Change snake case to camel case

Author: JimClarke5

tensorflow-framework/src/main/java/org/tensorflow/framework/initializers/Identity.java

@@ -75,24 +75,24 @@ public Operand<T> call(Operand<TInt64> dims, DataType<T> dtype) {
       throw new IllegalArgumentException("2D matrix required, got " + shape.numDimensions());
     }
     boolean isSquare = shape.size(0) == shape.size(1);
-    long diag_size = Math.min(shape.size(0), shape.size(1));
-    Shape diagShape = Shape.of(diag_size);
+    long diagSize = Math.min(shape.size(0), shape.size(1));
+    Shape diagShape = Shape.of(diagSize);
 
     Operand<T> op;
     Operand<T> zero = tf.dtypes.cast(tf.constant(0), dtype);
-    Operand<T> diag_ones =
+    Operand<T> diagOnes =
         tf.fill(tf.constant(diagShape.asArray()), tf.dtypes.cast(tf.constant(1.0), dtype));
     if (isSquare) {
       op =
           tf.linalg.matrixDiag(
-              diag_ones,
+              diagOnes,
               tf.constant(0), // don't cast here, expecting TInt32
               tf.constant((int) shape.size(0)),
               tf.constant((int) shape.size(1)),
               zero);
     } else {
-      Operand<T> zero_matrix = tf.zeros(dims, dtype);
-      op = tf.linalg.matrixSetDiag(zero_matrix, diag_ones, tf.constant(0));
+      Operand<T> zeroMatrix = tf.zeros(dims, dtype);
+      op = tf.linalg.matrixSetDiag(zeroMatrix, diagOnes, tf.constant(0));
     }
 
     return tf.math.mul(op, tf.dtypes.cast(tf.constant(gain), dtype));

tensorflow-framework/src/main/java/org/tensorflow/framework/initializers/Orthogonal.java

@@ -94,18 +94,18 @@ public Operand<T> call(Operand<TInt64> dims, DataType<T> dtype) {
           "The tensor to initialize must be at least two-dimensional, got "
               + dimsShape.numDimensions());
     }
-    long num_rows = 1;
+    long numRows = 1;
     int i = 0;
-    for (; i < dimsShape.numDimensions() - 1; i++) num_rows *= dimsShape.size(i);
-    long num_cols = dimsShape.size(i);
-    Shape flat_shape = Shape.of(Math.max(num_rows, num_cols), Math.min(num_rows, num_cols));
+    for (; i < dimsShape.numDimensions() - 1; i++) numRows *= dimsShape.size(i);
+    long numCols = dimsShape.size(i);
+    Shape flatShape = Shape.of(Math.max(numRows, numCols), Math.min(numRows, numCols));
     long[] seeds = {seed, 0};
     @SuppressWarnings("unchecked")
     DataType<U> numdType = (DataType<U>) dtype;
     @SuppressWarnings("unchecked")
     Operand<T> op =
         (Operand<T>)
-            tf.random.statelessRandomNormal(tf.constant(flat_shape), tf.constant(seeds), numdType);
+            tf.random.statelessRandomNormal(tf.constant(flatShape), tf.constant(seeds), numdType);
 
     Qr.Options qrOptions = Qr.fullMatrices(false);
     Qr<T> qrOp = tf.linalg.qr(op, qrOptions);
@@ -114,7 +114,7 @@ public Operand<T> call(Operand<TInt64> dims, DataType<T> dtype) {
     Operand<T> diagOp =
         tf.linalg.matrixDiagPart(ro, tf.constant(0), tf.dtypes.cast(tf.constant(0), dtype));
     Operand<T> qop = tf.math.mul(qo, tf.math.sign(diagOp));
-    if (num_rows < num_cols) qop = tf.linalg.transpose(qop, null);
+    if (numRows < numCols) qop = tf.linalg.transpose(qop, null);
 
     return tf.math.mul(qop, tf.dtypes.cast(tf.constant(this.gain), dtype));
   }

tensorflow-framework/src/main/java/org/tensorflow/framework/initializers/VarianceScaling.java

@@ -166,12 +166,12 @@ private double[] computeFans(Shape shape) {
       fanIn = dims[0];
       fanOut = dims[1];
     } else {
-      double receptive_field_size = 1.;
+      double receptiveFieldSize = 1.;
       for (int i = dims.length - 2; i >= 0; i--) {
-        receptive_field_size *= dims[i];
+        receptiveFieldSize *= dims[i];
       }
-      fanIn = dims[dims.length - 2] * receptive_field_size;
-      fanOut = dims[dims.length - 1] * receptive_field_size;
+      fanIn = dims[dims.length - 2] * receptiveFieldSize;
+      fanOut = dims[dims.length - 1] * receptiveFieldSize;
     }
 
     return new double[] {fanIn, fanOut};