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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 { ENGINE } from '../engine';
import { dispose, tidy } from '../globals';
import { add } from '../ops/add';
import { div } from '../ops/div';
import { mul } from '../ops/mul';
import { sqrt } from '../ops/ops';
import { square } from '../ops/square';
import { zerosLike } from '../ops/zeros_like';
import { Optimizer } from './optimizer';
/** @doclink Optimizer */
export class AdadeltaOptimizer extends Optimizer {
    /** @nocollapse */
    static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return 'Adadelta';
    }
    constructor(learningRate, rho, epsilon = null) {
        super();
        this.learningRate = learningRate;
        this.rho = rho;
        this.epsilon = epsilon;
        this.accumulatedGrads = [];
        this.accumulatedUpdates = [];
        if (epsilon == null) {
            this.epsilon = ENGINE.backend.epsilon();
        }
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ?
            variableGradients.map(item => item.name) :
            Object.keys(variableGradients);
        variableNames.forEach((name, i) => {
            const value = ENGINE.registeredVariables[name];
            const trainable = false;
            if (this.accumulatedGrads[i] == null) {
                this.accumulatedGrads[i] = {
                    originalName: `${name}/accum_grad`,
                    variable: tidy(() => zerosLike(value).variable(trainable))
                };
            }
            if (this.accumulatedUpdates[i] == null) {
                this.accumulatedUpdates[i] = {
                    originalName: `${name}/accum_var`,
                    variable: tidy(() => zerosLike(value).variable(trainable))
                };
            }
            const gradient = Array.isArray(variableGradients) ?
                variableGradients[i].tensor :
                variableGradients[name];
            if (gradient == null) {
                return;
            }
            const accumulatedGrad = this.accumulatedGrads[i].variable;
            const accumulatedUpdate = this.accumulatedUpdates[i].variable;
            tidy(() => {
                const newAccumulatedGrad = add(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
                const updates = mul(div(sqrt(add(accumulatedUpdate, this.epsilon)), sqrt(add(accumulatedGrad, this.epsilon))), gradient);
                const newAccumulatedUpdate = add(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));
                accumulatedGrad.assign(newAccumulatedGrad);
                accumulatedUpdate.assign(newAccumulatedUpdate);
                const newValue = add(mul(updates, -this.learningRate), value);
                value.assign(newValue);
            });
        });
        this.incrementIterations();
    }
    dispose() {
        if (this.accumulatedUpdates != null) {
            dispose(this.accumulatedGrads.map(v => v.variable));
            dispose(this.accumulatedUpdates.map(v => v.variable));
        }
    }
    async getWeights() {
        // Order matters for Python compatibility.
        const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        const variableCount = weightValues.length / 2;
        const trainable = false;
        this.accumulatedGrads =
            weightValues.slice(0, variableCount).map(v => ({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
        this.accumulatedUpdates =
            weightValues.slice(variableCount, variableCount * 2)
                .map(v => ({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
    }
    getConfig() {
        return {
            'learningRate': this.learningRate,
            'rho': this.rho,
            'epsilon': this.epsilon
        };
    }
    /** @nocollapse */
    static fromConfig(cls, config) {
        return new cls(config['learningRate'], config['rho'], config['epsilon']);
    }
}
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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 {ENGINE} from '../engine';\nimport {dispose, tidy} from '../globals';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/ops';\nimport {square} from '../ops/square';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n  /** @nocollapse */\n  static get className() {\n    // Name matters for Python compatibility.\n    // This is a getter instead of a property because when it's a property, it\n    // prevents the entire class from being tree-shaken.\n    return 'Adadelta';\n  }\n  private accumulatedGrads: OptimizerVariable[] = [];\n  private accumulatedUpdates: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected rho: number,\n      protected epsilon: number = null) {\n    super();\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const variableNames = Array.isArray(variableGradients) ?\n        variableGradients.map(item => item.name) :\n        Object.keys(variableGradients);\n\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedGrads[i] == null) {\n        this.accumulatedGrads[i] = {\n          originalName: `${name}/accum_grad`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedUpdates[i] == null) {\n        this.accumulatedUpdates[i] = {\n          originalName: `${name}/accum_var`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n\n      const gradient = Array.isArray(variableGradients) ?\n          variableGradients[i].tensor :\n          variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n\n      const accumulatedGrad = this.accumulatedGrads[i].variable;\n      const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n\n      tidy(() => {\n        const newAccumulatedGrad =\n            add(mul(accumulatedGrad, this.rho),\n                mul(square(gradient), 1 - this.rho));\n\n        const updates =\n            mul(div(sqrt(add(accumulatedUpdate, this.epsilon)),\n                    sqrt(add(accumulatedGrad, this.epsilon))),\n                gradient);\n\n        const newAccumulatedUpdate =\n            add(mul(accumulatedUpdate, this.rho),\n                mul(square(updates), 1 - this.rho));\n\n        accumulatedGrad.assign(newAccumulatedGrad);\n        accumulatedUpdate.assign(newAccumulatedUpdate);\n\n        const newValue = add(mul(updates, -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  override dispose(): void {\n    if (this.accumulatedUpdates != null) {\n      dispose(this.accumulatedGrads.map(v => v.variable));\n      dispose(this.accumulatedUpdates.map(v => v.variable));\n    }\n  }\n\n  override async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    const variables: OptimizerVariable[] =\n        [...this.accumulatedGrads, ...this.accumulatedUpdates];\n    return [await this.saveIterations()].concat(\n        variables.map(v => ({name: v.originalName, tensor: v.variable})));\n  }\n\n  override async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedGrads =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedUpdates =\n        weightValues.slice(variableCount, variableCount * 2)\n            .map(v => ({\n                   originalName: v.name,\n                   variable: v.tensor.variable(trainable)\n                 }));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'rho': this.rho,\n      'epsilon': this.epsilon\n    };\n  }\n\n  /** @nocollapse */\n  static override fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config['learningRate'], config['rho'], config['epsilon']);\n  }\n}\n"]}