{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # 设置日志级别为ERROR,以减少警告信息\n", "# 禁用 Gemini 的底层库(gRPC 和 Abseil)在初始化日志警告\n", "os.environ[\"GRPC_VERBOSITY\"] = \"ERROR\"\n", "os.environ[\"GLOG_minloglevel\"] = \"3\" # 0: INFO, 1: WARNING, 2: ERROR, 3: FATAL\n", "os.environ[\"GLOG_minloglevel\"] = \"true\"\n", "import logging\n", "import tensorflow as tf\n", "tf.get_logger().setLevel(logging.ERROR)\n", "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n", "!export TF_FORCE_GPU_ALLOW_GROWTH=true\n", "from pathlib import Path\n", "\n", "temp_dir = Path(\".temp\")\n", "temp_dir.mkdir(parents=True, exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "FhGuhbZ6M5tl" }, "outputs": [], "source": [ "# Copyright 2018 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "cellView": "form", "id": "AwOEIRJC6Une" }, "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": "code", "execution_count": 4, "metadata": { "cellView": "form", "id": "KyPEtTqk6VdG" }, "outputs": [], "source": [ "#@title MIT License\n", "#\n", "# Copyright (c) 2017 François Chollet\n", "#\n", "# Permission is hereby granted, free of charge, to any person obtaining a\n", "# copy of this software and associated documentation files (the \"Software\"),\n", "# to deal in the Software without restriction, including without limitation\n", "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n", "# and/or sell copies of the Software, and to permit persons to whom the\n", "# Software is furnished to do so, subject to the following conditions:\n", "#\n", "# The above copyright notice and this permission notice shall be included in\n", "# all copies or substantial portions of the Software.\n", "#\n", "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n", "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n", "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n", "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n", "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n", "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n", "# DEALINGS IN THE SOFTWARE." ] }, { "cell_type": "markdown", "metadata": { "id": "EIdT9iu_Z4Rb" }, "source": [ "# Basic regression: Predict fuel efficiency" ] }, { "cell_type": "markdown", "metadata": { "id": "bBIlTPscrIT9" }, "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": "AHp3M9ZmrIxj" }, "source": [ "Note: 我们的 TensorFlow 社区翻译了这些文档。因为社区翻译是尽力而为, 所以无法保证它们是最准确的,并且反映了最新的 [官方英文文档](https://tensorflow.google.cn/?hl=en)。如果您有改进此翻译的建议, 请提交 pull request 到 [tensorflow/docs](https://github.com/tensorflow/docs) GitHub 仓库。要志愿地撰写或者审核译文,请加入 [docs-zh-cn@tensorflow.org Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-zh-cn)。\n", "\n", "此教程使用经典的 [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) 数据集并演示了如何构建模型来预测 20 世纪 70 年代末和 20 世纪 80 年代初汽车的燃油效率。为此,您需要为模型提供该时期的许多汽车的描述。这种描述包括诸如气缸、排量、马力和重量等特性。\n", "\n", "此示例使用了 Keras API。(请访问 Keras [教程](https://tensorflow.google.cn/tutorials/keras)和[指南](https://tensorflow.google.cn/guide/keras)以了解更多信息。)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "moB4tpEHxKB3" }, "outputs": [], "source": [ "# Use seaborn for pairplot.\n", "!pip install -q seaborn" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "1rRo8oNqZ-Rj" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", "# Make NumPy printouts easier to read.\n", "np.set_printoptions(precision=3, suppress=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "9xQKvCJ85kCQ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.17.0\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "from tensorflow import keras\n", "from tensorflow.keras import layers\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "id": "F_72b0LCNbjx" }, "source": [ "## Auto MPG 数据集\n", "\n", "该数据集可以从 [UCI机器学习库](https://archive.ics.uci.edu/ml/) 中获取.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "gFh9ne3FZ-On" }, "source": [ "### 获取数据\n", "\n", "首先下载数据集。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "CiX2FI4gZtTt" }, "outputs": [], "source": [ "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n", "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n", " 'Acceleration', 'Model Year', 'Origin']\n", "\n", "raw_dataset = pd.read_csv(url, names=column_names,\n", " na_values='?', comment='\\t',\n", " sep=' ', skipinitialspace=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "2oY3pMPagJrO" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearOrigin
39327.04140.086.02790.015.6821
39444.0497.052.02130.024.6822
39532.04135.084.02295.011.6821
39628.04120.079.02625.018.6821
39731.04119.082.02720.019.4821
\n", "
" ], "text/plain": [ " MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", "393 27.0 4 140.0 86.0 2790.0 15.6 \n", "394 44.0 4 97.0 52.0 2130.0 24.6 \n", "395 32.0 4 135.0 84.0 2295.0 11.6 \n", "396 28.0 4 120.0 79.0 2625.0 18.6 \n", "397 31.0 4 119.0 82.0 2720.0 19.4 \n", "\n", " Model Year Origin \n", "393 82 1 \n", "394 82 2 \n", "395 82 1 \n", "396 82 1 \n", "397 82 1 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = raw_dataset.copy()\n", "dataset.tail()" ] }, { "cell_type": "markdown", "metadata": { "id": "3MWuJTKEDM-f" }, "source": [ "### 数据清洗\n", "\n", "数据集包含一些未知值:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "JEJHhN65a2VV" }, "outputs": [ { "data": { "text/plain": [ "MPG 0\n", "Cylinders 0\n", "Displacement 0\n", "Horsepower 6\n", "Weight 0\n", "Acceleration 0\n", "Model Year 0\n", "Origin 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.isna().sum()" ] }, { "cell_type": "markdown", "metadata": { "id": "9UPN0KBHa_WI" }, "source": [ "为了保证此初始教程简单,请删除这些行:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "4ZUDosChC1UN" }, "outputs": [], "source": [ "dataset = dataset.dropna()" ] }, { "cell_type": "markdown", "metadata": { "id": "8XKitwaH4v8h" }, "source": [ "`\"Origin\"` 列为分类数据,而不是数值数据。因此,下一步是使用 [pd.get_dummies](https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html) 对列中的值进行独热编码。\n", "\n", "注:您可以设置 `tf.keras.Model` 来为您执行这种转换,但这超出了本教程的范围。有关示例,请参阅[使用 Keras 预处理层对结构化数据进行分类](../structured_data/preprocessing_layers.ipynb)或[加载 CSV 数据](../load_data/csv.ipynb)教程。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "gWNTD2QjBWFJ" }, "outputs": [], "source": [ "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "ulXz4J7PAUzk" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MPGCylindersDisplacementHorsepowerWeightAccelerationModel YearEuropeJapanUSA
39327.04140.086.02790.015.6820.00.01.0
39444.0497.052.02130.024.6821.00.00.0
39532.04135.084.02295.011.6820.00.01.0
39628.04120.079.02625.018.6820.00.01.0
39731.04119.082.02720.019.4820.00.01.0
\n", "
" ], "text/plain": [ " MPG Cylinders Displacement Horsepower Weight Acceleration \\\n", "393 27.0 4 140.0 86.0 2790.0 15.6 \n", "394 44.0 4 97.0 52.0 2130.0 24.6 \n", "395 32.0 4 135.0 84.0 2295.0 11.6 \n", "396 28.0 4 120.0 79.0 2625.0 18.6 \n", "397 31.0 4 119.0 82.0 2720.0 19.4 \n", "\n", " Model Year Europe Japan USA \n", "393 82 0.0 0.0 1.0 \n", "394 82 1.0 0.0 0.0 \n", "395 82 0.0 0.0 1.0 \n", "396 82 0.0 0.0 1.0 \n", "397 82 0.0 0.0 1.0 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='', dtype=\"float32\")\n", "dataset.tail()" ] }, { "cell_type": "markdown", "metadata": { "id": "Cuym4yvk76vU" }, "source": [ "### 将数据拆分为训练集和测试集\n", "\n", "现在,将数据集拆分为训练集和测试集。您将在模型的最终评估中使用测试集。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "qn-IGhUE7_1H" }, "outputs": [], "source": [ "train_dataset = dataset.sample(frac=0.8, random_state=0)\n", "test_dataset = dataset.drop(train_dataset.index)" ] }, { "cell_type": "markdown", "metadata": { "id": "J4ubs136WLNp" }, "source": [ "### 数据检查\n", "\n", "检查训练集中几对列的联合分布。\n", "\n", "第一行表明燃油效率 (MPG) 是所有其他参数的函数。其他行表示它们是彼此的函数。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "oRKO_x8gWKv-" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')" ] }, { "cell_type": "markdown", "metadata": { "id": "gavKO_6DWRMP" }, "source": [ "让我们也查看一下总体统计信息。请注意每个特征覆盖大为不同的范围:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "yi2FzC3T21jR" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
MPG314.023.3105107.72865210.017.0022.028.9546.6
Cylinders314.05.4777071.6997883.04.004.08.008.0
Displacement314.0195.318471104.33158968.0105.50151.0265.75455.0
Horsepower314.0104.86942738.09621446.076.2594.5128.00225.0
Weight314.02990.251592843.8985961649.02256.502822.53608.005140.0
Acceleration314.015.5592362.7892308.013.8015.517.2024.8
Model Year314.075.8980893.67564270.073.0076.079.0082.0
Europe314.00.1783440.3834130.00.000.00.001.0
Japan314.00.1974520.3987120.00.000.00.001.0
USA314.00.6242040.4851010.00.001.01.001.0
\n", "
" ], "text/plain": [ " count mean std min 25% 50% \\\n", "MPG 314.0 23.310510 7.728652 10.0 17.00 22.0 \n", "Cylinders 314.0 5.477707 1.699788 3.0 4.00 4.0 \n", "Displacement 314.0 195.318471 104.331589 68.0 105.50 151.0 \n", "Horsepower 314.0 104.869427 38.096214 46.0 76.25 94.5 \n", "Weight 314.0 2990.251592 843.898596 1649.0 2256.50 2822.5 \n", "Acceleration 314.0 15.559236 2.789230 8.0 13.80 15.5 \n", "Model Year 314.0 75.898089 3.675642 70.0 73.00 76.0 \n", "Europe 314.0 0.178344 0.383413 0.0 0.00 0.0 \n", "Japan 314.0 0.197452 0.398712 0.0 0.00 0.0 \n", "USA 314.0 0.624204 0.485101 0.0 0.00 1.0 \n", "\n", " 75% max \n", "MPG 28.95 46.6 \n", "Cylinders 8.00 8.0 \n", "Displacement 265.75 455.0 \n", "Horsepower 128.00 225.0 \n", "Weight 3608.00 5140.0 \n", "Acceleration 17.20 24.8 \n", "Model Year 79.00 82.0 \n", "Europe 0.00 1.0 \n", "Japan 0.00 1.0 \n", "USA 1.00 1.0 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_dataset.describe().transpose()" ] }, { "cell_type": "markdown", "metadata": { "id": "Db7Auq1yXUvh" }, "source": [ "### 从标签中分离特征\n", "\n", "将目标值(“标签”)从特征中分离。此标签是您训练模型来预测的值。" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "t2sluJdCW7jN" }, "outputs": [], "source": [ "train_features = train_dataset.copy()\n", "test_features = test_dataset.copy()\n", "\n", "train_labels = train_features.pop('MPG')\n", "test_labels = test_features.pop('MPG')" ] }, { "cell_type": "markdown", "metadata": { "id": "mRklxK5s388r" }, "source": [ "## 归一化\n", "\n", "在统计信息表中,可以很轻松地看到每个特征的范围的不同:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "IcmY6lKKbkw8" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
meanstd
MPG23.3105107.728652
Cylinders5.4777071.699788
Displacement195.318471104.331589
Horsepower104.86942738.096214
Weight2990.251592843.898596
Acceleration15.5592362.789230
Model Year75.8980893.675642
Europe0.1783440.383413
Japan0.1974520.398712
USA0.6242040.485101
\n", "
" ], "text/plain": [ " mean std\n", "MPG 23.310510 7.728652\n", "Cylinders 5.477707 1.699788\n", "Displacement 195.318471 104.331589\n", "Horsepower 104.869427 38.096214\n", "Weight 2990.251592 843.898596\n", "Acceleration 15.559236 2.789230\n", "Model Year 75.898089 3.675642\n", "Europe 0.178344 0.383413\n", "Japan 0.197452 0.398712\n", "USA 0.624204 0.485101" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_dataset.describe().transpose()[['mean', 'std']]" ] }, { "cell_type": "markdown", "metadata": { "id": "-ywmerQ6dSox" }, "source": [ "使用不同的尺度和范围对特征归一化是好的实践。尽管模型*可能* 在没有特征归一化的情况下收敛,它会使得模型训练更加复杂,并会造成生成的模型依赖输入所使用的单位选择。\n", "\n", "归一化十分重要的一个原因是特征会与模型权重相乘。因此,输出尺度和梯度尺度受输入尺度的影响。\n", "\n", "尽管模型*可能*在没有特征归一化的情况下收敛,但归一化会使训练更加稳定。\n", "\n", "注:归一化独热特征没有任何好处,这里这样做是为了简单起见。有关如何使用预处理层的更多详细信息,请参阅[使用预处理层](https://tensorflow.google.cn/guide/keras/preprocessing_layers)指南和[使用 Keras 预处理层对结构化数据进行分类](../structured_data/preprocessing_layers.ipynb)教程。" ] }, { "cell_type": "markdown", "metadata": { "id": "aFJ6ISropeoo" }, "source": [ "### 归一化层\n", "\n", "`tf.keras.layers.Normalization` 是一种将特征归一化添加到模型中的简洁且简单的方法。\n", "\n", "第一步是创建层:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "JlC5ooJrgjQF" }, "outputs": [], "source": [ "normalizer = tf.keras.layers.Normalization(axis=-1)" ] }, { "cell_type": "markdown", "metadata": { "id": "XYA2Ap6nVOha" }, "source": [ "然后,通过调用 `Normalization.adapt` 以将预处理层的状态拟合到数据:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "CrBbbjbwV91f" }, "outputs": [], "source": [ "normalizer.adapt(np.array(train_features))" ] }, { "cell_type": "markdown", "metadata": { "id": "oZccMR5yV9YV" }, "source": [ "计算均值和方差,并将它们存储在层中。" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "GGn-ukwxSPtx" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 5.478 195.318 104.869 2990.252 15.559 75.898 0.178 0.197\n", " 0.624]]\n" ] } ], "source": [ "print(normalizer.mean.numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "oGWKaF9GSRuN" }, "source": [ "当层被调用时,它会返回输入数据,每个特征将单独归一化:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CylindersDisplacementHorsepowerWeightAccelerationModel YearEuropeJapanUSA
146490.075.02125.014.5740.00.01.0
\n", "
" ], "text/plain": [ " Cylinders Displacement Horsepower Weight Acceleration Model Year \\\n", "146 4 90.0 75.0 2125.0 14.5 74 \n", "\n", " Europe Japan USA \n", "146 0.0 0.0 1.0 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_features[:1]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "2l7zFL_XWIRu" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First example: [[ 4. 90. 75. 2125. 14.5 74. 0. 0. 1. ]]\n", "\n", "Normalized: [[-0.87 -1.01 -0.79 -1.03 -0.38 -0.52 -0.47 -0.5 0.78]]\n" ] } ], "source": [ "first = np.array(train_features[:1])\n", "\n", "with np.printoptions(precision=2, suppress=True):\n", " print('First example:', first)\n", " print()\n", " print('Normalized:', normalizer(first).numpy())" ] }, { "cell_type": "markdown", "metadata": { "id": "6o3CrycBXA2s" }, "source": [ "## 线性回归\n", "\n", "在构建深度神经网络模型之前,首先使用一个和多个变量进行线性回归。" ] }, { "cell_type": "markdown", "metadata": { "id": "lFby9n0tnHkw" }, "source": [ "### 使用一个变量进行线性回归\n", "\n", "从单变量线性回归开始,根据 `'Horsepower'` 预测 `'MPG'`。\n", "\n", "使用 `tf.keras` 训练模型通常从定义模型架构开始。使用 `tf.keras.Sequential` 模型,它[表示一系列步骤](https://tensorflow.google.cn/guide/keras/sequential_model)。\n", "\n", "单变量线性回归模型有两个步骤:\n", "\n", "- 使用 `tf.keras.layers.Normalization` 预处理层规一化 `'Horsepower'` 输入特征。\n", "- 应用线性变换 ($y = mx+b$) 以使用线性层 (`tf.keras.layers.Dense`) 生成 1 个输出。\n", "\n", "*输入*的数量可以由 `input_shape` 参数设置,也可以在模型第一次运行时自动设置。" ] }, { "cell_type": "markdown", "metadata": { "id": "Xp3gAFn3TPv8" }, "source": [ "首先,创建一个由 `'Horsepower'` 特征构成的 NumPy 数组。然后,实例化 `tf.keras.layers.Normalization` 并将其状态拟合到 `horsepower` 数据:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "1gJAy0fKs1TS" }, "outputs": [], "source": [ "horsepower = np.array(train_features['Horsepower'])\n", "\n", "horsepower_normalizer = layers.Normalization(axis=None)\n", "horsepower_normalizer.adapt(horsepower)" ] }, { "cell_type": "markdown", "metadata": { "id": "4NVlHJY2TWlC" }, "source": [ "构建 Keras 序贯模型:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "c0sXM7qLlKfZ" }, "outputs": [ { "data": { "text/html": [ "
Model: \"sequential\"\n",
              "
\n" ], "text/plain": [ "\u001b[1mModel: \"sequential\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)                     Output Shape                  Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ normalization_1 (Normalization) │ (None, 1)              │             3 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (Dense)                   │ (None, 1)              │             2 │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ normalization_1 (\u001b[38;5;33mNormalization\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m3\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m2\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Total params: 5 (24.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m5\u001b[0m (24.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Trainable params: 2 (8.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Non-trainable params: 3 (16.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3\u001b[0m (16.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "horsepower_model = tf.keras.Sequential([\n", " layers.Input(shape=(1,)),\n", " horsepower_normalizer,\n", " layers.Dense(units=1)\n", "])\n", "\n", "horsepower_model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "eObQu9fDnXGL" }, "source": [ "此模型将根据 `'Horsepower'` 预测 `'MPG'`。\n", "\n", "在前 10 个“马力”值上运行未经训练的模型。输出不会很好,但您会看到它具有预期的形状 `(10, 1)`:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "id": "UfV1HS6bns-s" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1729738825.939939 3280987 service.cc:146] XLA service 0x7f2c40004cf0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", "I0000 00:00:1729738825.939973 3280987 service.cc:154] StreamExecutor device (0): NVIDIA GeForce RTX 3090, Compute Capability 8.6\n", "I0000 00:00:1729738825.939981 3280987 service.cc:154] StreamExecutor device (1): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5\n", "I0000 00:00:1729738826.047359 3280987 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" ] }, { "data": { "text/plain": [ "array([[-0.019],\n", " [-0.011],\n", " [ 0.035],\n", " [-0.026],\n", " [-0.024],\n", " [-0.009],\n", " [-0.028],\n", " [-0.024],\n", " [-0.006],\n", " [-0.011]], dtype=float32)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "horsepower_model.predict(horsepower[:10])" ] }, { "cell_type": "markdown", "metadata": { "id": "CSkanJlmmFBX" }, "source": [ "构建模型后,使用 Keras `Model.compile` 方法配置训练过程。要编译的最重要参数是 `loss` 和 `optimizer`,因为它们定义了将要优化的内容 (`mean_absolute_error`) 以及优化的方法(使用 `tf.keras.optimizers.Adam`)。" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "id": "JxA_3lpOm-SK" }, "outputs": [], "source": [ "horsepower_model.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n", " loss='mean_absolute_error')" ] }, { "cell_type": "markdown", "metadata": { "id": "Z3q1I9TwnRSC" }, "source": [ "使用 Keras `Model.fit` 执行 100 个周期的训练:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "-iSrNy59nRAp" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.16 s, sys: 1.3 s, total: 8.46 s\n", "Wall time: 6.55 s\n" ] } ], "source": [ "%%time\n", "history = horsepower_model.fit(\n", " train_features['Horsepower'],\n", " train_labels,\n", " epochs=100,\n", " # Suppress logging.\n", " verbose=0,\n", " # Calculate validation results on 20% of the training data.\n", " validation_split = 0.2)" ] }, { "cell_type": "markdown", "metadata": { "id": "tQm3pc0FYPQB" }, "source": [ "使用 `history` 对象中存储的统计信息呈现模型的训练进度:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "id": "YCAwD_y4AdC3" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
lossval_lossepoch
953.8018834.18948795
963.8023294.19358696
973.8047664.19343397
983.8048024.18091498
993.8030754.19519699
\n", "
" ], "text/plain": [ " loss val_loss epoch\n", "95 3.801883 4.189487 95\n", "96 3.802329 4.193586 96\n", "97 3.804766 4.193433 97\n", "98 3.804802 4.180914 98\n", "99 3.803075 4.195196 99" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hist = pd.DataFrame(history.history)\n", "hist['epoch'] = history.epoch\n", "hist.tail()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "9E54UoZunqhc" }, "outputs": [], "source": [ "def plot_loss(history):\n", " plt.plot(history.history['loss'], label='loss')\n", " plt.plot(history.history['val_loss'], label='val_loss')\n", " plt.ylim([0, 10])\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Error [MPG]')\n", " plt.legend()\n", " plt.grid(True)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "yYsQYrIZyqjz" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(history)" ] }, { "cell_type": "markdown", "metadata": { "id": "CMNrt8X2ebXd" }, "source": [ "收集测试集上的结果,供后面使用:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "kDZ8EvNYrDtx" }, "outputs": [], "source": [ "test_results = {}\n", "\n", "test_results['horsepower_model'] = horsepower_model.evaluate(\n", " test_features['Horsepower'],\n", " test_labels, verbose=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "F0qutYAKwoda" }, "source": [ "由于这是一个单变量回归,很容易将模型的预测视为输入的函数:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "id": "xDS2JEtOn9Jn" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n" ] } ], "source": [ "x = tf.linspace(0.0, 250, 251)\n", "y = horsepower_model.predict(x)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "rttFCTU8czsI" }, "outputs": [], "source": [ "def plot_horsepower(x, y):\n", " plt.scatter(train_features['Horsepower'], train_labels, label='Data')\n", " plt.plot(x, y, color='k', label='Predictions')\n", " plt.xlabel('Horsepower')\n", " plt.ylabel('MPG')\n", " plt.legend()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "id": "7l9ZiAOEUNBL" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_horsepower(x, y)" ] }, { "cell_type": "markdown", "metadata": { "id": "Yk2RmlqPoM9u" }, "source": [ "### 使用多个输入进行线性回归" ] }, { "cell_type": "markdown", "metadata": { "id": "PribnwDHUksC" }, "source": [ "您可以使用几乎相同的设置根据多个输入进行预测。此模型仍然执行相同的 $y = mx+b$,只是 $m$ 是一个矩阵,而 $b$ 是一个向量。\n", "\n", "再次创建一个两步 Keras 序贯模型,第一层为您之前定义并拟合到整个数据集的 `normalizer` (`tf.keras.layers.Normalization(axis=-1)`):" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "id": "ssnVcKg7oMe6" }, "outputs": [], "source": [ "linear_model = tf.keras.Sequential([\n", " normalizer,\n", " layers.Dense(units=1)\n", "])" ] }, { "cell_type": "markdown", "metadata": { "id": "IHlx6WeIWyAr" }, "source": [ "当您对一批输入调用 `Model.predict` 时,它会为每个样本生成 `units=1` 输出。" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "id": "DynfJV18WiuT" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step\n" ] }, { "data": { "text/plain": [ "array([[-1.12 ],\n", " [-0.624],\n", " [ 1.936],\n", " [-2.406],\n", " [-0.512],\n", " [ 0.408],\n", " [-0.474],\n", " [ 0.402],\n", " [ 0.685],\n", " [ 0.963]], dtype=float32)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linear_model.predict(train_features[:10])" ] }, { "cell_type": "markdown", "metadata": { "id": "hvHKH3rPXHmq" }, "source": [ "当您调用模型时,将构建其权重矩阵 – 可以看到 `kernel` 权重($y=mx+b$ 中的 $m$)的形状为 `(9, 1)`:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "id": "DwJ4Fq0RXBQf" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linear_model.layers[1].kernel" ] }, { "cell_type": "markdown", "metadata": { "id": "eINAc6rZXzOt" }, "source": [ "使用 Keras `Model.compile` 配置模型并使用 `Model.fit` 训练 100 个周期:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "id": "A0Sv_Ybr0szp" }, "outputs": [], "source": [ "linear_model.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n", " loss='mean_absolute_error')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "id": "EZoOYORvoTSe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.04 s, sys: 1.22 s, total: 8.25 s\n", "Wall time: 6.44 s\n" ] } ], "source": [ "%%time\n", "history = linear_model.fit(\n", " train_features,\n", " train_labels,\n", " epochs=100,\n", " # Suppress logging.\n", " verbose=0,\n", " # Calculate validation results on 20% of the training data.\n", " validation_split = 0.2)" ] }, { "cell_type": "markdown", "metadata": { "id": "EdxiCbiNYK2F" }, "source": [ "使用此回归模型中的所有输入可以实现比 `horsepower_model`(具有一个输入)低得多的训练和验证误差:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "id": "4sWO3W0koYgu" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(history)" ] }, { "cell_type": "markdown", "metadata": { "id": "NyN49hIWe_NH" }, "source": [ "收集测试集上的结果,供后面使用:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "id": "jNC3D1DGsGgK" }, "outputs": [], "source": [ "test_results['linear_model'] = linear_model.evaluate(\n", " test_features, test_labels, verbose=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "SmjdzxKzEu1-" }, "source": [ "## 使用深度神经网络 (DNN) 进行回归" ] }, { "cell_type": "markdown", "metadata": { "id": "DT_aHPsrzO1t" }, "source": [ "在上一部分中,您为单输入和多输入实现了两个线性模型。\n", "\n", "在此,您将实现单输入和多输入 DNN 模型。\n", "\n", "除了将模型扩展为包括一些“隐藏”非线性层之外,代码基本相同。此处的名称“隐藏”仅表示不直接连接到输入或输出。" ] }, { "cell_type": "markdown", "metadata": { "id": "6SWtkIjhrZwa" }, "source": [ "这些模型包含的层比线性模型多一些:\n", "\n", "- 归一化层和以前一样(对于单输入模型使用 `horsepower_normalizer`,对于多输入模型使用 `normalizer`)。\n", "- 使用 ReLU (`relu`) 激活函数非线性的两个隐藏非线性 `Dense` 层。\n", "- 一个线性 `Dense` 单输出层。\n", "\n", "两个模型都将使用相同的训练过程,因此 `compile` 方法包含在下面的 `build_and_compile_model` 函数中。" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "id": "c26juK7ZG8j-" }, "outputs": [], "source": [ "def build_and_compile_model(norm):\n", " model = keras.Sequential([\n", " norm,\n", " layers.Dense(64, activation='relu'),\n", " layers.Dense(64, activation='relu'),\n", " layers.Dense(1)\n", " ])\n", "\n", " model.compile(loss='mean_absolute_error',\n", " optimizer=tf.keras.optimizers.Adam(0.001))\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "id": "6c51caebbc0d" }, "source": [ "### 使用 DNN 和单输入进行回归" ] }, { "cell_type": "markdown", "metadata": { "id": "xvu9gtxTZR5V" }, "source": [ "创建一个 DNN 模型,仅将 `'Horsepower'` 作为输入,将 `'Horsepower'`(之前定义)作为归一化层:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "id": "cGbPb-PHGbhs" }, "outputs": [], "source": [ "dnn_horsepower_model = build_and_compile_model(horsepower_normalizer)" ] }, { "cell_type": "markdown", "metadata": { "id": "Sj49Og4YGULr" }, "source": [ "此模型比线性模型的可训练参数多很多。" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "id": "ReAD0n6MsFK-" }, "outputs": [ { "data": { "text/html": [ "
Model: \"sequential_2\"\n",
              "
\n" ], "text/plain": [ "\u001b[1mModel: \"sequential_2\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)                     Output Shape                  Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ normalization_1 (Normalization) │ (None, 1)              │             3 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_2 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_3 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_4 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ normalization_1 (\u001b[38;5;33mNormalization\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m3\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_4 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Total params: 3 (16.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3\u001b[0m (16.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Trainable params: 0 (0.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Non-trainable params: 3 (16.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3\u001b[0m (16.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dnn_horsepower_model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "0-qWCsh6DlyH" }, "source": [ "使用 Keras `Model.fit` 训练模型:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "id": "sD7qHCmNIOY0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 18.3 s, sys: 1.67 s, total: 19.9 s\n", "Wall time: 9.41 s\n" ] } ], "source": [ "%%time\n", "history = dnn_horsepower_model.fit(\n", " train_features['Horsepower'],\n", " train_labels,\n", " validation_split=0.2,\n", " verbose=0, epochs=100)" ] }, { "cell_type": "markdown", "metadata": { "id": "dArGGxHxcKjN" }, "source": [ "此模型略优于线性单输入 `horsepower_model`:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "id": "NcF6UWjdCU8T" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(history)" ] }, { "cell_type": "markdown", "metadata": { "id": "TG1snlpR2QCK" }, "source": [ "如果您将预测值绘制为 `'Horsepower'` 的函数,应看到此模型如何利用隐藏层提供的非线性:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "id": "hPF53Rem14NS" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step\n" ] } ], "source": [ "x = tf.linspace(0.0, 250, 251)\n", "y = dnn_horsepower_model.predict(x)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "id": "rsf9rD8I17Wq" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_horsepower(x, y)" ] }, { "cell_type": "markdown", "metadata": { "id": "WxCJKIUpe4io" }, "source": [ "收集测试集上的结果,供后面使用:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "id": "bJjM0dU52XtN" }, "outputs": [], "source": [ "test_results['dnn_horsepower_model'] = dnn_horsepower_model.evaluate(\n", " test_features['Horsepower'], test_labels,\n", " verbose=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "S_2Btebp2e64" }, "source": [ "### 使用 DNN 和多输入进行回归" ] }, { "cell_type": "markdown", "metadata": { "id": "aKFtezDldLSf" }, "source": [ "使用所有输入重复前面的过程。模型的性能在验证数据集上会略有提高。" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "id": "c0mhscXh2k36" }, "outputs": [ { "data": { "text/html": [ "
Model: \"sequential_3\"\n",
              "
\n" ], "text/plain": [ "\u001b[1mModel: \"sequential_3\"\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃ Layer (type)                     Output Shape                  Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ normalization (Normalization)   │ (10, 9)                │            19 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_5 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_6 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_7 (Dense)                 │ ?                      │   0 (unbuilt) │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "
\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ normalization (\u001b[38;5;33mNormalization\u001b[0m) │ (\u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m9\u001b[0m) │ \u001b[38;5;34m19\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_7 (\u001b[38;5;33mDense\u001b[0m) │ ? │ \u001b[38;5;34m0\u001b[0m (unbuilt) │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Total params: 19 (80.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19\u001b[0m (80.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Trainable params: 0 (0.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 Non-trainable params: 19 (80.00 B)\n",
              "
\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m19\u001b[0m (80.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dnn_model = build_and_compile_model(normalizer)\n", "dnn_model.summary()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "id": "CXDENACl2tuW" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 9.77 s, sys: 1.29 s, total: 11.1 s\n", "Wall time: 8.31 s\n" ] } ], "source": [ "%%time\n", "history = dnn_model.fit(\n", " train_features,\n", " train_labels,\n", " validation_split=0.2,\n", " verbose=0, epochs=100)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "id": "-9Dbj0fX23RQ" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_loss(history)" ] }, { "cell_type": "markdown", "metadata": { "id": "hWoVYS34fJPZ" }, "source": [ "收集测试集上的结果:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "id": "-bZIa96W3c7K" }, "outputs": [], "source": [ "test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "uiCucdPLfMkZ" }, "source": [ "## 性能" ] }, { "cell_type": "markdown", "metadata": { "id": "rDf1xebEfWBw" }, "source": [ "所有模型都已经过训练,因此您可以查看它们的测试集性能:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "id": "e5_ooufM5iH2" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Mean absolute error [MPG]
horsepower_model3.646849
linear_model2.500054
dnn_horsepower_model2.905027
dnn_model1.643063
\n", "
" ], "text/plain": [ " Mean absolute error [MPG]\n", "horsepower_model 3.646849\n", "linear_model 2.500054\n", "dnn_horsepower_model 2.905027\n", "dnn_model 1.643063" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T" ] }, { "cell_type": "markdown", "metadata": { "id": "DABIVzsCf-QI" }, "source": [ "这些结果与训练期间看到的验证误差相匹配。" ] }, { "cell_type": "markdown", "metadata": { "id": "ft603OzXuEZC" }, "source": [ "### 做预测\n", "\n", "您现在可以使用 Keras `Model.predict`,在测试集上利用 `dnn_model` 进行预测并查看损失:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "id": "Xe7RXH3N3CWU" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 175ms/step\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_predictions = dnn_model.predict(test_features).flatten()\n", "\n", "a = plt.axes(aspect='equal')\n", "plt.scatter(test_labels, test_predictions)\n", "plt.xlabel('True Values [MPG]')\n", "plt.ylabel('Predictions [MPG]')\n", "lims = [0, 50]\n", "plt.xlim(lims)\n", "plt.ylim(lims)\n", "_ = plt.plot(lims, lims)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "19wyogbOSU5t" }, "source": [ "看起来模型预测得相当出色。\n", "\n", "现在,查看一下误差分布:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "id": "f-OHX4DiXd8x" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "error = test_predictions - test_labels\n", "plt.hist(error, bins=25)\n", "plt.xlabel('Prediction Error [MPG]')\n", "_ = plt.ylabel('Count')" ] }, { "cell_type": "markdown", "metadata": { "id": "KSyaHUfDT-mZ" }, "source": [ "如果您对模型感到满意,请使用 `Model.save` 将其保存以备后续使用:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "id": "4-WwLlmfT-mb" }, "outputs": [], "source": [ "dnn_model.save(temp_dir/'dnn_model.keras')" ] }, { "cell_type": "markdown", "metadata": { "id": "Benlnl8UT-me" }, "source": [ "如果您重新加载模型,它会给出相同的输出:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "id": "dyyyj2zVT-mf" }, "outputs": [], "source": [ "reloaded = tf.keras.models.load_model(temp_dir/'dnn_model.keras')\n", "\n", "test_results['reloaded'] = reloaded.evaluate(\n", " test_features, test_labels, verbose=0)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "id": "f_GchJ2tg-2o" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Mean absolute error [MPG]
horsepower_model3.646849
linear_model2.500054
dnn_horsepower_model2.905027
dnn_model1.643063
reloaded1.643063
\n", "
" ], "text/plain": [ " Mean absolute error [MPG]\n", "horsepower_model 3.646849\n", "linear_model 2.500054\n", "dnn_horsepower_model 2.905027\n", "dnn_model 1.643063\n", "reloaded 1.643063" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T" ] }, { "cell_type": "markdown", "metadata": { "id": "vgGQuV-yqYZH" }, "source": [ "## 结论\n", "\n", "本笔记本 (notebook) 介绍了一些处理回归问题的技术。\n", "\n", "- 均方误差 (MSE) (`tf.keras.losses.MeanSquaredError`) 和平均绝对误差 (MAE) (`tf.keras.losses.MeanAbsoluteError`) 是用于回归问题的常见损失函数。MAE 对异常值不那么敏感。不同的损失函数用于分类问题。\n", "- 类似的,用于回归的评估指标与分类不同。 常见的回归指标是平均绝对误差(MAE)。\n", "- 当数字输入数据特征的值存在不同范围时,每个特征应独立缩放到相同范围。\n", "- 过拟合是 DNN 模型的常见问题,但本教程不存在此问题。有关这方面的更多帮助,请访问[过拟合和欠拟合](overfit_and_underfit.ipynb)教程。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "regression.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "xxx", "language": "python", "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": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 0 }