@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('AdamOptimizer', ALL_ENVS, () => {
it('basic', async () => {
const learningRate = .1;
const beta1 = .8;
const beta2 = .9;
const optimizer = tf.train.adam(learningRate, beta1, beta2);
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]
// new_second_m = [
// beta2 * old_second_m_w1 + (1-beta2) * grad_w1**2,
// beta2 * old_second_m_w2 + (1-beta2) * grad_w2**2
// ] = [1.6, 6.4]
// m = [new_first_m/(1-acc_beta1)] = [4, 8]
// v = [new_second_m/(1-acc_beta2)] = [16, 64]
// x = [x - lr * m / sqrt(v)] = [1.9, 3.9]
//
expectArraysClose(await x.data(), [1.9, 3.9]);
cost.dispose();
numTensors = tf.memory().numTensors;
cost = optimizer.minimize(f, /* returnCost */ false);
// new_first_m = [
// beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
// beta1 * old_first_m_w2 + (1-beta1) * grad_w2
// ] = [1.4, 2.84]
// new_second_m = [
// beta2 * old_second_m_w1 + (1-beta2) * grad_w1**2,
// beta2 * old_second_m_w2 + (1-beta2) * grad_w2**2
// ] = [2.884, 11.884]
// m = [new_first_m/(1-acc_beta1)] = [3.888888, 7.88889]
// v = [new_second_m/(1-acc_beta2)] = [15.1789, 62.5473]
// x = [x - lr * m / sqrt(v)] = [1.8000001, 3.8002]
//
expectArraysClose(await x.data(), [1.8000001, 3.8002]);
// 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('Continue training after loading weights', async () => {
const learningRate = .1;
const beta1 = .8;
const beta2 = .9;
const optimizer1 = tf.train.adam(learningRate, beta1, beta2);
const x = tf.tensor1d([2, 4]).variable();
const f = () => x.square().sum();
let cost = optimizer1.minimize(f, /* returnCost */ true);
expect(optimizer1.iterations).toEqual(1);
expectArraysClose(await cost.data(), 20);
const weights = await optimizer1.getWeights();
expect(weights.length).toEqual(3);
expect(weights[0].name).toEqual('iter');
expect(weights[1].name).toEqual(`${x.name}/m`);
expect(weights[2].name).toEqual(`${x.name}/v`);
const optimizer2 = tf.train.adam(learningRate, beta1, beta2);
await optimizer2.setWeights(weights);
cost = optimizer2.minimize(f, /* returnCost */ true);
expectArraysClose(await cost.data(), 18.82);
expect(optimizer2.iterations).toEqual(2);
const optimizer3 = tf.train.adam(learningRate, beta1, beta2);
await optimizer3.setWeights(await optimizer2.getWeights());
cost = optimizer2.minimize(f, /* returnCost */ true);
expectArraysClose(await cost.data(), 17.681284);
expect(optimizer3.iterations).toEqual(2);
});
it('serialization round-trip', () => {
const originalOpt = tf.train.adam(0.1, 0.2, 0.3, 2e-8);
const reserialized = tf.AdamOptimizer.fromConfig(tf.AdamOptimizer, originalOpt.getConfig());
expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
});
});
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