From 79db89930aefa88667b43dcd2e6dd61adc3a2392 Mon Sep 17 00:00:00 2001
From: Vijay Janapa Reddi <vj@eecs.harvard.edu>
Date: Tue, 23 Sep 2025 08:24:50 -0400
Subject: [PATCH] Complete comprehensive testing for API simplification
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Added full test suite following TinyTorch testing conventions:

✅ UNIT TESTS (test_api_simplification.py):
- 23 comprehensive tests covering all API components
- Tests Parameter function, Module base class, Linear/Conv2d layers
- Tests functional interface (F.relu, F.flatten, F.max_pool2d)
- Tests optimizer integration and backward compatibility
- Tests complete model workflows (MLP, CNN)

✅ INTEGRATION TESTS (test_api_simplification_integration.py):
- Cross-component integration testing
- Complete workflow validation (model → optimizer → training setup)
- PyTorch compatibility verification
- Nested module parameter collection testing

✅ EXAMPLE FIXES:
- Fixed optimizer parameter names (lr → learning_rate)
- Examples demonstrate real-world usage patterns
- Show dramatic code simplification vs old API

🎯 TEST RESULTS:
- Unit Tests: 23/23 PASS ✅
- Integration Tests: 8/8 PASS ✅
- API simplification validated with comprehensive coverage

The testing validates that the API simplification maintains educational
value while providing clean PyTorch-compatible interfaces.
---
 examples/cifar10/train_cnn_modern_api.py      |   2 +-
 examples/xornet/train_xor_modern_api.py       |   2 +-
 .../test_api_simplification_integration.py    | 388 +++++++++++++++
 tests/test_api_simplification.py              | 443 ++++++++++++++++++
 4 files changed, 833 insertions(+), 2 deletions(-)
 create mode 100644 tests/integration/test_api_simplification_integration.py
 create mode 100644 tests/test_api_simplification.py

diff --git a/examples/cifar10/train_cnn_modern_api.py b/examples/cifar10/train_cnn_modern_api.py
index 42757ea3..a7c9edfc 100644
--- a/examples/cifar10/train_cnn_modern_api.py
+++ b/examples/cifar10/train_cnn_modern_api.py
@@ -74,7 +74,7 @@ def train_modern_cnn():
     model = ModernCNN()
     
     # Create optimizer - automatic parameter collection!
-    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    optimizer = optim.Adam(model.parameters(), learning_rate=0.001)
     criterion = CrossEntropyLoss()
     
     # Load data
diff --git a/examples/xornet/train_xor_modern_api.py b/examples/xornet/train_xor_modern_api.py
index 97c14e08..2eb56012 100644
--- a/examples/xornet/train_xor_modern_api.py
+++ b/examples/xornet/train_xor_modern_api.py
@@ -83,7 +83,7 @@ def train_xor_network():
     
     # Create model and optimizer - notice how clean this is!
     model = XORNet()
-    optimizer = optim.SGD(model.parameters(), lr=0.1)  # Auto parameter collection!
+    optimizer = optim.SGD(model.parameters(), learning_rate=0.1)  # Auto parameter collection!
     criterion = MSELoss()
     
     # Create dataset
diff --git a/tests/integration/test_api_simplification_integration.py b/tests/integration/test_api_simplification_integration.py
new file mode 100644
index 00000000..610bdb57
--- /dev/null
+++ b/tests/integration/test_api_simplification_integration.py
@@ -0,0 +1,388 @@
+"""
+Integration test for API Simplification
+
+Validates that the new PyTorch-compatible API integrates correctly across all components:
+- nn module with Module, Linear, Conv2d
+- nn.functional with relu, flatten, max_pool2d  
+- optim module with Adam, SGD
+- Complete workflow integration (model creation → optimizer → training setup)
+
+This follows TinyTorch testing conventions:
+1. Unit tests in test_api_simplification.py
+2. Integration tests here (cross-component)
+3. Examples as ultimate integration validation
+"""
+
+import sys
+import warnings
+import numpy as np
+
+
+def test_modern_api_integration():
+    """Test complete modern API integration across all components."""
+    
+    # Suppress warnings for cleaner test output
+    warnings.filterwarnings("ignore")
+    
+    results = {
+        "module_name": "api_simplification",
+        "integration_type": "modern_api_validation",
+        "tests": [],
+        "success": True,
+        "errors": []
+    }
+    
+    try:
+        # Test 1: Modern imports work
+        try:
+            import tinytorch.nn as nn
+            import tinytorch.nn.functional as F
+            import tinytorch.optim as optim
+            from tinytorch.core.tensor import Tensor, Parameter
+            
+            results["tests"].append({
+                "name": "modern_imports",
+                "status": "✅ PASS",
+                "description": "Modern PyTorch-like imports work"
+            })
+        except ImportError as e:
+            results["tests"].append({
+                "name": "modern_imports", 
+                "status": "❌ FAIL",
+                "description": f"Modern imports failed: {e}"
+            })
+            results["success"] = False
+            results["errors"].append(f"Import error: {e}")
+            return results
+        
+        # Test 2: Complete MLP workflow integration
+        try:
+            class SimpleMLP(nn.Module):
+                def __init__(self):
+                    super().__init__()
+                    self.fc1 = nn.Linear(4, 8)
+                    self.fc2 = nn.Linear(8, 2)
+                
+                def forward(self, x):
+                    x = F.relu(self.fc1(x))
+                    return self.fc2(x)
+            
+            # Create model and optimizer
+            model = SimpleMLP()
+            optimizer = optim.Adam(model.parameters(), learning_rate=0.001)
+            
+            # Test forward pass
+            x = Tensor([[1.0, 2.0, 3.0, 4.0]])
+            output = model(x)
+            
+            # Verify integration
+            assert len(list(model.parameters())) == 4, "Should have 4 parameters"
+            assert output.shape == (1, 2), f"Expected (1, 2), got {output.shape}"
+            assert len(optimizer.parameters) == 4, "Optimizer should have 4 parameters"
+            
+            results["tests"].append({
+                "name": "mlp_workflow_integration",
+                "status": "✅ PASS", 
+                "description": "Complete MLP workflow integrates correctly"
+            })
+        except Exception as e:
+            results["tests"].append({
+                "name": "mlp_workflow_integration",
+                "status": "❌ FAIL",
+                "description": f"MLP workflow failed: {e}"
+            })
+            results["success"] = False
+            results["errors"].append(f"MLP workflow error: {e}")
+        
+        # Test 3: Complete CNN workflow integration
+        try:
+            class SimpleCNN(nn.Module):
+                def __init__(self):
+                    super().__init__()
+                    self.conv1 = nn.Conv2d(3, 8, (3, 3))
+                    self.fc1 = nn.Linear(200, 10)  # Simplified calculation
+                
+                def forward(self, x):
+                    x = F.relu(self.conv1(x))
+                    x = F.flatten(x)
+                    return self.fc1(x)
+            
+            # Create model and optimizer
+            model = SimpleCNN()
+            optimizer = optim.SGD(model.parameters(), learning_rate=0.01)
+            
+            # Verify CNN integration
+            params = list(model.parameters())
+            assert len(params) == 4, f"Expected 4 parameters, got {len(params)}"
+            assert len(optimizer.parameters) == 4, "Optimizer should have 4 parameters"
+            
+            # Test parameter shapes
+            conv_weight = model.conv1.weight
+            conv_bias = model.conv1.bias
+            fc_weight = model.fc1.weights
+            fc_bias = model.fc1.bias
+            
+            assert conv_weight.shape == (8, 3, 3, 3), f"Conv weight shape: {conv_weight.shape}"
+            assert conv_bias.shape == (8,), f"Conv bias shape: {conv_bias.shape}"
+            assert fc_weight.shape == (200, 10), f"FC weight shape: {fc_weight.shape}"
+            assert fc_bias.shape == (10,), f"FC bias shape: {fc_bias.shape}"
+            
+            results["tests"].append({
+                "name": "cnn_workflow_integration",
+                "status": "✅ PASS",
+                "description": "Complete CNN workflow integrates correctly"
+            })
+        except Exception as e:
+            results["tests"].append({
+                "name": "cnn_workflow_integration", 
+                "status": "❌ FAIL",
+                "description": f"CNN workflow failed: {e}"
+            })
+            results["success"] = False
+            results["errors"].append(f"CNN workflow error: {e}")
+        
+        # Test 4: Functional interface integration
+        try:
+            x = Tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]])
+            
+            # Test relu
+            relu_out = F.relu(x)
+            expected_relu = np.array([[0.0, 0.0, 0.0, 1.0, 2.0]])
+            np.testing.assert_array_equal(relu_out.data, expected_relu)
+            
+            # Test flatten
+            x2 = Tensor([[[[1, 2], [3, 4]]]])  # (1, 1, 2, 2)
+            flat_out = F.flatten(x2)
+            assert flat_out.shape == (1, 4), f"Flatten shape: {flat_out.shape}"
+            
+            results["tests"].append({
+                "name": "functional_interface_integration",
+                "status": "✅ PASS",
+                "description": "Functional interface integrates correctly"
+            })
+        except Exception as e:
+            results["tests"].append({
+                "name": "functional_interface_integration",
+                "status": "❌ FAIL", 
+                "description": f"Functional interface failed: {e}"
+            })
+            results["success"] = False
+            results["errors"].append(f"Functional interface error: {e}")
+        
+        # Test 5: Backward compatibility integration
+        try:
+            # Test old names still work
+            from tinytorch.core.layers import Dense
+            from tinytorch.core.spatial import MultiChannelConv2D
+            
+            dense = Dense(5, 3)
+            conv = MultiChannelConv2D(3, 8, (3, 3))
+            
+            # Should be the same classes as new names
+            assert type(dense).__name__ == 'Linear', f"Dense should be Linear, got {type(dense).__name__}"
+            assert type(conv).__name__ == 'Conv2d', f"MultiChannelConv2D should be Conv2d, got {type(conv).__name__}"
+            
+            results["tests"].append({
+                "name": "backward_compatibility_integration",
+                "status": "✅ PASS",
+                "description": "Backward compatibility maintained"
+            })
+        except Exception as e:
+            results["tests"].append({
+                "name": "backward_compatibility_integration",
+                "status": "❌ FAIL",
+                "description": f"Backward compatibility failed: {e}"
+            })
+            results["success"] = False
+            results["errors"].append(f"Backward compatibility error: {e}")
+        
+        # Test 6: Cross-module parameter integration
+        try:
+            # Test that parameters flow correctly across modules
+            class ComplexModel(nn.Module):
+                def __init__(self):
+                    super().__init__()
+                    self.conv_block = nn.Module()
+                    self.conv_block.conv1 = nn.Conv2d(3, 16, (3, 3))
+                    self.conv_block.conv2 = nn.Conv2d(16, 32, (3, 3))
+                    
+                    self.classifier = nn.Module()
+                    self.classifier.fc1 = nn.Linear(512, 128)
+                    self.classifier.fc2 = nn.Linear(128, 10)
+                
+                def forward(self, x):
+                    return x  # Stub
+            
+            model = ComplexModel()
+            params = list(model.parameters())
+            
+            # Should collect from nested modules
+            assert len(params) == 8, f"Expected 8 parameters from nested modules, got {len(params)}"
+            
+            # Test optimizer works with nested parameters
+            optimizer = optim.Adam(model.parameters(), learning_rate=0.001)
+            assert len(optimizer.parameters) == 8, "Optimizer should get nested parameters"
+            
+            results["tests"].append({
+                "name": "cross_module_parameter_integration",
+                "status": "✅ PASS",
+                "description": "Cross-module parameter collection works"
+            })
+        except Exception as e:
+            results["tests"].append({
+                "name": "cross_module_parameter_integration",
+                "status": "❌ FAIL",
+                "description": f"Cross-module parameters failed: {e}"
+            })
+            results["success"] = False
+            results["errors"].append(f"Cross-module error: {e}")
+        
+    except Exception as e:
+        results["success"] = False
+        results["errors"].append(f"Unexpected error: {e}")
+        results["tests"].append({
+            "name": "unexpected_error",
+            "status": "❌ FAIL",
+            "description": f"Unexpected error: {e}"
+        })
+    
+    return results
+
+
+def test_pytorch_api_compatibility():
+    """Test that the API closely matches PyTorch patterns."""
+    
+    results = {
+        "module_name": "api_simplification",
+        "integration_type": "pytorch_compatibility",
+        "tests": [],
+        "success": True,
+        "errors": []
+    }
+    
+    try:
+        # Test PyTorch-like import patterns
+        import tinytorch.nn as nn
+        import tinytorch.nn.functional as F
+        import tinytorch.optim as optim
+        
+        # Test 1: PyTorch-like model definition
+        try:
+            class PyTorchLikeModel(nn.Module):
+                def __init__(self):
+                    super().__init__()
+                    self.features = nn.Module()
+                    self.features.conv1 = nn.Conv2d(3, 64, (3, 3))
+                    self.features.conv2 = nn.Conv2d(64, 128, (3, 3))
+                    
+                    self.classifier = nn.Module()
+                    self.classifier.fc1 = nn.Linear(2048, 512)
+                    self.classifier.fc2 = nn.Linear(512, 10)
+                
+                def forward(self, x):
+                    # Conv features
+                    x = F.relu(self.features.conv1(x))
+                    x = F.max_pool2d(x, (2, 2))
+                    x = F.relu(self.features.conv2(x))
+                    x = F.max_pool2d(x, (2, 2))
+                    
+                    # Classifier
+                    x = F.flatten(x)
+                    x = F.relu(self.classifier.fc1(x))
+                    x = self.classifier.fc2(x)
+                    return x
+            
+            model = PyTorchLikeModel()
+            optimizer = optim.Adam(model.parameters(), learning_rate=0.001)
+            
+            # Should work exactly like PyTorch
+            assert callable(model), "Model should be callable"
+            assert len(list(model.parameters())) > 0, "Should have parameters"
+            assert hasattr(optimizer, 'parameters'), "Optimizer should have parameters"
+            
+            results["tests"].append({
+                "name": "pytorch_like_model_definition",
+                "status": "✅ PASS",
+                "description": "PyTorch-like model definition works"
+            })
+        except Exception as e:
+            results["tests"].append({
+                "name": "pytorch_like_model_definition",
+                "status": "❌ FAIL", 
+                "description": f"PyTorch-like definition failed: {e}"
+            })
+            results["success"] = False
+            results["errors"].append(f"PyTorch compatibility error: {e}")
+        
+        # Test 2: PyTorch-like training setup pattern
+        try:
+            # This should look exactly like PyTorch code
+            model = nn.Linear(784, 10)
+            optimizer = optim.SGD(model.parameters(), learning_rate=0.01)
+            
+            # Test that syntax matches PyTorch
+            params = model.parameters()
+            assert hasattr(params, '__iter__'), "parameters() should be iterable"
+            
+            param_list = list(model.parameters())
+            assert len(param_list) == 2, "Linear should have weight + bias"
+            
+            results["tests"].append({
+                "name": "pytorch_training_setup",
+                "status": "✅ PASS",
+                "description": "PyTorch-like training setup works"
+            })
+        except Exception as e:
+            results["tests"].append({
+                "name": "pytorch_training_setup",
+                "status": "❌ FAIL",
+                "description": f"Training setup failed: {e}"
+            })
+            results["success"] = False
+            results["errors"].append(f"Training setup error: {e}")
+            
+    except Exception as e:
+        results["success"] = False
+        results["errors"].append(f"PyTorch compatibility test error: {e}")
+    
+    return results
+
+
+if __name__ == '__main__':
+    print("🧪 TinyTorch API Simplification Integration Tests")
+    print("=" * 60)
+    
+    # Run integration tests
+    api_results = test_modern_api_integration()
+    pytorch_results = test_pytorch_api_compatibility()
+    
+    # Print results
+    all_results = [api_results, pytorch_results]
+    total_tests = sum(len(r["tests"]) for r in all_results)
+    total_passed = sum(len([t for t in r["tests"] if t["status"] == "✅ PASS"]) for r in all_results)
+    total_failed = total_tests - total_passed
+    
+    print(f"\n📊 Integration Test Summary:")
+    print(f"   Total tests: {total_tests}")
+    print(f"   ✅ Passed: {total_passed}")
+    print(f"   ❌ Failed: {total_failed}")
+    
+    # Detailed results
+    for results in all_results:
+        print(f"\n🔍 {results['integration_type']}:")
+        for test in results["tests"]:
+            print(f"   {test['status']} {test['name']}: {test['description']}")
+        
+        if results["errors"]:
+            print(f"   Errors: {results['errors']}")
+    
+    # Overall success
+    overall_success = all(r["success"] for r in all_results)
+    if overall_success:
+        print("\n✅ All integration tests passed!")
+        print("🎉 API simplification integration is successful!")
+    else:
+        print("\n❌ Some integration tests failed!")
+        print("🔧 Fix integration issues before deployment.")
+    
+    sys.exit(0 if overall_success else 1)
\ No newline at end of file
diff --git a/tests/test_api_simplification.py b/tests/test_api_simplification.py
new file mode 100644
index 00000000..b8c00b97
--- /dev/null
+++ b/tests/test_api_simplification.py
@@ -0,0 +1,443 @@
+#!/usr/bin/env python3
+"""
+Test Suite for TinyTorch API Simplification
+
+This test suite validates all the new PyTorch-compatible API features
+introduced in the API simplification project.
+
+Test Hierarchy:
+1. Unit Tests: Individual components (Parameter, Module, Linear, Conv2d)
+2. Integration Tests: Components working together 
+3. End-to-End Tests: Complete workflows (model creation, training setup)
+"""
+
+import unittest
+import sys
+import os
+import numpy as np
+
+# Add project root to path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+class TestParameterFunction(unittest.TestCase):
+    """Unit tests for the Parameter() helper function."""
+    
+    def setUp(self):
+        from tinytorch.core.tensor import Parameter, Tensor
+        self.Parameter = Parameter
+        self.Tensor = Tensor
+    
+    def test_parameter_creation(self):
+        """Test basic Parameter creation."""
+        param = self.Parameter([[1.0, 2.0], [3.0, 4.0]])
+        
+        self.assertTrue(param.requires_grad, "Parameter should have requires_grad=True")
+        self.assertEqual(param.shape, (2, 2), "Parameter should preserve shape")
+        self.assertEqual(param.dtype, np.float32, "Parameter should default to float32")
+    
+    def test_parameter_vs_tensor(self):
+        """Test Parameter vs Tensor differences."""
+        data = [[1.0, 2.0]]
+        
+        tensor = self.Tensor(data)
+        param = self.Parameter(data)
+        
+        self.assertFalse(tensor.requires_grad, "Tensor should default requires_grad=False")
+        self.assertTrue(param.requires_grad, "Parameter should have requires_grad=True")
+    
+    def test_parameter_with_dtype(self):
+        """Test Parameter with explicit dtype."""
+        param = self.Parameter([1, 2, 3], dtype='int32')
+        self.assertEqual(param.dtype, np.int32, "Parameter should respect dtype")
+        self.assertTrue(param.requires_grad, "Parameter should still have requires_grad=True")
+
+
+class TestModuleBaseClass(unittest.TestCase):
+    """Unit tests for the Module base class."""
+    
+    def setUp(self):
+        import tinytorch.nn as nn
+        from tinytorch.core.tensor import Parameter
+        self.nn = nn
+        self.Parameter = Parameter
+    
+    def test_module_creation(self):
+        """Test basic Module creation."""
+        module = self.nn.Module()
+        
+        self.assertEqual(len(module._parameters), 0, "New module should have no parameters")
+        self.assertEqual(len(module._modules), 0, "New module should have no submodules")
+        self.assertEqual(len(list(module.parameters())), 0, "parameters() should return empty list")
+    
+    def test_parameter_registration(self):
+        """Test automatic parameter registration."""
+        Parameter = self.Parameter
+        
+        class TestModule(self.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.weight = Parameter([[1.0, 2.0]])
+                self.bias = Parameter([0.5])
+                self.non_param = "not a parameter"
+        
+        module = TestModule()
+        params = list(module.parameters())
+        
+        self.assertEqual(len(params), 2, "Should register 2 parameters")
+        self.assertTrue(all(p.requires_grad for p in params), "All parameters should require gradients")
+    
+    def test_submodule_registration(self):
+        """Test automatic submodule registration."""
+        Parameter = self.Parameter
+        
+        class SubModule(self.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.weight = Parameter([[1.0]])
+        
+        class MainModule(self.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.sub1 = SubModule()
+                self.sub2 = SubModule()
+                self.weight = Parameter([[2.0]])
+        
+        module = MainModule()
+        params = list(module.parameters())
+        
+        self.assertEqual(len(params), 3, "Should collect parameters from submodules: 2 + 1 = 3")
+        self.assertEqual(len(module._modules), 2, "Should register 2 submodules")
+    
+    def test_callable_interface(self):
+        """Test that modules are callable via __call__."""
+        class TestModule(self.nn.Module):
+            def forward(self, x):
+                return x + 1
+        
+        module = TestModule()
+        result = module(5)  # Should call forward(5)
+        
+        self.assertEqual(result, 6, "Module should be callable and delegate to forward()")
+
+
+class TestLinearLayer(unittest.TestCase):
+    """Unit tests for the Linear layer (renamed from Dense)."""
+    
+    def setUp(self):
+        import tinytorch.nn as nn
+        from tinytorch.core.tensor import Tensor
+        self.nn = nn
+        self.Tensor = Tensor
+    
+    def test_linear_creation(self):
+        """Test Linear layer creation."""
+        linear = self.nn.Linear(5, 3)
+        
+        self.assertEqual(linear.input_size, 5, "Should store input size")
+        self.assertEqual(linear.output_size, 3, "Should store output size")
+        self.assertEqual(linear.weights.shape, (5, 3), "Weights should have correct shape")
+        self.assertEqual(linear.bias.shape, (3,), "Bias should have correct shape")
+        self.assertTrue(linear.weights.requires_grad, "Weights should require gradients")
+        self.assertTrue(linear.bias.requires_grad, "Bias should require gradients")
+    
+    def test_linear_no_bias(self):
+        """Test Linear layer without bias."""
+        linear = self.nn.Linear(5, 3, use_bias=False)
+        
+        self.assertIsNone(linear.bias, "Should have no bias when use_bias=False")
+        self.assertEqual(len(list(linear.parameters())), 1, "Should only have weight parameter")
+    
+    def test_linear_forward(self):
+        """Test Linear layer forward pass."""
+        linear = self.nn.Linear(3, 2)
+        x = self.Tensor([[1.0, 2.0, 3.0]])
+        
+        output = linear(x)
+        
+        self.assertEqual(output.shape, (1, 2), "Output should have correct shape")
+        self.assertIsInstance(output, self.Tensor, "Output should be a Tensor")
+    
+    def test_linear_parameter_collection(self):
+        """Test that Linear parameters are properly collected."""
+        linear = self.nn.Linear(4, 2)
+        params = list(linear.parameters())
+        
+        self.assertEqual(len(params), 2, "Should have 2 parameters (weight + bias)")
+        shapes = [p.shape for p in params]
+        self.assertIn((4, 2), shapes, "Should include weight shape")
+        self.assertIn((2,), shapes, "Should include bias shape")
+
+
+class TestConv2dLayer(unittest.TestCase):
+    """Unit tests for the Conv2d layer (renamed from MultiChannelConv2D)."""
+    
+    def setUp(self):
+        import tinytorch.nn as nn
+        from tinytorch.core.tensor import Tensor
+        self.nn = nn
+        self.Tensor = Tensor
+    
+    def test_conv2d_creation(self):
+        """Test Conv2d layer creation."""
+        conv = self.nn.Conv2d(3, 16, (3, 3))
+        
+        self.assertEqual(conv.in_channels, 3, "Should store input channels")
+        self.assertEqual(conv.out_channels, 16, "Should store output channels")
+        self.assertEqual(conv.kernel_size, (3, 3), "Should store kernel size")
+        self.assertEqual(conv.weight.shape, (16, 3, 3, 3), "Weight should have correct shape")
+        self.assertEqual(conv.bias.shape, (16,), "Bias should have correct shape")
+    
+    def test_conv2d_parameter_collection(self):
+        """Test that Conv2d parameters are properly collected."""
+        conv = self.nn.Conv2d(3, 8, (3, 3))
+        params = list(conv.parameters())
+        
+        self.assertEqual(len(params), 2, "Should have 2 parameters (weight + bias)")
+        weight_params = [p for p in params if len(p.shape) == 4]
+        bias_params = [p for p in params if len(p.shape) == 1]
+        
+        self.assertEqual(len(weight_params), 1, "Should have 1 weight parameter")
+        self.assertEqual(len(bias_params), 1, "Should have 1 bias parameter")
+
+
+class TestFunctionalInterface(unittest.TestCase):
+    """Unit tests for the functional interface (F.relu, F.flatten, etc.)."""
+    
+    def setUp(self):
+        import tinytorch.nn.functional as F
+        from tinytorch.core.tensor import Tensor
+        self.F = F
+        self.Tensor = Tensor
+    
+    def test_relu_function(self):
+        """Test F.relu function."""
+        x = self.Tensor([[-2.0, -1.0, 0.0, 1.0, 2.0]])
+        output = self.F.relu(x)
+        
+        expected = np.array([[0.0, 0.0, 0.0, 1.0, 2.0]])
+        np.testing.assert_array_equal(output.data, expected, "ReLU should zero negative values")
+    
+    def test_flatten_function(self):
+        """Test F.flatten function."""
+        x = self.Tensor([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]])  # Shape: (1, 2, 2, 2)
+        output = self.F.flatten(x)
+        
+        self.assertEqual(output.shape, (1, 8), "Flatten should preserve batch dimension")
+        expected = np.array([[1, 2, 3, 4, 5, 6, 7, 8]])
+        np.testing.assert_array_equal(output.data, expected, "Flatten should preserve order")
+    
+    def test_flatten_start_dim(self):
+        """Test F.flatten with custom start_dim."""
+        x = self.Tensor([[[1, 2], [3, 4]]])  # Shape: (1, 2, 2) = 4 elements
+        output = self.F.flatten(x, start_dim=0)  # Flatten everything
+        
+        self.assertEqual(output.shape, (4,), "Should flatten from dimension 0")
+        expected = np.array([1, 2, 3, 4])
+        np.testing.assert_array_equal(output.data, expected, "Should flatten all dimensions")
+
+
+class TestOptimizerIntegration(unittest.TestCase):
+    """Integration tests for optimizers with the new API."""
+    
+    def setUp(self):
+        import tinytorch.nn as nn
+        import tinytorch.optim as optim
+        self.nn = nn
+        self.optim = optim
+    
+    def test_adam_with_model_parameters(self):
+        """Test Adam optimizer with model.parameters()."""
+        model = self.nn.Linear(5, 3)
+        optimizer = self.optim.Adam(model.parameters(), learning_rate=0.001)
+        
+        # Check that optimizer received the parameters
+        self.assertEqual(len(optimizer.parameters), 2, "Adam should receive 2 parameters")
+        
+        # Check parameter types
+        for param in optimizer.parameters:
+            self.assertTrue(param.requires_grad, "All optimizer parameters should require gradients")
+    
+    def test_sgd_with_model_parameters(self):
+        """Test SGD optimizer with model.parameters()."""
+        nn = self.nn
+        
+        class SimpleNet(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.fc1 = nn.Linear(4, 8)
+                self.fc2 = nn.Linear(8, 2)
+        
+        model = SimpleNet()
+        optimizer = self.optim.SGD(model.parameters(), learning_rate=0.01)
+        
+        # Should collect parameters from both layers
+        self.assertEqual(len(optimizer.parameters), 4, "SGD should receive 4 parameters (2 layers × 2 params)")
+
+
+class TestBackwardCompatibility(unittest.TestCase):
+    """Integration tests for backward compatibility."""
+    
+    def test_dense_alias_works(self):
+        """Test that Dense alias still works."""
+        from tinytorch.core.layers import Dense
+        
+        dense = Dense(5, 3)
+        self.assertEqual(dense.input_size, 5, "Dense alias should work")
+        self.assertEqual(dense.output_size, 3, "Dense alias should work")
+    
+    def test_multichannel_conv2d_alias_works(self):
+        """Test that MultiChannelConv2D alias still works."""
+        from tinytorch.core.spatial import MultiChannelConv2D
+        
+        conv = MultiChannelConv2D(3, 16, (3, 3))
+        self.assertEqual(conv.in_channels, 3, "MultiChannelConv2D alias should work")
+        self.assertEqual(conv.out_channels, 16, "MultiChannelConv2D alias should work")
+
+
+class TestCompleteModelWorkflow(unittest.TestCase):
+    """End-to-end integration tests for complete model workflows."""
+    
+    def setUp(self):
+        import tinytorch.nn as nn
+        import tinytorch.nn.functional as F
+        import tinytorch.optim as optim
+        from tinytorch.core.tensor import Tensor
+        
+        self.nn = nn
+        self.F = F
+        self.optim = optim
+        self.Tensor = Tensor
+    
+    def test_complete_mlp_workflow(self):
+        """Test complete MLP creation and setup."""
+        nn = self.nn
+        F = self.F
+        
+        class MLP(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.fc1 = nn.Linear(4, 8)
+                self.fc2 = nn.Linear(8, 2)
+            
+            def forward(self, x):
+                x = F.relu(self.fc1(x))
+                return self.fc2(x)
+        
+        # Create model
+        model = MLP()
+        
+        # Test parameter collection
+        params = list(model.parameters())
+        self.assertEqual(len(params), 4, "Should collect all parameters")
+        
+        # Test optimizer creation
+        optimizer = self.optim.Adam(model.parameters(), learning_rate=0.001)
+        self.assertIsNotNone(optimizer, "Should create optimizer")
+        
+        # Test forward pass
+        x = self.Tensor([[1.0, 2.0, 3.0, 4.0]])
+        output = model(x)
+        self.assertEqual(output.shape, (1, 2), "Should produce correct output shape")
+    
+    def test_complete_cnn_workflow(self):
+        """Test complete CNN creation and setup."""
+        nn = self.nn
+        F = self.F
+        
+        class CNN(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv1 = nn.Conv2d(3, 8, (3, 3))
+                self.fc1 = nn.Linear(128, 10)  # Simplified size
+            
+            def forward(self, x):
+                x = F.relu(self.conv1(x))
+                x = F.flatten(x)
+                return self.fc1(x)
+        
+        # Create model
+        model = CNN()
+        
+        # Test parameter collection
+        params = list(model.parameters())
+        self.assertEqual(len(params), 4, "Should collect conv + fc parameters")
+        
+        # Test optimizer creation  
+        optimizer = self.optim.Adam(model.parameters(), learning_rate=0.001)
+        self.assertIsNotNone(optimizer, "Should create optimizer")
+    
+    def test_pytorch_like_syntax(self):
+        """Test that the syntax matches PyTorch patterns."""
+        # This test verifies the API feels like PyTorch
+        import tinytorch.nn as nn
+        import tinytorch.nn.functional as F
+        import tinytorch.optim as optim
+        
+        # Should be able to write PyTorch-like code
+        model = nn.Linear(10, 1)
+        optimizer = optim.Adam(model.parameters(), learning_rate=0.001)
+        
+        # Test that this workflow doesn't crash
+        x = self.Tensor([[1.0] * 10])
+        output = model(x)
+        
+        # Should be able to chain operations
+        output = F.relu(output)
+        
+        self.assertIsNotNone(output, "PyTorch-like workflow should work")
+
+
+if __name__ == '__main__':
+    # Run the test suite
+    print("🧪 TinyTorch API Simplification Test Suite")
+    print("=" * 60)
+    
+    # Create test suite
+    suite = unittest.TestSuite()
+    
+    # Add all test classes
+    test_classes = [
+        TestParameterFunction,
+        TestModuleBaseClass, 
+        TestLinearLayer,
+        TestConv2dLayer,
+        TestFunctionalInterface,
+        TestOptimizerIntegration,
+        TestBackwardCompatibility,
+        TestCompleteModelWorkflow
+    ]
+    
+    for test_class in test_classes:
+        tests = unittest.TestLoader().loadTestsFromTestCase(test_class)
+        suite.addTests(tests)
+    
+    # Run tests
+    runner = unittest.TextTestRunner(verbosity=2)
+    result = runner.run(suite)
+    
+    # Print summary
+    print("\n" + "=" * 60)
+    print(f"🎯 Test Summary:")
+    print(f"   Tests run: {result.testsRun}")
+    print(f"   Failures: {len(result.failures)}")
+    print(f"   Errors: {len(result.errors)}")
+    
+    if result.failures:
+        print("\n❌ Failures:")
+        for test, traceback in result.failures:
+            print(f"   {test}: {traceback.split('AssertionError:')[-1].strip()}")
+    
+    if result.errors:
+        print("\n💥 Errors:")
+        for test, traceback in result.errors:
+            print(f"   {test}: {traceback.split('Exception:')[-1].strip()}")
+    
+    if result.wasSuccessful():
+        print("\n✅ All API simplification tests passed!")
+        print("🎉 PyTorch-compatible API is working correctly!")
+    else:
+        print("\n❌ Some tests failed!")
+        print("🔧 API needs fixes before deployment.")
+    
+    # Exit with proper code
+    sys.exit(0 if result.wasSuccessful() else 1)
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