|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/sparse_ops.py#L1331-L1414) | Computes [`tf.sparse.maximum`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/maximum) of elements across dimensions of a SparseTensor. (deprecated arguments) (deprecated arguments) #### View aliases **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.sparse.reduce_max`](https://www.tensorflow.org/api_docs/python/tf/compat/v1/sparse_reduce_max)
tf.compat.v1.sparse_reduce_max( sp_input, axis=None, keepdims=None, reduction_axes=None, keep_dims=None ) > [!WARNING] > **Deprecated:** SOME ARGUMENTS ARE DEPRECATED: `(keep_dims)`. They will be removed in a future version. Instructions for updating: keep_dims is deprecated, use keepdims instead > [!WARNING] > **Deprecated:** SOME ARGUMENTS ARE DEPRECATED: `(reduction_axes)`. They will be removed in a future version. Instructions for updating: reduction_axes is deprecated, use axis instead This is the reduction operation for the elementwise [`tf.sparse.maximum`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/maximum) op. This Op takes a SparseTensor and is the sparse counterpart to [`tf.reduce_max()`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/math/reduce_max). In particular, this Op also returns a dense `Tensor` instead of a sparse one. > [!NOTE] > **Note:** A gradient is not defined for this function, so it can't be used in training models that need gradient descent. Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_axes`. If `keepdims` 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, 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 `reduction_axes`. But, in case there are no values in `reduction_axes`, it will reduce to 0. See second example below.


| ## For example || |---|---| |
# 'x' represents \[\[1, ?, 2\] # \[?, 3, ?\]\] # where ? is implicitly-zero. x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 2, 3], [2, 3]) tf.sparse.reduce_max(x) tf.sparse.reduce_max(x, 0) tf.sparse.reduce_max(x, 1) tf.sparse.reduce_max(x, 1, keepdims=True) tf.sparse.reduce_max(x, [0, 1]) # 'y' represents \[\[-7, ?\] # \[ 4, 3\] # \[ ?, ?\] y = tf.sparse.SparseTensor([[0, 0,], [1, 0], [1, 1]], [-7, 4, 3], [3, 2]) tf.sparse.reduce_max(y, 1)
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| ## 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. | | `reduction_axes` | Deprecated name of `axis`. | | `keep_dims` | Deprecated alias for `keepdims`. |



| ## Returns || |---|---| | The reduced Tensor. ||