use sigmoid for attention

layers/attention.py

@@ -167,12 +167,12 @@ class AttentionRNNCell(nn.Module):
                 alignment[:, :back_win] = -float("inf")
             if front_win < memory.shape[1]:
                 alignment[:, front_win:] = -float("inf")
-        # Update the window
-        self.win_idx = torch.argmax(alignment,1).long()[0].item()
+            # Update the window
+            self.win_idx = torch.argmax(alignment,1).long()[0].item()
         # Normalize context weight
-        alignment = F.softmax(alignment, dim=-1)
+        # alignment = F.softmax(alignment, dim=-1)
         # alignment = 5 * alignment
-        # alignment = torch.sigmoid(alignment) / torch.sigmoid(alignment).sum(dim=1).unsqueeze(1)
+        alignment = torch.sigmoid(alignment) / torch.sigmoid(alignment).sum(dim=1).unsqueeze(1)
         # Attention context vector
         # (batch, 1, dim)
         # c_i = \sum_{j=1}^{T_x} \alpha_{ij} h_j