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@tensorflow/tfjs-core

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Hardware-accelerated JavaScript library for machine intelligence

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/** * @license * Copyright 2018 Google Inc. 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 {ENGINE} from '../engine'; import {Tensor, Tensor3D, Tensor4D} from '../tensor'; import {convertToTensor} from '../tensor_util_env'; import {TensorLike} from '../types'; import * as util from '../util'; import {op} from './operation'; /** * Normalizes the activation of a local neighborhood across or within * channels. * * @param x The input tensor. The 4-D input tensor is treated as a 3-D array * of 1D vectors (along the last dimension), and each vector is * normalized independently. * @param depthRadius The number of adjacent channels in the 1D normalization * window. * @param bias A constant bias term for the basis. * @param alpha A scale factor, usually positive. * @param beta An exponent. */ /** @doc {heading: 'Operations', subheading: 'Normalization'} */ function localResponseNormalization_<T extends Tensor3D|Tensor4D>( x: T|TensorLike, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T { const $x = convertToTensor(x, 'x', 'localResponseNormalization'); util.assert( $x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got rank ${$x.rank}.`); util.assert( util.isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an ` + `integer but got depthRadius ${depthRadius}.`); let x4D = $x as Tensor4D; let reshapedTo4D = false; if ($x.rank === 3) { reshapedTo4D = true; x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]); } const backward = (dy: Tensor4D, saved: Tensor[]) => { const [x4D, y] = saved; return { x4D: () => ENGINE.runKernel( backend => backend.LRNGrad( dy, x4D as Tensor4D, y as Tensor4D, depthRadius, bias, alpha, beta), {}) }; }; const res = ENGINE.runKernel((backend, save) => { const y = backend.localResponseNormalization4D( x4D, depthRadius, bias, alpha, beta); save([x4D, y]); return y; }, {x4D}, backward); if (reshapedTo4D) { return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T; } else { return res as T; } } export const localResponseNormalization = op({localResponseNormalization_});