What is Constraints module in Keras, How to use it?

 

In Keras, constraints are a set of functions that are used to add constraints on the weights of neural network layers during optimization. The "constraints" module in Keras provides several options for constraint, such as maximum norm, non-negativity, and unit norm.

Here's an example of how to use the constraints module in Keras:

python
from keras.models import Sequential
from keras.layers import Dense
from keras.constraints import max_norm

model = Sequential()
model.add(Dense(64, input_dim=100, activation='relu', kernel_constraint=max_norm(2.)))
model.add(Dense(32, activation='relu', kernel_constraint=NonNeg()))
model.add(Dense(1, activation='sigmoid'))

In the example above, we are creating a sequential model with three dense layers. The first layer has 64 units and is activated using the 'relu' activation function, and is constrained by the maximum norm of the weights set to 2. The second layer has 32 units and is also activated using the 'relu' activation function, but is constrained by non-negativity of the weights. The third and final layer has one unit and is activated using the sigmoid function.

By default, Keras does not apply any constraints to the model weights. However, you can choose to use any of the constraints available in the "constraints" module to add constraints to the weights of your neural network layers during optimization. Constraints can help prevent overfitting and improve the robustness of the model by limiting the range of the weights.

Keywords: Keras constraints, weight constraints, maximum norm, non-negativity, unit norm, overfitting, model robustness, how to use Keras constraints.