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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# <center/> 自定义调试体验文档"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 概述"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "本文将使用[快速入门](https://gitee.com/mindspore/docs/blob/master/tutorials/tutorial_code/lenet.py)作为样例，并通过构建自定义调试函数：`Callback`、`metrics`、`Print算子`、日志打印等，同时将构建的自定义调试函数添加进代码中，通过运行效果来展示具体如何使用MindSpore提供给我们的自定义调试能力，帮助快速调试训练网络。\n",
+    "体验过程如下：\n",
+    "1. 数据集准备。\n",
+    "2. 定义深度学习网络LeNet5。\n",
+    "3. 使用Callback回调函数构建StopAtTime类来控制训练停止时间。\n",
+    "4. 设置日志环境变量。\n",
+    "5. 定义模型并执行训练。\n",
+    "6. 执行测试。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 数据集准备"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 数据集的下载"
+   ]
+  },
+  {
+   "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："
+   ]
+  },
+  {
+   "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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 数据集的增强操作"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "下载的数据集，需要通过`mindspore.dataset`处理成适用于MindSpore框架的数据，再使用一系列框架中提供的工具进行数据增强操作来适应LeNet网络的数据处理需求。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import mindspore.dataset as ds\n",
+    "import mindspore.dataset.transforms.vision.c_transforms as CV\n",
+    "import mindspore.dataset.transforms.c_transforms as C\n",
+    "from mindspore.dataset.transforms.vision import Inter\n",
+    "from mindspore.common import dtype as mstype\n",
+    "\n",
+    "def create_dataset(data_path, batch_size=32, repeat_size=1,\n",
+    "                   num_parallel_workers=1):\n",
+    "    \"\"\" create dataset for train or test\n",
+    "    Args:\n",
+    "        data_path (str): Data path\n",
+    "        batch_size (int): The number of data records in each group\n",
+    "        repeat_size (int): The number of replicated data records\n",
+    "        num_parallel_workers (int): The number of parallel workers\n",
+    "    \"\"\"\n",
+    "    # define dataset\n",
+    "    mnist_ds = ds.MnistDataset(data_path)\n",
+    "\n",
+    "    # define operation parameters\n",
+    "    resize_height, resize_width = 32, 32\n",
+    "    rescale = 1.0 / 255.0\n",
+    "    shift = 0.0\n",
+    "    rescale_nml = 1 / 0.3081\n",
+    "    shift_nml = -1 * 0.1307 / 0.3081\n",
+    "\n",
+    "    # define map operations\n",
+    "    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)\n",
+    "    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)\n",
+    "    rescale_op = CV.Rescale(rescale, shift)\n",
+    "    hwc2chw_op = CV.HWC2CHW() \n",
+    "    type_cast_op = C.TypeCast(mstype.int32)\n",
+    "\n",
+    "    # apply map operations on images\n",
+    "    mnist_ds = mnist_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\n",
+    "    mnist_ds = mnist_ds.map(input_columns=\"image\", operations=resize_op, num_parallel_workers=num_parallel_workers)\n",
+    "    mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_op, num_parallel_workers=num_parallel_workers)\n",
+    "    mnist_ds = mnist_ds.map(input_columns=\"image\", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers)\n",
+    "    mnist_ds = mnist_ds.map(input_columns=\"image\", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers)\n",
+    "\n",
+    "    # apply DatasetOps\n",
+    "    buffer_size = 10000\n",
+    "    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)\n",
+    "    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)\n",
+    "    mnist_ds = mnist_ds.repeat(repeat_size)\n",
+    "\n",
+    "    return mnist_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 定义深度学习网络LeNet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "针对MNIST数据集我们采用的是LeNet5网络，先对卷积函数和全连接函数初始化，然后`construct`构建神经网络。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mindspore.common.initializer import TruncatedNormal\n",
+    "import mindspore.nn as nn\n",
+    "from mindspore.ops import operations as P\n",
+    "\n",
+    "def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\n",
+    "    \"\"\"Conv layer weight initial.\"\"\"\n",
+    "    weight = weight_variable()\n",
+    "    return nn.Conv2d(in_channels, out_channels,\n",
+    "                     kernel_size=kernel_size, stride=stride, padding=padding,\n",
+    "                     weight_init=weight, has_bias=False, pad_mode=\"valid\")\n",
+    "\n",
+    "\n",
+    "def fc_with_initialize(input_channels, out_channels):\n",
+    "    \"\"\"Fc layer weight initial.\"\"\"\n",
+    "    weight = weight_variable()\n",
+    "    bias = weight_variable()\n",
+    "    return nn.Dense(input_channels, out_channels, weight, bias)\n",
+    "\n",
+    "\n",
+    "def weight_variable():\n",
+    "    \"\"\"Weight initial.\"\"\"\n",
+    "    return TruncatedNormal(0.02)\n",
+    "\n",
+    "\n",
+    "class LeNet5(nn.Cell):\n",
+    "    \"\"\"Lenet network structure.\"\"\"\n",
+    "    def __init__(self):\n",
+    "        super(LeNet5, self).__init__()\n",
+    "        self.batch_size = 32\n",
+    "        self.conv1 = conv(1, 6, 5)\n",
+    "        self.conv2 = conv(6, 16, 5)\n",
+    "        self.fc1 = fc_with_initialize(16 * 5 * 5, 120)\n",
+    "        self.fc2 = fc_with_initialize(120, 84)\n",
+    "        self.fc3 = fc_with_initialize(84, 10)\n",
+    "        self.relu = nn.ReLU()\n",
+    "        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)\n",
+    "        self.flatten = nn.Flatten()\n",
+    "        \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.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.fc3(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 构建自定义回调函数StopAtTime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "使用回调函数的基类Callback，构建训练定时器`StopAtTime`，其基类（可在源码中找到位置在`/mindspore/nn/callback`）为："
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "class Callback():\n",
+    "    def begin(self, run_context):\n",
+    "        pass\n",
+    "    def epoch_begin(self, run_context):\n",
+    "        pass\n",
+    "    def epoch_end(self, run_context):\n",
+    "        pass\n",
+    "    def step_begin(self, run_context): \n",
+    "        pass\n",
+    "    def step_end(self, run_context):\n",
+    "        pass\n",
+    "    def end(self, run_context):\n",
+    "        pass\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- `begin`：表示训练开始时执行。\n",
+    "- `epoch_begin`：表示每个epoch开始时执行。\n",
+    "- `epoch_end`：表示每个epoch结束时执行。\n",
+    "- `step_begin`：表示每个step刚开始时执行。\n",
+    "- `step_end`：表示每个step结束时执行。\n",
+    "- `end`：表示训练结束时执行。\n",
+    "\n",
+    "了解上述基类的用法后，还有一个参数`run_context`，这是一个类，存储了模型训练中的各种参数，我们在这里使用`print(cb_params.list_callback)`将其放在`end`中打印（当然也可以使用`print(cb_param)`打印所有参数信息，由于参数信息太多，我们这里只选了一个参数举例），后续在执行完训练后，根据打印信息，会简单介绍`run_context`类中各参数的意义，我们开始构建训练定时器，如下："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mindspore.train.callback import Callback\n",
+    "import time\n",
+    "\n",
+    "class StopAtTime(Callback):\n",
+    "    def __init__(self, run_time):\n",
+    "        super(StopAtTime, self).__init__()\n",
+    "        self.run_time = run_time*60\n",
+    "\n",
+    "    def begin(self, run_context):\n",
+    "        cb_params = run_context.original_args()\n",
+    "        cb_params.init_time = time.time()\n",
+    "    \n",
+    "    def step_end(self, run_context):\n",
+    "        cb_params = run_context.original_args()\n",
+    "        epoch_num = cb_params.cur_epoch_num\n",
+    "        step_num = cb_params.cur_step_num\n",
+    "        loss = cb_params.net_outputs\n",
+    "        cur_time = time.time()\n",
+    "        if (cur_time - cb_params.init_time) > self.run_time:\n",
+    "                print(\"epoch: \", epoch_num, \" step: \", step_num, \" loss: \", loss)\n",
+    "                run_context.request_stop()\n",
+    "    def end(self, run_context):\n",
+    "        cb_params = run_context.original_args()\n",
+    "        print(cb_params.list_callback)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 设置日志环境变量"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MindSpore采用`glog`来输出日志，我们这里将日志输出到屏幕：\n",
+    "\n",
+    "`GlOG_v`：控制日志的级别，默认值为2，即WARNING级别，对应关系如下：0-DEBUG、1-INFO、2-WARNING、3-ERROR。本次设置为1。\n",
+    "\n",
+    "`GLOG_logtostderr`：控制日志输出方式，设置为`1`时，日志输出到屏幕；值设置为`0`时，日志输出到文件。设置输出屏幕时，日志部分的信息会显示成红色，设置成输出到文件时，会在`GLOG_log_dir`路径下生成`mindspore.log`文件。\n",
+    "\n",
+    "> 更多设置请参考官网：https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/customized_debugging_information.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'GLOG_v': '1', 'GLOG_logtostderr': '1'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from mindspore import log as logger\n",
+    "\n",
+    "os.environ['GLOG_v'] = '1'\n",
+    "os.environ['GLOG_logtostderr'] = '1'\n",
+    "os.environ['GLOG_log_dir'] = 'D:/' if os.name==\"nt\" else '/var/log/mindspore'\n",
+    "os.environ['logger_maxBytes'] = '5242880'\n",
+    "os.environ['logger_backupCount'] = '10'\n",
+    "print(logger.get_log_config())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "打印信息为`GLOG_v`的等级：`INFO`级别。\n",
+    "\n",
+    "输出方式`GLOG_logtostderr`：`1`表示屏幕输出。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 定义网络模型并执行训练"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 定义网络模型"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "此过程中先将之前生成的模型文件`.ckpt`和`.meta`的数据删除，并将模型需要用到的参数配置到`Model`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mindspore import context\n",
+    "from mindspore.train import Model\n",
+    "from mindspore.nn.metrics import Accuracy\n",
+    "from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits\n",
+    "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor\n",
+    "\n",
+    "# clean files\n",
+    "if os.name == \"nt\":\n",
+    "    os.system('del/f/s/q *.ckpt *.meta')\n",
+    "else:\n",
+    "    os.system('rm -f *.ckpt *.meta *.pb')\n",
+    "\n",
+    "context.set_context(mode=context.GRAPH_MODE, device_target=\"CPU\")\n",
+    "lr = 0.01\n",
+    "momentum = 0.9 \n",
+    "epoch_size = 3\n",
+    "train_data_path = \"./MNIST_Data/train\"\n",
+    "eval_data_path = \"./MNIST_Data/train\"\n",
+    "\n",
+    "net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n",
+    "repeat_size = epoch_size\n",
+    "network = LeNet5()\n",
+    "\n",
+    "metrics = {\n",
+    "    'accuracy': nn.Accuracy(),\n",
+    "    'loss': nn.Loss(),\n",
+    "    'precision': nn.Precision(),\n",
+    "    'recall': nn.Recall(),\n",
+    "    'f1_score': nn.F1()\n",
+    "    }\n",
+    "net_opt = nn.Momentum(network.trainable_params(), lr, momentum)\n",
+    "\n",
+    "config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)\n",
+    "\n",
+    "ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", config=config_ck)\n",
+    "\n",
+    "model = Model(network, net_loss, net_opt, metrics=metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 执行训练"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "在构建训练网络中，给`model.train`传入了三个回调函数，分别是`ckpoint_cb`，`LossMonitor`，`stop_cb`；其分别代表如下：\n",
+    "\n",
+    "`ckpoint_cb`：即是`ModelCheckpoint`，设置模型保存的回调函数。\n",
+    "\n",
+    "`LossMonitor`：loss值监视器，打印训练过程每步的loss值。\n",
+    "\n",
+    "`stop_cb`：即是`StopAtTime`，上面刚构建的训练定时器。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们将训练定时器`StopAtTime`设置成18秒，即`run_time=0.3`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============== Starting Training ==============\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO] ME(10004:11540,MainProcess):2020-07-22-16:52:22.904.779 [mindspore\\train\\serialization.py:308] Execute save the graph process.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 1 step 375, loss is 2.3015153408050537\n",
+      "epoch: 1 step 750, loss is 2.2981557846069336\n",
+      "epoch: 1 step 1125, loss is 2.304901361465454\n",
+      "epoch: 1 step 1500, loss is 0.27651622891426086\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO] ME(10004:11540,MainProcess):2020-07-22-16:52:33.315.965 [mindspore\\train\\serialization.py:119] Execute save checkpoint process.\n",
+      "[INFO] ME(10004:11540,MainProcess):2020-07-22-16:52:33.325.978 [mindspore\\train\\serialization.py:147] Save checkpoint process finish.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch: 1 step 1875, loss is 0.263612300157547\n",
+      "Epoch time: 11051.060, per step time: 5.894, avg loss: 1.702\n",
+      "************************************************************\n",
+      "epoch: 2 step 375, loss is 0.22589832544326782\n",
+      "epoch: 2 step 750, loss is 0.12003941088914871\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[INFO] ME(10004:11540,MainProcess):2020-07-22-16:52:40.282.209 [mindspore\\train\\serialization.py:119] Execute save checkpoint process.\n",
+      "[INFO] ME(10004:11540,MainProcess):2020-07-22-16:52:40.297.275 [mindspore\\train\\serialization.py:147] Save checkpoint process finish.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch:  2  step:  2927  loss:  0.26415202\n",
+      "Epoch time: 6953.909, per step time: 3.709, avg loss: 0.130\n",
+      "************************************************************\n",
+      "[<mindspore.train.callback._checkpoint.ModelCheckpoint object at 0x000001ACCDDA8688>, <mindspore.train.callback._loss_monitor.LossMonitor object at 0x000001ACBCF3AE08>, <__main__.StopAtTime object at 0x000001ACBD016148>]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"============== Starting Training ==============\")\n",
+    "ds_train = create_dataset(train_data_path, repeat_size = repeat_size)\n",
+    "stop_cb = StopAtTime(run_time=0.3)\n",
+    "model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(375), stop_cb], dataset_sink_mode=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "以上打印信息中，主要分为两部分：\n",
+    "- 日志信息部分：\n",
+    "    - `[INFO]`部分信息即为日志输出的信息，由于没有Warning信息，目前主要记录的是训练的几个重要步骤。\n",
+    "    \n",
+    "- 回调函数信息部分：\n",
+    "    - `LossMonitor`：每步的loss值。\n",
+    "    - `StopAtTime`：在每个epoch结束及训练时间结束时，打印当前epoch的训练总时间(单位为毫秒)，每步训练花费的时间以及平均loss值，另外在训练结束时还打印了`run_context.list_callback`的信息，这条信息表示本次训练过程中使用的回调函数；另外`run_conext.original_args`中还包含以下参数：\n",
+    "        - `train_network`：网络的各类参数。\n",
+    "        - `epoch_num`：训练的epoch数。\n",
+    "        - `batch_num`：一个epoch的step数。\n",
+    "        - `mode`：MODEL的模式。\n",
+    "        - `loss_fn`：使用的损失函数。\n",
+    "        - `optimizer`：使用的优化器。\n",
+    "        - `parallel_mode`：并行模式。\n",
+    "        - `device_number`：训练卡的数量。\n",
+    "        - `train_dataset`：训练的数据集。\n",
+    "        - `list_callback`：使用的回调函数。\n",
+    "        - `train_dataset_element`：打印当前batch的数据集。\n",
+    "        - `cur_step_num`：当前训练的step数。\n",
+    "        - `cur_epoch_num`：当前的epoch。\n",
+    "        - `net_outputs`：网络返回值。\n",
+    "\n",
+    "   几乎在训练中的所有重要数据，都可以从Callback中取得，所以Callback也是在自定义调试中比较常用的功能。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 执行测试"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "测试网络中我们的自定义函数`metrics`将在`model.eval`中被调用，除了模型的预测正确率外`recall`，`F1`等不同的检验标准下的预测正确率也会打印出来："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "============== Starting Testing ==============\n",
+      "============== Accuracy:{'accuracy': 0.9712666666666666, 'loss': 0.0918103571044902, 'precision': array([0.979007  , 0.9815034 , 0.9695254 , 0.99006449, 0.97207177,\n",
+      "       0.9750469 , 0.97660037, 0.9832609 , 0.91311292, 0.97466828]), 'recall': array([0.99206483, 0.98383269, 0.97717355, 0.92643941, 0.98305375,\n",
+      "       0.95867921, 0.9873268 , 0.97509976, 0.97709793, 0.95074802]), 'f1_score': array([0.98549266, 0.98266667, 0.97333445, 0.95719582, 0.97753191,\n",
+      "       0.96679379, 0.98193429, 0.97916333, 0.94402246, 0.96255956])} ==============\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"============== Starting Testing ==============\")\n",
+    "ds_eval = create_dataset(eval_data_path, repeat_size=repeat_size)\n",
+    "acc = model.eval(ds_eval,dataset_sink_mode = False)\n",
+    "print(\"============== Accuracy:{} ==============\".format(acc))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`[INFO]`部分为日志信息。\n",
+    "\n",
+    "`Accuracy`部分的信息即为`metric`控制输出的信息，模型的预测值正确率和其他标准下验证（0-9）的正确率值，至于不同的验证标准计算方法，大家可以去官网搜索`mindspore.nn`查找，这里就不多介绍了。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 总结\n",
+    "\n",
+    "我们使用了MNIST数据集，通过LeNet5神经网络进行训练，将自定义调试函数结合进其代码中进行调试，展示了使用方法和部分功能，并在过程中展示了训练过程中我们能够通过自定义调试函数输出的数据，来更好的认识自定义调试函数的方便性，以上就是本次的体验内容。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/notebook/quick_start.ipynb b/tutorials/notebook/quick_start.ipynb
index 558fd5bc0b20632de3236e16cebec89127e90ab4..8f425891f317ab34f53d1164e076343d890f39a1 100644
--- a/tutorials/notebook/quick_start.ipynb
+++ b/tutorials/notebook/quick_start.ipynb
@@ -204,7 +204,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -387,7 +387,7 @@
      "text": [
       "dict_keys(['label', 'image'])\n",
       "Tensor of image: (32, 1, 32, 32)\n",
-      "labels: [6 3 5 8 9 0 1 8 1 6 8 9 1 3 0 9 0 4 0 2 2 6 7 9 4 9 1 4 1 4 3 3]\n"
+      "labels: [7 4 0 6 6 6 5 8 3 5 4 8 5 3 2 9 3 7 3 0 9 2 0 6 3 3 6 2 5 9 2 0]\n"
      ]
     }
    ],
@@ -414,7 +414,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 32 Axes>"
       ]
@@ -637,7 +637,7 @@
    "metadata": {},
    "source": [
     "构建完成神经网络后，就可以着手进行训练网络的构建，模型训练函数为`Model.train`，参数主要包含：\n",
-    "1. 圈数`epoch size`（每圈需要遍历完成1875组图片）；\n",
+    "1. 每个`epoch`需要遍历完成图片的`batch`数：`epoch_size`；\n",
     "2. 数据集`ds_train`；\n",
     "3. 回调函数`callbacks`包含`ModelCheckpoint`、`LossMonitor`和`Callback`模型检测参数；\n",
     "4. 数据下沉模式`dataset_sink_mode`，此参数默认`True`需设置成`False`，因为此功能不支持CPU模式。"
@@ -661,7 +661,7 @@
     "    print(\"============== Starting Training ==============\")\n",
     "    # load training dataset\n",
     "    ds_train = create_dataset(os.path.join(mnist_path, \"train\"), 32, repeat_size)\n",
-    "    model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(), step_loss_info], dataset_sink_mode=False)"
+    "    model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(125), step_loss_info], dataset_sink_mode=False)"
    ]
   },
   {
@@ -717,1942 +717,22 @@
      "output_type": "stream",
      "text": [
       "============== Starting Training ==============\n",
-      "epoch: 1 step 1, loss is 2.3036417961120605\n",
-      "epoch: 1 step 2, loss is 2.298630714416504\n",
-      "epoch: 1 step 3, loss is 2.302391529083252\n",
-      "epoch: 1 step 4, loss is 2.304328441619873\n",
-      "epoch: 1 step 5, loss is 2.3027987480163574\n",
-      "epoch: 1 step 6, loss is 2.2987725734710693\n",
-      "epoch: 1 step 7, loss is 2.298673629760742\n",
-      "epoch: 1 step 8, loss is 2.30534029006958\n",
-      "epoch: 1 step 9, loss is 2.303036689758301\n",
-      "epoch: 1 step 10, loss is 2.2993321418762207\n",
-      "epoch: 1 step 11, loss is 2.305026054382324\n",
-      "epoch: 1 step 12, loss is 2.302748918533325\n",
-      "epoch: 1 step 13, loss is 2.3028924465179443\n",
-      "epoch: 1 step 14, loss is 2.2981364727020264\n",
-      "epoch: 1 step 15, loss is 2.3044252395629883\n",
-      "epoch: 1 step 16, loss is 2.2937963008880615\n",
-      "epoch: 1 step 17, loss is 2.302947759628296\n",
-      "epoch: 1 step 18, loss is 2.301767587661743\n",
-      "epoch: 1 step 19, loss is 2.302520275115967\n",
-      "epoch: 1 step 20, loss is 2.2997517585754395\n",
-      "epoch: 1 step 21, loss is 2.300358533859253\n",
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-      "epoch: 1 step 1844, loss is 0.2947942316532135\n",
-      "epoch: 1 step 1845, loss is 0.12519270181655884\n",
-      "epoch: 1 step 1846, loss is 0.03666522726416588\n",
-      "epoch: 1 step 1847, loss is 0.06074002757668495\n",
-      "epoch: 1 step 1848, loss is 0.0761866495013237\n",
-      "epoch: 1 step 1849, loss is 0.23314397037029266\n",
-      "epoch: 1 step 1850, loss is 0.029110223054885864\n",
-      "epoch: 1 step 1851, loss is 0.31116774678230286\n",
-      "epoch: 1 step 1852, loss is 0.055939022451639175\n",
-      "epoch: 1 step 1853, loss is 0.2014581859111786\n",
-      "epoch: 1 step 1854, loss is 0.019982852041721344\n",
-      "epoch: 1 step 1855, loss is 0.052759602665901184\n",
-      "epoch: 1 step 1856, loss is 0.21708421409130096\n",
-      "epoch: 1 step 1857, loss is 0.03414442390203476\n",
-      "epoch: 1 step 1858, loss is 0.037223730236291885\n",
-      "epoch: 1 step 1859, loss is 0.01988343521952629\n",
-      "epoch: 1 step 1860, loss is 0.03718801960349083\n",
-      "epoch: 1 step 1861, loss is 0.47103869915008545\n",
-      "epoch: 1 step 1862, loss is 0.0968848392367363\n",
-      "epoch: 1 step 1863, loss is 0.06905733793973923\n",
-      "epoch: 1 step 1864, loss is 0.13268259167671204\n",
-      "epoch: 1 step 1865, loss is 0.3494003713130951\n",
-      "epoch: 1 step 1866, loss is 0.0347442626953125\n",
-      "epoch: 1 step 1867, loss is 0.08115438371896744\n",
-      "epoch: 1 step 1868, loss is 0.12732252478599548\n",
-      "epoch: 1 step 1869, loss is 0.08057542145252228\n",
-      "epoch: 1 step 1870, loss is 0.05422119051218033\n",
-      "epoch: 1 step 1871, loss is 0.09048718959093094\n",
-      "epoch: 1 step 1872, loss is 0.13636641204357147\n",
-      "epoch: 1 step 1873, loss is 0.2992154359817505\n",
-      "epoch: 1 step 1874, loss is 0.30933624505996704\n",
-      "epoch: 1 step 1875, loss is 0.06503278017044067\n",
-      "Epoch time: 22519.637, per step time: 12.010, avg loss: 1.140\n",
+      "epoch: 1 step 125, loss is 2.3136098384857178\n",
+      "epoch: 1 step 250, loss is 2.303882598876953\n",
+      "epoch: 1 step 375, loss is 2.3046326637268066\n",
+      "epoch: 1 step 500, loss is 2.3024802207946777\n",
+      "epoch: 1 step 625, loss is 2.3106091022491455\n",
+      "epoch: 1 step 750, loss is 2.298833131790161\n",
+      "epoch: 1 step 875, loss is 2.3070852756500244\n",
+      "epoch: 1 step 1000, loss is 2.284291982650757\n",
+      "epoch: 1 step 1125, loss is 0.7130898237228394\n",
+      "epoch: 1 step 1250, loss is 0.17307262122631073\n",
+      "epoch: 1 step 1375, loss is 0.3248927891254425\n",
+      "epoch: 1 step 1500, loss is 0.09352534264326096\n",
+      "epoch: 1 step 1625, loss is 0.025928258895874023\n",
+      "epoch: 1 step 1750, loss is 0.0918595939874649\n",
+      "epoch: 1 step 1875, loss is 0.20610764622688293\n",
+      "Epoch time: 15709.893, per step time: 8.379, avg loss: 1.440\n",
       "************************************************************\n"
      ]
     }
@@ -2721,7 +801,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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46CJ4+eXC82+4IbxPntxuIYnUm5KBNJ9MrWDAgPrGIdJAlAyk+Vh0uyzduVRkGSUDaT7L6c9eJJf+K6T5WNEb6Yo0JSUDERFRMpAmVG7NQH0K0kSUDKT5ZJKBmotEllEykOalZCCyjJKBNB8lAZE8SgbSfJQMRPIoGYiIiJKBNCF1IIvkUTKQ5qMkIJJHyUCal5KCyDJKBtJ8FiwoPl9JQpqQkoGIiCgZiIiIkoE0s9zmoD32gJtvrksoIvWmZCCSMWoUHHlkvaMQqQslA2len35a7whEGoaSgYiIKBmIiIiSgYiIkHIyMLOhZva+mb2SMH8PM/vIzMZFryFpxiNSET3pTJpIl5TXfwNwFXBjkTJPufsBKcchIiJFpFozcPcngQ/S3IZITagWIE2uEfoMdjKz8Wb2oJn1SypkZoPNbIyZjZk7d257xici0unVOxm8BGzs7lsDfwX+k1TQ3a919/7u3r9Hjx7tFqB0QldckT9NNQNpcnVNBu6+0N0/joYfAFYws+71jEmawBpr5E9buLD94xBpIHVNBma2vlm4QYyZ7RDFM7+eMUkTWK7An/2WW7Z/HCINpOyzicxsPeAPQC9338/MtgR2cvfriyxzC7AH0N3MZgIXAisAuPs1wKHAT8xsCfAZcLi76uuSskLPK5g9u/3jEGkglZxaegMwDDg/Gn8TGAEkJgN3P6LYCt39KsKppyLtRw+vEclTSTNRd3e/DWgBcPclwNJUohJJU6lkoGQhTaiSZPCJma0DOICZDQA+SiUqkTQV6jMQaXKV/FecAYwE+prZM4Srik9NJSqRNMWTwahR8PWv1y8WkQZRdp+Bu79kZrsDXwEMeMPdv0wtMpG0fOMb2eGTT4ZJk+oXi0iDqORsomNyJm1nZrh7sfsOiTSevn2zwy0t9YtDpIFUcjZR7OcUKwF7Eq4gVjKQjktnMosAlTUTteofMLM1gZtqHpFIe3r99XpHINIQqjmt4lNg81oFIiIi9VNJn8G9RKeVEpLIlsBtaQQlIiLtq5I+g0tiw0uA6e4+s8bxiDQO9SdIE6mkz2BUmoGIiEj9lEwGZraIbPNQq1mAu3uB+wGLiEhHUjIZuPvq7RGIiIjUTyV9BgCY2bqE6wwAcPd3ahqRiIi0u7JPLTWzA81sMjANGAW8DTyYUlwiItKOKrnO4LfAAOBNd9+EcAXyM6lEJSIi7aqSZPClu88HljOz5dz9cWCblOISEZF2VEmfwQIzWw14EhhuZu8TrjcQEZEOrpKawUGEW1CcDvwfMBX4ThpBidSVnnQmTaiSmsFg4PboquN/pRSPiIjUQSU1gzWAh8zsKTM72czWSysokdT161fvCEQaStnJwN1/7e79gJOBXsAoM3s0tchE0vS1r1VWfvJkuPbadGIRaQBtuYX1+8B7wHxg3dqGI9JOKn3C2YABcOKJunmddFqVXHT2EzN7Avgv0B34sbvrSeLSMS2p8ES4Dz4I70oG0klV0oG8MXCau48rNNPM1nL3D2sTlkjKli5Nnvf558nzlAykk6rkFtbnlCjyX2C76sIRaSfFagbPPhveCx34lQykk6rmsZe5dHK2dBwPPVS6zJAh+dMq7WsQ6SBqmQz0k0k6l9Gj86epZiCdVC2TgUjHseqqbVtONQPppCp+nkERaiaSjmPx4vLKTZoE06eHW1S4q2YgnVbZycDM+gIz3X2xme0BfB240d0XREX2TCE+kXSUe2rpVluF98z9ipQMpJOqpJnoTmCpmW0GXA9sAtycmenuH9Q4NpH0rLJKZeUzyUDNRNJJVZIMWtx9CXAwcIW7nw70TCcskZSNGdO25VQzkE6qoofbmNkRwLHAfdG0FYotYGZDzex9M3slYb6Z2ZVmNsXMJpiZrlOQ9lHpvYkylAykk6okGRwP7AT83t2nmdkmwL9LLHMDsG+R+fsBm0evwcDVFcQj0n7UTCSdXCVXIL8K/AzCrSeA1d39TyWWedLM+hQpchChE9qB58ysm5n1dPfZ5cYl0q5UM5BOqpIb1T1hZmuY2drAeGCYmV1W5fY3AGbExmdG00QaS+ZeRkoG0klV0ky0prsvBL4HDHP37YG9qtx+oWsTCv63mdlgMxtjZmPmzp1b5WZF2kjNRNJJVZIMuphZT+Awsh3I1ZoJ9I6NbwjMKlTQ3a919/7u3r9Hjx412rw0tbYc2FUzkE6qkmTwG+AhYKq7v2hmmwKTq9z+SOCY6KyiAcBH6i+QdtOWB9+rZiCdVCUdyLcDt8fG3wIOKbaMmd0C7AF0N7OZwIVEp6O6+zXAA8AgYArwKeGMJZHGpZqBdFKV3I5iQ+CvwEBCu/7TwM/dfWbSMu5+RLF1RmcRnVxuDCJ1p2QgnVQlzUTDCM06vQhn/NwbTRPpnL797fxpaiaSTqqSZNDD3Ye5+5LodQOgnlzpvLbdNn+aagbSSVWSDOaZ2VFmtnz0OgqYn1ZgInV3yy350zLJYO5cmDevfeMRSVElyeAEwmml7wGzgUNRh690ZjNm5E/LNBOtuy7oFGfpRMpOBu7+jrsf6O493H1dd/8u4QI0keZxzz2qEUinVO1jL8+oSRQiHcVpp8EBB9Q7CpGaqzYZ6FGX0nymTEme98gj8PHH7ReLSI1Umwx0aoVIxvTpsPfecNxx9Y5EpGIlLzozs0UUPugbsHLNIxLpqDI1gtdeq28cIm1QMhm4++rtEYhIh6FrDaQTqraZSKT5JCUDJQnpwJQMRDJWWgnWWafty2eSQVvuhipSZ0oGIhnbbAPvv1/9eqpJBk88AXfcUX0MIhUq+66lIp3SE0/AzJlw1FFhfLkyfh99+GF68Xzzm+FdTU7SzpQMpLntvjvMmROGjz22vrGI1JGaiUTWWy/8Ej/ppNqv+/nnYYstdCGaNDwlA5FayW3aufjicOuKyZPhxRfzy95wA3zxRbuF18qMGSEukYiaiURqLdOB/L//mz8t44474PjjYepU+O1v2y+2jI02Cu/qm5CIagYitZI5sE6cmH+QzU0GmU7oTH+FSJ0pGYikYdGi8srpl7k0CCUDkWokHcy/+93iy+nCNGkwSgYi1Ui6Q+lTT7UeTzr4d7SagXu4NqOjxS0lKRmIVOPGG7PDlRwgM8mhkmVWWgkOPLD88mm49dZwYdzQofWNo1rHHAN/+Uu9o2goSgYitTBlCtx+e3a8UAfyFVeE988+a9s2Fi+Ge+9te4y18NZbrd87qptuCk+tk2V0aqlILfTrV/yagaVL4fTTw/CCBa3nPfAAdOkSHowjUieqGYjUQm4iyO0juOqqwvNffx323x/22af6GN56C846C1paql+XNB0lA5H2EL8bajxRjB5dfLnXX4chQwr3LTz2GFx3XXb8kEPg0kth0qTqYpWmpGQgkobcg3fu3VBLnVr6wQehc3r77cMVyoXubbTnnjB4cHZ8yZK2xSqC+gxE2kel1xUcfXToS2hUOrW001HNQKSUfv0qXyb34F9OzeBb38oOv/tu63nlHHx1gJYqKBmI5Fplldbjyy9f/TrjycCscDJ4/PHk5RvtQN+WK6iHD4e77659LFITaiYSyfXee+FU0LXWCuOlksE225ReZ27N4JFHKoupLRe0NZrM0+QaLbEJoJqBSL7VV4du3eCgg8L4qqsWLz9+fP603M7c3BvX3Xxz8XXmHjDj42+8ASusUHz5JIsWwYABbVu2Gh39IrUmoGQgkuTvf4ennw7JoVrPPpsdfvnl0uUnTGg9Hk8GX/1q4TOHyvnF/dhj4elr7e2DD9pvWzfeCPfc037b6yRSTwZmtq+ZvWFmU8zsnALzjzOzuWY2Lnr9KO2YRMrSqxcMHJjfxFOt/farfJn2bFpxh112qe0zoWu9D4s59tjSd42VPKl+Q2a2PPA3YD9gS+AIM9uyQNER7r5N9PpnmjGJVKw9D2RJykkG5fQVlLOe2bPhmWda34SvWo2wD0uZMAEWLqx3FHWT9je0AzDF3d9y9y+AW4GDUt6mSG11hANZOUaNgoMPTp7/ySfw+efl9UfMnBlOhc08sa2URt+H7rD11uHWIE0q7W9oA2BGbHxmNC3XIWY2wczuMLPehVZkZoPNbIyZjZk7d24asYoU1ghn59TiOoPbbis+f7XVYIst8j+vezj4x914YzgVtlRHeEYlp+fOnw+fflp++VpYujS8P/NMdev59rfDbUE6oLSTQaH/oty/2HuBPu7+deBR4F+FVuTu17p7f3fv36NHjxqHKVJEI/yqvfPO0mW+/DK8m4Vbapu1vq12OWbMyE8q114LvXvDSy9Vtq64SvZh9+6w7bZt31ZG0hXcn3+ePy1zo8EuVZ5t/+ijcNdd1a0jlztMm1bbdRaQ9l/5TCD+S39DYFa8gLvPd/fF0eh1wPYpxyRSmUZIBj/5SekykydnhzMH7mLPWEiSe9fTJ54I76+/nl9ryIw/9RT85jf565o/H+bMqXwfvvlmZeUL2X//whfynXlm/rRMMmjrKbtpuvJK2HRTGDcu1c2k/Vf+IrC5mW1iZisChwMj4wXMrGds9EDgtZRjEqlMIzQTVWKrreAHPwjDbYm90IN5Ss3bbTe48ML8dXXvDuuvX78LzebNy59W6Fd2rWoGbTF8OHz0UfL8J58M71OnphpGqsnA3ZcApwAPEQ7yt7n7JDP7jZllnt/3MzObZGbjgZ8Bx6UZk0jFGqFm0FbFDuRJcmsGtTiQ12od5XZYZ5SbDOuVDCZODFdmn3BC6bIpJ9TU/8rd/QF338Ld+7r776NpQ9x9ZDR8rrv3c/et3f2b7v562jGJVKTRk0Gx0yHjzTjXXFPe+pIOOkuX5l8MV66kB+5cdRXMmlV4Xq5LL4W114bp09sWQ0ahBFGvZqLMrcmL7YN2qpk2+F+5SANo9GRwxx2ly+y2W+HpL76YPy33wJ05GJ17bukzkpJce23+tGnT4NRTi5/uGjcyamF+++38eQsXhjgvvbT0egolu3rVDDL7utgBv52a2HSjOpFSVlyx3hEUd/nlyc0MpRLZDjvkH2ySfsXn3lYbyv/VeuWV+dMyt9SYP7+8dWS2lRvvCSfAyiuH4XJrPxmXXw79+8M667TeRnvJfJYG+MGhZCBSykUXwbBh9Y4i2SuvZM+Tz1VtB7J7OgfIpUuzNYJyf/kmJYNqvpszzgjvr7zS9nVUo5yagZqJRBpE9+71jqC0pGTQFptskh1+91347LPksmaVP27z/vvDM6ErfVZz5tdzGrfzLuegnGvqVLj66sLzHn649Pa++KKhagb1j0BEqpe54CxXtb8qe/cu/UCaTHt7rv/8p/D0Aw6Ahx6qPJakmkHclCmVrxfalgx22QV++lNYvDh/3j77FP+MRx0FXbtmt1tOMujoZxOJdAq//jWMHVvvKJIVOiBB+k0MZq37GOIHrGIdw/GzZ6ppJirUj1FomVKS+kkAdt8dBg/On17qttzFYrvlltbbVTORSAcxZAhst129o0iWlAzK+cVZzvMViokfSB97DN55p/QySTWZYjIHxfj2Ntyw+DLxq7KLKXZQfvJJuO66MDx2bPEHDxVaZ8att+ZfWZ1ZtpwD/mGHlS5TBSUDkc6gmppBtUkufjDca6/w8J1S4smg3JpBJrH98pflL3NO3iNUCi9bzi/0++4LZx4NHZq/vvvvz79GITcZHHEEfOUrhWOJJ+1hw8q/9qKGlAxEOoNCN1/LSLOtObeZCIp3OGdUkgyuvjp0NmcO1C++2Pa+gSTFmoky3ngjvL/6av6yF16Y35Fezjpzk9C8eeFU2fgDkNRMJNKA7r233hEUlnRWy3LLlXdQqkahg3mpZqCJE8tb97x5oZN2q61aHxQz1xXUSiaBFTvwJnX2trQUvkV3Ofs9XjMYPBguvjiMZ24ZfsklMGZMtvzf/pZcC6ySrjMQqcQBB6R37n01ku7yOXRoOJCm5Ve/KtyROnt28eUefDA7HE8mn3zSutz552eH42ct1bq2c/LJ4X3atHALandYa63wAJ+MzOfMTQbuhftm4skg6cK6eILJ9EtASKavvgpnn926/CmnhKukTzyx9GeqkGoGIp1BsXvqZC6sSsOsWeW3yxczdGh45vRqq2WnnXtu6wQwZ052uNa1nXjTzyGHwKGHwp57tm76yXB15GUAAA6wSURBVJwqOmJE62U33BCeey5/nfFrP5LOrErqq1i0CPr1K7xMSj9ElAxEOoNGa74qdC+iJDNmwA9/mF+b+NOfWjcnxS9SqyQZXHdd6zb4ShLV3/+ev83cG+UtWlR42XiMSbefzpRJehBPISldoKZmIpG2mD49XDS0cGF4VKS09oc/lF+22ME56dqOSpJBoWsEyhW/I2yltZH454ovG69ZtKWGk1LNQMlApC022ii8d+1a3ziaVTXNRG25+rnQNksdlM88M5Q5/fTWTUaHH54dbsttRNRMJNKAGq0juVm0tFR+IF28OHvlb7kuuKD1NuPKaW7K9NckxapkINJJKBnUR0sL3HBDZcuceiocfXTbtxlv9/+//0u+J1MhSgYinVxnSQYd7XO0tMCPflTZMqNHV9e8FD/4xzukSylWi6n0jq+gZCDSkGp1Zsejj9ZmPc0ifpZPuZYubbenhrWy3nrZx1vmaqAOZCUDkWrE/zELnW9frm99K/UbkXUqbUkGbfkVXgvz5iXPa0szUUqnlioZiFQjngx22y08tCVXsfsGFVqPpKPW9zOqhXolqAKUDESqkXsQ79ED1l679bRGf4ay1M8TT1S+TEpNXUoGItUo9Is+d1q5v/pPOaX87Z51VvllpXFVeqorpHbjQSUDkWoUar/deefs8Pe+V/66dt21/F998fPfpbkoGYg0oEK/+m+5Bc47L93tdqny5gHxu4ZKx6JkINKACiWDVVfNPj0s80v/uuvg+edrt91qk8E229QmDml/SgYiDSjpNL/cJPGjH8EOO1S27uOOaz0ev6tnqWRwySXF59fr7KURI+Coo+qz7c5CyUCkAZmF+96n4fjjW4+vv352OJ6EevTIX/bMM/On/fvf4X311aFbt+rja4vDDgs1p1J69kw/lo5KyUCkQRV6sEwtfnnHf/2vs05yuT/+sfS67r0XjjwSRo6EV14Jd1u9/vriy9x+e3lxVmqVVUqXeffddLbdGSgZiHQg++8PP/4xXHVV6+l/+Uv569h88+zwzTcnlyvnKtbMtQ/f+U729ttf+UrxZXr3Lr3etij1/OJDDulYF+HddVfh6fHHWMZttll121MyEOlAVlwxPO2rV6/W0089Nf+JXv/4R+F19OgR/vFfegn23jt//gEHhISz6aZhPLfpp2/f7HChA8jAgTBuXPJnSOmgU7Jm8POfp7PdWnjkkfxp3/1u4bJHHll4ejk1o2KUDEQa1De+Ed5zD/yFmLVu+3cv/CSuddfNlt922+z0556Dyy4Lw/feGxLOXnvBs8/mP5j+6adbb6eQrbdOfiRjoWV+8pPCZePinw9Ckjr00Ox4qT6DXXctvY00lPPZcmPfeefkWkzSc6mrvSI9nuRrSMlApFoXXhh+YW+9dW3WN3MmvPFG4Xk77hienJVrwID8g9L664eHykO4c2aSTM0C4OKLsw9k2Xjj/LL77JO8nqOPDldRT5kSmqX+3//Lzrv99mxyiTcTFdpGKTfdVPkyxTz1FLz3Xnk3v8s9wCc9e3qHHZLP+KqmCey550KNMA3unuoL2Bd4A5gCnFNgfldgRDT/eaBPqXVuv/32LtKhzZ7tPm1a7de77rrum22WHV+yxH3SpNLLhUO1e0tLeH36aZj+8MNh+oUXhvfXX8+WPekk9/POy44//HDrdc6fH6Z369Z6+ttvh+nPPNN625lXbkxnnZUd3nzzwssUe910U/H58e+h0PzLL88Ov/xyNo7XXktebtiwwtN79nTfc8/K4i+0b9oIGONJx+qkGbV4AcsDU4FNgRWB8cCWOWV+ClwTDR8OjCi1XiUDkQRLl4aDeaUefND9ySeT57e0uC9YEIZzD/DPPef+yiv5y3zwQeFkkCtzoBs92v2uu7LTf/979112CcObbOJ+wgnuixaF8enTyzt4du/eehuZV7du7r/4hfuYMa1jmTPHfe21Q5k113R/6KHWy0+eHN732afwZwD3Cy7IfgfvvOO+1VatD+b77JMfz5prtk544H7zza3LzJ5dfD+WoZ7JYCfgodj4ucC5OWUeAnaKhrsA8wArtl4lA5E6GjfO/b33Spf78kv3Ll3c//nP4uW6dQsH+0plaiV77x3e//lP94sucv/ii5AUe/UKtQL3/IPvAw8kr3f69PxaTvxgPnx4qPXkzl9xRfeBAwvvm3//2/3WW8PwwQdn1zdihPvEie59+7bexgsvhIQyZ477H/7gPmpU5fungGLJwML8dJjZocC+7v6jaPxoYEd3PyVW5pWozMxofGpUZl7OugYDgwE22mij7adPn55a3CLSjjJnx1Tz0JZ33smeMlvI8OGhD6NPH5gwAXbZpbL1T54cHpt57LGF58+cGS7mW3PN0uuaOzd0+g8blr1tyVtvhWc6DxgAgwZVFlsFzGysu/cvOC/lZPB9YJ+cZLCDu58aKzMpKhNPBju4+/yk9fbv39/HjBmTWtwiIp1RsWSQ9tlEM4H4lSsbArOSyphZF2BNIOccORERSVPayeBFYHMz28TMViR0EI/MKTMSyNS9DgUe8zSrKyIikqfK++AW5+5LzOwUQifx8sBQd59kZr8hdGSMBK4HbjKzKYQaweFpxiQiIvlSTQYA7v4A8EDOtCGx4c+B76cdh4iIJNMVyCIiomQgIiJKBiIigpKBiIiQ8kVnaTGzuUBbL0HuTrjlRSNr9BgbPT5QjLXQ6PFB48fYaPFt7O4FnpPaQZNBNcxsTNIVeI2i0WNs9PhAMdZCo8cHjR9jo8cXp2YiERFRMhARkeZMBtfWO4AyNHqMjR4fKMZaaPT4oPFjbPT4lmm6PgMREcnXjDUDERHJoWQgIiLNlQzMbF8ze8PMppjZOXWKobeZPW5mr5nZJDP7eTT9V2b2rpmNi16DYsucG8X8hpnt005xvm1mE6NYxkTT1jazR8xscvS+VjTdzOzKKMYJZrZdyrF9JbafxpnZQjM7rd770MyGmtn70dP7MtMq3mdmdmxUfrKZJTxaq6YxXmxmr0dx3G1m3aLpfczss9j+vCa2zPbR38eU6HNYivFV/L2m+b+eEOOIWHxvm9m4aHq778M2S3oeZmd7EW6hPRXYFFgRGA9sWYc4egLbRcOrA28CWwK/As4qUH7LKNauwCbRZ1i+HeJ8G+ieM+0i4Jxo+Bzgz9HwIOBBwIABwPPt/L2+B2xc730I7AZsB7zS1n0GrA28Fb2vFQ2vlXKMewNdouE/x2LsEy+Xs54XCM84t+hz7JdifBV9r2n/rxeKMWf+pcCQeu3Dtr6aqWawAzDF3d9y9y+AW4GD2jsId5/t7i9Fw4uA14ANiixyEHCruy9292nAFMJnqYeDgH9Fw/8CvhubfqMHzwHdzKxnO8W0JzDV3Ytdkd4u+9DdnyT/KX2V7rN9gEfc/QN3/xB4BNg3zRjd/WF3XxKNPkd4ImGiKM413P1ZD0e1G2Ofq+bxFZH0vab6v14sxujX/WHALcXWkeY+bKtmSgYbADNi4zMpfhBOnZn1AbYFno8mnRJV1YdmmhOoX9wOPGxmY81scDRtPXefDSGpAevWOUYID0OK/+M10j6EyvdZvf9OTyD8Ss3YxMxeNrNRZrZrNG2DKK6M9oixku+1nvtwV2COu0+OTWuUfVhUMyWDQu1xdTuv1sxWA+4ETnP3hcDVQF9gG2A2oaoJ9Yt7oLtvB+wHnGxmuxUpW5cYLTxK9UDg9mhSo+3DYpJiqlusZnY+sAQYHk2aDWzk7tsCZwA3m9kadYix0u+1nt/3EbT+cdIo+7CkZkoGM4HesfENgVn1CMTMViAkguHufheAu89x96Xu3gJcR7YZoy5xu/us6P194O4onjmZ5p/o/f16xkhIVC+5+5wo1obah5FK91ldYo06qg8AjoyaLYiaX+ZHw2MJ7fBbRDHGm5JSjbEN32u99mEX4HvAiMy0RtmH5WimZPAisLmZbRL9ojwcGNneQURtitcDr7n7ZbHp8Tb2g4HMmQojgcPNrKuZbQJsTuh4SjPGVc1s9cwwoYPxlSiWzNktxwL3xGI8JjpDZgDwUaZpJGWtfoU10j6MqXSfPQTsbWZrRc0he0fTUmNm+wK/AA50909j03uY2fLR8KaE/fZWFOciMxsQ/T0fE/tcacRX6fdar//1vYDX3X1Z80+j7MOy1LP3ur1fhDM43iRk5/PrFMMuhOrgBGBc9BoE3ARMjKaPBHrGljk/ivkN2uGMA8JZGOOj16TMvgLWAf4LTI7e146mG/C3KMaJQP92iHEVYD6wZmxaXfchITHNBr4k/PL7YVv2GaHdfkr0Or4dYpxCaGPP/D1eE5U9JPr+xwMvAd+Jrac/4aA8FbiK6G4GKcVX8fea5v96oRij6TcAJ+WUbfd92NaXbkchIiJN1UwkIiIJlAxERETJQERElAxERAQlAxERQclApCgzO9/C3WUnRHed3NHCHVJXqXdsIrWkU0tFEpjZTsBlwB7uvtjMuhPugjmacF3AvLoGKFJDqhmIJOsJzHP3xQDRwf9QoBfwuJk9DmBme5vZs2b2kpndHt13KvNMiD+b2QvRa7No+vfN7BUzG29mT9bno4m0ppqBSILooP404WrnR4ER7j7KzN4mqhlEtYW7CFe/fmJmvwC6uvtvonLXufvvzewY4DB3P8DMJgL7uvu7ZtbN3RfU5QOKxKhmIJLA3T8GtgcGA3OBEWZ2XE6xAYSHrDxj4elWxxIetJNxS+x9p2j4GeAGM/sx4UEsInXXpd4BiDQyd18KPAE8Ef2iz30MpREeRnNE0ipyh939JDPbEdgfGGdm23h0Z0uRelHNQCSBhWctbx6btA0wHVhEeGQphCeDDYz1B6xiZlvElvlB7P3ZqExfd3/e3YcA82h9u2WRulDNQCTZasBfLTwgfgnh7p6DCbfOftDMZrv7N6Omo1vMrGu03C8Jd8wE6GpmzxN+eGVqDxdHScYIdzId3y6fRqQIdSCLpCTe0VzvWERKUTORiIioZiAiIqoZiIgISgYiIoKSgYiIoGQgIiIoGYiICPD/Ad0SONj7cQZoAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2788,7 +868,7 @@
      "output_type": "stream",
      "text": [
       "============== Starting Testing ==============\n",
-      "============== Accuracy:{'Accuracy': 0.9613381410256411} ==============\n"
+      "============== Accuracy:{'Accuracy': 0.9553285256410257} ==============\n"
      ]
     }
    ],
@@ -2831,7 +911,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2915,19 +995,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Row 2, column 5 is incorrectly identified as 8, the correct value should be 3 \n",
-      "\n",
-      "Row 3, column 3 is incorrectly identified as 5, the correct value should be 8 \n",
+      "Row 1, column 7 is incorrectly identified as 2, the correct value should be 3 \n",
       "\n",
-      "Row 4, column 6 is incorrectly identified as 6, the correct value should be 0 \n",
+      "Row 4, column 3 is incorrectly identified as 0, the correct value should be 8 \n",
       "\n",
-      "[8 0 5 4 9 1 8 7 0 5 2 4 8 2 4 1 2 1 5 3 9 8 5 7 1 9 3 8 2 6 0 1] <--Predicted figures\n",
-      "[8 0 5 4 9 1 8 7 0 5 2 4 3 2 4 1 2 1 8 3 9 8 5 7 1 9 3 8 2 0 0 1] <--The right number\n"
+      "[2 7 7 0 6 3 2 3 2 5 2 1 8 7 8 3 0 5 2 1 0 8 2 2 1 8 0 3 6 8 8 2] <--Predicted figures\n",
+      "[2 7 7 0 6 3 3 3 2 5 2 1 8 7 8 3 0 5 2 1 0 8 2 2 1 8 8 3 6 8 8 2] <--The right number\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 32 Axes>"
       ]
@@ -2959,7 +1037,7 @@
     "        index = 0\n",
     "        print(\"Row {}, column {} is incorrectly identified as {}, the correct value should be {}\".format(int(i/8)+1, i%8+1, pred[i], labels[i]), '\\n')\n",
     "if index:\n",
-    "    print(\"All the figures in this group are predicted correctly！\")\n",
+    "    print(\"All the figures in this group are predicted correctly!\")\n",
     "print(pred, \"<--Predicted figures\") \n",
     "print(labels, \"<--The right number\")\n",
     "plt.show()"
@@ -2984,13 +1062,13 @@
      "output_type": "stream",
      "text": [
       "Figure 1 probability of corresponding numbers [0-9]:\n",
-      " [-3.4469228   2.0546532  -3.5465317  -1.3858355  -0.28758872  1.1322775\n",
-      " -0.8353202  -3.6402948   8.3553      1.3656969 ]\n"
+      " [-0.48477417  2.0016153  11.054499    2.3544474  -2.7436607  -3.630352\n",
+      " -3.7523592   0.9330094   2.6389365  -6.602851  ]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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moSlnKhEiPuzFD6e5LiXbFFGxKkLRIa7riJ+Jjfvlsq8uH7g5uXNOa2cl0JeAf4OD547bzJkzV/Tq1Svja505c2brXr16dart8fpx1q170MBNoqHYaapVL8raBJwBdPG+bj7AY7/BLtzVDegOrPB+PgToid3ns9L9wJt1qvCqMzZNdBS4AIcCf9UTa27pi++KyInAKNdl5JZrgff2+dnvsO9ri72vvzvAY68G7gLmA1Ox4T3Lu28WdoPlrcC33v0XxF1dxzblX/zl3uU1rRyWamcC/+u4hrymoeuCSAvg7+iYepKdAuw7o+dN4Brv+2uwF/rtax4QwfaEwQ6vNwKKgDIgBlQABcB9wK/irqxeYWzFjBfmdfUJEveDk28UwZDr8M9bGrpuvAB0cF1EflgHVG680A5YX80xi4DmwMXYpS7uAqLYoYYO2GVrLweWAIb4l8MwOyc/vSDcvEm0Wdzlp4YPGKM7T7ihoZtuItcAF7kuozrVjYiOAtoDvb3buwd47BPe43qw96TQn2NHRK+u8rOXveMzRwQ7fPAI8CWwDHjJu+9xYAZwB/B/2F7uA9gQfq5WrT9686qZ/qN2dUlqyYnrDDzouoh8pKGbTiJtgd+7LuNArmX/EVGAEdjYmUH1q6jPwcbPVGAm8G/sCOpW4DPsiGgUmI39sP4ScHNSK/8+bbDjsHhfS6o55hBs7/Uw7IjPhcD0fY55E/ADO7G/8Xjs28eu7332gX22BW+/fF2mLlz0U4Khga6LyDc6pphGowOM/L8gzTP1ne4U9pyzj8d84HjsKChAAPgX8FPsSKjBhm0R8DDwM+/79Dgf+Asw0vta3QmwfthZDaXY3ew/wgZspTC2b175dlI5LFs51lv98gYtm0Zm/vfhRScm/CukjgAvEgwdQ8C/w3UxNREhqbM+jKHGeb/3339/ydixY4uNMVx99dWl9913X3XjU3HJ1P//OUdGy4WjBjK85G5mzi2uU7Y58xR2iGAo1U+4OhqYCGzE9vvexU4IbQpcgu1DdgaaYT+8x3/ev7auAAYAC7G91xewYfsBdsrYB96fAULA9d73BdihhdOAY7BvEzdUafdp7Em4RthXwnjHnYgdC96fz2fWzRozt11RYcZ3bDpx4Ckdee3LL79sMHbs2OLp06fPnz9//tz33nuv+ezZs+sn2q6GbhrIaGkCPAmwsRG9j76ZttdcSDDsI+K4tBr9FFiKHVpohx3Z3Fc37NjtGcBZQC/2fIS623vso+wZEX0eOyL666RX+wp2CCEMrAKGUbmhgu2hTmDP7Aa/V0mlM7ADIbOxAyBVd7e/jT0zIMR7ntkceEjUhN99cPH69q3D1Y1lZKKf6h5r+5s9e3bDvn377mjatGmsqKiIE088cfu4ceOqf5eNg4ZueozCdr0socHY3gSaj2Tppx3I6BX+22D7gT5s32/qAY4bhh0FnYiNtX3PGn3lfT0SGIsdEZ2DjcJcM/yS9VPO7LetthtLZgIfmXZuMwP07t277Isvvmi6du3agu3bt/s++OCDZitXrqxX8yO/n4Zuislo6QxUu9jIrnocdcp1HPmDHxPcWVTDGRlHvq3y/b/Ye2ZDVZUDXd8Ar7P/PvGVvdww9qQa2H98GflLJ6B7x7LJj9+68hTXddTBKQRDl7suIpP07dt39/Dhw9cOGjToyIEDB3bp3r37rsLCxEeLNHRT7wH2/qy6N6Hgg8MJtBjJhn923++UeVpVNyJ6N3b0sifwMXumXqxh75kMl2Avnh2MHQFtUeW+N7Cnqg7GjoAO8NoU7FBErmhYP7po6p/nZ/Oedg8TDDV0XUQmGTFixIZ58+bND4VCC1u2bBnt0qWLrjKWyWS0+IEf1ebYcAEdLr+cDr3WMmnCXzi6VdkBztCk0CvV/GzYAY49mL3n7H76Pe1e6N0qPeLdcolgtk5/bn79xg1i2bxTQwfs++xo14VkitWrVxe2b98+snjx4nrvvPNO86lTpy5ItE0N3dR6GOK77HNmW04quYvSx/7LlOFfMCBFdamkMmbMyBWLunbYna71cVPpLoKhZwj4E17CMNlqM8Ur2c4///zDt2zZUlhYWGgef/zxb4qLi6M1P+r7aeimiIyWc4FT6/LYmI/i286m+LEBTP30RQ7tsO2761hVBrrklC3Ba87aeKrrOpKkMXAndkJK3ps2bdrCZLepY7qp85tEG/imOf07jqDxnWcwMWYnh6oMc3CritC4UUuz8cTZ97mFYCjZG2Uqj4ZuCshoOQd77ikJjXHQoydySuufM3tmG5YlpU2VFIUFsZWzxsw9osCXc/+PGlP9lGyVBLn2jyVT3J3sBjc3pGfvn9D+yksIVvgIJ7t9FS9TNvEPC3e1Oiia9hOeafI/BEOtHNcQi8VimbAU5gF59cXieYyGbpLJaOmPXX4gBY1T/5VjCDS/hxUfd2JuSp5D1cqvh62eNqDHzqNc15FCTYDbHdcwp7S0tFmmBm8sFpPS0tJm2Ot8ak33SEsyGS2vYRdmTS1DbOAKPn3r7/ibhGmc8ufLMC73SBvQY8fEz55ekGvjuNXZBBxKwO/kGpZp06aVFBYWPo+9JicTO4gxYE4kErn+2GOPrfVCOBq6SSSjpQuwgDT+AymMsmrsv1h7xZy9lsXKea5Ct1njyJx1b8w8sn6RSfhy0CxxEwH/s66LyCWZ+O6RzUaQ5tc0UsAhV16K/+ibmVzaiE3pfO584xNTOuvFea3yKHDhAJewq7rT0E0SGS0NgStdPf/cEk5scxexh0/gM1c15DYTff3+pas7tKnItznTRxMMney6iFyioZs8F2OXjHXGCK3v/gEnHHI7Xy5rzmqXteSaG87bMOmCk7bk6/KHP3FdQC7R0PWIyAoRmS0iM0QkVIcmhia9qDpafRD9Dh9Os9vOYmJU4pvOovZ3RPvdU5698+t83j33EoKhfbdZVnWkobu3gcaY3saYuE5KyWjpBGTWXlNCkyeO55RWP2fuV21Z6rqcbNWgXmzp9OfmZdPauKlQH7uQnEoCDd3kuJY4F7ZJl60NOKbvTRx6+WUEywuocF1PdjHbp/5pvq9po5huVb7/EsmqjjR09zDA+yIyTURurO2DZLQIe/ZyyUxCvX/2INB8JCs/OIzZrsvJFs+M+GbuMYeVdXZdR4YIEAzl20nElNDQ3eNEY0xf4GzgFhGp7eT3/tjN/TLe7iIO/8GP6XHydQS31yPjd3916Zzjtnzy0wtKj3ddRwbxYbe2UwnS0PUYY9Z4X9djd6bpX8uHXpSyolJB8E3qSKDFSLaN7cmXrsvJRCXNw9Pf/M0SnSa1Px1iSAINXUBEGotI08rvgR9Q++upsyt0PVEfB19zMf263cJn6xqzwXU9maLAZ9bMenFuh8ICClzXkoGOIxjq4LqIbKeha7UBJonITOyGt+8YY96r6UEyWo7CbnCbtRYUc0K7O/H99iQmu67FPVP+4WMLt7RpGdG1ZA/sLNcFZDsNXcAYs8wY08u79TDGPFDLh56b0sLSxAgt7z2dE9vdQWhJS1a5rseVe4asnXpq7x3dXdeR4c50XUC209BNTE6EbqW1TfF3uZUWN59DMN8uqujTZeenv7lhtY7j1mwQwZAOvSRAVxmrI2+thS183/bqWaxpOXMn/IV6/dbQxXUt1UnmKmNNGkbnr39jRueG9U2DZLSXB04k4Nc1PupIe7p1158cDVyA7fXp0f8GOl30Qz7ZXUC563pSRcRsmvH8vKYauHHRIYYEaOjW3YmuC0g5oeiNbpza/B5Wv3sEs1yXk3wm9o/7li0/vH25k8XQs9jprgvIZhq6dZf7oespL+Swc4dwzIBhfLq1Pttc15MsQ87Y9OnlAzcf67qOLNSXYKjQdRHZSsd068C79Hcj0CKtTzwFmO593wa4ACiqcv8W4A1gN3YjkdOxE9q+Af4NFGKXLWkFlAGvAlcR16oRBTG+ffYtVg6dUeuLR1Ii0THdDiXlU5ePm93PJ5m5ZkYWOJaAf3rNh6l9aU+3brqR7sDdBnwB3Ajcgrc70z7HTAR6YFc/vRR4x/v5Z8APgdOAUJVjTybuZXqiPtoNu5D+R97KlDVNKI3798gA9QpjK2a+OO8oDdyEOH3TzWYaunXjZmghBoSBqPe16T73C3x3yqu8yv0F3vFh7N/4JmyId6p7KYtbMeCQOygcHWBS3Vtxweyc/PSCcPMmUacLzueAfq4LyFYaunWT/h0EDgJOAH4PPAo0AI7Y55hTgVne/X8DzvF+fhLwNvA5tn8yARiUeElGaDFqICe1uZPpC1vxTeItpt6jN6+a6T9qV0ZOg8sy2tOtIw3duumW9mcsw+4zfBtwB1ABzNznmNnYt4M7gCHA69jecTvgBuyqv5uxPWAD/BN4DRJdb2x9E/p2/R9a3ziYYESIJtZa6gzssy14++XrTnBdR47oTjDU0HUR2UhDt266pv0Zl2FHkRtjhwu6ASv3OeYr7JguwKFABNhV5X6DHcsNAEFsz7gndqw4UUKj544l0GIkiz5vz8IktJhULZtGZv734UV5M+MkDXzs/1lL1YKGbpxktDTD9h3TqxmwCtvDNcByoLiaY5Z535diQ7dxlftnYGczNMSO74p3CyevzB316Tbgeg4/7wqCZYXsTl7LdefzmXWzxsxtV1SITnNKLh2mqQMN3filv5cLcAjQHfgz8Aw2eI8FPsIOO4BdkHI68EfsdLAL2TM7oXI4ovL0xwBgPHZ8N64d4WpBKHznKALNR7L2rSOZkeTW42TC7z64eH371uESt3XkJA3dOtB5unGS0XItMMZ1HVnDYPqtYdL7Y+nZvDx5W9TXdp7u8EvWTXz81pW13QVExecFAv7rXReRbbSnG7+sXj837QT5sj0nt/45Zc8em5TR41rr3rFssgZuSmlPtw40dON3sOsCslHUR9ubBnPc4T/j81VNWZfq52tYP7po6p/n90n18+Q5PZFWBxq68WvjuoBstqwlx3e4nQa/GMinqXoOwWyd/tz8+o0bxBql6jkUACUEQ3pVX5w0dOOnJ2QSZIRmDwQ4ufguvprfmq+T3LoZM3LFoq4ddndMbruqGoWk+3L4HKChGz/t6SbJhsb06X4LJUMvIBj2EUlGmxefsmXiNWdt1EtU02ffiYuqBhq68dOebjIJDcf0IdBiJEsmHcr8RJpq16oiNH7UUt1yJ710E884aejGQUZLC/ZeTFElyc56dD15KEeedRWf7CqkLN7HFxbEVs4eM/eIAp/+m04z7enGSf+BxqeV6wJymlDw3yM4tfk9rH+9K1/V/oGmbOIfFu5qdVC0eeqKUwegPd04aejGR8+Gp0G4gI6X/Ig+fW5i0saGbKnp+PuHrpk2oMfOo9JRm9pP45oPUVVp6MZHNy9MoxntOKnkLiqe6s+UAx0zoMeOib+4+tuT0lmX2ouuZxEnfcHicG6rkwoa+upPL49V+MpjFQXlsXBBuQkXlMcqCipi4YIKEy6siIUL7ddIYYUJF4ZNpCgcixRFiRW4rj8bxXyU3HoOJY8O4IuJY+h46DbaVt7XrHFkzsePLzzeZX1Kz3HES0M3Dv8+5vcNgL51eayxi1yEgbCBCJhwDBMxxkQMJhojFokZE42aWDRKLBo10WjUxGJRE41GTDQWNpFYxERN2ERiYRMx4VjYVJiIqYiFTYUJUxELm3L7lfJYBeWxsJSbCnbHwlIeq6i8+XYb+7U8Fvbt3vPm4Ss39k2kIlZRUG7fPKq8iUQKK2LeG4iJFEVMNO3/bla04LgOI9h692Q+/e2HnNSwMLxl1otLWtUvMvXSXYvai2ZInPQFi0+dh2NERIB6QD17CY/YxrL0eh5jTOUGQGGDiRhMJGa8r5hozMSiMWIR+8Zh30QiJhrbc4vEwvbNxIRN2FTEIqbChE1FLEzl1/JYmHITxnvDsG8knStk/HkF73xUtKR9hzYV6V9iU+1LMyRO+oLFR8fAPSJShPfRsnJ/x4K0voFUpPPJ1IHp8EKcNETio//TldpbzHUB2UZDNz47XRegVIZJcIe9/KOhGx/9B6bU3vT/RJw0dOOjPV2l9qahGycN3fho6Cq1t+2uC8g2Grrx0Xd1pfam/yfipKEbj4C/Au3tKlXVVtcFZBsN3fitdjuzRrcAAArRSURBVF2AUhlkresCso2GbvxWuS5AqQzyresCso2Gbvy0p6uUtcEbclNx0NCNn/Z0lbKSvKloftDQjZ+GrlKWhm4daOjGb4XrApTKEMtdF5CNNHTjN891AUplCP2/UAcauvH7Gr0KRymA2a4LyEYauvEK+A0w13UZSjkWQ/8f1ImGbt3McV2AUo4tI+Df5bqIbKShWzcauirf6dBCHWno1s1M1wUo5ZiGbh1p6NbNVOymjErlqymuC8hWGrp1YceyprsuQylHosBk10VkKw3dupvougClHJlBwK/TJutIQ7fuPnVdgFKOaIcjARq6dTcZMK6LUMoBDd0EaOjWVcC/CT2Dq/KPQT/lJURDNzH/dl2AUmn2JQH/RtdFZDMN3cS85boApdLsDdcFZDsN3cRMRbcrUflFQzdBGrqJsIvfvO26DKXSZBEB/3zXRWQ7Dd3E6RCDyhdvui4gF2joJm4CsM11EUqlgQ4tJIGGbqIC/t3AeNdlKJViS9H1FpJCQzc5xrguQKkUe8k7h6ESpKGbDAH/Z8Ai12UolSIx4CXXReQKDd3kecl1AUqlyAcE/KtcF5ErNHSTZyy2R6BUrtHhsyTS0E2WgH818K7rMpRKso3orIWk0tBNrsdcF6BUkv2RgL/cdRG5REM3mQL+j4FprstQKkl2A0+6LiLXaOgm36OuC1AqScYS8K93XUSu0dBNvn8CX7suQqkExdAOREpo6CZbwB8BnnBdhlIJeouAX+eep4CGbmo8C+jHMpXNfue6gFyloZsKAf9O4Leuy1Cqjv5FwP+F6yJylYZu6vwR0Kt4VLaJAve6LiKXaeimip3bOMp1GUrF6SUC/gWui8hlGrqp9RIwx3URStXSbrSjkHIauqkU8EeBu12XoVQtPakL26Sehm6qBfz/Qa9dd2rL9u1cet/P6frjS+l29WVMmTvru/se+cfLyKn92LBly36P+/irEL2HXfndrcEZJ/LGp58AMOTXv6Dn0Cu497mnvzv+/rHP8+akYMp/nxRZDdzvuoh8UOi6gDzxM+B0oInrQvLR8Kce5az+A3j1Vw9SEQ6za/duAFauX8sH06bSoU3bah83sI+fGS/8HYBN27ZyxJCL+UG/45m1dDEAs158hZNvvYGtO3awq3w3U+fP5f+uvj49v1TyDSfg3+66iHygPd10CPhXomNlTmzbuYOJM79i2LkXAFCvqIjmTZsCMOKp3/PQTbciSI3tvBqcwNnHDaBRgwYUFRZSVl5OLBajIhKmwOfjvhf/xK+G/iSlv0sKvUPA/5rrIvKFhm76PAHMqvEolVTL1qymuHlzrvvdaPpcP4TrH/o1O8vKeGtykPbFxfQ64shatfOPjz7gikFnAtCtY2c6lLSl7w1Xcfmpp7Nk9UqMgT5djkrlr5Iqu4BbXBeRT3R4IV0C/gjB0E+AyVCLrpVKikg0yvRFC3nyZ3dxXPejGf7kI4x66VkmzvqK9x9+qlZtfLtxA7OXLeHM/gO++9njt97x3feD7xnBn++4lwdefpGZSxdzhr8/N5x3UdJ/lxT5FQG/rhWSRtrTTaeAfwq65m5aHVJcwiHFJRzX/WgALg2cxvTFC1j+7Rp6DbuSTj88n1Wl6+l741Ws3bih2jbGf/wBF518KkWF+/dR3pwUxH9Ud3buLmPO8qWMH/VbXn7/P9+NG2e4aeiiNmmnoZt+9wIzXReRL9q2as2hJW1Y+M0KACZM+5K+Xbqy/o33WTHuLVaMe4tDikuY/uxfaduqdbVtvDLhfa447cz9fh6ORHjitX9w149+zK7duxGxH2BisRgV4XDKfqckKQOu8hZoUmmkoZtuAX8FcCV2IrpKgyd/didDfn0fPYdewYwli7j3qusOeGxowTyuf+jX3/15xbdrWFm6jkCvvvsd+/S/xnPNmefSqEEDeh7eBWMMx1z3I048ptd3J+sy2N165ZkbYoxuZe9EMHQr8AfXZai89G8C/sGui8hX2tN1JeB/EnjPdRkq76wBDtzVVymnoevW1cA3rotQeSMCXEnAX/0ZQ5UWGrouBfylwEXYkxpKpdptBPxZe51yrtDQdS3gnw4Mc12GynnPEfA/XfNhKtU0dDNBwP8K8LDrMlTOmoRedZYxNHQzx0jgv66LUDlnJXAJAX/GTxzOFxq6mSLgjwGXAdNdl6JyxlZgMAG/bpKaQTR0M4ldWu9sYLHrUlTWKwPOI+DXqx8zjIZuprG9kjOwi0orVRdh4FIC/kmuC1H709DNRHbVpzOBTa5LUVnHANcS8L/ruhBVPQ3dTBXwzwXOBba5LkVllZ8R8P/ddRHqwHTthUwXDPXDzmpo4boUldEMcDMB/59cF6K+n4ZuNgiGegIfACWuS1EZKQpcR8D/sutCVM00dLNFMNQV+BBo77oUlVEqgCsI+F93XYiqHQ3dbBIMHYYN3s6uS1EZoQy4mIBfV6vLInoiLZsE/MuA44EprktRzq0DBmngZh8N3Wxj5/EOBPQMdf6aBfQn4P/cdSEqfjq8kM2CofuAUejuwvnkbeyauDtcF6LqRnu62Szg/xVwBboeb754GLhQAze7aU83FwRDRwPjgW6uS1EpsRW4kYB/vOtCVOK0p5sLAv45gB8Y47oUlXRTgT4auLlDe7q5Jhi6Cvgj0MR1KSohBngE+F9dCze3aOjmomDoSOBv2N6vyj7rsIvW6HSwHKTDC7ko4F+Enc97D1DuuBoVnzFANw3c3KU93VxnLx9+FjjZdSnqey0FbiLgn+C6EJVa2tPNdQH/AiAA3AhscVyN2l8UO3Z7jAZuftCebj4JhlpjL6a4CSh0W4zCrqNxp26pk180dPORHXJ4CBjsupQ8NRe4i4D/P64LUemnoZvPgqFBwKNAb9el5Im1wC+BFwj4o66LUW5o6Oa7YEiAi4F7gb6Oq8lV67Fvbs/oJbxKQ1ftEQydhQ1fnemQHCuBx4BnCfh3uS5GZQYNXbW/YOhkYCRwFjrDpS5mYxeneYWAP+K6GJVZNHTVgQVDnYAbgKFAW7fFZLwy7KJDzxLwf+a6GJW5NHRVzYKhIuAC7FSz09D1e6uag7345GUCfp0HrWqkoaviEwx1BC71bseRnwG8DHgNeJWAf6rrYlR20dBVdRcMHcqeAB5AbgfwIuBV4DUC/umJNiYiI4DrsauJzQauM8bsTrRdlfk0dFVyBEMl2L3bTvNuh7ktKGGbgE+Aj4AJ3uXUSSEi7YFJQHdjTJmIjAfeNca8lKznUJlLLwVVyWE3zBzn3SpPwp0GnAIcC3QFChxVVxsrgenARGzQziTgT2WPpBBoKCJhoBGwJoXPpTKI9nRVegRDDYFe2Asw+njfHwa0SnMlZcBy7I6604GvgK8I+DemswgRGQ484NXzvjFmSDqfX7mjoavcCoaaAp2xAdwZ6ASUYMO4FdAcOMi71TtAK2HsPmJb9rltBL7GhuwK77YuxT3YGolIC+yJuB9i6/wn8Kox5q8u61LpoaGrVJqJyGXAWcaYYd6frwaON8bc7LYylQ56tZFS6fcNcLyINBIRwY59z3dck0oTDV2l0swY8wV2+tl07HQxH/YCC5UHdHhBKaXSSHu6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRhq6SimVRv8PxQO2cgY9S64AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3003,13 +1081,13 @@
      "output_type": "stream",
      "text": [
       "Figure 2 probability of corresponding numbers [0-9]:\n",
-      " [10.008167   -5.861808    1.4555234  -1.482836   -2.721113   -0.8507886\n",
-      "  3.199972   -2.2156045   0.32849124 -1.4580953 ]\n"
+      " [-1.916862   -0.16948226 -0.2352289  -0.5903556   0.8726251  -0.41480547\n",
+      " -3.0238853   4.210627   -0.70848167  1.8492212 ]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md
index 5df1e2da144f9a23353310778f83d36760d53431..1ad1416adb435c816444792446b600465af63990 100644
--- a/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md
+++ b/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md
@@ -14,6 +14,8 @@
 <!-- /TOC -->
 
 <a href="https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/advanced_use/customized_debugging_information.md" target="_blank"><img src="../_static/logo_source.png"></a>
+&nbsp;&nbsp;
+<a href="https://gitee.com/mindspore/docs/blob/master/tutorials/notebook/customized_debugging_information.ipynb" target="_blank"><img src="../_static/logo_notebook.png"></a>
 
 ## 概述