org.deeplearning4j.nn.conf.layers

Class FeedForwardLayer

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.FeedForwardLayer

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

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

public abstract class FeedForwardLayer
extends Layer

Created by jeffreytang on 7/21/15.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	FeedForwardLayer.Builder<T extends FeedForwardLayer.Builder<T>>

Field Summary

Fields

Modifier and Type	Field and Description
protected int	nIn
protected int	nOut

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer

activationFn, adamMeanDecay, adamVarDecay, biasInit, biasLearningRate, dist, dropOut, epsilon, gradientNormalization, gradientNormalizationThreshold, l1, l1Bias, l2, l2Bias, layerName, learningRate, learningRateSchedule, momentum, momentumSchedule, rho, rmsDecay, updater, weightInit

Constructor Summary

Constructors

Constructor and Description
FeedForwardLayer(FeedForwardLayer.Builder builder)

Method Summary

Modifier and Type	Method and Description
double	getL1ByParam(java.lang.String paramName) Get the L1 coefficient for the given parameter.
double	getL2ByParam(java.lang.String paramName) Get the L2 coefficient for the given parameter.
double	getLearningRateByParam(java.lang.String paramName) Get the (initial) learning rate coefficient for the given parameter.
InputType	getOutputType(int layerIndex, InputType inputType) For a given type of input to this layer, what is the type of the output?
InputPreProcessor	getPreProcessorForInputType(InputType inputType) For the given type of input to this layer, what preprocessor (if any) is required? Returns null if no preprocessor is required, otherwise returns an appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor
void	setNIn(InputType inputType, boolean override) Set the nIn value (number of inputs, or input depth for CNNs) based on the given input type

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer

clone, getUpdaterByParam, initializer, instantiate, resetLayerDefaultConfig

Methods inherited from class java.lang.Object

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

Field Detail

nIn

protected int nIn

nOut

protected int nOut

Constructor Detail

FeedForwardLayer

public FeedForwardLayer(FeedForwardLayer.Builder builder)

Method Detail

getOutputType

public InputType getOutputType(int layerIndex,
                               InputType inputType)

Description copied from class: Layer

For a given type of input to this layer, what is the type of the output?

Specified by:

getOutputType in class Layer

Parameters:

layerIndex - Index of the layer

inputType - Type of input for the layer

Returns:

Type of output from the layer

setNIn

public void setNIn(InputType inputType,
                   boolean override)

Description copied from class: Layer

Set the nIn value (number of inputs, or input depth for CNNs) based on the given input type

Specified by:

setNIn in class Layer

Parameters:

inputType - Input type for this layer

override - If false: only set the nIn value if it's not already set. If true: set it regardless of whether it's already set or not.

getPreProcessorForInputType

public InputPreProcessor getPreProcessorForInputType(InputType inputType)

Description copied from class: Layer

For the given type of input to this layer, what preprocessor (if any) is required?
Returns null if no preprocessor is required, otherwise returns an appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor

Specified by:

getPreProcessorForInputType in class Layer

Parameters:

inputType - InputType to this layer

Returns:

Null if no preprocessor is required, otherwise the type of preprocessor necessary for this layer/input combination

getL1ByParam

public double getL1ByParam(java.lang.String paramName)

Description copied from class: Layer

Get the L1 coefficient for the given parameter. Different parameters may have different L1 values, even for a single .l1(x) configuration. For example, biases generally aren't L1 regularized, even if weights are

Specified by:

getL1ByParam in class Layer

Parameters:

paramName - Parameter name

Returns:

L1 value for that parameter

getL2ByParam

public double getL2ByParam(java.lang.String paramName)

Description copied from class: Layer

Get the L2 coefficient for the given parameter. Different parameters may have different L2 values, even for a single .l2(x) configuration. For example, biases generally aren't L1 regularized, even if weights are

Specified by:

getL2ByParam in class Layer

Parameters:

paramName - Parameter name

Returns:

L2 value for that parameter

getLearningRateByParam

public double getLearningRateByParam(java.lang.String paramName)

Description copied from class: Layer

Get the (initial) learning rate coefficient for the given parameter. Different parameters may be configured to have different learning rates, though commonly all parameters will have the same learning rate

Specified by:

getLearningRateByParam in class Layer

Parameters:

paramName - Parameter name

Returns:

Initial learning rate value for that parameter