gx

chenyc

2025-06-12 7b72ac13a83764a662159d4a49b7fffb90476ecb

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/**
 * @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 initialTensors = tf.memory().numTensors;
        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 additional tensor remaining is the argument to variable().
        expect(tf.memory().numTensors).toBe(initialTensors + 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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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\n\ndescribeWithFlags('SGDOptimizer', ALL_ENVS, () => {\n  it('basic', async () => {\n    const initialTensors = tf.memory().numTensors;\n    const learningRate = .1;\n    const optimizer = tf.train.sgd(learningRate);\n\n    const x = tf.scalar(4).variable();\n\n    let numTensors = tf.memory().numTensors;\n\n    let cost = optimizer.minimize(() => x.square(), /* returnCost */ true);\n\n    // Cost should be the only additional arrays.\n    expect(tf.memory().numTensors).toBe(numTensors + 1);\n\n    // de/dx = 2x\n    const expectedValue1 = -2 * 4 * learningRate + 4;\n    expectArraysClose(await x.data(), [expectedValue1]);\n    expectArraysClose(await cost.data(), [Math.pow(4, 2)]);\n\n    cost.dispose();\n    numTensors = tf.memory().numTensors;\n\n    cost = optimizer.minimize(() => x.square(), /* returnCost */ false);\n    // There should be no new additional Tensors.\n    expect(tf.memory().numTensors).toBe(numTensors);\n\n    const expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;\n    expectArraysClose(await x.data(), [expectedValue2]);\n    expect(cost).toBe(null);\n\n    optimizer.dispose();\n    x.dispose();\n    // The only additional tensor remaining is the argument to variable().\n    expect(tf.memory().numTensors).toBe(initialTensors + 1);\n  });\n\n  it('Set and get weights: empty', async () => {\n    const x = tf.scalar(4).variable();\n\n    const learningRate = .1;\n    const optimizer1 = tf.train.sgd(learningRate);\n\n    let weights = await optimizer1.getWeights();\n    expect(optimizer1.iterations).toEqual(0);\n\n    optimizer1.minimize(() => x.square());\n\n    weights = await optimizer1.getWeights();\n    expect(optimizer1.iterations).toEqual(1);\n    expect(weights.length).toEqual(1);\n    expect(weights[0].name).toEqual('iter');\n    expectArraysClose(await weights[0].tensor.data(), 1);\n\n    const optimizer2 = tf.train.sgd(learningRate);\n    await optimizer2.setWeights(weights);\n    optimizer2.minimize(() => x.square());\n    expectArraysClose(await x.data(), 2.56);\n    expect(optimizer2.iterations).toEqual(2);\n\n    const optimizer3 = tf.train.sgd(learningRate);\n    await optimizer3.setWeights(await optimizer2.getWeights());\n    optimizer3.minimize(() => x.square());\n    expectArraysClose(await x.data(), 2.048);\n    expect(optimizer3.iterations).toEqual(3);\n  });\n\n  it('serialization round-trip', () => {\n    const learningRate = .1;\n    const originalOpt = tf.train.sgd(learningRate);\n    const reserialized =\n        tf.SGDOptimizer.fromConfig(tf.SGDOptimizer, originalOpt.getConfig());\n    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());\n  });\n});\n"]}