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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 { abs } from '../ops/abs';
import { add } from '../ops/add';
import { div } from '../ops/div';
import { maximum } from '../ops/maximum';
import { mul } from '../ops/mul';
import { scalar } from '../ops/scalar';
import { sub } from '../ops/sub';
import { zerosLike } from '../ops/zeros_like';
import { Optimizer } from './optimizer';
export class AdamaxOptimizer 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 'Adamax';
    }
    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0) {
        super();
        this.learningRate = learningRate;
        this.beta1 = beta1;
        this.beta2 = beta2;
        this.epsilon = epsilon;
        this.decay = decay;
        this.accumulatedFirstMoment = [];
        this.accumulatedWeightedInfNorm = [];
        tidy(() => {
            this.iteration = scalar(0).variable();
            this.accBeta1 = scalar(beta1).variable();
        });
        if (epsilon == null) {
            this.epsilon = ENGINE.backend.epsilon();
        }
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ?
            variableGradients.map(item => item.name) :
            Object.keys(variableGradients);
        tidy(() => {
            const oneMinusAccBeta1 = sub(1, this.accBeta1);
            const lr = div(-this.learningRate, add(mul(this.iteration, this.decay), 1));
            variableNames.forEach((name, i) => {
                const value = ENGINE.registeredVariables[name];
                const trainable = false;
                if (this.accumulatedFirstMoment[i] == null) {
                    this.accumulatedFirstMoment[i] = {
                        originalName: `${name}/m`,
                        variable: zerosLike(value).variable(trainable)
                    };
                }
                if (this.accumulatedWeightedInfNorm[i] == null) {
                    this.accumulatedWeightedInfNorm[i] = {
                        originalName: `${name}/v`,
                        variable: zerosLike(value).variable(trainable)
                    };
                }
                const gradient = Array.isArray(variableGradients) ?
                    variableGradients[i].tensor :
                    variableGradients[name];
                if (gradient == null) {
                    return;
                }
                const firstMoment = this.accumulatedFirstMoment[i].variable;
                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
                const ut0 = mul(weightedInfNorm, this.beta2);
                const ut1 = abs(gradient);
                const newWeightedInfNorm = maximum(ut0, ut1);
                firstMoment.assign(newFirstMoment);
                weightedInfNorm.assign(newWeightedInfNorm);
                const newValue = add(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))), value);
                value.assign(newValue);
            });
            this.iteration.assign(add(this.iteration, 1));
            this.accBeta1.assign(mul(this.accBeta1, this.beta1));
        });
        this.incrementIterations();
    }
    dispose() {
        this.accBeta1.dispose();
        this.iteration.dispose();
        if (this.accumulatedFirstMoment != null) {
            dispose(this.accumulatedFirstMoment.map(v => v.variable));
        }
        if (this.accumulatedWeightedInfNorm != null) {
            dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));
        }
    }
    async getWeights() {
        throw new Error('getWeights() is not implemented for Adamax yet.');
    }
    async setWeights(weightValues) {
        throw new Error('setWeights() is not implemented for Adamax yet.');
    }
    getConfig() {
        return {
            'learningRate': this.learningRate,
            'beta1': this.beta1,
            'beta2': this.beta2,
            'epsilon': this.epsilon,
            'decay': this.decay
        };
    }
    /** @nocollapse */
    static fromConfig(cls, config) {
        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon'], config['decay']);
    }
}
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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 {abs} from '../ops/abs';\nimport {add} from '../ops/add';\nimport {div} from '../ops/div';\nimport {maximum} from '../ops/maximum';\nimport {mul} from '../ops/mul';\nimport {scalar} from '../ops/scalar';\nimport {sub} from '../ops/sub';\nimport {zerosLike} from '../ops/zeros_like';\nimport {ConfigDict, Serializable, SerializableConstructor} from '../serialization';\nimport {Variable} from '../tensor';\nimport {NamedTensor, NamedVariableMap} from '../tensor_types';\n\nimport {Optimizer, OptimizerVariable} from './optimizer';\n\nexport class AdamaxOptimizer 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 'Adamax';\n  }\n  private accBeta1: Variable;\n  private iteration: Variable;\n\n  private accumulatedFirstMoment: OptimizerVariable[] = [];\n  private accumulatedWeightedInfNorm: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null,\n      protected decay = 0.0) {\n    super();\n\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\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    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const lr =\n          div(-this.learningRate, add(mul(this.iteration, this.decay), 1));\n\n      variableNames.forEach((name, i) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i] == null) {\n          this.accumulatedFirstMoment[i] = {\n            originalName: `${name}/m`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        if (this.accumulatedWeightedInfNorm[i] == null) {\n          this.accumulatedWeightedInfNorm[i] = {\n            originalName: `${name}/v`,\n            variable: 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 firstMoment = this.accumulatedFirstMoment[i].variable;\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n\n        const newFirstMoment =\n            add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n\n        const ut0 = mul(weightedInfNorm, this.beta2);\n        const ut1 = abs(gradient);\n\n        const newWeightedInfNorm = maximum(ut0, ut1);\n\n        firstMoment.assign(newFirstMoment);\n        weightedInfNorm.assign(newWeightedInfNorm);\n\n        const newValue =\n            add(mul(div(lr, oneMinusAccBeta1),\n                    div(newFirstMoment, add(newWeightedInfNorm, this.epsilon))),\n                value);\n\n        value.assign(newValue);\n      });\n\n      this.iteration.assign(add(this.iteration, 1));\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n    });\n    this.incrementIterations();\n  }\n\n  override dispose(): void {\n    this.accBeta1.dispose();\n    this.iteration.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map(v => v.variable));\n    }\n    if (this.accumulatedWeightedInfNorm != null) {\n      dispose(this.accumulatedWeightedInfNorm.map(v => v.variable));\n    }\n  }\n\n  override async getWeights(): Promise<NamedTensor[]> {\n    throw new Error('getWeights() is not implemented for Adamax yet.');\n  }\n\n  override async setWeights(weightValues: NamedTensor[]): Promise<void> {\n    throw new Error('setWeights() is not implemented for Adamax yet.');\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      'learningRate': this.learningRate,\n      'beta1': this.beta1,\n      'beta2': this.beta2,\n      'epsilon': this.epsilon,\n      'decay': this.decay\n    };\n  }\n\n  /** @nocollapse */\n  static override fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config['learningRate'], config['beta1'], config['beta2'],\n        config['epsilon'], config['decay']);\n  }\n}\n"]}