Update typings in demo and release new version (tensorflow#558)

demos/benchmarks/pool_benchmarks.ts

@@ -33,22 +33,19 @@ function getPoolingOp(option: string, math: NDArrayMath): (
     strides: [number, number]|number, pad: 'valid'|'same'|number) => Array3D {
   switch (option) {
     case 'max':
-      return (x: Array3D, filterSize: [number, number] | number,
-              strides: [number, number] | number,
-              pad: 'valid' | 'same' | number) => {
+      return (x: Array3D, filterSize: [number, number]|number,
+              strides: [number, number]|number, pad: 'valid'|'same'|number) => {
         return math.maxPool(x, filterSize, strides, pad);
       };
     case 'min':
-      return (x: Array3D, filterSize: [number, number] | number,
-              strides: [number, number] | number,
-              pad: 'valid' | 'same' | number) => {
+      return (x: Array3D, filterSize: [number, number]|number,
+              strides: [number, number]|number, pad: 'valid'|'same'|number) => {
         return math.minPool(x, filterSize, strides, pad);
       };
     case 'avg':
-      return (x: Array3D, filterSize: [number, number] | number,
-              strides: [number, number] | number,
-              pad: 'valid' | 'same' | number) => {
-        return math.avgPool(x, filterSize, strides, pad);
+      return (x: Array3D, filterSize: [number, number]|number,
+              strides: [number, number]|number, pad: 'valid'|'same'|number) => {
+        return math.avgPool(x.asType('float32'), filterSize, strides, pad);
       };
     default:
       throw new Error(`Not found such ops: ${option}`);

demos/benchmarks/reduction_ops_benchmark.ts

@@ -34,7 +34,7 @@ function getReductionOp(option: string, math: NDArrayMath): (input: NDArray) =>
     case 'sum':
       return input => math.sum(input) as Scalar;
     case 'logSumExp':
-      return input => math.logSumExp(input);
+      return input => math.logSumExp(input) as Scalar;
     default:
       throw new Error(`Not found such ops: ${option}`);
   }

package.json

@@ -1,6 +1,6 @@
 {
   "name": "deeplearn",
-  "version": "0.4.1",
+  "version": "0.4.2",
   "description": "Hardware-accelerated JavaScript library for machine intelligence",
   "private": false,
   "main": "dist/index.js",

src/math/math.ts

@@ -2426,11 +2426,10 @@ export class NDArrayMath implements NDArrayManager {
    *     number. If none is provided, it will not round and error if the output
    *     is of fractional size.
    */
-  avgPool<R extends '3'|'4', T1 extends NDArray<'int32'|'float32', R>,
-                                        T2 extends NDArray<'float32', R>>(
-      x: T1, filterSize: [number, number]|number,
+  avgPool<R extends '3'|'4'>(
+      x: NDArray<'int32'|'float32', R>, filterSize: [number, number]|number,
       strides: [number, number]|number, pad: 'valid'|'same'|number,
-      dimRoundingMode?: 'floor'|'round'|'ceil'): T2 {
+      dimRoundingMode?: 'floor'|'round'|'ceil'): RankMap<'float32'>[R] {
     let x4D = x as NDArray as Array4D;
     let reshapedTo4D = false;
     if (x.rank === 3) {
@@ -2458,10 +2457,10 @@ export class NDArrayMath implements NDArrayManager {
       const res = this.backendEngine.executeKernel(
           'AvgPool', {inputs: {x: x4D}, args: {convInfo}}, gradients);
       if (reshapedTo4D) {
-        return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as NDArray as
-            T2;
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as
+            RankMap<'float32'>[R];
       }
-      return res as NDArray as T2;
+      return res as RankMap<'float32'>[R];
     });
   }
 

src/version.ts

@@ -1,5 +1,5 @@
 /** @license See the LICENSE file. */
 
 // This code is auto-generated, do not modify this file!
-const version = '0.4.1';
+const version = '0.4.2';
 export {version};