# -*- 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)
本文链接：http://task.lmcjl.com/news/12634.html
