本文介绍了使用Tensorflow构建唐诗生成网络的实战过程，包括数据预处理、模型构建、训练及主函数。通过输入首字母，模型能够补全整首诗。在训练过程中，统计词频进行词到数字映射，然后利用RNNcell进行模型构建，并进行模型训练和诗歌生成。
版权声明：本文为博主原创文章，未经博主允许不得转载。
一、目录结构介绍
- checkpoints：是生成唐诗时自动创建的
- dataset：存放数据集和读取诗歌文件（poem.py：预处理古诗词）
- models：存放模型构建（model.py）这是歌词与唐诗的共用代码
- inference：存放模型训练，包括训练和生成
- 主函数，主要是命令行的参数的构建（main.py）
二、整体思路
- 输入首字母，补全整首诗
- 先统计古诗词中的词频，进行词到数字的映射。生成poems_vector（词向量）,word_to_int（词数字映射关系）,words（词表）
三、代码实现
【第一步】poems.py： 读取诗的数据集 / 预处理古诗词
主要有2个函数构成：
（1）process_poems：
· 读取诗歌数据集（诗歌：标题、内容）
· 排除一些不必要的数据
· 统计每个字出现的次数，获取常用字
· 将每个字映射成一个数字ID（word_int_map），从而获得诗歌矢量（poems_vector）
（2）generate_batch：每次取一个batch进行训练（这里取64），获得一个epoch内有多少个batch
· 在一个epoch内迭代，获取这个batch的所有poem中最长的poem的长度
· 填充其它短的诗，空的地方放空格对应获得index标号
import collections
import os
import sys
import numpy as np
start_token = 'G'
end_token = 'E'
def process_poems(file_name):
# 诗集
poems = []
with open(file_name, 'r', encoding='utf-8') as f:
for line in f.readlines():
try:
title, content = line.strip().split(':')
content = content.replace(' ', '')
if '_' in content or '(' in content or '（' in content or '《' in content or '[' in content or start_token in content or end_token in content:
continue
if len(content) < 5 or len(content) > 79:
continue
content = start_token + content + end_token
poems.append(content)
except ValueError as e:
pass
# 按诗的字数排序
poems = sorted(poems, key=lambda l: len(line))
# 统计每个字出现的次数
all_words = []
for poem in poems:
all_words += [word for word in poem]
# 这里根据包含了每个字对应的频率
counter = collections.Counter(all_words)
count_pairs = sorted(counter.items(), key=lambda x: -x[1])
words, _ = zip(*count_pairs)
# 取前多少个常用字
words = words[:len(words)] + (' ',)
# 每个字映射为一个数字ID
word_int_map = dict(zip(words, range(len(words))))
poems_vector = [list(map(lambda word: word_int_map.get(word, len(words)), poem)) for poem in poems]
return poems_vector, word_int_map, words
def generate_batch(batch_size, poems_vec, word_to_int):
n_chunk = len(poems_vec) // batch_size
x_batches = []
y_batches = []
for i in range(n_chunk):
start_index = i * batch_size
end_index = start_index + batch_size
batches = poems_vec[start_index: end_index]
# 找到这个batch 的所有poem 中最长的poem的长度
length = max(map(len, batches))
# 填充一个这么大小的空batch，空的地方放空格对应的index标号
x_data = np.full((batch_size, length), word_to_int[' '], np.int32)
for row in range(batch_size):
# 每一行就是一首诗，在原本的长度上把诗还原上去
x_data[row, :len(batches[row])] = batches[row]
y_data = np.copy(x_data)
# y的话就是x向左边也就是前面移动一个
y_data[: ,:-1] = x_data[: ,1:]
"""
x_data y_data
[6,2,4,6,9] [2,4,6,9,9]
[1,4,2,8,5] [4,2,8,5,5]
"""
x_batches.append(x_data)
y_batches.append(y_data)
return x_batches, y_batches
【第二步】model.py： 模型构建
主要构建RNN基本单元RNNcell；确定隐层、输出；初始化权重和偏置；
import tensorflow as tf
import numpy as np
def rnn_model(model, input_data, output_data, vocab_size, run_size=128, num_layers=2, batch_size=64, learning_rate=0.01):
"""
:param model: model class
:param input_data: input data placeholder
:param output_data: output data placeholder
:param vocab_size:
:param rnn_size:
:param num_layers:
:param batch_size:
:param learning_rate:
:return:
"""
end_points = {}
# 构建RNN基本单元RNNCell
if model == 'rnn':
cell_fun = tf.contrib.rnn.BasicRNNCell
elif model == 'gru':
cell_fun = tf.contrib.rnn.GRUCell
elif model == 'lstm':
cell_fun = tf.contrib.rnn.BasicLSTMCell
cell = cell_fun(run_size, state_is_tuple=True)
# 构建堆叠rnn，这里选用2层的rnn
cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers, state_is_tuple=True)
# 如果是训练模式，output_data不为None，则初始状态shape为[batch_size * rnn_size]
# 如果是生成模式，output_data为None， 则初始状态shape为[1， rnn_size]
if output_data is not None:
initial_state = cell.zero_state(batch_size, tf.float32)
else:
initial_state = cell.zero_state(1, tf.float32)
# 构建隐层
with tf.device('/cpu:0'):
embedding = tf.get_variable('embedding', initializer=tf.random_uniform([vocab_size+1, run_size], -1.0, 1.0))
inputs = tf.nn.embedding_lookup(embedding, input_data)
# [batch_size, ?, rnn_size] = [64, ?, 128]
outputs, last_state = tf.nn.dynamic_rnn(cell, inputs, initial_state=initial_state)
output = tf.reshape(outputs, [-1, run_size])
weights = tf.Variable(tf.truncated_normal([run_size, vocab_size + 1]))
bias = tf.Variable(tf.zeros(shape=[vocab_size + 1]))
logits = tf.nn.bias_add(tf.matmul(output, weights), bias=bias)
if output_data is not None:
# output_data must be one-hot encode
labels = tf.one_hot(tf.reshape(output_data, [-1]), depth=vocab_size+1)
# shuould be [?， vocab_size + 1 ]
loss = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)
# loss shape should be [？, vocab_size + 1 ]
total_loss = tf.reduce_mean(loss)
train_op = tf.train.AdamOptimizer(learning_rate).minimize(total_loss)
end_points['initial_state'] = initial_state
end_points['output'] = output
end_points['train_op'] = train_op
end_points['total_loss'] = total_loss
end_points['loss'] = loss
end_points['last_state'] = last_state
else:
prediction = tf.nn.softmax(logits)
end_points['initial_state'] = initial_state
end_points['last_state'] = last_state
end_points['prediction'] = prediction
return end_points
【第三步】tang_poems.py： 模型训练
（1）确定main函数的参数
（2）开始训练：处理数据，生成batch，构建模型，创建会话
（3）如果没有开头字，随机生成一个字开头
（4）生成诗歌：处理数据，构建模型，创建会话（加载已经训练好的模型，生成唐诗）
import collections
import os
import sys
import numpy as np
import tensorflow as tf
from models.model import rnn_model
from dataset.poems import process_poems, generate_batch
import heapq
# main 函数的参数
tf.app.flags.DEFINE_integer('batch_size',64,'batch size.')
tf.app.flags.DEFINE_float('learning_rate', 0.01, 'learning rate')
# set this to 'main.py relative path
tf.app.flags.DEFINE_string('checkpoints_dir', os.path.abspath('./checkpoints/poems/'), 'checkpoints save path.')
tf.app.flags.DEFINE_string('file_path', os.path.abspath('./dataset/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
start_token = 'G'
end_token = 'E'
def run_training():
if not os.path.exists(os.path.dirname(FLAGS.checkpoints_dir)):
os.mkdir(os.path.dirname(FLAGS.checkpoints_dir))
if not os.path.exists(FLAGS.checkpoints_dir):
os.mkdir(FLAGS.checkpoints_dir)
# 处理数据集
poems_vector, word_to_int, vocabularies = process_poems(FLAGS.file_path)
# 生成batch
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),
run_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 = 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 = 0
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoints_dir)
if checkpoint:
saver.restore(sess, checkpoint)
print("[INFO] restore from the checkpoint {0}".format(checkpoint))
start_epoch += int(checkpoint.split('-')[-1])
print('[INFO] start training...')
try:
for epoch in range(start_epoch, FLAGS.epochs):
n = 0
n_chunk = len(poems_vector) // FLAGS.batch_size
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
print('[INFO] Epoch:%d, batch: %d, training loss: %.6f'%(epoch, batch, loss))
if epoch % 6 == 0:
# saver.save(sess, './model/', global_step = epoch)
saver.save(sess, os.path.join(FLAGS.checkpoints_dir, FLAGS.model_prefix), global_step=epoch)
except KeyboardInterrupt:
# 如果Ctrl + C 中断，保存 checkpoint
print('[INFO] Interrupt manually，try saving checkpoint for now ...')
saver.save(sess, os.path.join(FLAGS.checkpoints_dir, FLAGS.model_prefix), global_step=epoch)
print('[INFO] Last epoch were saved，next time will staert from epoch {}'.format(epoch))
def to_word(predict, vocabs):
t = np.cumsum(predict)
s = np.sum(predict)
sample = int(np.searchsorted(t, np.random.rand(1)*s))
if sample > len(vocabs):
sample = len(vocabs) - 1
return vocabs[sample]
def gen_poem(begin_word):
batch_size = 1
print('[INFO] loading corpus from %s'%(FLAGS.file_path))
poems_vector,word_int_map, vocabularies = process_poems(FLAGS.file_path)
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),
run_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)
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoints_dir)
# saver.restore(sess, checkpoint)
saver.restore(sess, './checkpoints/poems/-24')
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})
# 如果指定开始的字
if begin_word:
word = begin_word
# 如果不指定开始的字，就按根据start_token生成第一个字
else:
word = to_word(predict, vocabularies)
poem = ''
while word != end_token:
print('runing 死循环？？')
poem += word
x = np.zeros((1, 1))
# 比如：指定第一个字为“白”，则x就为[[36]]，即batch_size为1，并且poems_length为1，生下第一个字
x[0, 0] = word_int_map[word]
# 传入input_data，此时没有output_data即为生成模式，并且传入初始状态为训练结束的状态
# state_shape 为[1, run_size]
[predict, last_state] = sess.run([end_points['prediction'], end_points['last_state']], feed_dict={input_data:x, end_points['initial_state']: last_state})
# 根据预测结果生成对应的字
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 + '。')
def main(is_train):
if is_train:
print('[INFO] train tang poem....')
run_training()
else:
print('[INFO] write tang poem....')
begin_word = input('输入起始字：')
poem2 = gen_poem(begin_word)
pretty_pretty_print_poem(poem2)
if __name__ == '__main__':
tf.app.run()
【第四步】main.py： 主函数
（1）第一步 训练模型：训练唐诗生成模型：直接在pyCharm中Run main
parser.set_defaults(train=True)
（2）第二步 验证模型：生成唐诗：将main( )函数中的train参数改为False
parser.set_defaults(train=False)
import argparse
def parse_args():
parser = argparse.ArgumentParser(description='Intelligence Poem and Lyric Writer.')
help_ = 'you can set this value in terminal --write value can be poem or lyric'
parser.add_argument('-w', '--write', default='poem', choices=['poem', 'lyric'], help=help_)
help_ = 'choose to train or generate'
parser.add_argument('--train', dest='train', action='store_true', help=help_)
parser.add_argument('--no-train', dest='train', action='store_false', help=help_)
# parser.set_defaults('--no-train', dest='train', action='store_false', help=help_)
# 第一步 训练模型：训练唐诗生成模型：直接在pyCharm中Run main
# parser.set_defaults(train=True)
# 第二步 验证模型：生成唐诗：将main()函数中的train参数改为False
parser.set_defaults(train=False)
args_ = parser.parse_args()
return args_
if __name__ =='__main__':
args = parse_args()
if args.write == 'poem':
from inference import tang_poems
if args.train:
tang_poems.main(True)
else:
tang_poems.main(False)
elif args.write == 'lyric':
from inference import song_lyrics
print(args.train)
if args.train:
song_lyrics.main(True)
else:
song_lyrics.main(False)
else:
print('[INFO] write option can only be poem or lyric right now.')
四、Bug
【报错描述】 ValueError: Can’t load save_path when it is None.
【原因分析】 ①不一定是你程序有 bug , 可能是模型还没生成完毕 !
②模型还没生成就引用了导致报错
【解决办法】 ①保存训练模型
checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoints_dir)
saver.restore(sess, checkpoint)
②重新训练模型 ——>保存模型——>验证模型
【报错描述】 输入唐诗的起始字，输不出结果，进入死循环一样？？
【原因分析】
【解决办法】
版权声明：本文为博主原创文章，未经博主允许不得转载。
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