style: apply consistent whitespace and formatting across codebase

tinytorch/tests/integration/test_module_05_dense.py

@@ -15,85 +15,85 @@ sys.path.insert(0, str(Path(__file__).parent.parent.parent))
 
 class TestDenseModuleExports:
     """Test that all expected exports are available."""
-    
+
     def test_dense_class_exports(self):
         """Test Dense class exports from layers module."""
         from tinytorch.core.layers import Linear as Dense
         assert Dense is not None, "Dense class should be exported"
-        
+
     def test_dense_is_callable(self):
         """Test Dense can be instantiated."""
         from tinytorch.core.layers import Linear as Dense
         layer = Linear(10, 5)
         assert layer is not None, "Should create Dense layer instance"
         assert hasattr(layer, 'forward'), "Dense should have forward method"
-        
+
 
 class TestDenseLayerFunctionality:
     """Test that Dense layer works correctly."""
-    
+
     def test_dense_forward_pass(self):
         """Test Dense layer forward pass produces correct shape."""
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.tensor import Tensor
-        
+
         # Create layer
         layer = Linear(10, 5)
-        
+
         # Create input
         batch_size = 32
         x = Tensor(np.random.randn(batch_size, 10))
-        
+
         # Forward pass
         output = layer.forward(x)
-        
+
         # Check output
         assert output.shape == (32, 5), f"Expected shape (32, 5), got {output.shape}"
         assert isinstance(output, Tensor), "Output should be a Tensor"
-    
+
     def test_dense_with_bias(self):
         """Test Dense layer with bias enabled."""
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.tensor import Tensor
-        
+
         layer = Linear(10, 5, bias=True)
         assert hasattr(layer, 'bias'), "Layer should have bias"
         assert layer.bias is not None, "Bias should be initialized"
-        
+
         x = Tensor(np.random.randn(1, 10))
         output = layer(x)
         assert output.shape == (1, 5), "Output shape should be correct with bias"
-    
+
     def test_dense_without_bias(self):
         """Test Dense layer with bias disabled."""
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.tensor import Tensor
-        
+
         layer = Linear(10, 5, bias=False)
         assert layer.bias is None, "Bias should be None when disabled"
-        
+
         x = Tensor(np.random.randn(1, 10))
         output = layer(x)
         assert output.shape == (1, 5), "Output shape should be correct without bias"
-    
+
     def test_dense_callable_interface(self):
         """Test Dense layer can be called directly."""
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.tensor import Tensor
-        
+
         layer = Linear(10, 5)
         x = Tensor(np.random.randn(4, 10))
-        
+
         # Test both forward() and __call__()
         output1 = layer.forward(x)
         output2 = layer(x)
-        
+
         assert output1.shape == output2.shape, "forward() and __call__() should produce same shape"
 
 
 class TestNetworkComposition:
     """Test building multi-layer networks."""
-    
+
     def test_sequential_exists(self):
         """Test if Sequential is available for network composition."""
         try:
@@ -106,31 +106,31 @@ class TestNetworkComposition:
                 assert Sequential is not None
             except ImportError:
                 pytest.skip("Sequential not yet implemented")
-    
+
     def test_multi_layer_network(self):
         """Test building a multi-layer network."""
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.activations import ReLU, Sigmoid
         from tinytorch.core.tensor import Tensor
-        
+
         # Build network manually (without Sequential)
         layer1 = Linear(784, 128)
         relu = ReLU()
         layer2 = Linear(128, 10)
         sigmoid = Sigmoid()
-        
+
         # Test forward pass through all layers
         x = Tensor(np.random.randn(32, 784))
-        
+
         h1 = layer1(x)
         assert h1.shape == (32, 128), "First layer output shape incorrect"
-        
+
         h1_activated = relu(h1)
         assert h1_activated.shape == (32, 128), "ReLU should preserve shape"
-        
+
         output = layer2(h1_activated)
         assert output.shape == (32, 10), "Second layer output shape incorrect"
-        
+
         final = sigmoid(output)
         assert final.shape == (32, 10), "Sigmoid should preserve shape"
         assert np.all(final.data >= 0) and np.all(final.data <= 1), "Sigmoid output should be in [0,1]"
@@ -138,28 +138,28 @@ class TestNetworkComposition:
 
 class TestXORCapability:
     """Test that the network can represent XOR problem."""
-    
+
     def test_xor_network_structure(self):
         """Test building XOR network structure."""
         from tinytorch.core.layers import Linear as Dense
         from tinytorch.core.activations import ReLU, Sigmoid
         from tinytorch.core.tensor import Tensor
-        
+
         # XOR network: 2 -> 4 -> 1
         hidden = Linear(2, 4)
         relu = ReLU()
         output = Linear(4, 1)
         sigmoid = Sigmoid()
-        
+
         # XOR inputs
         X = Tensor([[0, 0], [0, 1], [1, 0], [1, 1]])
-        
+
         # Forward pass
         h = hidden(X)
         h_activated = relu(h)
         out = output(h_activated)
         predictions = sigmoid(out)
-        
+
         assert predictions.shape == (4, 1), "XOR network should produce 4 predictions"
         assert np.all(predictions.data >= 0) and np.all(predictions.data <= 1), "Predictions should be probabilities"
 
@@ -178,7 +178,7 @@ if __name__ == "__main__":
         capture_output=True,
         text=True
     )
-    
+
     print(result.stdout)
     if result.returncode != 0:
         print(result.stderr)
@@ -186,4 +186,4 @@ if __name__ == "__main__":
     else:
         print("\n✅ All integration tests passed! Module 05 is working correctly.")
         print("🎯 Capability Unlocked: Neural Networks That Learn")
-        print("\nTry the demo: python capabilities/05_neural_networks/demonstrate.py")
+        print("\nTry the demo: python capabilities/05_neural_networks/demonstrate.py")