@Test
public void testGradientWithAsList() {
MultiLayerNetwork net1 = new MultiLayerNetwork(getConf());
MultiLayerNetwork net2 = new MultiLayerNetwork(getConf());
net1.init();
net2.init();
DataSet x1 = new IrisDataSetIterator(1, 150).next();
DataSet all = new IrisDataSetIterator(150, 150).next();
DataSet x2 = all.asList().get(0);
// x1 and x2 contain identical data
assertArrayEquals(asFloat(x1.getFeatureMatrix()), asFloat(x2.getFeatureMatrix()), 0.0f); // OK
assertArrayEquals(asFloat(x1.getLabels()), asFloat(x2.getLabels()), 0.0f); // OK
assertEquals(x1, x2); // Fails, DataSet doesn't override Object.equals()
// Set inputs/outputs so gradient can be calculated:
net1.feedForward(x1.getFeatureMatrix());
net2.feedForward(x2.getFeatureMatrix());
((OutputLayer) net1.getLayers()[1]).setLabels(x1.getLabels());
((OutputLayer) net2.getLayers()[1]).setLabels(x2.getLabels());
net1.gradient(); // OK
net2.gradient(); // IllegalArgumentException: Buffers must fill up specified length 29
}
private static void checkNinNoutForEachLayer(
int[] expNin, int[] expNout, MultiLayerConfiguration conf, MultiLayerNetwork network) {
// Check configuration
for (int i = 0; i < expNin.length; i++) {
NeuralNetConfiguration layerConf = conf.getConf(i);
assertTrue(layerConf.getNIn() == expNin[i]);
assertTrue(layerConf.getNOut() == expNout[i]);
}
// Check Layer
for (int i = 0; i < expNin.length; i++) {
Layer layer = network.getLayers()[i];
assertTrue(layer.conf().getNIn() == expNin[i]);
assertTrue(layer.conf().getNOut() == expNout[i]);
int[] weightShape = layer.getParam(DefaultParamInitializer.WEIGHT_KEY).shape();
assertTrue(weightShape[0] == expNin[i]);
assertTrue(weightShape[1] == expNout[i]);
}
}
