| import chainer | |
| import chainer.links as L | |
| import chainer.functions as F | |
| import numpy as np | |
| class CNN(chainer.Chain): | |
| def __init__(self): | |
| super(CNN, self).__init__() | |
| with self.init_scope(): |
| import chainer | |
| from chainer import Variable | |
| import numpy as np | |
| def f(x): | |
| y = x * x * x / 3 | |
| gx, = chainer.grad([y], [x], enable_double_backprop=True) | |
| z = gx - 2 * x + 1 # z = (x - 1) ** 2 | |
| return z |
| class ConvolutionalAutoEncoder(chainer.Chain): | |
| def __init__(self): | |
| super(ConvolutionalAutoEncoder).__init__( | |
| c1=L.Convolution2D(...), | |
| c2=L.Convolution2D(...), | |
| dc1=L.Deconvolution2D(...), | |
| dc2=L.Deconvolution2D(...), | |
| ) | |
| def convolve(self, x): |
The goal of this document is to explain the basic mechanism of the dataset in Chainer and how to prepare customized dataset that can work with Trainer.
See the official document for the full detail of the specifications.
In order to make the dataset be able to work with the Trainer, it must have two methods:
__getitem__, which is used for indexing like dataset[i] or slicing like dataset[i:j]__len__ , which enables us to feed it to len()
| import chainer | |
| import chainer.functions as F | |
| from chainer import initializers as I | |
| from chainer import reporter | |
| from chainer import training | |
| from chainer.training import extensions as E | |
| import numpy | |
| import scipy.misc | |
| from chainer import functions as F | |
| import cupy | |
| y = cupy.random.uniform(0, 1, (10, 5)).astype(cupy.float32) | |
| t = cupy.empty((10,), dtype=cupy.int32) | |
| p = F.softmax_cross_entropy(y, t) | |
| p.data.get() | |
| (pyenv-3.5.1) KANDEL /home/delta/dev/chainer-test/cupy% [16:11:58] | |
| %(master)[]git --no-pager show -s --format=%H | |
| 353ea32b10738fb1490e39a0864e4613fcf918bf | |
| (pyenv-3.5.1) KANDEL /home/delta/dev/chainer-test/cupy% [16:12:01] | |
| %(master)[]pip --no-cache-dir install -e . | |
| Obtaining file:///home/delta/dev/chainer-test/cupy | |
| Requirement already satisfied (use --upgrade to upgrade): filelock in /home/delta/.pyenv/versions/pyenv-3.5.1/lib/python3.5/site-packages (from cupy==1.0.0) | |
| Requirement already satisfied (use --upgrade to upgrade): nose in /home/delta/.pyenv/versions/pyenv-3.5.1/lib/python3.5/site-packages (from cupy==1.0.0) | |
| Requirement already satisfied (use --upgrade to upgrade): numpy>=1.9.0 in /home/delta/.pyenv/versions/pyenv-3.5.1/lib/python3.5/site-packages (from cupy==1.0.0) |
| import chainer | |
| from chainer import links as L | |
| from chainer import utils | |
| import warnings | |
| import time | |
| import numpy as np | |
| import cupy as cp | |
| warnings.simplefilter('ignore') |
| import chainer | |
| import chainer.links as L | |
| import chainer.functions as F | |
| import numpy as np | |
| class Wrapper(chainer.Chain): | |
| pass | |