TF之LSTM：利用基于顺序的LSTM回归算法对DIY数据集sin曲线(蓝虚)预测cos(红实)(matplotlib动态演示)—daiding

不念不忘少年蓝@ 2022-12-28 14:25 69阅读 0赞

**目录**

输出结果

代码设计

--------------------

## 输出结果 ##

![2020121721572280.gif][]

## 代码设计 ##

import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt
    
    BATCH_START = 0  
    TIME_STEPS = 20    
    BATCH_SIZE = 50     
    INPUT_SIZE = 1      
    OUTPUT_SIZE = 1    
    CELL_SIZE = 10      
    LR = 0.006       
    BATCH_START_TEST = 0
    
    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
        return [seq[:, :, np.newaxis], res[:, :, np.newaxis], xs]
    
    
    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)
                
        def add_input_layer(self,):  
            l_in_x = tf.reshape(self.xs, [-1, self.input_size], name='2_2D') 
            Ws_in = self._weight_variable([self.input_size, self.cell_size])
            bs_in = self._bias_variable([self.cell_size,])
            with tf.name_scope('Wx_plus_b'):
                l_in_y = tf.matmul(l_in_x, Ws_in) + bs_in
            self.l_in_y = tf.reshape(l_in_y, [-1, self.n_steps, self.cell_size], name='2_3D')
            
        def add_cell(self):       
            lstm_cell = tf.nn.rnn_cell.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)  
                
        def add_output_layer(self):  
            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, ])
            with tf.name_scope('Wx_plus_b'):
                self.pred = tf.matmul(l_out_x, Ws_out) + bs_out
    
        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)
        
    if __name__ == '__main__':  
        model = LSTMRNN(TIME_STEPS, INPUT_SIZE, OUTPUT_SIZE, CELL_SIZE, BATCH_SIZE)
        sess = tf.Session()
        merged=tf.summary.merge_all()
        writer=tf.summary.FileWriter("niu0127/logs0127",sess.graph)
        sess.run(tf.initialize_all_variables())
        
    plt.ion()  
    plt.show() 
         
    for i in range(200):
          seq, res, xs = get_batch() 
          if i == 0:
              feed_dict = {
                      model.xs: seq,
                      model.ys: res,
              }
          else:
              feed_dict = {
                  model.xs: seq,
                  model.ys: res,
                  model.cell_init_state: state  
              }
          _, cost, state, pred = sess.run(
              [model.train_op, model.cost, model.cell_final_state, model.pred],
              feed_dict=feed_dict)
    
          plt.plot(xs[0,:],res[0].flatten(),'r',xs[0,:],pred.flatten()[:TIME_STEPS],'g--')
          plt.title('Matplotlib,RNN,Efficient learning,Approach,Cosx --Jason Niu')
          plt.ylim((-1.2,1.2))
          plt.draw()
          plt.pause(0.1)

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[2020121721572280.gif]: /images/20221120/348f297f232a4c7f88c037b575c71831.png
[TF_RNN_matplotlib_RNN]: https://www.cnblogs.baidu.com/yunyaniu/articles/8365976.html