![]() ![]() Preprocessing of extracted mini-batches.When we want to train neural networks, we have to run training loops that update the parameters many times.Ī typical training loop consists of the following procedures: In the framework, the network is defined by running the chained graph, hence the name is Chainer. You can build a computational graph by dynamically ‘chaining’ various kinds of Link s and Function s to define a Chain. In short, the difference between these two objects, Link and Function, is whether it contains trainable parameters or not.Ī neural network model is typically described as a series of Function and Link. The parameters of the function performed inside the Link object are represented as Variable objects. Therefore, the function needs to keep trainable parameters inside, so that Chainer has Link class that can keep trainable parameters in the object of the class. When such function is a layer of neural network, the parameters of the function will be updated through training. We will review such amenities in later sections of this tutorial.Ĭhainer represents a network as an execution path on a computational graph.Ī computational graph is a series of function applications, so that it can be described with multiple Function objects. This strategy also makes it easy to write multi-GPU parallelization, since logic comes closer to network manipulation. We will show in this tutorial how to define networks dynamically. The Define-by-Run scheme is the core concept of Chainer. This strategy enables us to fully leverage the power of programming logic in Python.įor example, Chainer does not need any magic to introduce conditionals and loops into the network definitions. More precisely, Chainer stores the history of computation instead of programming logic. In contrast, Chainer adopts a “Define-by-Run” scheme, i.e., the network is defined on-the-fly via the actual forward computation. ![]()
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