{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model基本使用\n",
"\n",
"[](https://openi.pcl.ac.cn/MindSpore/docs/src/branch/r1.9/tutorials/source_zh_cn/advanced/model/model.ipynb?card=2&image=MindSpore1.8.1) [](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r1.9/tutorials/zh_cn/advanced/model/mindspore_model.ipynb) [](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r1.9/tutorials/zh_cn/advanced/model/mindspore_model.py) [](https://gitee.com/mindspore/docs/blob/r1.9/tutorials/source_zh_cn/advanced/model/model.ipynb)\n",
"\n",
"通常情况下，定义训练和评估网络并直接运行，已经可以满足基本需求。\n",
"\n",
"一方面，`Model`可以在一定程度上简化代码。例如：无需手动遍历数据集；在不需要自定义`nn.TrainOneStepCell`的场景下，可以借助`Model`自动构建训练网络；可以使用`Model`的`eval`接口进行模型评估，直接输出评估结果，无需手动调用评价指标的`clear`、`update`、`eval`函数等。\n",
"\n",
"另一方面，`Model`提供了很多高阶功能，如数据下沉、混合精度等，在不借助`Model`的情况下，使用这些功能需要花费较多的时间仿照`Model`进行自定义。\n",
"\n",
"本文档首先对MindSpore的Model进行基本介绍，然后重点讲解如何使用`Model`进行模型训练、评估和推理。\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mindspore\n",
"from mindspore import nn\n",
"from mindspore.dataset import MnistDataset, vision, transforms\n",
"from mindspore.train import Model, CheckpointConfig, ModelCheckpoint, LossMonitor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model基本介绍\n",
"\n",
"[Model](https://www.mindspore.cn/docs/en/r1.9/api_python/mindspore/mindspore.Model.html#mindspore.Model)是MindSpore提供的高阶API，可以进行模型训练、评估和推理。其接口的常用参数如下：\n",
"\n",
"- `network`：用于训练或推理的神经网络。\n",
"- `loss_fn`：所使用的损失函数。\n",
"- `optimizer`：所使用的优化器。\n",
"- `metrics`：用于模型评估的评价函数。\n",
"- `eval_network`：模型评估所使用的网络，未定义情况下，`Model`会使用`network`和`loss_fn`进行封装。\n",
"\n",
"`Model`提供了以下接口用于模型训练、评估和推理：\n",
"\n",
"- `fit`：边训练边评估模型。\n",
"- `train`：用于在训练集上进行模型训练。\n",
"- `eval`：用于在验证集上进行模型评估。\n",
"- `predict`：用于对输入的一组数据进行推理，输出预测结果。\n",
"\n",
"### 使用Model接口\n",
"\n",
"对于简单场景的神经网络，可以在定义`Model`时指定前向网络`network`、损失函数`loss_fn`、优化器`optimizer`和评价函数`metrics`。\n",
"\n",
"## 下载并处理数据集"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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\")\n",
"\n",
"\n",
"def datapipe(path, 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 = MnistDataset(path)\n",
" dataset = dataset.map(image_transforms, 'image')\n",
" dataset = dataset.map(label_transform, 'label')\n",
" dataset = dataset.batch(batch_size, drop_remainder=True)\n",
" return dataset\n",
"\n",
"train_dataset = datapipe('MNIST_Data/train', 64)\n",
"test_dataset = datapipe('MNIST_Data/test', 64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 创建模型"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2022-01-04T06:43:30.392367Z",
"start_time": "2022-01-04T06:43:28.436687Z"
}
},
"outputs": [],
"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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 定义损失函数和优化器\n",
"\n",
"要训练神经网络模型，需要定义损失函数和优化器函数。\n",
"\n",
"- 损失函数这里使用交叉熵损失函数`CrossEntropyLoss`。\n",
"- 优化器这里使用`SGD`。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": [
"## 训练及保存模型\n",
"\n",
"在开始训练之前，MindSpore需要提前声明网络模型在训练过程中是否需要保存中间过程和结果，因此使用`ModelCheckpoint`接口用于保存网络模型和参数，以便进行后续的Fine-tuning（微调）操作。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"steps_per_epoch = train_dataset.get_dataset_size()\n",
"config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch)\n",
"\n",
"ckpt_callback = ModelCheckpoint(prefix=\"mnist\", directory=\"./checkpoint\", config=config)\n",
"loss_callback = LossMonitor(steps_per_epoch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"通过MindSpore提供的`model.fit`接口可以方便地进行网络的训练，`LossMonitor`可以监控训练过程中`loss`值的变化。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 1 step: 937, loss is 0.5313149094581604\n",
"Eval result: epoch 1, metrics: {'accuracy': 0.8557692307692307}\n",
"epoch: 2 step: 937, loss is 0.2875961363315582\n",
"Eval result: epoch 2, metrics: {'accuracy': 0.9007411858974359}\n",
"epoch: 3 step: 937, loss is 0.19009104371070862\n",
"Eval result: epoch 3, metrics: {'accuracy': 0.9191706730769231}\n",
"epoch: 4 step: 937, loss is 0.24231787025928497\n",
"Eval result: epoch 4, metrics: {'accuracy': 0.9296875}\n",
"epoch: 5 step: 937, loss is 0.16016671061515808\n",
"Eval result: epoch 5, metrics: {'accuracy': 0.9386017628205128}\n",
"epoch: 6 step: 937, loss is 0.4830142855644226\n",
"Eval result: epoch 6, metrics: {'accuracy': 0.9444110576923077}\n",
"epoch: 7 step: 937, loss is 0.20778779685497284\n",
"Eval result: epoch 7, metrics: {'accuracy': 0.9508213141025641}\n",
"epoch: 8 step: 937, loss is 0.22020074725151062\n",
"Eval result: epoch 8, metrics: {'accuracy': 0.9540264423076923}\n",
"epoch: 9 step: 937, loss is 0.15951070189476013\n",
"Eval result: epoch 9, metrics: {'accuracy': 0.9575320512820513}\n",
"epoch: 10 step: 937, loss is 0.11161471903324127\n",
"Eval result: epoch 10, metrics: {'accuracy': 0.9608373397435898}\n"
]
}
],
"source": [
"trainer = Model(model, loss_fn=loss_fn, optimizer=optimizer, metrics={'accuracy'})\n",
"\n",
"trainer.fit(10, train_dataset, test_dataset, callbacks=[ckpt_callback, loss_callback])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"训练过程中会打印loss值，loss值会波动，但总体来说loss值会逐步减小，精度逐步提高。每个人运行的loss值有一定随机性，不一定完全相同。\n",
"\n",
"通过模型运行测试数据集得到的结果，验证模型的泛化能力：\n",
"\n",
"1. 使用`model.eval`接口读入测试数据集。\n",
"2. 使用保存后的模型参数进行推理。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2022-01-04T06:43:30.952190Z",
"start_time": "2022-01-04T06:43:30.525149Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'accuracy': 0.9607371794871795}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"acc = trainer.eval(test_dataset)\n",
"acc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"可以在打印信息中看出模型精度数据，示例中精度数据达到95%以上，模型质量良好。随着网络迭代次数增加，模型精度会进一步提高。"
]
}
],
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