Reputation: 391
I am trying to calculate the Frobenius Norm of my tensor
W = tf.Variable(tf.random_normal([3072,20],stddev=0.1))
temp = tf.matmul(tf.transpose(W),W)
fro_W = tf.norm(temp, ord ='fro')
This produces the following error:
ValueError: 'ord' must be a supported vector norm, got fro
I don't understand why it is treating my 2D tensor as a vector and not as matrix.
Am I missing something here?
Thank you
Upvotes: 2
Views: 1816
Reputation: 24591
From the documentation:
The Frobenius norm fro is not defined for vectors
Also,
If axis is
None
(the default), the input is considered a vector
Try this instead:
tf.norm(temp, ord='fro', axis=(0,1))
Upvotes: 4