tensorflow lstm (一)

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/luoganttcc/article/details/83384794

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

TIME_STEPS=10
BATCH_SIZE=128
HIDDEN_UNITS1=30
HIDDEN_UNITS=1
LEARNING_RATE=0.001
EPOCH=50


TRAIN_EXAMPLES=11000
TEST_EXAMPLES=1100

def generate(seq):
    X=[]
    y=[]
    for i in range(len(seq)-TIME_STEPS):
        X.append([seq[i:i+TIME_STEPS]])
        y.append([seq[i+TIME_STEPS]])
    return np.array(X,dtype=np.float32),np.array(y,dtype=np.float32)

seq_train=np.sin(np.linspace(start=0,stop=100,num=TRAIN_EXAMPLES,dtype=np.float32)) 
seq_test=np.sin(np.linspace(start=100,stop=110,num=TEST_EXAMPLES,dtype=np.float32))

X_train,y_train=generate(seq_train)
#print(X_train.shape,y_train.shape)
X_test,y_test=generate(seq_test)

X_train=np.reshape(X_train,newshape=(-1,TIME_STEPS,1))
X_test=np.reshape(X_test,newshape=(-1,TIME_STEPS,1))

plt.plot(range(1000),y_test[:1000,0],"r*")
graph=tf.Graph()
with graph.as_default():
     X_p=tf.placeholder(dtype=tf.float32,shape=(None,TIME_STEPS,1),name="input_placeholder") 
     y_p=tf.placeholder(dtype=tf.float32,shape=(None,1),name="pred_placeholder")     
     lstm_cell1=rnn.BasicLSTMCell(num_units=HIDDEN_UNITS1)
     lstm_cell=rnn.BasicLSTMCell(num_units=HIDDEN_UNITS)
     
     multi_lstm=rnn.MultiRNNCell(cells=[lstm_cell1,lstm_cell])
     
     init_state=multi_lstm.zero_state(batch_size=BATCH_SIZE,dtype=tf.float32)
     
     outputs,states=tf.nn.dynamic_rnn(cell=multi_lstm,inputs=X_p,initial_state=init_state,dtype=tf.float32)
     
     h=outputs[:,-1,:]
     mse=tf.losses.mean_squared_error(labels=y_p,predictions=h)
     
     optimizer=tf.train.AdamOptimizer(LEARNING_RATE).minimize(loss=mse)
     
     init=tf.global_variables_initializer()
     
with tf.Session(graph=graph) as sess: 
    sess.run(init) 
    for epoch in range(1,EPOCH+1): 
        results = np.zeros(shape=(TEST_EXAMPLES, 1)) 
        train_losses=[] 
        test_losses=[] 
        print("epoch:",epoch) 
        for j in range(TRAIN_EXAMPLES//BATCH_SIZE): 
            _,train_loss=sess.run( fetches=(optimizer,mse), 
                                  feed_dict={ X_p:X_train[j*BATCH_SIZE:(j+1)*BATCH_SIZE], 
                                            y_p:y_train[j*BATCH_SIZE:(j+1)*BATCH_SIZE] } )
    
            train_losses.append(train_loss)
        print("average training loss:", sum(train_losses) / len(train_losses))
        
        for j in range(TEST_EXAMPLES//BATCH_SIZE): 
            
            result,test_loss=sess.run( fetches=(h,mse),
                                      feed_dict={ X_p:X_test[j*BATCH_SIZE:(j+1)*BATCH_SIZE],                                                                                                         
                                        y_p:y_test[j*BATCH_SIZE:(j+1)*BATCH_SIZE] } )
            results[j*BATCH_SIZE:(j+1)*BATCH_SIZE]=result
            test_losses.append(test_loss)
            
        print("average test loss:", sum(test_losses) / len(test_losses))
        plt.plot(range(1000),results[:1000,0])
    plt.show()