第154章 敦煌计划（2/2）

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m = len(y)

A = sigmoid(np.dot(X, w) + b)

cost = -(1 / m) * np.sum(y * np.log(A) + (1 - y) * np.log(1 - A))

return cost

```python

def pute_gradient(X, y, w, b):

m = len(y)

A = sigmoid(np.dot(X, w) + b)

dz = A - y

dw = (1 / m) * np.dot(X.T, dz)

db = (1 / m) * np.sum(dz)

return dw, db

```

接下来，我们编写一个函数来更新权重和偏置项：

```python

def update_parameters(w, b, dw, db, learning_rate):

w = w - learning_rate * dw

b = b - learning_rate * db

return w, b

```*****

现在，我们将所有这些步骤整合到一个训练函数中，并设置迭代次数和学习率：

```python

def train_logistic_regression(X, y, num_iterations=2000, learning_rate=0.5):

dim = X.shape[1]

w, b = initialize_with_zeros(dim)

for i in range(num_iterations):

dw, db = pute_gradient(X, y, w, b)

w, b = update_parameters(w, b, dw, db, learning_rate)

if i % 100 == 0:

cost = pute_cost(X, y, w, b)

print(f"Cost after iteration {i}: {cost}")