Master commit of OpenFace.

matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN.m

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+%function test_example_NN
+load mnist_uint8;
+
+train_x = double(train_x) / 255;
+test_x  = double(test_x)  / 255;
+train_y = double(train_y);
+test_y  = double(test_y);
+
+% normalize
+[train_x, mu, sigma] = zscore(train_x);
+test_x = normalize(test_x, mu, sigma);
+
+%% ex1 vanilla neural net
+rand('state',0)
+nn = nnsetup([784 100 10]);
+opts.numepochs =  10;   %  Number of full sweeps through data
+opts.batchsize = 100;  %  Take a mean gradient step over this many samples
+[nn, L] = nntrain(nn, train_x, train_y, opts);
+
+[er, bad] = nntest(nn, test_x, test_y);
+
+assert(er < 0.08, 'Too big error');
+
+%% ex2 neural net with L2 weight decay
+rand('state',0)
+nn = nnsetup([784 100 10]);
+
+nn.weightPenaltyL2 = 1e-4;  %  L2 weight decay
+opts.numepochs =  1;        %  Number of full sweeps through data
+opts.batchsize = 100;       %  Take a mean gradient step over this many samples
+
+nn = nntrain(nn, train_x, train_y, opts);
+
+[er, bad] = nntest(nn, test_x, test_y);
+assert(er < 0.1, 'Too big error');
+
+
+%% ex3 neural net with dropout
+rand('state',0)
+nn = nnsetup([784 100 10]);
+
+nn.dropoutFraction = 0.5;   %  Dropout fraction 
+opts.numepochs =  1;        %  Number of full sweeps through data
+opts.batchsize = 100;       %  Take a mean gradient step over this many samples
+
+nn = nntrain(nn, train_x, train_y, opts);
+
+[er, bad] = nntest(nn, test_x, test_y);
+assert(er < 0.1, 'Too big error');
+
+%% ex4 neural net with sigmoid activation function
+rand('state',0)
+nn = nnsetup([784 100 10]);
+
+nn.activation_function = 'sigm';    %  Sigmoid activation function
+nn.learningRate = 1;                %  Sigm require a lower learning rate
+opts.numepochs =  1;                %  Number of full sweeps through data
+opts.batchsize = 100;               %  Take a mean gradient step over this many samples
+
+nn = nntrain(nn, train_x, train_y, opts);
+
+[er, bad] = nntest(nn, test_x, test_y);
+assert(er < 0.1, 'Too big error');
+
+%% ex5 plotting functionality
+rand('state',0)
+nn = nnsetup([784 20 10]);
+opts.numepochs         = 5;            %  Number of full sweeps through data
+nn.output              = 'softmax';    %  use softmax output
+opts.batchsize         = 1000;         %  Take a mean gradient step over this many samples
+opts.plot              = 1;            %  enable plotting
+
+nn = nntrain(nn, train_x, train_y, opts);
+
+[er, bad] = nntest(nn, test_x, test_y);
+assert(er < 0.1, 'Too big error');
+
+%% ex6 neural net with sigmoid activation and plotting of validation and training error
+% split training data into training and validation data
+vx   = train_x(1:10000,:);
+tx = train_x(10001:end,:);
+vy   = train_y(1:10000,:);
+ty = train_y(10001:end,:);
+
+rand('state',0)
+nn                      = nnsetup([784 20 10]);     
+nn.output               = 'softmax';                   %  use softmax output
+opts.numepochs          = 5;                           %  Number of full sweeps through data
+opts.batchsize          = 1000;                        %  Take a mean gradient step over this many samples
+opts.plot               = 1;                           %  enable plotting
+nn = nntrain(nn, tx, ty, opts, vx, vy);                %  nntrain takes validation set as last two arguments (optionally)
+
+[er, bad] = nntest(nn, test_x, test_y);
+assert(er < 0.1, 'Too big error');