tensorflow自动写诗

1、目录结构

2、入口类

# coding = utf-8
"""
注意：RNN使用的数据为序列化的数据
RNN网络：主要由多个LSTM计算单元组成，依靠BPTT算法进行时序展开
LSTM:含有保留门和忘记门，是一个多输入多输出的网络结构。
LSTM具备抑制梯度特性
"""
# import numpy as np
# import  tensorflow as tf
# from .models.model import rnn_model
# from .dataset.poems import process_poems,generate_batch
import argparse
import sys
sys.path.append(r'D:\study\python-数据分析\深度学习\RNN网络\inference')

def parse_args():
    """
    参数设定
    :return:
    """
    #参数描述
    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.'
    #训练
    parser.add_argument('--train', dest='train', action='store_true', help=help_)
    #测试
    parser.add_argument('--no-train', dest='train', action='store_false', help=help_)
    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) #训练
        else:
            tang_poems.main(False) #测试
    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.')

3、tang_poems.py

# -*- coding: utf-8 -*-
# file: 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

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():
    #模型保存路径配置
    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)
    #1、诗集数据处理
    poems_vector, word_to_int, vocabularies = process_poems(FLAGS.file_path)
    #2、生成批量数据用于训练
    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])
    #3、建立模型
    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())
    #4、开始训练
    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:
            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)
    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), 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)
        checkpoint = tf.train.latest_checkpoint('./model/')
        #saver.restore(sess, checkpoint)
        saver.restore(sess, './model/-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})
        if begin_word:
            word = begin_word
        else:
            word = to_word(predict, vocabularies)
        poem = ''
        while word != end_token:
            print ('runing')
            poem += word
            x = np.zeros((1, 1))
            x[0, 0] = word_int_map[word]
            [predict, last_state] = sess.run([end_points['prediction'], end_points['last_state']],
                                             feed_dict={input_data: x, end_points['initial_state']: last_state})
            word = to_word(predict, vocabularies)
        # word = words[np.argmax(probs_)]
        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:')
        #begin_word = '我'
        poem2 = gen_poem(begin_word)
        pretty_print_poem(poem2)

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

4、inference中poems.py

import numpy as np
import tensorflow as tf
from models.model import rnn_model
from dataset.poems import process_poems,generate_batch

tf.app.flags.DEFINE_integer('batch_size',64,'batch size = ?')
tf.app.flags.DEFINE_float('learning_rate',0.01,'learning_rate')
tf.app.flags.DEFINE_string('check_pointss_dir','./model/','check_pointss_dir')
tf.app.flags.DEFINE_string('file_path','./data/.txt','file_path')
tf.app.flags.DEFINE_integer('epoch',50,'train epoch')

start_token = 'G'
end_token = 'E'
FLAGS = tf.app.flags.FLAGS
def run_training():
    poems_vector,word_to_int,vocabularies = process_poems(FLAGS.file_path)
    batch_inputs,batch_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=input_data,output_data = output_targets,vocab_size = len(vocabularies)
                           ,run_size = 128,num_layers = 2,batch_size = 64,learning_rate = 0.01)

def main(is_train):
    if is_train:
        print ('training')
        run_training()
    else:
        print ('test')
        begin_word = input('word')

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

5、model.py

# -*- coding: utf-8 -*-
# file: 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):
    """
    construct rnn seq2seq model.
    :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 = {}

    #1、选择网络
    if model == 'rnn':
        cell_fun = tf.contrib.rnn.BasicRNNCell  #RNN API
    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)  #lstm api

    #2、lstm 状态初始化
    if output_data is not None:
        initial_state = cell.zero_state(batch_size, tf.float32)
    else:
        initial_state = cell.zero_state(1, tf.float32)

    #3、使用cpu运算
    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])
    #4、模型建立
    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]
    #5、损失以及优化
    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

6、dataset中poems.py

# -*- coding: utf-8 -*-
# file: poems.py

import collections
import os
import sys
import numpy as np

start_token = 'G'
end_token = 'E'

def process_poems(file_name):
    """
    诗数据处理，
    :param file_name: 文件名
    :return:
    """
    # 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(' ', '')
                #过滤不符合的诗，或者脏数据
                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):
    # 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

7、clean_cn.py

# -*- coding: utf-8 -*-
# file: clean_cn.py

"""
this script using for clean Chinese corpus.
you can set level for clean, i.e.:
level='all', will clean all character that not Chinese, include punctuations
level='normal', this will generate corpus like normal use, reserve alphabets and numbers
level='clean', this will remove all except Chinese and Chinese punctuations

besides, if you want remove complex Chinese characters, just set this to be true:
simple_only=True
"""
import numpy as np
import os
import string

cn_punctuation_set = ['，', '。', '！', '？', '"', '"', '、']
en_punctuation_set = [',', '.', '?', '!', '"', '"']

def clean_cn_corpus(file_name, clean_level='all', simple_only=True, is_save=True):
    """
    clean Chinese corpus.
    :param file_name:
    :param clean_level:
    :param simple_only:
    :param is_save:
    :return: clean corpus in list type.
    """
    if os.path.dirname(file_name):
        base_dir = os.path.dirname(file_name)
    else:
        print('not set dir. please check')

    save_file = os.path.join(base_dir, os.path.basename(file_name).split('.')[0] + '_cleaned.txt')
    with open(file_name, 'r+') as f:
        clean_content = []
        for l in f.readlines():
            l = l.strip()
            if l == '':
                pass
            else:
                l = list(l)
                should_remove_words = []
                for w in l:
                    if not should_reserve(w, clean_level):
                        should_remove_words.append(w)
                clean_line = [c for c in l if c not in should_remove_words]
                clean_line = ''.join(clean_line)
                if clean_line != '':
                    clean_content.append(clean_line)
    if is_save:
        with open(save_file, 'w+') as f:
            for l in clean_content:
                f.write(l + '\n')
        print('[INFO] cleaned file have been saved to %s.' % save_file)
    return clean_content

def should_reserve(w, clean_level):
    if w == ' ':
        return True
    else:
        if clean_level == 'all':
            # only reserve Chinese characters
            if w in cn_punctuation_set or w in string.punctuation or is_alphabet(w):
                return False
            else:
                return is_chinese(w)
        elif clean_level == 'normal':
            # reserve Chinese characters, English alphabet, number
            if is_chinese(w) or is_alphabet(w) or is_number(w):
                return True
            elif w in cn_punctuation_set or w in en_punctuation_set:
                return True
            else:
                return False
        elif clean_level == 'clean':
            if is_chinese(w):
                return True
            elif w in cn_punctuation_set:
                return True
            else:
                return False
        else:
            raise "clean_level not support %s, please set for all, normal, clean" % clean_level

def is_chinese(uchar):
    """is chinese"""
    if u'\u4e00' <= uchar <= u'\u9fa5':
        return True
    else:
        return False

def is_number(uchar):
    """is number"""
    if u'\u0030' <= uchar <= u'\u0039':
        return True
    else:
        return False

def is_alphabet(uchar):
    """is alphabet"""
    if (u'\u0041' <= uchar <= u'\u005a') or (u'\u0061' <= uchar <= u'\u007a'):
        return True
    else:
        return False

def semi_angle_to_sbc(uchar):
    """半角转全角"""
    inside_code = ord(uchar)
    if inside_code < 0x0020 or inside_code > 0x7e:
        return uchar
    if inside_code == 0x0020:
        inside_code = 0x3000
    else:
        inside_code += 0xfee0
    return chr(inside_code)

def sbc_to_semi_angle(uchar):
    """全角转半角"""
    inside_code = ord(uchar)
    if inside_code == 0x3000:
        inside_code = 0x0020
    else:
        inside_code -= 0xfee0
    if inside_code < 0x0020 or inside_code > 0x7e:
        return uchar
    return chr(inside_code)