Remove extra arguments.

Kumar-Tarun · May 16, 2020 · adc705e · adc705e
1 parent 050589e
commit adc705e
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Showing 3 changed files with 5 additions and 7 deletions.
diff --git a/main_toefl.py b/main_toefl.py
@@ -174,9 +174,8 @@
                                 char_vocab_size=len(c2idx), char_embed_dim=50,
                                 dropout1=0.5, dropout2=0, dropout3=0.1, name='toefl')
 
-Transformer_model = Transformer1(emb=300+1024+250+30, k=300, heads=1, depth=1, 
-                                seq_length=128, num_tokens=13845, num_classes=2,
-                                char_vocab_size=len(c2idx), char_embed_dim=50, name='toefl')
+Transformer_model = Transformer(emb=300+1024+250+30, k=300, heads=1, depth=1, 
+                                num_classes=2, char_vocab_size=len(c2idx), char_embed_dim=50, name='toefl')
 
 transformer_parameters = sum(p.numel() for p in Transformer_model.parameters() if p.requires_grad)
 rnn_parameters = sum(p.numel() for p in RNNseq_model.parameters() if p.requires_grad)

diff --git a/main_vua.py b/main_vua.py
@@ -178,8 +178,8 @@
                                 dropout1=0.5, dropout2=0, dropout3=0.1)
 
 Transformer_model = Transformer(emb=300+1024+250+30, k=300, heads=1, depth=1, 
-                                seq_length=128, num_tokens=13845, num_classes=2,
-                                char_vocab_size=len(c2idx), char_embed_dim=50)
+                                num_classes=2, char_vocab_size=len(c2idx), 
+                                char_embed_dim=50)
 
 transformer_parameters = sum(p.numel() for p in Transformer_model.parameters() if p.requires_grad)
 rnn_parameters = sum(p.numel() for p in RNNseq_model.parameters() if p.requires_grad)

diff --git a/model.py b/model.py
@@ -263,10 +263,9 @@ def forward(self, x):
     return {0:x, 1:pad_amounts}
 
 class Transformer(nn.Module):
-    def __init__(self, emb, k, heads, depth, seq_length, num_tokens, num_classes, char_vocab_size, char_embed_dim, name='vua'):
+    def __init__(self, emb, k, heads, depth, num_classes, char_vocab_size, char_embed_dim, name='vua'):
         super(Transformer1, self).__init__()
 
-        self.num_tokens = num_tokens
         self.char_emb = CharCNN(char_vocab_size, char_embed_dim)
         self.name = name