use of org.nd4j.linalg.heartbeat.reports.Task in project deeplearning4j by deeplearning4j.
the class WordVectorsImpl method update.
protected void update(Environment env, Event event) {
if (!initDone) {
initDone = true;
Heartbeat heartbeat = Heartbeat.getInstance();
Task task = new Task();
task.setNumFeatures(layerSize);
if (vocab != null)
task.setNumSamples(vocab.numWords());
task.setNetworkType(Task.NetworkType.DenseNetwork);
task.setArchitectureType(Task.ArchitectureType.WORDVECTORS);
heartbeat.reportEvent(event, env, task);
}
}
use of org.nd4j.linalg.heartbeat.reports.Task in project deeplearning4j by deeplearning4j.
the class ModelSerializer method taskByModel.
/**
*
* @param model
* @return
*/
public static Task taskByModel(Model model) {
Task task = new Task();
try {
task.setArchitectureType(Task.ArchitectureType.RECURRENT);
if (model instanceof ComputationGraph) {
task.setNetworkType(Task.NetworkType.ComputationalGraph);
ComputationGraph network = (ComputationGraph) model;
try {
if (network.getLayers() != null && network.getLayers().length > 0) {
for (Layer layer : network.getLayers()) {
if (layer instanceof RBM || layer instanceof org.deeplearning4j.nn.layers.feedforward.rbm.RBM) {
task.setArchitectureType(Task.ArchitectureType.RBM);
break;
}
if (layer.type().equals(Layer.Type.CONVOLUTIONAL)) {
task.setArchitectureType(Task.ArchitectureType.CONVOLUTION);
break;
} else if (layer.type().equals(Layer.Type.RECURRENT) || layer.type().equals(Layer.Type.RECURSIVE)) {
task.setArchitectureType(Task.ArchitectureType.RECURRENT);
break;
}
}
} else
task.setArchitectureType(Task.ArchitectureType.UNKNOWN);
} catch (Exception e) {
// do nothing here
}
} else if (model instanceof MultiLayerNetwork) {
task.setNetworkType(Task.NetworkType.MultilayerNetwork);
MultiLayerNetwork network = (MultiLayerNetwork) model;
try {
if (network.getLayers() != null && network.getLayers().length > 0) {
for (Layer layer : network.getLayers()) {
if (layer instanceof RBM || layer instanceof org.deeplearning4j.nn.layers.feedforward.rbm.RBM) {
task.setArchitectureType(Task.ArchitectureType.RBM);
break;
}
if (layer.type().equals(Layer.Type.CONVOLUTIONAL)) {
task.setArchitectureType(Task.ArchitectureType.CONVOLUTION);
break;
} else if (layer.type().equals(Layer.Type.RECURRENT) || layer.type().equals(Layer.Type.RECURSIVE)) {
task.setArchitectureType(Task.ArchitectureType.RECURRENT);
break;
}
}
} else
task.setArchitectureType(Task.ArchitectureType.UNKNOWN);
} catch (Exception e) {
// do nothing here
}
}
return task;
} catch (Exception e) {
task.setArchitectureType(Task.ArchitectureType.UNKNOWN);
task.setNetworkType(Task.NetworkType.DenseNetwork);
return task;
}
}
use of org.nd4j.linalg.heartbeat.reports.Task in project deeplearning4j by deeplearning4j.
the class MultiLayerTest method testCid.
@Test
@Ignore
public void testCid() throws Exception {
System.out.println(EnvironmentUtils.buildCId());
Environment environment = EnvironmentUtils.buildEnvironment();
environment.setSerialVersionID(EnvironmentUtils.buildCId());
Task task = TaskUtils.buildTask(Nd4j.create(new double[] { 1, 2, 3, 4, 5, 6 }));
Heartbeat.getInstance().reportEvent(Event.STANDALONE, environment, task);
Thread.sleep(25000);
}
use of org.nd4j.linalg.heartbeat.reports.Task in project deeplearning4j by deeplearning4j.
the class SparkComputationGraph method update.
private void update(int mr, long mg) {
Environment env = EnvironmentUtils.buildEnvironment();
env.setNumCores(mr);
env.setAvailableMemory(mg);
Task task = ModelSerializer.taskByModel(network);
Heartbeat.getInstance().reportEvent(Event.SPARK, env, task);
}
use of org.nd4j.linalg.heartbeat.reports.Task in project deeplearning4j by deeplearning4j.
the class SparkDl4jMultiLayer method update.
private void update(int mr, long mg) {
Environment env = EnvironmentUtils.buildEnvironment();
env.setNumCores(mr);
env.setAvailableMemory(mg);
Task task = ModelSerializer.taskByModel(network);
Heartbeat.getInstance().reportEvent(Event.SPARK, env, task);
}
Aggregations