def __init__(
self,
vars=None,
batch_size=None,
total_size=None,
step_size=1.0,
model=None,
random_seed=None,
minibatches=None,
minibatch_tensors=None,
**kwargs
):
warnings.warn(EXPERIMENTAL_WARNING)
model = modelcontext(model)
if vars is None:
vars = model.value_vars
else:
vars = [model.rvs_to_values.get(var, var) for var in vars]
vars = inputvars(vars)
self.model = model
self.vars = vars
self.batch_size = batch_size
self.total_size = total_size
_value_error(
total_size != None or batch_size != None,
"total_size and batch_size of training data have to be specified",
)
self.expected_iter = int(total_size / batch_size)
# set random stream
self.random = None
if random_seed is None:
self.random = at_rng()
else:
self.random = at_rng(random_seed)
self.step_size = step_size
shared = make_shared_replacements(vars, model)
self.updates = OrderedDict()
# XXX: This needs to be refactored
self.q_size = None # int(sum(v.dsize for v in self.vars))
# This seems to be the only place that `Model.flatten` is used.
# TODO: Why not _actually_ flatten the variables?
# E.g. `flat_vars = at.concatenate([var.ravel() for var in vars])`
# or `set_subtensor` the `vars` into a `at.vector`?
flat_view = model.flatten(vars)
self.inarray = [flat_view.input]
self.dlog_prior = prior_dlogp(vars, model, flat_view)
self.dlogp_elemwise = elemwise_dlogL(vars, model, flat_view)
# XXX: This needs to be refactored
self.q_size = None # int(sum(v.dsize for v in self.vars))
if minibatch_tensors is not None:
_check_minibatches(minibatch_tensors, minibatches)
self.minibatches = minibatches
# Replace input shared variables with tensors
def is_shared(t):
return isinstance(t, aesara.compile.sharedvalue.SharedVariable)
tensors = [(t.type() if is_shared(t) else t) for t in minibatch_tensors]
updates = OrderedDict(
{t: t_ for t, t_ in zip(minibatch_tensors, tensors) if is_shared(t)}
)
self.minibatch_tensors = tensors
self.inarray += self.minibatch_tensors
self.updates.update(updates)
self._initialize_values()
super().__init__(vars, shared)
def setup_method(self):
nr.seed(self.random_seed)
self.old_at_rng = at_rng()
set_at_rng(RandomStream(self.random_seed))