NetModel

NetModel["name"]

represents a neural net model with the specified name.

NetModel["name","prop"]

gives property prop of the model.

Details

  • Possible trained neural net models include:
  • "AdaIN-Style Trained on MS-COCO and Painter by Numbers Data"
    "Age Estimation VGG-16 Trained on IMDB-WIKI Data"
    "Gender Prediction VGG-16 Trained on IMDB-WIKI Data"
    "GloVe 100-Dimensional Word Vectors Trained on Wikipedia and Gigaword-5 Data"
    "GloVe 300-Dimensional Word Vectors Trained on Wikipedia and Gigaword-5 Data"
    "GloVe 50-Dimensional Word Vectors Trained on Wikipedia and Gigaword-5 Data"
    "Inception V1 Trained on Extended Salient Object Subitizing Data"
    "Inception V1 Trained on ImageNet Competition Data"
    "Inception V1 Trained on Places365 Data"
    "Inception V3 Trained on ImageNet Competition Data"
    "LeNet Trained on MNIST Data"
    "ResNet-101 Trained on Augmented Casia WebFace Data"
    "ResNet-101 Trained on ImageNet Competition Data"
    "ResNet-152 Trained on ImageNet Competition Data"
    "ResNet-50 Trained on ImageNet Competition Data"
    "SqueezeNet V1.1 Trained on ImageNet Competition Data"
    "VGG-16 Trained on ImageNet Competition Data"
    "VGG-19 Trained on ImageNet Competition Data"
    "Wolfram ImageIdentify Net for WL 11.1"
    "Yahoo Open NSFW Model V1"
  • Possible properties in NetModel[name,prop] include:
  • "ByteCount"size of the evaluation net in bytes
    "ConstructionNotebook"notebook containing explicit network construction
    "EvaluationNet"trained net suitable for evaluation
    "InputDomains"domain of inputs to net
    "SourceMetadata"information about the origin of the net
    "TaskType"category of learning task performed by the net
    "TrainingSetData"data resource containing training set used (if available)
    "TrainingSetInformation"information about the training set used to train the net
    "UninitializedEvaluationNet"untrained evaluation net
  • "SourceMetadata" consists of an association whose keys are based on a subset of properties from the Dublin Core metadata standard, including "Creator", "Date", "Rights", "Source".

Examples

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Basic Examples  (3)

Obtain the trained version of a specific neural net:

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Apply the trained net to a set of inputs:

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Produce class probabilities for a single input:

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Get an uninitialized network:

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Obtain a specific property of a net:

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Applications  (18)

See Also

NetGraph  NetChain  NetExtract  FeatureExtraction  ImageRestyle

Introduced in 2017
(11.1)
| Updated in 2017
(11.2)