106 lines
3.3 KiB
Java
106 lines
3.3 KiB
Java
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/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.nn.layers;
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import org.deeplearning4j.nn.api.Layer;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.gradient.DefaultGradient;
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import org.deeplearning4j.nn.gradient.Gradient;
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import org.nd4j.linalg.api.buffer.DataType;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.primitives.Pair;
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import org.deeplearning4j.nn.workspace.ArrayType;
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import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
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/**
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* Created by davekale on 12/7/16.
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*/
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public class DropoutLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.DropoutLayer> {
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public DropoutLayer(NeuralNetConfiguration conf, DataType dataType) {
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super(conf, dataType);
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}
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@Override
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public double calcRegularizationScore(boolean backpropParamsOnly){
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return 0;
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}
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@Override
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public Type type() {
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return Type.FEED_FORWARD;
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}
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@Override
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public void fit(INDArray input, LayerWorkspaceMgr workspaceMgr) {
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throw new UnsupportedOperationException("Not supported");
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}
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@Override
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public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
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INDArray delta = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon);
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if (maskArray != null) {
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delta.muliColumnVector(maskArray);
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}
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Gradient ret = new DefaultGradient();
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delta = backpropDropOutIfPresent(delta);
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return new Pair<>(ret, delta);
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}
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@Override
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public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
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assertInputSet(false);
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INDArray ret;
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if(!training){
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ret = input;
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} else {
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if(layerConf().getIDropout() != null){
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INDArray result;
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if(inputModificationAllowed){
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result = input;
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} else {
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result = workspaceMgr.createUninitialized(ArrayType.INPUT, input.dataType(), input.shape(), input.ordering());
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}
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ret = layerConf().getIDropout().applyDropout(input, result, getIterationCount(), getEpochCount(), workspaceMgr);
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} else {
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ret = workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, input);
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}
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}
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if (maskArray != null) {
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ret.muliColumnVector(maskArray);
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}
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ret = workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, ret);
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return ret;
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}
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@Override
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public boolean isPretrainLayer() {
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return false;
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}
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@Override
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public INDArray params() {
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return null;
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}
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}
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