How to import dataset using chainer

This recipe helps you import dataset using chainer

Recipe Objective - How to import dataset using chainer?

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 dataset to support getitem and len methods. In particular, the getitem method should support indexing by both an integer and a slice. We can easily support slice indexing by inheriting DatasetMixin, in which case users only have to implement get_example() method for indexing. Basically, datasets are considered as stateless objects, so that we do not need to save the dataset as a checkpoint of the training procedure.

chainer.dataset.DatasetMixin - Default implementation of dataset indexing.

chainer.dataset.TabularDataset - An abstract class that represents tabular dataset.

chainer.datasets.DictDataset - Dataset of a dictionary of datasets.

# Example 1
import numpy
from chainer import dataset
class DemoDataset(dataset.DatasetMixin):
 def __init__(self, values):
  self.values = values
 def __len__(self):
  return len(self.values)
 def get_example(self, i):
  return self.values[i]
data = DemoDataset([0,7,2,9,0,8,5])
print(data[1]) # Access by int
print(data[2:5]) # Access by slice
print(data[[-1, 0]]) # Access by one-dimensional integer list

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