Classes from the `tf.keras.constraints`

module allow setting constraints (eg. non-negativity)
on model parameters during training. They are per-variable projection functions
applied to the target variable after each gradient update (when using `fit()`

).

The exact API will depend on the layer, but the layers `Dense`

, `Conv1D`

, `Conv2D`

and `Conv3D`

have a unified API.

These layers expose two keyword arguments:

`kernel_constraint`

for the main weights matrix`bias_constraint`

for the bias.

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

`MaxNorm`

class```
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.

Also available via the shortcut function `keras.constraints.max_norm`

.

**Arguments**

**max_value**: the maximum norm value 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)`

.

`MinMaxNorm`

class```
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)`

.

`NonNeg`

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

Constrains the weights to be non-negative.

`UnitNorm`

class```
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)`

.

A weight constraint can be any callable that takes a tensor
and returns a tensor with the same shape and dtype. You would typically
implement your constraints as subclasses of `tf.keras.constraints.Constraint`

.

Here's a simple example: a constraint that forces weight tensors to be centered around a specific value on average.

```
class CenterAround(tf.keras.constraints.Constraint):
"""Constrains weight tensors to be centered around `ref_value`."""
def __init__(self, ref_value):
self.ref_value = ref_value
def __call__(self, w):
mean = tf.reduce_mean(w)
return w - mean + self.ref_value
def get_config(self):
return {'ref_value': self.ref_value}
```

Optionally, you an also implement the method `get_config`

and the class
method `from_config`

in order to support serialization — just like with
any Keras object. Note that we don't have to implement `from_config`

in the example above since the constructor arguments of the class
the keys in the config returned by `get_config`

are the same.
In this case, the default `from_config`

works fine.