@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
68 lines • 2.98 kB
JavaScript
/**
* @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('logSoftmax', ALL_ENVS, () => {
it('regular test', async () => {
const y = tf.logSoftmax(tf.tensor1d([2, 1, 3]));
expectArraysClose(await y.data(), [-1.407606, -2.4076061, -0.407606]);
});
it('Huge difference', async () => {
const y = tf.logSoftmax(tf.tensor1d([-1000, +1000]));
expectArraysClose(await y.data(), [-2000, 0]);
});
it('Propagates NaNs', async () => {
const a = tf.tensor1d([2, 1, NaN]);
const y = tf.logSoftmax(a);
expectArraysClose(await y.data(), [NaN, NaN, NaN]);
});
it('2D, axis=1', async () => {
const y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);
const expected = [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];
expect(y.rank).toBe(2);
expectArraysClose(await y.data(), expected);
});
it('2D, implicit axis=1', async () => {
const y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));
const expected = [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];
expect(y.rank).toBe(2);
expectArraysClose(await y.data(), expected);
});
it('1D gradient', async () => {
const x = tf.tensor1d([1, 2, 10]);
const dy = tf.tensor1d([1, 2, 3]);
const dx = tf.grad((x) => x.logSoftmax())(x, dy);
expect(dx.shape).toEqual(x.shape);
expectArraysClose(await dx.data(), [0.9992599, 1.9979881, -2.9972477]);
});
it('2D, axis=0 throws error', () => {
const f = () => {
tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);
};
expect(f).toThrowError();
});
it('throws when passed a non-tensor', () => {
expect(() => tf.logSoftmax({}))
.toThrowError(/Argument 'logits' passed to 'logSoftmax' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const y = tf.logSoftmax([2, 1, 3]);
expectArraysClose(await y.data(), [-1.407606, -2.4076061, -0.407606]);
});
});
//# sourceMappingURL=log_softmax_test.js.map