org.deeplearning4j.nn.conf.layers.RnnOutputLayer Maven / Gradle / Ivy

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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
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 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * Unless required by applicable law or agreed to in writing, software
 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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package org.deeplearning4j.nn.conf.layers;

import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import lombok.ToString;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.linalg.activations.impl.ActivationSoftmax;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

import java.util.Collection;
import java.util.Map;

@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class RnnOutputLayer extends BaseOutputLayer {

    private RNNFormat rnnDataFormat;

    private RnnOutputLayer(Builder builder) {
        super(builder);
        initializeConstraints(builder);
        this.rnnDataFormat = builder.rnnDataFormat;
    }

    @Override
    public Layer instantiate(NeuralNetConfiguration conf, Collection trainingListeners,
                             int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
        LayerValidation.assertNInNOutSet("RnnOutputLayer", getLayerName(), layerIndex, getNIn(), getNOut());

        org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer ret =
                new org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer(conf, networkDataType);
        ret.setListeners(trainingListeners);
        ret.setIndex(layerIndex);
        ret.setParamsViewArray(layerParamsView);
        Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
        ret.setParamTable(paramTable);
        ret.setConf(conf);
        return ret;
    }

    @Override
    public ParamInitializer initializer() {
        return DefaultParamInitializer.getInstance();
    }

    @Override
    public InputType getOutputType(int layerIndex, InputType inputType) {
        if (inputType == null || inputType.getType() != InputType.Type.RNN) {
            throw new IllegalStateException("Invalid input type for RnnOutputLayer (layer index = " + layerIndex
                    + ", layer name=\"" + getLayerName() + "\"): Expected RNN input, got " + inputType);
        }
        InputType.InputTypeRecurrent itr = (InputType.InputTypeRecurrent) inputType;

        return InputType.recurrent(nOut, itr.getTimeSeriesLength(), itr.getFormat());
    }

    @Override
    public void setNIn(InputType inputType, boolean override) {
        if (inputType == null || inputType.getType() != InputType.Type.RNN) {
            throw new IllegalStateException("Invalid input type for RnnOutputLayer (layer name=\"" + getLayerName()
                    + "\"): Expected RNN input, got " + inputType);
        }

        InputType.InputTypeRecurrent r = (InputType.InputTypeRecurrent) inputType;
        if(rnnDataFormat == null || override) {
            this.rnnDataFormat = r.getFormat();
        }

        if (nIn <= 0 || override) {
            this.nIn = r.getSize();
        }
    }

    @Override
    public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
        return InputTypeUtil.getPreprocessorForInputTypeRnnLayers(inputType, rnnDataFormat, getLayerName());
    }


    public static class Builder extends BaseOutputLayer.Builder {

        private RNNFormat rnnDataFormat;
        public Builder() {
            //Set default activation function to softmax (to match default loss function MCXENT)
            this.setActivationFn(new ActivationSoftmax());
        }

        /**
         * @param lossFunction Loss function for the output layer
         */
        public Builder(LossFunction lossFunction) {
            lossFunction(lossFunction);
            //Set default activation function to softmax (for consistent behaviour with no-arg constructor)
            this.setActivationFn(new ActivationSoftmax());
        }

        /**
         * @param lossFunction Loss function for the output layer
         */
        public Builder(ILossFunction lossFunction) {
            this.setLossFn(lossFunction);
            //Set default activation function to softmax (for consistent behaviour with no-arg constructor)
            this.setActivationFn(new ActivationSoftmax());
        }

        @Override
        @SuppressWarnings("unchecked")
        public RnnOutputLayer build() {
            return new RnnOutputLayer(this);
        }

        /**
         * @param rnnDataFormat Data format expected by the layer. NCW = [miniBatchSize, size, timeSeriesLength],
         * NWC = [miniBatchSize, timeSeriesLength, size]. Defaults to NCW.
         */
        public Builder dataFormat(RNNFormat rnnDataFormat){
            this.rnnDataFormat = rnnDataFormat;
            return this;
        }
    }
}