Optimization Level 18: Caching

Results:
- Perceptron: ✅ (1.86s) 100.0%
- XOR: ✅ (1.93s) 54.5%
- MNIST: ✅ (1.95s) 10.5%
- CIFAR: ❌ (60.00s)
- TinyGPT: ✅ (1.88s)

optimization_log_20250928_214329.txt

@@ -94,3 +94,23 @@ Testing Optimization Level 17: Compression
 [2025-09-28 21:46:39]     ✅ Complete in 1.82s
 [2025-09-28 21:46:39] 
 Committing results for Compression...
+[2025-09-28 21:46:40]   Committed results
+[2025-09-28 21:46:40] 
+Verifying previous optimizations still work...
+[2025-09-28 21:46:40]   Previous optimizations verified
+[2025-09-28 21:46:40] 
+Testing Optimization Level 18: Caching
+[2025-09-28 21:46:40] Description: Module 18: Caching and memory optimization
+[2025-09-28 21:46:40] ------------------------------------------------------------
+[2025-09-28 21:46:40]   Testing Perceptron with Caching...
+[2025-09-28 21:46:42]     ✅ Complete in 1.86s
+[2025-09-28 21:46:42]   Testing XOR with Caching...
+[2025-09-28 21:46:44]     ✅ Complete in 1.93s
+[2025-09-28 21:46:44]   Testing MNIST with Caching...
+[2025-09-28 21:46:46]     ✅ Complete in 1.95s
+[2025-09-28 21:46:46]   Testing CIFAR with Caching...
+[2025-09-28 21:47:16]     ⏱️ Timeout after 60s
+[2025-09-28 21:47:16]   Testing TinyGPT with Caching...
+[2025-09-28 21:47:18]     ✅ Complete in 1.88s
+[2025-09-28 21:47:18] 
+Committing results for Caching...

results_Caching.json

@@ -0,0 +1,34 @@
+{
+  "Perceptron": {
+    "success": true,
+    "time": 1.8569300174713135,
+    "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": 1.9278671741485596,
+    "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": 1.9451780319213867,
+    "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": 10.5
+  },
+  "CIFAR": {
+    "success": false,
+    "time": 60,
+    "timeout": true
+  },
+  "TinyGPT": {
+    "success": true,
+    "time": 1.8846020698547363,
+    "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": 0.3701
+  }
+}