www.pudn.com > deepmat-master-for-spectral.zip > mlp_classify.m, change:2014-07-28,size:2127b


% mlp_classify
% Copyright (C) 2011 KyungHyun Cho, Tapani Raiko, Alexander Ilin
%
%This program is free software; you can redistribute it and/or
%modify it under the terms of the GNU General Public License
%as published by the Free Software Foundation; either version 2
%of the License, or (at your option) any later version.
%
%This program is distributed in the hope that it will be useful,
%but WITHOUT ANY WARRANTY; without even the implied warranty of
%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%GNU General Public License for more details.
%
%You should have received a copy of the GNU General Public License
%along with this program; if not, write to the Free Software
%Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
%
function [c, posterior] = mlp_classify(M, x0, Q0, raw)

if nargin < 3
    Q0 = [];
end

if nargin < 4
    raw = 0;
end

layers = M.structure.layers;
n_layers = length(layers);

posterior = x0;

if isfield(M, 'dbm') && M.dbm.use
    for l = 2:n_layers
        if M.dropout.use && l > 2
            posterior = posterior * bsxfun(@times, (M.W{l-1}), 1 - M.dropout.probs{l-1});
        else
            posterior = posterior * M.W{l-1};
        end

        if l < n_layers-1
            posterior = posterior + Q0{l+1} * (M.dbm.W{l})';
        end
        posterior = bsxfun(@plus, posterior, M.biases{l}');

        if l < n_layers 
            posterior = sigmoid(posterior, M.hidden.use_tanh);
        end

        if l == n_layers && M.output.binary
            posterior = softmax(posterior);
        end
    end
else
    for l = 2:n_layers
        if M.dropout.use && l > 2
            posterior = bsxfun(@plus, posterior * (M.W{l-1}/2), M.biases{l}');
        else
            posterior = bsxfun(@plus, posterior * M.W{l-1}, M.biases{l}');
        end

        if l < n_layers 
            posterior = sigmoid(posterior, M.hidden.use_tanh);
        end

        if l == n_layers && M.output.binary
            posterior = softmax(posterior);
        end
    end
end

if raw
    c = posterior;
else
    [maxp, c] = max(posterior, [], 2);
end