@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('AdagradOptimizer', ALL_ENVS, () => {
it('basic', async () => {
const learningRate = .1;
const initialAccumulatorValue = .1;
const optimizer = tf.train.adagrad(learningRate, initialAccumulatorValue);
const x = tf.tensor1d([1, 2]).variable();
const f = () => x.square().sum();
let numTensors = tf.memory().numTensors;
let cost = optimizer.minimize(f, /* returnCost */ true);
// Cost & accumulator should be the only additional arrays.
expect(tf.memory().numTensors).toBe(numTensors + 2);
// epsilon = 1-e8
// newAccumulatedGrad = accumulatedGrad + grad^2
// x -= (learningRate * grad) / sqrt(newAccumulatedGrad + eps)
// de/dx = [2, 4]
// accumulatedGrad = [0.1, 0.1]
// newAccumulatedGrad = [4.1, 16.1]
// x = [0.9012270405, 1.900311042]
expectArraysClose(await x.data(), [0.9012270405, 1.9003110428]);
cost.dispose();
numTensors = tf.memory().numTensors;
cost = optimizer.minimize(f, /* returnCost */ false);
// de/dx = [1.802454081, 3.9501555214]
// accumulatedGrad = [4.1, 16.1]
// newAccumulatedGrad = [7.3488407141, 31.7037286432]
// x = [0.8347372764, 1.83015597828]
// TODO: Fix numerical precision.
expectArraysClose(await x.data(), [0.8347372764, 1.83015597828], 1e-2);
// 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 is the argument to variable().
expect(tf.memory().numTensors).toBe(1);
});
it('Continue training after loading weights', async () => {
const learningRate = .1;
const initialAccumulatorValue = .1;
const optimizer1 = tf.train.adagrad(learningRate, initialAccumulatorValue);
const x = tf.tensor1d([2, 4]).variable();
const f = () => x.square().sum();
let cost = optimizer1.minimize(f, /* returnCost */ true);
expectArraysClose(await cost.data(), 20);
const weights = await optimizer1.getWeights();
expect(weights.length).toEqual(2);
expect(weights[0].name).toEqual('iter');
expect(weights[1].name).toEqual(`${x.name}/accumulator`);
const optimizer2 = tf.train.adam(learningRate, initialAccumulatorValue);
await optimizer2.setWeights(weights);
cost = optimizer2.minimize(f, /* returnCost */ true);
expectArraysClose(await cost.data(), 18.82179);
expect(optimizer2.iterations).toEqual(2);
});
it('serialization round-trip', () => {
const originalOpt = tf.train.adagrad(0.1, 0.2);
const reserialized = tf.AdagradOptimizer.fromConfig(tf.AdagradOptimizer, originalOpt.getConfig());
expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
});
});
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