@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @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 { ENGINE } from '../engine';
import { LRN } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { op } from './operation';
import { reshape } from './reshape';
/**
* 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_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
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 = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
const inputs = { x: x4D };
const attrs = { depthRadius, bias, alpha, beta };
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(LRN, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
else {
return res;
}
}
export const localResponseNormalization = op({ localResponseNormalization_ });
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