78 lines
2.4 KiB
Mathematica
78 lines
2.4 KiB
Mathematica
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function [nn, L] = nntrain(nn, train_x, train_y, opts, val_x, val_y)
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%NNTRAIN trains a neural net
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% [nn, L] = nnff(nn, x, y, opts) trains the neural network nn with input x and
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% output y for opts.numepochs epochs, with minibatches of size
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% opts.batchsize. Returns a neural network nn with updated activations,
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% errors, weights and biases, (nn.a, nn.e, nn.W, nn.b) and L, the sum
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% squared error for each training minibatch.
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assert(isfloat(train_x), 'train_x must be a float');
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assert(nargin == 4 || nargin == 6,'number ofinput arguments must be 4 or 6')
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loss.train.e = [];
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loss.train.e_frac = [];
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loss.val.e = [];
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loss.val.e_frac = [];
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opts.validation = 0;
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if nargin == 6
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opts.validation = 1;
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end
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fhandle = [];
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if isfield(opts,'plot') && opts.plot == 1
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fhandle = figure();
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end
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m = size(train_x, 1);
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batchsize = opts.batchsize;
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numepochs = opts.numepochs;
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numbatches = floor(m / batchsize);
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assert(rem(numbatches, 1) == 0, 'numbatches must be a integer');
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L = zeros(numepochs*numbatches,1);
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n = 1;
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for i = 1 : numepochs
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tic;
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kk = randperm(m);
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for l = 1 : numbatches
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batch_x = train_x(kk((l - 1) * batchsize + 1 : l * batchsize), :);
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%Add noise to input (for use in denoising autoencoder)
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if(nn.inputZeroMaskedFraction ~= 0)
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batch_x = batch_x.*(rand(size(batch_x))>nn.inputZeroMaskedFraction);
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end
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batch_y = train_y(kk((l - 1) * batchsize + 1 : l * batchsize), :);
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nn = nnff(nn, batch_x, batch_y);
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nn = nnbp(nn);
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nn = nnapplygrads(nn);
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L(n) = nn.L;
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n = n + 1;
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end
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t = toc;
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if opts.validation == 1
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loss = nneval(nn, loss, train_x, train_y, val_x, val_y);
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str_perf = sprintf('; Full-batch train mse = %f, val mse = %f', loss.train.e(end), loss.val.e(end));
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else
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loss = nneval(nn, loss, train_x, train_y);
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str_perf = sprintf('; Full-batch train err = %f', loss.train.e(end));
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end
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if ishandle(fhandle)
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nnupdatefigures(nn, fhandle, loss, opts, i);
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end
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% disp(['epoch ' num2str(i) '/' num2str(opts.numepochs) '. Took ' num2str(t) ' seconds' '. Mini-batch mean squared error on training set is ' num2str(mean(L((n-numbatches):(n-1)))) str_perf]);
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nn.learningRate = nn.learningRate * nn.scaling_learningRate;
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end
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end
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