@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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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 = function (d, b) {
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 extendStatics(d, b);
};
return function (d, b) {
extendStatics(d, b);
function __() { this.constructor = d; }
d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
};
})();
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
Object.defineProperty(exports, "__esModule", { value: true });
var globals_1 = require("../globals");
var gradients_1 = require("../gradients");
var ops_1 = require("../ops/ops");
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;
if (varList != null) {
var gradArray = varList.map(function (v) { return ({ name: v.name, tensor: grads[v.name] }); });
this.applyGradients(gradArray);
}
else {
this.applyGradients(grads);
}
// Dispose gradients.
globals_1.dispose(grads);
if (returnCost) {
return value;
}
else {
value.dispose();
return null;
}
};
Object.defineProperty(Optimizer.prototype, "iterations", {
/**
* The number of iterations that this optimizer instance has been invoked for.
*/
get: function () {
if (this.iterations_ == null) {
this.iterations_ = 0;
}
return this.iterations_;
},
enumerable: true,
configurable: true
});
Optimizer.prototype.incrementIterations = function () {
this.iterations_ = this.iterations + 1;
};
/**
* 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 gradients_1.variableGrads(f, varList);
};
/**
* Dispose the variables (if any) owned by this optimizer instance.
*/
Optimizer.prototype.dispose = function () {
if (this.iterations_ != null) {
globals_1.dispose(this.iterations_);
}
};
Optimizer.prototype.saveIterations = function () {
return __awaiter(this, void 0, void 0, function () {
return __generator(this, function (_a) {
if (this.iterations_ == null) {
this.iterations_ = 0;
}
return [2 /*return*/, {
name: 'iter',
// TODO(cais): Use 'int64' type when available.
tensor: ops_1.scalar(this.iterations_, 'int32')
}];
});
});
};
Optimizer.prototype.getWeights = function () {
return __awaiter(this, void 0, void 0, function () {
return __generator(this, function (_a) {
throw new Error('getWeights() is not implemented for this optimizer yet.');
});
});
};
Optimizer.prototype.setWeights = function (weightValues) {
return __awaiter(this, void 0, void 0, function () {
return __generator(this, function (_a) {
throw new Error("setWeights() is not implemented for this optimizer class " +
("" + this.getClassName()));
});
});
};
/**
* Extract the first element of the weight values and set it
* as the iterations counter variable of this instance of optimizer.
*
* @param weightValues
* @returns Weight values with the first element consumed and excluded.
*/
Optimizer.prototype.extractIterations = function (weightValues) {
return __awaiter(this, void 0, void 0, function () {
var _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this;
return [4 /*yield*/, weightValues[0].tensor.data()];
case 1:
_a.iterations_ = (_b.sent())[0];
return [2 /*return*/, weightValues.slice(1)];
}
});
});
};
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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