Java Code Examples for org.nd4j.autodiff.samediff.SameDiff#setTrainingConfig()
The following examples show how to use
org.nd4j.autodiff.samediff.SameDiff#setTrainingConfig() .
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Example 1
| Source File: CheckpointListenerTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
public static SameDiff getModel(){
Nd4j.getRandom().setSeed(12345);
SameDiff sd = SameDiff.create();
SDVariable in = sd.placeHolder("in", DataType.FLOAT, -1, 4);
SDVariable label = sd.placeHolder("label", DataType.FLOAT, -1, 3);
SDVariable w = sd.var("W", Nd4j.rand(DataType.FLOAT, 4, 3));
SDVariable b = sd.var("b", DataType.FLOAT, 3);
SDVariable mmul = in.mmul(w).add(b);
SDVariable softmax = sd.nn().softmax(mmul);
SDVariable loss = sd.loss().logLoss("loss", label, softmax);
sd.setTrainingConfig(TrainingConfig.builder()
.dataSetFeatureMapping("in")
.dataSetLabelMapping("label")
.updater(new Adam(1e-2))
.weightDecay(1e-2, true)
.build());
return sd;
}
Example 2
| Source File: UIListenerTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
private static SameDiff getSimpleNet(){
Nd4j.getRandom().setSeed(12345);
SameDiff sd = SameDiff.create();
SDVariable in = sd.placeHolder("in", DataType.FLOAT, -1, 4);
SDVariable label = sd.placeHolder("label", DataType.FLOAT, -1, 3);
SDVariable w = sd.var("W", Nd4j.rand(DataType.FLOAT, 4, 3));
SDVariable b = sd.var("b", DataType.FLOAT, 1, 3);
SDVariable mmul = in.mmul(w).add(b);
SDVariable softmax = sd.nn.softmax("softmax", mmul);
SDVariable loss = sd.loss().logLoss("loss", label, softmax);
sd.setTrainingConfig(TrainingConfig.builder()
.dataSetFeatureMapping("in")
.dataSetLabelMapping("label")
.updater(new Adam(1e-1))
.weightDecay(1e-3, true)
.build());
return sd;
}
Example 3
| Source File: ListenerTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testCustomListener() {
SameDiff sd = SameDiff.create();
SDVariable in = sd.placeHolder("input", DataType.FLOAT, -1, 4);
SDVariable label = sd.placeHolder("label", DataType.FLOAT, -1, 3);
SDVariable w = sd.var("w", Nd4j.rand(DataType.FLOAT, 4, 3));
SDVariable b = sd.var("b", Nd4j.rand(DataType.FLOAT, 3));
SDVariable z = sd.nn().linear("z", in, w, b);
SDVariable out = sd.nn().softmax("out", z, 1);
SDVariable loss = sd.loss().softmaxCrossEntropy("loss", label, out, null);
//Create and set the training configuration
double learningRate = 1e-3;
TrainingConfig config = new TrainingConfig.Builder()
.l2(1e-4) //L2 regularization
.updater(new Adam(learningRate)) //Adam optimizer with specified learning rate
.dataSetFeatureMapping("input") //DataSet features array should be associated with variable "input"
.dataSetLabelMapping("label") //DataSet label array should be associated with variable "label
.addEvaluations(false,"out",0,new Evaluation())
.build();
sd.setTrainingConfig(config);
CustomListener listener = new CustomListener();
Map<String,INDArray> m = sd.output()
.data(new IrisDataSetIterator(150, 150))
.output("out")
.listeners(listener)
.exec();
assertEquals(1, m.size());
assertTrue(m.containsKey("out"));
assertNotNull(listener.z);
assertNotNull(listener.out);
}
Example 4
| Source File: ExecDebuggingListenerTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testExecDebugListener(){
SameDiff sd = SameDiff.create();
SDVariable in = sd.placeHolder("in", DataType.FLOAT, -1, 3);
SDVariable label = sd.placeHolder("label", DataType.FLOAT, 1, 2);
SDVariable w = sd.var("w", Nd4j.rand(DataType.FLOAT, 3, 2));
SDVariable b = sd.var("b", Nd4j.rand(DataType.FLOAT, 1, 2));
SDVariable sm = sd.nn.softmax("softmax", in.mmul(w).add(b));
SDVariable loss = sd.loss.logLoss("loss", label, sm);
INDArray i = Nd4j.rand(DataType.FLOAT, 1, 3);
INDArray l = Nd4j.rand(DataType.FLOAT, 1, 2);
sd.setTrainingConfig(TrainingConfig.builder()
.dataSetFeatureMapping("in")
.dataSetLabelMapping("label")
.updater(new Adam(0.001))
.build());
for(ExecDebuggingListener.PrintMode pm : ExecDebuggingListener.PrintMode.values()){
sd.setListeners(new ExecDebuggingListener(pm, -1, true));
// sd.output(m, "softmax");
sd.fit(new DataSet(i, l));
System.out.println("\n\n\n");
}
}
Example 5
| Source File: UIListenerTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testUIListenerBasic() throws Exception {
Nd4j.getRandom().setSeed(12345);
IrisDataSetIterator iter = new IrisDataSetIterator(150, 150);
SameDiff sd = getSimpleNet();
File dir = testDir.newFolder();
File f = new File(dir, "logFile.bin");
UIListener l = UIListener.builder(f)
.plotLosses(1)
.trainEvaluationMetrics("softmax", 0, Evaluation.Metric.ACCURACY, Evaluation.Metric.F1)
.updateRatios(1)
.build();
sd.setListeners(l);
sd.setTrainingConfig(TrainingConfig.builder()
.dataSetFeatureMapping("in")
.dataSetLabelMapping("label")
.updater(new Adam(1e-1))
.weightDecay(1e-3, true)
.build());
sd.fit(iter, 20);
//Test inference after training with UI Listener still around
Map<String, INDArray> m = new HashMap<>();
iter.reset();
m.put("in", iter.next().getFeatures());
INDArray out = sd.outputSingle(m, "softmax");
assertNotNull(out);
assertArrayEquals(new long[]{150, 3}, out.shape());
}
Example 6
| Source File: SameDiffRNNTestCases.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
public Object getConfiguration() throws Exception {
Nd4j.getRandom().setSeed(12345);
int miniBatchSize = 10;
int numLabelClasses = 6;
int nIn = 60;
int numUnits = 7;
int timeSteps = 3;
SameDiff sd = SameDiff.create();
SDVariable in = sd.placeHolder("in", DataType.FLOAT, miniBatchSize, timeSteps, nIn);
SDVariable label = sd.placeHolder("label", DataType.FLOAT, miniBatchSize, numLabelClasses);
SDVariable cLast = sd.var("cLast", Nd4j.zeros(DataType.FLOAT, miniBatchSize, numUnits));
SDVariable yLast = sd.var("yLast", Nd4j.zeros(DataType.FLOAT, miniBatchSize, numUnits));
LSTMLayerConfig c = LSTMLayerConfig.builder()
.lstmdataformat(LSTMDataFormat.NTS)
.directionMode(LSTMDirectionMode.FWD)
.gateAct(LSTMActivations.SIGMOID)
.cellAct(LSTMActivations.TANH)
.outAct(LSTMActivations.TANH)
.retFullSequence(true)
.retLastC(true)
.retLastH(true)
.build();
LSTMLayerOutputs outputs = new LSTMLayerOutputs(sd.rnn.lstmLayer(
in, cLast, yLast, null,
LSTMLayerWeights.builder()
.weights(sd.var("weights", Nd4j.rand(DataType.FLOAT, nIn, 4 * numUnits)))
.rWeights(sd.var("rWeights", Nd4j.rand(DataType.FLOAT, numUnits, 4 * numUnits)))
.peepholeWeights(sd.var("inputPeepholeWeights", Nd4j.rand(DataType.FLOAT, 3 * numUnits)))
.bias(sd.var("bias", Nd4j.rand(DataType.FLOAT, 4 * numUnits)))
.build(),
c), c);
// Behaviour with default settings: 3d (time series) input with shape
// [miniBatchSize, vectorSize, timeSeriesLength] -> 2d output [miniBatchSize, vectorSize]
SDVariable layer0 = outputs.getOutput();
SDVariable layer1 = layer0.mean(1);
SDVariable w1 = sd.var("w1", Nd4j.rand(DataType.FLOAT, numUnits, numLabelClasses));
SDVariable b1 = sd.var("b1", Nd4j.rand(DataType.FLOAT, numLabelClasses));
SDVariable out = sd.nn.softmax("out", layer1.mmul(w1).add(b1));
SDVariable loss = sd.loss.logLoss("loss", label, out);
//Also set the training configuration:
sd.setTrainingConfig(TrainingConfig.builder()
.updater(new Adam(5e-2))
.l1(1e-3).l2(1e-3)
.dataSetFeatureMapping("in") //features[0] -> "in" placeholder
.dataSetLabelMapping("label") //labels[0] -> "label" placeholder
.build());
return sd;
}
