## Usage of constraints

Functions from the `constraints`

module allow setting constraints (eg. non-negativity) on network parameters during optimization.

The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers `Dense`

, `Conv1D`

, `Conv2D`

and `Conv3D`

have a unified API.

These layers expose 2 keyword arguments:

`kernel_constraint`

for the main weights matrix`bias_constraint`

for the bias.

```
from keras.constraints import max_norm
model.add(Dense(64, kernel_constraint=max_norm(2.)))
```

## Available constraints

### MaxNorm

```
keras.constraints.MaxNorm(max_value=2, axis=0)
```

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**

### NonNeg

```
keras.constraints.NonNeg()
```

Constrains the weights to be non-negative.

### UnitNorm

```
keras.constraints.UnitNorm(axis=0)
```

Constrains the weights incident to each hidden unit to have unit norm.

**Arguments**

**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)`

.

### MinMaxNorm

```
keras.constraints.MinMaxNorm(min_value=0.0, max_value=1.0, rate=1.0, axis=0)
```

MinMaxNorm weight constraint.

Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.

**Arguments**

**min_value**: the minimum norm for the incoming weights.**max_value**: the maximum norm for the incoming weights.**rate**: rate for enforcing the constraint: weights will be rescaled to yield`(1 - rate) * norm + rate * norm.clip(min_value, max_value)`

. 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.**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)`

.