org.deeplearning4j.nn.conf.layers

Class ConvolutionLayer

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.FeedForwardLayer

org.deeplearning4j.nn.conf.layers.ConvolutionLayer

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

Convolution1DLayer

public class ConvolutionLayer
extends FeedForwardLayer

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ConvolutionLayer.AlgoMode
protected static class	ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>>
static class	ConvolutionLayer.Builder

Field Summary

Fields

Modifier and Type	Field and Description
protected ConvolutionMode	convolutionMode
protected ConvolutionLayer.AlgoMode	cudnnAlgoMode Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
protected int[]	kernelSize
protected int[]	padding
protected int[]	stride

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

nIn, 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

Modifier	Constructor and Description
protected	ConvolutionLayer(ConvolutionLayer.BaseConvBuilder<?> builder) ConvolutionLayer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the depth The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size

Method Summary

Modifier and Type	Method and Description
ConvolutionLayer	clone()
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
ParamInitializer	initializer()
Layer	instantiate(NeuralNetConfiguration conf, java.util.Collection<IterationListener> iterationListeners, int layerIndex, org.nd4j.linalg.api.ndarray.INDArray layerParamsView, boolean initializeParams)
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

getUpdaterByParam, resetLayerDefaultConfig

Methods inherited from class java.lang.Object

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

Field Detail

convolutionMode

protected ConvolutionMode convolutionMode

kernelSize

protected int[] kernelSize

stride

protected int[] stride

padding

protected int[] padding

cudnnAlgoMode

protected ConvolutionLayer.AlgoMode cudnnAlgoMode

Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.

Constructor Detail

ConvolutionLayer

protected ConvolutionLayer(ConvolutionLayer.BaseConvBuilder<?> builder)

ConvolutionLayer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the depth The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size

Method Detail

clone

public ConvolutionLayer clone()

Overrides:

clone in class Layer

instantiate

public Layer instantiate(NeuralNetConfiguration conf,
                         java.util.Collection<IterationListener> iterationListeners,
                         int layerIndex,
                         org.nd4j.linalg.api.ndarray.INDArray layerParamsView,
                         boolean initializeParams)

Specified by:

instantiate in class Layer

initializer

public ParamInitializer initializer()

Specified by:

initializer in class Layer

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?

Overrides:

getOutputType in class FeedForwardLayer

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

Overrides:

setNIn in class FeedForwardLayer

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

Overrides:

getPreProcessorForInputType in class FeedForwardLayer

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

Overrides:

getL1ByParam in class FeedForwardLayer

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

Overrides:

getL2ByParam in class FeedForwardLayer

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

Overrides:

getLearningRateByParam in class FeedForwardLayer

Parameters:

paramName - Parameter name

Returns:

Initial learning rate value for that parameter