Question

作者: Morvan 编辑: Morvan

• 学习资料:
  • 相关代码
  • 机器学习-简介系列 什么是 RNN
  • 机器学习-简介系列 什么是 LSTM RNN
  • Tensorflow 的 bptt 形式理解

本节的内容包括:

• 设置 RNN 的参数
• 数据生成
• 定义 LSTMRNN 的主体结构
• 训练 LSTMRNN

设置 RNN 的参数

这次我们会使用 RNN 来进行回归的训练 (Regression). 会继续使用到自己创建的 sin 曲线预测一条 cos 曲线. 接下来我们先确定 RNN 的各种参数(super-parameters):

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

BATCH_START = 0     # 建立 batch data 时候的 index
TIME_STEPS = 20     # backpropagation through time 的 time_steps
BATCH_SIZE = 50     
INPUT_SIZE = 1      # sin 数据输入 size
OUTPUT_SIZE = 1     # cos 数据输出 size
CELL_SIZE = 10      # RNN 的 hidden unit size 
LR = 0.006          # learning rate

数据生成

定义一个生成数据的 get_batch function:

def get_batch():
    global BATCH_START, TIME_STEPS
    # xs shape (50batch, 20steps)
    xs = np.arange(BATCH_START, BATCH_START+TIME_STEPS*BATCH_SIZE).reshape((BATCH_SIZE, TIME_STEPS)) / (10*np.pi)
    seq = np.sin(xs)
    res = np.cos(xs)
    BATCH_START += TIME_STEPS
    # returned seq, res and xs: shape (batch, step, input)
    return [seq[:, :, np.newaxis], res[:, :, np.newaxis], xs]

定义 LSTMRNN 的主体结构

使用一个 class 来定义这次的 LSTMRNN 会更加方便. 第一步定义 class 中的 __init__ 传入各种参数:

class LSTMRNN(object):
    def __init__(self, n_steps, input_size, output_size, cell_size, batch_size):
        self.n_steps = n_steps
        self.input_size = input_size
        self.output_size = output_size
        self.cell_size = cell_size
        self.batch_size = batch_size
        with tf.name_scope('inputs'):
            self.xs = tf.placeholder(tf.float32, [None, n_steps, input_size], name='xs')
            self.ys = tf.placeholder(tf.float32, [None, n_steps, output_size], name='ys')
        with tf.variable_scope('in_hidden'):
            self.add_input_layer()
        with tf.variable_scope('LSTM_cell'):
            self.add_cell()
        with tf.variable_scope('out_hidden'):
            self.add_output_layer()
        with tf.name_scope('cost'):
            self.compute_cost()
        with tf.name_scope('train'):
            self.train_op = tf.train.AdamOptimizer(LR).minimize(self.cost)

设置 add_input_layer 功能，添加 input_layer :

    def add_input_layer(self,):
        l_in_x = tf.reshape(self.xs, [-1, self.input_size], name='2_2D')  # (batch*n_step, in_size)
        # Ws (in_size, cell_size)
        Ws_in = self._weight_variable([self.input_size, self.cell_size])
        # bs (cell_size, )
        bs_in = self._bias_variable([self.cell_size,])
        # l_in_y = (batch * n_steps, cell_size)
        with tf.name_scope('Wx_plus_b'):
            l_in_y = tf.matmul(l_in_x, Ws_in) + bs_in
        # reshape l_in_y ==> (batch, n_steps, cell_size)
        self.l_in_y = tf.reshape(l_in_y, [-1, self.n_steps, self.cell_size], name='2_3D')

设置 add_cell 功能，添加 cell , 注意这里的 self.cell_init_state , 因为我们在 training 的时候，这个地方要特别说明。

    def add_cell(self):
        lstm_cell = tf.contrib.rnn.BasicLSTMCell(self.cell_size, forget_bias=1.0, state_is_tuple=True)
        with tf.name_scope('initial_state'):
            self.cell_init_state = lstm_cell.zero_state(self.batch_size, dtype=tf.float32)
        self.cell_outputs, self.cell_final_state = tf.nn.dynamic_rnn(
            lstm_cell, self.l_in_y, initial_state=self.cell_init_state, time_major=False)

设置 add_output_layer 功能，添加 output_layer :

    def add_output_layer(self):
        # shape = (batch * steps, cell_size)
        l_out_x = tf.reshape(self.cell_outputs, [-1, self.cell_size], name='2_2D')
        Ws_out = self._weight_variable([self.cell_size, self.output_size])
        bs_out = self._bias_variable([self.output_size, ])
        # shape = (batch * steps, output_size)
        with tf.name_scope('Wx_plus_b'):
            self.pred = tf.matmul(l_out_x, Ws_out) + bs_out

添加 RNN 中剩下的部分:

    def compute_cost(self):
        losses = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
            [tf.reshape(self.pred, [-1], name='reshape_pred')],
            [tf.reshape(self.ys, [-1], name='reshape_target')],
            [tf.ones([self.batch_size * self.n_steps], dtype=tf.float32)],
            average_across_timesteps=True,
            softmax_loss_function=self.ms_error,
            name='losses'
        )
        with tf.name_scope('average_cost'):
            self.cost = tf.div(
                tf.reduce_sum(losses, name='losses_sum'),
                self.batch_size,
                name='average_cost')
            tf.summary.scalar('cost', self.cost)

    def ms_error(self, y_target, y_pre):
        return tf.square(tf.sub(y_target, y_pre))

    def _weight_variable(self, shape, name='weights'):
        initializer = tf.random_normal_initializer(mean=0., stddev=1.,)
        return tf.get_variable(shape=shape, initializer=initializer, name=name)

    def _bias_variable(self, shape, name='biases'):
        initializer = tf.constant_initializer(0.1)
        return tf.get_variable(name=name, shape=shape, initializer=initializer)

训练 LSTMRNN

if __name__ == '__main__':
    # 搭建 LSTMRNN 模型
    model = LSTMRNN(TIME_STEPS, INPUT_SIZE, OUTPUT_SIZE, CELL_SIZE, BATCH_SIZE)
    sess = tf.Session()
    # sess.run(tf.initialize_all_variables()) # tf 马上就要废弃这种写法
    # 替换成下面的写法:
    sess.run(tf.global_variables_initializer())
    
    # 训练 200 次
    for i in range(200):
        seq, res, xs = get_batch()  # 提取 batch data
        if i == 0:
        # 初始化 data
            feed_dict = {
                    model.xs: seq,
                    model.ys: res,
            }
        else:
            feed_dict = {
                model.xs: seq,
                model.ys: res,
                model.cell_init_state: state    # 保持 state 的连续性
            }
        
        # 训练
        _, cost, state, pred = sess.run(
            [model.train_op, model.cost, model.cell_final_state, model.pred],
            feed_dict=feed_dict)
        
        # 打印 cost 结果
        if i % 20 == 0:
            print('cost: ', round(cost, 4))

最后 cost 结果如下:

cost:  48.4813
cost:  9.9825
cost:  7.9988
cost:  5.8154
cost:  3.9268
cost:  2.4393
cost:  2.9643
cost:  0.4856
cost:  0.5175
cost:  0.7858

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