diff --git a/README.md b/README.md
index f00a6b7d8..4ab2c8c52 100644
--- a/README.md
+++ b/README.md
@@ -56,7 +56,7 @@ The high-level design of API makes it easy to use. Only one click can initiate t
### Various Functions
-The function contains the visualization of training parameters, data samples, graph structures, histograms of tensors, PR curves and high-dimensional data.
+The function contains the visualization of training parameters, data samples, graph structures, histograms of tensors, PR curves and high-dimensional data distributions.
### High Compatibility
@@ -64,7 +64,7 @@ VisualDL provides the visualization of the mainstream model structures such as P
### Fully Support
-By Integrating into PaddlePaddle and related modules, VisualDL allows developers to use different components unobstructed, and thus have the best experience in the PaddlePaddle ecosystem.
+By Integrating into PaddlePaddle and related modules, VisualDL allows developers to use different components without obstructions, and thus to have the best experience in the PaddlePaddle ecosystem.
## Installation
@@ -112,12 +112,12 @@ class LogWriter(logdir=None,
| --------------- | ------- | ------------------------------------------------------------ |
| logdir | string | The path location of log file. VisualDL will create a log file under this path to record information generated by the training process. If not specified, the path will be `runs/${CURRENT_TIME}`as default. |
| comment | string | Add a suffix to the log folder name, which is invalid if logdir is already specified. |
-| max_queue | int | The maximum capacity of the data generated before recording in a log file. If the capacity is reached, the data is immediately written into the log file. |
-| flush_secs | int | The maximum cache time of the data generated before recording in a log file, when this time is reached, the data is immediately written to the log file. |
+| max_queue | int | The maximum capacity of the data generated before recording in a log file. If the capacity is reached, the data are immediately written into the log file. |
+| flush_secs | int | The maximum cache time of the data generated before recording in a log file, when this time is reached, the data are immediately written to the log file. |
| filename_suffix | string | Add a suffix to the default log file name. |
| write_to_disk | boolean | Write into disk or not. |
-| display_name | string | Set the name of different runs when `logdir` is too long or needed to be hidden. If not set, the default name is `logdir`. |
-| file_name | string | Set the name of the log file. If the file_name already exists, setting the file_name means to continue to add new records in the same log file. Note that the name should include 'vdlrecords'.|
+| display_name | string | Give a new name to `logdir` when `logdir` is too long or needed to be hidden. If not set, the default name is `logdir`. |
+| file_name | string | Set the name of the log file. If the file_name already exists, setting the file_name will be new records in the same log file, which will continue to be used. Note that the name should include 'vdlrecords'. |
@@ -145,7 +145,7 @@ with LogWriter(logdir="./log/scalar_test/train") as writer:
### 2. Launch Panel
-In the above example, the log has recorded three sets of scalar values. Developers can view the visualization results of the log file through launching the visualDL panel. There are two ways to launch a log file:
+In the above example, the log has recorded three sets of scalar values. Developers can view the visualization results of the log file through launching the visualDL panel. There are two ways to launch the log file:
#### Launch by Command Line
@@ -159,11 +159,11 @@ Parameter details:
| parameters | meaning |
| --------------- | ------------------------------------------------------------ |
-| --logdir | Set one or more directories of the log. VisualDL will search the log file recursively under this path to display the all experimental results. |
-| --model | Set path to the model file (not a directory). VisualDL will visualize the model file in Graph page. PaddlePaddle、ONNX、Keras、Core ML、Caffe and Other model formats are supported. Please refer to [Graph - Functional Instructions](./docs/components/UserGuide-en.md#functional-instructions-2). |
+| --logdir | Set one or more directories of the log. VisualDL will search the log file recursively under this path to display all the experimental results. |
+| --model | Set a path to the model file (not a directory). VisualDL will visualize the model file in Graph page. PaddlePaddle、ONNX、Keras、Core ML、Caffe and other model formats are supported. Please refer to [Graph - Functional Instructions](./docs/components/UserGuide-en.md#functional-instructions-2). |
| --host | Specify IP address. The default value is `127.0.0.1`. |
| --port | Set the port. The default value is `8040`. |
-| --cache-timeout | Cache time of the backend. During the cache time, the front end requests the same URL multiple times, and then the returned data is obtained from the cache. The default cache time is 20 seconds. |
+| --cache-timeout | Cache time of the backend. During the cache time, the front end requests the same URL multiple times, and then the returned data are obtained from the cache. The default cache time is 20 seconds. |
| --language | The language of the VisualDL panel. Language can be specified as 'en' or 'zh', and the default is the language used by the browser. |
| --public-path | The URL path of the VisualDL panel. The default path is '/app', meaning that the access address is 'http://<host>:<port>/app'. |
| --api-only | Decide whether or not to provide only API. If this parameter is set, VisualDL will only provides API service without displaying the web page, and the API address is 'http://<host>:<port>/<public_path>/api'. Additionally, If the public_path parameter is not specified, the default address is 'http://<host>:<port>/api'. |
@@ -197,11 +197,11 @@ The interface parameters are as follows:
| parameters | type | meaning |
| ------------- | -------------------------------------------------- | ------------------------------------------------------------ |
-| logdir | string or list[string_1, string_2, ... , string_n] | Set one or more directories of the log. VisualDL will search the log file recursively under this path to display the all experimental results. |
-| model | string | Set path to the model file (not a directory). VisualDL will visualize the model file in Graph page. |
+| logdir | string or list[string_1, string_2, ... , string_n] | Set one or more directories of the log. VisualDL will search the log file recursively under this path to display all the experimental results. |
+| model | string | Set a path to the model file (not a directory). VisualDL will visualize the model file in Graph page. |
| host | string | Specify IP address. The default value is `127.0.0.1`. |
| port | int | Set the port. The default value is `8040`. |
-| cache_timeout | int | Cache time of the backend. During the cache time, the front end requests the same URL multiple times, and then the returned data is obtained from the cache. The default cache time is 20 seconds. |
+| cache_timeout | int | Cache time of the backend. During the cache time, the front end requests the same URL multiple times, and then the returned data are obtained from the cache. The default cache time is 20 seconds. |
| language | string | The language of the VisualDL panel. Language can be specified as 'en' or 'zh', and the default is the language used by the browser. |
| public_path | string | The URL path of the VisualDL panel. The default path is '/app', meaning that the access address is 'http://<host>:<port>/app'. |
| api_only | boolean | Decide whether or not to provide only API. If this parameter is set, VisualDL will only provides API service without displaying the web page, and the API address is 'http://<host>:<port>/<public_path>/api'. Additionally, If the parameter public_path is not specified, the default address is 'http://<host>:<port>/api'. |
@@ -223,12 +223,12 @@ After launching the panel by one of the above methods, developers can see the vi
### 3. Read data in log files using LogReader
-VisualDL also provides `LogReader` interface to read raw data from log files.
+VisualDL also provides `LogReader` interface to read any data from log files.
```python
class LogReader(file_path='')
```
-#### interface parameters
+#### Interface Parameters
| parameters | type | meaning |
| ---------- | ------ | ------------------------------------ |
| file_path | string | File path of the log file. Required. |
@@ -257,7 +257,7 @@ For more information of `LogReader`, please refer to [LogReader](./docs/io/LogRe
### Scalar
-**Scalar** makes use of various charts to display how the parameters, such as accuracy, loss and learning rate, change during the training process. In this case, developers can observe not only the single but also the multiple groups of parameters in order to understand the training process and thus speed up the process of model tuning.
+**Scalar** makes use of various charts to display how the parameters, such as accuracy, loss and learning rate, changes during the training process. In this case, developers can observe not only the single but also the multiple groups of parameters in order to understand the training process and thus speed up the process of model tuning.
#### Dynamic Display
@@ -271,7 +271,7 @@ After the launch of VisualDL Board, the LogReader will continuously record the d
#### Comparison of Multiple Experiments
-Developers can compare with multiple experiments by specifying and uploading the path of each experiment at the same time so as to visualize the same parameters in the same chart.
+Developers can compare multiple experiments by specifying and uploading the path of each experiment at the same time so as to visualize the same parameters in the same chart.
@@ -281,7 +281,7 @@ Developers can compare with multiple experiments by specifying and uploading the
### Image
-**Image** provides real-time visualizations of the image data during the training process, allowing developers to observe the changes of images in different training stages and to deeply understand the effects of the training process.
+**Image** provides real-time visualizations of the image data during the training process, allowing developers to observe the changes of images at different training stages and to deeply understand the effects of the training process.
@@ -299,7 +299,7 @@ Developers can compare with multiple experiments by specifying and uploading the
### Graph
-**Graph** enables developers to visualize model structures by only one click. Moreover, **Graph** allows Developers to explore model attributes, node information, node input and output. aiding them analyze model structure quickly and understand the direction of data flow easily.
+**Graph** enables developers to visualize model structures by only one click. Moreover, **Graph** allows developers to explore model attributes, node information, node input and output. aiding them analyze model structures quickly and understand the direction of data flow easily.
@@ -308,7 +308,7 @@ Developers can compare with multiple experiments by specifying and uploading the
### Histogram
-**Histogram** displays how the trend of tensors (weight, bias, gradient, etc.) changes during the training process in the form of histogram. Developers can adjust the model structures accurately by having an in-depth understanding of the effect of each layer.
+**Histogram** displays how the trend of tensor (weight, bias, gradient, etc.) changes during the training process in the form of histogram. Developers can adjust the model structures accurately by having an in-depth understanding of the effect of each layer.
- Offset Mode
@@ -334,7 +334,7 @@ Developers can compare with multiple experiments by specifying and uploading the
### ROC Curve
-**ROC Curve** shows the performance of a classification model at all classification thresholds; the larger the area under the curve, the better the model performs, aiding developers to evaluate the model performance and choose an appropriate threshold.
+**ROC Curve** shows the performance of a classification model at all classification thresholds; the larger the area under the curve, the better the model performs, aiding developers in evaluating the model performance and choosing an appropriate threshold.
