#7376 Use util.sizeFromShape method and remove intermediate variables…

… in Orthogonal Initializer apply method

tfjs-layers/src/initializers.ts

@@ -8,7 +8,7 @@
  * =============================================================================
  */
 
-import {DataType, eye, linalg, mul, ones, randomUniform, scalar, serialization, Tensor, tidy, truncatedNormal, zeros} from '@tensorflow/tfjs-core';
+import {DataType, eye, linalg, mul, ones, randomUniform, scalar, serialization, Tensor, tidy, truncatedNormal, util, zeros} from '@tensorflow/tfjs-core';
 
 import * as K from './backend/tfjs_backend';
 import {checkDataFormat} from './common';
@@ -551,10 +551,7 @@ export class Orthogonal extends Initializer {
 
       // flatten the input shape with the last dimension remaining its
       // original shape so it works for conv2d
-      let numRows = 1;
-      for (const dim of shape.slice(0, -1)) {
-        numRows *= dim;
-      }
+      const numRows = util.sizeFromShape(shape.slice(0, -1));
       const numCols = shape[shape.length - 1];
       const numElements = numRows * numCols;
       if (numElements > this.ELEMENTS_WARN_SLOW) {
@@ -563,9 +560,8 @@ export class Orthogonal extends Initializer {
             `than ${this.ELEMENTS_WARN_SLOW} (${numElements}) elements: ` +
             `Slowness may result.`);
       }
-      const maxDim = Math.max(numCols, numRows);
-      const minDim = Math.min(numCols, numRows);
-      const flatShape = [maxDim, minDim];
+      const flatShape =
+          [Math.max(numCols, numRows), Math.min(numCols, numRows)];
 
       // Generate a random matrix
       const randNormalMat = K.randomNormal(flatShape, 0, 1, dtype, this.seed);
@@ -576,8 +572,9 @@ export class Orthogonal extends Initializer {
       const rMat = qr[1];
 
       // Make Q uniform
-      const diag =
-          rMat.flatten().stridedSlice([0], [minDim * minDim], [minDim + 1]);
+      const diag = rMat.flatten().stridedSlice(
+          [0], [Math.min(numCols, numRows) * Math.min(numCols, numRows)],
+          [Math.min(numCols, numRows) + 1]);
       qMat = mul(qMat, diag.sign());
       if (numRows < numCols) {
         qMat = qMat.transpose();