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79f8fe38d0
Created datasets/tiny/ for shipping small datasets with TinyTorch: New Structure: - datasets/tiny/digits_8x8.npz (67KB, 1,797 samples) - 8×8 handwritten digits from UCI/sklearn - Normalized to [0-1], ready for immediate use - Perfect for DataLoader learning (Module 08) - datasets/tiny/README.md - Full documentation and usage examples - Philosophy: tiny (learn) → full (practice) → custom (master) - datasets/tiny/create_digits_8x8.py - Extraction script showing how dataset was created - Reproducible from sklearn.datasets.load_digits() Updated .gitignore: - Ignore datasets/* (downloaded large files) - Allow datasets/tiny/ (shipped small files) - Allow datasets/README.md and download scripts - Selectively ignore .npz files (not in tiny/) Benefits: ✅ Zero download friction for Module 08 ✅ Offline-friendly (planes, classrooms, slow networks) ✅ Real handwritten digits (not synthetic noise) ✅ Git-friendly size (67KB vs 10MB MNIST) ✅ Same shape/format students will use for CNNs Progression: - Module 08: Learn DataLoader with 8×8 digits - Milestone 03: Train on full 28×28 MNIST - Milestone 04: Scale to CIFAR-10
54 lines
1.6 KiB
Python
54 lines
1.6 KiB
Python
#!/usr/bin/env python3
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"""
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Create 8x8 Digits Dataset
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=========================
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Extracts the 8×8 handwritten digits dataset from sklearn and saves it
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as a compact .npz file for TinyTorch.
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Source: UCI Machine Learning Repository
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Used by: sklearn.datasets.load_digits()
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Size: 1,797 samples, 8×8 grayscale images
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License: Public domain
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"""
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import numpy as np
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try:
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from sklearn.datasets import load_digits
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except ImportError:
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print("❌ sklearn not installed. Install with: pip install scikit-learn")
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exit(1)
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print("📥 Loading 8×8 digits from sklearn...")
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digits = load_digits()
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print(f"✅ Loaded {len(digits.images)} digit images")
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print(f" Shape: {digits.images.shape}")
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print(f" Classes: {np.unique(digits.target)}")
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# Normalize to [0, 1] range (original is 0-16)
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images_normalized = digits.images.astype(np.float32) / 16.0
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labels = digits.target.astype(np.int64)
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# Save as compressed .npz
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output_file = 'digits_8x8.npz'
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np.savez_compressed(output_file,
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images=images_normalized,
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labels=labels)
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# Check file size
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import os
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file_size_kb = os.path.getsize(output_file) / 1024
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print(f"\n💾 Saved to {output_file}")
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print(f" File size: {file_size_kb:.1f} KB")
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print(f" Images shape: {images_normalized.shape}")
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print(f" Labels shape: {labels.shape}")
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print(f" Value range: [{images_normalized.min():.2f}, {images_normalized.max():.2f}]")
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# Quick verification
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print(f"\n✅ Dataset ready for TinyTorch!")
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print(f" Total samples: {len(images_normalized)}")
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print(f" Samples per class: ~{len(images_normalized) // 10}")
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print(f" Perfect for DataLoader demos!")
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