@tensorflow/tfjs-core/dist/ops/neg_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('neg', ALL_ENVS, () => {
    it('basic', async () => {
        const a = tf.tensor1d([1, -3, 2, 7, -4]);
        const result = tf.neg(a);
        expectArraysClose(await result.data(), [-1, 3, -2, -7, 4]);
    });
    it('propagate NaNs', async () => {
        const a = tf.tensor1d([1, -3, 2, 7, NaN]);
        const result = tf.neg(a);
        const expected = [-1, 3, -2, -7, NaN];
        expectArraysClose(await result.data(), expected);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(4);
        const dy = tf.scalar(8);
        const da = tf.grad(a => tf.neg(a))(a, dy);
        expect(da.shape).toEqual(a.shape);
        expect(da.dtype).toEqual('float32');
        expectArraysClose(await da.data(), [8 * -1]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(4);
        const dy = tf.scalar(8);
        const da = tf.grad(a => tf.neg(a.clone()).clone())(a, dy);
        expect(da.shape).toEqual(a.shape);
        expect(da.dtype).toEqual('float32');
        expectArraysClose(await da.data(), [8 * -1]);
    });
    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.neg(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.neg(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.neg({}))
            .toThrowError(/Argument 'x' passed to 'neg' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const result = tf.neg([1, -3, 2, 7, -4]);
        expectArraysClose(await result.data(), [-1, 3, -2, -7, 4]);
    });
    it('throws for string tensor', () => {
        expect(() => tf.neg('q'))
            .toThrowError(/Argument 'x' passed to 'neg' must be numeric/);
    });
});
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