{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

基于LeNet5的手写数字识别

\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)等功能。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", "└── 脚本等文件\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.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", " 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": [ "
" ] }, "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中提供了现成的LeNet5模型，但当前ModelArts平台上暂未集成该模块。模型结构如下图所示：\n", "\n", "\n", "\n", "[1] 图片来源于http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class LeNet5(nn.Cell):\n", " def __init__(self):\n", " super(LeNet5, self).__init__()\n", " self.relu = nn.ReLU()\n", " self.conv1 = nn.Conv2d(1, 6, 5, stride=1, pad_mode='valid')\n", " self.conv2 = nn.Conv2d(6, 16, 5, stride=1, pad_mode='valid')\n", " self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n", " self.flatten = nn.Flatten()\n", " self.fc1 = nn.Dense(400, 120)\n", " self.fc2 = nn.Dense(120, 84)\n", " self.fc3 = nn.Dense(84, 10)\n", " \n", " def construct(self, input_x):\n", " output = self.conv1(input_x)\n", " output = self.relu(output)\n", " output = self.pool(output)\n", " output = self.conv2(output)\n", " output = self.relu(output)\n", " output = self.pool(output)\n", " output = self.flatten(output)\n", " output = self.fc1(output)\n", " output = self.fc2(output)\n", " output = self.fc3(output)\n", " \n", " return output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 训练\n", "\n", "使用MNIST数据集对上述定义的LeNet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", "### 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、训练和使用LeNet模型。通过对LeNet模型做几代的训练，然后使用训练后的LeNet模型对手写数字进行识别，识别准确率大于95%。即LeNet学习到了如何进行手写数字识别。" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, 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