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Computer Vision Toolkit
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CUDA Toolkit
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OneHot
Description
Produces a one-hot tensor based on inputs. The locations represented by the index values in the βindicesβ input tensor will have βon_valueβ and the other locations will have βoff_valueβ in the output tensor, where βon_valueβ and βoff_valueβ are specified as part of required input argument βvaluesβ, which is a two-element tensor of format [off_value, on_value]. The rank of the output tensor will be one greater than the rank of the input tensor. The additional dimension is for one-hot representation. The additional dimension will be inserted at the position specified by βaxisβ. If βaxisβ is not specified then then additional dimension will be inserted as the innermost dimension, i.e. axis=-1. The size of the additional dimension is specified by required scalar input βdepthβ. The type of the output tensor is the same as the type of the βvaluesβ input. Any entries in the βindicesβ input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all βoff_valueβ values in the output tensor.
when axis = 0: output[input[i, j, k], i, j, k] = 1 for all i, j, k and 0 otherwise.
when axis = -1: output[i, j, k, input[i, j, k]] = 1 for all i, j, k and 0 otherwise.
Input parameters
specified_outputs_name :Β array, this parameter lets you manually assign custom names to the output tensors of a node.
Β Graphs in :Β cluster, ONNX model architecture.
indicesΒ (heterogeneous) –Β T1 : object, input tensor containing indices. Any entries in the βindicesβ input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all βoff_valueβ values in the output tensor.InΒ case βindicesβ is of non-integer type, the values will be casted to int64 before use.
depth (heterogeneous) – T2 : object, scalar or Rank 1 tensor containing exactly one element, specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by βaxisβ attribute) added on in the output tensor. The values in the βindicesβ input tensor are expected to be in the range [-depth, depth-1]. In case βdepthβ is of non-integer type, it will be casted to int64 before use.
values (heterogeneous) – T3 : object, rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where βon_valueβ is the value used for filling locations specified in βindicesβ input tensor, and βoff_valueβ is the value used for filling locations other than those specified in βindicesβ input tensor.
Β Parameters : cluster,
axis :Β boolean, axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).
Default value β-1β.
Β training?Β :Β boolean, whether the layer is in training mode (can store data for backward).
Default value βTrueβ.
Β lda coeff :Β float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value β1β.
Β name (optional) :Β string, name of the node.
Output parameters
output (heterogeneous) – T3 : object, tensor of rank one greater than input tensor βindicesβ, i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input βvaluesβ is used.
Type Constraints
tensor(double),Β tensor(float),Β tensor(float16),Β tensor(int16),Β tensor(int32),Β tensor(int64),Β tensor(int8),tensor(uint16),Β tensor(uint32),Β tensor(uint64),Β tensor(uint8)) : Constrain input to only numeric types.
T2 in (tensor(double),Β tensor(float),Β tensor(float16),Β tensor(int16),Β tensor(int32),Β tensor(int64),Β tensor(int8),
tensor(uint16),Β tensor(uint32),Β tensor(uint64),Β tensor(uint8)) : Constrain input to only numeric types.
T3 in (tensor(bool),Β tensor(complex128),Β tensor(complex64),Β tensor(double),Β tensor(float),Β tensor(float16),Β tensor(int16),
tensor(int32),Β tensor(int64),Β tensor(int8),Β tensor(string),Β tensor(uint16),Β tensor(uint32),Β tensor(uint64),Β tensor(uint8)) : Constrain to any tensor type.