103 lines
3.4 KiB
Plaintext
103 lines
3.4 KiB
Plaintext
%function test_example_NN
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load mnist_uint8;
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train_x = double(train_x) / 255;
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test_x = double(test_x) / 255;
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train_y = double(train_y);
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test_y = double(test_y);
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train_y = train_y(:,1);
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test_y = test_y(:,1);
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% normalize
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[train_x, mu, sigma] = zscore(train_x);
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test_x = normalize(test_x, mu, sigma);
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%% ex1 vanilla neural net
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rand('state',0)
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nn = nnsetup([784 100 1]);
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nn.output = 'sigm';
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opts.numepochs = 5; % Number of full sweeps through data
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opts.batchsize = 100; % Take a mean gradient step over this many samples
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[nn, L] = nntrain(nn, train_x, train_y, opts);
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% [er, bad] = nntest(nn, test_x, test_y);
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nn = nnff(nn, test_x, zeros(size(test_x,1), nn.size(end)));
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% pred_y = nnpredict(nn, test_x);
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nn.a{end};
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fprintf('Prediction error %f\n', sqrt(mean((pred_y - test_y).^2)));
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% assert(er < 0.08, 'Too big error');
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%% ex2 neural net with L2 weight decay
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rand('state',0)
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nn = nnsetup([784 100 10]);
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nn.weightPenaltyL2 = 1e-4; % L2 weight decay
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opts.numepochs = 1; % Number of full sweeps through data
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opts.batchsize = 100; % Take a mean gradient step over this many samples
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nn = nntrain(nn, train_x, train_y, opts);
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[er, bad] = nntest(nn, test_x, test_y);
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assert(er < 0.1, 'Too big error');
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%% ex3 neural net with dropout
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rand('state',0)
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nn = nnsetup([784 100 10]);
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nn.dropoutFraction = 0.5; % Dropout fraction
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opts.numepochs = 1; % Number of full sweeps through data
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opts.batchsize = 100; % Take a mean gradient step over this many samples
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nn = nntrain(nn, train_x, train_y, opts);
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[er, bad] = nntest(nn, test_x, test_y);
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assert(er < 0.1, 'Too big error');
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%% ex4 neural net with sigmoid activation function
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rand('state',0)
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nn = nnsetup([784 100 10]);
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nn.activation_function = 'sigm'; % Sigmoid activation function
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nn.learningRate = 1; % Sigm require a lower learning rate
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opts.numepochs = 1; % Number of full sweeps through data
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opts.batchsize = 100; % Take a mean gradient step over this many samples
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nn = nntrain(nn, train_x, train_y, opts);
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[er, bad] = nntest(nn, test_x, test_y);
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assert(er < 0.1, 'Too big error');
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%% ex5 plotting functionality
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rand('state',0)
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nn = nnsetup([784 20 10]);
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opts.numepochs = 5; % Number of full sweeps through data
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nn.output = 'softmax'; % use softmax output
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opts.batchsize = 1000; % Take a mean gradient step over this many samples
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opts.plot = 1; % enable plotting
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nn = nntrain(nn, train_x, train_y, opts);
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[er, bad] = nntest(nn, test_x, test_y);
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assert(er < 0.1, 'Too big error');
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%% ex6 neural net with sigmoid activation and plotting of validation and training error
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% split training data into training and validation data
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vx = train_x(1:10000,:);
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tx = train_x(10001:end,:);
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vy = train_y(1:10000,:);
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ty = train_y(10001:end,:);
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rand('state',0)
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nn = nnsetup([784 20 10]);
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nn.output = 'softmax'; % use softmax output
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opts.numepochs = 5; % Number of full sweeps through data
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opts.batchsize = 1000; % Take a mean gradient step over this many samples
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opts.plot = 1; % enable plotting
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nn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)
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[er, bad] = nntest(nn, test_x, test_y);
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assert(er < 0.1, 'Too big error');
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