org.nd4j.autodiff.samediff.ops

Class SDCNN

java.lang.Object

org.nd4j.autodiff.samediff.ops.SDOps

org.nd4j.autodiff.samediff.ops.SDCNN

public class SDCNN
extends SDOps

Field Summary

Fields inherited from class org.nd4j.autodiff.samediff.ops.SDOps

sd

Constructor Summary

Constructors

Constructor and Description
SDCNN(SameDiff sameDiff)

Method Summary

Modifier and Type	Method and Description
SDVariable	avgPooling2d(SDVariable input, Pooling2DConfig Pooling2DConfig) 2D Convolution layer operation - average pooling 2d
SDVariable	avgPooling2d(String name, SDVariable input, Pooling2DConfig Pooling2DConfig) 2D Convolution layer operation - average pooling 2d
SDVariable	avgPooling3d(SDVariable input, Pooling3DConfig Pooling3DConfig) 3D convolution layer operation - average pooling 3d
SDVariable	avgPooling3d(String name, SDVariable input, Pooling3DConfig Pooling3DConfig) 3D convolution layer operation - average pooling 3d
SDVariable	batchToSpace(SDVariable x, int[] blocks, int[] croppingTop, int... croppingBottom) Convolution 2d layer batch to space operation on 4d input. Reduces input batch dimension by rearranging data into a larger spatial dimensions
SDVariable	batchToSpace(String name, SDVariable x, int[] blocks, int[] croppingTop, int... croppingBottom) Convolution 2d layer batch to space operation on 4d input. Reduces input batch dimension by rearranging data into a larger spatial dimensions
SDVariable	col2Im(SDVariable in, Conv2DConfig Conv2DConfig) col2im operation for use in 2D convolution operations.
SDVariable	col2Im(String name, SDVariable in, Conv2DConfig Conv2DConfig) col2im operation for use in 2D convolution operations.
SDVariable	conv1d(SDVariable input, SDVariable weights, Conv1DConfig Conv1DConfig) Conv1d operation.
SDVariable	conv1d(SDVariable input, SDVariable weights, SDVariable bias, Conv1DConfig Conv1DConfig) Conv1d operation.
SDVariable	conv1d(String name, SDVariable input, SDVariable weights, Conv1DConfig Conv1DConfig) Conv1d operation.
SDVariable	conv1d(String name, SDVariable input, SDVariable weights, SDVariable bias, Conv1DConfig Conv1DConfig) Conv1d operation.
SDVariable	conv2d(SDVariable layerInput, SDVariable weights, Conv2DConfig Conv2DConfig) 2D Convolution operation with optional bias
SDVariable	conv2d(SDVariable layerInput, SDVariable weights, SDVariable bias, Conv2DConfig Conv2DConfig) 2D Convolution operation with optional bias
SDVariable	conv2d(String name, SDVariable layerInput, SDVariable weights, Conv2DConfig Conv2DConfig) 2D Convolution operation with optional bias
SDVariable	conv2d(String name, SDVariable layerInput, SDVariable weights, SDVariable bias, Conv2DConfig Conv2DConfig) 2D Convolution operation with optional bias
SDVariable	conv3d(SDVariable input, SDVariable weights, Conv3DConfig Conv3DConfig) Convolution 3D operation with optional bias
SDVariable	conv3d(SDVariable input, SDVariable weights, SDVariable bias, Conv3DConfig Conv3DConfig) Convolution 3D operation with optional bias
SDVariable	conv3d(String name, SDVariable input, SDVariable weights, Conv3DConfig Conv3DConfig) Convolution 3D operation with optional bias
SDVariable	conv3d(String name, SDVariable input, SDVariable weights, SDVariable bias, Conv3DConfig Conv3DConfig) Convolution 3D operation with optional bias
SDVariable	deconv2d(SDVariable layerInput, SDVariable weights, DeConv2DConfig DeConv2DConfig) 2D deconvolution operation with optional bias
SDVariable	deconv2d(SDVariable layerInput, SDVariable weights, SDVariable bias, DeConv2DConfig DeConv2DConfig) 2D deconvolution operation with optional bias
SDVariable	deconv2d(String name, SDVariable layerInput, SDVariable weights, DeConv2DConfig DeConv2DConfig) 2D deconvolution operation with optional bias
SDVariable	deconv2d(String name, SDVariable layerInput, SDVariable weights, SDVariable bias, DeConv2DConfig DeConv2DConfig) 2D deconvolution operation with optional bias
SDVariable	deconv3d(SDVariable input, SDVariable weights, DeConv3DConfig DeConv3DConfig) 3D CNN deconvolution operation with or without optional bias
SDVariable	deconv3d(SDVariable input, SDVariable weights, SDVariable bias, DeConv3DConfig DeConv3DConfig) 3D CNN deconvolution operation with or without optional bias
SDVariable	deconv3d(String name, SDVariable input, SDVariable weights, DeConv3DConfig DeConv3DConfig) 3D CNN deconvolution operation with or without optional bias
SDVariable	deconv3d(String name, SDVariable input, SDVariable weights, SDVariable bias, DeConv3DConfig DeConv3DConfig) 3D CNN deconvolution operation with or without optional bias
SDVariable	depthToSpace(SDVariable x, int blockSize, DataFormat dataFormat) Convolution 2d layer batch to space operation on 4d input. Reduces input channels dimension by rearranging data into a larger spatial dimensions Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2] = [mb, 2, 4, 4]
SDVariable	depthToSpace(String name, SDVariable x, int blockSize, DataFormat dataFormat) Convolution 2d layer batch to space operation on 4d input. Reduces input channels dimension by rearranging data into a larger spatial dimensions Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2] = [mb, 2, 4, 4]
SDVariable	depthWiseConv2d(SDVariable layerInput, SDVariable depthWeights, Conv2DConfig Conv2DConfig) Depth-wise 2D convolution operation with optional bias
SDVariable	depthWiseConv2d(SDVariable layerInput, SDVariable depthWeights, SDVariable bias, Conv2DConfig Conv2DConfig) Depth-wise 2D convolution operation with optional bias
SDVariable	depthWiseConv2d(String name, SDVariable layerInput, SDVariable depthWeights, Conv2DConfig Conv2DConfig) Depth-wise 2D convolution operation with optional bias
SDVariable	depthWiseConv2d(String name, SDVariable layerInput, SDVariable depthWeights, SDVariable bias, Conv2DConfig Conv2DConfig) Depth-wise 2D convolution operation with optional bias
SDVariable	dilation2D(SDVariable df, SDVariable weights, int[] strides, int[] rates, boolean isSameMode) TODO doc string
SDVariable	dilation2D(String name, SDVariable df, SDVariable weights, int[] strides, int[] rates, boolean isSameMode) TODO doc string
SDVariable	extractImagePatches(SDVariable input, int kH, int kW, int sH, int sW, int rH, int rW, boolean sameMode) Extract image patches
SDVariable	extractImagePatches(String name, SDVariable input, int kH, int kW, int sH, int sW, int rH, int rW, boolean sameMode) Extract image patches
SDVariable	im2Col(SDVariable in, Conv2DConfig Conv2DConfig) im2col operation for use in 2D convolution operations.
SDVariable	im2Col(String name, SDVariable in, Conv2DConfig Conv2DConfig) im2col operation for use in 2D convolution operations.
SDVariable	localResponseNormalization(SDVariable input, LocalResponseNormalizationConfig LocalResponseNormalizationConfig) 2D convolution layer operation - local response normalization
SDVariable	localResponseNormalization(String name, SDVariable input, LocalResponseNormalizationConfig LocalResponseNormalizationConfig) 2D convolution layer operation - local response normalization
SDVariable	maxPooling2d(SDVariable input, Pooling2DConfig Pooling2DConfig) 2D Convolution layer operation - max pooling 2d
SDVariable	maxPooling2d(String name, SDVariable input, Pooling2DConfig Pooling2DConfig) 2D Convolution layer operation - max pooling 2d
SDVariable	maxPooling3d(SDVariable input, Pooling3DConfig Pooling3DConfig) 3D convolution layer operation - max pooling 3d operation.
SDVariable	maxPooling3d(String name, SDVariable input, Pooling3DConfig Pooling3DConfig) 3D convolution layer operation - max pooling 3d operation.
SDVariable[]	maxPoolWithArgmax(SDVariable input, Pooling2DConfig Pooling2DConfig) 2D Convolution layer operation - Max pooling on the input and outputs both max values and indices
SDVariable[]	maxPoolWithArgmax(String[] names, SDVariable input, Pooling2DConfig Pooling2DConfig) 2D Convolution layer operation - Max pooling on the input and outputs both max values and indices
SDVariable	separableConv2d(SDVariable layerInput, SDVariable depthWeights, SDVariable pointWeights, Conv2DConfig Conv2DConfig) Separable 2D convolution operation with optional bias
SDVariable	separableConv2d(SDVariable layerInput, SDVariable depthWeights, SDVariable pointWeights, SDVariable bias, Conv2DConfig Conv2DConfig) Separable 2D convolution operation with optional bias
SDVariable	separableConv2d(String name, SDVariable layerInput, SDVariable depthWeights, SDVariable pointWeights, Conv2DConfig Conv2DConfig) Separable 2D convolution operation with optional bias
SDVariable	separableConv2d(String name, SDVariable layerInput, SDVariable depthWeights, SDVariable pointWeights, SDVariable bias, Conv2DConfig Conv2DConfig) Separable 2D convolution operation with optional bias
SDVariable	spaceToBatch(SDVariable x, int[] blocks, int[] paddingTop, int... paddingBottom) Convolution 2d layer space to batch operation on 4d input. Increases input batch dimension by rearranging data from spatial dimensions into batch dimension
SDVariable	spaceToBatch(String name, SDVariable x, int[] blocks, int[] paddingTop, int... paddingBottom) Convolution 2d layer space to batch operation on 4d input. Increases input batch dimension by rearranging data from spatial dimensions into batch dimension
SDVariable	spaceToDepth(SDVariable x, int blockSize, DataFormat dataFormat) Convolution 2d layer space to depth operation on 4d input. Increases input channels (reduced spatial dimensions) by rearranging data into a larger channels dimension Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2] = [mb, 2, 4, 4]
SDVariable	spaceToDepth(String name, SDVariable x, int blockSize, DataFormat dataFormat) Convolution 2d layer space to depth operation on 4d input. Increases input channels (reduced spatial dimensions) by rearranging data into a larger channels dimension Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2] = [mb, 2, 4, 4]
SDVariable	upsampling2d(SDVariable input, int scale) Upsampling layer for 2D inputs. scale is used for both height and width dimensions.
SDVariable	upsampling2d(SDVariable input, int scaleH, int scaleW, boolean nchw) 2D Convolution layer operation - Upsampling 2d
SDVariable	upsampling2d(String name, SDVariable input, int scale) Upsampling layer for 2D inputs. scale is used for both height and width dimensions.
SDVariable	upsampling2d(String name, SDVariable input, int scaleH, int scaleW, boolean nchw) 2D Convolution layer operation - Upsampling 2d
SDVariable	upsampling3d(SDVariable input, boolean ncdhw, int scaleD, int scaleH, int scaleW) 3D Convolution layer operation - Upsampling 3d
SDVariable	upsampling3d(String name, SDVariable input, boolean ncdhw, int scaleD, int scaleH, int scaleW) 3D Convolution layer operation - Upsampling 3d

Methods inherited from class java.lang.Object

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

Constructor Detail

SDCNN

public SDCNN(SameDiff sameDiff)

Method Detail

avgPooling2d

public SDVariable avgPooling2d(SDVariable input,
                               Pooling2DConfig Pooling2DConfig)

2D Convolution layer operation - average pooling 2d

Parameters:

input - the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

Pooling2DConfig - Configuration Object

Returns:

output Result after applying average pooling on the input (NUMERIC type)

avgPooling2d

public SDVariable avgPooling2d(String name,
                               SDVariable input,
                               Pooling2DConfig Pooling2DConfig)

2D Convolution layer operation - average pooling 2d

Parameters:

name - name May be null. Name for the output variable

input - the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

Pooling2DConfig - Configuration Object

Returns:

output Result after applying average pooling on the input (NUMERIC type)

avgPooling3d

public SDVariable avgPooling3d(SDVariable input,
                               Pooling3DConfig Pooling3DConfig)

3D convolution layer operation - average pooling 3d

Parameters:

input - the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)

Pooling3DConfig - Configuration Object

Returns:

output after applying average pooling on the input (NUMERIC type)

avgPooling3d

public SDVariable avgPooling3d(String name,
                               SDVariable input,
                               Pooling3DConfig Pooling3DConfig)

3D convolution layer operation - average pooling 3d

Parameters:

name - name May be null. Name for the output variable

input - the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)

Pooling3DConfig - Configuration Object

Returns:

output after applying average pooling on the input (NUMERIC type)

batchToSpace

public SDVariable batchToSpace(SDVariable x,
                               int[] blocks,
                               int[] croppingTop,
                               int... croppingBottom)

Convolution 2d layer batch to space operation on 4d input.
Reduces input batch dimension by rearranging data into a larger spatial dimensions

Parameters:

x - Input variable. 4d input (NUMERIC type)

blocks - Block size, in the height/width dimension (Size: Exactly(count=2))

croppingTop - (Size: Exactly(count=2))

croppingBottom - (Size: Exactly(count=2))

Returns:

output Output variable (NUMERIC type)

batchToSpace

public SDVariable batchToSpace(String name,
                               SDVariable x,
                               int[] blocks,
                               int[] croppingTop,
                               int... croppingBottom)

Convolution 2d layer batch to space operation on 4d input.
Reduces input batch dimension by rearranging data into a larger spatial dimensions

Parameters:

name - name May be null. Name for the output variable

x - Input variable. 4d input (NUMERIC type)

blocks - Block size, in the height/width dimension (Size: Exactly(count=2))

croppingTop - (Size: Exactly(count=2))

croppingBottom - (Size: Exactly(count=2))

Returns:

output Output variable (NUMERIC type)

col2Im

public SDVariable col2Im(SDVariable in,
                         Conv2DConfig Conv2DConfig)

col2im operation for use in 2D convolution operations. Outputs a 4d array with shape
[minibatch, inputChannels, height, width]

Parameters:

in - Input - rank 6 input with shape [minibatch, inputChannels, kernelHeight, kernelWidth, outputHeight, outputWidth] (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output Col2Im output variable (NUMERIC type)

col2Im

public SDVariable col2Im(String name,
                         SDVariable in,
                         Conv2DConfig Conv2DConfig)

col2im operation for use in 2D convolution operations. Outputs a 4d array with shape
[minibatch, inputChannels, height, width]

Parameters:

name - name May be null. Name for the output variable

in - Input - rank 6 input with shape [minibatch, inputChannels, kernelHeight, kernelWidth, outputHeight, outputWidth] (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output Col2Im output variable (NUMERIC type)

conv1d

public SDVariable conv1d(SDVariable input,
                         SDVariable weights,
                         SDVariable bias,
                         Conv1DConfig Conv1DConfig)

Conv1d operation.

Parameters:

input - the inputs to conv1d (NUMERIC type)

weights - weights for conv1d op - rank 3 array with shape [kernelSize, inputChannels, outputChannels] (NUMERIC type)

bias - bias for conv1d op - rank 1 array with shape [outputChannels]. May be null. (NUMERIC type)

Conv1DConfig - Configuration Object

Returns:

output result of conv1d op (NUMERIC type)

conv1d

public SDVariable conv1d(String name,
                         SDVariable input,
                         SDVariable weights,
                         SDVariable bias,
                         Conv1DConfig Conv1DConfig)

Conv1d operation.

Parameters:

name - name May be null. Name for the output variable

input - the inputs to conv1d (NUMERIC type)

weights - weights for conv1d op - rank 3 array with shape [kernelSize, inputChannels, outputChannels] (NUMERIC type)

bias - bias for conv1d op - rank 1 array with shape [outputChannels]. May be null. (NUMERIC type)

Conv1DConfig - Configuration Object

Returns:

output result of conv1d op (NUMERIC type)

conv1d

public SDVariable conv1d(SDVariable input,
                         SDVariable weights,
                         Conv1DConfig Conv1DConfig)

Conv1d operation.

Parameters:

input - the inputs to conv1d (NUMERIC type)

weights - weights for conv1d op - rank 3 array with shape [kernelSize, inputChannels, outputChannels] (NUMERIC type)

Conv1DConfig - Configuration Object

Returns:

output result of conv1d op (NUMERIC type)

conv1d

public SDVariable conv1d(String name,
                         SDVariable input,
                         SDVariable weights,
                         Conv1DConfig Conv1DConfig)

Conv1d operation.

Parameters:

name - name May be null. Name for the output variable

input - the inputs to conv1d (NUMERIC type)

weights - weights for conv1d op - rank 3 array with shape [kernelSize, inputChannels, outputChannels] (NUMERIC type)

Conv1DConfig - Configuration Object

Returns:

output result of conv1d op (NUMERIC type)

conv2d

public SDVariable conv2d(SDVariable layerInput,
                         SDVariable weights,
                         SDVariable bias,
                         Conv2DConfig Conv2DConfig)

2D Convolution operation with optional bias

Parameters:

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type)

weights - Weights for the convolution operation. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, outputChannels] (NUMERIC type)

bias - Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of conv2d op (NUMERIC type)

conv2d

public SDVariable conv2d(String name,
                         SDVariable layerInput,
                         SDVariable weights,
                         SDVariable bias,
                         Conv2DConfig Conv2DConfig)

2D Convolution operation with optional bias

Parameters:

name - name May be null. Name for the output variable

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type)

weights - Weights for the convolution operation. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, outputChannels] (NUMERIC type)

bias - Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of conv2d op (NUMERIC type)

conv2d

public SDVariable conv2d(SDVariable layerInput,
                         SDVariable weights,
                         Conv2DConfig Conv2DConfig)

2D Convolution operation with optional bias

Parameters:

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type)

weights - Weights for the convolution operation. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, outputChannels] (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of conv2d op (NUMERIC type)

conv2d

public SDVariable conv2d(String name,
                         SDVariable layerInput,
                         SDVariable weights,
                         Conv2DConfig Conv2DConfig)

2D Convolution operation with optional bias

Parameters:

name - name May be null. Name for the output variable

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type)

weights - Weights for the convolution operation. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, outputChannels] (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of conv2d op (NUMERIC type)

conv3d

public SDVariable conv3d(SDVariable input,
                         SDVariable weights,
                         SDVariable bias,
                         Conv3DConfig Conv3DConfig)

Convolution 3D operation with optional bias

Parameters:

input - the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)

weights - Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)

bias - Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)

Conv3DConfig - Configuration Object

Returns:

output Conv3d output variable (NUMERIC type)

conv3d

public SDVariable conv3d(String name,
                         SDVariable input,
                         SDVariable weights,
                         SDVariable bias,
                         Conv3DConfig Conv3DConfig)

Convolution 3D operation with optional bias

Parameters:

name - name May be null. Name for the output variable

input - the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)

weights - Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)

bias - Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)

Conv3DConfig - Configuration Object

Returns:

output Conv3d output variable (NUMERIC type)

conv3d

public SDVariable conv3d(SDVariable input,
                         SDVariable weights,
                         Conv3DConfig Conv3DConfig)

Convolution 3D operation with optional bias

Parameters:

input - the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)

weights - Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)

Conv3DConfig - Configuration Object

Returns:

output Conv3d output variable (NUMERIC type)

conv3d

public SDVariable conv3d(String name,
                         SDVariable input,
                         SDVariable weights,
                         Conv3DConfig Conv3DConfig)

Convolution 3D operation with optional bias

Parameters:

name - name May be null. Name for the output variable

input - the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)

weights - Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)

Conv3DConfig - Configuration Object

Returns:

output Conv3d output variable (NUMERIC type)

deconv2d

public SDVariable deconv2d(SDVariable layerInput,
                           SDVariable weights,
                           SDVariable bias,
                           DeConv2DConfig DeConv2DConfig)

2D deconvolution operation with optional bias

Parameters:

layerInput - the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

weights - Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)

bias - Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)

DeConv2DConfig - Configuration Object

Returns:

output result of deconv2d op (NUMERIC type)

deconv2d

public SDVariable deconv2d(String name,
                           SDVariable layerInput,
                           SDVariable weights,
                           SDVariable bias,
                           DeConv2DConfig DeConv2DConfig)

2D deconvolution operation with optional bias

Parameters:

name - name May be null. Name for the output variable

layerInput - the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

weights - Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)

bias - Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)

DeConv2DConfig - Configuration Object

Returns:

output result of deconv2d op (NUMERIC type)

deconv2d

public SDVariable deconv2d(SDVariable layerInput,
                           SDVariable weights,
                           DeConv2DConfig DeConv2DConfig)

2D deconvolution operation with optional bias

Parameters:

layerInput - the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

weights - Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)

DeConv2DConfig - Configuration Object

Returns:

output result of deconv2d op (NUMERIC type)

deconv2d

public SDVariable deconv2d(String name,
                           SDVariable layerInput,
                           SDVariable weights,
                           DeConv2DConfig DeConv2DConfig)

2D deconvolution operation with optional bias

Parameters:

name - name May be null. Name for the output variable

layerInput - the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

weights - Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)

DeConv2DConfig - Configuration Object

Returns:

output result of deconv2d op (NUMERIC type)

deconv3d

public SDVariable deconv3d(SDVariable input,
                           SDVariable weights,
                           SDVariable bias,
                           DeConv3DConfig DeConv3DConfig)

3D CNN deconvolution operation with or without optional bias

Parameters:

input - Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) (NUMERIC type)

weights - Weights array - shape [kD, kH, kW, oC, iC] (NUMERIC type)

bias - Bias array - optional, may be null. If non-null, must have shape [outputChannels] (NUMERIC type)

DeConv3DConfig - Configuration Object

Returns:

output result of 3D CNN deconvolution operation (NUMERIC type)

deconv3d

public SDVariable deconv3d(String name,
                           SDVariable input,
                           SDVariable weights,
                           SDVariable bias,
                           DeConv3DConfig DeConv3DConfig)

3D CNN deconvolution operation with or without optional bias

Parameters:

name - name May be null. Name for the output variable

input - Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) (NUMERIC type)

weights - Weights array - shape [kD, kH, kW, oC, iC] (NUMERIC type)

bias - Bias array - optional, may be null. If non-null, must have shape [outputChannels] (NUMERIC type)

DeConv3DConfig - Configuration Object

Returns:

output result of 3D CNN deconvolution operation (NUMERIC type)

deconv3d

public SDVariable deconv3d(SDVariable input,
                           SDVariable weights,
                           DeConv3DConfig DeConv3DConfig)

3D CNN deconvolution operation with or without optional bias

Parameters:

input - Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) (NUMERIC type)

weights - Weights array - shape [kD, kH, kW, oC, iC] (NUMERIC type)

DeConv3DConfig - Configuration Object

Returns:

output result of 3D CNN deconvolution operation (NUMERIC type)

deconv3d

public SDVariable deconv3d(String name,
                           SDVariable input,
                           SDVariable weights,
                           DeConv3DConfig DeConv3DConfig)

3D CNN deconvolution operation with or without optional bias

Parameters:

name - name May be null. Name for the output variable

input - Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW) (NUMERIC type)

weights - Weights array - shape [kD, kH, kW, oC, iC] (NUMERIC type)

DeConv3DConfig - Configuration Object

Returns:

output result of 3D CNN deconvolution operation (NUMERIC type)

depthToSpace

public SDVariable depthToSpace(SDVariable x,
                               int blockSize,
                               DataFormat dataFormat)

Convolution 2d layer batch to space operation on 4d input.
Reduces input channels dimension by rearranging data into a larger spatial dimensions
Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
= [mb, 2, 4, 4]

Parameters:

x - the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

blockSize - Block size, in the height/width dimension

dataFormat - Data format: "NCHW" or "NHWC"

Returns:

output Output variable (NUMERIC type)

depthToSpace

public SDVariable depthToSpace(String name,
                               SDVariable x,
                               int blockSize,
                               DataFormat dataFormat)

Convolution 2d layer batch to space operation on 4d input.
Reduces input channels dimension by rearranging data into a larger spatial dimensions
Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
= [mb, 2, 4, 4]

Parameters:

name - name May be null. Name for the output variable

x - the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

blockSize - Block size, in the height/width dimension

dataFormat - Data format: "NCHW" or "NHWC"

Returns:

output Output variable (NUMERIC type)

depthWiseConv2d

public SDVariable depthWiseConv2d(SDVariable layerInput,
                                  SDVariable depthWeights,
                                  SDVariable bias,
                                  Conv2DConfig Conv2DConfig)

Depth-wise 2D convolution operation with optional bias

Parameters:

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type)

depthWeights - Depth-wise conv2d weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)

bias - Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of depthwise conv2d op (NUMERIC type)

depthWiseConv2d

public SDVariable depthWiseConv2d(String name,
                                  SDVariable layerInput,
                                  SDVariable depthWeights,
                                  SDVariable bias,
                                  Conv2DConfig Conv2DConfig)

Depth-wise 2D convolution operation with optional bias

Parameters:

name - name May be null. Name for the output variable

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type)

depthWeights - Depth-wise conv2d weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)

bias - Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of depthwise conv2d op (NUMERIC type)

depthWiseConv2d

public SDVariable depthWiseConv2d(SDVariable layerInput,
                                  SDVariable depthWeights,
                                  Conv2DConfig Conv2DConfig)

Depth-wise 2D convolution operation with optional bias

Parameters:

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type)

depthWeights - Depth-wise conv2d weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of depthwise conv2d op (NUMERIC type)

depthWiseConv2d

public SDVariable depthWiseConv2d(String name,
                                  SDVariable layerInput,
                                  SDVariable depthWeights,
                                  Conv2DConfig Conv2DConfig)

Depth-wise 2D convolution operation with optional bias

Parameters:

name - name May be null. Name for the output variable

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (NUMERIC type)

depthWeights - Depth-wise conv2d weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of depthwise conv2d op (NUMERIC type)

dilation2D

public SDVariable dilation2D(SDVariable df,
                             SDVariable weights,
                             int[] strides,
                             int[] rates,
                             boolean isSameMode)

TODO doc string

Parameters:

df - (NUMERIC type)

weights - df (NUMERIC type)

strides - weights (Size: Exactly(count=2))

rates - strides (Size: Exactly(count=2))

isSameMode - isSameMode

Returns:

output Computed the grayscale dilation of 4-D input and 3-D filters tensors. (NUMERIC type)

dilation2D

public SDVariable dilation2D(String name,
                             SDVariable df,
                             SDVariable weights,
                             int[] strides,
                             int[] rates,
                             boolean isSameMode)

TODO doc string

Parameters:

name - name May be null. Name for the output variable

df - (NUMERIC type)

weights - df (NUMERIC type)

strides - weights (Size: Exactly(count=2))

rates - strides (Size: Exactly(count=2))

isSameMode - isSameMode

Returns:

output Computed the grayscale dilation of 4-D input and 3-D filters tensors. (NUMERIC type)

extractImagePatches

public SDVariable extractImagePatches(SDVariable input,
                                      int kH,
                                      int kW,
                                      int sH,
                                      int sW,
                                      int rH,
                                      int rW,
                                      boolean sameMode)

Extract image patches

Parameters:

input - Input array. Must be rank 4, with shape [minibatch, height, width, channels] (NUMERIC type)

kH - Kernel height

kW - Kernel width

sH - Stride height

sW - Stride width

rH - Rate height

rW - Rate width

sameMode - If true: use same mode padding. If false

Returns:

output The result is a 4D tensor which is indexed by batch, row, and column. (NUMERIC type)

extractImagePatches

public SDVariable extractImagePatches(String name,
                                      SDVariable input,
                                      int kH,
                                      int kW,
                                      int sH,
                                      int sW,
                                      int rH,
                                      int rW,
                                      boolean sameMode)

Extract image patches

Parameters:

name - name May be null. Name for the output variable

input - Input array. Must be rank 4, with shape [minibatch, height, width, channels] (NUMERIC type)

kH - Kernel height

kW - Kernel width

sH - Stride height

sW - Stride width

rH - Rate height

rW - Rate width

sameMode - If true: use same mode padding. If false

Returns:

output The result is a 4D tensor which is indexed by batch, row, and column. (NUMERIC type)

im2Col

public SDVariable im2Col(SDVariable in,
                         Conv2DConfig Conv2DConfig)

im2col operation for use in 2D convolution operations. Outputs a 6d array with shape
[minibatch, inputChannels, kernelHeight, kernelWidth, outputHeight, outputWidth]

Parameters:

in - Input - rank 4 input with shape [minibatch, inputChannels, height, width] (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output Im2Col output variable (NUMERIC type)

im2Col

public SDVariable im2Col(String name,
                         SDVariable in,
                         Conv2DConfig Conv2DConfig)

im2col operation for use in 2D convolution operations. Outputs a 6d array with shape
[minibatch, inputChannels, kernelHeight, kernelWidth, outputHeight, outputWidth]

Parameters:

name - name May be null. Name for the output variable

in - Input - rank 4 input with shape [minibatch, inputChannels, height, width] (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output Im2Col output variable (NUMERIC type)

localResponseNormalization

public SDVariable localResponseNormalization(SDVariable input,
                                             LocalResponseNormalizationConfig LocalResponseNormalizationConfig)

2D convolution layer operation - local response normalization

Parameters:

input - the inputs to lrn (NUMERIC type)

LocalResponseNormalizationConfig - Configuration Object

Returns:

output Result after Local Response Normalization (NUMERIC type)

localResponseNormalization

public SDVariable localResponseNormalization(String name,
                                             SDVariable input,
                                             LocalResponseNormalizationConfig LocalResponseNormalizationConfig)

2D convolution layer operation - local response normalization

Parameters:

name - name May be null. Name for the output variable

input - the inputs to lrn (NUMERIC type)

LocalResponseNormalizationConfig - Configuration Object

Returns:

output Result after Local Response Normalization (NUMERIC type)

maxPoolWithArgmax

public SDVariable[] maxPoolWithArgmax(SDVariable input,
                                      Pooling2DConfig Pooling2DConfig)

2D Convolution layer operation - Max pooling on the input and outputs both max values and indices

Parameters:

input - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

Pooling2DConfig - Configuration Object

maxPoolWithArgmax

public SDVariable[] maxPoolWithArgmax(String[] names,
                                      SDVariable input,
                                      Pooling2DConfig Pooling2DConfig)

2D Convolution layer operation - Max pooling on the input and outputs both max values and indices

Parameters:

names - names May be null. Arrays of names for the output variables.

input - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

Pooling2DConfig - Configuration Object

maxPooling2d

public SDVariable maxPooling2d(SDVariable input,
                               Pooling2DConfig Pooling2DConfig)

2D Convolution layer operation - max pooling 2d

Parameters:

input - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

Pooling2DConfig - Configuration Object

Returns:

output Result after applying max pooling on the input (NUMERIC type)

maxPooling2d

public SDVariable maxPooling2d(String name,
                               SDVariable input,
                               Pooling2DConfig Pooling2DConfig)

2D Convolution layer operation - max pooling 2d

Parameters:

name - name May be null. Name for the output variable

input - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

Pooling2DConfig - Configuration Object

Returns:

output Result after applying max pooling on the input (NUMERIC type)

maxPooling3d

public SDVariable maxPooling3d(SDVariable input,
                               Pooling3DConfig Pooling3DConfig)

3D convolution layer operation - max pooling 3d operation.

Parameters:

input - the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)

Pooling3DConfig - Configuration Object

Returns:

output Result after applying max pooling on the input (NUMERIC type)

maxPooling3d

public SDVariable maxPooling3d(String name,
                               SDVariable input,
                               Pooling3DConfig Pooling3DConfig)

3D convolution layer operation - max pooling 3d operation.

Parameters:

name - name May be null. Name for the output variable

input - the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)

Pooling3DConfig - Configuration Object

Returns:

output Result after applying max pooling on the input (NUMERIC type)

separableConv2d

public SDVariable separableConv2d(SDVariable layerInput,
                                  SDVariable depthWeights,
                                  SDVariable pointWeights,
                                  SDVariable bias,
                                  Conv2DConfig Conv2DConfig)

Separable 2D convolution operation with optional bias

Parameters:

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

depthWeights - Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)

pointWeights - Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)

bias - Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of separable convolution 2d operation (NUMERIC type)

separableConv2d

public SDVariable separableConv2d(String name,
                                  SDVariable layerInput,
                                  SDVariable depthWeights,
                                  SDVariable pointWeights,
                                  SDVariable bias,
                                  Conv2DConfig Conv2DConfig)

Separable 2D convolution operation with optional bias

Parameters:

name - name May be null. Name for the output variable

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

depthWeights - Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)

pointWeights - Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)

bias - Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of separable convolution 2d operation (NUMERIC type)

separableConv2d

public SDVariable separableConv2d(SDVariable layerInput,
                                  SDVariable depthWeights,
                                  SDVariable pointWeights,
                                  Conv2DConfig Conv2DConfig)

Separable 2D convolution operation with optional bias

Parameters:

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

depthWeights - Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)

pointWeights - Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of separable convolution 2d operation (NUMERIC type)

separableConv2d

public SDVariable separableConv2d(String name,
                                  SDVariable layerInput,
                                  SDVariable depthWeights,
                                  SDVariable pointWeights,
                                  Conv2DConfig Conv2DConfig)

Separable 2D convolution operation with optional bias

Parameters:

name - name May be null. Name for the output variable

layerInput - the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

depthWeights - Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)

pointWeights - Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)

Conv2DConfig - Configuration Object

Returns:

output result of separable convolution 2d operation (NUMERIC type)

spaceToBatch

public SDVariable spaceToBatch(SDVariable x,
                               int[] blocks,
                               int[] paddingTop,
                               int... paddingBottom)

Convolution 2d layer space to batch operation on 4d input.
Increases input batch dimension by rearranging data from spatial dimensions into batch dimension

Parameters:

x - Input variable. 4d input (NUMERIC type)

blocks - Block size, in the height/width dimension (Size: Exactly(count=2))

paddingTop - Optional 2d int[] array for padding the result: values [[pad top, pad bottom], [pad left, pad right]] (Size: Exactly(count=2))

paddingBottom - Optional 2d int[] array for padding the result: values [[pad top, pad bottom], [pad left, pad right]] (Size: Exactly(count=2))

Returns:

output Output variable (NUMERIC type)

spaceToBatch

public SDVariable spaceToBatch(String name,
                               SDVariable x,
                               int[] blocks,
                               int[] paddingTop,
                               int... paddingBottom)

Convolution 2d layer space to batch operation on 4d input.
Increases input batch dimension by rearranging data from spatial dimensions into batch dimension

Parameters:

name - name May be null. Name for the output variable

x - Input variable. 4d input (NUMERIC type)

blocks - Block size, in the height/width dimension (Size: Exactly(count=2))

paddingTop - Optional 2d int[] array for padding the result: values [[pad top, pad bottom], [pad left, pad right]] (Size: Exactly(count=2))

paddingBottom - Optional 2d int[] array for padding the result: values [[pad top, pad bottom], [pad left, pad right]] (Size: Exactly(count=2))

Returns:

output Output variable (NUMERIC type)

spaceToDepth

public SDVariable spaceToDepth(SDVariable x,
                               int blockSize,
                               DataFormat dataFormat)

Convolution 2d layer space to depth operation on 4d input.
Increases input channels (reduced spatial dimensions) by rearranging data into a larger channels dimension
Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
= [mb, 2, 4, 4]

Parameters:

x - the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

blockSize - Block size, in the height/width dimension

dataFormat - Data format: "NCHW" or "NHWC"

Returns:

output Output variable (NUMERIC type)

spaceToDepth

public SDVariable spaceToDepth(String name,
                               SDVariable x,
                               int blockSize,
                               DataFormat dataFormat)

Convolution 2d layer space to depth operation on 4d input.
Increases input channels (reduced spatial dimensions) by rearranging data into a larger channels dimension
Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]
= [mb, 2, 4, 4]

Parameters:

name - name May be null. Name for the output variable

x - the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)

blockSize - Block size, in the height/width dimension

dataFormat - Data format: "NCHW" or "NHWC"

Returns:

output Output variable (NUMERIC type)

upsampling2d

public SDVariable upsampling2d(SDVariable input,
                               int scale)

Upsampling layer for 2D inputs.
scale is used for both height and width dimensions.

Parameters:

input - Input in NCHW format (NUMERIC type)

scale - The scale for both height and width dimensions.

Returns:

output Upsampled input (NUMERIC type)

upsampling2d

public SDVariable upsampling2d(String name,
                               SDVariable input,
                               int scale)

Upsampling layer for 2D inputs.
scale is used for both height and width dimensions.

Parameters:

name - name May be null. Name for the output variable

input - Input in NCHW format (NUMERIC type)

scale - The scale for both height and width dimensions.

Returns:

output Upsampled input (NUMERIC type)

upsampling2d

public SDVariable upsampling2d(SDVariable input,
                               int scaleH,
                               int scaleW,
                               boolean nchw)

2D Convolution layer operation - Upsampling 2d

Parameters:

input - Input in NCHW format (NUMERIC type)

scaleH - Scale to upsample in height dimension

scaleW - Scale to upsample in width dimension

nchw - If true: input is in NCHW (minibatch, channels, height, width) format. False: NHWC format

Returns:

output Upsampled input (NUMERIC type)

upsampling2d

public SDVariable upsampling2d(String name,
                               SDVariable input,
                               int scaleH,
                               int scaleW,
                               boolean nchw)

2D Convolution layer operation - Upsampling 2d

Parameters:

name - name May be null. Name for the output variable

input - Input in NCHW format (NUMERIC type)

scaleH - Scale to upsample in height dimension

scaleW - Scale to upsample in width dimension

nchw - If true: input is in NCHW (minibatch, channels, height, width) format. False: NHWC format

Returns:

output Upsampled input (NUMERIC type)

upsampling3d

public SDVariable upsampling3d(SDVariable input,
                               boolean ncdhw,
                               int scaleD,
                               int scaleH,
                               int scaleW)

3D Convolution layer operation - Upsampling 3d

Parameters:

input - Input in NCHW format (NUMERIC type)

ncdhw - If true: input is in NCDHW (minibatch, channels, depth, height, width) format. False: NDHWC format

scaleD - Scale to upsample in depth dimension

scaleH - Scale to upsample in height dimension

scaleW - Scale to upsample in width dimension

Returns:

output Upsampled input (NUMERIC type)

upsampling3d

public SDVariable upsampling3d(String name,
                               SDVariable input,
                               boolean ncdhw,
                               int scaleD,
                               int scaleH,
                               int scaleW)

3D Convolution layer operation - Upsampling 3d

Parameters:

name - name May be null. Name for the output variable

input - Input in NCHW format (NUMERIC type)

ncdhw - If true: input is in NCDHW (minibatch, channels, depth, height, width) format. False: NDHWC format

scaleD - Scale to upsample in depth dimension

scaleH - Scale to upsample in height dimension

scaleW - Scale to upsample in width dimension

Returns:

output Upsampled input (NUMERIC type)