@tensorflow/tfjs-core/dist/ops/losses/log_loss_test.js

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @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('logLoss', ALL_ENVS, () => {
    it('1D', async () => {
        const labels = tf.tensor1d([1, 2, 3]);
        const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
        const y = tf.losses.logLoss(labels, predictions);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 2.668788);
    });
    it('1D - Check for negative values', async () => {
        const labels = tf.tensor1d([1, 2, 3]);
        const predictions = tf.tensor1d([0.3, -0.6, -0.1]);
        const y = tf.losses.logLoss(labels, predictions);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), NaN);
    });
    it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
        const labels = tf.tensor1d([1, 2, 3]);
        const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
        const weights = tf.tensor1d([0.1, 0.2, 0.3]);
        const y = tf.losses.logLoss(labels, predictions, weights);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 0.7168596);
    });
    it('1D - weighted - Reduction.NONE', async () => {
        const labels = tf.tensor1d([1, 2, 3]);
        const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
        const weights = tf.tensor1d([0.1, 0.2, 0.3]);
        const y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
        expect(y.shape).toEqual([3]);
        expectArraysClose(await y.data(), [0.12039725, 0.02107204, 2.0091095]);
    });
    it('1D - Reduction.MEAN', async () => {
        const labels = tf.tensor1d([1, 2, 3]);
        const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
        const y = tf.losses.logLoss(labels, predictions, undefined, undefined, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 2.668788);
    });
    it('1D - weighted - Reduction.MEAN', async () => {
        const labels = tf.tensor1d([1, 2, 3]);
        const predictions = tf.tensor1d([0.3, 0.6, 0.1]);
        const weights = tf.tensor1d([0.1, 0.2, 0.3]);
        const y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 3.5842977);
    });
    it('2D', async () => {
        const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
        const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
        const y = tf.losses.logLoss(labels, predictions);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 0.60019904);
    });
    it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', async () => {
        const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
        const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
        const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
        const y = tf.losses.logLoss(labels, predictions, weights);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 1.8866577);
    });
    it('2D - weighted - Reduction.NONE', async () => {
        const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
        const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
        const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
        const y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
        expect(y.shape).toEqual([2, 3]);
        expectArraysClose(await y.data(), [2.9527497, 0., 1.8451363, 0., 1.3829476, 1.3657978]);
    });
    it('2D - Reduction.MEAN', async () => {
        const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
        const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
        const y = tf.losses.logLoss(labels, predictions, undefined, undefined, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 0.60019904);
    });
    it('2D - weighted - Reduction.MEAN', async () => {
        const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
        const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
        const weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
        const y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.MEAN);
        expect(y.shape).toEqual([]);
        expectArraysClose(await y.data(), 0.53904504);
    });
    it('throws when passed label as a non-tensor', () => {
        const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
        const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
        const e = /Argument 'labels' passed to 'logLoss' must be a Tensor/;
        expect(() => tf.losses.logLoss({}, predictions, weights, tf.Reduction.MEAN))
            .toThrowError(e);
    });
    it('throws when passed label as a non-tensor', () => {
        const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
        const weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
        const e = new RegExp('Argument \'predictions\' passed to \'logLoss\' ' +
            'must be a Tensor');
        expect(() => tf.losses.logLoss(labels, {}, weights, tf.Reduction.MEAN))
            .toThrowError(e);
    });
    it('throws when passed weights as a non-tensor', () => {
        const labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
        const predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
        const e = /Argument 'weights' passed to 'logLoss' must be a Tensor/;
        expect(() => tf.losses.logLoss(labels, predictions, {}, tf.Reduction.MEAN))
            .toThrowError(e);
    });
    it('accepts a tensor-like object', async () => {
        const labels = [1, 2, 3];
        const predictions = [0.3, 0.6, 0.1];
        const weights = [0.1, 0.2, 0.3];
        const y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
        expect(y.shape).toEqual([3]);
        expectArraysClose(await y.data(), [0.12039725, 0.02107204, 2.0091095]);
    });
});
//# sourceMappingURL=log_loss_test.js.map