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Java example source code file (DefaultLayerFactory.java)
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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);
}
}
@Override
public <E extends Layer> E create(NeuralNetConfiguration conf, Collection iterationListeners, int index,
INDArray layerParamsView, boolean initializeParams) {
Layer ret = getInstance(conf);
ret.setListeners(iterationListeners);
ret.setIndex(index);
ret.setParamsViewArray(layerParamsView);
ret.setParamTable(getParams(conf, layerParamsView, initializeParams));
ret.setConf(conf);
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;
}
@Override
public ParamInitializer initializer() {
return new DefaultParamInitializer();
}
@Override
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);
}
@Override
public int hashCode() {
return layerConfig != null ? layerConfig.hashCode() : 0;
}
}
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