Today we would like to introduce you to Chainer. It is a popular open-source, cross-platform deep learning framework. Also, the framework was developed by Seiya Tokui with the collaboration with Japanese company preferred networks in partnership with IBM, Intel, Microsoft, and NVidia. It is a robust, flexible, and intuitive deep learning framework.
Chainer is based on Python and was first released in the year of 2015. It is prevalent in Japan since its first version. As it is gaining its popularity very fast, we have decided to make you updated on this topic with our knowledge and the research-based information. But before we start a discussion on Chainer, you should know something about neural networks. So, let’s begin.
What is The Neural Network Framework?
Artificial neural networks are an integral part of the process of deep learning. The neurological structure of the human brain inspires these systems. Deep learning focuses on five core neural networks which are:
- Multi-layer perception.
- Radial basis network.
- Recurrent neural networks.
- Generative adversarial network.
- Convolutional neural network.
To fulfil the work demand, neural networks rely on their complex structures which comprises of artificial neurons. Neurons can influence each other.
Each of these neurons is capable of taking many inputs at a time or at different times. And they provide a single output. This is the major use of neural networks – to convert inputs into a meaningful output.
There is one or more than one hidden layer in-between the input and output layer of the neural network. The flow of information occurs in two ways. One refers to Feed-forward networks, where signals travel only in one direction. Moreover, the other one is Feed-back networks. Here the recurrent networks use their internal memory to process the given inputs.
Features of Chainer
- It supports Define-by-run scheme.
- Chainer supports CUDA computation. It can leverage a GPU with a few lines of code. Running of many GPUs is also possible with a little bit of extra effort. Chainer offers these facilities with the help of PyCUDA.
- Chainer supports different network architectures. For instance, feed-forward, nets, converts, recurrent nets, recursive nets, per-batch architecture.
- It supports multi-dimensional array and layer implementations.
- Forward computation can include any control flow statements of Python without losing the potential of backward propagation.
- It can control flow statements and can predefine the functions.
Here in the first point, we have mentioned the define-by run scheme. The following of this scheme is quite important for the neural frameworks. We will now make you understand what this feature is and its importance.
What is Define-by-run?
Chainer was the first deep learning framework that used this Define-by-run method. Before it got introduced by Chainer, the network training had two phases. First, defining the fixed connection between mathematical operations like multiplication of matrix and non-linear activations within the network. And then running the actual training calculation. We call this in a common term the define-and-learn method.
TensorFlow and Theano use this approach to train. In comparison, in the define-by-learn approach, the connection in the network is determined at the time of actual calculation, not the time of starting.
Here, first, a computational graph is constructed. It is periodically fed with minibatches to do forward/backwards. Also, its interpreter does this forward/backward computation. The forward compulsion is regular program code. Also, the graph is used for backward compulsion.
Advantages of Define-by-run Approach over The Define-and-run Approach
The advantages of the approach over the traditional method are flexibility and intuition. The flexibility is especially useful for the implementation of the recurrent neural network.
The define-by-run approach uses native constructs of the programming language. The use of a define-by-run approach gives Chainer flexibility. Hence, it is known as a flexible neural network.
Another advantage is the easy process of debugging. In the traditional method, error in the training calculation was hard to find. But in the define-by-run method, the user can only suspend the calculation by the built-in debugger. And one can inspect the data to find the fault.
Chainer as a Flexible Framework
As we have mentioned earlier, Chainer is a powerful and flexible neural network framework. We will discuss in elaborate regarding this. Currently, Chainer uses the version 6.3.0. This version has released in the year of 2019.
Chainer supports multi-dimensional array, different network architectures, layer implementations. So it is a perfect example of a neural framework. Now the unique feature is the define-by-run approach of learning. It makes this neural network flexible. Thus, it is an advantage over the other neural networks.
Chainer has four extension libraries which are: – ChainerMN, ChainerRL, ChainerCV, and ChainerUI. The first one, ChainerMN, is the key to the use of Chainer in multiple GPUs.
It also allows Chainer to give faster performance than the other deep learning frameworks. ChainerRL is another important library. The library has a state of art algorithms for deep reinforcement learning. ChainerUI is also a management tool.
Also Read: Top 5 Python Libraries for Machine Learning
How to Use Chainer?
So we have almost informed you everything about Chainer. Now you should also know how to install and use it. To install it, you first have to prepare a python 2.7 environment with pip. The Chainer is now ready for install.
You can use it after the completion of installing. If you want to use GPUs, you have to install CUDA and corresponding NVIDIA drivers. Before you start using Chainer after opening it, we will suggest you read the user manual. It is kept inside the Chainer in the form of PDF. It will also help you run the app.
We have included all the necessary information regarding Chainer. It will show you direction if you are going to use it. Another notable example of the application of Chainer is PaintsChainer. It is used to do automatic colourization of black and white lines only. And then draft drawing like tasks without or minimal user input.