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@tensorflow/tfjs-core

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Hardware-accelerated JavaScript library for machine intelligence

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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 { convertToTensor } from '../tensor_util_env'; import { parseAxisParam } from '../util'; import { add } from './add'; import { expandShapeToKeepDim } from './axis_util'; import { exp } from './exp'; import { log } from './log'; import { max } from './max'; import { op } from './operation'; import { reshape } from './reshape'; import { sub } from './sub'; import { sum } from './sum'; /** * Computes the log(sum(exp(elements across the reduction dimensions)). * * Reduces the input along the dimensions given in `axis`. Unless `keepDims` * is true, the rank of the array is reduced by 1 for each entry in `axis`. * If `keepDims` is true, the reduced dimensions are retained with length 1. * If `axis` has no entries, all dimensions are reduced, and an array with a * single element is returned. * * ```js * const x = tf.tensor1d([1, 2, 3]); * * x.logSumExp().print(); // or tf.logSumExp(x) * ``` * * ```js * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); * * const axis = 1; * x.logSumExp(axis).print(); // or tf.logSumExp(a, axis) * ``` * @param x The input tensor. * @param axis The dimension(s) to reduce. If null (the default), * reduces all dimensions. * @param keepDims If true, retains reduced dimensions with length * of 1. Defaults to false. * * @doc {heading: 'Operations', subheading: 'Reduction'} */ function logSumExp_(x, axis = null, keepDims = false) { const $x = convertToTensor(x, 'x', 'logSumExp'); const axes = parseAxisParam(axis, $x.shape); const xMax = max($x, axes, true /* keepDims */); const a = sub($x, xMax); const b = exp(a); const c = sum(b, axes); const d = log(c); const res = add(reshape(xMax, d.shape), d); if (keepDims) { const newShape = expandShapeToKeepDim(res.shape, axes); return reshape(res, newShape); } return res; } export const logSumExp = op({ logSumExp_ }); //# sourceMappingURL=log_sum_exp.js.map