gx

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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('AdadeltaOptimizer', ALL_ENVS, () => {
    it('basic', async () => {
        const initialTensors = tf.memory().numTensors;
        const learningRate = .1;
        const rho = .95;
        const optimizer = tf.train.adadelta(learningRate, rho);
        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 & 2 accumulators should be the only additional arrays.
        expect(tf.memory().numTensors).toBe(numTensors + 3);
        // epsilon = 1-e8
        // newAccumulatedGrad = rho * accumulatedGrad + (1 - rho) * grad ^ 2
        // updates = -grad * sqrt(accumulatedUpdate + epsilon) /
        //     sqrt(accumulatedGrad + epsilon)
        // newAccumulatedUpdate = rho * accumulatedUpdate + (1 - rho) * updates ^ 2
        // x += learningRate * updates
        //
        // de/dx = [2, 4]
        // accumulatedGrad = [0, 0]
        // newAccumulatedGrad = [.2, .8]
        // updates = [-2, -4]
        // newAccumulatedUpdate = [.2, .8]
        // x = [0.8, 1.6]
        expectArraysClose(await x.data(), [0.8, 1.6]);
        cost.dispose();
        numTensors = tf.memory().numTensors;
        cost = optimizer.minimize(f, /* returnCost */ false);
        // de/dx = [1.6, 3.2]
        // accumulatedGrad = [.2, .8]
        // accumulatedUpdate = [.2, .8]
        // newAccumulatedGrad = [0.318, 1.272]
        // updates = [-1.6, -3.2]
        // x = [0.64, 1.28]
        expectArraysClose(await x.data(), [0.64, 1.28]);
        // 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 is the argument to variable().
        expect(tf.memory().numTensors).toBe(initialTensors + 1);
    });
    it('Save, load weights and continue training', async () => {
        const learningRate = .1;
        const rho = .95;
        const optimizer1 = tf.train.adadelta(learningRate, rho);
        const x = tf.tensor1d([1, 2]).variable();
        const f = () => x.square().sum();
        let cost = optimizer1.minimize(f, /* returnCost */ true);
        expectArraysClose(await cost.data(), 5);
        expectArraysClose(await x.data(), [0.8, 1.6]);
        const weights = await optimizer1.getWeights();
        expect(weights.length).toEqual(3);
        expect(weights[0].name).toEqual('iter');
        const optimizer2 = tf.train.adadelta(learningRate, rho);
        await optimizer2.setWeights(weights);
        cost = optimizer2.minimize(f, /* returnCost */ true);
        expectArraysClose(await cost.data(), 3.2);
        expectArraysClose(await x.data(), [0.64, 1.28]);
        expect(optimizer2.iterations).toEqual(2);
    });
    it('serialization round-trip', () => {
        const originalOpt = tf.train.adadelta(0.1, 0.2, 2e-8);
        const reserialized = tf.AdadeltaOptimizer.fromConfig(tf.AdadeltaOptimizer, 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('AdadeltaOptimizer', ALL_ENVS, () => {\n  it('basic', async () => {\n    const initialTensors = tf.memory().numTensors;\n    const learningRate = .1;\n    const rho = .95;\n    const optimizer = tf.train.adadelta(learningRate, rho);\n\n    const x = tf.tensor1d([1, 2]).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\n    // epsilon = 1-e8\n    // newAccumulatedGrad = rho * accumulatedGrad + (1 - rho) * grad ^ 2\n    // updates = -grad * sqrt(accumulatedUpdate + epsilon) /\n    //     sqrt(accumulatedGrad + epsilon)\n    // newAccumulatedUpdate = rho * accumulatedUpdate + (1 - rho) * updates ^ 2\n    // x += learningRate * updates\n    //\n    // de/dx = [2, 4]\n    // accumulatedGrad = [0, 0]\n    // newAccumulatedGrad = [.2, .8]\n    // updates = [-2, -4]\n    // newAccumulatedUpdate = [.2, .8]\n    // x = [0.8, 1.6]\n    expectArraysClose(await x.data(), [0.8, 1.6]);\n\n    cost.dispose();\n    numTensors = tf.memory().numTensors;\n\n    cost = optimizer.minimize(f, /* returnCost */ false);\n\n    // de/dx = [1.6, 3.2]\n    // accumulatedGrad = [.2, .8]\n    // accumulatedUpdate = [.2, .8]\n    // newAccumulatedGrad = [0.318, 1.272]\n    // updates = [-1.6, -3.2]\n    // x = [0.64, 1.28]\n    expectArraysClose(await x.data(), [0.64, 1.28]);\n\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 is the argument to variable().\n    expect(tf.memory().numTensors).toBe(initialTensors + 1);\n  });\n\n  it('Save, load weights and continue training', async () => {\n    const learningRate = .1;\n    const rho = .95;\n    const optimizer1 = tf.train.adadelta(learningRate, rho);\n\n    const x = tf.tensor1d([1, 2]).variable();\n    const f: () => tf.Scalar = () => x.square().sum();\n\n    let cost = optimizer1.minimize(f, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 5);\n    expectArraysClose(await x.data(), [0.8, 1.6]);\n\n    const weights = await optimizer1.getWeights();\n    expect(weights.length).toEqual(3);\n    expect(weights[0].name).toEqual('iter');\n\n    const optimizer2 = tf.train.adadelta(learningRate, rho);\n    await optimizer2.setWeights(weights);\n\n    cost = optimizer2.minimize(f, /* returnCost */ true);\n    expectArraysClose(await cost.data(), 3.2);\n    expectArraysClose(await x.data(), [0.64, 1.28]);\n    expect(optimizer2.iterations).toEqual(2);\n  });\n\n  it('serialization round-trip', () => {\n    const originalOpt = tf.train.adadelta(0.1, 0.2, 2e-8);\n    const reserialized = tf.AdadeltaOptimizer.fromConfig(\n        tf.AdadeltaOptimizer, originalOpt.getConfig());\n    expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());\n  });\n});\n"]}