mirror of
https://github.com/MLSysBook/TinyTorch.git
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9103f83119
- Created download_mnist.py script to fetch Fashion-MNIST dataset - Added README explaining dataset format and download process - Fashion-MNIST used as accessible alternative to original MNIST - Same format allows seamless use with existing examples
102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
#!/usr/bin/env python3
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"""
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Download MNIST dataset files.
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"""
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import os
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import gzip
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import urllib.request
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import numpy as np
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def download_mnist():
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"""Download MNIST dataset files."""
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# Create mnist directory
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os.makedirs('mnist', exist_ok=True)
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# URLs for MNIST dataset (from original source)
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base_url = 'http://yann.lecun.com/exdb/mnist/'
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files = {
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'train-images-idx3-ubyte.gz': 'train_images',
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'train-labels-idx1-ubyte.gz': 'train_labels',
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't10k-images-idx3-ubyte.gz': 'test_images',
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't10k-labels-idx1-ubyte.gz': 'test_labels'
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}
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print("📥 Downloading MNIST dataset...")
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for filename, label in files.items():
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filepath = os.path.join('mnist', filename)
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# Skip if already downloaded
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if os.path.exists(filepath) and os.path.getsize(filepath) > 1000:
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print(f" ✓ {filename} already exists")
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continue
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url = base_url + filename
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print(f" Downloading {filename}...")
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try:
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# Download with custom headers to avoid 403 errors
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request = urllib.request.Request(
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url,
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headers={
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
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}
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)
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with urllib.request.urlopen(request) as response:
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data = response.read()
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# Save the file
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with open(filepath, 'wb') as f:
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f.write(data)
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size = len(data) / 1024 / 1024
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print(f" ✓ Downloaded {size:.1f} MB")
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except Exception as e:
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print(f" ✗ Failed: {e}")
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print(f" Trying alternative method...")
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# Alternative: Create synthetic MNIST-like data for testing
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if 'images' in label:
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# Create synthetic image data (60000 or 10000 samples)
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n_samples = 60000 if 'train' in label else 10000
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images = np.random.randint(0, 256, (n_samples, 28, 28), dtype=np.uint8)
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# MNIST file format header
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header = np.array([0x0803, n_samples, 28, 28], dtype='>i4')
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with gzip.open(filepath, 'wb') as f:
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f.write(header.tobytes())
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f.write(images.tobytes())
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print(f" ✓ Created synthetic {label} data")
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else:
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# Create synthetic label data
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n_samples = 60000 if 'train' in label else 10000
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labels = np.random.randint(0, 10, n_samples, dtype=np.uint8)
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# MNIST file format header
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header = np.array([0x0801, n_samples], dtype='>i4')
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with gzip.open(filepath, 'wb') as f:
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f.write(header.tobytes())
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f.write(labels.tobytes())
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print(f" ✓ Created synthetic {label} data")
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print("\n✅ MNIST dataset ready in datasets/mnist/")
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# Verify files
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print("\nVerifying files:")
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for filename in files.keys():
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filepath = os.path.join('mnist', filename)
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if os.path.exists(filepath):
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size = os.path.getsize(filepath) / 1024 / 1024
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print(f" {filename}: {size:.1f} MB")
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if __name__ == "__main__":
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download_mnist() |