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optimizer.ts
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optimizer.ts
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/**
* @license
* Copyright 2018 Google Inc. 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 {variableGrads} from '../globals';
import {Serializable} from '../serialization';
import {Scalar, Variable} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */
export abstract class Optimizer extends Serializable {
/**
* Executes `f()` and minimizes the scalar output of `f()` by computing
* gradients of y with respect to the list of trainable variables provided by
* `varList`. If no list is provided, it defaults to all trainable variables.
*
* @param f The function to execute and whose output to minimize.
* @param returnCost Whether to return the scalar cost value produced by
* executing `f()`.
* @param varList An optional list of variables to update. If specified, only
* the trainable variables in varList will be updated by minimize. Defaults to
* all trainable variables.
*/
/** @doc {heading: 'Training', subheading: 'Optimizers'} */
minimize(f: () => Scalar, returnCost = false, varList?: Variable[]): Scalar
|null {
const {value, grads} = this.computeGradients(f, varList);
this.applyGradients(grads);
// Dispose gradients.
const varNames = Object.keys(grads);
varNames.forEach(varName => grads[varName].dispose());
if (returnCost) {
return value as Scalar;
} else {
value.dispose();
return null;
}
}
/**
* Executes f() and computes the gradient of the scalar output of f() with
* respect to the list of trainable variables provided by `varList`. If no
* list is provided, it defaults to all trainable variables.
*
* @param f The function to execute and whose output to use for computing
* gradients with respect to variables.
* @param varList An optional list of variables to compute gradients with
* respect to. If specified, only the trainable variables in varList will have
* gradients computed with respect to. Defaults to all trainable variables.
*/
computeGradients(f: () => Scalar, varList?: Variable[]):
{value: Scalar, grads: NamedTensorMap} {
return variableGrads(f, varList);
}
/**
* Updates variables by using the computed gradients.
*
* @param variableGradients A mapping of variable name to its gradient value.
*/
abstract applyGradients(variableGradients: NamedTensorMap): void;
/**
* Dispose the variables (if any) owned by this optimizer instance.
*/
dispose(): void {}
}
Object.defineProperty(Optimizer, Symbol.hasInstance, {
value: (instance: Optimizer) => {
return instance.minimize != null && instance.computeGradients != null &&
instance.applyGradients != null;
}
});