/** * Evaluate the (single output layer only) network for regression performance * @param iterator Data to evaluate on * @return Regression evaluation */ public RegressionEvaluation evaluateRegression(MultiDataSetIterator iterator, List<String> columnNames) { return doEvaluation(iterator, new RegressionEvaluation(columnNames))[0]; }
/** * Evaluate the (single output layer only) network for regression performance * @param iterator Data to evaluate on * @param columnNames Column names for the regression evaluation. May be null. * @return Regression evaluation */ public RegressionEvaluation evaluateRegression(DataSetIterator iterator, List<String> columnNames) { return doEvaluation(iterator, new RegressionEvaluation(columnNames))[0]; }
/** * Evaluate the network for regression performance * @param iterator Data to evaluate on * @return */ public RegressionEvaluation evaluateRegression(DataSetIterator iterator) { return doEvaluation(iterator, new RegressionEvaluation(iterator.totalOutcomes()))[0]; }
RegressionEvaluation regressionEvaluation = new RegressionEvaluation(numClasses); while (iterator.hasNext()) { DataSet next;
@Override public String evaluate(FederatedDataSet federatedDataSet) { //evaluate the model on the test set DataSet testData = (DataSet) federatedDataSet.getNativeDataSet(); RegressionEvaluation eval = new RegressionEvaluation(12); INDArray output = model.output(testData.getFeatureMatrix()); eval.eval(testData.getLabels(), output); return "MSE: " + eval.meanSquaredError(11) + "\nScore: " + model.score(); }
LOGGER.info("Epoch " + i + " complete. Time series evaluation:"); RegressionEvaluation evaluation = new RegressionEvaluation(2);
RegressionEvaluation evaluation = new RegressionEvaluation(1); INDArray features = testData.getFeatures();
evaluations[i] = new RegressionEvaluation();