用numpy库实现softmax回归

可以使用以下代码实现softmax回归：

import numpy as np

def softmax_regression(X, y, num_classes, learning_rate, num_iterations):
    # Initialize weights and biases
    num_features = X.shape[1]
    W = np.zeros((num_features, num_classes))
    b = np.zeros((1, num_classes))

    # Gradient descent
    for i in range(num_iterations):
        # Forward propagation
        Z = np.dot(X, W) + b
        A = np.exp(Z) / np.sum(np.exp(Z), axis=1, keepdims=True)

        # Backward propagation
        dZ = A - y
        dW = np.dot(X.T, dZ)
        db = np.sum(dZ, axis=0, keepdims=True)

        # Update weights and biases
        W -= learning_rate * dW
        b -= learning_rate * db

    return W, b

其中，X是输入特征矩阵，y是标签矩阵，num_classes是分类数，learning_rate是学习率，num_iterations是迭代次数。函数返回训练好的权重矩阵W和偏置矩阵b。