Complex neural networks made easy by Chainer

Chainer is an open source framework designed for efficient research into and development of deep learning algorithms. In this post, we briefly introduce Chainer with a few examples and compare with other frameworks such as Caffe, Theano, Torch, and Tensorflow. Most existing frameworks construct a computational graph in advance of training. This approach is fairly straightforward, especially for implementing fixed and layer-wise neural networks like convolutional neural networks. However, state-of-the-art performance and new applications are now coming from more complex networks, such as recurrent or stochastic neural networks. Though existing frameworks can be used for these kinds of complex networks, it sometimes requires (dirty) hacks that can reduce development efficiency and maintainability of the code.