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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![在线运行](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0/resource/_static/logo_modelarts.png)](https://authoring-modelarts-cnnorth4.huaweicloud.com/console/lab?share-url-b64=aHR0cHM6Ly9taW5kc3BvcmUtd2Vic2l0ZS5vYnMuY24tbm9ydGgtNC5teWh1YXdlaWNsb3VkLmNvbS9ub3RlYm9vay9yMi4wL3R1dG9yaWFscy96aF9jbi9iZWdpbm5lci9taW5kc3BvcmVfcXVpY2tfc3RhcnQuaXB5bmI==&imageid=e225a9aa-230a-4ea5-a538-b5faed64a6a6)&emsp;[![下载Notebook](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0/resource/_static/logo_notebook.png)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.0/tutorials/zh_cn/beginner/mindspore_quick_start.ipynb)&emsp;[![下载样例代码](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0/resource/_static/logo_download_code.png)](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.0/tutorials/zh_cn/beginner/mindspore_quick_start.py)&emsp;[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0/resource/_static/logo_source.png)](https://gitee.com/mindspore/docs/blob/r2.0/tutorials/source_zh_cn/beginner/quick_start.ipynb)\n",
    "\n",
    "[基本介绍](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/introduction.html) || **快速入门** || [张量 Tensor](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/tensor.html) || [数据集 Dataset](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/dataset.html) || [数据变换 Transforms](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/transforms.html) || [网络构建](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/model.html) || [函数式自动微分](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/autograd.html) || [模型训练](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/train.html) || [保存与加载](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/save_load.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 快速入门\n",
    "\n",
    "本节通过MindSpore的API来快速实现一个简单的深度学习模型。若想要深入了解MindSpore的使用方法，请参阅各节最后提供的参考链接。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore\n",
    "from mindspore import nn\n",
    "from mindspore.dataset import vision, transforms\n",
    "from mindspore.dataset import MnistDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 处理数据集\n",
    "\n",
    "MindSpore提供基于Pipeline的[数据引擎](https://www.mindspore.cn/docs/zh-CN/r2.0/design/data_engine.html)，通过[数据集（Dataset）](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/dataset.html)和[数据变换（Transforms）](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/transforms.html)实现高效的数据预处理。在本教程中，我们使用Mnist数据集，自动下载完成后，使用`mindspore.dataset`提供的数据变换进行预处理。\n",
    "\n",
    "> 本章节中的示例代码依赖`download`，可使用命令`pip install download`安装。如本文档以Notebook运行时，完成安装后需要重启kernel才能执行后续代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)\n",
      "\n",
      "file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:01<00:00, 6.73MB/s]\n",
      "Extracting zip file...\n",
      "Successfully downloaded / unzipped to ./\n"
     ]
    }
   ],
   "source": [
    "# Download data from open datasets\n",
    "from download import download\n",
    "\n",
    "url = \"https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/\" \\\n",
    "      \"notebook/datasets/MNIST_Data.zip\"\n",
    "path = download(url, \"./\", kind=\"zip\", replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据下载完成后，获得数据集对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = MnistDataset('MNIST_Data/train')\n",
    "test_dataset = MnistDataset('MNIST_Data/test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印数据集中包含的数据列名，用于dataset的预处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['image', 'label']\n"
     ]
    }
   ],
   "source": [
    "print(train_dataset.get_col_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MindSpore的dataset使用数据处理流水线（Data Processing Pipeline），需指定map、batch、shuffle等操作。这里我们使用map对图像数据及标签进行变换处理，将输入的图像缩放为1/255，根据均值0.1307和标准差值0.3081进行归一化处理，然后将处理好的数据集打包为大小为64的batch。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datapipe(dataset, batch_size):\n",
    "    image_transforms = [\n",
    "        vision.Rescale(1.0 / 255.0, 0),\n",
    "        vision.Normalize(mean=(0.1307,), std=(0.3081,)),\n",
    "        vision.HWC2CHW()\n",
    "    ]\n",
    "    label_transform = transforms.TypeCast(mindspore.int32)\n",
    "\n",
    "    dataset = dataset.map(image_transforms, 'image')\n",
    "    dataset = dataset.map(label_transform, 'label')\n",
    "    dataset = dataset.batch(batch_size)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map vision transforms and batch dataset\n",
    "train_dataset = datapipe(train_dataset, 64)\n",
    "test_dataset = datapipe(test_dataset, 64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用`create_tuple_iterator`或`create_dict_iterator`对数据集进行迭代。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of image [N, C, H, W]: (64, 1, 28, 28) Float32\n",
      "Shape of label: (64,) Int32\n"
     ]
    }
   ],
   "source": [
    "for image, label in test_dataset.create_tuple_iterator():\n",
    "    print(f\"Shape of image [N, C, H, W]: {image.shape} {image.dtype}\")\n",
    "    print(f\"Shape of label: {label.shape} {label.dtype}\")\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of image [N, C, H, W]: (64, 1, 28, 28) Float32\n",
      "Shape of label: (64,) Int32\n"
     ]
    }
   ],
   "source": [
    "for data in test_dataset.create_dict_iterator():\n",
    "    print(f\"Shape of image [N, C, H, W]: {data['image'].shape} {data['image'].dtype}\")\n",
    "    print(f\"Shape of label: {data['label'].shape} {data['label'].dtype}\")\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更多细节详见[数据集 Dataset](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/dataset.html)与[数据变换 Transforms](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/transforms.html)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 网络构建\n",
    "\n",
    "`mindspore.nn`类是构建所有网络的基类，也是网络的基本单元。当用户需要自定义网络时，可以继承`nn.Cell`类，并重写`__init__`方法和`construct`方法。`__init__`包含所有网络层的定义，`construct`中包含数据（[Tensor](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/tensor.html)）的变换过程（即[计算图](https://www.mindspore.cn/tutorials/zh-CN/r2.0/advanced/compute_graph.html)的构造过程）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network<\n",
      "  (flatten): Flatten<>\n",
      "  (dense_relu_sequential): SequentialCell<\n",
      "    (0): Dense<input_channels=784, output_channels=512, has_bias=True>\n",
      "    (1): ReLU<>\n",
      "    (2): Dense<input_channels=512, output_channels=512, has_bias=True>\n",
      "    (3): ReLU<>\n",
      "    (4): Dense<input_channels=512, output_channels=10, has_bias=True>\n",
      "    >\n",
      "  >\n"
     ]
    }
   ],
   "source": [
    "# Define model\n",
    "class Network(nn.Cell):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.dense_relu_sequential = nn.SequentialCell(\n",
    "            nn.Dense(28*28, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Dense(512, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Dense(512, 10)\n",
    "        )\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.flatten(x)\n",
    "        logits = self.dense_relu_sequential(x)\n",
    "        return logits\n",
    "\n",
    "model = Network()\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更多细节详见[网络构建](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/model.html)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate loss function and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = nn.SGD(model.trainable_params(), 1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在模型训练中，一个完整的训练过程（step）需要实现以下三步：\n",
    "\n",
    "1. **正向计算**：模型预测结果（logits），并与正确标签（label）求预测损失（loss）。\n",
    "2. **反向传播**：利用自动微分机制，自动求模型参数（parameters）对于loss的梯度（gradients）。\n",
    "3. **参数优化**：将梯度更新到参数上。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MindSpore使用函数式自动微分机制，因此针对上述步骤需要实现：\n",
    "\n",
    "1. 正向计算函数定义。\n",
    "2. 通过函数变换获得梯度计算函数。\n",
    "3. 训练函数定义，执行正向计算、反向传播和参数优化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define forward function\n",
    "def forward_fn(data, label):\n",
    "    logits = model(data)\n",
    "    loss = loss_fn(logits, label)\n",
    "    return loss, logits\n",
    "\n",
    "# Get gradient function\n",
    "grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)\n",
    "\n",
    "# Define function of one-step training\n",
    "def train_step(data, label):\n",
    "    (loss, _), grads = grad_fn(data, label)\n",
    "    optimizer(grads)\n",
    "    return loss\n",
    "\n",
    "def train(model, dataset):\n",
    "    size = dataset.get_dataset_size()\n",
    "    model.set_train()\n",
    "    for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):\n",
    "        loss = train_step(data, label)\n",
    "\n",
    "        if batch % 100 == 0:\n",
    "            loss, current = loss.asnumpy(), batch\n",
    "            print(f\"loss: {loss:>7f}  [{current:>3d}/{size:>3d}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "除训练外，我们定义测试函数，用来评估模型的性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model, dataset, loss_fn):\n",
    "    num_batches = dataset.get_dataset_size()\n",
    "    model.set_train(False)\n",
    "    total, test_loss, correct = 0, 0, 0\n",
    "    for data, label in dataset.create_tuple_iterator():\n",
    "        pred = model(data)\n",
    "        total += len(data)\n",
    "        test_loss += loss_fn(pred, label).asnumpy()\n",
    "        correct += (pred.argmax(1) == label).asnumpy().sum()\n",
    "    test_loss /= num_batches\n",
    "    correct /= total\n",
    "    print(f\"Test: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练过程需多次迭代数据集，一次完整的迭代称为一轮（epoch）。在每一轮，遍历训练集进行训练，结束后使用测试集进行预测。打印每一轮的loss值和预测准确率（Accuracy），可以看到loss在不断下降，Accuracy在不断提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "-------------------------------\n",
      "loss: 2.302088  [  0/938]\n",
      "loss: 2.290692  [100/938]\n",
      "loss: 2.266338  [200/938]\n",
      "loss: 2.205240  [300/938]\n",
      "loss: 1.907198  [400/938]\n",
      "loss: 1.455603  [500/938]\n",
      "loss: 0.861103  [600/938]\n",
      "loss: 0.767219  [700/938]\n",
      "loss: 0.422253  [800/938]\n",
      "loss: 0.513922  [900/938]\n",
      "Test: \n",
      " Accuracy: 83.8%, Avg loss: 0.529534 \n",
      "\n",
      "Epoch 2\n",
      "-------------------------------\n",
      "loss: 0.580867  [  0/938]\n",
      "loss: 0.479347  [100/938]\n",
      "loss: 0.677991  [200/938]\n",
      "loss: 0.550141  [300/938]\n",
      "loss: 0.226565  [400/938]\n",
      "loss: 0.314738  [500/938]\n",
      "loss: 0.298739  [600/938]\n",
      "loss: 0.459540  [700/938]\n",
      "loss: 0.332978  [800/938]\n",
      "loss: 0.406709  [900/938]\n",
      "Test: \n",
      " Accuracy: 90.2%, Avg loss: 0.334828 \n",
      "\n",
      "Epoch 3\n",
      "-------------------------------\n",
      "loss: 0.461890  [  0/938]\n",
      "loss: 0.242303  [100/938]\n",
      "loss: 0.281414  [200/938]\n",
      "loss: 0.207835  [300/938]\n",
      "loss: 0.206000  [400/938]\n",
      "loss: 0.409646  [500/938]\n",
      "loss: 0.193608  [600/938]\n",
      "loss: 0.217575  [700/938]\n",
      "loss: 0.212817  [800/938]\n",
      "loss: 0.202862  [900/938]\n",
      "Test: \n",
      " Accuracy: 91.9%, Avg loss: 0.280962 \n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "epochs = 3\n",
    "for t in range(epochs):\n",
    "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
    "    train(model, train_dataset)\n",
    "    test(model, test_dataset, loss_fn)\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更多细节详见[模型训练](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/train.html)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存模型\n",
    "\n",
    "模型训练完成后，需要将其参数进行保存。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved Model to model.ckpt\n"
     ]
    }
   ],
   "source": [
    "# Save checkpoint\n",
    "mindspore.save_checkpoint(model, \"model.ckpt\")\n",
    "print(\"Saved Model to model.ckpt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载保存的权重分为两步：\n",
    "\n",
    "1. 重新实例化模型对象，构造模型。\n",
    "2. 加载模型参数，并将其加载至模型上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "# Instantiate a random initialized model\n",
    "model = Network()\n",
    "# Load checkpoint and load parameter to model\n",
    "param_dict = mindspore.load_checkpoint(\"model.ckpt\")\n",
    "param_not_load, _ = mindspore.load_param_into_net(model, param_dict)\n",
    "print(param_not_load)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `param_not_load`是未被加载的参数列表，为空时代表所有参数均加载成功。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载后的模型可以直接用于预测推理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: \"Tensor(shape=[10], dtype=Int32, value= [3, 9, 6, 1, 6, 7, 4, 5, 2, 2])\", Actual: \"Tensor(shape=[10], dtype=Int32, value= [3, 9, 6, 1, 6, 7, 4, 5, 2, 2])\"\n"
     ]
    }
   ],
   "source": [
    "model.set_train(False)\n",
    "for data, label in test_dataset:\n",
    "    pred = model(data)\n",
    "    predicted = pred.argmax(1)\n",
    "    print(f'Predicted: \"{predicted[:10]}\", Actual: \"{label[:10]}\"')\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "更多细节详见[保存与加载](https://www.mindspore.cn/tutorials/zh-CN/r2.0/beginner/save_load.html)。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "MindSpore",
   "language": "python",
   "name": "mindspore"
  },
  "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 (default, Oct 25 2019, 15:51:11) \n[GCC 7.3.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "8c9da313289c39257cb28b126d2dadd33153d4da4d524f730c81a4aaccbd2ca7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

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