protected INDArray createUserWeightMatrix(NeuralNetConfiguration conf, INDArray weightParamView, boolean initializeParameters) { FeedForwardLayer layerConf = (FeedForwardLayer) conf.getLayer(); if (initializeParameters) { Distribution dist = Distributions.createDistribution(layerConf.getDist()); return createWeightMatrix(numUsers, layerConf.getNOut(), layerConf.getWeightInit(), dist, weightParamView, true); } else { return createWeightMatrix(numUsers, layerConf.getNOut(), null, null, weightParamView, false); } }
protected INDArray createWeightMatrix(NeuralNetConfiguration conf, INDArray weightParamView, boolean initializeParameters) { org.deeplearning4j.nn.conf.layers.FeedForwardLayer layerConf = (org.deeplearning4j.nn.conf.layers.FeedForwardLayer) conf.getLayer(); if (initializeParameters) { Distribution dist = Distributions.createDistribution(layerConf.getDist()); return createWeightMatrix(layerConf.getNIn(), layerConf.getNOut(), layerConf.getWeightInit(), dist, weightParamView, true); } else { return createWeightMatrix(layerConf.getNIn(), layerConf.getNOut(), null, null, weightParamView, false); } }
double forgetGateInit = layerConf.getForgetGateBiasInit(); Distribution dist = Distributions.createDistribution(layerConf.getDist());
double forgetGateInit = layerConf.getForgetGateBiasInit(); Distribution dist = Distributions.createDistribution(layerConf.getDist());
double forgetGateInit = layerConf.getForgetGateBiasInit(); Distribution dist = Distributions.createDistribution(layerConf.getDist());
(org.deeplearning4j.nn.conf.layers.ConvolutionLayer) conf.getLayer(); if (initializeParams) { Distribution dist = Distributions.createDistribution(layerConf.getDist()); int[] kernel = layerConf.getKernelSize(); int[] stride = layerConf.getStride();
Distribution dist = Distributions.createDistribution(layer.getDist());