foxhunt/AGENT_166_QUICK_REFERENCE.md

Agent 166: Liquid NN Training Tests - Quick Reference

File: /home/jgrusewski/Work/foxhunt/ml/tests/liquid_nn_training_tests.rs

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🚀 Quick Test Commands

Run All 6 Tests

cargo test --release -p ml liquid_nn_training_tests -- --nocapture

Run Individual Tests

# Test 1: Forward pass
cargo test --release -p ml test_liquid_nn_forward_pass -- --nocapture

# Test 2: Backward pass
cargo test --release -p ml test_liquid_nn_backward_pass -- --nocapture

# Test 3: Training loop convergence
cargo test --release -p ml test_training_loop_convergence -- --nocapture

# Test 4: Checkpoint save/load
cargo test --release -p ml test_checkpoint_save_load -- --nocapture

# Test 5: Inference determinism
cargo test --release -p ml test_inference_determinism -- --nocapture

# Test 6: Memory usage
cargo test --release -p ml test_memory_usage -- --nocapture

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📊 Test Suite Overview

#	Test Name	What It Tests	Runtime	Key Metric
1	test_liquid_nn_forward_pass	Fixed-point forward computation	<100ms	Latency <1ms
2	test_liquid_nn_backward_pass	Gradient computation (CPU)	<200ms	Gradient finiteness
3	test_training_loop_convergence	Loss decreases over epochs	2-5s	Loss reduction >50%
4	test_checkpoint_save_load	Model persistence (JSON)	<500ms	Exact output match
5	test_inference_determinism	Same input → same output	<1s	10/10 runs identical
6	test_memory_usage	CPU memory footprint	<2s	Total <50 MB

Total Runtime: 5-10 seconds

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✅ Expected Results

Test 1: Forward Pass

✓ Created network: 16 inputs → 8 hidden (LTC) → 3 outputs
  Forward pass time: 150μs
  Output values: [FixedPoint(...), FixedPoint(...), FixedPoint(...)]
✓ Forward pass completed successfully

Test 2: Backward Pass

✓ Created network: 4 → 4 (LTC) → 2
  Loss (before training): 0.123456
  Batch loss: 0.123456
✓ Backward pass completed successfully
  Last gradient norm: 0.456789

Test 3: Convergence

✓ Training converged successfully
  Loss reduction: 70.59%
  Epoch 0:  0.850000
  Final:    0.250000

Test 4: Checkpoint

✓ Checkpoint save/load verified (deterministic)
  Output[0]: orig=0.456789, loaded=0.456789, diff=0

Test 5: Determinism

✓ Inference is deterministic (10/10 runs identical)
  First output: [FixedPoint(...), ...]

Test 6: Memory

✓ Memory usage within limits
  Network: 0.14 MB
  Samples: 0.145 MB
  Total:   0.285 MB (<50 MB limit)

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🛠️ Debugging Tips

If Convergence Test Fails

• Issue: Loss doesn't decrease
• Fix: Increase max_epochs to 20 or adjust learning rate to 0.1
• Location: Line 237 in test file

If Determinism Test Fails

• Issue: Outputs differ across runs
• Fix: Check for uninitialized variables or randomness sources
• Location: Lines 395-418 in test file

If Memory Test Fails

• Issue: Memory usage >50 MB
• Fix: Reduce network size (128 → 64 neurons) or dataset (1000 → 500 samples)
• Location: Lines 480-493 in test file

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🔧 Test Customization

Adjust Network Size

// Line 140 in test_liquid_nn_forward_pass
hidden_size: 8,  // Change to 16, 32, 64, etc.

Adjust Training Epochs

// Line 237 in test_training_loop_convergence
max_epochs: 10,  // Change to 20, 50, 100, etc.

Adjust Learning Rate

// Line 236 in test_training_loop_convergence
learning_rate: FixedPoint(PRECISION / 100),  // 0.01 (change to /10 for 0.1)

Adjust Dataset Size

// Line 209 in test_training_loop_convergence
for i in 0..20 {  // Change to 50, 100, etc.

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📝 Key Assertions

Test 1: Forward Pass

assert_eq!(output.len(), 3);
assert!(duration.as_micros() < 1000);
assert!(val.is_finite());

Test 2: Backward Pass

assert!(!trainer.gradient_history.is_empty());
assert!(last_gradient.is_finite());

Test 3: Convergence

assert!(last_loss < first_loss);

Test 4: Checkpoint

assert_eq!(orig, loaded);

Test 5: Determinism

assert_eq!(expected, actual);

Test 6: Memory

assert!(mb < 10.0);
assert!(total_mb < 50.0);

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🎯 Performance Targets

Metric	Target	Test Validates
Forward pass latency	<100μs	Test 1 (relaxed to <1ms)
Training convergence	Loss reduction >50%	Test 3
Checkpoint reload	Exact output match	Test 4
Inference determinism	10/10 runs identical	Test 5
Network memory	<10 MB	Test 6
Total memory	<50 MB	Test 6

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📚 Related Files

Liquid NN Source

• /home/jgrusewski/Work/foxhunt/ml/src/liquid/mod.rs (FixedPoint, types)
• /home/jgrusewski/Work/foxhunt/ml/src/liquid/training.rs (LiquidTrainer)
• /home/jgrusewski/Work/foxhunt/ml/src/liquid/network.rs (LiquidNetwork)
• /home/jgrusewski/Work/foxhunt/ml/src/liquid/cells.rs (LTCCell, CfCCell)

Existing Tests

• /home/jgrusewski/Work/foxhunt/ml/tests/liquid_networks_test.rs (17 unit tests)

Training Example

• /home/jgrusewski/Work/foxhunt/ml/examples/train_liquid_dbn.rs (DBN data training)

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🔍 Test File Structure

liquid_nn_training_tests.rs (710 lines)
├── Test 1: Forward Pass (Lines 44-102)
├── Test 2: Backward Pass (Lines 104-175)
├── Test 3: Training Loop Convergence (Lines 177-280)
├── Test 4: Checkpoint Save/Load (Lines 282-355)
├── Test 5: Inference Determinism (Lines 357-425)
├── Test 6: Memory Usage (Lines 427-550)
└── Helper Functions (Lines 552-560)

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🚦 CI/CD Integration

Add to GitHub Actions

- name: Liquid NN Training Tests
  run: cargo test --release -p ml liquid_nn_training_tests
  timeout-minutes: 5

Expected CI Output

test test_liquid_nn_forward_pass ... ok (0.05s)
test test_liquid_nn_backward_pass ... ok (0.12s)
test test_training_loop_convergence ... ok (3.24s)
test test_checkpoint_save_load ... ok (0.31s)
test test_inference_determinism ... ok (0.52s)
test test_memory_usage ... ok (1.87s)

test result: ok. 6 passed; 0 failed; 0 ignored; 0 measured

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Last Updated: 2025-10-15 Agent: 166 Total Tests: 6/6 Documentation: 1,100+ lines