Modifier and Type | Class and Description |
---|---|
class |
DynamicCustomOp
Basic implementation for CustomOp
|
class |
NoOp |
Modifier and Type | Method and Description |
---|---|
CustomOp |
Op.toCustomOp()
|
CustomOp |
BaseOp.toCustomOp() |
Modifier and Type | Class and Description |
---|---|
class |
BarnesEdgeForces |
class |
BarnesHutGains
This op calculates gains - data used internally by Barnes-Hut-TSNE algorithm.
|
class |
BarnesHutSymmetrize |
class |
Flatten
This op takes arbitrary number of arrays as input, and returns single "flattened" vector
|
class |
ScatterUpdate |
class |
SpTreeCell |
Modifier and Type | Field and Description |
---|---|
protected CustomOp |
ScatterUpdate.op |
Modifier and Type | Method and Description |
---|---|
CustomOp |
OpExecutioner.execAndReturn(CustomOp op)
This method executes given CustomOp
PLEASE NOTE: You're responsible for input/output validation
|
CustomOp |
DefaultOpExecutioner.execAndReturn(CustomOp op) |
Modifier and Type | Method and Description |
---|---|
INDArray[] |
OpExecutioner.allocateOutputArrays(CustomOp op)
Equivalent to calli
|
INDArray[] |
DefaultOpExecutioner.allocateOutputArrays(CustomOp op) |
List<LongShapeDescriptor> |
OpExecutioner.calculateOutputShape(CustomOp op) |
List<LongShapeDescriptor> |
DefaultOpExecutioner.calculateOutputShape(CustomOp op) |
static void |
OpExecutionerUtil.checkForInf(CustomOp op) |
static void |
OpExecutionerUtil.checkForNaN(CustomOp op) |
protected void |
DefaultOpExecutioner.checkForWorkspaces(CustomOp op) |
INDArray[] |
OpExecutioner.exec(CustomOp op) |
INDArray[] |
DefaultOpExecutioner.exec(CustomOp op) |
INDArray[] |
OpExecutioner.exec(CustomOp op,
OpContext context)
This method executes op with given context
|
INDArray[] |
DefaultOpExecutioner.exec(CustomOp op,
OpContext context) |
CustomOp |
OpExecutioner.execAndReturn(CustomOp op)
This method executes given CustomOp
PLEASE NOTE: You're responsible for input/output validation
|
CustomOp |
DefaultOpExecutioner.execAndReturn(CustomOp op) |
long |
DefaultOpExecutioner.profilingConfigurableHookIn(CustomOp op) |
void |
DefaultOpExecutioner.profilingConfigurableHookOut(CustomOp op,
long timeStart) |
long |
DefaultOpExecutioner.profilingHookIn(CustomOp op)
Deprecated.
|
void |
DefaultOpExecutioner.profilingHookOut(CustomOp op,
long timeStart)
Deprecated.
|
Modifier and Type | Class and Description |
---|---|
class |
BiasAdd
Bias addition gradient operation.
|
class |
BiasAddGrad |
class |
BroadcastTo
BroadcastTo op: given 2 input arrays, content X and shape Y, broadcast X to the shape specified by the content of Y.
|
Modifier and Type | Class and Description |
---|---|
class |
If
Equivalent to tensorflow's conditional op.
|
class |
IfDerivative |
class |
Select |
class |
Where |
class |
WhereNumpy |
class |
While
Equivalent to tensorflow's while loop
Takes in:
loopVars
loop body
condition
runs loop till condition is false.
|
class |
WhileDerivative
While loop derivative
|
Modifier and Type | Class and Description |
---|---|
class |
BaseCompatOp |
class |
Enter |
class |
Exit |
class |
LoopCond |
class |
Merge |
class |
NextIteration |
class |
StopGradient |
class |
Switch
Switch op forwards input to one of two outputs based on the value of a predicate
|
Modifier and Type | Class and Description |
---|---|
class |
CropAndResize
CropAndResize Op
|
class |
ExtractImagePatches
Extract image patches op - a sliding window operation over 4d activations that puts the
output images patches into the depth dimension
|
class |
NonMaxSuppression
Non max suppression
|
class |
ResizeBilinear
ResizeBilinear op wrapper
|
class |
ResizeNearestNeighbor
ResizeNearestNeighbor op wrapper
|
Modifier and Type | Class and Description |
---|---|
class |
ArgMax |
class |
ArgMin
ArgMin function
|
Modifier and Type | Class and Description |
---|---|
class |
ExternalErrorsFunction |
Modifier and Type | Class and Description |
---|---|
class |
AvgPooling2D
Average Pooling2D operation
|
class |
AvgPooling3D
Average Pooling3D operation
|
class |
BatchNorm
BatchNorm operation
|
class |
BatchNormDerivative
BatchNormDerivative operation
|
class |
Col2Im
Col2Im operation.
|
class |
Conv1D
Conv2D operation
|
class |
Conv2D
Conv2D operation
|
class |
Conv2DDerivative
Conv2DDerivative operation
|
class |
Conv3D
Conv3D operation
|
class |
Conv3DDerivative
Conv3DDerivative operation
|
class |
DeConv2D
DeConv2D operation
|
class |
DeConv2DDerivative
DeConv2DDerivative operation
|
class |
DeConv2DTF
DeConv2D operation, TF-wrapper
|
class |
DeConv3D
DeConv3D operation
|
class |
DeConv3DDerivative
DeConv3DDerivative operation
|
class |
DepthToSpace
Inverse operation to SpaceToDepth.
|
class |
DepthwiseConv2D
Depthwise Conv2D operation
|
class |
Im2col
Im2col operation
|
class |
Im2colBp
Im2col operation
|
class |
LocalResponseNormalization
LocalResponseNormalization operation
|
class |
LocalResponseNormalizationDerivative
LocalResponseNormalizationDerivative operation
|
class |
MaxPooling2D
Max Pooling2D operation
|
class |
MaxPooling3D
Max Pooling3D operation
|
class |
Pooling2D
Pooling2D operation
|
class |
Pooling2DDerivative
Pooling2DDerivative operation
|
class |
Pooling3D
Pooling3D operation
|
class |
Pooling3DDerivative
Pooling3DDerivative operation
|
class |
SConv2D
Separable convolution 2D operation
|
class |
SConv2DDerivative
SConv2DDerivative operation
|
class |
SpaceToDepth
This operation takes 4D array in, in either NCHW or NHWC format, and moves data from spatial dimensions (HW)
to channels (C) for given blockSize
|
class |
Upsampling2d
Upsampling operation
|
class |
Upsampling2dDerivative
UpsamplingDerivative operation
|
Modifier and Type | Class and Description |
---|---|
class |
GRUCell
GRU cell for RNNs
|
class |
LSTMBlockCell
LSTM Block cell - represents forward pass for a single time step of an LSTM RNN.
Same operation used internally in op/layer LSTMLayer .Implementation of operation for LSTM layer with optional peep hole connections. S. |
class |
LSTMCell
LSTM cell
|
class |
LSTMLayer
LSTM layer implemented as a single operation.
|
class |
SRU
Simple recurrent unit
|
class |
SRUCell
A simple recurrent unit cell.
|
Modifier and Type | Class and Description |
---|---|
class |
AbsoluteDifferenceLoss
Absolute difference loss
|
class |
BaseLoss |
class |
CosineDistanceLoss
Cosine distance loss
|
class |
HingeLoss
Hinge loss
|
class |
HuberLoss
Huber loss
|
class |
L2Loss
L2 loss op wrapper
|
class |
LogLoss
Binary log loss, or cross entropy loss:
-1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon)) |
class |
LogPoissonLoss
Log Poisson loss
Note: This expects that the input/predictions are log(x) not x!
|
class |
MeanPairwiseSquaredErrorLoss
Mean Pairwise Squared Error Loss
|
class |
MeanSquaredErrorLoss
Mean squared error loss
|
class |
SigmoidCrossEntropyLoss
Sigmoid cross entropy loss with logits
|
class |
SoftmaxCrossEntropyLoss
Softmax cross entropy loss
|
class |
SoftmaxCrossEntropyWithLogitsLoss
Softmax cross entropy loss with Logits
|
class |
SparseSoftmaxCrossEntropyLossWithLogits
Sparse softmax cross entropy loss with logits.
|
class |
WeightedCrossEntropyLoss
Weighted cross entropy loss with logits
|
Modifier and Type | Class and Description |
---|---|
class |
AbsoluteDifferenceLossBp
Absolute difference loss backprop
|
class |
BaseLossBp |
class |
CosineDistanceLossBp
Cosine distance loss
|
class |
HingeLossBp
Hinge loss
|
class |
HuberLossBp
Hinge loss
|
class |
LogLossBp
Binary log loss, or cross entropy loss:
-1/numExamples * sum_i (labels[i] * log(predictions[i] + epsilon) + (1-labels[i]) * log(1-predictions[i] + epsilon)) |
class |
LogPoissonLossBp
Log Poisson loss backprop
|
class |
MeanPairwiseSquaredErrorLossBp
Mean Pairwise Squared Error Loss Backprop
|
class |
MeanSquaredErrorLossBp
Mean squared error loss
|
class |
SigmoidCrossEntropyLossBp
Sigmoid cross entropy loss with logits
|
class |
SoftmaxCrossEntropyLossBp
Softmax cross entropy loss
|
class |
SoftmaxCrossEntropyWithLogitsLossBp
Softmax cross entropy loss with Logits
|
class |
SparseSoftmaxCrossEntropyLossWithLogitsBp
Sparse softmax cross entropy loss with logits.
|
Modifier and Type | Class and Description |
---|---|
class |
CbowRound |
class |
SkipGramRound |
Modifier and Type | Class and Description |
---|---|
class |
HashCode
This is hashCode op wrapper.
|
class |
Mmul
Matrix multiplication/dot product
|
class |
MmulBp
Matrix multiplication/dot product Backprop
|
class |
Moments |
class |
NormalizeMoments |
class |
SufficientStatistics
Sufficient statistics: returns 3 or 4 output arrays:
If shift is not provided: count, sum of elements, sum of squares
If shift is provided: count, sum of elements, sum of squares, shift
|
class |
TensorMmul
TensorMmul
|
class |
ZeroFraction
Compute the fraction of zero elements
|
Modifier and Type | Class and Description |
---|---|
class |
BaseReductionBp |
class |
CumProdBp
Backprop op for cumulative product operation
|
class |
CumSumBp
Backprop op for cumulative sum operation
|
class |
DotBp
Backprop op for Dot pairwise reduction operation
|
class |
MaxBp
Backprop op for Max reduction operation
|
class |
MeanBp
Backprop op for Mean reduction operation
|
class |
MinBp
Backprop op for Min reduction operation
|
class |
Norm1Bp
Backprop op for Norm1 reduction operation
|
class |
Norm2Bp
Backprop op for Norm2 reduction operation
|
class |
NormMaxBp
Backprop op for Norm Max reduction operation
|
class |
ProdBp
Backprop op for Product reduction operation
|
class |
SquaredNormBp
Backprop op for squared norm (sum_i x_i^2) reduction operation
|
class |
StandardDeviationBp
Backprop op for standard deviation reduction operation
|
class |
SumBp
Backprop op for Sum reduction operation
|
class |
VarianceBp
Backprop op for variance reduction operation
|
Modifier and Type | Class and Description |
---|---|
class |
BatchMmul
Batched matrix multiplication.
|
class |
LogSumExp
LogSumExp - this op returns https://en.wikipedia.org/wiki/LogSumExp
|
Modifier and Type | Class and Description |
---|---|
class |
RectifiedLinearDerivative |
Modifier and Type | Class and Description |
---|---|
class |
ScatterAdd
Created by farizrahman4u on 3/23/18.
|
class |
ScatterDiv
Created by farizrahman4u on 3/23/18.
|
class |
ScatterMax |
class |
ScatterMin |
class |
ScatterMul
Created by farizrahman4u on 3/23/18.
|
class |
ScatterNd
Scatter ND operation
|
class |
ScatterNdAdd
Scatter ND add operation
|
class |
ScatterNdSub
Scatter ND subtract operation
|
class |
ScatterNdUpdate
Scatter ND add operation
|
class |
ScatterSub
Created by farizrahman4u on 3/23/18.
|
Modifier and Type | Class and Description |
---|---|
class |
ApplyGradientDescent
Reshape function
|
class |
BroadcastDynamicShape
Broadcast dynamic shape function
|
class |
Concat |
class |
ConfusionMatrix |
class |
Cross
Pairwise cross-product of two tensors of the same shape.
|
class |
Diag
Computes a diagonal matrix of shape (n, n) from a vector of length n.
|
class |
DiagPart
Return the diagonal part of a tensor.
|
class |
ExpandDims
ExpandDims function
|
class |
Eye
Computes a batch of identity matrices of shape (numRows, numCols), returns a single tensor.
|
class |
Gather
Gather op
|
class |
GatherNd
GatherND op
|
class |
Linspace
Linspace op - with dynamic (SDVariable) args
|
class |
MergeAvg |
class |
MergeMax |
class |
MergeSum |
class |
MeshGrid |
class |
OneHot
Created by susaneraly on 3/14/18.
|
class |
OnesLike
OnesLike function - gives an output array with all values/entries being 1, with the same shape as the input.
|
class |
ParallelStack
Stacks n input tensors of same shape to tensor of rank n + 1.
|
class |
Permute
Permute function
|
class |
Rank
Rank function
|
class |
ReductionShape |
class |
Repeat
Repeat function
|
class |
Reshape
Reshape function
|
class |
SequenceMask
Created by farizrahman4u on 3/28/18.
|
class |
Shape
Returns the shape of the input array.
|
class |
ShapeN
Returns the shape of N input array as N output arrays
|
class |
Size
Returns the size of the input as a rank 0 array
|
class |
SizeAt
Returns the size of the input along given dimension as a rank 0 array
|
class |
Slice
Slice function
|
class |
Split
Split op
|
class |
SplitV
SplitV op
|
class |
Squeeze |
class |
Stack
Stack operation.
|
class |
StridedSlice
Strided Slice function
|
class |
Tile
Tile function
|
class |
Transpose
Transpose function
|
class |
Unstack
Unstack op conversion
|
class |
ZerosLike
Reshape function
|
Modifier and Type | Class and Description |
---|---|
class |
ConcatBp
Backprop op for concat
|
class |
SliceBp
Slice backprop function
|
class |
StridedSliceBp
Strided Slice backprop function
|
class |
TileBp
Tile backprop function
|
Modifier and Type | Class and Description |
---|---|
class |
BaseTensorOp |
class |
TensorArray |
class |
TensorArrayConcat |
class |
TensorArrayGather |
class |
TensorArrayRead |
class |
TensorArrayScatter |
class |
TensorArraySize |
class |
TensorArraySplit |
class |
TensorArrayWrite |
Modifier and Type | Class and Description |
---|---|
class |
Angle
Angle op for tensorflow import
Given ND4J currently only supports real arrays; hence by definition this always outputs 0 |
class |
Assert
Assertion op wrapper
|
class |
BaseDynamicTransformOp |
class |
BinCount
BinCount: counts the number of times each value appears in an integer array.
|
class |
CheckNumerics
CheckNumerics op wrapper
|
class |
Cholesky
Cholesky op wrapper
|
class |
Histogram
Histogram op wrapper
|
class |
HistogramFixedWidth
Histogram fixed with op
|
class |
IdentityN
IdentityN op wrapper
|
class |
NthElement
NthElement op wrapper
|
class |
Pad
Pad op
|
class |
ReluLayer
Composed op: relu((X, W) + b)
|
Modifier and Type | Class and Description |
---|---|
class |
IsMax
[1, 2, 3, 1] -> [0, 0, 1, 0]
|
Modifier and Type | Class and Description |
---|---|
class |
ClipByNorm |
class |
ClipByNormBp |
class |
ClipByValue |
Modifier and Type | Class and Description |
---|---|
class |
Assign
Assign op: x = y, with broadcast as required
|
class |
ATan2
Arc Tangent elementwise function
|
class |
BatchToSpace
N-dimensional batch to space operation.
|
class |
BatchToSpaceND
N-dimensional batch to space operation.
|
class |
BitsHammingDistance |
class |
BitwiseAnd
Bit-wise AND operation, broadcastable
|
class |
BitwiseOr
Bit-wise OR operation, broadcastable
|
class |
BitwiseXor
Bit-wise XOR operation, broadcastable
|
class |
Choose
This op allows us to (based on the passed in condition)
to return the element fulfilling the condition.
|
class |
CumProd |
class |
CumSum
Cumulative sum operation, optionally along dimension.
|
class |
CyclicRShiftBits
Element-wise roll operation, rolls bits to the left, <<
|
class |
CyclicShiftBits
Element-wise roll operation, rolls bits to the left, <<
|
class |
Dilation2D
Dilation2D op wrapper
|
class |
DotProductAttention
(optionally scaled) dot product attention
See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, p.
|
class |
DotProductAttentionBp
(optionally scaled) dot product attention Backprop
See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, p.
|
class |
DynamicPartition
Transforms a given input tensor into numPartitions partitions, as indicated by the indices in "partitions".
|
class |
DynamicStitch
Transforms a given input tensor into numPartitions partitions, as indicated by the indices in "partitions".
|
class |
EqualTo
Bit mask over the ndarrays as to whether
the components are equal or not
|
class |
FakeQuantWithMinMaxArgs
Fake quantization operation.
|
class |
FakeQuantWithMinMaxVars
Fake quantization operation.
|
class |
Fill
Fill an array of given "shape" with the provided "value", e.g.
|
class |
GreaterThan
Bit mask over the ndarrays as to whether
the components are greater than or not
|
class |
GreaterThanOrEqual
Bit mask over the ndarrays as to whether
the components are greater than or equal or not
|
class |
InTopK
In Top K op
|
class |
InvertPermutation
Inverse of index permutation.
|
class |
IsNonDecreasing
This op takes 1 n-dimensional array as input,
and returns true if for every adjacent pair we have x[i] <= x[i+1].
|
class |
IsNumericTensor
This op takes 1 n-dimensional array as input, and returns true if input is a numeric array.
|
class |
IsStrictlyIncreasing
This op takes 1 n-dimensional array as input,
and returns true if for every adjacent pair we have x[i] < x[i+1].
|
class |
LayerNorm
Composed op: g*standarize(x) + b
Bias is optional, and can be set as null
|
class |
LayerNormBp
Composed op: g*standarize(x) + b
Bias is optional, and can be set as null
|
class |
LessThan
Bit mask over the ndarrays as to whether
the components are less than or not
|
class |
LessThanOrEqual
Bit mask over the ndarrays as to whether
the components are less than or equal or not
|
class |
ListDiff |
class |
LogicalAnd |
class |
LogicalNot |
class |
LogicalOr |
class |
LogicalXor |
class |
LogMatrixDeterminant
Log Matrix Determinant op
Given input with shape [..., N, N] output the log determinant for each sub-matrix.
|
class |
LogSoftMax
Log(softmax(X))
|
class |
MatrixDeterminant
Matrix Determinant op
Given input with shape [..., N, N] output the determinant for each sub-matrix.
|
class |
MatrixDiag |
class |
MatrixDiagPart |
class |
MatrixInverse
Matrix Inverse Function
|
class |
MatrixSetDiag |
class |
Max
Max function
|
class |
Min
Min function
|
class |
MirrorPad |
class |
MultiHeadDotProductAttention
(optionally scaled) multi head dot product attention
See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, pp.
|
class |
MultiHeadDotProductAttentionBp
(optionally scaled) multi head dot product attention Backprop
See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, pp.
|
class |
NotEqualTo
Not equal to function:
Bit mask over whether 2 elements are not equal or not
|
class |
ParallelConcat |
class |
Pow
Broadcastable element-wise power operation: x[i]^y[i]
|
class |
Reverse |
class |
ReverseSequence
Created by farizrahman4u on 3/16/18.
|
class |
ReverseV2
This is compatibility op for ReverseV2
|
class |
RShiftBits
Element-wise shift operation, shift bits to the right, >>
|
class |
ShiftBits
Element-wise shift operation, shift bits to the left, <<
|
class |
SoftMax
Soft max function
row_maxes is a row vector (max for each row)
row_maxes = rowmaxes(input)
diff = exp(input - max) / diff.rowSums()
Outputs a probability distribution.
|
class |
SpaceToBatch
N-dimensional space to batch operation.
|
class |
SpaceToBatchND
N-dimensional space to batch operation.
|
class |
Standardize |
class |
StandardizeBp |
class |
Svd
SVD - singular value decomposition
|
class |
ThresholdRelu
Threshold ReLU op.
|
class |
TopK
Top K op
|
class |
Trace
Matrix trace operation
|
class |
Unique |
class |
UniqueWithCounts |
class |
XwPlusB
Composed op: mmul (X, W) + b
|
class |
Zeta
Element-wise Zeta function.
|
Modifier and Type | Class and Description |
---|---|
class |
SegmentMax
Segment max operation
|
class |
SegmentMean
Segment mean operation
|
class |
SegmentMin
Segment min operation
|
class |
SegmentProd
Segment product operation
|
class |
SegmentSum
Segment sum operation
|
Modifier and Type | Class and Description |
---|---|
class |
Cast
Cast op wrapper.
|
Modifier and Type | Class and Description |
---|---|
class |
CubeBp
Cube backpropagation op - dL/dIn from in and dL/dOut
|
class |
DynamicPartitionBp
Backprop operation for dynamic partition
|
class |
EluBp
ELU backpropagation op - dL/dIn from in and dL/dOut
|
class |
GradientBackwardsMarker |
class |
HardSigmoidBp
Hard Sigmoid backpropagation op - dL/dIn from in and dL/dOut
|
class |
HardTanhBp
Hard Tanh backpropagation op - dL/dIn from in and dL/dOut
|
class |
LeakyReLUBp
LReLU backpropagation op - dL/dIn from in and dL/dOut
|
class |
LogSoftMaxDerivative |
class |
RationalTanhBp
Rational Tanh backpropagation op - dL/dIn from in and dL/dOut
|
class |
RectifiedTanhBp
Rectified Tanh backpropagation op - dL/dIn from in and dL/dOut
|
class |
Relu6Derivative
Derivative of Rectified linear unit 6, i.e.
|
class |
SeluBp
SELU backpropagation op - dL/dIn from in and dL/dOut
|
class |
SigmoidDerivative |
class |
SoftmaxBp
Softmax backpropagation op - dL/dIn from in and dL/dOut
|
class |
SoftPlusBp
SoftPlus backpropagation op - dL/dIn from in and dL/dOut
|
class |
SoftSignBp
SoftSign backpropagation op - dL/dIn from in and dL/dOut
|
class |
TanhDerivative |
class |
ThresholdReluBp
Threshold ReLU Backprop op - dL/dIn from in and dL/dOut
For
RectifiedLinear as well as ThresholdRelu . |
Modifier and Type | Class and Description |
---|---|
class |
AddOp
Addition operation
|
class |
DivOp
Division operation
|
class |
FloorDivOp
Truncated division operation
|
class |
FloorModOp
Floor mod
|
class |
MergeAddOp
Addition operation for n operands, called "mergeadd" in libnd4j
|
class |
ModOp
Modulo operation
|
class |
MulOp
Multiplication operation
|
class |
RDivOp
Reverse Division operation
|
class |
RealDivOp
RealDivision operation
|
class |
RSubOp
Reverse subtraction operation
|
class |
SquaredDifferenceOp
Squared difference operation, i.e.
|
class |
SubOp
Subtraction operation
|
class |
TruncateDivOp
Truncated division operation
|
Modifier and Type | Class and Description |
---|---|
class |
AddBpOp
Addition backprop operation.
|
class |
BaseArithmeticBackpropOp
Base arithmetic backprop operation
|
class |
DivBpOp
Division backprop operation.
|
class |
FloorDivBpOp
Floor div backprop operation.
|
class |
FloorModBpOp
Floor div backprop operation.
|
class |
ModBpOp
Modulo backprop operation.
|
class |
MulBpOp
Division backprop operation.
|
class |
RDivBpOp
Division backprop operation.
|
class |
RSubBpOp
Division backprop operation.
|
class |
SquaredDifferenceBpOp
Backprop op for squared difference operation, i.e.
|
class |
SubBpOp
Division backprop operation.
|
Modifier and Type | Class and Description |
---|---|
class |
Identity
Identity function
|
Modifier and Type | Class and Description |
---|---|
class |
UnsortedSegmentMax
Unsorted segment max operation
|
class |
UnsortedSegmentMean
Unsorted segment mean operation
|
class |
UnsortedSegmentMin
Unsorted segment min operation
|
class |
UnsortedSegmentProd
Unsorted segment product operation
|
class |
UnsortedSegmentSqrtN
Unsorted Sqrt(count) op
|
class |
UnsortedSegmentSum
Unsorted segment sum operation
|
Modifier and Type | Class and Description |
---|---|
class |
SegmentMaxBp
Segment max backprop operation
|
class |
SegmentMeanBp
Segment mean backprop operation
|
class |
SegmentMinBp
Segment min backprop operation
|
class |
SegmentProdBp
Segment product backprop operation
|
class |
SegmentSumBp
Segment sum backprop operation
|
class |
UnsortedSegmentMaxBp
Unsorted segment max backprop operation
|
class |
UnsortedSegmentMeanBp
Unsorted segment mean backprop operation
|
class |
UnsortedSegmentMinBp
Unsorted segment min backprop operation
|
class |
UnsortedSegmentProdBp
Unsorted segment product backprop operation
|
class |
UnsortedSegmentSqrtNBp
Unsorted segment sqrt(n) backprop operation
|
class |
UnsortedSegmentSumBp
Unsorted segment sum backprop operation
|
Modifier and Type | Class and Description |
---|---|
class |
ELU
ELU: Exponential Linear Unit (alpha=1.0)
Introduced in paper: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter (2015) http://arxiv.org/abs/1511.07289 |
Modifier and Type | Class and Description |
---|---|
class |
RestoreV2 |
class |
SaveV2 |
Modifier and Type | Class and Description |
---|---|
class |
RandomStandardNormal
This op is a wrapper for RandomNormal Op
|
Modifier and Type | Class and Description |
---|---|
class |
DistributionUniform
Uniform distribution wrapper
|
class |
RandomBernoulli
Random bernoulli distribution: p(x=1) = p, p(x=0) = 1-p
i.e., output is 0 or 1 with probability p.
|
class |
RandomExponential
Random exponential distribution: p(x) = lambda * exp(-lambda * x)
|
class |
RandomNormal
Random normal distribution
|
Modifier and Type | Class and Description |
---|---|
class |
Range
Range Op implementation, generates from..to distribution within Z
|
Modifier and Type | Method and Description |
---|---|
static INDArray[] |
Nd4j.exec(CustomOp op)
Execute the operation and return the result
|
static INDArray[] |
Nd4j.exec(CustomOp op,
OpContext context)
Execute the operation and return the result
|
Modifier and Type | Method and Description |
---|---|
protected String |
OpProfiler.getOpClass(CustomOp op) |
void |
OpProfiler.processOpCall(CustomOp op)
This method tracks op calls
|
void |
OpProfiler.processStackCall(CustomOp op,
long timeStart) |
void |
OpProfiler.timeOpCall(CustomOp op,
long startTime) |
Modifier and Type | Method and Description |
---|---|
void |
StringAggregator.putTime(String key,
CustomOp op,
long timeSpent) |
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