Python의 오토인코더란 무엇입니까?

from keras.layers import Input, Dense
from keras.models import Model

# 定义编码器
input_img = Input(shape=(784,))
encoded = Dense(128, activation='relu')(input_img)
encoded = Dense(64, activation='relu')(encoded)
encoded_output = Dense(32, activation='relu')(encoded)

# 定义解码器
decoded = Dense(64, activation='relu')(encoded_output)
decoded = Dense(128, activation='relu')(decoded)
decoded_output = Dense(784, activation='sigmoid')(decoded)

# 定义自动编码器模型
autoencoder = Model(inputs=input_img, outputs=decoded_output)

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

# 加载数据
from keras.datasets import mnist
import numpy as np

(x_train, _), (x_test, _) = mnist.load_data()
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))

# 压缩图像
encoded_imgs = encoder.predict(x_test)
# 解压缩图像
decoded_imgs = decoder.predict(encoded_imgs)

# 可视化图像
import matplotlib.pyplot as plt
n = 10  # 选择要可视化的图像数量
plt.figure(figsize=(20, 4))
for i in range(n):
    # 原始图像
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    # 压缩后的图像
    ax = plt.subplot(2, n, i + 1 + n)
    plt.imshow(decoded_imgs[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()