Add embedding rnn example

dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/embedding/RNNEmbedding.java

@@ -0,0 +1,67 @@
+package org.deeplearning4j.examples.misc.embedding;
+
+import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.*;
+import org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor;
+import org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.ndarray.INDArray;
+
+import java.util.Random;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+/** Feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1)
+ * as input. This input has shape [numExamples,1] instead of [numExamples,numClasses] for the equivalent one-hot representation.
+ * Mathematically, EmbeddingLayer is equivalent to using a DenseLayer with a one-hot representation for the input; however,
+ * it can be much more efficient with a large number of classes (as a dense layer + one-hot input does a matrix multiply
+ * with all but one value being zero).<br>
+ * <b>Note</b>: can only be used as the first layer for a network<br>
+ * <b>Note 2</b>: For a given example index i, the output is activationFunction(weights.getRow(i) + bias), hence the
+ * weight rows can be considered a vector/embedding for each example.
+ *
+ * @author Alex Black
+ */
+public class RNNEmbedding {
+    public static void main(String[] args) throws Exception {
+
+        int nClassesIn = 10;
+        int batchSize = 3;
+        int timeSeriesLength = 8;
+        INDArray inEmbedding = Nd4j.create(batchSize, 1, timeSeriesLength);
+        INDArray outLabels = Nd4j.create(batchSize, 4, timeSeriesLength);
+
+        Random r = new Random(12345);
+        for (int i = 0; i < batchSize; i++) {
+            for (int j = 0; j < timeSeriesLength; j++) {
+                int classIdx = r.nextInt(nClassesIn);
+                inEmbedding.putScalar(new int[]{i, 0, j}, classIdx);
+                int labelIdx = r.nextInt(4);
+                outLabels.putScalar(new int[]{i, labelIdx, j}, 1.0);
+            }
+        }
+
+        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
+            .activation(Activation.RELU)
+            .list()
+            .layer(0, new EmbeddingLayer.Builder().nIn(nClassesIn).nOut(5).build())
+            .layer(1, new GravesLSTM.Builder().nIn(5).nOut(7).activation(Activation.SOFTSIGN).build())
+            .layer(2, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(7).nOut(4).activation(Activation.SOFTMAX).build())
+            .inputPreProcessor(0, new RnnToFeedForwardPreProcessor())
+            .inputPreProcessor(1, new FeedForwardToRnnPreProcessor())
+            .build();
+
+        MultiLayerNetwork net = new MultiLayerNetwork(conf);
+        net.init();
+
+        net.setInput(inEmbedding);
+        net.setLabels(outLabels);
+
+        net.computeGradientAndScore();
+        System.out.println(net.score());
+
+
+    }
+}