Update examples to reflect new imports, exceptions

Author: agibsonccc

dl4j-examples/src/main/java/org/deeplearning4j/examples/advanced/features/transferlearning/editlastlayer/presave/FitFromFeaturized.java

@@ -23,8 +23,6 @@
 import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.graph.ComputationGraph;
-import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
-import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
 import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration;
 import org.deeplearning4j.nn.transferlearning.TransferLearning;
 import org.deeplearning4j.nn.transferlearning.TransferLearningHelper;
@@ -61,7 +59,7 @@ public class FitFromFeaturized {
     protected static final int numClasses = 5;
     protected static final int nEpochs = 3;
 
-    public static void main(String [] args) throws IOException, InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
+    public static void main(String [] args) throws IOException {
 
         //Import vgg
         //Note that the model imported does not have an output layer (check printed summary)

dl4j-examples/src/main/java/org/deeplearning4j/examples/advanced/features/transferlearning/iterators/FlowerDataSetIteratorFeaturized.java

@@ -20,8 +20,8 @@
 package org.deeplearning4j.examples.advanced.features.transferlearning.iterators;
 
 import org.deeplearning4j.examples.advanced.features.transferlearning.editlastlayer.presave.FeaturizedPreSave;
-import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
-import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
+import org.deeplearning4j.frameworkimport.keras.keras.exceptions.InvalidKerasConfigurationException;
+import org.deeplearning4j.frameworkimport.keras.keras.exceptions.UnsupportedKerasConfigurationException;
 import org.nd4j.linalg.dataset.AsyncDataSetIterator;
 import org.nd4j.linalg.dataset.ExistingMiniBatchDataSetIterator;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
@@ -39,7 +39,7 @@ public class FlowerDataSetIteratorFeaturized {
 
     private static String featureExtractorLayer = FeaturizedPreSave.featurizeExtractionLayer;
 
-    public static DataSetIterator trainIterator() throws UnsupportedKerasConfigurationException, IOException, InvalidKerasConfigurationException {
+    public static DataSetIterator trainIterator() throws  IOException {
         runFeaturize();
         DataSetIterator existingTrainingData = new ExistingMiniBatchDataSetIterator(new File("trainFolder"),"flowers-"+featureExtractorLayer+"-train-%d.bin");
         DataSetIterator asyncTrainIter = new AsyncDataSetIterator(existingTrainingData);
@@ -51,7 +51,7 @@ public static DataSetIterator testIterator() {
         return asyncTestIter;
     }
 
-    private static void runFeaturize() throws InvalidKerasConfigurationException, IOException, UnsupportedKerasConfigurationException {
+    private static void runFeaturize() throws IOException {
         File trainDir = new File("trainFolder","flowers-"+featureExtractorLayer+"-train-0.bin");
         if (!trainDir.isFile()) {
             log.info("\n\tFEATURIZED DATA NOT FOUND. \n\t\tRUNNING \"FeaturizedPreSave\" first to do presave of featurized data");

dl4j-examples/src/main/java/org/deeplearning4j/examples/quickstart/modeling/feedforward/regression/MathFunctionsModel.java

@@ -19,7 +19,7 @@
 
 package org.deeplearning4j.examples.quickstart.modeling.feedforward.regression;
 
-import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator;
+import org.deeplearning4j.datasets.iterator.utilty.ListDataSetIterator;
 import org.deeplearning4j.examples.quickstart.modeling.feedforward.regression.mathfunctions.MathFunction;
 import org.deeplearning4j.examples.quickstart.modeling.feedforward.regression.mathfunctions.SinXDivXMathFunction;
 import org.nd4j.linalg.activations.Activation;

dl4j-examples/src/main/java/org/deeplearning4j/examples/quickstart/modeling/feedforward/regression/SumModel.java

@@ -19,7 +19,7 @@
 
 package org.deeplearning4j.examples.quickstart.modeling.feedforward.regression;
 
-import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator;
+import org.deeplearning4j.datasets.iterator.utilty.ListDataSetIterator;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
@@ -44,7 +44,7 @@
  */
 @SuppressWarnings({"DuplicatedCode", "FieldCanBeLocal"})
 public class SumModel {
-    //Random number generator seed, for reproducability
+    //Random number generator seed, for reproduceability
     public static final int seed = 12345;
     //Number of epochs (full passes of the data)
     public static final int nEpochs = 200;

dl4j-examples/src/main/java/org/deeplearning4j/examples/quickstart/modeling/recurrent/MemorizeSequence.java

@@ -150,7 +150,7 @@ public static void main(String[] args) {
                 // first process the last output of the network to a concrete
                 // neuron, the neuron with the highest output has the highest
                 // chance to get chosen
-                int sampledCharacterIdx = Nd4j.getExecutioner().exec(new ArgMax(output, 1))[0].getInt(0);
+                int sampledCharacterIdx = Nd4j.getExecutioner().exec(new ArgMax(new INDArray[]{output},false,new int[]{1}))[0].getInt(0);
 
                 // print the chosen output
                 System.out.print(LEARNSTRING_CHARS_LIST.get(sampledCharacterIdx));

dl4j-examples/src/main/java/org/deeplearning4j/examples/utils/PlotUtil.java

@@ -19,7 +19,7 @@
 
 package org.deeplearning4j.examples.utils;
 
-import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator;
+import org.deeplearning4j.datasets.iterator.utilty.ListDataSetIterator;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.jfree.chart.ChartPanel;
 import org.jfree.chart.ChartUtilities;
@@ -144,7 +144,7 @@ private static XYDataset createDataSetTrain(INDArray features, INDArray labels)
 
         XYSeries[] series = new XYSeries[nClasses];
         for (int i = 0; i < series.length; i++) series[i] = new XYSeries("Class " + i);
-        INDArray argMax = Nd4j.getExecutioner().exec(new ArgMax(labels, 1))[0];
+        INDArray argMax = Nd4j.getExecutioner().exec(new ArgMax(new INDArray[]{labels},false,new int[]{1}))[0];
         for (int i = 0; i < nRows; i++) {
             int classIdx = (int) argMax.getDouble(i);
             series[classIdx].add(features.getDouble(i, 0), features.getDouble(i, 1));