Computes the max of elements across dimensions of a SparseTensor.

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` . |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/SparseReduceMax