Chainer modules



Comparing with caffe

If you are using caffe, it is easy to get accustomed to chainer modules. Several variable’s functionality is similar and you can see below table for its correspondence.

Chainer Caffe Comment
datasets Data layers Input data can be formatted to this class for the model input.
It covers most of the use case of input data structure.
variable blob It is an input and output of functions/links/Chain.
functions layers Framework supports widely used functions in deep learning. Ex sigmoid, tanh, ReLU etc.
links layers Framework supports widely used layers in deep learning. Ex Linear layer, Convolutional layer etc.
Chain net links and functions (layers) are jointed to form a ”model”.
This group of layers are Chain/net
optimizers solver Specify what kind of gradient descent method to be used for tuning model parameter. Ex. SGD, AdaGrad, Adam.
serializers   Save/load training state. Ex. model, optimizer etc.
iterators  –  Defines each minibatch construction, used in Trainer. 
training.updater  –  Defines each forward-backward propagation followed by parameter update procedure, used in Trainer.
training.Trainer  –  It manages training procedure.




Variable, Function, Link and Chain is explained in Chainer basic module introduction.

Optimizer and Serializer is explained in Chainer basic module introduction 2.




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