python:vuetify.VTextField( # M_sw v_model=("MS", 15), label="M_sw", value = True, )怎么让他显示三个变量
时间: 2024-01-03 19:03:32 浏览: 24
这个代码片段中,`vuetify.VTextField` 是 Vue.js 框架中的一个组件,它是用来创建文本输入框的。在这个组件的定义中,`v_model`、`label` 和 `value` 都是该组件的属性,用于控制文本输入框的行为和显示。
如果你想让这个组件显示三个变量的值,可以使用字符串拼接的方式将它们连接起来,然后将拼接后的字符串赋值给 `value` 属性。
例如,如果你有三个变量 `var1`、`var2` 和 `var3`,可以这样显示它们:
```
vuetify.VTextField(
v_model=("MS", 15),
label="M_sw",
value=f"{var1}, {var2}, {var3}"
)
```
在这个例子中,`f"{var1}, {var2}, {var3}"` 是一个带有变量的格式化字符串,它会将三个变量的值连接起来,并用逗号和空格分隔开来。最终的结果会显示在文本输入框中。
相关问题
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) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数
好的,以下是使用LDAM损失函数进行模型训练的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from ldam_loss import LDAMLoss # 导入LDAM损失函数
# 定义模型
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.fc1 = nn.Linear(256 * 4 * 4, 1024)
self.fc2 = nn.Linear(1024, 10)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.max_pool2d(x, 2)
x = F.relu(self.bn2(self.conv2(x)))
x = F.max_pool2d(x, 2)
x = F.relu(self.bn3(self.conv3(x)))
x = F.max_pool2d(x, 2)
x = x.view(-1, 256 * 4 * 4)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义数据集和数据加载器
train_dataset = ... # 定义训练集
test_dataset = ... # 定义测试集
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)
# 定义LDAM损失函数
cls_num_list = ... # 定义每个类别的样本数量
criterion = LDAMLoss(cls_num_list, max_m=0.5, s=30)
# 定义优化器和学习率调度器
model = MyModel().to('cuda')
optimizer = optim.Adam(model.parameters(), lr=1e-3)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
# 训练模型
for epoch in range(50):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to('cuda'), target.to('cuda')
data, target_a, target_b, lam = mixup_fn(data, target)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target_a, target_b, lam) # 使用LDAM损失函数
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) # 防止梯度爆炸
optimizer.step()
model.eval()
with torch.no_grad():
total = 0
correct = 0
for data, target in test_loader:
data, target = data.to('cuda'), target.to('cuda')
output = model(data)
_, predicted = torch.max(output.data, 1)
total += target.size(0)
correct += (predicted == target).sum().item()
acc = 100 * correct / total
print('Epoch {}: Test Acc = {}%'.format(epoch+1, acc))
scheduler.step()
```
在以上代码中,我们首先定义了一个名为`MyModel`的模型,然后定义了训练集和测试集的数据加载器。接着,我们使用LDAM损失函数来定义模型的损失函数,然后使用Adam优化器来训练模型。在每个epoch结束后,我们使用测试集来评估模型的性能,并使用学习率调度器来调整学习率。
# In[]收缩损失函数 # def contractive_loss(y_pred, y_true,lam=1e-4): # mse = K.mean(K.square(y_true - y_pred), axis=1) # W = K.variable(value=model.get_layer('encoded').get_weights()[0]) # N x N_hidden # W = K.transpose(W) # N_hidden x N # h = model.get_layer('encoded').output # dh = h * (1 - h) # N_batch x N_hidden # contractive = lam * K.sum(dh**2 * K.sum(W**2, axis=1), axis=1) # return mse + contractive将这段代码修改为适合tensorflow最高版本的形式
可以将这段代码修改为适合TensorFlow最高版本的形式,如下所示:
```python
def contractive_loss(y_pred, y_true, lam=1e-4):
mse = tf.reduce_mean(tf.square(y_true - y_pred), axis=1)
W = tf.transpose(model.get_layer('encoded').get_weights()[0]) # N_hidden x N
h = model.get_layer('encoded').output # N_batch x N_hidden
dh = h * (1 - h) # N_batch x N_hidden
contractive = lam * tf.reduce_sum(tf.square(dh) * tf.reduce_sum(tf.square(W), axis=1), axis=1)
return mse + contractive
```
需要注意的是,TensorFlow的函数名和用法与Keras略有不同,例如`K.mean`改为`tf.reduce_mean`,`K.square`改为`tf.square`,`K.variable`改为直接从`model.get_layer`获取权重等。