https://github.com/pengsun/MexConv3D
Tip revision: b84a25240acc21dd2dc90c91074baa43139d02dc authored by Peng Sun on 21 November 2016, 04:04:21 UTC
add example/example_simplenet/*
add example/example_simplenet/*
Tip revision: b84a252
mex_conv3d.m
%MEX_CONV3D Volume convolution for 3D convnet
% Y = MEX_CONV3D(X, F, B); forward pass
% [dZdX, dZdF, dZdB] = MEX_CONV3D(X, F, B dZdY); backward pass
% [...] =MEX_CONV3D(..., 'stride', s, 'pad', p); the options
%
% Input:
% X: [H,W,D,P,N]. Input volume or feature maps. H, W, D are volume's
% height, width and depth, respectively. P is #volumes (or #feature maps).
% N is #instances.
% F: [FH,FW,FD,P,Q]. Filter kernels (a volume). FH, FW, FD are kernel's
% height, width and depth, respectively. P is #input feature map and Q is
% #output feature map
% B: [1, Q]. Bias.
% dZdY: [Ho,Wo,Do,Q,N]. Output feature maps. Subscript "o" means output.
% s: [1] or [a,b,c]. the stride. default 1
% p: [1] or [a,b,c]. pad. default 0
%
% Output:
% Y: [Ho,Wo,Do,Q,N]. Output feature maps
% dZdX: [H,W,D,P,N]. Delta w.r.t X where Z means loss.
% dZdF: [FH,FW,FD,P,Q]. Delta w.r.t. F
% dZdB: [1, Q]. Delta w.r.t. B
%