@Test
public void testJson() throws Exception {
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.list()
.layer(0, new RBM.Builder().dist(new NormalDistribution(1, 1e-1)).build())
.inputPreProcessor(0, new ReshapePreProcessor())
.build();
String json = conf.toJson();
MultiLayerConfiguration from = MultiLayerConfiguration.fromJson(json);
assertEquals(conf.getConf(0), from.getConf(0));
Properties props = new Properties();
props.put("json", json);
String key = props.getProperty("json");
assertEquals(json, key);
File f = new File("props");
f.deleteOnExit();
BufferedOutputStream bos = new BufferedOutputStream(new FileOutputStream(f));
props.store(bos, "");
bos.flush();
bos.close();
BufferedInputStream bis = new BufferedInputStream(new FileInputStream(f));
Properties props2 = new Properties();
props2.load(bis);
bis.close();
assertEquals(props2.getProperty("json"), props.getProperty("json"));
String json2 = props2.getProperty("json");
MultiLayerConfiguration conf3 = MultiLayerConfiguration.fromJson(json2);
assertEquals(conf.getConf(0), conf3.getConf(0));
}
@Test
public void testListOverloads() {
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.seed(12345)
.list()
.layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build())
.layer(1, new OutputLayer.Builder().nIn(4).nOut(5).build())
.pretrain(false)
.backprop(true)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
DenseLayer dl = (DenseLayer) conf.getConf(0).getLayer();
assertEquals(3, dl.getNIn());
assertEquals(4, dl.getNOut());
OutputLayer ol = (OutputLayer) conf.getConf(1).getLayer();
assertEquals(4, ol.getNIn());
assertEquals(5, ol.getNOut());
MultiLayerConfiguration conf2 =
new NeuralNetConfiguration.Builder()
.seed(12345)
.list()
.layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build())
.layer(1, new OutputLayer.Builder().nIn(4).nOut(5).build())
.pretrain(false)
.backprop(true)
.build();
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init();
MultiLayerConfiguration conf3 =
new NeuralNetConfiguration.Builder()
.seed(12345)
.list(
new DenseLayer.Builder().nIn(3).nOut(4).build(),
new OutputLayer.Builder().nIn(4).nOut(5).build())
.pretrain(false)
.backprop(true)
.build();
MultiLayerNetwork net3 = new MultiLayerNetwork(conf3);
net3.init();
assertEquals(conf, conf2);
assertEquals(conf, conf3);
}
@Test
public void testConvnetJson() {
final int numRows = 75;
final int numColumns = 75;
int nChannels = 3;
int outputNum = 6;
int batchSize = 500;
int iterations = 10;
int seed = 123;
// setup the network
MultiLayerConfiguration.Builder builder =
new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iterations)
.regularization(true)
.l1(1e-1)
.l2(2e-4)
.useDropConnect(true)
.dropOut(0.5)
.miniBatch(true)
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.list()
.layer(
0,
new ConvolutionLayer.Builder(5, 5)
.nOut(5)
.dropOut(0.5)
.weightInit(WeightInit.XAVIER)
.activation("relu")
.build())
.layer(
1,
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2})
.build())
.layer(
2,
new ConvolutionLayer.Builder(3, 3)
.nOut(10)
.dropOut(0.5)
.weightInit(WeightInit.XAVIER)
.activation("relu")
.build())
.layer(
3,
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {2, 2})
.build())
.layer(4, new DenseLayer.Builder().nOut(100).activation("relu").build())
.layer(
5,
new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum)
.weightInit(WeightInit.XAVIER)
.activation("softmax")
.build())
.backprop(true)
.pretrain(false);
new ConvolutionLayerSetup(builder, numRows, numColumns, nChannels);
MultiLayerConfiguration conf = builder.build();
String json = conf.toJson();
MultiLayerConfiguration conf2 = MultiLayerConfiguration.fromJson(json);
assertEquals(conf, conf2);
}
@Test
public void testClone() {
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.list()
.layer(0, new RBM.Builder().build())
.layer(1, new OutputLayer.Builder().build())
.inputPreProcessor(1, new ReshapePreProcessor(new int[] {1, 2}, new int[] {3, 4}))
.build();
MultiLayerConfiguration conf2 = conf.clone();
assertEquals(conf, conf2);
assertNotSame(conf, conf2);
assertNotSame(conf.getConfs(), conf2.getConfs());
for (int i = 0; i < conf.getConfs().size(); i++) {
assertNotSame(conf.getConf(i), conf2.getConf(i));
}
assertNotSame(conf.getInputPreProcessors(), conf2.getInputPreProcessors());
for (Integer layer : conf.getInputPreProcessors().keySet()) {
assertNotSame(conf.getInputPreProcess(layer), conf2.getInputPreProcess(layer));
}
}
