@tensorflow/tfjs-core/dist/ops/sin_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('sin', 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.sin(a);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = Math.sin(values[i]);
        }
        expectArraysClose(await result.data(), expected);
    });
    it('propagates NaNs', async () => {
        const a = tf.tensor1d([4, NaN, 0]);
        const res = tf.sin(a);
        expectArraysClose(await res.data(), [Math.sin(4), NaN, Math.sin(0)]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.sin(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 * Math.cos(5)]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.sin(a.clone()).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 * Math.cos(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.sin(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [1 * Math.cos(-1), 2 * Math.cos(2), 3 * Math.cos(3), 4 * Math.cos(-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.sin(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [1 * Math.cos(-3), 2 * Math.cos(1), 3 * Math.cos(2), 4 * Math.cos(3)], 1e-1);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.sin({}))
            .toThrowError(/Argument 'x' passed to 'sin' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const values = [1, -3, 2, 7, -4];
        const result = tf.sin(values);
        const expected = [];
        for (let i = 0; i < values.length; i++) {
            expected[i] = Math.sin(values[i]);
        }
        expectArraysClose(await result.data(), expected);
    });
    it('throws for string tensor', () => {
        expect(() => tf.sin('q'))
            .toThrowError(/Argument 'x' passed to 'sin' must be float32/);
    });
    it('throws for int32 tensor', () => {
        expect(() => tf.sin(tf.tensor1d([1], 'int32')))
            .toThrowError(/Argument 'x' passed to 'sin' must be float32/);
    });
});
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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('sin', ALL_ENVS, () => {\n  it('basic', async () => {\n    // Covers every 1/4pi range from -4pi to 4pi.\n    const values = [1, 3, 4, 6, 7, 9, 10, 12, -1, -3, -4, -6, -7, -9, -10, -12];\n    const a = tf.tensor1d(values);\n    const result = tf.sin(a);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = Math.sin(values[i]);\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('propagates NaNs', async () => {\n    const a = tf.tensor1d([4, NaN, 0]);\n    const res = tf.sin(a);\n    expectArraysClose(await res.data(), [Math.sin(4), NaN, Math.sin(0)]);\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(5);\n    const dy = tf.scalar(8);\n\n    const gradients = tf.grad(a => tf.sin(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [8 * Math.cos(5)]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.scalar(5);\n    const dy = tf.scalar(8);\n\n    const gradients = tf.grad(a => tf.sin(a.clone()).clone())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [8 * Math.cos(5)]);\n  });\n\n  it('gradients: Tensor1D', async () => {\n    const a = tf.tensor1d([-1, 2, 3, -5]);\n    const dy = tf.tensor1d([1, 2, 3, 4]);\n\n    const gradients = tf.grad(a => tf.sin(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(),\n        [1 * Math.cos(-1), 2 * Math.cos(2), 3 * Math.cos(3), 4 * Math.cos(-5)],\n        1e-1);\n  });\n\n  it('gradients: Tensor2D', async () => {\n    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);\n    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n\n    const gradients = tf.grad(a => tf.sin(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(),\n        [1 * Math.cos(-3), 2 * Math.cos(1), 3 * Math.cos(2), 4 * Math.cos(3)],\n        1e-1);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.sin({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'sin' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const values = [1, -3, 2, 7, -4];\n    const result = tf.sin(values);\n\n    const expected = [];\n    for (let i = 0; i < values.length; i++) {\n      expected[i] = Math.sin(values[i]);\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.sin('q'))\n        .toThrowError(/Argument 'x' passed to 'sin' must be float32/);\n  });\n\n  it('throws for int32 tensor', () => {\n    expect(() => tf.sin(tf.tensor1d([1], 'int32')))\n        .toThrowError(/Argument 'x' passed to 'sin' must be float32/);\n  });\n});\n"]}