www.gusucode.com > 有监督的 CNN 网络完成对MNIST 数字的识别 > 有监督的 CNN 网络完成对MNIST 数字的识别/CNN—卷积神经网络数字识别/@cnn/cnn_size.m
function sz = cnn_size(cnet) %cnn_size Calculate the total number of all trainable parameters % % Syntax % % sz = cnn_size(cnet) % % Description % Input: % cnet - Convolutional neural network class object % Output: % sz - number of all trainable parameters sz = 0; %Loop through the fully-connected layers for k=cnet.numLayers:-1:(cnet.numLayers-cnet.numFLayers+1) sz = sz + numel(cnet.FLayer{k}.W)+numel(cnet.FLayer{k}.B); end %All other layers for k=(cnet.numLayers-cnet.numFLayers):-1:2 %first layer is dummy if(rem(k,2)) %Parity check %Subsampling layer sz = sz + numel(cnet.SLayer{k}.WS)*numel(cnet.SLayer{k}.WS{1})+numel(cnet.SLayer{k}.BS)*numel(cnet.SLayer{k}.BS{1}); else %Convolutional layer sz = sz + numel(cnet.CLayer{k}.WC)*numel(cnet.CLayer{k}.WC{1})+numel(cnet.CLayer{k}.BC)*numel(cnet.CLayer{k}.BC{1}); end end end