TensorFlow 1 version | View source on GitHub |
Computes the maximum of elements across dimensions of a tensor.
tf.math.reduce_max(
input_tensor, axis=None, keepdims=False, name=None
)
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
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. |