@Test
public void testDBNCalculation() {
Environment.getInstance().setUseWeightsSharedMemory(true);
DBN dbn = NNFactory.dbn(new int[] {3, 2, 2}, true);
dbn.setLayerCalculator(NNFactory.lcWeightedSum(dbn, null));
RBM firstRBM = dbn.getFirstNeuralNetwork();
Tensor t = firstRBM.getMainConnections().getWeights();
float[] e1 = t.getElements();
t.forEach(i -> e1[i] = 0.2f);
t = firstRBM.getVisibleBiasConnections().getWeights();
float[] e2 = t.getElements();
t.forEach(i -> e2[i] = 0.1f);
t = firstRBM.getHiddenBiasConnections().getWeights();
float[] e3 = t.getElements();
t.forEach(i -> e3[i] = 0.3f);
RBM secondRBM = dbn.getLastNeuralNetwork();
t = secondRBM.getMainConnections().getWeights();
float[] e4 = t.getElements();
t.forEach(i -> e4[i] = 0.4f);
t = secondRBM.getVisibleBiasConnections().getWeights();
float[] e5 = t.getElements();
t.forEach(i -> e5[i] = 0.8f);
t = secondRBM.getHiddenBiasConnections().getWeights();
float[] e6 = t.getElements();
t.forEach(i -> e6[i] = 0.5f);
Set<Layer> calculatedLayers = new HashSet<>();
calculatedLayers.add(dbn.getInputLayer());
ValuesProvider results = TensorFactory.tensorProvider(dbn, 1, true);
results.get(dbn.getInputLayer()).set(1, 0, 0);
results.get(dbn.getInputLayer()).set(0, 1, 0);
results.get(dbn.getInputLayer()).set(1, 2, 0);
dbn.getLayerCalculator().calculate(dbn, dbn.getOutputLayer(), calculatedLayers, results);
assertEquals(1.06, results.get(dbn.getOutputLayer()).get(0, 0), 0.00001);
assertEquals(1.06, results.get(dbn.getOutputLayer()).get(1, 0), 0.00001);
}
@Test
public void testSAECalculation() {
Environment.getInstance().setUseWeightsSharedMemory(true);
StackedAutoencoder sae = NNFactory.sae(new int[] {3, 2, 2}, true);
sae.setLayerCalculator(NNFactory.lcWeightedSum(sae, null));
Autoencoder firstAE = sae.getFirstNeuralNetwork();
Tensor t =
((FullyConnected) firstAE.getConnection(firstAE.getInputLayer(), firstAE.getHiddenLayer()))
.getWeights();
float[] e1 = t.getElements();
t.forEach(i -> e1[i] = 0.2f);
t =
((FullyConnected)
firstAE.getConnection(firstAE.getHiddenBiasLayer(), firstAE.getHiddenLayer()))
.getWeights();
float[] e2 = t.getElements();
t.forEach(i -> e2[i] = 0.3f);
t =
((FullyConnected) firstAE.getConnection(firstAE.getHiddenLayer(), firstAE.getOutputLayer()))
.getWeights();
float[] e3 = t.getElements();
t.forEach(i -> e3[i] = 0.8f);
t =
((FullyConnected)
firstAE.getConnection(firstAE.getOutputBiasLayer(), firstAE.getOutputLayer()))
.getWeights();
float[] e4 = t.getElements();
t.forEach(i -> e4[i] = 0.9f);
Autoencoder secondAE = sae.getLastNeuralNetwork();
t =
((FullyConnected)
secondAE.getConnection(secondAE.getInputLayer(), secondAE.getHiddenLayer()))
.getWeights();
float[] e5 = t.getElements();
t.forEach(i -> e5[i] = 0.4f);
t =
((FullyConnected)
secondAE.getConnection(secondAE.getHiddenBiasLayer(), secondAE.getHiddenLayer()))
.getWeights();
float[] e6 = t.getElements();
t.forEach(i -> e6[i] = 0.5f);
t =
((FullyConnected)
secondAE.getConnection(secondAE.getHiddenLayer(), secondAE.getOutputLayer()))
.getWeights();
float[] e7 = t.getElements();
t.forEach(i -> e7[i] = 0.7f);
t =
((FullyConnected)
secondAE.getConnection(secondAE.getOutputBiasLayer(), secondAE.getOutputLayer()))
.getWeights();
float[] e8 = t.getElements();
t.forEach(i -> e8[i] = 0.9f);
Set<Layer> calculatedLayers = new HashSet<>();
calculatedLayers.add(sae.getInputLayer());
ValuesProvider results = TensorFactory.tensorProvider(sae, 1, true);
results.get(sae.getInputLayer()).set(1, 0, 0);
results.get(sae.getInputLayer()).set(0, 1, 0);
results.get(sae.getInputLayer()).set(1, 2, 0);
sae.getLayerCalculator().calculate(sae, sae.getOutputLayer(), calculatedLayers, results);
assertEquals(1.06, results.get(sae.getOutputLayer()).get(0, 0), 0.00001);
assertEquals(1.06, results.get(sae.getOutputLayer()).get(1, 0), 0.00001);
}
