第六章（1.7）深度学习实战——用lstm做小说预测

#!/bash/bin
# -*-coding=utf-8-*-
import tensorflow as tf
import codecs
import os
import jieba
import collections
import re

"""
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, filename):
    word_to_id, id_to_word, data = _build_vocab(filename)
    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

#!/bash/bin
# -*-coding=utf-8-*-
import tensorflow as tf
from data import *
import numpy as np
import random


def random_distribution():
    """Generate a random column of probabilities."""
    b = np.random.uniform(0.0, 1.0, size=[1, vocab_size])
    return b / np.sum(b, 1)[:, None]


def sample_distribution(distribution):  # choose under the probabilities
    """Sample one element from a distribution assumed to be an array of normalized
    probabilities.
    """
    r = random.uniform(0, 1)
    s = 0
    for i in range(len(distribution[0])):
        s += distribution[0][i]
        if s >= r:
            return i
    return len(distribution) - 1


def sample(prediction):
    d = sample_distribution(prediction)
    re = []
    re.append(d)
    return re


# 模型参数设置
learning_rate = 1.0
num_steps = 35
hidden_size = 300
keep_prob = 1.0
lr_decay = 0.5
batch_size = 20
num_layers = 3
max_epoch = 14

# 语料文件
filename = 'novel.txt'

x, y, id_to_word = dataproducer(batch_size, num_steps, filename)
vocab_size = len(id_to_word)

size = hidden_size

# 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)

initial_state = cell.zero_state(batch_size, tf.float32)
state = initial_state
embedding = tf.get_variable('embedding', [vocab_size, size])
input_data = x
targets = y

test_input = tf.placeholder(tf.int32, shape=[1])
test_initial_state = cell.zero_state(1, tf.float32)

inputs = tf.nn.embedding_lookup(embedding, input_data)
test_inputs = tf.nn.embedding_lookup(embedding, test_input)

outputs = []
initializer = tf.random_uniform_initializer(-0.1, 0.1)

# 根据训练数据输出误差反向调整模型,tensorflow主要通过变量空间来实现共享变量
with tf.variable_scope("Model", reuse=None, initializer=initializer):
    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.contrib.legacy_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)

        cost = tf.reduce_sum(loss) / batch_size
        # 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)

# 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(100000):
        _, 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)

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