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)

## Arguments

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. |

## Details

`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.

## Custom constraints

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:

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()`

.

## See also