PyBrain网络中所有节点的激活值 [英] activation values for all nodes in a PyBrain network

问题描述

我觉得这应该是微不足道的，但是我一直在努力在PyBrain文档中(无论是在此处还是在其他地方)找到任何有用的东西.

I feel like this should be trivial, but I've struggled to find anything useful in the PyBrain documentation, on here, or elsewhere.

问题是这样的:

我在PyBrain中构建并训练了一个三层(输入，隐藏，输出)前馈网络.每层有三个节点.我想用新颖的输入激活网络，并将节点的最终激活值存储在隐藏层.据我所知，net.activate()和net.activateOnDataset()将仅返回输出层节点的激活值，并且是激活网络的唯一方法.

I have a three layer (input, hidden, output) feedforward network built and trained in PyBrain. Each layer has three nodes. I want to activate the network with novel inputs and store the resultant activation values of the nodes at the hidden layer. As far as I can tell, net.activate() and net.activateOnDataset() will only return the activation values of output layer nodes and are the only ways to activate a network.

我如何获得PyBrain网络的隐藏层激活?

How do I get at the hidden layer activations of a PyBrain network?

我不确定示例代码在这种情况下是否能帮上大忙，但还是有一些(使用简化的训练集):

I'm not sure example code will help that much in this case, but here's some anyway (with a cut-down training set) :

from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer

net = buildNetwork(3, 3, 3)

dataSet = SupervisedDataSet(3, 3)
dataSet.addSample((0, 0, 0), (0, 0, 0))
dataSet.addSample((1, 1, 1), (0, 0, 0))
dataSet.addSample((1, 0, 0), (1, 0, 0))
dataSet.addSample((0, 1, 0), (0, 1, 0))
dataSet.addSample((0, 0, 1), (0, 0, 1))

trainer = BackpropTrainer(net, dataSet)
trained = False
acceptableError = 0.001

# train until acceptable error reached
while trained == False :
    error = trainer.train()
    if error < acceptableError :
        trained = True

result = net.activate([0.5, 0.4, 0.7])
print result

在这种情况下，所需的功能是打印隐藏层的激活值列表.

In this case, desired functionality is to print a list of the hidden layer's activation values.

推荐答案

看起来应该可以:

net['in'].outputbuffer[net['in'].offset]
net['hidden0'].outputbuffer[net['hidden0'].offset]

纯粹基于查看源代码.

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