@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2017 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('softmax', ALL_ENVS, () => {
it('regular test', async () => {
const y = tf.softmax(tf.tensor1d([2, 1, 3]));
expectArraysClose(await y.data(), [0.24472847, 0.09003057, 0.66524095]);
expectArraysClose(await y.sum().data(), 1);
});
it('overflow', async () => {
const y = tf.softmax(tf.tensor1d([100, 100]));
expectArraysClose(await y.data(), [0.5, 0.5]);
});
it('underflow', async () => {
const y = tf.softmax(tf.tensor1d([-100, -100]));
expectArraysClose(await y.data(), [0.5, 0.5]);
});
it('Huge difference between probabilities', async () => {
const y = tf.softmax(tf.tensor1d([-1000, +1000]));
expectArraysClose(await y.data(), [0, 1]);
});
it('Propagates NaNs', async () => {
const a = tf.tensor1d([2, 1, NaN]);
const y = tf.softmax(a);
expectArraysClose(await y.data(), [NaN, NaN, NaN]);
});
it('2D, dim=1', async () => {
const y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);
const expected = [
0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847
];
expect(y.rank).toBe(2);
expectArraysClose(await y.data(), expected);
});
it('2D, implicit dim=1', async () => {
const y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));
const expected = [
0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847
];
expect(y.rank).toBe(2);
expectArraysClose(await y.data(), expected);
});
it('2D, dim=0 throws error', () => {
const f = () => {
tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);
};
expect(f).toThrowError();
});
it('1D gradient', async () => {
const x = tf.tensor1d([10, 0, -1]);
const y = tf.softmax(x);
const dy = tf.tensor1d([1, 2, 3]);
const dx = tf.grad((x) => x.softmax())(x, dy);
const totalSum = tf.sum(tf.mul(dy, y));
const dyVals = await dy.array();
const sumVals = await totalSum.array();
const yVals = await y.array();
expect(dx.shape).toEqual(x.shape);
expectArraysClose(await dx.data(), [
(dyVals[0] - sumVals) * yVals[0],
(dyVals[1] - sumVals) * yVals[1],
(dyVals[2] - sumVals) * yVals[2],
]);
});
it('gradient with clones', () => {
const x = tf.tensor1d([10, 0, -1]);
const dx = tf.grad((x) => x.clone().softmax().clone())(x);
expect(dx.shape).toEqual(x.shape);
expect(dx.dtype).toBe('float32');
});
it('2D gradient', async () => {
const x = tf.tensor2d([10, 0, -1, 5, 4, 3], [2, 3]);
const y = tf.softmax(x);
const dy = tf.tensor2d([3, 2, 1, 1, 2, 3], [2, 3]);
const dx = tf.grad((x) => x.softmax())(x, dy);
const axis = -1;
const totalSum = tf.sum(tf.mul(dy, y), axis);
const dyVals = await dy.array();
const sumVals = await totalSum.array();
const yVals = await y.array();
expect(dx.shape).toEqual(x.shape);
expectArraysClose(await dx.data(), [
(dyVals[0][0] - sumVals[0]) * yVals[0][0],
(dyVals[0][1] - sumVals[0]) * yVals[0][1],
(dyVals[0][2] - sumVals[0]) * yVals[0][2],
(dyVals[1][0] - sumVals[1]) * yVals[1][0],
(dyVals[1][1] - sumVals[1]) * yVals[1][1],
(dyVals[1][2] - sumVals[1]) * yVals[1][2]
]);
});
it('throws when passed a non-tensor', () => {
expect(() => tf.softmax({}))
.toThrowError(/Argument 'logits' passed to 'softmax' must be a Tensor/);
});
it('accepts a tensor-like object', async () => {
const y = tf.softmax([2, 1, 3]);
expectArraysClose(await y.data(), [0.24472847, 0.09003057, 0.66524095]);
expectArraysClose(await y.sum().data(), 1);
});
});
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