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2025-06-12 7b72ac13a83764a662159d4a49b7fffb90476ecb

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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 { Softplus } from '@tensorflow/tfjs-core';
import { unaryKernelFunc } from '../utils/unary_utils';
// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX
// epsilon is the difference between 1.0 and the next representable float.
// For a single precision 32 bit float this should be 2^-23, see:
// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm
const epsilon = 1.1920928955078125e-7;
const threshold = Math.log(epsilon) + 2.0;
export const softplus = unaryKernelFunc(Softplus, (xi) => {
    // Value above which exp(x) may overflow, but softplus(x) == x
    // is within machine epsilon.
    const tooLarge = xi > -threshold;
    // Value below which exp(x) may underflow, but softplus(x) == exp(x)
    // is within machine epsilon.
    const tooSmall = xi < threshold;
    const expX = Math.exp(xi);
    let result;
    if (tooSmall) {
        result = expX;
    }
    else if (tooLarge) {
        result = xi;
    }
    else {
        result = Math.log(1.0 + expX);
    }
    return result;
});
export const softplusConfig = {
    kernelName: Softplus,
    backendName: 'cpu',
    kernelFunc: softplus,
};
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