org.deeplearning4j.nn.layers

Class FrozenLayer

java.lang.Object

org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

org.deeplearning4j.nn.layers.FrozenLayer

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model, Trainable

public class FrozenLayer
extends BaseWrapperLayer

For purposes of transfer learning A frozen layers wraps another dl4j layer within it. The params of the layer within it are "frozen" or in other words held constant During the forward pass the frozen layer behaves as the layer within it would during test regardless of the training/test mode the network is in. Backprop is skipped since parameters are not be updated.

Author:

susaneraly

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 inherited from class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

underlying

Constructor Summary

Constructors

Constructor and Description
FrozenLayer(Layer insideLayer)

Method Summary

Modifier and Type	Method and Description
INDArray	activate(boolean training, LayerWorkspaceMgr workspaceMgr) Perform forward pass and return the activations array with the last set input
INDArray	activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr) Perform forward pass and return the activations array with the specified input
void	applyConstraints(int iteration, int epoch) Apply any constraints to the model
Pair<Gradient,INDArray>	backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) Calculate the gradient relative to the error in the next layer
double	calcRegularizationScore(boolean backpropParamsOnly) Calculate the regularization component of the score, for the parameters in this layer For example, the L1, L2 and/or weight decay components of the loss function
void	computeGradientAndScore(LayerWorkspaceMgr workspaceMgr) Update the score
void	fit() All models have a fit method
void	fit(INDArray data, LayerWorkspaceMgr workspaceMgr) Fit the model to the given data
TrainingConfig	getConfig()
Layer	getInsideLayer()
Gradient	gradient() Get the gradient.
Pair<Gradient,Double>	gradientAndScore() Get the gradient and score
void	init() Init the model
protected String	layerId()
void	logTestMode(boolean training)
void	logTestMode(Layer.TrainingMode training)
void	setBackpropGradientsViewArray(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	setCacheMode(CacheMode mode) This method sets given CacheMode for current layer
void	update(Gradient gradient) Update layer weights and biases with gradient change
void	update(INDArray gradient, String paramType) Perform one update applying the gradient

Methods inherited from class org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

addListeners, allowInputModification, batchSize, clear, clearNoiseWeightParams, close, conf, feedForwardMaskArray, getEpochCount, getGradientsViewArray, getHelper, getIndex, getInputMiniBatchSize, getIterationCount, getListeners, getMaskArray, getOptimizer, getParam, input, isPretrainLayer, numParams, numParams, params, paramTable, paramTable, score, setConf, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setIterationCount, setListeners, setListeners, setMaskArray, setParam, setParams, setParamsViewArray, setParamTable, type, updaterDivideByMinibatch

Methods inherited from class java.lang.Object

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

Constructor Detail

FrozenLayer

public FrozenLayer(Layer insideLayer)

Method Detail

setCacheMode

public void setCacheMode(CacheMode mode)

Description copied from interface: Layer

This method sets given CacheMode for current layer

Specified by:

setCacheMode in interface Layer

Overrides:

setCacheMode in class BaseWrapperLayer

layerId

protected String layerId()

calcRegularizationScore

public double calcRegularizationScore(boolean backpropParamsOnly)

Description copied from interface: Layer

Calculate the regularization component of the score, for the parameters in this layer
For example, the L1, L2 and/or weight decay components of the loss function

Specified by:

calcRegularizationScore in interface Layer

Overrides:

calcRegularizationScore in class BaseWrapperLayer

Parameters:

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

Returns:

the regularization score of

backpropGradient

public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon,
                                                LayerWorkspaceMgr workspaceMgr)

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 BaseWrapperLayer

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.

workspaceMgr - Workspace manager

Returns:

Pair where Gradient is gradient for this layer, INDArray is epsilon (activation gradient) 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() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the ArrayType.ACTIVATION_GRAD workspace via the workspace manager

activate

public INDArray activate(boolean training,
                         LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Perform forward pass and return the activations array with the last set input

Specified by:

activate in interface Layer

Overrides:

activate in class BaseWrapperLayer

Parameters:

training - training or test mode

workspaceMgr - Workspace manager

Returns:

the activation (layer output) of the last specified input. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager

activate

public INDArray activate(INDArray input,
                         boolean training,
                         LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Perform forward pass and return the activations array with the specified input

Specified by:

activate in interface Layer

Overrides:

activate in class BaseWrapperLayer

Parameters:

input - the input to use

training - train or test mode

workspaceMgr - Workspace manager.

Returns:

Activations array. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager

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 BaseWrapperLayer

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 BaseWrapperLayer

update

public void update(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 BaseWrapperLayer

Parameters:

gradient - the gradient to apply

computeGradientAndScore

public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Model

Update the score

Specified by:

computeGradientAndScore in interface Model

Overrides:

computeGradientAndScore in class BaseWrapperLayer

setBackpropGradientsViewArray

public void setBackpropGradientsViewArray(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 BaseWrapperLayer

Parameters:

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

fit

public void fit(INDArray data,
                LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Model

Fit the model to the given data

Specified by:

fit in interface Model

Overrides:

fit in class BaseWrapperLayer

Parameters:

data - the data to fit the model to

gradient

public Gradient gradient()

Description copied from interface: Model

Get the gradient. Note that this method will not calculate the gradient, it will rather return the gradient that has been computed before. For calculating the gradient, see Model.computeGradientAndScore(LayerWorkspaceMgr) } .

Specified by:

gradient in interface Model

Overrides:

gradient in class BaseWrapperLayer

Returns:

the gradient for this model, as calculated before

gradientAndScore

public Pair<Gradient,Double> gradientAndScore()

Description copied from interface: Model

Get the gradient and score

Specified by:

gradientAndScore in interface Model

Overrides:

gradientAndScore in class BaseWrapperLayer

Returns:

the gradient and score

applyConstraints

public void applyConstraints(int iteration,
                             int epoch)

Description copied from interface: Model

Apply any constraints to the model

Specified by:

applyConstraints in interface Model

Overrides:

applyConstraints in class BaseWrapperLayer

init

public void init()

Init the model

Specified by:

init in interface Model

Overrides:

init in class BaseWrapperLayer

logTestMode

public void logTestMode(boolean training)

logTestMode

public void logTestMode(Layer.TrainingMode training)

getInsideLayer

public Layer getInsideLayer()

getConfig

public TrainingConfig getConfig()

Specified by:

getConfig in interface Trainable

Overrides:

getConfig in class BaseWrapperLayer

Returns:

Training configuration