# DiscriminatorLSGAN¶

Inheritance Diagram

class ashpy.losses.gan.DiscriminatorLSGAN[source]

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

https://arxiv.org/abs/1611.04076

Methods

 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.
class LeastSquareLoss[source]

Bases: tensorflow.python.keras.losses.Loss

Least Square Loss as tf.keras.losses.Loss.

__init__()[source]

Initialize the Loss.

Return type

None

call(d_real, d_fake)[source]

Compute the Least Square Loss.

Parameters
Return type

Tensor

Returns

tf.Tensor – Loss.

property reduction

Return the reduction type for this loss.

Return type

ReductionV2

Returns

tf.keras.losses.Reduction – Reduction.

__init__()[source]

Initialize loss.

Return type

None