Optimization Level 19: Benchmarking

Results:
- Perceptron: ✅ (1.87s) 100.0%
- XOR: ✅ (1.92s) 54.5%
- MNIST: ✅ (2.04s) 7.5%
- CIFAR: ❌ (60.00s)
- TinyGPT: ✅ (1.88s)

optimization_log_20250928_214329.txt

@@ -114,3 +114,23 @@ Testing Optimization Level 18: Caching
 [2025-09-28 21:47:18]     ✅ Complete in 1.88s
 [2025-09-28 21:47:18] 
 Committing results for Caching...
+[2025-09-28 21:47:18]   Committed results
+[2025-09-28 21:47:18] 
+Verifying previous optimizations still work...
+[2025-09-28 21:47:18]   Previous optimizations verified
+[2025-09-28 21:47:18] 
+Testing Optimization Level 19: Benchmarking
+[2025-09-28 21:47:18] Description: Module 19: Advanced benchmarking suite
+[2025-09-28 21:47:18] ------------------------------------------------------------
+[2025-09-28 21:47:18]   Testing Perceptron with Benchmarking...
+[2025-09-28 21:47:20]     ✅ Complete in 1.87s
+[2025-09-28 21:47:20]   Testing XOR with Benchmarking...
+[2025-09-28 21:47:22]     ✅ Complete in 1.92s
+[2025-09-28 21:47:22]   Testing MNIST with Benchmarking...
+[2025-09-28 21:47:24]     ✅ Complete in 2.04s
+[2025-09-28 21:47:24]   Testing CIFAR with Benchmarking...
+[2025-09-28 21:47:54]     ⏱️ Timeout after 60s
+[2025-09-28 21:47:54]   Testing TinyGPT with Benchmarking...
+[2025-09-28 21:47:56]     ✅ Complete in 1.88s
+[2025-09-28 21:47:56] 
+Committing results for Benchmarking...

results_Benchmarking.json

@@ -0,0 +1,34 @@
+{
+  "Perceptron": {
+    "success": true,
+    "time": 1.8683466911315918,
+    "output_preview": "ion\n\n\ud83d\ude80 Next Steps:\n   \u2022 Continue to XOR 1969 milestone after Module 06 (Autograd)\n   \u2022 YOUR foundation enables solving non-linear problems!\n   \u2022 With 100.0% accuracy, YOUR perceptron works perfectly!\n",
+    "loss": 0.2038,
+    "accuracy": 100.0
+  },
+  "XOR": {
+    "success": true,
+    "time": 1.9171321392059326,
+    "output_preview": "ayer networks\n\n\ud83d\ude80 Next Steps:\n   \u2022 Continue to MNIST MLP after Module 08 (Training)\n   \u2022 YOUR XOR solution scales to real vision problems!\n   \u2022 Hidden layers principle powers all modern deep learning!\n",
+    "loss": 0.2497,
+    "accuracy": 54.5
+  },
+  "MNIST": {
+    "success": true,
+    "time": 2.0394182205200195,
+    "output_preview": " a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n  one_hot[i, int(labels_np[i])] = 1.0\n",
+    "loss": 0.0,
+    "accuracy": 7.5
+  },
+  "CIFAR": {
+    "success": false,
+    "time": 60,
+    "timeout": true
+  },
+  "TinyGPT": {
+    "success": true,
+    "time": 1.876978874206543,
+    "output_preview": "ining\n   \u2022 Complete transformer architecture from first principles\n\n\ud83c\udfed Production Note:\n   Real PyTorch uses optimized CUDA kernels for attention,\n   but you built and understand the core mathematics!\n",
+    "loss": 0.3195
+  }
+}