137 lines
4.4 KiB
Matlab
137 lines
4.4 KiB
Matlab
clear
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root_loc = 'F:/datasets/detection_validation/';
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location = [root_loc, '/prep_data/'];
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faceCheckersLoc = dir([location 'face_checker_general_training_large_68_*']);
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training_ratio = 0.7;
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rng(0);
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for i=1:numel(faceCheckersLoc)
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load([location faceCheckersLoc(i).name]);
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% set a max value to the error
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errors(errors > 3) = 3;
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num_examples = size(examples, 1);
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training_cutoff = round(num_examples * training_ratio);
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% picking training data for SVM (positive and negative samples)
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examples_train = examples(1:training_cutoff,:);
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% Extract the mean and standard deviation and normalise by it
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mean_ex = mean(examples_train);
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std_ex = std(examples_train);
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examples_train = bsxfun(@times, bsxfun(@minus, examples_train, mean_ex), 1./std_ex);
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examples_valid = examples(training_cutoff+1:end,:);
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examples_valid = bsxfun(@times, bsxfun(@minus, examples_valid, mean_ex), 1./std_ex);
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errors_train = errors(1:training_cutoff);
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% Normalise to 0-1 labels
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labels_train = errors(1:training_cutoff) / 3;
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labels_valid = errors(training_cutoff+1:end,:)/3;
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% liblinear SVR training
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addpath(genpath('DeepLearnToolbox'));
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layer_depth = [1, 2];
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neural_layers = [5, 10, 20, 50];
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res = zeros(numel(neural_layers), numel(layer_depth));
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corrs = zeros(numel(neural_layers), numel(layer_depth));
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for n=1:numel(neural_layers)
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for l=1:numel(layer_depth)
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%% Vanilla neural net
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rand(0);
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% input layer
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layers = size(examples_train,2);
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% add hidden layers between input and output
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for nl=1:layer_depth(l)
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layers = cat(1, layers, neural_layers(n));
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end
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% output layer
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layers = cat(1, layers, 1);
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nn = nnsetup(layers);
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nn.output = 'sigm';
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opts.numepochs = 100; % Number of full sweeps through data
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opts.batchsize = 200; % Take a mean gradient step over this many samples
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opts.plot = 0;
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[nn, L] = nntrain(nn, examples_train, labels_train, opts, examples_valid, labels_valid);
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% [er, bad] = nntest(nn, test_x, test_y);
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nn = nnff(nn, examples_valid, zeros(size(examples_valid,1), nn.size(end)));
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% pred_y = nnpredict(nn, test_x);
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pred_y = nn.a{end};
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rms_valid = sqrt(mean((labels_valid - pred_y).^2));
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res(n, l) = rms_valid;
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corrs(n, l) = corr(labels_valid, pred_y);
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end
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end
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[val,~] = min(res(:));
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[a, b] = ind2sub(size(res), find(res == val));
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layers = size(examples_train,2);
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% add hidden layers between input and output
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for nl=1:layer_depth(b)
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layers = cat(1, layers, neural_layers(a));
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end
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% output layer
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layers = cat(1, layers, 1);
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nn = nnsetup(layers);
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examples = bsxfun(@times, bsxfun(@minus, examples, mean_ex), 1./std_ex);
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[nn, L] = nntrain(nn, examples, errors/3, opts);
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face_check_nns(i).nn = nn;
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face_check_nns(i).destination = shape(:,1:2);
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face_check_nns(i).triangulation = triangulation;
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face_check_nns(i).triX = triX;
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face_check_nns(i).mask = mask;
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face_check_nns(i).centres = centres;
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face_check_nns(i).alphas = alphas;
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face_check_nns(i).betas = betas;
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face_check_nns(i).minX = minX;
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face_check_nns(i).minY = minY;
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face_check_nns(i).nPix = nPix;
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face_check_nns(i).mean_ex = mean_ex;
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face_check_nns(i).std_ex = std_ex;
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end
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%the actual testing before it is released
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% Create the function
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face_check_fun = @(img, shape, global_params) face_check_nn(img, shape, global_params, face_check_nns);
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[ predictions, gts, rmse, corr_coeff ] = Test_face_checker(face_check_fun);
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locationOut = [root_loc, './trained/face_check_nn_68.txt'];
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locationOutM = [root_loc, './trained/face_check_nn_68.mat'];
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save(locationOutM, 'face_check_nns', 'rmse', 'gts', 'predictions', 'corr_coeff');
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WriteOutFaceCheckersNNbinary(locationOut, face_check_nns);
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% WriteOutFaceCheckers(locationOut, locationOutM, faceCheckers);
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% as a side effect write out a triangulation file as well
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% WriteOutTriangulation('./trained/tris_68.txt', './trained/tris_68.mat', faceCheckers);
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% Results, with global normalisation |