forked from tensorflow/tfjs
-
Notifications
You must be signed in to change notification settings - Fork 0
/
EluGrad.ts
49 lines (42 loc) · 1.69 KB
/
EluGrad.ts
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import {EluGrad, EluGradInputs, KernelConfig, KernelFunc, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
import {MathBackendCPU} from '../backend_cpu';
import {assertNotComplex} from '../cpu_util';
export function eluGrad(args: {inputs: EluGradInputs, backend: MathBackendCPU}):
TensorInfo {
const {inputs, backend} = args;
const {dy, y} = inputs;
assertNotComplex([dy, y], 'eluGrad');
const resultValues = new Float32Array(util.sizeFromShape(y.shape));
const values = backend.data.get(y.dataId).values as TypedArray;
const dyValues = backend.data.get(dy.dataId).values as TypedArray;
for (let i = 0; i < values.length; ++i) {
const v = values[i];
if (v >= 1) {
resultValues[i] = dyValues[i];
} else {
resultValues[i] = dyValues[i] * (v + 1);
}
}
return backend.makeTensorInfo(y.shape, 'float32', resultValues);
}
export const eluGradConfig: KernelConfig = {
kernelName: EluGrad,
backendName: 'cpu',
kernelFunc: eluGrad as unknown as KernelFunc
};