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
False, or a
Note: A gradient is not defined for this function, so it can't be used in training models that need gradient descent.
sp_input along the dimensions given in
keepdims is true, the rank of the tensor is reduced by 1 for each entry in
keepdims is true, the reduced dimensions are retained
with length 1.
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
axis. But, in case there are no values in
axis, it will reduce to 0. See second example below.
# '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) ==> [, ] tf.sparse.reduce_max(x, [0, 1]) ==> 3 # 'y' represents [[-7, ?] # [ 4, 3] # [ ?, ?] tf.sparse.reduce_max(x, 1) ==> [-7, 4, 0]
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
SparseTensorinstead of a dense
name: A name for the operation (optional).
The reduced Tensor or the reduced SparseTensor if