用Keras搭建神经网络 简单模版（五）——RNN LSTM Regressor 循环神经网络

# -*- coding: utf-8 -*-

import numpy as np

np.random.seed(1337)

import matplotlib.pyplot as plt

from keras.models import Sequential

from keras.layers import LSTM,TimeDistributed,Dense

from keras.optimizers import Adam

BATCH_START = 0

TIME_STEPS = 20

BATCH_SIZE = 50

INPUT_SIZE = 1

OUTPUT_SIZE = 1

CELL_SIZE = 20

LR = 0.006

def get_batch():

    global BATCH_START,TIME_STEPS

    # xs shape(50,20,)

    #xs=np.arange(0,0+20*50).reshape(50,20)

    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

    #plt.plot(xs[0,:],res[0,:],'r',xs[0,:],seq[0,:],'b--')

    #plt.show()

    return [seq[:,:,np.newaxis], res[:,:,np.newaxis],xs]

#get_batch()

#exit()

model = Sequential()

model.add(LSTM(output_dim=CELL_SIZE,

               return_sequences=True, # 每一个时间点都输出一个output

               batch_input_shape=(BATCH_SIZE,TIME_STEPS,INPUT_SIZE),

               stateful = True,# batch和batch之间是否有联系

               # 前一个batch的最后一步和后一个batch的第一步是有联系的

        )) 

model.add(TimeDistributed(Dense(OUTPUT_SIZE))) # dense对每一个output连接，对每一个时间点都要计算

adam = Adam(LR)

model.compile(optimizer = adam,

              loss = 'mse',)

print('Training ------------')

for step in range(501):

    # data shape = (batch_num,steps,inputs/output)

    X_batch, Y_batch, xs = get_batch()

    cost = model.train_on_batch(X_batch, Y_batch)

    pred = model.predict(X_batch,BATCH_SIZE)

    plt.plot(xs[0,:], Y_batch[0].flatten(),'r',xs[0,:],pred.flatten()[:TIME_STEPS],'b--')

    plt.ylim((-1.2,1.2))

    plt.draw()

    plt.pause(0.5)

    if step % 10 == 0:

        print('train cost',cost)