|TensorFlow 1 version||View source on GitHub|
Computes the Euclidean norm of elements across dimensions of a tensor.
Compat aliases for migration
See Migration guide for more details.
tf.math.reduce_euclidean_norm( input_tensor, axis=None, keepdims=False, name=None )
input_tensor along the dimensions given in
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.
axis is None, all dimensions are reduced, and a
tensor with a single element is returned.
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)
||The tensor to reduce. Should have numeric type.|
The dimensions to reduce. If
||If true, retains reduced dimensions with length 1.|
||A name for the operation (optional).|
|The reduced tensor, of the same dtype as the input_tensor.|