zea.models.generative¶
Generative models for zea.
Classes
|
Base class for deep generative models. |
Abstract base class for generative models. |
- class zea.models.generative.DeepGenerativeModel(*args, **kwargs)[source]¶
Bases:
BaseModel
,GenerativeModel
Base class for deep generative models.
Inherits from both GenerativeModel and BaseModel to combine generative capabilities with Keras model functionality.
- class zea.models.generative.GenerativeModel[source]¶
Bases:
ABC
Abstract base class for generative models.
- fit(data, **kwargs)[source]¶
Fit the model to the data.
- Parameters:
data – The data to fit the model to.
**kwargs – Additional arguments to pass to the fitting procedure.
- log_density(data, **kwargs)[source]¶
Compute the log-density $log p(x)$ of the data under the model.
- Parameters:
data – The data $x$ to compute the log-density for.
**kwargs – Additional arguments.
- Returns:
Log-density $log p(x)$ of the data.
- posterior_sample(measurements, n_samples=1, **kwargs)[source]¶
Draw samples $z sim p(z mid x)$ from the posterior given measurements.
- Parameters:
measurements – The measurements $x$ to condition the posterior on.
n_samples – Number of posterior samples to generate. This will add an additional dimension to the output. For instance, if measurements has shape (batch_size, …), the output will have shape (batch_size, n_samples, …).
**kwargs – Additional arguments to pass to the sampling procedure.
- Returns:
Samples $z$ from the posterior $p(z mid x)$.