FactorVAE¶

This module is the implementation of the FactorVAE proposed in (https://arxiv.org/abs/1802.05983). This model adds a new parameter to the VAE loss function balancing the weight of the reconstruction term and the Total Correlation.

Available samplers¶

`NormalSampler`	Samples from a Standard normal distribution in the Autoencoder’s latent space.
`GaussianMixtureSampler`	Fits a Gaussian Mixture in the Autoencoder’s latent space.
`TwoStageVAESampler`	Fits a second VAE in the Autoencoder’s latent space.
`MAFSampler`	Fits a Masked Autoregressive Flow in the Autoencoder’s latent space.
`IAFSampler`	Fits an Inverse Autoregressive Flow in the Autoencoder’s latent space.

class pythae.models.FactorVAEConfig(input_dim=None, latent_dim=10, uses_default_encoder=True, uses_default_decoder=True, reconstruction_loss='mse', gamma=2.0, uses_default_discriminator=True)[source]¶

FactorVAE model config config class

Parameters

input_dim (tuple) – The input_data dimension.
latent_dim (int) – The latent space dimension. Default: None.
reconstruction_loss (str) – The reconstruction loss to use [‘bce’, ‘mse’]. Default: ‘mse’
gamma (float) – The balancing factor before the Total Correlation. Default: 0.5

class pythae.models.FactorVAE(model_config, encoder=None, decoder=None)[source]¶

FactorVAE model.

Parameters

model_config (FactorVAEConfig) – The Variational Autoencoder configuration setting the main parameters of the model.
encoder (BaseEncoder) – An instance of BaseEncoder (inheriting from torch.nn.Module which plays the role of encoder. This argument allows you to use your own neural networks architectures if desired. If None is provided, a simple Multi Layer Preception (https://en.wikipedia.org/wiki/Multilayer_perceptron) is used. Default: None.
decoder (BaseDecoder) – An instance of BaseDecoder (inheriting from torch.nn.Module which plays the role of decoder. This argument allows you to use your own neural networks architectures if desired. If None is provided, a simple Multi Layer Preception (https://en.wikipedia.org/wiki/Multilayer_perceptron) is used. Default: None.

Note

For high dimensional data we advice you to provide you own network architectures. With the provided MLP you may end up with a MemoryError.

forward(inputs, **kwargs)[source]¶

The VAE model

Parameters: inputs (BaseDataset) – The training dataset with labels
Returns: An instance of ModelOutput containing all the relevant parameters
Return type: ModelOutput

interpolate(starting_inputs, ending_inputs, granularity=10)[source]¶

This function performs a linear interpolation in the latent space of the autoencoder from starting inputs to ending inputs. It returns the interpolation trajectories.

Parameters

starting_inputs (torch.Tensor) – The starting inputs in the interpolation of shape [B x input_dim]
ending_inputs (torch.Tensor) – The starting inputs in the interpolation of shape [B x input_dim]
granularity (int) – The granularity of the interpolation.

Returns

A tensor of shape [B x granularity x input_dim] containing the interpolation trajectories.

Return type

torch.Tensor

reconstruct(inputs)[source]¶

This function returns the reconstructions of given input data.

Parameters

inputs (torch.Tensor) – The inputs data to be reconstructed of shape [B x input_dim]
ending_inputs (torch.Tensor) – The starting inputs in the interpolation of shape

Returns

A tensor of shape [B x input_dim] containing the reconstructed samples.

Return type

torch.Tensor

set_discriminator(discriminator)[source]¶

This method is called to set the discriminator network

Parameters: discriminator (BaseDiscriminator) – The discriminator module that needs to be set to the model.