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Java example source code file (DefaultParamInitializer.java)
The DefaultParamInitializer.java Java example source code/* * * * Copyright 2015 Skymind,Inc. * * * * Licensed under the Apache License, Version 2.0 (the "License"); * * you may not use this file except in compliance with the License. * * You may obtain a copy of the License at * * * * http://www.apache.org/licenses/LICENSE-2.0 * * * * Unless required by applicable law or agreed to in writing, software * * distributed under the License is distributed on an "AS IS" BASIS, * * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * * See the License for the specific language governing permissions and * * limitations under the License. * */ package org.deeplearning4j.nn.params; import org.canova.api.conf.Configuration; import org.deeplearning4j.nn.weights.WeightInitUtil; import org.deeplearning4j.nn.api.ParamInitializer; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.conf.distribution.Distributions; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.rng.distribution.Distribution; import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.indexing.NDArrayIndex; import java.util.LinkedHashMap; import java.util.Map; /** * Static weight initializer with just a weight matrix and a bias * @author Adam Gibson */ public class DefaultParamInitializer implements ParamInitializer { public final static String WEIGHT_KEY = "W"; public final static String BIAS_KEY = "b"; @Override public int numParams(NeuralNetConfiguration conf, boolean backprop) { org.deeplearning4j.nn.conf.layers.FeedForwardLayer layerConf = (org.deeplearning4j.nn.conf.layers.FeedForwardLayer) conf.getLayer(); int nIn = layerConf.getNIn(); int nOut = layerConf.getNOut(); return nIn*nOut + nOut; //weights + bias } @Override public void init(Map<String, INDArray> params, NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParameters) { if(!(conf.getLayer() instanceof org.deeplearning4j.nn.conf.layers.FeedForwardLayer)) throw new IllegalArgumentException("unsupported layer type: " + conf.getLayer().getClass().getName()); int length = numParams(conf,true); if(paramsView.length() != length) throw new IllegalStateException("Expected params view of length " + length + ", got length " + paramsView.length()); org.deeplearning4j.nn.conf.layers.FeedForwardLayer layerConf = (org.deeplearning4j.nn.conf.layers.FeedForwardLayer) conf.getLayer(); int nIn = layerConf.getNIn(); int nOut = layerConf.getNOut(); int nWeightParams = nIn*nOut; INDArray weightView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0,nWeightParams)); INDArray biasView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nWeightParams, nWeightParams + nOut)); params.put(WEIGHT_KEY,createWeightMatrix(conf, weightView, initializeParameters)); params.put(BIAS_KEY,createBias(conf, biasView, initializeParameters)); conf.addVariable(WEIGHT_KEY); conf.addVariable(BIAS_KEY); } @Override public Map<String, INDArray> getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) { org.deeplearning4j.nn.conf.layers.FeedForwardLayer layerConf = (org.deeplearning4j.nn.conf.layers.FeedForwardLayer) conf.getLayer(); int nIn = layerConf.getNIn(); int nOut = layerConf.getNOut(); int nWeightParams = nIn*nOut; INDArray weightGradientView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0,nWeightParams)).reshape('f',nIn,nOut); INDArray biasView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nWeightParams, nWeightParams + nOut)); //Already a row vector Map<String,INDArray> out = new LinkedHashMap<>(); out.put(WEIGHT_KEY, weightGradientView); out.put(BIAS_KEY, biasView); return out; } protected INDArray createBias(NeuralNetConfiguration conf, INDArray biasParamView, boolean initializeParameters) { org.deeplearning4j.nn.conf.layers.FeedForwardLayer layerConf = (org.deeplearning4j.nn.conf.layers.FeedForwardLayer) conf.getLayer(); if(initializeParameters) { INDArray ret = Nd4j.valueArrayOf(layerConf.getNOut(), layerConf.getBiasInit()); biasParamView.assign(ret); } return biasParamView; } 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()); INDArray ret = WeightInitUtil.initWeights( layerConf.getNIn(), layerConf.getNOut(), layerConf.getWeightInit(), dist, weightParamView); return ret; } else { return WeightInitUtil.reshapeWeights(new int[]{layerConf.getNIn(), layerConf.getNOut()}, weightParamView); } } } Other Java examples (source code examples)Here is a short list of links related to this Java DefaultParamInitializer.java source code file: |
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