mirror of
https://github.com/hanzoai/jube-fork.git
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richard-churchman
cc0a2bee21
Included open-source Accord.net library code natively to the solution given the deprecated state of that project. Accord.net will be supported inside Jube for own needs (such as upgrading to .Net 7+). Accord.net libraries modified to compile to .Net 6. Modified Accord.net libraries to better support Newtonsoft Json.net serialisation. Included annotations in the Accord.net libraries to house important dependency statistics. Serialised json can be used independently of Jube to recall a Neural Network. Removal of Binary Serializer totally as a precursor to the projects support of .Net 7+. Extensive bug fixing in crucial functions of Neural Network training and recall. While this part of the system remains in Alpha, it is substantially more fit for purpose.
107 lines
3.7 KiB
C#
107 lines
3.7 KiB
C#
// Accord Neural Net Library
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// The Accord.NET Framework
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// http://accord-framework.net
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//
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// Copyright © César Souza, 2009-2017
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// cesarsouza at gmail.com
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//
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// This library is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 2.1 of the License, or (at your option) any later version.
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//
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// This library is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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// Lesser General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License along with this library; if not, write to the Free Software
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// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
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//
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namespace Accord.Neuro
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{
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using System;
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using AForge;
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using Accord.Statistics.Distributions.Univariate;
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/// <summary>
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/// Nguyen-Widrow weight initialization.
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/// </summary>
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///
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/// <remarks>
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/// <para>The Nguyen-Widrow initialization algorithm chooses values in
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/// order to distribute the active region of each neuron in the layer
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/// approximately evenly across the layers' input space.</para>
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///
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/// <para>The values contain a degree of randomness, so they are not the
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/// same each time this function is called.</para>
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/// </remarks>
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///
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public class NguyenWidrow
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{
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private ActivationNetwork network;
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private Accord.Range randRange;
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private double beta;
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/// <summary>
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/// Constructs a new Nguyen-Widrow Weight initialization.
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/// </summary>
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///
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/// <param name="network">The activation network whose weights will be initialized.</param>
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///
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public NguyenWidrow(ActivationNetwork network)
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{
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this.network = network;
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int hiddenNodes = network.Layers[0].Neurons.Length;
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int inputNodes = network.Layers[0].InputsCount;
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randRange = new Accord.Range(-0.5f, 0.5f);
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beta = 0.7 * Math.Pow(hiddenNodes, 1.0 / inputNodes);
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}
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/// <summary>
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/// Randomizes (initializes) the weights of
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/// the network using Nguyen-Widrow method's.
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/// </summary>
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///
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public void Randomize(int layerIndex)
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{
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for (int j = 0; j < network.Layers[layerIndex].Neurons.Length; j++)
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{
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ActivationNeuron neuron = network.Layers[layerIndex].Neurons[j] as ActivationNeuron;
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neuron.RandGenerator = new UniformContinuousDistribution(randRange);
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neuron.Randomize();
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double norm = 0.0;
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// Calculate the Euclidean Norm for the weights
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for (int k = 0; k < neuron.Weights.Length; k++)
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norm += neuron.Weights[k] * neuron.Weights[k];
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norm += neuron.Threshold * neuron.Threshold;
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norm = System.Math.Sqrt(norm);
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// Rescale the weights using beta and the norm
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for (int k = 0; k < neuron.InputsCount; k++)
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neuron.Weights[k] = beta * neuron.Weights[k] / norm;
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neuron.Threshold = beta * neuron.Threshold / norm;
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}
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}
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/// <summary>
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/// Randomizes (initializes) the weights of
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/// the network using a Gaussian distribution.
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/// </summary>
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///
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public void Randomize()
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{
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for (int i = 0; i < network.Layers.Length; i++)
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Randomize(i);
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}
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}
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}
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