Rename Shape.size(int) to get, add toListOrNull

rnett · rnett · commit fb3c103cefa4 · 2021-05-14T18:11:22.000-07:00
Signed-off-by: Ryan Nett &lt;JNett96@gmail.com&gt;
diff --git a/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/Signature.java b/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/Signature.java
@@ -121,7 +121,7 @@ private static TensorInfo toTensorInfo(Output<?> operand) {
       Shape shape = operand.shape();
       TensorShapeProto.Builder tensorShapeBuilder = TensorShapeProto.newBuilder();
       for (int i = 0; i < shape.numDimensions(); ++i) {
-        tensorShapeBuilder.addDim(Dim.newBuilder().setSize(shape.size(i)));
+        tensorShapeBuilder.addDim(Dim.newBuilder().setSize(shape.get(i)));
       }
       return TensorInfo.newBuilder()
           .setDtype(operand.dataType())
diff --git a/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/op/nn/SigmoidCrossEntropyWithLogits.java b/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/op/nn/SigmoidCrossEntropyWithLogits.java
@@ -95,8 +95,8 @@ public static <T extends TNumber> Operand<T> sigmoidCrossEntropyWithLogits(
   private static boolean isCompatible(Shape shape, Shape other) {
     if (shape.numDimensions() != other.numDimensions()) return false;
     for (int i = 0; i < shape.numDimensions(); i++) {
-      long aShapeDim = shape.size(i);
-      long bShapeDim = other.size(i);
+      long aShapeDim = shape.get(i);
+      long bShapeDim = other.get(i);
       if (aShapeDim == bShapeDim
           || (aShapeDim == Shape.UNKNOWN_SIZE || bShapeDim == Shape.UNKNOWN_SIZE)) {
         continue;
diff --git a/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/op/nn/SoftmaxCrossEntropyWithLogits.java b/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/op/nn/SoftmaxCrossEntropyWithLogits.java
@@ -14,7 +14,6 @@
 import org.tensorflow.types.TFloat32;
 import org.tensorflow.types.TInt64;
 import org.tensorflow.types.family.TNumber;
-import org.tensorflow.types.family.TType;
 
 import java.util.Arrays;
 import java.util.List;
@@ -124,10 +123,10 @@ public static <T extends TNumber, U extends TNumber> Operand<T> softmaxCrossEntr
         axis = shape.numDimensions() + axis;
       }
       for (int i = 0; i < axis; i++) {
-        newArray[i] = shape.size(i);
+        newArray[i] = shape.get(i);
       }
       for (int i = axis + 1; i < shape.numDimensions(); i++) {
-        newArray[i - 1] = shape.size(i);
+        newArray[i - 1] = shape.get(i);
       }
       cost = Reshape.create(scope, cost, Constant.vectorOf(scope, newArray));
     }
@@ -152,15 +151,15 @@ private static <T extends TNumber> Operand<T> flattenOuterDims(Scope scope, Oper
       long product = 1L;
       boolean productValid = true;
       for (int i = ndims - 2; i >= 0; i--) {
-        long d = shape.size(i);
+        long d = shape.get(i);
         if (d == org.tensorflow.ndarray.Shape.UNKNOWN_SIZE) {
           productValid = false;
           break;
         }
         product *= d;
       }
       if (productValid) {
-        return Reshape.create(scope, logits, Constant.arrayOf(scope, product, shape.size(-1)));
+        return Reshape.create(scope, logits, Constant.arrayOf(scope, product, shape.get(-1)));
       }
     }
 
diff --git a/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/op/nn/SparseSoftmaxCrossEntropyWithLogits.java b/tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/op/nn/SparseSoftmaxCrossEntropyWithLogits.java
@@ -140,7 +140,7 @@ public static <T extends TNumber, U extends TNumber> Operand sparseSoftmaxCrossE
     }
 
     // Reshape logits to 2 dims, labels to 1 dim.
-    long numClassses = logitsShape.size(-1);
+    long numClassses = logitsShape.get(-1);
 
     preciseLogits = Reshape.create(scope, preciseLogits, Constant.arrayOf(scope, -1L, numClassses));
     labels = Reshape.create(scope, labels, Constant.scalarOf(scope, -1));
diff --git a/tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/EagerOperationTest.java b/tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/EagerOperationTest.java
@@ -57,8 +57,8 @@ public void outputDataTypeAndShape() {
               .setAttr("value", t)
               .build();
       assertEquals(DataType.DT_INT32, op.dtype(0));
-      assertEquals(2, op.shape(0).size(0));
-      assertEquals(3, op.shape(0).size(1));
+      assertEquals(2, op.shape(0).get(0));
+      assertEquals(3, op.shape(0).get(1));
     }
   }
 
diff --git a/tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/GraphOperationBuilderTest.java b/tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/GraphOperationBuilderTest.java
@@ -129,8 +129,8 @@ public void setAttrShape() {
               .build()
               .output(0);
       assertEquals(2, n.shape().numDimensions());
-      assertEquals(-1, n.shape().size(0));
-      assertEquals(784, n.shape().size(1));
+      assertEquals(-1, n.shape().get(0));
+      assertEquals(784, n.shape().get(1));
       assertEquals(DataType.DT_FLOAT, n.dataType());
     }
   }
diff --git a/tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/SavedModelBundleTest.java b/tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/SavedModelBundleTest.java
@@ -146,7 +146,7 @@ public void exportFunctionWithVariables() throws IOException {
       assertNotNull(inputInfo);
       assertEquals(xyShape.numDimensions(), inputInfo.getTensorShape().getDimCount());
       for (int i = 0; i < xyShape.numDimensions(); ++i) {
-        assertEquals(xyShape.size(i), inputInfo.getTensorShape().getDim(i).getSize());
+        assertEquals(xyShape.get(i), inputInfo.getTensorShape().getDim(i).getSize());
       }
 
       TensorInfo outputInfo = signatureDef.getOutputsMap().get("reducedSum");
diff --git a/tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/TensorTest.java b/tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/TensorTest.java
@@ -325,7 +325,7 @@ public void nDimensional() {
       assertEquals(TFloat64.class, t.type());
       assertEquals(DataType.DT_DOUBLE, t.dataType());
       assertEquals(1, t.shape().numDimensions());
-      assertEquals(3, t.shape().size(0));
+      assertEquals(3, t.shape().get(0));
       assertEquals(vector, t);
     }
 
@@ -334,8 +334,8 @@ public void nDimensional() {
       assertEquals(TInt32.class, t.type());
       assertEquals(DataType.DT_INT32, t.dataType());
       assertEquals(2, t.shape().numDimensions());
-      assertEquals(2, t.shape().size(0));
-      assertEquals(3, t.shape().size(1));
+      assertEquals(2, t.shape().get(0));
+      assertEquals(3, t.shape().get(1));
       assertEquals(matrix, t);
     }
 
@@ -346,9 +346,9 @@ public void nDimensional() {
       assertEquals(TInt64.class, t.type());
       assertEquals(DataType.DT_INT64, t.dataType());
       assertEquals(3, t.shape().numDimensions());
-      assertEquals(2, t.shape().size(0));
-      assertEquals(5, t.shape().size(1));
-      assertEquals(1, t.shape().size(2));
+      assertEquals(2, t.shape().get(0));
+      assertEquals(5, t.shape().get(1));
+      assertEquals(1, t.shape().get(2));
       assertEquals(threeD, t);
     }
 
@@ -361,10 +361,10 @@ public void nDimensional() {
       assertEquals(TBool.class, t.type());
       assertEquals(DataType.DT_BOOL, t.dataType());
       assertEquals(4, t.shape().numDimensions());
-      assertEquals(3, t.shape().size(0));
-      assertEquals(1, t.shape().size(1));
-      assertEquals(2, t.shape().size(2));
-      assertEquals(4, t.shape().size(3));
+      assertEquals(3, t.shape().get(0));
+      assertEquals(1, t.shape().get(1));
+      assertEquals(2, t.shape().get(2));
+      assertEquals(4, t.shape().get(3));
       assertEquals(fourD, t);
     }
   }
@@ -381,8 +381,8 @@ public void testNDimensionalStringTensor() {
       assertEquals(TString.class, t.type());
       assertEquals(DataType.DT_STRING, t.dataType());
       assertEquals(2, t.shape().numDimensions());
-      assertEquals(4, t.shape().size(0));
-      assertEquals(3, t.shape().size(1));
+      assertEquals(4, t.shape().get(0));
+      assertEquals(3, t.shape().get(1));
       assertEquals(matrix, t);
     }
 
@@ -392,8 +392,8 @@ public void testNDimensionalStringTensor() {
       assertEquals(TString.class, t.type());
       assertEquals(DataType.DT_STRING, t.dataType());
       assertEquals(2, t.shape().numDimensions());
-      assertEquals(4, t.shape().size(0));
-      assertEquals(3, t.shape().size(1));
+      assertEquals(4, t.shape().get(0));
+      assertEquals(3, t.shape().get(1));
       assertEquals(byteMatrix, t.asBytes());
       assertEquals(matrix, t);
     }
@@ -406,7 +406,7 @@ public void testUint8TensorFromArray() {
       assertEquals(TUint8.class, t.type());
       assertEquals(DataType.DT_UINT8, t.dataType());
       assertEquals(1, t.shape().numDimensions());
-      assertEquals(4, t.shape().size(0));
+      assertEquals(4, t.shape().get(0));
 
       byte[] got = new byte[4];
       t.read(DataBuffers.of(got));
@@ -421,7 +421,7 @@ public void testCreateFromArrayOfBoxed() {
       assertEquals(TInt32.class, t.type());
       assertEquals(DataType.DT_INT32, t.dataType());
       assertEquals(1, t.shape().numDimensions());
-      assertEquals(4, t.shape().size(0));
+      assertEquals(4, t.shape().get(0));
 
       Integer[] got = new Integer[4];
       t.read(DataBuffers.ofObjects(got));
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/initializers/Identity.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/initializers/Identity.java
@@ -70,8 +70,8 @@ public Operand<T> call(Operand<TInt64> dims, Class<T> type) {
     if (shape.numDimensions() != 2) {
       throw new IllegalArgumentException("2D matrix required, got " + shape.numDimensions());
     }
-    boolean isSquare = shape.size(0) == shape.size(1);
-    long diagSize = Math.min(shape.size(0), shape.size(1));
+    boolean isSquare = shape.get(0) == shape.get(1);
+    long diagSize = Math.min(shape.get(0), shape.get(1));
     Shape diagShape = Shape.of(diagSize);
 
     Operand<T> op;
@@ -83,8 +83,8 @@ public Operand<T> call(Operand<TInt64> dims, Class<T> type) {
           tf.linalg.matrixDiag(
               diagOnes,
               tf.constant(0), // don't cast here, expecting TInt32
-              tf.constant((int) shape.size(0)),
-              tf.constant((int) shape.size(1)),
+              tf.constant((int) shape.get(0)),
+              tf.constant((int) shape.get(1)),
               zero);
     } else {
       Operand<T> zeroMatrix = tf.zeros(dims, type);
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/initializers/Orthogonal.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/initializers/Orthogonal.java
@@ -90,8 +90,8 @@ public Operand<T> call(Operand<TInt64> dims, Class<T> type) {
     }
     long numRows = 1;
     int i = 0;
-    for (; i < dimsShape.numDimensions() - 1; i++) numRows *= dimsShape.size(i);
-    long numCols = dimsShape.size(i);
+    for (; i < dimsShape.numDimensions() - 1; i++) numRows *= dimsShape.get(i);
+    long numCols = dimsShape.get(i);
     Shape flatShape = Shape.of(Math.max(numRows, numCols), Math.min(numRows, numCols));
     long[] seeds = {seed, 0};
     Operand<T> op =
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/losses/Losses.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/losses/Losses.java
@@ -566,7 +566,7 @@ public static <T extends TNumber> Operand<T> sparseCategoricalCrossentropy(
           tf.reshape(
               predictions,
               tf.constant(
-                  new long[] {-1L, predictionsShape.size(predictionsShape.numDimensions() - 1)}));
+                  new long[] {-1L, predictionsShape.get(predictionsShape.numDimensions() - 1)}));
     }
 
     @SuppressWarnings("unchecked")
@@ -643,7 +643,7 @@ private static <T extends TNumber> Operand<T> smoothCategoricalLabels(
     Operand<T> smoothing = cast(tf, tf.constant(labelSmoothing), labelType);
     Shape labelsShape = labels.shape();
     int numDims = labelsShape.numDimensions();
-    Operand<T> numClasses = cast(tf, tf.constant(labelsShape.size(numDims - 1)), labelType);
+    Operand<T> numClasses = cast(tf, tf.constant(labelsShape.get(numDims - 1)), labelType);
     Operand<T> oneMinusSmoothing = cast(tf, tf.constant(1.f - labelSmoothing), labelType);
     return tf.math.add(tf.math.mul(labels, oneMinusSmoothing), tf.math.div(smoothing, numClasses));
   }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/losses/impl/LossesHelper.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/losses/impl/LossesHelper.java
@@ -101,7 +101,7 @@ public static <T extends TNumber> LossTuple<T> squeezeOrExpandDimensions(
       long labelsRank = labelsShape.numDimensions();
       if (labelsRank != Shape.UNKNOWN_SIZE && predictionsRank != Shape.UNKNOWN_SIZE) {
         // Use static rank for 'label' and 'prediction'.
-        if (predictionsRank - labelsRank != 1 || predictionsShape.size(-1) == 1) {
+        if (predictionsRank - labelsRank != 1 || predictionsShape.get(-1) == 1) {
           lossTuple = removeSqueezableDimensions(tf, labels, predictions);
         }
       } else { // use dynamic rank
@@ -213,9 +213,9 @@ public static <T extends TNumber> LossTuple<T> removeSqueezableDimensions(
     if (predictionsRank != Shape.UNKNOWN_SIZE || labelsRank != Shape.UNKNOWN_SIZE) {
       // Use static rank.
       int rankDiff = predictionsRank - labelsRank;
-      if (rankDiff == expectedRankDiff + 1 && Shape.isCompatible(predictionsShape.size(-1), 1)) {
+      if (rankDiff == expectedRankDiff + 1 && Shape.isCompatible(predictionsShape.get(-1), 1)) {
         predictions = tf.squeeze(predictions);
-      } else if (rankDiff == expectedRankDiff - 1 && Shape.isCompatible(labelsShape.size(-1), 1)) {
+      } else if (rankDiff == expectedRankDiff - 1 && Shape.isCompatible(labelsShape.get(-1), 1)) {
         labels = tf.squeeze(labels);
       }
       return new LossTuple<>(labels, predictions);
@@ -224,15 +224,15 @@ public static <T extends TNumber> LossTuple<T> removeSqueezableDimensions(
 
     // TODO: hold for lazy select feature,
     //  Operand<TInt32> rankDiff = tf.math.sub(tf.rank(predictions), tf.rank(labels));
-    if (predictionsRank == Shape.UNKNOWN_SIZE && Shape.isCompatible(predictionsShape.size(-1), 1)) {
+    if (predictionsRank == Shape.UNKNOWN_SIZE && Shape.isCompatible(predictionsShape.get(-1), 1)) {
       /*
        * TODO, if we ever get a select that does lazy evaluation, but for now do the tf.squeeze
        * predictions = tf.select( tf.math.equal(tf.constant(expectedRankDiff+1),rankDiff ),
        * tf.squeeze(predictions, Squeeze.axis(Arrays.asList(-1L))), predictions ); *
        */
       predictions = tf.squeeze(predictions, Squeeze.axis(Collections.singletonList(-1L)));
     }
-    if (labelsRank == Shape.UNKNOWN_SIZE && Shape.isCompatible(labelsShape.size(-1), 1)) {
+    if (labelsRank == Shape.UNKNOWN_SIZE && Shape.isCompatible(labelsShape.get(-1), 1)) {
       /*
        * TODO, if we ever get a select that does lazy evaluation labels = tf.select(
        * tf.math.equal(tf.constant(expectedRankDiff+1),rankDiff ), tf.squeeze(labels,
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/metrics/impl/MetricsHelper.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/metrics/impl/MetricsHelper.java
@@ -109,8 +109,8 @@ public static <T extends TNumber> Op assertBroadcastable(
       }
 
       for (int i = 0; i < valuesRankStatic; i++) {
-        if (valuesShapeStatic.size(i) != weightsShapeStatic.size(i)
-            && weightsShapeStatic.size(i) != 1) {
+        if (valuesShapeStatic.get(i) != weightsShapeStatic.get(i)
+            && weightsShapeStatic.get(i) != 1) {
           throw new NotBroadcastableException(
               String.format(
                   "%s Mismatch at dim %d. values.shape=%s weights.shape=%s.",
diff --git a/tensorflow-framework/src/test/java/org/tensorflow/framework/utils/ND.java b/tensorflow-framework/src/test/java/org/tensorflow/framework/utils/ND.java
@@ -75,7 +75,7 @@ private static long[] getCoordinates(Shape shape, long index) {
     int numDims = shape.numDimensions();
     int i = numDims - 1;
     for (; i >= 0; i--) {
-      long size = shape.size(i);
+      long size = shape.get(i);
       long mod = index % size;
       coordinates[i] = mod;
       index -= mod;
@@ -676,7 +676,7 @@ public static FloatNdArray sum(FloatNdArray a, int axis, boolean keepDims) {
     int nDims = shape.numDimensions();
     int xis = nDims - 1 - axis;
     long totalSize = shape.size();
-    long axisSize = shape.size(xis);
+    long axisSize = shape.get(xis);
     final float[] sums = new float[(int) axisSize];
 
     a.scalars()
@@ -767,7 +767,7 @@ public static DoubleNdArray sum(DoubleNdArray a, int axis, boolean keepDims) {
     int nDims = shape.numDimensions();
     int xis = nDims - 1 - axis;
     long totalSize = shape.size();
-    long axisSize = shape.size(xis);
+    long axisSize = shape.get(xis);
     final double[] sums = new double[(int) axisSize];
 
     a.scalars()
diff --git a/tensorflow-kotlin-parent/tensorflow-core-kotlin/src/main/kotlin/org/tensorflow/ndarray/NDArayUtils.kt b/tensorflow-kotlin-parent/tensorflow-core-kotlin/src/main/kotlin/org/tensorflow/ndarray/NDArayUtils.kt

Original file line number	Diff line number	Diff line change
`@@ -121,7 +121,7 @@ private static TensorInfo toTensorInfo(Output<?> operand) {`
`121`	`121`	`Shape shape = operand.shape();`
`122`	`122`	`TensorShapeProto.Builder tensorShapeBuilder = TensorShapeProto.newBuilder();`
`123`	`123`	`for (int i = 0; i < shape.numDimensions(); ++i) {`
`124`		`- tensorShapeBuilder.addDim(Dim.newBuilder().setSize(shape.size(i)));`
	`124`	`+ tensorShapeBuilder.addDim(Dim.newBuilder().setSize(shape.get(i)));`
`125`	`125`	`}`
`126`	`126`	`return TensorInfo.newBuilder()`
`127`	`127`	`.setDtype(operand.dataType())`
Original file line number	Diff line number	Diff line change
`@@ -14,7 +14,6 @@`
`14`	`14`	`import org.tensorflow.types.TFloat32;`
`15`	`15`	`import org.tensorflow.types.TInt64;`
`16`	`16`	`import org.tensorflow.types.family.TNumber;`
`17`		`-import org.tensorflow.types.family.TType;`
`18`	`17`
`19`	`18`	`import java.util.Arrays;`
`20`	`19`	`import java.util.List;`
`@@ -124,10 +123,10 @@ public static <T extends TNumber, U extends TNumber> Operand<T> softmaxCrossEntr`
`124`	`123`	`axis = shape.numDimensions() + axis;`
`125`	`124`	`}`
`126`	`125`	`for (int i = 0; i < axis; i++) {`
`127`		`- newArray[i] = shape.size(i);`
	`126`	`+ newArray[i] = shape.get(i);`
`128`	`127`	`}`
`129`	`128`	`for (int i = axis + 1; i < shape.numDimensions(); i++) {`
`130`		`- newArray[i - 1] = shape.size(i);`
	`129`	`+ newArray[i - 1] = shape.get(i);`
`131`	`130`	`}`
`132`	`131`	`cost = Reshape.create(scope, cost, Constant.vectorOf(scope, newArray));`
`133`	`132`	`}`
`@@ -152,15 +151,15 @@ private static <T extends TNumber> Operand<T> flattenOuterDims(Scope scope, Oper`
`152`	`151`	`long product = 1L;`
`153`	`152`	`boolean productValid = true;`
`154`	`153`	`for (int i = ndims - 2; i >= 0; i--) {`
`155`		`- long d = shape.size(i);`
	`154`	`+ long d = shape.get(i);`
`156`	`155`	`if (d == org.tensorflow.ndarray.Shape.UNKNOWN_SIZE) {`
`157`	`156`	`productValid = false;`
`158`	`157`	`break;`
`159`	`158`	`}`
`160`	`159`	`product *= d;`
`161`	`160`	`}`
`162`	`161`	`if (productValid) {`
`163`		`- return Reshape.create(scope, logits, Constant.arrayOf(scope, product, shape.size(-1)));`
	`162`	`+ return Reshape.create(scope, logits, Constant.arrayOf(scope, product, shape.get(-1)));`
`164`	`163`	`}`
`165`	`164`	`}`
`166`	`165`
Original file line number	Diff line number	Diff line change
`@@ -140,7 +140,7 @@ public static <T extends TNumber, U extends TNumber> Operand sparseSoftmaxCrossE`
`140`	`140`	`}`
`141`	`141`
`142`	`142`	`// Reshape logits to 2 dims, labels to 1 dim.`
`143`		`- long numClassses = logitsShape.size(-1);`
	`143`	`+ long numClassses = logitsShape.get(-1);`
`144`	`144`
`145`	`145`	`preciseLogits = Reshape.create(scope, preciseLogits, Constant.arrayOf(scope, -1L, numClassses));`
`146`	`146`	`labels = Reshape.create(scope, labels, Constant.scalarOf(scope, -1));`
Original file line number	Diff line number	Diff line change
`@@ -57,8 +57,8 @@ public void outputDataTypeAndShape() {`
`57`	`57`	`.setAttr("value", t)`
`58`	`58`	`.build();`
`59`	`59`	`assertEquals(DataType.DT_INT32, op.dtype(0));`
`60`		`- assertEquals(2, op.shape(0).size(0));`
`61`		`- assertEquals(3, op.shape(0).size(1));`
	`60`	`+ assertEquals(2, op.shape(0).get(0));`
	`61`	`+ assertEquals(3, op.shape(0).get(1));`
`62`	`62`	`}`
`63`	`63`	`}`
`64`	`64`
Original file line number	Diff line number	Diff line change
`@@ -129,8 +129,8 @@ public void setAttrShape() {`
`129`	`129`	`.build()`
`130`	`130`	`.output(0);`
`131`	`131`	`assertEquals(2, n.shape().numDimensions());`
`132`		`- assertEquals(-1, n.shape().size(0));`
`133`		`- assertEquals(784, n.shape().size(1));`
	`132`	`+ assertEquals(-1, n.shape().get(0));`
	`133`	`+ assertEquals(784, n.shape().get(1));`
`134`	`134`	`assertEquals(DataType.DT_FLOAT, n.dataType());`
`135`	`135`	`}`
`136`	`136`	`}`
Original file line number	Diff line number	Diff line change
`@@ -146,7 +146,7 @@ public void exportFunctionWithVariables() throws IOException {`
`146`	`146`	`assertNotNull(inputInfo);`
`147`	`147`	`assertEquals(xyShape.numDimensions(), inputInfo.getTensorShape().getDimCount());`
`148`	`148`	`for (int i = 0; i < xyShape.numDimensions(); ++i) {`
`149`		`- assertEquals(xyShape.size(i), inputInfo.getTensorShape().getDim(i).getSize());`
	`149`	`+ assertEquals(xyShape.get(i), inputInfo.getTensorShape().getDim(i).getSize());`
`150`	`150`	`}`
`151`	`151`
`152`	`152`	`TensorInfo outputInfo = signatureDef.getOutputsMap().get("reducedSum");`