gorar commited on
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a1f8718
1 Parent(s): 6736bca

Add initial dataset

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.gitattributes CHANGED
@@ -52,3 +52,7 @@ saved_model/**/* 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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  *.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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+ data/t10k-images-idx3-ubyte.gz filter=lfs diff=lfs merge=lfs -text
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+ data/t10k-labels-idx1-ubyte.gz filter=lfs diff=lfs merge=lfs -text
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+ data/train-images-idx3-ubyte.gz filter=lfs diff=lfs merge=lfs -text
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+ data/train-labels-idx1-ubyte.gz filter=lfs diff=lfs merge=lfs -text
.gitignore ADDED
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+ .idea/
a-mnist.py ADDED
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+ """Augmented MNIST Data Set"""
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+
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+
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+ import struct
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+
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+ import numpy as np
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+
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+ import datasets
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+ from datasets.tasks import ImageClassification
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+
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+ _DESCRIPTION = """\
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+ The dataset is built on top of MNIST.
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+ It consists from 130K of images in 10 classes - 120K training and 10K test samples.
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+ The training set was augmented with additional 60K images.
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+ """
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+
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+ _URLS = {
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+ "train_images": "data/train-images-idx3-ubyte.gz",
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+ "train_labels": "data/train-labels-idx1-ubyte.gz",
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+ "test_images": "data/t10k-images-idx3-ubyte.gz",
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+ "test_labels": "data/t10k-labels-idx1-ubyte.gz",
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+ }
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+
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+
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+ class AMNIST(datasets.GeneratorBasedBuilder):
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+ """A-MNIST Data Set"""
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+
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(
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+ name="amnist",
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+ version=datasets.Version("1.0.0"),
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+ description=_DESCRIPTION,
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+ )
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "image": datasets.Image(),
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+ "label": datasets.features.ClassLabel(names=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]),
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+ }
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+ ),
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+ supervised_keys=("image", "label"),
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+ task_templates=[
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+ ImageClassification(
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+ image_column="image",
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+ label_column="label",
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+ )
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+ ],
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ urls_to_download = _URLS
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+ downloaded_files = dl_manager.download_and_extract(urls_to_download)
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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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+ "filepath": [downloaded_files["train_images"],
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+ downloaded_files["train_labels"]],
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+ "split": "train",
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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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+ "filepath": [downloaded_files["test_images"],
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+ downloaded_files["test_labels"]],
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+ "split": "test",
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath, split):
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+ """This function returns the examples in the raw form."""
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+ # Images
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+ with open(filepath[0], "rb") as f:
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+ # First 16 bytes contain some metadata
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+ _ = f.read(4)
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+ size = struct.unpack(">I", f.read(4))[0]
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+ _ = f.read(8)
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+ images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
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+
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+ # Labels
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+ with open(filepath[1], "rb") as f:
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+ # First 8 bytes contain some metadata
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+ _ = f.read(8)
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+ labels = np.frombuffer(f.read(), dtype=np.uint8)
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+
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+ for idx in range(size):
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+ yield idx, {"image": images[idx], "label": str(labels[idx])}
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