/******************************************************************************* * 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.*; 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; /** * Autoencoder layer. Adds noise to input and learn a reconstruction function. */ @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); } } }