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)
```

- max_value
The maximum norm for the incoming weights.

- axis
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`

.- min_value
The minimum norm for the incoming weights.

- rate
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.

`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.

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()`

.