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finishes feedforward samples.
Signed-off-by: Robert Altena <[email protected]>
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‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistSingleLayerExample.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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package org.deeplearning4j.examples.feedforward.mnist;
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import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.layers.DenseLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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import org.nd4j.evaluation.classification.Evaluation;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.learning.config.Nesterovs;

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistTwoLayerExample.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
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import org.deeplearning4j.eval.Evaluation;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.layers.DenseLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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import org.nd4j.evaluation.classification.Evaluation;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.learning.config.Nadam;

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/RegressionMathFunctions.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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import org.jfree.chart.plot.PlotOrientation;
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import org.jfree.data.xy.XYSeries;
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import org.jfree.data.xy.XYSeriesCollection;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.factory.Nd4j;
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import org.nd4j.linalg.learning.config.Nesterovs;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
@@ -62,16 +60,16 @@ public class RegressionMathFunctions {
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//Number of epochs (full passes of the data)
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public static final int nEpochs = 2000;
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//How frequently should we plot the network output?
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public static final int plotFrequency = 500;
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private static final int plotFrequency = 500;
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//Number of data points
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public static final int nSamples = 1000;
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private static final int nSamples = 1000;
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//Batch size: i.e., each epoch has nSamples/batchSize parameter updates
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public static final int batchSize = 100;
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//Network learning rate
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public static final double learningRate = 0.01;
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public static final Random rng = new Random(seed);
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public static final int numInputs = 1;
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public static final int numOutputs = 1;
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private static final int numOutputs = 1;
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public static void main(final String[] args){
@@ -127,13 +125,14 @@ private static MultiLayerConfiguration getDeepDenseLayerNetworkConfiguration() {
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* @param batchSize Batch size (number of examples for every call of DataSetIterator.next())
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* @param rng Random number generator (for repeatability)
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*/
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@SuppressWarnings("SameParameterValue")
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private static DataSetIterator getTrainingData(final INDArray x, final MathFunction function, final int batchSize, final Random rng) {
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final INDArray y = function.getFunctionValues(x);
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final DataSet allData = new DataSet(x,y);
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final List<DataSet> list = allData.asList();
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Collections.shuffle(list,rng);
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return new ListDataSetIterator(list,batchSize);
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return new ListDataSetIterator<>(list,batchSize);
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}
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//Plot the data

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/RegressionSum.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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* Created by Anwar on 3/15/2016.
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* An example of regression neural network for performing addition
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*/
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@SuppressWarnings({"DuplicatedCode", "FieldCanBeLocal"})
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public class RegressionSum {
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//Random number generator seed, for reproducability
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public static final int seed = 12345;
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//Number of epochs (full passes of the data)
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public static final int nEpochs = 200;
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//Number of data points
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public static final int nSamples = 1000;
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private static final int nSamples = 1000;
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//Batch size: i.e., each epoch has nSamples/batchSize parameter updates
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public static final int batchSize = 100;
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//Network learning rate
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public static final double learningRate = 0.01;
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// The range of the sample data, data in range (0-1 is sensitive for NN, you can try other ranges and see how it effects the results
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// also try changing the range along with changing the activation function
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public static int MIN_RANGE = 0;
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public static int MAX_RANGE = 3;
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private static int MIN_RANGE = 0;
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private static int MAX_RANGE = 3;
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public static final Random rng = new Random(seed);
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net.fit(iterator);
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}
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// Test the addition of 2 numbers (Try different numbers here)
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final INDArray input = Nd4j.create(new double[] { 0.111111, 0.3333333333333 }, new int[] { 1, 2 });
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final INDArray input = Nd4j.create(new double[] { 0.111111, 0.3333333333333 }, 1, 2);
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INDArray out = net.output(input, false);
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System.out.println(out);
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}
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@SuppressWarnings("SameParameterValue")
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private static DataSetIterator getTrainingData(int batchSize, Random rand){
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double [] sum = new double[nSamples];
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double [] input1 = new double[nSamples];
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input2[i] = MIN_RANGE + (MAX_RANGE - MIN_RANGE) * rand.nextDouble();
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sum[i] = input1[i] + input2[i];
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}
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INDArray inputNDArray1 = Nd4j.create(input1, new int[]{nSamples,1});
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INDArray inputNDArray2 = Nd4j.create(input2, new int[]{nSamples,1});
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INDArray inputNDArray1 = Nd4j.create(input1, nSamples,1);
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INDArray inputNDArray2 = Nd4j.create(input2, nSamples,1);
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INDArray inputNDArray = Nd4j.hstack(inputNDArray1,inputNDArray2);
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INDArray outPut = Nd4j.create(sum, new int[]{nSamples, 1});
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INDArray outPut = Nd4j.create(sum, nSamples, 1);
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DataSet dataSet = new DataSet(inputNDArray, outPut);
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List<DataSet> listDs = dataSet.asList();
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Collections.shuffle(listDs,rng);
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return new ListDataSetIterator(listDs,batchSize);
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return new ListDataSetIterator<>(listDs,batchSize);
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}
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}

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/function/SawtoothMathFunction.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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* @author Unknown
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* Documentation added by ERRM
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*/
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public class SawtoothMathFunction implements MathFunction {
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@Override
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for (int i = 0; i < xd2.length; i++) { //Using the sawtooth wave function, find the values at the given intervals
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yd2[i] = 2 * (xd2[i] / sawtoothPeriod - Math.floor(xd2[i] / sawtoothPeriod + 0.5));
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}
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return Nd4j.create(yd2, new int[]{xd2.length, 1}); //Column vector
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return Nd4j.create(yd2, xd2.length, 1); //Column vector
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}
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@Override

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/function/SinMathFunction.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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package org.deeplearning4j.examples.feedforward.regression.function;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.api.ops.impl.transforms.strict.Sin;
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import org.nd4j.linalg.factory.Nd4j;
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import org.nd4j.linalg.ops.transforms.Transforms;
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/**

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/function/SinXDivXMathFunction.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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package org.deeplearning4j.examples.feedforward.regression.function;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.api.ops.impl.transforms.strict.Sin;
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import org.nd4j.linalg.factory.Nd4j;
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import org.nd4j.linalg.ops.transforms.Transforms;
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/**

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/function/SquareWaveMathFunction.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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package org.deeplearning4j.examples.feedforward.regression.function;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.api.ops.impl.transforms.same.Sign;
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import org.nd4j.linalg.api.ops.impl.transforms.strict.Sin;
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import org.nd4j.linalg.factory.Nd4j;
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import org.nd4j.linalg.ops.transforms.Transforms;
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/**

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/function/TriangleWaveMathFunction.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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for (int i = 0; i < xd.length; i++) {
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yd[i] = Math.abs(2 * (xd[i] / period - Math.floor(xd[i] / period + 0.5)));
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}
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return Nd4j.create(yd, new int[]{xd.length, 1}); //Column vector
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return Nd4j.create(yd, xd.length, 1); //Column vector
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}
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@Override

‎dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/xor/XorExample.java

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/*******************************************************************************
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/* *****************************************************************************
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* Copyright (c) 2015-2019 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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import org.deeplearning4j.nn.conf.layers.DenseLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.api.ndarray.INDArray;
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.nOut(4)
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.activation(Activation.SIGMOID)
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// random initialize weights with values between 0 and 1
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.weightInit(new UniformDistribution(0, 1))
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.weightInit(new UniformDistribution(0, 1))
128127
.build())
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.layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
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.nOut(2)

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