docs/guides/beginner/quick_start_cn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7fb27b941602401d91542211134fc71a",
   "metadata": {},
   "source": [
    "\n",
    "# 10分钟快速上手飞桨\n",
    "\n",
    "从完成一个简单的『手写数字识别任务』开始，可快速了解深度学习模型开发的大致流程，并初步掌握飞桨框架 API 的使用方法。\n",
    "\n",
    "## 一、快速安装飞桨\n",
    "\n",
    "如果已经安装好飞桨那么可以跳过此步骤。飞桨支持很多种安装方式，这里介绍其中一种简单的安装命令。\n",
    "\n",
    "> 注：目前飞桨支持 Python 3.6 ~ 3.9 版本，pip3 要求 20.2.2 或更高版本，请提前安装对应版本的 Python 和 pip 工具。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acae54e37e7d407bbb7b55eff062a284",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-20T07:51:58.100025Z",
     "iopub.status.busy": "2021-12-20T07:51:58.099277Z",
     "iopub.status.idle": "2021-12-20T07:51:59.862320Z",
     "shell.execute_reply": "2021-12-20T07:51:59.861499Z",
     "shell.execute_reply.started": "2021-12-20T07:51:58.099995Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 使用 pip 工具安装飞桨 CPU 版\n",
    "! python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
   "metadata": {},
   "source": [
    "该命令用于安装 CPU 版本的飞桨。如果要安装其他计算平台或操作系统支持的版本，可点击 [ 快速安装]( <https://www.paddlepaddle.org.cn/install/quick>)  查看安装引导。\n",
    "\n",
    "## 二、导入飞桨\n",
    "\n",
    "安装完成后，需要在Python解释器中使用 import 导入飞桨，即可开始实践深度学习任务。\n",
    "\n",
    "若操作成功，会输出飞桨的版本号。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8dd0d8092fe74a7c96281538738b07e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-16T04:18:43.298378Z",
     "iopub.status.busy": "2021-12-16T04:18:43.297973Z",
     "iopub.status.idle": "2021-12-16T04:18:43.302570Z",
     "shell.execute_reply": "2021-12-16T04:18:43.301792Z",
     "shell.execute_reply.started": "2021-12-16T04:18:43.298346Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.1\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "\n",
    "print(paddle.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72eea5119410473aa328ad9291626812",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## 三、实践：手写数字识别任务\n",
    "\n",
    "『手写数字识别』是深度学习里的 Hello World 任务，用于对 0 ~ 9 的十类数字进行分类，即输入手写数字的图片，可识别出这个图片中的数字。\n",
    "\n",
    "本任务用到的数据集为 [MNIST 手写数字数据集](http://yann.lecun.com/exdb/mnist/)，用于训练和测试模型。该数据集包含 60000 张训练图片、 10000 张测试图片、以及对应的分类标签文件，每张图片上是一个 0 ~ 9 的手写数字，分辨率为 28 * 28。部分图像和对应的分类标签如下图所示。\n",
    "\n",
    "![](images/mnist.png)\n",
    "\n",
    "图 1：MNIST 数据集样例\n",
    "\n",
    "开始之前，需要使用下面的命令安装 Python 的 matplotlib 库和 numpy 库，matplotlib 库用于可视化图片，numpy 库用于处理数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8edb47106e1a46a883d545849b8ab81b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-16T06:31:04.356444Z",
     "iopub.status.busy": "2021-12-16T06:31:04.355435Z",
     "iopub.status.idle": "2021-12-16T06:31:06.274730Z",
     "shell.execute_reply": "2021-12-16T06:31:06.273827Z",
     "shell.execute_reply.started": "2021-12-16T06:31:04.356403Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 使用 pip 工具安装 matplotlib 和 numpy\n",
    "! python3 -m pip install matplotlib numpy -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10185d26023b46108eb7d9f57d49d2b3",
   "metadata": {},
   "source": [
    "下面是手写数字识别任务的完整代码，如果想直接运行代码，可以拷贝下面的完整代码到一个Python文件中运行。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8763a12b2bbd4a93a75aff182afb95dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-16T06:31:11.340019Z",
     "iopub.status.busy": "2021-12-16T06:31:11.338995Z",
     "iopub.status.idle": "2021-12-16T06:33:13.307650Z",
     "shell.execute_reply": "2021-12-16T06:33:13.306938Z",
     "shell.execute_reply.started": "2021-12-16T06:31:11.339980Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
      "Epoch 1/5\n",
      "step 938/938 [==============================] - loss: 0.0519 - acc: 0.9344 - 14ms/step          \n",
      "Epoch 2/5\n",
      "step 938/938 [==============================] - loss: 0.0239 - acc: 0.9767 - 14ms/step          \n",
      "Epoch 3/5\n",
      "step 938/938 [==============================] - loss: 0.0416 - acc: 0.9811 - 14ms/step          \n",
      "Epoch 4/5\n",
      "step 938/938 [==============================] - loss: 0.0084 - acc: 0.9837 - 14ms/step          \n",
      "Epoch 5/5\n",
      "step 938/938 [==============================] - loss: 0.0838 - acc: 0.9860 - 14ms/step          \n",
      "Eval begin...\n",
      "step 157/157 [==============================] - loss: 1.7577e-04 - acc: 0.9844 - 6ms/step         \n",
      "Eval samples: 10000\n",
      "true label: 7, pred label: 7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7feb8a585dd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\n",
    "import numpy as np\n",
    "from paddle.vision.transforms import Normalize\n",
    "\n",
    "transform = Normalize(mean=[127.5], std=[127.5], data_format=\"CHW\")\n",
    "# 下载数据集并初始化 DataSet\n",
    "train_dataset = paddle.vision.datasets.MNIST(mode=\"train\", transform=transform)\n",
    "test_dataset = paddle.vision.datasets.MNIST(mode=\"test\", transform=transform)\n",
    "\n",
    "# 模型组网并初始化网络\n",
    "lenet = paddle.vision.models.LeNet(num_classes=10)\n",
    "model = paddle.Model(lenet)\n",
    "\n",
    "# 模型训练的配置准备，准备损失函数，优化器和评价指标\n",
    "model.prepare(\n",
    "    paddle.optimizer.Adam(parameters=model.parameters()),\n",
    "    paddle.nn.CrossEntropyLoss(),\n",
    "    paddle.metric.Accuracy(),\n",
    ")\n",
    "\n",
    "# 模型训练\n",
    "model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)\n",
    "# 模型评估\n",
    "model.evaluate(test_dataset, batch_size=64, verbose=1)\n",
    "\n",
    "# 保存模型\n",
    "model.save(\"./output/mnist\")\n",
    "# 加载模型\n",
    "model.load(\"output/mnist\")\n",
    "\n",
    "# 从测试集中取出一张图片\n",
    "img, label = test_dataset[0]\n",
    "# 将图片shape从1*28*28变为1*1*28*28，增加一个batch维度，以匹配模型输入格式要求\n",
    "img_batch = np.expand_dims(img.astype(\"float32\"), axis=0)\n",
    "\n",
    "# 执行推理并打印结果，此处predict_batch返回的是一个list，取出其中数据获得预测结果\n",
    "out = model.predict_batch(img_batch)[0]\n",
    "pred_label = out.argmax()\n",
    "print(\"true label: {}, pred label: {}\".format(label[0], pred_label))\n",
    "# 可视化图片\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.imshow(img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7623eae2785240b9bd12b16a66d81610",
   "metadata": {},
   "source": [
    "以上代码使用 MNIST 数据集训练并测试了 LeNet 模型，并最终成功推理出了一张手写数字图片的标签，该图片推理结果是 7 （ pred label: 7），真实标签也是7 （true label: 7）。\n",
    "\n",
    "简单地说，深度学习任务一般分为以下几个核心步骤：\n",
    "\n",
    "\n",
    "1. 数据集定义与加载\n",
    "2. 模型组网\n",
    "3. 模型训练与评估\n",
    "4. 模型推理\n",
    "\n",
    "接下来逐个步骤介绍，帮助你快速掌握使用飞桨框架实践深度学习任务的方法。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cdc8c89c7104fffa095e18ddfef8986",
   "metadata": {},
   "source": [
    "###  3.1 数据集定义与加载\n",
    "\n",
    "飞桨在 [paddle.vision.datasets](../../api/paddle/vision/Overview_cn.html#api) 下内置了计算机视觉（Computer Vision，CV）领域常见的数据集，如 MNIST、Cifar10、Cifar100、FashionMNIST 和 VOC2012 等。在本任务中，先后加载了 MNIST 训练集（`mode='train'`）和测试集（`mode='test'`），训练集用于训练模型，测试集用于评估模型效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b118ea5561624da68c537baed56e602f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-17T06:55:43.185059Z",
     "iopub.status.busy": "2021-12-17T06:55:43.184142Z",
     "iopub.status.idle": "2021-12-17T06:55:53.018229Z",
     "shell.execute_reply": "2021-12-17T06:55:53.017346Z",
     "shell.execute_reply.started": "2021-12-17T06:55:43.185027Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 images in train_dataset, 10000 images in test_dataset\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "from paddle.vision.transforms import Normalize\n",
    "\n",
    "transform = Normalize(mean=[127.5], std=[127.5], data_format=\"CHW\")\n",
    "# 下载数据集并初始化 DataSet\n",
    "train_dataset = paddle.vision.datasets.MNIST(mode=\"train\", transform=transform)\n",
    "test_dataset = paddle.vision.datasets.MNIST(mode=\"test\", transform=transform)\n",
    "\n",
    "# 打印数据集里图片数量\n",
    "print(\n",
    "    \"{} images in train_dataset, {} images in test_dataset\".format(\n",
    "        len(train_dataset), len(test_dataset)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "938c804e27f84196a10c8828c723f798",
   "metadata": {},
   "source": [
    "飞桨除了内置了 CV 领域常见的数据集，还在 [paddle.text](../../api/paddle/text/Overview_cn.html#api) 下内置了自然语言处理（Natural Language Processing，NLP）领域常见的数据集，并提供了自定义数据集与加载功能的 [paddle.io.Dataset](../../api/paddle/io/Dataset_cn.html#dataset) 和 [paddle.io.DataLoader](../../api/paddle/io/DataLoader_cn.html#dataloader) API，详细使用方法可参考『数据集定义与加载』 章节。\n",
    "\n",
    "\n",
    "另外在 [paddle.vision.transforms](../..api/paddle/vision/Overview_cn.html#about-transforms) 下提供了一些常用的图像变换操作，如对图像的翻转、裁剪、调整亮度等处理，可实现数据增强，以增加训练样本的多样性，提升模型的泛化能力。本任务在初始化 MNIST 数据集时通过 `transform` 字段传入了 `Normalize` 变换对图像进行归一化，对图像进行归一化可以加快模型训练的收敛速度。该功能的具体使用方法可参考『数据预处理』 章节。\n",
    "\n",
    "\n",
    "更多参考：\n",
    "* [数据集定义与加载](data_load_cn.html)\n",
    "* [数据预处理](data_preprocessing_cn.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504fb2a444614c0babb325280ed9130a",
   "metadata": {},
   "source": [
    "###  3.2 模型组网\n",
    "\n",
    "飞桨的模型组网有多种方式，既可以直接使用飞桨内置的模型，也可以自定义组网。\n",
    "\n",
    "\n",
    "『手写数字识别任务』比较简单，普通的神经网络就能达到很高的精度，在本任务中使用了飞桨内置的 LeNet 作为模型。飞桨在 [paddle.vision.models](../../api/paddle/vision/Overview_cn.html#about-models) 下内置了 CV 领域的一些经典模型，LeNet 就是其中之一，调用很方便，只需一行代码即可完成 LeNet 的网络构建和初始化。`num_classes` 字段中定义分类的类别数，因为需要对 0 ~ 9 的十类数字进行分类，所以设置为 10。\n",
    "\n",
    "另外通过 [paddle.summary](../../api/paddle/summary_cn.html#summary) 可方便地打印网络的基础结构和参数信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59bbdb311c014d738909a11f9e486628",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-17T06:59:47.978121Z",
     "iopub.status.busy": "2021-12-17T06:59:47.977323Z",
     "iopub.status.idle": "2021-12-17T06:59:47.990123Z",
     "shell.execute_reply": "2021-12-17T06:59:47.989596Z",
     "shell.execute_reply.started": "2021-12-17T06:59:47.978088Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------\n",
      " Layer (type)       Input Shape          Output Shape         Param #    \n",
      "===========================================================================\n",
      "   Conv2D-1       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60       \n",
      "    ReLU-1        [[1, 6, 28, 28]]      [1, 6, 28, 28]           0       \n",
      "  MaxPool2D-1     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0       \n",
      "   Conv2D-2       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416     \n",
      "    ReLU-2       [[1, 16, 10, 10]]     [1, 16, 10, 10]           0       \n",
      "  MaxPool2D-2    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0       \n",
      "   Linear-1          [[1, 400]]            [1, 120]           48,120     \n",
      "   Linear-2          [[1, 120]]            [1, 84]            10,164     \n",
      "   Linear-3          [[1, 84]]             [1, 10]              850      \n",
      "===========================================================================\n",
      "Total params: 61,610\n",
      "Trainable params: 61,610\n",
      "Non-trainable params: 0\n",
      "---------------------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.11\n",
      "Params size (MB): 0.24\n",
      "Estimated Total Size (MB): 0.35\n",
      "---------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'total_params': 61610, 'trainable_params': 61610}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型组网并初始化网络\n",
    "lenet = paddle.vision.models.LeNet(num_classes=10)\n",
    "\n",
    "# 可视化模型组网结构和参数\n",
    "paddle.summary(lenet, (1, 1, 28, 28))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43b363d81ae4b689946ece5c682cd59",
   "metadata": {},
   "source": [
    "通过飞桨的 [paddle.nn.Sequential](../../api/paddle/nn/Sequential_cn.html) 和 [paddle.nn.Layer](../../api/paddle/nn/Layer_cn.html) API 可以更灵活方便的组建自定义的神经网络，详细使用方法可参考『模型组网』章节。\n",
    "\n",
    "更多参考：\n",
    "* [模型组网](model_cn.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a65eabff63a45729fe45fb5ade58bdc",
   "metadata": {},
   "source": [
    "### 3.3 模型训练与评估\n",
    "\n",
    "#### 3.3.1 模型训练\n",
    "\n",
    "模型训练需完成如下步骤：\n",
    "\n",
    "1. **使用 [paddle.Model](../../api/paddle/Model_cn.html) 封装模型。**  将网络结构组合成可快速使用 [飞桨高层 API](../../practices/quick_start/high_level_api.html) 进行训练、评估、推理的实例，方便后续操作。\n",
    "2. **使用 [paddle.Model.prepare](../../api/paddle/Model_cn.html#prepare-optimizer-none-loss-none-metrics-none-amp-configs-none) 完成训练的配置准备工作。**  包括损失函数、优化器和评价指标等。飞桨在 [paddle.optimizer](../../api/paddle/optimizer/Overview_cn.html#api) 下提供了优化器算法相关 API，在 [paddle.nn Loss层](../../api/paddle/nn/Overview_cn.html#loss) 提供了损失函数相关 API，在 [paddle.metric](../../api/paddle/metric/Overview_cn.html) 下提供了评价指标相关 API。\n",
    "3. **使用 [paddle.Model.fit](../../api/paddle/Model_cn.html#fit-train-data-none-eval-data-none-batch-size-1-epochs-1-eval-freq-1-log-freq-10-save-dir-none-save-freq-1-verbose-2-drop-last-false-shuffle-true-num-workers-0-callbacks-none) 配置循环参数并启动训练。**  配置参数包括指定训练的数据源 `train_dataset`、训练的批大小 `batch_size`、训练轮数 `epochs` 等，执行后将自动完成模型的训练循环。\n",
    "\n",
    "\n",
    "因为是分类任务，这里损失函数使用常见的 [CrossEntropyLoss](../../api/paddle/nn/CrossEntropyLoss_cn.html#crossentropyloss) （交叉熵损失函数），优化器使用 [Adam](../../api/paddle/optimizer/Adam_cn.html#adam)，评价指标使用 [Accuracy](../../api/paddle/metric/accuracy_cn.html#accuracy) 来计算模型在训练集上的精度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3933fab20d04ec698c2621248eb3be0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-17T04:01:43.380962Z",
     "iopub.status.busy": "2021-12-17T04:01:43.380575Z",
     "iopub.status.idle": "2021-12-17T04:03:17.852495Z",
     "shell.execute_reply": "2021-12-17T04:03:17.851918Z",
     "shell.execute_reply.started": "2021-12-17T04:01:43.380928Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n",
      "Epoch 1/5\n",
      "step 938/938 [==============================] - loss: 0.0011 - acc: 0.9865 - 14ms/step          \n",
      "Epoch 2/5\n",
      "step 938/938 [==============================] - loss: 0.0045 - acc: 0.9885 - 14ms/step          \n",
      "Epoch 3/5\n",
      "step 938/938 [==============================] - loss: 0.0519 - acc: 0.9896 - 14ms/step          \n",
      "Epoch 4/5\n",
      "step 938/938 [==============================] - loss: 4.1989e-05 - acc: 0.9912 - 14ms/step      \n",
      "Epoch 5/5\n",
      "step 938/938 [==============================] - loss: 0.0671 - acc: 0.9918 - 15ms/step            \n"
     ]
    }
   ],
   "source": [
    "# 封装模型，便于进行后续的训练、评估和推理\n",
    "model = paddle.Model(lenet)\n",
    "\n",
    "# 模型训练的配置准备，准备损失函数，优化器和评价指标\n",
    "model.prepare(\n",
    "    paddle.optimizer.Adam(parameters=model.parameters()),\n",
    "    paddle.nn.CrossEntropyLoss(),\n",
    "    paddle.metric.Accuracy(),\n",
    ")\n",
    "\n",
    "# 开始训练\n",
    "model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dd4641cc4064e0191573fe9c69df29b",
   "metadata": {},
   "source": [
    "从训练过程的打印日志中，可观察到损失函数值 loss 逐渐变小，精度 acc 逐渐上升的趋势，反映出不错的训练效果。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8309879909854d7188b41380fd92a7c3",
   "metadata": {},
   "source": [
    "#### 3.3.2 模型评估\n",
    "\n",
    "模型训练完成之后，调用 [paddle.Model.evaluate](../../api/paddle/Model_cn.html#evaluate-eval-data-batch-size-1-log-freq-10-verbose-2-num-workers-0-callbacks-none) ，使用预先定义的测试数据集，来评估训练好的模型效果，评估完成后将输出模型在测试集上的损失函数值 loss 和精度 acc。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ed186c9a28b402fb0bc4494df01f08d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eval begin...\n",
      "step 157/157 [==============================] - loss: 5.7177e-04 - acc: 0.9859 - 6ms/step         \n",
      "Eval samples: 10000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'loss': [0.00057177414], 'acc': 0.9859}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 进行模型评估\n",
    "model.evaluate(test_dataset, batch_size=64, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb1e1581032b452c9409d6c6813c49d1",
   "metadata": {},
   "source": [
    "从结果可以看到，初步训练得到的模型精度在98%附近，在逐渐熟悉深度学习模型开发和训练技巧后，可以通过调整其中的训练参数来进一步提升模型的精度。\n",
    "\n",
    "更多参考：\n",
    "* [模型训练、评估与推理](train_eval_predict_cn.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379cbbc1e968416e875cc15c1202d7eb",
   "metadata": {},
   "source": [
    "### 3.4 模型推理\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "277c27b1587741f2af2001be3712ef0d",
   "metadata": {},
   "source": [
    "#### 3.4.1 模型保存\n",
    "\n",
    "模型训练完成后，通常需要将训练好的模型参数和优化器等信息，持久化保存到参数文件中，便于后续执行推理验证。\n",
    "\n",
    "在飞桨中可通过调用 [paddle.Model.save](../../api/paddle/Model_cn.html#save-path-training-true) 保存模型。代码示例如下，其中 output 为模型保存的文件夹名称，minst 为保存的模型文件名称。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db7b79bc585a40fcaf58bf750017e135",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-17T04:11:42.623618Z",
     "iopub.status.busy": "2021-12-17T04:11:42.622719Z",
     "iopub.status.idle": "2021-12-17T04:11:42.637039Z",
     "shell.execute_reply": "2021-12-17T04:11:42.636284Z",
     "shell.execute_reply.started": "2021-12-17T04:11:42.623579Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 保存模型，文件夹会自动创建\n",
    "model.save(\"./output/mnist\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916684f9a58a4a2aa5f864670399430d",
   "metadata": {},
   "source": [
    "以上代码执行后会在`output`目录下保存两个文件，`mnist.pdopt`为优化器的参数，`mnist.pdparams`为模型的参数。\n",
    "```bash\n",
    "output\n",
    "├── mnist.pdopt     # 优化器的参数\n",
    "└── mnist.pdparams  # 模型的参数\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1671c31a24314836a5b85d7ef7fbf015",
   "metadata": {},
   "source": [
    "#### 3.4.2 模型加载并执行推理\n",
    "\n",
    "执行模型推理时，可调用 [paddle.Model.load](../../api/paddle/Model_cn.html#load-path-skip-mismatch-false-reset-optimizer-false) 加载模型，然后即可通过 [paddle.Model.predict_batch](../../api/paddle/Model_cn.html#predict-batch-inputs) 执行推理操作。\n",
    "\n",
    "如下示例中，针对前面创建的 `model` 网络加载保存的参数文件 `output/mnist`，并选择测试集中的一张图片 `test_dataset[0]` 作为输入，执行推理并打印结果，可以看到推理的结果与可视化图片一致。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "33b0902fd34d4ace834912fa1002cf8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true label: 7, pred label: 7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f853e6f0e50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 加载模型\n",
    "model.load(\"output/mnist\")\n",
    "\n",
    "# 从测试集中取出一张图片\n",
    "img, label = test_dataset[0]\n",
    "# 将图片shape从1*28*28变为1*1*28*28，增加一个batch维度，以匹配模型输入格式要求\n",
    "img_batch = np.expand_dims(img.astype(\"float32\"), axis=0)\n",
    "\n",
    "# 执行推理并打印结果，此处predict_batch返回的是一个list，取出其中数据获得预测结果\n",
    "out = model.predict_batch(img_batch)[0]\n",
    "pred_label = out.argmax()\n",
    "print(\"true label: {}, pred label: {}\".format(label[0], pred_label))\n",
    "# 可视化图片\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.imshow(img[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6fa52606d8c4a75a9b52967216f8f3f",
   "metadata": {},
   "source": [
    "更多参考：\n",
    "* [模型保存与加载](model_save_load_cn.html)\n",
    "* [模型训练、评估与推理](train_eval_predict_cn.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5a1fa73e5044315a093ec459c9be902",
   "metadata": {},
   "source": [
    "## 四、总结\n",
    "\n",
    "至此通过飞桨几个简单的API完成了一个深度学习任务，总结整个流程和用到的关键 API 如下图所示。\n",
    "\n",
    "![](images/model_develop_flow.png)\n",
    "\n",
    "图 2：模型开发流程\n",
    "\n",
    "如果想要完成更复杂的深度学习任务，开发更强大的模型，飞桨提供了功能丰富的 API 帮助开发者完成任务，比如对数据集应用数据增强、使用更大的 CNN 模型、调优性能等。飞桨官网提供了丰富的教程与案例可供参考，欢迎一起探索深度学习的世界。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.10"
  },
  "toc-autonumbering": false,
  "toc-showcode": false,
  "toc-showmarkdowntxt": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}