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tensorflow::ops::SparseSoftmax
#include <sparse_ops.h>
Applies softmax to a batched N-D SparseTensor.
Summary
The inputs represent an N-D SparseTensor with logical shape [..., B, C] (where N >= 2), and with indices sorted in the canonical lexicographic order.
This op is equivalent to applying the normal tf.nn.softmax() to each innermost logical submatrix with shape [B, C], but with the catch that the implicitly zero elements do not participate. Specifically, the algorithm is equivalent to the following:
(1) Applies tf.nn.softmax() to a densified view of each innermost submatrix with shape [B, C], along the size-C dimension; (2) Masks out the original implicitly-zero locations; (3) Renormalizes the remaining elements.
Hence, the SparseTensor result has exactly the same non-zero indices and shape.
Args:
- scope: A Scope object
- sp_indices: 2-D.
NNZ x R matrix with the indices of non-empty values in a SparseTensor, in canonical ordering.
- sp_values: 1-D.
NNZ non-empty values corresponding to sp_indices.
- sp_shape: 1-D. Shape of the input SparseTensor.
Returns:
Output: 1-D. The NNZ values for the result SparseTensor.
Public attributes
Public functions
node
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tensorflow::ops::SparseSoftmax Class Reference\n\ntensorflow::ops::SparseSoftmax\n==============================\n\n`#include \u003csparse_ops.h\u003e`\n\nApplies softmax to a batched N-D `SparseTensor`.\n\nSummary\n-------\n\nThe inputs represent an N-D SparseTensor with logical shape `[..., B, C]` (where `N \u003e= 2`), and with indices sorted in the canonical lexicographic order.\n\nThis op is equivalent to applying the normal `tf.nn.softmax()` to each innermost logical submatrix with shape `[B, C]`, but with the catch that *the implicitly zero elements do not participate*. Specifically, the algorithm is equivalent to the following:\n\n(1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix with shape `[B, C]`, along the size-C dimension; (2) Masks out the original implicitly-zero locations; (3) Renormalizes the remaining elements.\n\nHence, the `SparseTensor` result has exactly the same non-zero indices and shape.\n\nArgs:\n\n- scope: A [Scope](/versions/r2.14/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a SparseTensor, in canonical ordering.\n- sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`.\n- sp_shape: 1-D. Shape of the input SparseTensor.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.14/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): 1-D. The `NNZ` values for the result `SparseTensor`.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [SparseSoftmax](#classtensorflow_1_1ops_1_1_sparse_softmax_1a64ec9c22eb2f8d50797cfb39eb94009d)`(const ::`[tensorflow::Scope](/versions/r2.14/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.14/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` sp_indices, ::`[tensorflow::Input](/versions/r2.14/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` sp_values, ::`[tensorflow::Input](/versions/r2.14/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` sp_shape)` ||\n\n| ### Public attributes ||\n|--------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_sparse_softmax_1ad2dc43b15de20c26df875d2e2f5e9191) | [Operation](/versions/r2.14/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_sparse_softmax_1a94b1fda8269b6888396b9c165fdd28b1) | `::`[tensorflow::Output](/versions/r2.14/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|--------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_sparse_softmax_1aabb6b649a7d5f3c8a9db2dea2c44ef1a)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_sparse_softmax_1af6f0269e4c290ac6b8234ba881dafe13)`() const ` | |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_sparse_softmax_1a1fccadd0a530764ea2d1691045ebf2a5)`() const ` | |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### output\n\n```text\n::tensorflow::Output output\n``` \n\nPublic functions\n----------------\n\n### SparseSoftmax\n\n```gdscript\n SparseSoftmax(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input sp_indices,\n ::tensorflow::Input sp_values,\n ::tensorflow::Input sp_shape\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n```"]]
