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org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder Maven / Gradle / Ivy
/*******************************************************************************
* 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.layers.feedforward.autoencoder;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.layers.BasePretrainNetwork;
import org.deeplearning4j.nn.params.PretrainParamInitializer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.nn.workspace.ArrayType;
/**
* Autoencoder.
* Add Gaussian noise to input and learn
* a reconstruction function.
*
* @author Adam Gibson
*
*/
public class AutoEncoder extends BasePretrainNetwork {
public AutoEncoder(NeuralNetConfiguration conf, DataType dataType) {
super(conf, dataType);
}
@Override
public Pair sampleHiddenGivenVisible(INDArray v) {
setInput(v, LayerWorkspaceMgr.noWorkspaces()); //TODO
INDArray ret = encode(v, true, LayerWorkspaceMgr.noWorkspaces()); //TODO
return new Pair<>(ret, ret);
}
@Override
public Pair sampleVisibleGivenHidden(INDArray h) {
INDArray ret = decode(h, LayerWorkspaceMgr.noWorkspaces()); //TODO
return new Pair<>(ret, ret);
}
// Encode
public INDArray encode(INDArray v, boolean training, LayerWorkspaceMgr workspaceMgr) {
INDArray W = getParamWithNoise(PretrainParamInitializer.WEIGHT_KEY, training, workspaceMgr);
INDArray hBias = getParamWithNoise(PretrainParamInitializer.BIAS_KEY, training, workspaceMgr);
INDArray ret = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, W.dataType(), v.size(0), W.size(1));
INDArray preAct = v.castTo(W.dataType()).mmuli(W, ret).addiRowVector(hBias);
ret = layerConf().getActivationFn().getActivation(preAct, training);
return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, ret);
}
// Decode
public INDArray decode(INDArray y, LayerWorkspaceMgr workspaceMgr) {
INDArray W = getParamWithNoise(PretrainParamInitializer.WEIGHT_KEY, true, workspaceMgr);
INDArray vBias = getParamWithNoise(PretrainParamInitializer.VISIBLE_BIAS_KEY, true, workspaceMgr);
INDArray preAct = y.mmul(W.transpose()).addiRowVector(vBias);
return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, layerConf().getActivationFn().getActivation(preAct, true));
}
@Override
public INDArray activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr) {
setInput(input, workspaceMgr);
return encode(input, training, workspaceMgr);
}
@Override
public boolean isPretrainLayer() {
return true;
}
@Override
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
return encode(input, training, workspaceMgr);
}
@Override
public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr) {
INDArray W = getParamWithNoise(PretrainParamInitializer.WEIGHT_KEY, true, workspaceMgr);
double corruptionLevel = layerConf().getCorruptionLevel();
INDArray corruptedX = corruptionLevel > 0 ? getCorruptedInput(input, corruptionLevel) : input;
setInput(corruptedX, workspaceMgr);
INDArray y = encode(corruptedX, true, workspaceMgr);
INDArray z = decode(y, workspaceMgr);
INDArray visibleLoss = input.sub(z);
INDArray hiddenLoss = layerConf().getSparsity() == 0 ? visibleLoss.mmul(W).muli(y).muli(y.rsub(1))
: visibleLoss.mmul(W).muli(y).muli(y.add(-layerConf().getSparsity()));
INDArray wGradient = corruptedX.transpose().mmul(hiddenLoss).addi(visibleLoss.transpose().mmul(y));
INDArray hBiasGradient = hiddenLoss.sum(0);
INDArray vBiasGradient = visibleLoss.sum(0);
gradient = createGradient(wGradient, vBiasGradient, hBiasGradient);
setScoreWithZ(z);
}
}