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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('sigmoidCrossEntropy', ALL_ENVS, () => {
| it('All wrong', async () => {
| const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const y = tf.losses.sigmoidCrossEntropy(label, predictions);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 6.6667123);
| });
| it('All right', async () => {
| const label = tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]], [3, 3]);
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const y = tf.losses.sigmoidCrossEntropy(label, predictions);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 0);
| });
| it('Weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
| const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
| const y = tf.losses.sigmoidCrossEntropy(label, predictions, weights);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 1.3333424);
| });
| it('Weighted - Reduction.NONE', async () => {
| const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
| const y = tf.losses.sigmoidCrossEntropy(label, predictions, weights, undefined, tf.Reduction.NONE);
| expect(y.shape).toEqual([3, 3]);
| expectArraysClose(await y.data(), [
| 1.0000046, 9.0797803e-06, 3.0000138e+00, 1.0000046e+00, 2.0000093e+00,
| 1.3619671e-05, 4.5398901e-06, 2.0000093e+00, 3.0000138e+00
| ]);
| });
| it('Reduction.MEAN', async () => {
| const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const y = tf.losses.sigmoidCrossEntropy(label, predictions, undefined, undefined, tf.Reduction.MEAN);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 6.6667123);
| });
| it('Weighted - Reduction.MEAN', async () => {
| const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
| const y = tf.losses.sigmoidCrossEntropy(label, predictions, weights, undefined, tf.Reduction.MEAN);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 6.666712284088135);
| });
| it('Label Smoothing - Weighted - Reduction.MEAN', async () => {
| const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
| const labelSmoothing = 0.3;
| const y = tf.losses.sigmoidCrossEntropy(label, predictions, weights, labelSmoothing, tf.Reduction.MEAN);
| expect(y.shape).toEqual([]);
| expectArraysClose(await y.data(), 6.1667128);
| });
| it('throws when multiClassLabels and logits are of different shapes', () => {
| const multiClassLabels = tf.tensor2d([10, 10, 10, 10, 10, 10, 10, 10, 10], [3, 3]);
| const logits = tf.tensor2d([10, 10, 10, 10, 10, 10], [2, 3]);
| const e = new RegExp('Error in sigmoidCrossEntropy: Shapes 3,3 and 2,3 must match');
| expect(() => tf.losses.sigmoidCrossEntropy(multiClassLabels, logits))
| .toThrowError(e);
| });
| it('throws when passed multiClassLabels as a non-tensor', () => {
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
| const e = new RegExp('Argument \'multiClassLabels\' passed to \'sigmoidCrossEntropy\' ' +
| 'must be a Tensor');
| expect(() => tf.losses.sigmoidCrossEntropy({}, predictions, weights, tf.Reduction.MEAN))
| .toThrowError(e);
| });
| it('throws when passed logits as a non-tensor', () => {
| const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
| const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
| const e = new RegExp('Argument \'logits\' passed to \'sigmoidCrossEntropy\' ' +
| 'must be a Tensor');
| expect(() => tf.losses.sigmoidCrossEntropy(label, {}, weights, tf.Reduction.MEAN))
| .toThrowError(e);
| });
| it('throws when passed weights as a non-tensor', () => {
| const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
| const predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
| const e = /Argument 'weights' passed to 'sigmoidCrossEntropy' must be a Tensor/;
| expect(() => tf.losses.sigmoidCrossEntropy(label, predictions, {}, tf.Reduction.MEAN))
| .toThrowError(e);
| });
| });
| //# 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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 */\nimport * as tf from '../../index';\nimport {ALL_ENVS, describeWithFlags} from '../../jasmine_util';\nimport {expectArraysClose} from '../../test_util';\n\ndescribeWithFlags('sigmoidCrossEntropy', ALL_ENVS, () => {\n  it('All wrong', async () => {\n    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n\n    const y = tf.losses.sigmoidCrossEntropy(label, predictions);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 6.6667123);\n  });\n\n  it('All right', async () => {\n    const label = tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]], [3, 3]);\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n\n    const y = tf.losses.sigmoidCrossEntropy(label, predictions);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 0);\n  });\n\n  it('Weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {\n    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n\n    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);\n\n    const y = tf.losses.sigmoidCrossEntropy(label, predictions, weights);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 1.3333424);\n  });\n\n  it('Weighted - Reduction.NONE', async () => {\n    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);\n\n    const y = tf.losses.sigmoidCrossEntropy(\n        label, predictions, weights, undefined, tf.Reduction.NONE);\n\n    expect(y.shape).toEqual([3, 3]);\n    expectArraysClose(await y.data(), [\n      1.0000046, 9.0797803e-06, 3.0000138e+00, 1.0000046e+00, 2.0000093e+00,\n      1.3619671e-05, 4.5398901e-06, 2.0000093e+00, 3.0000138e+00\n    ]);\n  });\n\n  it('Reduction.MEAN', async () => {\n    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n\n    const y = tf.losses.sigmoidCrossEntropy(\n        label, predictions, undefined, undefined, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 6.6667123);\n  });\n\n  it('Weighted - Reduction.MEAN', async () => {\n    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);\n\n    const y = tf.losses.sigmoidCrossEntropy(\n        label, predictions, weights, undefined, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(\n        await y.data(),\n        6.666712284088135,\n    );\n  });\n\n  it('Label Smoothing - Weighted - Reduction.MEAN', async () => {\n    const label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);\n    const labelSmoothing = 0.3;\n\n    const y = tf.losses.sigmoidCrossEntropy(\n        label, predictions, weights, labelSmoothing, tf.Reduction.MEAN);\n\n    expect(y.shape).toEqual([]);\n    expectArraysClose(await y.data(), 6.1667128);\n  });\n\n  it('throws when multiClassLabels and logits are of different shapes', () => {\n    const multiClassLabels =\n        tf.tensor2d([10, 10, 10, 10, 10, 10, 10, 10, 10], [3, 3]);\n    const logits = tf.tensor2d([10, 10, 10, 10, 10, 10], [2, 3]);\n\n    const e = new RegExp(\n        'Error in sigmoidCrossEntropy:  Shapes 3,3 and 2,3 must match');\n    expect(() => tf.losses.sigmoidCrossEntropy(multiClassLabels, logits))\n        .toThrowError(e);\n  });\n\n  it('throws when passed multiClassLabels as a non-tensor', () => {\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n    const weights = tf.tensor2d([[0.1, 0.2, 0.3]]);\n\n    const e = new RegExp(\n        'Argument \\'multiClassLabels\\' passed to \\'sigmoidCrossEntropy\\' ' +\n        'must be a Tensor');\n\n    expect(\n        () => tf.losses.sigmoidCrossEntropy(\n            {} as tf.Tensor, predictions, weights, tf.Reduction.MEAN))\n        .toThrowError(e);\n  });\n\n  it('throws when passed logits as a non-tensor', () => {\n    const label = 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 e = new RegExp(\n        'Argument \\'logits\\' passed to \\'sigmoidCrossEntropy\\' ' +\n        'must be a Tensor');\n    expect(\n        () => tf.losses.sigmoidCrossEntropy(\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 label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);\n    const predictions = tf.tensor2d(\n        [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]],\n        [3, 3]);\n\n    const e =\n        /Argument 'weights' passed to 'sigmoidCrossEntropy' must be a Tensor/;\n    expect(\n        () => tf.losses.sigmoidCrossEntropy(\n            label, predictions, {} as tf.Tensor, tf.Reduction.MEAN))\n        .toThrowError(e);\n  });\n});\n"]}
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