tf.raw_ops.SparseReduceMax

Computes the max of elements across dimensions of a SparseTensor.

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tf.compat.v1.raw_ops.SparseReduceMax

tf.raw_ops.SparseReduceMax(
    input_indices, input_values, input_shape, reduction_axes, keep_dims=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 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.
name	A name for the operation (optional).

Returns
A Tensor. Has the same type as input_values.