@tensorflow-models/coco-ssd
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Object detection model (coco-ssd) in TensorFlow.js
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JavaScript
;
/**
* @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.
* =============================================================================
*/
var __extends = (this && this.__extends) || (function () {
var extendStatics = Object.setPrototypeOf ||
({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
return function (d, b) {
extendStatics(d, b);
function __() { this.constructor = d; }
d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
};
})();
Object.defineProperty(exports, "__esModule", { value: true });
var globals_1 = require("../globals");
var serialization_1 = require("../serialization");
/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */
var Optimizer = /** @class */ (function (_super) {
__extends(Optimizer, _super);
function Optimizer() {
return _super !== null && _super.apply(this, arguments) || this;
}
/**
* 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'} */
Optimizer.prototype.minimize = function (f, returnCost, varList) {
if (returnCost === void 0) { returnCost = false; }
var _a = this.computeGradients(f, varList), value = _a.value, grads = _a.grads;
this.applyGradients(grads);
// Dispose gradients.
var varNames = Object.keys(grads);
varNames.forEach(function (varName) { return grads[varName].dispose(); });
if (returnCost) {
return value;
}
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.
*/
Optimizer.prototype.computeGradients = function (f, varList) {
return globals_1.variableGrads(f, varList);
};
/**
* Dispose the variables (if any) owned by this optimizer instance.
*/
Optimizer.prototype.dispose = function () { };
return Optimizer;
}(serialization_1.Serializable));
exports.Optimizer = Optimizer;
Object.defineProperty(Optimizer, Symbol.hasInstance, {
value: function (instance) {
return instance.minimize != null && instance.computeGradients != null &&
instance.applyGradients != null;
}
});
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