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gene_poetry_head.py
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gene_poetry_head.py
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#-*- coding: UTF-8 -*-
import os
import collections
import numpy as np
import tensorflow as tf
'''
test_acrostic_poetry.py 生成藏头诗(五言或七言) win10 python3.6.1 tensorflow1.2.1
'''
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#-------------------------------数据预处理---------------------------#
poetry_file ='poetry.txt'
# 诗集
poetrys = []
with open(poetry_file, "r", encoding='utf-8') as f:
for line in f:
try:
#line = line.decode('UTF-8')
line = line.strip(u'\n')
title, content = line.strip(u' ').split(u':')
content = content.replace(u' ',u'')
if u'_' in content or u'(' in content or u'（' in content or u'《' in content or u'[' in content:
continue
if len(content) < 5 or len(content) > 79:
continue
content = u'[' + content + u']'
poetrys.append(content)
except Exception as e:
pass
# 按诗的字数排序
poetrys = sorted(poetrys,key=lambda line: len(line))
print('唐诗总数: ', len(poetrys))
# 统计每个字出现次数
all_words = []
for poetry in poetrys:
all_words += [word for word in poetry]
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_num_map = dict(zip(words, range(len(words))))
# 把诗转换为向量形式，参考TensorFlow练习1
to_num = lambda word: word_num_map.get(word, len(words))
poetrys_vector = [ list(map(to_num, poetry)) for poetry in poetrys]
#[[314, 3199, 367, 1556, 26, 179, 680, 0, 3199, 41, 506, 40, 151, 4, 98, 1],
#[339, 3, 133, 31, 302, 653, 512, 0, 37, 148, 294, 25, 54, 833, 3, 1, 965, 1315, 377, 1700, 562, 21, 37, 0, 2, 1253, 21, 36, 264, 877, 809, 1]
#....]
# 每次取64首诗进行训练
batch_size = 1
n_chunk = len(poetrys_vector) // batch_size
class DataSet(object):
def __init__(self, data_size):
self._data_size = data_size
self._epochs_completed = 0
self._index_in_epoch = 0
self._data_index = np.arange(data_size)
def next_batch(self,batch_size):
start = self._index_in_epoch
if start + batch_size > self._data_size:
np.random.shuffle(self._data_index)
self._epochs_completed = self._epochs_completed + 1
self._index_in_epoch = batch_size
full_batch_features ,full_batch_labels = self.data_batch(0, batch_size)
return full_batch_features , full_batch_labels
else:
self._index_in_epoch += batch_size
end = self._index_in_epoch
full_batch_features ,full_batch_labels = self.data_batch(start, end)
if self._index_in_epoch == self._data_size:
self._index_in_epoch = 0
self._epochs_completed = self._epochs_completed + 1
np.random.shuffle(self._data_index)
return full_batch_features,full_batch_labels
def data_batch(self, start, end):
batches = []
for i in range(start, end):
batches.append(poetrys_vector[self._data_index[i]])
length = max(map(len, batches))
xdata = np.full((end - start,length), word_num_map[' '], np.int32)
for row in range(end - start):
xdata[row,:len(batches[row])] = batches[row]
ydata = np.copy(xdata)
ydata[:, :-1] = xdata[:, 1:]
return xdata, ydata
#---------------------------------------RNN--------------------------------------#
input_data = tf.placeholder(tf.int32, [batch_size, None])
output_targets = tf.placeholder(tf.int32, [batch_size, None])
# 定义RNN
def neural_network(model = 'lstm', rnn_size = 128, num_layers = 2):
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(rnn_size, state_is_tuple = True)
cell = tf.contrib.rnn.MultiRNNCell([cell] * num_layers, state_is_tuple = True)
initial_state = cell.zero_state(batch_size, tf.float32)
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w", [rnn_size, len(words)])
softmax_b = tf.get_variable("softmax_b", [len(words)])
embedding = tf.get_variable("embedding", [len(words), rnn_size])
inputs = tf.nn.embedding_lookup(embedding, input_data)
outputs, last_state = tf.nn.dynamic_rnn(cell, inputs, initial_state=initial_state, scope = 'rnnlm')
output = tf.reshape(outputs,[-1, rnn_size])
logits = tf.matmul(output, softmax_w) + softmax_b
probs = tf.nn.softmax(logits)
return logits, last_state, probs, cell, initial_state
#-------------------------------生成古诗---------------------------------#
# 使用训练完成的模型
def gen_head_poetry(heads, type):
if type != 5 and type != 7:
print('The second para has to be 5 or 7!')
return
def to_word(weights):
t = np.cumsum(weights)
s = np.sum(weights)
sample = int(np.searchsorted(t, np.random.rand(1)*s))
return words[sample]
_, last_state, probs, cell, initial_state = neural_network()
Session_config = tf.ConfigProto(allow_soft_placement = True)
Session_config.gpu_options.allow_growth = True
with tf.Session(config = Session_config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
#saver.restore(sess, 'model/poetry.module-99')
ckpt = tf.train.get_checkpoint_state('./model/')
checkpoint_suffix = ""
if tf.__version__ > "0.12":
checkpoint_suffix = ".index"
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path + checkpoint_suffix):
#print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
return None
poem = ''
for head in heads:
flag = True
while flag:
state_ = sess.run(cell.zero_state(1, tf.float32))
x = np.array([list(map(word_num_map.get, u'['))])
[probs_, state_] = sess.run([probs, last_state], feed_dict={input_data: x, initial_state: state_})
sentence = head
x = np.zeros((1, 1))
x[0,0] = word_num_map[sentence]
[probs_, state_] = sess.run([probs, last_state], feed_dict={input_data: x, initial_state: state_})
word = to_word(probs_)
sentence += word
while word != u'。':
x = np.zeros((1, 1))
x[0,0] = word_num_map[word]
[probs_, state_] = sess.run([probs, last_state], feed_dict={input_data: x, initial_state: state_})
word = to_word(probs_)
sentence += word
if len(sentence) == 2 + 2 * type:
sentence += u'\n'
poem += sentence
flag = False
return poem
print(gen_head_poetry(u'王佳晟牛逼', 5))