Adapt to TFJava 0.4.0

tensorflow · Nov 28, 2021 · ed75c1c · ed75c1c
1 parent 0489e6b
commit ed75c1c
Show file tree

Hide file tree

Showing 7 changed files with 44 additions and 79 deletions.
diff --git a/tensorflow-examples/pom.xml b/tensorflow-examples/pom.xml
@@ -12,18 +12,19 @@
     <!-- The maven compiler plugin defaults to a lower version -->
     <maven.compiler.source>1.8</maven.compiler.source>
     <maven.compiler.target>1.8</maven.compiler.target>
+    <tensorflow.version>0.4.0</tensorflow.version>
   </properties>
 
   <dependencies>
     <dependency>
       <groupId>org.tensorflow</groupId>
       <artifactId>tensorflow-core-platform</artifactId>
-      <version>0.3.1</version>
+      <version>${tensorflow.version}</version>
     </dependency>
     <dependency>
       <groupId>org.tensorflow</groupId>
       <artifactId>tensorflow-framework</artifactId>
-      <version>0.3.1</version>
+      <version>${tensorflow.version}</version>
     </dependency>
   </dependencies>
 

diff --git a/...xamples/src/main/java/org/tensorflow/model/examples/cnn/fastrcnn/FasterRcnnInception.java b/...xamples/src/main/java/org/tensorflow/model/examples/cnn/fastrcnn/FasterRcnnInception.java
@@ -101,11 +101,15 @@ The given SavedModel SignatureDef contains the following output(s):
 */
 
 
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.Map;
+import java.util.TreeMap;
 import org.tensorflow.Graph;
+import org.tensorflow.Operand;
 import org.tensorflow.SavedModelBundle;
 import org.tensorflow.Session;
 import org.tensorflow.Tensor;
-import org.tensorflow.Operand;
 import org.tensorflow.ndarray.FloatNdArray;
 import org.tensorflow.ndarray.Shape;
 import org.tensorflow.op.Ops;
@@ -121,12 +125,6 @@ The given SavedModel SignatureDef contains the following output(s):
 import org.tensorflow.types.TUint8;
 
 
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.Map;
-import java.util.TreeMap;
-
-
 /**
  * Loads an image using ReadFile and DecodeJpeg and then uses the saved model
  * faster_rcnn/inception_resnet_v2_1024x1024/1 to detect objects with a detection score greater than 0.3
@@ -254,7 +252,6 @@ public static void main(String[] params) {
             Constant<TString> fileName = tf.constant(imagePath);
             ReadFile readFile = tf.io.readFile(fileName);
             Session.Runner runner = s.runner();
-            s.run(tf.init());
             DecodeJpeg.Options options = DecodeJpeg.channels(3L);
             DecodeJpeg decodeImage = tf.image.decodeJpeg(readFile.contents(), options);
             //fetch image from file

diff --git a/tensorflow-examples/src/main/java/org/tensorflow/model/examples/cnn/lenet/CnnMnist.java b/tensorflow-examples/src/main/java/org/tensorflow/model/examples/cnn/lenet/CnnMnist.java
@@ -22,9 +22,20 @@
 import org.tensorflow.Graph;
 import org.tensorflow.Operand;
 import org.tensorflow.Session;
-import org.tensorflow.Tensor;
+import org.tensorflow.framework.optimizers.AdaDelta;
+import org.tensorflow.framework.optimizers.AdaGrad;
+import org.tensorflow.framework.optimizers.AdaGradDA;
+import org.tensorflow.framework.optimizers.Adam;
+import org.tensorflow.framework.optimizers.GradientDescent;
+import org.tensorflow.framework.optimizers.Momentum;
+import org.tensorflow.framework.optimizers.Optimizer;
+import org.tensorflow.framework.optimizers.RMSProp;
 import org.tensorflow.model.examples.datasets.ImageBatch;
 import org.tensorflow.model.examples.datasets.mnist.MnistDataset;
+import org.tensorflow.ndarray.ByteNdArray;
+import org.tensorflow.ndarray.FloatNdArray;
+import org.tensorflow.ndarray.Shape;
+import org.tensorflow.ndarray.index.Indices;
 import org.tensorflow.op.Op;
 import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Constant;
@@ -38,20 +49,8 @@
 import org.tensorflow.op.nn.MaxPool;
 import org.tensorflow.op.nn.Relu;
 import org.tensorflow.op.nn.Softmax;
-import org.tensorflow.op.nn.raw.SoftmaxCrossEntropyWithLogits;
+import org.tensorflow.op.nn.SoftmaxCrossEntropyWithLogits;
 import org.tensorflow.op.random.TruncatedNormal;
-import org.tensorflow.ndarray.Shape;
-import org.tensorflow.ndarray.ByteNdArray;
-import org.tensorflow.ndarray.FloatNdArray;
-import org.tensorflow.ndarray.index.Indices;
-import org.tensorflow.framework.optimizers.AdaDelta;
-import org.tensorflow.framework.optimizers.AdaGrad;
-import org.tensorflow.framework.optimizers.AdaGradDA;
-import org.tensorflow.framework.optimizers.Adam;
-import org.tensorflow.framework.optimizers.GradientDescent;
-import org.tensorflow.framework.optimizers.Momentum;
-import org.tensorflow.framework.optimizers.Optimizer;
-import org.tensorflow.framework.optimizers.RMSProp;
 import org.tensorflow.types.TFloat32;
 import org.tensorflow.types.TUint8;
 
@@ -75,7 +74,6 @@ public class CnnMnist {
   public static final String TARGET = "target";
   public static final String TRAIN = "train";
   public static final String TRAINING_LOSS = "training_loss";
-  public static final String INIT = "init";
 
   private static final String TRAINING_IMAGES_ARCHIVE = "mnist/train-images-idx3-ubyte.gz";
   private static final String TRAINING_LABELS_ARCHIVE = "mnist/train-labels-idx1-ubyte.gz";
@@ -160,8 +158,7 @@ public static Graph build(String optimizerName) {
     // Loss function & regularization
     OneHot<TFloat32> oneHot = tf
         .oneHot(labels, tf.constant(10), tf.constant(1.0f), tf.constant(0.0f));
-    SoftmaxCrossEntropyWithLogits<TFloat32> batchLoss = tf.nn.raw
-            .softmaxCrossEntropyWithLogits(logits, oneHot);
+    SoftmaxCrossEntropyWithLogits<TFloat32> batchLoss = tf.nn.softmaxCrossEntropyWithLogits(logits, oneHot);
     Mean<TFloat32> labelLoss = tf.math.mean(batchLoss.loss(), tf.constant(0));
     Add<TFloat32> regularizers = tf.math.add(tf.nn.l2Loss(fc1Weights), tf.math
         .add(tf.nn.l2Loss(fc1Biases),
@@ -197,19 +194,13 @@ public static Graph build(String optimizerName) {
       default:
         throw new IllegalArgumentException("Unknown optimizer " + optimizerName);
     }
-    logger.info("Optimizer = " + optimizer.toString());
+    logger.info("Optimizer = " + optimizer);
     Op minimize = optimizer.minimize(loss, TRAIN);
 
-    tf.init();
-
     return graph;
   }
 
   public static void train(Session session, int epochs, int minibatchSize, MnistDataset dataset) {
-    // Initialises the parameters.
-    session.runner().addTarget(INIT).run();
-    logger.info("Initialised the model parameters");
-
     int interval = 0;
     // Train the model
     for (int i = 0; i < epochs; i++) {
@@ -274,7 +265,7 @@ public static void test(Session session, int minibatchSize, MnistDataset dataset
       sb.append("\n");
     }
 
-    System.out.println(sb.toString());
+    System.out.println(sb);
   }
 
   /**

diff --git a/...low-examples/src/main/java/org/tensorflow/model/examples/cnn/vgg/VGG11OnFashionMNIST.java b/...low-examples/src/main/java/org/tensorflow/model/examples/cnn/vgg/VGG11OnFashionMNIST.java
@@ -16,9 +16,8 @@
  */
 package org.tensorflow.model.examples.cnn.vgg;
 
-import org.tensorflow.model.examples.datasets.mnist.MnistDataset;
-
 import java.util.logging.Logger;
+import org.tensorflow.model.examples.datasets.mnist.MnistDataset;
 
 /**
  * Trains and evaluates VGG'11 model on FashionMNIST dataset.

diff --git a/tensorflow-examples/src/main/java/org/tensorflow/model/examples/cnn/vgg/VGGModel.java b/tensorflow-examples/src/main/java/org/tensorflow/model/examples/cnn/vgg/VGGModel.java
@@ -16,14 +16,20 @@
  */
 package org.tensorflow.model.examples.cnn.vgg;
 
+import java.util.Arrays;
+import java.util.logging.Level;
+import java.util.logging.Logger;
 import org.tensorflow.Graph;
 import org.tensorflow.Operand;
 import org.tensorflow.Session;
-import org.tensorflow.Tensor;
 import org.tensorflow.framework.optimizers.Adam;
 import org.tensorflow.framework.optimizers.Optimizer;
 import org.tensorflow.model.examples.datasets.ImageBatch;
 import org.tensorflow.model.examples.datasets.mnist.MnistDataset;
+import org.tensorflow.ndarray.ByteNdArray;
+import org.tensorflow.ndarray.FloatNdArray;
+import org.tensorflow.ndarray.Shape;
+import org.tensorflow.ndarray.index.Indices;
 import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Constant;
 import org.tensorflow.op.core.OneHot;
@@ -35,19 +41,11 @@
 import org.tensorflow.op.nn.Conv2d;
 import org.tensorflow.op.nn.MaxPool;
 import org.tensorflow.op.nn.Relu;
-import org.tensorflow.op.nn.raw.SoftmaxCrossEntropyWithLogits;
+import org.tensorflow.op.nn.SoftmaxCrossEntropyWithLogits;
 import org.tensorflow.op.random.TruncatedNormal;
-import org.tensorflow.ndarray.Shape;
-import org.tensorflow.ndarray.ByteNdArray;
-import org.tensorflow.ndarray.FloatNdArray;
-import org.tensorflow.ndarray.index.Indices;
 import org.tensorflow.types.TFloat32;
 import org.tensorflow.types.TUint8;
 
-import java.util.Arrays;
-import java.util.logging.Level;
-import java.util.logging.Logger;
-
 /**
  * Describes the VGGModel.
  */
@@ -64,7 +62,6 @@ public class VGGModel implements AutoCloseable {
     public static final String TARGET = "target";
     public static final String TRAIN = "train";
     public static final String TRAINING_LOSS = "training_loss";
-    public static final String INIT = "init";
 
     private static final Logger logger = Logger.getLogger(VGGModel.class.getName());
 
@@ -127,8 +124,6 @@ public static Graph compile() {
 
         optimizer.minimize(loss, TRAIN);
 
-        tf.init();
-
         return graph;
     }
 
@@ -159,8 +154,7 @@ public static Add<TFloat32> buildFCLayersAndRegularization(Ops tf, Placeholder<T
         // Loss function & regularization
         OneHot<TFloat32> oneHot = tf
                 .oneHot(labels, tf.constant(10), tf.constant(1.0f), tf.constant(0.0f));
-        SoftmaxCrossEntropyWithLogits<TFloat32> batchLoss = tf.nn.raw
-                .softmaxCrossEntropyWithLogits(logits, oneHot);
+        SoftmaxCrossEntropyWithLogits<TFloat32> batchLoss = tf.nn.softmaxCrossEntropyWithLogits(logits, oneHot);
         Mean<TFloat32> labelLoss = tf.math.mean(batchLoss.loss(), tf.constant(0));
         Add<TFloat32> regularizers = tf.math.add(tf.nn.l2Loss(fc1Weights), tf.math
                 .add(tf.nn.l2Loss(fc1Biases),
@@ -193,10 +187,6 @@ public static Relu<TFloat32> vggConv2DLayer(String layerName, Ops tf, Operand<TF
     }
 
     public void train(MnistDataset dataset, int epochs, int minibatchSize) {
-        // Initialises the parameters.
-        session.runner().addTarget(INIT).run();
-        logger.info("Initialised the model parameters");
-
         int interval = 0;
         // Train the model
         for (int i = 0; i < epochs; i++) {
@@ -261,7 +251,7 @@ public void test(MnistDataset dataset, int minibatchSize) {
             sb.append("\n");
         }
 
-        System.out.println(sb.toString());
+        System.out.println(sb);
     }
 
     /**

diff --git a/tensorflow-examples/src/main/java/org/tensorflow/model/examples/dense/SimpleMnist.java b/tensorflow-examples/src/main/java/org/tensorflow/model/examples/dense/SimpleMnist.java
@@ -19,19 +19,18 @@
 import org.tensorflow.Graph;
 import org.tensorflow.Operand;
 import org.tensorflow.Session;
-import org.tensorflow.Tensor;
 import org.tensorflow.framework.optimizers.GradientDescent;
 import org.tensorflow.framework.optimizers.Optimizer;
 import org.tensorflow.model.examples.datasets.ImageBatch;
 import org.tensorflow.model.examples.datasets.mnist.MnistDataset;
+import org.tensorflow.ndarray.ByteNdArray;
+import org.tensorflow.ndarray.Shape;
 import org.tensorflow.op.Op;
 import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Placeholder;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.op.math.Mean;
 import org.tensorflow.op.nn.Softmax;
-import org.tensorflow.ndarray.Shape;
-import org.tensorflow.ndarray.ByteNdArray;
 import org.tensorflow.types.TFloat32;
 import org.tensorflow.types.TInt64;
 
@@ -61,16 +60,11 @@ public void run() {
 
     // Create weights with an initial value of 0
     Shape weightShape = Shape.of(dataset.imageSize(), MnistDataset.NUM_CLASSES);
-    Variable<TFloat32> weights = tf.variable(weightShape, TFloat32.class);
-    tf.initAdd(tf.assign(weights, tf.zerosLike(weights)));
+    Variable<TFloat32> weights = tf.variable(tf.zeros(tf.constant(weightShape), TFloat32.class));
 
     // Create biases with an initial value of 0
     Shape biasShape = Shape.of(MnistDataset.NUM_CLASSES);
-    Variable<TFloat32> biases = tf.variable(biasShape, TFloat32.class);
-    tf.initAdd(tf.assign(biases, tf.zerosLike(biases)));
-
-    // Register all variable initializers for single execution
-    tf.init();
+    Variable<TFloat32> biases = tf.variable(tf.zeros(tf.constant(biasShape), TFloat32.class));
 
     // Predict the class of each image in the batch and compute the loss
     Softmax<TFloat32> softmax =
@@ -103,9 +97,6 @@ public void run() {
     // Run the graph
     try (Session session = new Session(graph)) {
 
-      // Initialize variables
-      session.run(tf.init());
-
       // Train the model
       for (ImageBatch trainingBatch : dataset.trainingBatches(TRAINING_BATCH_SIZE)) {
         try (TFloat32 batchImages = preprocessImages(trainingBatch.images());
@@ -163,8 +154,8 @@ private static TFloat32 preprocessLabels(ByteNdArray rawLabels) {
     ).asTensor();
   }
 
-  private Graph graph;
-  private MnistDataset dataset;
+  private final Graph graph;
+  private final MnistDataset dataset;
 
   private SimpleMnist(Graph graph, MnistDataset dataset) {
     this.graph = graph;

diff --git a/...rc/main/java/org/tensorflow/model/examples/regression/linear/LinearRegressionExample.java b/...rc/main/java/org/tensorflow/model/examples/regression/linear/LinearRegressionExample.java
@@ -16,11 +16,13 @@
  */
 package org.tensorflow.model.examples.regression.linear;
 
+import java.util.List;
+import java.util.Random;
 import org.tensorflow.Graph;
 import org.tensorflow.Session;
-import org.tensorflow.Tensor;
 import org.tensorflow.framework.optimizers.GradientDescent;
 import org.tensorflow.framework.optimizers.Optimizer;
+import org.tensorflow.ndarray.Shape;
 import org.tensorflow.op.Op;
 import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Placeholder;
@@ -29,12 +31,8 @@
 import org.tensorflow.op.math.Div;
 import org.tensorflow.op.math.Mul;
 import org.tensorflow.op.math.Pow;
-import org.tensorflow.ndarray.Shape;
 import org.tensorflow.types.TFloat32;
 
-import java.util.List;
-import java.util.Random;
-
 /**
  * In this example TensorFlow finds the weight and bias of the linear regression during 1 epoch,
  * training on observations one by one.
@@ -89,8 +87,6 @@ public static void main(String[] args) {
             Op minimize = optimizer.minimize(mse);
 
             try (Session session = new Session(graph)) {
-                // Initialize graph variables
-                session.run(tf.init());
 
                 // Train the model on data
                 for (int i = 0; i < xValues.length; i++) {