Generative Adversarial Networks Projects

Training the discriminator network

Perform the following steps to train the discriminator network:

1. Start by sampling a batch of noise vectors from a normal distribution, as follows:

z_noise = np.random.normal(0, 1, size=(batch_size, z_shape))

To sample the values, use the normal() method from the np.random module in the Numpy library.

2. Next, sample a batch of real images from the set of all images:

image_batch = X[index * batch_size:(index + 1) * batch_size]

3. Next, generate a batch of fake images using the generator network:

generated_images = gen_model.predict_on_batch(z_noise)

4. Next, create real labels and fake labels:

y_real = np.ones(batch_size) - np.random.random_sample(batch_size) * 0.2y_fake = np.random.random_sample(batch_size) ...