{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Ndo4ERqnwQOU" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2023-11-07T19:41:15.748569Z", "iopub.status.busy": "2023-11-07T19:41:15.748069Z", "iopub.status.idle": "2023-11-07T19:41:15.751940Z", "shell.execute_reply": "2023-11-07T19:41:15.751245Z" }, "id": "MTKwbguKwT4R" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "xfNT-mlFwxVM" }, "source": [ "# 自编码器简介" ] }, { "cell_type": "markdown", "metadata": { "id": "0TD5ZrvEMbhZ" }, "source": [ "\n", " \n", " \n", " \n", " \n", "
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
" ] }, { "cell_type": "markdown", "metadata": { "id": "ITZuApL56Mny" }, "source": [ "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。\n", "\n", "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。\n", "\n", "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》](https://www.deeplearningbook.org/)一书的第 14 章。" ] }, { "cell_type": "markdown", "metadata": { "id": "e1_Y75QXJS6h" }, "source": [ "## 导入 TensorFlow 和其他库" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:41:15.755885Z", "iopub.status.busy": "2023-11-07T19:41:15.755444Z", "iopub.status.idle": "2023-11-07T19:41:18.701185Z", "shell.execute_reply": "2023-11-07T19:41:18.700017Z" }, "id": "YfIk2es3hJEd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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\n", "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\n", "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\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score\n", "from sklearn.model_selection import train_test_split\n", "from tensorflow.keras import layers, losses\n", "from tensorflow.keras.datasets import fashion_mnist\n", "from tensorflow.keras.models import Model" ] }, { "cell_type": "markdown", "metadata": { "id": "iYn4MdZnKCey" }, "source": [ "## 加载数据集\n", "\n", "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。 " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:41:18.706290Z", "iopub.status.busy": "2023-11-07T19:41:18.705320Z", "iopub.status.idle": "2023-11-07T19:41:19.678647Z", "shell.execute_reply": "2023-11-07T19:41:19.677938Z" }, "id": "YZm503-I_tji" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 8192/29515 [=======>......................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "29515/29515 [==============================] - 0s 0us/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 8192/26421880 [..............................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4202496/26421880 [===>..........................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "16523264/26421880 [=================>............] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "24494080/26421880 [==========================>...] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "26421880/26421880 [==============================] - 0s 0us/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "5148/5148 [==============================] - 0s 0us/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 8192/4422102 [..............................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "4422102/4422102 [==============================] - 0s 0us/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(60000, 28, 28)\n", "(10000, 28, 28)\n" ] } ], "source": [ "(x_train, _), (x_test, _) = fashion_mnist.load_data()\n", "\n", "x_train = x_train.astype('float32') / 255.\n", "x_test = x_test.astype('float32') / 255.\n", "\n", "print (x_train.shape)\n", "print (x_test.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "VEdCXSwCoKok" }, "source": [ "## 第一个示例：基本自编码器\n", "\n", "![Basic autoencoder results](images/intro_autoencoder_result.png)\n", "\n", "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`。\n", "\n", "要定义模型，请使用 [Keras Model Subclassing API](https://tensorflow.google.cn/guide/keras/custom_layers_and_models)。\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:41:19.682302Z", "iopub.status.busy": "2023-11-07T19:41:19.682036Z", "iopub.status.idle": "2023-11-07T19:41:21.921821Z", "shell.execute_reply": "2023-11-07T19:41:21.921094Z" }, "id": "0MUxidpyChjX" }, "outputs": [], "source": [ "latent_dim = 64 \n", "\n", "class Autoencoder(Model):\n", " def __init__(self, latent_dim):\n", " super(Autoencoder, self).__init__()\n", " self.latent_dim = latent_dim \n", " self.encoder = tf.keras.Sequential([\n", " layers.Flatten(),\n", " layers.Dense(latent_dim, activation='relu'),\n", " ])\n", " self.decoder = tf.keras.Sequential([\n", " layers.Dense(784, activation='sigmoid'),\n", " layers.Reshape((28, 28))\n", " ])\n", "\n", " def call(self, x):\n", " encoded = self.encoder(x)\n", " decoded = self.decoder(encoded)\n", " return decoded\n", " \n", "autoencoder = Autoencoder(latent_dim) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:41:21.926340Z", "iopub.status.busy": "2023-11-07T19:41:21.925601Z", "iopub.status.idle": "2023-11-07T19:41:22.100956Z", "shell.execute_reply": "2023-11-07T19:41:22.100271Z" }, "id": "9I1JlqEIDCI4" }, "outputs": [], "source": [ "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())" ] }, { "cell_type": "markdown", "metadata": { "id": "7oJSeMTroABs" }, "source": [ "使用 `x_train` 作为输入和目标来训练模型。`encoder` 会学习将数据集从 784 个维度压缩到隐空间，而 `decoder` 将学习重构原始图像。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:41:22.105322Z", "iopub.status.busy": "2023-11-07T19:41:22.104646Z", "iopub.status.idle": "2023-11-07T19:42:06.715250Z", "shell.execute_reply": "2023-11-07T19:42:06.714597Z" }, "id": "h1RI9OfHDBsK" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1699386084.268834 448569 device_compiler.h:186] Compiled cluster using XLA! 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1869/1875 [============================>.] - ETA: 0s - loss: 0.0095" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0095 - val_loss: 0.0094\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 6s - loss: 0.0101" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 27/1875 [..............................] - ETA: 3s - loss: 0.0092" ] }, { "name": 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0s - loss: 0.0091" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0091 - val_loss: 0.0091\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 7/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 5s - loss: 0.0080" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 25/1875 [..............................] - ETA: 3s - loss: 0.0091" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 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0s - loss: 0.0089" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0089 - val_loss: 0.0090\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 9/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 5s - loss: 0.0086" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 27/1875 [..............................] - ETA: 3s - loss: 0.0089" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1829/1875 [============================>.] - ETA: 0s - loss: 0.0088" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1856/1875 [============================>.] - ETA: 0s - loss: 0.0088" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0088 - val_loss: 0.0089\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autoencoder.fit(x_train, x_train,\n", " epochs=10,\n", " shuffle=True,\n", " validation_data=(x_test, x_test))" ] }, { "cell_type": "markdown", "metadata": { "id": "wAM1QBhtoC-n" }, "source": [ "现在，模型已经训练完成，我们通过对测试集中的图像进行编码和解码来测试该模型。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:06.718986Z", "iopub.status.busy": "2023-11-07T19:42:06.718398Z", "iopub.status.idle": "2023-11-07T19:42:06.831634Z", "shell.execute_reply": "2023-11-07T19:42:06.830902Z" }, "id": "Pbr5WCj7FQUi" }, "outputs": [], "source": [ "encoded_imgs = autoencoder.encoder(x_test).numpy()\n", "decoded_imgs = autoencoder.decoder(encoded_imgs).numpy()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:06.835683Z", "iopub.status.busy": "2023-11-07T19:42:06.835080Z", "iopub.status.idle": "2023-11-07T19:42:07.323315Z", "shell.execute_reply": "2023-11-07T19:42:07.322653Z" }, "id": "s4LlDOS6FUA1" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 10\n", "plt.figure(figsize=(20, 4))\n", "for i in range(n):\n", " # display original\n", " ax = plt.subplot(2, n, i + 1)\n", " plt.imshow(x_test[i])\n", " plt.title(\"original\")\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " # display reconstruction\n", " ax = plt.subplot(2, n, i + 1 + n)\n", " plt.imshow(decoded_imgs[i])\n", " plt.title(\"reconstructed\")\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "r4gv6G8PoRQE" }, "source": [ "## 第二个示例：图像降噪\n", "\n", "![Image denoising results](https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/generative/images/image_denoise_fmnist_results.png?raw=true)\n", "\n", "经过训练后，自编码器还可以去除图像中的噪点。在以下部分中，您将通过对每个图像应用随机噪声来创建有噪版本的 Fashion MNIST 数据集。随后，您将使用有噪图像作为输入并以原始图像作为目标来训练自编码器。\n", "\n", "我们重新导入数据集以忽略之前所做的修改：" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:07.326981Z", "iopub.status.busy": "2023-11-07T19:42:07.326684Z", "iopub.status.idle": "2023-11-07T19:42:07.702780Z", "shell.execute_reply": "2023-11-07T19:42:07.701933Z" }, "id": "gDYHJA2PCQ3m" }, "outputs": [], "source": [ "(x_train, _), (x_test, _) = fashion_mnist.load_data()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:07.707244Z", "iopub.status.busy": "2023-11-07T19:42:07.706553Z", "iopub.status.idle": "2023-11-07T19:42:07.797087Z", "shell.execute_reply": "2023-11-07T19:42:07.796068Z" }, "id": "uJZ-TcaqDBr5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(60000, 28, 28, 1)\n" ] } ], "source": [ "x_train = x_train.astype('float32') / 255.\n", "x_test = x_test.astype('float32') / 255.\n", "\n", "x_train = x_train[..., tf.newaxis]\n", "x_test = x_test[..., tf.newaxis]\n", "\n", "print(x_train.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "aPZl_6P65_8R" }, "source": [ "向图像添加随机噪声：" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:07.800852Z", "iopub.status.busy": "2023-11-07T19:42:07.800222Z", "iopub.status.idle": "2023-11-07T19:42:08.778106Z", "shell.execute_reply": "2023-11-07T19:42:08.777254Z" }, "id": "axSMyxC354fc" }, "outputs": [], "source": [ "noise_factor = 0.2\n", "x_train_noisy = x_train + noise_factor * tf.random.normal(shape=x_train.shape) \n", "x_test_noisy = x_test + noise_factor * tf.random.normal(shape=x_test.shape) \n", "\n", "x_train_noisy = tf.clip_by_value(x_train_noisy, clip_value_min=0., clip_value_max=1.)\n", "x_test_noisy = tf.clip_by_value(x_test_noisy, clip_value_min=0., clip_value_max=1.)" ] }, { "cell_type": "markdown", "metadata": { "id": "wRxHe4XXltNd" }, "source": [ "绘制有噪图像：\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:08.782291Z", "iopub.status.busy": "2023-11-07T19:42:08.782023Z", "iopub.status.idle": "2023-11-07T19:42:09.589975Z", "shell.execute_reply": "2023-11-07T19:42:09.589291Z" }, "id": "thKUmbVVCQpt" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 10\n", "plt.figure(figsize=(20, 2))\n", "for i in range(n):\n", " ax = plt.subplot(1, n, i + 1)\n", " plt.title(\"original + noise\")\n", " plt.imshow(tf.squeeze(x_test_noisy[i]))\n", " plt.gray()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "Sy9SY8jGl5aP" }, "source": [ "### 定义卷积自编码器" ] }, { "cell_type": "markdown", "metadata": { "id": "vT_BhZngWMwp" }, "source": [ "在此示例中，您将使用 `encoder` 中的 [Conv2D](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Conv2D) 层和 `decoder` 中的 [Conv2DTranspose](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Conv2DTranspose) 层来训练卷积自编码器。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:09.594182Z", "iopub.status.busy": "2023-11-07T19:42:09.593928Z", "iopub.status.idle": "2023-11-07T19:42:09.637716Z", "shell.execute_reply": "2023-11-07T19:42:09.637018Z" }, "id": "R5KjoIlYCQko" }, "outputs": [], "source": [ "class Denoise(Model):\n", " def __init__(self):\n", " super(Denoise, self).__init__()\n", " self.encoder = tf.keras.Sequential([\n", " layers.Input(shape=(28, 28, 1)),\n", " layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=2),\n", " layers.Conv2D(8, (3, 3), activation='relu', padding='same', strides=2)])\n", "\n", " self.decoder = tf.keras.Sequential([\n", " layers.Conv2DTranspose(8, kernel_size=3, strides=2, activation='relu', padding='same'),\n", " layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same'),\n", " layers.Conv2D(1, kernel_size=(3, 3), activation='sigmoid', padding='same')])\n", "\n", " def call(self, x):\n", " encoded = self.encoder(x)\n", " decoded = self.decoder(encoded)\n", " return decoded\n", "\n", "autoencoder = Denoise()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:09.640985Z", "iopub.status.busy": "2023-11-07T19:42:09.640565Z", "iopub.status.idle": "2023-11-07T19:42:09.648790Z", "shell.execute_reply": "2023-11-07T19:42:09.648202Z" }, "id": "QYKbiDFYCQfj" }, "outputs": [], "source": [ "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:42:09.651609Z", "iopub.status.busy": "2023-11-07T19:42:09.651371Z", "iopub.status.idle": "2023-11-07T19:43:20.431688Z", "shell.execute_reply": "2023-11-07T19:43:20.430937Z" }, "id": "IssFr1BNCQX3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 1:24:33 - loss: 0.1709" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1870/1875 [============================>.] - ETA: 0s - loss: 0.0086" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0086 - val_loss: 0.0081\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 8s - loss: 0.0078" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 16/1875 [..............................] - ETA: 6s - loss: 0.0081" ] }, { "name": 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1868/1875 [============================>.] - ETA: 0s - loss: 0.0072" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0072 - val_loss: 0.0071\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 8s - loss: 0.0065" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 17/1875 [..............................] - ETA: 6s - loss: 0.0073" ] }, { "name": 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0069 - val_loss: 0.0069\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 7/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 9s - loss: 0.0068" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 17/1875 [..............................] - ETA: 6s - loss: 0.0069" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 33/1875 [..............................] - ETA: 6s - loss: 0.0068" ] }, { "name": 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4ms/step - loss: 0.0067 - val_loss: 0.0067\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 10/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/1875 [..............................] - ETA: 8s - loss: 0.0057" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 16/1875 [..............................] - ETA: 6s - loss: 0.0068" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 32/1875 [..............................] - ETA: 6s - loss: 0.0067" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 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"autoencoder.fit(x_train_noisy, x_train,\n", " epochs=10,\n", " shuffle=True,\n", " validation_data=(x_test_noisy, x_test))" ] }, { "cell_type": "markdown", "metadata": { "id": "G85xUVBGTAKp" }, "source": [ "我们来看一下编码器的摘要。请注意图像是如何从 28x28 像素下采样为 7x7 像素的。" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:20.435686Z", "iopub.status.busy": "2023-11-07T19:43:20.435070Z", "iopub.status.idle": "2023-11-07T19:43:20.445867Z", "shell.execute_reply": "2023-11-07T19:43:20.445277Z" }, "id": "oEpxlX6sTEQz" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_2\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Layer (type) Output Shape Param # \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d (Conv2D) (None, 14, 14, 16) 160 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d_1 (Conv2D) (None, 7, 7, 8) 1160 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total params: 1320 (5.16 KB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Trainable params: 1320 (5.16 KB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Non-trainable params: 0 (0.00 Byte)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] } ], "source": [ "autoencoder.encoder.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "DDZBfMx1UtXx" }, "source": [ "解码器将图像从 7x7 像素上采样为 28x28 像素。" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:20.452417Z", "iopub.status.busy": "2023-11-07T19:43:20.451779Z", "iopub.status.idle": "2023-11-07T19:43:20.463894Z", "shell.execute_reply": "2023-11-07T19:43:20.463280Z" }, "id": "pbeQtYMaUpro" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_3\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Layer (type) Output Shape Param # \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d_transpose (Conv2DTr (None, 14, 14, 8) 584 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " anspose) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d_transpose_1 (Conv2D (None, 28, 28, 16) 1168 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Transpose) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " conv2d_2 (Conv2D) (None, 28, 28, 1) 145 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total params: 1897 (7.41 KB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Trainable params: 1897 (7.41 KB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Non-trainable params: 0 (0.00 Byte)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] } ], "source": [ "autoencoder.decoder.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "A7-VAuEy_N6M" }, "source": [ "绘制由自编码器生成的有噪图像和去噪图像。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:20.471101Z", "iopub.status.busy": "2023-11-07T19:43:20.470524Z", "iopub.status.idle": "2023-11-07T19:43:22.667921Z", "shell.execute_reply": "2023-11-07T19:43:22.667159Z" }, "id": "t5IyPi1fCQQz" }, "outputs": [], "source": [ "encoded_imgs = autoencoder.encoder(x_test_noisy).numpy()\n", "decoded_imgs = autoencoder.decoder(encoded_imgs).numpy()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:22.672153Z", "iopub.status.busy": "2023-11-07T19:43:22.671595Z", "iopub.status.idle": "2023-11-07T19:43:23.306977Z", "shell.execute_reply": "2023-11-07T19:43:23.306277Z" }, "id": "sfxr9NdBCP_x" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n = 10\n", "plt.figure(figsize=(20, 4))\n", "for i in range(n):\n", "\n", " # display original + noise\n", " ax = plt.subplot(2, n, i + 1)\n", " plt.title(\"original + noise\")\n", " plt.imshow(tf.squeeze(x_test_noisy[i]))\n", " plt.gray()\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", " # display reconstruction\n", " bx = plt.subplot(2, n, i + n + 1)\n", " plt.title(\"reconstructed\")\n", " plt.imshow(tf.squeeze(decoded_imgs[i]))\n", " plt.gray()\n", " bx.get_xaxis().set_visible(False)\n", " bx.get_yaxis().set_visible(False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "ErGrTnWHoUYl" }, "source": [ "## 第三个示例：异常检测\n", "\n", "## 概述\n", "\n", "在此示例中，您将训练自编码器来检测 [ECG5000 数据集](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000)上的异常。此数据集包含 5,000 个[心电图](https://en.wikipedia.org/wiki/Electrocardiography)，每个心电图拥有 140 个数据点。您将使用简化版的数据集，其中每个样本都被标记为 `0`（对应于异常心律）或 `1`（对应于正常心律）。您需要关注如何识别异常心律。\n", "\n", "注：这是一个有标签的数据集，因此您可以将其表述为一个监督学习问题。此示例的目标是说明可应用于没有可用标签的大型数据集的异常检测概念（例如，如果您有成千上万个正常心律，而只有少量异常心律）。\n", "\n", "您将如何使用自编码器检测异常？回想一下，自编码器经过训练后可最大程度地减少重构误差。您将只基于正常心律训练自编码器，随后使用它来重构所有数据。我们的假设是，异常心律存在更高的重构误差。随后，如果重构误差超过固定阈值，则将心律分类为异常。" ] }, { "cell_type": "markdown", "metadata": { "id": "i5estNaur_Mh" }, "source": [ "### 加载心电图数据" ] }, { "cell_type": "markdown", "metadata": { "id": "y35nsXLPsDNX" }, "source": [ "您将使用的数据集基于 [timeseriesclassification.com](http://www.timeseriesclassification.com/description.php?Dataset=ECG5000) 中的数据集。\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.310907Z", "iopub.status.busy": "2023-11-07T19:43:23.310609Z", "iopub.status.idle": "2023-11-07T19:43:23.569398Z", "shell.execute_reply": "2023-11-07T19:43:23.568725Z" }, "id": "KmKRDJWgsFYa" }, "outputs": [ { "data": { "text/html": [ "
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-1.436250 1.0 \n", "2 -0.713683 -0.532197 0.321097 0.904227 -0.421797 1.0 \n", "3 0.952074 0.990133 1.086798 1.403011 -0.383564 1.0 \n", "4 1.277392 0.960304 0.971020 1.614392 1.421456 1.0 \n", "\n", "[5 rows x 141 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Download the dataset\n", "dataframe = pd.read_csv('http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv', header=None)\n", "raw_data = dataframe.values\n", "dataframe.head()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.572665Z", "iopub.status.busy": "2023-11-07T19:43:23.572363Z", "iopub.status.idle": "2023-11-07T19:43:23.579570Z", "shell.execute_reply": "2023-11-07T19:43:23.578989Z" }, "id": "UmuCPVYKsKKx" }, "outputs": [], "source": [ "# The last element contains the labels\n", "labels = raw_data[:, -1]\n", "\n", "# The other data points are the electrocadriogram data\n", "data = raw_data[:, 0:-1]\n", "\n", "train_data, test_data, train_labels, test_labels = train_test_split(\n", " data, labels, test_size=0.2, random_state=21\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "byK2vP7hsMbz" }, "source": [ "将数据归一化为 `[0,1]`。\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.583230Z", "iopub.status.busy": "2023-11-07T19:43:23.582630Z", "iopub.status.idle": "2023-11-07T19:43:23.609706Z", "shell.execute_reply": "2023-11-07T19:43:23.609065Z" }, "id": "tgMZVWRKsPx6" }, "outputs": [], "source": [ "min_val = tf.reduce_min(train_data)\n", "max_val = tf.reduce_max(train_data)\n", "\n", "train_data = (train_data - min_val) / (max_val - min_val)\n", "test_data = (test_data - min_val) / (max_val - min_val)\n", "\n", "train_data = tf.cast(train_data, tf.float32)\n", "test_data = tf.cast(test_data, tf.float32)" ] }, { "cell_type": "markdown", "metadata": { "id": "BdSYr2IPsTiz" }, "source": [ "您将仅使用正常心律训练自编码器，在此数据集中，正常心律被标记为 `1`。将正常心律与异常心律分开。" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.613340Z", "iopub.status.busy": "2023-11-07T19:43:23.612711Z", "iopub.status.idle": "2023-11-07T19:43:23.647786Z", "shell.execute_reply": "2023-11-07T19:43:23.647176Z" }, "id": "VvK4NRe8sVhE" }, "outputs": [], "source": [ "train_labels = train_labels.astype(bool)\n", "test_labels = test_labels.astype(bool)\n", "\n", "normal_train_data = train_data[train_labels]\n", "normal_test_data = test_data[test_labels]\n", "\n", "anomalous_train_data = train_data[~train_labels]\n", "anomalous_test_data = test_data[~test_labels]" ] }, { "cell_type": "markdown", "metadata": { "id": "wVcTBDo-CqFS" }, "source": [ "绘制正常的心电图。 " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.650705Z", "iopub.status.busy": "2023-11-07T19:43:23.650458Z", "iopub.status.idle": "2023-11-07T19:43:23.795632Z", "shell.execute_reply": "2023-11-07T19:43:23.795011Z" }, "id": "ZTlMIrpmseYe" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.grid()\n", "plt.plot(np.arange(140), normal_train_data[0])\n", "plt.title(\"A Normal ECG\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "QpI9by2ZA0NN" }, "source": [ "绘制异常的心电图。" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.799178Z", "iopub.status.busy": "2023-11-07T19:43:23.798554Z", "iopub.status.idle": "2023-11-07T19:43:23.927498Z", "shell.execute_reply": "2023-11-07T19:43:23.926925Z" }, "id": "zrpXREF2siBr" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.grid()\n", "plt.plot(np.arange(140), anomalous_train_data[0])\n", "plt.title(\"An Anomalous ECG\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "0DS6QKZJslZz" }, "source": [ "### 构建模型" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.930845Z", "iopub.status.busy": "2023-11-07T19:43:23.930433Z", "iopub.status.idle": "2023-11-07T19:43:23.947173Z", "shell.execute_reply": "2023-11-07T19:43:23.946572Z" }, "id": "bf6owZQDsp9y" }, "outputs": [], "source": [ "class AnomalyDetector(Model):\n", " def __init__(self):\n", " super(AnomalyDetector, self).__init__()\n", " self.encoder = tf.keras.Sequential([\n", " layers.Dense(32, activation=\"relu\"),\n", " layers.Dense(16, activation=\"relu\"),\n", " layers.Dense(8, activation=\"relu\")])\n", " \n", " self.decoder = tf.keras.Sequential([\n", " layers.Dense(16, activation=\"relu\"),\n", " layers.Dense(32, activation=\"relu\"),\n", " layers.Dense(140, activation=\"sigmoid\")])\n", " \n", " def call(self, x):\n", " encoded = self.encoder(x)\n", " decoded = self.decoder(encoded)\n", " return decoded\n", "\n", "autoencoder = AnomalyDetector()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.950701Z", "iopub.status.busy": "2023-11-07T19:43:23.950108Z", "iopub.status.idle": "2023-11-07T19:43:23.958489Z", "shell.execute_reply": "2023-11-07T19:43:23.957851Z" }, "id": "gwRpBBbg463S" }, "outputs": [], "source": [ "autoencoder.compile(optimizer='adam', loss='mae')" ] }, { "cell_type": "markdown", "metadata": { "id": "zuTy60STBEy4" }, "source": [ "请注意，自编码器仅使用正常的心电图进行训练，但使用完整的测试集进行评估。" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:23.961723Z", "iopub.status.busy": "2023-11-07T19:43:23.961489Z", "iopub.status.idle": "2023-11-07T19:43:27.549059Z", "shell.execute_reply": "2023-11-07T19:43:27.548273Z" }, "id": "V6NFSs-jsty2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 8s - loss: 0.0582" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 2s 47ms/step - loss: 0.0575 - val_loss: 0.0530\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0559" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 16ms/step - loss: 0.0548 - val_loss: 0.0510\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0525" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 15ms/step - loss: 0.0509 - val_loss: 0.0492\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 4/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0483" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 15ms/step - loss: 0.0464 - val_loss: 0.0469\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0432" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 15ms/step - loss: 0.0416 - val_loss: 0.0444\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 6/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0389" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 15ms/step - loss: 0.0373 - val_loss: 0.0420\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 7/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0346" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 15ms/step - loss: 0.0337 - val_loss: 0.0406\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 8/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0312" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 15ms/step - loss: 0.0217 - val_loss: 0.0338\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 19/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0216" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 15ms/step - loss: 0.0212 - val_loss: 0.0335\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 20/20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/5 [=====>........................] - ETA: 0s - loss: 0.0207" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "5/5 [==============================] - 0s 15ms/step - loss: 0.0209 - val_loss: 0.0334\n" ] } ], "source": [ "history = autoencoder.fit(normal_train_data, normal_train_data, \n", " epochs=20, \n", " batch_size=512,\n", " validation_data=(test_data, test_data),\n", " shuffle=True)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:27.552504Z", "iopub.status.busy": "2023-11-07T19:43:27.552213Z", "iopub.status.idle": "2023-11-07T19:43:27.723722Z", "shell.execute_reply": "2023-11-07T19:43:27.722980Z" }, "id": "OEexphFwwTQS" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n", "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": { "id": "ceI5lKv1BT-A" }, "source": [ "如果重构误差比正常训练样本大一个标准差，您可以快速地将心电图归类为异常。首先，我们绘制训练集中的一个正常心电图，随后绘制自编码器对其进行编码和解码后的重构以及重构误差。" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:27.726960Z", "iopub.status.busy": "2023-11-07T19:43:27.726712Z", "iopub.status.idle": "2023-11-07T19:43:27.879083Z", "shell.execute_reply": "2023-11-07T19:43:27.878490Z" }, "id": "hmsk4DuktxJ2" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "encoded_data = autoencoder.encoder(normal_test_data).numpy()\n", "decoded_data = autoencoder.decoder(encoded_data).numpy()\n", "\n", "plt.plot(normal_test_data[0], 'b')\n", "plt.plot(decoded_data[0], 'r')\n", "plt.fill_between(np.arange(140), decoded_data[0], normal_test_data[0], color='lightcoral')\n", "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "ocA_q9ufB_aF" }, "source": [ "创建一个类似的绘图，这次是一个异常的测试样本。" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:27.882481Z", "iopub.status.busy": "2023-11-07T19:43:27.882203Z", "iopub.status.idle": "2023-11-07T19:43:28.028792Z", "shell.execute_reply": "2023-11-07T19:43:28.028062Z" }, "id": "vNFTuPhLwTBn" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "encoded_data = autoencoder.encoder(anomalous_test_data).numpy()\n", "decoded_data = autoencoder.decoder(encoded_data).numpy()\n", "\n", "plt.plot(anomalous_test_data[0], 'b')\n", "plt.plot(decoded_data[0], 'r')\n", "plt.fill_between(np.arange(140), decoded_data[0], anomalous_test_data[0], color='lightcoral')\n", "plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "ocimg3MBswdS" }, "source": [ "### 检测异常" ] }, { "cell_type": "markdown", "metadata": { "id": "Xnh8wmkDsypN" }, "source": [ "通过计算重构损失是否大于固定阈值来检测异常。在本教程中，您将计算训练集中正常样本的平均误差，如果重构误差比训练集大一个标准差，则将未来的样本分类为异常。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "TeuT8uTA5Y_w" }, "source": [ "根据训练集中的正常心电图绘制重构误差：" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:28.032321Z", "iopub.status.busy": "2023-11-07T19:43:28.032047Z", "iopub.status.idle": "2023-11-07T19:43:28.488572Z", "shell.execute_reply": "2023-11-07T19:43:28.487931Z" }, "id": "N7FltOnHu4-l" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/74 [..............................] - ETA: 8s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "37/74 [==============>...............] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "74/74 [==============================] - 0s 1ms/step\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reconstructions = autoencoder.predict(normal_train_data)\n", "train_loss = tf.keras.losses.mae(reconstructions, normal_train_data)\n", "\n", "plt.hist(train_loss[None,:], bins=50)\n", "plt.xlabel(\"Train loss\")\n", "plt.ylabel(\"No of examples\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "mh-3ChEF5hog" }, "source": [ "选择一个比平均值高一个标准差的阈值。" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:28.491952Z", "iopub.status.busy": "2023-11-07T19:43:28.491679Z", "iopub.status.idle": "2023-11-07T19:43:28.496009Z", "shell.execute_reply": "2023-11-07T19:43:28.495376Z" }, "id": "82hkl0Chs3P_" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Threshold: 0.032788377\n" ] } ], "source": [ "threshold = np.mean(train_loss) + np.std(train_loss)\n", "print(\"Threshold: \", threshold)" ] }, { "cell_type": "markdown", "metadata": { "id": "uEGlA1Be50Nj" }, "source": [ "注：还有其他可用来选择阈值的策略，高于该阈值时，应将测试样本分类为异常，正确的方式将取决于您的数据集。您可以通过本教程末尾的链接了解更多信息。 " ] }, { "cell_type": "markdown", "metadata": { "id": "zpLSDAeb51D_" }, "source": [ "如果检查测试集中异常样本的重构误差，您会注意到大多数异常样本的重构误差都比阈值大。通过更改阈值，您可以调整分类器的[精确率](https://developers.google.com/machine-learning/glossary#precision)和[召回率](https://developers.google.com/machine-learning/glossary#recall)。 " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:28.499643Z", "iopub.status.busy": "2023-11-07T19:43:28.499070Z", "iopub.status.idle": "2023-11-07T19:43:28.770597Z", "shell.execute_reply": "2023-11-07T19:43:28.769884Z" }, "id": "sKVwjQK955Wy" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/14 [=>............................] - ETA: 0s" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "14/14 [==============================] - 0s 1ms/step\n" ] }, { "data": { "image/png": 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j4+Xv76+MjAylpKRIkg4cOKBjx44pISHBozUAAAA7VOumke6Slpamvn37Kjo6WmfPntWqVau0bds2ffjhhwoNDdWIESOUmpqq8PBwhYSEaMyYMUpISKjwjCUAAPCvxeUgc/nyZc2ePVtr167VsWPHVFJSUmb96dOnq7yvkydPaujQocrLy1NoaKg6d+6sDz/8UP/+7/8uSZo9e7Z8fHyUkpKi4uJiJSUlaeHCha6WDAAA6iiXryMzefJkvfrqqxo/frwmTZqkZ599VkeOHNGGDRs0efJkjR071lO1VktVz0MH6jobrxFTFVxHBqibPHYdmTfeeEOLFy/W+PHj5efnp4ceekivvvqqJk+erJ07d15T0QAAAK5wOcjk5+erU6dOkqQGDRqosLBQkvSb3/xGGzfWzf/xAQCA2snlINO8eXPl5eVJklq3bq3NmzdLkvbs2cP1WwAAQI1yOcgMGjRIGRkZkqQxY8boueeeU5s2bTR06FA9+uijbi8QAACgIi6ftTR9+nTnnwcPHqyYmBh9+umnatOmjfr37+/W4gAAAK7mmq8j06NHD+d1XYwxcjgc11wUAABAVbj81dLw4cN1/vz5K8aPHDmiO+64wy1FAQAAVIXLQeb//u//1LlzZ2VlZTnHVqxYoS5duqhx48ZuLQ4AAOBqXP5qaffu3XrmmWd01113afz48Tp06JA++OADzZo1SyNHjvREjQAAAOVyOcj4+/vrhRdeUL169fSHP/xBfn5+yszM5EaOAACgxrn81dKlS5c0fvx4zZgxQ2lpaUpISFBycrL++te/eqI+AACACrk8I3PzzTfrwoUL2rZtm3r06CFjjGbOnKnk5GQ9+uij3NQRAADUGJdnZG6++Wbl5OQ4T7l2OByaMGGCsrKy9PHHH7u9QAAAgIq4PCOzZMmScse7du2q7Ozsay4IAACgqlyekZGk1157TT179lRUVJSOHj0qSZozZ442bdrk1uIAAACuxuUgs2jRIqWmpuree+/VmTNndPnyZUlSWFiY5syZ4+76AAAAKuRykJk/f74WL16sZ599Vr6+vs7xm2++WZ999plbiwMAALgal4NMbm6uunbtesV4YGBgubcuAAAA8BSXg0yrVq2Uk5NzxfimTZvUvn17d9QEAABQJS6ftZSamqpRo0bp4sWLMsZo9+7devPNNzVt2jS9+uqrnqgRQCVaTtzo7RK8piq9H5nerwYqAeANLgeZ3/3udwoODtakSZN04cIF/ed//qeioqI0d+5cPfjgg56oEQAAoFwuBxlJGjJkiIYMGaILFy7o3Llzatq0qbvrAgAAqFS1gsyv6tWrp3r16rmrFgAAAJdU64J4AAAAtQFBBgAAWIsgAwAArFWlIBMeHq4ffvhBkvToo4/q7NmzHi0KAACgKqoUZEpKSlRUVCRJWrFihS5evOjRogAAAKqiSmctJSQk6L777lN8fLyMMRo7dqyCg4PL3Xbp0qVuLRAAAKAiVQoyr7/+umbPnq3Dhw/L4XCosLCQWRkAAOB1VQoyERERmj59uqRf7rX02muvqVGjRh4tDAAAoDIuXxAvNzfXE3UAAAC4rFqnX2dmZqp///6KjY1VbGysBgwYoO3bt7u7NgAAgKtyOci8/vrrSkxMVL169TR27Fjngb+9evXSqlWrPFEjAABAuRzGGOPKE9q3b6/HHntM48aNKzM+a9YsLV68WF999ZVbC7xWRUVFCg0NVWFhoUJCQrxdDuARLSdu9HYJtdqR6f28XQIAF1X197fLMzLffvut+vfvf8X4gAEDOH4GAADUKJeDTIsWLZSRkXHF+EcffaQWLVq4pSgAAICqcPmspfHjx2vs2LHKycnRrbfeKknasWOHli9frrlz57q9QAAAgIq4HGR+//vfKzIyUi+++KLWrl0r6ZfjZtasWaOBAwe6vUAAAICKuBxkJGnQoEEaNGiQu2sBAABwSbWuIwMAAFAbEGQAAIC1CDIAAMBaBBkAAGCtawoyxhi5eGFgAAAAt6lWkFm5cqU6deqk4OBgBQcHq3PnznrttdfcXRsAAMBVuXz69axZs/Tcc89p9OjR6tmzpyTpk08+0eOPP64ffvjhinswAQAAeIrLQWb+/PlatGiRhg4d6hwbMGCAOnTooClTphBkAABAjXH5q6W8vDznrQn+0a233qq8vDy3FAUAAFAVLgeZ2NhY560J/tGaNWvUpk0btxQFAABQFS5/tTR16lQNHjxYH3/8sfMYmR07digjI6PcgAMAAOApLs/IpKSkaNeuXWrcuLE2bNigDRs2qHHjxtq9ezf3XwIAADWqWjeNjI+P1+uvv+7uWgAAAFzClX0BAIC1qjwj4+PjI4fDcdVtHA6Hfv7552suCgAAoCqqHGTeeeedCtdlZWVp3rx5Ki0tdenFp02bpvXr1+vrr79WcHCwbr31Vs2YMUPt2rVzbnPx4kWNHz9eq1evVnFxsZKSkrRw4UJFRES49FoAAKDuqXKQGThw4BVjBw4c0MSJE/X+++9ryJAhev7551168czMTI0aNUq33HKLfv75Zz3zzDPq3bu3vvzyS9WvX1+SNG7cOG3cuFHr1q1TaGioRo8ereTkZO3YscOl1wIAAHVPtQ72PXHihNLT07VixQolJSUpJydHHTt2dHk/mzZtKvN4+fLlatq0qbKzs3XHHXeosLBQS5Ys0apVq3TPPfdIkpYtW6b27dtr586d6tGjR3XKBwAAdYRLB/sWFhZqwoQJio2N1RdffKGMjAy9//771QoxFe1fksLDwyVJ2dnZunTpkhITE53bxMXFKTo6WllZWeXuo7i4WEVFRWUWAABQN1V5RmbmzJmaMWOGIiMj9eabb5b7VdO1KC0t1RNPPKGePXs6g1F+fr4CAgIUFhZWZtuIiAjl5+eXu59p06Zp6tSpbq0NACSp5cSNlW5zZHq/GqgEwK+qHGQmTpyo4OBgxcbGasWKFVqxYkW5261fv75ahYwaNUqff/65Pvnkk2o9/1dpaWlKTU11Pi4qKlKLFi2uaZ8AAKB2qnKQGTp0aKWnX1fX6NGj9Ze//EUff/yxmjdv7hyPjIxUSUmJzpw5U2ZWpqCgQJGRkeXuKzAwUIGBgR6pEwAA1C5VDjLLly93+4sbYzRmzBi988472rZtm1q1alVmfXx8vPz9/ZWRkaGUlBRJv5wpdezYMSUkJLi9HgAAYJdqnbXkLqNGjdKqVav07rvvqmHDhs7jXkJDQxUcHKzQ0FCNGDFCqampCg8PV0hIiMaMGaOEhATOWAIAAN4NMosWLZIk3XXXXWXGly1bpuHDh0uSZs+eLR8fH6WkpJS5IB4AAIBXg4wxptJtgoKCtGDBAi1YsKAGKgIAADbxapABgJrAadNA3cXdrwEAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIuzlgBAVTuzCUDtw4wMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYy8/bBQC4upYTN3q7BACotZiRAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsJZXg8zHH3+s/v37KyoqSg6HQxs2bCiz3hijyZMnq1mzZgoODlZiYqIOHjzonWIBAECt49Ugc/78eXXp0kULFiwod/3MmTM1b948vfTSS9q1a5fq16+vpKQkXbx4sYYrBQAAtZGfN1+8b9++6tu3b7nrjDGaM2eOJk2apIEDB0qSVq5cqYiICG3YsEEPPvhguc8rLi5WcXGx83FRUZH7CwcAALWCV4PM1eTm5io/P1+JiYnOsdDQUHXv3l1ZWVkVBplp06Zp6tSpNVUmUKGWEzdWus2R6f1qoBIAqLtq7cG++fn5kqSIiIgy4xEREc515UlLS1NhYaFzOX78uEfrBAAA3lNrZ2SqKzAwUIGBgd4uAwAA1IBaOyMTGRkpSSooKCgzXlBQ4FwHAAD+tdXaINOqVStFRkYqIyPDOVZUVKRdu3YpISHBi5UBAIDawqtfLZ07d06HDh1yPs7NzVVOTo7Cw8MVHR2tJ554Qn/84x/Vpk0btWrVSs8995yioqJ03333ea9oAABQa3g1yOzdu1d3332383FqaqokadiwYVq+fLmefvppnT9/Xo899pjOnDmj2267TZs2bVJQUJC3SgYAALWIV4PMXXfdJWNMhesdDoeef/55Pf/88zVYFQAAsEWtPUYGAACgMgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFjLqzeNBIB/RS0nbqx0myPT+9VAJYD9mJEBAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWp18DgBtV5dRqAO7DjAwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1uKmkUA1uOvGgNxgEACuDTMyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADW4vRr4J9wSjRqg6r8PTwyvV8NVFJ1NtYM+zEjAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWpy1BACW4iyha8d7eHU2vD/MyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIvTr69BTZ+W5q7Xs+F0uuqoq30B16K23QS1Jn9Oa7J3G//9qW1/N6qLGRkAAGAtggwAALCWFUFmwYIFatmypYKCgtS9e3ft3r3b2yUBAIBaoNYHmTVr1ig1NVXp6enat2+funTpoqSkJJ08edLbpQEAAC+r9UFm1qxZGjlypB555BHdcMMNeumll1SvXj0tXbrU26UBAAAvq9VnLZWUlCg7O1tpaWnOMR8fHyUmJiorK6vc5xQXF6u4uNj5uLCwUJJUVFTk9vpKiy9Uuo07X9ddr1fTddeUmnx/AHiOu/79cdfPcl39d7Um359r2a8x5uobmlrs+++/N5LMp59+Wmb8qaeeMt26dSv3Oenp6UYSCwsLCwsLSx1Yjh8/ftWsUKtnZKojLS1NqampzselpaU6ffq0GjVqJIfDIemXlNeiRQsdP35cISEh3irVY+p6f1Ld75H+7FbX+5Pqfo/0533GGJ09e1ZRUVFX3a5WB5nGjRvL19dXBQUFZcYLCgoUGRlZ7nMCAwMVGBhYZiwsLKzcbUNCQmrtB+gOdb0/qe73SH92q+v9SXW/R/rzrtDQ0Eq3qdUH+wYEBCg+Pl4ZGRnOsdLSUmVkZCghIcGLlQEAgNqgVs/ISFJqaqqGDRumm2++Wd26ddOcOXN0/vx5PfLII94uDQAAeFmtDzKDBw/W3//+d02ePFn5+fm68cYbtWnTJkVERFR7n4GBgUpPT7/iK6i6oq73J9X9HunPbnW9P6nu90h/9nAYU9l5TQAAALVTrT5GBgAA4GoIMgAAwFoEGQAAYC2CDAAAsFadCTILFixQy5YtFRQUpO7du2v37t1X3X7dunWKi4tTUFCQOnXqpL/+9a9l1q9fv169e/d2XhE4JyfHg9VXzp39Xbp0SRMmTFCnTp1Uv359RUVFaejQoTpx4oSn26iQuz+/KVOmKC4uTvXr19d1112nxMRE7dq1y5MtXJW7+/tHjz/+uBwOh+bMmePmql3j7h6HDx8uh8NRZunTp48nW7gqT3yGX331lQYMGKDQ0FDVr19ft9xyi44dO+apFq7K3f3982f36/LCCy94so0Kubu/c+fOafTo0WrevLmCg4OdNzX2Jnf3WFBQoOHDhysqKkr16tVTnz59dPDgQU+2UD3uuSuSd61evdoEBASYpUuXmi+++MKMHDnShIWFmYKCgnK337Fjh/H19TUzZ840X375pZk0aZLx9/c3n332mXOblStXmqlTp5rFixcbSWb//v011M2V3N3fmTNnTGJiolmzZo35+uuvTVZWlunWrZuJj4+vybacPPH5vfHGG2bLli3m8OHD5vPPPzcjRowwISEh5uTJkzXVlpMn+vvV+vXrTZcuXUxUVJSZPXu2hzupmCd6HDZsmOnTp4/Jy8tzLqdPn66plsrwRH+HDh0y4eHh5qmnnjL79u0zhw4dMu+++26F+/QkT/T3j59bXl6eWbp0qXE4HObw4cM11ZaTJ/obOXKkad26tdm6davJzc01L7/8svH19TXvvvtuTbVVhrt7LC0tNT169DC333672b17t/n666/NY489ZqKjo825c+dqsrVK1Ykg061bNzNq1Cjn48uXL5uoqCgzbdq0crd/4IEHTL9+/cqMde/e3fzXf/3XFdvm5uZ6Pch4sr9f7d6920gyR48edU/RLqiJ/goLC40k89FHH7mnaBd4qr/vvvvO/Nu//Zv5/PPPTUxMjFeDjCd6HDZsmBk4cKBH6nWVJ/obPHiw+e1vf+uZgl1UEz+DAwcONPfcc497CnaRJ/rr0KGDef7558tsc9NNN5lnn33WjZVXnbt7PHDggJFkPv/88zL7bNKkiVm8eLEHOqg+679aKikpUXZ2thITE51jPj4+SkxMVFZWVrnPycrKKrO9JCUlJVW4vTfVVH+FhYVyOBwV3pfKU2qiv5KSEr3yyisKDQ1Vly5d3Fd8FXiqv9LSUj388MN66qmn1KFDB88UX0We/Ay3bdumpk2bql27dvr973+vU6dOub+BSniiv9LSUm3cuFFt27ZVUlKSmjZtqu7du2vDhg0e66MiNfEzWFBQoI0bN2rEiBHuK7yKPNXfrbfeqvfee0/ff/+9jDHaunWrvvnmG/Xu3dszjVyFJ3osLi6WJAUFBZXZZ2BgoD755BN3t3BNrA8yP/zwgy5fvnzFlX4jIiKUn59f7nPy8/Nd2t6baqK/ixcvasKECXrooYdq/OZhnuzvL3/5ixo0aKCgoCDNnj1bW7ZsUePGjd3bQCU81d+MGTPk5+ensWPHur9oF3mqxz59+mjlypXKyMjQjBkzlJmZqb59++ry5cvub+IqPNHfyZMnde7cOU2fPl19+vTR5s2bNWjQICUnJyszM9MzjVSgJv6NWbFihRo2bKjk5GT3FO0CT/U3f/583XDDDWrevLkCAgLUp08fLViwQHfccYf7m6iEJ3qMi4tTdHS00tLS9OOPP6qkpEQzZszQd999p7y8PM80Uk21/hYF8KxLly7pgQcekDFGixYt8nY5bnX33XcrJydHP/zwgxYvXqwHHnhAu3btUtOmTb1d2jXJzs7W3LlztW/fPjkcDm+X4zEPPvig88+dOnVS586d1bp1a23btk29evXyYmXXrrS0VJI0cOBAjRs3TpJ044036tNPP9VLL72kO++805vlud3SpUs1ZMiQMv+7t938+fO1c+dOvffee4qJidHHH3+sUaNGKSoq6oqZDhv5+/tr/fr1GjFihMLDw+Xr66vExET17dtXppbdEMD6GZnGjRvL19dXBQUFZcYLCgoUGRlZ7nMiIyNd2t6bPNnfryHm6NGj2rJli1du5e7J/urXr6/Y2Fj16NFDS5YskZ+fn5YsWeLeBirhif62b9+ukydPKjo6Wn5+fvLz89PRo0c1fvx4tWzZ0iN9XE1N/Qxef/31aty4sQ4dOnTtRbvAE/01btxYfn5+uuGGG8ps0759+xo/a8nTn9/27dt14MAB/e53v3Nf0S7wRH8//fSTnnnmGc2aNUv9+/dX586dNXr0aA0ePFh/+tOfPNPIVXjqM4yPj1dOTo7OnDmjvLw8bdq0SadOndL111/v/iaugfVBJiAgQPHx8crIyHCOlZaWKiMjQwkJCeU+JyEhocz2krRly5YKt/cmT/X3a4g5ePCgPvroIzVq1MgzDVSiJj+/0tJS5/e+NcUT/T388MP629/+ppycHOcSFRWlp556Sh9++KHnmqlATX2G3333nU6dOqVmzZq5p/Aq8kR/AQEBuuWWW3TgwIEy23zzzTeKiYlxcwdX5+nPb8mSJYqPj6/x49N+5Yn+Ll26pEuXLsnHp+yvUF9fX+dsW03y9GcYGhqqJk2a6ODBg9q7d68GDhzo3gaulZcPNnaL1atXm8DAQLN8+XLz5Zdfmscee8yEhYWZ/Px8Y4wxDz/8sJk4caJz+x07dhg/Pz/zpz/9yXz11VcmPT39ilPrTp06Zfbv3282btxoJJnVq1eb/fv3m7y8POv7KykpMQMGDDDNmzc3OTk5ZU6RLC4utr6/c+fOmbS0NJOVlWWOHDli9u7dax555BETGBhY5gh8W/srj7fPWnJ3j2fPnjVPPvmkycrKMrm5ueajjz4yN910k2nTpo25ePGi9f0Z88up8/7+/uaVV14xBw8eNPPnzze+vr5m+/btdaI/Y345W7BevXpm0aJFNdrPP/NEf3feeafp0KGD2bp1q/n222/NsmXLTFBQkFm4cGGN92eMZ3pcu3at2bp1qzl8+LDZsGGDiYmJMcnJyTXeW2XqRJAxxpj58+eb6OhoExAQYLp162Z27tzpXHfnnXeaYcOGldl+7dq1pm3btiYgIMB06NDBbNy4scz6ZcuWGUlXLOnp6TXQzZXc2d+vp5SXt2zdurWGOirLnf399NNPZtCgQSYqKsoEBASYZs2amQEDBpjdu3fXVDtXcPffz3/m7SBjjHt7vHDhgundu7dp0qSJ8ff3NzExMWbkyJHOf5S9wROf4ZIlS0xsbKwJCgoyXbp0MRs2bPB0GxXyRH8vv/yyCQ4ONmfOnPF0+ZVyd395eXlm+PDhJioqygQFBZl27dqZF1980ZSWltZEO+Vyd49z5841zZs3N/7+/iY6OtpMmjTJK//ZrYzDmFp21A4AAEAVWX+MDAAA+NdFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgA6DOOHLkiBwOh3JycrxdCoAaQpAB4DYOh+Oqy5QpU65p3xs2bHBbrQDqBj9vFwCg7sjLy3P+ec2aNZo8eXKZOzw3aNDAG2UBqMOYkQHgNpGRkc4lNDRUDoejzNjq1avVvn17BQUFKS4uTgsXLnQ+t6SkRKNHj1azZs0UFBSkmJgYTZs2TZLUsmVLSdKgQYPkcDicj6siMzNT3bp1U2BgoJo1a6aJEyfq559/dq5/66231KlTJwUHB6tRo0ZKTEzU+fPnJUnbtm1Tt27dVL9+fYWFhalnz546evTotb9RANyGGRkANeKNN97Q5MmT9ec//1ldu3bV/v37NXLkSNWvX1/Dhg3TvHnz9N5772nt2rWKjo7W8ePHdfz4cUnSnj171LRpUy1btkx9+vSRr69vlV7z+++/17333qvhw4dr5cqV+vrrrzVy5EgFBQVpypQpysvL00MPPaSZM2dq0KBBOnv2rLZv3y5jjH7++Wfdd999GjlypN58802VlJRo9+7dcjgcnnybALiIIAOgRqSnp+vFF19UcnKyJKlVq1b68ssv9fLLL2vYsGE6duyY2rRpo9tuu00Oh0MxMTHO5zZp0kSSFBYWpsjIyCq/5sKFC9WiRQv9+c9/lsPhUFxcnE6cOKEJEyZo8uTJysvL088//6zk5GTn63Xq1EmSdPr0aRUWFuo3v/mNWrduLUlq3769W94LAO7DV0sAPO78+fM6fPiwRowYoQYNGjiXP/7xjzp8+LAkafjw4crJyVG7du00duxYbd68+Zpf96uvvlJCQkKZWZSePXvq3Llz+u6779SlSxf16tVLnTp10n/8x39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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reconstructions = autoencoder.predict(anomalous_test_data)\n", "test_loss = tf.keras.losses.mae(reconstructions, anomalous_test_data)\n", "\n", "plt.hist(test_loss[None, :], bins=50)\n", "plt.xlabel(\"Test loss\")\n", "plt.ylabel(\"No of examples\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "PFVk_XGE6AX2" }, "source": [ "如果重构误差大于阈值，则将心电图分类为异常。" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:28.774141Z", "iopub.status.busy": "2023-11-07T19:43:28.773889Z", "iopub.status.idle": "2023-11-07T19:43:28.778302Z", "shell.execute_reply": "2023-11-07T19:43:28.777707Z" }, "id": "mkgJZfhh6CHr" }, "outputs": [], "source": [ "def predict(model, data, threshold):\n", " reconstructions = model(data)\n", " loss = tf.keras.losses.mae(reconstructions, data)\n", " return tf.math.less(loss, threshold)\n", "\n", "def print_stats(predictions, labels):\n", " print(\"Accuracy = {}\".format(accuracy_score(labels, predictions)))\n", " print(\"Precision = {}\".format(precision_score(labels, predictions)))\n", " print(\"Recall = {}\".format(recall_score(labels, predictions)))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T19:43:28.781707Z", "iopub.status.busy": "2023-11-07T19:43:28.781204Z", "iopub.status.idle": "2023-11-07T19:43:28.798588Z", "shell.execute_reply": "2023-11-07T19:43:28.797999Z" }, "id": "sOcfXfXq6FBd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy = 0.943\n", "Precision = 0.9921722113502935\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Recall = 0.9053571428571429\n" ] } ], "source": [ "preds = predict(autoencoder, test_data, threshold)\n", "print_stats(preds, test_labels)" ] }, { "cell_type": "markdown", "metadata": { "id": "HrJRef8Ln945" }, "source": [ "## 后续步骤\n", "\n", "要详细了解如何使用自编码器检测异常，请查看 Victor Dibia 使用 TensorFlow.js 构建的出色[交互式示例](https://anomagram.fastforwardlabs.com/#/)。对于真实用例，您可以了解 [Airbus 如何使用 TensorFlow 检测 ISS 遥测数据中的异常](https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html)。要详细了解基础知识，请考虑阅读 François Chollet 撰写的这篇[博文](https://blog.keras.io/building-autoencoders-in-keras.html)。有关更多详细信息，请查看 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 撰写的[《深度学习》](https://www.deeplearningbook.org/)一书的第 14 章。\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "autoencoder.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 0 }