Documentation for v0.1 version (#199)

* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>

tests/test_layers/test_residual.py

@@ -1,26 +1,69 @@
-from pina.model.layers import ResidualBlock
+from pina.model.layers import ResidualBlock, EnhancedLinear
 import torch
+import torch.nn as nn
 
 
-def test_constructor():
+def test_constructor_residual_block():
+
+    res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
 
-    res_block = ResidualBlock(input_dim=10,
-                              output_dim=3,
-                              hidden_dim=4)
-    
     res_block = ResidualBlock(input_dim=10,
                               output_dim=3,
                               hidden_dim=4,
                               spectral_norm=True)
 
 
-def test_forward():
+def test_forward_residual_block():
+
+    res_block = ResidualBlock(input_dim=10, output_dim=3, hidden_dim=4)
 
-    res_block = ResidualBlock(input_dim=10,
-                              output_dim=3,
-                              hidden_dim=4)
-    
     x = torch.rand(size=(80, 10))
     y = res_block(x)
-    assert y.shape[1]==3
-    assert y.shape[0]==x.shape[0]
+    assert y.shape[1] == 3
+    assert y.shape[0] == x.shape[0]
+
+def test_constructor_no_activation_no_dropout():
+    linear_layer = nn.Linear(10, 20)
+    enhanced_linear = EnhancedLinear(linear_layer)
+
+    assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters()))
+
+def test_constructor_with_activation_no_dropout():
+    linear_layer = nn.Linear(10, 20)
+    activation = nn.ReLU()
+    enhanced_linear = EnhancedLinear(linear_layer, activation)
+
+    assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + len(list(activation.parameters()))
+
+def test_constructor_no_activation_with_dropout():
+    linear_layer = nn.Linear(10, 20)
+    dropout_prob = 0.5
+    enhanced_linear = EnhancedLinear(linear_layer, dropout=dropout_prob)
+
+    assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters()))
+
+def test_constructor_with_activation_with_dropout():
+    linear_layer = nn.Linear(10, 20)
+    activation = nn.ReLU()
+    dropout_prob = 0.5
+    enhanced_linear = EnhancedLinear(linear_layer, activation, dropout_prob)
+
+    assert len(list(enhanced_linear.parameters())) == len(list(linear_layer.parameters())) + len(list(activation.parameters()))
+
+def test_forward_enhanced_linear_no_dropout():
+
+    enhanced_linear = EnhancedLinear(nn.Linear(10, 3))
+
+    x = torch.rand(size=(80, 10))
+    y = enhanced_linear(x)
+    assert y.shape[1] == 3
+    assert y.shape[0] == x.shape[0]
+
+def test_forward_enhanced_linear_dropout():
+
+    enhanced_linear = EnhancedLinear(nn.Linear(10, 3), dropout=0.5)
+
+    x = torch.rand(size=(80, 10))
+    y = enhanced_linear(x)
+    assert y.shape[1] == 3
+    assert y.shape[0] == x.shape[0]