dl简单模板，无pretraining过程

layer_dimensions    =   [11 22 33 22 11]';
ld_size             =   size(layer_dimensions   ,   1);
% what is deal
[x rx dx rdx y ry dy rdy dphi] = deal( cell( ld_size    ,   1 ) );
[W rW dW rdW]                  = deal( cell( ld_size-1  , 1 ) );

numsamples  =   100;
%initialize weight and x,y, always allocate memory first
for i   =   1:ld_size
    x{i}    =   zeros(  layer_dimensions(   i  )   ,  numsamples );
    y{i}    =   x{i};
    if i<ld_size
        W{i}    =   rand(   layer_dimensions(   i  ) ,layer_dimensions(   i+1 )   );
    end
end
%initialize
inputs  =   0*ones(layer_dimensions(1),numsamples);
y{1}    =   inputs;
dphi{1} =   zeros(size(y{1}));
ry{1}   =   dphi{1};

numepoches  =   100;
numbatches  =   10;
for iii =   1:numepoches
    for ii  =   1:numbatches
        % f0 pass
        for i   =   2:ld_size
            x{i}    =   W{i-1}'*y{i-1};
            y{i}    =   sigmoid(x{i});
            dphi{i} =   y{i}.*(1-y{i});  %for backpropagate
        end
        % r1 pass
        dy{ld_size}     =   y{ld_size}  -   y{1};
        error=sum(sum(abs(dy{ld_size})))
        dx{ld_size}     =   dphi{ld_size}   .*  dy{ld_size};
        dW{ld_size-1}   =   y{ld_size-1}    *   dx{ld_size}';
        for i   =   ld_size-1:-1:2
            dy{i}   =   W{i}    *   dx{i+1};
            W{i}    =   W{i}    -   0.3 *   dW{i};
            dx{i}   =   dphi{i} .*   dy{i};
            dW{i-1}   =   y{i-1}  *   dx{i}';
        end
        % f1 pass

    end
end

%test
numtestsamples=3;
for i   =   1:ld_size
    x{i}    =   zeros(  layer_dimensions(   i  )   ,  numtestsamples );
    y{i}    =   x{i};
end
inputs  =   zeros(layer_dimensions(1),numtestsamples);
y{1}    =   inputs;
dphi{1} =   zeros(size(y{1}));
ry{1}   =   dphi{1};
for i   =   2:ld_size
            x{i}    =   W{i-1}'*y{i-1};
            y{i}    =   sigmoid(x{i});
            dphi{i} =   y{i}.*(1-y{i});  %for backpropagate
end
y{ld_size}