tf.compat.v1.sparse_reduce_max_sparse
Computes the max of elements across dimensions of a SparseTensor. (deprecated arguments)
tf.compat.v1.sparse_reduce_max_sparse(
sp_input, axis=None, keepdims=None, reduction_axes=None, keep_dims=None
)
This Op takes a SparseTensor and is the sparse counterpart to tf.reduce_max(). In contrast to SparseReduceSum, this Op returns a SparseTensor.
Note: A gradient is not defined for this function, so it can't be used in training models that need gradient descent.
Reduces sp_input along the dimensions given in reduction_axes. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in reduction_axes. If keepdims is true, the reduced dimensions are retained with length 1.
If reduction_axes has no entries, all dimensions are reduced, and a tensor with a single element is returned. Additionally, the axes can be negative, which are interpreted according to the indexing rules in Python.
| Args | |
|---|---|
sp_input |
The SparseTensor to reduce. Should have numeric type. |
axis |
The dimensions to reduce; list or scalar. If None (the default), reduces all dimensions. |
keepdims |
If true, retain reduced dimensions with length 1. |
reduction_axes |
Deprecated name of axis. |
keep_dims |
Deprecated alias for keepdims. |
| Returns | |
|---|---|
| The reduced SparseTensor. |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/sparse_reduce_max_sparse