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
时间: 2023-04-10 12:05:00 浏览: 66
以下是一段读取mat格式的脑电数据使用自动编码器分类的代码:
```python
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_layer = Input(shape=(X_train.shape[1],))
encoded = Dense(64, activation='relu')(input_layer)
decoded = Dense(X_train.shape[1], activation='sigmoid')(encoded)
autoencoder = Model(input_layer, decoded)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
# 训练自动编码器
autoencoder.fit(X_train, X_train, epochs=50, batch_size=256, shuffle=True, validation_data=(X_test, X_test))
# 提取编码器部分
encoder = Model(input_layer, encoded)
# 使用编码器进行分类
input_layer = Input(shape=(X_train.shape[1],))
encoded = encoder(input_layer)
output_layer = Dense(1, activation='sigmoid')(encoded)
classifier = Model(input_layer, output_layer)
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# 训练分类器
classifier.fit(X_train, y_train, epochs=50, batch_size=256, shuffle=True, validation_data=(X_test, y_test))
```
希望这段代码能够帮助你解决问题。