Added missing HelperUtilsTest.java

cavis-dnn/cavis-dnn-nn/src/test/java/org/deeplearning4j/nn/layers/HelperUtilsTest.java

@@ -0,0 +1,92 @@
+/*
+ *  ******************************************************************************
+ *  *
+ *  *
+ *  * 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 org.deeplearning4j.nn.layers;
+
+import org.deeplearning4j.BaseDL4JTest;
+import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
+import org.deeplearning4j.nn.api.OptimizationAlgorithm;
+import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
+import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.layers.ActivationLayer;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
+import org.deeplearning4j.nn.conf.layers.*;
+import org.deeplearning4j.nn.graph.ComputationGraph;
+import org.deeplearning4j.nn.layers.convolution.ConvolutionHelper;
+import org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingHelper;
+import org.deeplearning4j.nn.layers.mkldnn.*;
+import org.deeplearning4j.nn.layers.normalization.BatchNormalizationHelper;
+import org.deeplearning4j.nn.layers.normalization.LocalResponseNormalizationHelper;
+import org.deeplearning4j.nn.layers.recurrent.LSTMHelper;
+import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
+import org.deeplearning4j.nn.weights.WeightInit;
+import org.junit.jupiter.api.DisplayName;
+import org.junit.jupiter.api.Tag;
+import org.junit.jupiter.api.Test;
+import org.nd4j.common.tests.tags.NativeTag;
+import org.nd4j.common.tests.tags.TagNames;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.activations.impl.ActivationELU;
+import org.nd4j.linalg.activations.impl.ActivationRationalTanh;
+import org.nd4j.linalg.activations.impl.ActivationSoftmax;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.dataset.DataSet;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+/**
+ */
+@DisplayName("Activation Layer Test")
+@NativeTag
+@Tag(TagNames.CUSTOM_FUNCTIONALITY)
+@Tag(TagNames.DL4J_OLD_API)
+public class HelperUtilsTest extends BaseDL4JTest {
+
+    @Override
+    public DataType getDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Test
+    @DisplayName("Test instance creation of various helpers")
+    public void testOneDnnHelperCreation() {
+        assertNotNull(HelperUtils.createHelper("",
+                MKLDNNLSTMHelper.class.getName(), LSTMHelper.class,"layername",getDataType()));
+        assertNotNull(HelperUtils.createHelper("", MKLDNNBatchNormHelper.class.getName(),
+                BatchNormalizationHelper.class,"layername",getDataType()));
+        assertNotNull(HelperUtils.createHelper("", MKLDNNLocalResponseNormalizationHelper.class.getName(),
+                LocalResponseNormalizationHelper.class,"layername",getDataType()));
+        assertNotNull(HelperUtils.createHelper("", MKLDNNSubsamplingHelper.class.getName(),
+                SubsamplingHelper.class,"layername",getDataType()));
+        assertNotNull(HelperUtils.createHelper("", MKLDNNConvHelper.class.getName(),
+                ConvolutionHelper.class,"layername",getDataType()));
+
+
+    }
+
+
+}