FlatNetwork (encog-java-core 3.4 API)

java.lang.Object
- org.encog.neural.flat.FlatNetwork

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

FlatNetworkRBF
```
public class FlatNetwork
extends java.lang.Object
implements java.io.Serializable, java.lang.Cloneable
```
Implements a flat (vector based) neural network in the Encog Engine. This is meant to be a very highly efficient feedforward, or simple recurrent, neural network. It uses a minimum of objects and is designed with one principal in mind-- SPEED. Readability, code reuse, object oriented programming are all secondary in consideration. Vector based neural networks are also very good for GPU processing. The flat network classes will make use of the GPU if you have enabled GPU processing. See the Encog class for more info.

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static double`	`DEFAULT_BIAS_ACTIVATION` The default bias activation.
`static double`	`NO_BIAS_ACTIVATION` The value that indicates that there is no bias activation.

Constructor Summary

Constructors
Constructor and Description
`FlatNetwork()` Default constructor.
`FlatNetwork(FlatLayer[] layers)` Create a flat network from an array of layers.
`FlatNetwork(FlatLayer[] layers, boolean dropout)`
`FlatNetwork(int input, int hidden1, int hidden2, int output, boolean tanh)` Construct a flat neural network.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`calculateError(MLDataSet data)` Calculate the error for this neural network.
`void`	`clearConnectionLimit()` Clear any connection limits.
`void`	`clearContext()` Clear any context neurons.
`FlatNetwork`	`clone()` Clone the network.
`void`	`cloneFlatNetwork(FlatNetwork result)` Clone into the flat network passed in.
`void`	`compute(double[] input, double[] output)` Calculate the output for the given input.
`protected void`	`computeLayer(int currentLayer)` Calculate a layer.
`void`	`decodeNetwork(double[] data)` Decode the specified data into the weights of the neural network.
`double[]`	`encodeNetwork()` Encode the neural network to an array of doubles.
`ActivationFunction[]`	`getActivationFunctions()`
`int`	`getBeginTraining()`
`double[]`	`getBiasActivation()`
`double`	`getConnectionLimit()`
`int[]`	`getContextTargetOffset()`
`int[]`	`getContextTargetSize()`
`int`	`getEncodeLength()`
`int`	`getEndTraining()`
`boolean`	`getHasContext()`
`int`	`getInputCount()`
`int[]`	`getLayerContextCount()`
`int[]`	`getLayerCounts()`
`double[]`	`getLayerDropoutRates()`
`int[]`	`getLayerFeedCounts()`
`int[]`	`getLayerIndex()`
`int`	`getLayerNeuronCount(int l)` Get the neuron count.
`double[]`	`getLayerOutput()`
`double[]`	`getLayerSums()`
`int`	`getLayerTotalNeuronCount(int l)` Get the total (including bias and context) neuron cont for a layer.
`int`	`getNeuronCount()`
`int`	`getOutputCount()`
`double`	`getWeight(int fromLayer, int fromNeuron, int toNeuron)` Get the weight between the two layers.
`int[]`	`getWeightIndex()`
`double[]`	`getWeights()`
`java.lang.Class<?>`	`hasSameActivationFunction()` Neural networks with only one type of activation function offer certain optimization options.
`void`	`init(FlatLayer[] layers, boolean dropout)` Construct a flat network.
`boolean`	`isLimited()`
`void`	`randomize()` Perform a simple randomization of the weights of the neural network between -1 and 1.
`void`	`randomize(double hi, double lo)` Perform a simple randomization of the weights of the neural network between the specified hi and lo.
`void`	`setActivationFunctions(ActivationFunction[] af)` Set the activation functions.
`void`	`setBeginTraining(int beginTraining)`
`void`	`setBiasActivation(double[] biasActivation)` Set the bias activation.
`void`	`setConnectionLimit(double connectionLimit)`
`void`	`setContextTargetOffset(int[] contextTargetOffset)` Set the context target offset.
`void`	`setContextTargetSize(int[] contextTargetSize)` Set the context target size.
`void`	`setEndTraining(int endTraining)`
`void`	`setHasContext(boolean hasContext)` Set the hasContext property.
`void`	`setInputCount(int inputCount)` Set the input count.
`void`	`setLayerContextCount(int[] layerContextCount)` Set the layer context count.
`void`	`setLayerCounts(int[] layerCounts)` Set the layer counts.
`void`	`setLayerDropoutRates(double[] layerDropoutRates)`
`void`	`setLayerFeedCounts(int[] layerFeedCounts)`
`void`	`setLayerIndex(int[] i)` Set the layer index.
`void`	`setLayerOutput(double[] layerOutput)` Set the layer output.
`void`	`setLayerSums(double[] d)` Set the layer sums.
`void`	`setOutputCount(int outputCount)` Set the output count.
`void`	`setWeightIndex(int[] weightIndex)` Set the weight index.
`void`	`setWeights(double[] weights)` Set the weights.
`void`	`validateNeuron(int targetLayer, int neuron)` Validate the the specified targetLayer and neuron are valid.

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - DEFAULT_BIAS_ACTIVATION
```
public static final double DEFAULT_BIAS_ACTIVATION
```
    The default bias activation.
    
    See Also:
    
    Constant Field Values
  - NO_BIAS_ACTIVATION
```
public static final double NO_BIAS_ACTIVATION
```
    The value that indicates that there is no bias activation.
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - FlatNetwork
```
public FlatNetwork()
```
    Default constructor.
  - FlatNetwork
```
public FlatNetwork(FlatLayer[] layers)
```
    Create a flat network from an array of layers.
    
    Parameters:
    
    layers - The layers.
  - FlatNetwork
```
public FlatNetwork(FlatLayer[] layers,
                   boolean dropout)
```
  - FlatNetwork
```
public FlatNetwork(int input,
                   int hidden1,
                   int hidden2,
                   int output,
                   boolean tanh)
```
    Construct a flat neural network.
    
    Parameters:
    
    input - Neurons in the input layer.
    
    hidden1 - Neurons in the first hidden layer. Zero for no first hidden layer.
    
    hidden2 - Neurons in the second hidden layer. Zero for no second hidden layer.
    
    output - Neurons in the output layer.
    
    tanh - True if this is a tanh activation, false for sigmoid.
- Method Detail
  - calculateError
```
public double calculateError(MLDataSet data)
```
    Calculate the error for this neural network. The error is calculated using root-mean-square(RMS).
    
    Parameters:
    
    data - The training set.
    
    Returns:
    
    The error percentage.
  - clearConnectionLimit
```
public void clearConnectionLimit()
```
    Clear any connection limits.
  - clearContext
```
public void clearContext()
```
    Clear any context neurons.
  - clone
```
public FlatNetwork clone()
```
    Clone the network.
    
    Overrides:
    
    clone in class java.lang.Object
    
    Returns:
    
    A clone of the network.
  - cloneFlatNetwork
```
public void cloneFlatNetwork(FlatNetwork result)
```
    Clone into the flat network passed in.
    
    Parameters:
    
    result - The network to copy into.
  - compute
```
public void compute(double[] input,
                    double[] output)
```
    Calculate the output for the given input.
    
    Parameters:
    
    input - The input.
    
    output - Output will be placed here.
  - computeLayer
```
protected void computeLayer(int currentLayer)
```
    Calculate a layer.
    
    Parameters:
    
    currentLayer - The layer to calculate.
  - decodeNetwork
```
public void decodeNetwork(double[] data)
```
    Decode the specified data into the weights of the neural network. This method performs the opposite of encodeNetwork.
    
    Parameters:
    
    data - The data to be decoded.
  - encodeNetwork
```
public double[] encodeNetwork()
```
    Encode the neural network to an array of doubles. This includes the network weights. To read this into a neural network, use the decodeNetwork method.
    
    Returns:
    
    The encoded network.
  - getActivationFunctions
```
public ActivationFunction[] getActivationFunctions()
```
    Returns:
    
    The activation functions.
  - getBeginTraining
```
public int getBeginTraining()
```
    Returns:
    
    the beginTraining
  - getBiasActivation
```
public double[] getBiasActivation()
```
    Returns:
    
    The bias activation.
  - getConnectionLimit
```
public double getConnectionLimit()
```
    Returns:
    
    the connectionLimit
  - getContextTargetOffset
```
public int[] getContextTargetOffset()
```
    Returns:
    
    The offset of the context target for each layer.
  - getContextTargetSize
```
public int[] getContextTargetSize()
```
    Returns:
    
    The context target size for each layer. Zero if the layer does not feed a context layer.
  - getEncodeLength
```
public int getEncodeLength()
```
    Returns:
    
    The length of the array the network would encode to.
  - getEndTraining
```
public int getEndTraining()
```
    Returns:
    
    the endTraining
  - getHasContext
```
public boolean getHasContext()
```
    Returns:
    
    True if this network has context.
  - getInputCount
```
public int getInputCount()
```
    Returns:
    
    The number of input neurons.
  - getLayerContextCount
```
public int[] getLayerContextCount()
```
    Returns:
    
    The layer context count.
  - getLayerCounts
```
public int[] getLayerCounts()
```
    Returns:
    
    The number of neurons in each layer.
  - getLayerFeedCounts
```
public int[] getLayerFeedCounts()
```
    Returns:
    
    The number of neurons in each layer that are fed by the previous layer.
  - getLayerIndex
```
public int[] getLayerIndex()
```
    Returns:
    
    Indexes into the weights for the start of each layer.
  - getLayerOutput
```
public double[] getLayerOutput()
```
    Returns:
    
    The output for each layer.
  - getNeuronCount
```
public int getNeuronCount()
```
    Returns:
    
    The neuron count.
  - getOutputCount
```
public int getOutputCount()
```
    Returns:
    
    The number of output neurons.
  - getWeightIndex
```
public int[] getWeightIndex()
```
    Returns:
    
    The index of each layer in the weight and threshold array.
  - getWeights
```
public double[] getWeights()
```
    Returns:
    
    The index of each layer in the weight and threshold array.
  - hasSameActivationFunction
```
public java.lang.Class<?> hasSameActivationFunction()
```
    Neural networks with only one type of activation function offer certain optimization options. This method determines if only a single activation function is used.
    
    Returns:
    
    The number of the single activation function, or -1 if there are no activation functions or more than one type of activation function.
  - init
```
public void init(FlatLayer[] layers,
                 boolean dropout)
```
    Construct a flat network.
    
    Parameters:
    
    layers - The layers of the network to create.
    
    dropout - Flag to enable dropout rate for each layer.
  - isLimited
```
public boolean isLimited()
```
    Returns:
    
    the isLimited
  - randomize
```
public void randomize()
```
    Perform a simple randomization of the weights of the neural network between -1 and 1.
  - randomize
```
public void randomize(double hi,
                      double lo)
```
    Perform a simple randomization of the weights of the neural network between the specified hi and lo.
    
    Parameters:
    
    hi - The network high.
    
    lo - The network low.
  - setActivationFunctions
```
public void setActivationFunctions(ActivationFunction[] af)
```
    Set the activation functions.
    
    Parameters:
    
    af - The activation functions.
  - setBeginTraining
```
public void setBeginTraining(int beginTraining)
```
    Parameters:
    
    beginTraining - the beginTraining to set
  - setBiasActivation
```
public void setBiasActivation(double[] biasActivation)
```
    Set the bias activation.
    
    Parameters:
    
    biasActivation - The bias activation.
  - setConnectionLimit
```
public void setConnectionLimit(double connectionLimit)
```
    Parameters:
    
    connectionLimit - the connectionLimit to set
  - setContextTargetOffset
```
public void setContextTargetOffset(int[] contextTargetOffset)
```
    Set the context target offset.
    
    Parameters:
    
    contextTargetOffset - The context target offset.
  - setContextTargetSize
```
public void setContextTargetSize(int[] contextTargetSize)
```
    Set the context target size.
    
    Parameters:
    
    contextTargetSize - The context target size.
  - setEndTraining
```
public void setEndTraining(int endTraining)
```
    Parameters:
    
    endTraining - the endTraining to set
  - setHasContext
```
public void setHasContext(boolean hasContext)
```
    Set the hasContext property.
    
    Parameters:
    
    hasContext - True if the network has context.
  - setInputCount
```
public void setInputCount(int inputCount)
```
    Set the input count.
    
    Parameters:
    
    inputCount - The input count.
  - setLayerContextCount
```
public void setLayerContextCount(int[] layerContextCount)
```
    Set the layer context count.
    
    Parameters:
    
    layerContextCount - The layer context count.
  - setLayerCounts
```
public void setLayerCounts(int[] layerCounts)
```
    Set the layer counts.
    
    Parameters:
    
    layerCounts - The layer counts.
  - setLayerFeedCounts
```
public void setLayerFeedCounts(int[] layerFeedCounts)
```
  - setLayerIndex
```
public void setLayerIndex(int[] i)
```
    Set the layer index.
    
    Parameters:
    
    i - The layer index.
  - setLayerOutput
```
public void setLayerOutput(double[] layerOutput)
```
    Set the layer output.
    
    Parameters:
    
    layerOutput - The layer output.
  - setOutputCount
```
public void setOutputCount(int outputCount)
```
    Set the output count.
    
    Parameters:
    
    outputCount - The output count.
  - setWeightIndex
```
public void setWeightIndex(int[] weightIndex)
```
    Set the weight index.
    
    Parameters:
    
    weightIndex - The weight index.
  - setWeights
```
public void setWeights(double[] weights)
```
    Set the weights.
    
    Parameters:
    
    weights - The weights.
  - getLayerSums
```
public double[] getLayerSums()
```
    Returns:
    
    the layerSums
  - setLayerSums
```
public void setLayerSums(double[] d)
```
    Set the layer sums.
    
    Parameters:
    
    d - The layer sums.
  - getLayerDropoutRates
```
public double[] getLayerDropoutRates()
```
  - setLayerDropoutRates
```
public void setLayerDropoutRates(double[] layerDropoutRates)
```
  - getWeight
```
public double getWeight(int fromLayer,
                        int fromNeuron,
                        int toNeuron)
```
    Get the weight between the two layers.
    
    Parameters:
    
    fromLayer - The from layer.
    
    fromNeuron - The from neuron.
    
    toNeuron - The to neuron.
    
    Returns:
    
    The weight value.
  - validateNeuron
```
public void validateNeuron(int targetLayer,
                           int neuron)
```
    Validate the the specified targetLayer and neuron are valid.
    
    Parameters:
    
    targetLayer - The target layer.
    
    neuron - The target neuron.
  - getLayerTotalNeuronCount
```
public int getLayerTotalNeuronCount(int l)
```
    Get the total (including bias and context) neuron cont for a layer.
    
    Parameters:
    
    l - The layer.
    
    Returns:
    
    The count.
  - getLayerNeuronCount
```
public int getLayerNeuronCount(int l)
```
    Get the neuron count.
    
    Parameters:
    
    l - The layer.
    
    Returns:
    
    The neuron count.

Class FlatNetwork

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_BIAS_ACTIVATION

NO_BIAS_ACTIVATION

Constructor Detail

FlatNetwork

FlatNetwork

FlatNetwork

FlatNetwork

Method Detail

calculateError

clearConnectionLimit

clearContext

clone

cloneFlatNetwork

compute

computeLayer

decodeNetwork

encodeNetwork

getActivationFunctions

getBeginTraining

getBiasActivation

getConnectionLimit

getContextTargetOffset

getContextTargetSize

getEncodeLength

getEndTraining

getHasContext

getInputCount

getLayerContextCount

getLayerCounts

getLayerFeedCounts

getLayerIndex

getLayerOutput

getNeuronCount

getOutputCount

getWeightIndex

getWeights

hasSameActivationFunction

init

isLimited

randomize

randomize

setActivationFunctions

setBeginTraining

setBiasActivation

setConnectionLimit

setContextTargetOffset

setContextTargetSize

setEndTraining

setHasContext

setInputCount

setLayerContextCount

setLayerCounts

setLayerFeedCounts

setLayerIndex

setLayerOutput

setOutputCount

setWeightIndex

setWeights

getLayerSums

setLayerSums

getLayerDropoutRates

setLayerDropoutRates

getWeight

validateNeuron

getLayerTotalNeuronCount

getLayerNeuronCount