@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 * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose, expectArraysEqual } from '../test_util';
describeWithFlags('norm', ALL_ENVS, () => {
it('scalar norm', async () => {
const a = tf.scalar(-22.0);
const norm = tf.norm(a);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 22);
});
it('vector inf norm', async () => {
const a = tf.tensor1d([1, -2, 3, -4]);
const norm = tf.norm(a, Infinity);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 4);
});
it('vector -inf norm', async () => {
const a = tf.tensor1d([1, -2, 3, -4]);
const norm = tf.norm(a, -Infinity);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 1);
});
it('vector 1 norm', async () => {
const a = tf.tensor1d([1, -2, 3, -4]);
const norm = tf.norm(a, 1);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 10);
});
it('vector euclidean norm', async () => {
const a = tf.tensor1d([1, -2, 3, -4]);
const norm = tf.norm(a, 'euclidean');
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 5.4772);
});
it('vector 2-norm', async () => {
const a = tf.tensor1d([1, -2, 3, -4]);
const norm = tf.norm(a, 2);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 5.4772);
});
it('vector >2-norm to throw error', () => {
const a = tf.tensor1d([1, -2, 3, -4]);
expect(() => tf.norm(a, 3)).toThrowError();
});
it('matrix inf norm', async () => {
const a = tf.tensor2d([1, 2, -3, 1, 0, 1], [3, 2]);
const norm = tf.norm(a, Infinity, [0, 1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 4);
});
it('matrix -inf norm', async () => {
const a = tf.tensor2d([1, 2, -3, 1, 0, 1], [3, 2]);
const norm = tf.norm(a, -Infinity, [0, 1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 1);
});
it('matrix 1 norm', async () => {
const a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
const norm = tf.norm(a, 1, [0, 1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 5);
});
it('matrix euclidean norm', async () => {
const a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
const norm = tf.norm(a, 'euclidean', [0, 1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 4.123);
});
it('matrix fro norm', async () => {
const a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
const norm = tf.norm(a, 'fro', [0, 1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 4.123);
});
it('matrix other norm to throw error', () => {
const a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
expect(() => tf.norm(a, 2, [0, 1])).toThrowError();
});
it('propagates NaNs for norm', async () => {
const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
const norm = tf.norm(a);
expect(norm.dtype).toBe('float32');
expectArraysEqual(await norm.data(), NaN);
});
it('axis=null in 2D array norm', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const norm = tf.norm(a, Infinity);
expect(norm.shape).toEqual([]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3]);
});
it('2D array norm with keep dim', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const norm = tf.norm(a, Infinity, null, true /* keepDims */);
expect(norm.shape).toEqual([1, 1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3]);
});
it('axis=0 in 2D array norm', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const norm = tf.norm(a, Infinity, [0]);
expect(norm.shape).toEqual([2]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3, 2]);
});
it('axis=1 in 2D array norm', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const norm = tf.norm(a, Infinity, [1]);
expect(norm.dtype).toBe('float32');
expect(norm.shape).toEqual([3]);
expectArraysClose(await norm.data(), [2, 3, 1]);
});
it('axis=1 keepDims in 2D array norm', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const norm = tf.norm(a, Infinity, [1], true);
expect(norm.dtype).toBe('float32');
expect(norm.shape).toEqual([3, 1]);
expectArraysClose(await norm.data(), [2, 3, 1]);
});
it('2D norm with axis=1 provided as number', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
const norm = tf.norm(a, Infinity, 1);
expect(norm.shape).toEqual([2]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3, 1]);
});
it('axis=0,1 in 2D array norm', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const norm = tf.norm(a, Infinity, [0, 1]);
expect(norm.shape).toEqual([]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3]);
});
it('axis=0,1 keepDims in 2D array norm', async () => {
const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
const norm = tf.norm(a, Infinity, [0, 1], true);
expect(norm.shape).toEqual([1, 1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3]);
});
it('3D norm axis=0,1, matrix inf norm', async () => {
const a = tf.tensor3d([1, 2, -3, 1, 0, 1], [3, 2, 1]);
const norm = tf.norm(a, Infinity, [0, 1]);
expect(norm.shape).toEqual([1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [4]);
});
it('axis=0,1 keepDims in 3D array norm', async () => {
const a = tf.tensor3d([1, 2, 3, 0, 0, 1], [3, 2, 1]);
const norm = tf.norm(a, Infinity, [0, 1], true);
expect(norm.shape).toEqual([1, 1, 1]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3]);
});
it('axis=0,1 keepDims in 3D array norm', async () => {
const a = tf.tensor3d([1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1], [3, 2, 2]);
const norm = tf.norm(a, Infinity, [0, 1], true);
expect(norm.shape).toEqual([1, 1, 2]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [4, 3]);
});
it('axis=null in 3D array norm', async () => {
const a = tf.tensor3d([1, 2, 3, 0, 0, 1], [3, 2, 1]);
const norm = tf.norm(a, Infinity);
expect(norm.shape).toEqual([]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3]);
});
it('axis=null in 4D array norm', async () => {
const a = tf.tensor4d([1, 2, 3, 0, 0, 1], [3, 2, 1, 1]);
const norm = tf.norm(a, Infinity);
expect(norm.shape).toEqual([]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [3]);
});
it('axis=0,1 in 4D array norm', async () => {
const a = tf.tensor4d([
1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1,
1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1
], [3, 2, 2, 2]);
const norm = tf.norm(a, Infinity, [0, 1]);
expect(norm.shape).toEqual([2, 2]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [4, 3, 4, 3]);
});
it('axis=0,1 in 4D array norm', async () => {
const a = tf.tensor4d([
1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1,
1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1
], [3, 2, 2, 2]);
const norm = tf.norm(a, Infinity, [0, 1], true);
expect(norm.shape).toEqual([1, 1, 2, 2]);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), [4, 3, 4, 3]);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.norm({}))
.toThrowError(/Argument 'x' passed to 'norm' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const norm = tf.norm([1, -2, 3, -4], 1);
expect(norm.dtype).toBe('float32');
expectArraysClose(await norm.data(), 10);
});
it('throws error for string tensors', () => {
expect(() => tf.norm([
'a', 'b'
])).toThrowError(/Argument 'x' passed to 'norm' must be numeric tensor/);
});
});
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