Class InputOutputDeepArchitecture
source code
An InputOutputDeepArchitecture (IODA) is a specialization of the DNN,
where the layers are divided into three categories : the input layers,
the link layer and the output layers. It has been specifically designed
for cases where both the input and the output spaces are high-dimensional.
The input and output layers are pretrained on the training example
(x) and the training labels (y),
respectively, using a Stacked AutoEncoder strategy, as for DNNs.
The link layer can optionally be pretrained, using as input and output data
the hidden representations of the deepmost input and output autoencoders,
respectively.
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Inherited from PretrainedMLP:
prepareParams,
pretrainInputAutoEncoders,
pretrainOutputAutoEncoders,
train
Inherited from MultiLayerPerceptron:
checkBadmoveHook,
checkBatchHook,
checkEpochHook,
checkLearningParameters,
defaultLearningParameters,
finetune,
getGeometry,
getParameters,
initBadmoveHook,
initBatchHook,
initEpochHook,
setParameters
Inherited from module.Sequential:
prepareGeometry,
prepareOutput
Inherited from module.Container:
add
Inherited from module.Module:
criterionFunction,
forward,
forwardFunction,
holdFunction,
linkInputs,
linkModule,
prepare,
prepareBackup,
restoreFunction,
save,
trainFunction
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__init__(self,
nUnitsInput,
nUnitsOutput,
outputActivation=<class crino.module.Sigmoid at 0x2b85740f5db8>)
(Constructor)
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Constructs a new InputOutputDeepArchitecture.
- Parameters:
nUnitsInput (int list) - The sizes of the (input and hidden) representations on the input side.nUnitsOutput (int list) - The sizes of the (hidden and output) representations on the output side.outputActivation (class derived from Activation) - The type of activation for the output layer. - Overrides:
module.Module.__init__
Attention:
outputActivation parameter is not an instance but a class.
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