DL4J Feedforward Learner (Pretraining)


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.

Input Ports

  1. Type: DL4J Model Finished configuration of a deep learning network.
  2. Type: Data Data table containing training data.

Output Ports

  1. Type: DL4J Model Trained Deep Learning Model

Find here

KNIME Labs > Deep Learning > DL4J > Learn > Unsupervised

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