本文实例为大家分享了Pytorch入门之mnist分类的具体代码，供大家参考，具体内容如下

创新互联建站专注于企业成都全网营销推广、网站重做改版、项城网站定制设计、自适应品牌网站建设、H5建站、成都商城网站开发、集团公司官网建设、成都外贸网站建设、高端网站制作、响应式网页设计等建站业务，价格优惠性价比高，为项城等各大城市提供网站开发制作服务。

#!/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, 3, 1, 1),
  torch.nn.ReLU(),
  torch.nn.MaxPool2d(2)
 )
 self.conv3 = torch.nn.Sequential(
  torch.nn.Conv2d(64, 64, 3, 1, 1),
  torch.nn.ReLU(),
  torch.nn.MaxPool2d(2)
 )
 self.dense = torch.nn.Sequential(
  torch.nn.Linear(64 * 3 * 3, 128),
  torch.nn.ReLU(),
  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))))

另外有需要云服务器可以了解下创新互联scvps.cn，海内外云服务器15元起步，三天无理由+7*72小时售后在线，公司持有idc许可证，提供“云服务器、裸金属服务器、高防服务器、香港服务器、美国服务器、虚拟主机、免备案服务器”等云主机租用服务以及企业上云的综合解决方案，具有“安全稳定、简单易用、服务可用性高、性价比高”等特点与优势，专为企业上云打造定制，能够满足用户丰富、多元化的应用场景需求。

分享文章：Pytorch入门之mnist分类实例-创新互联
网页路径：http://scyanting.com/article/cccseg.html