diff --git a/docs/source_zh_cn/design/mindinsight/training_visual_design.md b/docs/source_zh_cn/design/mindinsight/training_visual_design.md
index 1c12dc9413ac6abd3d994ea15ebb64d844120cf2..b0a68bfc4fd26fae3661cf5139136a8846700589 100644
--- a/docs/source_zh_cn/design/mindinsight/training_visual_design.md
+++ b/docs/source_zh_cn/design/mindinsight/training_visual_design.md
@@ -24,7 +24,7 @@
 
 [MindInsight](https://gitee.com/mindspore/mindinsight)是MindSpore的可视化调试调优组件。通过MindInsight可以完成训练可视、性能调优、精度调优等任务。
 
-训练可视功能主要包括训练看板、模型溯源、数据溯源等功能，训练看板中又包括标量、参数分布图、计算图、数据图、数据抽样等子功能。
+训练可视功能主要包括训练看板、模型溯源、数据溯源等功能，训练看板中又包括标量、参数分布图、计算图、数据图、数据抽样、张量等子功能。
 
 本文主要介绍MindInsight训练可视功能的逻辑架构、代码组织和数据模型。
 
diff --git a/tutorials/source_en/advanced_use/dashboard.md b/tutorials/source_en/advanced_use/dashboard.md
index 7fae18e20f8adad443c29d5da19518d1e464974d..d471f49878e7501a6597735913c2c9f7b4ab2c26 100644
--- a/tutorials/source_en/advanced_use/dashboard.md
+++ b/tutorials/source_en/advanced_use/dashboard.md
@@ -18,7 +18,7 @@
 
 ## Overview
 
-Training dashboard is an important part of mindinsight's visualization component, and its tags include scalar visualization, parameter distribution visualization, computational visualization, data visualization and image visualization.
+Training dashboard is an important part of mindinsight's visualization component, and its tags include scalar visualization, parameter distribution visualization, computational visualization, data visualization, image visualization and tensor visualization.
 
 Access the Training Dashboard by selecting a specific training from the training list.
 
@@ -195,6 +195,6 @@ Figure 13 shows tensors recorded by a user in a form of a histogram. Click the u
 
    Remarks: The method of estimating the space usage of `TensorSummary` is as follows:
 
-   The size of a `TensorSummary` data = the number of values in the tensor * 4 bytes. Assuming that the size of the tensor recorded by `TensorSummary` is 32 * 1 * 256 * 256, then a `TensorSummary` data needs about 32 * 1 * 256 * 256 * 4 bytes = 8,388,608 bytes = 8MiB. Also suppose that the collect_freq of `SummaryCollector` is set to 1, and 50 iterations are trained. Then the required space when recording these 50 sets of data is about 50 * 8 MiB = 400MiB. It should be noted that due to the overhead of data structure and other factors, the actual storage space used will be slightly larger than 400MiB.
+   The size of a `TensorSummary` data = the number of values in the tensor * 4 bytes. Assuming that the size of the tensor recorded by `TensorSummary` is 32 * 1 * 256 * 256, then a `TensorSummary` data needs about 32 * 1 * 256 * 256 * 4 bytes = 8,388,608 bytes = 8MiB. `TensorSummary` will record data of 20 steps by default. Then the required space when recording these 20 sets of data is about 20 * 8 MiB = 160MiB. It should be noted that due to the overhead of data structure and other factors, the actual storage space used will be slightly larger than 160MiB.
 
 6. The training log file is large when using `TensorSummary` because the complete tensor data is recorded. MindInsight needs more time to parse the training log file, please be patient.
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diff --git a/tutorials/source_zh_cn/advanced_use/dashboard.md b/tutorials/source_zh_cn/advanced_use/dashboard.md
index 2f5d47e7119fe53a4bcc7538a7f0e0e4680c1122..c3b1f01f67760755b2178682184b0761674f875e 100644
--- a/tutorials/source_zh_cn/advanced_use/dashboard.md
+++ b/tutorials/source_zh_cn/advanced_use/dashboard.md
@@ -19,7 +19,7 @@
 
 ## 概述
 
-训练看板是MindInsight的可视化组件的重要组成部分，而训练看板的标签包含：标量可视化、参数分布图可视化、计算图可视化、数据图可视化和图像可视化等。
+训练看板是MindInsight的可视化组件的重要组成部分，而训练看板的标签包含：标量可视化、参数分布图可视化、计算图可视化、数据图可视化、图像可视化和张量可视化等。
 
 用户从训练列表中选择指定的训练，进入训练看板。
 
@@ -193,5 +193,5 @@
 
    备注：估算`TensorSummary`空间使用量的方法如下：
 
-   一个`TensorSummary`数据的大小 ＝ Tensor中的数值个数 * 4 bytes。假设使用`TensorSummary`记录的Tensor大小为32 * 1 * 256 * 256，则一个`TensorSummary`数据大约需要32 * 1 * 256 * 256 * 4 bytes = 8,388,608 bytes = 8MiB。又假设`SummaryCollector`的collect_freq设置为1，且训练了50个迭代。则记录这50组数据需要的空间约为50 * 8 MiB ＝ 400MiB。需要注意的是，由于数据结构等因素的开销，实际使用的存储空间会略大于400MiB。
+   一个`TensorSummary`数据的大小 ＝ Tensor中的数值个数 * 4 bytes。假设使用`TensorSummary`记录的Tensor大小为32 * 1 * 256 * 256，则一个`TensorSummary`数据大约需要32 * 1 * 256 * 256 * 4 bytes = 8,388,608 bytes = 8MiB。`TensorSummary`默认会记录20个步骤的数据，则记录这20组数据需要的空间约为20 * 8 MiB ＝ 160MiB。需要注意的是，由于数据结构等因素的开销，实际使用的存储空间会略大于160MiB。
 6. 当使用`TensorSummary`时，由于记录完整Tensor数据，训练日志文件较大，MindInsight需要更多时间解析训练日志文件，请耐心等待。
\ No newline at end of file