def tag_dropout(self, variables, rng=None, **hyperparameters):
from blocks.roles import INPUT
from blocks.filter import VariableFilter
bricks_ = [brick for brick in util.all_bricks([self.mlp])
if isinstance(brick, bricks.Linear)]
variables = (VariableFilter(roles=[INPUT], bricks=bricks_)
(theano.gof.graph.ancestors(variables)))
graph.add_transform(
variables,
graph.DropoutTransform("classifier_dropout", rng=rng),
reason="regularization")
def tag_dropout(self, variables, rng=None, **hyperparameters):
from blocks.roles import INPUT
from blocks.filter import VariableFilter
rng = util.get_rng(seed=1)
bricks_ = [brick for brick in util.all_bricks(self.emitters)
if isinstance(brick, bricks.Linear)]
variables = (VariableFilter(roles=[INPUT], bricks=bricks_)
(theano.gof.graph.ancestors(variables)))
graph.add_transform(
variables,
graph.DropoutTransform("classifier_dropout", rng=rng),
reason="regularization")
def tag_attention_dropout(self, variables, rng=None, **hyperparameters):
from blocks.roles import INPUT, has_roles
bricks_ = [
brick for brick in util.all_bricks([self.patch_transform])
if isinstance(brick, (bricks.Linear, conv2d.Convolutional,
conv3d.Convolutional))
]
variables = [
var for var in graph.deep_ancestors(variables)
if (has_roles(var, [INPUT]) and any(brick in var.tag.annotations
for brick in bricks_))
]
graph.add_transform(variables,
graph.DropoutTransform("attention_dropout",
rng=rng),
reason="regularization")
