fix bug

Author: L1aoXingyu

05-Recurrent Neural Network/recurrent_network.py

@@ -13,12 +13,11 @@
 num_epoches = 20
 
 # 下载训练集 MNIST 手写数字训练集
-train_dataset = datasets.MNIST(root='./data', train=True,
-                               transform=transforms.ToTensor(),
-                               download=True)
+train_dataset = datasets.MNIST(
+    root='./data', train=True, transform=transforms.ToTensor(), download=True)
 
-test_dataset = datasets.MNIST(root='./data', train=False,
-                              transform=transforms.ToTensor())
+test_dataset = datasets.MNIST(
+    root='./data', train=False, transform=transforms.ToTensor())
 
 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
 test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
@@ -30,15 +29,14 @@ def __init__(self, in_dim, hidden_dim, n_layer, n_class):
         super(Rnn, self).__init__()
         self.n_layer = n_layer
         self.hidden_dim = hidden_dim
-        self.lstm = nn.LSTM(in_dim, hidden_dim, n_layer,
-                            batch_first=True)
+        self.lstm = nn.LSTM(in_dim, hidden_dim, n_layer, batch_first=True)
         self.classifier = nn.Linear(hidden_dim, n_class)
 
     def forward(self, x):
         # h0 = Variable(torch.zeros(self.n_layer, x.size(1),
-                                #   self.hidden_dim)).cuda()
+        #   self.hidden_dim)).cuda()
         # c0 = Variable(torch.zeros(self.n_layer, x.size(1),
-                                #   self.hidden_dim)).cuda()
+        #   self.hidden_dim)).cuda()
         out, _ = self.lstm(x)
         out = out[:, -1, :]
         out = self.classifier(out)
@@ -55,8 +53,8 @@ def forward(self, x):
 
 # 开始训练
 for epoch in range(num_epoches):
-    print('epoch {}'.format(epoch+1))
-    print('*'*10)
+    print('epoch {}'.format(epoch + 1))
+    print('*' * 10)
     running_loss = 0.0
     running_acc = 0.0
     for i, data in enumerate(train_loader, 1):
@@ -87,15 +85,11 @@ def forward(self, x):
 
         if i % 300 == 0:
             print('[{}/{}] Loss: {:.6f}, Acc: {:.6f}'.format(
-                epoch+1, num_epoches,
-                running_loss/(batch_size*i),
-                running_acc/(batch_size*i)
-            ))
+                epoch + 1, num_epoches, running_loss / (batch_size * i),
+                running_acc / (batch_size * i)))
     print('Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}'.format(
-        epoch+1,
-        running_loss/(len(train_dataset)),
-        running_acc/(len(train_dataset))
-    ))
+        epoch + 1, running_loss / (len(train_dataset)), running_acc / (len(
+            train_dataset))))
     model.eval()
     eval_loss = 0
     eval_acc = 0
@@ -111,18 +105,16 @@ def forward(self, x):
             img = Variable(img, volatile=True).cuda()
             label = Variable(label, volatile=True).cuda()
         else:
-            img = Variabel(img, volatile=True)
+            img = Variable(img, volatile=True)
             label = Variable(label, volatile=True)
         out = model(img)
         loss = criterion(out, label)
-        eval_loss += loss.data[0]*label.size(0)
+        eval_loss += loss.data[0] * label.size(0)
         _, pred = torch.max(out, 1)
         num_correct = (pred == label).sum()
         eval_acc += num_correct.data[0]
-    print('Test Loss: {:.6f}, Acc: {:.6f}'.format(
-        eval_loss/(len(test_dataset)),
-        eval_acc/(len(test_dataset))
-    ))
+    print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
+        test_dataset)), eval_acc / (len(test_dataset))))
     print()
 
 # 保存模型