diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,4 +1,6 @@
# AI实战-[practicalAI](https://github.com/GokuMohandas/practicalAI) 中文版
+[](https://github.com/GokuMohandas/practicalAI#notebooks)
+[](https://github.com/GokuMohandas/practicalAI/blob/master/LICENSE)
让你有能力使用机器学习从数据中获取有价值的见解。
- 🔥 使用 [PyTorch](https://pytorch.org/) 实现基本的机器学习算法和深度神经网络。
@@ -13,7 +15,7 @@
|🔢 [NumPy](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/02_NumPy.ipynb)|🔎 [数据和模型 Data & Models](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/09_Data_and_Models.ipynb)|🔮 自编码器 Autoencoders|🏘️ Topic Modeling|
| 🐼 [Pandas](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/03_Pandas.ipynb) |📦 [面向对象的机器学习 Object-Oriented ML](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/10_Object_Oriented_ML.ipynb)|🎭 生成对抗网络 Generative Adversarial Networks|🛒 推荐系统 Recommendation Systems|
|📈 [线性回归 Linear Regression](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/04_Linear_Regression.ipynb)|🖼️ [卷积神经网络 Convolutional Neural Networks](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/11_Convolutional_Neural_Networks.ipynb)|🐝 空间变换模型 Spatial Transformer Networks|🗣️ 预训练语言模型 Pretrained Language Modeling|
-|📊 [Logistic Regression](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/05_Logistic_Regression.ipynb)|📝 [嵌入层 Embeddings](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/12_Embeddings.ipynb)||🤷 多任务学习 Multitask Learning|
+|📊 [逻辑回归 Logistic Regression](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/05_Logistic_Regression.ipynb)|📝 [嵌入层 Embeddings](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/12_Embeddings.ipynb)||🤷 多任务学习 Multitask Learning|
|🌳 [随机森林 Random Forests](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/06_Random_Forests.ipynb)|📗 [递归神经网络 Recurrent Neural Networks](https://colab.research.google.com/github/MLEveryday/practicalAI-cn/blob/master/notebooks/13_Recurrent_Neural_Networks.ipynb)||🎯 Low Shot Learning|
|💥 k-均值聚类 KMeans Clustering|||🍒 强化学习 Reinforcement Learning|
diff --git a/notebooks/10_Object_Oriented_ML.ipynb b/notebooks/10_Object_Oriented_ML.ipynb
new file mode 100644
index 0000000..11f01c6
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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "10_Object_Oriented_ML-cn",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "metadata": {
+ "id": "bOChJSNXtC9g",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# 面向对象的机器学习"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "OLIxEDq6VhvZ",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "![](\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/logo.png\")
\n",
+ "\n",
+ "本文中，我们将学习如何适当地创建和使用类与函数，基于 PyTorch 完成机器学习任务。在后续的 notebooks中，都会按照这样的实现结构。\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "VoMq0eFRvugb",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# 概览"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "qWro5T5qTJJL",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "我们将会用到的类和函数总计如下："
+ ]
+ },
+ {
+ "metadata": {
+ "id": "f88fhoHFLIKg",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "* **词汇表 Vocabulary**：用于原始输入和数字形式的相互转换。\n",
+ "* **向量器 Vectorizer**：输入和输出的 vocabulary 类实例，并为模型把数据向量化。\n",
+ "* **数据集 Dataset**：处理和拆分数据的 vectorizers。\n",
+ "* **模型 Model**：处理输入和返回预测值的 PyTorch模型。\n",
+ "\n",
+ "下面的以及后续课程中的代码实现结构，全部归功于 PyTorch 的[贡献者](https://github.com/joosthub/PyTorchNLPBook/graphs/contributors)。"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "I_1BrSavU0ek",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# 配置"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "b6B0GSOuY_ty",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "首先，我们配置可重现的环境、参数、种子等。"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "WnV34uLSY4bZ",
+ "colab_type": "code",
+ "outputId": "923141ef-0d88-47ba-8b6a-ad14e50b789d",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# 加载 PyTorch 库\n",
+ "!pip3 install torch"
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (1.0.0)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "0-dXQiLlTIgz",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "from argparse import Namespace\n",
+ "import collections\n",
+ "import json\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import re\n",
+ "import torch"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "hnID97KCXuT-",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# 设置 Numpy 和 PyTorch 种子\n",
+ "def set_seeds(seed, cuda):\n",
+ " np.random.seed(seed)\n",
+ " torch.manual_seed(seed)\n",
+ " if cuda:\n",
+ " torch.cuda.manual_seed_all(seed)\n",
+ " \n",
+ "# 创建目录\n",
+ "def create_dirs(dirpath):\n",
+ " if not os.path.exists(dirpath):\n",
+ " os.makedirs(dirpath)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "wmpwPJr5VtKP",
+ "colab_type": "code",
+ "outputId": "4ede8242-2dcd-4f30-aaa2-88090713b1a6",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# 参数\n",
+ "args = Namespace(\n",
+ " seed=1234,\n",
+ " cuda=False,\n",
+ " shuffle=True,\n",
+ " data_file=\"names.csv\",\n",
+ " split_data_file=\"split_names.csv\",\n",
+ " vectorizer_file=\"vectorizer.json\",\n",
+ " model_state_file=\"model.pth\",\n",
+ " save_dir=\"names\",\n",
+ " train_size=0.7,\n",
+ " val_size=0.15,\n",
+ " test_size=0.15,\n",
+ " num_epochs=20,\n",
+ " early_stopping_criteria=5,\n",
+ " learning_rate=1e-3,\n",
+ " batch_size=64,\n",
+ " hidden_dim=300,\n",
+ " dropout_p=0.1,\n",
+ ")\n",
+ "\n",
+ "# 设置种子\n",
+ "set_seeds(seed=args.seed, cuda=args.cuda)\n",
+ "\n",
+ "# 创建保存路径\n",
+ "create_dirs(args.save_dir)\n",
+ "\n",
+ "# 展开文件路径\n",
+ "args.vectorizer_file = os.path.join(args.save_dir, args.vectorizer_file)\n",
+ "args.model_state_file = os.path.join(args.save_dir, args.model_state_file)\n",
+ "\n",
+ "# 检查 CUDA\n",
+ "if not torch.cuda.is_available():\n",
+ " args.cuda = False\n",
+ "args.device = torch.device(\"cuda\" if args.cuda else \"cpu\")\n",
+ "print(\"Using CUDA: {}\".format(args.cuda))\n",
+ " "
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Using CUDA: False\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "1U_jyWA-ZIZF",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# 数据"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "R_Fg-TslU2Bs",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "我们的任务是得到指定姓名对应的国籍，包括数据预处理，以及拆分数据为训练、验证和测试部分。"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "M7H6_m8XZkFX",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "import re\n",
+ "import urllib"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "Vq8Ooa6RZWhX",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# 从 GitHub 上传数据到 notebook 的目录\n",
+ "url = \"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/data/surnames.csv\"\n",
+ "response = urllib.request.urlopen(url)\n",
+ "html = response.read()\n",
+ "with open(args.data_file, 'wb') as fp:\n",
+ " fp.write(html)"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "QkkdVRivZKd8",
+ "colab_type": "code",
+ "outputId": "79d7d550-f7e0-4eb7-9a62-ebac2c514fd1",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 204
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "# 原始数据\n",
+ "df = pd.read_csv(args.data_file, header=0)\n",
+ "df.head()"
+ ],
+ "execution_count": 0,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " surname | \n",
+ " nationality | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Woodford | \n",
+ " English | \n",
+ "
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+ " \n",
+ " | 1 | \n",
+ " Coté | \n",
+ " French | \n",
+ "
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+ " \n",
+ " | 2 | \n",
+ " Kore | \n",
+ " English | \n",
+ "
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+ " \n",
+ " | 3 | \n",
+ " Koury | \n",
+ " Arabic | \n",
+ "
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+ " \n",
+ " | 4 | \n",
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+ " Russian | \n",
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+ "\n",
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+ " Arabic | \n",
+ " train | \n",
+ " bishara | \n",
+ "
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+ " \n",
+ " | 1 | \n",
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+ " train | \n",
+ " nahas | \n",
+ "
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+ " \n",
+ " | 2 | \n",
+ " Arabic | \n",
+ " train | \n",
+ " ghanem | \n",
+ "
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+ " \n",
+ " | 3 | \n",
+ " Arabic | \n",
+ " train | \n",
+ " tannous | \n",
+ "
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+ " \n",
+ " | 4 | \n",
+ " Arabic | \n",
+ " train | \n",
+ " mikhail | \n",
+ "
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+ " \n",
+ "
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+ "