Package org.jetbrains.kotlinx.dl.api.core.model

Functions

resnet101
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fun resnet101(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet101 architecture as a Functional model.
resnet101Light
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fun resnet101Light(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet101 architecture without BatchNorm layers as a Functional model.
resnet101v2
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fun resnet101v2(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet101V2 architecture as a Functional model.
resnet101v2Light
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fun resnet101v2Light(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet101V2 architecture without BatchNorm layers as a Functional model.
resnet152
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fun resnet152(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet152 architecture as a Functional model.
resnet152Light
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fun resnet152Light(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet152 architecture without BatchNorm layers as a Functional model.
resnet152v2
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fun resnet152v2(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet152V2 architecture as a Functional model.
resnet152v2Light
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fun resnet152v2Light(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet152V2 architecture without BatchNorm layers as a Functional model.
resnet50
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fun resnet50(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet50 architecture as a Functional model.
resnet50Light
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fun resnet50Light(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet50 architecture without BatchNorm layers as a Functional model.
resnet50v2
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fun resnet50v2(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet50V2 architecture as a Functional model.
resnet50v2Light
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fun resnet50v2Light(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Functional
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Instantiates the ResNet50V2 architecture without BatchNorm layers as a Functional model.
vgg16
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fun vgg16(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Sequential
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Instantiates the VGG16 model.
vgg19
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fun vgg19(imageSize: Long = 224, numberOfClasses: Int = 10, numberOfInputChannels: Long = 3, lastLayerActivation: Activations = Activations.Linear): Sequential
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Instantiates the VGG19 model.