Hands-On Machine Learning for Algorithmic Trading

Adversarial training

Adversarial training iterates over the epochs, generates random image and noise input, and trains both the discriminator and the generator (as part of the combined model):

valid = np.ones((batch_size, 1))fake = np.zeros((batch_size, 1))for epoch in range(epochs):    # Select a random half of images    idx = np.random.randint(0, X_train.shape[0], batch_size)    imgs = X_train[idx]    # Sample noise and generate a batch of new images    noise = np.random.normal(0, 1, (batch_size, latent_dim))    gen_imgs = generator.predict(noise)    # Train the discriminator (real classified as ones and generated as zeros)    d_loss_real = discriminator.train_on_batch(imgs, valid)    d_loss_fake = discriminator.train_on_batch(gen_imgs, fake) d_loss = 0.5 * np.add(d_loss_real, ...