Functions that impose constraints on weight values.
constraint_maxnorm(max_value = 2, axis = 0)constraint_nonneg()
constraint_unitnorm(axis = 0)
constraint_minmaxnorm(min_value = 0, max_value = 1, rate = 1, 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.
The minimum norm for the incoming weights.
The rate for enforcing the constraint: weights will be rescaled to yield (1 - rate) * norm + rate * norm.clip(low, high). Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval.
You can implement your own constraint functions in R. A custom
constraint is an R function that takes weights (w) as input
and returns modified weights. Note that keras backend() tensor
functions (e.g. k_greater_equal()) should be used in the
implementation of custom constraints. For example:
nonneg_constraint <- function(w) {
w * k_cast(k_greater_equal(w, 0), k_floatx())
}layer_dense(units = 32, input_shape = c(784),
kernel_constraint = nonneg_constraint)
Note that models which use custom constraints cannot be serialized using
save_model_hdf5(). Rather, the weights of the model should be saved
and restored using save_model_weights_hdf5().
constraint_maxnorm() constrains the weights incident to each
hidden unit to have a norm less than or equal to a desired value.
constraint_nonneg() constraints the weights to be non-negative
constraint_unitnorm() constrains the weights incident to each hidden
unit to have unit norm.
constraint_minmaxnorm() constrains the weights incident to each
hidden unit to have the norm between a lower bound and an upper bound.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014