RNN Language Models — Chainer 7.8.1 documentation
RNN Language Models¶ 0. Introduction¶ The language model is modeling the probability of generating natural language sentences or documents. You can use the language model to estimate how natural a sentence or a document is. Also, with the language model, you can generate new sentences or documents. Let’s start with modeling the probability of generating sentences. We represent a sentence as \({\bf
Trainer extensions — Chainer 3.4.0 documentation
Trainer extension to exponentially shift an optimizer attribute.
chainer.functions.concat — Chainer 7.8.1 documentation
chainer.functions.concat¶ chainer.functions.concat(xs, axis=1)[source]¶ Concatenates given variables along an axis. Parameters xs (tuple of Variable or N-dimensional array) – Input variables to be concatenated. The variables must have the same shape, except in the dimension corresponding to axis. axis (int) – The axis along which the arrays will be joined. Default is 1. Returns The concatenated va
Upgrade Guide — Chainer 7.8.1 documentation
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 7.8.1 documentation
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
chainer.functions.pooling.max_pooling_2d — Chainer 1.20.0.1 documentation
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 .
CUDA and Backend Utilities — Chainer 5.4.0 documentation
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 — Chainer 7.8.1 documentation
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
Visualization of Computational Graph — Chainer 7.8.1 documentation
Visualization of Computational Graph¶ As neural networks get larger and complicated, it gets much harder to confirm if their architectures are constructed properly. Chainer supports visualization of computational graphs. Users can generate computational graphs by invoking build_computational_graph(). Generated computational graphs are dumped to specified format (Currently Dot Language is supported
chainer.functions.activation.lstm — Chainer 1.6.0 documentation
import numpy import six from chainer import cuda from chainer import function from chainer.utils import type_check def _extract_gates ( x ): r = x . reshape (( x . shape [ 0 ], x . shape [ 1 ] // 4 , 4 ) + x . shape [ 2 :]) return ( r [:, :, i ] for i in six . moves . range ( 4 )) def _sigmoid ( x ): return 1 / ( 1 + numpy . exp ( - x )) def _grad_sigmoid ( x ): return x * ( 1 - x ) def _grad_tanh
Chainer – A flexible framework of neural networks — Chainer 7.8.1 documentation
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
Chainer – A flexible framework of neural networks — Chainer 7.8.1 documentation
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
Recurrent Nets and their Computational Graph — Chainer 1.3.2 documentation
Recurrent Nets and their Computational Graph¶ In this section, you will learn how to write recurrent nets with full backprop, recurrent nets with truncated backprop, evaluation of networks with few memory. After reading this section, you will be able to: Handle input sequences of variable length Truncate upper stream of the network during forward computation Use volatile variables to prevent netwo
Optimizers — Chainer 7.8.1 documentation
Optimizer/UpdateRule hook function for weight decay regularization.
Standard Function implementations — Chainer 1.4.1 documentation
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
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Chainer – A flexible framework of neural networks — Chainer 2.0.0 documentation
Chainer – A flexible framework of neural networks — Chainer 2.0.0 documentation
Environment variables — Chainer 3.4.0 documentation
Reference — Chainer 4.5.0 documentation
CuPy Reference Manual — Chainer 1.5.1 documentation