📄 biasedlinearlayer.java
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package org.joone.engine;/** * This layer consists of linear neurons, i.e. neurons that sum up their inputs * (actually this is done by the (full) synapse in Joone) along with their biases. * In the learning process the biases are adjusted in an attempt to output a value * closer to the desired output. * * This layer differs from LinearLayer in two ways: * - This layer uses biases. These biases can/will also be adjusted in the * learning process. * - It has no scalar beta parameter. * * @author Boris Jansen */public class BiasedLinearLayer extends SimpleLayer implements LearnableLayer { /** Creates a new instance of BiasedLinearLayer */ public BiasedLinearLayer() { super(); } /** * Creates a new instance of BiasedLinearLayer. * * @param The name of the layer. */ public BiasedLinearLayer(String anElemName) { super(anElemName); } public void backward(double[] pattern) { int x; int n = getRows(); for (x = 0; x < n; ++x) { gradientOuts[x] = pattern[x]; } myLearner.requestBiasUpdate(gradientOuts); } public void forward(double[] pattern) { int x; int n = getRows(); for (x = 0; x < n; ++x) { outs[x] = pattern[x] + bias.value[x][0]; } } /** @deprecated - Used only for backward compatibility */ public Learner getLearner() { learnable = true; return super.getLearner(); }}
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