@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('abs', ALL_ENVS, () => {
it('basic', async () => {
const a = tf.tensor1d([1, -2, 0, 3, -0.1]);
const result = tf.abs(a);
expectArraysClose(await result.data(), [1, 2, 0, 3, 0.1]);
});
it('5D', async () => {
const a = tf.tensor5d([1, -2, 0, -3], [1, 2, 2, 1, 1]);
const result = tf.abs(a);
expectArraysClose(await result.data(), [1, 2, 0, 3]);
});
it('6D', async () => {
const a = tf.tensor6d([1, -2, 5, -3, -1, 4, 7, 8], [1, 2, 2, 2, 1, 1]);
const result = tf.abs(a);
expectArraysClose(await result.data(), [1, 2, 5, 3, 1, 4, 7, 8]);
});
it('complex64 rank-1', async () => {
const a = tf.complex([-2, -1, 0, 1, 2], [1, 2, 3, 0, -1]);
const result = tf.abs(a);
expectArraysClose(await result.data(), [
Math.sqrt(-2 * -2 + 1 * 1), Math.sqrt(-1 * -1 + 2 * 2),
Math.sqrt(0 * 0 + 3 * 3), Math.sqrt(1 * 1 + 0 * 0),
Math.sqrt(2 * 2 + -1 * -1)
]);
expect(result.shape).toEqual([5]);
});
it('complex64 rank-2', async () => {
const a = tf.complex([[-3, -2, -1], [0, 1, 2]], [[4, 1, 2], [3, 0, -1]]);
const result = tf.abs(a);
expectArraysClose(await result.data(), [
Math.sqrt(-3 * -3 + 4 * 4), Math.sqrt(-2 * -2 + 1 * 1),
Math.sqrt(-1 * -1 + 2 * 2), Math.sqrt(0 * 0 + 3 * 3),
Math.sqrt(1 * 1 + 0 * 0), Math.sqrt(2 * 2 + -1 * -1)
]);
expect(result.shape).toEqual([2, 3]);
});
it('complex64 rank-3', async () => {
const a = tf.complex([[[-3, -2], [-1, 0]], [[1, 2], [3, 4]]], [[[4, 1], [2, 3]], [[0, -1], [-3, -4]]]);
const result = tf.abs(a);
expectArraysClose(await result.data(), [
Math.sqrt(-3 * -3 + 4 * 4), Math.sqrt(-2 * -2 + 1 * 1),
Math.sqrt(-1 * -1 + 2 * 2), Math.sqrt(0 * 0 + 3 * 3),
Math.sqrt(1 * 1 + 0 * 0), Math.sqrt(2 * 2 + -1 * -1),
Math.sqrt(3 * 3 + -3 * -3), Math.sqrt(4 * 4 + -4 * -4)
]);
expect(result.shape).toEqual([2, 2, 2]);
});
it('is underflow-safe for complex64', async () => {
const floatBits = tf.backend().floatPrecision();
let small;
switch (floatBits) {
case 32:
small = 1e-30;
break;
case 16:
small = 1e-4;
break;
default:
throw new Error(`Test not implemented for ENV.engine.floatPrecision()=${floatBits}.`);
}
const a = tf.complex([small, 0, small, 0], [small, small, 0, 0]);
const result = tf.abs(a);
expectArraysClose(await result.data(), [
Math.hypot(small, small), Math.hypot(0, small), Math.hypot(small, 0),
Math.hypot(0, 0)
],
/*tolerance=*/ small / 100);
expect(result.shape).toEqual([4]);
});
it('propagates NaNs', async () => {
const a = tf.tensor1d([1, -2, 0, 3, -0.1, NaN]);
const result = tf.abs(a);
expectArraysClose(await result.data(), [1, 2, 0, 3, 0.1, NaN]);
});
it('gradients: Scalar', async () => {
const a = tf.scalar(4);
const dy = tf.scalar(8);
const da = tf.grad(a => tf.abs(a))(a, dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
expectArraysClose(await da.data(), [8 * 1]);
});
it('gradient with clones', () => {
const a = tf.scalar(4);
const dy = tf.scalar(8);
const da = tf.grad(a => a.clone().abs().clone())(a, dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
});
it('gradients: Tensor1D', async () => {
const a = tf.tensor1d([1, 2, -3, 5]);
const dy = tf.tensor1d([1, 2, 3, 4]);
const da = tf.grad(a => tf.abs(a))(a, dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
expectArraysClose(await da.data(), [1 * 1, 2 * 1, 3 * -1, 4 * 1]);
});
it('gradients: Tensor2D', async () => {
const a = tf.tensor2d([3, -1, -2, 3], [2, 2]);
const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
const da = tf.grad(a => tf.abs(a))(a, dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
expectArraysClose(await da.data(), [1 * 1, 2 * -1, 3 * -1, 4 * 1]);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.abs({}))
.toThrowError(/Argument 'x' passed to 'abs' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const result = tf.abs([1, -2, 0, 3, -0.1]);
expectArraysClose(await result.data(), [1, 2, 0, 3, 0.1]);
});
it('throws for string tensor', () => {
expect(() => tf.abs('q'))
.toThrowError(/Argument 'x' passed to 'abs' must be numeric/);
});
});
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