DL4J Feedforward Predictor (deprecated)
This node uses the supplied trained Deep Learning Model to create predictions or activations for the supplied test data. The network output activation will be appended to the training data as a collection column where the collection has the same length as the number of output units of the network, which is usually specified in the Output Layer. The numbers contained in the collection are the raw output activations of the last layer of the network. If specified and the activation of the last layer is 'softmax' the raw activation can be interpreted as class probabilities and be associated with a class label taken from the Deeplearning Model the model was trained on. The supplied data table needs to be in the same format as the table used for learning, meaning it needs to contain columns of the same name and type.
- Type: DL4J Model Trained Deep Learning Model.
- Type: Data Data table containing testing data.
- Type: Data Data Table containing original testing data with appended network output. The network output is given by a collection column containing output activations for each test example. Additionally, softmax prediction may be appended.