update tensorflow java to 1.0.0

wayang-api/wayang-api-scala-java/pom.xml

@@ -34,7 +34,7 @@
 
     <properties>
         <java-module-name>org.apache.wayang.api</java-module-name>
-        <tensorflow.version>0.4.2</tensorflow.version>
+        <tensorflow.version>1.0.0-rc.2</tensorflow.version>
     </properties>
 
     <dependencyManagement>

wayang-platforms/wayang-tensorflow/pom.xml

@@ -39,7 +39,7 @@
         <maven.compiler.source>11</maven.compiler.source>
         <maven.compiler.target>11</maven.compiler.target>
         <wayang.version>0.7.1</wayang.version>
-        <tensorflow.version>0.4.2</tensorflow.version>
+        <tensorflow.version>1.0.0-rc.2</tensorflow.version>
     </properties>
 
     <dependencies>

wayang-platforms/wayang-tensorflow/src/main/java/org/apache/wayang/tensorflow/model/TensorflowModel.java

@@ -22,10 +22,7 @@
 import org.apache.wayang.basic.model.op.Input;
 import org.apache.wayang.basic.model.op.Op;
 import org.apache.wayang.basic.model.optimizer.Optimizer;
-import org.tensorflow.Graph;
-import org.tensorflow.Operand;
-import org.tensorflow.Session;
-import org.tensorflow.Tensor;
+import org.tensorflow.*;
 import org.tensorflow.ndarray.*;
 import org.tensorflow.ndarray.index.Indices;
 import org.tensorflow.op.Ops;
@@ -122,14 +119,12 @@ void train(XT x, YT y, int epoch, int batchSize) {
                     if (accuracyCalculation != null) {
                         runner.fetch(accuracyCalculation.getName());
                     }
-                    List<Tensor> ret = runner.run();
-                    try (TFloat32 loss = (TFloat32) ret.get(0)) {
+                    try (Result ret = runner.run()) {
+                        TFloat32 loss = (TFloat32) ret.get(0);
                         System.out.printf("[epoch %d, batch %d] loss: %f ", i + 1, start / batchSize + 1, loss.getFloat());
-                    }
-                    if (accuracyCalculation != null) {
-                        try (TFloat32 acc = (TFloat32) ret.get(1)) {
-                            System.out.printf("accuracy: %f ", acc.getFloat());
-                        }
+
+                        TFloat32 acc = (TFloat32) ret.get(1);
+                        System.out.printf("accuracy: %f ", acc.getFloat());
                     }
                     System.out.println();
                 }

wayang-platforms/wayang-tensorflow/src/test/java/org/apache/wayang/tensorflow/model/TensorflowModelTest.java

@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * 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.
+ */
+
+package org.apache.wayang.tensorflow.model;
+
+import org.apache.wayang.basic.model.DLModel;
+import org.apache.wayang.basic.model.op.*;
+import org.apache.wayang.basic.model.op.nn.CrossEntropyLoss;
+import org.apache.wayang.basic.model.op.nn.Linear;
+import org.apache.wayang.basic.model.op.nn.Sigmoid;
+import org.apache.wayang.basic.model.optimizer.GradientDescent;
+import org.apache.wayang.basic.model.optimizer.Optimizer;
+import org.junit.Test;
+import org.tensorflow.ndarray.FloatNdArray;
+import org.tensorflow.ndarray.IntNdArray;
+import org.tensorflow.ndarray.NdArrays;
+import org.tensorflow.ndarray.Shape;
+import org.tensorflow.op.Ops;
+import org.tensorflow.types.TFloat32;
+import org.tensorflow.types.TInt32;
+public class TensorflowModelTest {
+    @Test
+    public void test() {
+        FloatNdArray x = NdArrays.ofFloats(Shape.of(6, 4))
+                .set(NdArrays.vectorOf(5.1f, 3.5f, 1.4f, 0.2f), 0)
+                .set(NdArrays.vectorOf(4.9f, 3.0f, 1.4f, 0.2f), 1)
+                .set(NdArrays.vectorOf(6.9f, 3.1f, 4.9f, 1.5f), 2)
+                .set(NdArrays.vectorOf(5.5f, 2.3f, 4.0f, 1.3f), 3)
+                .set(NdArrays.vectorOf(5.8f, 2.7f, 5.1f, 1.9f), 4)
+                .set(NdArrays.vectorOf(6.7f, 3.3f, 5.7f, 2.5f), 5)
+                ;
+        IntNdArray y = NdArrays.vectorOf(0, 0, 1, 1, 2, 2);
+        Op l1 = new Linear(4, 64, true);
+        Op s1 = new Sigmoid();
+        Op l2 = new Linear(64, 3, true);
+        s1.with(l1.with(new Input(Input.Type.FEATURES)));
+        l2.with(s1);
+        DLModel model = new DLModel(l2);
+        Op criterion = new CrossEntropyLoss(3);
+        criterion.with(
+                new Input(Input.Type.PREDICTED, Op.DType.FLOAT32),
+                new Input(Input.Type.LABEL, Op.DType.INT32)
+        );
+        Op acc = new Mean(0);
+        acc.with(new Cast(Op.DType.FLOAT32).with(new Eq().with(
+                new ArgMax(1).with(new Input(Input.Type.PREDICTED, Op.DType.FLOAT32)),
+                new Input(Input.Type.LABEL, Op.DType.INT32)
+        )));
+        Optimizer optimizer = new GradientDescent(0.02f);
+        try (TensorflowModel tfModel = new TensorflowModel(model, criterion, optimizer, acc)) {
+            System.out.println(tfModel.getOut().getName());
+            tfModel.train(x, y, 100, 6);
+            TFloat32 predicted = tfModel.predict(x);
+            Ops tf = Ops.create();
+            org.tensorflow.op.math.ArgMax<TInt32> argMax = tf.math.argMax(tf.constantOf(predicted), tf.constant(1), TInt32.class);
+            final TInt32 tensor = argMax.asTensor();
+            System.out.print("[ ");
+            for (int i = 0; i < tensor.shape().size(0); i++) {
+                System.out.print(tensor.getInt(i) + " ");
+            }
+            System.out.println("]");
+        }
+        System.out.println();
+    }
+}

wayang-tests-integration/src/test/java/org/apache/wayang/tests/TensorflowIntegrationIT.java

@@ -30,7 +30,6 @@
 import org.apache.wayang.core.plan.wayangplan.WayangPlan;
 import org.apache.wayang.java.Java;
 import org.apache.wayang.tensorflow.Tensorflow;
-import org.junit.Ignore;
 import org.junit.Test;
 
 import java.util.ArrayList;
@@ -68,7 +67,7 @@ public class TensorflowIntegrationIT {
 
     public static String[] LABELS = new String[]{"Iris-setosa", "Iris-versicolor", "Iris-virginica"};
 
-    @Ignore
+    @Test
     public void test() {
         /* training features */
         CollectionSource<float[]> trainXSource = new CollectionSource<>(trainX, float[].class);

wayang-tests-integration/src/test/java/org/apache/wayang/tests/TensorflowIrisIT.java

@@ -24,22 +24,22 @@
 import org.apache.wayang.basic.model.op.nn.Linear;
 import org.apache.wayang.basic.model.op.nn.Sigmoid;
 import org.apache.wayang.basic.model.optimizer.Adam;
-import org.apache.wayang.basic.model.optimizer.GradientDescent;
 import org.apache.wayang.basic.model.optimizer.Optimizer;
 import org.apache.wayang.basic.operators.*;
 import org.apache.wayang.core.api.WayangContext;
 import org.apache.wayang.core.plan.wayangplan.Operator;
 import org.apache.wayang.core.plan.wayangplan.WayangPlan;
 import org.apache.wayang.core.util.Tuple;
-import org.apache.wayang.core.util.WayangCollections;
 import org.apache.wayang.java.Java;
 import org.apache.wayang.tensorflow.Tensorflow;
-import org.junit.Ignore;
 import org.junit.Test;
 
 import java.net.URI;
 import java.net.URISyntaxException;
-import java.util.*;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
 
 /**
  * Test the Tensorflow integration with Wayang.
@@ -56,7 +56,7 @@ public class TensorflowIrisIT {
             "Iris-virginica", 2
     );
 
-    @Ignore
+    @Test
     public void test() {
         final Tuple<Operator, Operator> trainSource = fileOperation(TRAIN_PATH, true);
         final Tuple<Operator, Operator> testSource = fileOperation(TEST_PATH, false);