# tf.math.reduce_max

Computes the maximum of elements across dimensions of a tensor.

### Used in the notebooks

Used in the guide Used in the tutorials

Reduces `input_tensor` along the dimensions given in `axis`. Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each entry in `axis`. 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.

#### Usage example:

````x = tf.constant([5, 1, 2, 4])`
`print(tf.reduce_max(x))`
`tf.Tensor(5, shape=(), dtype=int32)`
`x = tf.constant([-5, -1, -2, -4])`
`print(tf.reduce_max(x))`
`tf.Tensor(-1, shape=(), dtype=int32)`
`x = tf.constant([4, float('nan')])`
`print(tf.reduce_max(x))`
`tf.Tensor(4.0, shape=(), dtype=float32)`
`x = tf.constant([float('nan'), float('nan')])`
`print(tf.reduce_max(x))`
`tf.Tensor(-inf, shape=(), dtype=float32)`
`x = tf.constant([float('-inf'), float('inf')])`
`print(tf.reduce_max(x))`
`tf.Tensor(inf, shape=(), dtype=float32)`
```

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.