View source on GitHub |
Computes the Euclidean norm of elements across dimensions of a tensor.
tf.math.reduce_euclidean_norm(
input_tensor, axis=None, keepdims=False, name=None
)
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.
For example:
x = tf.constant([[1, 2, 3], [1, 1, 1]]) # x.dtype is tf.int32
tf.math.reduce_euclidean_norm(x) # returns 4 as dtype is tf.int32
y = tf.constant([[1, 2, 3], [1, 1, 1]], dtype = tf.float32)
tf.math.reduce_euclidean_norm(y) # returns 4.1231055 which is sqrt(17)
tf.math.reduce_euclidean_norm(y, 0) # [sqrt(2), sqrt(5), sqrt(10)]
tf.math.reduce_euclidean_norm(y, 1) # [sqrt(14), sqrt(3)]
tf.math.reduce_euclidean_norm(y, 1, keepdims=True) # [[sqrt(14)], [sqrt(3)]]
tf.math.reduce_euclidean_norm(y, [0, 1]) # sqrt(17)
Returns | |
---|---|
The reduced tensor, of the same dtype as the input_tensor. |