Forked from tomthetrainer/MnistImagePipelineExampleSave.java
Created
March 4, 2019 12:40
-
-
Save adrianprecub/aa5d1994fa268e12ef51f297b8ccd16b to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
package org.deeplearning4j.examples.dataExamples; | |
import org.datavec.api.io.labels.ParentPathLabelGenerator; | |
import org.datavec.api.split.FileSplit; | |
import org.datavec.image.loader.NativeImageLoader; | |
import org.datavec.image.recordreader.ImageRecordReader; | |
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator; | |
import org.deeplearning4j.eval.Evaluation; | |
import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
import org.deeplearning4j.nn.conf.MultiLayerConfiguration; | |
import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
import org.deeplearning4j.nn.conf.Updater; | |
import org.deeplearning4j.nn.conf.inputs.InputType; | |
import org.deeplearning4j.nn.conf.layers.DenseLayer; | |
import org.deeplearning4j.nn.conf.layers.OutputLayer; | |
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; | |
import org.deeplearning4j.nn.weights.WeightInit; | |
import org.deeplearning4j.optimize.listeners.ScoreIterationListener; | |
import org.deeplearning4j.util.ModelSerializer; | |
import org.nd4j.linalg.api.ndarray.INDArray; | |
import org.nd4j.linalg.dataset.DataSet; | |
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator; | |
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization; | |
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler; | |
import org.nd4j.linalg.lossfunctions.LossFunctions; | |
import org.slf4j.Logger; | |
import org.slf4j.LoggerFactory; | |
import java.io.File; | |
import java.util.Random; | |
/** | |
* Created by tomhanlon on 11/7/16. | |
*/ | |
public class MnistImagePipelineExampleSave { | |
private static Logger log = LoggerFactory.getLogger(MnistImagePipelineExampleSave.class); | |
public static void main(String[] args) throws Exception { | |
// image information | |
// 28 * 28 grayscale | |
// grayscale implies single channel | |
int height = 28; | |
int width = 28; | |
int channels = 1; | |
int rngseed = 123; | |
Random randNumGen = new Random(rngseed); | |
int batchSize = 128; | |
int outputNum = 10; | |
int numEpochs = 15; | |
// Define the File Paths | |
File trainData = new File("/Users/tomhanlon/SkyMind/java/dl4j-examples62/dl4j-examples/src/main/resources/mnist_png/training"); | |
File testData = new File("/Users/tomhanlon/SkyMind/java/dl4j-examples62/dl4j-examples/src/main/resources/mnist_png/testing"); | |
// Define the FileSplit(PATH, ALLOWED FORMATS,random) | |
FileSplit train = new FileSplit(trainData, NativeImageLoader.ALLOWED_FORMATS,randNumGen); | |
FileSplit test = new FileSplit(testData,NativeImageLoader.ALLOWED_FORMATS,randNumGen); | |
// Extract the parent path as the image label | |
ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator(); | |
ImageRecordReader recordReader = new ImageRecordReader(height,width,channels,labelMaker); | |
// Initialize the record reader | |
// add a listener, to extract the name | |
recordReader.initialize(train); | |
//recordReader.setListeners(new LogRecordListener()); | |
// DataSet Iterator | |
DataSetIterator dataIter = new RecordReaderDataSetIterator(recordReader,batchSize,1,outputNum); | |
// Scale pixel values to 0-1 | |
DataNormalization scaler = new ImagePreProcessingScaler(0,1); | |
scaler.fit(dataIter); | |
dataIter.setPreProcessor(scaler); | |
// Build Our Neural Network | |
log.info("**** Build Model ****"); | |
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() | |
.seed(rngseed) | |
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) | |
.iterations(1) | |
.learningRate(0.006) | |
.updater(Updater.NESTEROVS).momentum(0.9) | |
.regularization(true).l2(1e-4) | |
.list() | |
.layer(0, new DenseLayer.Builder() | |
.nIn(height * width) | |
.nOut(100) | |
.activation("relu") | |
.weightInit(WeightInit.XAVIER) | |
.build()) | |
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) | |
.nIn(100) | |
.nOut(outputNum) | |
.activation("softmax") | |
.weightInit(WeightInit.XAVIER) | |
.build()) | |
.pretrain(false).backprop(true) | |
.setInputType(InputType.convolutional(height,width,channels)) | |
.build(); | |
MultiLayerNetwork model = new MultiLayerNetwork(conf); | |
model.init(); | |
model.setListeners(new ScoreIterationListener(10)); | |
log.info("*****TRAIN MODEL********"); | |
for(int i = 0; i<numEpochs; i++){ | |
model.fit(dataIter); | |
} | |
log.info("******SAVE TRAINED MODEL******"); | |
// Details | |
// Where to save model | |
File locationToSave = new File("trained_mnist_model.zip"); | |
// boolean save Updater | |
boolean saveUpdater = false; | |
// ModelSerializer needs modelname, saveUpdater, Location | |
ModelSerializer.writeModel(model,locationToSave,saveUpdater); | |
/* | |
log.info("******EVALUATE MODEL******"); | |
recordReader.reset(); | |
recordReader.initialize(test); | |
DataSetIterator testIter = new RecordReaderDataSetIterator(recordReader,batchSize,1,outputNum); | |
scaler.fit(testIter); | |
testIter.setPreProcessor(scaler); | |
// Create Eval object with 10 possible classes | |
Evaluation eval = new Evaluation(outputNum); | |
while(testIter.hasNext()){ | |
DataSet next = testIter.next(); | |
INDArray output = model.output(next.getFeatureMatrix()); | |
eval.eval(next.getLabels(),output); | |
} | |
log.info(eval.stats()); | |
*/ | |
} | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment