gx

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 } from '../test_util';
describeWithFlags('sign', ALL_ENVS, () => {
    it('basic', async () => {
        const a = tf.tensor1d([1.5, 0, NaN, -1.4]);
        const r = tf.sign(a);
        expectArraysClose(await r.data(), [1, 0, 0, -1]);
    });
    it('does not propagate NaNs', async () => {
        const a = tf.tensor1d([1.5, NaN, -1.4]);
        const r = tf.sign(a);
        expectArraysClose(await r.data(), [1, 0, -1]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(5.2);
        const dy = tf.scalar(3);
        const gradients = tf.grad(a => tf.sign(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [0]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(5.2);
        const dy = tf.scalar(3);
        const gradients = tf.grad(a => tf.sign(a.clone()).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [0]);
    });
    it('gradients: Tensor1D', async () => {
        const a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
        const dy = tf.tensor1d([-1, 1, 1, -1]);
        const gradients = tf.grad(a => tf.sign(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
    });
    it('gradients: Tensor2D', async () => {
        const a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
        const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
        const gradients = tf.grad(a => tf.sign(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [0, 0, 0, 0]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.sign({}))
            .toThrowError(/Argument 'x' passed to 'sign' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const r = tf.sign([1.5, 0, NaN, -1.4]);
        expectArraysClose(await r.data(), [1, 0, 0, -1]);
    });
    it('throws for string tensor', () => {
        expect(() => tf.sign('q'))
            .toThrowError(/Argument 'x' passed to 'sign' must be numeric/);
    });
});
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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('sign', ALL_ENVS, () => {\n  it('basic', async () => {\n    const a = tf.tensor1d([1.5, 0, NaN, -1.4]);\n    const r = tf.sign(a);\n    expectArraysClose(await r.data(), [1, 0, 0, -1]);\n  });\n\n  it('does not propagate NaNs', async () => {\n    const a = tf.tensor1d([1.5, NaN, -1.4]);\n    const r = tf.sign(a);\n    expectArraysClose(await r.data(), [1, 0, -1]);\n  });\n\n  it('gradients: Scalar', async () => {\n    const a = tf.scalar(5.2);\n    const dy = tf.scalar(3);\n\n    const gradients = tf.grad(a => tf.sign(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [0]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.scalar(5.2);\n    const dy = tf.scalar(3);\n\n    const gradients = tf.grad(a => tf.sign(a.clone()).clone())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [0]);\n  });\n\n  it('gradients: Tensor1D', async () => {\n    const a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);\n    const dy = tf.tensor1d([-1, 1, 1, -1]);\n\n    const gradients = tf.grad(a => tf.sign(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);\n  });\n\n  it('gradients: Tensor2D', async () => {\n    const a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);\n    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n\n    const gradients = tf.grad(a => tf.sign(a))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [0, 0, 0, 0]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.sign({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'sign' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const r = tf.sign([1.5, 0, NaN, -1.4]);\n    expectArraysClose(await r.data(), [1, 0, 0, -1]);\n  });\n\n  it('throws for string tensor', () => {\n    expect(() => tf.sign('q'))\n        .toThrowError(/Argument 'x' passed to 'sign' must be numeric/);\n  });\n});\n"]}