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)
