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)

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)

目录

输出结果

代码设计

输出结果

更新……

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(-title class_ inherited__">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)  

相关文章
TF之RNN：matplotlib动态演示之基于顺序的RNN回归案例实现高效学习逐步逼近余弦曲线