def loss(self, y: np.ndarray, prob: np.ndarray)->float: ''' Compute cross entropy loss. Parameters: y is the true label. y is a one dimensional array. prob is the predicted label probability. prob is a one dimensional array. Return: cross entropy loss ''' #### write your code below #### #### you must think of how to deal with the case that prob contains 1 or 0 #### return #### write your code above ####。请帮我完成需要填写的代码
时间: 2024-02-10 17:17:17 浏览: 34
好的,根据您提供的代码,以下是需要填写的代码:
```python
# compute cross entropy loss
epsilon = 1e-5
loss = -np.mean(y * np.log(prob + epsilon) + (1 - y) * np.log(1 - prob + epsilon))
return loss
```
以上代码会计算交叉熵损失函数,其中为了避免在计算对数时出现极小值,加上了一个非常小的值 epsilon。此外,为了适应在梯度下降时的求导计算,使用了概率值的对数形式。
希望对您有帮助!
相关问题
def accuracy(self, y: np.ndarray, prob: np.ndarray)-> float: ''' compute accuracy Parameters: y is the true label. y is a one dimensional array. prob is the predicted label probability. prob is a one dimensional array. Return: acc is the accuracy value ''' #### write your code below #### #### write your code above ####。请帮我完成需要填写的代码
好的,根据您提供的代码,以下是需要填写的代码:
```python
# compute accuracy
pred_label = (prob >= 0.5).astype(int)
acc = np.mean(pred_label == y)
return acc
```
以上代码会将模型预测的概率值转换为标签,规定概率大于等于 0.5 的为正类(1),小于 0.5 的为负类(0),然后计算分类的准确率。
希望对您有帮助!
def gradient(self, trainX: np.ndarray, trainY: np.ndarray)->np.ndarray: ''' Compute gradient of logistic regression. Parameters: trainX is the training data input. trainX is a two two dimensional numpy array. trainY is the training data label. trainY is a one dimensional numpy array. Return: a one dimensional numpy array representing the gradient ''' x = self.add_feature(trainX) #### write your code below #### return #### write your code above ####。请帮我完成需要填写的代码
好的,根据您提供的代码,以下是需要填写的代码:
```python
# compute gradient of logistic regression
prob = self.sigmoid(x)
grad = np.mean((prob - trainY)[:, None] * x, axis=0)
return grad
```
以上代码会先为输入数据添加全为1的一列,然后计算模型预测的标签概率,最后使用梯度下降的公式计算梯度。其中 `(prob - trainY)[:, None]` 的作用是将一维数组转换为二维数组,方便后续计算。
希望对您有帮助!