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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 { fill } from '../ops/fill';
import { mul } from '../ops/mul';
import { sqrt } from '../ops/sqrt';
import { square } from '../ops/square';
import { Optimizer } from './optimizer';
/** @doclink Optimizer */
export class AdagradOptimizer 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 'Adagrad';
    }
    constructor(learningRate, initialAccumulatorValue = 0.1) {
        super();
        this.learningRate = learningRate;
        this.initialAccumulatorValue = initialAccumulatorValue;
        this.accumulatedGrads = [];
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ?
            variableGradients.map(item => item.name) :
            Object.keys(variableGradients);
        variableNames.forEach((name, i) => {
            const value = ENGINE.registeredVariables[name];
            if (this.accumulatedGrads[i] == null) {
                const trainable = false;
                this.accumulatedGrads[i] = {
                    originalName: `${name}/accumulator`,
                    variable: tidy(() => fill(value.shape, this.initialAccumulatorValue)
                        .variable(trainable))
                };
            }
            const gradient = Array.isArray(variableGradients) ?
                variableGradients[i].tensor :
                variableGradients[name];
            if (gradient == null) {
                return;
            }
            const accumulatedGrad = this.accumulatedGrads[i].variable;
            tidy(() => {
                const newAccumulatedGrad = add(accumulatedGrad, square(gradient));
                accumulatedGrad.assign(newAccumulatedGrad);
                const newValue = add(mul(div(gradient, sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);
                value.assign(newValue);
            });
        });
        this.incrementIterations();
    }
    dispose() {
        if (this.accumulatedGrads != null) {
            dispose(this.accumulatedGrads.map(v => v.variable));
        }
    }
    async getWeights() {
        // Order matters for Python compatibility.
        return [await this.saveIterations()].concat(this.accumulatedGrads.map(v => ({ name: v.originalName, tensor: v.variable })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        const trainable = false;
        this.accumulatedGrads = weightValues.map(v => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));
    }
    getConfig() {
        return {
            'learningRate': this.learningRate,
            'initialAccumulatorValue': this.initialAccumulatorValue,
        };
    }
    /** @nocollapse */
    static fromConfig(cls, config) {
        return new cls(config['learningRate'], config['initialAccumulatorValue']);
    }
}
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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 {fill} from '../ops/fill';\nimport {mul} from '../ops/mul';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\nimport {ConfigDict, Serializable, SerializableConstructor} from '../serialization';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdagradOptimizer 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 'Adagrad';\n  }\n\n  private accumulatedGrads: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, private initialAccumulatorValue = 0.1) {\n    super();\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      if (this.accumulatedGrads[i] == null) {\n        const trainable = false;\n        this.accumulatedGrads[i] = {\n          originalName: `${name}/accumulator`,\n          variable: tidy(\n              () => fill(value.shape, this.initialAccumulatorValue)\n                        .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\n      tidy(() => {\n        const newAccumulatedGrad = add(accumulatedGrad, square(gradient));\n        accumulatedGrad.assign(newAccumulatedGrad);\n\n        const newValue = add(\n            mul(div(gradient,\n                    sqrt(add(newAccumulatedGrad, ENGINE.backend.epsilon()))),\n                -this.learningRate),\n            value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n\n  override dispose(): void {\n    if (this.accumulatedGrads != null) {\n      dispose(this.accumulatedGrads.map(v => v.variable));\n    }\n  }\n\n  override async getWeights(): Promise<NamedTensor[]> {\n    // Order matters for Python compatibility.\n    return [await this.saveIterations()].concat(this.accumulatedGrads.map(\n        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 trainable = false;\n    this.accumulatedGrads = weightValues.map(\n        v => ({originalName: v.name, variable: v.tensor.variable(trainable)}));\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'initialAccumulatorValue': this.initialAccumulatorValue,\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['initialAccumulatorValue']);\n  }\n}\n"]}