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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('AdamOptimizer', ALL_ENVS, () => {
    it('basic', async () => {
        const initialTensors = tf.memory().numTensors;
        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 additional tensor remaining should be the argument to
        // variable().
        expect(tf.memory().numTensors).toBe(initialTensors + 1);
    });
    it('Continue training after loading weights', async () => {
        const initialTensors = tf.memory().numTensors;
        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(initialTensors + 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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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('AdamOptimizer', ALL_ENVS, () => {\n  it('basic', async () => {\n    const initialTensors = tf.memory().numTensors;\n    const learningRate = .1;\n    const beta1 = .8;\n    const beta2 = .9;\n    const optimizer = tf.train.adam(learningRate, beta1, beta2);\n\n    const x = tf.tensor1d([2, 4]).variable();\n\n    const f: () => tf.Scalar = () => x.square().sum();\n\n    let numTensors = tf.memory().numTensors;\n\n    let cost = optimizer.minimize(f, /* returnCost */ true);\n\n    // Cost & 2 accumulators should be the only additional arrays.\n    expect(tf.memory().numTensors).toBe(numTensors + 3);\n    // new_first_m = [\n    //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,\n    //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2\n    // ] = [.8, 1.6]\n    // new_second_m = [\n    //    beta2 * old_second_m_w1 + (1-beta2) * grad_w1**2,\n    //    beta2 * old_second_m_w2 + (1-beta2) * grad_w2**2\n    // ] = [1.6, 6.4]\n    // m = [new_first_m/(1-acc_beta1)] = [4, 8]\n    // v = [new_second_m/(1-acc_beta2)] = [16, 64]\n    // x = [x - lr * m / sqrt(v)] = [1.9, 3.9]\n    //\n    expectArraysClose(await x.data(), [1.9, 3.9]);\n\n    cost.dispose();\n    numTensors = tf.memory().numTensors;\n\n    cost = optimizer.minimize(f, /* returnCost */ false);\n\n    // new_first_m = [\n    //    beta1 * old_first_m_w1 + (1-beta1) * grad_w1,\n    //    beta1 * old_first_m_w2 + (1-beta1) * grad_w2\n    // ] = [1.4, 2.84]\n    // new_second_m = [\n    //    beta2 * old_second_m_w1 + (1-beta2) * grad_w1**2,\n    //    beta2 * old_second_m_w2 + (1-beta2) * grad_w2**2\n    // ] = [2.884, 11.884]\n    // m = [new_first_m/(1-acc_beta1)] = [3.888888, 7.88889]\n    // v = [new_second_m/(1-acc_beta2)] = [15.1789, 62.5473]\n    // x = [x - lr * m / sqrt(v)] = [1.8000001, 3.8002]\n    //\n    expectArraysClose(await x.data(), [1.8000001, 3.8002]);\n    // There should be no new additional Tensors.\n    expect(tf.memory().numTensors).toBe(numTensors);\n\n    expect(cost).toBe(null);\n\n    x.dispose();\n    optimizer.dispose();\n\n    // The only additional tensor remaining should be the argument to\n    // variable().\n    expect(tf.memory().numTensors).toBe(initialTensors + 1);\n  });\n\n  it('Continue training after loading weights', async () => {\n    const initialTensors = tf.memory().numTensors;\n    const learningRate = .1;\n    const beta1 = .8;\n    const beta2 = .9;\n    const optimizer1 = tf.train.adam(learningRate, beta1, beta2);\n\n    const x = tf.tensor1d([2, 4]).variable();\n    const f: () => tf.Scalar = () => x.square().sum();\n    let cost = optimizer1.minimize(f, /* returnCost */ true);\n    expect(optimizer1.iterations).toEqual(1);\n    expectArraysClose(await cost.data(), 20);\n\n    const weights = await optimizer1.getWeights();\n    expect(weights.length).toEqual(3);\n    expect(weights[0].name).toEqual('iter');\n    expect(weights[1].name).toEqual(`${x.name}/m`);\n    expect(weights[2].name).toEqual(`${x.name}/v`);\n\n    const optimizer2 = tf.train.adam(learningRate, beta1, beta2);\n    await optimizer2.setWeights(weights);\n\n    cost = optimizer2.minimize(f, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 18.82);\n    expect(optimizer2.iterations).toEqual(2);\n\n    const optimizer3 = tf.train.adam(learningRate, beta1, beta2);\n    await optimizer3.setWeights(await optimizer2.getWeights());\n    cost = optimizer2.minimize(f, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 17.681284);\n    expect(optimizer3.iterations).toEqual(initialTensors + 2);\n  });\n\n  it('serialization round-trip', () => {\n    const originalOpt = tf.train.adam(0.1, 0.2, 0.3, 2e-8);\n    const reserialized =\n        tf.AdamOptimizer.fromConfig(tf.AdamOptimizer, originalOpt.getConfig());\n    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());\n  });\n});\n"]}