public class KerasEfficientNet extends AbstractZooModel
Constructor and Description |
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KerasEfficientNet()
Instantiate the model.
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Modifier and Type | Method and Description |
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org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler |
getImagePreprocessingScaler()
Get the preprocessor to process this model's data with
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int[] |
getInputShape()
Get the input shape of this zoomodel
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EfficientNet.VARIATION |
getVariation()
Get the current variation of the zoo model (e.g., Resnet50 or Resnet101)
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org.deeplearning4j.nn.graph.ComputationGraph |
init(int numLabels,
long seed,
int[] shape,
boolean filterMode)
Initialize the ZooModel as MLP.
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void |
setVariation(EfficientNet.VARIATION var) |
getChannelsLast, getDefaultGraph, getExtraLayersToRemove, getFeatureExtractionLayer, getNumFExtractOutputs, getOptions, getOutputlayer, getPretrainedType, getPrettyName, initZooModel, isPretrained, isRequiresPooling, listOptions, requiresPreProcessing, setChannelsLast, setExtraLayersToRemove, setFeatureExtractionLayer, setNumFExtractOutputs, setOptions, setOutputLayer, setPretrainedType, setRequiresPooling
@OptionMetadata(description="The model variation to use.", displayName="Model Variation", commandLineParamName="variation", commandLineParamSynopsis="-variation <String>") public EfficientNet.VARIATION getVariation()
AbstractZooModel
getVariation
in class AbstractZooModel
public org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler getImagePreprocessingScaler()
AbstractZooModel
getImagePreprocessingScaler
in class AbstractZooModel
public void setVariation(EfficientNet.VARIATION var)
public org.deeplearning4j.nn.graph.ComputationGraph init(int numLabels, long seed, int[] shape, boolean filterMode)
AbstractZooModel
init
in class AbstractZooModel
numLabels
- Number of labels to adjust the outputseed
- Seedshape
- shapefilterMode
- True if creating for feature extractionpublic int[] getInputShape()
AbstractZooModel
getInputShape
in class AbstractZooModel