])
# 构建MyDataset实例
train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform)
# 构建DataLoder
train_loader = DataLoader(dataset=train_data,
batch_size=BATCH_SIZE,
shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)
# ============================ step 2.数据增强方法/5 模型 ============================
net = LeNet(classes=2)
net.initialize_weights()
# ============================ step 3/5 损失函数 ============================
criterion = nn.CrossEntropyLoss() # 选择损失函数
# ============================ step 4/5 优化器 ============================
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9) # 选择优化器
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10,
gamma=0.1) # 设置学习率下降策略
# ============================ step 5/5 训练 ============================
train_curve = list()
valid_curve = list()
for epoch in range(MAX_EPOCH):
def train_lenet(split_dir):
"""训练lenet"""
set_seed() # 设置随机种子
rmb_label = {"1": 0, "100": 1}
# 参数设置
MAX_EPOCH = 10
BATCH_SIZE = 16
LR = 0.01
log_interval = 10
val_interval = 1
"""Step 1: 数据读取"""
train_dir = os.path.join(split_dir, "train")
valid_dir = os.path.join(split_dir, "valid")
test_dir = os.path.join(split_dir, "test")
norm_mean = [0.485, 0.456, 0.406]
norm_std = [0.229, 0.224, 0.225]
# 对训练数据进行变换,添加RandomCrop进行数据增强
train_transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
# 对验证数据进行变换
valid_transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std),
])
# 构建RMBDataset实例
train_data = RMBDataset(data_dir=train_dir, transform=train_transform)
valid_data = RMBDataset(data_dir=valid_dir, transform=valid_transform)
# 构建DataLoader
train_loader = DataLoader(dataset=train_data,
batch_size=BATCH_SIZE,
shuffle=True)
valid_loader = DataLoader(dataset=valid_data, batch_size=BATCH_SIZE)
"""Step 2: 模型"""
net = LeNet(classes=2)
net.initialize_weights()
"""Step 3: 损失函数"""
criterion = nn.CrossEntropyLoss()
"""Step 4: 优化器"""
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=10,
gamma=0.1)
"""Step 5: 训练"""
train_curve = []
valid_curve = []
figure_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'result')
for epoch in range(MAX_EPOCH):
loss_mean = 0.
correct = 0.
total = 0.
net.train()
for i, data in enumerate(train_loader):
# forward
inputs, labels = data
outputs = net(inputs)
# backward
optimizer.zero_grad()
loss = criterion(outputs, labels)
loss.backward()
# update weights
optimizer.step()
# 统计分类情况
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).squeeze().sum().numpy()
# 打印训练信息
loss_mean += loss.item()
train_curve.append(loss.item())
if (i + 1) % log_interval == 0:
loss_mean = loss_mean / log_interval
print(
"Training:Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}"
.format(epoch, MAX_EPOCH, i + 1, len(train_loader),
loss_mean, correct / total))
loss_mean = 0.
scheduler.step() # 更新学习率
# validate the model
if (epoch + 1) % val_interval == 0:
correct_val = 0.
total_val = 0.
loss_val = 0.
net.eval()
with torch.no_grad():
for j, data in enumerate(valid_loader):
inputs, labels = data
outputs = net(inputs)
loss = criterion(outputs, labels)
_, predicted = torch.max(outputs.data, 1)
total_val += labels.size(0)
correct_val += (
predicted == labels).squeeze().sum().numpy()
loss_val += loss.item()
valid_curve.append(loss_val / valid_loader.__len__())
print(
"Valid:\t Epoch[{:0>3}/{:0>3}] Iteration[{:0>3}/{:0>3}] Loss: {:.4f} Acc:{:.2%}"
.format(epoch, MAX_EPOCH, j + 1, len(valid_loader),
loss_val, correct_val / total_val))
train_x = range(len(train_curve))
train_y = train_curve
train_iters = len(train_loader)
valid_x = np.arange(
1,
len(valid_curve) + 1
) * train_iters * val_interval # 由于valid中记录的是epochloss，需要对记录点进行转换到iterations
valid_y = valid_curve
plt.plot(train_x, train_y, label='Train')
plt.plot(valid_x, valid_y, label='Valid')
plt.legend(loc='upper right')
plt.ylabel('loss value')
plt.xlabel('Iteration')
figure_path = os.path.join(figure_dir, '0201.png')
plt.savefig(figure_path)
plt.close()
