Java Code Examples for org.deeplearning4j.nn.multilayer.MultiLayerNetwork#toComputationGraph()
The following examples show how to use
org.deeplearning4j.nn.multilayer.MultiLayerNetwork#toComputationGraph() .
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Example 1
| Source File: TestLrChanges.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testChangeLSGD() {
//Simple test for no updater nets
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.activation(Activation.TANH)
.seed(12345)
.updater(new Sgd(0.1))
.list()
.layer(new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(new OutputLayer.Builder().nIn(10).nOut(10).lossFunction(LossFunctions.LossFunction.MSE).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setLearningRate(1.0);
net.setLearningRate(1, 0.5);
assertEquals(1.0, net.getLearningRate(0), 0.0);
assertEquals(0.5, net.getLearningRate(1), 0.0);
ComputationGraph cg = net.toComputationGraph();
cg.setLearningRate(2.0);
cg.setLearningRate("1", 2.5);
assertEquals(2.0, cg.getLearningRate("0"), 0.0);
assertEquals(2.5, cg.getLearningRate("1"), 0.0);
}
Example 2
| Source File: CNNGradientCheckTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnnWithSpaceToBatch() {
Nd4j.getRandom().setSeed(12345);
int nOut = 4;
int[] minibatchSizes = {2, 4};
int width = 5;
int height = 5;
int inputDepth = 1;
int[] kernel = {2, 2};
int[] blocks = {2, 2};
String[] activations = {"sigmoid", "tanh"};
SubsamplingLayer.PoolingType[] poolingTypes =
new SubsamplingLayer.PoolingType[]{SubsamplingLayer.PoolingType.MAX,
SubsamplingLayer.PoolingType.AVG, SubsamplingLayer.PoolingType.PNORM};
boolean nchw = format == CNN2DFormat.NCHW;
for (String afn : activations) {
for (SubsamplingLayer.PoolingType poolingType : poolingTypes) {
for (int minibatchSize : minibatchSizes) {
long[] inShape = nchw ? new long[]{minibatchSize, inputDepth, height, width} : new long[]{minibatchSize, height, width, inputDepth};
INDArray input = Nd4j.rand(DataType.DOUBLE, inShape);
INDArray labels = Nd4j.zeros(4 * minibatchSize, nOut);
for (int i = 0; i < 4 * minibatchSize; i++) {
labels.putScalar(new int[]{i, i % nOut}, 1.0);
}
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.updater(new NoOp()).weightInit(new NormalDistribution(0, 1))
.list()
.layer(new ConvolutionLayer.Builder(kernel).nIn(inputDepth).nOut(3).build())
.layer(new SpaceToBatchLayer.Builder(blocks).build()) //trivial space to batch
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nOut(nOut).build())
.setInputType(InputType.convolutional(height, width, inputDepth, format))
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
String msg = format + " - poolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn="
+ afn;
if (PRINT_RESULTS) {
System.out.println(msg);
// for (int j = 0; j < net.getnLayers(); j++)
// System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
}
boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
assertTrue(msg, gradOK);
//Also check compgraph:
ComputationGraph cg = net.toComputationGraph();
gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(cg).inputs(new INDArray[]{input})
.labels(new INDArray[]{labels}));
assertTrue(msg + " - compgraph", gradOK);
TestUtils.testModelSerialization(net);
}
}
}
}
Example 3
| Source File: UtilLayerGradientChecks.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testFrozenWithBackprop(){
for( int minibatch : new int[]{1,5}) {
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.seed(12345)
.updater(Updater.NONE)
.list()
.layer(new DenseLayer.Builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build())
.layer(new FrozenLayerWithBackprop(new DenseLayer.Builder().nIn(10).nOut(10)
.activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()))
.layer(new FrozenLayerWithBackprop(
new DenseLayer.Builder().nIn(10).nOut(10).activation(Activation.TANH)
.weightInit(WeightInit.XAVIER).build()))
.layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf2);
net.init();
INDArray in = Nd4j.rand(minibatch, 10);
INDArray labels = TestUtils.randomOneHot(minibatch, 10);
Set<String> excludeParams = new HashSet<>();
excludeParams.addAll(Arrays.asList("1_W", "1_b", "2_W", "2_b"));
boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(in)
.labels(labels).excludeParams(excludeParams));
assertTrue(gradOK);
TestUtils.testModelSerialization(net);
//Test ComputationGraph equivalent:
ComputationGraph g = net.toComputationGraph();
boolean gradOKCG = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(g)
.minAbsoluteError(1e-6)
.inputs(new INDArray[]{in}).labels(new INDArray[]{labels}).excludeParams(excludeParams));
assertTrue(gradOKCG);
TestUtils.testModelSerialization(g);
}
}
Example 4
| Source File: TestNetConversion.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testMlnToCompGraph() {
Nd4j.getRandom().setSeed(12345);
for( int i=0; i<3; i++ ){
MultiLayerNetwork n;
switch (i){
case 0:
n = getNet1(false);
break;
case 1:
n = getNet1(true);
break;
case 2:
n = getNet2();
break;
default:
throw new RuntimeException();
}
INDArray in = (i <= 1 ? Nd4j.rand(new int[]{8, 3, 10, 10}) : Nd4j.rand(new int[]{8, 5, 10}));
INDArray labels = (i <= 1 ? Nd4j.rand(new int[]{8, 10}) : Nd4j.rand(new int[]{8, 10, 10}));
ComputationGraph cg = n.toComputationGraph();
INDArray out1 = n.output(in);
INDArray out2 = cg.outputSingle(in);
assertEquals(out1, out2);
n.setInput(in);
n.setLabels(labels);
cg.setInputs(in);
cg.setLabels(labels);
n.computeGradientAndScore();
cg.computeGradientAndScore();
assertEquals(n.score(), cg.score(), 1e-6);
assertEquals(n.gradient().gradient(), cg.gradient().gradient());
n.fit(in, labels);
cg.fit(new INDArray[]{in}, new INDArray[]{labels});
assertEquals(n.params(), cg.params());
}
}
Example 5
| Source File: TestCompGraphUnsupervised.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void compareImplementations() throws Exception {
for(WorkspaceMode wsm : new WorkspaceMode[]{WorkspaceMode.NONE, WorkspaceMode.ENABLED}) {
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder()
.seed(12345)
.updater(new Adam(1e-3))
.weightInit(WeightInit.XAVIER)
.inferenceWorkspaceMode(wsm)
.trainingWorkspaceMode(wsm)
.list()
.layer(new VariationalAutoencoder.Builder()
.nIn(784)
.nOut(32)
.encoderLayerSizes(16)
.decoderLayerSizes(16)
.activation(Activation.TANH)
.pzxActivationFunction(Activation.SIGMOID)
.reconstructionDistribution(new BernoulliReconstructionDistribution(Activation.SIGMOID))
.build())
.layer(new VariationalAutoencoder.Builder()
.nIn(32)
.nOut(8)
.encoderLayerSizes(16)
.decoderLayerSizes(16)
.activation(Activation.TANH)
.pzxActivationFunction(Activation.SIGMOID)
.reconstructionDistribution(new GaussianReconstructionDistribution(Activation.TANH))
.build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf2);
net.init();
ComputationGraph cg = net.toComputationGraph();
cg.getConfiguration().setInferenceWorkspaceMode(wsm);
cg.getConfiguration().setTrainingWorkspaceMode(wsm);
DataSetIterator ds = new EarlyTerminationDataSetIterator(new MnistDataSetIterator(1, true, 12345), 1);
Nd4j.getRandom().setSeed(12345);
net.pretrainLayer(0, ds);
ds = new EarlyTerminationDataSetIterator(new MnistDataSetIterator(1, true, 12345), 1);
Nd4j.getRandom().setSeed(12345);
cg.pretrainLayer("0", ds);
assertEquals(net.params(), cg.params());
}
}
Example 6
| Source File: TestListeners.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testListenerCalls(){
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.list()
.layer(new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
TestListener tl = new TestListener();
net.setListeners(tl);
DataSetIterator irisIter = new IrisDataSetIterator(50, 150);
net.fit(irisIter, 2);
List<Triple<Call,Integer,Integer>> exp = new ArrayList<>();
exp.add(new Triple<>(Call.EPOCH_START, 0, 0));
exp.add(new Triple<>(Call.ON_FWD, 0, 0));
exp.add(new Triple<>(Call.ON_BWD, 0, 0));
exp.add(new Triple<>(Call.ON_GRAD, 0, 0));
exp.add(new Triple<>(Call.ITER_DONE, 0, 0));
exp.add(new Triple<>(Call.ON_FWD, 1, 0));
exp.add(new Triple<>(Call.ON_BWD, 1, 0));
exp.add(new Triple<>(Call.ON_GRAD, 1, 0));
exp.add(new Triple<>(Call.ITER_DONE, 1, 0));
exp.add(new Triple<>(Call.ON_FWD, 2, 0));
exp.add(new Triple<>(Call.ON_BWD, 2, 0));
exp.add(new Triple<>(Call.ON_GRAD, 2, 0));
exp.add(new Triple<>(Call.ITER_DONE, 2, 0));
exp.add(new Triple<>(Call.EPOCH_END, 3, 0)); //Post updating iter count, pre update epoch count
exp.add(new Triple<>(Call.EPOCH_START, 3, 1));
exp.add(new Triple<>(Call.ON_FWD, 3, 1));
exp.add(new Triple<>(Call.ON_BWD, 3, 1));
exp.add(new Triple<>(Call.ON_GRAD, 3, 1));
exp.add(new Triple<>(Call.ITER_DONE, 3, 1));
exp.add(new Triple<>(Call.ON_FWD, 4, 1));
exp.add(new Triple<>(Call.ON_BWD, 4, 1));
exp.add(new Triple<>(Call.ON_GRAD, 4, 1));
exp.add(new Triple<>(Call.ITER_DONE, 4, 1));
exp.add(new Triple<>(Call.ON_FWD, 5, 1));
exp.add(new Triple<>(Call.ON_BWD, 5, 1));
exp.add(new Triple<>(Call.ON_GRAD, 5, 1));
exp.add(new Triple<>(Call.ITER_DONE, 5, 1));
exp.add(new Triple<>(Call.EPOCH_END, 6, 1));
assertEquals(exp, tl.getCalls());
tl = new TestListener();
ComputationGraph cg = net.toComputationGraph();
cg.setListeners(tl);
cg.fit(irisIter, 2);
assertEquals(exp, tl.getCalls());
}
Example 7
| Source File: TestConvolutionalListener.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore //Should be run manually
public void testUI() throws Exception {
int nChannels = 1; // Number of input channels
int outputNum = 10; // The number of possible outcomes
int batchSize = 64; // Test batch size
DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345) // Training iterations as above
.l2(0.0005).weightInit(WeightInit.XAVIER)
.updater(new Nesterovs(0.01, 0.9)).list()
.layer(0, new ConvolutionLayer.Builder(5, 5)
//nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
.nIn(nChannels).stride(1, 1).nOut(20).activation(Activation.IDENTITY).build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
.stride(2, 2).build())
.layer(2, new ConvolutionLayer.Builder(5, 5)
//Note that nIn need not be specified in later layers
.stride(1, 1).nOut(50).activation(Activation.IDENTITY).build())
.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
.stride(2, 2).build())
.layer(4, new DenseLayer.Builder().activation(Activation.RELU).nOut(500).build())
.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum).activation(Activation.SOFTMAX).build())
.setInputType(InputType.convolutionalFlat(28, 28, 1)) //See note below
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setListeners(new ConvolutionalIterationListener(1), new ScoreIterationListener(1));
for (int i = 0; i < 10; i++) {
net.fit(mnistTrain.next());
Thread.sleep(1000);
}
ComputationGraph cg = net.toComputationGraph();
cg.setListeners(new ConvolutionalIterationListener(1), new ScoreIterationListener(1));
for (int i = 0; i < 10; i++) {
cg.fit(mnistTrain.next());
Thread.sleep(1000);
}
Thread.sleep(100000);
}
