import torch
import torchvision as tv
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import argparse
# 定义是否使用GPU
device = torch.device(
"cuda" if torch.cuda.is_available()
else "cpu")
# 定义网络结构
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Sequential(
#input_size=(3*200*200)
nn.Conv2d(3, 6, kernel_size=5),
#padding=2保证输入输出尺寸相同
nn.ReLU(),
#input_size=(6*196*196)
nn.MaxPool2d(kernel_size=2, stride=2),
#output_size=(6*98*98)
)
self.conv2 =
nn.Sequential(
nn.Conv2d(6, 16, kernel_size=5
),
nn.ReLU(), #input_size=(16*94*94)
nn.MaxPool2d(2, 2)
#output_size=(16*47*47)
)
self.fc1 =
nn.Sequential(
nn.Linear(16*47*47, 120
),
nn.ReLU()
)
self.fc2 =
nn.Sequential(
nn.Linear(120, 84
),
nn.ReLU()
)
self.fc3 = nn.Linear(84, 2
)
# 定义前向传播过程，输入为x
def forward(self, x):
x =
self.conv1(x)
x =
self.conv2(x)
# nn.Linear()的输入输出都是维度为一的值，所以要把多维度的tensor展平成一维
x = x.view(x.size(0), -1
)
x =
self.fc1(x)
x =
self.fc2(x)
x =
self.fc3(x)
return x
#使得我们能够手动输入命令行参数，就是让风格变得和Linux命令行差不多
parser =
argparse.ArgumentParser()
parser.add_argument('--outf', default=
'./model/', help=
'folder to output images and model checkpoints')
#模型保存路径
parser.add_argument(
'--net', default=
'./model/net.pth', help=
"path to netG (to continue training)")
#模型加载路径
opt =
parser.parse_args()
# 超参数设置
EPOCH = 8
#遍历数据集次数
BATCH_SIZE = 64
#批处理尺寸(batch_size)
LR = 0.001
#学习率
# 定义数据预处理方式
transform =
transforms.ToTensor()
# 定义训练数据集
trainset =
tv.datasets.ImageFolder(
root=
'./train',
transform=
transform)
# 定义训练批处理数据
trainloa
der =
torch.utils.data.DataLoader(
trainset,
batch_size=
BATCH_SIZE,
shuffle=
True,
)
# 定义测试数据集
testset =
tv.datasets.ImageFolder(
root=
'./test',
transform=
transform)
# 定义测试批处理数据
testloader =
torch.utils.data.DataLoader(
testset,
batch_size=
BATCH_SIZE,
shuffle=
False,
)
# 定义损失函数loss function 和优化方式（采用SGD）
net =
LeNet().to(device)
criterion = nn.CrossEntropyLoss()
# 交叉熵损失函数，通常用于多分类问题上
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9
)
# 训练
if __name__ ==
"__main__":
for epoch
in range(EPOCH):
sum_loss = 0.0
# 数据读取
for i, data
in enumerate(trainloader):
inputs, labels =
data
inputs, labels =
inputs.to(device), labels.to(device)
# 梯度清零
optimizer.zero_grad()
# forward + backward
outputs =
net(inputs)
loss =
criterion(outputs, labels)
loss.backward()
optimizer.step()
# 每训练100个batch打印一次平均loss
sum_loss +=
loss.item()
if i % 100 == 99
:
print(
'[%d, %d] loss: %.03f'
% (epoch + 1, i + 1, sum_loss / 100
))
sum_loss = 0.0
# 每跑完一次epoch测试一下准确率
with torch.no_grad():
correct =
0
total =
0
for data
in testloader:
images, labels =
data
images, labels =
images.to(device), labels.to(device)
outputs =
net(images)
# 取得分最高的那个类
_, predicted = torch.max(outputs.data, 1
)
total +=
labels.size(0)
correct += (predicted ==
labels).sum()
print(
'第%d个epoch的识别准确率为：%d%%' % (epoch + 1, (100 * correct / total)))
转载于:https://www.cnblogs.com/Sunnyside-Bao/p/11231958.html
相关资源：1维卷积神经网络demo
