pytorch教程實(shí)現(xiàn)mnist手寫(xiě)數(shù)字識(shí)別代碼示例
發(fā)布日期：2022-01-20 15:02 | 文章來(lái)源：CSDN
1.構(gòu)建網(wǎng)絡(luò)
nn.Moudle是pytorch官方指定的編寫(xiě)Net模塊，在init函數(shù)中添加需要使用的層，在foeword中定義網(wǎng)絡(luò)流向。
下面詳細(xì)解釋各層：
conv1層：輸入channel = 1 ，輸出chanael = 10，濾波器5*5
maxpooling = 2*2
conv2層：輸入channel = 10 ，輸出chanael = 20，濾波器5*5,
dropout
maxpooling = 2*2
fc1層：輸入320 個(gè)神經(jīng)單元，輸出50個(gè)神經(jīng)單元
fc1層：輸入50個(gè)神經(jīng)單元 ，輸出10個(gè)神經(jīng)單元
class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 10, kernel_size=5) self.conv2 = nn.Conv2d(10, 20, kernel_size=5) self.conv2_drop = nn.Dropout2d() self.fc1 = nn.Linear(320, 50) self.fc2 = nn.Linear(50, 10) def forward(self, x): #x.size() = 28*28*1 x = F.relu(F.max_pool2d(self.conv1(x), 2)) x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) #x.size() =12*12*10 x = x.view(-1, 320) #x.size() =1*320 x = F.relu(self.fc1(x)) x = F.dropout(x, training=self.training) x = self.fc2(x) return F.log_softmax(x, dim=1)
2.編寫(xiě)訓(xùn)練代碼
model = Net() #調(diào)用寫(xiě)好的網(wǎng)絡(luò)
if args.cuda: #如果有GPU使用CPU
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)#設(shè)置SGD隨機(jī)梯度下降算法
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()#梯度初始化為O
output = model(data)
loss = F.nll_loss(output, target) #簡(jiǎn)歷loss function
loss.backward()#反向傳播，計(jì)算梯度
optimizer.step() #更新權(quán)重
if batch_idx % args.log_interval == 0: #輸出信息
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data[0]))
3.編寫(xiě)測(cè)試代碼
def test():
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).long().cpu().sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
4.指導(dǎo)程序train和test
for epoch in range(1, args.epochs + 1): train(epoch) #訓(xùn)練N個(gè)epoch test() #檢驗(yàn)在測(cè)試集上的表現(xiàn)
5.完整代碼
# -*- coding: utf-8 -*-
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
print (x.size())
x = F.relu(F.max_pool2d(self.conv1(x), 2))
print(x.size())
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
print(x.size())
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x, dim=1)
model = Net() #調(diào)用寫(xiě)好的網(wǎng)絡(luò)
if args.cuda: #如果有GPU使用CPU
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)#設(shè)置SGD隨機(jī)梯度下降算法
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()#梯度初始化為O
output = model(data)
loss = F.nll_loss(output, target) #簡(jiǎn)歷loss function
loss.backward()#反向傳播，計(jì)算梯度
optimizer.step() #更新權(quán)重
if batch_idx % args.log_interval == 0: #輸出信息
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data[0]))
def test():
model.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(target.data.view_as(pred)).long().cpu().sum()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
for epoch in range(1, args.epochs + 1):
train(epoch)
test()
以上就是pytorch教程實(shí)現(xiàn)mnist手寫(xiě)數(shù)字識(shí)別代碼示例的詳細(xì)內(nèi)容，更多關(guān)于pytorch實(shí)現(xiàn)mnist手寫(xiě)數(shù)字識(shí)別的資料請(qǐng)關(guān)注本站其它相關(guān)文章！
版權(quán)聲明：本站文章來(lái)源標(biāo)注為YINGSOO的內(nèi)容版權(quán)均為本站所有，歡迎引用、轉(zhuǎn)載，請(qǐng)保持原文完整并注明來(lái)源及原文鏈接。禁止復(fù)制或仿造本網(wǎng)站，禁止在非maisonbaluchon.cn所屬的服務(wù)器上建立鏡像，否則將依法追究法律責(zé)任。本站部分內(nèi)容來(lái)源于網(wǎng)友推薦、互聯(lián)網(wǎng)收集整理而來(lái)，僅供學(xué)習(xí)參考，不代表本站立場(chǎng)，如有內(nèi)容涉嫌侵權(quán)，請(qǐng)聯(lián)系alex-e#qq.com處理。
相關(guān)文章
關(guān)注官方微信