1-LeNet5_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center\">基于LeNet5的手写数字识别</h1>\n",
    "\n",
    "## 实验介绍\n",
    "\n",
    "LeNet5 + MINST被誉为深度学习领域的“Hello world”。本实验主要介绍使用MindSpore在MNIST数据集上开发和训练一个LeNet5模型，并验证模型精度。\n",
    "\n",
    "## 实验目的\n",
    "\n",
    "- 了解如何使用MindSpore进行简单卷积神经网络的开发。\n",
    "- 了解如何使用MindSpore进行简单图片分类任务的训练。\n",
    "- 了解如何使用MindSpore进行简单图片分类任务的验证。\n",
    "\n",
    "## 预备知识\n",
    "\n",
    "- 熟练使用Python，了解Shell及Linux操作系统基本知识。\n",
    "- 具备一定的深度学习理论知识，如卷积神经网络、损失函数、优化器，训练策略等。\n",
    "- 了解华为云的基本使用方法，包括[OBS（对象存储）](https://www.huaweicloud.com/product/obs.html)、[ModelArts（AI开发平台）](https://www.huaweicloud.com/product/modelarts.html)、[Notebook（开发工具）](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0033.html)、[训练作业](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0046.html)等服务。华为云官网：https://www.huaweicloud.com\n",
    "- 了解并熟悉MindSpore AI计算框架，MindSpore官网：https://www.mindspore.cn\n",
    "\n",
    "## 实验环境\n",
    "\n",
    "- MindSpore 0.2.0（MindSpore版本会定期更新，本指导也会定期刷新，与版本配套）；\n",
    "- 华为云ModelArts：ModelArts是华为云提供的面向开发者的一站式AI开发平台，集成了昇腾AI处理器资源池，用户可以在该平台下体验MindSpore。ModelArts官网：https://www.huaweicloud.com/product/modelarts.html\n",
    "\n",
    "## 实验准备\n",
    "\n",
    "### 创建OBS桶\n",
    "\n",
    "本实验需要使用华为云OBS存储实验脚本和数据集，可以参考[快速通过OBS控制台上传下载文件](https://support.huaweicloud.com/qs-obs/obs_qs_0001.html)了解使用OBS创建桶、上传文件、下载文件的使用方法。\n",
    "\n",
    "> **提示：** 华为云新用户使用OBS时通常需要创建和配置“访问密钥”，可以在使用OBS时根据提示完成创建和配置。也可以参考[获取访问密钥并完成ModelArts全局配置](https://support.huaweicloud.com/prepare-modelarts/modelarts_08_0002.html)获取并配置访问密钥。\n",
    "\n",
    "创建OBS桶的参考配置如下：\n",
    "\n",
    "- 区域：华北-北京四\n",
    "- 数据冗余存储策略：单AZ存储\n",
    "- 桶名称：如ms-course\n",
    "- 存储类别：标准存储\n",
    "- 桶策略：公共读\n",
    "- 归档数据直读：关闭\n",
    "- 企业项目、标签等配置：免\n",
    "\n",
    "### 数据集准备\n",
    "\n",
    "MNIST是一个手写数字数据集，训练集包含60000张手写数字，测试集包含10000张手写数字，共10类。MNIST数据集的官网：[THE MNIST DATABASE](http://yann.lecun.com/exdb/mnist/)。\n",
    "\n",
    "从MNIST官网下载如下4个文件到本地并解压：\n",
    "\n",
    "```\n",
    "train-images-idx3-ubyte.gz:  training set images (9912422 bytes)\n",
    "train-labels-idx1-ubyte.gz:  training set labels (28881 bytes)\n",
    "t10k-images-idx3-ubyte.gz:   test set images (1648877 bytes)\n",
    "t10k-labels-idx1-ubyte.gz:   test set labels (4542 bytes)\n",
    "```\n",
    "\n",
    "### 脚本准备\n",
    "\n",
    "从[课程gitee仓库](https://gitee.com/mindspore/course)上下载本实验相关脚本。\n",
    "\n",
    "### 上传文件\n",
    "\n",
    "将脚本和数据集上传到OBS桶中，组织为如下形式：\n",
    "\n",
    "```\n",
    "experiment_1\n",
    "├── MNIST\n",
    "│   ├── test\n",
    "│   │   ├── t10k-images-idx3-ubyte\n",
    "│   │   └── t10k-labels-idx1-ubyte\n",
    "│   └── train\n",
    "│       ├── train-images-idx3-ubyte\n",
    "│       └── train-labels-idx1-ubyte\n",
    "├── *.ipynb\n",
    "└── main.py\n",
    "```\n",
    "\n",
    "## 实验步骤（方案一）\n",
    "\n",
    "### 创建Notebook\n",
    "\n",
    "可以参考[创建并打开Notebook](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0034.html)来创建并打开本实验的Notebook脚本。\n",
    "\n",
    "创建Notebook的参考配置：\n",
    "\n",
    "- 计费模式：按需计费\n",
    "- 名称：experiment_1\n",
    "- 工作环境：Python3\n",
    "- 资源池：公共资源\n",
    "- 类型：Ascend\n",
    "- 规格：单卡1*Ascend 910\n",
    "- 存储位置：对象存储服务（OBS）->选择上述新建的OBS桶中的experiment_1文件夹\n",
    "- 自动停止等配置：默认\n",
    "\n",
    "> **注意：**\n",
    "> - 打开Notebook前，在Jupyter Notebook文件列表页面，勾选目录里的所有文件/文件夹（实验脚本和数据集），并点击列表上方的“Sync OBS”按钮，使OBS桶中的所有文件同时同步到Notebook工作环境中，这样Notebook中的代码才能访问数据集。参考[使用Sync OBS功能](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0038.html)。\n",
    "> - 打开Notebook后，选择MindSpore环境作为Kernel。\n",
    "\n",
    "> **提示：** 上述数据集和脚本的准备工作也可以在Notebook环境中完成，在Jupyter Notebook文件列表页面，点击右上角的\"New\"->\"Terminal\"，进入Notebook环境所在终端，进入`work`目录，可以使用常用的linux shell命令，如`wget, gzip, tar, mkdir, mv`等，完成数据集和脚本的下载和准备。\n",
    "\n",
    "> **提示：** 请从上至下阅读提示并执行代码框进行体验。代码框执行过程中左侧呈现[\\*]，代码框执行完毕后左侧呈现如[1]，[2]等。请等上一个代码框执行完毕后再执行下一个代码框。\n",
    "\n",
    "导入MindSpore模块和辅助模块："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# os.environ['DEVICE_ID'] = '0'\n",
    "import matplotlib.pyplot as plt\n",
    "import mindspore as ms\n",
    "import mindspore.context as context\n",
    "import mindspore.dataset.transforms.c_transforms as C\n",
    "import mindspore.dataset.transforms.vision.c_transforms as CV\n",
    "\n",
    "from mindspore import nn\n",
    "from mindspore.model_zoo.lenet import LeNet5\n",
    "from mindspore.train import Model\n",
    "from mindspore.train.callback import LossMonitor\n",
    "\n",
    "context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据处理\n",
    "\n",
    "在使用数据集训练网络前，首先需要对数据进行预处理，如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR_TRAIN = \"MNIST/train\" # 训练集信息\n",
    "DATA_DIR_TEST = \"MNIST/test\" # 测试集信息\n",
    "\n",
    "def create_dataset(training=True, num_epoch=1, batch_size=32, resize=(32, 32),\n",
    "                   rescale=1/(255*0.3081), shift=-0.1307/0.3081, buffer_size=64):\n",
    "    ds = ms.dataset.MnistDataset(DATA_DIR_TRAIN if training else DATA_DIR_TEST)\n",
    "    \n",
    "    ds = ds.map(input_columns=\"image\", operations=[CV.Resize(resize), CV.Rescale(rescale, shift), CV.HWC2CHW()])\n",
    "    ds = ds.map(input_columns=\"label\", operations=C.TypeCast(ms.int32))\n",
    "    ds = ds.shuffle(buffer_size=buffer_size).batch(batch_size, drop_remainder=True).repeat(num_epoch)\n",
    "    \n",
    "    return ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对其中几张图片进行可视化，可以看到图片中的手写数字，图片的大小为32x32。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = create_dataset(training=False)\n",
    "data = ds.create_dict_iterator().get_next()\n",
    "images = data['image']\n",
    "labels = data['label']\n",
    "\n",
    "for i in range(1, 5):\n",
    "    plt.subplot(2, 2, i)\n",
    "    plt.imshow(images[i][0])\n",
    "    plt.title('Number: %s' % labels[i])\n",
    "    plt.xticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 定义模型\n",
    "\n",
    "MindSpore model_zoo中提供了多种常见的模型，可以直接使用。这里使用其中的LeNet5模型，模型结构如下图所示：\n",
    "\n",
    "<img src=\"https://www.mindspore.cn/tutorial/zh-CN/master/_images/LeNet_5.jpg\">\n",
    "\n",
    "[1] 图片来源于http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练\n",
    "\n",
    "使用MNIST数据集对上述定义的LeNet5模型进行训练。训练策略如下表所示，可以调整训练策略并查看训练效果，要求验证精度大于95%。\n",
    "\n",
    "| batch size | number of epochs | learning rate | optimizer |\n",
    "| -- | -- | -- | -- |\n",
    "| 32 | 3 | 0.01 | Momentum 0.9 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 step: 1875 ,loss is 2.3086565\n",
      "epoch: 2 step: 1875 ,loss is 0.22017351\n",
      "epoch: 3 step: 1875 ,loss is 0.025683485\n",
      "Metrics: {'acc': 0.9742588141025641, 'loss': 0.08628832848253062}\n"
     ]
    }
   ],
   "source": [
    "ds_train = create_dataset(num_epoch=3)\n",
    "ds_eval = create_dataset(training=False)\n",
    "\n",
    "net = LeNet5()\n",
    "loss = nn.loss.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n",
    "opt = nn.Momentum(net.trainable_params(), 0.01, 0.9)\n",
    "\n",
    "loss_cb = LossMonitor(per_print_times=1)\n",
    "\n",
    "model = Model(net, loss, opt, metrics={'acc', 'loss'})\n",
    "model.train(3, ds_train, callbacks=[loss_cb])\n",
    "metrics = model.eval(ds_eval)\n",
    "print('Metrics:', metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验步骤（方案二）\n",
    "\n",
    "除了Notebook，ModelArts还提供了训练作业服务。相比Notebook，训练作业资源池更大，且具有作业排队等功能，适合大规模并发使用。使用训练作业时，也会有修改代码和调试的需求，有如下三个方案：\n",
    "\n",
    "1. 在本地修改代码后重新上传；\n",
    "\n",
    "2. 使用[PyCharm ToolKit](https://support.huaweicloud.com/tg-modelarts/modelarts_15_0001.html)配置一个本地Pycharm+ModelArts的开发环境，便于上传代码、提交训练作业和获取训练日志。\n",
    "\n",
    "3. 在ModelArts上创建Notebook，然后设置[Sync OBS功能](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0038.html)，可以在线修改代码并自动同步到OBS中。因为只用Notebook来编辑代码，所以创建CPU类型最低规格的Notebook就行。\n",
    "\n",
    "### 代码梳理\n",
    "\n",
    "创建训练作业时，运行参数会通过脚本传参的方式输入给脚本代码，脚本必须解析传参才能在代码中使用相应参数。如data_url和train_url，分别对应数据存储路径(OBS路径)和训练输出路径(OBS路径)。脚本对传参进行解析后赋值到`args`变量里，在后续代码里可以使用。\n",
    "\n",
    "```python\n",
    "import argparse\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--data_url', required=True, default=None, help='Location of data.')\n",
    "parser.add_argument('--train_url', required=True, default=None, help='Location of training outputs.')\n",
    "parser.add_argument('--num_epochs', type=int, default=1, help='Number of training epochs.')\n",
    "args, unknown = parser.parse_known_args()\n",
    "```\n",
    "\n",
    "MindSpore暂时没有提供直接访问OBS数据的接口，需要通过MoXing提供的API与OBS交互。将OBS中存储的数据拷贝至执行容器：\n",
    "\n",
    "```python\n",
    "import moxing as mox\n",
    "mox.file.copy_parallel(src_url=args.data_url, dst_url='MNIST/')\n",
    "```\n",
    "\n",
    "如需将训练输出（如模型Checkpoint）从执行容器拷贝至OBS，请参考：\n",
    "\n",
    "```python\n",
    "import moxing as mox\n",
    "mox.file.copy_parallel(src_url='output', dst_url='s3://OBS/PATH')\n",
    "```\n",
    "\n",
    "其他代码分析请参考方案一。\n",
    "\n",
    "### 创建训练作业\n",
    "\n",
    "可以参考[使用常用框架训练模型](https://support.huaweicloud.com/engineers-modelarts/modelarts_23_0238.html)来创建并启动训练作业。\n",
    "\n",
    "创建训练作业的参考配置：\n",
    "\n",
    "- 算法来源：常用框架->Ascend-Powered-Engine->MindSpore\n",
    "- 代码目录：选择上述新建的OBS桶中的experiment_1目录\n",
    "- 启动文件：选择上述新建的OBS桶中的experiment_1目录下的`main.py`\n",
    "- 数据来源：数据存储位置->选择上述新建的OBS桶中的experiment_1目录下的MNIST目录\n",
    "- 训练输出位置：选择上述新建的OBS桶中的experiment_1目录并在其中创建output目录\n",
    "- 作业日志路径：同训练输出位置\n",
    "- 规格：Ascend:1*Ascend 910\n",
    "- 其他均为默认\n",
    "\n",
    "启动并查看训练过程：\n",
    "\n",
    "1. 点击提交以开始训练；\n",
    "2. 在训练作业列表里可以看到刚创建的训练作业，在训练作业页面可以看到版本管理；\n",
    "3. 点击运行中的训练作业，在展开的窗口中可以查看作业配置信息，以及训练过程中的日志，日志会不断刷新，等训练作业完成后也可以下载日志到本地进行查看；\n",
    "4. 在训练日志中可以看到`epoch: 3 step: 1875 ,loss is 0.025683485`等字段，即训练过程的loss值；\n",
    "5. 在训练日志中可以看到`Metrics: {'acc': 0.9742588141025641, 'loss': 0.08628832848253062}`字段，即训练完成后的验证精度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实验小结\n",
    "\n",
    "本实验展示了如何使用MindSpore进行手写数字识别，以及开发和训练LeNet5模型。通过对LeNet5模型做几代的训练，然后使用训练后的LeNet5模型对手写数字进行识别，识别准确率大于95%。即LeNet5学习到了如何进行手写数字识别。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}