生成用Mobielnetv3网络对高光谱图像进行分类代码

以下是使用MobileNetV3网络对高光谱图像进行分类的示例代码，其中使用Keras深度学习框架和Scikit-learn机器学习库：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report

from keras.models import Model
from keras.layers import Input, Dense, GlobalAveragePooling2D, Dropout
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from keras.applications.mobilenet_v3 import MobileNetV3Small

# 加载数据集
data = pd.read_csv('high_spectra_dataset.csv')

# 将数据按比例分成训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.iloc[:, :-1].values, data.iloc[:, -1].values, test_size=0.2, random_state=42)

# 数据归一化
X_train = X_train / 255.0
X_test = X_test / 255.0

# 将数据转换为高光谱图像格式
X_train = np.reshape(X_train, (-1, 33, 33, 224))
X_test = np.reshape(X_test, (-1, 33, 33, 224))

# 构建MobileNetV3模型
input = Input(shape=(33, 33, 224))
model = MobileNetV3Small(input_tensor=input, weights=None, classes=10)
model.summary()

# 编译模型
model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

# 设定回调函数
early_stop = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='min', restore_best_weights=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1, mode='min', min_lr=0.00001)

# 训练模型
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50, batch_size=32, callbacks=[early_stop, reduce_lr])

# 模型评估
score = model.evaluate(X_test, y_test, verbose=0)
print('Test accuracy:', score[1])

# 绘制训练和验证的损失值和准确率曲线
plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.plot(history.history['accuracy'], label='train_accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.legend()
plt.show()

# 生成混淆矩阵和分类报告
y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
print('Confusion matrix:\n', confusion_matrix(y_test, y_pred))
print('\nClassification report:\n', classification_report(y_test, y_pred))

此示例代码假设您的高光谱图像数据已经准备好，保存在名为“high_spectra_dataset.csv”的CSV文件中，其中最后一列是类标签。该代码将数据加载到Pandas DataFrame中，然后将其拆分为训练集和测试集。然后，数据被归一化并转换为高光谱图像格式。接下来，使用Keras框架中的MobileNetV3Small模型进行分类，并编译模型。在训练期间，使用早期停止和学习率调整回调函数来避免过拟合和提高模型性能。最后，模型被评估，并绘制训练和验证的损失值和准确率曲线，同时生成混淆矩阵和分类报告。