org.platanios.tensorflow.api.tensors.ops
Basic
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package ops
$OpDocBasicBatchToSpace
$OpDocBasicBatchToSpace
4-dimensional input tensor with shape [batch, height, width, depth].
Block size which must be greater than 1.
2-dimensional tensor containing non-negative integers with shape [2, 2].
Result as a new tensor.
$OpDocBasicBatchToSpaceND
$OpDocBasicBatchToSpaceND
N-dimensional tensor with shape inputShape = [batch] + spatialShape + remainingShape, where
spatialShape has M dimensions.
One-dimensional tensor with shape [M] whose elements must all be >= 1.
Two-dimensional 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).
Result as a new tensor.
$OpDocBasicBooleanMask
$OpDocBasicBooleanMask
N-dimensional tensor.
K-dimensional boolean tensor, where K <= N and K must be known statically.
Result as a new tensor.
$OpDocBasicCheckNumerics
$OpDocBasicCheckNumerics
Input tensor.
Prefix to print for the error message.
Result as a new tensor which has the same value as the input tensor.
$OpDocBasicConcatenate
$OpDocBasicConcatenate
Input tensors to be concatenated.
Dimension along which to concatenate the input tensors.
Result as a new tensor.
$OpDocBasicDepthToSpace
$OpDocBasicDepthToSpace
4-dimensional input tensor with shape [batch, height, width, depth].
Block size which must be greater than 1.
Format of the input and output data.
Result as a new tensor.
$OpDocBasicEditDistance
$OpDocBasicEditDistance
Sparse tensor that contains the hypothesis sequences.
Sparse tensor that contains the truth sequences.
Optional boolean value indicating whether to normalize the Levenshtein distance by the
length of truth.
Result as a new tensor.
$OpDocBasicExpandDims
$OpDocBasicExpandDims
Input tensor.
Dimension index at which to expand the shape of input.
Result as a new tensor.
$OpDocBasicGather
$OpDocBasicGather
Tensor from which to gather values.
Tensor containing indices to gather.
Tensor containing the axis along which to gather.
Result as a new tensor.
$OpDocBasicGatherND
$OpDocBasicGatherND
Tensor from which to gather values.
Tensor containing indices to gather.
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)::).
$OpDocBasicIndexedSlicesMask
$OpDocBasicIndexedSlicesMask
Input indexed slices.
One-dimensional tensor containing the indices of the elements to mask.
Result as a new tensor indexed slices object.
$OpDocBasicInvertPermutation
$OpDocBasicInvertPermutation
One-dimensional input tensor.
Result as a new tensor.
$OpDocBasicListDiff
$OpDocBasicListDiff
One-dimensional tensor containing the values to keep.
One-dimensional tensor containing the values to remove.
Data type to use for the output indices of this op.
Tuple containing output and indices, from the method description.
$OpDocBasicListDiff
$OpDocBasicListDiff
One-dimensional tensor containing the values to keep.
One-dimensional tensor containing the values to remove.
Tuple containing output and indices, from the method description.
$OpDocBasicMatrixTranspose
$OpDocBasicMatrixTranspose
Input tensor to transpose.
If true, then the complex conjugate of the transpose result is returned.
Result as a new tensor.
InvalidShapeException If the input tensor has rank <= 2.
$OpDocBasicOneHot
$OpDocBasicOneHot
Tensor containing the indices for the "on" values.
Scalar tensor defining the depth of the one-hot dimension.
Scalar tensor defining the value to fill in the output ith value, when indices[j] = i.
Defaults to the value 1 with type dataType.
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 to fill. Defaults to -1, representing the last axis.
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.
Result as a new tensor.
$OpDocBasicPad
$OpDocBasicPad
Input tensor to be padded.
Tensor containing the paddings.
Padding mode to use.
Result as a new tensor.
$OpDocBasicParallelStack
$OpDocBasicParallelStack
Input tensors to be stacked.
Result as a new tensor.
$OpDocBasicPreventGradient
$OpDocBasicPreventGradient
Input tensor.
Message to print along with the error.
Result as a new tensor which has the same value as the input tensor.
$OpDocBasicRank
$OpDocBasicRank
Tensor whose rank to return.
Result as a new tensor.
$OpDocBasicRequiredSpaceToBatchPaddingsAndCrops
$OpDocBasicRequiredSpaceToBatchPaddingsAndCrops
Tensor with shape [N].
Tensor with shape [N].
Optional tensor with shape [N, 2] that specifies the minimum amount of padding to use. All
elements must be non-negative. Defaults to a tensor containing all zeros.
Tuple containing the paddings and crops required.
InvalidShapeException If inputShape, blockShape, or basePaddings, has invalid shape.
$OpDocBasicReshape
$OpDocBasicReshape
Input tensor.
Shape of the output tensor.
Result as a new tensor.
$OpDocBasicReverse
$OpDocBasicReverse
Input tensor to reverse. It must have rank at most 8.
Dimensions of the input tensor to reverse.
Result as a new tensor which has the same shape as input.
$OpDocBasicReverseSequence
$OpDocBasicReverseSequence
Input tensor to reverse.
One-dimensional tensor with length input.shape(batchAxis) and
max(sequenceLengths) <= input.shape(sequenceAxis).
Tensor dimension which is partially reversed.
Tensor dimension along which the reversal is performed.
Result as a new tensor which has the same shape as input.
$OpDocBasicScatterND
$OpDocBasicScatterND
Indices tensor.
Updates to scatter into the output tensor.
One-dimensional tensor specifying the shape of the output tensor.
Result as a new tensor.
$OpDocBasicSequenceMask
$OpDocBasicSequenceMask
One-dimensional integer tensor containing the lengths to keep for each row. If maxLength is
provided, then all values in lengths must be smaller than maxLength.
Scalar integer tensor representing the maximum length of each row. Defaults to the maximum value
in lengths.
Result as a new tensor.
IllegalArgumentException If maxLength is not a scalar.
$OpDocBasicShape
$OpDocBasicShape
Tensor whose shape to return.
Optional data type to use for the output of this op.
Result as a new tensor.
$OpDocBasicShape
$OpDocBasicShape
Tensor whose shape to return.
Result as a new tensor.
$OpDocBasicShapeN
$OpDocBasicShapeN
Tensors whose shapes to return.
Optional data type to use for the outputs of this op.
Result as a sequence of new tensors.
$OpDocBasicShapeN
$OpDocBasicShapeN
Tensors whose shapes to return.
Result as a sequence of new tensors.
$OpDocBasicSize
$OpDocBasicSize
Tensor whose size to return.
Optional data type to use for the output of this op.
Result as a new tensor.
$OpDocBasicSize
$OpDocBasicSize
Tensor whose size to return.
Result as a new tensor.
$OpDocBasicSpaceToBatch
$OpDocBasicSpaceToBatch
4-dimensional input tensor with shape [batch, height, width, depth].
Block size which must be greater than 1.
2-dimensional tensor containing non-negative integers with shape [2, 2].
Result as a new tensor.
$OpDocBasicSpaceToBatchND
$OpDocBasicSpaceToBatchND
N-dimensional tensor with shape inputShape = [batch] + spatialShape + remainingShape, where
spatialShape has M dimensions.
One-dimensional tensor with shape [M] whose elements must all be >= 1.
Two-dimensional 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.
Result as a new tensor.
$OpDocBasicSpaceToDepth
$OpDocBasicSpaceToDepth
4-dimensional input tensor with shape [batch, height, width, depth].
Block size which must be greater than 1.
Format of the input and output data.
Result as a new tensor.
$OpDocBasicSplit
$OpDocBasicSplit
Input tensor to split.
Sizes for the splits to obtain.
Dimension along which to split the input tensor.
Result as a new tensor.
$OpDocBasicSplitEvenly
$OpDocBasicSplitEvenly
Input tensor to split.
Number of splits to obtain along the axis dimension.
Dimension along which to split the input tensor.
Result as a sequence of new tensors.
$OpDocBasicSqueeze
$OpDocBasicSqueeze
Input tensor.
Dimensions of size 1 to squeeze. If this argument is not provided, then all dimensions of size 1 will be squeezed.
Result as a new tensor.
$OpDocBasicStack
$OpDocBasicStack
Input tensors to be stacked.
Dimension along which to stack the input tensors.
Result as a new tensor.
$OpDocBasicStopGradient
$OpDocBasicStopGradient
Input tensor.
Result as a new tensor which has the same value as the input tensor.
$OpDocBasicTile
$OpDocBasicTile
Tensor to tile.
One-dimensional tensor containing the tiling multiples. Its length must be the same as the rank
of input.
Result as a new tensor.
$OpDocBasicTranspose
$OpDocBasicTranspose
Input tensor to transpose.
Permutation of the input tensor dimensions.
If true, then the complex conjugate of the transpose result is returned.
Result as a new tensor.
$OpDocBasicUnique
$OpDocBasicUnique
One-dimensional input tensor.
Data type of the returned indices.
Tuple containing output and indices.
$OpDocBasicUnique
$OpDocBasicUnique
One-dimensional input tensor.
Tuple containing output and indices.
$OpDocBasicUniqueWithCounts
$OpDocBasicUniqueWithCounts
One-dimensional input tensor.
Data type of the returned indices.
Tuple containing output, indices, and counts.
$OpDocBasicUniqueWithCounts
$OpDocBasicUniqueWithCounts
One-dimensional input tensor.
Tuple containing output, indices, and counts.
$OpDocBasicUnstack
$OpDocBasicUnstack
Rank R > 0 Tensor to be unstacked.
Number of tensors to unstack. If set to -1 (the default value), its value will be inferred.
Dimension along which to unstack the input tensor.
Result as a sequence of new tensors.
$OpDocBasicWhere
$OpDocBasicWhere
Input boolean tensor.
Result as a new tensor.