From 528d746f07cc4937d17b99b27363cb0d69fe2a9c Mon Sep 17 00:00:00 2001
From: lvmingfu <lvmingfu@huawei.com>
Date: Tue, 24 Nov 2020 17:31:44 +0800
Subject: [PATCH] modify notebook files in tutorials for master

---
 tutorials/notebook/README.md                  |   1 +
 ...spore_convert_dataset_to_mindrecord.ipynb} |  54 ++++--
 ... mindspore_data_loading_enhancement.ipynb} |  55 +++++-
 ...indspore_debugging_in_pynative_mode.ipynb} | 116 ++++++++-----
 ..._evaluate_the_model_during_training.ipynb} |  81 ++++++---
 ....ipynb => mindspore_mixed_precision.ipynb} | 160 ++++++++++--------
 .../advanced_use/convert_dataset.md           |   2 +-
 .../advanced_use/debug_in_pynative_mode.md    |   2 +-
 .../advanced_use/enable_mixed_precision.md    |   4 +-
 .../evaluate_the_model_during_training.md     |   2 +-
 10 files changed, 308 insertions(+), 169 deletions(-)
 rename tutorials/notebook/convert_dataset_to_mindrecord/{convert_dataset_to_mindrecord.ipynb => mindspore_convert_dataset_to_mindrecord.ipynb} (91%)
 rename tutorials/notebook/data_loading_enhance/{data_loading_enhancement.ipynb => mindspore_data_loading_enhancement.ipynb} (99%)
 rename tutorials/notebook/{debugging_in_pynative_mode.ipynb => mindspore_debugging_in_pynative_mode.ipynb} (99%)
 rename tutorials/notebook/{evaluate_the_model_during_training.ipynb => mindspore_evaluate_the_model_during_training.ipynb} (96%)
 rename tutorials/notebook/{mixed_precision.ipynb => mindspore_mixed_precision.ipynb} (97%)

diff --git a/tutorials/notebook/README.md b/tutorials/notebook/README.md
index 53ab5ca549..dff7cfcdd7 100644
--- a/tutorials/notebook/README.md
+++ b/tutorials/notebook/README.md
@@ -78,3 +78,4 @@
 | 优化训练性能           | 混合精度         | [mixed_precision.ipynb](https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/mixed_precision.ipynb)       | - 了解混合精度训练的原理  <br/> - 学习在MindSpore中使用混合精度训练 <br/> - 对比单精度训练和混合精度训练的对模型训练的影响
 | 模型安全和隐私        | 模型安全         | [model_security.ipynb](https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/model_security.ipynb)       | - 了解AI算法的安全威胁的概念和影响<br/> - 介绍MindArmour提供的模型安全防护手段<br/> - 学习如何模拟攻击训练模型<br/> - 学习针对被攻击模型进行对抗性防御
 
+>
\ No newline at end of file
diff --git a/tutorials/notebook/convert_dataset_to_mindrecord/convert_dataset_to_mindrecord.ipynb b/tutorials/notebook/convert_dataset_to_mindrecord/mindspore_convert_dataset_to_mindrecord.ipynb
similarity index 91%
rename from tutorials/notebook/convert_dataset_to_mindrecord/convert_dataset_to_mindrecord.ipynb
rename to tutorials/notebook/convert_dataset_to_mindrecord/mindspore_convert_dataset_to_mindrecord.ipynb
index 592b296d2a..2667dc5582 100644
--- a/tutorials/notebook/convert_dataset_to_mindrecord/convert_dataset_to_mindrecord.ipynb
+++ b/tutorials/notebook/convert_dataset_to_mindrecord/mindspore_convert_dataset_to_mindrecord.ipynb
@@ -96,7 +96,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- 在jupyter工作目录下创建`./datasets/convert_dataset_to_mindrecord/datas_to_mindrecord`目录，本次体验将所有的转换数据集都放在该目录下。"
+    "下载需要处理的图片数据`tansform.jpg`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget https://gitee.com/mindspore/docs/raw/master/tutorials/notebook/convert_dataset_to_mindrecord/datasets/convert_dataset_to_mindrecord/images/transform.jpg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "创建文件夹`./datasets/convert_dataset_to_mindrecord/datas_to_mindrecord/`用于存放本次体验中所有的转换数据集。  \n",
+    "创建文件夹`./datasets/convert_dataset_to_mindrecord/images/`用于存放下载下来的图片数据。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir -p ./datasets/convert_dataset_to_mindrecord/datas_to_mindrecord/\n",
+    "!mkdir -p ./datasets/convert_dataset_to_mindrecord/images/\n",
+    "!mv -f ./transform.jpg ./datasets/convert_dataset_to_mindrecord/images/"
    ]
   },
   {
@@ -119,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -156,7 +184,7 @@
        "0"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -175,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -206,7 +234,7 @@
        "MSRStatus.SUCCESS"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -238,7 +266,7 @@
        "MSRStatus.SUCCESS"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -280,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -289,7 +317,7 @@
        "MSRStatus.SUCCESS"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -323,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -339,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
diff --git a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb b/tutorials/notebook/data_loading_enhance/mindspore_data_loading_enhancement.ipynb
similarity index 99%
rename from tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb
rename to tutorials/notebook/data_loading_enhance/mindspore_data_loading_enhancement.ipynb
index e7d144150e..3566917f6e 100644
--- a/tutorials/notebook/data_loading_enhance/data_loading_enhancement.ipynb
+++ b/tutorials/notebook/data_loading_enhance/mindspore_data_loading_enhancement.ipynb
@@ -437,9 +437,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "2. 使用一类图片当作数据，体验操作。在一个数据量比较大的图片数据集中，例如数据集名称叫`images`，它的存储方式是在`images`文件夹下，有不同子类别的文件夹，一个子类别文件夹中的图片属于同一类。所以我们本次体验所使用的图片放置方法，就需要创建`enhance_images`文件夹，接着在`enhance_images`下建一个名为`sample`的子类别文件夹，将图片放在`sample`文件夹中即可。如果有更多类别图片，可以在`enhance_images`下创建对应的子类别文件夹，将图片放入即可。\n",
-    "\n",
-    "   增强体验使用的数据位置在<https://gitee.com/mindspore/docs/tree/master/tutorials/notebook/data_loading_enhance/enhance_images/sample>中，使用过程中可以在此路径下找到图片数据，并参照本次体验中图片放置的位置来新建文件夹。"
+    "2. 使用一类图片当作数据，体验操作。在一个数据量比较大的图片数据集中，例如数据集名称叫`images`，它的存储方式是在`images`文件夹下，有不同子类别的文件夹，一个子类别文件夹中的图片属于同一类。所以我们本次体验所使用的图片放置方法，就需要创建`enhance_images`文件夹，接着在`enhance_images`下建一个名为`sample`的子类别文件夹，将图片放在`sample`文件夹中即可。如果有更多类别图片，可以在`enhance_images`下创建对应的子类别文件夹，将图片放入即可。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "增强体验使用的图片数据下载，执行如下命令："
    ]
   },
   {
@@ -447,6 +452,40 @@
    "execution_count": 11,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "!wget https://gitee.com/mindspore/docs/raw/master/tutorials/notebook/data_loading_enhance/enhance_images/sample/sample1.png\n",
+    "!wget https://gitee.com/mindspore/docs/raw/master/tutorials/notebook/data_loading_enhance/enhance_images/sample/sample2.png"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "生成图片数据的保存路径`./enhance_images/sample/`，并将下载下来的图片数据`sample1.png`和`sample2.png`移入其中。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir -p ./enhance_images/sample/\n",
+    "!mv sample1.png sample2.png -t  ./enhance_images/sample/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用过程中可以在此路径下找到图片数据，并参照本次体验中图片放置的位置来新建文件夹。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "DATA_DIR = \"./enhance_images\"\n",
     "ds1 = ds.ImageFolderDataset(DATA_DIR, decode=True)"
@@ -461,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -471,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -533,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -550,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
diff --git a/tutorials/notebook/debugging_in_pynative_mode.ipynb b/tutorials/notebook/mindspore_debugging_in_pynative_mode.ipynb
similarity index 99%
rename from tutorials/notebook/debugging_in_pynative_mode.ipynb
rename to tutorials/notebook/mindspore_debugging_in_pynative_mode.ipynb
index 2d25ba6b5f..0bdd0bf7f9 100644
--- a/tutorials/notebook/debugging_in_pynative_mode.ipynb
+++ b/tutorials/notebook/mindspore_debugging_in_pynative_mode.ipynb
@@ -55,21 +55,56 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "这里我们需要将MNIST数据集中随机取出一张图片，并增强成适合LeNet网络的数据格式（如何处理请参考[quick_start.ipynb](https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/quick_start.ipynb)），训练数据集下载地址：{\"<http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz>\", \"<http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz>\"} 。\n",
-    "<br/>数据集放在----Jupyter工作目录+\\MNIST_Data\\train\\，如下图结构："
+    "下载并解压数据集数据。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/datasets/MNIST_Data.zip\n",
+    "!unzip MNIST_Data.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将解压后的数据集移动到指定位置。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir -p ./datasets/\n",
+    "!mv -f ./MNIST_Data/ ./datasets/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "执行完成上述命令后，数据存放形式应如下："
    ]
   },
   {
    "cell_type": "raw",
    "metadata": {},
    "source": [
-    "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"
+    "datasets\n",
+    "│\n",
+    "└── MNIST_Data\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"
    ]
   },
   {
@@ -88,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -219,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,11 +281,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LeNet5<\n",
+      "  (conv1): Conv2d<input_channels=1, output_channels=6, kernel_size=(5, 5),stride=(1, 1),  pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False,weight_init=Parameter (name=conv1.weight), bias_init=None>\n",
+      "  (conv2): Conv2d<input_channels=6, output_channels=16, kernel_size=(5, 5),stride=(1, 1),  pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False,weight_init=Parameter (name=conv2.weight), bias_init=None>\n",
+      "  (fc1): Dense<in_channels=400, out_channels=120, weight=Parameter (name=fc1.weight), has_bias=True, bias=Parameter (name=fc1.bias)>\n",
+      "  (fc2): Dense<in_channels=120, out_channels=84, weight=Parameter (name=fc2.weight), has_bias=True, bias=Parameter (name=fc2.bias)>\n",
+      "  (fc3): Dense<in_channels=84, out_channels=10, weight=Parameter (name=fc3.weight), has_bias=True, bias=Parameter (name=fc3.bias)>\n",
+      "  (relu): ReLU<>\n",
+      "  (max_pool2d): MaxPool2d<kernel_size=2, stride=2, pad_mode=VALID>\n",
+      "  >\n"
+     ]
+    }
+   ],
    "source": [
     "import mindspore.nn as nn\n",
     "import mindspore.ops as ops\n",
@@ -318,31 +367,8 @@
     "        x = self.relu(x)\n",
     "        x = self.fc3(x)\n",
     "        self.switch -= 1\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LeNet5<\n",
-      "  (conv1): Conv2d<input_channels=1, output_channels=6, kernel_size=(5, 5),stride=(1, 1),  pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False,weight_init=Parameter (name=conv1.weight), bias_init=None>\n",
-      "  (conv2): Conv2d<input_channels=6, output_channels=16, kernel_size=(5, 5),stride=(1, 1),  pad_mode=valid, padding=0, dilation=(1, 1), group=1, has_bias=False,weight_init=Parameter (name=conv2.weight), bias_init=None>\n",
-      "  (fc1): Dense<in_channels=400, out_channels=120, weight=Parameter (name=fc1.weight), has_bias=True, bias=Parameter (name=fc1.bias)>\n",
-      "  (fc2): Dense<in_channels=120, out_channels=84, weight=Parameter (name=fc2.weight), has_bias=True, bias=Parameter (name=fc2.bias)>\n",
-      "  (fc3): Dense<in_channels=84, out_channels=10, weight=Parameter (name=fc3.weight), has_bias=True, bias=Parameter (name=fc3.bias)>\n",
-      "  (relu): ReLU<>\n",
-      "  (max_pool2d): MaxPool2d<kernel_size=2, stride=2, pad_mode=VALID>\n",
-      "  >\n"
-     ]
-    }
-   ],
-   "source": [
+    "        return x\n",
+    "\n",
     "print(LeNet5())"
    ]
   },
@@ -356,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -388,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "scrolled": false
    },
@@ -523,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "scrolled": false
    },
diff --git a/tutorials/notebook/evaluate_the_model_during_training.ipynb b/tutorials/notebook/mindspore_evaluate_the_model_during_training.ipynb
similarity index 96%
rename from tutorials/notebook/evaluate_the_model_during_training.ipynb
rename to tutorials/notebook/mindspore_evaluate_the_model_during_training.ipynb
index 5d71a8ce78..e80be1c4f6 100644
--- a/tutorials/notebook/evaluate_the_model_during_training.ipynb
+++ b/tutorials/notebook/mindspore_evaluate_the_model_during_training.ipynb
@@ -51,25 +51,56 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "训练数据集下载地址：{\"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz \", \"http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz \"}。\n",
-    "\n",
-    "测试数据集：{\"<http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz>\", \"<http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz>\"}\n",
-    "<br/>数据集放在----*Jupyter工作目录+\\datasets\\MNIST_Data\\*，如下图结构："
+    "下载并解压数据集数据。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/datasets/MNIST_Data.zip\n",
+    "!unzip MNIST_Data.zip"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "```\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",
-    "```"
+    "将解压后的数据集移动到指定位置。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mkdir -p ./datasets/\n",
+    "!mv -f ./MNIST_Data/ ./datasets/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "执行完成上述命令后，数据存放形式应如下："
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "datasets\n",
+    "│\n",
+    "└── MNIST_Data\n",
+    "        ├── test\n",
+    "        │   ├── t10k-images-idx3-ubyte\n",
+    "        │   └── t10k-labels-idx1-ubyte\n",
+    "        └── train\n",
+    "            ├── train-images-idx3-ubyte\n",
+    "            └── train-labels-idx1-ubyte "
    ]
   },
   {
@@ -88,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,17 +213,11 @@
     "\n",
     "    # use the preceding operators to construct networks\n",
     "    def construct(self, x):\n",
-    "        x = self.conv1(x)\n",
-    "        x = self.relu(x)\n",
-    "        x = self.max_pool2d(x)\n",
-    "        x = self.conv2(x)\n",
-    "        x = self.relu(x)\n",
-    "        x = self.max_pool2d(x)\n",
+    "        x = self.max_pool2d(self.relu(self.conv1(x)))\n",
+    "        x = self.max_pool2d(self.relu(self.conv2(x)))\n",
     "        x = self.flatten(x)\n",
-    "        x = self.fc1(x)\n",
-    "        x = self.relu(x)\n",
-    "        x = self.fc2(x)\n",
-    "        x = self.relu(x)\n",
+    "        x = self.relu(self.fc1(x))\n",
+    "        x = self.relu(self.fc2(x))\n",
     "        x = self.fc3(x)\n",
     "        return x"
    ]
@@ -226,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -421,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
diff --git a/tutorials/notebook/mixed_precision.ipynb b/tutorials/notebook/mindspore_mixed_precision.ipynb
similarity index 97%
rename from tutorials/notebook/mixed_precision.ipynb
rename to tutorials/notebook/mindspore_mixed_precision.ipynb
index 972ae34d83..b866ca7255 100644
--- a/tutorials/notebook/mixed_precision.ipynb
+++ b/tutorials/notebook/mindspore_mixed_precision.ipynb
@@ -56,7 +56,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![image](https://gitee.com/mindspore/docs/raw/master/tutorials/training/source_zh_cn/advanced_use/images/mix_precision.jpg)"
+    "![image](https://gitee.com/mindspore/docs/raw/master/tutorials/training/source_zh_cn/advanced_use/images/mix_precision.PNG)"
    ]
   },
   {
@@ -90,31 +90,58 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "数据集下载地址：<https://www.cd.toronto.edu/~kriz/cifar-10-python.tar.gz>。\n",
-    "\n",
-    "数据集下载后，解压至jupyter的工作路径+/datasets/cifar10，由于测试数据集和训练数据集在一个文件夹中，需要你分开两个文件夹存放，存放形式如下。"
+    "下载并解压数据集数据。"
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!wget https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/datasets/cifar10.zip\n",
+    "!unzip cifar10.zip"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "将解压后的数据集移动到指定位置。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "cifar10\n",
-    "├── test\n",
-    "│   └── test_batch.bin\n",
-    "└── train\n",
-    "    ├── data_batch_1.bin\n",
-    "    ├── data_batch_2.bin\n",
-    "    ├── data_batch_3.bin\n",
-    "    ├── data_batch_4.bin\n",
-    "    └── data_batch_5.bin\n"
+    "!mkdir -p ./datasets/\n",
+    "!mv -f ./cifar10/ ./datasets/"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "如果放置正确，可以在开启的jupyter的首页网址+`/tree/datasets/cifar10`，找到`test`和`train`文件夹。"
+    "执行完成上述命令后，数据存放形式应如下："
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "datasets\n",
+    "│\n",
+    "└── cifar10\n",
+    "    ├── test\n",
+    "    │   └── test_batch.bin\n",
+    "    └── train\n",
+    "        ├── data_batch_1.bin\n",
+    "        ├── data_batch_2.bin\n",
+    "        ├── data_batch_3.bin\n",
+    "        ├── data_batch_4.bin\n",
+    "        └── data_batch_5.bin"
    ]
   },
   {
@@ -133,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -187,60 +214,16 @@
     "### 定义数据增强函数"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import mindspore.common.dtype as mstype\n",
-    "import mindspore.dataset.engine as de\n",
-    "import mindspore.dataset.vision.c_transforms as C\n",
-    "import mindspore.dataset.transforms.c_transforms as C2\n",
-    "\n",
-    "def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target=\"GPU\"):\n",
-    "    \n",
-    "    ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)\n",
-    "    \n",
-    "    # define map operations\n",
-    "    trans = []\n",
-    "    if do_train:\n",
-    "        trans += [\n",
-    "            C.RandomCrop((32, 32), (4, 4, 4, 4)),\n",
-    "            C.RandomHorizontalFlip(prob=0.5)\n",
-    "        ]\n",
-    "\n",
-    "    trans += [\n",
-    "        C.Resize((224, 224)),\n",
-    "        C.Rescale(1.0 / 255.0, 0.0),\n",
-    "        C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),\n",
-    "        C.HWC2CHW()\n",
-    "    ]\n",
-    "\n",
-    "    type_cast_op = C2.TypeCast(mstype.int32)\n",
-    "\n",
-    "    ds = ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=8)\n",
-    "    ds = ds.map(operations=trans, input_columns=\"image\", num_parallel_workers=8)\n",
-    "\n",
-    "    # apply batch operations\n",
-    "    ds = ds.batch(batch_size, drop_remainder=True)\n",
-    "    # apply dataset repeat operation\n",
-    "    ds = ds.repeat(repeat_num)\n",
-    "\n",
-    "    return ds"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "定义完成数据集增强函数后，我们来看一下，数据集增强后的效果是如何的："
+    "定义数据集增强函数，并使用该函数对原始数据集进行增强操作，取出其中一个batch中的一张图片数据进行可视化，查看数据集增强后的效果是如何的。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -278,6 +261,43 @@
     }
    ],
    "source": [
+    "import os\n",
+    "import mindspore.common.dtype as mstype\n",
+    "import mindspore.dataset.engine as de\n",
+    "import mindspore.dataset.vision.c_transforms as C\n",
+    "import mindspore.dataset.transforms.c_transforms as C2\n",
+    "\n",
+    "def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target=\"GPU\"):\n",
+    "    \n",
+    "    ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)\n",
+    "    \n",
+    "    # define map operations\n",
+    "    trans = []\n",
+    "    if do_train:\n",
+    "        trans += [\n",
+    "            C.RandomCrop((32, 32), (4, 4, 4, 4)),\n",
+    "            C.RandomHorizontalFlip(prob=0.5)\n",
+    "        ]\n",
+    "\n",
+    "    trans += [\n",
+    "        C.Resize((224, 224)),\n",
+    "        C.Rescale(1.0 / 255.0, 0.0),\n",
+    "        C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),\n",
+    "        C.HWC2CHW()\n",
+    "    ]\n",
+    "\n",
+    "    type_cast_op = C2.TypeCast(mstype.int32)\n",
+    "\n",
+    "    ds = ds.map(operations=type_cast_op, input_columns=\"label\", num_parallel_workers=8)\n",
+    "    ds = ds.map(operations=trans, input_columns=\"image\", num_parallel_workers=8)\n",
+    "\n",
+    "    # apply batch operations\n",
+    "    ds = ds.batch(batch_size, drop_remainder=True)\n",
+    "    # apply dataset repeat operation\n",
+    "    ds = ds.repeat(repeat_num)\n",
+    "\n",
+    "    return ds\n",
+    "\n",
     "ds = create_dataset(train_path, do_train=True, repeat_num=1, batch_size=32, target=\"GPU\")\n",
     "print(\"the cifar dataset size is:\", ds.get_dataset_size())\n",
     "dict1 = ds.create_dict_iterator()\n",
@@ -293,7 +313,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "cifar10通过数据增强后的，变成了一共有1562个batch，张量为(32,3,224,224)的数据集。"
+    "cifar10通过数据增强后的，变成了一共有1562个batch，图片张量为(32,3,224,224)的数据集。"
    ]
   },
   {
@@ -312,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -651,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -702,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -897,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -990,4 +1010,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/tutorials/training/source_zh_cn/advanced_use/convert_dataset.md b/tutorials/training/source_zh_cn/advanced_use/convert_dataset.md
index 21c0729e1c..7510892bac 100644
--- a/tutorials/training/source_zh_cn/advanced_use/convert_dataset.md
+++ b/tutorials/training/source_zh_cn/advanced_use/convert_dataset.md
@@ -13,7 +13,7 @@
 <!-- /TOC -->
 
 <a href="https://gitee.com/mindspore/docs/blob/master/tutorials/training/source_zh_cn/advanced_use/convert_dataset.md" target="_blank"><img src="../_static/logo_source.png"></a>&nbsp;&nbsp;
-<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/convert_dataset_to_mindrecord/convert_dataset_to_mindrecord.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
+<a href="https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/master/convert_dataset_to_mindrecord/mindspore_convert_dataset_to_mindrecord.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
 
 ## 概述
 
diff --git a/tutorials/training/source_zh_cn/advanced_use/debug_in_pynative_mode.md b/tutorials/training/source_zh_cn/advanced_use/debug_in_pynative_mode.md
index 0784052004..ea7e46aa27 100644
--- a/tutorials/training/source_zh_cn/advanced_use/debug_in_pynative_mode.md
+++ b/tutorials/training/source_zh_cn/advanced_use/debug_in_pynative_mode.md
@@ -15,7 +15,7 @@
 
 <a href="https://gitee.com/mindspore/docs/blob/master/tutorials/training/source_zh_cn/advanced_use/debug_in_pynative_mode.md" target="_blank"><img src="../_static/logo_source.png"></a>
 &nbsp;&nbsp;
-<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/debugging_in_pynative_mode.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
+<a href="https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/master/mindspore_debugging_in_pynative_mode.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
 
 ## 概述
 
diff --git a/tutorials/training/source_zh_cn/advanced_use/enable_mixed_precision.md b/tutorials/training/source_zh_cn/advanced_use/enable_mixed_precision.md
index 5df7ce1f91..146f48b881 100644
--- a/tutorials/training/source_zh_cn/advanced_use/enable_mixed_precision.md
+++ b/tutorials/training/source_zh_cn/advanced_use/enable_mixed_precision.md
@@ -14,7 +14,7 @@
 <!-- /TOC -->
 
 <a href="https://gitee.com/mindspore/docs/blob/master/tutorials/training/source_zh_cn/advanced_use/enable_mixed_precision.md" target="_blank"><img src="../_static/logo_source.png"></a>&nbsp;&nbsp;
-<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/mixed_precision.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
+<a href="https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/master/mindspore_mixed_precision.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
 
 ## 概述
 
@@ -229,4 +229,4 @@ output = train_network(predict, label)
 
 ## 约束
 
-使用混合精度时，只能由自动微分功能生成反向网络，不能由用户自定义生成反向网络，否则可能会导致MindSpore产生数据格式不匹配的异常信息。
\ No newline at end of file
+使用混合精度时，只能由自动微分功能生成反向网络，不能由用户自定义生成反向网络，否则可能会导致MindSpore产生数据格式不匹配的异常信息。
diff --git a/tutorials/training/source_zh_cn/advanced_use/evaluate_the_model_during_training.md b/tutorials/training/source_zh_cn/advanced_use/evaluate_the_model_during_training.md
index 4a3fcd4b59..8d15166811 100644
--- a/tutorials/training/source_zh_cn/advanced_use/evaluate_the_model_during_training.md
+++ b/tutorials/training/source_zh_cn/advanced_use/evaluate_the_model_during_training.md
@@ -15,7 +15,7 @@
 
 <a href="https://gitee.com/mindspore/docs/blob/master/tutorials/training/source_zh_cn/advanced_use/evaluate_the_model_during_training.md" target="_blank"><img src="../_static/logo_source.png"></a>
 &nbsp;&nbsp;
-<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/evaluate_the_model_during_training.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
+<a href="https://obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/master/mindspore_evaluate_the_model_during_training.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
 
 ## 概述
 
-- 
Gitee