/**
 * @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 { pow } from '../ops/pow';
import { scalar } from '../ops/scalar';
import { sqrt } from '../ops/sqrt';
import { square } from '../ops/square';
import { sub } from '../ops/sub';
import { zerosLike } from '../ops/zeros_like';
import { Optimizer } from './optimizer';
export class AdamOptimizer 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 'Adam';
    }
    constructor(learningRate, beta1, beta2, epsilon = null) {
        super();
        this.learningRate = learningRate;
        this.beta1 = beta1;
        this.beta2 = beta2;
        this.epsilon = epsilon;
        this.accumulatedFirstMoment = [];
        this.accumulatedSecondMoment = [];
        tidy(() => {
            // accB* will be updated by batch.
            this.accBeta1 = scalar(beta1).variable();
            this.accBeta2 = scalar(beta2).variable();
        });
        if (epsilon == null) {
            this.epsilon = ENGINE.backend.epsilon();
        }
    }
    applyGradients(variableGradients) {
        const varNames = Array.isArray(variableGradients) ?
            variableGradients.map(v => v.name) :
            Object.keys(variableGradients);
        tidy(() => {
            const oneMinusAccBeta1 = sub(1, this.accBeta1);
            const oneMinusAccBeta2 = sub(1, this.accBeta2);
            varNames.forEach((name, i) => {
                const value = ENGINE.registeredVariables[name];
                const trainable = false;
                if (this.accumulatedFirstMoment[i] == null) {
                    this.accumulatedFirstMoment[i] = {
                        originalName: `${name}/m`,
                        variable: tidy(() => zerosLike(value).variable(trainable))
                    };
                }
                if (this.accumulatedSecondMoment[i] == null) {
                    this.accumulatedSecondMoment[i] = {
                        originalName: `${name}/v`,
                        variable: tidy(() => 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 secondMoment = this.accumulatedSecondMoment[i].variable;
                const newFirstMoment = add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
                const newSecondMoment = add(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
                const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
                const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);
                firstMoment.assign(newFirstMoment);
                secondMoment.assign(newSecondMoment);
                const newValue = add(mul(div(biasCorrectedFirstMoment, add(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
                value.assign(newValue);
            });
            this.accBeta1.assign(mul(this.accBeta1, this.beta1));
            this.accBeta2.assign(mul(this.accBeta2, this.beta2));
        });
        this.incrementIterations();
    }
    dispose() {
        this.accBeta1.dispose();
        this.accBeta2.dispose();
        if (this.accumulatedFirstMoment != null) {
            dispose(this.accumulatedFirstMoment.map(v => v.variable));
        }
        if (this.accumulatedSecondMoment != null) {
            dispose(this.accumulatedSecondMoment.map(v => v.variable));
        }
    }
    async getWeights() {
        // Order matters for Python compatibility.
        const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
        return [await this.saveIterations()].concat(variables.map(v => ({ name: v.originalName, tensor: v.variable })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        tidy(() => {
            this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));
            this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));
        });
        const variableCount = weightValues.length / 2;
        const trainable = false;
        this.accumulatedFirstMoment =
            weightValues.slice(0, variableCount).map(v => ({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
        this.accumulatedSecondMoment =
            weightValues.slice(variableCount, variableCount * 2)
                .map(v => ({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
    }
    getConfig() {
        return {
            'learningRate': this.learningRate,
            'beta1': this.beta1,
            'beta2': this.beta2,
            'epsilon': this.epsilon,
        };
    }
    /** @nocollapse */
    static fromConfig(cls, config) {
        return new cls(config['learningRate'], config['beta1'], config['beta2'], config['epsilon']);
    }
}
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* @license\n * Copyright 2018 Google LLC. 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 {pow} from '../ops/pow';\nimport {scalar} from '../ops/scalar';\nimport {sqrt} from '../ops/sqrt';\nimport {square} from '../ops/square';\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 AdamOptimizer 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 'Adam';\n  }\n  private accBeta1: Variable;\n  private accBeta2: Variable;\n\n  private accumulatedFirstMoment: OptimizerVariable[] = [];\n  private accumulatedSecondMoment: OptimizerVariable[] = [];\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null) {\n    super();\n    tidy(() => {\n      // accB* will be updated by batch.\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n\n    if (epsilon == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n\n  applyGradients(variableGradients: NamedVariableMap|NamedTensor[]) {\n    const varNames = Array.isArray(variableGradients) ?\n        variableGradients.map(v => v.name) :\n        Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const oneMinusAccBeta2 = sub(1, this.accBeta2);\n\n      varNames.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: `${name}/m`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        if (this.accumulatedSecondMoment[i] == null) {\n          this.accumulatedSecondMoment[i] = {\n            originalName: `${name}/v`,\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 firstMoment = this.accumulatedFirstMoment[i].variable;\n        const secondMoment = this.accumulatedSecondMoment[i].variable;\n\n        const newFirstMoment =\n            add(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const newSecondMoment =\n            add(mul(secondMoment, this.beta2),\n                mul(square(gradient), 1 - this.beta2));\n\n        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n        const biasCorrectedSecondMoment =\n            div(newSecondMoment, oneMinusAccBeta2);\n\n        firstMoment.assign(newFirstMoment);\n        secondMoment.assign(newSecondMoment);\n\n        const newValue =\n            add(mul(div(biasCorrectedFirstMoment,\n                        add(sqrt(biasCorrectedSecondMoment), this.epsilon)),\n                    -this.learningRate),\n                value);\n        value.assign(newValue);\n      });\n\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n      this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n    });\n    this.incrementIterations();\n  }\n\n  override dispose(): void {\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map(v => v.variable));\n    }\n    if (this.accumulatedSecondMoment != null) {\n      dispose(this.accumulatedSecondMoment.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.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\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    tidy(() => {\n      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n    });\n\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedFirstMoment =\n        weightValues.slice(0, variableCount).map(v => ({\n                                                   originalName: v.name,\n                                                   variable: v.tensor.variable(\n                                                       trainable)\n                                                 }));\n    this.accumulatedSecondMoment =\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      'beta1': this.beta1,\n      'beta2': this.beta2,\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(\n        config['learningRate'], config['beta1'], config['beta2'],\n        config['epsilon']);\n  }\n}\n"]}