BaseNeuralNetwork (deeplearning4j-core 0.0.3.2.7 API)

java.lang.Object
- org.deeplearning4j.nn.BaseNeuralNetwork

All Implemented Interfaces:

Serializable, Cloneable, Model, NeuralNetwork, Persistable, IterationListener

Direct Known Subclasses:

AutoEncoder, DenoisingAutoEncoder, RBM
```
public abstract class BaseNeuralNetwork
extends Object
implements NeuralNetwork, Persistable
```
Baseline class for any Neural Network used as a layer in a deep network such as an DBN

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class BaseNeuralNetwork.Builder<E extends BaseNeuralNetwork>
- Nested classes/interfaces inherited from interface org.deeplearning4j.nn.api.NeuralNetwork
  NeuralNetwork.OptimizationAlgorithm

Nested Classes
Modifier and Type	Class and Description
`static class`	`BaseNeuralNetwork.Builder<E extends BaseNeuralNetwork>`

Field Summary

Fields
Modifier and Type	Field and Description
`protected NeuralNetConfiguration`	`conf`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`doMask`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`hBias`
`protected org.nd4j.linalg.learning.AdaGrad`	`hBiasAdaGrad`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`hBiasGradient`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`input`
`protected int`	`lastMiniBatchSize`
`protected NeuralNetworkOptimizer`	`optimizer`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`vBias`
`protected org.nd4j.linalg.learning.AdaGrad`	`vBiasAdaGrad`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`vBiasGradient`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`W`
`protected org.nd4j.linalg.learning.AdaGrad`	`wAdaGrad`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`wGradient`

Constructor Summary

Constructors
Modifier	Constructor and Description
`protected`	`BaseNeuralNetwork()`
	`BaseNeuralNetwork(org.nd4j.linalg.api.ndarray.INDArray input, org.nd4j.linalg.api.ndarray.INDArray W, org.nd4j.linalg.api.ndarray.INDArray hbias, org.nd4j.linalg.api.ndarray.INDArray vbias, NeuralNetConfiguration conf)`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`protected void`	`applyDropOutIfNecessary(org.nd4j.linalg.api.ndarray.INDArray input)`
`protected void`	`applySparsity(org.nd4j.linalg.api.ndarray.INDArray hBiasGradient)` Applies sparsity to the passed in hbias gradient
`void`	`backProp(double lr, int iterations, Object[] extraParams)` Backprop with the output being the reconstruction
`void`	`clearInput()` Clears the input from the neural net
`NeuralNetwork`	`clone()`
`NeuralNetConfiguration`	`conf()`
`void`	`fit(org.nd4j.linalg.api.ndarray.INDArray data)` Fit the model to the given data
`org.nd4j.linalg.learning.AdaGrad`	`getAdaGrad()`
`org.nd4j.linalg.api.ndarray.INDArray`	`gethBias()`
`org.nd4j.linalg.learning.AdaGrad`	`gethBiasAdaGrad()`
`org.nd4j.linalg.api.ndarray.INDArray`	`getInput()`
`org.nd4j.linalg.api.ndarray.INDArray`	`getvBias()`
`org.nd4j.linalg.learning.AdaGrad`	`getVBiasAdaGrad()`
`org.nd4j.linalg.api.ndarray.INDArray`	`getW()`
`org.nd4j.linalg.api.ndarray.INDArray`	`hBiasMean()`
`protected void`	`initWeights()` Initialize weights.
`void`	`iterationDone(int iteration)` Event listener for each iteration
`double`	`l2RegularizedCoefficient()`
`void`	`load(InputStream is)` Load (using `ObjectInputStream`
`void`	`merge(NeuralNetwork network, int batchSize)` Performs a network merge in the form of a += b - a / n where a is a matrix here b is a matrix on the incoming network and n is the batch size
`int`	`numParams()` The number of parameters for the model
`org.nd4j.linalg.api.ndarray.INDArray`	`params()` Returns the parameters of the neural network
`protected org.nd4j.linalg.api.ndarray.INDArray`	`preProcessInput(org.nd4j.linalg.api.ndarray.INDArray input)`
`double`	`score()`
`void`	`setAdaGrad(org.nd4j.linalg.learning.AdaGrad adaGrad)`
`void`	`setConf(NeuralNetConfiguration conf)`
`void`	`sethBias(org.nd4j.linalg.api.ndarray.INDArray hBias)`
`void`	`setHbiasAdaGrad(org.nd4j.linalg.learning.AdaGrad adaGrad)`
`void`	`setInput(org.nd4j.linalg.api.ndarray.INDArray input)`
`void`	`setParams(org.nd4j.linalg.api.ndarray.INDArray params)` Set the parameters for this model.
`void`	`setvBias(org.nd4j.linalg.api.ndarray.INDArray vBias)`
`void`	`setVBiasAdaGrad(org.nd4j.linalg.learning.AdaGrad adaGrad)`
`void`	`setW(org.nd4j.linalg.api.ndarray.INDArray w)`
`abstract org.nd4j.linalg.api.ndarray.INDArray`	`transform(org.nd4j.linalg.api.ndarray.INDArray x)` All neural networks are based on this idea of minimizing reconstruction error.
`NeuralNetwork`	`transpose()`
`void`	`update(BaseNeuralNetwork n)` Copies params from the passed in network to this one
`protected void`	`updateGradientAccordingToParams(NeuralNetworkGradient gradient, int iteration, double learningRate)` Update the gradient according to the configuration such as adagrad, momentum, and sparsity
`void`	`write(OutputStream os)` Write this to an object output stream

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

Methods inherited from interface org.deeplearning4j.nn.api.NeuralNetwork
getGradient, hiddenActivation, sampleHiddenGivenVisible, sampleVisibleGivenHidden

Methods inherited from interface org.deeplearning4j.nn.api.Model
fit, iterate

- Field Detail
  - W
```
protected org.nd4j.linalg.api.ndarray.INDArray W
```
  - hBias
```
protected org.nd4j.linalg.api.ndarray.INDArray hBias
```
  - vBias
```
protected org.nd4j.linalg.api.ndarray.INDArray vBias
```
  - input
```
protected org.nd4j.linalg.api.ndarray.INDArray input
```
  - optimizer
```
protected transient NeuralNetworkOptimizer optimizer
```
  - doMask
```
protected org.nd4j.linalg.api.ndarray.INDArray doMask
```
  - wGradient
```
protected org.nd4j.linalg.api.ndarray.INDArray wGradient
```
  - vBiasGradient
```
protected org.nd4j.linalg.api.ndarray.INDArray vBiasGradient
```
  - hBiasGradient
```
protected org.nd4j.linalg.api.ndarray.INDArray hBiasGradient
```
  - lastMiniBatchSize
```
protected int lastMiniBatchSize
```
  - wAdaGrad
```
protected org.nd4j.linalg.learning.AdaGrad wAdaGrad
```
  - hBiasAdaGrad
```
protected org.nd4j.linalg.learning.AdaGrad hBiasAdaGrad
```
  - vBiasAdaGrad
```
protected org.nd4j.linalg.learning.AdaGrad vBiasAdaGrad
```
  - conf
```
protected NeuralNetConfiguration conf
```
- Constructor Detail
  - BaseNeuralNetwork
```
protected BaseNeuralNetwork()
```
  - BaseNeuralNetwork
```
public BaseNeuralNetwork(org.nd4j.linalg.api.ndarray.INDArray input,
                         org.nd4j.linalg.api.ndarray.INDArray W,
                         org.nd4j.linalg.api.ndarray.INDArray hbias,
                         org.nd4j.linalg.api.ndarray.INDArray vbias,
                         NeuralNetConfiguration conf)
```
- Method Detail
  - params
```
public org.nd4j.linalg.api.ndarray.INDArray params()
```
    Returns the parameters of the neural network
    
    Specified by:
    
    params in interface Model
    
    Returns:
    
    the parameters of the neural network
  - l2RegularizedCoefficient
```
public double l2RegularizedCoefficient()
```
  - initWeights
```
protected void initWeights()
```
    Initialize weights. This includes steps for doing a random initialization of W as well as the vbias and hbias
  - numParams
```
public int numParams()
```
    The number of parameters for the model
    
    Specified by:
    
    numParams in interface Model
    
    Returns:
    
    the number of parameters for the model
  - setParams
```
public void setParams(org.nd4j.linalg.api.ndarray.INDArray params)
```
    Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model
    
    Specified by:
    
    setParams in interface Model
    
    Parameters:
    
    params - the parameters for the model
  - backProp
```
public void backProp(double lr,
                     int iterations,
                     Object[] extraParams)
```
    Backprop with the output being the reconstruction
    
    Specified by:
    
    backProp in interface NeuralNetwork
    
    Parameters:
    
    lr - the learning rate to use
    
    iterations - the max number of epochs to run
    
    extraParams - implementation specific params
  - fit
```
public void fit(org.nd4j.linalg.api.ndarray.INDArray data)
```
    Fit the model to the given data
    
    Specified by:
    
    fit in interface Model
    
    Parameters:
    
    data - the data to fit the model to
  - applySparsity
```
protected void applySparsity(org.nd4j.linalg.api.ndarray.INDArray hBiasGradient)
```
    Applies sparsity to the passed in hbias gradient
    
    Parameters:
    
    hBiasGradient - the hbias gradient to apply to
  - updateGradientAccordingToParams
```
protected void updateGradientAccordingToParams(NeuralNetworkGradient gradient,
                                               int iteration,
                                               double learningRate)
```
    Update the gradient according to the configuration such as adagrad, momentum, and sparsity
    
    Parameters:
    
    gradient - the gradient to modify
    
    iteration - the current iteration
    
    learningRate - the learning rate for the current iteration
  - score
```
public double score()
```
    Specified by:
    
    score in interface Model
  - clearInput
```
public void clearInput()
```
    Clears the input from the neural net
    
    Specified by:
    
    clearInput in interface NeuralNetwork
  - getAdaGrad
```
public org.nd4j.linalg.learning.AdaGrad getAdaGrad()
```
    Specified by:
    
    getAdaGrad in interface NeuralNetwork
  - setAdaGrad
```
public void setAdaGrad(org.nd4j.linalg.learning.AdaGrad adaGrad)
```
    Specified by:
    
    setAdaGrad in interface NeuralNetwork
  - transpose
```
public NeuralNetwork transpose()
```
    Specified by:
    
    transpose in interface NeuralNetwork
  - conf
```
public NeuralNetConfiguration conf()
```
    Specified by:
    
    conf in interface NeuralNetwork
  - setConf
```
public void setConf(NeuralNetConfiguration conf)
```
    Specified by:
    
    setConf in interface NeuralNetwork
  - clone
```
public NeuralNetwork clone()
```
    Specified by:
    
    clone in interface NeuralNetwork
    
    Overrides:
    
    clone in class Object
  - merge
```
public void merge(NeuralNetwork network,
                  int batchSize)
```
    Description copied from interface: NeuralNetwork
    
    Performs a network merge in the form of a += b - a / n where a is a matrix here b is a matrix on the incoming network and n is the batch size
    
    Specified by:
    
    merge in interface NeuralNetwork
    
    Parameters:
    
    network - the network to merge with
    
    batchSize - the batch size (number of training examples) to average by
  - update
```
public void update(BaseNeuralNetwork n)
```
    Copies params from the passed in network to this one
    
    Parameters:
    
    n - the network to copy
  - load
```
public void load(InputStream is)
```
    Load (using ObjectInputStream
    
    Specified by:
    
    load in interface Persistable
    
    Parameters:
    
    is - the input stream to load from (usually a file)
  - getW
```
public org.nd4j.linalg.api.ndarray.INDArray getW()
```
    Specified by:
    
    getW in interface NeuralNetwork
  - setW
```
public void setW(org.nd4j.linalg.api.ndarray.INDArray w)
```
    Specified by:
    
    setW in interface NeuralNetwork
  - gethBias
```
public org.nd4j.linalg.api.ndarray.INDArray gethBias()
```
    Specified by:
    
    gethBias in interface NeuralNetwork
  - sethBias
```
public void sethBias(org.nd4j.linalg.api.ndarray.INDArray hBias)
```
    Specified by:
    
    sethBias in interface NeuralNetwork
  - getvBias
```
public org.nd4j.linalg.api.ndarray.INDArray getvBias()
```
    Specified by:
    
    getvBias in interface NeuralNetwork
  - setvBias
```
public void setvBias(org.nd4j.linalg.api.ndarray.INDArray vBias)
```
    Specified by:
    
    setvBias in interface NeuralNetwork
  - getInput
```
public org.nd4j.linalg.api.ndarray.INDArray getInput()
```
    Specified by:
    
    getInput in interface NeuralNetwork
  - setInput
```
public void setInput(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Specified by:
    
    setInput in interface NeuralNetwork
  - gethBiasAdaGrad
```
public org.nd4j.linalg.learning.AdaGrad gethBiasAdaGrad()
```
    Specified by:
    
    gethBiasAdaGrad in interface NeuralNetwork
  - setHbiasAdaGrad
```
public void setHbiasAdaGrad(org.nd4j.linalg.learning.AdaGrad adaGrad)
```
    Specified by:
    
    setHbiasAdaGrad in interface NeuralNetwork
  - getVBiasAdaGrad
```
public org.nd4j.linalg.learning.AdaGrad getVBiasAdaGrad()
```
    Specified by:
    
    getVBiasAdaGrad in interface NeuralNetwork
  - setVBiasAdaGrad
```
public void setVBiasAdaGrad(org.nd4j.linalg.learning.AdaGrad adaGrad)
```
    Specified by:
    
    setVBiasAdaGrad in interface NeuralNetwork
  - write
```
public void write(OutputStream os)
```
    Write this to an object output stream
    
    Specified by:
    
    write in interface Persistable
    
    Parameters:
    
    os - the output stream to write to
  - transform
```
public abstract org.nd4j.linalg.api.ndarray.INDArray transform(org.nd4j.linalg.api.ndarray.INDArray x)
```
    All neural networks are based on this idea of minimizing reconstruction error. Both RBMs and Denoising AutoEncoders have a component for reconstructing, ala different implementations.
    
    Specified by:
    
    transform in interface Model
    
    Parameters:
    
    x - the input to transform
    
    Returns:
    
    the reconstructed input
  - applyDropOutIfNecessary
```
protected void applyDropOutIfNecessary(org.nd4j.linalg.api.ndarray.INDArray input)
```
  - hBiasMean
```
public org.nd4j.linalg.api.ndarray.INDArray hBiasMean()
```
    Specified by:
    
    hBiasMean in interface NeuralNetwork
  - preProcessInput
```
protected org.nd4j.linalg.api.ndarray.INDArray preProcessInput(org.nd4j.linalg.api.ndarray.INDArray input)
```
  - iterationDone
```
public void iterationDone(int iteration)
```
    Description copied from interface: IterationListener
    
    Event listener for each iteration
    
    Specified by:
    
    iterationDone in interface NeuralNetwork
    
    Specified by:
    
    iterationDone in interface IterationListener

Class BaseNeuralNetwork

Nested Class Summary

Nested classes/interfaces inherited from interface org.deeplearning4j.nn.api.NeuralNetwork

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface org.deeplearning4j.nn.api.NeuralNetwork

Methods inherited from interface org.deeplearning4j.nn.api.Model

Field Detail

W

hBias

vBias

input

optimizer

doMask

wGradient

vBiasGradient

hBiasGradient

lastMiniBatchSize

wAdaGrad

hBiasAdaGrad

vBiasAdaGrad

conf

Constructor Detail

BaseNeuralNetwork

BaseNeuralNetwork

Method Detail

params

l2RegularizedCoefficient

initWeights

numParams

setParams

backProp

fit

applySparsity

updateGradientAccordingToParams

score

clearInput

getAdaGrad

setAdaGrad

transpose

conf

setConf

clone

merge

update

load

getW

setW

gethBias

sethBias

getvBias

setvBias

getInput

setInput

gethBiasAdaGrad

setHbiasAdaGrad

getVBiasAdaGrad

setVBiasAdaGrad

write

transform

applyDropOutIfNecessary

hBiasMean

preProcessInput

iterationDone