@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2018 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('AdamaxOptimizer', ALL_ENVS, () => {
it('basic', async () => {
const learningRate = 0.1;
const beta1 = 0.8;
const beta2 = 0.9;
const decay = 0.1;
const optimizer = tf.train.adamax(learningRate, beta1, beta2, undefined, decay);
const x = tf.tensor1d([2, 4]).variable();
const f = () => x.square().sum();
let numTensors = tf.memory().numTensors;
let cost = optimizer.minimize(f, /* returnCost */ true);
// Cost & 2 accumulators should be the only additional arrays.
expect(tf.memory().numTensors).toBe(numTensors + 3);
// new_first_m = [
// beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
// beta1 * old_first_m_w2 + (1-beta1) * grad_w2
// ] = [.8, 1.6]
//
// ut_0 = beta2 * old_weighted_inf_norm = [0, 0]
// u1_1 = [
// abs(grad_w1),
// abs(grad_w2)
// ] = [4, 8]
// new_weighted_inf_norm = max(ut_0, ut_1) = [4, 8]
//
// coefficient = alpha / (1-beta1) = 0.5
// updates = coefficient * [
// new_first_m1 / new_weighted_inf_norm1,
// new_first_m2 / new_weighted_inf_norm2
// ] = [0.1, 0.1]
// w = [
// w1_old - updates_1,
// w2_old - updates_2
// ] = [1.9, 3.9]
//
expectArraysClose(await x.data(), [1.9, 3.9]);
cost.dispose();
numTensors = tf.memory().numTensors;
cost = optimizer.minimize(f, /* returnCost */ false);
// gradient = [3.8, 7.8]
// new_first_m = [
// beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
// beta1 * old_first_m_w2 + (1-beta1) * grad_w2
// ] = [
// 0.8 * 0.8 + 0.2 * 3.8,
// 0.8 * 1.6 + 0.2 * 7.8
// ] = [1.4, 2.84]
//
// ut_0 = beta2 * old_weighted_inf_norm = [
// 0.9 * 4,
// 0.9 * 8
// ] = [3.6, 7.2]
// u1_1 = [
// abs(grad_w1),
// abs(grad_w2)
// ] = [3.8, 7.8]
// new_weighted_inf_norm = max(ut_0, ut_1) = [3.8, 7.8]
//
// alpha = 0.1 / (1 + 0.1 * 1) = 0.0909090909
//
// coefficient = alpha / (1 - beta1*beta1) = 0.25252525
// updates = coefficient * [
// new_first_m1 / new_weighted_inf_norm1,
// new_first_m2 / new_weighted_inf_norm2
// ] = [0.09303, 0.09194]
// w = [
// w1_old - updates_1,
// w2_old - updates_2
// ] = [1.80697, 3.8086]
//
expectArraysClose(await x.data(), [1.80697, 3.8086]);
// There should be no new additional Tensors.
expect(tf.memory().numTensors).toBe(numTensors);
expect(cost).toBe(null);
x.dispose();
optimizer.dispose();
// The only tensor remaining should be the argument to variable().
expect(tf.memory().numTensors).toBe(1);
});
it('serialization round-trip', () => {
const originalOpt = tf.train.adamax(0.1, 0.2, 0.3, 2e-8, 0.1);
const reserialized = tf.AdamaxOptimizer.fromConfig(tf.AdamaxOptimizer, originalOpt.getConfig());
expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
});
});
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