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</p>
</p>
### Text
**Text** visualizes the text output of NLP models within any stage, aiding developers to compare the changes of outputs so as to deeply understand the training process and simply evaluate the performance of the model.
**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.
**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.
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</p>
</p>
### Hyper Parameters
**Hyper Parameters** visualize the relationship between hyperparameters and model metrics (such as accuracy and loss) in a rich view, helping you identify the best hyperparameters in an efficient way.
**Text** visualizes the text output of NLP models within any stage, aiding developers to compare the changes of outputs so as to deeply understand the training process and simply evaluate the performance of the model.
**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 structure quickly and understand the direction of data flow easily.
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**High Dimensional** provides two approaches--T-SNE and PCA--to do the dimensionality reduction, allowing developers to have an in-depth analysis of the relationship between high-dimensional data and to optimize algorithms based on the analysis.
**High Dimensional** provides two approaches--T-SNE and PCA--to do the dimensionality reduction, allowing developers to have an in-depth analysis of the relationship between high-dimensional data and to optimize algorithms based on the analysis.
**Hyper Parameters** visualize the relationship between hyperparameters and model metrics (such as accuracy and loss) in a rich view, helping you identify the best hyperparameters in an efficient way.
VisualDL is a visualization tool designed for Deep Learning. VisualDL provides a variety of charts to show the trends of parameters. It enables users to understand the training process and model structures of Deep Learning models more clearly and intuitively so as to optimize models efficiently.
VisualDL is a visualization tool designed for Deep Learning. VisualDL provides a variety of charts to show the trends of parameters. It enables users to understand the training process and model structures of Deep Learning models more clearly and intuitively so as to optimize models efficiently.
Currently, VisualDL provides seven components: scalar, image, audio, graph, histogram, pr curve, ROC curve and high dimensional. VisualDL iterates rapidly and new functions will be continuously added.
Currently, VisualDL provides ten components: scalar, image, audio, text, graph, histogram, pr curve, ROC curve, high dimensional and hyper parameters. VisualDL iterates rapidly and new functions will be continuously added.
| [ Scalar](#Scalar--Line-Chart) | Line Chart | Display scalar data such as loss and accuracy dynamically. |
| [ Scalar](#Scalar--Line-Chart) | Line Chart | Display scalar data such as loss and accuracy dynamically. |
| [Image](#Image--Image-Visualization) | Image Visualization | Display images, visualizing the input and the output and making it easy to view the changes in the intermediate process. |
| [Image](#Image--Image-Visualization) | Image Visualization | Display images, visualizing the input and the output and making it easy to view the changes in the intermediate process. |
| [Audio](#Audio--Audio-Play) | Audio Play | Play the audio during the training process, making it easy to monitor the process of speech recognition and text-to-speech. |
| [Audio](#Audio--Audio-Play) | Audio Play | Play the audio during the training process, making it easy to monitor the process of speech recognition and text-to-speech. |
| [Text](#Text) | Text Visualization | Visualizes the text output of NLP models within any stage, aiding developers to compare the changes of outputs so as to deeply understand the training process and simply evaluate the performance of the model. |
| [Text](#Text) | Text Visualization | Visualize the text output of NLP models within any stage, aiding developers to compare the changes of outputs so as to deeply understand the training process and simply evaluate the performance of the model. |
| [Graph](#Graph--Network-Structure) | Network Structure | Visualize network structures, node attributes and data flow, assisting developers to learn and to optimize network structures. |
| [Graph](#Graph--Network-Structure) | Network Structure | Visualize network structures, node attributes and data flow, assisting developers to learn and to optimize network structures. |
| [Histogram](#Histogram--Distribution-of-Tensors) | Distribution of Tensors | Present the changes of distributions of tensors, such as weights/gradients/bias, during the training process. |
| [Histogram](#Histogram--Distribution-of-Tensors) | Distribution of Tensors | Present the changes of distributions of tensors, such as weights/gradients/bias, during the training process. |
| [PR Curve](#PR-Curve) | Precision & Recall Curve | Display precision-recall curves across training steps, clarifying the tradeoff between precision and recall when comparing models. |
| [PR Curve](#PR-Curve) | Precision & Recall Curve | Display precision-recall curves across training steps, clarifying the tradeoff between precision and recall when comparing models. |
| [ROC Curve](#ROC-Curve) | Receiver Operating Characteristic curve | Shows the performance of a classification model at all classification thresholds. |
| [ROC Curve](#ROC-Curve) | Receiver Operating Characteristic curve | Show the performance of a classification model at all classification thresholds. |
| [High Dimensional](#High-Dimensional--Data-Dimensionality-Reduction) | Data Dimensionality Reduction | Project high-dimensional data into 2D/3D space for embedding visualization, making it convenient to observe the correlation between data. |
| [High Dimensional](#High-Dimensional--Data-Dimensionality-Reduction) | Data Dimensionality Reduction | Project high-dimensional data into 2D/3D space for embedding visualization, making it convenient to observe the correlation between data. |
| [Hyper Parameters](#hyperparameters--hyperparameter-visualization) | HyperParameter Visualization | Visualize the relationship between hyperparameters and model metrics (such as accuracy and loss) in a rich view, helping you identify the best hyperparameters in an efficient way. |
At the same time, VisualDL provides [VDL.service](#vdlservice) , which allows developers to easily save, track and share visualization results of experiments with anyone for free.
At the same time, VisualDL provides [VDL.service](#vdlservice) , which allows developers to easily save, track and share visualization results of experiments with anyone for free.
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HyperParameters visualize the relationship between hyperparameters and model metrics (such as accuracy and loss) in a rich view, helping you identify the best hyperparameters in an efficient way.
### Record Interface
The interface of the HyperParameters is slightly different from other components'. Firstly, you need to use the `add_hparams` to record the hyperparameter data(`hparams_dict`) and specify the name of the metrics(`metrics_list`). Then, for the metrics you just added, you need to record those metrics values by using `add_scalar`. In this way you can get all data for HpyerParameters Visualization.
| hparam_dict | dict | name and data of hparams. |
| metric_list | list | The metrics name to be recorded later corresponds to the `tag` parameter in the `add_scalar` interface, and VisualDL corresponds to the indicator data through the `tag`. |
| walltime | int | Record the time stamp of the data, the default is the current time stamp. |
### Demo
The following shows an example of how to use HyperParameters component, and script can be found in [HyperParameters Demo](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/hparams_test.py)
```python
fromvisualdlimportLogWriter
# This demo demonstrates the hyperparameter records of two experiments. Take the first
# experiment data as an example, First, record the data of the hyperparameter `hparams`
# in the `add_hparams` interface. Then specify the name of `metrics` to be recorded later.
# Finally, use `add_scalar` to specifically record the data of `metrics`. Note that the
# `metrics_list` parameter in the `add_hparams` interface needs to include the `tag`
- The table view can be displayed in a sorted order.
- Trial ID represents a specific experiment name, the column name displayed in other normal fonts is the hyperparameter name, and the column displayed in bold font is the metric name.
- The position of hyperparameters and metrics can be customized by dragging.
- The column width of the table view can be adjusted by dragging.
- You can click to expand to view the scalar of the metrics.
