make the specification of tensor visualization more clear.

tutorials/source_en/advanced_use/dashboard_and_lineage.md

@@ -578,5 +578,5 @@ To ensure performance, MindInsight implements scalars comparision with the cache
 - The maximum of 5 trainings can be selected for scalars comparision at the same time.
 
 Since `TensorSummary` will record complete tensor data, the amount of data is usually relatively large. In order to limit memory usage and ensure performance, MindInsight make the following restrictions with the size of tensor and the number of value responsed and displayed on the front end:
-- Support tensor containing up to 10 million values.
-- In the tensor-visible table view, the maximum number of value responsed to the front end for each step of each label is 100,000.
+- MindInsight supports loading tensor containing up to 10 million values.
+- After the tensor is loaded, in the tensor-visible table view, you can view a maximum of 100,000 values. If the value obtained by the selected dimension query exceeds this limit, it cannot be displayed.
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tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md

@@ -579,5 +579,5 @@ model.train(cnn_network, callbacks=[confusion_martrix])
 - 用户最多同时对比5个训练的标量曲线。
 
 由于`TensorSummary`会记录完整Tensor数据，数据量通常会比较大，为了控制内存占用和出于性能上的考虑，MindInsight对Tensor的大小以及返回前端展示的数值个数进行以下限制：
-- 最大支持含有1千万个数值的Tensor。
-- 在张量可视的表格视图下，每个标签的每个步骤返回前端的数值个数最大为10万。
+- MindInsight最大支持加载含有1千万个数值的Tensor。
+- Tensor加载后，在张量可视的表格视图下，最大支持查看10万个数值，如果所选择的维度查询得到的数值超过这一限制，则无法显示。
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