BasicOps

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

   Definition Classes

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4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def batchToSpace(blockSize: Int, crops: Output): Output

   

   $OpDocBasicBatchToSpace

   $OpDocBasicBatchToSpace

   blockSize

   Block size which must be greater than 1.

   crops

   2-dimensional INT32 or INT64 tensor containing non-negative integers with shape [2, 2].

   returns

   Result as a new tensor.

6. def batchToSpaceND(blockShape: Output, crops: Output): Output

   

   $OpDocBasicBatchToSpaceND

   $OpDocBasicBatchToSpaceND

   blockShape

   One-dimensional INT32 or INT64 tensor with shape [M] whose elements must all be >= 1.

   crops

   Two-dimensional INT32 or INT64 tensor with shape [M, 2] whose elements must all be non-negative. crops(i) = [cropStart, cropEnd] specifies the amount to crop from input dimension i + 1, which corresponds to spatial dimension i. It is required that cropStart(i) + cropEnd(i) <= blockShape(i) * inputShape(i + 1).

   returns

   Result as a new tensor.

7. def booleanMask(mask: Output): Output

   

   $OpDocBasicBooleanMask

   $OpDocBasicBooleanMask

   mask

   K-dimensional boolean tensor, where K <= N and K must be known statically.

   returns

   Result as a new tensor.

8. def checkNumerics(message: String = ""): Output

   

   $OpDocBasicCheckNumerics

   $OpDocBasicCheckNumerics

   message

   Prefix to print for the error message.

   returns

   Result as a new tensor which has the same value as the input tensor.

9. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

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   @throws( ... )

10. def depthToSpace(blockSize: Int, dataFormat: NN.CNNDataFormat = CNNDataFormat.default): Output

    

    $OpDocBasicDepthToSpace

    $OpDocBasicDepthToSpace

    blockSize

    Block size which must be greater than 1.

    dataFormat

    Format of the input and output data.

    returns

    Result as a new tensor.

11. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

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12. def expandDims(axis: Output): Output

    

    $OpDocBasicExpandDims

    $OpDocBasicExpandDims

    axis

    Dimension index at which to expand the shape of this tensor.

    returns

    Result as a new tensor.

13. def finalize(): Unit

    

    Attributes

    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

14. def gather(indices: Output, axis: Output = 0): Output

    

    $OpDocBasicGather

    $OpDocBasicGather

    indices

    Output containing indices to gather.

    axis

    Output containing the axis along which to gather.

    returns

    Result as a new tensor.

15. def gatherND(indices: Output): Output

    

    $OpDocBasicGatherND

    $OpDocBasicGatherND

    indices

    Output containing indices to gather.

    returns

    Result as a new tensor which contains the values from input gathered from indices given by indices, with shape indices.shape(::-1) + input.shape(indices.shape(-1)::).

16. final def getClass(): Class[_]

    

    Definition Classes

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17. def invertPermutation(): Output

    

    $OpDocBasicInvertPermutation

    $OpDocBasicInvertPermutation

    returns

    Result as a new tensor.

18. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

19. def listDiff(other: Output, indicesDataType: types.DataType = INT32): (Output, Output)

    

    $OpDocBasicListDiff

    $OpDocBasicListDiff

    other

    One-dimensional tensor containing the values to remove.

    indicesDataType

    Data type to use for the output indices of this op. Must be INT32 or INT64.

    returns

    Tuple containing output and indices, from the method description.

20. def matrixTranspose(conjugate: Boolean = false): Output

    

    $OpDocBasicMatrixTranspose

    $OpDocBasicMatrixTranspose

    conjugate

    If true, then the complex conjugate of the transpose result is returned.

    returns

    Result as a new tensor.

21. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

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22. final def notify(): Unit

    

    Definition Classes

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23. final def notifyAll(): Unit

    

    Definition Classes

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24. def oneHot(depth: Output, onValue: Output = null, offValue: Output = null, axis: Int = 1, dataType: types.DataType = null): Output

    

    $OpDocBasicOneHot

    $OpDocBasicOneHot

    depth

    Scalar tensor defining the depth of the one-hot dimension.

    onValue

    Scalar tensor defining the value to fill in the output ith value, when indices[j] = i. Defaults to the value 1 with type dataType.

    offValue

    Scalar tensor defining the value to fill in the output ith value, when indices[j] != i. Defaults to the value 0 with type dataType.

    axis

    Axis to fill. Defaults to -1, representing the last axis.

    dataType

    Data type of the output tensor. If not provided, the function will attempt to assume the data type of onValue or offValue, if one or both are passed in. If none of onValue, offValue, or dataType are provided, dataType will default to the FLOAT32 data type.

    returns

    Result as a new tensor.

25. val output: Output

    

26. def pad(paddings: Output, mode: Basic.PaddingMode = ConstantPadding(0)): Output

    

    $OpDocBasicPad

    $OpDocBasicPad

    paddings

    INT32 or INT64 tensor containing the paddings.

    mode

    Padding mode to use.

    returns

    Result as a new tensor.

27. def preventGradient(message: String = ""): Output

    

    $OpDocBasicPreventGradient

    $OpDocBasicPreventGradient

    message

    Message to print along with the error.

    returns

    Result as a new tensor which has the same value as this tensor.

28. def reshape(shape: Output): Output

    

    $OpDocBasicReshape

    $OpDocBasicReshape

    shape

    Shape of the output tensor.

    returns

    Result as a new tensor.

29. def reverse(axes: Output): Output

    

    $OpDocBasicReverse

    $OpDocBasicReverse

    axes

    Dimensions of the input tensor to reverse. Has to be INT32 or INT64.

    returns

    Result as a new tensor which has the same shape as input.

30. def reverseSequence(sequenceLengths: Output, sequenceAxis: Int, batchAxis: Int = 0): Output

    

    $OpDocBasicReverseSequence

    $OpDocBasicReverseSequence

    sequenceLengths

    One-dimensional tensor with length input.shape(batchAxis) and max(sequenceLengths) <= input.shape(sequenceAxis).

    sequenceAxis

    Output dimension which is partially reversed.

    batchAxis

    Output dimension along which the reversal is performed.

    returns

    Result as a new tensor which has the same shape as input.

31. def scatterND(updates: Output, shape: Output): Output

    

    $OpDocBasicScatterND

    $OpDocBasicScatterND

    updates

    Updates to scatter into the output tensor.

    shape

    One-dimensional INT32 or INT64 tensor specifying the shape of the output tensor.

    returns

    Result as a new tensor.

32. def sequenceMask(maxLength: Output = null, dataType: types.DataType = BOOLEAN): Output

    

    $OpDocBasicSequenceMask

    $OpDocBasicSequenceMask

    maxLength

    Scalar integer tensor representing the maximum length of each row. Defaults to the maximum value in this tensor.

    dataType

    Data type for the output tensor.

    returns

    Result as a new tensor.

33. def slice(indexers: core.Indexer*): Output

    

34. def spaceToBatch(blockSize: Int, paddings: Output): Output

    

    $OpDocBasicSpaceToBatch

    $OpDocBasicSpaceToBatch

    blockSize

    Block size which must be greater than 1.

    paddings

    2-dimensional INT32 or INT64 tensor containing non-negative integers with shape [2, 2].

    returns

    Result as a new tensor.

35. def spaceToBatchND(blockShape: Output, paddings: Output): Output

    

    $OpDocBasicSpaceToBatchND

    $OpDocBasicSpaceToBatchND

    blockShape

    One-dimensional INT32 or INT64 tensor with shape [M] whose elements must all be >= 1.

    paddings

    Two-dimensional INT32 or INT64 tensor with shape [M, 2] whose elements must all be non-negative. paddings(i) = [padStart, padEnd] specifies the padding for input dimension i + 1, which corresponds to spatial dimension i. It is required that blockShape(i) divides inputShape(i + 1) + padStart + padEnd.

    returns

    Result as a new tensor.

36. def spaceToDepth(blockSize: Int, dataFormat: NN.CNNDataFormat = CNNDataFormat.default): Output

    

    $OpDocBasicSpaceToDepth

    $OpDocBasicSpaceToDepth

    blockSize

    Block size which must be greater than 1.

    dataFormat

    Format of the input and output data.

    returns

    Result as a new tensor.

37. def split(splitSizes: Output, axis: Output = 0): Seq[Output]

    

    $OpDocBasicSplit

    $OpDocBasicSplit

    splitSizes

    Sizes for the splits to obtain.

    axis

    Dimension along which to split the input tensor.

    returns

    Result as a new tensor.

38. def splitEvenly(numSplits: Int, axis: Output = 0): Seq[Output]

    

    $OpDocBasicSplitEvenly

    $OpDocBasicSplitEvenly

    numSplits

    Number of splits to obtain along the axis dimension.

    axis

    Dimension along which to split the input tensor.

    returns

    Result as a sequence of new tensors.

39. def squeeze(axes: Seq[Int] = null): Output

    

    $OpDocBasicSqueeze

    $OpDocBasicSqueeze

    axes

    Dimensions of size 1 to squeeze. If this argument is not provided, then all dimensions of size 1 will be squeezed.

    returns

    Result as a new tensor.

40. def stopGradient(): Output

    

    $OpDocBasicStopGradient

    $OpDocBasicStopGradient

    returns

    Result as a new tensor which has the same value as this tensor.

41. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

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42. def tile(multiples: Output): Output

    

    $OpDocBasicTile

    $OpDocBasicTile

    multiples

    One-dimensional tensor containing the tiling multiples. Its length must be the same as the rank of input.

    returns

    Result as a new tensor.

43. def transpose(permutation: Output = null, conjugate: Boolean = false): Output

    

    $OpDocBasicTranspose

    $OpDocBasicTranspose

    permutation

    Permutation of the input tensor dimensions.

    conjugate

    If true, then the complex conjugate of the transpose result is returned.

    returns

    Result as a new tensor.

44. def unique(axis: Output, indicesDataType: types.DataType = INT32): (Output, Output)

    

    $OpDocBasicUnique

    $OpDocBasicUnique

    axis

    Axis along which to compute the unique values.

    indicesDataType

    Data type of the returned indices. Must be INT32 or INT64.

    returns

    Tuple containing output and indices.

45. def uniqueWithCounts(axis: Output = 0, indicesDataType: types.DataType = INT32): (Output, Output, Output)

    

    $OpDocBasicUniqueWithCounts

    $OpDocBasicUniqueWithCounts

    axis

    Axis along which to count the unique elements.

    indicesDataType

    Data type of the returned indices. Must be INT32 or INT64.

    returns

    Tuple containing output, indices, and counts.

46. def unstack(number: Int, axis: Int = 0): Seq[Output]

    

    $OpDocBasicUnstack

    $OpDocBasicUnstack

    number

    Number of tensors to unstack. If set to -1 (the default value), its value will be inferred.

    axis

    Dimension along which to unstack the input tensor.

    returns

    Result as a new tensor.

47. final def wait(): Unit

    

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    @throws( ... )

48. final def wait(arg0: Long, arg1: Int): Unit

    

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    @throws( ... )

49. final def wait(arg0: Long): Unit

    

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    @throws( ... )

50. def where(): Output

    

    $OpDocBasicWhere

    $OpDocBasicWhere

    returns

    Result as a new tensor.