@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('SGDOptimizer', ALL_ENVS, () => {
it('basic', async () => {
const learningRate = .1;
const optimizer = tf.train.sgd(learningRate);
const x = tf.scalar(4).variable();
let numTensors = tf.memory().numTensors;
let cost = optimizer.minimize(() => x.square(), /* returnCost */ true);
// Cost should be the only additional arrays.
expect(tf.memory().numTensors).toBe(numTensors + 1);
// de/dx = 2x
const expectedValue1 = -2 * 4 * learningRate + 4;
expectArraysClose(await x.data(), [expectedValue1]);
expectArraysClose(await cost.data(), [Math.pow(4, 2)]);
cost.dispose();
numTensors = tf.memory().numTensors;
cost = optimizer.minimize(() => x.square(), /* returnCost */ false);
// There should be no new additional Tensors.
expect(tf.memory().numTensors).toBe(numTensors);
const expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;
expectArraysClose(await x.data(), [expectedValue2]);
expect(cost).toBe(null);
optimizer.dispose();
x.dispose();
// The only tensor remaining is the argument to variable().
expect(tf.memory().numTensors).toBe(1);
});
it('Set and get weights: empty', async () => {
const x = tf.scalar(4).variable();
const learningRate = .1;
const optimizer1 = tf.train.sgd(learningRate);
let weights = await optimizer1.getWeights();
expect(optimizer1.iterations).toEqual(0);
optimizer1.minimize(() => x.square());
weights = await optimizer1.getWeights();
expect(optimizer1.iterations).toEqual(1);
expect(weights.length).toEqual(1);
expect(weights[0].name).toEqual('iter');
expectArraysClose(await weights[0].tensor.data(), 1);
const optimizer2 = tf.train.sgd(learningRate);
await optimizer2.setWeights(weights);
optimizer2.minimize(() => x.square());
expectArraysClose(await x.data(), 2.56);
expect(optimizer2.iterations).toEqual(2);
const optimizer3 = tf.train.sgd(learningRate);
await optimizer3.setWeights(await optimizer2.getWeights());
optimizer3.minimize(() => x.square());
expectArraysClose(await x.data(), 2.048);
expect(optimizer3.iterations).toEqual(3);
});
it('serialization round-trip', () => {
const learningRate = .1;
const originalOpt = tf.train.sgd(learningRate);
const reserialized = tf.SGDOptimizer.fromConfig(tf.SGDOptimizer, originalOpt.getConfig());
expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
});
});
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