Upped lr and dropout

discriminator.py

@@ -13,7 +13,7 @@ class Model(nn.Module):
             input_size=8,
             hidden_size=16,
             num_layers=3,
-            dropout=0.05,
+            dropout=0.1,
         )
         self.fc = nn.Linear(16, 1)
         self.out = nn.Sigmoid()
@@ -32,7 +32,7 @@ def train(model, seq_len=16*64):
     model.train()
 
     criterion = nn.BCELoss()
-    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    optimizer = optim.Adam(model.parameters(), lr=0.01)
 
     for epoch in range(1024):
         state_h, state_c = model.init_state(seq_len)