org.deeplearning4j.nn.layers

Class BasePretrainNetwork<LayerConfT extends BasePretrainNetwork>

java.lang.Object

org.deeplearning4j.nn.layers.BaseLayer<LayerConfT>

org.deeplearning4j.nn.layers.BasePretrainNetwork<LayerConfT>

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Layer, Model

Direct Known Subclasses:

AutoEncoder, RBM

public abstract class BasePretrainNetwork<LayerConfT extends BasePretrainNetwork>
extends BaseLayer<LayerConfT>

Baseline class for any Neural Network used as a layer in a deep network *

See Also:

Serialized Form

Nested Class Summary

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

Layer.TrainingMode, Layer.Type

Field Summary

Fields

Modifier and Type	Field and Description
protected java.util.Collection<TrainingListener>	trainingListeners

Fields inherited from class org.deeplearning4j.nn.layers.BaseLayer

conf, dropoutApplied, dropoutMask, gradient, gradientsFlattened, gradientViews, index, input, iterationListeners, maskArray, maskState, optimizer, params, paramsFlattened, score, solver

Constructor Summary

Constructors

Constructor and Description
BasePretrainNetwork(NeuralNetConfiguration conf)
BasePretrainNetwork(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)

Method Summary

Modifier and Type	Method and Description
Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray>	backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon) Calculate the gradient relative to the error in the next layer
double	calcL1(boolean backpropParamsOnly) Calculate the l1 regularization term 0.0 if regularization is not used.
double	calcL2(boolean backpropParamsOnly) Calculate the l2 regularization term 0.0 if regularization is not used.
protected Gradient	createGradient(org.nd4j.linalg.api.ndarray.INDArray wGradient, org.nd4j.linalg.api.ndarray.INDArray vBiasGradient, org.nd4j.linalg.api.ndarray.INDArray hBiasGradient)
org.nd4j.linalg.api.ndarray.INDArray	getCorruptedInput(org.nd4j.linalg.api.ndarray.INDArray x, double corruptionLevel) Corrupts the given input by doing a binomial sampling given the corruption level
int	numParams() The number of parameters for the model, for backprop (i.e., excluding visible bias)
int	numParams(boolean backwards) the number of parameters for the model
org.nd4j.linalg.api.ndarray.INDArray	params() Returns the parameters of the neural network as a flattened row vector
java.util.Map<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray>	paramTable(boolean backpropParamsOnly) Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
abstract Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	sampleHiddenGivenVisible(org.nd4j.linalg.api.ndarray.INDArray v) Sample the hidden distribution given the visible
abstract Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray>	sampleVisibleGivenHidden(org.nd4j.linalg.api.ndarray.INDArray h) Sample the visible distribution given the hidden
void	setListeners(java.util.Collection<IterationListener> listeners) Set the iteration listeners for this layer.
void	setListeners(IterationListener... listeners) Set the iteration listeners for this layer.
void	setParams(org.nd4j.linalg.api.ndarray.INDArray params) Set the parameters for this model.
protected void	setScoreWithZ(org.nd4j.linalg.api.ndarray.INDArray z)

Methods inherited from class org.deeplearning4j.nn.layers.BaseLayer

accumulateScore, activate, activate, activate, activate, activate, activate, activationMean, applyDropOutIfNecessary, applyLearningRateScoreDecay, applyMask, batchSize, calcGradient, clear, clone, computeGradientAndScore, conf, createGradient, derivativeActivation, error, feedForwardMaskArray, fit, fit, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, getOptimizer, getParam, gradient, gradientAndScore, init, initParams, input, iterate, layerConf, layerNameAndIndex, merge, paramTable, preOutput, preOutput, preOutput, preOutput, score, setBackpropGradientsViewArray, setConf, setIndex, setInput, setInputMiniBatchSize, setMaskArray, setParam, setParams, setParamsViewArray, setParamTable, toString, transpose, type, update, update, validateInput

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

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

isPretrainLayer

Field Detail

trainingListeners

protected java.util.Collection<TrainingListener> trainingListeners

Constructor Detail

BasePretrainNetwork

public BasePretrainNetwork(NeuralNetConfiguration conf)

BasePretrainNetwork

public BasePretrainNetwork(NeuralNetConfiguration conf,
                           org.nd4j.linalg.api.ndarray.INDArray input)

Method Detail

setListeners

public void setListeners(java.util.Collection<IterationListener> listeners)

Description copied from interface: Layer

Set the iteration listeners for this layer.

Specified by:

setListeners in interface Layer

Specified by:

setListeners in interface Model

Overrides:

setListeners in class BaseLayer<LayerConfT extends BasePretrainNetwork>

setListeners

public void setListeners(IterationListener... listeners)

Description copied from interface: Layer

Set the iteration listeners for this layer.

Specified by:

setListeners in interface Layer

Specified by:

setListeners in interface Model

Overrides:

setListeners in class BaseLayer<LayerConfT extends BasePretrainNetwork>

getCorruptedInput

public org.nd4j.linalg.api.ndarray.INDArray getCorruptedInput(org.nd4j.linalg.api.ndarray.INDArray x,
                                                              double corruptionLevel)

Corrupts the given input by doing a binomial sampling given the corruption level

Parameters:

x - the input to corrupt

corruptionLevel - the corruption value

Returns:

the binomial sampled corrupted input

createGradient

protected Gradient createGradient(org.nd4j.linalg.api.ndarray.INDArray wGradient,
                                  org.nd4j.linalg.api.ndarray.INDArray vBiasGradient,
                                  org.nd4j.linalg.api.ndarray.INDArray hBiasGradient)

numParams

public int numParams(boolean backwards)

Description copied from interface: Model

the number of parameters for the model

Specified by:

numParams in interface Model

Overrides:

numParams in class BaseLayer<LayerConfT extends BasePretrainNetwork>

Returns:

the number of parameters for the model

sampleHiddenGivenVisible

public abstract Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray> sampleHiddenGivenVisible(org.nd4j.linalg.api.ndarray.INDArray v)

Sample the hidden distribution given the visible

Parameters:

v - the visible to sample from

Returns:

the hidden mean and sample

sampleVisibleGivenHidden

public abstract Pair<org.nd4j.linalg.api.ndarray.INDArray,org.nd4j.linalg.api.ndarray.INDArray> sampleVisibleGivenHidden(org.nd4j.linalg.api.ndarray.INDArray h)

Sample the visible distribution given the hidden

Parameters:

h - the hidden to sample from

Returns:

the mean and sample

setScoreWithZ

protected void setScoreWithZ(org.nd4j.linalg.api.ndarray.INDArray z)

Overrides:

setScoreWithZ in class BaseLayer<LayerConfT extends BasePretrainNetwork>

paramTable

public java.util.Map<java.lang.String,org.nd4j.linalg.api.ndarray.INDArray> paramTable(boolean backpropParamsOnly)

Description copied from interface: Model

Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters

Specified by:

paramTable in interface Model

Overrides:

paramTable in class BaseLayer<LayerConfT extends BasePretrainNetwork>

Parameters:

backpropParamsOnly - If true, return backprop params only. If false: return all params (equivalent to paramsTable())

params

public org.nd4j.linalg.api.ndarray.INDArray params()

Description copied from class: BaseLayer

Returns the parameters of the neural network as a flattened row vector

Specified by:

params in interface Model

Overrides:

params in class BaseLayer<LayerConfT extends BasePretrainNetwork>

Returns:

the parameters of the neural network

numParams

public int numParams()

The number of parameters for the model, for backprop (i.e., excluding visible bias)

Specified by:

numParams in interface Model

Overrides:

numParams in class BaseLayer<LayerConfT extends BasePretrainNetwork>

Returns:

the number of parameters for the model (ex. visible bias)

setParams

public void setParams(org.nd4j.linalg.api.ndarray.INDArray params)

Description copied from interface: Model

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

Overrides:

setParams in class BaseLayer<LayerConfT extends BasePretrainNetwork>

Parameters:

params - the parameters for the model

backpropGradient

public Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray> backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon)

Description copied from interface: Layer

Calculate the gradient relative to the error in the next layer

Specified by:

backpropGradient in interface Layer

Overrides:

backpropGradient in class BaseLayer<LayerConfT extends BasePretrainNetwork>

Parameters:

epsilon - w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C is cost function a=sigma(z) is activation.

Returns:

Pair where Gradient is gradient for this layer, INDArray is epsilon needed by next layer, but before element-wise multiply by sigmaPrime(z). So for standard feed-forward layer, if this layer is L, then return.getSecond() == (w^(L)*(delta^(L))^T)^T

calcL2

public double calcL2(boolean backpropParamsOnly)

Description copied from interface: Layer

Calculate the l2 regularization term
0.0 if regularization is not used. Or 0.5 * l2Coeff * l2Magnitude otherwise.
Note that this does not divide by mini-batch size

Specified by:

calcL2 in interface Layer

Overrides:

calcL2 in class BaseLayer<LayerConfT extends BasePretrainNetwork>

Parameters:

backpropParamsOnly - If true: calculate L2 based on backprop params only. If false: calculate based on all params (including pretrain params, if any)

Returns:

the l2 regularization term for this layer.

calcL1

public double calcL1(boolean backpropParamsOnly)

Description copied from interface: Layer

Calculate the l1 regularization term
0.0 if regularization is not used. Or l1Coeff * l1Magnitude otherwise.
Note that this does not divide by mini-batch size

Specified by:

calcL1 in interface Layer

Overrides:

calcL1 in class BaseLayer<LayerConfT extends BasePretrainNetwork>

Parameters:

backpropParamsOnly - If true: calculate L1 based on backprop params only. If false: calculate based on all params (including pretrain params, if any)

Returns:

the l1 regularization term for this layer.