5.9 Descriptive Information

Each deepnet has an associated name, description, category, and tags. The following subsections provide a brief description for each concept. In Figure 5.104 , you can see the options the More info menu provides to edit them.

5.9.1 Deepnet Name

Each deepnet has a name that is displayed in the deepnet list view and also on the top bar of the deepnet view. Deepnet’s names are indexed to be used in searches. When you create a deepnet, it gets a default name which is your dataset name. Change it using the More info menu option on the right corner of the deepnet view. The name of a deepnet cannot be longer than 256 characters. More than one deepnet can have the same name even within the same project, but they will always have different identifiers.

5.9.2 Deepnet Description

Each deepnet also has a description that it is very useful for documenting your Machine Learning projects. Deepnets take the description of the datasets used to create them by default.

Descriptions can be written using plain text and also markdown. BigML provides a simple markdown editor that accepts a subset of markdown syntax. (See Figure 5.105 .)

Descriptions cannot be longer than 8192 characters and can use almost any character.

5.9.3 Deepnet Category

A category, taken from the dataset used to create it, is associated with each deepnet. Categories are useful to classify deepnet according to the domain which your data comes from. This is useful when you use BigML to solve problems across industries or multiple customers.

A deepnet category must be one of the 24 categories listed on Table 5.1 .

5.9.4 Deepnet Tags

A deepnet can also have a number of tags associated with it that can help to retrieve it via the BigML API or to provide deepnet with some extra information. A deepnet inherits the tags from the dataset used to create it. Each tag is limited to a maximum of 128 characters. Each deepnet can have up to 32 different tags.

5.9.5 Deepnet Counters

For each deepnet, BigML also stores a number of counters to track the number of other resources that have been created using the deepnet as a starting point. In the deepnet view, you can see a menu option that displays counters for evaluations, single and batch predictions. It also allows you to quickly jump to all the resources of one type. (See Figure 5.106 .)