Variable roles

blocks.roles.add_role(var, role)

Add a role to a given Theano variable.

Parameters:

• var (TensorVariable) – The variable to assign the new role to.
• role (VariableRole instance) –

Notes

Some roles are subroles of others (e.g. WEIGHT is a subrole of PARAMETER). This function will not add a role if a more specific role has already been added. If you need to replace a role with a parent role (e.g. replace WEIGHT with PARAMETER) you must do so manually.

Examples

>>> from theano import tensor
>>> W = tensor.matrix()
>>> from blocks.roles import PARAMETER, WEIGHT
>>> add_role(W, PARAMETER)
>>> print(*W.tag.roles)
PARAMETER
>>> add_role(W, WEIGHT)
>>> print(*W.tag.roles)
WEIGHT
>>> add_role(W, PARAMETER)
>>> print(*W.tag.roles)
WEIGHT

Roles

All roles are implemented as subclasses of VariableRole.

class blocks.roles.VariableRole

Base class for all variable roles.

The actual roles are instances of the different subclasses of VariableRole. They are:

blocks.roles.INPUT = INPUT

The input of a Brick

blocks.roles.OUTPUT = OUTPUT

The output of a Brick

blocks.roles.AUXILIARY = AUXILIARY

Variables added to the graph as annotations

blocks.roles.COST = COST

A scalar cost that can be used to train or regularize

blocks.roles.PARAMETER = PARAMETER

A parameter of the model

blocks.roles.WEIGHT = WEIGHT

The weight matrices of linear transformations

blocks.roles.BIAS = BIAS

Biases of linear transformations

blocks.roles.FILTER = FILTER

The filters (kernels) of a convolution operation