使用自动编码器对运动想象分类的代码

可以使用以下代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model

# 定义输入层
input_layer = Input(shape=(784,))

# 定义编码层
encoded = Dense(128, activation='relu')(input_layer)
encoded = Dense(64, activation='relu')(encoded)
encoded = Dense(32, activation='relu')(encoded)

# 定义解码层
decoded = Dense(64, activation='relu')(encoded)
decoded = Dense(128, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)

# 定义自动编码器模型
autoencoder = Model(input_layer, decoded)

# 编译模型
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# 数据预处理
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))

# 训练模型
autoencoder.fit(x_train, x_train,
                epochs=50,
                batch_size=256,
                shuffle=True,
                validation_data=(x_test, x_test))

# 使用训练好的模型进行分类
encoded_imgs = autoencoder.predict(x_test)

这段代码使用自动编码器对 MNIST 手写数字进行分类。