age=18 number=18 cancer = '\u264B' where_I_will_go='Russia' def introduce(): print(f'我的年龄是{age}\n学号是{number}\n星座是{cancer}\n想去的地方是{where_I_will_go}') if __name__=='_main_': introduce()
时间: 2024-02-15 16:04:11 浏览: 14
你的代码有几个小错误,第一个是 `_main_` 应该是 `__main__`,第二个是 `introduce()` 函数缺少括号。下面是修正后的代码:
```python
age = 18
number = 18
cancer = '\u264B'
where_I_will_go = 'Russia'
def introduce():
print(f'我的年龄是{age}\n学号是{number}\n星座是{cancer}\n想去的地方是{where_I_will_go}')
if __name__ == '__main__':
introduce()
```
这个程序会输出:
```
我的年龄是18
学号是18
星座是♋
想去的地方是Russia
```
其中 `\u264B` 是 Unicode 编码,表示的是巨蟹座。
相关问题
class Person: def __init__(self, name, age): self.name = name self.age = age
def introduce(self):
print(f"My name is {self.name} and I am {self.age} years old.")
class Student(Person):
def __init__(self, name, age, major):
super().__init__(name, age)
self.major = major
def study(self):
print(f"I am studying {self.major}.")
class Teacher(Person):
def __init__(self, name, age, subject):
super().__init__(name, age)
self.subject = subject
def teach(self):
print(f"I am teaching {self.subject}.")
s = Student("John", 20, "Computer Science")
s.introduce() # Output: My name is John and I am 20 years old.
s.study() # Output: I am studying Computer Science.
t = Teacher("Jane", 35, "Math")
t.introduce() # Output: My name is Jane and I am 35 years old.
t.teach() # Output: I am teaching Math.
out = self.inp_prelu(self.inp_snorm(self.inp_conv(x)))
This code represents a neural network layer where an input tensor x is passed through a series of operations:
1. The first operation is inp_conv, which performs a convolution operation on the input tensor with some learnable filters.
2. The output of the convolution operation is then passed through inp_snorm, which performs a spatial normalization operation to normalize the output tensor across channels and spatial dimensions.
3. The normalized output is then passed through inp_prelu, which applies a parametric rectified linear unit (PReLU) activation function to introduce non-linearity.
4. Finally, the output of the PReLU activation function is returned as the output of the layer.
Overall, this layer can be used as a building block for a deeper neural network architecture to learn more complex representations of input data.