@tensorflow/tfjs-core/dist/ops/log_test.js

/**
 * @license
 * Copyright 2020 Google Inc. 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('log', ALL_ENVS, () => {
    it('log', async () => {
        const a = tf.tensor1d([1, 2]);
        const r = tf.log(a);
        expectArraysClose(await r.data(), [Math.log(1), Math.log(2)]);
    });
    it('log 6D', async () => {
        const a = tf.range(1, 65).reshape([2, 2, 2, 2, 2, 2]);
        const r = tf.log(a);
        const expectedResult = [];
        for (let i = 1; i < 65; i++) {
            expectedResult[i - 1] = Math.log(i);
        }
        expectArraysClose(await r.data(), expectedResult);
    });
    it('log propagates NaNs', async () => {
        const a = tf.tensor1d([1, NaN]);
        const r = tf.log(a);
        expectArraysClose(await r.data(), [Math.log(1), NaN]);
    });
    it('gradients: Scalar', async () => {
        const a = tf.scalar(5);
        const dy = tf.scalar(3);
        const gradients = tf.grad(a => tf.log(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [3 / 5]);
    });
    it('gradient with clones', async () => {
        const a = tf.scalar(5);
        const dy = tf.scalar(3);
        const gradients = tf.grad(a => tf.log(a.clone()).clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [3 / 5]);
    });
    it('gradients: Tensor1D', async () => {
        const a = tf.tensor1d([-1, 2, 3, -5]);
        const dy = tf.tensor1d([1, 2, 3, 4]);
        const gradients = tf.grad(a => tf.log(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [1 / -1, 2 / 2, 3 / 3, 4 / -5]);
    });
    it('gradients: Tensor2D', async () => {
        const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
        const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
        const gradients = tf.grad(a => tf.log(a))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [1 / -3, 2 / 1, 3 / 2, 4 / 3]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.log({}))
            .toThrowError(/Argument 'x' passed to 'log' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const r = tf.log([1, 2]);
        expectArraysClose(await r.data(), [Math.log(1), Math.log(2)]);
    });
    it('throws for string tensor', () => {
        expect(() => tf.log('q'))
            .toThrowError(/Argument 'x' passed to 'log' must be numeric/);
    });
});
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