写一段读取mat格式的脑电数据使用自动编码器分类的代码

以下是读取mat格式的脑电数据使用自动编码器分类的代码：

import scipy.io as sio
import numpy as np
from keras.layers import Input, Dense
from keras.models import Model

# 读取mat格式的脑电数据
data = sio.loadmat('eeg_data.mat')
X_train = data['X_train']
X_test = data['X_test']
y_train = data['y_train']
y_test = data['y_test']

# 自动编码器模型
input_data = Input(shape=(X_train.shape[1],))
encoded = Dense(64, activation='relu')(input_data)
decoded = Dense(X_train.shape[1], activation='sigmoid')(encoded)
autoencoder = Model(input_data, decoded)

# 编码器模型
encoder = Model(input_data, encoded)

# 训练自动编码器
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.fit(X_train, X_train, epochs=50, batch_size=256, shuffle=True, validation_data=(X_test, X_test))

# 使用编码器进行分类
encoded_X_train = encoder.predict(X_train)
encoded_X_test = encoder.predict(X_test)
input_data = Input(shape=(encoded_X_train.shape[1],))
output = Dense(1, activation='sigmoid')(input_data)
classifier = Model(input_data, output)
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
classifier.fit(encoded_X_train, y_train, epochs=50, batch_size=256, shuffle=True, validation_data=(encoded_X_test, y_test))

希望对您有所帮助！