脉冲神经网络BP

这一段代码是整个过程的宏观表现

#method to train the neural network, given the training data (inputS) or the input time
#sequence of spikes for the input neurons and the expected spiking times (outputS)
@classmethod
def train(self, network, inputS, outputS, learningR, epochs):
   global learningRate    #学习率
   learningRate = learningR
   lenTimeSeq, inNeurons = inputS.shape      #时间序列长度、


   #this should be done for a number of epochs as well and at the end of each epoch the resetSpikeTimes
   #method should be called      #在每一次迭代的最后重置脉冲时间


   print ('%%%%%%%%%%%%%%%%%%%%Start of simulation%%%%%%%%%%%%%%%%%%%%%%%%')


   for e in range(epochs):
      error = 0
      inputS, outputS = DataProc.shuffleInUnison(inputS, outputS)          #随机打乱inputS和outputS的下标
      # network.displaySNN()
      #inIndex represents the index of the spikes in the training data time sequence
      for inIndex in range(lenTimeSeq):
         inLayer = inputS[inIndex,:]
         print ('The input layer is ', inLayer)
         expSpikes = outputS[inIndex,:]
         print ('The expected spikes are ', expSpikes)
         predSpikes = np.zeros((lenTimeSeq))


         print ('The forward propagation phase started *********')
         predSpikes = self.forwardProp(network, inLayer, expSpikes) #前馈
         print ('The predicted spike times are ++++++++ ', predSpikes)


         scale = 10 if e < 200 else 1


         network = self.backProp(network, expSpikes, inLayer)   #反馈


         network.resetSpikeTimeNet()       #重置


         predSpikes = self.forwardProp(network, inLayer, expSpikes) #反馈后又前馈一次
         print ('The predicted spike times are ++++++++ ', predSpikes)
         # network.displaySNN()
         # network.resetSpikeTimeLayer(network.layers[-1])
         error += self.errorFMSE(expSpikes, predSpikes)    #计算误差


         #the spikes should be reset after each example
         network.resetSpikeTimeNet()


      print ('The error is ', error)
      if error == 0:
         break

然后解析里面的语句 forwaidProp这个语句是关于层的前馈，这个属于先更新一层，然后根据更新的这一层把后面的所有层都更新一次。

#function to simulate a forward pass through the network
@classmethod
def forwardProp(self, network, inLayer, expSpikes):       #前馈    这是整个网络的前馈，所以会以层的传输为主
   global codingInterval  #编码时间间隙
   global timeStep          #时间步长
   noLayers = len(network.layers)    #层数


   # print 'The simulation time is ', time
   time = 0      #仿真时间


   while time <= codingInterval:
      #compute the update for the first layer, using the input spikes
      updatedLayer = self.forwardPropL(inLayer, network.layers[0], 0, time)  #返回的是一个对象，是一个异步神经元类型
      network.layers[0] = updatedLayer   #更新层变成当前层


      for layer in range(1,noLayers):       #传递到最后一层
         # print 'layer is ', layer, time
         #check for updates for the layers of the network
         updatedLayer = self.forwardPropL(network.layers[layer-1], network.layers[layer], layer, time)
         network.layers[layer] = updatedLayer
      time += timeStep


   predSpikes = SNNetwork.getFireTimesLayer(network.layers[-1])