softmax分类器代码python

以下是一个简单的softmax分类器的Python代码示例：

import numpy as np

class SoftmaxClassifier:
    def __init__(self, num_classes, input_size, learning_rate=.1):
        self.num_classes = num_classes
        self.input_size = input_size
        self.learning_rate = learning_rate
        self.weights = np.random.randn(input_size, num_classes)
        self.biases = np.zeros(num_classes)

    def train(self, X, y, num_epochs=100):
        for epoch in range(num_epochs):
            # Forward pass
            scores = np.dot(X, self.weights) + self.biases
            exp_scores = np.exp(scores)
            probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)

            # Backward pass
            delta = probs
            delta[range(X.shape[]), y] -= 1
            dW = np.dot(X.T, delta)
            db = np.sum(delta, axis=)

            # Update weights and biases
            self.weights -= self.learning_rate * dW
            self.biases -= self.learning_rate * db

    def predict(self, X):