org.deeplearning4j.nn.layers

Class BaseLayer<LayerConfT extends BaseLayer>

java.lang.Object

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

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

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model

Direct Known Subclasses:

BaseOutputLayer, BasePretrainNetwork, BaseRecurrentLayer, BatchNormalization, ConvolutionLayer, DenseLayer, DropoutLayer, EmbeddingLayer, LossLayer

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

A layer with parameters

Author:

Adam Gibson

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 Gradient	gradient
protected org.nd4j.linalg.api.ndarray.INDArray	gradientsFlattened
protected Map<String,org.nd4j.linalg.api.ndarray.INDArray>	gradientViews
protected ConvexOptimizer	optimizer
protected Map<String,org.nd4j.linalg.api.ndarray.INDArray>	params
protected org.nd4j.linalg.api.ndarray.INDArray	paramsFlattened
protected double	score
protected Solver	solver

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

cacheMode, conf, dropoutApplied, dropoutMask, index, input, iterationListeners, maskArray, maskState, preOutput

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
void	accumulateScore(double accum) Sets a rolling tally for the score.
org.nd4j.linalg.api.ndarray.INDArray	activate(boolean training) Trigger an activation with the last specified input
org.nd4j.linalg.api.ndarray.INDArray	activate(org.nd4j.linalg.api.ndarray.INDArray input, Layer.TrainingMode training) Initialize the layer with the given input and return the activation for this layer given this input
org.nd4j.linalg.api.ndarray.INDArray	activate(Layer.TrainingMode training) Trigger an activation with the last specified input
org.nd4j.linalg.api.ndarray.INDArray	activationMean() Calculate the mean representation for the activation for this layer
void	applyLearningRateScoreDecay() Update learningRate using for this model.
org.nd4j.linalg.primitives.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
Gradient	calcGradient(Gradient layerError, org.nd4j.linalg.api.ndarray.INDArray activation) Calculate the gradient
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.
Layer	clone() Clone the layer
void	computeGradientAndScore() Update the score
Gradient	error(org.nd4j.linalg.api.ndarray.INDArray errorSignal) Calculate error with respect to the current layer.
void	fit() All models have a fit method
void	fit(org.nd4j.linalg.api.ndarray.INDArray input) Fit the model to the given data
org.nd4j.linalg.api.ndarray.INDArray	getGradientsViewArray()
ConvexOptimizer	getOptimizer() Returns this models optimizer
org.nd4j.linalg.api.ndarray.INDArray	getParam(String param) Get the parameter
Gradient	gradient() Calculate a gradient
void	initParams() Initialize the parameters
void	iterate(org.nd4j.linalg.api.ndarray.INDArray input) iterate one iteration of the network
LayerConfT	layerConf()
void	merge(Layer l, int batchSize) Averages the given logistic regression from a mini batch into this layer
int	numParams() 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
Map<String,org.nd4j.linalg.api.ndarray.INDArray>	paramTable() The param table
Map<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
org.nd4j.linalg.api.ndarray.INDArray	preOutput(boolean training)
org.nd4j.linalg.api.ndarray.INDArray	preOutput(org.nd4j.linalg.api.ndarray.INDArray x, Layer.TrainingMode training) Raw activations
double	score() Objective function: the specified objective
void	setBackpropGradientsViewArray(org.nd4j.linalg.api.ndarray.INDArray gradients) Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
void	setParam(String key, org.nd4j.linalg.api.ndarray.INDArray val) Set the parameter with a new ndarray
void	setParams(org.nd4j.linalg.api.ndarray.INDArray params) Set the parameters for this model.
protected void	setParams(org.nd4j.linalg.api.ndarray.INDArray params, char order)
void	setParamsViewArray(org.nd4j.linalg.api.ndarray.INDArray params) Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
void	setParamTable(Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable) Setter for the param table
protected void	setScoreWithZ(org.nd4j.linalg.api.ndarray.INDArray z)
String	toString()
Layer	transpose() Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)
void	update(Gradient gradient) Update layer weights and biases with gradient change
void	update(org.nd4j.linalg.api.ndarray.INDArray gradient, String paramType) Perform one update applying the gradient

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

activate, activate, activate, addListeners, applyDropOutIfNecessary, applyMask, batchSize, clear, conf, derivativeActivation, feedForwardMaskArray, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, gradientAndScore, init, input, layerId, numParams, preOutput, preOutput, setCacheMode, setConf, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, type, 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

paramsFlattened

protected org.nd4j.linalg.api.ndarray.INDArray paramsFlattened

gradientsFlattened

protected org.nd4j.linalg.api.ndarray.INDArray gradientsFlattened

params

protected Map<String,org.nd4j.linalg.api.ndarray.INDArray> params

gradientViews

protected transient Map<String,org.nd4j.linalg.api.ndarray.INDArray> gradientViews

score

protected double score

optimizer

protected ConvexOptimizer optimizer

gradient

protected Gradient gradient

solver

protected Solver solver

Constructor Detail

BaseLayer

public BaseLayer(NeuralNetConfiguration conf)

BaseLayer

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

Method Detail

layerConf

public LayerConfT layerConf()

Overrides:

layerConf in class AbstractLayer<LayerConfT extends BaseLayer>

error

public Gradient error(org.nd4j.linalg.api.ndarray.INDArray errorSignal)

Description copied from interface: Layer

Calculate error with respect to the current layer. This gradient will contain the error signal

Specified by:

error in interface Layer

Overrides:

error in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

errorSignal - the gradient for the forward layer If this is the final layer, it will start with the error from the output. This is on the user to initialize.

Returns:

the gradient wrt the parameters on the current layer

calcGradient

public Gradient calcGradient(Gradient layerError,
                             org.nd4j.linalg.api.ndarray.INDArray activation)

Description copied from interface: Layer

Calculate the gradient

Specified by:

calcGradient in interface Layer

Overrides:

calcGradient in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

layerError - the layer error

Returns:

the gradient

backpropGradient

public org.nd4j.linalg.primitives.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

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

fit

public void fit()

Description copied from interface: Model

All models have a fit method

Specified by:

fit in interface Model

Overrides:

fit in class AbstractLayer<LayerConfT extends BaseLayer>

computeGradientAndScore

public void computeGradientAndScore()

Description copied from interface: Model

Update the score

Specified by:

computeGradientAndScore in interface Model

Overrides:

computeGradientAndScore in class AbstractLayer<LayerConfT extends BaseLayer>

setScoreWithZ

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

preOutput

public org.nd4j.linalg.api.ndarray.INDArray preOutput(org.nd4j.linalg.api.ndarray.INDArray x,
                                                      Layer.TrainingMode training)

Description copied from interface: Layer

Raw activations

Specified by:

preOutput in interface Layer

Overrides:

preOutput in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

x - the input to transform

Returns:

the raw activation for this layer

activate

public org.nd4j.linalg.api.ndarray.INDArray activate(Layer.TrainingMode training)

Description copied from interface: Layer

Trigger an activation with the last specified input

Specified by:

activate in interface Layer

Overrides:

activate in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

training - training or test mode

Returns:

the activation of the last specified input

activate

public org.nd4j.linalg.api.ndarray.INDArray activate(org.nd4j.linalg.api.ndarray.INDArray input,
                                                     Layer.TrainingMode training)

Description copied from interface: Layer

Initialize the layer with the given input and return the activation for this layer given this input

Specified by:

activate in interface Layer

Overrides:

activate in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

input - the input to use

training - train or test mode

Returns:

score

public double score()

Objective function: the specified objective

Specified by:

score in interface Model

Overrides:

score in class AbstractLayer<LayerConfT extends BaseLayer>

Returns:

the score for the objective

gradient

public Gradient gradient()

Description copied from interface: Model

Calculate a gradient

Specified by:

gradient in interface Model

Overrides:

gradient in class AbstractLayer<LayerConfT extends BaseLayer>

Returns:

the gradient for this model

iterate

public void iterate(org.nd4j.linalg.api.ndarray.INDArray input)

iterate one iteration of the network

Specified by:

iterate in interface Model

Overrides:

iterate in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

input - the input to iterate on

update

public void update(Gradient gradient)

Description copied from interface: Model

Update layer weights and biases with gradient change

Specified by:

update in interface Model

Overrides:

update in class AbstractLayer<LayerConfT extends BaseLayer>

update

public void update(org.nd4j.linalg.api.ndarray.INDArray gradient,
                   String paramType)

Description copied from interface: Model

Perform one update applying the gradient

Specified by:

update in interface Model

Overrides:

update in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

gradient - the gradient to apply

getOptimizer

public ConvexOptimizer getOptimizer()

Description copied from interface: Model

Returns this models optimizer

Specified by:

getOptimizer in interface Model

Overrides:

getOptimizer in class AbstractLayer<LayerConfT extends BaseLayer>

Returns:

this models optimizer

params

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

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

Specified by:

params in interface Model

Overrides:

params in class AbstractLayer<LayerConfT extends BaseLayer>

Returns:

the parameters of the neural network

getParam

public org.nd4j.linalg.api.ndarray.INDArray getParam(String param)

Description copied from interface: Model

Get the parameter

Specified by:

getParam in interface Model

Overrides:

getParam in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

param - the key of the parameter

Returns:

the parameter vector/matrix with that particular key

setParam

public void setParam(String key,
                     org.nd4j.linalg.api.ndarray.INDArray val)

Description copied from interface: Model

Set the parameter with a new ndarray

Specified by:

setParam in interface Model

Overrides:

setParam in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

key - the key to se t

val - the new ndarray

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 AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

params - the parameters for the model

setParams

protected void setParams(org.nd4j.linalg.api.ndarray.INDArray params,
                         char order)

Overrides:

setParams in class AbstractLayer<LayerConfT extends BaseLayer>

setParamsViewArray

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

Description copied from interface: Model

Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.

Specified by:

setParamsViewArray in interface Model

Overrides:

setParamsViewArray in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

params - a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array

getGradientsViewArray

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

Specified by:

getGradientsViewArray in interface Model

Overrides:

getGradientsViewArray in class AbstractLayer<LayerConfT extends BaseLayer>

setBackpropGradientsViewArray

public void setBackpropGradientsViewArray(org.nd4j.linalg.api.ndarray.INDArray gradients)

Description copied from interface: Model

Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.

Specified by:

setBackpropGradientsViewArray in interface Model

Overrides:

setBackpropGradientsViewArray in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

gradients - a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array

setParamTable

public void setParamTable(Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable)

Description copied from interface: Model

Setter for the param table

Specified by:

setParamTable in interface Model

Overrides:

setParamTable in class AbstractLayer<LayerConfT extends BaseLayer>

initParams

public void initParams()

Description copied from interface: Model

Initialize the parameters

Specified by:

initParams in interface Model

Overrides:

initParams in class AbstractLayer<LayerConfT extends BaseLayer>

paramTable

public Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable()

Description copied from interface: Model

The param table

Specified by:

paramTable in interface Model

Overrides:

paramTable in class AbstractLayer<LayerConfT extends BaseLayer>

Returns:

paramTable

public Map<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 AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

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

preOutput

public org.nd4j.linalg.api.ndarray.INDArray preOutput(boolean training)

Specified by:

preOutput in class AbstractLayer<LayerConfT extends BaseLayer>

activate

public org.nd4j.linalg.api.ndarray.INDArray activate(boolean training)

Description copied from interface: Layer

Trigger an activation with the last specified input

Parameters:

training - training or test mode

Returns:

the activation of the last specified input

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 AbstractLayer<LayerConfT extends BaseLayer>

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 AbstractLayer<LayerConfT extends BaseLayer>

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.

activationMean

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

Description copied from interface: Layer

Calculate the mean representation for the activation for this layer

Returns:

the activation mean for this layer

merge

public void merge(Layer l,
                  int batchSize)

Averages the given logistic regression from a mini batch into this layer

Specified by:

merge in interface Layer

Overrides:

merge in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

l - the logistic regression layer to average into this layer

batchSize - the batch size

clone

public Layer clone()

Description copied from interface: Layer

Clone the layer

Specified by:

clone in interface Layer

Specified by:

clone in class AbstractLayer<LayerConfT extends BaseLayer>

Returns:

numParams

public int numParams()

The number of parameters for the model

Specified by:

numParams in interface Model

Overrides:

numParams in class AbstractLayer<LayerConfT extends BaseLayer>

Returns:

the number of parameters for the model

fit

public void fit(org.nd4j.linalg.api.ndarray.INDArray input)

Description copied from interface: Model

Fit the model to the given data

Specified by:

fit in interface Model

Overrides:

fit in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

input - the data to fit the model to

toString

public String toString()

Overrides:

toString in class Object

transpose

public Layer transpose()

Description copied from interface: Layer

Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)

Specified by:

transpose in interface Layer

Overrides:

transpose in class AbstractLayer<LayerConfT extends BaseLayer>

Returns:

the transposed layer

accumulateScore

public void accumulateScore(double accum)

Description copied from interface: Model

Sets a rolling tally for the score. This is useful for mini batch learning when you are accumulating error across a dataset.

Specified by:

accumulateScore in interface Model

Overrides:

accumulateScore in class AbstractLayer<LayerConfT extends BaseLayer>

Parameters:

accum - the amount to accum

applyLearningRateScoreDecay

public void applyLearningRateScoreDecay()

Description copied from interface: Model

Update learningRate using for this model. Use the learningRateScoreBasedDecay to adapt the score if the Eps termination condition is met

Specified by:

applyLearningRateScoreDecay in interface Model

Overrides:

applyLearningRateScoreDecay in class AbstractLayer<LayerConfT extends BaseLayer>