DiscriminatorLSGAN

Inheritance Diagram

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class ashpy.losses.gan.DiscriminatorLSGAN

Bases: ashpy.losses.gan.DiscriminatorAdversarialLoss

Least square Loss for discriminator.

Reference: Least Squares Generative Adversarial Networks [1] .

Basically the Mean Squared Error between the discriminator output when evaluated in fake samples and 0 and the discriminator output when evaluated in real samples and 1: For the unconditioned case this is:

\[L_{D} = \frac{1}{2} E[(D(x) - 1)^2 + (0 - D(G(z))^2]\]

where x are real samples and z is the latent vector.

For the conditioned case this is:

\[L_{D} = \frac{1}{2} E[(D(x, c) - 1)^2 + (0 - D(G(c), c)^2]\]

where c is the condition and x are real samples.

[1]	Least Squares Generative Adversarial Networks https://arxiv.org/abs/1611.04076

Methods

__init__()

Initialize loss.

Attributes

fn	Return the Keras loss function to execute.
global_batch_size	Global batch size comprises the batch size for each cpu.
weight	Return the loss weight.

__init__()

Initialize loss.

Return type:None