@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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TypeScript
/**
* @license
* Copyright 2022 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.
* =============================================================================
*/
/// <amd-module name="@tensorflow/tfjs-core/dist/ops/euclidean_norm" />
import { Tensor } from '../tensor';
import { TensorLike } from '../types';
/**
* Computes the Euclidean norm of scalar, vectors, and matrices.
*
* ```js
* const x = tf.tensor1d([1, 2, 3, 4]);
*
* x.euclideanNorm().print(); // or tf.euclideanNorm(x)
* ```
*
* @param x The input array.
* @param axis Optional. If axis is null (the default), the input is
* considered a vector and a single vector norm is computed over the entire
* set of values in the Tensor, i.e. euclideanNorm(x) is equivalent
* to euclideanNorm(x.reshape([-1])). If axis is an integer, the input
* is considered a batch of vectors, and axis determines the axis in x
* over which to compute vector norms. If axis is a 2-tuple of integer it is
* considered a batch of matrices and axis determines the axes in NDArray
* over which to compute a matrix norm.
* @param keepDims Optional. If true, the norm has the same dimensionality
* as the input.
*
* @doc {heading: 'Operations', subheading: 'Matrices'}
*/
declare function euclideanNorm_(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): Tensor;
export declare const euclideanNorm: typeof euclideanNorm_;
export {};