> ## Documentation Index
> Fetch the complete documentation index at: https://docs.abbyy.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Set up a Deep Learning activity

> Add a Deep Learning activity, select fields, label documents, and train it on a semi-structured document set in Advanced Designer.

You can use a separate document set to train your Deep Learning activity. To do so, select the Deep Learning activity from the drop-down list next to the skill name. Then, in the drop-down list to the left of the **Upload** button, select the necessary document set or click **Create Set...** to create a new one. You can upload, delete, and rotate documents on this tab as described in [Documents](/vantage/documentation/advanced-designer/document-skills/documents).

<Steps>
  <Step title="Add the activity">
    On the **Activities** tab, add a Deep Learning activity for semi-structured documents to your document processing flow.
  </Step>

  <Step title="Select fields">
    In the **Activity Properties** pane, select the fields to be trained using this activity.

    You can select one of the following:

    * Up to 50 fields of type **Text**, regardless of their nesting level.
    * One table with up to 32 columns.

    If you need to train more fields, you can add more Deep Learning activities and use them to select additional fields. For example, if you need to train several text fields and a table, create two Deep Learning activities.

    The following fields cannot be trained:

    * Fields of type other than **Text**
    * Groups with multiple items, tables, or text fields with multiple items nested in a group with multiple items
    * Tables with more than 32 columns

    You will need to set up extraction of such fields using other activities, e.g. an Extraction Rules activity.
  </Step>

  <Step title="Label documents">
    Click **Activity Editor** and go to the **Fields** tab to label your documents. The labeling process in the Activity Editor is identical to the regular document labeling process.

    Use the following guidelines to determine the size of the document set:

    * If the training set contains only the minimum 10 documents, you can start the deep learning training, but uploading additional documents is recommended to achieve higher accuracy.
    * If your training set includes only 10 documents, you can still begin training your model. However, Advanced Designer will display a warning recommending that you add more than 500 labeled documents for optimal training results.
    * If your training set contains between 500 and 10,000 documents, you can begin training your activity immediately. This is the recommended number of documents to have in your training set.
    * If the training set contains more than 10,000 documents, Advanced Designer will display a warning saying that the skill may become unstable.
  </Step>

  <Step title="Train the activity">
    Once you have uploaded and labeled your documents, click **Train Activity**.
  </Step>

  <Step title="Monitor training progress">
    Go to the **Results** tab to evaluate the training progress. If necessary, adjust the training length or stop the training.

    For more information, see [Monitoring and adjusting activity training](/vantage/documentation/advanced-designer/activities/deep-learning-monitoring).
  </Step>
</Steps>

## Post-training steps

Once the activity has been trained, activity testing will start automatically. If you stop the training, you will be prompted to start testing the activity manually.

When the testing is complete, analyze the field extraction results in the **Activity Test Results** section of the **Results** tab. Statistics for this activity are identical to the general statistics for the skill displayed on the [Results](/vantage/documentation/advanced-designer/document-skills/results) tab. If you are not satisfied with the quality of field extraction, you have the following options:

* Add more documents to the training set and resume the training process. The training results obtained so far will be preserved and the neural network will be additionally trained using the updated document set.
* Adjust the labeling and restart training. The training results obtained so far will be discarded and the neural network will be trained from scratch.
* Create a Hypothesis Filtering container with an Extraction Rules activity, which will allow you to set conditions for the output of the Deep Learning activity.

The activity can only be trained and tested using documents with confirmed labeling. Documents have unconfirmed labeling if the reference labeling was generated automatically based on the predicted labeling, unless you copy predicted labeling to reference using the corresponding option in the document context menu. You can check the labeling status for each document on the **Documents** tab. To confirm labeling for a document, you should review it on the **Fields** tab.

<Note>
  Beginning with Advanced Designer v. 2.3.1, the field limitations for the Deep Learning activity have changed. If your skill uses a trained Deep Learning activity that extracts more than 50 fields, you can continue processing documents with that skill. However, when you open such a skill for editing, the existing Deep Learning activity will be split into several Deep Learning activities, which you may need to retrain. You will also have to route the activities in the document processing workflow.
</Note>