@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 } from '../test_util';
describeWithFlags('max', ALL_ENVS, () => {
it('with one element dominating', async () => {
const a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
const r = tf.max(a);
expectArraysClose(await r.data(), 100);
});
it('with all elements being the same', async () => {
const a = tf.tensor1d([3, 3, 3]);
const r = tf.max(a);
expectArraysClose(await r.data(), 3);
});
it('with a large dimension', async () => {
const aData = new Float32Array(1000);
aData[0] = 1;
const a = tf.tensor1d(aData);
const r = tf.max(a);
expectArraysClose(await r.data(), 1);
});
it('ignores NaNs', async () => {
expectArraysClose(await tf.max([3, NaN, 2]).data(), 3);
});
it('2D', async () => {
const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
expectArraysClose(await tf.max(a).data(), 100);
});
it('2D axis=[0,1]', async () => {
const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
expectArraysClose(await tf.max(a, [0, 1]).data(), 100);
});
it('2D, axis=0', async () => {
const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
const r = tf.max(a, [0]);
expect(r.shape).toEqual([3]);
expectArraysClose(await r.data(), [100, -1, 2]);
});
it('2D, axis=0, keepDims', async () => {
const a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
const r = tf.max(a, [0], true /* keepDims */);
expect(r.shape).toEqual([1, 3]);
expectArraysClose(await r.data(), [100, -1, 2]);
});
it('2D, axis=1 provided as a number', async () => {
const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
const r = tf.max(a, 1);
expectArraysClose(await r.data(), [5, 100]);
});
it('2D, axis = -1 provided as a number', async () => {
const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
const r = tf.max(a, -1);
expectArraysClose(await r.data(), [5, 100]);
});
it('2D, axis=[1]', async () => {
const a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
const r = tf.max(a, [1]);
expectArraysClose(await r.data(), [5, 100]);
});
it('6D, axis=[5]', async () => {
const a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
const r = tf.max(a, [5]);
const expectedResult = [
1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31,
33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63
];
expectArraysClose(await r.data(), expectedResult);
});
it('axis permutation does not change input', async () => {
const input = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
const inputDataBefore = await input.data();
tf.max(input, [1, 0]);
const inputDataAfter = await input.data();
expectArraysClose(inputDataBefore, inputDataAfter);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.max({}))
.toThrowError(/Argument 'x' passed to 'max' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const r = tf.max([3, -1, 0, 100, -7, 2]);
expectArraysClose(await r.data(), 100);
});
it('max gradient: Scalar', async () => {
const x = tf.scalar(42);
const dy = tf.scalar(-1);
const gradients = tf.grad(v => tf.max(v))(x, dy);
expectArraysClose(await gradients.data(), [-1]);
});
it('gradient with clones', async () => {
const x = tf.scalar(42);
const dy = tf.scalar(-1);
const gradients = tf.grad(v => tf.max(v.clone()).clone())(x, dy);
expectArraysClose(await gradients.data(), [-1]);
});
it('max gradient: 1D, ties', async () => {
const x = tf.tensor1d([1, 3, 7, 7]);
const dy = tf.scalar(-1);
const gradients = tf.grad(v => tf.max(v))(x, dy);
expectArraysClose(await gradients.data(), [0, 0, -1, -1]);
});
it('max gradient: 2D, axes=-1, keepDims=false', async () => {
const x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);
const dy = tf.tensor1d([-1, -1]);
const axis = -1;
const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
expect(gradients.shape).toEqual([2, 3]);
});
it('max gradient: ties, 2D, axes=-1, keepDims=false', async () => {
const x = tf.tensor2d([[0, 20, 20], [-10, -30, -10]]);
const dy = tf.tensor1d([-1, -1]);
const axis = -1;
const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, -1]);
expect(gradients.shape).toEqual([2, 3]);
});
it('max gradient: 2D, axes=0, keepDims=false', async () => {
const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
const dy = tf.tensor1d([-1, -1, -1]);
const axis = 0;
const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
expect(gradients.shape).toEqual([2, 3]);
});
it('max gradient: 2D, axes=-1, keepDims=true', async () => {
const x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);
const dy = tf.tensor2d([[-1], [-1]]);
const axis = -1;
const keepDims = true;
const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, 0, -1, 0, 0]);
expect(gradients.shape).toEqual([2, 3]);
});
it('max gradient: 2D, axes=0, keepDims=true', async () => {
const x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
const dy = tf.tensor2d([[-1, -1, -1]]);
const axis = 0;
const keepDims = true;
const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
expectArraysClose(await gradients.data(), [-1, -1, 0, 0, 0, -1]);
expect(gradients.shape).toEqual([2, 3]);
});
it('max gradient: 3D, axis=1 keepDims=false', async () => {
const x = tf.ones([2, 1, 250]);
const axis = 1;
const gradients = tf.grad(v => tf.max(v, axis))(x);
expect(gradients.shape).toEqual(x.shape);
});
it('max gradient: 3D, axes=[1, 2], keepDims=false', async () => {
const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
const dy = tf.tensor1d([-1, -1]);
const axis = [1, 2];
const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, 0, 0, -1, 0, 0, 0]);
expect(gradients.shape).toEqual([2, 2, 2]);
});
it('max gradient: ties, 3D, axes=[1, 2], keepDims=false', async () => {
const x = tf.tensor3d([[[0, 20], [20, 20]], [[-10, -30], [-10, -15]]]);
const dy = tf.tensor1d([-1, -1]);
const axis = [1, 2];
const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, -1, -1, -1, 0, -1, 0]);
expect(gradients.shape).toEqual([2, 2, 2]);
});
it('max gradient: 3D, axes=2, keepDims=false', async () => {
const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
const dy = tf.tensor2d([[-1, -1], [-1, -1]]);
const axis = 2;
const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
expect(gradients.shape).toEqual([2, 2, 2]);
});
it('max gradient: 3D, axes=2, keepDims=true', async () => {
const x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
const dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);
const axis = 2;
const keepDims = true;
const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, 0, -1, -1, 0, 0, -1]);
expect(gradients.shape).toEqual([2, 2, 2]);
});
it('max gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', async () => {
const x = tf.tensor4d([
[[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],
[[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]
]);
const dy = tf.tensor1d([-1, -1]);
const axis = [1, 2, 3];
const gradients = tf.grad(v => tf.max(v, axis))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1]);
expect(gradients.shape).toEqual([2, 2, 2, 2]);
});
it('max gradient: ties, 4D, axes=[2, 3], keepDims=true', async () => {
const x = tf.tensor4d([
[[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],
[[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]
]);
const dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);
const axis = [2, 3];
const keepDims = true;
const gradients = tf.grad(v => tf.max(v, axis, keepDims))(x, dy);
expectArraysClose(await gradients.data(), [0, -1, -1, -1, -2, 0, -2, 0, -3, 0, 0, 0, 0, -4, 0, -4]);
expect(gradients.shape).toEqual([2, 2, 2, 2]);
});
it('throws error for string tensor', () => {
expect(() => tf.max(['a']))
.toThrowError(/Argument 'x' passed to 'max' must be numeric tensor/);
});
});
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