define activation function at the beginning

BIMSBbioinfo · Apr 4, 2024 · f2db9d6 · f2db9d6
1 parent 75f5b6e
commit f2db9d6
Showing 1 changed file with 7 additions and 7 deletions.
diff --git a/flexynesis/modules.py b/flexynesis/modules.py
@@ -19,19 +19,19 @@ class Encoder(nn.Module):
     def __init__(self, input_dim, hidden_dims, latent_dim):
         super(Encoder, self).__init__()
 
-        self.LeakyReLU = nn.LeakyReLU(0.2)
+        self.act = nn.LeakyReLU(0.2)
 
         hidden_layers = []
 
         hidden_layers.append(nn.Linear(input_dim, hidden_dims[0]))
         nn.init.xavier_uniform_(hidden_layers[-1].weight)
-        hidden_layers.append(self.LeakyReLU)
+        hidden_layers.append(self.act)
         hidden_layers.append(nn.BatchNorm1d(hidden_dims[0]))
 
         for i in range(len(hidden_dims)-1):
             hidden_layers.append(nn.Linear(hidden_dims[i], hidden_dims[i+1]))
             nn.init.xavier_uniform_(hidden_layers[-1].weight)
-            hidden_layers.append(self.LeakyReLU)
+            hidden_layers.append(self.act)
             hidden_layers.append(nn.BatchNorm1d(hidden_dims[i+1]))
 
         self.hidden_layers = nn.Sequential(*hidden_layers)
@@ -68,19 +68,19 @@ class Decoder(nn.Module):
     def __init__(self, latent_dim, hidden_dims, output_dim):
         super(Decoder, self).__init__()
 
-        self.LeakyReLU = nn.LeakyReLU(0.2)
+        self.act = nn.LeakyReLU(0.2)
 
         hidden_layers = []
 
         hidden_layers.append(nn.Linear(latent_dim, hidden_dims[0]))
         nn.init.xavier_uniform_(hidden_layers[-1].weight)
-        hidden_layers.append(self.LeakyReLU)
+        hidden_layers.append(self.act)
         hidden_layers.append(nn.BatchNorm1d(hidden_dims[0]))
 
         for i in range(len(hidden_dims) - 1):
             hidden_layers.append(nn.Linear(hidden_dims[i], hidden_dims[i + 1]))
             nn.init.xavier_uniform_(hidden_layers[-1].weight)
-            hidden_layers.append(self.LeakyReLU)
+            hidden_layers.append(self.act)
             hidden_layers.append(nn.BatchNorm1d(hidden_dims[i+1]))
 
         self.hidden_layers = nn.Sequential(*hidden_layers)
@@ -99,7 +99,7 @@ def forward(self, x):
             x_hat (torch.Tensor): The reconstructed output tensor.
         """
         h = self.hidden_layers(x)
-        x_hat = torch.tanh(self.FC_output(h))
+        x_hat = torch.sigmoid(self.FC_output(h))
         return x_hat