What is a Parameter Container in PyBrain

This recipe explains what is a Parameter Container in PyBrain

Recipe Objective - What is a Parameter Container in PyBrain?

There is a desire for adaptive parameters that an external learning algorithm can train in all neural networks. Structures that contain these parameters are implemented using subclasses of ParameterContainer. Due to Python's ability to have multiple inheritances, you can add this to your subclass list. We will now consider this using the FullConnection example. The linear connection is a connection that connects two layers and processes the data by multiplying it with a weight matrix. This type of connection is more common in neural networks.

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from scipy import reshape, dot, outer
from connection import Connection
from pybrain.structure.parametercontainer import ParameterContainer

class FullConnection(Connection, ParameterContainer):

   def __init__(self, *args, **kwargs):
     Connection.__init__(self, *args, **kwargs)
     ParameterContainer.__init__(self, self.indim*self.outdim)

   def _forwardImplementation(self, inbuf, outbuf):
     outbuf += dot(reshape(self.params, (self.outdim, self.indim)), inbuf)

   def _backwardImplementation(self, outerr, inerr, inbuf):
     inerr += dot(reshape(self.params, (self.outdim, self.indim)).T, outerr)
     self.derivs += outer(inbuf, outerr).T.flatten()

In lines 8 and 9, we see what the constructors of the superclasses are called. ParameterContainer expects an integer argument N representing the number of parameters that FullConnection needs, the product of the size of the incoming modules, and the size of the outgoing modules. For this reason, the constructor of ParameterContainer gives the object two fields: params and drifts, which are two arrays of size N. These are used to hold parameters and possibly derivatives. In the case of backpropagation, learning occurs while _backwardImplementation () is called. In line 16, we see how the derivatives of the field are modified.

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