update RNN GAN example code

Author: keon

07-순차적인_데이터를_처리하는_RNN/sequence_to_sequence.ipynb

@@ -126,8 +126,7 @@
     }
    ],
    "source": [
-    "seq2seq = Seq2Seq(vocab_size, 16)\n",
-    "print(seq2seq)"
+    "seq2seq = Seq2Seq(vocab_size, 16)"
    ]
   },
   {

07-순차적인_데이터를_처리하는_RNN/sequence_to_sequence.py

@@ -70,7 +70,6 @@ def _init_state(self, batch_size=1):
 
 
 seq2seq = Seq2Seq(vocab_size, 16)
-print(seq2seq)
 
 
 criterion = nn.CrossEntropyLoss()

07-순차적인_데이터를_처리하는_RNN/text_classification.ipynb

@@ -194,8 +194,7 @@
    ],
    "source": [
     "model = BasicGRU(1, 256, vocab_size, 128, n_classes, 0.5).to(DEVICE)\n",
-    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
-    "print(model)"
+    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)"
    ]
   },
   {

07-순차적인_데이터를_처리하는_RNN/text_classification.py

@@ -104,7 +104,6 @@ def evaluate(model, val_iter):
 
 model = BasicGRU(1, 256, vocab_size, 128, n_classes, 0.5).to(DEVICE)
 optimizer = torch.optim.Adam(model.parameters(), lr=lr)
-print(model)
 
 
 best_val_loss = None