pygan.discriminativemodel.autoencodermodel.convolutionalautoencoder.convolutionalladdernetworks package

Submodules

pygan.discriminativemodel.autoencodermodel.convolutionalautoencoder.convolutionalladdernetworks.seq_cln_model module

class pygan.discriminativemodel.autoencodermodel.convolutionalautoencoder.convolutionalladdernetworks.seq_cln_model.SeqCLNModel(convolutional_auto_encoder=None, batch_size=10, channel=1, learning_rate=1e-10, learning_attenuate_rate=0.1, attenuate_epoch=50, opt_params=None, feature_matching_layer=0)[source]

Bases: pygan.discriminativemodel.autoencodermodel.convolutionalautoencoder.convolutional_ladder_networks.ConvolutionalLadderNetworks

Ladder Networks with a Stacked convolutional Auto-Encoder as a Discriminator..

This model observes sequencal data as image-like data.

If the length of sequence is T and the dimension is D, image-like matrix will be configured as a T × D matrix.

References

  • Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Advances in neural information processing systems (pp. 153-160).
  • Dumoulin, V., & V,kisin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285.
  • Erhan, D., Bengio, Y., Courville, A., Manzagol, P. A., Vincent, P., & Bengio, S. (2010). Why does unsupervised pre-training help deep learning?. Journal of Machine Learning Research, 11(Feb), 625-660.
  • Erhan, D., Courville, A., & Bengio, Y. (2010). Understanding representations learned in deep architectures. Department dInformatique et Recherche Operationnelle, University of Montreal, QC, Canada, Tech. Rep, 1355, 1.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning (adaptive computation and machine learning series). Adaptive Computation and Machine Learning series, 800.
  • Manisha, P., & Gujar, S. (2018). Generative Adversarial Networks (GANs): What it can generate and What it cannot?. arXiv preprint arXiv:1804.00140.
  • Masci, J., Meier, U., Cireşan, D., & Schmidhuber, J. (2011, June). Stacked convolutional auto-encoders for hierarchical feature extraction. In International Conference on Artificial Neural Networks (pp. 52-59). Springer, Berlin, Heidelberg.
  • Rasmus, A., Berglund, M., Honkala, M., Valpola, H., & Raiko, T. (2015). Semi-supervised learning with ladder networks. In Advances in neural information processing systems (pp. 3546-3554).
  • Valpola, H. (2015). From neural PCA to deep unsupervised learning. In Advances in Independent Component Analysis and Learning Machines (pp. 143-171). Academic Press.
  • Zhao, J., Mathieu, M., & LeCun, Y. (2016). Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126.
feature_matching_forward(observed_arr)[source]

Forward propagation in only first or intermediate layer for so-called Feature matching.

Parameters:observed_arr – np.ndarray of observed data points.
Returns:np.ndarray of outputs.
inference(observed_arr)[source]

Draws samples from the true distribution.

Parameters:observed_arr – np.ndarray of observed data points.
Returns:np.ndarray of inferenced.
learn(grad_arr, fix_opt_flag=False)[source]

Update this Discriminator by ascending its stochastic gradient.

Parameters:
  • grad_arr – np.ndarray of gradients.
  • fix_opt_flag – If False, no optimization in this model will be done.
Returns:

np.ndarray of delta or gradients.

Module contents