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Java example source code file (LayerFactories.java)

This example Java source code file (LayerFactories.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

batchnormalizationlayerfactory, class, convolutionlayerfactory, defaultlayerfactory, emptyfactory, layerfactories, layerfactory, pretrainlayerfactory, subsamplelayerfactory

The LayerFactories.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.LayerFactory;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.*;

 * Static method for finding which layer factory to use
 * @author Adam Gibson
public class LayerFactories {
     * Get the factory based on the passed in class
     * @param conf the clazz to get the layer factory for
     * @return the layer factory for the particular layer
    public static LayerFactory getFactory(NeuralNetConfiguration conf) {
        return getFactory(conf.getLayer());

     * Get the factory based on the passed in class
     * @param layer the clazz to get the layer factory for
     * @return the layer factory for the particular layer
    public static LayerFactory getFactory(Layer layer) {
        Class<? extends Layer> clazz = layer.getClass();
        	return new GravesLSTMLayerFactory(GravesLSTM.class);
        else if (clazz.equals(GravesBidirectionalLSTM.class))
            return new GravesBidirectionalLSTMLayerFactory(GravesBidirectionalLSTM.class);
        else if(clazz.equals(GRU.class))
        	return new GRULayerFactory(GRU.class);
        else if(BasePretrainNetwork.class.isAssignableFrom(clazz))
            return new PretrainLayerFactory(clazz);
        else if(ConvolutionLayer.class.isAssignableFrom(clazz))
            return new ConvolutionLayerFactory(clazz);
        else if(SubsamplingLayer.class.isAssignableFrom(clazz))
            return new SubsampleLayerFactory(clazz);
        else if(BatchNormalization.class.isAssignableFrom(clazz))
            return new BatchNormalizationLayerFactory(clazz);
        else if(LocalResponseNormalization.class.isAssignableFrom(clazz))
            return new EmptyFactory(clazz);
        else if(ActivationLayer.class.isAssignableFrom(clazz))
            return new EmptyFactory(clazz);
        return new DefaultLayerFactory(clazz);


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