DLeastSquare

Inheritance Diagram

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class ashpy.keras.losses.DLeastSquare

Bases: tensorflow.python.keras.losses.Loss

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

Methods

__init__()	Least square Loss for Discriminator.
call(d_real, d_fake)	Compute the Least Square Loss.

Attributes

reduction

Return the reduction type for this loss.

__init__()

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

Return type

None

call(d_real, d_fake)

Compute the Least Square Loss.

Parameters

• d_real (tf.Tensor) – Discriminator evaluated in real samples.

• d_fake (tf.Tensor) – Discriminator evaluated in fake samples.

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.