diff --git a/untitled.txt b/untitled.txt
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+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch import optim
+
+import torchvision
+from matplotlib import pyplot as plt
+import utils
+
+def plot_curve(data):
+    fig = plt.figure()
+    plt.plot(range(len(data)), data, color='blue')
+    plt.legend(['value'], loc='upper right')
+    plt.xlabel('step')
+    plt.ylabel('value')
+    plt.show()
+
+
+def plot_image(img, label, name):
+
+    fig = plt.figure()
+    for i in range(6):
+        plt.subplot(2, 3, i+1)
+        plt.tight_layout()
+        plt.imshow(img[i, 0]*0.3081+0.1307, cmap='gray', interpolation='none')
+        plt.title("{}: {}".format(name,label[i].item()))
+        plt.xticks([])
+        plt.yticks([])
+    plt.show()
+
+def one_hot(label, depth=10):
+    out = torch.zeros(label.size(0), depth)
+    idx = torch.LongTensor(label).view(-1, 1)
+    out.scatter_(dim=1, index=idx, value=1)
+    return out
+
+batch_size=512
+train_loader=torch.utils.data.DataLoader(torchvision.datasets.MNIST('mnist_data',train=True,download=True,transform=torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.1307,),(0.3081,))])),batch_size=batch_size,shuffle=True)
+
+test_loader=torch.utils.data.DataLoader(torchvision.datasets.MNIST('mnist_data/',train=False,download=True,transform=torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize((0.1307,),(0.3081,))])),batch_size=batch_size,shuffle=True)
+
+x,y=next(iter(train_loader))
+print(x.shape,y.shape,x.min(),x.max())
+plot_image(x,y,'image_sample')
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net,self).__init__()
+        self.fc1=nn.Linear(28*28,256)
+        self.fc2=nn.Linear(256,64)
+        self.fc3=nn.Linear(64,10)
+    
+    def forward(self,x):
+        x=F.relu(self.fc1(x))
+        x=F.relu(self.fc2(x))
+        x=self.fc3(x)
+        return x
+    
+net=Net()
+optimizer=optim.SGD(net.parameters(),lr=0.01,momentum=0.9)
+train_loss=[]
+for epoch in range(3):
+    for batch_idx,(x,y) in enumerate(train_loader):
+        x=x.view(x.size(0),28*28)
+        out=net(x)
+        y_onehot=one_hot(y)
+        loss=F.mse_loss(out,y_onehot)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        train_loss.append(loss.item())
+        
+        if batch_idx %10==0:
+            print(epoch,batch_idx,loss.item())
+
+plot_curve(train_loss)
+
+total_correct=0
+for x,y in test_loader:
+    x=x.view(x.size(0),28*28)
+    out=net(x)
+    pred=out.argmax(dim=1)
+    correct=pred.eq(y).sum().float().item()
+    total_correct+=correct
+total_num=len(test_loader.dataset)
+acc=total_correct/total_num
+print("test acc:",acc)
+
+x,y=next(iter(test_loader))
+out=net(x.view(x.size(0),28*28))
+pred=out.argmax(dim=1)
+plot_image(x,pred,'test')
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