Use torch.nn.Parameter(List) in decode.

src/icosagon/decode.py

@@ -20,11 +20,11 @@ class DEDICOMDecoder(torch.nn.Module):
         self.keep_prob = keep_prob
         self.activation = activation
 
-        self.global_interaction = init_glorot(input_dim, input_dim)
-        self.local_variation = [
-            torch.flatten(init_glorot(input_dim, 1)) \
+        self.global_interaction = torch.nn.Parameter(init_glorot(input_dim, input_dim))
+        self.local_variation = torch.nn.ParameterList([
+            torch.nn.Parameter(torch.flatten(init_glorot(input_dim, 1))) \
                 for _ in range(num_relation_types)
-        ]
+        ])
 
     def forward(self, inputs_row, inputs_col, relation_index):
         inputs_row = dropout(inputs_row, self.keep_prob)
@@ -53,10 +53,10 @@ class DistMultDecoder(torch.nn.Module):
         self.keep_prob = keep_prob
         self.activation = activation
 
-        self.relation = [
-            torch.flatten(init_glorot(input_dim, 1)) \
+        self.relation = torch.nn.ParameterList([
+            torch.nn.Parameter(torch.flatten(init_glorot(input_dim, 1))) \
                 for _ in range(num_relation_types)
-        ]
+        ])
 
     def forward(self, inputs_row, inputs_col, relation_index):
         inputs_row = dropout(inputs_row, self.keep_prob)
@@ -83,10 +83,10 @@ class BilinearDecoder(torch.nn.Module):
         self.keep_prob = keep_prob
         self.activation = activation
 
-        self.relation = [
-            init_glorot(input_dim, input_dim) \
+        self.relation = torch.nn.ParameterList([
+            torch.nn.Parameter(init_glorot(input_dim, input_dim)) \
                 for _ in range(num_relation_types)
-        ]
+        ])
 
     def forward(self, inputs_row, inputs_col, relation_index):
         inputs_row = dropout(inputs_row, self.keep_prob)