Neural network 如何为带有两个顶部blob的caffe python层编写向后函数？_Neural Network_Deep Learning_Caffe_Gradient Descent_Pycaffe

Neural network 如何为带有两个顶部blob的caffe python层编写向后函数？

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Neural network 如何为带有两个顶部blob的caffe python层编写向后函数？,neural-network,deep-learning,caffe,gradient-descent,pycaffe,Neural Network,Deep Learning,Caffe,Gradient Descent,Pycaffe,在ubuntu 16.04上安装caffe之后。我成功地训练了cifar10快速模型，然后添加了一个简单的python层，如下所示： import caffe import numpy as np class My_Custom_Layer(caffe.Layer): def setup(self, bottom, top): if len(bottom) != 1: raise Exception("Wrong number of bott

在ubuntu 16.04上安装caffe之后。我成功地训练了cifar10快速模型，然后添加了一个简单的python层，如下所示：

import caffe
import numpy as np

class My_Custom_Layer(caffe.Layer):  
    def setup(self, bottom, top):
        if len(bottom) != 1:
            raise Exception("Wrong number of bottom blobs")  

    def forward(self, bottom, top):
        top[0].data[...] = bottom[0].data

    def reshape(self, bottom, top):
        top[0].reshape(*bottom[0].shape)        

    def backward(self, top, propagate_down, bottom):
        bottom[0].diff[...] = top[0].diff

现在我想添加另一个顶部blob，顶部[1]，底部[0]有一些更改，但我不知道如何更改向后函数