Convolutional Neural Network
Also known as CNN.
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Also known as CNN.
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1D convolution layer. Expects input activations of shape [minibatch,channels,sequenceLength]
2D convolution layer
3D convolution layer configuration
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
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
Set stride size for 3D convolutions in (depth, height, width) order
param stride kernel size
return 3D convolution layer builder
padding
Set padding size for 3D convolutions in (depth, height, width) order
param padding kernel size
return 3D convolution layer builder
dilation
Set dilation size for 3D convolutions in (depth, height, width) order
param dilation kernel size
return 3D convolution layer builder
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
Set kernel size for 3D convolutions in (depth, height, width) order
param kernelSize kernel size
setStride
Set stride size for 3D convolutions in (depth, height, width) order
param stride kernel size
setPadding
Set padding size for 3D convolutions in (depth, height, width) order
param padding kernel size
setDilation
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.
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
Set the convolution mode for the Convolution layer. See {- link ConvolutionMode} for more details
param convolutionMode Convolution mode for layer
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
param cropTopBottom Amount of cropping to apply to both the top and the bottom of the input activations
setCropping
Cropping amount for top/bottom (in that order). Must be length 1 or 2 array.
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
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
Cropping amount for top/bottom/left/right (in that order). A length 4 array.
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
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
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
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
See the paper by Matt Zeiler for details:
For an intuitive guide to convolution arithmetic and shapes, see: