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chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @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, TEST_EPSILON_FLOAT16 } from '../test_util';
describeWithFlags('tan', ALL_ENVS, () => {
    it('basic', async () => {
        const values = [1, -3, 2, 7, -4];
        const a = tf.tensor1d(values);
        const result = tf.tan(a);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = Math.tan(values[i]);
        }
        expectArraysClose(await result.data(), expected);
    });
    it('numbers exceed float32 precision', async () => {
        const values = [
            -608065414.8781943,
            781902002.7943993,
            -470910673.97399473,
            1786759246.171617,
            1873777868.5510726,
            -1015107953.8969269,
            830023227.6215034,
        ];
        const a = tf.tensor1d(values, 'float32');
        const result = tf.tan(a);
        const expected = [...new Float32Array(values).map((v) => Math.tan(v))];
        expectArraysClose(await result.data(), expected);
    });
    it('propagates NaNs', async () => {
        const a = tf.tensor1d([4, NaN, 0]);
        const res = tf.tan(a);
        expectArraysClose(await res.data(), [Math.tan(4), NaN, Math.tan(0)]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(0.5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.tan(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 / (Math.cos(0.5) * Math.cos(0.5))]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(0.5);
        const dy = tf.scalar(8);
        const gradients = tf.grad(a => tf.tan(a.clone()).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [8 / (Math.cos(0.5) * Math.cos(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.tan(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = dyValues[i] / (Math.cos(aValues[i]) * Math.cos(aValues[i]));
        }
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        // The grad(tan(x)) which relies on 1/cos(x) is less precise on Windows.
        expectArraysClose(await gradients.data(), expected, TEST_EPSILON_FLOAT16);
    });
    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.tan(a))(a, dy);
        const expected = [];
        for (let i = 0; i < a.size; i++) {
            expected[i] = dyValues[i] / (Math.cos(aValues[i]) * Math.cos(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.tan({}))
            .toThrowError(/Argument 'x' passed to 'tan' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const values = [1, -3, 2, 7, -4];
        const result = tf.tan(values);
        const expected = [];
        for (let i = 0; i < values.length; i++) {
            expected[i] = Math.tan(values[i]);
        }
        expectArraysClose(await result.data(), expected);
    });
    it('throws for string tensor', () => {
        expect(() => tf.tan('q'))
            .toThrowError(/Argument 'x' passed to 'tan' must be float32/);
    });
    it('throws for int32 tensor', () => {
        expect(() => tf.tan(tf.tensor1d([1], 'int32')))
            .toThrowError(/Argument 'x' passed to 'tan' 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, TEST_EPSILON_FLOAT16} from '../test_util';\n\ndescribeWithFlags('tan', ALL_ENVS, () => {\n  it('basic', async () => {\n    const values = [1, -3, 2, 7, -4];\n    const a = tf.tensor1d(values);\n    const result = tf.tan(a);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = Math.tan(values[i]);\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('numbers exceed float32 precision', async () => {\n    const values = [\n      -608065414.8781943,\n      781902002.7943993,\n      -470910673.97399473,\n      1786759246.171617,\n      1873777868.5510726,\n      -1015107953.8969269,\n      830023227.6215034,\n    ];\n    const a = tf.tensor1d(values, 'float32');\n    const result = tf.tan(a);\n\n    const expected = [...new Float32Array(values).map((v) => Math.tan(v))];\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.tan(a);\n    expectArraysClose(await res.data(), [Math.tan(4), NaN, Math.tan(0)]);\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(0.5);\n    const dy = tf.scalar(8);\n\n    const gradients = tf.grad(a => tf.tan(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(), [8 / (Math.cos(0.5) * Math.cos(0.5))]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.scalar(0.5);\n    const dy = tf.scalar(8);\n\n    const gradients = tf.grad(a => tf.tan(a.clone()).clone())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(\n        await gradients.data(), [8 / (Math.cos(0.5) * Math.cos(0.5))]);\n  });\n\n  it('gradients: Tensor1D', async () => {\n    const aValues = [-1, 2, 3, -5];\n    const dyValues = [1, 2, 3, 4];\n    const a = tf.tensor1d(aValues);\n    const dy = tf.tensor1d(dyValues);\n\n    const gradients = tf.grad(a => tf.tan(a))(a, dy);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = dyValues[i] / (Math.cos(aValues[i]) * Math.cos(aValues[i]));\n    }\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    // The grad(tan(x)) which relies on 1/cos(x) is less precise on Windows.\n    expectArraysClose(await gradients.data(), expected, TEST_EPSILON_FLOAT16);\n  });\n\n  it('gradients: Tensor2D', async () => {\n    const aValues = [-3, 1, 2, 3];\n    const dyValues = [1, 2, 3, 4];\n    const a = tf.tensor2d(aValues, [2, 2]);\n    const dy = tf.tensor2d(dyValues, [2, 2]);\n\n    const gradients = tf.grad(a => tf.tan(a))(a, dy);\n\n    const expected = [];\n    for (let i = 0; i < a.size; i++) {\n      expected[i] = dyValues[i] / (Math.cos(aValues[i]) * Math.cos(aValues[i]));\n    }\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), expected);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.tan({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'tan' 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.tan(values);\n\n    const expected = [];\n    for (let i = 0; i < values.length; i++) {\n      expected[i] = Math.tan(values[i]);\n    }\n    expectArraysClose(await result.data(), expected);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.tan('q'))\n        .toThrowError(/Argument 'x' passed to 'tan' must be float32/);\n  });\n\n  it('throws for int32 tensor', () => {\n    expect(() => tf.tan(tf.tensor1d([1], 'int32')))\n        .toThrowError(/Argument 'x' passed to 'tan' must be float32/);\n  });\n});\n"]}