tf.math.reduce_euclidean_norm

Computes the Euclidean norm 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.

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)

input_tensor The tensor to reduce. Should have 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,