Question

我测试了一个虚拟程序，以便从网络的隐藏层激活。

from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets import SupervisedDataSet, SequentialDataSet
from pybrain.supervised.trainers import BackpropTrainer, RPropMinusTrainer
from pybrain.structure.modules import SigmoidLayer, LSTMLayer,LinearLayer

net = buildNetwork(3,20,3, hiddenclass=SigmoidLayer,outclass=LinearLayer, bias=True, outputbias=False, recurrent=False)
dataSet = SequentialDataSet(3, 3)
dataSet.newSequence()
dataSet.appendLinked([0, 0, 0], [0, 0, 0])
dataSet.newSequence()
dataSet.appendLinked([1, 1, 1], [0, 0, 0])
dataSet.newSequence()
dataSet.appendLinked([1, 0, 0], [1, 0, 0])
dataSet.newSequence()
dataSet.appendLinked([0, 1, 0], [0, 1, 0])
dataSet.newSequence()
dataSet.appendLinked([0, 0, 1], [0, 0, 1])

trainer = RPropMinusTrainer(net, dataset=dataSet, verbose=True, weightdecay=0.01)

for i in range(10):    
        trainer.train()

result = net.activate([0.5, 0.4, 0.7])
print net['in'].outputbuffer[net['in'].offset]
print net['hidden0'].outputbuffer[net['hidden0'].offset]

上面的代码输出

[ 0.5  0.4  0.7]
[ 0.67309695  0.56123959  0.50724186  0.08670971  0.53251197  0.42582582
  0.36052132  0.04995797  0.85588161  0.19565005  0.44142276  0.72340318
  0.27358051  0.53198858  0.2181886   0.30581747  0.591014    0.86583721
  0.67234109  0.64745772]

请注意，当我更改recurrent = True和hiddenclass = LSTMLayer

时

from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets import SupervisedDataSet, SequentialDataSet
from pybrain.supervised.trainers import BackpropTrainer, RPropMinusTrainer
from pybrain.structure.modules import SigmoidLayer, LSTMLayer,LinearLayer

net = buildNetwork(3,20,3, hiddenclass=LSTMLayer,outclass=LinearLayer, bias=True, outputbias=False, recurrent=True)
dataSet = SequentialDataSet(3, 3)
dataSet.newSequence()
dataSet.appendLinked([0, 0, 0], [0, 0, 0])
dataSet.newSequence()
dataSet.appendLinked([1, 1, 1], [0, 0, 0])
dataSet.newSequence()
dataSet.appendLinked([1, 0, 0], [1, 0, 0])
dataSet.newSequence()
dataSet.appendLinked([0, 1, 0], [0, 1, 0])
dataSet.newSequence()
dataSet.appendLinked([0, 0, 1], [0, 0, 1])

trainer = RPropMinusTrainer(net, dataset=dataSet, verbose=True, weightdecay=0.01)

for i in range(10):    
        trainer.train()

result = net.activate([0.5, 0.4, 0.7])
print net['in'].outputbuffer[net['in'].offset]
print net['hidden0'].outputbuffer[net['hidden0'].offset]

净输出

[ 0.  0.  0.]
[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
  0.  0.]

我无法理解为什么网络会以这种方式运行。希望有更好的pybrain库知识的人可以帮助解决这个问题。谢谢。

pybrain递归网络的激活值为零

0 个答案: