@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
155 lines • 8.41 kB
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 __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 };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
jasmine_util_1.describeWithFlags('SGDOptimizer', jasmine_util_1.ALL_ENVS, function () {
it('basic', function () { return __awaiter(_this, void 0, void 0, function () {
var learningRate, optimizer, x, numTensors, cost, expectedValue1, _a, _b, expectedValue2, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
learningRate = .1;
optimizer = tf.train.sgd(learningRate);
x = tf.scalar(4).variable();
numTensors = tf.memory().numTensors;
cost = optimizer.minimize(function () { return x.square(); }, /* returnCost */ true);
// Cost should be the only additional arrays.
expect(tf.memory().numTensors).toBe(numTensors + 1);
expectedValue1 = -2 * 4 * learningRate + 4;
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, x.data()];
case 1:
_a.apply(void 0, [_d.sent(), [expectedValue1]]);
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, cost.data()];
case 2:
_b.apply(void 0, [_d.sent(), [Math.pow(4, 2)]]);
cost.dispose();
numTensors = tf.memory().numTensors;
cost = optimizer.minimize(function () { return x.square(); }, /* returnCost */ false);
// There should be no new additional Tensors.
expect(tf.memory().numTensors).toBe(numTensors);
expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, x.data()];
case 3:
_c.apply(void 0, [_d.sent(), [expectedValue2]]);
expect(cost).toBe(null);
optimizer.dispose();
x.dispose();
// The only tensor remaining is the argument to variable().
expect(tf.memory().numTensors).toBe(1);
return [2 /*return*/];
}
});
}); });
it('Set and get weights: empty', function () { return __awaiter(_this, void 0, void 0, function () {
var x, learningRate, optimizer1, weights, _a, optimizer2, _b, optimizer3, _c, _d, _e;
return __generator(this, function (_f) {
switch (_f.label) {
case 0:
x = tf.scalar(4).variable();
learningRate = .1;
optimizer1 = tf.train.sgd(learningRate);
return [4 /*yield*/, optimizer1.getWeights()];
case 1:
weights = _f.sent();
expect(optimizer1.iterations).toEqual(0);
optimizer1.minimize(function () { return x.square(); });
return [4 /*yield*/, optimizer1.getWeights()];
case 2:
weights = _f.sent();
expect(optimizer1.iterations).toEqual(1);
expect(weights.length).toEqual(1);
expect(weights[0].name).toEqual('iter');
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, weights[0].tensor.data()];
case 3:
_a.apply(void 0, [_f.sent(), 1]);
optimizer2 = tf.train.sgd(learningRate);
return [4 /*yield*/, optimizer2.setWeights(weights)];
case 4:
_f.sent();
optimizer2.minimize(function () { return x.square(); });
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, x.data()];
case 5:
_b.apply(void 0, [_f.sent(), 2.56]);
expect(optimizer2.iterations).toEqual(2);
optimizer3 = tf.train.sgd(learningRate);
_d = (_c = optimizer3).setWeights;
return [4 /*yield*/, optimizer2.getWeights()];
case 6: return [4 /*yield*/, _d.apply(_c, [_f.sent()])];
case 7:
_f.sent();
optimizer3.minimize(function () { return x.square(); });
_e = test_util_1.expectArraysClose;
return [4 /*yield*/, x.data()];
case 8:
_e.apply(void 0, [_f.sent(), 2.048]);
expect(optimizer3.iterations).toEqual(3);
return [2 /*return*/];
}
});
}); });
it('serialization round-trip', function () {
var learningRate = .1;
var originalOpt = tf.train.sgd(learningRate);
var reserialized = tf.SGDOptimizer.fromConfig(tf.SGDOptimizer, originalOpt.getConfig());
expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
});
});
//# sourceMappingURL=sgd_optimizer_test.js.map