优化这段代码实现self.id累计 def __init__(self, id): self.id = id
时间: 2024-05-15 11:16:53 浏览: 12
可以使用类变量来实现self.id的累加:
```python
class MyClass:
# 类变量count用于记录对象个数
count = 0
def __init__(self, id):
self.id = id
MyClass.count += 1
# self.id = MyClass.count # 可以直接使用count作为id
a = MyClass(1)
b = MyClass(2)
c = MyClass(3)
print(a.id) # 输出1
print(b.id) # 输出2
print(c.id) # 输出3
print(MyClass.count) # 输出3
```
相关问题
如何实现self.id累积 def __init__(self, id): self.id = id
可以在类的定义中增加一个类变量来记录当前的最大id值,然后在每次创建实例时将该类变量的值加1,作为新实例的id值。
代码实现如下:
```
class MyClass:
max_id = 0 # 类变量,记录当前的最大id值
def __init__(self, id):
self.id = MyClass.max_id + 1 # 将最大id值加1作为新实例的id
MyClass.max_id += 1 # 更新最大id值
obj1 = MyClass(0)
print(obj1.id) # 输出1
obj2 = MyClass(0)
print(obj2.id) # 输出2
obj3 = MyClass(0)
print(obj3.id) # 输出3
```
上述代码中,我们定义了一个类变量`max_id`,用于记录当前的最大id值。在每次创建实例时,我们将`max_id`加1作为新实例的id,并更新`max_id`的值,以便下一次创建实例时使用。
这段代码哪里错了 class my_BiGRU(torch.nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def _setup_BiGRU_layers(self): self.BiGRU_layers = my_BiGRU(input_size=self.args.capsule_out_dim, output_size=self.args.apsule_out_features, hidden_size=self.args.BiGRU_hiddensize, num_layers=self.args.BiGRU_layers, gru_dropout=self.args.BiGRU_dropout, )
这段代码存在两个问题:
1.函数 `_setup_BiGRU_layers` 中的代码应该是在类的构造函数 `__init__` 中进行初始化,而不是在另外一个函数中进行初始化。
2.在调用 `my_BiGRU` 类时,参数名称有误,应该将 `apsule_out_features` 改为 `capsule_out_features`。
改正后的代码如下所示:
```
class my_BiGRU(torch.nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, gru_dropout, capsule_out_features):
super(my_BiGRU, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.gru_dropout = gru_dropout
self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True)
self.fc = torch.nn.Linear(hidden_size * 2, output_size)
self.BiGRU_layers = my_BiGRU(input_size=self.input_size,
output_size=capsule_out_features,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
gru_dropout=self.gru_dropout,
)
```
注意:这里假设 `capsule_out_dim` 和 `args` 都已经在代码中被定义好了。