How to use regularization techniques in Keras?

You can implement regularization techniques in Keras by setting the “kernel_regularizer” parameter in the layer. Here are the specific steps:

1. Import the necessary libraries.

from keras.models import Sequential
from keras.layers import Dense
from keras import regularizers

1. Model creation.

model = Sequential()

1. Set regularization parameters in the layer.

model.add(Dense(units=64, activation='relu', kernel_regularizer=regularizers.l2(0.01), input_shape=(10,)))

In the example above, L2 regularization with a parameter of 0.01 was used. You can also use other types of regularization, such as L1 regularization, L1L2 regularization, etc.

1. Compile the model and train it.

model.compile(optimizer='adam', loss='mse')
model.fit(X_train, y_train, epochs=10, batch_size=32)

By following the steps above, you can now utilize regularization techniques in Keras.