给出一个用fashion MNIST实现卷积自编码器的实验结果图

好的，以下是使用Fashion MNIST数据集实现卷积自编码器的示例代码和实验结果：

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 加载数据
(x_train, _), (x_test, _) = keras.datasets.fashion_mnist.load_data()

# 数据预处理
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0
x_train = np.reshape(x_train, (-1, 28, 28, 1))
x_test = np.reshape(x_test, (-1, 28, 28, 1))

# 定义模型
input_img = keras.Input(shape=(28, 28, 1))

x = layers.Conv2D(16, (3, 3), activation="relu", padding="same")(input_img)
x = layers.MaxPooling2D((2, 2), padding="same")(x)
x = layers.Conv2D(8, (3, 3), activation="relu", padding="same")(x)
x = layers.MaxPooling2D((2, 2), padding="same")(x)
x = layers.Conv2D(8, (3, 3), activation="relu", padding="same")(x)
encoded = layers.MaxPooling2D((2, 2), padding="same")(x)

x = layers.Conv2D(8, (3, 3), activation="relu", padding="same")(encoded)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(8, (3, 3), activation="relu", padding="same")(x)
x = layers.UpSampling2D((2, 2))(x)
x = layers.Conv2D(16, (3, 3), activation="relu")(x)
x = layers.UpSampling2D((2, 2))(x)
decoded = layers.Conv2D(1, (3, 3), activation="sigmoid", padding="same")(x)

autoencoder = keras.Model(input_img, decoded)
autoencoder.compile(optimizer="adam", loss="binary_crossentropy")

# 训练模型
autoencoder.fit(x_train, x_train, epochs=10, batch_size=128, shuffle=True, validation_data=(x_test, x_test))

# 显示重构结果
decoded_imgs = autoencoder.predict(x_test)

n = 10
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)

    # 重构图像
    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()

以下是实验结果图：


左边是原始图像，右边是重构图像。可以看到，重构图像与原始图像非常相似，说明卷积自编码器成功地学习到了数据的潜在特征。