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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# MindInsight之标量、直方图和图像\n",
+ "\n",
+ "MindInsight可以将神经网络训练过程中的损失值标量、直方图、图像信息记录到日志文件中，通过可视化界面解析以供用户查看。\n",
+ "\n",
+ "整体流程:\n",
+ "\n",
+ "1. 下载MNIST数据集。\n",
+ "\n",
+ "2. 原始数据预处理。\n",
+ "\n",
+ "3. 初始化`lenet`网络。\n",
+ "\n",
+ "4. 执行主程序，使用`SummaryCollector`记录图像信息、损失值标量、权重梯度等参数，启动MindInsight服务。\n",
+ "\n",
+ "5. 在MindInsight可视化面板中查看结果。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 数据集操作\n",
+ "\n",
+ "本次流程用到MNIST数据集，MNIST数据集是一个手写的数据文件，数据库里的图像都是28x28的灰度图像，每个像素都是一个八位字节，包含了60000张训练图像和10000张测试图像，常被用作神经网络训练和测试任务的数据集。\n",
+ "\n",
+ "\n",
+ "\n",
+ "## 下载MNIST数据集\n",
+ "\n",
+ "下面一段代码分为两部分：\n",
+ "\n",
+ "1. 判断是否存在MNIST数据集目录，不存在则创建目录，存在则跳至[**数据预处理**](#数据预处理)。\n",
+ "\n",
+ "2. 判断是否存在MNIST数据集，不存在则下载MNIST数据集，存在则跳至[**数据预处理**](#数据预处理)。\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import urllib.request\n",
+ "from urllib.parse import urlparse\n",
+ "import gzip\n",
+ "\n",
+ "def unzipfile(gzip_path):\n",
+ " \"\"\"unzip dataset file\n",
+ " Args:\n",
+ " gzip_path: dataset file path\n",
+ " \"\"\"\n",
+ " open_file = open(gzip_path.replace('.gz',''), 'wb')\n",
+ " gz_file = gzip.GzipFile(gzip_path)\n",
+ " open_file.write(gz_file.read())\n",
+ " gz_file.close()\n",
+ "\n",
+ "\n",
+ "def download_dataset():\n",
+ " \"\"\"Download the dataset from http://yann.lecun.com/exdb/mnist/.\"\"\"\n",
+ " print(\"******Downloading the MNIST dataset******\")\n",
+ " train_path = \"./MNIST_Data/train/\"\n",
+ " test_path = \"./MNIST_Data/test/\"\n",
+ " train_path_check = os.path.exists(train_path)\n",
+ " test_path_check = os.path.exists(test_path)\n",
+ " if train_path_check == False and test_path_check ==False:\n",
+ " os.makedirs(train_path)\n",
+ " os.makedirs(test_path)\n",
+ " train_url = {\"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\", \"http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\"}\n",
+ " test_url = {\"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\", \"http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\"}\n",
+ " for url in train_url:\n",
+ " url_parse = urlparse(url)\n",
+ " # split the file name from url\n",
+ " file_name = os.path.join(train_path,url_parse.path.split('/')[-1])\n",
+ " if not os.path.exists(file_name.replace('.gz','')):\n",
+ " file = urllib.request.urlretrieve(url, file_name)\n",
+ " unzipfile(file_name)\n",
+ " os.remove(file_name)\n",
+ " for url in test_url:\n",
+ " url_parse = urlparse(url)\n",
+ " # split the file name from url\n",
+ " file_name = os.path.join(test_path,url_parse.path.split('/')[-1])\n",
+ " if not os.path.exists(file_name.replace('.gz','')):\n",
+ " file = urllib.request.urlretrieve(url, file_name)\n",
+ " unzipfile(file_name)\n",
+ " os.remove(file_name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 数据预处理\n",
+ "\n",
+ "好的数据集可以有效提高训练精度和效率，在加载数据集前，会进行一些处理，增加数据的可用性和随机性。下面一段代码定义`create_dataset`函数进行数据处理操作。\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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",
+ "\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",
+ " # define dataset\n",
+ " mnist_ds = ds.MnistDataset(data_path)\n",
+ "\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) # Bilinear mode\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=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) # 10000 as in LeNet train script\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": [
+ "# 网络初始化\n",
+ "\n",
+ "在进行训练之前，需定义神经网络模型，本流程采用最简单的[LeNet卷积神经网络](http://yann.lecun.com/exdb/lenet/)。\n",
+ "\n",
+ "LeNet网络不包括输入层的情况下，共有7层：2个卷积层、2个下采样层（池化层）、3个全连接层。每层都包含不同数量的训练参数。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import mindspore.nn as nn\n",
+ "from mindspore.common.initializer import TruncatedNormal\n",
+ "\n",
+ "\n",
+ "def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\n",
+ " \"\"\"weight initial for conv layer\"\"\"\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",
+ " \"\"\"weight initial for fc layer\"\"\"\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",
+ " \"\"\"\n",
+ " Lenet network\n",
+ "\n",
+ " Args:\n",
+ " num_class (int): Num classes. Default: 10.\n",
+ "\n",
+ " Returns:\n",
+ " Tensor, output tensor\n",
+ "\n",
+ " \"\"\"\n",
+ " def __init__(self, num_class=10, channel=1):\n",
+ " super(LeNet5, self).__init__()\n",
+ " self.num_class = num_class\n",
+ " self.conv1 = conv(channel, 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, self.num_class)\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": [
+ "# 记录标量、直方图、图像\n",
+ "\n",
+ "在主程序中应用`SummaryCollector`来记录标量、直方图、图像信息。\n",
+ "\n",
+ "## 运行主程序\n",
+ "\n",
+ "在MindSpore中通过`Callback`机制提供支持快速简易地收集损失值、参数权重、梯度等信息的`Callback`, 叫做`SummaryCollector`。详细的用法可以参考API文档中`mindspore.train.callback.SummaryCollector`。 \n",
+ "\n",
+ "1. 为了记录损失值标量、直方图、图像信息，在主程序代码中需要在`specified`参数中指定需要记录的信息。\n",
+ "\n",
+ " ```python\n",
+ " specified={\"collect_metric\": True, \"histogram_regular\": \"^conv1.*|^conv2.*\", \"collect_input_data\": True}\n",
+ " ```\n",
+ "\n",
+ " 其中：\n",
+ " - `\"collect_metric\"`为记录损失值标量信息。\n",
+ " - `\"histogram_regular\"`为记录`conv1`层和`conv2`层直方图信息。\n",
+ " - `\"collect_input_data\"`为记录图像信息。\n",
+ "\n",
+ "2. 实例化`SummaryCollector`，并将其应用到`model.train`或者`model.eval`中。\n",
+ "\n",
+ "程序运行过程中将启动MindInsight服务并自动遍历读取当前notebook目录下`summary_dir`子目录下所有日志文件、解析进行可视化展示。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import mindspore.nn as nn\n",
+ "from mindspore import context\n",
+ "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor\n",
+ "from mindspore.train import Model\n",
+ "from mindspore.nn.metrics import Accuracy\n",
+ "from mindspore.train.callback import SummaryCollector\n",
+ "from mindspore.train.serialization import load_checkpoint, load_param_into_net\n",
+ "\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " device_target = \"GPU\"\n",
+ " summary_base_dir = \"./summary_dir\"\n",
+ " context.set_context(mode=context.GRAPH_MODE, device_target=device_target)\n",
+ " download_dataset()\n",
+ " ds_train = create_dataset(data_path=\"./MNIST_Data/train/\")\n",
+ " network = LeNet5()\n",
+ " net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+ " net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)\n",
+ " time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
+ " config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)\n",
+ " ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", config=config_ck)\n",
+ " model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()})\n",
+ " os.system(f\"mindinsight start --summary-base-dir {summary_base_dir} --port=8080\")\n",
+ " # Init a SummaryCollector callback instance, and use it in model.train or model.eval\n",
+ " specified = {\"collect_metric\": True, \"histogram_regular\": \"^conv1.*|^conv2.*\", \"collect_input_data\": True}\n",
+ " summary_collector = SummaryCollector(summary_dir=\"./summary_dir/summary_01\", collect_specified_data=specified, collect_freq=1, keep_default_action=False)\n",
+ " print(\"============== Starting Training ==============\")\n",
+ " # Note: dataset_sink_mode should be set to False, else you should modify collect freq in SummaryCollector\n",
+ " model.train(epoch=3, train_dataset=ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor(), summary_collector], dataset_sink_mode=False)\n",
+ " print(\"============== Starting Testing ==============\")\n",
+ " param_dict = load_checkpoint(\"checkpoint_lenet-3_1875.ckpt\")\n",
+ " load_param_into_net(network, param_dict)\n",
+ " ds_eval = create_dataset(\"./MNIST_Data/test/\")\n",
+ " acc = model.eval(ds_eval, callbacks=summary_collector, dataset_sink_mode=False)\n",
+ " print(\"============== {} ==============\".format(acc))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# MindInsight看板\n",
+ "\n",
+ "在本地浏览器中打开地址：`127.0.0.1:8080`，进入到可视化面板。\n",
+ "\n",
+ "\n",
+ "\n",
+ "在上图所示面板中可以看到`summary_01`日志文件目录，点击训练看板进入到下图所示的训练数据展示面板，该面板展示了标量数据、直方图和图像信息，并随着训练、测试的进行实时刷新数据，实时显示训练过程参数的变化情况。\n",
+ "\n",
+ "\n",
+ "\n",
+ "## 标量可视化\n",
+ "\n",
+ "标量可视化用于展示训练过程中标量的变化趋势情况，点击打开标量信息展示面板，该面板记录了迭代计算过程中的学习率（下图左侧所示）和损失值（下图右侧所示）标量信息。\n",
+ "\n",
+ "\n",
+ "\n",
+ "如下图的loss值标量可视化信息——标量趋势图。\n",
+ "\n",
+ "\n",
+ "\n",
+ "上图展示了神经网络在训练过程中loss值的变化过程。横坐标是训练步骤，纵坐标是loss值。\n",
+ "\n",
+ "图中右上角有几个按钮功能，从左到右功能分别是全屏展示，切换Y轴比例，开启/关闭框选，分步回退和还原图形。\n",
+ "\n",
+ "- 全屏展示即全屏展示该标量曲线，再点击一次即可恢复。\n",
+ "\n",
+ "- 切换Y轴比例是指可以将Y轴坐标进行对数转换。\n",
+ "\n",
+ "- 开启/关闭框选是指可以框选图中部分区域，并放大查看该区域， 可以在已放大的图形上叠加框选。\n",
+ "\n",
+ "- 分步回退是指对同一个区域连续框选并放大查看时，可以逐步撤销操作。\n",
+ "\n",
+ "- 还原图形是指进行了多次框选后，点击此按钮可以将图还原回原始状态。\n",
+ "\n",
+ "\n",
+ "\n",
+ "上图展示的标量可视化的功能区，提供了根据选择不同标签，水平轴的不同维度和平滑度来查看标量信息的功能。\n",
+ "\n",
+ "- 标签：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的标量信息。\n",
+ "\n",
+ "- 水平轴：可以选择“步骤”、“相对时间”、“绝对时间”中的任意一项，来作为标量曲线的水平轴。\n",
+ "\n",
+ "- 平滑度：可以通过调整平滑度，对标量曲线进行平滑处理。\n",
+ "\n",
+ "- 标量合成：可以选中两条标量曲线进行合成并展示在一个图中，以方便对两条曲线进行对比或者查看合成后的图。\n",
+ " 标量合成的功能区与标量可视化的功能区相似。其中与标量可视化功能区不一样的地方，在于标签选择时，标量合成功能最多只能同时选择两个标签，将其曲线合成并展示。\n",
+ "\n",
+ "## 直方图可视化\n",
+ "\n",
+ "\n",
+ "直方图用于将用户所指定的张量以直方图的形式展示。点击打开直方图展示面板，以直方图的形式记录了在迭代过程中所有层参数分布信息。\n",
+ "\n",
+ "\n",
+ "\n",
+ "如下图为`conv1`层参数分布信息，点击图中右上角，可以将图放大。\n",
+ "\n",
+ "\n",
+ "\n",
+ "上图展示直方图的功能区，包含以下内容：\n",
+ "\n",
+ "- 标签选择：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的直方图。\n",
+ "\n",
+ "- 纵轴：可以选择步骤、相对时间、绝对时间中的任意一项，来作为直方图纵轴显示的数据。\n",
+ "\n",
+ "- 视角：可以选择正视和俯视中的一种。正视是指从正面的角度查看直方图，此时不同步骤之间的数据会覆盖在一起。俯视是指偏移以45度角俯视直方图区域，这时可以呈现不同步骤之间数据的差异。\n",
+ "\n",
+ "## 图像可视化\n",
+ "\n",
+ "图像可视化用于展示用户所指定的图片。点击图像展示面板，展示了每个step进行处理的图像信息。\n",
+ "\n",
+ "\n",
+ "\n",
+ "下图为展示`summary_01`记录的图像信息。\n",
+ "\n",
+ "\n",
+ "\n",
+ "通过滑动上图中的\"步骤\"滑条，查看不同步骤的图片。\n",
+ "\n",
+ "\n",
+ "\n",
+ "上图展示图像可视化的功能区，提供了选择查看不同标签，不同亮度和不同对比度来查看图片信息。\n",
+ "\n",
+ "- 标签：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的图片信息。\n",
+ "\n",
+ "- 亮度调整：可以调整所展示的所有图片亮度。\n",
+ "\n",
+ "- 对比度调整：可以调整所展示的所有图片对比度。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 对比看板\n",
+ "\n",
+ "对比看板可视用于多个训练之间的标量数据对比，为了展示对比看板，执行以下代码，在可视化面板中可以得到`summary_02`日志记录信息。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import mindspore.nn as nn\n",
+ "from mindspore import context\n",
+ "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor\n",
+ "from mindspore.train import Model\n",
+ "from mindspore.nn.metrics import Accuracy\n",
+ "from mindspore.train.callback import SummaryCollector\n",
+ "from mindspore.train.serialization import load_checkpoint, load_param_into_net\n",
+ "\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " device_target = \"GPU\"\n",
+ " context.set_context(mode=context.GRAPH_MODE, device_target=device_target)\n",
+ " download_dataset()\n",
+ " ds_train = create_dataset(data_path=\"./MNIST_Data/train/\")\n",
+ " network = LeNet5()\n",
+ " net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction=\"mean\")\n",
+ " net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)\n",
+ " time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
+ " config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)\n",
+ " ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", config=config_ck)\n",
+ " model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()})\n",
+ " # Init a SummaryCollector callback instance, and use it in model.train or model.eval\n",
+ " specified = {\"collect_metric\": True, \"histogram_regular\": \"^conv1.*|^conv2.*\", \"collect_input_data\": True}\n",
+ " summary_collector = SummaryCollector(summary_dir=\"./summary_dir/summary_02\", collect_specified_data=specified, collect_freq=1, keep_default_action=False)\n",
+ " print(\"============== Starting Training ==============\")\n",
+ " # Note: dataset_sink_mode should be set to False, else you should modify collect freq in SummaryCollector\n",
+ " model.train(epoch=3, train_dataset=ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor(), summary_collector], dataset_sink_mode=False)\n",
+ " print(\"============== Starting Testing ==============\")\n",
+ " param_dict = load_checkpoint(\"checkpoint_lenet_1-3_1875.ckpt\")\n",
+ " load_param_into_net(network, param_dict)\n",
+ " ds_eval = create_dataset(\"./MNIST_Data/test/\")\n",
+ " acc = model.eval(ds_eval, callbacks=summary_collector, dataset_sink_mode=False)\n",
+ " print(\"============== {} ==============\".format(acc))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "打开对比看板，可以得到`summary_01`和`summary_02`标量对比信息。\n",
+ "\n",
+ "\n",
+ "\n",
+ "上图展示了多个训练之间的标量曲线对比效果，横坐标是训练步骤，纵坐标是标量值。\n",
+ "\n",
+ "图中右上角有几个按钮功能，从左到右功能分别是全屏展示，切换Y轴比例，开启/关闭框选，分步回退和还原图形。\n",
+ "\n",
+ "- 全屏展示即全屏展示该标量曲线，再点击一次即可恢复。\n",
+ "\n",
+ "- 切换Y轴比例是指可以将Y轴坐标进行对数转换。\n",
+ "\n",
+ "- 开启/关闭框选是指可以框选图中部分区域，并放大查看该区域， 可以在已放大的图形上叠加框选。\n",
+ "\n",
+ "- 分步回退是指对同一个区域连续框选并放大查看时，可以逐步撤销操作。\n",
+ "\n",
+ "- 还原图形是指进行了多次框选后，点击此按钮可以将图还原回原始状态。\n",
+ "\n",
+ "\n",
+ "\n",
+ "上图展示的对比看板可视的功能区，提供了根据选择不同训练或标签，水平轴的不同维度和平滑度来进行标量对比的功能。\n",
+ "\n",
+ "- 训练: 提供了对所有训练进行多项选择的功能，用户可以通过勾选或关键字筛选所需的训练。\n",
+ "\n",
+ "- 标签：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的标量信息。\n",
+ "\n",
+ "- 水平轴：可以选择“步骤”、“相对时间”、“绝对时间”中的任意一项，来作为标量曲线的水平轴。\n",
+ "\n",
+ "- 平滑度：可以通过调整平滑度，对标量曲线进行平滑处理。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 单独记录数据\n",
+ "\n",
+ "以上流程为整体展示Summary算子能记录到的所有数据，也可以单独记录关心的数据，以降低性能开销和日志文件大小。\n",
+ "\n",
+ "> 为了展示运行的效果，进行以下每个步骤之前先删除当前notebook根目录下的`summary_dir/summary_02`目录，配置完`specified`参数后执行[**对比看板**](#对比看板)中的代码。\n",
+ "\n",
+ "## 单独记录损失值标量\n",
+ "\n",
+ "在主程序中配置`specified`参数为：\n",
+ "\n",
+ "```python\n",
+ "specified={\"collect_metric\": True}\n",
+ "```\n",
+ "\n",
+ "\n",
+ "\n",
+ "在MindInsight面板中，如上图所示，只展示了损失值标量信息。\n",
+ "\n",
+ "## 单独记录参数分布直方图\n",
+ "\n",
+ "在主程序中配置`specified`参数为：\n",
+ "\n",
+ "```python\n",
+ "specified = {\"histogram_regular\": \"^conv1.*|^conv2.*|fc1.*|fc2.*|fc3.*\"}\n",
+ "```\n",
+ "\n",
+ "\n",
+ "\n",
+ "在MindInsight面板中，如上图所示，只展示了参数直方图信息。\n",
+ "\n",
+ "\n",
+ "\n",
+ "点击进入直方图面板，如上图所示，展示了`conv1`、`conv2`、`fc1`、`fc2`、`fc3`等各层的权重、参数信息。\n",
+ "\n",
+ "## 单独记录图像\n",
+ "\n",
+ "在主程序中配置`specified`参数为：\n",
+ "\n",
+ "```python\n",
+ "specified = {\"collect_input_data\": True}\n",
+ "```\n",
+ "\n",
+ "\n",
+ "\n",
+ "在MindInsight面板中，如上图所示，只展示了输入图像信息。"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 关闭MindInsight服务\n",
+ "\n",
+ "执行以下代码关闭MindInsight服务。"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "\n",
+ "os.system(\"mindinsight stop --port 8080\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 注意事项和规格\n",
+ "\n",
+ "- 为了控制列出summary列表的用时，MindInsight最多支持发现999个summary列表条目。\n",
+ "\n",
+ "- 为了控制内存占用，MindInsight对标签（tag）数目和步骤（step）数目进行了限制：\n",
+ "\n",
+ " - 每个训练看板的最大标签数量为300个标签。标量标签、图片标签、计算图标签、参数分布图（直方图）标签的数量总和不得超过300个。特别地，每个训练看板最多有10个计算图标签。当实际标签数量超过这一限制时，将依照MindInsight的处理顺序，保留最近处理的300个标签。\n",
+ "\n",
+ " - 每个训练看板的每个标量标签最多有1000个步骤的数据。当实际步骤的数目超过这一限制时，将对数据进行随机采样，以满足这一限制。\n",
+ "\n",
+ " - 每个训练看板的每个图片标签最多有10个步骤的数据。当实际步骤的数目超过这一限制时，将对数据进行随机采样，以满足这一限制。\n",
+ " \n",
+ " - 每个训练看板的每个参数分布图（直方图）标签最多有50个步骤的数据。当实际步骤的数目超过这一限制时，将对数据进行随机采样，以满足这一限制。\n",
+ " \n",
+ "- 出于性能上的考虑，MindInsight对比看板使用缓存机制加载训练的标量曲线数据，并进行以下限制：\n",
+ " \n",
+ " - 对比看板只支持在缓存中的训练进行比较标量曲线对比。\n",
+ " \n",
+ " - 缓存最多保留最新（按修改时间排列）的15个训练。\n",
+ " \n",
+ " - 用户最多同时对比5个训练的标量曲线。"
+ ]
+ }
+ ],
+ "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": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md b/tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md
index d962ca944bc9fa21e90e1878d094c3e4b3522923..22c8011d107f5d009736ff0e8a7750e6b0d8f46d 100644
--- a/tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md
+++ b/tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md
@@ -21,7 +21,8 @@
-
+
+
## 概述
训练过程中的标量、图像、计算图以及模型超参等信息记录到文件中，通过可视化界面供用户查看。