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Add ASCII visualizations to Module 08 for understanding image data

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Added educational ASCII art showing:

1. **Actual pixel values** - What 8×8 digit images look like as numbers
   - Shows digits 5, 3, and 8 with real pixel values (0-16 range)
   - Helps students understand images are just 2D arrays

2. **Visual representation** - How humans see the digits
   - ASCII art showing recognizable digit shapes
   - Connects abstract numbers to concrete patterns

3. **Shape transformations** - How DataLoader batches data
   - Individual: (8, 8) → Batched: (32, 8, 8)
   - Shows what the model actually receives

4. **Complete example** - Loading and using tiny digits dataset
   - Real code showing datasets/tiny/digits_8x8.npz usage
   - Demonstrates the full DataLoader workflow

Benefits:
✅ Students visualize what image data IS
✅ Understand DataLoader's batching transformation
✅ See connection between numbers and visual patterns
✅ Ready to work with real datasets in milestones

This makes the abstract concept of 'image tensors' concrete and visual.
This commit is contained in:
Vijay Janapa Reddi
2025-09-30 15:22:30 -04:00
parent 38b089b52f
commit b6f4a0bee6
1 changed files with 48 additions and 3 deletions
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51
modules/source/08_dataloader/dataloader_dev.py
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@@ -357,7 +357,7 @@ def test_unit_tensordataset():
# Test basic functionality
features = Tensor([[1, 2], [3, 4], [5, 6]]) # 3 samples, 2 features
labels = Tensor([0, 1, 0]) # 3 labels
labels = Tensor([0, 1, 0]) # 3 labels
dataset = TensorDataset(features, labels)
@@ -645,6 +645,45 @@ Module 08 (DataLoader) Examples & Milestones
(Learn mechanics) (Apply to real data)
```
### Understanding Image Data
**What does image data actually look like?**
Images are just 2D arrays of numbers (pixels). Here are actual 8×8 handwritten digits:
```
Digit "5" (8×8): Digit "3" (8×8): Digit "8" (8×8):
0 0 12 13 5 0 0 0 0 0 11 12 0 0 0 0 0 0 10 14 8 1 0 0
0 0 13 15 10 0 0 0 0 2 16 16 16 7 0 0 0 0 16 15 15 9 0 0
0 3 15 13 16 7 0 0 0 0 8 16 8 0 0 0 0 0 15 5 5 13 0 0
0 8 13 6 15 4 0 0 0 0 0 12 13 0 0 0 0 1 16 5 5 13 0 0
0 0 0 6 16 5 0 0 0 0 1 16 15 9 0 0 0 6 16 16 16 16 1 0
0 0 5 15 16 9 0 0 0 0 14 16 16 16 7 0 1 16 3 1 1 15 1 0
0 0 9 16 9 0 0 0 0 5 16 8 8 16 0 0 0 9 16 16 16 15 0 0
0 0 0 0 0 0 0 0 0 3 16 16 16 12 0 0 0 0 0 0 0 0 0 0
Visual representation:
░█████░ ░█████░ ░█████░
░█░░░█░ ░░░░░█░ █░░░░█░
░░░░█░░ ░░███░░ ░█████░
░░░█░░░ ░░░░█░░ █░░░░█░
░░█░░░░ ░█████░ ░█████░
```
**Shape transformations in DataLoader:**
```
Individual Sample (from Dataset):
image: (8, 8) ← Single 8×8 image
label: scalar ← Single digit (0-9)
After DataLoader batching (batch_size=32):
images: (32, 8, 8) ← Stack of 32 images
labels: (32,) ← Array of 32 labels
This is what your model sees during training!
```
### Quick Start with Real Data
**Tiny Datasets (ships with TinyTorch):**
@@ -657,12 +696,18 @@ labels = Tensor(data['labels']) # (1797,)
dataset = TensorDataset(images, labels)
loader = DataLoader(dataset, batch_size=32, shuffle=True)
# Each batch contains real digit images!
for batch_images, batch_labels in loader:
# batch_images: (32, 8, 8) - 32 digit images
# batch_labels: (32,) - their labels (0-9)
break
```
**Full Datasets (for serious training):**
```python
# See milestones/03_mlp_revival_1986/ for MNIST download
# See milestones/04_cnn_revolution_1998/ for CIFAR-10 download
# See milestones/03_mlp_revival_1986/ for MNIST download (28×28 images)
# See milestones/04_cnn_revolution_1998/ for CIFAR-10 download (32×32×3 images)
```
### What You've Accomplished