@tensorflow/tfjs-core/dist/ops/sinh_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('sinh', ALL_ENVS, () => {
    it('basic', async () => {
        const values = [1, -3, 2, -1, -4];
        const a = tf.tensor1d(values);
        const result = tf.sinh(a);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = Math.sinh(values[i]);
        }
        expectArraysClose(await result.data(), expected);
    });
    it('propagates NaNs', async () => {
        const a = tf.tensor1d([4, NaN, 0]);
        const res = tf.sinh(a);
        expectArraysClose(await res.data(), [Math.sinh(4), NaN, Math.sinh(0)]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(0.5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.sinh(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 * Math.cosh(0.5)]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(0.5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.sinh(a.clone()).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 * Math.cosh(0.5)]);
    });
    it('gradients: Tensor1D', async () => {
        const aValues = [-1, 2, 3, -5];
        const dyValues = [1, 2, 3, 4];
        const a = tf.tensor1d(aValues);
        const dy = tf.tensor1d(dyValues);
        const gradients = tf.grad(a => tf.sinh(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = dyValues[i] * Math.cosh(aValues[i]);
        }
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), expected);
    });
    it('gradients: Tensor2D', async () => {
        const aValues = [-3, 1, 2, 3];
        const dyValues = [1, 2, 3, 4];
        const a = tf.tensor2d(aValues, [2, 2]);
        const dy = tf.tensor2d(dyValues, [2, 2]);
        const gradients = tf.grad(a => tf.sinh(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = dyValues[i] * Math.cosh(aValues[i]);
        }
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), expected);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.sinh({}))
            .toThrowError(/Argument 'x' passed to 'sinh' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const values = [1, -3, 2, -1, -4];
        const result = tf.sinh(values);
        const expected = [];
        for (let i = 0; i < values.length; i++) {
            expected[i] = Math.sinh(values[i]);
        }
        expectArraysClose(await result.data(), expected);
    });
    it('throws for string tensor', () => {
        expect(() => tf.sinh('q'))
            .toThrowError(/Argument 'x' passed to 'sinh' must be numeric/);
    });
});
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