def get_ge(net_name, model_parameters, load_parameters): args = util.EmptySpace() for key, value in load_parameters.items(): setattr(args, key, value) folder = "/media/rico/Data/TU/thesis/runs{}/{}".format( args.experiment, util.generate_folder_name(args)) ge_x, ge_y = [], [] lta, lva, ltl, lvl = [], [], [], [] for run in runs: filename = '{}/model_r{}_{}'.format( folder, run, get_save_name(net_name, model_parameters)) ge_path = '{}.exp'.format(filename) y_r = util.load_csv(ge_path, delimiter=' ', dtype=np.float) x_r = range(len(y_r)) ge_x.append(x_r) ge_y.append(y_r) if show_losses or show_acc: ta, va, tl, vl = util.load_loss_acc(filename) lta.append(ta) lva.append(va) ltl.append(tl) lvl.append(vl) return ge_x, ge_y, (lta, lva, ltl, lvl)
def get_ge(net_name, model_parameters): folder = '/media/rico/Data/TU/thesis/runs{}/{}/subkey_{}/{}{}{}_SF{}_' \ 'E{}_BZ{}_LR{}{}{}/train{}/'.format( '3' if not experiment else '', str(data_set), sub_key_index, '' if unmask else 'masked/', '' if desync is 0 else 'desync{}/'.format(desync), type_network, spread_factor, epochs, batch_size, '%.2E' % Decimal(lr), '' if np.math.ceil(l2_penalty) <= 0 else '_L2_{}'.format(l2_penalty), init, train_size) ge_x, ge_y = [], [] lta, lva, ltl, lvl = [], [], [], [] for run in runs: filename = '{}/model_r{}_{}'.format( folder, run, get_save_name(net_name, model_parameters)) ge_path = '{}.exp'.format(filename) y_r = util.load_csv(ge_path, delimiter=' ', dtype=np.float) x_r = range(len(y_r)) ge_x.append(x_r) ge_y.append(y_r) if show_losses or show_acc: ta, va, tl, vl = util.load_loss_acc(filename) lta.append(ta) lva.append(va) ltl.append(tl) lvl.append(vl) return ge_x, ge_y, (lta, lva, ltl, lvl)
def get_ge(net_name, model_parameters, load_parameters): folder = '/media/rico/Data/TU/thesis/runs{}/{}/subkey_{}/{}{}{}_SF{}_' \ 'E{}_BZ{}_LR{}{}/train{}/'.format( load_parameters["experiment"], load_parameters["data_set"], load_parameters["subkey"], load_parameters["masked"], load_parameters["desync"], load_parameters["hw"], load_parameters["spread"], load_parameters["epochs"], load_parameters["batch_size"], load_parameters["lr"], load_parameters["l2"], load_parameters["train_size"]) ge_x, ge_y = [], [] lta, lva, ltl, lvl = [], [], [], [] for run in runs: filename = '{}/model_r{}_{}'.format( folder, run, get_save_name(net_name, model_parameters)) ge_path = '{}.exp'.format(filename) y_r = util.load_csv(ge_path, delimiter=' ', dtype=np.float) x_r = range(len(y_r)) ge_x.append(x_r) ge_y.append(y_r) if show_losses or show_acc: ta, va, tl, vl = util.load_loss_acc(filename) lta.append(ta) lva.append(va) ltl.append(tl) lvl.append(vl) return ge_x, ge_y, (lta, lva, ltl, lvl)
def get_ge(net_name, model_parameters): folder = "{}/{}".format('/media/rico/Data/TU/thesis/runs/', util.generate_folder_name(args)) ge_x, ge_y = [], [] lta, lva, ltl, lvl = [], [], [], [] for run in runs: filename = '{}/model_r{}_{}'.format( folder, run, get_save_name(net_name, model_parameters)) ge_path = '{}.exp'.format(filename) y_r = util.load_csv(ge_path, delimiter=' ', dtype=np.float) x_r = range(len(y_r)) ge_x.append(x_r) ge_y.append(y_r) if show_losses or show_acc: ta, va, tl, vl = util.load_loss_acc(filename) lta.append(ta) lva.append(va) ltl.append(tl) lvl.append(vl) return ge_x, ge_y, (lta, lva, ltl, lvl)