Tensorflow生成唐诗和歌词（上）

整个工程使用的是Windows版pyCharm和tensorflow。

源码地址：https://github.com/Irvinglove/tensorflow_poems/tree/master

唐诗生成

一、读取诗的数据集（poems.py）

import collections
import os
import sys
import numpy as np
import codecs
start_token = 'G'
end_token = 'E'

def process_poems(file_name):
    # 诗集
    poems = []
    with codecs.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 line: len(line))

    # 统计每个字出现次数
    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]
    poems_vector = [list(map(word_int_map.get, poem)) for poem in poems]
    return poems_vector, word_int_map, words

def generate_batch(batch_size, poems_vec, word_to_int):
    # 每次取64首诗进行训练
    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

这段代码主要是有两个函数构成：

　　　　　　　　　　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；

　　　　　　　　　　　将每个字映射为一个数字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长度；

　　　　　　　　　　　填充它，空的地方放空格对应获得index标号

二、模型构建（model.py）

import tensorflow as tf
import numpy as np

def rnn_model(model, input_data, output_data, vocab_size, rnn_size=128, num_layers=2, batch_size=64,
              learning_rate=0.01):

    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
    else:
        cell_fun = tf.contrib.rnn.BasicLSTMCell

    cell = cell_fun(rnn_size, state_is_tuple=True)
    # 构建堆叠rnn，这里选用两层的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, rnn_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, rnn_size])

    weights = tf.Variable(tf.truncated_normal([rnn_size, vocab_size + 1]))
    bias = tf.Variable(tf.zeros(shape=[vocab_size + 1]))
    logits = tf.nn.bias_add(tf.matmul(output, weights), bias=bias)
    # [?, vocab_size+1]

    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)
        # should 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

主要构建RNN基本单元RNNcell；确立隐层、输出；初始化权重和偏置

三、模型训练（tang_poems.py）

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('begin_word', '白', 'file name of poems.')

tf.app.flags.DEFINE_string('model_prefix', 'poems', 'model save prefix.')

tf.app.flags.DEFINE_integer('epochs', 20, '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])

    # 构建模型
    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 = 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)
        # 从上次中断的checkpoint开始训练
        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, 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 start from epoch {}.'.format(epoch))

def to_word(predict, vocabs):
    t = np.cumsum(predict)
    s = np.sum(predict)
    # 改代码是作者写的，t的长度为vocab_size + 1, 随机生成一个数然后判断能插入第几个位置
    # 个人感觉这使得训练变得毫无意义
    # sample = int(np.searchsorted(t, np.random.rand(1) * s))
    # 而实际上输出的预测向量predict，随着训练过程应该逐渐向one-hot编码靠拢，所以应该取argmax函数
    sample = np.argmax(predict)
    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), 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)

        checkpoint = tf.train.latest_checkpoint(FLAGS.checkpoints_dir)
        saver.restore(sess, checkpoint)

        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
            #print(type(begin_word.dtype))
        # 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:
            poem += word
            x = np.zeros((1, 1))
            # 比如，指定第一个字为“白”，则x就为x[[36]]，即batch_size为1，并且poems_length为1，生成下一个字
            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,rnn_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...')

        poem2 = gen_poem(FLAGS.begin_word.encode('utf-8').decode(encoding="utf-8"))
        pretty_print_poem(poem2)

if __name__ == '__main__':
    tf.app.run()

　　　　　　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，随机生成一个字作为开头

　　　　　　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）

源代码出现的问题如下：

其实很好改 ，只要将字节转换为字符串即可，修改如下，

poem2 = gen_poem(FLAGS.begin_word.encode('utf-8').decode(encoding="utf-8"))

四、主函数（main.py）

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(train=True)

    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.')

1. 训练唐诗生成模型。直接在pyCharm中Run main

2. 生成唐诗，将main函数中的train参数改成False

parser.set_defaults(train=False)

开头是"白"

白日无人识，清风不可攀。
何时得相见，不觉此时同。
不觉人间事，何时更有诗。
无因不可见，不觉此心违。
不得闲人去，无因得见诗。

当然也可以更换开头字

五、小结

关于歌词的生成将在下一篇中介绍：Tensorflow生成唐诗和歌词（下）

整个工程分为5个文件夹。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）。

　　　　　　　　　　　 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

　　　　　　　　　　　最后是主函数，主要是命令行的参数的构建