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。请帮我完成需要填写的代码
时间: 2024-02-10 13:17:39 浏览: 28
Softmax函数的计算公式如下:
$$
\sigma(z_j) = \frac{e^{z_j}}{\sum_{k=1}^{K} e^{z_k}}
$$
其中,$z_j$表示输入向量的第$j$个元素,$K$表示向量的长度。
因此,可以使用以下代码实现Softmax函数:
```
def value(self, x: np.ndarray) -> np.ndarray:
# 计算指数
exp_x = np.exp(x)
# 计算每行元素的和
sum_exp_x = np.sum(exp_x, axis=1, keepdims=True)
# 计算Softmax函数值
return exp_x / sum_exp_x
```
其中,`np.exp`函数可以对数组进行指数运算;`np.sum`函数可以计算每行元素的和,同时保持二维数组的维度不变,因此`keepdims=True`;最后将指数和除以每行元素的和即可得到Softmax函数的值。
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)
```
class CBOW(torch.nn.Module): def __init__(self, word_size, embedding_dim): super(CBOW, self).__init__() self.embeddings = nn.Embedding(word_size, embedding_dim) self.linear1 = nn.Linear(embedding_dim, 128) self.activation_function1 = nn.ReLU() self.linear2 = nn.Linear(128, word_size) self.activation_function2 = nn.LogSoftmax(dim = -1)
这段代码定义了一个CBOW(Continuous Bag-of-Words)模型的类CBOW。CBOW模型是一种用于自然语言处理的神经网络模型。
在__init__方法中,模型的初始化操作被定义。它接受两个参数:word_size和embedding_dim。word_size表示词汇表的大小,embedding_dim表示词向量的维度。
在模型的初始化过程中,首先创建了一个Embedding层self.embeddings,它将输入的单词索引映射为词向量。Embedding层的大小为(word_size, embedding_dim),其中word_size是词汇表的大小,embedding_dim是词向量的维度。
接下来,定义了两个线性层self.linear1和self.linear2。self.linear1将词向量映射到一个大小为128的隐藏层,然后通过激活函数self.activation_function1(这里使用了ReLU)进行非线性变换。self.linear2将隐藏层的输出映射为最终的预测结果,大小为word_size。最后,通过self.activation_function2(这里使用了LogSoftmax)对预测结果进行归一化。
这样,CBOW模型的架构就定义好了,可以用于训练和预测。