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Pure-Java code for the TensorFlow machine intelligence library.
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package org.tensorflow.op;
import org.tensorflow.Operand;
import org.tensorflow.Shape;
import org.tensorflow.op.sparse.AddManySparseToTensorsMap;
import org.tensorflow.op.sparse.AddSparseToTensorsMap;
import org.tensorflow.op.sparse.DenseToDenseSetOperation;
import org.tensorflow.op.sparse.DenseToSparseSetOperation;
import org.tensorflow.op.sparse.DeserializeSparse;
import org.tensorflow.op.sparse.SparseAccumulatorApplyGradient;
import org.tensorflow.op.sparse.SparseAccumulatorTakeGradient;
import org.tensorflow.op.sparse.SparseAdd;
import org.tensorflow.op.sparse.SparseAddGrad;
import org.tensorflow.op.sparse.SparseConcat;
import org.tensorflow.op.sparse.SparseConditionalAccumulator;
import org.tensorflow.op.sparse.SparseCross;
import org.tensorflow.op.sparse.SparseDenseCwiseAdd;
import org.tensorflow.op.sparse.SparseDenseCwiseDiv;
import org.tensorflow.op.sparse.SparseDenseCwiseMul;
import org.tensorflow.op.sparse.SparseFillEmptyRows;
import org.tensorflow.op.sparse.SparseFillEmptyRowsGrad;
import org.tensorflow.op.sparse.SparseMatMul;
import org.tensorflow.op.sparse.SparseReduceMax;
import org.tensorflow.op.sparse.SparseReduceMaxSparse;
import org.tensorflow.op.sparse.SparseReduceSum;
import org.tensorflow.op.sparse.SparseReduceSumSparse;
import org.tensorflow.op.sparse.SparseReorder;
import org.tensorflow.op.sparse.SparseReshape;
import org.tensorflow.op.sparse.SparseSegmentMean;
import org.tensorflow.op.sparse.SparseSegmentMeanGrad;
import org.tensorflow.op.sparse.SparseSegmentMeanWithNumSegments;
import org.tensorflow.op.sparse.SparseSegmentSqrtN;
import org.tensorflow.op.sparse.SparseSegmentSqrtNGrad;
import org.tensorflow.op.sparse.SparseSegmentSqrtNWithNumSegments;
import org.tensorflow.op.sparse.SparseSegmentSum;
import org.tensorflow.op.sparse.SparseSegmentSumWithNumSegments;
import org.tensorflow.op.sparse.SparseSlice;
import org.tensorflow.op.sparse.SparseSliceGrad;
import org.tensorflow.op.sparse.SparseSoftmax;
import org.tensorflow.op.sparse.SparseSparseMaximum;
import org.tensorflow.op.sparse.SparseSparseMinimum;
import org.tensorflow.op.sparse.SparseSplit;
import org.tensorflow.op.sparse.SparseTensorDenseAdd;
import org.tensorflow.op.sparse.SparseTensorDenseMatMul;
import org.tensorflow.op.sparse.SparseToDense;
import org.tensorflow.op.sparse.SparseToSparseSetOperation;
import org.tensorflow.op.sparse.TakeManySparseFromTensorsMap;
/**
* An API for building {@code sparse} operations as {@link Op Op}s
*
* @see {@link Ops}
*/
public final class SparseOps {
private final Scope scope;
SparseOps(Scope scope) {
this.scope = scope;
}
/**
* Builds an {@link SparseReorder} operation
*
* @param inputIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param inputValues 1-D. `N` non-empty values corresponding to `input_indices`.
* @param inputShape 1-D. Shape of the input SparseTensor.
* @return a new instance of SparseReorder
* @see org.tensorflow.op.sparse.SparseReorder
*/
public SparseReorder sparseReorder(Operand inputIndices, Operand inputValues,
Operand inputShape) {
return SparseReorder.create(scope, inputIndices, inputValues, inputShape);
}
/**
* Builds an {@link SparseSegmentSqrtNGrad} operation
*
* @param grad gradient propagated to the SparseSegmentSqrtN op.
* @param indices indices passed to the corresponding SparseSegmentSqrtN op.
* @param segmentIds segment_ids passed to the corresponding SparseSegmentSqrtN op.
* @param outputDim0 dimension 0 of "data" passed to SparseSegmentSqrtN op.
* @return a new instance of SparseSegmentSqrtNGrad
* @see org.tensorflow.op.sparse.SparseSegmentSqrtNGrad
*/
public SparseSegmentSqrtNGrad sparseSegmentSqrtNGrad(Operand grad,
Operand indices, Operand segmentIds, Operand outputDim0) {
return SparseSegmentSqrtNGrad.create(scope, grad, indices, segmentIds, outputDim0);
}
/**
* Builds an {@link SparseAccumulatorApplyGradient} operation
*
* @param handle The handle to a accumulator.
* @param localStep The local_step value at which the sparse gradient was computed.
* @param gradientIndices Indices of the sparse gradient to be accumulated. Must be a
* @param gradientValues Values are the non-zero slices of the gradient, and must have
* @param gradientShape Shape of the sparse gradient to be accumulated.
* @param hasKnownShape Boolean indicating whether gradient_shape is unknown, in which
* @return a new instance of SparseAccumulatorApplyGradient
* @see org.tensorflow.op.sparse.SparseAccumulatorApplyGradient
*/
public SparseAccumulatorApplyGradient sparseAccumulatorApplyGradient(Operand handle,
Operand localStep, Operand gradientIndices, Operand gradientValues,
Operand gradientShape, Boolean hasKnownShape) {
return SparseAccumulatorApplyGradient.create(scope, handle, localStep, gradientIndices, gradientValues, gradientShape, hasKnownShape);
}
/**
* Builds an {@link SparseFillEmptyRows} operation
*
* @param indices 2-D. the indices of the sparse tensor.
* @param values 1-D. the values of the sparse tensor.
* @param denseShape 1-D. the shape of the sparse tensor.
* @param defaultValue 0-D. default value to insert into location `[row, 0, ..., 0]`
* @return a new instance of SparseFillEmptyRows
* @see org.tensorflow.op.sparse.SparseFillEmptyRows
*/
public SparseFillEmptyRows sparseFillEmptyRows(Operand indices, Operand values,
Operand denseShape, Operand defaultValue) {
return SparseFillEmptyRows.create(scope, indices, values, denseShape, defaultValue);
}
/**
* Builds an {@link SparseSegmentSqrtN} operation
*
* @param data
* @param indices A 1-D tensor. Has same rank as `segment_ids`.
* @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
* @return a new instance of SparseSegmentSqrtN
* @see org.tensorflow.op.sparse.SparseSegmentSqrtN
*/
public SparseSegmentSqrtN sparseSegmentSqrtN(Operand data,
Operand indices, Operand segmentIds) {
return SparseSegmentSqrtN.create(scope, data, indices, segmentIds);
}
/**
* Builds an {@link SparseTensorDenseAdd} operation
*
* @param aIndices 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`.
* @param aValues 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`.
* @param aShape 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`.
* @param b `ndims`-D Tensor. With shape `a_shape`.
* @return a new instance of SparseTensorDenseAdd
* @see org.tensorflow.op.sparse.SparseTensorDenseAdd
*/
public SparseTensorDenseAdd sparseTensorDenseAdd(Operand aIndices,
Operand aValues, Operand aShape, Operand b) {
return SparseTensorDenseAdd.create(scope, aIndices, aValues, aShape, b);
}
/**
* Builds an {@link SparseTensorDenseMatMul} operation
*
* @param aIndices 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix.
* @param aValues 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector.
* @param aShape 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector.
* @param b 2-D. A dense Matrix.
* @param options carries optional attributes values
* @return a new instance of SparseTensorDenseMatMul
* @see org.tensorflow.op.sparse.SparseTensorDenseMatMul
*/
public SparseTensorDenseMatMul sparseTensorDenseMatMul(Operand aIndices,
Operand aValues, Operand aShape, Operand b,
SparseTensorDenseMatMul.Options... options) {
return SparseTensorDenseMatMul.create(scope, aIndices, aValues, aShape, b, options);
}
/**
* Builds an {@link DeserializeSparse} operation
*
* @param serializedSparse The serialized `SparseTensor` objects. The last dimension
* @param dtype The `dtype` of the serialized `SparseTensor` objects.
* @return a new instance of DeserializeSparse
* @see org.tensorflow.op.sparse.DeserializeSparse
*/
public DeserializeSparse deserializeSparse(Operand serializedSparse,
Class dtype) {
return DeserializeSparse.create(scope, serializedSparse, dtype);
}
/**
* Builds an {@link SparseSegmentSqrtNWithNumSegments} operation
*
* @param data
* @param indices A 1-D tensor. Has same rank as `segment_ids`.
* @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
* @param numSegments Should equal the number of distinct segment IDs.
* @return a new instance of SparseSegmentSqrtNWithNumSegments
* @see org.tensorflow.op.sparse.SparseSegmentSqrtNWithNumSegments
*/
public SparseSegmentSqrtNWithNumSegments sparseSegmentSqrtNWithNumSegments(Operand data,
Operand indices, Operand segmentIds, Operand numSegments) {
return SparseSegmentSqrtNWithNumSegments.create(scope, data, indices, segmentIds, numSegments);
}
/**
* Builds an {@link TakeManySparseFromTensorsMap} operation
*
* @param sparseHandles 1-D, The `N` serialized `SparseTensor` objects.
* @param dtype The `dtype` of the `SparseTensor` objects stored in the
* @param options carries optional attributes values
* @return a new instance of TakeManySparseFromTensorsMap
* @see org.tensorflow.op.sparse.TakeManySparseFromTensorsMap
*/
public TakeManySparseFromTensorsMap takeManySparseFromTensorsMap(Operand sparseHandles,
Class dtype, TakeManySparseFromTensorsMap.Options... options) {
return TakeManySparseFromTensorsMap.create(scope, sparseHandles, dtype, options);
}
/**
* Builds an {@link SparseReduceMaxSparse} operation
*
* @param inputIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param inputValues 1-D. `N` non-empty values corresponding to `input_indices`.
* @param inputShape 1-D. Shape of the input SparseTensor.
* @param reductionAxes 1-D. Length-`K` vector containing the reduction axes.
* @param options carries optional attributes values
* @return a new instance of SparseReduceMaxSparse
* @see org.tensorflow.op.sparse.SparseReduceMaxSparse
*/
public SparseReduceMaxSparse sparseReduceMaxSparse(Operand inputIndices,
Operand inputValues, Operand inputShape, Operand reductionAxes,
SparseReduceMaxSparse.Options... options) {
return SparseReduceMaxSparse.create(scope, inputIndices, inputValues, inputShape, reductionAxes, options);
}
/**
* Builds an {@link AddSparseToTensorsMap} operation
*
* @param sparseIndices 2-D. The `indices` of the `SparseTensor`.
* @param sparseValues 1-D. The `values` of the `SparseTensor`.
* @param sparseShape 1-D. The `shape` of the `SparseTensor`.
* @param options carries optional attributes values
* @return a new instance of AddSparseToTensorsMap
* @see org.tensorflow.op.sparse.AddSparseToTensorsMap
*/
public AddSparseToTensorsMap addSparseToTensorsMap(Operand sparseIndices,
Operand sparseValues, Operand sparseShape,
AddSparseToTensorsMap.Options... options) {
return AddSparseToTensorsMap.create(scope, sparseIndices, sparseValues, sparseShape, options);
}
/**
* Builds an {@link SparseAdd} operation
*
* @param aIndices 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix.
* @param aValues 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector.
* @param aShape 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector.
* @param bIndices 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.
* @param bValues 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector.
* @param bShape 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector.
* @param thresh 0-D. The magnitude threshold that determines if an output value/index
* @return a new instance of SparseAdd
* @see org.tensorflow.op.sparse.SparseAdd
*/
public SparseAdd sparseAdd(Operand aIndices, Operand aValues,
Operand aShape, Operand bIndices, Operand bValues, Operand bShape,
Operand thresh) {
return SparseAdd.create(scope, aIndices, aValues, aShape, bIndices, bValues, bShape, thresh);
}
/**
* Builds an {@link SparseSegmentMeanGrad} operation
*
* @param grad gradient propagated to the SparseSegmentMean op.
* @param indices indices passed to the corresponding SparseSegmentMean op.
* @param segmentIds segment_ids passed to the corresponding SparseSegmentMean op.
* @param outputDim0 dimension 0 of "data" passed to SparseSegmentMean op.
* @return a new instance of SparseSegmentMeanGrad
* @see org.tensorflow.op.sparse.SparseSegmentMeanGrad
*/
public SparseSegmentMeanGrad sparseSegmentMeanGrad(Operand grad,
Operand indices, Operand segmentIds, Operand outputDim0) {
return SparseSegmentMeanGrad.create(scope, grad, indices, segmentIds, outputDim0);
}
/**
* Builds an {@link SparseMatMul} operation
*
* @param a
* @param b
* @param options carries optional attributes values
* @return a new instance of SparseMatMul
* @see org.tensorflow.op.sparse.SparseMatMul
*/
public SparseMatMul sparseMatMul(Operand a, Operand b,
SparseMatMul.Options... options) {
return SparseMatMul.create(scope, a, b, options);
}
/**
* Builds an {@link SparseFillEmptyRowsGrad} operation
*
* @param reverseIndexMap 1-D. The reverse index map from SparseFillEmptyRows.
* @param gradValues 1-D. The gradients from backprop.
* @return a new instance of SparseFillEmptyRowsGrad
* @see org.tensorflow.op.sparse.SparseFillEmptyRowsGrad
*/
public SparseFillEmptyRowsGrad sparseFillEmptyRowsGrad(Operand reverseIndexMap,
Operand gradValues) {
return SparseFillEmptyRowsGrad.create(scope, reverseIndexMap, gradValues);
}
/**
* Builds an {@link SparseDenseCwiseDiv} operation
*
* @param spIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param spValues 1-D. `N` non-empty values corresponding to `sp_indices`.
* @param spShape 1-D. Shape of the input SparseTensor.
* @param dense `R`-D. The dense Tensor operand.
* @return a new instance of SparseDenseCwiseDiv
* @see org.tensorflow.op.sparse.SparseDenseCwiseDiv
*/
public SparseDenseCwiseDiv sparseDenseCwiseDiv(Operand spIndices,
Operand spValues, Operand spShape, Operand dense) {
return SparseDenseCwiseDiv.create(scope, spIndices, spValues, spShape, dense);
}
/**
* Builds an {@link SparseSlice} operation
*
* @param indices 2-D tensor represents the indices of the sparse tensor.
* @param values 1-D tensor represents the values of the sparse tensor.
* @param shape 1-D. tensor represents the shape of the sparse tensor.
* @param start 1-D. tensor represents the start of the slice.
* @param size 1-D. tensor represents the size of the slice.
* @return a new instance of SparseSlice
* @see org.tensorflow.op.sparse.SparseSlice
*/
public SparseSlice sparseSlice(Operand indices, Operand values,
Operand shape, Operand start, Operand size) {
return SparseSlice.create(scope, indices, values, shape, start, size);
}
/**
* Builds an {@link SparseReduceSumSparse} operation
*
* @param inputIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param inputValues 1-D. `N` non-empty values corresponding to `input_indices`.
* @param inputShape 1-D. Shape of the input SparseTensor.
* @param reductionAxes 1-D. Length-`K` vector containing the reduction axes.
* @param options carries optional attributes values
* @return a new instance of SparseReduceSumSparse
* @see org.tensorflow.op.sparse.SparseReduceSumSparse
*/
public SparseReduceSumSparse sparseReduceSumSparse(Operand inputIndices,
Operand inputValues, Operand inputShape, Operand reductionAxes,
SparseReduceSumSparse.Options... options) {
return SparseReduceSumSparse.create(scope, inputIndices, inputValues, inputShape, reductionAxes, options);
}
/**
* Builds an {@link SparseReduceSum} operation
*
* @param inputIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param inputValues 1-D. `N` non-empty values corresponding to `input_indices`.
* @param inputShape 1-D. Shape of the input SparseTensor.
* @param reductionAxes 1-D. Length-`K` vector containing the reduction axes.
* @param options carries optional attributes values
* @return a new instance of SparseReduceSum
* @see org.tensorflow.op.sparse.SparseReduceSum
*/
public SparseReduceSum sparseReduceSum(Operand inputIndices, Operand inputValues,
Operand inputShape, Operand reductionAxes,
SparseReduceSum.Options... options) {
return SparseReduceSum.create(scope, inputIndices, inputValues, inputShape, reductionAxes, options);
}
/**
* Builds an {@link SparseSegmentSumWithNumSegments} operation
*
* @param data
* @param indices A 1-D tensor. Has same rank as `segment_ids`.
* @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
* @param numSegments Should equal the number of distinct segment IDs.
* @return a new instance of SparseSegmentSumWithNumSegments
* @see org.tensorflow.op.sparse.SparseSegmentSumWithNumSegments
*/
public SparseSegmentSumWithNumSegments sparseSegmentSumWithNumSegments(Operand data,
Operand indices, Operand segmentIds, Operand numSegments) {
return SparseSegmentSumWithNumSegments.create(scope, data, indices, segmentIds, numSegments);
}
/**
* Builds an {@link SparseSparseMinimum} operation
*
* @param aIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param aValues 1-D. `N` non-empty values corresponding to `a_indices`.
* @param aShape 1-D. Shape of the input SparseTensor.
* @param bIndices counterpart to `a_indices` for the other operand.
* @param bValues counterpart to `a_values` for the other operand; must be of the same dtype.
* @param bShape counterpart to `a_shape` for the other operand; the two shapes must be equal.
* @return a new instance of SparseSparseMinimum
* @see org.tensorflow.op.sparse.SparseSparseMinimum
*/
public SparseSparseMinimum sparseSparseMinimum(Operand aIndices, Operand aValues,
Operand aShape, Operand bIndices, Operand bValues, Operand bShape) {
return SparseSparseMinimum.create(scope, aIndices, aValues, aShape, bIndices, bValues, bShape);
}
/**
* Builds an {@link AddManySparseToTensorsMap} operation
*
* @param sparseIndices 2-D. The `indices` of the minibatch `SparseTensor`.
* @param sparseValues 1-D. The `values` of the minibatch `SparseTensor`.
* @param sparseShape 1-D. The `shape` of the minibatch `SparseTensor`.
* @param options carries optional attributes values
* @return a new instance of AddManySparseToTensorsMap
* @see org.tensorflow.op.sparse.AddManySparseToTensorsMap
*/
public AddManySparseToTensorsMap addManySparseToTensorsMap(Operand sparseIndices,
Operand sparseValues, Operand sparseShape,
AddManySparseToTensorsMap.Options... options) {
return AddManySparseToTensorsMap.create(scope, sparseIndices, sparseValues, sparseShape, options);
}
/**
* Builds an {@link SparseSegmentMean} operation
*
* @param data
* @param indices A 1-D tensor. Has same rank as `segment_ids`.
* @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
* @return a new instance of SparseSegmentMean
* @see org.tensorflow.op.sparse.SparseSegmentMean
*/
public SparseSegmentMean sparseSegmentMean(Operand data,
Operand indices, Operand segmentIds) {
return SparseSegmentMean.create(scope, data, indices, segmentIds);
}
/**
* Builds an {@link SparseToSparseSetOperation} operation
*
* @param set1Indices 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
* @param set1Values 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
* @param set1Shape 1D `Tensor`, shape of a `SparseTensor`. `set1_shape[0...n-1]` must
* @param set2Indices 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
* @param set2Values 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
* @param set2Shape 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
* @param setOperation
* @param options carries optional attributes values
* @return a new instance of SparseToSparseSetOperation
* @see org.tensorflow.op.sparse.SparseToSparseSetOperation
*/
public SparseToSparseSetOperation sparseToSparseSetOperation(Operand set1Indices,
Operand set1Values, Operand set1Shape, Operand set2Indices,
Operand set2Values, Operand set2Shape, String setOperation,
SparseToSparseSetOperation.Options... options) {
return SparseToSparseSetOperation.create(scope, set1Indices, set1Values, set1Shape, set2Indices, set2Values, set2Shape, setOperation, options);
}
/**
* Builds an {@link DenseToSparseSetOperation} operation
*
* @param set1 `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
* @param set2Indices 2D `Tensor`, indices of a `SparseTensor`. Must be in row-major
* @param set2Values 1D `Tensor`, values of a `SparseTensor`. Must be in row-major
* @param set2Shape 1D `Tensor`, shape of a `SparseTensor`. `set2_shape[0...n-1]` must
* @param setOperation
* @param options carries optional attributes values
* @return a new instance of DenseToSparseSetOperation
* @see org.tensorflow.op.sparse.DenseToSparseSetOperation
*/
public DenseToSparseSetOperation denseToSparseSetOperation(Operand set1,
Operand set2Indices, Operand set2Values, Operand set2Shape,
String setOperation, DenseToSparseSetOperation.Options... options) {
return DenseToSparseSetOperation.create(scope, set1, set2Indices, set2Values, set2Shape, setOperation, options);
}
/**
* Builds an {@link SparseSplit} operation
*
* @param splitDim 0-D. The dimension along which to split. Must be in the range
* @param indices 2-D tensor represents the indices of the sparse tensor.
* @param values 1-D tensor represents the values of the sparse tensor.
* @param shape 1-D. tensor represents the shape of the sparse tensor.
* @param numSplit The number of ways to split.
* @return a new instance of SparseSplit
* @see org.tensorflow.op.sparse.SparseSplit
*/
public SparseSplit sparseSplit(Operand splitDim, Operand indices,
Operand values, Operand shape, Long numSplit) {
return SparseSplit.create(scope, splitDim, indices, values, shape, numSplit);
}
/**
* Builds an {@link DenseToDenseSetOperation} operation
*
* @param set1 `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set2`.
* @param set2 `Tensor` with rank `n`. 1st `n-1` dimensions must be the same as `set1`.
* @param setOperation
* @param options carries optional attributes values
* @return a new instance of DenseToDenseSetOperation
* @see org.tensorflow.op.sparse.DenseToDenseSetOperation
*/
public DenseToDenseSetOperation denseToDenseSetOperation(Operand set1, Operand set2,
String setOperation, DenseToDenseSetOperation.Options... options) {
return DenseToDenseSetOperation.create(scope, set1, set2, setOperation, options);
}
/**
* Builds an {@link SparseAccumulatorTakeGradient} operation
*
* @param handle The handle to a SparseConditionalAccumulator.
* @param numRequired Number of gradients required before we return an aggregate.
* @param dtype The data type of accumulated gradients. Needs to correspond to the type
* @return a new instance of SparseAccumulatorTakeGradient
* @see org.tensorflow.op.sparse.SparseAccumulatorTakeGradient
*/
public SparseAccumulatorTakeGradient sparseAccumulatorTakeGradient(Operand handle,
Operand numRequired, Class dtype) {
return SparseAccumulatorTakeGradient.create(scope, handle, numRequired, dtype);
}
/**
* Builds an {@link SparseDenseCwiseMul} operation
*
* @param spIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param spValues 1-D. `N` non-empty values corresponding to `sp_indices`.
* @param spShape 1-D. Shape of the input SparseTensor.
* @param dense `R`-D. The dense Tensor operand.
* @return a new instance of SparseDenseCwiseMul
* @see org.tensorflow.op.sparse.SparseDenseCwiseMul
*/
public SparseDenseCwiseMul sparseDenseCwiseMul(Operand spIndices,
Operand spValues, Operand spShape, Operand dense) {
return SparseDenseCwiseMul.create(scope, spIndices, spValues, spShape, dense);
}
/**
* Builds an {@link SparseConditionalAccumulator} operation
*
* @param dtype The type of the value being accumulated.
* @param shape The shape of the values.
* @param options carries optional attributes values
* @return a new instance of SparseConditionalAccumulator
* @see org.tensorflow.op.sparse.SparseConditionalAccumulator
*/
public SparseConditionalAccumulator sparseConditionalAccumulator(Class dtype, Shape shape,
SparseConditionalAccumulator.Options... options) {
return SparseConditionalAccumulator.create(scope, dtype, shape, options);
}
/**
* Builds an {@link SparseReduceMax} operation
*
* @param inputIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param inputValues 1-D. `N` non-empty values corresponding to `input_indices`.
* @param inputShape 1-D. Shape of the input SparseTensor.
* @param reductionAxes 1-D. Length-`K` vector containing the reduction axes.
* @param options carries optional attributes values
* @return a new instance of SparseReduceMax
* @see org.tensorflow.op.sparse.SparseReduceMax
*/
public SparseReduceMax sparseReduceMax(Operand inputIndices,
Operand inputValues, Operand inputShape, Operand reductionAxes,
SparseReduceMax.Options... options) {
return SparseReduceMax.create(scope, inputIndices, inputValues, inputShape, reductionAxes, options);
}
/**
* Builds an {@link SparseDenseCwiseAdd} operation
*
* @param spIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param spValues 1-D. `N` non-empty values corresponding to `sp_indices`.
* @param spShape 1-D. Shape of the input SparseTensor.
* @param dense `R`-D. The dense Tensor operand.
* @return a new instance of SparseDenseCwiseAdd
* @see org.tensorflow.op.sparse.SparseDenseCwiseAdd
*/
public SparseDenseCwiseAdd sparseDenseCwiseAdd(Operand spIndices,
Operand spValues, Operand spShape, Operand dense) {
return SparseDenseCwiseAdd.create(scope, spIndices, spValues, spShape, dense);
}
/**
* Builds an {@link SparseToDense} operation
*
* @param sparseIndices 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete
* @param outputShape 1-D. Shape of the dense output tensor.
* @param sparseValues 1-D. Values corresponding to each row of `sparse_indices`,
* @param defaultValue Scalar value to set for indices not specified in
* @param options carries optional attributes values
* @return a new instance of SparseToDense
* @see org.tensorflow.op.sparse.SparseToDense
*/
public SparseToDense sparseToDense(Operand sparseIndices,
Operand outputShape, Operand sparseValues, Operand defaultValue,
SparseToDense.Options... options) {
return SparseToDense.create(scope, sparseIndices, outputShape, sparseValues, defaultValue, options);
}
/**
* Builds an {@link SparseCross} operation
*
* @param indices 2-D. Indices of each input `SparseTensor`.
* @param values 1-D. values of each `SparseTensor`.
* @param shapes 1-D. Shapes of each `SparseTensor`.
* @param denseInputs 2-D. Columns represented by dense `Tensor`.
* @param hashedOutput If true, returns the hash of the cross instead of the string.
* @param numBuckets It is used if hashed_output is true.
* @param hashKey Specify the hash_key that will be used by the `FingerprintCat64`
* @param outType
* @param internalType
* @return a new instance of SparseCross
* @see org.tensorflow.op.sparse.SparseCross
*/
public SparseCross sparseCross(Iterable> indices,
Iterable> values, Iterable> shapes, Iterable> denseInputs,
Boolean hashedOutput, Long numBuckets, Long hashKey, Class outType,
Class internalType) {
return SparseCross.create(scope, indices, values, shapes, denseInputs, hashedOutput, numBuckets, hashKey, outType, internalType);
}
/**
* Builds an {@link SparseSliceGrad} operation
*
* @param backpropValGrad 1-D. The gradient with respect to
* @param inputIndices 2-D. The `indices` of the input `SparseTensor`.
* @param inputStart 1-D. tensor represents the start of the slice.
* @param outputIndices 2-D. The `indices` of the sliced `SparseTensor`.
* @return a new instance of SparseSliceGrad
* @see org.tensorflow.op.sparse.SparseSliceGrad
*/
public SparseSliceGrad sparseSliceGrad(Operand backpropValGrad,
Operand inputIndices, Operand inputStart, Operand outputIndices) {
return SparseSliceGrad.create(scope, backpropValGrad, inputIndices, inputStart, outputIndices);
}
/**
* Builds an {@link SparseSegmentMeanWithNumSegments} operation
*
* @param data
* @param indices A 1-D tensor. Has same rank as `segment_ids`.
* @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
* @param numSegments Should equal the number of distinct segment IDs.
* @return a new instance of SparseSegmentMeanWithNumSegments
* @see org.tensorflow.op.sparse.SparseSegmentMeanWithNumSegments
*/
public SparseSegmentMeanWithNumSegments sparseSegmentMeanWithNumSegments(Operand data,
Operand indices, Operand segmentIds, Operand numSegments) {
return SparseSegmentMeanWithNumSegments.create(scope, data, indices, segmentIds, numSegments);
}
/**
* Builds an {@link SparseAddGrad} operation
*
* @param backpropValGrad 1-D with shape `[nnz(sum)]`. The gradient with respect to
* @param aIndices 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`.
* @param bIndices 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`.
* @param sumIndices 2-D. The `indices` of the sum `SparseTensor`, size
* @return a new instance of SparseAddGrad
* @see org.tensorflow.op.sparse.SparseAddGrad
*/
public SparseAddGrad sparseAddGrad(Operand backpropValGrad, Operand aIndices,
Operand bIndices, Operand sumIndices) {
return SparseAddGrad.create(scope, backpropValGrad, aIndices, bIndices, sumIndices);
}
/**
* Builds an {@link SparseSoftmax} operation
*
* @param spIndices 2-D. `NNZ x R` matrix with the indices of non-empty values in a
* @param spValues 1-D. `NNZ` non-empty values corresponding to `sp_indices`.
* @param spShape 1-D. Shape of the input SparseTensor.
* @return a new instance of SparseSoftmax
* @see org.tensorflow.op.sparse.SparseSoftmax
*/
public SparseSoftmax sparseSoftmax(Operand spIndices,
Operand spValues, Operand spShape) {
return SparseSoftmax.create(scope, spIndices, spValues, spShape);
}
/**
* Builds an {@link SparseConcat} operation
*
* @param indices 2-D. Indices of each input `SparseTensor`.
* @param values 1-D. Non-empty values of each `SparseTensor`.
* @param shapes 1-D. Shapes of each `SparseTensor`.
* @param concatDim Dimension to concatenate along. Must be in range [-rank, rank),
* @return a new instance of SparseConcat
* @see org.tensorflow.op.sparse.SparseConcat
*/
public SparseConcat sparseConcat(Iterable> indices,
Iterable> values, Iterable> shapes, Long concatDim) {
return SparseConcat.create(scope, indices, values, shapes, concatDim);
}
/**
* Builds an {@link SparseSparseMaximum} operation
*
* @param aIndices 2-D. `N x R` matrix with the indices of non-empty values in a
* @param aValues 1-D. `N` non-empty values corresponding to `a_indices`.
* @param aShape 1-D. Shape of the input SparseTensor.
* @param bIndices counterpart to `a_indices` for the other operand.
* @param bValues counterpart to `a_values` for the other operand; must be of the same dtype.
* @param bShape counterpart to `a_shape` for the other operand; the two shapes must be equal.
* @return a new instance of SparseSparseMaximum
* @see org.tensorflow.op.sparse.SparseSparseMaximum
*/
public SparseSparseMaximum sparseSparseMaximum(Operand aIndices,
Operand aValues, Operand aShape, Operand bIndices, Operand bValues,
Operand bShape) {
return SparseSparseMaximum.create(scope, aIndices, aValues, aShape, bIndices, bValues, bShape);
}
/**
* Builds an {@link SparseSegmentSum} operation
*
* @param data
* @param indices A 1-D tensor. Has same rank as `segment_ids`.
* @param segmentIds A 1-D tensor. Values should be sorted and can be repeated.
* @return a new instance of SparseSegmentSum
* @see org.tensorflow.op.sparse.SparseSegmentSum
*/
public SparseSegmentSum sparseSegmentSum(Operand data,
Operand indices, Operand segmentIds) {
return SparseSegmentSum.create(scope, data, indices, segmentIds);
}
/**
* Builds an {@link SparseReshape} operation
*
* @param inputIndices 2-D. `N x R_in` matrix with the indices of non-empty values in a
* @param inputShape 1-D. `R_in` vector with the input SparseTensor's dense shape.
* @param newShape 1-D. `R_out` vector with the requested new dense shape.
* @return a new instance of SparseReshape
* @see org.tensorflow.op.sparse.SparseReshape
*/
public SparseReshape sparseReshape(Operand inputIndices, Operand inputShape,
Operand newShape) {
return SparseReshape.create(scope, inputIndices, inputShape, newShape);
}
}
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