Add TAR and AR (#172)

* Add TAR and AR

common/trainers/reuters_trainer.py

@@ -58,7 +58,9 @@ class ReutersTrainer(Trainer):
                 loss = F.binary_cross_entropy_with_logits(scores, batch.label.float())
 
             if hasattr(self.model, 'TAR') and self.model.TAR:
-                loss = loss + (rnn_outs[1:] - rnn_outs[:-1]).pow(2).mean()
+                loss = loss + self.model.TAR*(rnn_outs[1:] - rnn_outs[:-1]).pow(2).mean()
+            if hasattr(self.model, 'AR') and self.model.AR:
+                loss = loss + self.model.AR*(rnn_outs[:]).pow(2).mean()
 
             n_total += batch.batch_size
             train_acc = 100. * n_correct / n_total

lstm_regularization/args.py

@@ -33,7 +33,8 @@ def get_args():
                         default=os.path.join(os.pardir, 'Castor-data', 'embeddings', 'word2vec'))
     parser.add_argument('--word_vectors_file', help='word vectors filename', default='GoogleNews-vectors-negative300.txt')
     parser.add_argument('--trained_model', type=str, default="")
-    parser.add_argument('--TAR', action='store_true')
+    parser.add_argument('--TAR', type=float, default=0.0, help="Hyperparameter for Temporal Activation Regularization")
+    parser.add_argument('--AR', type=float, default=0.0, help="Hyperparameter for Activation Regularization")
     parser.add_argument('--weight_decay', type=float, default=0)
     parser.add_argument('--beta_ema', type=float, default = 0, help="for temporal averaging")
     parser.add_argument('--wdrop', type=float, default=0.0, help="for weight-drop")

lstm_regularization/model.py

@@ -17,6 +17,7 @@ class LSTMBaseline(nn.Module):
         self.has_bottleneck_layer = config.bottleneck_layer
         self.mode = config.mode
         self.TAR = config.TAR
+        self.AR = config.AR
         self.beta_ema = config.beta_ema  ## Temporal averaging
         self.wdrop = config.wdrop ## Weight dropping
         self.embed_droprate = config.embed_droprate ## Embedding dropout 
@@ -84,11 +85,11 @@ class LSTMBaseline(nn.Module):
         if self.has_bottleneck_layer:
             x = F.relu(self.fc1(x))
             # x = self.dropout(x)
-            if self.TAR:
+            if self.TAR or self.AR:
                 return self.fc2(x), rnn_outs.permute(1,0,2)
             return self.fc2(x)
         else:
-            if self.TAR:
+            if self.TAR or self.AR:
                 return self.fc1(x), rnn_outs.permute(1,0,2)
             return self.fc1(x)