任务描述:
通过简单的自定义神经网络,实现CIFAR10数据集图像分类任务
python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils
import torch.utils.data
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
def show_img(img):
"""显示图片
"""
img = img/2 + 0.5
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1,2,0)))
plt.show()
# torchvision输出的是PILImage, 值的范围是[0, 1]
# 我们将其转化为张量数据, 并归一化为[-1, 1]
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
])
# 下载训练集
trainset = torchvision.datasets.CIFAR10(
root= "./data",
train= True,
download=True,
transform=transform
)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=False, num_workers=2)
classes = ["plane", "car", "brid", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 输入为3通道, 输出为6通道, 卷积核为5
self.conv1 = nn.Conv2d(3,6,5)
# 输入为6通道,输出为16通道,卷积核为5
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 = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
net = Net()
# 交叉熵损失函数
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
if __name__ == "__main__":
for epoch in range(1):
running_loss = 0.0
for i,data in enumerate(trainloader, 0):
inputs, labels = data
# 梯度清零
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i%2000 == 1999:
print(epoch+1, i+1, running_loss/2000)
running_loss = 0
print("Finished Training")
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)
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
value, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted==labels).sum()
print(correct/total)
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(classes[i], 100*class_correct[i]/class_total[i])