Update app/Hackathon_setup/exp_recognition_model.py

app/Hackathon_setup/exp_recognition_model.py

@@ -16,17 +16,17 @@ classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5:
 class facExpRec(torch.nn.Module):
     def __init__(self, out_features=7):
         super().__init__()
-    
+
         self.conv1 = self.convlayer(in_channels=1, out_channels=64, kernel_size=3)
         self.conv2 = self.convlayer(in_channels=64, out_channels=128, kernel_size=5)
         self.conv3 = self.convlayer(in_channels=128, out_channels=512, kernel_size=3)
         self.conv4 = self.convlayer(in_channels=512, out_channels=512, kernel_size=3, max_pool=1)
-    
+
         self.fc1 = self.fclayer(512, 256)
         self.fc2 = self.fclayer(256, 512)
         self.fc3 = nn.Linear(512, out_features)
-      
-      def convlayer(self, in_channels, out_channels, kernel_size, max_pool=2):
+    
+    def convlayer(self, in_channels, out_channels, kernel_size, max_pool=2):
         return nn.Sequential(
             nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=1),
             nn.BatchNorm2d(out_channels),
@@ -34,17 +34,17 @@ class facExpRec(torch.nn.Module):
             nn.Dropout2d(),
             nn.MaxPool2d(kernel_size=max_pool),
         )
-    
-      def fclayer(self, in_features, out_features):
+
+    def fclayer(self, in_features, out_features):
         return nn.Sequential(
             nn.Linear(in_features, out_features),
             nn.BatchNorm1d(out_features),
             nn.Dropout1d(0.4),
             nn.ReLU(),
         )
+
     
-      
-      def forward(self, x):
+    def forward(self, x):
         x = self.conv1(x)
         x = self.conv2(x)
         x = self.conv3(x)