Fixed missing imports
Signed-off-by: brian <brian@brutex.de>enhance-build-infrastructure
parent
d0342fc939
commit
e27fb8422f
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@ -21,14 +21,14 @@
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package net.brutex.gan;
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import java.awt.BorderLayout;
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import java.awt.Dimension;
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import java.awt.GridLayout;
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import java.awt.Image;
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import java.awt.*;
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import java.awt.image.BufferedImage;
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import java.io.File;
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import java.io.IOException;
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import java.util.Arrays;
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import java.util.Random;
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import java.util.UUID;
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import javax.imageio.ImageIO;
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import javax.swing.ImageIcon;
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import javax.swing.JFrame;
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import javax.swing.JLabel;
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@ -82,9 +82,9 @@ public class App {
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private static final int X_DIM = 20 ;
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private static final int Y_DIM = 20;
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private static final int CHANNELS = 3;
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private static final int batchSize = 50;
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private static final int INPUT = 128;
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private static final int CHANNELS = 1;
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private static final int batchSize = 1;
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private static final int INPUT = 10;
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private static final int OUTPUT_PER_PANEL = 16;
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@ -96,6 +96,8 @@ public class App {
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private static JPanel panel;
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private static JPanel panel2;
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private static final String OUTPUT_DIR = "C:/temp/output/";
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private static LayerConfiguration[] genLayers() {
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return new LayerConfiguration[] {
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DenseLayer.builder().nIn(INPUT).nOut(X_DIM*Y_DIM*CHANNELS).weightInit(WeightInit.NORMAL).build(),
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@ -103,6 +105,7 @@ public class App {
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DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
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ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
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DropoutLayer.builder(1 - 0.5).build(),
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DenseLayer.builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM).build(),
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ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
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@ -207,6 +210,12 @@ public class App {
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@Test
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public void runTest() throws Exception {
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if(! log.isDebugEnabled()) {
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log.info("Logging is not set to DEBUG");
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}
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else {
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log.info("Logging is set to DEBUG");
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}
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main();
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}
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@ -240,25 +249,25 @@ public class App {
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MultiLayerNetwork gan = new MultiLayerNetwork(gan());
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gen.init(); log.debug("Generator network: {}", gen);
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dis.init(); log.debug("Discriminator network: {}", dis);
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gan.init(); log.debug("Complete GAN network: {}", gan);
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gan.init(); log.info("Complete GAN network: {}", gan);
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copyParams(gen, dis, gan);
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gen.addTrainingListeners(new PerformanceListener(15, true));
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//dis.addTrainingListeners(new PerformanceListener(10, true));
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//gan.addTrainingListeners(new PerformanceListener(10, true));
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//gen.addTrainingListeners(new PerformanceListener(15, true, "GEN"));
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dis.addTrainingListeners(new PerformanceListener(10, true, "DIS"));
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gan.addTrainingListeners(new PerformanceListener(10, true, "GAN"));
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//gan.addTrainingListeners(new ScoreToChartListener("gan"));
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//dis.setListeners(new ScoreToChartListener("dis"));
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System.out.println(gan.toString());
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gan.fit(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1));
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//System.out.println(gan.toString());
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//gan.fit(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1));
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//gan.fit(new DataSet(trainData.next().getFeatures(), Nd4j.zeros(batchSize, 1)));
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//trainData.reset();
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int j = 0;
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for (int i = 0; i < 201; i++) { //epoch
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for (int i = 0; i < 51; i++) { //epoch
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while (trainData.hasNext()) {
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j++;
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@ -282,6 +291,9 @@ public class App {
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// int batchSize = (int) real.shape()[0];
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INDArray fakeIn = Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM);
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//INDArray fakeIn = Nd4j.rand(new int[]{batchSize, X_DIM*Y_DIM}); //hack for MNIST only, use above otherwise
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INDArray fake = gan.activateSelectedLayers(0, gen.getLayers().length - 1, fakeIn);
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fake = fake.reshape(batchSize, CHANNELS, X_DIM, Y_DIM);
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@ -299,11 +311,11 @@ public class App {
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updateGan(gen, dis, gan);
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//gan.fit(new DataSet(Nd4j.rand(batchSize, INPUT), Nd4j.zeros(batchSize, 1)));
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gan.fit(new DataSet(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1)));
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gan.fit(new DataSet(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.ones(batchSize, 1)));
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//Visualize and reporting
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if (j % 10 == 1) {
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System.out.println("Iteration " + j + " Visualizing...");
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System.out.println("Epoch " + i + " Iteration " + j + " Visualizing...");
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INDArray[] samples = batchSize > OUTPUT_PER_PANEL ? new INDArray[OUTPUT_PER_PANEL] : new INDArray[batchSize];
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@ -330,11 +342,12 @@ public class App {
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}
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// Copy the GANs generator to gen.
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updateGen(gen, gan);
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gen.save(new File("mnist-mlp-generator.dlj"));
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}
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gen.save(new File("mnist-mlp-generator.dlj"));
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}
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private static void copyParams(MultiLayerNetwork gen, MultiLayerNetwork dis, MultiLayerNetwork gan) {
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@ -342,10 +355,10 @@ public class App {
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for (int i = 0; i < gan.getLayers().length; i++) {
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if (i < genLayerCount) {
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if(gan.getLayer(i).getParams() != null)
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gen.getLayer(i).setParams(gan.getLayer(i).getParams());
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gan.getLayer(i).setParams(gen.getLayer(i).getParams());
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} else {
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if(gan.getLayer(i).getParams() != null)
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dis.getLayer(i - genLayerCount).setParams(gan.getLayer(i).getParams());
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gan.getLayer(i ).setParams(dis.getLayer(i- genLayerCount).getParams());
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}
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}
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}
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@ -411,14 +424,50 @@ public class App {
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}
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}
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}
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ImageIcon orig = new ImageIcon(bi);
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Image imageScaled = orig.getImage().getScaledInstance((4 * X_DIM), (4 * Y_DIM), Image.SCALE_DEFAULT);
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ImageIcon scaled = new ImageIcon(imageScaled);
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if(! isOrig) saveImage(imageScaled, batchElement, isOrig);
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return new JLabel(scaled);
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}
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private static void saveImage(Image image, int batchElement, boolean isOrig) {
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String outputDirectory = OUTPUT_DIR; // Set the output directory where the images will be saved
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try {
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// Save the images to disk
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saveImage(image, outputDirectory, UUID.randomUUID().toString()+".png");
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log.debug("Images saved successfully.");
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} catch (IOException e) {
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log.error("Error saving the images: {}", e.getMessage());
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}
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}
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private static void saveImage(Image image, String outputDirectory, String fileName) throws IOException {
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File directory = new File(outputDirectory);
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if (!directory.exists()) {
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directory.mkdir();
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}
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File outputFile = new File(directory, fileName);
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ImageIO.write(imageToBufferedImage(image), "png", outputFile);
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}
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public static BufferedImage imageToBufferedImage(Image image) {
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if (image instanceof BufferedImage) {
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return (BufferedImage) image;
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}
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// Create a buffered image with the same dimensions and transparency as the original image
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BufferedImage bufferedImage = new BufferedImage(image.getWidth(null), image.getHeight(null), BufferedImage.TYPE_INT_ARGB);
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// Draw the original image onto the buffered image
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Graphics2D g2d = bufferedImage.createGraphics();
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g2d.drawImage(image, 0, 0, null);
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g2d.dispose();
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return bufferedImage;
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}
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}
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@ -25,7 +25,7 @@
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# Default logging detail level for all instances of SimpleLogger.
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# Must be one of ("trace", "debug", "info", "warn", or "error").
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# If not specified, defaults to "info".
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org.slf4j.simpleLogger.defaultLogLevel=trace
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org.slf4j.simpleLogger.defaultLogLevel=debug
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# Logging detail level for a SimpleLogger instance named "xxxxx".
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# Must be one of ("trace", "debug", "info", "warn", or "error").
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@ -42,8 +42,8 @@ org.slf4j.simpleLogger.defaultLogLevel=trace
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# If the format is not specified or is invalid, the default format is used.
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# The default format is yyyy-MM-dd HH:mm:ss:SSS Z.
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#org.slf4j.simpleLogger.dateTimeFormat=yyyy-MM-dd HH:mm:ss:SSS Z
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org.slf4j.simpleLogger.dateTimeFormat=yyyy-MM-dd HH:mm:ss
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#org.slf4j.simpleLogger.dateTimeFormat=yyyy-MM-dd HH:mm:ss
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# Set to true if you want to output the current thread name.
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# Defaults to true.
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org.slf4j.simpleLogger.showThreadName=true
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#org.slf4j.simpleLogger.showThreadName=true
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@ -23,6 +23,7 @@ package org.deeplearning4j.nn.modelimport.keras.configurations;
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import org.deeplearning4j.nn.conf.InputPreProcessor;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.nn.conf.serde.CavisMapper;
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import org.deeplearning4j.nn.modelimport.keras.preprocessors.KerasFlattenRnnPreprocessor;
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import org.deeplearning4j.nn.modelimport.keras.preprocessors.PermutePreprocessor;
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import org.deeplearning4j.nn.modelimport.keras.preprocessors.ReshapePreprocessor;
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@ -55,6 +55,8 @@ public class PerformanceListener extends BaseTrainingListener implements Seriali
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private boolean reportEtl = true;
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private boolean reportTime = true;
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private final String name;
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public PerformanceListener(int frequency) {
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@ -66,14 +68,22 @@ public class PerformanceListener extends BaseTrainingListener implements Seriali
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}
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public PerformanceListener(int frequency, boolean reportScore, boolean reportGC) {
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this(frequency, reportScore, reportGC, "no-name");
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}
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public PerformanceListener(int frequency, boolean reportScore, boolean reportGC, String name) {
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Preconditions.checkArgument(frequency > 0, "Invalid frequency, must be > 0: Got " + frequency);
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this.frequency = frequency;
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this.reportScore = reportScore;
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this.reportGC = reportGC;
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this.name = name;
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lastTime.set(System.currentTimeMillis());
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}
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public PerformanceListener(int frequency, boolean reportScore, String name) {
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this(frequency, reportScore, false, name);
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}
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@Override
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public void iterationDone(IModel model, int iteration, int epoch) {
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// we update lastTime on every iteration
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@ -116,6 +126,7 @@ public class PerformanceListener extends BaseTrainingListener implements Seriali
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StringBuilder builder = new StringBuilder();
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builder.append("Name: '"+name+"'");
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if (Nd4j.getAffinityManager().getNumberOfDevices() > 1)
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builder.append("Device: [").append(Nd4j.getAffinityManager().getDeviceForCurrentThread()).append("]; ");
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Binary file not shown.
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@ -1,5 +1,5 @@
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distributionBase=GRADLE_USER_HOME
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distributionPath=wrapper/dists
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distributionUrl=https\://services.gradle.org/distributions/gradle-7.5.1-bin.zip
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distributionUrl=https\://services.gradle.org/distributions/gradle-8.2.1-bin.zip
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zipStoreBase=GRADLE_USER_HOME
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zipStorePath=wrapper/dists
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@ -116,7 +116,6 @@ esac
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CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
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# Determine the Java command to use to start the JVM.
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if [ -n "$JAVA_HOME" ]; then
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if [ -x "$JAVA_HOME/jre/sh/java" ]; then
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@ -145,12 +144,14 @@ if ! "$cygwin" && ! "$darwin" && ! "$nonstop" ; then
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max*)
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MAX_FD=$(ulimit -H -n) ||
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warn "Could not query maximum file descriptor limit"
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;;
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esac
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case $MAX_FD in #(
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'' | soft) : ;; #(
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*)
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ulimit -n "$MAX_FD" ||
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warn "Could not set maximum file descriptor limit to $MAX_FD"
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;;
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esac
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fi
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@ -170,12 +171,14 @@ if "$cygwin" || "$msys" ; then
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JAVACMD=$(cygpath --unix "$JAVACMD")
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# Now convert the arguments - kludge to limit ourselves to /bin/sh
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for arg do
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for arg; do
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if
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case $arg in #(
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-*) false ;; # don't mess with options #(
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/?*) t=${arg#/} t=/${t%%/*} # looks like a POSIX filepath
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[ -e "$t" ] ;; #(
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/?*)
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t=${arg#/} t=/${t%%/*} # looks like a POSIX filepath
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[ -e "$t" ]
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;; #(
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*) false ;;
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esac
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then
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@ -205,6 +208,11 @@ set -- \
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org.gradle.wrapper.GradleWrapperMain \
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"$@"
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# Stop when "xargs" is not available.
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if ! command -v xargs >/dev/null 2>&1; then
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die "xargs is not available"
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fi
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# Use "xargs" to parse quoted args.
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#
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# With -n1 it outputs one arg per line, with the quotes and backslashes removed.
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@ -40,7 +40,7 @@ if defined JAVA_HOME goto findJavaFromJavaHome
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set JAVA_EXE=java.exe
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%JAVA_EXE% -version >NUL 2>&1
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if "%ERRORLEVEL%" == "0" goto execute
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if %ERRORLEVEL% equ 0 goto execute
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echo.
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echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
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@ -75,13 +75,15 @@ set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
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:end
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@rem End local scope for the variables with windows NT shell
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if "%ERRORLEVEL%"=="0" goto mainEnd
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if %ERRORLEVEL% equ 0 goto mainEnd
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:fail
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rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
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rem the _cmd.exe /c_ return code!
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if not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
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exit /b 1
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set EXIT_CODE=%ERRORLEVEL%
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if %EXIT_CODE% equ 0 set EXIT_CODE=1
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if not ""=="%GRADLE_EXIT_CONSOLE%" exit %EXIT_CODE%
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exit /b %EXIT_CODE%
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:mainEnd
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if "%OS%"=="Windows_NT" endlocal
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@ -70,7 +70,7 @@ apply from: "chooseBackend.gradle"
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rootProject.name = "Cavis"
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enableFeaturePreview("TYPESAFE_PROJECT_ACCESSORS")
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enableFeaturePreview("VERSION_CATALOGS")
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//enableFeaturePreview("VERSION_CATALOGS") //only needed for gradle <8
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sourceControl {
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