Java Code Examples for org.nd4j.linalg.activations.Activation#SELU
The following examples show how to use
org.nd4j.linalg.activations.Activation#SELU .
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Example 1
| Source File: KerasActivationUtils.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
/**
* Map Keras to DL4J activation functions.
*
* @param conf Keras layer configuration
* @param kerasActivation String containing Keras activation function name
* @return Activation enum value containing DL4J activation function name
*/
public static Activation mapToActivation(String kerasActivation, KerasLayerConfiguration conf)
throws UnsupportedKerasConfigurationException {
Activation dl4jActivation;
if (kerasActivation.equals(conf.getKERAS_ACTIVATION_SOFTMAX())) {
dl4jActivation = Activation.SOFTMAX;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_SOFTPLUS())) {
dl4jActivation = Activation.SOFTPLUS;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_SOFTSIGN())) {
dl4jActivation = Activation.SOFTSIGN;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_RELU())) {
dl4jActivation = Activation.RELU;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_RELU6())) {
dl4jActivation = Activation.RELU6;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_ELU())) {
dl4jActivation = Activation.ELU;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_SELU())) {
dl4jActivation = Activation.SELU;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_TANH())) {
dl4jActivation = Activation.TANH;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_SIGMOID())) {
dl4jActivation = Activation.SIGMOID;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_HARD_SIGMOID())) {
dl4jActivation = Activation.HARDSIGMOID;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_LINEAR())) {
dl4jActivation = Activation.IDENTITY;
} else if (kerasActivation.equals(conf.getKERAS_ACTIVATION_SWISH())) {
dl4jActivation = Activation.SWISH;
} else {
throw new UnsupportedKerasConfigurationException(
"Unknown Keras activation function " + kerasActivation);
}
return dl4jActivation;
}
Example 2
| Source File: OutputLayerTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnnLossLayer(){
for(WorkspaceMode ws : WorkspaceMode.values()) {
log.info("*** Testing workspace: " + ws);
for (Activation a : new Activation[]{Activation.TANH, Activation.SELU}) {
//Check that (A+identity) is equal to (identity+A), for activation A
//i.e., should get same output and weight gradients for both
MultiLayerConfiguration conf1 =
new NeuralNetConfiguration.Builder().seed(12345L)
.updater(new NoOp())
.convolutionMode(ConvolutionMode.Same)
.inferenceWorkspaceMode(ws)
.trainingWorkspaceMode(ws)
.list()
.layer(new ConvolutionLayer.Builder().nIn(3).nOut(4).activation(Activation.IDENTITY)
.kernelSize(2, 2).stride(1, 1)
.dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build())
.layer(new CnnLossLayer.Builder(LossFunction.MSE)
.activation(a)
.build())
.build();
MultiLayerConfiguration conf2 =
new NeuralNetConfiguration.Builder().seed(12345L)
.updater(new NoOp())
.convolutionMode(ConvolutionMode.Same)
.inferenceWorkspaceMode(ws)
.trainingWorkspaceMode(ws)
.list()
.layer(new ConvolutionLayer.Builder().nIn(3).nOut(4).activation(a)
.kernelSize(2, 2).stride(1, 1)
.dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build())
.layer(new CnnLossLayer.Builder(LossFunction.MSE)
.activation(Activation.IDENTITY)
.build())
.build();
MultiLayerNetwork mln = new MultiLayerNetwork(conf1);
mln.init();
MultiLayerNetwork mln2 = new MultiLayerNetwork(conf2);
mln2.init();
mln2.setParams(mln.params());
INDArray in = Nd4j.rand(new int[]{3, 3, 5, 5});
INDArray out1 = mln.output(in);
INDArray out2 = mln2.output(in);
assertEquals(out1, out2);
INDArray labels = Nd4j.rand(out1.shape());
mln.setInput(in);
mln.setLabels(labels);
mln2.setInput(in);
mln2.setLabels(labels);
mln.computeGradientAndScore();
mln2.computeGradientAndScore();
assertEquals(mln.score(), mln2.score(), 1e-6);
assertEquals(mln.gradient().gradient(), mln2.gradient().gradient());
//Also check computeScoreForExamples
INDArray in2a = Nd4j.rand(new int[]{1, 3, 5, 5});
INDArray labels2a = Nd4j.rand(new int[]{1, 4, 5, 5});
INDArray in2 = Nd4j.concat(0, in2a, in2a);
INDArray labels2 = Nd4j.concat(0, labels2a, labels2a);
INDArray s = mln.scoreExamples(new DataSet(in2, labels2), false);
assertArrayEquals(new long[]{2, 1}, s.shape());
assertEquals(s.getDouble(0), s.getDouble(1), 1e-6);
TestUtils.testModelSerialization(mln);
}
}
}
Example 3
| Source File: OutputLayerTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testCnnLossLayerCompGraph(){
for(WorkspaceMode ws : WorkspaceMode.values()) {
log.info("*** Testing workspace: " + ws);
for (Activation a : new Activation[]{Activation.TANH, Activation.SELU}) {
//Check that (A+identity) is equal to (identity+A), for activation A
//i.e., should get same output and weight gradients for both
ComputationGraphConfiguration conf1 =
new NeuralNetConfiguration.Builder().seed(12345L)
.updater(new NoOp())
.convolutionMode(ConvolutionMode.Same)
.inferenceWorkspaceMode(ws)
.trainingWorkspaceMode(ws)
.graphBuilder()
.addInputs("in")
.addLayer("0", new ConvolutionLayer.Builder().nIn(3).nOut(4).activation(Activation.IDENTITY)
.kernelSize(2, 2).stride(1, 1)
.dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build(), "in")
.addLayer("1", new CnnLossLayer.Builder(LossFunction.MSE)
.activation(a)
.build(), "0")
.setOutputs("1")
.build();
ComputationGraphConfiguration conf2 =
new NeuralNetConfiguration.Builder().seed(12345L)
.updater(new NoOp())
.convolutionMode(ConvolutionMode.Same)
.inferenceWorkspaceMode(ws)
.trainingWorkspaceMode(ws)
.graphBuilder()
.addInputs("in")
.addLayer("0", new ConvolutionLayer.Builder().nIn(3).nOut(4).activation(a)
.kernelSize(2, 2).stride(1, 1)
.dist(new NormalDistribution(0, 1.0))
.updater(new NoOp()).build(), "in")
.addLayer("1", new CnnLossLayer.Builder(LossFunction.MSE)
.activation(Activation.IDENTITY)
.build(), "0")
.setOutputs("1")
.build();
ComputationGraph graph = new ComputationGraph(conf1);
graph.init();
ComputationGraph graph2 = new ComputationGraph(conf2);
graph2.init();
graph2.setParams(graph.params());
INDArray in = Nd4j.rand(new int[]{3, 3, 5, 5});
INDArray out1 = graph.outputSingle(in);
INDArray out2 = graph2.outputSingle(in);
assertEquals(out1, out2);
INDArray labels = Nd4j.rand(out1.shape());
graph.setInput(0,in);
graph.setLabels(labels);
graph2.setInput(0,in);
graph2.setLabels(labels);
graph.computeGradientAndScore();
graph2.computeGradientAndScore();
assertEquals(graph.score(), graph2.score(), 1e-6);
assertEquals(graph.gradient().gradient(), graph2.gradient().gradient());
//Also check computeScoreForExamples
INDArray in2a = Nd4j.rand(new int[]{1, 3, 5, 5});
INDArray labels2a = Nd4j.rand(new int[]{1, 4, 5, 5});
INDArray in2 = Nd4j.concat(0, in2a, in2a);
INDArray labels2 = Nd4j.concat(0, labels2a, labels2a);
INDArray s = graph.scoreExamples(new DataSet(in2, labels2), false);
assertArrayEquals(new long[]{2, 1}, s.shape());
assertEquals(s.getDouble(0), s.getDouble(1), 1e-6);
TestUtils.testModelSerialization(graph);
}
}
}
