pymc.FullRankADVI.fit#
- FullRankADVI.fit(n=10000, score=None, callbacks=None, progressbar=True, progressbar_theme=<rich.theme.Theme object>, **kwargs)#
Perform Operator Variational Inference.
- Parameters:
- Returns:
- Other Parameters:
- obj_n_mc: int
Number of monte carlo samples used for approximation of objective gradients
- tf_n_mc: `int`
Number of monte carlo samples used for approximation of test function gradients
- obj_optimizer: function (grads, params) -> updates
Optimizer that is used for objective params
- test_optimizer: function (grads, params) -> updates
Optimizer that is used for test function params
- more_obj_params: `list`
Add custom params for objective optimizer
- more_tf_params: `list`
Add custom params for test function optimizer
- more_updates: `dict`
Add custom updates to resulting updates
- total_grad_norm_constraint: `float`
Bounds gradient norm, prevents exploding gradient problem
- fn_kwargs: `dict`
Add kwargs to pytensor.function (e.g. {‘profile’: True})
- more_replacements: `dict`
Apply custom replacements before calculating gradients