public class SDCNN extends SDOps
SameDiff.cnn()
SDNN
(accessible via SameDiff.nn()
for general neural network ops.SDRNN
(accessible via SameDiff.rnn()
for recurrent neural network ops.Modifier and Type | Method and Description |
---|---|
SDVariable |
avgPooling2d(SDVariable input,
Pooling2DConfig pooling2DConfig)
|
SDVariable |
avgPooling2d(String name,
SDVariable input,
Pooling2DConfig pooling2DConfig)
2D Convolution layer operation - average pooling 2d
|
SDVariable |
avgPooling3d(SDVariable input,
Pooling3DConfig pooling3DConfig)
|
SDVariable |
avgPooling3d(String name,
SDVariable input,
Pooling3DConfig pooling3DConfig)
3D convolution layer operation - average pooling 3d
|
SDVariable |
batchToSpace(SDVariable x,
int[] blocks,
int[][] crops) |
SDVariable |
batchToSpace(String name,
SDVariable x,
int[] blocks,
int[][] crops)
Convolution 2d layer batch to space operation on 4d input.
|
SDVariable |
col2Im(SDVariable in,
Conv2DConfig config)
|
SDVariable |
col2Im(String name,
SDVariable in,
Conv2DConfig config)
col2im operation for use in 2D convolution operations.
|
SDVariable |
conv1d(SDVariable input,
SDVariable weights,
Conv1DConfig conv1DConfig)
|
SDVariable |
conv1d(SDVariable input,
SDVariable weights,
SDVariable bias,
Conv1DConfig conv1DConfig)
|
SDVariable |
conv1d(String name,
SDVariable input,
SDVariable weights,
Conv1DConfig conv1DConfig)
|
SDVariable |
conv1d(String name,
SDVariable input,
SDVariable weights,
SDVariable bias,
Conv1DConfig conv1DConfig)
Conv1d operation.
|
SDVariable |
conv2d(SDVariable[] inputs,
Conv2DConfig config)
|
SDVariable |
conv2d(SDVariable layerInput,
SDVariable weights,
Conv2DConfig config)
|
SDVariable |
conv2d(SDVariable layerInput,
SDVariable weights,
SDVariable bias,
Conv2DConfig config)
|
SDVariable |
conv2d(String name,
SDVariable[] inputs,
Conv2DConfig config)
2D Convolution operation with optional bias
|
SDVariable |
conv2d(String name,
SDVariable layerInput,
SDVariable weights,
Conv2DConfig config)
|
SDVariable |
conv2d(String name,
SDVariable layerInput,
SDVariable weights,
SDVariable bias,
Conv2DConfig config)
2D Convolution operation with optional bias
|
SDVariable |
conv3d(SDVariable input,
SDVariable weights,
Conv3DConfig conv3DConfig)
|
SDVariable |
conv3d(SDVariable input,
SDVariable weights,
SDVariable bias,
Conv3DConfig conv3DConfig)
|
SDVariable |
conv3d(String name,
SDVariable input,
SDVariable weights,
Conv3DConfig conv3DConfig)
|
SDVariable |
conv3d(String name,
SDVariable input,
SDVariable weights,
SDVariable bias,
Conv3DConfig conv3DConfig)
Convolution 3D operation with optional bias
|
SDVariable |
deconv2d(SDVariable[] inputs,
DeConv2DConfig deconv2DConfig)
|
SDVariable |
deconv2d(SDVariable layerInput,
SDVariable weights,
DeConv2DConfig deconv2DConfig)
|
SDVariable |
deconv2d(SDVariable layerInput,
SDVariable weights,
SDVariable bias,
DeConv2DConfig deconv2DConfig)
|
SDVariable |
deconv2d(String name,
SDVariable[] inputs,
DeConv2DConfig deconv2DConfig)
2D deconvolution operation with or without optional bias
|
SDVariable |
deconv2d(String name,
SDVariable layerInput,
SDVariable weights,
DeConv2DConfig deconv2DConfig)
|
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 config)
|
SDVariable |
deconv3d(SDVariable input,
SDVariable weights,
SDVariable bias,
DeConv3DConfig config)
|
SDVariable |
deconv3d(String name,
SDVariable input,
SDVariable weights,
DeConv3DConfig config)
|
SDVariable |
deconv3d(String name,
SDVariable input,
SDVariable weights,
SDVariable bias,
DeConv3DConfig config)
3D CNN deconvolution operation with or without optional bias
|
SDVariable |
depthToSpace(SDVariable x,
int blockSize,
String dataFormat)
|
SDVariable |
depthToSpace(String name,
SDVariable x,
int blockSize,
String 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[] inputs,
Conv2DConfig depthConv2DConfig)
|
SDVariable |
depthWiseConv2d(SDVariable layerInput,
SDVariable depthWeights,
Conv2DConfig config)
|
SDVariable |
depthWiseConv2d(SDVariable layerInput,
SDVariable depthWeights,
SDVariable bias,
Conv2DConfig config)
|
SDVariable |
depthWiseConv2d(String name,
SDVariable[] inputs,
Conv2DConfig depthConv2DConfig)
Depth-wise convolution 2D operation.
|
SDVariable |
depthWiseConv2d(String name,
SDVariable layerInput,
SDVariable depthWeights,
Conv2DConfig config)
|
SDVariable |
depthWiseConv2d(String name,
SDVariable layerInput,
SDVariable depthWeights,
SDVariable bias,
Conv2DConfig config)
Depth-wise 2D convolution operation with optional bias
|
SDVariable |
dilation2D(SDVariable df,
SDVariable weights,
int[] strides,
int[] rates,
boolean isSameMode)
|
SDVariable |
dilation2D(String name,
SDVariable df,
SDVariable weights,
int[] strides,
int[] rates,
boolean isSameMode)
TODO doc string
|
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 config)
|
SDVariable |
im2Col(String name,
SDVariable in,
Conv2DConfig config)
im2col operation for use in 2D convolution operations.
|
SDVariable |
localResponseNormalization(SDVariable inputs,
LocalResponseNormalizationConfig lrnConfig)
|
SDVariable |
localResponseNormalization(String name,
SDVariable input,
LocalResponseNormalizationConfig lrnConfig)
2D convolution layer operation - local response normalization
|
SDVariable |
maxPooling2d(SDVariable input,
Pooling2DConfig pooling2DConfig)
|
SDVariable |
maxPooling2d(String name,
SDVariable input,
Pooling2DConfig pooling2DConfig)
2D Convolution layer operation - max pooling 2d
|
SDVariable |
maxPooling3d(SDVariable input,
Pooling3DConfig pooling3DConfig)
|
SDVariable |
maxPooling3d(String name,
SDVariable input,
Pooling3DConfig pooling3DConfig)
3D convolution layer operation - max pooling 3d operation.
|
SDVariable |
sconv2d(SDVariable[] inputs,
Conv2DConfig conv2DConfig)
|
SDVariable |
sconv2d(String name,
SDVariable[] inputs,
Conv2DConfig conv2DConfig)
Separable 2D convolution operation with/without optional bias
|
SDVariable |
separableConv2d(SDVariable layerInput,
SDVariable depthWeights,
SDVariable pointWeights,
Conv2DConfig config)
|
SDVariable |
separableConv2d(SDVariable layerInput,
SDVariable depthWeights,
SDVariable pointWeights,
SDVariable bias,
Conv2DConfig config)
|
SDVariable |
separableConv2d(String name,
SDVariable layerInput,
SDVariable depthWeights,
SDVariable pointWeights,
Conv2DConfig config)
|
SDVariable |
separableConv2d(String name,
SDVariable layerInput,
SDVariable depthWeights,
SDVariable pointWeights,
SDVariable bias,
Conv2DConfig config)
Separable 2D convolution operation with optional bias
|
SDVariable |
spaceToBatch(SDVariable x,
int[] blocks,
int[][] padding) |
SDVariable |
spaceToBatch(String name,
SDVariable x,
int[] blocks,
int[][] padding)
Convolution 2d layer space to batch operation on 4d input.
|
SDVariable |
spaceToDepth(SDVariable x,
int blockSize,
String dataFormat) |
SDVariable |
spaceToDepth(String name,
SDVariable x,
int blockSize,
String 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,
boolean nchw,
int scaleH,
int scaleW)
|
SDVariable |
upsampling2d(SDVariable input,
int scale)
See
upsampling2d(String, SDVariable, boolean, int, int) ,
scale is used for both height and width dimensions. |
SDVariable |
upsampling2d(String name,
SDVariable input,
boolean nchw,
int scaleH,
int scaleW)
2D Convolution layer operation - Upsampling 2d
|
SDVariable |
upsampling2d(String name,
SDVariable input,
int scale)
See
upsampling2d(String, SDVariable, boolean, int, int) ,
scale is used for both height and width dimensions. |
f, updateVariableNameAndReference
public SDCNN(SameDiff sameDiff)
public SDVariable avgPooling2d(@NonNull SDVariable input, @NonNull Pooling2DConfig pooling2DConfig)
public SDVariable avgPooling2d(String name, @NonNull SDVariable input, @NonNull Pooling2DConfig pooling2DConfig)
name
- name of the operation in SameDiffinput
- 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])pooling2DConfig
- the configurationpublic SDVariable avgPooling3d(@NonNull SDVariable input, @NonNull Pooling3DConfig pooling3DConfig)
public SDVariable avgPooling3d(String name, @NonNull SDVariable input, @NonNull Pooling3DConfig pooling3DConfig)
name
- name of the operation in SameDiffinput
- 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])pooling3DConfig
- the configurationpublic SDVariable batchToSpace(@NonNull SDVariable x, @NonNull int[] blocks, @NonNull int[][] crops)
public SDVariable batchToSpace(String name, @NonNull SDVariable x, @NonNull int[] blocks, @NonNull int[][] crops)
name
- Output variable namex
- Input variable. 4d inputblocks
- Block size, in the height/width dimensioncrops
- Optional 2d int[] array: values [[crop top, crop bottom], [crop left, crop right]]spaceToBatch(String, SDVariable, int[], int[][])
public SDVariable col2Im(@NonNull SDVariable in, @NonNull Conv2DConfig config)
public SDVariable col2Im(String name, @NonNull SDVariable in, @NonNull Conv2DConfig config)
name
- Name of the output variablein
- Input - rank 6 input with shape [minibatch, inputChannels, kernelHeight, kernelWidth, outputHeight, outputWidth]config
- Convolution configuration for the col2im operationpublic SDVariable conv1d(@NonNull SDVariable input, @NonNull SDVariable weights, @NonNull Conv1DConfig conv1DConfig)
public SDVariable conv1d(String name, @NonNull SDVariable input, @NonNull SDVariable weights, @NonNull Conv1DConfig conv1DConfig)
public SDVariable conv1d(@NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull Conv1DConfig conv1DConfig)
public SDVariable conv1d(String name, @NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull Conv1DConfig conv1DConfig)
name
- name of the operation in SameDiffinput
- the inputs to conv1dweights
- weights for conv1d op - rank 3 array with shape [kernelSize, inputChannels, outputChannels]bias
- bias for conv1d op - rank 1 array with shape [outputChannels]. May be null.conv1DConfig
- the configurationpublic SDVariable conv2d(@NonNull SDVariable layerInput, @NonNull SDVariable weights, @NonNull Conv2DConfig config)
public SDVariable conv2d(String name, @NonNull SDVariable layerInput, @NonNull SDVariable weights, @NonNull Conv2DConfig config)
public SDVariable conv2d(@NonNull SDVariable layerInput, @NonNull SDVariable weights, SDVariable bias, @NonNull Conv2DConfig config)
public SDVariable conv2d(String name, @NonNull SDVariable layerInput, @NonNull SDVariable weights, SDVariable bias, @NonNull Conv2DConfig config)
name
- name of the operation in SameDifflayerInput
- 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])weights
- Weights for the convolution operation. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, outputChannels]bias
- Optional 1D bias array with shape [outputChannels]. May be null.config
- Conv2DConfig configurationpublic SDVariable conv2d(@NonNull SDVariable[] inputs, @NonNull Conv2DConfig config)
public SDVariable conv2d(String name, @NonNull SDVariable[] inputs, @NonNull Conv2DConfig config)
name
- Name of the output SDVariableinputs
- an array with either 2 elements (layerInput, weights) or 3 elements (layerInput, weights, bias) as
described in conv2d(SDVariable, SDVariable, SDVariable, Conv2DConfig)
config
- Conv2DConfig configurationpublic SDVariable conv3d(@NonNull SDVariable input, @NonNull SDVariable weights, @NonNull Conv3DConfig conv3DConfig)
public SDVariable conv3d(String name, @NonNull SDVariable input, @NonNull SDVariable weights, @NonNull Conv3DConfig conv3DConfig)
public SDVariable conv3d(@NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull Conv3DConfig conv3DConfig)
public SDVariable conv3d(String name, @NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull Conv3DConfig conv3DConfig)
name
- Name of the output variableinput
- 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])weights
- Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels].bias
- Optional 1D bias array with shape [outputChannels]. May be null.conv3DConfig
- the configurationpublic SDVariable deconv2d(@NonNull SDVariable layerInput, @NonNull SDVariable weights, @NonNull DeConv2DConfig deconv2DConfig)
public SDVariable deconv2d(String name, @NonNull SDVariable layerInput, @NonNull SDVariable weights, @NonNull DeConv2DConfig deconv2DConfig)
public SDVariable deconv2d(@NonNull SDVariable layerInput, @NonNull SDVariable weights, SDVariable bias, @NonNull DeConv2DConfig deconv2DConfig)
public SDVariable deconv2d(String name, @NonNull SDVariable layerInput, @NonNull SDVariable weights, SDVariable bias, @NonNull DeConv2DConfig deconv2DConfig)
name
- name of the operation in SameDifflayerInput
- 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])weights
- Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth].bias
- Optional 1D bias array with shape [outputChannels]. May be null.deconv2DConfig
- DeConv2DConfig configurationpublic SDVariable deconv2d(@NonNull SDVariable[] inputs, @NonNull DeConv2DConfig deconv2DConfig)
public SDVariable deconv2d(String name, @NonNull SDVariable[] inputs, @NonNull DeConv2DConfig deconv2DConfig)
name
- Name of the output variableinputs
- Inputs to the deconvolution 2d operation - input array of length 2 (layerInput, weights)
or length 3 (layerInput, weights, bias) as described in deconv2d(SDVariable[], DeConv2DConfig)
deconv2DConfig
- the configurationpublic SDVariable deconv3d(@NonNull SDVariable input, @NonNull SDVariable weights, @NonNull DeConv3DConfig config)
public SDVariable deconv3d(String name, @NonNull SDVariable input, @NonNull SDVariable weights, @NonNull DeConv3DConfig config)
public SDVariable deconv3d(@NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull DeConv3DConfig config)
public SDVariable deconv3d(String name, @NonNull SDVariable input, @NonNull SDVariable weights, SDVariable bias, @NonNull DeConv3DConfig config)
name
- Name of the output variableinput
- Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)weights
- Weights array - shape [kD, kH, kW, oC, iC]bias
- Bias array - optional, may be null. If non-null, must have shape [outputChannels]config
- Configurationpublic SDVariable depthToSpace(@NonNull SDVariable x, @NonNull int blockSize, @NonNull String dataFormat)
public SDVariable depthToSpace(String name, @NonNull SDVariable x, @NonNull int blockSize, @NonNull String dataFormat)
name
- Output variable namex
- 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])blockSize
- Block size, in the height/width dimensiondataFormat
- Data format: "NCHW" or "NHWC"depthToSpace(String, SDVariable, int, String)
public SDVariable depthWiseConv2d(@NonNull SDVariable layerInput, @NonNull SDVariable depthWeights, @NonNull Conv2DConfig config)
public SDVariable depthWiseConv2d(String name, @NonNull SDVariable layerInput, @NonNull SDVariable depthWeights, @NonNull Conv2DConfig config)
public SDVariable depthWiseConv2d(@NonNull SDVariable layerInput, @NonNull SDVariable depthWeights, SDVariable bias, @NonNull Conv2DConfig config)
public SDVariable depthWiseConv2d(String name, @NonNull SDVariable layerInput, @NonNull SDVariable depthWeights, SDVariable bias, @NonNull Conv2DConfig config)
name
- name of the operation in SameDifflayerInput
- 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])depthWeights
- Depth-wise conv2d weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier]bias
- Optional 1D bias array with shape [outputChannels]. May be null.config
- Conv2DConfig configurationpublic SDVariable depthWiseConv2d(@NonNull SDVariable[] inputs, @NonNull Conv2DConfig depthConv2DConfig)
public SDVariable depthWiseConv2d(String name, @NonNull SDVariable[] inputs, @NonNull Conv2DConfig depthConv2DConfig)
name
- name of the output variableinputs
- the inputs to depth-wise conv2d. An array with either 2 elements (layerInput, depthWeights)
or 3 elements (layerInput, depthWeights, bias) as described in
depthWiseConv2d(SDVariable, SDVariable, SDVariable, Conv2DConfig)
depthConv2DConfig
- the configurationpublic SDVariable dilation2D(@NonNull SDVariable df, @NonNull SDVariable weights, @NonNull int[] strides, @NonNull int[] rates, @NonNull boolean isSameMode)
public SDVariable dilation2D(String name, @NonNull SDVariable df, @NonNull SDVariable weights, @NonNull int[] strides, @NonNull int[] rates, @NonNull boolean isSameMode)
name
- df
- weights
- strides
- rates
- isSameMode
- public SDVariable extractImagePatches(String name, @NonNull SDVariable input, int kH, int kW, int sH, int sW, int rH, int rW, boolean sameMode)
name
- Name of the output variableinput
- Input array. Must be rank 4, with shape [minibatch, height, width, channels]kH
- Kernel heightkW
- Kernel widthsH
- Stride heightsW
- Stride widthrH
- Rate heightrW
- Rate widthsameMode
- If true: use same mode padding. If falsepublic SDVariable im2Col(@NonNull SDVariable in, @NonNull Conv2DConfig config)
public SDVariable im2Col(String name, @NonNull SDVariable in, @NonNull Conv2DConfig config)
name
- Name of the output variablein
- Input - rank 4 input with shape [minibatch, inputChannels, height, width]config
- Convolution configuration for the im2col operationpublic SDVariable localResponseNormalization(@NonNull SDVariable inputs, @NonNull LocalResponseNormalizationConfig lrnConfig)
public SDVariable localResponseNormalization(String name, @NonNull SDVariable input, @NonNull LocalResponseNormalizationConfig lrnConfig)
name
- name of the operation in SameDiffinput
- the inputs to lrnlrnConfig
- the configurationpublic SDVariable maxPooling2d(@NonNull SDVariable input, @NonNull Pooling2DConfig pooling2DConfig)
public SDVariable maxPooling2d(String name, @NonNull SDVariable input, @NonNull Pooling2DConfig pooling2DConfig)
name
- name of the operation in SameDiffinput
- 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])pooling2DConfig
- the configurationpublic SDVariable maxPooling3d(@NonNull SDVariable input, @NonNull Pooling3DConfig pooling3DConfig)
public SDVariable maxPooling3d(String name, @NonNull SDVariable input, @NonNull Pooling3DConfig pooling3DConfig)
name
- name of the operation in SameDiffinput
- 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])pooling3DConfig
- the configurationpublic SDVariable separableConv2d(SDVariable layerInput, @NonNull SDVariable depthWeights, SDVariable pointWeights, @NonNull Conv2DConfig config)
public SDVariable separableConv2d(String name, @NonNull SDVariable layerInput, @NonNull SDVariable depthWeights, SDVariable pointWeights, @NonNull Conv2DConfig config)
public SDVariable separableConv2d(@NonNull SDVariable layerInput, @NonNull SDVariable depthWeights, SDVariable pointWeights, SDVariable bias, @NonNull Conv2DConfig config)
public SDVariable separableConv2d(String name, @NonNull SDVariable layerInput, @NonNull SDVariable depthWeights, SDVariable pointWeights, SDVariable bias, @NonNull Conv2DConfig config)
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])depthWeights
- Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier]pointWeights
- Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]
May be nullbias
- Optional bias, rank 1 with shape [outputChannels]. May be null.config
- Conv2DConfig configurationpublic SDVariable sconv2d(@NonNull SDVariable[] inputs, @NonNull Conv2DConfig conv2DConfig)
public SDVariable sconv2d(String name, @NonNull SDVariable[] inputs, @NonNull Conv2DConfig conv2DConfig)
name
- name of the output variableinputs
- the inputs to separable conv2 operation. Should be length 3 (layerInput, depthWeights, pointWeights)
or length 4 (layerInput, depthWeights, pointWeights, bias) as described in separableConv2d(SDVariable, SDVariable, SDVariable, SDVariable, Conv2DConfig)
conv2DConfig
- the configurationpublic SDVariable spaceToBatch(@NonNull SDVariable x, @NonNull int[] blocks, @NonNull int[][] padding)
public SDVariable spaceToBatch(String name, @NonNull SDVariable x, @NonNull int[] blocks, @NonNull int[][] padding)
name
- Output variable namex
- Input variable. 4d inputblocks
- Block size, in the height/width dimensionpadding
- Optional 2d int[] array for padding the result: values [[pad top, pad bottom], [pad left, pad right]]batchToSpace(String, SDVariable, int[], int[][])
public SDVariable spaceToDepth(@NonNull SDVariable x, int blockSize, @NonNull String dataFormat)
public SDVariable spaceToDepth(String name, @NonNull SDVariable x, int blockSize, @NonNull String dataFormat)
name
- Output variable namex
- 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])blockSize
- Block size, in the height/width dimensiondataFormat
- Data format: "NCHW" or "NHWC"depthToSpace(String, SDVariable, int, String)
public SDVariable upsampling2d(@NonNull SDVariable input, int scale)
upsampling2d(String, SDVariable, boolean, int, int)
,
scale is used for both height and width dimensions.scale
- The scale for both height and width dimensions.public SDVariable upsampling2d(String name, @NonNull SDVariable input, int scale)
upsampling2d(String, SDVariable, boolean, int, int)
,
scale is used for both height and width dimensions.scale
- The scale for both height and width dimensions.public SDVariable upsampling2d(@NonNull SDVariable input, boolean nchw, int scaleH, int scaleW)
public SDVariable upsampling2d(String name, @NonNull SDVariable input, boolean nchw, int scaleH, int scaleW)
input
- Input, in NCHW formatnchw
- If true: input is in NCHW (minibatch, channels, height, width) format. False: NHWC formatscaleH
- Scale to upsample in height dimensionscaleW
- Scale to upsample in width dimensionCopyright © 2019. All rights reserved.