/TensorFlow 2.4

# tf.math.reduce_max

Computes the maximum of elements across dimensions of a tensor.

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.

#### 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(nan, shape=(), dtype=float32)
x = tf.constant([float('nan'), float('nan')])
print(tf.reduce_max(x))
tf.Tensor(nan, 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.

Args
`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).
Returns
The reduced tensor.