This node supplies means to learn the network configuration specified by the Deep Learning Model. Thereby, the model can be either trained supervised or unsupervised using several training methods like Stochastic Gradient Descent. The output layer of the network, which can be configured in the node dialog, will be automatically added by this node. Additionally, the node supplies further methods for regularization, gradient normalization and learning refinements. In order to learn the network, inputs will be automatically converted into a network understandable vector format. For the model input there are two options. If the supplied model is untrained it will be trained normally by the learner. If the model was trained by a previous learner the node will try to use the network parameters of the trained model to initialise the parameters of the new network for the new training run, because the network configuration can be changed between learner nodes. This way methods like Transfer Learning can be implemented. The output of the node is a learned Deep Learning Model containing the original configuration and tuned network weights and biases.
- Type: DL4J ModelDeep Learning ModelFinished configuration of a deep learning network.
- Type: TableData TableData table containing training data.