What is a convolutional neural network?

Each layer in a neural network configuration represents a unit of hidden units. When layers are stacked together, they represent a deep neural network.

Available layers

Convolution1D

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1D convolution layer. Expects input activations of shape [minibatch,channels,sequenceLength]

Convolution2D

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2D convolution layer

Convolution3D

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3D convolution layer configuration

hasBias

public boolean hasBias() 

An optional dataFormat: “NDHWC” or “NCDHW”. Defaults to “NCDHW”. The data format of the input and outputdata. For “NCDHW” (also known as ‘channels first’ format), the data storage order is: [batchSize,inputChannels, inputDepth, inputHeight, inputWidth]. For “NDHWC” (‘channels last’ format), the data is storedin the order of: [batchSize, inputDepth, inputHeight, inputWidth, inputChannels].

kernelSize

public Builder kernelSize(int... kernelSize) 

The data format for input and output activations. NCDHW: activations (in/out) should have shape[minibatch, channels, depth, height, width] NDHWC: activations (in/out) should have shape [minibatch,depth, height, width, channels]

stride

public Builder stride(int... stride) 

Set stride size for 3D convolutions in (depth, height, width) order

• param stride kernel size
• return 3D convolution layer builder

padding

public Builder padding(int... padding) 

Set padding size for 3D convolutions in (depth, height, width) order

• param padding kernel size
• return 3D convolution layer builder

dilation

public Builder dilation(int... dilation) 

Set dilation size for 3D convolutions in (depth, height, width) order

• param dilation kernel size
• return 3D convolution layer builder

dataFormat

public Builder dataFormat(DataFormat dataFormat) 

The data format for input and output activations. NCDHW: activations (in/out) should have shape[minibatch, channels, depth, height, width] NDHWC: activations (in/out) should have shape [minibatch,depth, height, width, channels]

• param dataFormat Data format to use for activations

setKernelSize

public void setKernelSize(int... kernelSize) 

Set kernel size for 3D convolutions in (depth, height, width) order

• param kernelSize kernel size

setStride

public void setStride(int... stride) 

Set stride size for 3D convolutions in (depth, height, width) order

• param stride kernel size

setPadding

public void setPadding(int... padding) 

Set padding size for 3D convolutions in (depth, height, width) order

• param padding kernel size

setDilation

public void setDilation(int... dilation) 

Set dilation size for 3D convolutions in (depth, height, width) order

• param dilation kernel size

Deconvolution2D

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2D deconvolution layer configuration

Deconvolutions are also known as transpose convolutions or fractionally strided convolutions. In essence,deconvolutions swap forward and backward pass with regular 2D convolutions.

See the paper by Matt Zeiler for details: http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf

For an intuitive guide to convolution arithmetic and shapes, see:https://arxiv.org/abs/1603.07285v1

hasBias

public boolean hasBias() 

Deconvolution2D layer nIn in the input layer is the number of channels nOut is the number of filters to be usedin the net or in other words the channels The builder specifies the filter/kernel size, the stride and paddingThe pooling layer takes the kernel size

convolutionMode

public Builder convolutionMode(ConvolutionMode convolutionMode) 

Set the convolution mode for the Convolution layer. See {- link ConvolutionMode} for more details

• param convolutionMode Convolution mode for layer

kernelSize

public Builder kernelSize(int... kernelSize) 

Size of the convolution rows/columns

• param kernelSize the height and width of the kernel

Cropping1D

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Cropping layer for convolutional (1d) neural networks. Allows cropping to be done separately for top/bottom

getOutputType

public InputType getOutputType(int layerIndex, InputType inputType) 

• param cropTopBottom Amount of cropping to apply to both the top and the bottom of the input activations

setCropping

public void setCropping(int... cropping) 

Cropping amount for top/bottom (in that order). Must be length 1 or 2 array.

build

public Cropping1D build() 

• param cropping Cropping amount for top/bottom (in that order). Must be length 1 or 2 array.

Cropping2D

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Cropping layer for convolutional (2d) neural networks. Allows cropping to be done separately fortop/bottom/left/right

getOutputType

public InputType getOutputType(int layerIndex, InputType inputType) 

• param cropTopBottom Amount of cropping to apply to both the top and the bottom of the input activations
• param cropLeftRight Amount of cropping to apply to both the left and the right of the input activations

setCropping

public void setCropping(int... cropping) 

Cropping amount for top/bottom/left/right (in that order). A length 4 array.

build

public Cropping2D build() 

• param cropping Cropping amount for top/bottom/left/right (in that order). Must be length 4 array.

Cropping3D

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Cropping layer for convolutional (3d) neural networks. Allows cropping to be done separately for upper and lowerbounds of depth, height and width dimensions.

getOutputType

public InputType getOutputType(int layerIndex, InputType inputType) 

• param cropDepth Amount of cropping to apply to both depth boundaries of the input activations
• param cropHeight Amount of cropping to apply to both height boundaries of the input activations
• param cropWidth Amount of cropping to apply to both width boundaries of the input activations

setCropping

public void setCropping(int... cropping) 

Cropping amount, a length 6 array, i.e. crop left depth, crop right depth, crop left height, crop right height, crop left width, crop right width

build

public Cropping3D build() 

• param cropping Cropping amount, must be length 3 or 6 array, i.e. either crop depth, crop height, crop widthor crop left depth, crop right depth, crop left height, crop right height, crop left width, crop right width