Convolutional Neural Network
Also known as CNN.
1D convolution layer. Expects input activations of shape [minibatch,channels,sequenceLength]
2D convolution layer
3D convolution layer configuration
hasBias
public boolean hasBias()
An optional dataFormat: “NDHWC” or “NCDHW”. Defaults to “NCDHW ”.
The data format of the input and output data.
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 stored in 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
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 used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The 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
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.
Cropping layer for convolutional (2d) neural networks. Allows cropping to be done separately for top/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.
Cropping layer for convolutional (3d) neural networks. Allows cropping to be done separately for upper and lower bounds 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 width or crop left depth, crop right depth, crop left height, crop right height, crop left width, crop right width
Last modified 3yr ago