save(sess, os.path.join(FLAGS.model_dir, FLAGS.model_prefix), global_step=epoch)
except KeyboardInterrupt:
print('## Interrupt manually, try saving checkpoint for now...')
saver.save(sess, os.path.join(FLAGS.model_dir, FLAGS.model_prefix), global_step=epoch)
print('## Last epoch were saved, next time will start from epoch {}.'.format(epoch))
开始训练（也可以跳过训练，直接调用模型42进行预测）：
def main(_):
run_training()
if __name__ == '__main__':
tf.app.run() def main(_):
run_training()
if __name__ == '__main__':
tf.app.run()
导入预测相关包并加载checkpoints：
import numpy as np
start_token = 'B'
end_token = 'E'
model_dir = './model/'
corpus_file = './data/poems.txt'
lr = 0.0002
def 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):
tf.reset_default_graph()
batch_size = 1
print('## loading corpus from %s' % model_dir)
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)#,reuse=True
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)
saver.restore(sess, "/home/ma-user/work/model/poems-48")
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 = 0
while word != end_token:
poem_ += word
i += 1
if i > 24:
break
x = 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 + '。')
调用模型生成诗歌
poem = gen_poem('人')
pretty_print_poem(poem_=poem)
至此，本次实现先告一段落，关于多个字的藏头诗生成还没进行探索，欢迎在评论区分享指导，谢谢！