GROUPWORK_added comments

p1/net.py

@@ -10,7 +10,7 @@ from torch.nn.modules.dropout import Dropout
 from torch.nn.modules.pooling import MaxPool2d
 # import matplotlib.pyplot as plt
 
-## Basic Model
+## Basic MLP Network with 2 hidden layers
 class NN(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()
@@ -30,6 +30,7 @@ class NN(nn.Module):
         x = self.classifier(x)
         return x
 
+## VGG Convolutional Network with 2 hidden layers
 class CNN_VGG(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()
@@ -67,6 +68,7 @@ class CNN_VGG(nn.Module):
 
 #Problem 1 Part 2:
 ## 2 Networks: Classifier + Comparer (Weight Sharing)
+## MLP Network with 2 hidden layers for classification only
 class NN_Classification(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()
@@ -86,6 +88,7 @@ class NN_Classification(nn.Module):
         x2 = self.classifier(x2.view(-1, 196))
         return torch.cat((x1, x2), 1)
 
+## Convolutional Network with 2 hidden layers for classification only
 class CNN_Classification(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()
@@ -126,7 +129,7 @@ class CNN_Classification(nn.Module):
         x2 = self.classifier(x2)
         return torch.cat((x1, x2), 1)
 
-
+## MLP Network with 2 hidden layers for comparison only
 class MLP_Comparer(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()
@@ -145,11 +148,10 @@ class MLP_Comparer(nn.Module):
         )
 
     def forward(self, x):
-        # x = self.classifier(torch.cat((x1, x2), 1))
         x = self.classifier(x)
-        # return torch.cat((x1, x2), 1)
         return x
 
+## MLP Siamese Network with classification and comparison combined
 class NN_WS(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()
@@ -160,6 +162,7 @@ class NN_WS(nn.Module):
         x = self.comparer(self.classifier(x[:, 0], x[:,1]))
         return x
 
+## Convolutional Siamese Network with classification and comparison combined
 class CNN_WS(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()
@@ -167,12 +170,10 @@ class CNN_WS(nn.Module):
         self.comparer = MLP_Comparer(nb_hidden)
 
     def forward(self, x):
-        # x = self.classifier(torch.cat((x1, x2), 1))
         x = self.comparer(self.classifier(x[:, 0], x[:,1]))
-        # return torch.cat((x1, x2), 1)
         return x
 
-## NN WS+AL
+## MLP Siamese Network with classification and comparison combined + Auxiliary Loss
 class NN_WS_AL(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()
@@ -187,7 +188,7 @@ class NN_WS_AL(nn.Module):
         # keep both, for Auxiliary Loss
         return out, res
 
-## CNN WS + AL
+## Convolutional Siamese Network with classification and comparison combined + Auxiliary Loss
 class CNN_WS_AL(nn.Module):
     def __init__(self, nb_hidden):
         super().__init__()