训练的脚本 ，用于训练和测试。lib.train.py:
import time
from load_imags import train_loader, train_num
from nets import *
def main():
# 定义网络
print('Please choose a network:')
print('1. ResNet18')
print('2. VGG')
# 选择网络
while True:
net_choose = input('')
if net_choose == '1':
net = resnet18_model().to(device)
print('You choose ResNet18，now start training')
break
elif net_choose == '2':
net = vgg_model().to(device)
print('You choose VGG，now start training')
break
else:
print('Please input a correct number!')
# 定义损失函数和优化器
loss_func = nn.CrossEntropyLoss() # 交叉熵损失函数
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate) # 优化器使用Adam
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.9) # 学习率衰减, 每5个epoch，学习率乘以0.9
# 训练模型
for epoch in range(num_epoches):
trained_num = 0 # 记录训练过的图片数量
total_correct = 0 # 记录正确数量
print('-' * 100)
print('Epoch {}/{}'.format(epoch + 1, num_epoches))
begin_time = time.time() # 记录开始时间
net.train() # 训练模式
for i, (images, labels) in enumerate(train_loader):
images = images.to(device) # 每batch_size个图像的数据
labels = labels.to(device) # 每batch_size个图像的标签
trained_num += images.size(0) # 记录训练过的图片数量
outputs = net(images) # 前向传播
loss = loss_func(outputs, labels) # 计算损失
optimizer.zero_grad() # 梯度清零
loss.backward() # 反向传播
optimizer.step() # 优化器更新参数
_, predicted = torch.max(outputs.data, 1) # 预测结果
correct = predicted.eq(labels).cpu().sum() # 计算本batch_size的正确数量
total_correct += correct # 记录正确数量
if (i + 1) % 50 == 0: # 每50个batch_size打印一次
print('trained: {}/{}'.format(trained_num, train_num))
print('Loss: {:.4f}, Accuracy: {:.2f}%'.format(loss.item(), 100 * correct / images.size(0)))
# 每5个epoch，学习率衰减
scheduler.step()
end_time = time.time() # 记录结束时间
print('Each train_epoch take time: {} s'.format(end_time - begin_time))
print('This train_epoch accuracy: {:.2f}%'.format(100 * total_correct / train_num))
if __name__ == '__main__':
main()
C:\Users\DY\.conda\envs\torch\python.exe E:\AI_test\image_classification\lib\train.py
Please choose a network:
1. ResNet18
2. VGG
2
You choose VGG，now start training
----------------------------------------------------------------------------------------------------
Epoch 1/100
trained: 6400/50000
Loss: 2.3902, Accuracy: 10.16%
trained: 12800/50000
Loss: 2.3063, Accuracy: 11.72%
trained: 19200/50000
Loss: 2.1875, Accuracy: 18.75%
trained: 25600/50000
Loss: 2.1349, Accuracy: 19.53%
trained: 32000/50000
Loss: 1.9848, Accuracy: 26.56%
trained: 38400/50000
Loss: 2.0000, Accuracy: 16.41%
trained: 44800/50000
Loss: 2.0151, Accuracy: 25.78%
Each train_epoch take time: 71.04850149154663 s
This train_epoch accuracy: 19.34%
----------------------------------------------------------------------------------------------------
Epoch 2/100
trained: 6400/50000
Loss: 1.8815, Accuracy: 28.12%
trained: 12800/50000
Loss: 1.8677, Accuracy: 34.38%
trained: 19200/50000
Loss: 1.7808, Accuracy: 39.06%
trained: 25600/50000
Loss: 1.9118, Accuracy: 29.69%
trained: 32000/50000
Loss: 1.6296, Accuracy: 39.84%
trained: 38400/50000
Loss: 1.6648, Accuracy: 35.94%
trained: 44800/50000
Loss: 1.7854, Accuracy: 33.59%
Each train_epoch take time: 66.71016025543213 s
This train_epoch accuracy: 33.65%
----------------------------------------------------------------------------------------------------
Epoch 3/100
trained: 6400/50000
Loss: 1.4987, Accuracy: 44.53%
trained: 12800/50000
Loss: 1.6677, Accuracy: 41.41%
trained: 19200/50000
Loss: 1.6952, Accuracy: 43.75%
trained: 25600/50000
Loss: 1.6941, Accuracy: 38.28%
trained: 32000/50000
Loss: 1.4057, Accuracy: 49.22%
trained: 38400/50000
Loss: 1.5183, Accuracy: 44.53%
trained: 44800/50000
Loss: 1.6591, Accuracy: 37.50%
Each train_epoch take time: 68.37232995033264 s
This train_epoch accuracy: 41.65%
----------------------------------------------------------------------------------------------------
Epoch 4/100
trained: 6400/50000
Loss: 1.6636, Accuracy: 43.75%
trained: 12800/50000
Loss: 1.5985, Accuracy: 42.19%
trained: 19200/50000
Loss: 1.4054, Accuracy: 52.34%
trained: 25600/50000
Loss: 1.4520, Accuracy: 40.62%
trained: 32000/50000
Loss: 1.4574, Accuracy: 46.09%
trained: 38400/50000
Loss: 1.4711, Accuracy: 42.19%
trained: 44800/50000
Loss: 1.4806, Accuracy: 43.75%
Each train_epoch take time: 68.32443571090698 s
This train_epoch accuracy: 46.48%
----------------------------------------------------------------------------------------------------
Epoch 5/100
trained: 6400/50000
Loss: 1.2265, Accuracy: 57.03%
trained: 12800/50000
Loss: 1.3454, Accuracy: 52.34%
trained: 19200/50000
Loss: 1.3527, Accuracy: 49.22%
trained: 25600/50000
Loss: 1.2874, Accuracy: 53.12%
trained: 32000/50000
Loss: 1.3666, Accuracy: 55.47%
trained: 38400/50000
Loss: 1.4465, Accuracy: 50.00%
trained: 44800/50000
Loss: 1.2802, Accuracy: 52.34%
Each train_epoch take time: 68.22098922729492 s
This train_epoch accuracy: 50.72%
----------------------------------------------------------------------------------------------------
Epoch 6/100
trained: 6400/50000
Loss: 1.3402, Accuracy: 51.56%
trained: 12800/50000
Loss: 1.2873, Accuracy: 53.91%
trained: 19200/50000
Loss: 1.3183, Accuracy: 52.34%
trained: 25600/50000
Loss: 1.3688, Accuracy: 48.44%
trained: 32000/50000
Loss: 1.2143, Accuracy: 55.47%
trained: 38400/50000
Loss: 1.2132, Accuracy: 56.25%
trained: 44800/50000
Loss: 1.3172, Accuracy: 53.12%
Each train_epoch take time: 68.76534986495972 s
This train_epoch accuracy: 54.53%
----------------------------------------------------------------------------------------------------
Epoch 7/100
trained: 6400/50000
Loss: 1.3156, Accuracy: 53.12%
trained: 12800/50000
Loss: 1.1412, Accuracy: 60.16%
trained: 19200/50000
Loss: 1.1978, Accuracy: 57.03%
trained: 25600/50000
Loss: 1.0312, Accuracy: 55.47%
trained: 32000/50000
Loss: 1.3486, Accuracy: 50.00%
trained: 38400/50000
Loss: 1.1591, Accuracy: 60.16%
trained: 44800/50000
Loss: 1.0707, Accuracy: 63.28%
Each train_epoch take time: 68.1180489063263 s
This train_epoch accuracy: 56.99%
----------------------------------------------------------------------------------------------------
Epoch 8/100
看得出，模型是在逐步收敛的。下一步，完善训练脚本，加入测试的代码。
