# tf.math.reduce_max

Computes `tf.math.maximum` of elements across dimensions of a tensor.

This is the reduction operation for the elementwise `tf.math.maximum` op.

Reduces `input_tensor` along the dimensions given in `axis`. Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each of the entries in `axis`, which must be unique. If `keepdims` is true, the reduced dimensions are retained with length 1.

If `axis` is None, all dimensions are reduced, and a tensor with a single element is returned.

``````>>> x = tf.constant([5, 1, 2, 4])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=5>
>>> x = tf.constant([-5, -1, -2, -4])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=int32, numpy=-1>
>>> x = tf.constant([4, float('nan')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('nan'), float('nan')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=nan>
>>> x = tf.constant([float('-inf'), float('inf')])
>>> tf.reduce_max(x)
<tf.Tensor: shape=(), dtype=float32, numpy=inf>
``````

See the numpy docs for `np.amax` and `np.nanmax` behavior.

`input_tensor` The tensor to reduce. Should have real numeric type.
`axis` The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range ```[-rank(input_tensor), rank(input_tensor))```.
`keepdims` If true, retains reduced dimensions with length 1.
`name` A name for the operation (optional).

The reduced tensor.

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