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| import torch
import torchvision
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
import time
train_data = torchvision.datasets.CIFAR10(root = "./data", train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10(root = "./data", train=False, transform=torchvision.transforms.ToTensor(), download=True)
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集的长度为: {}".format(train_data_size))
print("测试数据集的长度为: {}".format(test_data_size))
train_dataloader = DataLoader(train_data, 64)
test_dataloader = DataLoader(test_data, 64)
class Tudui(nn.Module):
def __init__(self):
super(Tudui, self).__init__()
self.model = nn.Sequential(
nn.Conv2d(3, 32, 5, 1, 2),
nn.MaxPool2d(2),
nn.Conv2d(32, 32, 5, 1, 2),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 5, 1, 2),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(64*4*4, 64),
nn.Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
if __name__ == '__main__':
tudui = Tudui()
input = torch.ones((64, 3, 32, 32))
output = tudui(input)
print(output.shape)
tudui = Tudui()
if torch.cuda.is_available():
tudui = tudui.cuda()
loss_fn = nn.CrossEntropyLoss()
if torch.cuda.is_available():
loss_fn = loss_fn.cuda()
learning_rate = 1e-2
optimizer = torch.optim.SGD(tudui.parameters(), lr=learning_rate)
total_train_step = 0
total_test_step = 0
epoch = 10
writer = SummaryWriter("logs_train")
start_time = time.time()
for i in range(epoch):
print("-----------第 {} 轮训练开始----------".format(i + 1))
tudui.train()
for data in train_dataloader:
imgs, targets = data
if torch.cuda.is_available():
imgs = imgs.cuda()
targets = targets.cuda()
output = tudui(imgs)
loss = loss_fn(output, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
end_time = time.time()
print(end_time - start_time)
print("训练次数 {}, Loss: {}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
tudui.eval()
total_test_loss = 0
total_accuracy = 0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
if torch.cuda.is_available():
imgs = imgs.cuda()
targets = targets.cuda()
outputs = tudui(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
print("整体测试集上的Loss: {}".format(total_test_loss))
print("整体测试集上的正确率: {}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy, total_test_step)
total_test_step = total_test_step + 1
torch.save(tudui, "tudui_{}.pth".format(i))
print("模型已保存")
writer.close()
'''
-----------第 1 轮训练开始----------
6.235116958618164
训练次数 100, Loss: 2.292055368423462
'''
|
