117 lines
2.4 KiB
Python
117 lines
2.4 KiB
Python
from glob import glob
|
|
|
|
import numpy as np
|
|
|
|
from aux_functions import load_pickle, save_pickle
|
|
|
|
EXP_NO = 2
|
|
|
|
MODEL_SIZE = 1
|
|
|
|
CRITERIA = ""
|
|
|
|
data_type = {"cifar": "CIFAR", "mnist": "MNIST"}
|
|
|
|
experiment_criterias = {
|
|
"Early": "/Early_Stopping",
|
|
"Efficiency": "/Efficiency_Stopping",
|
|
"100": "/100_epoch",
|
|
"Accuracy": "/Accuracy_Bound",
|
|
"Energy": "/Energy_Bound",
|
|
}
|
|
|
|
model_type = {"bayes": "bayes_exp_*.pkl", "freq": "freq_exp_data_*.pkl"}
|
|
|
|
EXPERIMENT_PATH = f"./EXPERIMENT_{EXP_NO}_DATA"
|
|
|
|
models_exp = {
|
|
"cifar": {
|
|
"bayes": {
|
|
1: None,
|
|
2: None,
|
|
3: None,
|
|
4: None,
|
|
5: None,
|
|
6: None,
|
|
7: None,
|
|
},
|
|
"freq": {
|
|
1: None,
|
|
2: None,
|
|
3: None,
|
|
4: None,
|
|
5: None,
|
|
6: None,
|
|
7: None,
|
|
},
|
|
},
|
|
"mnist": {
|
|
"bayes": {
|
|
1: None,
|
|
2: None,
|
|
3: None,
|
|
4: None,
|
|
5: None,
|
|
6: None,
|
|
7: None,
|
|
},
|
|
"freq": {
|
|
1: None,
|
|
2: None,
|
|
3: None,
|
|
4: None,
|
|
5: None,
|
|
6: None,
|
|
7: None,
|
|
},
|
|
},
|
|
}
|
|
|
|
path = (
|
|
f"{EXPERIMENT_PATH}"
|
|
f"{experiment_criterias['Efficiency']}"
|
|
f"/{data_type['cifar']}"
|
|
f"/{model_type['bayes']}"
|
|
)
|
|
|
|
path = glob(path)
|
|
for t in zip(range(1, 8), path):
|
|
models_exp["cifar"]["bayes"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
|
|
|
|
path = (
|
|
f"{EXPERIMENT_PATH}"
|
|
f"{experiment_criterias['Efficiency']}"
|
|
f"/{data_type['mnist']}"
|
|
f"/{model_type['bayes']}"
|
|
)
|
|
|
|
path = glob(path)
|
|
for t in zip(range(1, 8), path):
|
|
models_exp["mnist"]["bayes"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
|
|
|
|
path = (
|
|
f"{EXPERIMENT_PATH}"
|
|
f"{experiment_criterias['Efficiency']}"
|
|
f"/{data_type['cifar']}"
|
|
f"/{model_type['freq']}"
|
|
)
|
|
|
|
path = glob(path)
|
|
for t in zip(range(1, 8), path):
|
|
models_exp["cifar"]["freq"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
|
|
|
|
path = (
|
|
f"{EXPERIMENT_PATH}"
|
|
f"{experiment_criterias['Efficiency']}"
|
|
f"/{data_type['mnist']}"
|
|
f"/{model_type['freq']}"
|
|
)
|
|
|
|
path = glob(path)
|
|
for t in zip(range(1, 8), path):
|
|
models_exp["mnist"]["freq"][t[0]] = np.concatenate(load_pickle(t[1]), axis=0)
|
|
|
|
# print(models_exp)
|
|
file_name = f"Experiment_{EXP_NO}_accuracy_data.pkl"
|
|
save_pickle(file_name, models_exp)
|