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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('hingeLoss', ALL_ENVS, () => {
| it('1D', async () => {
| const predictions = tf.tensor1d([0, 0, 1, 1]);
| const label = tf.tensor1d([0, 1, 0, 1]);
| const y = tf.losses.hingeLoss(label, predictions);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 1.0);
| });
| it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
| const predictions = tf.tensor1d([0, 0, 1, 1]);
| const label = tf.tensor1d([0, 1, 0, 1]);
| const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
| const y = tf.losses.hingeLoss(label, predictions, weights);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 0.225);
| });
| it('1D - weighted - Reduction.NONE', async () => {
| const predictions = tf.tensor1d([0, 0, 1, 1]);
| const label = tf.tensor1d([0, 1, 0, 1]);
| const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
| const y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
| expect(y.shape).toEqual([4]);
| expectArraysClose(await y.data(), [0.1, 0.2, 0.6, 0.0]);
| });
| it('1D - Reduction.MEAN', async () => {
| const predictions = tf.tensor1d([0, 0, 1, 1]);
| const label = tf.tensor1d([0, 1, 0, 1]);
| const y = tf.losses.hingeLoss(label, predictions, undefined, tf.Reduction.MEAN);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 1.0);
| });
| it('1D - weighted - Reduction.MEAN', async () => {
| const predictions = tf.tensor1d([0, 0, 1, 1]);
| const label = tf.tensor1d([0, 1, 0, 1]);
| const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
| const y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.MEAN);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 0.9);
| });
| it('2D', async () => {
| const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
| const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
| const y = tf.losses.hingeLoss(label, predictions);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 0.8333333);
| });
| it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
| const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
| const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
| const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
| const y = tf.losses.hingeLoss(label, predictions, weights);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 0.26666668);
| });
| it('2D - weighted - Reduction.NONE', async () => {
| const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
| const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
| const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
| const y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
| expect(y.shape).toEqual([2, 3]);
| expectArraysClose(await y.data(), [0.1, 0.2, 0.3, 0, 1, 0]);
| });
| it('2D - Reduction.MEAN', async () => {
| const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
| const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
| const y = tf.losses.hingeLoss(label, predictions, undefined, tf.Reduction.MEAN);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 0.8333333);
| });
| it('2D - weighted - Reduction.MEAN', async () => {
| const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
| const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
| const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
| const y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.MEAN);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 0.76190484);
| });
| it('throws when passed label as a non-tensor', () => {
| const predictions = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
| const weights = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
| const e = /Argument 'labels' passed to 'hingeLoss' must be a Tensor/;
| expect(() => tf.losses.hingeLoss({}, predictions, weights, tf.Reduction.MEAN))
| .toThrowError(e);
| });
| it('throws when passed label as a non-tensor', () => {
| const label = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
| const weights = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
| const e = new RegExp('Argument \'predictions\' passed to \'hingeLoss\' ' +
| 'must be a Tensor');
| expect(() => tf.losses.hingeLoss(label, {}, weights, tf.Reduction.MEAN))
| .toThrowError(e);
| });
| it('throws when passed weights as a non-tensor', () => {
| const predictions = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
| const label = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
| const e = /Argument 'weights' passed to 'hingeLoss' must be a Tensor/;
| expect(() => tf.losses.hingeLoss(label, predictions, {}, tf.Reduction.MEAN))
| .toThrowError(e);
| });
| it('accepts a tensor-like object', async () => {
| const predictions = [0, 0, 1, 1];
| const label = [0, 1, 0, 1];
| const weights = [0.1, 0.2, 0.3, 0.4];
| const y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
| expect(y.shape).toEqual([4]);
| expectArraysClose(await y.data(), [0.1, 0.2, 0.6, 0.0]);
| });
| });
| //# 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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('hingeLoss', ALL_ENVS, () => {\n  it('1D', async () => {\n    const predictions = tf.tensor1d([0, 0, 1, 1]);\n    const label = tf.tensor1d([0, 1, 0, 1]);\n\n    const y = tf.losses.hingeLoss(label, predictions);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 1.0);\n  });\n\n  it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {\n    const predictions = tf.tensor1d([0, 0, 1, 1]);\n    const label = tf.tensor1d([0, 1, 0, 1]);\n    const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);\n\n    const y = tf.losses.hingeLoss(label, predictions, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 0.225);\n  });\n\n  it('1D - weighted - Reduction.NONE', async () => {\n    const predictions = tf.tensor1d([0, 0, 1, 1]);\n    const label = tf.tensor1d([0, 1, 0, 1]);\n    const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);\n\n    const y =\n        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([4]);\n    expectArraysClose(await y.data(), [0.1, 0.2, 0.6, 0.0]);\n  });\n\n  it('1D - Reduction.MEAN', async () => {\n    const predictions = tf.tensor1d([0, 0, 1, 1]);\n    const label = tf.tensor1d([0, 1, 0, 1]);\n\n    const y =\n        tf.losses.hingeLoss(label, predictions, undefined, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 1.0);\n  });\n\n  it('1D - weighted - Reduction.MEAN', async () => {\n    const predictions = tf.tensor1d([0, 0, 1, 1]);\n    const label = tf.tensor1d([0, 1, 0, 1]);\n    const weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);\n\n    const y =\n        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 0.9);\n  });\n\n  it('2D', async () => {\n    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);\n    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);\n\n    const y = tf.losses.hingeLoss(label, predictions);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 0.8333333);\n  });\n\n  it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {\n    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);\n    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);\n    const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);\n\n    const y = tf.losses.hingeLoss(label, predictions, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 0.26666668);\n  });\n\n  it('2D - weighted - Reduction.NONE', async () => {\n    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);\n    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);\n    const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);\n\n    const y =\n        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([2, 3]);\n    expectArraysClose(await y.data(), [0.1, 0.2, 0.3, 0, 1, 0]);\n  });\n\n  it('2D - Reduction.MEAN', async () => {\n    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);\n    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);\n\n    const y =\n        tf.losses.hingeLoss(label, predictions, undefined, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 0.8333333);\n  });\n\n  it('2D - weighted - Reduction.MEAN', async () => {\n    const predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);\n    const label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);\n    const weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);\n\n    const y =\n        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 0.76190484);\n  });\n\n  it('throws when passed label as a non-tensor', () => {\n    const predictions = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);\n    const weights = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);\n\n    const e = /Argument 'labels' passed to 'hingeLoss' must be a Tensor/;\n    expect(\n        () => tf.losses.hingeLoss(\n            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))\n        .toThrowError(e);\n  });\n\n  it('throws when passed label as a non-tensor', () => {\n    const label = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);\n    const weights = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);\n\n    const e = new RegExp(\n        'Argument \\'predictions\\' passed to \\'hingeLoss\\' ' +\n        'must be a Tensor');\n    expect(\n        () => tf.losses.hingeLoss(\n            label, {} as tf.Tensor, weights, tf.Reduction.MEAN))\n        .toThrowError(e);\n  });\n\n  it('throws when passed weights as a non-tensor', () => {\n    const predictions = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);\n    const label = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);\n\n    const e = /Argument 'weights' passed to 'hingeLoss' must be a Tensor/;\n    expect(\n        () => tf.losses.hingeLoss(\n            label, predictions, {} as tf.Tensor, tf.Reduction.MEAN))\n        .toThrowError(e);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const predictions = [0, 0, 1, 1];\n    const label = [0, 1, 0, 1];\n    const weights = [0.1, 0.2, 0.3, 0.4];\n\n    const y =\n        tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([4]);\n    expectArraysClose(await y.data(), [0.1, 0.2, 0.6, 0.0]);\n  });\n});\n"]}
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