[MNT] minor modification

abl/reasoning/__init__.py

@@ -1,2 +1,2 @@
 from .reasoner import ReasonerBase
-from .kb import KBBase
+from .kb import KBBase, prolog_KB

abl/reasoning/reasoner.py

@@ -28,6 +28,9 @@ class ReasonerBase(abc.ABC):
             )
         else:
             self.mapping = mapping
+        self.set_remapping()
+    
+    def set_remapping(self):
         self.remapping = dict(zip(self.mapping.values(), self.mapping.keys()))
 
     def _get_cost_list(self, pseudo_label, pred_res_prob, candidates):

examples/models/nn.py

@@ -70,7 +70,7 @@ class SymbolNet(nn.Module):
         num_features = 64 * (image_size[0] // 4 - 1) * (image_size[1] // 4 - 1)
         self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU())
         self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU())
-        self.fc3 = nn.Linear(84, num_classes)
+        self.fc3 = nn.Sequential(nn.Linear(84, num_classes), nn.Softmax(dim=1))
 
     def forward(self, x):
         x = self.conv1(x)
@@ -86,6 +86,7 @@ class SymbolNetAutoencoder(nn.Module):
     def __init__(self, num_classes=4, image_size=(28, 28, 1)):
         super(SymbolNetAutoencoder, self).__init__()
         self.base_model = SymbolNet(num_classes, image_size)
+        self.softmax = nn.Softmax(dim=1)
         self.fc1 = nn.Sequential(nn.Linear(num_classes, 100), nn.ReLU())
         self.fc2 = nn.Sequential(
             nn.Linear(100, image_size[0] * image_size[1]), nn.ReLU()
@@ -93,7 +94,7 @@ class SymbolNetAutoencoder(nn.Module):
 
     def forward(self, x):
         x = self.base_model(x)
-        x = nn.Softmax(x, dim=1)
+        # x = self.softmax(x)
         x = self.fc1(x)
         x = self.fc2(x)
         return x