基于Tensorflow的LSTM代码分析-回归

清疚 2022-04-17 03:57 398阅读 0赞

### 前言 ###

最近在学习LSTM，看了一些博客和基于tensorflow的实战代码，并和朋友交流了一下，感觉学的也还不错，主要是对思路的理解，把代码注释一下，方便大家理解，一篇博客记录下来，方便其他人，非常感谢参考的博客，大家可以看看本文的参考博客，了解一下内容，自己学习一下。

### 代码 ###

环境说明：Windows 10 平台 + TensorFlow 1.5.0  
代码目的：用sin()函数的值作为输入x，利用cos()的值作为输出y，模拟x到y的映射关系，来训练这么一个模型。

*  引用的包
 *      import tensorflow as tf
        import numpy as np
        import matplotlib.pyplot as plt
 *  变量
 *      batch_start = 0     # 建立 batch data 时候的 index
        time_step = 20     # backpropagation through time 的 time_steps，样本的序列长度
        batch_size = 50    # 每次处理的样本数量
        input_size = 1      # sin 数据输入 size   一个序列维度，[1,input_size]
        out_size = 1        # cos 数据输出 size
        cell_size = 10     # RNN 的 hidden unit size   
        lr = 0.006           # learning rate
 *  数据语料
 *      def get_batch():
            global batch_start, time_step
            # xs shape (50batch, 20steps)
            xs = np.arange(batch_start, batch_start+time_step*batch_size).reshape((batch_size, time_step)) / (10*np.pi)
            seq = np.sin(xs)
            res = np.cos(xs)
            batch_start += time_step
            # returned seq, res and xs: shape (batch, step, input)
            return [seq[:, :, np.newaxis], res[:, :, np.newaxis], xs]
        # seq[:, :, np.newaxis]  加一个第三维度
 *  LSTM主体，这个重点了解一下结构
 *      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'):   #variable_scope 区分不同name下的变量
                    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)
                    
                    
            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,])
                # input_layer_y = (batch * n_steps, cell_size)
                with tf.name_scope('Wx_plus_b'):
                    input_layer_y = tf.matmul(l_in_x, Ws_in) + bs_in
                # reshape input_layer_y ==> (batch, n_steps, cell_size)    神经网络的cell 输入
                self.input_layer_y = tf.reshape(input_layer_y, [-1, self.n_steps, self.cell_size], name='2_3D')
                
            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.input_layer_y, initial_state=self.cell_init_state, time_major=False)
            
            
            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
            
            
            def compute_cost(self):
                # 这个地方好好看看，这个是关于误差的分析
                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, labels, logits):
                return tf.square(tf.subtract(labels, logits))
        
            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)
 *  主函数代码
 *      if __name__ == '__main__':
            # 搭建 LSTMRNN 模型
            model = LSTMRNN(time_step, input_size, out_size, cell_size, batch_size)
            sess = tf.Session()
            # 初始化全部变量
            sess.run(tf.global_variables_initializer())
            
            # 训练 400 次
            for i in range(400):
                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.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))
            tf.reset_default_graph()
    
    ### 参考博客 ###
    
    [莫烦大神的博客+视频讲解][Link 1]  
    [零基础入门深度学习(6) - 长短时记忆网络(LSTM)][6_ - _LSTM]

[Link 1]: https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/5-09-RNN3/
[6_ - _LSTM]: https://zybuluo.com/hanbingtao/note/581764