梯度下降图像分类python
时间: 2023-12-20 19:32:10 浏览: 43
以下是使用梯度下降算法进行图像分类的Python示例代码:
```python
import numpy as np
# 定义sigmoid函数
def sigmoid(z):
return 1 / (1 + np.exp(-z))
# 定义梯度下降函数
def gradient_descent(X, y, alpha, num_iterations):
m, n = X.shape
theta = np.zeros(n)
for iteration in range(num_iterations):
h = sigmoid(np.dot(X, theta))
error = h - y
gradient = np.dot(X.T, error) / m
theta -= alpha * gradient
return theta
# 加载数据集
data = np.loadtxt('data.txt', delimiter=',')
X = data[:, :-1]
y = data[:, -1]
# 添加偏置项
X = np.c_[np.ones(len(X)), X]
# 设置学习率和迭代次数
alpha = 0.01
num_iterations = 1000
# 调用梯度下降函数进行训练
theta = gradient_descent(X, y, alpha, num_iterations)
# 进行预测
predictions = sigmoid(np.dot(X, theta))
predictions[predictions >= 0.5] = 1
predictions[predictions < 0.5] = 0
# 输出预测结果
print(predictions)
```