removing demo files

Signed-off-by: brian <brian@brutex.de>

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/api/Animal.java

@@ -1,68 +0,0 @@
-/*
- *
- *    ******************************************************************************
- *    *
- *    * This program and the accompanying materials are made available under the
- *    * terms of the Apache License, Version 2.0 which is available at
- *    * https://www.apache.org/licenses/LICENSE-2.0.
- *    *
- *    *  See the NOTICE file distributed with this work for additional
- *    *  information regarding copyright ownership.
- *    * Unless required by applicable law or agreed to in writing, software
- *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *    * License for the specific language governing permissions and limitations
- *    * under the License.
- *    *
- *    * SPDX-License-Identifier: Apache-2.0
- *    *****************************************************************************
- *
- */
-
-package net.brutex.ai.dnn.api;
-
-public class Animal {
-
-  private String animalString;
-
-  protected Animal(AnimalBuilder<?, ?> b) {
-    this.animalString = b.animalString;
-  }
-
-  public static AnimalBuilder<?, ?> builder() {
-    return new AnimalBuilderImpl();
-  }
-
-  public static abstract class AnimalBuilder<C extends Animal, B extends AnimalBuilder<C, B>> {
-
-    private String animalString;
-
-    public B animalString(String animalString) {
-      this.animalString = animalString;
-      return self();
-    }
-
-    protected abstract B self();
-
-    public abstract C build();
-
-    public String toString() {
-      return "Animal.AnimalBuilder(animalString=" + this.animalString + ")";
-    }
-  }
-
-  private static final class AnimalBuilderImpl extends
-      AnimalBuilder<Animal, AnimalBuilderImpl> {
-
-    private AnimalBuilderImpl() {
-    }
-
-    protected AnimalBuilderImpl self() {
-      return this;
-    }
-
-    public Animal build() {
-      return new Animal(this);
-    }
-  }
-}

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/api/NN.java

@@ -35,9 +35,5 @@ public class NN {
     return NeuralNetConfiguration.builder();
   }
 
-  void test() {
+
-    Dog.DogBuilder builder = Dog.builder()
-        .animalString("")
-        .dogString("");
-  }
 }

cavis-dnn/cavis-dnn-nn/src/test/java/net/brutex/ai/dnn/api/dnnTest.java

@@ -29,9 +29,11 @@ import org.apache.commons.lang3.RandomUtils;
 import org.deeplearning4j.datasets.iterator.FloatsDataSetIterator;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.deeplearning4j.nn.conf.layers.ActivationLayer;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.conf.weightnoise.WeightNoise;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInitXavier;
 import org.deeplearning4j.optimize.listeners.ScoreToChartListener;
@@ -48,9 +50,9 @@ class dnnTest {
 
   @Test
   void testFFLayer() {
-    int numFeatures = 6;
+    int numFeatures = 600;
-    int batchSize = 5;
+    int batchSize = 5000;
-    int numRows = 100;
+    int numRows = 10000;
     AtomicInteger cnt = new AtomicInteger(0);
     FloatsDataSetIterator iterator = new FloatsDataSetIterator(floatIterable(numRows, numFeatures), batchSize);
 
@@ -85,8 +87,9 @@ class dnnTest {
             .gradientNormalizationThreshold(100)
             .weightInit(new WeightInitXavier())
             .activation(new ActivationSigmoid())
+            .weightNoise(new WeightNoise(new NormalDistribution(0.5, 0.7)))
             // .inputType(InputType.convolutional(28, 28, 1))
-            .layer(new DenseLayer.Builder().nIn(6).nOut(20).build())
+            .layer(new DenseLayer.Builder().nIn(numFeatures).nOut(20).build())
             .layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
             .layer(new DenseLayer.Builder().nIn(20).nOut(40).build())
             .layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
@@ -94,14 +97,18 @@ class dnnTest {
             .layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
             .layer(new DenseLayer.Builder().nIn(12).nOut(8).build())
             .layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())
-            .layer(new OutputLayer.Builder(LossFunctions.LossFunction.SQUARED_LOSS).activation(Activation.SIGMOID).nOut(6).build())
+            .layer(
+                new OutputLayer.Builder(LossFunctions.LossFunction.SQUARED_LOSS)
+                    .activation(Activation.SIGMOID)
+                    .nOut(numFeatures)
+                    .build())
             .build();
 
     MultiLayerNetwork net = new MultiLayerNetwork(network);
     net.addTrainingListeners(new ScoreToChartListener("dnnTest"));
     FloatsDataSetIterator dset = new FloatsDataSetIterator(floatIterable(numRows, numFeatures), batchSize);
 
-    for (int i = 0; i < 2000000; i++) {
+    for (int i = 0; i < 20000000; i++) {
       net.fit(dset);
       System.out.println("Score: " + net.getScore());
     }