Pytorch入门之mnist分类实例

#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'denny'
__time__ = '2017-9-9 9:03'

import torch
import torchvision
from torch.autograd import Variable
import torch.utils.data.dataloader as Data

train_data = torchvision.datasets.MNIST(
 './mnist',train=True,transform=torchvision.transforms.ToTensor(),download=True
)
test_data = torchvision.datasets.MNIST(
 './mnist',train=False,transform=torchvision.transforms.ToTensor()
)
print("train_data:",train_data.train_data.size())
print("train_labels:",train_data.train_labels.size())
print("test_data:",test_data.test_data.size())

train_loader = Data.DataLoader(dataset=train_data,batch_size=64,shuffle=True)
test_loader = Data.DataLoader(dataset=test_data,batch_size=64)


class Net(torch.nn.Module):
 def __init__(self):
 super(Net,self).__init__()
 self.conv1 = torch.nn.Sequential(
  torch.nn.Conv2d(1,32,3,1,1),torch.nn.ReLU(),torch.nn.MaxPool2d(2))
 self.conv2 = torch.nn.Sequential(
  torch.nn.Conv2d(32,64,torch.nn.MaxPool2d(2)
 )
 self.conv3 = torch.nn.Sequential(
  torch.nn.Conv2d(64,torch.nn.MaxPool2d(2)
 )
 self.dense = torch.nn.Sequential(
  torch.nn.Linear(64 * 3 * 3,128),torch.nn.Linear(128,10)
 )

 def forward(self,x):
 conv1_out = self.conv1(x)
 conv2_out = self.conv2(conv1_out)
 conv3_out = self.conv3(conv2_out)
 res = conv3_out.view(conv3_out.size(0),-1)
 out = self.dense(res)
 return out


model = Net()
print(model)

optimizer = torch.optim.Adam(model.parameters())
loss_func = torch.nn.CrossEntropyLoss()

for epoch in range(10):
 print('epoch {}'.format(epoch + 1))
 # training-----------------------------
 train_loss = 0.
 train_acc = 0.
 for batch_x,batch_y in train_loader:
 batch_x,batch_y = Variable(batch_x),Variable(batch_y)
 out = model(batch_x)
 loss = loss_func(out,batch_y)
 train_loss += loss.data[0]
 pred = torch.max(out,1)[1]
 train_correct = (pred == batch_y).sum()
 train_acc += train_correct.data[0]
 optimizer.zero_grad()
 loss.backward()
 optimizer.step()
 print('Train Loss: {:.6f},Acc: {:.6f}'.format(train_loss / (len(
 train_data)),train_acc / (len(train_data))))

 # evaluation--------------------------------
 model.eval()
 eval_loss = 0.
 eval_acc = 0.
 for batch_x,batch_y in test_loader:
 batch_x,batch_y = Variable(batch_x,volatile=True),Variable(batch_y,volatile=True)
 out = model(batch_x)
 loss = loss_func(out,batch_y)
 eval_loss += loss.data[0]
 pred = torch.max(out,1)[1]
 num_correct = (pred == batch_y).sum()
 eval_acc += num_correct.data[0]
 print('Test Loss: {:.6f},Acc: {:.6f}'.format(eval_loss / (len(
 test_data)),eval_acc / (len(test_data))))