Package org.deeplearning4j.nn.conf.inputs

Class InputType

java.lang.Object

org.deeplearning4j.nn.conf.inputs.InputType

All Implemented Interfaces:

Serializable

Direct Known Subclasses:

InputType.InputTypeConvolutional, InputType.InputTypeConvolutional3D, InputType.InputTypeConvolutionalFlat, InputType.InputTypeFeedForward, InputType.InputTypeRecurrent

public abstract class InputType
extends Object
implements Serializable

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	InputType.InputTypeConvolutional
static class	InputType.InputTypeConvolutional3D
static class	InputType.InputTypeConvolutionalFlat
static class	InputType.InputTypeFeedForward
static class	InputType.InputTypeRecurrent
static class	InputType.Type	The type of activations in/out of a given GraphVertex FF: Standard feed-foward (2d minibatch, 1d per example) data RNN: Recurrent neural network (3d minibatch) time series data CNN: 2D Convolutional neural network (4d minibatch, [miniBatchSize, channels, height, width]) CNNFlat: Flattened 2D conv net data (2d minibatch, [miniBatchSize, height * width * channels]) CNN3D: 3D convolutional neural network (5d minibatch, [miniBatchSize, channels, height, width, channels])

Constructor Summary

Constructors

Constructor	Description
InputType()

Method Summary

Modifier and Type	Method	Description
abstract long	arrayElementsPerExample()
static InputType	convolutional(long height, long width, long depth)	Input type for convolutional (CNN) data, that is 4d with shape [miniBatchSize, channels, height, width].
static InputType	convolutional(long height, long width, long depth, CNN2DFormat format)
static InputType	convolutional3D(long depth, long height, long width, long channels)	Deprecated. Use convolutional3D(Convolution3D.DataFormat, long, long, long, long)
static InputType	convolutional3D(Convolution3D.DataFormat dataFormat, long depth, long height, long width, long channels)	Input type for 3D convolutional (CNN3D) 5d data: If NDHWC format [miniBatchSize, depth, height, width, channels] If NDCWH
static InputType	convolutionalFlat(long height, long width, long depth)	Input type for convolutional (CNN) data, where the data is in flattened (row vector) format.
static InputType	feedForward(long size)	InputType for feed forward network data
static InputType	feedForward(long size, DataFormat timeDistributedFormat)
static CNN2DFormat	getDefaultCNN2DFormat()
long[]	getShape()	Returns the shape of this InputType without minibatch dimension in the returned array
abstract long[]	getShape(boolean includeBatchDim)	Returns the shape of this InputType
abstract InputType.Type	getType()
static InputType	inferInputType(INDArray inputArray)
static InputType[]	inferInputTypes(INDArray... inputArrays)
static InputType	recurrent(long size)	InputType for recurrent neural network (time series) data
static InputType	recurrent(long size, long timeSeriesLength)	InputType for recurrent neural network (time series) data
static InputType	recurrent(long size, long timeSeriesLength, RNNFormat format)
static InputType	recurrent(long size, RNNFormat format)
static void	setDefaultCNN2DFormat(CNN2DFormat defaultCNN2DFormat)
abstract String	toString()

Methods inherited from class java.lang.Object

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

Constructor Detail

InputType

public InputType()

Method Detail

getDefaultCNN2DFormat

public static CNN2DFormat getDefaultCNN2DFormat()

setDefaultCNN2DFormat

public static void setDefaultCNN2DFormat(CNN2DFormat defaultCNN2DFormat)

getType

public abstract InputType.Type getType()

toString

public abstract String toString()

Overrides:

toString in class Object

arrayElementsPerExample

public abstract long arrayElementsPerExample()

getShape

public abstract long[] getShape(boolean includeBatchDim)

Returns the shape of this InputType

Parameters:

includeBatchDim - Whether to include minibatch in the return shape array

Returns:

int[]

getShape

public long[] getShape()

Returns the shape of this InputType without minibatch dimension in the returned array

Returns:

int[]

feedForward

public static InputType feedForward(long size)

InputType for feed forward network data

Parameters:

size - The size of the activations

Returns:

InputTypeFeedForward

feedForward

public static InputType feedForward(long size,
                                    DataFormat timeDistributedFormat)

recurrent

public static InputType recurrent(long size)

InputType for recurrent neural network (time series) data

Parameters:

size - The size of the activations

Returns:

InputTypeRecurrent

recurrent

public static InputType recurrent(long size,
                                  long timeSeriesLength)

InputType for recurrent neural network (time series) data

Parameters:

size - The size of the activations

timeSeriesLength - Length of the input time series

Returns:

InputTypeRecurrent

recurrent

public static InputType recurrent(long size,
                                  RNNFormat format)

recurrent

public static InputType recurrent(long size,
                                  long timeSeriesLength,
                                  RNNFormat format)

convolutional

public static InputType convolutional(long height,
                                      long width,
                                      long depth)

Input type for convolutional (CNN) data, that is 4d with shape [miniBatchSize, channels, height, width]. For CNN data that has been flattened, use convolutionalFlat(long, long, long)

Parameters:

height - height of the input

width - Width of the input

depth - Depth, or number of channels

Returns:

InputTypeConvolutional

convolutional

public static InputType convolutional(long height,
                                      long width,
                                      long depth,
                                      CNN2DFormat format)

convolutional3D

@Deprecated
public static InputType convolutional3D(long depth,
                                        long height,
                                        long width,
                                        long channels)

Deprecated.

Use convolutional3D(Convolution3D.DataFormat, long, long, long, long)

Input type for 3D convolutional (CNN3D) data in NDHWC format, that is 5d with shape [miniBatchSize, depth, height, width, channels].

Parameters:

height - height of the input

width - Width of the input

depth - Depth of the input

channels - Number of channels of the input

Returns:

InputTypeConvolutional3D

convolutional3D

public static InputType convolutional3D(Convolution3D.DataFormat dataFormat,
                                        long depth,
                                        long height,
                                        long width,
                                        long channels)

Input type for 3D convolutional (CNN3D) 5d data:
If NDHWC format [miniBatchSize, depth, height, width, channels]
If NDCWH

Parameters:

height - height of the input

width - Width of the input

depth - Depth of the input

channels - Number of channels of the input

Returns:

InputTypeConvolutional3D

convolutionalFlat

public static InputType convolutionalFlat(long height,
                                          long width,
                                          long depth)

Input type for convolutional (CNN) data, where the data is in flattened (row vector) format. Expect data with shape [miniBatchSize, height * width * channels]. For CNN data in 4d format, use convolutional(long, long, long)

Parameters:

height - Height of the (unflattened) data represented by this input type

width - Width of the (unflattened) data represented by this input type

depth - Depth of the (unflattened) data represented by this input type

Returns:

InputTypeConvolutionalFlat

inferInputType

public static InputType inferInputType(INDArray inputArray)

inferInputTypes

public static InputType[] inferInputTypes(INDArray... inputArrays)