!436 add tensorsummary example

Merge pull request !436 from wangshuide/wsd_tensor_visual

tutorials/source_en/advanced_use/dashboard_and_lineage.md

@@ -203,10 +203,14 @@ class Net(nn.Cell):
 
         # Init ImageSummary
         self.sm_image = P.ImageSummary()
+        # Init TensorSummary
+        self.sm_tensor = P.TensorSummary()
 
     def construct(self, data):
         # Record image by Summary operator
         self.sm_image("image", data)
+        # Record tensor by Summary operator
+        self.sm_tensor("tensor", data)
         ......
         return out
 ```
@@ -327,9 +331,7 @@ In the saved files, `ms_output_after_hwopt.pb` is the computational graph after
 
    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. 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.
 
 ## Visualization Components
 

tutorials/source_zh_cn/advanced_use/dashboard_and_lineage.md

@@ -204,10 +204,14 @@ class Net(nn.Cell):
 
         # Init ImageSummary
         self.sm_image = P.ImageSummary()
+        # Init TensorSummary
+        self.sm_tensor = P.TensorSummary()
 
     def construct(self, data):
         # Record image by Summary operator
         self.sm_image("image", data)
+        # Record tensor by Summary operator
+        self.sm_tensor("tensor", data)
         ......
         return out
 
@@ -329,9 +333,7 @@ model.train(cnn_network, callbacks=[confusion_martrix])
 
    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。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。
 
 ## 可视化组件