基于BP神经网络的高光谱分类+印度松树数据集，给出具体代码和注释

好的，以下是基于BP神经网络的高光谱分类+印度松树数据集的代码和注释。这里我假设您已经熟悉了Python和神经网络的基本知识。

# 导入必要的库
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 读取数据集
data = pd.read_csv('indian_pines.csv')

# 提取特征和标签
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 数据标准化
sc = StandardScaler()
X = sc.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 构建神经网络模型
class NeuralNetwork:
    def __init__(self, layers, alpha=0.1):
        self.W = []
        self.layers = layers
        self.alpha = alpha
        for i in range(0, len(layers) - 2):
            w = np.random.randn(layers[i] + 1, layers[i + 1] + 1)
            self.W.append(w / np.sqrt(layers[i]))
        w = np.random.randn(layers[-2] + 1, layers[-1])
        self.W.append(w / np.sqrt(layers[-2]))

    def __sigmoid(self, x):
        return 1.0 / (1 + np.exp(-x))

    def __sigmoid_deriv(self, x):
        return x * (1 - x)

    def fit(self, X, y, epochs=1000, displayUpdate=100):
        X = np.c_[X, np.ones((X.shape[0]))]
        for epoch in range(0, epochs):
            for (x, target) in zip(X, y):
                self.fit_partial(x, target)
            if epoch == 0 or (epoch + 1) % displayUpdate == 0:
                loss = self.calculate_loss(X, y)
                print("[INFO] epoch={}, loss={:.7f}".format(epoch + 1, loss))

    def predict(self, X, addBias=True):
        p = np.atleast_2d(X)
        if addBias:
            p = np.c_[p, np.ones((p.shape[0]))]
        for layer in range(0, len(self.W)):
            p = self.__sigmoid(np.dot(p, self.W[layer]))
        return p

    def fit_partial(self, x, y):
        A = [np.atleast_2d(x)]
        for layer in range(0, len(self.W)):
            net = A[layer].dot(self.W[layer])
            out = self.__sigmoid(net)
            A.append(out)
        error = A[-1] - y
        D = [error * self.__sigmoid_deriv(A[-1])]
        for layer in range(len(A) - 2, 0, -1):
            delta = D[-1].dot(self.W[layer].T)
            delta = delta * self.__sigmoid_deriv(A[layer])
            D.append(delta)
        D = D[::-1]
        for layer in range(0, len(self.W)):
            self.W[layer] += -self.alpha * A[layer].T.dot(D[layer])

    def calculate_loss(self, X, targets):
        targets = np.atleast_2d(targets)
        predictions = self.predict(X, addBias=False)
        loss = 0.5 * np.sum((predictions - targets) ** 2)
        return loss

# 定义神经网络模型的层数和每层的神经元数
nn = NeuralNetwork([200, 100, 16])

# 训练神经网络模型
nn.fit(X_train, y_train, epochs=2000)

# 在测试集上评估模型的准确率
predictions = nn.predict(X_test)
predictions = np.argmax(predictions, axis=1)
accuracy = np.sum(predictions == y_test) / float(len(y_test))
print("Accuracy: {:.2f}%".format(accuracy * 100))

代码中使用了一个包含3个隐藏层的神经网络模型，每个隐藏层包含分别包含200、100和16个神经元，输出层包含16个神经元（对应16个类别）。将数据集划分为训练集和测试集，并使用标准化处理对数据进行预处理。训练神经网络模型的代码包含了反向传播算法的实现。最后在测试集上评估模型的准确率。