This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_max(). In particular, this Op also returns a dense Tensor
instead of a sparse one.
Reduces sp_input along the dimensions given in reduction_axes. Unless
keep_dims is true, the rank of the tensor is reduced by 1 for each entry in
reduction_axes. If keep_dims 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
input_indices
A Tensor of type int64.
2-D. N x R matrix with the indices of non-empty values in a
SparseTensor, possibly not in canonical ordering.
input_values
A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64.
1-D. N non-empty values corresponding to input_indices.
input_shape
A Tensor of type int64.
1-D. Shape of the input SparseTensor.
reduction_axes
A Tensor of type int32.
1-D. Length-K vector containing the reduction axes.
keep_dims
An optional bool. Defaults to False.
If true, retain reduced dimensions with length 1.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]