@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('cos', ALL_ENVS, () => {
it('basic', async () => {
// Covers every 1/4pi range from -4pi to 4pi.
const values = [1, 3, 4, 6, 7, 9, 10, 12, -1, -3, -4, -6, -7, -9, -10, -12];
const a = tf.tensor1d(values);
const result = tf.cos(a);
const expected = [];
for (let i = 0; i < a.size; i++) {
expected[i] = Math.cos(values[i]);
}
expectArraysClose(await result.data(), expected);
});
it('propagates NaNs', async () => {
const a = tf.tensor1d([4, NaN, 0]);
const res = tf.cos(a);
expectArraysClose(await res.data(), [Math.cos(4), NaN, Math.cos(0)]);
});
it('gradients: Scalar', async () => {
const a = tf.scalar(5);
const dy = tf.scalar(8);
const gradients = tf.grad(a => tf.cos(a))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [8 * Math.sin(5) * -1]);
});
it('gradient with clones', async () => {
const a = tf.scalar(5);
const dy = tf.scalar(8);
const gradients = tf.grad(a => tf.cos(a.clone()).clone())(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [8 * Math.sin(5) * -1]);
});
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.cos(a))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [
1 * Math.sin(-1) * -1, 2 * Math.sin(2) * -1, 3 * Math.sin(3) * -1,
4 * Math.sin(-5) * -1
], 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.cos(a))(a, dy);
expect(gradients.shape).toEqual(a.shape);
expect(gradients.dtype).toEqual('float32');
expectArraysClose(await gradients.data(), [
1 * Math.sin(-3) * -1, 2 * Math.sin(1) * -1, 3 * Math.sin(2) * -1,
4 * Math.sin(3) * -1
], 1e-1);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.cos({}))
.toThrowError(/Argument 'x' passed to 'cos' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const values = [1, -3, 2, 7, -4];
const result = tf.cos(values);
const expected = [];
for (let i = 0; i < values.length; i++) {
expected[i] = Math.cos(values[i]);
}
expectArraysClose(await result.data(), expected);
});
it('throws for string tensor', () => {
expect(() => tf.cos('q'))
.toThrowError(/Argument 'x' passed to 'cos' must be numeric/);
});
});
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