elektronn2.neuromancer.variables module

class elektronn2.neuromancer.variables.VariableParam(value=None, name=None, apply_train=True, apply_reg=True, dtype=None, strict=False, allow_downcast=None, borrow=False, broadcastable=None)

Bases: theano.tensor.sharedvar.TensorSharedVariable

Extension of theano TensorSharedVariable. Additional features are described by the parameters, otherwise identical

Parameters:

• value –
• name (str) –
• flag (apply_train) – whether to apply regularisation (e.g. L2) on this param
• flag – whether to train this parameter (as opposed to a meta-parameter or a parameter that is kept const. during a training phase)
• dtype –
• strict (bool) –
• allow_downcast (bool) –
• borrow (bool) –
• broadcastable –

clone()

Return a new Variable like self.

Returns:A new Variable instance (or subclass instance) with no owner or index. Return type:Variable instance

Notes

Tags are copied to the returned instance.

Name is copied to the returned instance.

updates

class elektronn2.neuromancer.variables.VariableWeight(shape=None, init_kwargs=None, value=None, name=None, apply_train=True, apply_reg=True, dtype=None, strict=False, allow_downcast=None, borrow=False, broadcastable=None)

Bases: elektronn2.neuromancer.variables.VariableParam

set_value(new_value, borrow=False)

Set the non-symbolic value associated with this SharedVariable.

Parameters:

• borrow (bool) – True to use the new_value directly, potentially creating problems related to aliased memory.
• to this value will be visible to all functions using (Changes) –
• SharedVariable. (this) –

class elektronn2.neuromancer.variables.ConstantParam(value, name=None, dtype=None, make_singletons_broadcastable=True)

Bases: theano.tensor.var.TensorConstant

Identical to theano VariableParam except that there are two two addition attributes apply_train and apply_reg`, which are both false. This is just to tell ELEKTRONN2 that this parameter is to be exempted from training. Obviously the set_value method raises an exception because this is a real constant. Constants are faster in the theano graph.

clone()

We clone this object, but we don’t clone the data to lower memory requirement. We suppose that the data will never change.

get_value(borrow=False)

set_value(new_value, borrow=False)

updates

elektronn2.neuromancer.variables.initweights(shape, dtype='float64', scale='glorot', mode='normal', pool=None, spatial_axes=None)