This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow
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?
class MaxNorm(Constraint): """MaxNorm weight constraint.
It seems that the default parameter is not suitable for conv2d?