human
I am working on the file "11_deep_learning.ipynb".
The first task is:
The implementation above is straightforward and it works fine, but it is a bit messy. A better approach is to define a max_norm_regularizer() function:
Can you write Python code for it?
assistant
#Here is the Python code you requested.

def max_norm_regularizer(threshold, axes=1, name="max_norm",
                         collection="max_norm"):
    def max_norm(weights):
        clipped = tf.clip_by_norm(weights, clip_norm=threshold, axes=axes)
        clip_weights = tf.assign(weights, clipped, name=name)
        tf.add_to_collection(collection, clip_weights)
        return None # there is no regularization loss term
    return max_norm