fix float bug for python 2.7

Author: L1aoXingyu

02-Logistic Regression/Logistic_Regression.py

@@ -14,12 +14,11 @@
 num_epoches = 100
 
 # 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)
@@ -36,7 +35,7 @@ def forward(self, x):
         return out
 
 
-model = Logstic_Regression(28*28, 10)  # 图片大小是28x28
+model = Logstic_Regression(28 * 28, 10)  # 图片大小是28x28
 use_gpu = torch.cuda.is_available()  # 判断是否有GPU加速
 if use_gpu:
     model = model.cuda()
@@ -46,8 +45,8 @@ 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):
@@ -73,18 +72,14 @@ 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
+    eval_loss = 0.
+    eval_acc = 0.
     for data in test_loader:
         img, label = data
         img = img.view(img.size(0), -1)
@@ -96,14 +91,12 @@ def forward(self, x):
             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

03-Neural Network/neural_network.py

@@ -13,17 +13,14 @@
 num_epoches = 50
 
 # 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)
+train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
 
 
 # 定义简单的前馈神经网络
@@ -41,16 +38,16 @@ def forward(self, x):
         return x
 
 
-model = Neuralnetwork(28*28, 300, 100, 10)
+model = Neuralnetwork(28 * 28, 300, 100, 10)
 if torch.cuda.is_available():
     model = model.cuda()
 
 criterion = nn.CrossEntropyLoss()
 optimizer = optim.SGD(model.parameters(), lr=learning_rate)
 
 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):
@@ -76,18 +73,14 @@ 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
+    eval_loss = 0.
+    eval_acc = 0.
     for data in test_loader:
         img, label = data
         img = img.view(img.size(0), -1)
@@ -99,14 +92,12 @@ def forward(self, x):
             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