/*
* Encog(tm) Core v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.networks.layers;
import java.io.Serializable;
import org.encog.engine.network.activation.ActivationFunction;
import org.encog.engine.network.activation.ActivationTANH;
import org.encog.neural.flat.FlatLayer;
import org.encog.neural.networks.BasicNetwork;
/**
* Basic functionality that most of the neural layers require. The basic layer
* is often used by itself to implement forward or recurrent layers. Other layer
* types are based on the basic layer as well.
*
* The following summarizes how basic layers calculate the output for a neural
* network.
*
* Example of a simple XOR network.
*
* Input: BasicLayer: 2 Neurons, null biasWeights, null biasActivation
*
* Hidden: BasicLayer: 2 Neurons, 2 biasWeights, 1 biasActivation
*
* Output: BasicLayer: 1 Neuron, 1 biasWeights, 1 biasActivation
*
* Input1Output and Input2Output are both provided.
*
* Synapse 1: Input to Hidden Hidden1Activation = (Input1Output *
* Input1->Hidden1Weight) + (Input2Output * Input2->Hidden1Weight) +
* (HiddenBiasActivation * Hidden1BiasWeight)
*
* Hidden1Output = calculate(Hidden1Activation, HiddenActivationFunction)
*
* Hidden2Activation = (Input1Output * Input1->Hidden2Weight) + (Input2Output *
* Input2->Hidden2Weight) + (HiddenBiasActivation * Hidden2BiasWeight)
*
* Hidden2Output = calculate(Hidden2Activation, HiddenActivationFunction)
*
* Synapse 2: Hidden to Output
*
* Output1Activation = (Hidden1Output * Hidden1->Output1Weight)
* + (Hidden2Output *
* Hidden2->Output1Weight) + (OutputBiasActivation * Output1BiasWeight)
*
* Output1Output = calculate(Output1Activation, OutputActivationFunction)
*
* @author jheaton
*/
public class BasicLayer extends FlatLayer implements Layer, Serializable {
/**
* The serial id.
*/
private static final long serialVersionUID = -5682296868750703898L;
/**
* The network that this layer belongs to.
*/
private BasicNetwork network;
/**
* Construct this layer with a non-default activation function, also
* determine if a bias is desired or not.
*
* @param activationFunction
* The activation function to use.
* @param neuronCount
* How many neurons in this layer.
* @param hasBias
* True if this layer has a bias.
*/
public BasicLayer(final ActivationFunction activationFunction,
final boolean hasBias, final int neuronCount) {
super(activationFunction, neuronCount,
hasBias?1.0:0.0);
}
/**
* Construct this layer with a sigmoid activation function.
*
* @param neuronCount
* How many neurons in this layer.
*/
public BasicLayer(final int neuronCount) {
this(new ActivationTANH(), true, neuronCount);
}
/**
* @return The network that owns this layer.
*/
public BasicNetwork getNetwork() {
return this.network;
}
/**
* Set the network for this layer.
*
* @param network
* The network for this layer.
*/
public void setNetwork(final BasicNetwork network) {
this.network = network;
}
public int getNeuronCount() {
return this.getCount();
}
@Override
public ActivationFunction getActivationFunction() {
return super.getActivation();
}
}