Explain the functions of Link and Chains in chainer

This recipe explains what the functions of Link and Chains in chainer

Recipe Objective - Explain the functions of Link and Chains in chainer?

Chainer provides many Link implementations in the chainer.links package.

Link is a building block of neural network models that support various features like handling parameters, defining network fragments, serialization, etc.

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Link is the primitive structure for the model definitions. It supports management of parameter variables and persistent values that should be incorporated to serialization.

Composability is one of the most important features of neural nets. Neural net models consist of many reusable fragments, and each model itself might be embedded into a larger learnable system. Chain enables us to write a neural net based on composition, without bothering about routine works like collecting parameters, serialization, copying the structure with parameters shared, etc.

Each chain itself is also a link. Therefore, one can combine chains into higher-level chains. In this way, links and chains construct a link hierarchy. Link hierarchy forms a tree structure, where each node is identified by the path from the root.

This is a simple example of custom chain definition. Chainer itself also provides some chains defined under the links module. They might serve as examples, too.

Consider we want to define a multi-layer perceptron consisting of two hidden layers with rectifiers as activation functions. We can use the Linear link as a building block:

import chainer
import chainer.functions as F
import chainer.links as link
class MultiLayerPerceptronClass(chainer.Chain):
 def __init__(self, in_param, hidden_param, out_param):
  super(MultiLayerPerceptronClass, self).__init__()
  with self.init_scope():
  self.layer1 = link.Linear(in_param, hidden_param)
  self.layer2 = link.Linear(hidden_param, hidden_param)
  self.layer3 = link.Linear(hidden_param, out_param)
 def forward(self, x): # Forward propagation
  l1 = F.relu(self.layer1(x))
  l2 = F.relu(self.layer2(l1))
  return self.layer3(l2)

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