Categoryencoding

tfjs-layers/src/layers/preprocessing/category_encoding_test.ts

@@ -125,9 +125,9 @@ describeMathCPUAndGPU('Category Encoding', () => {
     const encodingLayer = new CategoryEncoding({numTokens,
                                         outputMode: 'multiHot'});
     expect(() => encodingLayer.apply(categoryData))
-    .toThrowError(`When outputMode is not 'int', maximum output rank is 2
-    Received outputMode ${'multiHot'} and input shape ${categoryData.shape}
-    which would result in output rank ${categoryData.rank}.`);
+    .toThrowError(`When outputMode is not int, maximum output rank is 2`
+    + ` Received outputMode ${'multiHot'} and input shape ${categoryData.shape}`
+    + ` which would result in output rank ${categoryData.rank}.`);
   });
 
   it('Raises Value Error if max input value !<= numTokens', () => {

tfjs-layers/src/layers/preprocessing/preprocessing_utils_test.ts

@@ -12,7 +12,7 @@ import { describeMathCPUAndGPU, expectTensorsClose} from '../../utils/test_utils
 import { Tensor, tensor, tensor1d} from '@tensorflow/tfjs-core';
 import * as utils from './preprocessing_utils';
 
-describeMathCPUAndGPU('Tests for preprocessing utils', () => {
+describeMathCPUAndGPU('Preprocessing Utils', () => {
 
   it('Peforms int encoding correctly', () => {
     const inputs = tensor([0, 1, 2], [3], 'int32');