我们已经了解了如何定义神经网络,计算损失并对网络的权重进行更新。

接下来的问题就是:

一、What about data?

通常处理图像、文本、音频或视频数据时,可以使用标准的python包将数据加载到numpy数组中。然后你可以将这个数组转换成一个torch.Tensor.

对于图片, 涉及到的库有Pillowh和OpenCV。

对于音频,涉及到的库有scipy和librosa

对于文本,无论是原始的Python还是基于Cython的加载,都会用到NLTK或者SpaCy。

我们已经创建了一个名为torchvision的软件包。

torchvision为像Imagenet、CIFAR10、MNIST等普通数据集提供数据加载器,并为图像、viz、torchvision提供数据转换器,也就是torchvision.datasets torch.utils.data.DataLoader.

我们在这里使用的是CIFAR10数据集。它的类包括:“飞机”、“汽车”、“鸟”、“猫”、“鹿”、“狗”、“青蛙”、“马”、“船”、“卡车”。cifar - 10中的图像大小为3x32x32,即3 - channel彩色图像,大小为32x32像素。

二、Training an image classifier

我们将按顺序进行以下步骤:

1使用torchvision对CIFAR10训练和测试数据集进行加载和规范化

2.定义一个卷积神经网络

3.定义一个损失函数

4.在训练数据上训练神经网络

5.在测试数据上测试神经网络

1加载并规范化CIFAR10

 import相关类:

import torch
import torchvision
import torchvision.transforms as transforms
创建transform来处理图像数据
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

下载训练数据集到./data/目录下:

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)

 查看下载的数据:

classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
image, label = trainset[0] 
print(image.size())
print(label)
print(classes[label])

输出结果:

torch.Size([3, 32, 32])
6
frog

torchvision数据集的输出是范围[0,1]的PILImage图像。我们将它们转换为标准化范围的Tensor[- 1,1]:

trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)

同理,我们下载测试数据集并将其转化为Tensor:

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)

2.定义一个卷积神经网络

PyTorch教程之Neural Networks复制代码,然后修改成获取3通道图像(而不是原本定义为1通道的图像)。

from torch.autograd import Variable
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()

3.定义一个损失函数

这里使用的损失函数为Classification Cross-Entropy loss and SGD with momentum:

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

4.在训练数据上训练神经网络

我们只需要对数据迭代器进行循环,并将输入反馈到网络并进行优化。

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
# wrap them in Variable
inputs, labels = Variable(inputs), Variable(labels)
# 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.data[0]
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')

输出结果:

[1,  2000] loss: 2.224
[1, 4000] loss: 1.896
[1, 6000] loss: 1.721
[1, 8000] loss: 1.591
[1, 10000] loss: 1.542
[1, 12000] loss: 1.471
[2, 2000] loss: 1.411
[2, 4000] loss: 1.377
[2, 6000] loss: 1.334
[2, 8000] loss: 1.316
[2, 10000] loss: 1.290
[2, 12000] loss: 1.281

5.在测试数据上测试神经网络

我们将通过预测神经网络输出的类标签来检查它,如果预测是正确的,我们将样本添加到正确预测的列表中。

获取前四个测试数据的GroundTruth:

dataiter = iter(testloader)
images, labels = dataiter.next() print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))

输出结果:

GroundTruth:    cat  ship  ship plane

神经网络输出是10类分别对应的energy,一个类的 energy量越高,神经网络就认为图像属于该类可能性越高,我们将energy最高的类作为我们预测结果:

outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1) print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
for j in range(4)))

输出结果:

Predicted:    cat  ship  ship  ship

我们在整个测试数据集上进行测试:

correct = 0
total = 0
for data in testloader:
images, labels = data
outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum() print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))

输出结果显示正确率为56%

Accuracy of the network on the 10000 test images: 56 %

我们对不同的类识别效果进行分别统计:

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
for data in testloader:
images, labels = data
outputs = net(Variable(images))
_, predicted = torch.max(outputs.data, 1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i]
class_total[label] += 1 for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))

结果显示:

Accuracy of plane : 52 %
Accuracy of car : 73 %
Accuracy of bird : 45 %
Accuracy of cat : 26 %
Accuracy of deer : 39 %
Accuracy of dog : 42 %
Accuracy of frog : 73 %
Accuracy of horse : 73 %
Accuracy of ship : 75 %
Accuracy of truck : 63 %
05-11 11:28