diff --git a/tutorial-contents/306_optimizer.py b/tutorial-contents/306_optimizer.py
index b961866..9b5e4ff 100644
--- a/tutorial-contents/306_optimizer.py
+++ b/tutorial-contents/306_optimizer.py
@@ -43,43 +43,44 @@ class Net(torch.nn.Module):
         x = self.predict(x)             # linear output
         return x
 
-# different nets
-net_SGD         = Net()
-net_Momentum    = Net()
-net_RMSprop     = Net()
-net_Adam        = Net()
-nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
+if __name__ == '__main__':
+    # different nets
+    net_SGD         = Net()
+    net_Momentum    = Net()
+    net_RMSprop     = Net()
+    net_Adam        = Net()
+    nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
 
-# different optimizers
-opt_SGD         = torch.optim.SGD(net_SGD.parameters(), lr=LR)
-opt_Momentum    = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)
-opt_RMSprop     = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)
-opt_Adam        = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))
-optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]
+    # different optimizers
+    opt_SGD         = torch.optim.SGD(net_SGD.parameters(), lr=LR)
+    opt_Momentum    = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)
+    opt_RMSprop     = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)
+    opt_Adam        = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))
+    optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]
 
-loss_func = torch.nn.MSELoss()
-losses_his = [[], [], [], []]   # record loss
+    loss_func = torch.nn.MSELoss()
+    losses_his = [[], [], [], []]   # record loss
 
-# training
-for epoch in range(EPOCH):
-    print('Epoch: ', epoch)
-    for step, (batch_x, batch_y) in enumerate(loader):          # for each training step
-        b_x = Variable(batch_x)
-        b_y = Variable(batch_y)
+    # training
+    for epoch in range(EPOCH):
+        print('Epoch: ', epoch)
+        for step, (batch_x, batch_y) in enumerate(loader):          # for each training step
+            b_x = Variable(batch_x)
+            b_y = Variable(batch_y)
 
-        for net, opt, l_his in zip(nets, optimizers, losses_his):
-            output = net(b_x)              # get output for every net
-            loss = loss_func(output, b_y)  # compute loss for every net
-            opt.zero_grad()                # clear gradients for next train
-            loss.backward()                # backpropagation, compute gradients
-            opt.step()                     # apply gradients
-            l_his.append(loss.data[0])     # loss recoder
+            for net, opt, l_his in zip(nets, optimizers, losses_his):
+                output = net(b_x)              # get output for every net
+                loss = loss_func(output, b_y)  # compute loss for every net
+                opt.zero_grad()                # clear gradients for next train
+                loss.backward()                # backpropagation, compute gradients
+                opt.step()                     # apply gradients
+                l_his.append(loss.data[0])     # loss recoder
 
-labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
-for i, l_his in enumerate(losses_his):
-    plt.plot(l_his, label=labels[i])
-plt.legend(loc='best')
-plt.xlabel('Steps')
-plt.ylabel('Loss')
-plt.ylim((0, 0.2))
-plt.show()
+    labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
+    for i, l_his in enumerate(losses_his):
+        plt.plot(l_his, label=labels[i])
+    plt.legend(loc='best')
+    plt.xlabel('Steps')
+    plt.ylabel('Loss')
+    plt.ylim((0, 0.2))
+    plt.show()