init_operation_def.m~

Standard model object recognition matlab code

% init_operation_def.m
%
% Define necessary parameters (namely p, q, r, and c), depending on
% the operation type.  In general, the operation will be of the
% form:
%    y = sum(f1.*(x.^p))./(c+sum(f2.*(x.^q)).^r);
% When p < qr, the operation will make a bell-shaped tuning, and
% when p > qr, the operation will be max-like (in fact, p=q+1, r=1 define
% soft-max operation).  
%
% Available operations are
% fulmax: full max (with do_sp1)
% nullop: null operation (with do_sp1)
% gaussf: gaussian function (with do_sp1)
% sftmax: soft max
% normsp: normalized (sqrt of summed squares)
% normdp: tuning operation
% 
% Note: operation def are in comp_crop_or_zeropad and
% comp_get_next_layer.

switch type
  case ('gaussf')
  case ('fulmax')
  case ('normsp')
    % This is L2-norm dot-product.
    p = 1;
    q = 2;
    r = 1/2;
    c = repmat(eps, [1 siz(4)]);
    y_max = repmat(1, [1 siz(4)]);

  case ('normdp')
    % Normalized dot product, where [p,q,r] can be some other
    % values.  c and y_max values are obtained by taking first
    % derivative.
    p = 1;
    q = 2;
    r = 1;
    w = f1.^(q-p);
    c = (sum(w.^(q/(q-p))).^r)*(q*r/p-1);
    y_max = p/q/r./(sum(w.^(q/(q-p))).^(r-1));

  case ('sftmax')
    % Softmax.
    p = 3; 
    q = 2;
    r = 1;
    c = repmat(eps, [1 siz(4)]);
    y_max = repmat(1, [1 siz(4)]);
    
  otherwise
    error('error: undefined operation');
end

sigmoid_a = -1; % No sigmoid for <0.
if (type=='normdp')
  sigmoid_a = sqrt(size(f1,1)^(q*r/p));
  sigmoid_a = sqrt(size(f1,1));
  sigmoid_b = 0.9;
  sigmoid_b = 0.2;
end