Advanced Deep Learning with Keras

Improved GANs
The preceding table shows the comparison of the loss functions between GAN and
LSGAN. Minimizing Equation 5.2.1 or the discriminator loss function implies that
the MSE between real data classification and true label 1.0 should be close to zero.
In addition, the MSE between the fake data classification and the true label 0.0
should be close to zero.
Similar to GANs, the LSGAN discriminator is trained to classify real from fake
data samples. Minimizing Equation 5.2.2 means fooling the discriminator to think
that the generated fake sample data are real with label 1.0.
Implementing LSGAN using the DCGAN code in the previous chapter as the
basis requires few changes only. As shown in Listing 5.2.1, the discriminator
sigmoid activation is removed. The discriminator is built by calling:
discriminator = gan.discriminator(inputs, activation=None)
The generator is similar to the original DCGAN:
generator = gan.generator(inputs, image_size)
Both the discriminator and adversarial loss functions are replaced by mse. All the
network parameters are the same as in DCGAN. The network model of LSGAN
in Keras is similar to Figure 4.2.1 except that there is no linear or output activation.
The training process is similar to that seen in DCGAN and is provided by the utility
function:
gan.train(models, x_train, params)
Listing 5.2.1, lsgan-mnist-5.2.1.py shows how the discriminator and generator
are the same in DCGAN except for the discriminator output activation and the use
of MSE loss function:
def build_and_train_models():
# MNIST dataset
(x_train, _), (_, _) = mnist.load_data()
# reshape data for CNN as (28, 28, 1) and normalize
image_size = x_train.shape[1]
x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
model_name = "lsgan_mnist"
# network parameters
# the latent or z vector is 100-dim
latent_size = 100
[ 144 ]