@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('rsqrt', ALL_ENVS, () => {
it('rsqrt', async () => {
const a = tf.tensor1d([2, 4]);
const r = tf.rsqrt(a);
expectArraysClose(await r.data(), [1 / Math.sqrt(2), 1 / Math.sqrt(4)]);
});
it('rsqrt propagates NaNs', async () => {
const a = tf.tensor1d([1, NaN]);
const r = tf.rsqrt(a);
expectArraysClose(await r.data(), [1 / Math.sqrt(1), NaN]);
});
it('gradients: Scalar', async () => {
const a = tf.scalar(4);
const dy = tf.scalar(8);
const da = tf.grad(a => tf.rsqrt(a))(a, dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
expectArraysClose(await da.data(), [(-1 * 8) / (2 * Math.pow(4, 1.5))]);
});
it('gradients: Scalar', async () => {
const a = tf.scalar(4);
const dy = tf.scalar(8);
const da = tf.grad(a => tf.rsqrt(a.clone()).clone())(a, dy);
expect(da.shape).toEqual(a.shape);
expect(da.dtype).toEqual('float32');
expectArraysClose(await da.data(), [(-1 * 8) / (2 * Math.pow(4, 1.5))]);
});
it('gradients: Tensor1D', async () => {
const a = tf.tensor1d([1, 2, 3, 5]);
const dy = tf.tensor1d([1, 2, 3, 4]);
const gradients = tf.grad(a => tf.rsqrt(a))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [
-1 * 1 / (2 * Math.pow(1, 1.5)), -1 * 2 / (2 * Math.pow(2, 1.5)),
-1 * 3 / (2 * Math.pow(3, 1.5)), -1 * 4 / (2 * Math.pow(5, 1.5))
], 1e-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 gradients = tf.grad(a => tf.rsqrt(a))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [
-1 * 1 / (2 * Math.pow(3, 1.5)), -1 * 2 / (2 * Math.pow(1, 1.5)),
-1 * 3 / (2 * Math.pow(2, 1.5)), -1 * 4 / (2 * Math.pow(3, 1.5))
], 1e-1);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.rsqrt({}))
.toThrowError(/Argument 'x' passed to 'rsqrt' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const r = tf.rsqrt([2, 4]);
expectArraysClose(await r.data(), [1 / Math.sqrt(2), 1 / Math.sqrt(4)]);
});
it('throws for string tensor', () => {
expect(() => tf.rsqrt('q'))
.toThrowError(/Argument 'x' passed to 'rsqrt' must be numeric/);
});
});
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