/** * Pretrain network with multiple inputs and/or outputs */ public void pretrain(MultiDataSetIterator iter) { if (!configuration.isPretrain()) return; if (flattenedGradients == null) { initGradientsView(); } //Assume here that all layers are pretrainable layers for (int i = 0; i < topologicalOrder.length; i++) { if (!vertices[i].hasLayer()) continue; if (vertices[i].getLayer() instanceof IOutputLayer) continue; //Don't pretrain output layer if (!vertices[i].getLayer().isPretrainLayer()) continue; //Skip layers that aren't pretrainable pretrainLayer(vertices[i].getVertexName(), iter); } }
public GraphBuilder(ComputationGraphConfiguration newConf, NeuralNetConfiguration.Builder globalConfiguration) { ComputationGraphConfiguration clonedConf = newConf.clone(); this.vertices = clonedConf.getVertices(); this.vertexInputs = clonedConf.getVertexInputs(); this.networkInputs = clonedConf.getNetworkInputs(); this.networkOutputs = clonedConf.getNetworkOutputs(); this.pretrain = clonedConf.isPretrain(); this.backprop = clonedConf.isBackprop(); this.backpropType = clonedConf.getBackpropType(); this.tbpttFwdLength = clonedConf.getTbpttFwdLength(); this.tbpttBackLength = clonedConf.getTbpttBackLength(); this.globalConfiguration = globalConfiguration; //this.getGlobalConfiguration().setSeed(clonedConf.getDefaultConfiguration().getSeed()); }
if (!configuration.isPretrain()) return; if (flattenedGradients == null) {
if (configuration.isPretrain()) { pretrain(dataSetIterator);
multiDataSetIterator = multi; if (configuration.isPretrain()) { pretrain(multiDataSetIterator);
workspaceCache); if (configuration.isPretrain()) { MultiDataSetIterator iter = new SingletonMultiDataSetIterator(new org.nd4j.linalg.dataset.MultiDataSet(inputs, labels,