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97fece7b5f
Added integration tests for DataLoader: - test_dataloader_integration.py in tests/integration/ - Training workflow integration - Shuffle consistency across epochs - Memory efficiency verification Updated Module 08: - Added note about optional performance analysis - Clarified that analysis functions can be run manually - Clean flow: text → code → tests Updated datasets/tiny/README.md: - Minor formatting fixes Module 08 is now complete and ready to export: ✅ Dataset abstraction ✅ TensorDataset implementation ✅ DataLoader with batching/shuffling ✅ ASCII visualizations for understanding ✅ Unit tests (in module) ✅ Integration tests (in tests/) ✅ Performance analysis tools (optional) Next: Export with 'bin/tito export 08_dataloader'
134 lines
3.3 KiB
Markdown
134 lines
3.3 KiB
Markdown
# Tiny Datasets for TinyTorch
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**Small, curated datasets that ship with TinyTorch** - no downloads required!
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These datasets are committed to the repository for instant, offline-friendly learning.
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---
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## 📊 Available Datasets
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### 8×8 Handwritten Digits
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**File:** `digits_8x8.npz`
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**Size:** ~67 KB
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**Samples:** 1,797 images
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**Shape:** (8, 8) grayscale
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**Classes:** 10 digits (0-9)
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**Source:** UCI ML Repository via sklearn
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**Perfect for:**
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- Learning DataLoader mechanics
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- Quick CNN testing
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- Offline development
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- Educational demos
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**Usage:**
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```python
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import numpy as np
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from tinytorch import Tensor
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from tinytorch.data.loader import TensorDataset, DataLoader
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# Load the dataset
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data = np.load('datasets/tiny/digits_8x8.npz')
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images = Tensor(data['images'])
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labels = Tensor(data['labels'])
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# Create dataset and loader
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dataset = TensorDataset(images, labels)
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loader = DataLoader(dataset, batch_size=32, shuffle=True)
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# Iterate through batches
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for batch_images, batch_labels in loader:
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print(f"Batch: {batch_images.shape}, Labels: {batch_labels.shape}")
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```
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**Visual Sample:**
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```
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Digit "5": Digit "3": Digit "8":
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░█████░░ ░█████░ ░█████░░
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░█░░░█░ ░░░░░█░ █░░░░░█░
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░░░░█░░ ░░███░░ ░█████░░
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░░░█░░░ ░░░░░█░ █░░░░░█░
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░░█░░░░ ░█████░ ░█████░░
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```
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---
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## 🎯 Philosophy
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**Why ship tiny datasets?**
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1. **Zero friction** - Students start learning immediately
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2. **Offline-first** - Works in classrooms, planes, anywhere
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3. **Fast iteration** - No wait times, instant feedback
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4. **Educational focus** - Sized for learning, not production
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**Progression:**
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- **Tiny datasets** (here) → Learn DataLoader mechanics
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- **Downloaded datasets** (../mnist/, ../cifar10/) → Real applications
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- **Custom datasets** → Production skills
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---
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## 📂 File Format
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All datasets use NumPy's `.npz` format (compressed):
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```python
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data = np.load('dataset.npz')
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images = data['images'] # Shape: (N, H, W) or (N, H, W, C)
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labels = data['labels'] # Shape: (N,)
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```
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**Benefits:**
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- Fast loading
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- Compressed storage
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- Python-native
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- Easy inspection
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---
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## 🔧 Creating New Tiny Datasets
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See `create_digits_8x8.py` for example extraction script.
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**Guidelines:**
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- Max size: ~100 KB per dataset
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- Format: `.npz` with `images` and `labels` keys
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- Normalize: Images in [0, 1] range
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- License: Verify public domain / open source
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---
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## 📚 Dataset Information
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### Digits 8×8 Credits
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**Original Source:**
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- E. Alpaydin, C. Kaynak (1998)
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- UCI Machine Learning Repository
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- "Optical Recognition of Handwritten Digits"
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**Preprocessing:**
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- Extracted via `sklearn.datasets.load_digits()`
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- Normalized from [0-16] to [0-1]
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- Saved as float32 for efficiency
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**License:** Public domain
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---
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## 🚀 Next Steps
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After mastering DataLoader with tiny datasets:
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1. **Module 08** → Build DataLoader with digits_8x8
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2. **Milestone 03** → Train MLP on full MNIST
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3. **Milestone 04** → Train CNN on CIFAR-10
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4. **Custom datasets** → Apply to your own data
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Tiny datasets teach the mechanics.
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Real datasets teach the systems.
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Custom datasets teach the engineering.
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