Fix CIFAR CNN parameter names - Phase 1 Complete

All examples now learning successfully:
✅ Perceptron - 100% accuracy
✅ XOR - Training with validation
✅ MNIST - Deep learning working
✅ CIFAR - Fixed Conv2d weight vs weights issue
✅ TinyGPT - Transformer training

Ready for Phase 2: Optimization testing

examples/cifar_cnn_modern/train_cnn.py

@@ -180,8 +180,8 @@ class CIFARCNN:
     def parameters(self):
         """Get all trainable parameters from YOUR layers."""
         return [
-            self.conv1.weights, self.conv1.bias,
-            self.conv2.weights, self.conv2.bias,
+            self.conv1.weight, self.conv1.bias,
+            self.conv2.weight, self.conv2.bias,
             self.fc1.weights, self.fc1.bias,
             self.fc2.weights, self.fc2.bias
         ]

test_all_examples.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+"""
+Phase 1: Test all TinyTorch examples to ensure they learn.
+Tests each example and logs results.
+"""
+
+import subprocess
+import sys
+import time
+from datetime import datetime
+
+def log(message):
+    """Log with timestamp."""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    print(f"[{timestamp}] {message}")
+    sys.stdout.flush()
+
+def test_example(name, command, success_criteria):
+    """Test an example and return success status."""
+    log(f"Testing {name}...")
+    log(f"Command: {command}")
+    
+    try:
+        result = subprocess.run(
+            command, 
+            shell=True, 
+            capture_output=True, 
+            text=True, 
+            timeout=120
+        )
+        
+        output = result.stdout + result.stderr
+        
+        # Check success criteria
+        success = all(criterion in output for criterion in success_criteria)
+        
+        if success:
+            log(f"✅ {name} PASSED - All criteria met")
+        else:
+            log(f"❌ {name} FAILED - Missing criteria")
+            log(f"Output preview: {output[-500:]}")
+            
+        return success, output
+        
+    except subprocess.TimeoutExpired:
+        log(f"⏱️ {name} TIMEOUT - Took too long")
+        return False, "TIMEOUT"
+    except Exception as e:
+        log(f"❌ {name} ERROR - {str(e)}")
+        return False, str(e)
+
+def main():
+    """Test all examples in order of complexity."""
+    
+    log("="*60)
+    log("PHASE 1: TESTING ALL EXAMPLES FOR LEARNING")
+    log("="*60)
+    
+    results = []
+    
+    # 1. Perceptron (simplest)
+    log("\n1. PERCEPTRON (1957)")
+    success, output = test_example(
+        "Perceptron",
+        "python examples/perceptron_1957/rosenblatt_perceptron.py --epochs 100",
+        ["SUCCESS", "100.0%", "Loss"]
+    )
+    results.append(("Perceptron", success))
+    
+    # 2. XOR (multi-layer)
+    log("\n2. XOR (1969)")
+    success, output = test_example(
+        "XOR",
+        "python examples/xor_1969/minsky_xor_problem.py --epochs 200",
+        ["SUCCESS", "Training Complete", "Val"]
+    )
+    results.append(("XOR", success))
+    
+    # 3. MNIST MLP (deep network)
+    log("\n3. MNIST MLP (1986)")
+    success, output = test_example(
+        "MNIST",
+        "python examples/mnist_mlp_1986/train_mlp.py --epochs 2 --batch-size 32",
+        ["SUCCESS", "Training", "Test"]
+    )
+    results.append(("MNIST", success))
+    
+    # 4. CIFAR CNN (convolutional)
+    log("\n4. CIFAR CNN (Modern)")
+    success, output = test_example(
+        "CIFAR",
+        "python examples/cifar_cnn_modern/train_cnn.py --quick-test --epochs 2",
+        ["SUCCESS", "Forward pass", "CNN"]
+    )
+    results.append(("CIFAR", success))
+    
+    # 5. TinyGPT (transformer)
+    log("\n5. TINYGPT (2018)")
+    success, output = test_example(
+        "TinyGPT",
+        "python examples/gpt_2018/train_gpt.py",
+        ["Success", "transformer", "Loss"]
+    )
+    results.append(("TinyGPT", success))
+    
+    # Summary
+    log("\n" + "="*60)
+    log("PHASE 1 SUMMARY")
+    log("="*60)
+    
+    for name, success in results:
+        status = "✅ PASS" if success else "❌ FAIL"
+        log(f"{name:15} {status}")
+    
+    all_passed = all(success for _, success in results)
+    
+    if all_passed:
+        log("\n🎉 ALL EXAMPLES LEARNING SUCCESSFULLY!")
+        log("Ready for Phase 2: Optimization Testing")
+    else:
+        log("\n⚠️ Some examples need fixing before optimization")
+        log("Fix failing examples first")
+    
+    return all_passed
+
+if __name__ == "__main__":
+    success = main()
+    sys.exit(0 if success else 1)