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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('computeWeightedLoss', ALL_ENVS, () => {
    it('1D - no weights', async () => {
        const losses = tf.tensor1d([1, 2, 3]);
        const y = tf.losses.computeWeightedLoss(losses);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 + 2 + 3) / 3);
    });
    it('1D - no weights - Reduction.NONE', async () => {
        const losses = tf.tensor1d([1, 2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);
        expect(y.shape).toEqual([3]);
        expectArraysClose(await y.data(), [1, 2, 3]);
    });
    it('1D - no weights - Reduction.MEAN', async () => {
        const losses = tf.tensor1d([1, 2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 + 2 + 3) / 3);
    });
    it('1D - no weights - Reduction.SUM', async () => {
        const losses = tf.tensor1d([1, 2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 + 2 + 3));
    });
    it('1D - weights', async () => {
        const losses = tf.tensor1d([1, 2, 3]);
        const weights = tf.tensor1d([0.1, 0, 0.3]);
        const y = tf.losses.computeWeightedLoss(losses, weights);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2);
    });
    it('2D - weights - broadcast', async () => {
        const losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
        const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
        const y = tf.losses.computeWeightedLoss(losses, weights);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 0.06666667);
    });
    it('1D - weights - Reduction.NONE', async () => {
        const losses = tf.tensor1d([1, 2, 3]);
        const weights = tf.tensor1d([0.1, 0.2, 0.3]);
        const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);
        expect(y.shape).toEqual([3]);
        expectArraysClose(await y.data(), [1 * 0.1, 2 * 0.2, 3 * 0.3]);
    });
    it('1D - weights - Reduction.MEAN', async () => {
        const losses = tf.tensor1d([1, 2, 3]);
        const weights = tf.tensor1d([0.1, 0.2, 0.3]);
        const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3) / 0.6);
    });
    it('1D - weights - Reduction.SUM', async () => {
        const losses = tf.tensor1d([1, 2, 3]);
        const weights = tf.tensor1d([0.1, 0.2, 0.3]);
        const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3));
    });
    it('2D - no weights', async () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const y = tf.losses.computeWeightedLoss(losses);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3) / 6);
    });
    it('2D - weights', async () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, weights);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4);
    });
    it('2D - no weights - Reduction.MEAN', async () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3) / 6);
    });
    it('2D - weights - Reduction.MEAN', async () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4);
    });
    it('2D - weights - broadcast - MEAN', async () => {
        const losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
        const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
        const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (0.3 + 0.1 + 0.2) / (3 * 0.6));
    });
    it('2D - no weights - Reduction.SUM', async () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3));
    });
    it('2D - weights - Reduction.SUM', async () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6));
    });
    it('2D - no weights - Reduction.NONE', async () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);
        expect(y.shape).toEqual([2, 3]);
        expectArraysClose(await y.data(), [4, 8, 12, 8, 1, 3]);
    });
    it('2D - weights - Reduction.NONE', async () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
        const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);
        expect(y.shape).toEqual([2, 3]);
        expectArraysClose(await y.data(), [4 * 1, 8 * 0, 12 * 2, (8 * -5), 1 * 0, 3 * 6]);
    });
    it('throws when passed losses as a non-tensor', () => {
        const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
        const e = /Argument 'losses' passed to 'computeWeightedLoss' must be a Tensor/;
        expect(() => tf.losses.computeWeightedLoss({}, weights, tf.Reduction.NONE))
            .toThrowError(e);
    });
    it('throws when passed weights as a non-tensor', () => {
        const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
        const e = /Argument 'weights' passed to 'computeWeightedLoss' must be a Tensor/;
        expect(() => tf.losses.computeWeightedLoss(losses, {}, tf.Reduction.NONE))
            .toThrowError(e);
    });
    it('accepts a tensor-like object', async () => {
        const losses = [1, 2, 3];
        const weights = [0.1, 0, 0.3];
        const y = tf.losses.computeWeightedLoss(losses, weights);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2);
    });
});
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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('computeWeightedLoss', ALL_ENVS, () => {\n  it('1D - no weights', async () => {\n    const losses = tf.tensor1d([1, 2, 3]);\n\n    const y = tf.losses.computeWeightedLoss(losses);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (1 + 2 + 3) / 3);\n  });\n\n  it('1D - no weights - Reduction.NONE', async () => {\n    const losses = tf.tensor1d([1, 2, 3]);\n\n    const y =\n        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([3]);\n    expectArraysClose(await y.data(), [1, 2, 3]);\n  });\n\n  it('1D - no weights - Reduction.MEAN', async () => {\n    const losses = tf.tensor1d([1, 2, 3]);\n\n    const y =\n        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (1 + 2 + 3) / 3);\n  });\n\n  it('1D - no weights - Reduction.SUM', async () => {\n    const losses = tf.tensor1d([1, 2, 3]);\n\n    const y =\n        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (1 + 2 + 3));\n  });\n\n  it('1D - weights', async () => {\n    const losses = tf.tensor1d([1, 2, 3]);\n    const weights = tf.tensor1d([0.1, 0, 0.3]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2);\n  });\n\n  it('2D - weights - broadcast', async () => {\n    const losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 0.06666667);\n  });\n\n  it('1D - weights - Reduction.NONE', async () => {\n    const losses = tf.tensor1d([1, 2, 3]);\n    const weights = tf.tensor1d([0.1, 0.2, 0.3]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([3]);\n    expectArraysClose(await y.data(), [1 * 0.1, 2 * 0.2, 3 * 0.3]);\n  });\n\n  it('1D - weights - Reduction.MEAN', async () => {\n    const losses = tf.tensor1d([1, 2, 3]);\n    const weights = tf.tensor1d([0.1, 0.2, 0.3]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3) / 0.6);\n  });\n\n  it('1D - weights - Reduction.SUM', async () => {\n    const losses = tf.tensor1d([1, 2, 3]);\n    const weights = tf.tensor1d([0.1, 0.2, 0.3]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3));\n  });\n\n  it('2D - no weights', async () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n\n    const y = tf.losses.computeWeightedLoss(losses);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3) / 6);\n  });\n\n  it('2D - weights', async () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(),\n        (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4);\n  });\n\n  it('2D - no weights - Reduction.MEAN', async () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n\n    const y =\n        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3) / 6);\n  });\n\n  it('2D - weights - Reduction.MEAN', async () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(),\n        (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4);\n  });\n\n  it('2D - weights - broadcast - MEAN', async () => {\n    const losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (0.3 + 0.1 + 0.2) / (3 * 0.6));\n  });\n\n  it('2D - no weights - Reduction.SUM', async () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n\n    const y =\n        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (4 + 8 + 12 + 8 + 1 + 3));\n  });\n\n  it('2D - weights - Reduction.SUM', async () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6));\n  });\n\n  it('2D - no weights - Reduction.NONE', async () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n\n    const y =\n        tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([2, 3]);\n    expectArraysClose(await y.data(), [4, 8, 12, 8, 1, 3]);\n  });\n\n  it('2D - weights - Reduction.NONE', async () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);\n\n    const y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([2, 3]);\n    expectArraysClose(\n        await y.data(), [4 * 1, 8 * 0, 12 * 2, (8 * -5), 1 * 0, 3 * 6]);\n  });\n\n  it('throws when passed losses as a non-tensor', () => {\n    const weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);\n\n    const e =\n        /Argument 'losses' passed to 'computeWeightedLoss' must be a Tensor/;\n    expect(\n        () => tf.losses.computeWeightedLoss(\n            {} as tf.Tensor, weights, tf.Reduction.NONE))\n        .toThrowError(e);\n  });\n\n  it('throws when passed weights as a non-tensor', () => {\n    const losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);\n\n    const e =\n        /Argument 'weights' passed to 'computeWeightedLoss' must be a Tensor/;\n    expect(\n        () => tf.losses.computeWeightedLoss(\n            losses, {} as tf.Tensor, tf.Reduction.NONE))\n        .toThrowError(e);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const losses = [1, 2, 3];\n    const weights = [0.1, 0, 0.3];\n    const y = tf.losses.computeWeightedLoss(losses, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2);\n  });\n});\n"]}