使用以下代码:
tf = open('defl_07h.csv','r')
for line in tf.readlines():
data = [float(x) for x in line.strip().split(';') if x != '']
indata = tuple(data[:1])
outdata = tuple(data[1:])
ds.addSample(indata,outdata)
net = buildNetwork(ds.indim,20,ds.outdim,recurrent=True)
t = BackpropTrainer(net,learningrate=0.01,momentum=0.5,verbose=True)
t.trainOnDataset(ds,10)
t.testOnData(verbose=True)
获得如下相同的输出:
out:[3.479] 更正:[11.86]错误:35.12389858 输出:[3.479] 更正:[12.1]错误:37.16423359 输出:[3.479] 更正:[12.28]错误:38.73228485
然后创建网络结构:
Module: in
-connection to hidden0
- parameters [-1.9647867 -0.41898579 -0.24047698 0.6445537 0.06084947 -3.17343892
0.25454776 -0.45578641 0.70865416 -0.40517853 -0.22026247 -0.13106284
-0.71012557 -0.61140289 -0.00752148 -0.61770292 -0.50631486 0.95803659
-1.07403163 -0.87359713]
Recurrent connections
Module: bias
-connection to out
- parameters [ 0.55130311] …