part8_net

part8.m

net_arch = {...
    struct('type','input','inInd',0,'outInd',1,'blobSize',[H,W,B,batch_size],'fName',train_images_file,'scale',1/256,'dataType','uint8'), ...
    struct('type','input','inInd',0,'outInd',2,'blobSize',[k,batch_size],'fName',train_labels_file,'scale',1,'dataType','uint8'), ...
    ... 
    struct('type','conv','inInd',1,'outInd',3,'kernelsize',conv_kernel,'stride',conv_stride,'nOutChannels',conv_channels1,'bias_filler',0),...
    struct('type','relu','inInd',3,'outInd',3),  ...
    struct('type','maxpool','inInd',3,'outInd',4,'kernelsize',pool_kernel,'stride',pool_stride), ...
    ...
    struct('type','conv','inInd',4,'outInd',5,'kernelsize',conv_kernel,'stride',conv_stride,'nOutChannels',conv_channels2,'bias_filler',0),...
    struct('type','relu','inInd',5,'outInd',5),  ...
    struct('type','maxpool','inInd',5,'outInd',6,'kernelsize',pool_kernel,'stride',pool_stride), ...
    ...
    struct('type','flatten','inInd',6,'outInd',6), ...
    struct('type','affine','inInd',6,'outInd',7,'nOutChannels',k1,'bias_filler',0), ...
    struct('type','relu','inInd',7,'outInd',7),  ...
    ...
    struct('type','affine','inInd',7,'outInd',8,'nOutChannels',k2,'bias_filler',0), ...
    ...
    struct('type','loss','inInd',[8 2],'outInd',10,'lossType','MCLogLoss')};
net = ConvNet(net_arch,GPU, affine_weights);  % Xavier initialization of conv and affine layers