Java example source code file (GravesLSTMParamInitializer.java)

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Java - Java tags/keywords

bias_key, distribution, expected, illegalstateexception, indarray, indarrayindex, input_weight_key, map, neuralnetconfiguration, order, override, paraminitializer, recurrent_weight_key, string, util

The GravesLSTMParamInitializer.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.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.distribution.Distributions;
import org.deeplearning4j.nn.weights.WeightInitUtil;
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.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.util.LinkedHashMap;
import java.util.Map;

/**LSTM Parameter initializer, for LSTM based on
 * Graves: Supervised Sequence Labelling with Recurrent Neural Networks
 * http://www.cs.toronto.edu/~graves/phd.pdf
 */
public class GravesLSTMParamInitializer implements ParamInitializer {
	/** Weights for previous time step -> current time step connections */
    public final static String RECURRENT_WEIGHT_KEY = "RW";
    public final static String BIAS_KEY = DefaultParamInitializer.BIAS_KEY;
    public final static String INPUT_WEIGHT_KEY = DefaultParamInitializer.WEIGHT_KEY;

    @Override
    public int numParams(NeuralNetConfiguration conf, boolean backprop) {
        org.deeplearning4j.nn.conf.layers.GravesLSTM layerConf =
                (org.deeplearning4j.nn.conf.layers.GravesLSTM) conf.getLayer();

        int nL = layerConf.getNOut();	//i.e., n neurons in this layer
        int nLast = layerConf.getNIn();	//i.e., n neurons in previous layer

        int nParams = nLast * (4*nL)   //"input" weights
                    + nL * (4 * nL + 3) //recurrent weights
                    + 4*nL;             //bias

        return nParams;
    }

    @Override
    public void init(Map<String, INDArray> params, NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams) {
        org.deeplearning4j.nn.conf.layers.GravesLSTM layerConf =
                (org.deeplearning4j.nn.conf.layers.GravesLSTM) conf.getLayer();
        double forgetGateInit = layerConf.getForgetGateBiasInit();

        Distribution dist = Distributions.createDistribution(layerConf.getDist());

        int nL = layerConf.getNOut();	//i.e., n neurons in this layer
        int nLast = layerConf.getNIn();	//i.e., n neurons in previous layer
        
        conf.addVariable(INPUT_WEIGHT_KEY);
        conf.addVariable(RECURRENT_WEIGHT_KEY);
        conf.addVariable(BIAS_KEY);

        int length = numParams(conf,true);
        if(paramsView.length() != length) throw new IllegalStateException("Expected params view of length " + length + ", got length " + paramsView.length());

        int nParamsIn = nLast * (4*nL);
        int nParamsRecurrent = nL * (4*nL+3);
        int nBias = 4*nL;
        INDArray inputWeightView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, nParamsIn));
        INDArray recurrentWeightView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nParamsIn, nParamsIn + nParamsRecurrent));
        INDArray biasView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nParamsIn+nParamsRecurrent, nParamsIn+nParamsRecurrent+nBias));

        if(initializeParams) {
            params.put(INPUT_WEIGHT_KEY, WeightInitUtil.initWeights(nLast, 4 * nL, layerConf.getWeightInit(), dist, inputWeightView));
            params.put(RECURRENT_WEIGHT_KEY, WeightInitUtil.initWeights(nL, 4 * nL + 3, layerConf.getWeightInit(), dist, recurrentWeightView));
            biasView.put(new INDArrayIndex[]{NDArrayIndex.point(0), NDArrayIndex.interval(nL, 2 * nL)}, Nd4j.ones(1, nL).muli(forgetGateInit));   //Order: input, forget, output, input modulation, i.e., IFOG}
            /*The above line initializes the forget gate biases to specified value.
             * See Sutskever PhD thesis, pg19:
             * "it is important for [the forget gate activations] to be approximately 1 at the early stages of learning,
             *  which is accomplished by initializing [the forget gate biases] to a large value (such as 5). If it is
             *  not done, it will be harder to learn long range dependencies because the smaller values of the forget
             *  gates will create a vanishing gradients problem."
             *  http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
             */
            params.put(BIAS_KEY, biasView);
        } else {
            params.put(INPUT_WEIGHT_KEY, WeightInitUtil.reshapeWeights(new int[]{nLast, 4 * nL}, inputWeightView));
            params.put(RECURRENT_WEIGHT_KEY, WeightInitUtil.reshapeWeights(new int[]{nL, 4 * nL + 3}, recurrentWeightView));
            params.put(BIAS_KEY, biasView);
        }

    }

    @Override
    public Map<String, INDArray> getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {
        org.deeplearning4j.nn.conf.layers.GravesLSTM layerConf = (org.deeplearning4j.nn.conf.layers.GravesLSTM) conf.getLayer();

        int nL = layerConf.getNOut();	//i.e., n neurons in this layer
        int nLast = layerConf.getNIn();	//i.e., n neurons in previous layer

        int length = numParams(conf,true);
        if(gradientView.length() != length) throw new IllegalStateException("Expected gradient view of length " + length + ", got length " + gradientView.length());

        int nParamsIn = nLast * (4*nL);
        int nParamsRecurrent = nL * (4*nL+3);
        int nBias = 4*nL;
        INDArray inputWeightGradView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, nParamsIn)).reshape('f',nLast, 4*nL);
        INDArray recurrentWeightGradView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nParamsIn, nParamsIn + nParamsRecurrent)).reshape('f', nL, 4*nL + 3);
        INDArray biasGradView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nParamsIn+nParamsRecurrent, nParamsIn+nParamsRecurrent+nBias));   //already a row vector

        Map<String,INDArray> out = new LinkedHashMap<>();
        out.put(INPUT_WEIGHT_KEY,inputWeightGradView);
        out.put(RECURRENT_WEIGHT_KEY, recurrentWeightGradView);
        out.put(BIAS_KEY, biasGradView);

        return out;
    }
}