Chainer

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• ID : Q34978225

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1. Chainer supports CUDA computation.
2. Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets.
3. Chainer is a flexible Python-based framework for neural networks.
4. That is why Chainer’s design is based on the principle “Define-by-Run” - network is not pre-defined at the beginning, but is dynamically defined on-the-fly.
5. Chainer is powerful, flexible and intuitive framework of neural networks that supports almost arbitrary architectures.
6. To get more information you can visit Chainer website or browse its code in Github repository.
7. Chainer is a Python-based deep learning framework aiming at flexibility.
8. In this post we’ll go through the process of training your first neural network in Python using an exceptionally readable framework called Chainer.
9. Should you wish to execute the code examples below, you will need to install Chainer, Matplotlib, and NumPy.
10. To start, we will begin with a discussion of the three basic objects in Chainer, the chainer.
11. Let’s begin by making two Chainer variables, which are just wrapped NumPy arrays, named a and b .
12. Chainer™ is a Python-based deep learning framework developed and provided by PFN.
13. First open-sourced in June 2015, Chainer has supported PFN’s growth as its deep learning research and development platform.
14. Chainer was the first to adopt the define-by-run approach that allows developers to build complex neural networks in intuitive and flexible ways.
15. Chainer moved into a maintenance phase in December 2019 with the last v7 update.
16. Chainer is an open source framework designed for efficient research into and development of deep learning algorithms.
17. It is also easy to debug and refactor Chainer-based code with a standard debugger and profiler, since Chainer provides an imperative API in plain Python and NumPy.
18. Unlike other frameworks with a Python interface such as Theano and TensorFlow, Chainer provides imperative ways of declaring neural networks by supporting Numpy-compatible operations between arrays.
19. Standard neural network operations such as fully connected linear and convolutional layers are implemented in Chainer as an instance of Link .
20. Chainer’s training framework aims at maximal flexibility, while keeps the simplicity for the typical usages.
21. Chainer is written in Python.
22. While it is similar to Keras in its intent and place in the stack, it is distinguished by its dynamic computation graph, similar to Pytorch and Chainer, and unlike TensorFlow or Caffe.
23. In this paper, we introduce Chainer, a Pythonbased, standalone open source framework for deep learning models.
24. LEARNING POWERFUL Chainer supports CUDA computation.
25. FLEXIBLE Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets.
26. The PredNet*, written in Keras, was ported to Chainer and reconstructed for convenience.
27. Chainer is a neural network framework written almost entirely in Python.
28. Chainer was the first framework to provide the “define-by-run” neural network definition, which allows for dynamic changes in the network.
29. Define-by-run simplifies the debugging process since Chainer provides an imperative API in Python.
30. Since Chainer was created from the start in Python, the code is inspectable with Python tools, and can be customized if required.
31. The AMIs now come with support for Chainer, a flexible and intuitive framework for neural networks.
32. Chainer uses a "define-by-run" approach that enables developers to define their deep learning network architecture on the fly.
33. Today we would like to introduce you to Chainer.
34. Chainer is based on Python and was first released in the year of 2015.
35. But before we start a discussion on Chainer, you should know something about neural networks.
36. Chainer supports different network architectures.
37. means that the model was trained on and then converted to Chainer.
38. Chainer Chemistry is a deep learning framework (based on Chainer) with applications in Biology and Chemistry.
39. We used FunctionNode in this PR, which is introduced after chainer v3.
40. Chainer v6 is released and ChainerX is newly introduced.
41. In order to support this new feature & API, we broke backward compatibility for chainer chemistry v0.6.0 release.
42. Chainer is an open source neural network framework that supports CUDA computation (only requires few lines of codes to leverage a GPU and runs on multiple GPUs with a little effort).
43. Chainer is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries.
44. ChainerMN enables Chainer to be used on multiple GPUs with performance significantly faster than other deep learning frameworks.
45. A supercomputer running Chainer on 1024 GPUs processed 90 epochs of ImageNet dataset on ResNet-50 network in 15 minutes, which is four times faster than the previous record held by Facebook.
46. In this second and third part of this series with Chainer, we are going to train an autoencoder.
47. This is not specific to Chainer but simple Python using NumPy.
48. You might, however, need to get used to the assertion errors that Chainer throws at you when the model layers aren’t compatible or variable dimensions aren’t what the framework expects.
49. You can wrap all of the network definitions such as the layers and the loss functions in a class that inherits chainer.
50. Chainer can be deployed on systems consisting of Central Processing Units and Graphics Processing Units efficiently.
51. In addition, it is possible to run Chainer on systems containing Intel Xeon Phi coprocessors.
52. Nonetheless, Chainer can only be deployed on Intel Xeon Phi Knights Landing, not Knights Corner.
53. For that reason, Chainer cannot fully exploit the computing power of such systems, which leads to the demand for supporting Chainer run on them.