Uniform initialization和初始化列表的区别
时间: 2023-05-31 22:03:23 浏览: 60
Uniform initialization(统一初始化)是C++11引入的一种初始化方式,使用花括号{}来初始化变量或对象。例如:
```
int x{42};
std::string str{"hello"};
```
初始化列表是在构造函数中使用冒号:后跟花括号{}来初始化成员变量。例如:
```
class Foo {
public:
Foo(int x, std::string str) : x_{x}, str_{str} {}
private:
int x_;
std::string str_;
};
Foo foo{42, "hello"};
```
它们的区别在于,uniform initialization可以用于任何类型的变量或对象的初始化,而初始化列表只能用于类的构造函数中初始化成员变量。此外,uniform initialization会优先选择列表初始化,只有当列表初始化不可用时才会考虑其他形式的初始化。
相关问题
cpp11的 Uniform initialization
C++11引入了Uniform initialization,它提供了一种更加简洁和一致的初始化语法。Uniform initialization可以用于初始化各种类型的对象,包括基本类型、数组、STL容器和用户自定义类型等。
Uniform initialization有以下两种语法:
1. 使用花括号
使用花括号来初始化对象,可以自动推断出对象的类型。例如:
```c++
int x{ 10 }; // 初始化整型变量
double d{ 3.14 }; // 初始化双精度浮点型变量
int arr[]{ 1, 2, 3 }; // 初始化整型数组
vector<int> vec{ 1, 2, 3 }; // 初始化vector容器
```
2. 使用等号和圆括号
使用等号和圆括号来初始化对象,语法类似于函数调用。例如:
```c++
int x(10); // 初始化整型变量
double d(3.14); // 初始化双精度浮点型变量
int arr[] = { 1, 2, 3 }; // 初始化整型数组
vector<int> vec = { 1, 2, 3 }; // 初始化vector容器
```
Uniform initialization的优点:
1. 语法简洁:使用花括号或等号和圆括号来初始化对象,语法更加简洁易懂。
2. 类型安全:Uniform initialization可以防止隐式类型转换,从而提高类型安全性。
3. 通用性:Uniform initialization可以用于各种类型的对象的初始化,包括基本类型、数组、STL容器和用户自定义类型等。
总之,Uniform initialization是C++11中一个非常有用的新特性,它可以让代码更加简洁易懂,提高代码的可读性和可维护性。
pytorch里面的vgg16模型有多少种参数初始化方法?请举实际代码例子。
在PyTorch中,VGG16模型可以使用预训练模型或自定义初始化方法进行参数初始化。以下是两种不同的参数初始化方法:
1. 使用预训练模型初始化参数:
```python
import torch
import torchvision.models as models
# Load the pre-trained VGG16 model
vgg16_pretrained = models.vgg16(pretrained=True)
# Print the model architecture
print(vgg16_pretrained)
```
2. 自定义初始化方法进行参数初始化:
```python
import torch
import torch.nn as nn
class VGG16(nn.Module):
def __init__(self):
super(VGG16, self).__init__()
# Initialize the convolution layers with Xavier initialization
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv1.weight)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv2.weight)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv3.weight)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv4.weight)
self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv5.weight)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv6.weight)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv7.weight)
self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv8.weight)
self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv9.weight)
self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv10.weight)
self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv11.weight)
self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv12.weight)
self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
nn.init.xavier_uniform_(self.conv13.weight)
# Initialize the fully connected layers with Gaussian initialization
self.fc1 = nn.Linear(512 * 7 * 7, 4096)
nn.init.normal_(self.fc1.weight, mean=0, std=0.01)
self.fc2 = nn.Linear(4096, 4096)
nn.init.normal_(self.fc2.weight, mean=0, std=0.01)
self.fc3 = nn.Linear(4096, 1000)
nn.init.normal_(self.fc3.weight, mean=0, std=0.01)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.max_pool2d(x, kernel_size=2, stride=2)
x = nn.functional.relu(self.conv2(x))
x = nn.functional.max_pool2d(x, kernel_size=2, stride=2)
x = nn.functional.relu(self.conv3(x))
x = nn.functional.relu(self.conv4(x))
x = nn.functional.max_pool2d(x, kernel_size=2, stride=2)
x = nn.functional.relu(self.conv5(x))
x = nn.functional.relu(self.conv6(x))
x = nn.functional.relu(self.conv7(x))
x = nn.functional.max_pool2d(x, kernel_size=2, stride=2)
x = nn.functional.relu(self.conv8(x))
x = nn.functional.relu(self.conv9(x))
x = nn.functional.relu(self.conv10(x))
x = nn.functional.max_pool2d(x, kernel_size=2, stride=2)
x = nn.functional.relu(self.conv11(x))
x = nn.functional.relu(self.conv12(x))
x = nn.functional.relu(self.conv13(x))
x = x.view(-1, 512 * 7 * 7)
x = nn.functional.relu(self.fc1(x))
x = nn.functional.dropout(x, training=self.training)
x = nn.functional.relu(self.fc2(x))
x = nn.functional.dropout(x, training=self.training)
x = self.fc3(x)
return x
# Create an instance of the VGG16 model
vgg16_custom = VGG16()
# Print the model architecture
print(vgg16_custom)
```
以上是两种初始化方法的实际代码例子。第一种方法使用了PyTorch中内置的预训练模型来初始化参数,第二种方法则使用了自定义的初始化方法。