写一段基于python的高光谱KSC数据集的3DCNN卷积网络

好的，您的问题是关于高光谱KSC数据集的3DCNN卷积网络，以下是示例代码，希望对您有帮助：

import tensorflow as tf
from tensorflow.keras import layers

# load data
x_train = ...
y_train = ...
x_test = ...
y_test = ...

# define model architecture
input_shape = (x_train.shape[1], x_train.shape[2], x_train.shape[3], 1)

model = tf.keras.Sequential()
model.add(layers.Conv3D(filters=32, kernel_size=(3,3,3), activation='relu', input_shape=input_shape))
model.add(layers.MaxPooling3D(pool_size=(2,2,2)))
model.add(layers.BatchNormalization())
model.add(layers.Conv3D(filters=64, kernel_size=(3,3,3), activation='relu'))
model.add(layers.MaxPooling3D(pool_size=(2,2,2)))
model.add(layers.BatchNormalization())
model.add(layers.Flatten())
model.add(layers.Dense(units=512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(units=10, activation='softmax'))

# compile model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# train model
history = model.fit(x_train, y_train, batch_size=32, epochs=50, validation_data=(x_test, y_test))

# evaluate model
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

注：上述代码仅为示例，实际的模型架构应根据具体数据集和任务进行调整。