public <T> SparseReorder<T> sparseReorder(Operand<Long> inputIndices, Operand<T> inputValues, Operand<Long> inputShape)
SparseReorder operationinputIndices - 2-D. `N x R` matrix with the indices of non-empty values in ainputValues - 1-D. `N` non-empty values corresponding to `input_indices`.inputShape - 1-D. Shape of the input SparseTensor.SparseReorderpublic <T extends Number,U extends Number> SparseSegmentSqrtNGrad<T> sparseSegmentSqrtNGrad(Operand<T> grad, Operand<U> indices, Operand<Integer> segmentIds, Operand<Integer> outputDim0)
SparseSegmentSqrtNGrad operationgrad - gradient propagated to the SparseSegmentSqrtN op.indices - indices passed to the corresponding SparseSegmentSqrtN op.segmentIds - segment_ids passed to the corresponding SparseSegmentSqrtN op.outputDim0 - dimension 0 of "data" passed to SparseSegmentSqrtN op.SparseSegmentSqrtNGradpublic <T extends Number,U extends Number> SparseSegmentSqrtN<T> sparseSegmentSqrtN(Operand<T> data, Operand<U> indices, Operand<Integer> segmentIds)
SparseSegmentSqrtN operationdata - indices - A 1-D tensor. Has same rank as `segment_ids`.segmentIds - A 1-D tensor. Values should be sorted and can be repeated.SparseSegmentSqrtNpublic <T> SparseFillEmptyRows<T> sparseFillEmptyRows(Operand<Long> indices, Operand<T> values, Operand<Long> denseShape, Operand<T> defaultValue)
SparseFillEmptyRows operationindices - 2-D. the indices of the sparse tensor.values - 1-D. the values of the sparse tensor.denseShape - 1-D. the shape of the sparse tensor.defaultValue - 0-D. default value to insert into location `[row, 0, ..., 0]`SparseFillEmptyRowspublic <T> SparseAccumulatorApplyGradient sparseAccumulatorApplyGradient(Operand<String> handle, Operand<Long> localStep, Operand<Long> gradientIndices, Operand<T> gradientValues, Operand<Long> gradientShape, Boolean hasKnownShape)
SparseAccumulatorApplyGradient operationhandle - The handle to a accumulator.localStep - The local_step value at which the sparse gradient was computed.gradientIndices - Indices of the sparse gradient to be accumulated. Must be agradientValues - Values are the non-zero slices of the gradient, and must havegradientShape - Shape of the sparse gradient to be accumulated.hasKnownShape - Boolean indicating whether gradient_shape is unknown, in whichSparseAccumulatorApplyGradientpublic <U,T extends Number> SparseTensorDenseMatMul<U> sparseTensorDenseMatMul(Operand<T> aIndices, Operand<U> aValues, Operand<Long> aShape, Operand<U> b, SparseTensorDenseMatMul.Options... options)
SparseTensorDenseMatMul operationaIndices - 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.aValues - 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector.aShape - 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector.b - 2-D. A dense Matrix.options - carries optional attributes valuesSparseTensorDenseMatMulpublic <U,T extends Number> SparseTensorDenseAdd<U> sparseTensorDenseAdd(Operand<T> aIndices, Operand<U> aValues, Operand<T> aShape, Operand<U> b)
SparseTensorDenseAdd operationaIndices - 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`.aValues - 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`.aShape - 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`.b - `ndims`-D Tensor. With shape `a_shape`.SparseTensorDenseAddpublic <T extends Number,U extends Number,V extends Number> SparseSegmentSqrtNWithNumSegments<T> sparseSegmentSqrtNWithNumSegments(Operand<T> data, Operand<U> indices, Operand<Integer> segmentIds, Operand<V> numSegments)
SparseSegmentSqrtNWithNumSegments operationdata - indices - A 1-D tensor. Has same rank as `segment_ids`.segmentIds - A 1-D tensor. Values should be sorted and can be repeated.numSegments - Should equal the number of distinct segment IDs.SparseSegmentSqrtNWithNumSegmentspublic <U,T> DeserializeSparse<U> deserializeSparse(Operand<T> serializedSparse, Class<U> dtype)
DeserializeSparse operationserializedSparse - The serialized `SparseTensor` objects. The last dimensiondtype - The `dtype` of the serialized `SparseTensor` objects.DeserializeSparsepublic <T> TakeManySparseFromTensorsMap<T> takeManySparseFromTensorsMap(Operand<Long> sparseHandles, Class<T> dtype, TakeManySparseFromTensorsMap.Options... options)
TakeManySparseFromTensorsMap operationsparseHandles - 1-D, The `N` serialized `SparseTensor` objects.dtype - The `dtype` of the `SparseTensor` objects stored in theoptions - carries optional attributes valuesTakeManySparseFromTensorsMappublic <T extends Number> SparseReduceMaxSparse<T> sparseReduceMaxSparse(Operand<Long> inputIndices, Operand<T> inputValues, Operand<Long> inputShape, Operand<Integer> reductionAxes, SparseReduceMaxSparse.Options... options)
SparseReduceMaxSparse operationinputIndices - 2-D. `N x R` matrix with the indices of non-empty values in ainputValues - 1-D. `N` non-empty values corresponding to `input_indices`.inputShape - 1-D. Shape of the input SparseTensor.reductionAxes - 1-D. Length-`K` vector containing the reduction axes.options - carries optional attributes valuesSparseReduceMaxSparsepublic <T,U extends Number> SparseAdd<T> sparseAdd(Operand<Long> aIndices, Operand<T> aValues, Operand<Long> aShape, Operand<Long> bIndices, Operand<T> bValues, Operand<Long> bShape, Operand<U> thresh)
SparseAdd operationaIndices - 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.aValues - 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector.aShape - 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector.bIndices - 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.bValues - 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector.bShape - 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector.thresh - 0-D. The magnitude threshold that determines if an output value/indexSparseAddpublic <T> AddSparseToTensorsMap addSparseToTensorsMap(Operand<Long> sparseIndices, Operand<T> sparseValues, Operand<Long> sparseShape, AddSparseToTensorsMap.Options... options)
AddSparseToTensorsMap operationsparseIndices - 2-D. The `indices` of the `SparseTensor`.sparseValues - 1-D. The `values` of the `SparseTensor`.sparseShape - 1-D. The `shape` of the `SparseTensor`.options - carries optional attributes valuesAddSparseToTensorsMappublic <T extends Number,U extends Number> SparseSegmentMeanGrad<T> sparseSegmentMeanGrad(Operand<T> grad, Operand<U> indices, Operand<Integer> segmentIds, Operand<Integer> outputDim0)
SparseSegmentMeanGrad operationgrad - gradient propagated to the SparseSegmentMean op.indices - indices passed to the corresponding SparseSegmentMean op.segmentIds - segment_ids passed to the corresponding SparseSegmentMean op.outputDim0 - dimension 0 of "data" passed to SparseSegmentMean op.SparseSegmentMeanGradpublic <T extends Number,U extends Number> SparseMatMul sparseMatMul(Operand<T> a, Operand<U> b, SparseMatMul.Options... options)
SparseMatMul operationa - b - options - carries optional attributes valuesSparseMatMulpublic <T> SparseFillEmptyRowsGrad<T> sparseFillEmptyRowsGrad(Operand<Long> reverseIndexMap, Operand<T> gradValues)
SparseFillEmptyRowsGrad operationreverseIndexMap - 1-D. The reverse index map from SparseFillEmptyRows.gradValues - 1-D. The gradients from backprop.SparseFillEmptyRowsGradpublic <T> SparseSlice<T> sparseSlice(Operand<Long> indices, Operand<T> values, Operand<Long> shape, Operand<Long> start, Operand<Long> size)
SparseSlice operationindices - 2-D tensor represents the indices of the sparse tensor.values - 1-D tensor represents the values of the sparse tensor.shape - 1-D. tensor represents the shape of the sparse tensor.start - 1-D. tensor represents the start of the slice.size - 1-D. tensor represents the size of the slice.SparseSlicepublic <T> SparseReduceSumSparse<T> sparseReduceSumSparse(Operand<Long> inputIndices, Operand<T> inputValues, Operand<Long> inputShape, Operand<Integer> reductionAxes, SparseReduceSumSparse.Options... options)
SparseReduceSumSparse operationinputIndices - 2-D. `N x R` matrix with the indices of non-empty values in ainputValues - 1-D. `N` non-empty values corresponding to `input_indices`.inputShape - 1-D. Shape of the input SparseTensor.reductionAxes - 1-D. Length-`K` vector containing the reduction axes.options - carries optional attributes valuesSparseReduceSumSparsepublic <T> SparseDenseCwiseDiv<T> sparseDenseCwiseDiv(Operand<Long> spIndices, Operand<T> spValues, Operand<Long> spShape, Operand<T> dense)
SparseDenseCwiseDiv operationspIndices - 2-D. `N x R` matrix with the indices of non-empty values in aspValues - 1-D. `N` non-empty values corresponding to `sp_indices`.spShape - 1-D. Shape of the input SparseTensor.dense - `R`-D. The dense Tensor operand.SparseDenseCwiseDivpublic <T> SparseReduceSum<T> sparseReduceSum(Operand<Long> inputIndices, Operand<T> inputValues, Operand<Long> inputShape, Operand<Integer> reductionAxes, SparseReduceSum.Options... options)
SparseReduceSum operationinputIndices - 2-D. `N x R` matrix with the indices of non-empty values in ainputValues - 1-D. `N` non-empty values corresponding to `input_indices`.inputShape - 1-D. Shape of the input SparseTensor.reductionAxes - 1-D. Length-`K` vector containing the reduction axes.options - carries optional attributes valuesSparseReduceSumpublic <T> SparseSparseMinimum<T> sparseSparseMinimum(Operand<Long> aIndices, Operand<T> aValues, Operand<Long> aShape, Operand<Long> bIndices, Operand<T> bValues, Operand<Long> bShape)
SparseSparseMinimum operationaIndices - 2-D. `N x R` matrix with the indices of non-empty values in aaValues - 1-D. `N` non-empty values corresponding to `a_indices`.aShape - 1-D. Shape of the input SparseTensor.bIndices - counterpart to `a_indices` for the other operand.bValues - counterpart to `a_values` for the other operand; must be of the same dtype.bShape - counterpart to `a_shape` for the other operand; the two shapes must be equal.SparseSparseMinimumpublic <T extends Number,U extends Number,V extends Number> SparseSegmentSumWithNumSegments<T> sparseSegmentSumWithNumSegments(Operand<T> data, Operand<U> indices, Operand<Integer> segmentIds, Operand<V> numSegments)
SparseSegmentSumWithNumSegments operationdata - indices - A 1-D tensor. Has same rank as `segment_ids`.segmentIds - A 1-D tensor. Values should be sorted and can be repeated.numSegments - Should equal the number of distinct segment IDs.SparseSegmentSumWithNumSegmentspublic <T extends Number,U extends Number> SparseSegmentMean<T> sparseSegmentMean(Operand<T> data, Operand<U> indices, Operand<Integer> segmentIds)
SparseSegmentMean operationdata - indices - A 1-D tensor. Has same rank as `segment_ids`.segmentIds - A 1-D tensor. Values should be sorted and can be repeated.SparseSegmentMeanpublic <T> AddManySparseToTensorsMap addManySparseToTensorsMap(Operand<Long> sparseIndices, Operand<T> sparseValues, Operand<Long> sparseShape, AddManySparseToTensorsMap.Options... options)
AddManySparseToTensorsMap operationsparseIndices - 2-D. The `indices` of the minibatch `SparseTensor`.sparseValues - 1-D. The `values` of the minibatch `SparseTensor`.sparseShape - 1-D. The `shape` of the minibatch `SparseTensor`.options - carries optional attributes valuesAddManySparseToTensorsMappublic <T> SparseToSparseSetOperation<T> sparseToSparseSetOperation(Operand<Long> set1Indices, Operand<T> set1Values, Operand<Long> set1Shape, Operand<Long> set2Indices, Operand<T> set2Values, Operand<Long> set2Shape, String setOperation, SparseToSparseSetOperation.Options... options)
SparseToSparseSetOperation operationset1Indices - 2D `Tensor`, indices of a `SparseTensor`. Must be in row-majorset1Values - 1D `Tensor`, values of a `SparseTensor`. Must be in row-majorset1Shape - 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` mustset2Indices - 2D `Tensor`, indices of a `SparseTensor`. Must be in row-majorset2Values - 1D `Tensor`, values of a `SparseTensor`. Must be in row-majorset2Shape - 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` mustsetOperation - options - carries optional attributes valuesSparseToSparseSetOperationpublic <T> DenseToSparseSetOperation<T> denseToSparseSetOperation(Operand<T> set1, Operand<Long> set2Indices, Operand<T> set2Values, Operand<Long> set2Shape, String setOperation, DenseToSparseSetOperation.Options... options)
DenseToSparseSetOperation operationset1 - `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.set2Indices - 2D `Tensor`, indices of a `SparseTensor`. Must be in row-majorset2Values - 1D `Tensor`, values of a `SparseTensor`. Must be in row-majorset2Shape - 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` mustsetOperation - options - carries optional attributes valuesDenseToSparseSetOperationpublic <T> SparseSplit<T> sparseSplit(Operand<Long> splitDim, Operand<Long> indices, Operand<T> values, Operand<Long> shape, Long numSplit)
SparseSplit operationsplitDim - 0-D. The dimension along which to split. Must be in the rangeindices - 2-D tensor represents the indices of the sparse tensor.values - 1-D tensor represents the values of the sparse tensor.shape - 1-D. tensor represents the shape of the sparse tensor.numSplit - The number of ways to split.SparseSplitpublic <T> DenseToDenseSetOperation<T> denseToDenseSetOperation(Operand<T> set1, Operand<T> set2, String setOperation, DenseToDenseSetOperation.Options... options)
DenseToDenseSetOperation operationset1 - `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.set2 - `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`.setOperation - options - carries optional attributes valuesDenseToDenseSetOperationpublic <T> SparseAccumulatorTakeGradient<T> sparseAccumulatorTakeGradient(Operand<String> handle, Operand<Integer> numRequired, Class<T> dtype)
SparseAccumulatorTakeGradient operationhandle - The handle to a SparseConditionalAccumulator.numRequired - Number of gradients required before we return an aggregate.dtype - The data type of accumulated gradients. Needs to correspond to the typeSparseAccumulatorTakeGradientpublic <T> SparseDenseCwiseMul<T> sparseDenseCwiseMul(Operand<Long> spIndices, Operand<T> spValues, Operand<Long> spShape, Operand<T> dense)
SparseDenseCwiseMul operationspIndices - 2-D. `N x R` matrix with the indices of non-empty values in aspValues - 1-D. `N` non-empty values corresponding to `sp_indices`.spShape - 1-D. Shape of the input SparseTensor.dense - `R`-D. The dense Tensor operand.SparseDenseCwiseMulpublic <T> SparseConditionalAccumulator sparseConditionalAccumulator(Class<T> dtype, Shape shape, SparseConditionalAccumulator.Options... options)
SparseConditionalAccumulator operationdtype - The type of the value being accumulated.shape - The shape of the values.options - carries optional attributes valuesSparseConditionalAccumulatorpublic <T extends Number> SparseReduceMax<T> sparseReduceMax(Operand<Long> inputIndices, Operand<T> inputValues, Operand<Long> inputShape, Operand<Integer> reductionAxes, SparseReduceMax.Options... options)
SparseReduceMax operationinputIndices - 2-D. `N x R` matrix with the indices of non-empty values in ainputValues - 1-D. `N` non-empty values corresponding to `input_indices`.inputShape - 1-D. Shape of the input SparseTensor.reductionAxes - 1-D. Length-`K` vector containing the reduction axes.options - carries optional attributes valuesSparseReduceMaxpublic <U,T extends Number> SparseToDense<U> sparseToDense(Operand<T> sparseIndices, Operand<T> outputShape, Operand<U> sparseValues, Operand<U> defaultValue, SparseToDense.Options... options)
SparseToDense operationsparseIndices - 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the completeoutputShape - 1-D. Shape of the dense output tensor.sparseValues - 1-D. Values corresponding to each row of `sparse_indices`,defaultValue - Scalar value to set for indices not specified inoptions - carries optional attributes valuesSparseToDensepublic <T> SparseDenseCwiseAdd<T> sparseDenseCwiseAdd(Operand<Long> spIndices, Operand<T> spValues, Operand<Long> spShape, Operand<T> dense)
SparseDenseCwiseAdd operationspIndices - 2-D. `N x R` matrix with the indices of non-empty values in aspValues - 1-D. `N` non-empty values corresponding to `sp_indices`.spShape - 1-D. Shape of the input SparseTensor.dense - `R`-D. The dense Tensor operand.SparseDenseCwiseAddpublic <T,U> SparseCross<T> sparseCross(Iterable<Operand<Long>> indices, Iterable<Operand<?>> values, Iterable<Operand<Long>> shapes, Iterable<Operand<?>> denseInputs, Boolean hashedOutput, Long numBuckets, Long hashKey, Class<T> outType, Class<U> internalType)
SparseCross operationindices - 2-D. Indices of each input `SparseTensor`.values - 1-D. values of each `SparseTensor`.shapes - 1-D. Shapes of each `SparseTensor`.denseInputs - 2-D. Columns represented by dense `Tensor`.hashedOutput - If true, returns the hash of the cross instead of the string.numBuckets - It is used if hashed_output is true.hashKey - Specify the hash_key that will be used by the `FingerprintCat64`outType - internalType - SparseCrosspublic <T> SparseSliceGrad<T> sparseSliceGrad(Operand<T> backpropValGrad, Operand<Long> inputIndices, Operand<Long> inputStart, Operand<Long> outputIndices)
SparseSliceGrad operationbackpropValGrad - 1-D. The gradient with respect toinputIndices - 2-D. The `indices` of the input `SparseTensor`.inputStart - 1-D. tensor represents the start of the slice.outputIndices - 2-D. The `indices` of the sliced `SparseTensor`.SparseSliceGradpublic <T extends Number,U extends Number,V extends Number> SparseSegmentMeanWithNumSegments<T> sparseSegmentMeanWithNumSegments(Operand<T> data, Operand<U> indices, Operand<Integer> segmentIds, Operand<V> numSegments)
SparseSegmentMeanWithNumSegments operationdata - indices - A 1-D tensor. Has same rank as `segment_ids`.segmentIds - A 1-D tensor. Values should be sorted and can be repeated.numSegments - Should equal the number of distinct segment IDs.SparseSegmentMeanWithNumSegmentspublic <T extends Number> SparseSoftmax<T> sparseSoftmax(Operand<Long> spIndices, Operand<T> spValues, Operand<Long> spShape)
SparseSoftmax operationspIndices - 2-D. `NNZ x R` matrix with the indices of non-empty values in aspValues - 1-D. `NNZ` non-empty values corresponding to `sp_indices`.spShape - 1-D. Shape of the input SparseTensor.SparseSoftmaxpublic <T> SparseConcat<T> sparseConcat(Iterable<Operand<Long>> indices, Iterable<Operand<T>> values, Iterable<Operand<Long>> shapes, Long concatDim)
SparseConcat operationindices - 2-D. Indices of each input `SparseTensor`.values - 1-D. Non-empty values of each `SparseTensor`.shapes - 1-D. Shapes of each `SparseTensor`.concatDim - Dimension to concatenate along. Must be in range [-rank, rank),SparseConcatpublic <T> SparseAddGrad<T> sparseAddGrad(Operand<T> backpropValGrad, Operand<Long> aIndices, Operand<Long> bIndices, Operand<Long> sumIndices)
SparseAddGrad operationbackpropValGrad - 1-D with shape `[nnz(sum)]`. The gradient with respect toaIndices - 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.bIndices - 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.sumIndices - 2-D. The `indices` of the sum `SparseTensor`, sizeSparseAddGradpublic <T extends Number> SparseSparseMaximum<T> sparseSparseMaximum(Operand<Long> aIndices, Operand<T> aValues, Operand<Long> aShape, Operand<Long> bIndices, Operand<T> bValues, Operand<Long> bShape)
SparseSparseMaximum operationaIndices - 2-D. `N x R` matrix with the indices of non-empty values in aaValues - 1-D. `N` non-empty values corresponding to `a_indices`.aShape - 1-D. Shape of the input SparseTensor.bIndices - counterpart to `a_indices` for the other operand.bValues - counterpart to `a_values` for the other operand; must be of the same dtype.bShape - counterpart to `a_shape` for the other operand; the two shapes must be equal.SparseSparseMaximumpublic <T extends Number,U extends Number> SparseSegmentSum<T> sparseSegmentSum(Operand<T> data, Operand<U> indices, Operand<Integer> segmentIds)
SparseSegmentSum operationdata - indices - A 1-D tensor. Has same rank as `segment_ids`.segmentIds - A 1-D tensor. Values should be sorted and can be repeated.SparseSegmentSumpublic SparseReshape sparseReshape(Operand<Long> inputIndices, Operand<Long> inputShape, Operand<Long> newShape)
SparseReshape operationinputIndices - 2-D. `N x R_in` matrix with the indices of non-empty values in ainputShape - 1-D. `R_in` vector with the input SparseTensor's dense shape.newShape - 1-D. `R_out` vector with the requested new dense shape.SparseReshapeCopyright © 2022. All rights reserved.