Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.
constraint_maxnorm(max_value = 2, axis = 0)The maximum norm for the incoming weights.
The 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 convolution 2D layer with dim_ordering="tf", the weight tensor has
shape rows, cols, input_depth, output_depth, set axis to c(0, 1, 2)
to constrain the weights of each filter tensor of size rows, cols, input_depth.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014
Other constraints: constraint_minmaxnorm,
constraint_nonneg,
constraint_unitnorm