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Upgrade Guide¶ This is a list of changes introduced in each release that users should be aware of when migrating from older versions. Most changes are carefully designed not to break existing code; however changes that may possibly break them are highlighted with a box. Chainer v7¶ Dropping Support of Python 2.7¶ In Chainer v7, Python 2.7 is no longer supported as it reaches its end-of-life (EOL)
Contribution Guide¶ Chainer is an open source software hosted on GitHub and welcomes contributors to take part in the development of the framework. This is a document aimed towards such contributors. Anyone who for instance would like to file an issue or send a pull request (PR) is encouraged to go through it. Note As announced, Chainer is under the maintenance phase and further development will b
import numpy from chainer import cuda from chainer.functions.pooling import pooling_2d from chainer.utils import conv if cuda . cudnn_enabled : cudnn = cuda . cudnn libcudnn = cudnn . cudnn class MaxPooling2D ( pooling_2d . Pooling2D ): """Max pooling over a set of 2d planes.""" def forward_cpu ( self , x ): col = conv . im2col_cpu ( x [ 0 ], self . kh , self . kw , self . sy , self . sx , self .
Set 0 to disable cuDNN in Chainer. Otherwise cuDNN is enabled automatically. Default seed value of random number generators for CUDA. If it is not set, the seed value is generated from Python random module. Set an integer value in decimal format.
CUDA¶ Device, context and memory management on CuPy. Note The package chainer.cuda has been renamed to chainer.backends.cuda as of v4.0.0, but the previous module path chainer.cuda is also available. Chainer uses CuPy (with very thin wrapper) to exploit the speed of GPU computation. Following modules and classes defined in CuPy are imported to chainer.backends.cuda module for convenience (refer to
Datasets¶ Dataset Abstraction (chainer.dataset)¶ Chainer supports a common interface for training and validation of datasets. The dataset support consists of three components: datasets, iterators, and batch conversion functions. Dataset represents a set of examples. The interface is only determined by combination with iterators you want to use on it. The built-in iterators of Chainer require the d
CuPy Reference Manual ¶ This is the official documentation of CuPy, a multi-dimensional array on CUDA with a subset of NumPy interface.
Installation¶ Recommended Environments¶ We recommend the following Linux distributions. Ubuntu 14.04 / 16.04 LTS (64-bit) CentOS 7 (64-bit) Note We are automatically testing Chainer on all the recommended environments above. We cannot guarantee that Chainer works on other environments including Windows and macOS (especially with CUDA support), even if Chainer may seem to be running correctly. Requ
Functions¶ Chainer provides variety of built-in function implementations in chainer.functions package. These functions usually return a Variable object or a tuple of multiple Variable objects. For a Variable argument of a function, an N-dimensional array can be passed if you do not need its gradient. Some functions additionally supports scalar arguments. Note Functions implemented in Chainer consi
Optimizer/UpdateRule hook function for weight decay regularization.
Chainer – A flexible framework of neural networks¶ Chainer is a powerful, flexible and intuitive deep learning framework. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports
© Copyright 2015, Preferred Networks, inc. and Preferred Infrastructure, inc. Revision 536cda7c.
Introduction to Chainer¶ This is the first section of the Chainer Tutorial. In this section, you will learn about the following things: Pros and cons of existing frameworks and why we are developing Chainer Simple example of forward and backward computation Usage of parameterized functions and their gradient computation Management of a set of parameterized functions (a.k.a. “model” in most framewo
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