这篇文章我是基于一个博主文章改造的，但是这个博主有些方面没有说的很清楚，也可能是我比较愚昧的，所以关键的地方我都加了注释，让大家都知道，要想训练一个rnn，到底怎么组织输入，怎么利用输出（当然了，这是基于tensorflow，不同的框架可能有不同）。### 原博主文章在这
代码如下，如果有人看到觉得有用，并且给我点赞评论，那我将会好好修改修改，毕竟这位是动力嘛，
为了让大家清楚，附上一张图，一定要搞清楚LSTM的c和a（有些图叫h，没有区别，名字而已），这两个状态，切记切记

3. 我的代码[^code]

#写在最前面的话。LSTM（只要有两个状态都差不多）的输入一般是[batch_size, steps, one_hot编码或者embeeding]，
#dynamic_rnn的outputs是[batch_size, steps, hidden_units]，state(只包含最后两个状态，c，h，他们的shape一样)shape是[batch_size,hidden_units]
# 一般此时的W权重为[hidden_units, classes(需要分的类别数)]，
#如果需要每一个输出，那么就有outputs
#最后重要的一点，因为我们是一个batch一个batch的输入，所以每次output都是针对batch的

#还有一个很简单的一点，因为x和h都住一个公式中运算然后相加，那么他们与各自的W相乘，shape肯定是一样的，
#现在单个x是[150, 83]，那么Wx肯定是[83, 512]，结果为[150,512]，所以c（h也一样， c是由x和a得到的）也是[150, 512]，那么推出
#Wa是[512, 512]

import time
from collections import namedtuple
import numpy as np
import tensorflow as tf

with open('data/shakespeare.txt', 'r') as f:
    text = f.read()
vocab = set(text)
# vocab_to_int = {c: i for i, c in enumerate(vocab)}
# int_to_vocab = dict(enumerate(vocab))
# int_to_vocab = {i: c for i, c in enumerate(vocab)}

f = open('data/vocab_to_int.txt','r')
a = f.read()
vocab_to_int = eval(a)

f = open('data/int_to_vocab.txt','r')
a = f.read()
int_to_vocab = eval(a)


#对文本进行转码，就是将每个字符都转换为对应的数字
encoded = np.array([vocab_to_int[c] for c in text], dtype=np.int32)
#转码前
print(text[: 100])
#转码后
print(encoded[: 100])


#得到一个batch的数据
def get_batches(arr, n_seqs, n_steps):
    '''
    #对已有的数组进行mini-batch分割
    arr: 待分割的数组
    n_seqs: 一个batch中序列的个数
    n_steps: 单个序列包含的字符数
    '''
    batch_size = n_steps * n_steps
    n_batches = int(len(arr) / batch_size)
    #这里我们仅保留完整的batch，对于多余的部分舍弃
    arr = arr[: batch_size * n_batches]
    arr = arr.reshape((n_seqs, -1))

    for n in range(0, arr.shape[1], n_steps):
        #inputs
        x = arr[:, n: n+n_steps]
        #targets
        y = np.zeros_like(x)
        y[:, : -1], y[:, -1] = x[:, 1:], x[:, 0]
        yield x, y

batches = get_batches(encoded, 10, 50)
x, y = next(batches)

# print('x\n', x)
# print('\ny\n', y)

#构建输入层
def build_inputs(num_seqs, num_steps):
    '''
    构建输入层
    num_seqs: 每个batch中的序列个数
    num_steps: 每个序列包含的字符数
    '''
    inputs = tf.placeholder(tf.int32, shape=(num_seqs, num_steps), name='inputs')
    targets = tf.placeholder(tf.int32, shape=(num_seqs, num_steps), name='targets')

    #加入keep_prob，就是dropout选择随机失活节点比例
    keep_prob = tf.placeholder(tf.float32, name='keep_prob')

    return inputs, targets, keep_prob


#构建LSTM层
def build_lstm(lstm_size, num_layers, batch_size, keep_prob):
    '''
    构建LSTM层
    lstm_size: lstm中隐层节点数量
    num_layers: lstm的隐层数目，这是堆叠lstm，有好几层
    batch_size:
    keep_prob:
    '''
    lstm_cells = []
    for i in range(num_layers):
        #构建一个基本的lstm单元
        lstm = tf.nn.rnn_cell.BasicLSTMCell(lstm_size)
        #添加dropout
        drop = tf.nn.rnn_cell.DropoutWrapper(lstm, output_keep_prob=keep_prob)
        lstm_cells.append(drop)
        #堆叠
        cell = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)
        # state_size是我们在定义MultiRNNCell的时就设置好了的，
        # 只是我们的输入input shape=[batch_size, num_steps]，
        # 我们刚刚定义好的cell会依次接收num_steps个输入然后产生最后的state（n-tuple，n表示堆叠的层数）
        # 但是一个batch内有batch_size这样的seq，因此就需要[batch_size，s]来存储整个batch每个seq的状态。
        initial_state = cell.zero_state(batch_size, tf.float32)

    return cell, initial_state


#构造输出层
def build_output(lstm_output, in_size, out_size):
    '''
    构造输出层
    lstm_output: lstm层的输出结果
    in_size: lstm输出层重塑后的size
    out_size: sotfmax层的四则
    '''
    #将lstm的输出按照列concate，例如[[1, 2, 3], [7, 8, 9]]
    #tf.concat的结果是[1, 2, 3, 7, 8, 9]
    print('我是lstm_output：', lstm_output.shape) #(150, 100, 512)
    seq_output = tf.concat(lstm_output, axis=1)
    #reshape
    x = tf.reshape(seq_output, [-1, in_size])
    print('我是reshape之后的output：', x) #(15000, 512)

    #及那个lstm层与sotfmax层全连接
    with tf.variable_scope('sotfmax'):
        sotfmax_w = tf.Variable(tf.truncated_normal([in_size, out_size], stddev=0.1))
        sotfmax_b = tf.Variable(tf.zeros(out_size))
    #计算logits
    logits = tf.matmul(x, sotfmax_w) + sotfmax_b

    #softmax层返回概率分布
    out = tf.nn.softmax(logits=logits, name='predictions')

    return out, logits


#根据logits和targets计算损失
def build_loss(logits, targets, lstm_size, num_classes):
    '''
    根据logits和targets计算损失
    logits: 全连接层的输出结果（不经过softmax）
    targets:
    lstm_size:
    num_classes:
    '''
    #one-hot编码
    y_one_hot = tf.one_hot(targets, num_classes)
    y_reshaped = tf.reshape(y_one_hot, logits.get_shape())

    #softmax cross entropy loss
    loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped)
    loss = tf.reduce_mean(loss)

    return loss

#构造optimizer
def build_optimizer(loss, learning_rate, grad_clip):
    '''
    构造optimizer
    loss: 损失
    learning_rate: 学习率
    '''
    #使用clipping gradients
    tvars = tf.trainable_variables()
    grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), grad_clip)
    train_op = tf.train.AdamOptimizer(learning_rate)
    optimizer = train_op.apply_gradients(zip(grads, tvars))

    return optimizer


#模型组合
class CharRNN:
    def __init__(self, num_classes, batch_size=64, num_steps=50,
                 lstm_size=128, num_layers=2, learning_rate=0.001,
                 grad_clip=5, sampling=False):
        #如果sampling是True，则采用SGD
        if sampling == True:
            batch_size, num_steps = 1, 1
        else:
            batch_size, num_steps = batch_size, num_steps

        tf.reset_default_graph()

        #输入层
        self.inputs, self.targets, self.keep_prob = build_inputs(batch_size, num_steps)

        #LSTM层
        cell, self.initial_state = build_lstm(lstm_size, num_layers, batch_size, self.keep_prob)

        #对输入进行one-hot编码
        x_one_hot = tf.one_hot(self.inputs, num_classes)
        print('我是x_one_hot:', x_one_hot.shape) #(150, 100, 83)

        #运行RNN
        #outputs输出的是最上面一层的输出(考虑了堆叠lstm的情况)，states保存的是最后一个时间输出的states
        #inputs是一整个tensor，num_steps是inputs的一个维度
        #tf.nn.dynamic_rnn的x_in必须是3维的输入
        outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=self.initial_state)
        print('我是dynamic_rnn之后的outputs：', outputs.shape) #(150, 100, 512)
        self.final_state = state
        print('我是state：', state) #元祖，包含c和h,第一个是c，第二个是和，他们shape相同，(150, 512)

        #预测结果
        self.prediction, self.logits = build_output(outputs, lstm_size, num_classes)
        print('我是prediction,logits：',   self.prediction, self.logits) #(15000, 83)

        #Loss和optimizer（with gradient clipping）
        self.loss = build_loss(self.logits, self.targets, lstm_size, num_classes)
        self.optimizer = build_optimizer(self.loss, learning_rate, grad_clip)

#模型训练
# num_seqs: 单个batch中序列的个数
# num_steps: 单个序列中字符数目
# lstm_size: 隐层结点个数
# num_layers: LSTM层个数
# learning_rate: 学习率
# keep_prob: dropout层中保留结点比例
batch_size = 150
num_steps = 100
lstm_size = 512
num_layers = 2
learning_rate = 0.001
keep_prob = 0.5

epochs = 20
#每n轮进行一次变量保存
save_every_n = 200

model = CharRNN(len(vocab), batch_size=batch_size, num_steps=num_steps,
                lstm_size=lstm_size, num_layers=num_layers,
                learning_rate=learning_rate)
saver = tf.train.Saver(max_to_keep=10)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    counter = 0
    for e in range(epochs):
        #Train network
        new_state = sess.run(model.initial_state)
        loss = 0
        for x, y in get_batches(encoded, batch_size, num_steps):
            counter += 1
            start = time.time()
            feed = {
                model.inputs : x,
                model.targets : y,
                model.keep_prob : keep_prob,
                model.initial_state : new_state
            }
            batch_loss, new_state, _ = sess.run([model.loss,
                                                 model.final_state,
                                                 model.optimizer],
                                                feed_dict=feed)
            end = time.time()
            #control the print lines
            if counter % 100 == 0:
                print('轮数：{}/{}...'.format(e+1, epochs),
                      '训练步数：{}...'.format(counter),
                      '训练误差：{:.4f}...'.format(batch_loss),
                      '{:.4f} sec/batch'.format((end-start)))
            if counter % save_every_n == 0:
                saver.save(sess, 'checkpoints/i{}_1{}.ckpt'.format(counter, lstm_size))

    saver.save(sess, 'checkpoints/i{}_1{}.ckpt'.format(counter, lstm_size))


#查看checkpoints
print(tf.train.get_checkpoint_state('checkpoints'))


#从预测结果中选取前top_n个最有可能的字符
def pick_top_n(preds, vocab_size, top_n = 5):
    '''
    从预测结果中选取钱top_n个最有可能的字符
    preds: 预测结果
    vocab_size:
    top_n:
    '''
    p = np.squeeze(preds)
    #将除了top_n个预测值的位置都置为0
    p[np.argsort(p)[: -top_n]] = 0     #np.argsort()返回的是数组从小到大的索引值
    #归一化概率
    p = p / np.sum(p)
    #随机选取一个字符
    c = np.random.choice(vocab_size, 1, p=p)[0]
    return c

#生成新文本
def sample(checkpoint, n_samples, lstm_size, vocab_size, prime='The '):
    '''
    生成新文本
    checkpoint: 某一轮迭代的参数文件
    n_samples: 新文本的字符长度
    lstm_size: 隐层节点数
    vocab_size:
    prime: 起始文本
    '''
    #将输入的单词转换为单个字符组成的list
    samples = [c for c in prime]
    #sampling=True意味着batch的size=1 x 1
    model = CharRNN(len(vocab), lstm_size=lstm_size, sampling=True)
    saver = tf.train.Saver()
    with tf.Session() as sess:
        #加载模型参数，恢复训练
        saver.restore(sess, checkpoint)
        new_state = sess.run(model.initial_state)
        for c in prime:
            x = np.zeros((1, 1))
            #输入单个字符
            x[0, 0] = vocab_to_int[c]
            feed = {
                model.inputs: x,
                model.keep_prob: 1.,
                model.initial_state: new_state
            }
            preds, new_state = sess.run([
                model.prediction, model.final_state
            ], feed_dict=feed)

            c = pick_top_n(preds, len(vocab))
            #添加字符到samples中
            samples.append(int_to_vocab[c])

            #不断生成字符，直到达到指定数目
            for i in range(n_samples):
                x[0, 0] = c
                feed = {
                    model.inputs : x,
                    model.keep_prob : 1.,
                    model.initial_state : new_state
                }
                preds, new_state = sess.run([model.prediction, model.final_state],
                                            feed_dict=feed)
                c = pick_top_n(preds, len(vocab))
                samples.append(int_to_vocab[c])

    return ''.join(samples)


print(tf.train.latest_checkpoint('checkpoints'))

#选用最终的训练参数作为输入进行文本生成
checkpoints = tf.train.latest_checkpoint('checkpoints')
samp = sample(checkpoints, 500, lstm_size, len(vocab), prime='Shu Xu ')
print(samp)
#
# checkpoint = 'checkpoints\\i3960_1512.ckpt'
# samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime="Far")
# print(samp)
#
# checkpoint = 'checkpoints\\i3960_1512.ckpt'
# samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime="Shu ")
# print(samp)