本文整理汇总了Java中org.deeplearning4j.nn.graph.ComputationGraph.getLayer方法的典型用法代码示例。如果您正苦于以下问题：Java ComputationGraph.getLayer方法的具体用法？Java ComputationGraph.getLayer怎么用？Java ComputationGraph.getLayer使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.deeplearning4j.nn.graph.ComputationGraph的用法示例。
在下文中一共展示了ComputationGraph.getLayer方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: getVaeLayer
import org.deeplearning4j.nn.graph.ComputationGraph; //导入方法依赖的package包/类
@Override
public VariationalAutoencoder getVaeLayer() {
ComputationGraph network =
new ComputationGraph(ComputationGraphConfiguration.fromJson((String) jsonConfig.getValue()));
network.init();
INDArray val = ((INDArray) params.value()).unsafeDuplication();
if (val.length() != network.numParams(false))
throw new IllegalStateException(
"Network did not have same number of parameters as the broadcasted set parameters");
network.setParams(val);
Layer l = network.getLayer(0);
if (!(l instanceof VariationalAutoencoder)) {
throw new RuntimeException(
"Cannot use CGVaeReconstructionErrorWithKeyFunction on network that doesn't have a VAE "
+ "layer as layer 0. Layer type: " + l.getClass());
}
return (VariationalAutoencoder) l;
}
示例2: getVaeLayer
import org.deeplearning4j.nn.graph.ComputationGraph; //导入方法依赖的package包/类
@Override
public VariationalAutoencoder getVaeLayer() {
ComputationGraph network =
new ComputationGraph(ComputationGraphConfiguration.fromJson((String) jsonConfig.getValue()));
network.init();
INDArray val = ((INDArray) params.value()).unsafeDuplication();
if (val.length() != network.numParams(false))
throw new IllegalStateException(
"Network did not have same number of parameters as the broadcasted set parameters");
network.setParams(val);
Layer l = network.getLayer(0);
if (!(l instanceof VariationalAutoencoder)) {
throw new RuntimeException(
"Cannot use CGVaeReconstructionProbWithKeyFunction on network that doesn't have a VAE "
+ "layer as layer 0. Layer type: " + l.getClass());
}
return (VariationalAutoencoder) l;
}
示例3: testLastTimeStepVertex
import org.deeplearning4j.nn.graph.ComputationGraph; //导入方法依赖的package包/类
@Test
public void testLastTimeStepVertex() {
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
.addLayer("lastTS", new LastTimeStep(new SimpleRnn.Builder()
.nIn(5).nOut(6).build()), "in")
.setOutputs("lastTS")
.build();
ComputationGraph graph = new ComputationGraph(conf);
graph.init();
//First: test without input mask array
Nd4j.getRandom().setSeed(12345);
Layer l = graph.getLayer("lastTS");
INDArray in = Nd4j.rand(new int[]{3, 5, 6});
INDArray outUnderlying = ((LastTimeStepLayer)l).getUnderlying().activate(in);
INDArray expOut = outUnderlying.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(5));
//Forward pass:
INDArray outFwd = l.activate(in);
assertEquals(expOut, outFwd);
//Second: test with input mask array
INDArray inMask = Nd4j.zeros(3, 6);
inMask.putRow(0, Nd4j.create(new double[]{1, 1, 1, 0, 0, 0}));
inMask.putRow(1, Nd4j.create(new double[]{1, 1, 1, 1, 0, 0}));
inMask.putRow(2, Nd4j.create(new double[]{1, 1, 1, 1, 1, 0}));
graph.setLayerMaskArrays(new INDArray[]{inMask}, null);
expOut = Nd4j.zeros(3, 6);
expOut.putRow(0, outUnderlying.get(NDArrayIndex.point(0), NDArrayIndex.all(), NDArrayIndex.point(2)));
expOut.putRow(1, outUnderlying.get(NDArrayIndex.point(1), NDArrayIndex.all(), NDArrayIndex.point(3)));
expOut.putRow(2, outUnderlying.get(NDArrayIndex.point(2), NDArrayIndex.all(), NDArrayIndex.point(4)));
outFwd = l.activate(in);
assertEquals(expOut, outFwd);
TestUtils.testModelSerialization(graph);
}