def train(train_features, test_features, train_labels, test_labels, num_epochs=400): loss = nn.MSELoss(reduction='none') input_shape = train_features.shape[-1] # 不设置偏置,因为我们已经在多项式中实现了它 net = nn.Sequential(nn.Linear(input_shape, 1, bias=False)) batch_size = min(10, train_labels.shape[0]) train_iter = d2l.load_array((train_features, train_labels.reshape(-1,1)), batch_size) test_iter = d2l.load_array((test_features, test_labels.reshape(-1,1)), batch_size, is_train=False) trainer = torch.optim.SGD(net.parameters(), lr=0.01) animator = d2l.Animator(xlabel='epoch', ylabel='loss', yscale='log', xlim=[1, num_epochs], ylim=[1e-3, 1e2], legend=['train', 'test']) for epoch in range(num_epochs): d2l.train_epoch_ch3(net, train_iter, loss, trainer) if epoch == 0 or (epoch + 1) % 20 == 0: animator.add(epoch + 1, (evaluate_loss(net, train_iter, loss), evaluate_loss(net, test_iter, loss))) print('weight:', net[0].weight.data.numpy())
时间: 2024-04-29 10:19:36 浏览: 17
这是一个用于训练线性回归模型的函数,包含五个参数:
- train_features: 训练数据的特征,类型为numpy.ndarray
- test_features: 测试数据的特征,类型为numpy.ndarray
- train_labels: 训练数据的标签,类型为numpy.ndarray
- test_labels: 测试数据的标签,类型为numpy.ndarray
- num_epochs: 训练轮数,默认为400
该函数的主要实现逻辑如下:
1. 定义损失函数为均方误差(MSE)损失函数
2. 定义线性回归模型,其中偏置项(bias)被设置为False,因为已经在多项式中实现了它
3. 设置batch_size为10或者训练集样本数中的最小值,定义训练集和测试集的数据迭代器
4. 定义优化器为随机梯度下降(SGD)优化器,学习率为0.01
5. 定义一个绘图工具,用于绘制损失函数的变化曲线
6. 循环训练num_epochs轮,每轮训练中使用d2l.train_epoch_ch3函数进行训练,该函数实现了一个标准的训练过程,包括向前传播、计算损失、反向传播、更新参数等步骤
7. 在每20轮训练后,记录并绘制训练集和测试集的损失值
8. 输出训练后的权重
训练完成后,可以通过该函数返回的net对象来进行预测。
相关问题
def train(model, train_features, train_labels, test_features, test_labels, num_epochs, learning_rate, weight_decay, batch_size): 帮我加个注释
# 定义训练函数,接收模型、训练特征、训练标签、测试特征、测试标签、训练轮数、学习率、权重衰减和批大小等参数
def train(model, train_features, train_labels, test_features, test_labels,
num_epochs, learning_rate, weight_decay, batch_size):
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# 定义训练和测试数据集
train_dataset = TensorDataset(train_features, train_labels)
test_dataset = TensorDataset(test_features, test_labels)
# 定义训练和测试数据集的加载器
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# 开始训练模型
for epoch in range(num_epochs):
# 训练模式
model.train()
train_loss = 0.0
train_acc = 0.0
for features, labels in train_loader:
# 将特征和标签放入设备中
features = features.to(device)
labels = labels.to(device)
# 清空优化器的梯度
optimizer.zero_grad()
# 前向传播
outputs = model(features)
# 计算损失
loss = criterion(outputs, labels)
# 反向传播
loss.backward()
optimizer.step()
# 计算训练损失
train_loss += loss.item() * features.size(0)
# 计算训练准确率
_, preds = torch.max(outputs, 1)
train_acc += torch.sum(preds == labels.data)
# 计算平均训练损失和准确率
train_loss = train_loss / len(train_features)
train_acc = train_acc / len(train_features)
# 测试模式
model.eval()
test_loss = 0.0
test_acc = 0.0
with torch.no_grad():
for features, labels in test_loader:
# 将特征和标签放入设备中
features = features.to(device)
labels = labels.to(device)
# 前向传播
outputs = model(features)
# 计算损失
loss = criterion(outputs, labels)
# 计算测试损失
test_loss += loss.item() * features.size(0)
# 计算测试准确率
_, preds = torch.max(outputs, 1)
test_acc += torch.sum(preds == labels.data)
# 计算平均测试损失和准确率
test_loss = test_loss / len(test_features)
test_acc = test_acc / len(test_features)
# 输出训练和测试结果
print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'
.format(epoch+1, num_epochs, train_loss, train_acc, test_loss, test_acc))
# 返回训练好的模型
return model
LDAM损失函数pytorch代码如下:class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下:# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) # 读取数据集 dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform) dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数
好的,下面是使用LDAM损失函数训练模型的代码:
```python
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.utils.data import DataLoader
# 定义LDAM损失函数
class LDAMLoss(nn.Module):
def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30):
super(LDAMLoss, self).__init__()
m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list))
m_list = m_list * (max_m / np.max(m_list))
m_list = torch.cuda.FloatTensor(m_list)
self.m_list = m_list
assert s > 0
self.s = s
if weight is not None:
weight = torch.FloatTensor(weight).cuda()
self.weight = weight
self.cls_num_list = cls_num_list
def forward(self, x, target):
index = torch.zeros_like(x, dtype=torch.uint8)
index_float = index.type(torch.cuda.FloatTensor)
batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1
batch_m = batch_m.view((x.size(0), 1)) # size=(batch_size, 1)
x_m = x - batch_m
output = torch.where(index, x_m, x)
if self.weight is not None:
output = output * self.weight[None, :]
target = torch.flatten(target) # 将 target 转换成 1D Tensor
logit = output * self.s
return F.cross_entropy(logit, target, weight=self.weight)
# 定义模型
model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, classes)
model.to(DEVICE)
# 定义优化器和学习率调整器
optimizer = optim.Adam(model.parameters(), lr=model_lr)
scheduler = CosineAnnealingLR(optimizer, T_max=EPOCHS, eta_min=1e-6)
# 定义LDAM损失函数
cls_num_list = [len(dataset_train[dataset_train.targets == t]) for t in range(classes)]
criterion = LDAMLoss(cls_num_list)
# 定义数据加载器
train_loader = DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, num_workers=4, pin_memory=True)
test_loader = DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False, num_workers=4, pin_memory=True)
# 训练模型
best_acc = 0.0
for epoch in range(start_epoch, EPOCHS + 1):
model.train()
train_loss = 0.0
train_corrects = 0
for inputs, labels in train_loader:
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
if use_dp:
inputs, labels = dp(inputs, labels)
if use_amp:
with amp.autocast():
inputs, labels = mixup_fn(inputs, labels)
outputs = model(inputs)
loss = criterion(outputs, labels)
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
scaler.step(optimizer)
scaler.update()
else:
inputs, labels_a, labels_b, lam = mixup_fn(inputs, labels)
outputs = model(inputs)
loss = mixup_criterion(criterion, outputs, labels_a, labels_b, lam)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
optimizer.step()
optimizer.zero_grad()
train_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
train_corrects += torch.sum(preds == labels.data)
train_loss /= len(dataset_train)
train_acc = train_corrects.double() / len(dataset_train)
model.eval()
test_loss = 0.0
test_corrects = 0
with torch.no_grad():
for inputs, labels in test_loader:
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
outputs = model(inputs)
loss = criterion(outputs, labels)
test_loss += loss.item() * inputs.size(0)
_, preds = torch.max(outputs, 1)
test_corrects += torch.sum(preds == labels.data)
test_loss /= len(dataset_test)
test_acc = test_corrects.double() / len(dataset_test)
# 更新最佳模型
if test_acc > best_acc:
if use_ema:
ema_model.load_state_dict(model.state_dict())
best_acc = test_acc
# 更新学习率
scheduler.step()
# 打印训练结果
print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(
epoch, EPOCHS, train_loss, train_acc, test_loss, test_acc))
```