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Java example source code file (DefaultLayerFactory.java)

This example Java source code file (DefaultLayerFactory.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.

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Java - Java tags/keywords

collection, defaultlayerfactory, defaultparaminitializer, denselayer, embeddinglayer, indarray, layer, linkedhashmap, map, override, paraminitializer, runtimeexception, util

The DefaultLayerFactory.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.factory;

import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.LayerFactory;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.layers.feedforward.embedding.EmbeddingLayer;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.optimize.api.IterationListener;
import org.nd4j.linalg.api.ndarray.INDArray;

import java.util.*;

 * Default layer factory: create a bias and a weight matrix
 * @author Adam Gibson
public class DefaultLayerFactory implements LayerFactory {

    protected org.deeplearning4j.nn.conf.layers.Layer layerConfig;

    public DefaultLayerFactory(Class<? extends org.deeplearning4j.nn.conf.layers.Layer> layerConfig) {
        try {
            this.layerConfig = layerConfig.newInstance();
        } catch (Exception e) {
            throw new RuntimeException(e);

    public <E extends Layer> E create(NeuralNetConfiguration conf, Collection iterationListeners, int index,
                                      INDArray layerParamsView, boolean initializeParams) {
        Layer ret = getInstance(conf);
        ret.setParamTable(getParams(conf, layerParamsView, initializeParams));
        return (E) ret;

    protected Layer getInstance(NeuralNetConfiguration conf) {
        if (layerConfig instanceof DenseLayer)
            return new org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.AutoEncoder)
            return new org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.RBM)
            return new org.deeplearning4j.nn.layers.feedforward.rbm.RBM(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.GravesLSTM)
            return new org.deeplearning4j.nn.layers.recurrent.GravesLSTM(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM)
            return new org.deeplearning4j.nn.layers.recurrent.GravesBidirectionalLSTM(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.GRU)
            return new org.deeplearning4j.nn.layers.recurrent.GRU(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.OutputLayer)
            return new org.deeplearning4j.nn.layers.OutputLayer(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.RnnOutputLayer)
            return new org.deeplearning4j.nn.layers.recurrent.RnnOutputLayer(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.ConvolutionLayer)
            return new org.deeplearning4j.nn.layers.convolution.ConvolutionLayer(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.SubsamplingLayer)
            return new org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.BatchNormalization)
            return new org.deeplearning4j.nn.layers.normalization.BatchNormalization(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.LocalResponseNormalization)
            return new org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.EmbeddingLayer)
            return new EmbeddingLayer(conf);
        if (layerConfig instanceof org.deeplearning4j.nn.conf.layers.ActivationLayer)
            return new org.deeplearning4j.nn.layers.ActivationLayer(conf);
        throw new RuntimeException("unknown layer type: " + layerConfig);

    protected Map<String, INDArray> getParams(NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams) {
        ParamInitializer init = initializer();
        Map<String, INDArray> params = Collections.synchronizedMap(new LinkedHashMap());
        init.init(params, conf, paramsView, initializeParams);
        return params;

    public ParamInitializer initializer() {
        return new DefaultParamInitializer();

    public boolean equals(Object o) {
        if (this == o) return true;
        if (!(o instanceof DefaultLayerFactory)) return false;

        DefaultLayerFactory that = (DefaultLayerFactory) o;

        return !(layerConfig != null ? !layerConfig.equals(that.layerConfig) : that.layerConfig != null);

    public int hashCode() {
        return layerConfig != null ? layerConfig.hashCode() : 0;

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