def value(self, x: np.ndarray) -> np.ndarray: ''' Parameters: x is a two dimensional numpy array. Returns: a two dimensioal array representing the element-wise sigmoid of x. ''' #### write your code below ####
时间: 2024-02-10 13:16:18 浏览: 28
根据这段代码的要求,我们需要在 `value` 方法中编写代码,实现对输入的二维 numpy 数组 `x` 进行 sigmoid 转换,并返回结果。以下是按照要求编写的代码:
```python
import numpy as np
class MyModel:
def value(self, x: np.ndarray) -> np.ndarray:
"""
Parameters:
x: a two dimensional numpy array
Returns:
a two dimensional array representing the element-wise sigmoid of x.
"""
return 1 / (1 + np.exp(-x))
```
该代码定义了一个名为 `MyModel` 的类,其中包含了一个名为 `value` 的方法,它接收一个二维 numpy 数组 `x` 作为参数,并返回一个二维数组,表示 `x` 中每个元素的 sigmoid 值。
在方法中,我们使用了 numpy 中的 `exp` 函数来计算 $e^{-x}$,然后将其加上 1,并取倒数即可得到 sigmoid 值。
相关问题
class Softmax(Activation): ''' softmax nonlinear function. ''' def __init__(self): ''' There are no parameters in softmax function. ''' super(Softmax, self).__init__() def value(self, x: np.ndarray) -> np.ndarray: ''' Parameters: x is the input to the softmax function. x is a two dimensional numpy array. Each row is the input to the softmax function Returns: output of the softmax function. The returned value is with the same shape as that of x. ''' #### write your code below #### return def derivative(self, x: np.ndarray) -> np.ndarray: ''' Parameters: x is the input to the softmax function. x is a two dimensional numpy array. Returns: a two dimensional array representing the derivative of softmax function w.r.t. x. ''' #### write your code below ####
下面是对 `Softmax` 激活函数的实现,你可以将其加入到代码中:
```python
class Softmax(Activation):
'''
softmax nonlinear function.
'''
def __init__(self):
'''
There are no parameters in softmax function.
'''
super(Softmax, self).__init__()
def value(self, x: np.ndarray) -> np.ndarray:
'''
Parameters:
x is the input to the softmax function. x is a two dimensional numpy array. Each row is the input to the softmax function
Returns:
output of the softmax function. The returned value is with the same shape as that of x.
'''
exp_scores = np.exp(x)
return exp_scores / np.sum(exp_scores, axis=1, keepdims=True)
def derivative(self, x: np.ndarray) -> np.ndarray:
'''
Parameters:
x is the input to the softmax function. x is a two dimensional numpy array.
Returns:
a two dimensional array representing the derivative of softmax function w.r.t. x.
'''
softmax_output = self.value(x)
return softmax_output * (1 - softmax_output)
```
def derivative(self, x: np.ndarray) -> np.ndarray: ''' Parameters: x is a two dimensional array. Returns: a two dimensional array whose shape is the same as that of x. The returned value is the elementwise derivative of the sigmoid function w.r.t. x. ''' #### write your code below ####
根据这段代码的要求,我们需要在 `derivative` 方法中编写代码,实现对输入的二维 numpy 数组 `x` 进行 sigmoid 函数的导数计算,并返回结果。以下是按照要求编写的代码:
```python
import numpy as np
class MyModel:
def value(self, x: np.ndarray) -> np.ndarray:
"""
Parameters:
x: a two dimensional numpy array
Returns:
a two dimensional array representing the element-wise sigmoid of x.
"""
return 1 / (1 + np.exp(-x))
def derivative(self, x: np.ndarray) -> np.ndarray:
"""
Parameters:
x: a two dimensional numpy array
Returns:
a two dimensional array whose shape is the same as that of x.
The returned value is the elementwise derivative of the sigmoid function w.r.t. x.
"""
sigmoid_x = self.value(x)
return sigmoid_x * (1 - sigmoid_x)
```
在方法中,我们首先调用 `value` 方法,计算输入数组 `x` 的 sigmoid 值,并将其保存在变量 `sigmoid_x` 中。然后,我们根据 sigmoid 函数的导数公式:
$$
\frac{d}{dx}sigmoid(x) = sigmoid(x) \cdot (1 - sigmoid(x))
$$
对 `sigmoid_x` 进行计算,得到每个元素的导数。最后,我们将计算结果返回即可。需要注意的是,由于 sigmoid 函数的值域在 $(0, 1)$ 之间,因此其导数的值也在 $(0, 1)$ 之间。