Java Code Examples for org.deeplearning4j.nn.graph.ComputationGraph#setLayerMaskArrays()
The following examples show how to use
org.deeplearning4j.nn.graph.ComputationGraph#setLayerMaskArrays() .
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Example 1
| Source File: GradientCheckTestsComputationGraph.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testLSTMWithReverseTimeSeriesVertex() {
Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration conf =
new NeuralNetConfiguration.Builder().seed(12345)
.dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.updater(new NoOp()).graphBuilder()
.addInputs("input").setOutputs("out")
.addLayer("lstm_a",
new LSTM.Builder().nIn(2).nOut(3)
.activation(Activation.TANH).build(),
"input")
.addVertex("input_rev", new ReverseTimeSeriesVertex("input"), "input")
.addLayer("lstm_b",
new LSTM.Builder().nIn(2).nOut(3)
.activation(Activation.TANH).build(),
"input_rev")
.addVertex("lstm_b_rev", new ReverseTimeSeriesVertex("input"), "lstm_b")
.addLayer("out", new RnnOutputLayer.Builder().nIn(3 + 3).nOut(2)
.activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build(),
"lstm_a", "lstm_b_rev")
.build();
ComputationGraph graph = new ComputationGraph(conf);
graph.init();
Random r = new Random(12345);
INDArray input = Nd4j.rand(new int[] {2, 2, 4});
INDArray labels = TestUtils.randomOneHotTimeSeries(2, 2, 4);
if (PRINT_RESULTS) {
System.out.println("testLSTMWithReverseTimeSeriesVertex()");
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
String msg = "testLSTMWithDuplicateToTimeSeries()";
assertTrue(msg, gradOK);
//Second: test with input mask arrays.
INDArray inMask = Nd4j.zeros(3, 5);
inMask.putRow(0, Nd4j.create(new double[] {1, 1, 1, 0, 0}));
inMask.putRow(1, Nd4j.create(new double[] {1, 1, 0, 1, 0}));
inMask.putRow(2, Nd4j.create(new double[] {1, 1, 1, 1, 1}));
graph.setLayerMaskArrays(new INDArray[] {inMask}, null);
gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
assertTrue(msg, gradOK);
TestUtils.testModelSerialization(graph);
}
Example 2
| Source File: GradientCheckTestsComputationGraph.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testBasicStackUnstackVariableLengthTS() {
int layerSizes = 2;
Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
.dataType(DataType.DOUBLE)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.dist(new NormalDistribution(0, 1))
.activation(Activation.TANH).updater(new NoOp()).graphBuilder()
.addInputs("in1", "in2")
.addLayer("d0", new SimpleRnn.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in1")
.addLayer("d1", new SimpleRnn.Builder().nIn(layerSizes).nOut(layerSizes).build(), "in2")
.addVertex("stack", new StackVertex(), "d0", "d1")
.addLayer("d2", new SimpleRnn.Builder().nIn(layerSizes).nOut(layerSizes).build(), "stack")
.addVertex("u1", new UnstackVertex(0, 2), "d2").addVertex("u2", new UnstackVertex(1, 2), "d2")
.addLayer("p1", new GlobalPoolingLayer.Builder(PoolingType.AVG).build(), "u1")
.addLayer("p2", new GlobalPoolingLayer.Builder(PoolingType.AVG).build(), "u2")
.addLayer("out1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2)
.nIn(layerSizes).nOut(layerSizes).activation(Activation.IDENTITY).build(), "p1")
.addLayer("out2", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2)
.nIn(layerSizes).nOut(2).activation(Activation.IDENTITY).build(), "p2")
.setOutputs("out1", "out2").build();
ComputationGraph graph = new ComputationGraph(conf);
graph.init();
Nd4j.getRandom().setSeed(12345);
long nParams = graph.numParams();
INDArray newParams = Nd4j.rand(new long[]{1, nParams});
graph.setParams(newParams);
int[] mbSizes = new int[] {1, 2, 3};
for (int minibatch : mbSizes) {
INDArray in1 = Nd4j.rand(new int[] {minibatch, layerSizes, 4});
INDArray in2 = Nd4j.rand(new int[] {minibatch, layerSizes, 5});
INDArray inMask1 = Nd4j.zeros(minibatch, 4);
inMask1.get(NDArrayIndex.all(), NDArrayIndex.interval(0, 3)).assign(1);
INDArray inMask2 = Nd4j.zeros(minibatch, 5);
inMask2.get(NDArrayIndex.all(), NDArrayIndex.interval(0, 4)).assign(1);
INDArray labels1 = Nd4j.rand(new int[] {minibatch, 2});
INDArray labels2 = Nd4j.rand(new int[] {minibatch, 2});
String testName = "testBasicStackUnstackVariableLengthTS() - minibatch = " + minibatch;
if (PRINT_RESULTS) {
System.out.println(testName);
// for (int j = 0; j < graph.getNumLayers(); j++)
// System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
}
graph.setLayerMaskArrays(new INDArray[] {inMask1, inMask2}, null);
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
.labels(new INDArray[]{labels1, labels2}).inputMask(new INDArray[]{inMask1, inMask2}));
assertTrue(testName, gradOK);
TestUtils.testModelSerialization(graph);
}
}
Example 3
| Source File: TestLastTimeStepLayer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testLastTimeStepVertex() {
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
.addLayer("lastTS", new LastTimeStep(new SimpleRnn.Builder()
.nIn(5).nOut(6).dataFormat(rnnDataFormat).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;
if (rnnDataFormat == RNNFormat.NCW){
in = Nd4j.rand(3, 5, 6);
}
else{
in = Nd4j.rand(3, 6, 5);
}
INDArray outUnderlying = ((LastTimeStepLayer)l).getUnderlying().activate(in, false, LayerWorkspaceMgr.noWorkspaces());
INDArray expOut;
if (rnnDataFormat == RNNFormat.NCW){
expOut = outUnderlying.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(5));
}
else{
expOut = outUnderlying.get(NDArrayIndex.all(), NDArrayIndex.point(5), NDArrayIndex.all());
}
//Forward pass:
INDArray outFwd = l.activate(in, false, LayerWorkspaceMgr.noWorkspaces());
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);
if (rnnDataFormat == RNNFormat.NCW){
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)));
}
else{
expOut.putRow(0, outUnderlying.get(NDArrayIndex.point(0), NDArrayIndex.point(2), NDArrayIndex.all()));
expOut.putRow(1, outUnderlying.get(NDArrayIndex.point(1), NDArrayIndex.point(3), NDArrayIndex.all()));
expOut.putRow(2, outUnderlying.get(NDArrayIndex.point(2), NDArrayIndex.point(4), NDArrayIndex.all()));
}
outFwd = l.activate(in, false, LayerWorkspaceMgr.noWorkspaces());
assertEquals(expOut, outFwd);
TestUtils.testModelSerialization(graph);
}
Example 4
| Source File: TestGraphNodes.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testLastTimeStepVertex() {
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
.addVertex("lastTS", new LastTimeStepVertex("in"), "in")
.addLayer("out", new OutputLayer.Builder().nIn(1).nOut(1).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build(), "lastTS").setOutputs("out")
.build();
ComputationGraph graph = new ComputationGraph(conf);
graph.init();
//First: test without input mask array
Nd4j.getRandom().setSeed(12345);
INDArray in = Nd4j.rand(new int[] {3, 5, 6});
INDArray expOut = in.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(5));
GraphVertex gv = graph.getVertex("lastTS");
gv.setInputs(in);
//Forward pass:
INDArray outFwd = gv.doForward(true, LayerWorkspaceMgr.noWorkspaces());
assertEquals(expOut, outFwd);
//Backward pass:
gv.setEpsilon(expOut);
Pair<Gradient, INDArray[]> pair = gv.doBackward(false, LayerWorkspaceMgr.noWorkspaces());
INDArray eps = pair.getSecond()[0];
assertArrayEquals(in.shape(), eps.shape());
assertEquals(Nd4j.zeros(3, 5, 5),
eps.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4, true)));
assertEquals(expOut, eps.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(5)));
//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, 5);
expOut.putRow(0, in.get(NDArrayIndex.point(0), NDArrayIndex.all(), NDArrayIndex.point(2)));
expOut.putRow(1, in.get(NDArrayIndex.point(1), NDArrayIndex.all(), NDArrayIndex.point(3)));
expOut.putRow(2, in.get(NDArrayIndex.point(2), NDArrayIndex.all(), NDArrayIndex.point(4)));
gv.setInputs(in);
outFwd = gv.doForward(true, LayerWorkspaceMgr.noWorkspaces());
assertEquals(expOut, outFwd);
String json = conf.toJson();
ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
assertEquals(conf, conf2);
}
