init

.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

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+__pycache__/

data/test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eff039c809f429c2de65bc69adc3f7a6ebd2ecd286a7e7e6ec39f114b64e7443
+size 18299664

data/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:71f0e2ff351343e4d1217bc2e73c3127e219789f4df0fafa08ea19ad38057da4
+size 109636215

mnist.py

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+import csv
+import datasets
+import numpy as np
+
+_HOMEPAGE = 'http://yann.lecun.com/exdb/mnist/'
+_LICENSE = '-'
+_DESCRIPTION = '''\
+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.
+It is a subset of a larger set available from NIST.
+The digits have been size-normalized and centered in a fixed-size image.
+'''
+_CITATION = '''-'''
+_NAMES = list('0123456789')
+
+
+class MNISTConfig(datasets.BuilderConfig):
+    '''Builder Config for MNIST'''
+
+    def __init__(
+        self, description, homepage, **kwargs
+    ):
+        super(MNISTConfig, self).__init__(
+            version=datasets.Version('1.0.0', ''),
+            **kwargs
+        )
+        self.description = description
+        self.homepage = homepage
+        self.train_image_url = 'data/train.csv'
+        self.test_image_url = 'data/test.csv'
+
+
+class MNIST(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        MNISTConfig(
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features({
+            'image': datasets.Image(mode='L', decode=True, id=None),
+            'label': datasets.ClassLabel(names=_NAMES)
+        })
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        train_image_path = dl_manager.download(
+            self.config.train_image_url
+        )
+        test_image_path = dl_manager.download(
+            self.config.test_image_url
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'data_path': f'{train_image_path}'
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    'data_path': f'{test_image_path}'
+                }
+            )
+        ]
+
+    def _generate_examples(self, data_path):
+        idx = 0
+        with open(data_path, newline='', encoding='utf-8') as csvfile:
+            csvreader = csv.reader(csvfile)
+            next(csvreader)
+            for row in csvreader:
+                example = {
+                    'image': np.array(row[1:], np.uint8).reshape(28, 28),
+                    'label': row[0]
+                }
+                yield idx, example
+                idx += 1