diff --git a/discriminator.py b/discriminator.py
index a185f96..4c088f0 100644
--- a/discriminator.py
+++ b/discriminator.py
@@ -23,6 +23,7 @@ class Model(nn.Module):
         output, state = self.lstm(x, prev_state)
         logits = self.fc(output)
         val = self.out(logits)
+        #print(str(logits.item())+" > "+str(val.item()))
         return val, state
 
     def init_state(self, sequence_length):
@@ -31,17 +32,18 @@ class Model(nn.Module):
 
 def train(model, seq_len=16*64):
     tid = str(int(random.random()*99999)).zfill(5)
+    print("[i] I am "+str(tid))
     ltLoss = 50
     lltLoss = 51
     model.train()
 
     criterion = nn.BCELoss()
-    optimizer = optim.Adam(model.parameters(), lr=0.1)
+    optimizer = optim.Adam(model.parameters(), lr=0.0001)
 
     for epoch in range(1024):
         state_h, state_c = model.init_state(seq_len)
 
-        blob, y = shark.getSample(seq_len, epoch%2)
+        blob, y = shark.getSample(min(seq_len, 16*(epoch+1)), epoch%2)
         optimizer.zero_grad()
         for i in range(len(blob)):
             x = torch.tensor([[[float(d) for d in bin(blob[i])[2:].zfill(8)]]], dtype=torch.float32)
@@ -54,9 +56,10 @@ def train(model, seq_len=16*64):
             loss.backward()
             optimizer.step()
 
+        correct = round(y_pred.item()) == y
         ltLoss = ltLoss*0.9 + 0.1*loss.item()
         lltLoss = lltLoss*0.9 + 0.1*ltLoss
-        print({ 'epoch': epoch, 'loss': loss.item(), 'ltLoss': ltLoss})
+        print({ 'epoch': epoch, 'loss': loss.item(), 'ltLoss': ltLoss, 'correct?': correct})
         if ltLoss < 0.20 and lltLoss < 0.225:
             print("[*] Hell Yeah! Poccing! Got sup")
         if epoch % 8 == 0:
diff --git a/shark.py b/shark.py
index 4549590..6b9b986 100644
--- a/shark.py
+++ b/shark.py
@@ -21,6 +21,7 @@ def enc(plaintext, key, iv):
         m.update(xor(key, iv + i.to_bytes(bs, byteorder='big')))
         k = m.digest()
         ciphertext += xor(k, plaintext[bs*i:][:bs].ljust(bs, b'0'))
+        iv = (int.from_bytes(iv, byteorder='big')+1).to_bytes(bs, byteorder='big')
     return ciphertext
 
 def dec(ciphertext, key, iv):