add models and layers backward test

tests/test_layers/test_residual.py

@@ -22,6 +22,17 @@ def test_forward_residual_block():
     assert y.shape[1] == 3
     assert y.shape[0] == x.shape[0]
 
+def test_backward_residual_block():
+    
+    res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
+
+    x = torch.rand(size=(80, 10))
+    x.requires_grad = True
+    y = res_block(x)
+    l = torch.mean(y)
+    l.backward()
+    assert x._grad.shape == torch.Size([80,10])
+
 def test_constructor_no_activation_no_dropout():
     linear_layer = nn.Linear(10, 20)
     enhanced_linear = EnhancedLinear(linear_layer)
@@ -59,6 +70,17 @@ def test_forward_enhanced_linear_no_dropout():
     assert y.shape[1] == 3
     assert y.shape[0] == x.shape[0]
 
+def test_backward_enhanced_linear_no_dropout():
+    
+    enhanced_linear = EnhancedLinear(nn.Linear(10, 3))
+
+    x = torch.rand(size=(80, 10))
+    x.requires_grad = True
+    y = enhanced_linear(x)
+    l = torch.mean(y)
+    l.backward()
+    assert x._grad.shape == torch.Size([80, 10])
+
 def test_forward_enhanced_linear_dropout():
 
     enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5)
@@ -66,4 +88,15 @@ def test_forward_enhanced_linear_dropout():
     x = torch.rand(size=(80, 10))
     y = enhanced_linear(x)
     assert y.shape[1] == 3
-    assert y.shape[0] == x.shape[0]
+    assert y.shape[0] == x.shape[0]
+
+def test_backward_enhanced_linear_dropout():
+    
+    enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5)
+
+    x = torch.rand(size=(80, 10))
+    x.requires_grad = True
+    y = enhanced_linear(x)
+    l = torch.mean(y)
+    l.backward()
+    assert x._grad.shape == torch.Size([80, 10])