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提交 e466caf5 编写于 作者: M mindspore-ci-bot 提交者： Gitee
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!4 use model_zoo.lenet in experiment_1 

Merge pull request !4 from dyonghan/experiment_1
上级 90d94759 0edad40f
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experiment_1/1-LeNet5_MNIST.ipynb
浏览文件 @ e466caf5
......@@ -20,7 +20,7 @@
"\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",
......@@ -76,7 +76,8 @@
"│   └── train\n",
"│   ├── train-images-idx3-ubyte\n",
"│   └── train-labels-idx1-ubyte\n",
"└── 脚本等文件\n",
"├── *.ipynb\n",
"└── main.py\n",
"```\n",
"\n",
"## 实验步骤（方案一）\n",
......@@ -122,6 +123,7 @@
"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",
......@@ -149,6 +151,7 @@
"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",
......@@ -201,53 +204,20 @@
"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",
"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": "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",
"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",
......@@ -292,6 +262,14 @@
"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",
......@@ -351,7 +329,7 @@
"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。"
"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。"
]
}
],
......
experiment_1/main.py
浏览文件 @ e466caf5
......@@ -9,6 +9,7 @@ import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as CV
from mindspore import nn
from mindspore.model_zoo.lenet import LeNet5
from mindspore.train import Model
from mindspore.train.callback import LossMonitor
......@@ -21,6 +22,7 @@ DATA_DIR_TEST = "MNIST/test" # 测试集信息
def create_dataset(training=True, num_epoch=1, batch_size=32, resize=(32, 32),
rescale=1/(255*0.3081), shift=-0.1307/0.3081, buffer_size=64):
ds = ms.dataset.MnistDataset(DATA_DIR_TRAIN if training else DATA_DIR_TEST)
ds = ds.map(input_columns="image", operations=[CV.Resize(resize), CV.Rescale(rescale, shift), CV.HWC2CHW()])
ds = ds.map(input_columns="label", operations=C.TypeCast(ms.int32))
ds = ds.shuffle(buffer_size=buffer_size).batch(batch_size, drop_remainder=True).repeat(num_epoch)
......@@ -28,33 +30,6 @@ def create_dataset(training=True, num_epoch=1, batch_size=32, resize=(32, 32),
return ds
class LeNet5(nn.Cell):
def __init__(self):
super(LeNet5, self).__init__()
self.relu = nn.ReLU()
self.conv1 = nn.Conv2d(1, 6, 5, stride=1, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, 5, stride=1, pad_mode='valid')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.flatten = nn.Flatten()
self.fc1 = nn.Dense(400, 120)
self.fc2 = nn.Dense(120, 84)
self.fc3 = nn.Dense(84, 10)
def construct(self, input_x):
output = self.conv1(input_x)
output = self.relu(output)
output = self.pool(output)
output = self.conv2(output)
output = self.relu(output)
output = self.pool(output)
output = self.flatten(output)
output = self.fc1(output)
output = self.fc2(output)
output = self.fc3(output)
return output
def test_train(lr=0.01, momentum=0.9, num_epoch=3, ckpt_name="a_lenet"):
ds_train = create_dataset(num_epoch=num_epoch)
ds_eval = create_dataset(training=False)
......
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