DL4J SameDiff loss function (#251)
* Copied and pasted RegressionTest100b4.java to RegressionTest100b6.java with renamed b4->b6 * SameDiffLoss draft * very very draft * Copied and pasted RegressionTest100b4.java to RegressionTest100b6.java with renamed b4->b6 * temporary commit for clarification Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com> * temporary commit for clarification v2 Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com> * temporary commit for clarification v3 Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com> * temporary commit for clarification v3 Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com> * Copied and pasted RegressionTest100b4.java to RegressionTest100b6.java with renamed b4->b6 * very very draft * temporary commit for clarification Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com> * temporary commit for clarification v2 Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com> * temporary commit for clarification v3 Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com> * temporary commit for clarification v3 Signed-off-by: atuzhykov <andrewtuzhukov@gmail.com> * SDLoss after requested changes but with questions in comments Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com> * added requested changes * small fixes Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com> * Fixes Signed-off-by: Alex Black <blacka101@gmail.com> * Javadoc Signed-off-by: Alex Black <blacka101@gmail.com> * Test fix Signed-off-by: Alex Black <blacka101@gmail.com> Co-authored-by: Andrii Tuzhykov <andrew@unrealists.com> Co-authored-by: atuzhykov <andrewtuzhukov@gmail.com> Co-authored-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>master
Alex Black
GitHub
parent
5fbb04531d
commit
18d4eaa68d
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@ -19,6 +19,8 @@ package org.deeplearning4j.gradientcheck;
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import lombok.extern.slf4j.Slf4j;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.TestUtils;
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import org.deeplearning4j.gradientcheck.sdlosscustom.SDLossMAE;
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import org.deeplearning4j.gradientcheck.sdlosscustom.SDLossMSE;
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import org.deeplearning4j.nn.api.OptimizationAlgorithm;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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@ -83,7 +85,8 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
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LossMixtureDensity.builder().gaussians(2).labelWidth(3).build(),
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LossMixtureDensity.builder().gaussians(2).labelWidth(3).build(),
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new LossMultiLabel(), new LossWasserstein(),
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new LossSparseMCXENT()
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new LossSparseMCXENT(),
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new SDLossMAE(), new SDLossMSE()
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};
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Activation[] outputActivationFn = new Activation[] {Activation.SIGMOID, //xent
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@ -119,6 +122,12 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
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Activation.TANH, // MultiLabel, doesn't require any special activation, but tanh was used in paper
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Activation.IDENTITY, // Wasserstein
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Activation.SOFTMAX, //sparse MCXENT
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Activation.SOFTMAX, // SDLossMAE
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Activation.SIGMOID, // SDLossMAE
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Activation.TANH, // SDLossMAE
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Activation.SOFTMAX, // SDLossMSE
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Activation.SIGMOID, // SDLossMSE
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Activation.TANH //SDLossMSE
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};
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int[] nOut = new int[] {1, //xent
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@ -154,6 +163,12 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
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10, // MultiLabel
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2, // Wasserstein
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4, //sparse MCXENT
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3, // SDLossMAE
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3, // SDLossMAE
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3, // SDLossMAE
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3, // SDLossMSE
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3, // SDLossMSE
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3, // SDLossMSE
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};
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int[] minibatchSizes = new int[] {1, 3};
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@ -520,6 +535,8 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
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break;
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case "LossMAE":
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case "LossMSE":
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case "SDLossMAE":
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case "SDLossMSE":
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case "LossL1":
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case "LossL2":
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ret[1] = Nd4j.rand(labelsShape).muli(2).subi(1);
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@ -0,0 +1,30 @@
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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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.gradientcheck.sdlosscustom;
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import lombok.EqualsAndHashCode;
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import org.nd4j.autodiff.samediff.SDVariable;
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import org.nd4j.autodiff.samediff.SameDiff;
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import org.nd4j.linalg.lossfunctions.SameDiffLoss;
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@EqualsAndHashCode(callSuper = false)
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public class SDLossMAE extends SameDiffLoss {
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@Override
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public SDVariable defineLoss(SameDiff sd, SDVariable layerInput, SDVariable labels) {
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return sd.math.abs(labels.sub(layerInput)).mean(1);
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}
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}
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@ -0,0 +1,30 @@
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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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.gradientcheck.sdlosscustom;
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import lombok.EqualsAndHashCode;
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import org.nd4j.autodiff.samediff.SDVariable;
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import org.nd4j.autodiff.samediff.SameDiff;
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import org.nd4j.linalg.lossfunctions.*;
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@EqualsAndHashCode(callSuper = false)
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public class SDLossMSE extends SameDiffLoss {
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@Override
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public SDVariable defineLoss(SameDiff sd, SDVariable layerInput, SDVariable labels) {
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return labels.squaredDifference(layerInput).mean(1);
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}
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}
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@ -111,7 +111,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
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assertEquals(dtype, net.getLayerWiseConfigurations().getDataType());
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assertEquals(dtype, net.params().dataType());
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boolean eq = outExp.equalsWithEps(outAct, 0.01);
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assertTrue(outExp + " vs " + outAct, eq);
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assertTrue("Test for dtype: " + dtypeName + " - " + outExp + " vs " + outAct, eq);
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}
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}
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@ -28,6 +28,7 @@ import org.nd4j.base.Preconditions;
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import org.nd4j.imports.NoOpNameFoundException;
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import org.nd4j.linalg.api.buffer.DataBuffer;
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import org.nd4j.linalg.api.buffer.DataType;
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import org.nd4j.linalg.api.memory.MemoryWorkspace;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.api.shape.Shape;
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import org.nd4j.linalg.exception.ND4JIllegalStateException;
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@ -348,7 +349,9 @@ public abstract class BaseOp extends DifferentialFunction implements Op {
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if (dimensions == null || dimensions.length == 0)
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dimensions = new int[]{Integer.MAX_VALUE};
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this.dimensionz = Shape.ndArrayDimFromInt(dimensions);
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try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
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this.dimensionz = Shape.ndArrayDimFromInt(dimensions);
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}
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}
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public INDArray dimensions() {
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@ -0,0 +1,186 @@
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/*******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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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.nd4j.linalg.lossfunctions;
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import org.nd4j.autodiff.samediff.SDVariable;
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import org.nd4j.autodiff.samediff.SameDiff;
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import org.nd4j.base.Preconditions;
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import org.nd4j.linalg.activations.IActivation;
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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 java.util.HashMap;
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import java.util.Map;
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/**
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* SameDiff loss function.
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*
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* This class can be extended to create Deeplearning4j loss functions by defining one single method only:
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* {@link #defineLoss(SameDiff, SDVariable, SDVariable)}. This method is used to define the loss function on a
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* <i>per example</i> basis - i.e., the output should be an array with shape [minibatch].<br>
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* <br>
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* For example, the mean squared error (MSE) loss function can be defined using:<br>
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* {@code return labels.squaredDifference(layerInput).mean(1);}
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*
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*/
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public abstract class SameDiffLoss implements ILossFunction {
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protected transient SameDiff sd;
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protected transient SDVariable scoreVariable;
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protected SameDiffLoss() {
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}
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/**
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* Define the loss function.<br>
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* <b>NOTE</b>: The score on a *per example* basis - should return a SDVariable with shape [minibatch], where out[i]
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* is the score for the ith minibatch
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*
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* @param sd SameDiff instance to define the loss on
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* @param layerInput Input to the SameDiff loss function
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* @param labels Labels placeholder
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* @return The score on a per example basis (SDVariable with shape [minibatch])
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*/
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public abstract SDVariable defineLoss(SameDiff sd, SDVariable layerInput, SDVariable labels);
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protected void createSameDiffInstance(DataType dataType){
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sd = SameDiff.create();
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SDVariable layerInput = sd.placeHolder("layerInput", dataType, -1);
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SDVariable labels = sd.placeHolder("labels", dataType, -1);
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scoreVariable = this.defineLoss(sd, layerInput, labels);
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sd.createGradFunction("layerInput");
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}
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/**
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* Compute the score (loss function value) for the given inputs.
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*
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* @param labels Label/expected preOutput
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* @param preOutput Output of the model (neural network)
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* @param activationFn Activation function that should be applied to preOutput
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* @param mask Mask array; may be null
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* @param average Whether the score should be averaged (divided by number of rows in labels/preOutput) or not @return Loss function value
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*/
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@Override
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public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average) {
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if(sd == null){
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createSameDiffInstance(preOutput.dataType());
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}
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INDArray scoreArr = computeScoreArray(labels, preOutput, activationFn, mask);
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double score = scoreArr.sumNumber().doubleValue();
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if (average) {
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score /= scoreArr.size(0);
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}
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return score;
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}
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/**
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* Compute the score (loss function value) for each example individually.
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* For input [numExamples,nOut] returns scores as a column vector: [numExamples,1]
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*
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* @param labels Labels/expected output
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* @param preOutput Output of the model (neural network)
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* @param activationFn Activation function that should be applied to preOutput
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* @param mask @return Loss function value for each example; column vector
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*/
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@Override
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public INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
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if(sd == null){
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createSameDiffInstance(preOutput.dataType());
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}
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Preconditions.checkArgument((labels.size(1) == preOutput.size(1)), "Labels array numColumns (size(1) = %s) does not match output layer number of outputs (nOut = %s)", labels.size(1), preOutput.size(1));
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INDArray output = activationFn.getActivation(preOutput.dup(), true);
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Map<String, INDArray> m = new HashMap<>();
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m.put("labels", labels);
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m.put("layerInput", output);
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INDArray scoreArr = sd.outputSingle(m,scoreVariable.name());
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if (mask != null) {
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LossUtil.applyMask(scoreArr, mask);
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}
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return scoreArr;
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}
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/**
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* Compute the gradient of the loss function with respect to the inputs: dL/dOutput
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*
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* @param labels Label/expected output
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* @param preOutput Output of the model (neural network), before the activation function is applied
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* @param activationFn Activation function that should be applied to preOutput
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* @param mask Mask array; may be null
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* @return Gradient dL/dPreOut
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*/
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@Override
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public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
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if(sd == null){
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createSameDiffInstance(preOutput.dataType());
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}
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Map<String, INDArray> m = new HashMap<>();
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INDArray output = activationFn.getActivation(preOutput.dup(), true);
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m.put("labels", labels);
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m.put("layerInput", output);
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Map<String, INDArray> grads = sd.calculateGradients(m, "layerInput");
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INDArray gradAtActivationOutput = grads.get("layerInput");
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INDArray gradAtInput = activationFn.backprop(preOutput.dup(), gradAtActivationOutput).getFirst();
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if (mask != null) {
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LossUtil.applyMask(gradAtInput, mask);
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}
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return gradAtInput;
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}
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/**
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* Compute both the score (loss function value) and gradient. This is equivalent to calling {@link #computeScore(INDArray, INDArray, IActivation, INDArray, boolean)}
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* and {@link #computeGradient(INDArray, INDArray, IActivation, INDArray)} individually
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*
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* @param labels Label/expected output
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* @param preOutput Output of the model (neural network)
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* @param activationFn Activation function that should be applied to preOutput
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* @param mask Mask array; may be null
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* @param average Whether the score should be averaged (divided by number of rows in labels/output) or not
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* @return The score (loss function value) and gradient
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*/
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@Override
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public Pair<Double, INDArray> computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn,
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INDArray mask, boolean average) {
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Pair<Double, INDArray> GradientAndScore = new Pair<>();
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GradientAndScore.setFirst(this.computeScore(labels, preOutput, activationFn, mask, average));
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GradientAndScore.setSecond(this.computeGradient(labels, preOutput, activationFn, mask));
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return GradientAndScore;
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}
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@Override
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public String name() {
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return getClass().getSimpleName();
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}
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}
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@ -683,7 +683,7 @@ public class BasicWorkspaceTests extends BaseNd4jTest {
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workspace.initializeWorkspace();
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long reqMemory = 12 * Nd4j.sizeOfDataType(arrayCold.dataType());
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long reqMemory = 11 * Nd4j.sizeOfDataType(arrayCold.dataType());
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assertEquals(reqMemory + reqMemory % 8, workspace.getCurrentSize());
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