RProp MLP Learner
Implementation of the RProp algorithm for multilayer feedforward networks.
RPROP performs a local adaptation of the weight-updates according to the
behavior of the error function.
For further details see: Riedmiller, M. Braun, H. : "A direct adaptive method for faster
backpropagation learning: theRPROP algorithm",Proceedings of the IEEE
International Conference on Neural Networks (ICNN) (Vol. 16, pp. 586-591).
Piscataway, NJ: IEEE.
This node provides a view of the error plot.
If the optional PMML inport is connected and contains
preprocessing operations in the TransformationDictionary those are
added to the learned model.
Dialog Options
- Maximum number of iterations
-
The number of learning iterations.
- Number of hidden layers
-
Specifies the number of hidden layers in the architecture of the neural
network.
- Number of hidden neurons per layer
-
Specifies the number of neurons contained in each hidden layer.
- Class column
-
Choose the column that contains the target variable: it can either be
nominal or numerical. All nominal class values are extracted and assigned to
output neurons.
If you use a numerical target variable (regression), please make sure it is normalized!
- Ignore missing values
-
If this checkbox is set, rows with missing values will not be used for
training.
- Use seed for random initialization
-
If this checkbox is set, a seed (see next field) can be set for initializing the weights and thresholds can be set.
- Random seed
-
Seed for the random number generator.
Ports
Input Ports
0 |
Datatable with training data |
1 |
Optional PMML port object
containing preprocessing operations. |
Output Ports
0 |
RProp trained Neural Network |
Views
- Error Plot
-
Displays the error for each iteration.
This node is contained in KNIME Base Nodes
provided by KNIME GmbH, Konstanz, Germany.