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LeakyRelu.ts
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LeakyRelu.ts
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/**
* @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 {KernelConfig, KernelFunc, LeakyRelu, LeakyReluAttrs, LeakyReluInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';
import {MathBackendCPU} from '../backend_cpu';
import {assertNotComplex} from '../cpu_util';
export function leakyRelu(args: {
inputs: LeakyReluInputs,
backend: MathBackendCPU,
attrs: LeakyReluAttrs
}): TensorInfo {
const {inputs, backend, attrs} = args;
const {x} = inputs;
const {alpha} = attrs;
assertNotComplex([x], 'leakyRelu');
const xSize = util.sizeFromShape(x.shape);
const xVals = backend.data.get(x.dataId).values as TypedArray;
const outVals = util.getTypedArrayFromDType('float32', xSize);
for (let i = 0; i < xVals.length; i++) {
outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];
}
return backend.makeTensorInfo(x.shape, 'float32', outVals);
}
export const leakyReluConfig: KernelConfig = {
kernelName: LeakyRelu,
backendName: 'cpu',
kernelFunc: leakyRelu as unknown as KernelFunc
};