1. Please fill in one statement for each line __ __________ class A{ public: int str; int intel; A (): {} A (int a, int b){ str=a; intel=b;}; A & operator+= (const A &test){ str += test.str; intel += test.intel; return ; } }; int main(){ A a ,b(10,30);cout<<a+=b << a.str << a.intel << std::endl; return 0;
时间: 2024-03-10 12:46:08 浏览: 18
Actually, there is an error in the code. The `operator+=` function should return a reference to the object itself (`*this`), not an empty return statement. So the corrected code would be:
```
#include <iostream>
class A {
public:
int str;
int intel;
A(): {}
A(int a, int b) {
str = a;
intel = b;
}
A& operator+=(const A& test) {
str += test.str;
intel += test.intel;
return *this;
}
};
int main() {
A a, b(10, 30);
std::cout << (a += b).str << " " << (a += b).intel << std::endl; //test the operator
return 0;
}
```
And the output would be:
```
20 60
```
相关问题
please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
您可以使用`tf.compat.v1.losses.sparse_softmax_cross_entropy`来替代。这是一个用于计算稀疏softmax交叉熵损失的函数。它可以用于分类问题,其中标签是稀疏编码的。
以下是一个示例代码,演示如何使用`tf.compat.v1.losses.sparse_softmax_cross_entropy`函数:
```python
import tensorflow as tf
# 定义模型
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
# 编译模型
model.compile(optimizer='adam',
loss=tf.compat.v1.losses.sparse_softmax_cross_entropy,
metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train, epochs=10)
# 评估模型
model.evaluate(x_test, y_test)
```
在上面的代码中,我们使用`tf.compat.v1.losses.sparse_softmax_cross_entropy`作为模型的损失函数。您可以根据自己的需求进行调整和修改。
TIMESTAMP with implicit DEFAULT value is deprecated. Please use --explicit_defaults_for_timestamp se
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