自编码器简介

在 TensorFlow.org 上查看 在 Google Colab 中运行 在 GitHub 上查看源代码 下载笔记本

本教程通过以下三个示例介绍自编码器：基础知识、图像降噪和异常检测。

自编码器是一种特殊类型的神经网络，经过训练后可将其输入复制到其输出。例如，给定一个手写数字的图像，自编码器首先将图像编码为低维的潜在表示，然后将该潜在表示解码回图像。自编码器学习压缩数据，同时最大程度地减少重构误差。

要详细了解自编码器，请考虑阅读 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 撰写的《深度学习》一书的第 14 章。

导入 TensorFlow 和其他库

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf

from sklearn.metrics import accuracy_score, precision_score, recall_score
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers, losses
from tensorflow.keras.datasets import fashion_mnist
from tensorflow.keras.models import Model

2023-11-07 19:41:16.708459: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-11-07 19:41:16.708505: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-11-07 19:41:16.710018: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

加载数据集

首先，您将使用 Fashion MNIST 数据集训练基本自编码器。此数据集中的每个图像均为 28x28 像素。 

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

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.

print (x_train.shape)
print (x_test.shape)

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz
29515/29515 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz
26421880/26421880 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz
5148/5148 [==============================] - 0s 0us/step
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz
4422102/4422102 [==============================] - 0s 0us/step
(60000, 28, 28)
(10000, 28, 28)

第一个示例：基本自编码器

Basic autoencoder results

定义一个具有两个密集层的自编码器：一个将图像压缩为 64 维隐向量的 encoder，以及一个从隐空间重构原始图像的 decoder。

要定义模型，请使用 Keras Model Subclassing API。

latent_dim = 64 

class Autoencoder(Model):
  def __init__(self, latent_dim):
    super(Autoencoder, self).__init__()
    self.latent_dim = latent_dim   
    self.encoder = tf.keras.Sequential([
      layers