accelbrainbase.controllablemodel._mxnet.gancontroller package

Submodules

accelbrainbase.controllablemodel._mxnet.gancontroller.aae_controller module

class accelbrainbase.controllablemodel._mxnet.gancontroller.aae_controller.AAEController(true_sampler, generative_model, discriminative_model, generator_loss, discriminator_loss, reconstruction_loss, feature_matching_loss=None, optimizer_name='SGD', learning_rate=1e-05, learning_attenuate_rate=1.0, attenuate_epoch=50, hybridize_flag=True, scale=1.0, ctx=gpu(0), initializer=None, **kwargs)

Bases: accelbrainbase.controllablemodel._mxnet.gan_controller.GANController

The Adversarial Auto-Encoder(AAE).

The Generative Adversarial Networks(GANs) (Goodfellow et al., 2014) framework establishes a min-max adversarial game between two neural networks – a generative model, G, and a discriminative model, D. The discriminator model, D(x), is a neural network that computes the probability that a observed data point x in data space is a sample from the data distribution (positive samples) that we are trying to model, rather than a sample from our generative model (negative samples).

Concurrently, the generator uses a function G(z) that maps samples z from the prior p(z) to the data space. G(z) is trained to maximally confuse the discriminator into believing that samples it generates come from the data distribution. The generator is trained by leveraging the gradient of D(x) w.r.t. x, and using that to modify its parameters.

The Conditional GANs (or cGANs) is a simple extension of the basic GAN model which allows the model to condition on external information. This makes it possible to engage the learned generative model in different “modes” by providing it with different contextual information (Gauthier, J. 2014).

This model can be constructed by simply feeding the data, y, to condition on to both the generator and discriminator. In an unconditioned generative model, because the maps samples z from the prior p(z) are drawn from uniform or normal distribution, there is no control on modes of the data being generated. On the other hand, it is possible to direct the data generation process by conditioning the model on additional information (Mirza, M., & Osindero, S. 2014).

This library also provides the Adversarial Auto-Encoders(AAEs), which is a probabilistic Auto-Encoder that uses GANs to perform variational inference by matching the aggregated posterior of the feature points in hidden layer of the Auto-Encoder with an arbitrary prior distribution(Makhzani, A., et al., 2015). Matching the aggregated posterior to the prior ensures that generating from any part of prior space results in meaningful samples. As a result, the decoder of the Adversarial Auto-Encoder learns a deep generative model that maps the imposed prior to the data distribution.

References

  • Gauthier, J. (2014). Conditional generative adversarial nets for convolutional face generation. Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester, 2014(5), 2.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
  • Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015). Adversarial autoencoders. arXiv preprint arXiv:1511.05644.
  • Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784.
  • Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. In Advances in neural information processing systems (pp. 2234-2242).
  • Zhao, J., Mathieu, M., & LeCun, Y. (2016). Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126.
  • Warde-Farley, D., & Bengio, Y. (2016). Improving generative adversarial networks with denoising feature matching.
train_by_feature_matching(k_step)
train_discriminator(k_step)

Training for discriminator.

Parameters:k_step – int of the number of learning of the discriminative_model.
Returns:Tuple data. - discriminative loss. - discriminative posterior.
train_generator()

Train generator.

Returns:Tuple data. - generative loss. - discriminative posterior.

accelbrainbase.controllablemodel._mxnet.gancontroller.ebgan_controller module

class accelbrainbase.controllablemodel._mxnet.gancontroller.ebgan_controller.EBGANController(true_sampler, generative_model, discriminative_model, discriminator_loss, feature_matching_loss=None, optimizer_name='SGD', learning_rate=1e-05, learning_attenuate_rate=1.0, attenuate_epoch=50, hybridize_flag=True, scale=1.0, ctx=gpu(0), initializer=None, **kwargs)

Bases: accelbrainbase.controllablemodel._mxnet.gan_controller.GANController

The energy-based Generative Adversarial Networks(EBGANs).

The Generative Adversarial Networks(GANs) (Goodfellow et al., 2014) framework establishes a min-max adversarial game between two neural networks – a generative model, G, and a discriminative model, D. The discriminator model, D(x), is a neural network that computes the probability that a observed data point x in data space is a sample from the data distribution (positive samples) that we are trying to model, rather than a sample from our generative model (negative samples).

Concurrently, the generator uses a function G(z) that maps samples z from the prior p(z) to the data space. G(z) is trained to maximally confuse the discriminator into believing that samples it generates come from the data distribution. The generator is trained by leveraging the gradient of D(x) w.r.t. x, and using that to modify its parameters.

The Conditional GANs (or cGANs) is a simple extension of the basic GAN model which allows the model to condition on external information. This makes it possible to engage the learned generative model in different “modes” by providing it with different contextual information (Gauthier, J. 2014).

This model can be constructed by simply feeding the data, y, to condition on to both the generator and discriminator. In an unconditioned generative model, because the maps samples z from the prior p(z) are drawn from uniform or normal distribution, there is no control on modes of the data being generated. On the other hand, it is possible to direct the data generation process by conditioning the model on additional information (Mirza, M., & Osindero, S. 2014).

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

  • Gauthier, J. (2014). Conditional generative adversarial nets for convolutional face generation. Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester, 2014(5), 2.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
  • Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015). Adversarial autoencoders. arXiv preprint arXiv:1511.05644.
  • Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784.
  • Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. In Advances in neural information processing systems (pp. 2234-2242).
  • Zhao, J., Mathieu, M., & LeCun, Y. (2016). Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126.
  • Warde-Farley, D., & Bengio, Y. (2016). Improving generative adversarial networks with denoising feature matching.
train_generator()

Train generator.

Returns:Tuple data. - generative loss. - discriminative posterior.

Module contents