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dae.m
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% dae - training a single-layer DAE
% 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 [D] = dae(D, patches, valid_patches, valid_portion);
if nargin < 3
early_stop = 0;
valid_patches = [];
valid_portion = 0;
else
early_stop = 1;
valid_err = -Inf;
valid_best_err = -Inf;
end
actual_lrate = D.learning.lrate;
n_samples = size(patches, 1);
if D.structure.n_visible ~= size(patches, 2)
error('Data is not properly aligned');
end
vbias_grad_old = zeros(size(D.vbias'));
hbias_grad_old = zeros(size(D.hbias'));
W_grad_old = zeros(size(D.W));
minibatch_sz = D.learning.minibatch_sz;
n_minibatches = ceil(n_samples / minibatch_sz);
n_epochs = D.iteration.n_epochs;
momentum = D.learning.momentum;
weight_decay = D.learning.weight_decay;
n_visible = D.structure.n_visible;
n_hidden = D.structure.n_hidden;
min_recon_error = Inf;
min_recon_error_update_idx = 0;
stopping = 0;
do_normalize = D.do_normalize;
do_normalize_std = D.do_normalize_std;
if D.data.binary == 0
if do_normalize == 1
% make it zero-mean
patches_mean = mean(patches, 1);
patches = bsxfun(@minus, patches, patches_mean);
end
if do_normalize_std ==1
% make it unit-variance
patches_std = std(patches, [], 1);
patches = bsxfun(@rdivide, patches, patches_std);
end
end
anneal_counter = 0;
actual_lrate0 = actual_lrate;
if D.debug.do_display == 1
figure(D.debug.display_fid);
end
try
use_gpu = gpuDeviceCount;
catch errgpu
use_gpu = false;
disp(['Could not use CUDA. Error: ' errgpu.identifier])
end
for step=1:n_epochs
if D.verbose
fprintf(2, 'Epoch %d/%d: ', step, n_epochs)
end
if use_gpu
% push
D.W = gpuArray(single(D.W));
D.vbias = gpuArray(single(D.vbias));
D.hbias = gpuArray(single(D.hbias));
if D.adagrad.use
D.adagrad.W = gpuArray(single(D.adagrad.W));
D.adagrad.vbias = gpuArray(single(D.adagrad.vbias));
D.adagrad.hbias = gpuArray(single(D.adagrad.hbias));
elseif D.adadelta.use
D.adadelta.gW = gpuArray(single(D.adadelta.gW));
D.adadelta.gvbias = gpuArray(single(D.adadelta.gvbias));
D.adadelta.ghbias = gpuArray(single(D.adadelta.ghbias));
D.adadelta.W = gpuArray(single(D.adadelta.W));
D.adadelta.vbias = gpuArray(single(D.adadelta.vbias));
D.adadelta.hbias = gpuArray(single(D.adadelta.hbias));
end
end
for mb=1:n_minibatches
D.iteration.n_updates = D.iteration.n_updates + 1;
% p_0
v0 = patches((mb-1) * minibatch_sz + 1:min(mb * minibatch_sz, n_samples), :);
mb_sz = size(v0,1);
if use_gpu > 0
v0 = gpuArray(single(v0));
end
% add error
v0_clean = v0;
if D.data.binary == 0 && D.noise.level > 0
v0 = v0 + D.noise.level * gpuArray(randn(size(v0)));
end
if D.noise.drop > 0
mask = binornd(1, 1 - D.noise.drop, size(v0));
v0 = v0 .* mask;
clear mask;
end
h0 = bsxfun(@plus, v0 * D.W, D.hbias');
if D.hidden.binary
h0 = sigmoid(h0, D.hidden.use_tanh);
end
% compute reconstruction error
hr = bsxfun(@plus, v0_clean * D.W, D.hbias');
if D.hidden.binary
hr = sigmoid(hr, D.hidden.use_tanh);
end
vr = bsxfun(@plus,hr * D.W',D.vbias');
if D.data.binary
vr = sigmoid(vr, D.visible.use_tanh);
end
if D.data.binary && ~D.visible.use_tanh
rerr = -mean(sum(v0_clean .* log(max(vr, 1e-16)) + (1 - v0_clean) .* log(max(1 - vr, 1e-16)), 2));
else
rerr = mean(sum((v0_clean - vr).^2,2));
end
if use_gpu > 0
rerr = gather(rerr);
end
D.signals.recon_errors = [D.signals.recon_errors rerr];
% get gradient
vr = bsxfun(@plus,h0 * D.W',D.vbias');
if D.data.binary
vr = sigmoid(vr, D.visible.use_tanh);
end
deltao = vr - v0_clean;
if D.data.binary && D.visible.use_tanh
deltao = deltao .* dsigmoid(vr, D.visible.use_tanh);
end
vbias_grad = mean(deltao, 1);
clear hr vr;
deltah = deltao * D.W;
if D.hidden.binary
deltah = deltah .* dsigmoid(h0, D.hidden.use_tanh);
end
hbias_grad = mean(deltah, 1);
W_grad = ((deltao' * h0) + (v0' * deltah)) / size(v0,1);
clear deltao deltah;
if D.sparsity.cost > 0 && D.hidden.use_tanh == 0
diff_sp = (h0 - D.sparsity.target);
hbias_grad = hbias_grad + D.sparsity.cost * mean(diff_sp, 1);
%W_grad = W_grad + (D.sparsity.cost/size(v0,1)) * (v0_clean' * diff_sp);
W_grad = W_grad + (D.sparsity.cost/size(v0,1)) * (v0' * diff_sp);
clear diff_sp;
end
if D.cae.cost > 0 && D.hidden.use_tanh == 0
W_cae1 = bsxfun(@times, D.W, mean(h0 .* (1 - h0).^2, 1));
W_cae2 = D.W.^2 .* (v0' * (...
(1 - 2 * h0) .* h0 .* (1 - h0).^2 ...
) / size(v0, 1));
W_cae = W_cae1 + W_cae2;
W_grad = W_grad + D.cae.cost * W_cae;
clear W_cae1 W_cae2 W_cae;
hbias_cae = sum(bsxfun(@times, D.W, mean(h0 .* (1 - h0).^2 .* (1 - 2 * h0),1)), 1);
hbias_grad = hbias_grad + D.cae.cost * hbias_cae;
clear hbias_cae;
end
if D.rica.cost > 0
W_rica = v0_clean' * tanh(hr);
W_grad = W_grad + D.rica.cost * W_rica;
clear W_rica;
% hbias_rica = mean(tanh(hr), 1);
% hbias_grad = hbias_grad + D.rica.cost * hbias_rica;
% clear W_rica;
end
if D.adagrad.use
vbias_grad_old = (1-momentum) * vbias_grad + momentum * vbias_grad_old;
hbias_grad_old = (1-momentum) * hbias_grad + momentum * hbias_grad_old;
W_grad_old = (1-momentum) * W_grad + momentum * W_grad_old;
D.adagrad.W = D.adagrad.W + W_grad_old.^2;
D.adagrad.vbias = D.adagrad.vbias + vbias_grad_old.^2';
D.adagrad.hbias = D.adagrad.hbias + hbias_grad_old.^2';
if D.rica.cost <= 0
D.vbias = D.vbias - D.learning.lrate * (vbias_grad_old' + weight_decay * D.vbias) ./ sqrt(D.adagrad.vbias + D.adagrad.epsilon);
D.hbias = D.hbias - D.learning.lrate * (hbias_grad_old' + weight_decay * D.hbias) ./ sqrt(D.adagrad.hbias + D.adagrad.epsilon);
end
D.W = D.W - D.learning.lrate * (W_grad_old + weight_decay * D.W) ./ sqrt(D.adagrad.W + D.adagrad.epsilon);
elseif D.adadelta.use
vbias_grad_old = (1-momentum) * vbias_grad + momentum * vbias_grad_old;
hbias_grad_old = (1-momentum) * hbias_grad + momentum * hbias_grad_old;
W_grad_old = (1-momentum) * W_grad + momentum * W_grad_old;
if D.iteration.n_updates == 1
adamom = 0;
else
adamom = D.adadelta.momentum;
end
D.adadelta.gW = adamom * D.adadelta.gW + (1 - adamom) * W_grad_old.^2;
D.adadelta.gvbias = adamom * D.adadelta.gvbias + (1 - adamom) * vbias_grad_old.^2';
D.adadelta.ghbias = adamom * D.adadelta.ghbias + (1 - adamom) * hbias_grad_old.^2';
if D.rica.cost <= 0
dvbias = -(vbias_grad_old' + ...
weight_decay * D.vbias) .* (sqrt(D.adadelta.vbias + D.adadelta.epsilon) ./ sqrt(D.adadelta.gvbias + D.adadelta.epsilon));
dhbias = -(hbias_grad_old' + ...
weight_decay * D.hbias) .* (sqrt(D.adadelta.hbias + D.adadelta.epsilon) ./ sqrt(D.adadelta.ghbias + D.adadelta.epsilon));
D.vbias = D.vbias + dvbias;
D.hbias = D.hbias + dhbias;
end
dW = -(W_grad_old + weight_decay * D.W) .* ...
(sqrt(D.adadelta.W + D.adadelta.epsilon) ./ sqrt(D.adadelta.gW + D.adadelta.epsilon));
D.W = D.W + dW;
D.adadelta.W = adamom * D.adadelta.W + (1 - adamom) * dW.^2;
clear dW;
if D.rica.cost <= 0
D.adadelta.vbias = adamom * D.adadelta.vbias + (1 - adamom) * dvbias.^2;
D.adadelta.hbias = adamom * D.adadelta.hbias + (1 - adamom) * dhbias.^2;
clear dvbias dhbias;
end
else
if D.learning.lrate_anneal > 0 && (step >= D.learning.lrate_anneal * n_epochs)
anneal_counter = anneal_counter + 1;
actual_lrate = actual_lrate0 / anneal_counter;
else
if D.learning.lrate0 > 0
actual_lrate = D.learning.lrate / (1 + D.iteration.n_updates / D.learning.lrate0);
else
actual_lrate = D.learning.lrate;
end
actual_lrate0 = actual_lrate;
end
D.signals.lrates = [D.signals.lrates actual_lrate];
% update
vbias_grad_old = (1-momentum) * vbias_grad + momentum * vbias_grad_old;
hbias_grad_old = (1-momentum) * hbias_grad + momentum * hbias_grad_old;
W_grad_old = (1-momentum) * W_grad + momentum * W_grad_old;
if D.rica.cost <= 0
D.vbias = D.vbias - actual_lrate * (vbias_grad_old' + weight_decay * D.vbias);
D.hbias = D.hbias - actual_lrate * (hbias_grad_old' + weight_decay * D.hbias);
end
D.W = D.W - actual_lrate * (W_grad_old + weight_decay * D.W);
end
if D.verbose == 1
fprintf(2, '.');
end
if use_gpu > 0
clear v0 h0 v0_clean vr hr deltao deltah
end
if early_stop
n_valid = size(valid_patches, 1);
rndidx = randperm(n_valid);
v0valid = valid_patches(rndidx(1:round(n_valid * valid_portion)),:);
if use_gpu > 0
v0valid = gpuArray(single(v0valid));
end
hr = bsxfun(@plus, v0valid * D.W, D.hbias');
if D.hidden.binary
hr = sigmoid(hr, D.hidden.use_tanh);
end
vr = bsxfun(@plus,hr * D.W',D.vbias');
if D.data.binary
vr = sigmoid(vr, D.visible.use_tanh);
end
if D.data.binary && ~D.visible.use_tanh
rerr = -mean(sum(v0valid .* log(max(vr, 1e-16)) + (1 - v0valid) .* log(max(1 - vr, 1e-16)), 2));
else
rerr = mean(sum((v0valid - vr).^2,2));
end
if use_gpu > 0
rerr = gather(rerr);
end
D.signals.valid_errors = [D.signals.valid_errors rerr];
if valid_err == -Inf
valid_err = rerr;
valid_best_err = rerr;
else
prev_err = valid_err;
valid_err = 0.99 * valid_err + 0.01 * rerr;
if step > D.valid_min_epochs && (1.1 * valid_best_err) < valid_err
fprintf(2, 'Early-stop! %f, %f\n', valid_err, prev_err);
stopping = 1;
break;
end
if valid_err < valid_best_err
valid_best_err = valid_err;
end
end
else
if D.stop.criterion > 0
if D.stop.criterion == 1
if min_recon_error > D.signals.recon_errors(end)
min_recon_error = D.signals.recon_errors(end);
min_recon_error_update_idx = D.iteration.n_updates;
else
if D.iteration.n_updates > min_recon_error_update_idx + D.stop.recon_error.tolerate_count
fprintf(2, '\nStopping criterion reached (recon error) %f > %f\n', ...
D.signals.recon_errors(end), min_recon_error);
stopping = 1;
break;
end
end
else
error ('Unknown stopping criterion %d', D.stop.criterion);
end
end
end
if length(D.hook.per_update) > 1
err = D.hook.per_update{1}(D, D.hook.per_update{2});
if err == -1
stopping = 1;
break;
end
end
if D.debug.do_display == 1 && mod(D.iteration.n_updates, D.debug.display_interval) == 0
D.debug.display_function (D.debug.display_fid, D, v0, v1, h0, h1, W_grad, vbias_grad, hbias_grad);
drawnow;
end
end
if use_gpu > 0
% pull
D.W = gather(D.W);
D.vbias = gather(D.vbias);
D.hbias = gather(D.hbias);
if D.adagrad.use
D.adagrad.W = gather(D.adagrad.W);
D.adagrad.vbias = gather(D.adagrad.vbias);
D.adagrad.hbias = gather(D.adagrad.hbias);
elseif D.adadelta.use
D.adadelta.W = gather(D.adadelta.W);
D.adadelta.vbias = gather(D.adadelta.vbias);
D.adadelta.hbias = gather(D.adadelta.hbias);
D.adadelta.gW = gather(D.adadelta.gW);
D.adadelta.gvbias = gather(D.adadelta.gvbias);
D.adadelta.ghbias = gather(D.adadelta.ghbias);
end
end
if length(D.hook.per_epoch) > 1
err = D.hook.per_epoch{1}(D, D.hook.per_epoch{2});
if err == -1
stopping = 1;
end
end
if stopping == 1
break;
end
if D.verbose == 1
fprintf(2, '\n');
end
fprintf(2, 'Epoch %d/%d - recon_error: %f norms: %f/%f/%f\n', step, n_epochs, ...
D.signals.recon_errors(end), ...
D.W(:)' * D.W(:) / length(D.W(:)), ...
D.vbias' * D.vbias / length(D.vbias), ...
D.hbias' * D.hbias / length(D.hbias));
end
if use_gpu > 0
% pull
D.W = gather(D.W);
D.vbias = gather(D.vbias);
D.hbias = gather(D.hbias);
if D.adagrad.use
D.adagrad.W = gather(D.adagrad.W);
D.adagrad.vbias = gather(D.adagrad.vbias);
D.adagrad.hbias = gather(D.adagrad.hbias);
elseif D.adadelta.use
D.adadelta.W = gather(D.adadelta.W);
D.adadelta.vbias = gather(D.adadelta.vbias);
D.adadelta.hbias = gather(D.adadelta.hbias);
D.adadelta.gW = gather(D.adadelta.gW);
D.adadelta.gvbias = gather(D.adadelta.gvbias);
D.adadelta.ghbias = gather(D.adadelta.ghbias);
end
end