Optimization Level 0: Baseline

Results:
- Perceptron: ✅ (1.86s) 100.0%
- XOR: ✅ (1.92s) 54.5%
- MNIST: ✅ (2.03s) 15.0%
- CIFAR: ❌ (60.00s)
- TinyGPT: ✅ (1.85s)

optimization_log_20250928_213019.txt

@@ -14,3 +14,4 @@ Testing Optimization Level 0: Baseline
 [2025-09-28 21:31:27]     ✅ Complete in 2.00s
 [2025-09-28 21:31:27] 
 Committing results for Baseline...
+[2025-09-28 21:31:27]   Committed results

optimization_log_20250928_214132.txt

@@ -0,0 +1,16 @@
+[2025-09-28 21:41:32] 
+Testing Optimization Level 0: Baseline
+[2025-09-28 21:41:32] Description: No optimizations
+[2025-09-28 21:41:32] ------------------------------------------------------------
+[2025-09-28 21:41:32]   Testing Perceptron with Baseline...
+[2025-09-28 21:41:34]     ✅ Complete in 1.86s
+[2025-09-28 21:41:34]   Testing XOR with Baseline...
+[2025-09-28 21:41:36]     ✅ Complete in 1.92s
+[2025-09-28 21:41:36]   Testing MNIST with Baseline...
+[2025-09-28 21:41:38]     ✅ Complete in 2.03s
+[2025-09-28 21:41:38]   Testing CIFAR with Baseline...
+[2025-09-28 21:42:38]     ⏱️ Timeout after 60s
+[2025-09-28 21:42:38]   Testing TinyGPT with Baseline...
+[2025-09-28 21:42:40]     ✅ Complete in 1.85s
+[2025-09-28 21:42:40] 
+Committing results for Baseline...

results_Baseline.json

@@ -1,24 +1,24 @@
 {
   "Perceptron": {
     "success": true,
-    "time": 1.8516879081726074,
+    "time": 1.8552052974700928,
     "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": 1.9161672592163086,
+    "time": 1.9177570343017578,
     "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": 2.042603015899658,
+    "time": 2.02604603767395,
     "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": 9.0
+    "accuracy": 15.0
   },
   "CIFAR": {
     "success": false,
@@ -27,8 +27,8 @@
   },
   "TinyGPT": {
     "success": true,
-    "time": 2.003012180328369,
+    "time": 1.851945161819458,
     "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": 0.2696
+    "loss": 0.3688
   }
 }