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Java example source code file (GravesLSTM.java)

This example Java source code file (GravesLSTM.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

fwdpassreturn, gradient, graveslstm, indarray, layer, lstms, not, override, pair, shape, state_key_prev_memcell, string, type, unsupportedoperationexception

The GravesLSTM.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.layers.recurrent;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.params.GravesLSTMParamInitializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.ops.transforms.Transforms;

import static org.nd4j.linalg.indexing.NDArrayIndex.interval;

/**
 * LSTM layer implementation.
 * Based on Graves: Supervised Sequence Labelling with Recurrent Neural Networks
 * http://www.cs.toronto.edu/~graves/phd.pdf
 * See also for full/vectorized equations (and a comparison to other LSTM variants):
 * Greff et al. 2015, "LSTM: A Search Space Odyssey", pg11. This is the "vanilla" variant in said paper
 * http://arxiv.org/pdf/1503.04069.pdf
 *
 * @author Alex Black
 */
public class GravesLSTM extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.layers.GravesLSTM> {
    public static final String STATE_KEY_PREV_ACTIVATION = "prevAct";
    public static final String STATE_KEY_PREV_MEMCELL = "prevMem";

    public GravesLSTM(NeuralNetConfiguration conf) {
        super(conf);
    }

    public GravesLSTM(NeuralNetConfiguration conf, INDArray input) {
        super(conf, input);
    }

    @Override
    public Gradient gradient() {
        throw new UnsupportedOperationException("gradient() method for layerwise pretraining: not supported for LSTMs (pretraining not possible)");
    }

    @Override
    public Gradient calcGradient(Gradient layerError, INDArray activation) {
        throw new UnsupportedOperationException("Not supported");
    }

    @Override
    public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon) {
        return backpropGradientHelper(epsilon, false, -1);
    }

    @Override
    public Pair<Gradient, INDArray> tbpttBackpropGradient(INDArray epsilon, int tbpttBackwardLength) {
        return backpropGradientHelper(epsilon, true, tbpttBackwardLength);
    }


    private Pair<Gradient, INDArray> backpropGradientHelper(final INDArray epsilon,final boolean truncatedBPTT,final int tbpttBackwardLength) {

        final INDArray inputWeights = getParam(GravesLSTMParamInitializer.INPUT_WEIGHT_KEY);
        final INDArray recurrentWeights = getParam(GravesLSTMParamInitializer.RECURRENT_WEIGHT_KEY);    //Shape: [hiddenLayerSize,4*hiddenLayerSize+3]; order: [wI,wF,wO,wG,wFF,wOO,wGG]

        //First: Do forward pass to get gate activations, zs etc.
        FwdPassReturn fwdPass;
        if (truncatedBPTT) {
            fwdPass = activateHelper(true, stateMap.get(STATE_KEY_PREV_ACTIVATION), stateMap.get(STATE_KEY_PREV_MEMCELL), true);
            //Store last time step of output activations and memory cell state in tBpttStateMap
            tBpttStateMap.put(STATE_KEY_PREV_ACTIVATION, fwdPass.lastAct);
            tBpttStateMap.put(STATE_KEY_PREV_MEMCELL, fwdPass.lastMemCell);
        } else {
            fwdPass = activateHelper(true, null, null, true);
        }


        return LSTMHelpers.backpropGradientHelper(this.conf,
                this.input,
                recurrentWeights,
                inputWeights,
                epsilon,
                truncatedBPTT,
                tbpttBackwardLength,
                fwdPass,
                true,
                GravesLSTMParamInitializer.INPUT_WEIGHT_KEY,
                GravesLSTMParamInitializer.RECURRENT_WEIGHT_KEY,
                GravesLSTMParamInitializer.BIAS_KEY,
                gradientViews);
    }



    @Override
    public INDArray preOutput(INDArray x) {
        return activate(x, true);
    }

    @Override
    public INDArray preOutput(INDArray x, boolean training) {
        return activate(x, training);
    }

    @Override
    public INDArray activate(INDArray input, boolean training) {
        setInput(input);
        return activateHelper(training, null, null, false).fwdPassOutput;
    }

    @Override
    public INDArray activate(INDArray input) {
        setInput(input);
        return activateHelper(true, null, null, false).fwdPassOutput;
    }

    @Override
    public INDArray activate(boolean training) {
        return activateHelper(training, null, null, false).fwdPassOutput;
    }

    @Override
    public INDArray activate() {

        return activateHelper(false, null, null, false).fwdPassOutput;
    }

    private FwdPassReturn activateHelper(final boolean training,
                                         final INDArray prevOutputActivations,
                                         final INDArray prevMemCellState,
                                         boolean forBackprop) {

        final INDArray recurrentWeights = getParam(GravesLSTMParamInitializer.RECURRENT_WEIGHT_KEY);    //Shape: [hiddenLayerSize,4*hiddenLayerSize+3]; order: [wI,wF,wO,wG,wFF,wOO,wGG]
        final INDArray inputWeights = getParam(GravesLSTMParamInitializer.INPUT_WEIGHT_KEY);            //Shape: [n^(L-1),4*hiddenLayerSize]; order: [wi,wf,wo,wg]
        final INDArray biases = getParam(GravesLSTMParamInitializer.BIAS_KEY); //by row: IFOG			//Shape: [4,hiddenLayerSize]; order: [bi,bf,bo,bg]^T

        return LSTMHelpers.activateHelper(this,this.conf,this.input,recurrentWeights,inputWeights,biases,training,prevOutputActivations,prevMemCellState,forBackprop,true,GravesLSTMParamInitializer.INPUT_WEIGHT_KEY);
    }

    @Override
    public INDArray activationMean() {
        return activate();
    }

    @Override
    public Type type() {
        return Type.RECURRENT;
    }

    @Override
    public Layer transpose() {
        throw new UnsupportedOperationException("Not supported");
    }

    @Override
    public double calcL2() {
        if (!conf.isUseRegularization() || conf.getLayer().getL2() <= 0.0) return 0.0;

        double l2Norm = getParam(GravesLSTMParamInitializer.RECURRENT_WEIGHT_KEY).norm2Number().doubleValue();
        double sumSquaredWeights = l2Norm*l2Norm;

        l2Norm = getParam(GravesLSTMParamInitializer.INPUT_WEIGHT_KEY).norm2Number().doubleValue();
        sumSquaredWeights += l2Norm*l2Norm;

        return 0.5 * conf.getLayer().getL2() * sumSquaredWeights;
    }

    @Override
    public double calcL1() {
        if (!conf.isUseRegularization() || conf.getLayer().getL1() <= 0.0) return 0.0;
        double l1 = getParam(GravesLSTMParamInitializer.RECURRENT_WEIGHT_KEY).norm1Number().doubleValue()
                + getParam(GravesLSTMParamInitializer.INPUT_WEIGHT_KEY).norm1Number().doubleValue();
        return conf.getLayer().getL1() * l1;
    }

    @Override
    public INDArray rnnTimeStep(INDArray input) {
        setInput(input);
        FwdPassReturn fwdPass = activateHelper(false, stateMap.get(STATE_KEY_PREV_ACTIVATION), stateMap.get(STATE_KEY_PREV_MEMCELL), false);
        INDArray outAct = fwdPass.fwdPassOutput;
        //Store last time step of output activations and memory cell state for later use:
        stateMap.put(STATE_KEY_PREV_ACTIVATION, fwdPass.lastAct);
        stateMap.put(STATE_KEY_PREV_MEMCELL, fwdPass.lastMemCell);

        return outAct;
    }



    @Override
    public INDArray rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT) {
        setInput(input);
        FwdPassReturn fwdPass = activateHelper(training, stateMap.get(STATE_KEY_PREV_ACTIVATION), stateMap.get(STATE_KEY_PREV_MEMCELL), false);
        INDArray outAct = fwdPass.fwdPassOutput;
        if (storeLastForTBPTT) {
            //Store last time step of output activations and memory cell state in tBpttStateMap
            tBpttStateMap.put(STATE_KEY_PREV_ACTIVATION, fwdPass.lastAct);
            tBpttStateMap.put(STATE_KEY_PREV_MEMCELL, fwdPass.lastMemCell);
        }

        return outAct;
    }
}

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