KNIME Deeplearning4J Integration version 4.3.1.v202101261633 by KNIME AG, Zurich, Switzerland
This node performs unsupervised pretraining of a feedforward deep learning model. Thereby, the learning procedure can be adjusted using several training methods and parameters, which can be customized in the node dialog. Additionally, the node supplies further methods for regularization, gradient normalization and learning refinements. The learner node automatically adds an output layer to the network configuration, which can be also configured in the node dialog. For pretraining the network architecture needs to contain layers which are can be trained unsupervised. Such layers are for example an RBM or a Autoencoder. Usually, this node is used together with a classification learner node which performs finetuning of the output layer after the network was pretrained. The output of the node is a pretrained deep learning model.
To use this node in KNIME, install KNIME Deeplearning4J Integration (64bit only) from the following update site:
A zipped version of the software site can be downloaded here.
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