tf.compat.v1.sparse_reduce_max_sparse

Computes the max of elements across dimensions of a SparseTensor. (deprecated arguments)

View aliases

Compat aliases for migration

tf.compat.v1.sparse.reduce_max_sparse

tf.compat.v1.sparse_reduce_max_sparse(
    sp_input, axis=None, keepdims=None, reduction_axes=None, keep_dims=None
)

Warning: SOME ARGUMENTS ARE DEPRECATED: (keep_dims). They will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead

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.