| Class | Description |
|---|---|
| Assign |
Assign op: x = y, with broadcast as required
|
| ATan2 |
Arc Tangent elementwise function
|
| BatchToSpace |
N-dimensional batch to space operation.
|
| BatchToSpaceND |
N-dimensional batch to space operation.
|
| BitsHammingDistance | |
| BitwiseAnd |
Bit-wise AND operation, broadcastable
|
| BitwiseOr |
Bit-wise OR operation, broadcastable
|
| BitwiseXor |
Bit-wise XOR operation, broadcastable
|
| Choose |
This op allows us to (based on the passed in condition)
to return the element fulfilling the condition.
|
| CumProd | |
| CumSum |
Cumulative sum operation, optionally along dimension.
|
| CyclicRShiftBits |
Element-wise roll operation, rolls bits to the left, <<
|
| CyclicShiftBits |
Element-wise roll operation, rolls bits to the left, <<
|
| Dilation2D |
Dilation2D op wrapper
|
| DotProductAttention |
(optionally scaled) dot product attention
See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, p.
|
| DotProductAttentionBp |
(optionally scaled) dot product attention Backprop
See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, p.
|
| DynamicPartition |
Transforms a given input tensor into numPartitions partitions, as indicated by the indices in "partitions".
|
| DynamicStitch |
Transforms a given input tensor into numPartitions partitions, as indicated by the indices in "partitions".
|
| EqualTo |
Bit mask over the ndarrays as to whether
the components are equal or not
|
| FakeQuantWithMinMaxArgs |
Fake quantization operation.
|
| FakeQuantWithMinMaxVars |
Fake quantization operation.
|
| Fill |
Fill an array of given "shape" with the provided "value", e.g.
|
| GreaterThan |
Bit mask over the ndarrays as to whether
the components are greater than or not
|
| GreaterThanOrEqual |
Bit mask over the ndarrays as to whether
the components are greater than or equal or not
|
| InTopK |
In Top K op
|
| InvertPermutation |
Inverse of index permutation.
|
| 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].
|
| IsNumericTensor |
This op takes 1 n-dimensional array as input, and returns true if input is a numeric array.
|
| 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].
|
| LayerNorm |
Composed op: g*standarize(x) + b
Bias is optional, and can be set as null
|
| LayerNormBp |
Composed op: g*standarize(x) + b
Bias is optional, and can be set as null
|
| LessThan |
Bit mask over the ndarrays as to whether
the components are less than or not
|
| LessThanOrEqual |
Bit mask over the ndarrays as to whether
the components are less than or equal or not
|
| ListDiff | |
| LogicalAnd | |
| LogicalNot | |
| LogicalOr | |
| LogicalXor | |
| LogMatrixDeterminant |
Log Matrix Determinant op
Given input with shape [..., N, N] output the log determinant for each sub-matrix.
|
| LogSoftMax |
Log(softmax(X))
|
| MatrixDeterminant |
Matrix Determinant op
Given input with shape [..., N, N] output the determinant for each sub-matrix.
|
| MatrixDiag | |
| MatrixDiagPart | |
| MatrixInverse |
Matrix Inverse Function
|
| MatrixSetDiag | |
| Max |
Max function
|
| Min |
Min function
|
| MirrorPad | |
| MultiHeadDotProductAttention |
(optionally scaled) multi head dot product attention
See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, pp.
|
| MultiHeadDotProductAttentionBp |
(optionally scaled) multi head dot product attention Backprop
See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, pp.
|
| NotEqualTo |
Not equal to function:
Bit mask over whether 2 elements are not equal or not
|
| ParallelConcat | |
| Pow |
Broadcastable element-wise power operation: x[i]^y[i]
|
| Reverse | |
| ReverseSequence |
Created by farizrahman4u on 3/16/18.
|
| ReverseV2 |
This is compatibility op for ReverseV2
|
| RShiftBits |
Element-wise shift operation, shift bits to the right, >>
|
| ShiftBits |
Element-wise shift operation, shift bits to the left, <<
|
| 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.
|
| SpaceToBatch |
N-dimensional space to batch operation.
|
| SpaceToBatchND |
N-dimensional space to batch operation.
|
| Standardize | |
| StandardizeBp | |
| Svd |
SVD - singular value decomposition
|
| ThresholdRelu |
Threshold ReLU op.
|
| TopK |
Top K op
|
| Trace |
Matrix trace operation
|
| Unique | |
| UniqueWithCounts | |
| XwPlusB |
Composed op: mmul (X, W) + b
|
| Zeta |
Element-wise Zeta function.
|
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