Datasets:
whyen-wang
commited on
Commit
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c5c3e8b
1
Parent(s):
d1051c9
init
Browse files- .gitattributes +1 -0
- .gitignore +1 -0
- __init__.py +0 -0
- data/test.csv +3 -0
- data/train.csv +3 -0
- mnist.py +86 -0
.gitattributes
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*.csv filter=lfs diff=lfs merge=lfs -text
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.gitignore
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__pycache__/
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__init__.py
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data/test.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:eff039c809f429c2de65bc69adc3f7a6ebd2ecd286a7e7e6ec39f114b64e7443
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size 18299664
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data/train.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:71f0e2ff351343e4d1217bc2e73c3127e219789f4df0fafa08ea19ad38057da4
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size 109636215
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mnist.py
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import csv
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import datasets
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import numpy as np
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_HOMEPAGE = 'http://yann.lecun.com/exdb/mnist/'
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_LICENSE = '-'
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_DESCRIPTION = '''\
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The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples.
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It is a subset of a larger set available from NIST.
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The digits have been size-normalized and centered in a fixed-size image.
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'''
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_CITATION = '''-'''
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_NAMES = list('0123456789')
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class MNISTConfig(datasets.BuilderConfig):
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'''Builder Config for MNIST'''
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def __init__(
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self, description, homepage, **kwargs
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):
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super(MNISTConfig, self).__init__(
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version=datasets.Version('1.0.0', ''),
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**kwargs
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)
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self.description = description
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self.homepage = homepage
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self.train_image_url = 'data/train.csv'
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self.test_image_url = 'data/test.csv'
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class MNIST(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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MNISTConfig(
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description=_DESCRIPTION,
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homepage=_HOMEPAGE
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)
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]
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def _info(self):
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features = datasets.Features({
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'image': datasets.Image(mode='L', decode=True, id=None),
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'label': datasets.ClassLabel(names=_NAMES)
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})
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION
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)
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def _split_generators(self, dl_manager):
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train_image_path = dl_manager.download(
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self.config.train_image_url
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)
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test_image_path = dl_manager.download(
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self.config.test_image_url
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)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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'data_path': f'{train_image_path}'
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}
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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'data_path': f'{test_image_path}'
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}
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)
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]
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def _generate_examples(self, data_path):
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idx = 0
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with open(data_path, newline='', encoding='utf-8') as csvfile:
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csvreader = csv.reader(csvfile)
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next(csvreader)
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for row in csvreader:
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example = {
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'image': np.array(row[1:], np.uint8).reshape(28, 28),
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'label': row[0]
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}
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yield idx, example
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idx += 1
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