@tensorflow/tfjs-core/dist/ops/reciprocal_test.js

/**
 * @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('reciprocal', ALL_ENVS, () => {
    it('1D array', async () => {
        const a = tf.tensor1d([2, 3, 0, NaN]);
        const r = tf.reciprocal(a);
        expectArraysClose(await r.data(), [1 / 2, 1 / 3, Infinity, NaN]);
    });
    it('2D array', async () => {
        const a = tf.tensor2d([1, Infinity, 0, NaN], [2, 2]);
        const r = tf.reciprocal(a);
        expect(r.shape).toEqual([2, 2]);
        expectArraysClose(await r.data(), [1 / 1, 0, Infinity, NaN]);
    });
    it('reciprocal propagates NaNs', async () => {
        const a = tf.tensor1d([1.5, NaN]);
        const r = tf.reciprocal(a);
        expectArraysClose(await r.data(), [1 / 1.5, NaN]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.reciprocal(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [-1 * 8 * (1 / (5 * 5))]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.reciprocal(a.clone()).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [-1 * 8 * (1 / (5 * 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.reciprocal(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [
            -1 * 1 * (1 / (-1 * -1)), -1 * 2 * (1 / (2 * 2)), -1 * 3 * (1 / (3 * 3)),
            -1 * 4 * (1 / (-5 * -5))
        ]);
    });
    it('gradients: Tensor2D', async () => {
        const a = tf.tensor2d([-1, 2, 3, -5], [2, 2]);
        const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
        const gradients = tf.grad(a => tf.reciprocal(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [
            -1 * 1 * (1 / (-1 * -1)), -1 * 2 * (1 / (2 * 2)), -1 * 3 * (1 / (3 * 3)),
            -1 * 4 * (1 / (-5 * -5))
        ]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.reciprocal({}))
            .toThrowError(/Argument 'x' passed to 'reciprocal' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const r = tf.reciprocal([2, 3, 0, NaN]);
        expectArraysClose(await r.data(), [1 / 2, 1 / 3, Infinity, NaN]);
    });
    it('throws for string tensor', () => {
        expect(() => tf.reciprocal('q'))
            .toThrowError(/Argument 'x' passed to 'reciprocal' must be numeric/);
    });
});
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