登录
TensorFlow 1 version
|
View source on GitHub
|
Computes the max of elements across dimensions of a SparseTensor.
tf.sparse.reduce_max(
sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None
)
This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_max(). In particular, this Op also returns a dense Tensor
if output_is_sparse is False, or a SparseTensor if output_is_sparse
is True.
Reduces sp_input along the dimensions given in axis. Unless
keepdims is true, the rank of the tensor is reduced by 1 for each entry in
axis. If keepdims is true, the reduced dimensions are retained
with length 1.
If axis has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
similar to the indexing rules in Python.
The values not defined in sp_input don't participate in the reduce max,
as opposed to be implicitly assumed 0 -- hence it can return negative values
for sparse axis. But, in case there are no values in
axis, it will reduce to 0. See second example below.
For example:
# 'x' represents [[1, ?, 2]
# [?, 3, ?]]
# where ? is implicitly-zero.
tf.sparse.reduce_max(x) ==> 3
tf.sparse.reduce_max(x, 0) ==> [1, 3, 2]
tf.sparse.reduce_max(x, 1) ==> [2, 3] # Can also use -1 as the axis.
tf.sparse.reduce_max(x, 1, keepdims=True) ==> [[2], [3]]
tf.sparse.reduce_max(x, [0, 1]) ==> 3
# 'y' represents [[-7, ?]
# [ 4, 3]
# [ ?, ?]
tf.sparse.reduce_max(x, 1) ==> [-7, 4, 0]
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. |
output_is_sparse
|
If true, returns a SparseTensor instead of a dense
Tensor (the default).
|
name
|
A name for the operation (optional). |
Returns | |
|---|---|
The reduced Tensor or the reduced SparseTensor if output_is_sparse is
True.
|