Tensorflow lstm实现的小说撰写预测

本文主要是介绍Tensorflow lstm实现的小说撰写预测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

最近，在研究深度学习方面的知识，结合Tensorflow，完成了基于lstm的小说预测程序demo。

lstm是改进的RNN，具有长期记忆功能，相对于RNN,增加了多个门来控制输入与输出。原理方面的知识网上很多，在此，我只是将我短暂学习的tensorflow写一个预测小说的demo,如果有错误，还望大家指出。

1、将小说进行分词，去除空格，建立词汇表与id的字典，生成初始输入模型的x与y

def readfile(file_path):
f = codecs.open(file_path, 'r', 'utf-8')
alltext = f.read()
alltext = re.sub(r'\s','', alltext)
seglist = list(jieba.cut(alltext, cut_all = False))
return seglist

def _build_vocab(filename):
data = readfile(filename)
counter = collections.Counter(data)
count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))

words, _ = list(zip(*count_pairs))
word_to_id = dict(zip(words, range(len(words))))
id_to_word = dict(zip(range(len(words)),words))
dataids = []
for w in data:
dataids.append(word_to_id[w])
return word_to_id, id_to_word,dataids

def dataproducer(batch_size, num_steps):
word_to_id, id_to_word, data = _build_vocab('F:\\ml\\code\\lstm\\1.txt')
datalen = len(data)
batchlen = datalen//batch_size
epcho_size = (batchlen - 1)//num_steps

data = tf.reshape(data[0: batchlen*batch_size], [batch_size,batchlen])
i = tf.train.range_input_producer(epcho_size, shuffle=False).dequeue()
x = tf.slice(data, [0,i*num_steps],[batch_size, num_steps])
y = tf.slice(data, [0,i*num_steps+1],[batch_size, num_steps])
x.set_shape([batch_size, num_steps])
y.set_shape([batch_size, num_steps])
return x,y,id_to_word

2、建立lstm模型：

lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(size, forget_bias = 0.5)
lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob = keep_prob)
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell], num_layers)

3、根据训练数据输出误差反向调整模型

with tf.variable_scope("Model", reuse = None, initializer = initializer):#tensorflow主要通过变量空间来实现共享变量
with tf.variable_scope("r", reuse = None, initializer = initializer):
softmax_w = tf.get_variable('softmax_w', [size, vocab_size])
softmax_b = tf.get_variable('softmax_b', [vocab_size])
with tf.variable_scope("RNN", reuse = None, initializer = initializer):
for time_step in range(num_steps):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(inputs[:, time_step, :], state,)
outputs.append(cell_output)

output = tf.reshape(outputs, [-1,size])

logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.nn.seq2seq.sequence_loss_by_example([logits], [tf.reshape(targets,[-1])], [tf.ones([batch_size*num_steps])])

global_step = tf.Variable(0)
learning_rate = tf.train.exponential_decay(
10.0, global_step, 5000, 0.1, staircase=True)
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
gradients, v = zip(*optimizer.compute_gradients(loss))
gradients, _ = tf.clip_by_global_norm(gradients, 1.25)
optimizer = optimizer.apply_gradients(zip(gradients, v), global_step=global_step)

4、预测新一轮输出

teststate = test_initial_state
(celloutput,teststate)= cell(test_inputs, teststate)
partial_logits = tf.matmul(celloutput, softmax_w) + softmax_b
partial_logits = tf.nn.softmax(partial_logits)

5、根据之前建立的操作，运行tensorflow会话

sv = tf.train.Supervisor(logdir=None)
with sv.managed_session() as session:
costs = 0
iters = 0
for i in range(1000):
_,l= session.run([optimizer, cost])
costs += l
iters +=num_steps
perplextity = np.exp(costs / iters)
if i%20 == 0:
print(perplextity)
if i%100 == 0:
p = random_distribution()
b = sample(p)
sentence = id_to_word[b[0]]
for j in range(200):
test_output = session.run(partial_logits, feed_dict={test_input:b})
b = sample(test_output)
sentence += id_to_word[b[0]]
print(sentence)