"NeuralNet" (Resource Object Type)
Details
- Neural net resources contain neural networks in content elements that can be accessed with NetModel .
Properties
- There are standard ResourceObject properties common to all resource types ». Additionally, each resource type defines additional special properties.
- Special properties for neural net resources associated with the content include:
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"ContentElements" list of content element names available via NetModel ["name","elem"]"ContentElementLocations" storage locations of content elements"DefaultContentElement" name of content element available via NetModel ["name"]"Format" formats of the content elements"ParameterizationData" for parameterized net models, stores internally used information
- Special properties associated with the resource metadata include:
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"TrainingSetData" link to training data"TrainingSetInformation" description of the data used to train the net
- The "ContentElementLocations" property is an Association with content element names for keys and locations for values. Each value can be a CloudObject , LocalObject , File or URL .
- Properties used for sorting data resources include:
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"InputDomains" list input types ("Image", "Text", etc.)"TaskType" type of task performed by the net ("Classification","Regression")
- All neural net resources have the property "ResourceType""NeuralNet".
- Commonly used standard ResourceObject properties for data resources include:
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"ExampleNotebook" notebook of example inputs and outputs
- The "SourceMetadata" value is an Association that can include the following keys:
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"Citation" source/reference citation"Creator" name of the author or creator"Date" original publication date"Rights" rights for the source"Source" link to the original source
Using a NeuralNet Resource
- The nets within a ResourceObject are accessed with NetModel .
- Properties can be accessed using ResourceObject […]["prop"].
- Often, neural net resources have a notebook demonstrating the construction process available via NetModel ["name","ConstructionNotebook"].
- Neural net resources can include a single trained net or select from a parameterized collection of nets based on additional inputs to NetModel .
Examples
open all close allBasic Examples (1)
Retrieve a neural net resource from the public repository:
The resource has type "NeuralNet":
Retrieve the default neural net:
Retrieve an input and use the net:
Scope (2)
Explore the metadata for a neural net resource with one trained net:
See the names of the content elements:
See the locations and formats of the data files:
Open the example notebook:
See the task type:
See the input domains:
Read descriptions of the net and the training set:
Explore the metadata for a parameterized neural net resource with multiple trained nets available:
See the parameterization information:
See the names of the content elements. Note multiple evaluation nets:
Specify parameters to retrieve a net:
Retrieve the default net for comparison:
For the same input, the two parameterizations give different probabilities: