Source code for pygan.discriminativemodel.auto_encoder_model

# -*- coding: utf-8 -*-
from abc import ABCMeta, abstractmethod
from pygan.discriminative_model import DiscriminativeModel


[docs]class AutoEncoderModel(DiscriminativeModel): ''' Auto-Encoder as a Discriminative Model which discriminates `true` from `fake`. The Energy-based GAN framework considers the discriminator as an energy function, which assigns low energy values to real data and high to fake data. The generator is a trainable parameterized function that produces samples in regions to which the discriminator assigns low energy. References: - Manisha, P., & Gujar, S. (2018). Generative Adversarial Networks (GANs): What it can generate and What it cannot?. arXiv preprint arXiv:1804.00140. - Zhao, J., Mathieu, M., & LeCun, Y. (2016). Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126. '''
[docs] @abstractmethod def pre_learn(self, true_sampler, epochs=1000): ''' Pre learning. Args: true_sampler: is-a `TrueSampler`. epochs: Epochs. ''' raise NotImplementedError()