mlts-experiment-data
Version:
Machine learning experiment data downloader.
39 lines • 1.76 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var path = require("path");
var downloader = require("../utils/downloader");
var Data_1 = require("../Data");
var idx = require("idx-data");
var utils_1 = require("../utils/utils");
var dataRemoteLocation = 'https://rawgit.com/andnp/ml_data/master/gs_cifar10.tar.gz';
function download(location) {
if (location === void 0) { location = '.tmp'; }
return downloader.download(dataRemoteLocation, location);
}
exports.download = download;
function load(location) {
if (location === void 0) { location = '.tmp'; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var root, _a, dataX, dataY, _b, x, t, _c, y, ty;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, download(location)];
case 1:
_d.sent();
root = path.join(location, 'cifar');
return [4 /*yield*/, Promise.all([
idx.loadBits(path.join(root, 'cifar_data.idx')),
idx.loadBits(path.join(root, 'cifar_labels.idx')),
])];
case 2:
_a = tslib_1.__read.apply(void 0, [_d.sent(), 2]), dataX = _a[0], dataY = _a[1];
_b = tslib_1.__read(utils_1.splitTensor(dataX, 50000), 2), x = _b[0], t = _b[1];
_c = tslib_1.__read(utils_1.splitTensor(dataY, 50000), 2), y = _c[0], ty = _c[1];
return [2 /*return*/, new Data_1.Dataset(x, y, t, ty)];
}
});
});
}
exports.load = load;
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