写了半天的稿子没有保存！！！

 

 

下面例子为PTB完整的自然语言处理训练程序

# -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf

# 训练数据路径
TRAIN_DATA = "ptb.train."
# 验证数据路径
EVAL_DATA = "ptb.valid"
# 测试数据路径
TEST_DATA = "ptb.test"
# 隐藏层规模
HIDDEN_SIZE = 300
# 深层循环神经网络中LSTM结构的层数
NUM_LAYERS = 2
# 词典规模
VOCAB_SIZE = 10000
# 训练数据batch的大小
TRAIN_BATCH_SIZE = 20
# 训练数据截断长度
TRAIN_NUM_STEP = 35

# 测试数据batch的大小
EVAL_BATCH_SIZE = 1
# 测试数据截断长度
EVAL_NUM_STEP = 1
# 使用训练数据的轮数
NUM_EPOCH = 5
# LSTM节点不被dropout的概率
LSTM_KEEP_PROB = 0.9
# 词向量不被dropout的概率
EMBEDDING_KEEP_PROB = 0.9
# 用于控制梯度膨胀的梯度大小上限
MAX_GRAD_NORM = 5
# 在Softmax层和词向量层之间共享参数
SHARE_EMB_AND_SOFTMAX = True


# 通过一个PTBModel类来描述模型，这样方便维护循环神经网络中的状态。
class PTBModel(object):
    def __init__(self, is_training, batch_size, num_steps):
        # 记录使用的batch大小和截断长度
        self.batch_size = batch_size#训练时20
        self.num_steps = num_steps#训练时35

        # 定义每一步的输入和预期输出。两者的维度都是[batch_size, num_steps]
        self.input_data = tf.placeholder(tf.int32, [batch_size, num_steps])#[20,35]
        self.targets = tf.placeholder(tf.int32, [batch_size, num_steps])#[20,35]

        # 定义使用LSTM结构为循环体结构且使用dropout的深层循环神经网络。
        dropout_keep_prob = LSTM_KEEP_PROB if is_training else 1.0
        lstm_cells = [
            tf.nn.rnn_cell.DropoutWrapper(tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_SIZE), output_keep_prob=dropout_keep_prob)
            for _ in range(NUM_LAYERS)
        ]
        cell = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)

        # 初始化最近的状态，即全零的向量。这个量只在每个epoch初始化第一个batch时使用。
        self.initial_state = cell.zero_state(batch_size, tf.float32)

        # 定义单词的词向量矩阵
        embedding = tf.get_variable("embedding", [VOCAB_SIZE, HIDDEN_SIZE])#shape=(10000, 300)

        # 将输入单词转化为词向量
        inputs = tf.nn.embedding_lookup(embedding, self.input_data)  #shape=(20, 35, 300)

        # 只在训练时使用dropout
        if is_training:
            inputs = tf.nn.dropout(inputs, EMBEDDING_KEEP_PROB)

        # 定义输出列表。在这里先将不同时刻LSTM结构的输出收集起来，再一起提供给softmax层。
        outputs = []
        state = self.initial_state
        with tf.variable_scope("RNN"):
            for time_step in range(num_steps):#35
                if time_step > 0:
                    tf.get_variable_scope().reuse_variables()
                cell_output, state = cell(inputs[:, time_step, :], state)
                outputs.append(cell_output)

        # 把输出队列展开成[batch, hidden_size * num_steps]的形状[20,300*35]
        #然后再reshape成[batch*num_steps, hidden_size]的形状。[20*35,300]
        output = tf.reshape(tf.concat(outputs, 1), [-1, HIDDEN_SIZE])

        # Softmax层：将RNN在每个位置上的输出转化为各个单词的logits
        if SHARE_EMB_AND_SOFTMAX:
            weight = tf.transpose(embedding)
        else:
            weight = tf.get_variable("weight", [HIDDEN_SIZE, VOCAB_SIZE])
        bias = tf.get_variable("bias", [VOCAB_SIZE])
        logits = tf.matmul(output, weight) + bias

        # 定义交叉熵损失函数和平均损失
        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
            labels=tf.reshape(self.targets, [-1]),
            logits=logits
        )
        self.cost = tf.reduce_sum(loss) / batch_size
        self.final_state = state

        # 只在训练模型时定义反向传播操作
        if not is_training:
            return

        trainable_variables = tf.trainable_variables()
        # 控制梯度大小，定义优化方法和训练步骤
        grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, trainable_variables), MAX_GRAD_NORM)
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
        self.train_op = optimizer.apply_gradients(zip(grads, trainable_variables))


# 使用给定的模型model在数据data上运行train_op并返回在全部数据上的perplexity值
def run_epoch(session, model, batches, train_op, output_log, step):
    # 计算平均perplexity的辅助变量
    total_costs = 0.0
    iters = 0
    state = session.run(model.initial_state)
    # 训练一个epoch
    for x, y in batches:
        #print('x',tf.shape(x))#shape=(2,)
        #print(tf.shape(y))#shape=(2,)
        # 在当前batch上运行train_op并计算损失值。交叉熵损失函数计算的就是下一个单词为给定单词的概率
        cost, state, _ = session.run(
            [model.cost, model.final_state, train_op],
            {model.input_data: x, model.targets: y, model.initial_state: state}
        )
        total_costs += cost
        iters += model.num_steps#35

        # 只有在训练时输出日志
        if output_log and step % 100 == 0:
            print("After %d steps, perplexity is %.3f" % (step, np.exp(total_costs / iters)))

        step += 1

    # 返回给定模型在给定数据上的perplexity值
    return step, np.exp(total_costs / iters)


# 从文件中读取数据，并返回包含单词编号的数组
def read_data(file_path):
    with open(file_path, "r") as fin:
        # 将整个文档读进一个长字符串
        id_string = ' '.join([line.strip() for line in fin.readlines()])
    # 将读取的单词编号转为整数
    id_list = [int(w) for w in id_string.split()]
    return id_list


def make_batches(id_list, batch_size, num_step):
    # batch_size: 一个batch中样本的数量
    # num_batches：batch的个数
    # num_step: 一个样本的序列长度
    # 计算总的batch数量。每个batch包含的单词数量是batch_size * num_step
    num_batches = (len(id_list) - 1) // (batch_size * num_step)

    # 将数据整理成一个维度为[batch_size, num_batches*num_step]的二维数组
    data = np.array(id_list[: num_batches * batch_size * num_step])
    data = np.reshape(data, [batch_size, num_batches * num_step])

    # 沿着第二个维度将数据切分成num_batches个batch，存入一个数组。
    data_batches = np.split(data, num_batches, axis=1)

    # 重复上述操作，但是每个位置向右移动一位，这里得到的是RNN每一步输出所需要预测的下一个单词
    label = np.array(id_list[1: num_batches * batch_size * num_step + 1])
    label = np.reshape(label, [batch_size, num_batches * num_step])
    label_batches = np.split(label, num_batches, axis=1)
    # 返回一个长度为num_batches的数组，其中每一项包括一个data矩阵和一个label矩阵
    # print(len(id_list))
    # print(num_batches * batch_size * num_step)
    return list(zip(data_batches, label_batches))


def main():
    # 定义初始化函数
    initializer = tf.random_uniform_initializer(-0.05, 0.05)

    # 定义训练用的循环神经网络模型
    with tf.variable_scope("language_model", reuse=None, initializer=initializer):
        train_model = PTBModel(True, TRAIN_BATCH_SIZE, TRAIN_NUM_STEP)

    # 定义测试用的循环神经网络模型。它与train_model共用参数，但是没有dropout
    with tf.variable_scope("language_model", reuse=True, initializer=initializer):
        eval_model = PTBModel(False, EVAL_BATCH_SIZE, EVAL_NUM_STEP)

    # 训练模型
    with tf.Session() as session:
        tf.global_variables_initializer().run()
        train_batches = make_batches(read_data(TRAIN_DATA), TRAIN_BATCH_SIZE, TRAIN_NUM_STEP)
        eval_batches = make_batches(read_data(EVAL_DATA), EVAL_BATCH_SIZE, EVAL_NUM_STEP)
        test_batches = make_batches(read_data(TEST_DATA), EVAL_BATCH_SIZE, EVAL_NUM_STEP)

        step = 0
        for i in range(NUM_EPOCH):#共5轮
            print("In iteration: %d" % (i + 1))
            step, train_pplx = run_epoch(session, train_model, train_batches, train_model.train_op, True, step)
            print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_pplx))

            _, eval_pplx = run_epoch(session, eval_model, eval_batches, tf.no_op(), False, 0)
            print("Epoch: %d Eval Perplexity: %.3f" % (i + 1, eval_pplx))

        _, test_pplx = run_epoch(session, eval_model, test_batches, tf.no_op(), False, 0)
        print("Test Perplexity: %.3f" % test_pplx)


if __name__ == '__main__':
    main()