Caffe中net.layers.blobs和net.params之间有什么区别？

Question

我正在使用Python Caffe,并与net.layers [layer_index] .blobs和net.params [layer_type]混淆.如果我理解的话,net.params包含所有网络参数.以LeNet为例,net.params ['conv1']代表'conv1'层的网络系数.然后net.layer [layer_index] .blobs应该表示相同.但是,我发现它们并不完全相同.我使用以下代码来测试它:

def _differ_square_sum(self,blobs):
    import numpy as np
    gradients = np.sum(np.multiply(blobs[0].diff,blobs[0].diff)) + np.sum(np.multiply(blobs[1].diff,blobs[1].diff))
    return gradients


def _calculate_objective(self, iteration, solver):
    net = solver.net
    params = net.params
    params_value_list = list(params.keys())
    [print(k,v.data.shape) for k,v in net.blobs.items()]

    layer_num = len(net.layers)
    j = 0
    for layer_index in range(layer_num):
        if(len(net.layers[layer_index].blobs)>0):
            cur_gradient = self._differ_square_sum(net.layers[layer_index].blobs)
            key = params_value_list[j]
            cur_gradient2 = self._differ_square_sum(params[key])
            print([cur_gradient,cur_gradient2])
            assert(cur_gradient == cur_gradient2)

关于他们之间的区别的任何想法？谢谢.

Answer 1

您正在将可训练的网络参数(存储在其中net.params)和输入数据混合到网络中(存储在其中net.blobs):
完成模型训练后,net.params固定并且不会更改.但是,对于每个要输入网络的新输入示例,net.blobs将存储不同图层对该特定输入的响应.