Java Code Examples for org.deeplearning4j.nn.conf.MultiLayerConfiguration#getConf()
The following examples show how to use
org.deeplearning4j.nn.conf.MultiLayerConfiguration#getConf() .
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Example 1
| Source File: TestVAE.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testForwardPass() {
int[][] encLayerSizes = new int[][] {{12}, {12, 13}, {12, 13, 14}};
for (int i = 0; i < encLayerSizes.length; i++) {
MultiLayerConfiguration mlc = new NeuralNetConfiguration.Builder().list().layer(0,
new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder().nIn(10)
.nOut(5).encoderLayerSizes(encLayerSizes[i]).decoderLayerSizes(13).build())
.build();
NeuralNetConfiguration c = mlc.getConf(0);
org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder vae =
(org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) c.getLayer();
MultiLayerNetwork net = new MultiLayerNetwork(mlc);
net.init();
INDArray in = Nd4j.rand(1, 10);
// net.output(in);
List<INDArray> out = net.feedForward(in);
assertArrayEquals(new long[] {1, 10}, out.get(0).shape());
assertArrayEquals(new long[] {1, 5}, out.get(1).shape());
}
}
Example 2
| Source File: TestVAE.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testInitialization() {
MultiLayerConfiguration mlc =
new NeuralNetConfiguration.Builder().list()
.layer(0, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
.nIn(10).nOut(5).encoderLayerSizes(12).decoderLayerSizes(13)
.build())
.build();
NeuralNetConfiguration c = mlc.getConf(0);
org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder vae =
(org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) c.getLayer();
long allParams = vae.initializer().numParams(c);
// Encoder Encoder -> p(z|x) Decoder //p(x|z)
int expNumParams = (10 * 12 + 12) + (12 * (2 * 5) + (2 * 5)) + (5 * 13 + 13) + (13 * (2 * 10) + (2 * 10));
assertEquals(expNumParams, allParams);
MultiLayerNetwork net = new MultiLayerNetwork(mlc);
net.init();
System.out.println("Exp num params: " + expNumParams);
assertEquals(expNumParams, net.getLayer(0).params().length());
Map<String, INDArray> paramTable = net.getLayer(0).paramTable();
int count = 0;
for (INDArray arr : paramTable.values()) {
count += arr.length();
}
assertEquals(expNumParams, count);
assertEquals(expNumParams, net.getLayer(0).numParams());
}
Example 3
| Source File: TestVAE.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testPretrainSimple() {
int inputSize = 3;
MultiLayerConfiguration mlc = new NeuralNetConfiguration.Builder().list()
.layer(0, new org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder.Builder()
.nIn(inputSize).nOut(4).encoderLayerSizes(5).decoderLayerSizes(6).build())
.build();
NeuralNetConfiguration c = mlc.getConf(0);
org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder vae =
(org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) c.getLayer();
long allParams = vae.initializer().numParams(c);
MultiLayerNetwork net = new MultiLayerNetwork(mlc);
net.init();
net.initGradientsView(); //TODO this should happen automatically
Map<String, INDArray> paramTable = net.getLayer(0).paramTable();
Map<String, INDArray> gradTable =
((org.deeplearning4j.nn.layers.variational.VariationalAutoencoder) net.getLayer(0))
.getGradientViews();
assertEquals(paramTable.keySet(), gradTable.keySet());
for (String s : paramTable.keySet()) {
assertEquals(paramTable.get(s).length(), gradTable.get(s).length());
assertArrayEquals(paramTable.get(s).shape(), gradTable.get(s).shape());
}
System.out.println("Num params: " + net.numParams());
INDArray data = Nd4j.rand(1, inputSize);
net.pretrainLayer(0, data);
}
Example 4
| Source File: TestMultiLayerSpace.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testVariationalAutoencoderLayerSpaceBasic() {
MultiLayerSpace mls =
new MultiLayerSpace.Builder()
.updater(new Sgd(0.005)).seed(
12345)
.addLayer(new VariationalAutoencoderLayerSpace.Builder()
.nIn(new IntegerParameterSpace(50, 75)).nOut(200)
.encoderLayerSizes(234, 567).decoderLayerSizes(123, 456)
.reconstructionDistribution(
new DiscreteParameterSpace<ReconstructionDistribution>(
new GaussianReconstructionDistribution(),
new BernoulliReconstructionDistribution()))
.build())
.build();
int numParams = mls.numParameters();
//Assign numbers to each leaf ParameterSpace object (normally done by candidate generator - manual here for testing)
List<ParameterSpace> noDuplicatesList = LeafUtils.getUniqueObjects(mls.collectLeaves());
//Second: assign each a number
int c = 0;
for (ParameterSpace ps : noDuplicatesList) {
int np = ps.numParameters();
if (np == 1) {
ps.setIndices(c++);
} else {
int[] values = new int[np];
for (int j = 0; j < np; j++)
values[c++] = j;
ps.setIndices(values);
}
}
double[] zeros = new double[numParams];
DL4JConfiguration configuration = mls.getValue(zeros);
MultiLayerConfiguration conf = configuration.getMultiLayerConfiguration();
assertEquals(1, conf.getConfs().size());
NeuralNetConfiguration nnc = conf.getConf(0);
VariationalAutoencoder vae = (VariationalAutoencoder) nnc.getLayer();
assertEquals(50, vae.getNIn());
assertEquals(200, vae.getNOut());
assertArrayEquals(new int[] {234, 567}, vae.getEncoderLayerSizes());
assertArrayEquals(new int[] {123, 456}, vae.getDecoderLayerSizes());
assertTrue(vae.getOutputDistribution() instanceof GaussianReconstructionDistribution);
double[] ones = new double[numParams];
for (int i = 0; i < ones.length; i++)
ones[i] = 1.0;
configuration = mls.getValue(ones);
conf = configuration.getMultiLayerConfiguration();
assertEquals(1, conf.getConfs().size());
nnc = conf.getConf(0);
vae = (VariationalAutoencoder) nnc.getLayer();
assertEquals(75, vae.getNIn());
assertEquals(200, vae.getNOut());
assertArrayEquals(new int[] {234, 567}, vae.getEncoderLayerSizes());
assertArrayEquals(new int[] {123, 456}, vae.getDecoderLayerSizes());
assertTrue(vae.getOutputDistribution() instanceof BernoulliReconstructionDistribution);
}
