A question about max_norm

[Yang Wang]

class MaxNorm(Constraint):
"""MaxNorm weight constraint.

Constrains the weights incident to each hidden unit
to have a norm less than or equal to a desired value.

# Arguments
    m: the maximum norm for the incoming weights.
    axis: integer, axis along which to calculate weight norms.
        For instance, in a `Dense` layer the weight matrix
        has shape `(input_dim, output_dim)`,
        set `axis` to `0` to constrain each weight vector
        of length `(input_dim,)`.
        In a `Conv2D` layer with `data_format="channels_last"`,
        the weight tensor has shape
        `(rows, cols, input_depth, output_depth)`,
        set `axis` to `[0, 1, 2]`
        to constrain the weights of each filter tensor of size
        `(rows, cols, input_depth)`.

# References
    - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014](http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
"""

def __init__(self, max_value=2, axis=0):
    self.max_value = max_value
    self.axis = axis

def __call__(self, w):
    norms = K.sqrt(K.sum(K.square(w), axis=self.axis, keepdims=True))
    desired = K.clip(norms, 0, self.max_value)
    w *= (desired / (K.epsilon() + norms))
    return w

def get_config(self):
    return {'max_value': self.max_value,
            'axis': self.axis}

It seems that the default parameter is not suitable for conv2d?

[vlawhern]

Closing this issue as it's been resolved by the original developers.