org.deeplearning4j.zoo.model.SimpleCNN 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.zoo.model;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.NoArgsConstructor;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.zoo.ModelMetaData;
import org.deeplearning4j.zoo.PretrainedType;
import org.deeplearning4j.zoo.ZooModel;
import org.deeplearning4j.zoo.ZooType;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.learning.config.AdaDelta;
import org.nd4j.linalg.learning.config.IUpdater;
/**
* A simple convolutional network for generic image classification.
* Reference: https://github.com/oarriaga/face_classification/
*
* @author Justin Long (crockpotveggies)
*/
@AllArgsConstructor
@Builder
public class SimpleCNN extends ZooModel {
@Builder.Default private long seed = 1234;
@Builder.Default private int[] inputShape = new int[] {3, 48, 48};
@Builder.Default private int numClasses = 0;
@Builder.Default private IUpdater updater = new AdaDelta();
@Builder.Default private CacheMode cacheMode = CacheMode.NONE;
@Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.ENABLED;
@Builder.Default private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
private SimpleCNN() {}
@Override
public String pretrainedUrl(PretrainedType pretrainedType) {
return null;
}
@Override
public long pretrainedChecksum(PretrainedType pretrainedType) {
return 0L;
}
@Override
public Class modelType() {
return MultiLayerNetwork.class;
}
public MultiLayerConfiguration conf() {
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().seed(seed)
.activation(Activation.IDENTITY)
.weightInit(WeightInit.RELU)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(updater)
.cacheMode(cacheMode)
.trainingWorkspaceMode(workspaceMode)
.inferenceWorkspaceMode(workspaceMode)
.convolutionMode(ConvolutionMode.Same)
.list()
// block 1
.layer(0, new ConvolutionLayer.Builder(new int[] {7, 7}).name("image_array")
.nIn(inputShape[0]).nOut(16).build())
.layer(1, new BatchNormalization.Builder().build())
.layer(2, new ConvolutionLayer.Builder(new int[] {7, 7}).nIn(16).nOut(16)
.build())
.layer(3, new BatchNormalization.Builder().build())
.layer(4, new ActivationLayer.Builder().activation(Activation.RELU).build())
.layer(5, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG,
new int[] {2, 2}).build())
.layer(6, new DropoutLayer.Builder(0.5).build())
// block 2
.layer(7, new ConvolutionLayer.Builder(new int[] {5, 5}).nOut(32).build())
.layer(8, new BatchNormalization.Builder().build())
.layer(9, new ConvolutionLayer.Builder(new int[] {5, 5}).nOut(32).build())
.layer(10, new BatchNormalization.Builder().build())
.layer(11, new ActivationLayer.Builder().activation(Activation.RELU).build())
.layer(12, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG,
new int[] {2, 2}).build())
.layer(13, new DropoutLayer.Builder(0.5).build())
// block 3
.layer(14, new ConvolutionLayer.Builder(new int[] {3, 3}).nOut(64).build())
.layer(15, new BatchNormalization.Builder().build())
.layer(16, new ConvolutionLayer.Builder(new int[] {3, 3}).nOut(64).build())
.layer(17, new BatchNormalization.Builder().build())
.layer(18, new ActivationLayer.Builder().activation(Activation.RELU).build())
.layer(19, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG,
new int[] {2, 2}).build())
.layer(20, new DropoutLayer.Builder(0.5).build())
// block 4
.layer(21, new ConvolutionLayer.Builder(new int[] {3, 3}).nOut(128).build())
.layer(22, new BatchNormalization.Builder().build())
.layer(23, new ConvolutionLayer.Builder(new int[] {3, 3}).nOut(128).build())
.layer(24, new BatchNormalization.Builder().build())
.layer(25, new ActivationLayer.Builder().activation(Activation.RELU).build())
.layer(26, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG,
new int[] {2, 2}).build())
.layer(27, new DropoutLayer.Builder(0.5).build())
// block 5
.layer(28, new ConvolutionLayer.Builder(new int[] {3, 3}).nOut(256).build())
.layer(29, new BatchNormalization.Builder().build())
.layer(30, new ConvolutionLayer.Builder(new int[] {3, 3}).nOut(numClasses)
.build())
.layer(31, new GlobalPoolingLayer.Builder(PoolingType.AVG).build())
.layer(32, new ActivationLayer.Builder().activation(Activation.SOFTMAX).build())
.setInputType(InputType.convolutional(inputShape[2], inputShape[1],
inputShape[0]))
.build();
return conf;
}
@Override
public Model init() {
MultiLayerNetwork network = new MultiLayerNetwork(conf());
network.init();
return network;
}
@Override
public ModelMetaData metaData() {
return new ModelMetaData(new int[][] {inputShape}, 1, ZooType.CNN);
}
@Override
public void setInputShape(int[][] inputShape) {
this.inputShape = inputShape[0];
}
}
© 2015 - 2024 Weber Informatics LLC | Privacy Policy