pytorch4学习4:训练一个分类器
2018-05-07 本文已影响65人
bdb87b292706
现在已经知道了一个网络的结构搭建,正向反向传播以及梯度下降的训练方法。那么如何读入一组数据?
首先使用现有的python工具包将训练数据读入存为numpy的形式,之后将numpy转换为pytorch使用的tensor:
-图像:使用Pillow或者OpenCV
-音频:使用scipy或者librosa
在pytorch中提供了一个数据库的读取包torchvision,可以读取并使用一些常用的数据库,例如imagenet,cifar10,mnist等。
下面将以cifar10为例,介绍如何训练一个分类器:
首先使用torchvision工具包下载并调用cifar10数据库:(这一部分将在后面详细介绍,就不在此展开讲)
import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
接下来,定义我们的分类网络:
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
下面定义一下loss的计算,和反向传播的方法:
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
接下来,开始我们的训练吧:
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
接下来在测试集上进行一下测试:
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
可以得到输出的结果:
Accuracy of the network on the 10000 test images: 53 %
下面再测试一下每一类的结果:
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))
输出为:
Accuracy of plane : 60 %
Accuracy of car : 75 %
Accuracy of bird : 33 %
Accuracy of cat : 50 %
Accuracy of deer : 26 %
Accuracy of dog : 47 %
Accuracy of frog : 54 %
Accuracy of horse : 66 %
Accuracy of ship : 48 %
Accuracy of truck : 70 %
如果使用GPU训练,可以简单的将net,和输入数据都放到GPU上即可。这个matlab的操作类似。
首先使用
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
得到GPU的编号
之后使用
net.to(device)
inputs, labels = inputs.to(device), labels.to(device)
将网络和数据放到GPU上。代码中测试的时候使用
images, labels = images.to(device), labels.to(device)