mirror of
https://github.com/hanzoai/jube-fork.git
synced 2026-10-30 18:33:33 +00:00
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.
133 lines
4.3 KiB
C#
133 lines
4.3 KiB
C#
// Accord Neural Net Library
|
|
// The Accord.NET Framework
|
|
// http://accord-framework.net
|
|
//
|
|
// Copyright © César Souza, 2009-2017
|
|
// cesarsouza at gmail.com
|
|
//
|
|
// This library is free software; you can redistribute it and/or
|
|
// modify it under the terms of the GNU Lesser General Public
|
|
// License as published by the Free Software Foundation; either
|
|
// version 2.1 of the License, or (at your option) any later version.
|
|
//
|
|
// This library is distributed in the hope that it will be useful,
|
|
// but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
|
|
// Lesser General Public License for more details.
|
|
//
|
|
// You should have received a copy of the GNU Lesser General Public
|
|
// License along with this library; if not, write to the Free Software
|
|
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
|
|
//
|
|
|
|
namespace Accord.Neuro.Neurons
|
|
{
|
|
using System;
|
|
using Accord.Neuro.ActivationFunctions;
|
|
using Accord.Compat;
|
|
|
|
/// <summary>
|
|
/// Stochastic Activation Neuron.
|
|
/// </summary>
|
|
///
|
|
/// <remarks>
|
|
/// The Stochastic Activation Neuron is an activation neuron
|
|
/// which activates (returns 1) only within a given probability.
|
|
/// The neuron has a random component in the activation function,
|
|
/// and the neuron fires only if the total sum, after applied
|
|
/// to a logistic activation function, is greater than a randomly
|
|
/// sampled value.
|
|
/// </remarks>
|
|
///
|
|
[Serializable]
|
|
public class StochasticNeuron : ActivationNeuron
|
|
{
|
|
|
|
private double sample;
|
|
private new IStochasticFunction function;
|
|
|
|
/// <summary>
|
|
/// Gets the neuron sample value generated in the last
|
|
/// call of any of the <see cref="Generate(double[])"/> methods.
|
|
/// </summary>
|
|
///
|
|
public double Sample
|
|
{
|
|
get { return sample; }
|
|
}
|
|
|
|
/// <summary>
|
|
/// Gets or sets the stochastic activation
|
|
/// function for this stochastic neuron.
|
|
/// </summary>
|
|
///
|
|
public new IStochasticFunction ActivationFunction
|
|
{
|
|
get { return function; }
|
|
set { base.function = this.function = value; }
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
/// Initializes a new instance of the <see cref="StochasticNeuron"/> class.
|
|
/// </summary>
|
|
///
|
|
/// <param name="inputs">Number of inputs for the neuron.</param>
|
|
/// <param name="function">Activation function for the neuron.</param>
|
|
///
|
|
public StochasticNeuron(int inputs, IStochasticFunction function)
|
|
: base(inputs, function)
|
|
{
|
|
this.ActivationFunction = function;
|
|
this.Threshold = 0; // Ruslan Salakhutdinov and Geoff Hinton
|
|
// also start with zero thresholds
|
|
}
|
|
|
|
/// <summary>
|
|
/// Samples the neuron output considering
|
|
/// the stochastic activation function.
|
|
/// </summary>
|
|
///
|
|
/// <param name="input">An input vector.</param>
|
|
///
|
|
/// <returns>A possible output for the neuron drawn
|
|
/// from the neuron's stochastic function.</returns>
|
|
///
|
|
public double Generate(double[] input)
|
|
{
|
|
double sum = threshold;
|
|
for (int i = 0; i < weights.Length; i++)
|
|
sum += weights[i] * input[i];
|
|
|
|
double output = function.Function(sum);
|
|
double sample = function.Generate2(output);
|
|
|
|
this.output = output;
|
|
this.sample = sample;
|
|
|
|
return sample;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Samples the neuron output considering
|
|
/// the stochastic activation function.
|
|
/// </summary>
|
|
///
|
|
/// <param name="output">The (previously computed) neuron output.</param>
|
|
///
|
|
/// <returns>A possible output for the neuron drawn
|
|
/// from the neuron's stochastic function.</returns>
|
|
///
|
|
public double Generate(double output)
|
|
{
|
|
double sample = function.Generate2(output);
|
|
|
|
this.output = output;
|
|
this.sample = sample;
|
|
|
|
return sample;
|
|
}
|
|
|
|
}
|
|
}
|