<jupyter_start><jupyter_text><jupyter_code>!pip3 install torch
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
input_size =28*28
num_classes =10
num_epochs=5
batch_size=100
learning_rate=0.001

train_dataset = torchvision.datasets.MNIST(root='../../data', 
                                           train=True, 
                                           transform=transforms.ToTensor(),
                                           download=True)

test_dataset = torchvision.datasets.MNIST(root='../../data', 
                                          train=False, 
                                          transform=transforms.ToTensor())

# Data loader (input pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
                                           batch_size=batch_size, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
                                          batch_size=batch_size, 
                                           shuffle=False)
model= nn.Linear(input_size, num_classes)
criterion=nn.CrossEntropyLoss()
optimizer=torch.optim.SGD(model.parameters(), lr=learning_rate)
total_step=len(train_loader)
for epoch in range(num_epochs):
  for i, (images, labels) in enumerate(train_loader):
    images=images.reshape(-1,input_size)
    outputs=model(images)
    loss=criterion(outputs,labels)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if(i+1) %100 ==0:
      print ('Epoch [{}/{}, Step[{}/{}],Loss:{:.4f}'.format(epoch+1,num_epochs,i+1,total_step,loss.item()))
with torch.no_grad():
  correct=0
  total =0
  for images, labels in test_loader:
      images= images.reshape(-1, input_size)
      outputs=model(images)
      _,predicted=torch.max(outputs.data,1)
      total+=labels.size(0)
      correct+=(predicted==labels).sum()
  print('Accuracy of the model on the 10000 test images: {}%'.format(100*correct/total))

torch.save(model.state_dict(), 'model.ckpt')<jupyter_output><empty_output>
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