Adversarial training is a technique used to improve the robustness of machine learning models. The basic idea in adversarial training is to introduce carefully crafted adversarial examples into the training data. These adversarial examples are created by adding small perturbations to the input data in a way that is designed to fool the model into making incorrect predictions.
By training models on adversarial examples, we can improve their ability to generalize to new and unseen data. This is because the adversarial examples represent a kind of "worst-case scenario" for the model, and by training on them we are essentially making the model more resilient to attacks or other unexpected behavior.
In Keras, adversarial training can be implemented using a technique called adversarial training with Keras, which is implemented in the popular Keras Adversarial Library (KALI). The basic idea in KALI is to define a new loss function and train the model on both the original data and adversarial examples computed using a separate generator network.
Here’s an example of how adversarial training might be implemented in Keras using KALI:
from keras.models import Sequential
from keras.layers import Dense
from keras.datasets import mnist
from keras.optimizers import Adam
from keras_adversarial import AdversarialModel, simple_gan, gan_targets
# Load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# Preprocess data
X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.
# Define generator and discriminator models
generator = Sequential([Dense(128, input_shape=(100,), activation='relu'),
Dense(28*28, activation='sigmoid')])
discriminator = Sequential([Dense(128, input_shape=(28*28,), activation='relu'),
Dense(1, activation='sigmoid')])
gan = simple_gan(generator, discriminator, normal_latent_sampling((100,)))
# Define the adversarial model
adversarial_model = AdversarialModel(base_model=gan,
player_params=[generator.trainable_weights,
discriminator.trainable_weights])
# Compile the adversarial model
adversarial_model.compile(optimizer=Adam(1e-4, decay=1e-4),
loss=gan_targets)
# Train the adversarial model
adversarial_model.fit(X_train, gan_targets(X_train.shape[0]), epochs=20, batch_size=32)This code defines a generator model and a discriminator model for training a GAN (Generative Adversarial Network) on the MNIST dataset. The adversarial model is created by passing both the generator and discriminator to the ‘adversarial_model‘ function. The adversarial loss function is defined in the ‘gan_targets‘ function, which computes the target values for the generator and discriminator during training.
By training the adversarial model on both the original MNIST data and adversarial examples generated by the generator network, we can improve the robustness of the model and make it less susceptible to attacks.