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

Go to download
Show more of this group Show more artifacts with this name
Show all versions of deeplearning4j-nn Show documentation
There is a newer version: 1.0.0-M2.1
/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * 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.
 *
 * 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.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

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.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;

/**
 * A version of {@link OutputLayer} for recurrent neural networks. Expects inputs of size [minibatch,nIn,sequenceLength]
 * and labels of shape [minibatch,nOut,sequenceLength]. It also supports mask arrays.
 * 

 * Note that RnnOutputLayer can also be used for 1D CNN layers, which also have [minibatch,nOut,sequenceLength]
 * activations/labels shape.
 *
 * See also: {@link RnnLossLayer}
 */
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class RnnOutputLayer extends BaseOutputLayer {

    private RnnOutputLayer(Builder builder) {
        super(builder);
        initializeConstraints(builder);
    }

    @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());
    }

    @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);
        }

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

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


    public static class Builder extends BaseOutputLayer.Builder {

        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);
        }
    }
}