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dgm.pt

Pytorch code for building deep generative models

Install https://github.com/probabll/dists.pt

Then

pip install -r requirements.txt
python setup.py develop

Register parameterization

Use decorators to register a parameterization of a distribution, e.g.

@register_conditional_parameterization(Normal)
def make_normal(inputs, event_size):
    params = torch.split(inputs, event_size, -1)
    return Normal(loc=params[0], scale=F.softplus(params[1]))

Use conditioners to parameterize components. For example, here we show the prior, approximate posterior, and likelihood of the classic MNIST VAE:

p_z = PriorLayer(
    event_shape=z_size,
    dist_type=Normal,
    params=[0., 1.]
)
q_z = ConditionalLayer(
    event_size=z_size,
    dist_type=Normal,
    conditioner=FFConditioner(
        input_size=x_size,
        output_size=z_size * 2,  # Gaussians take two parameters per unit
        hidden_sizes=[x_size // 2]
    )
)
p_x_given_z = FullyFactorizedLikelihood(
    event_size=x_size, 
    dist_type=Bernoulli, 
    conditioner=FFConditioner(
        input_size=z_size, 
        output_size=x_size * 1,   # Bernoullis take one parameter per unit
        hidden_sizes=[x_size // 2]
    )            
)

Design

  • Bijection: an invertible transformation (which also computes log det jacobian)
  • Conditioner: maps from data to D-dimensional output
  • ConditionalLayer: parameterises a certain distribution (using a conditioner)
  • LikelihoodLayer: parameterises a certain distribution (using a conditioner) and also provides a dedicated sampling procedure
  • NF: a type of distribution

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Pytorch code for deep generative models

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