/
AutoEncoder.java
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/
AutoEncoder.java
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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.
* *
* * 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
* * 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.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.params.PretrainParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Collection;
import java.util.Map;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class AutoEncoder extends BasePretrainNetwork {
protected double corruptionLevel;
protected double sparsity;
// Builder
private AutoEncoder(Builder builder) {
super(builder);
this.corruptionLevel = builder.corruptionLevel;
this.sparsity = builder.sparsity;
initializeConstraints(builder);
}
@Override
public Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners,
int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder ret =
new org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder(conf, networkDataType);
ret.setListeners(trainingListeners);
ret.setIndex(layerIndex);
ret.setParamsViewArray(layerParamsView);
Map<String, INDArray> paramTable = initializer().init(conf, layerParamsView, initializeParams);
ret.setParamTable(paramTable);
ret.setConf(conf);
return ret;
}
@Override
public ParamInitializer initializer() {
return PretrainParamInitializer.getInstance();
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
//Because of supervised + unsupervised modes: we'll assume unsupervised, which has the larger memory requirements
InputType outputType = getOutputType(-1, inputType);
val actElementsPerEx = outputType.arrayElementsPerExample() + inputType.arrayElementsPerExample();
val numParams = initializer().numParams(this);
val updaterStateSize = (int) getIUpdater().stateSize(numParams);
int trainSizePerEx = 0;
if (getIDropout() != null) {
if (false) {
//TODO drop connect
//Dup the weights... note that this does NOT depend on the minibatch size...
} else {
//Assume we dup the input
trainSizePerEx += inputType.arrayElementsPerExample();
}
}
//Also, during backprop: we do a preOut call -> gives us activations size equal to the output size
// which is modified in-place by loss function
trainSizePerEx += actElementsPerEx;
return new LayerMemoryReport.Builder(layerName, AutoEncoder.class, inputType, outputType)
.standardMemory(numParams, updaterStateSize).workingMemory(0, 0, 0, trainSizePerEx)
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching
.build();
}
@AllArgsConstructor
@Getter
@Setter
public static class Builder extends BasePretrainNetwork.Builder<Builder> {
/**
* Level of corruption - 0.0 (none) to 1.0 (all values corrupted)
*
*/
private double corruptionLevel = 3e-1f;
/**
* Autoencoder sparity parameter
*
*/
private double sparsity = 0f;
public Builder() {}
/**
* Builder - sets the level of corruption - 0.0 (none) to 1.0 (all values corrupted)
*
* @param corruptionLevel Corruption level (0 to 1)
*/
public Builder(double corruptionLevel) {
this.setCorruptionLevel(corruptionLevel);
}
/**
* Level of corruption - 0.0 (none) to 1.0 (all values corrupted)
*
* @param corruptionLevel Corruption level (0 to 1)
*/
public Builder corruptionLevel(double corruptionLevel) {
this.setCorruptionLevel(corruptionLevel);
return this;
}
/**
* Autoencoder sparity parameter
*
* @param sparsity Sparsity
*/
public Builder sparsity(double sparsity) {
this.setSparsity(sparsity);
return this;
}
@Override
@SuppressWarnings("unchecked")
public AutoEncoder build() {
return new AutoEncoder(this);
}
}
}