Optimization Level 0: Baseline

Results:
- Perceptron: ✅ (1.76s) 100.0%
- XOR: ✅ (1.88s) 54.5%
- MNIST: ✅ (1.89s) 9.0%
- CIFAR: ❌ (3.85s)
- TinyGPT: ✅ (1.84s)

optimization_log_20250928_220325.txt

@@ -0,0 +1,16 @@
+[2025-09-28 22:03:25] 
+Testing Optimization Level 0: Baseline
+[2025-09-28 22:03:25] Description: No optimizations
+[2025-09-28 22:03:25] ------------------------------------------------------------
+[2025-09-28 22:03:25]   Testing Perceptron with Baseline...
+[2025-09-28 22:03:27]     ✅ Complete in 1.76s
+[2025-09-28 22:03:27]   Testing XOR with Baseline...
+[2025-09-28 22:03:29]     ✅ Complete in 1.88s
+[2025-09-28 22:03:29]   Testing MNIST with Baseline...
+[2025-09-28 22:03:30]     ✅ Complete in 1.89s
+[2025-09-28 22:03:30]   Testing CIFAR with Baseline...
+[2025-09-28 22:03:34]     ✅ Complete in 3.85s
+[2025-09-28 22:03:34]   Testing TinyGPT with Baseline...
+[2025-09-28 22:03:36]     ✅ Complete in 1.84s
+[2025-09-28 22:03:36] 
+Committing results for Baseline...

optimization_test_framework.py

@@ -118,8 +118,8 @@ class OptimizationTester:
             env['TINYTORCH_OPT'] = optimization['module']
         
         try:
-            # Use shorter timeout for CIFAR architecture test
-            timeout_val = 30 if example['name'] == 'CIFAR' else 60
+            # Use longer timeout for CIFAR since Conv2D operations are slow in pure Python
+            timeout_val = 120 if example['name'] == 'CIFAR' else 60
             cmd = f"python {example['path']} {example['args']}"
             result = subprocess.run(
                 cmd,

results_Baseline.json

@@ -1,34 +1,34 @@
 {
   "Perceptron": {
     "success": true,
-    "time": 1.924880027770996,
+    "time": 1.7636549472808838,
     "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": 1.8728010654449463,
+    "time": 1.8759121894836426,
     "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": 1.9613378047943115,
+    "time": 1.8865001201629639,
     "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": 11.5
+    "accuracy": 9.0
   },
   "CIFAR": {
     "success": false,
-    "time": 60,
-    "timeout": true
+    "time": 3.8529930114746094,
+    "output_preview": "\n   Total parameters: 612,042\n\n\ud83e\uddea ARCHITECTURE TEST MODE\n   Using minimal dataset for optimization testing framework...\n\u2705 Forward pass successful! Shape: (1, 10)\n\u2705 YOUR CNN + DataLoader work together!\n"
   },
   "TinyGPT": {
     "success": true,
-    "time": 1.9189341068267822,
+    "time": 1.8408770561218262,
     "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": 0.3419
+    "loss": 0.2969
   }
 }