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results.py
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results.py
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import argparse
import os
from copy import deepcopy
import pandas as pd
from matplotlib import pyplot as plt
import tensorboard as tb
import numpy as np
# arg parse
parser = argparse.ArgumentParser(description='re-sln results')
parser.add_argument('--experiment_id', type=str, help='the TensorboardDEV experiment id', required=True)
results_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "results")
hp_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hp")
experiments = \
{
"cifar10":
{
"ce":
{
"symmetric":
{
"paper": "exp_2021-11-25 13:17:26.851200", "custom": "exp_2021-11-25 20:30:28.794160"
},
"asymmetric":
{
"paper": "exp_2021-11-26 09:21:19.524188", "custom": "exp_2021-11-26 14:03:18.975684"
},
"dependent":
{
"paper": "exp_2021-11-26 20:14:40.983299"
},
"openset":
{
"custom": "exp_2021-11-27 13:35:23.026659"
},
},
"sln":
{
"symmetric":
{
"paper": "exp_2021-11-25 15:38:09.361059", "custom": "exp_2021-11-25 20:31:37.546765"
},
"asymmetric":
{
"paper": "exp_2021-11-26 11:41:56.758060", "custom": "exp_2021-11-26 16:11:00.844488"
},
"dependent":
{
"paper": "exp_2021-11-27 11:07:55.847340"
},
"openset":
{
"custom": "exp_2021-11-27 13:44:37.885816"
},
},
"sln-mo":
{
"symmetric":
{
"paper": "exp_2021-11-25 16:46:29.066838", "custom": "exp_2021-11-26 09:18:14.291265"
},
"asymmetric":
{
"paper": "exp_2021-11-26 11:44:27.727904", "custom": "exp_2021-11-26 16:14:06.628600"
},
"dependent":
{
"paper": "exp_2021-11-27 11:11:07.020347"
},
"openset":
{
"custom": "exp_2021-11-27 13:46:43.777573"
},
},
"sln-mo-lc":
{
"symmetric":
{
"paper": "exp_2021-11-26 11:18:36.051172", "custom": "exp_2021-11-26 13:51:03.590616"
},
"asymmetric":
{
"paper": "exp_2021-11-26 13:57:45.567433", "custom": "exp_2021-11-26 16:16:06.031597"
},
"dependent":
{
"paper": "exp_2021-11-27 11:14:24.120092"
},
"openset":
{
"custom": "exp_2021-11-28 16:34:38.935269"
},
},
},
"cifar100":
{
"ce":
{
"symmetric":
{
"paper": "exp_2021-11-29 13:02:42.947124", "custom": "exp_2021-11-29 15:14:24.277293"
},
"asymmetric":
{
"paper": "exp_2021-12-02 17:15:05.141925", "custom": "exp_2021-12-02 20:50:30.272408"
},
"dependent":
{
"paper": "exp_2021-12-03 12:09:50.374569"
},
},
"sln":
{
"symmetric":
{
"paper": "exp_2021-11-29 13:12:28.474547", "custom": "exp_2021-11-29 15:15:36.143703"
},
"asymmetric":
{
"paper": "exp_2021-12-02 17:34:08.440889", "custom": "exp_2021-12-02 20:55:53.387841"
},
"dependent":
{
"paper": "exp_2021-12-03 14:37:51.783033"
},
},
"sln-mo":
{
"symmetric":
{
"paper": "exp_2021-11-29 13:16:11.590910", "custom": "exp_2021-11-29 22:15:08.652843"
},
"asymmetric":
{
"paper": "exp_2021-12-02 17:39:34.952358", "custom": "exp_2021-12-03 11:53:37.290785"
},
"dependent":
{
"paper": "exp_2021-12-03 14:43:27.237441"
},
},
"sln-mo-lc":
{
"symmetric":
{
"paper": "exp_2021-11-29 22:04:19.910053", "custom": "exp_2021-11-29 22:26:18.532929"
},
"asymmetric":
{
"paper": "exp_2021-12-02 20:43:32.204172", "custom": "exp_2021-12-03 12:01:04.662910"
},
"dependent":
{
"paper": "exp_2021-12-03 14:51:11.441549"
},
},
},
"animal-10n":
{
"ce":
{
"real":
{
"run1": "exp_2021-12-08 11:38:16.477097", "run2": "exp_2021-12-08 20:23:04.646474"
}
},
"sln":
{
"real":
{
"run1": "exp_2021-12-08 13:06:15.220761", "run2": "exp_2021-12-08 18:29:16.424429"
}
},
"sln-mo":
{
"real":
{
"run1": "exp_2021-12-08 14:43:36.523374", "run2": "exp_2021-12-08 18:29:27.093767"
}
},
"sln-mo-lc":
{
"real":
{
"run1": "exp_2021-12-07 21:55:00.730335", "run2": "exp_2021-12-08 18:35:55.422639"
}
}
}
}
tags = \
{
"loss":
{
"train": "loss/train", "test": "loss/test"
},
"accuracy":
{
"train": "accuracy/train", "test": "accuracy/test"
}
}
suffixes = \
{
"loss":
{
"train":
{
"all": "/loss_train_all", "clean": "/loss_train_clean", "noisy": "/loss_train_noisy"
}
}
}
hp = \
{
"cifar10":
{
"sln":
{
"sym":
{
"hp_2021-12-03_13-18-02":
{
"0.1": "hp_71b1c_00000_0_sigma=0.1_2021-12-03_13-18-03",
"0.2": "hp_71b1c_00001_1_sigma=0.2_2021-12-03_13-18-03",
"0.5": "hp_71b1c_00002_2_sigma=0.5_2021-12-03_13-18-03",
"1.0": "hp_71b1c_00003_3_sigma=1.0_2021-12-03_15-11-40"
},
"hp_2021-12-04_17-04-54":
{
"0.1": "hp_4d8ac_00000_0_sigma=0.1_2021-12-04_17-04-55",
"0.2": "hp_4d8ac_00001_1_sigma=0.2_2021-12-04_17-04-55",
"0.5": "hp_4d8ac_00002_2_sigma=0.5_2021-12-04_17-04-55",
"1.0": "hp_4d8ac_00003_3_sigma=1.0_2021-12-04_18-59-42"
}
},
"asym":
{
"hp_2021-12-05_10-55-09":
{
"0.1": "hp_d09c0_00000_0_sigma=0.1_2021-12-05_10-55-10",
"0.2": "hp_d09c0_00001_1_sigma=0.2_2021-12-05_10-55-10",
"0.5": "hp_d09c0_00002_2_sigma=0.5_2021-12-05_10-55-10",
"1.0": "hp_d09c0_00003_3_sigma=1.0_2021-12-05_12-44-35"
},
"hp_2021-12-05_14-46-12":
{
"0.1": "hp_17553_00000_0_sigma=0.1_2021-12-05_14-46-12",
"0.2": "hp_17553_00001_1_sigma=0.2_2021-12-05_14-46-12",
"0.5": "hp_17553_00002_2_sigma=0.5_2021-12-05_14-46-12",
"1.0": "hp_17553_00003_3_sigma=1.0_2021-12-05_16-35-42"
}
}
}
}
}
ablation_runs = \
{
"cifar10":
{
"sln":
{
"symmetric":
{
"0.0": ["exp_2021-11-25 13:17:26.851200", "exp_2021-11-25 20:30:28.794160"],
"0.2": ["exp_2021-12-04 18:44:30.809125", "exp_2021-12-04 18:45:29.413332"],
"0.4": ["exp_2021-12-04 20:16:53.822991", "exp_2021-12-04 20:17:35.698730"],
"0.6": ["exp_2021-12-05 10:32:08.543830", "exp_2021-12-05 10:33:02.145316"],
"0.8": ["exp_2021-12-05 14:47:21.250193", "exp_2021-12-05 14:47:51.111383"],
"1.0": ["exp_2021-11-25 15:38:09.361059", "exp_2021-11-25 20:31:37.546765"],
"1.2": ["exp_2021-12-05 17:40:34.580201", "exp_2021-12-05 17:41:13.978731"],
"1.4": ["exp_2021-12-06 21:07:47.205424", "exp_2021-12-06 21:07:59.931017"],
"1.6": ["exp_2021-12-07 20:08:48.682079", "exp_2021-12-07 20:09:03.085870"],
"1.8": ["exp_2021-12-08 11:28:45.265396", "exp_2021-12-08 11:29:11.334586"],
"2.0": ["exp_2021-12-08 13:04:50.380869", "exp_2021-12-08 13:01:27.453031"]
},
}
}
}
def get_accuracy(dataset_name, model, noise_mode, if_train):
if_train_str = "train" if if_train else "test"
if len(experiments[dataset_name][model][noise_mode]) == 2:
e1 = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode]["paper"],
df["tag"] == tags["accuracy"][if_train_str])]["value"])
e2 = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode]["custom"],
df["tag"] == tags["accuracy"][if_train_str])]["value"])
e = (e1 + e2) / 2
mean = e[-1]
std = np.sqrt(((mean - e1[-1])**2 + (mean - e2[-1])**2)/2)
else:
key = list(experiments[dataset_name][model][noise_mode].keys())[0]
e = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode][key],
df["tag"] == tags["accuracy"][if_train_str])]["value"])
mean = e[-1]
std = 0
return e, mean, std
def get_accuracy_animal(model, if_train):
if_train_str = "train" if if_train else "test"
e1 = \
np.array(df[np.logical_and(
df["run"] == experiments["animal-10n"][model]["real"]["run1"],
df["tag"] == tags["accuracy"][if_train_str])]["value"])
e2 = \
np.array(df[np.logical_and(
df["run"] == experiments["animal-10n"][model]["real"]["run2"],
df["tag"] == tags["accuracy"][if_train_str])]["value"])
e = (e1 + e2) / 2
mean = e[-1]
std = np.sqrt(((mean - e1[-1])**2 + (mean - e2[-1])**2)/2)
return e, mean, std
def get_loss_train(dataset_name, model, noise_mode, loss_type):
if len(experiments[dataset_name][model][noise_mode]) == 2:
e1 = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode]["paper"] + suffixes["loss"]["train"][loss_type],
df["tag"] == tags["loss"]["train"])]["value"])
e2 = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode]["custom"] + suffixes["loss"]["train"][loss_type],
df["tag"] == tags["loss"]["train"])]["value"])
e = (e1 + e2) / 2
else:
key = list(experiments[dataset_name][model][noise_mode].keys())[0]
e = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode][key] + suffixes["loss"]["train"][loss_type],
df["tag"] == tags["loss"]["train"])]["value"])
return e
def get_loss_test(dataset_name, model, noise_mode):
if_train_str = "train" if if_train else "test"
if len(experiments[dataset_name][model][noise_mode]) == 2:
e1 = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode]["paper"],
df["tag"] == tags["loss"]["test"])]["value"])
e2 = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode]["custom"],
df["tag"] == tags["loss"]["test"])]["value"])
e = (e1 + e2) / 2
else:
key = list(experiments[dataset_name][model][noise_mode].keys())[0]
e = \
np.array(df[np.logical_and(
df["run"] == experiments[dataset_name][model][noise_mode][key],
df["tag"] == tags["loss"]["test"])]["value"])
return e
def get_hp_table(results_path, hp_path, noise_mode):
model = "sln"
dataset_name = "cifar10"
hp_results = deepcopy(hp)
progress_table_name = "progress.csv"
experiments = hp[dataset_name][model][noise_mode]
for exp_id in experiments.keys():
exp_path = os.path.join(hp_path, exp_id)
for exp, path in experiments[exp_id].items():
trial_path = os.path.join(exp_path, path)
trial_table_path = os.path.join(trial_path, progress_table_name)
df_trial = pd.read_csv(trial_table_path)
hp_results[dataset_name][model][noise_mode][exp_id][exp] = list(df_trial["accuracy_val"])[-1]*100
experiment_results = hp_results[dataset_name][model][noise_mode]
df = pd.DataFrame(data=experiment_results)
df["mean"] = df.mean(axis=1)
df["std"] = df.std(axis=1)
table_name = f"hp_{dataset_name}_{model}_{noise_mode}.csv"
path_save = os.path.join(results_path, table_name)
df.to_csv(path_save, float_format='%.2f')
def get_ablation_results(df, results_path):
dataset_name = "cifar10"
model = "sln"
noise_mode = "symmetric"
data = {}
steps = np.round(np.arange(0,2.2,0.2),2)
for step in steps:
e1 = \
np.array(df[np.logical_and(
df["run"] == ablation_runs[dataset_name][model][noise_mode][str(step)][0],
df["tag"] == tags["accuracy"]["test"])]["value"])*100
e2 = \
np.array(df[np.logical_and(
df["run"] == ablation_runs[dataset_name][model][noise_mode][str(step)][1],
df["tag"] == tags["accuracy"]["test"])]["value"])*100
e = (e1 + e2) / 2
mean = e[-1]
std = np.sqrt(((mean - e1[-1])**2 + (mean - e2[-1])**2)/2)
data[str(step)] = {"mean": mean, "std": std}
df_res = pd.DataFrame(data=data)
step_max = steps[np.argmax(np.array(df_res.iloc[0]))]
val_max = np.max(np.array(df_res.iloc[0]))
table_name = f"ablation_{dataset_name}_{model}_{noise_mode}.csv"
df_res.to_csv(os.path.join(results_path, table_name), float_format='%.2f')
plt.figure(figsize=(6,4))
plt.errorbar(x=steps, y=df_res.iloc[0], yerr=df_res.iloc[1], label="sym")
plt.plot(step_max, val_max, color="blue", marker="o")
plt.xlabel(r"$\sigma$")
plt.ylabel("Test Accuracy")
plt.title(r"Ablation on CIFAR-10 with SLN: the performance of SLN w.r.t. $\sigma$")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.tight_layout()
fig_name = "ablation-c10-sln-sym.png"
plt.savefig(os.path.join(results_path, fig_name))
if __name__ == "__main__":
# args parse
args = parser.parse_args()
experiment = tb.data.experimental.ExperimentFromDev(args.experiment_id)
df = experiment.get_scalars(pivot=False)
print("retrieved data from TensorboardDEV")
# c10 ce test acc
dataset_name, model, noise_mode, if_train = "cifar10", "ce", "symmetric", False
c10_ce_sym_acc, c10_ce_sym_acc_mean, c10_ce_sym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "ce", "asymmetric", False
c10_ce_asym_acc, c10_ce_asym_acc_mean, c10_ce_asym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "ce", "dependent", False
c10_ce_dep_acc, c10_ce_dep_acc_mean, c10_ce_dep_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "ce", "openset", False
c10_ce_openset_acc, c10_ce_openset_acc_mean, c10_ce_openset_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
plt.figure(figsize=(6, 4))
plt.plot(c10_ce_sym_acc*100, color="blue", label="sym")
plt.plot(c10_ce_asym_acc*100, color="orange", label="asym")
plt.plot(c10_ce_dep_acc*100, color="green", label="dep")
plt.plot(c10_ce_openset_acc*100, color="red", label="openset")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (CE) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(50, 87)
plt.tight_layout()
fig_name = "c10-ce-test-acc.png"
plt.savefig(os.path.join(results_path, fig_name))
# c10 sln test acc
dataset_name, model, noise_mode, if_train = "cifar10", "sln", "symmetric", False
c10_sln_sym_acc, c10_sln_sym_acc_mean, c10_sln_sym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln", "asymmetric", False
c10_sln_asym_acc, c10_sln_asym_acc_mean, c10_sln_asym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln", "dependent", False
c10_sln_dep_acc, c10_sln_dep_acc_mean, c10_sln_dep_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln", "openset", False
c10_sln_openset_acc, c10_sln_openset_acc_mean, c10_sln_openset_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
plt.figure(figsize=(6, 4))
plt.plot(c10_sln_sym_acc*100, color="blue", label="sym")
plt.plot(c10_sln_asym_acc*100, color="orange", label="asym")
plt.plot(c10_sln_dep_acc*100, color="green", label="dep")
plt.plot(c10_sln_openset_acc*100, color="red", label="openset")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (SLN) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(50, 87)
plt.tight_layout()
fig_name = "c10-sln-test-acc.png"
plt.savefig(os.path.join(results_path, fig_name))
# c10 sln-mo test acc
dataset_name, model, noise_mode, if_train = "cifar10", "sln-mo", "symmetric", False
c10_sln_mo_sym_acc, c10_sln_mo_sym_acc_mean, c10_sln_mo_sym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln-mo", "asymmetric", False
c10_sln_mo_asym_acc, c10_sln_mo_asym_acc_mean, c10_sln_mo_asym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln-mo", "dependent", False
c10_sln_mo_dep_acc, c10_sln_mo_dep_acc_mean, c10_sln_mo_dep_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln-mo", "openset", False
c10_sln_mo_openset_acc, c10_sln_mo_openset_acc_mean, c10_sln_mo_openset_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
plt.figure(figsize=(6, 4))
plt.plot(c10_sln_mo_sym_acc*100, color="blue", label="sym")
plt.plot(c10_sln_mo_asym_acc*100, color="orange", label="asym")
plt.plot(c10_sln_mo_dep_acc*100, color="green", label="dep")
plt.plot(c10_sln_mo_openset_acc*100, color="red", label="openset")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (SLN-MO) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(50, 92)
plt.tight_layout()
fig_name = "c10-sln-mo-test-acc.png"
plt.savefig(os.path.join(results_path, fig_name))
# c10 sln-mo-lc test acc
dataset_name, model, noise_mode, if_train = "cifar10", "sln-mo-lc", "symmetric", False
c10_sln_mo_lc_sym_acc, c10_sln_mo_lc_sym_acc_mean, c10_sln_mo_lc_sym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln-mo-lc", "asymmetric", False
c10_sln_mo_lc_asym_acc, c10_sln_mo_lc_asym_acc_mean, c10_sln_mo_lc_asym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln-mo-lc", "dependent", False
c10_sln_mo_lc_dep_acc, c10_sln_mo_lc_dep_acc_mean, c10_sln_mo_lc_dep_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar10", "sln-mo-lc", "openset", False
c10_sln_mo_lc_openset_acc, c10_sln_mo_lc_openset_acc_mean, c10_sln_mo_lc_openset_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
plt.figure(figsize=(6, 4))
plt.plot(c10_sln_mo_lc_sym_acc*100, color="blue", label="sym")
plt.plot(c10_sln_mo_lc_asym_acc*100, color="orange", label="asym")
plt.plot(c10_sln_mo_lc_dep_acc*100, color="green", label="dep")
plt.plot(c10_sln_mo_lc_openset_acc*100, color="red", label="openset")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (SLN-MO-LC) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(50, 92)
plt.tight_layout()
fig_name = "c10-sln-mo-lc-test-acc.png"
plt.savefig(os.path.join(results_path, fig_name))
plt.figure(figsize=(6, 4))
plt.plot(c10_sln_mo_lc_sym_acc*100, color="blue", label="sym")
plt.plot(c10_sln_mo_lc_asym_acc*100, color="orange", label="asym")
plt.plot(c10_sln_mo_lc_dep_acc*100, color="green", label="dep")
plt.plot(c10_sln_mo_lc_openset_acc*100, color="red", label="openset")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (SLN-MO-LC) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(220, 300)
plt.ylim(80, 92)
plt.tight_layout()
fig_name = "c10-sln-mo-lc-test-acc-zoom.png"
plt.savefig(os.path.join(results_path, fig_name))
# c10 test acc table
data_cifar10 = {
"model": ["ce", "sln", "sln-mo", "sln-mo-lc"],
"symmetric": [
str(round(c10_ce_sym_acc_mean*100,2))+u"\u00B1"+str(round(c10_ce_sym_acc_std*100,2)),
str(round(c10_sln_sym_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_sym_acc_std*100,2)),
str(round(c10_sln_mo_sym_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_mo_sym_acc_std*100,2)),
str(round(c10_sln_mo_lc_sym_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_mo_lc_sym_acc_std*100,2)),
],
"asymmetric": [
str(round(c10_ce_asym_acc_mean*100,2))+u"\u00B1"+str(round(c10_ce_asym_acc_std*100,2)),
str(round(c10_sln_asym_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_asym_acc_std*100,2)),
str(round(c10_sln_mo_asym_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_mo_asym_acc_std*100,2)),
str(round(c10_sln_mo_lc_asym_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_mo_lc_asym_acc_std*100,2)),
],
"dependent": [
str(round(c10_ce_dep_acc_mean*100,2))+u"\u00B1"+str(round(c10_ce_dep_acc_std*100,2)),
str(round(c10_sln_dep_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_dep_acc_std*100,2)),
str(round(c10_sln_mo_dep_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_mo_dep_acc_std*100,2)),
str(round(c10_sln_mo_lc_dep_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_mo_lc_dep_acc_std*100,2)),
],
"openset": [
str(round(c10_ce_openset_acc_mean*100,2))+u"\u00B1"+str(round(c10_ce_openset_acc_std*100,2)),
str(round(c10_sln_openset_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_openset_acc_std*100,2)),
str(round(c10_sln_mo_openset_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_mo_openset_acc_std*100,2)),
str(round(c10_sln_mo_lc_openset_acc_mean*100,2))+u"\u00B1"+str(round(c10_sln_mo_lc_openset_acc_std*100,2)),
],
}
df_table_cifar10 = pd.DataFrame(data=data_cifar10)
table_name = "c10-test-acc-table.csv"
df_table_cifar10.to_csv(os.path.join(results_path, table_name))
# c10 ce training losses
dataset_name, model, noise_mode, loss_type = "cifar10", "ce", "symmetric", "clean"
c10_ce_sym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "ce", "symmetric", "noisy"
c10_ce_sym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "ce", "asymmetric", "clean"
c10_ce_asym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "ce", "asymmetric", "noisy"
c10_ce_asym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "ce", "dependent", "clean"
c10_ce_dep_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "ce", "dependent", "noisy"
c10_ce_dep_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "ce", "openset", "clean"
c10_ce_openset_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "ce", "openset", "noisy"
c10_ce_openset_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
plt.figure(figsize=(6, 4))
plt.plot(c10_ce_sym_loss_train_clean, color="blue", label="sym clean")
plt.plot(c10_ce_sym_loss_train_noisy, color="blue", linestyle='dashed', label="sym noisy")
plt.plot(c10_ce_asym_loss_train_clean, color="orange", label="asym clean")
plt.plot(c10_ce_asym_loss_train_noisy, color="orange", linestyle='dashed', label="asym noisy")
plt.plot(c10_ce_dep_loss_train_clean, color="green", label="dep clean")
plt.plot(c10_ce_dep_loss_train_noisy, color="green", linestyle='dashed', label="dep noisy")
plt.plot(c10_ce_openset_loss_train_clean, color="red", label="openset clean")
plt.plot(c10_ce_openset_loss_train_noisy, color="red", linestyle='dashed', label="openset noisy")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("CE Training Loss (CE) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(0, 3)
plt.tight_layout()
fig_name = "c10-ce-train-loss.png"
plt.savefig(os.path.join(results_path, fig_name))
# c10 sln train losses
dataset_name, model, noise_mode, loss_type = "cifar10", "sln", "symmetric", "clean"
c10_sln_sym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln", "symmetric", "noisy"
c10_sln_sym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln", "asymmetric", "clean"
c10_sln_asym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln", "asymmetric", "noisy"
c10_sln_asym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln", "dependent", "clean"
c10_sln_dep_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln", "dependent", "noisy"
c10_sln_dep_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln", "openset", "clean"
c10_sln_openset_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln", "openset", "noisy"
c10_sln_openset_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
plt.figure(figsize=(6, 4))
plt.plot(c10_sln_sym_loss_train_clean, color="blue", label="sym clean")
plt.plot(c10_sln_sym_loss_train_noisy, color="blue", linestyle='dashed', label="sym noisy")
plt.plot(c10_sln_asym_loss_train_clean, color="orange", label="asym clean")
plt.plot(c10_sln_asym_loss_train_noisy, color="orange", linestyle='dashed', label="asym noisy")
plt.plot(c10_sln_dep_loss_train_clean, color="green", label="dep clean")
plt.plot(c10_sln_dep_loss_train_noisy, color="green", linestyle='dashed', label="dep noisy")
plt.plot(c10_sln_openset_loss_train_clean, color="red", label="openset clean")
plt.plot(c10_sln_openset_loss_train_noisy, color="red", linestyle='dashed', label="openset noisy")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("CE Training Loss (SLN) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(0, 3)
plt.tight_layout()
fig_name = "c10-sln-train-loss.png"
plt.savefig(os.path.join(results_path, fig_name))
# c10 sln-mo train losses
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo", "symmetric", "clean"
c10_sln_mo_sym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo", "symmetric", "noisy"
c10_sln_mo_sym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo", "asymmetric", "clean"
c10_sln_mo_asym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo", "asymmetric", "noisy"
c10_sln_mo_asym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo", "dependent", "clean"
c10_sln_mo_dep_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo", "dependent", "noisy"
c10_sln_mo_dep_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo", "openset", "clean"
c10_sln_mo_openset_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo", "openset", "noisy"
c10_sln_mo_openset_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
plt.figure(figsize=(6, 4))
plt.plot(c10_sln_mo_sym_loss_train_clean, color="blue", label="sym clean")
plt.plot(c10_sln_mo_sym_loss_train_noisy, color="blue", linestyle='dashed', label="sym noisy")
plt.plot(c10_sln_mo_asym_loss_train_clean, color="orange", label="asym clean")
plt.plot(c10_sln_mo_asym_loss_train_noisy, color="orange", linestyle='dashed', label="asym noisy")
plt.plot(c10_sln_mo_dep_loss_train_clean, color="green", label="dep clean")
plt.plot(c10_sln_mo_dep_loss_train_noisy, color="green", linestyle='dashed', label="dep noisy")
plt.plot(c10_sln_mo_openset_loss_train_clean, color="red", label="openset clean")
plt.plot(c10_sln_mo_openset_loss_train_noisy, color="red", linestyle='dashed', label="openset noisy")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("CE Training Loss (SLN-MO) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(0, 3)
plt.tight_layout()
fig_name = "c10-sln-mo-train-loss.png"
plt.savefig(os.path.join(results_path, fig_name))
# c10 sln-mo-lc train losses
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo-lc", "symmetric", "clean"
c10_sln_mo_lc_sym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo-lc", "symmetric", "noisy"
c10_sln_mo_lc_sym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo-lc", "asymmetric", "clean"
c10_sln_mo_lc_asym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo-lc", "asymmetric", "noisy"
c10_sln_mo_lc_asym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo-lc", "dependent", "clean"
c10_sln_mo_lc_dep_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo-lc", "dependent", "noisy"
c10_sln_mo_lc_dep_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo-lc", "openset", "clean"
c10_sln_mo_lc_openset_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar10", "sln-mo-lc", "openset", "noisy"
c10_sln_mo_lc_openset_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
plt.figure(figsize=(6, 4))
plt.plot(c10_sln_mo_lc_sym_loss_train_clean, color="blue", label="sym clean")
plt.plot(c10_sln_mo_lc_sym_loss_train_noisy, color="blue", linestyle='dashed', label="sym noisy")
plt.plot(c10_sln_mo_lc_asym_loss_train_clean, color="orange", label="asym clean")
plt.plot(c10_sln_mo_lc_asym_loss_train_noisy, color="orange", linestyle='dashed', label="asym noisy")
plt.plot(c10_sln_mo_lc_dep_loss_train_clean, color="green", label="dep clean")
plt.plot(c10_sln_mo_lc_dep_loss_train_noisy, color="green", linestyle='dashed', label="dep noisy")
plt.plot(c10_sln_mo_lc_openset_loss_train_clean, color="red", label="openset clean")
plt.plot(c10_sln_mo_lc_openset_loss_train_noisy, color="red", linestyle='dashed', label="openset noisy")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("CE Training Loss (SLN-MO-LC) on CIFAR-10")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(0, 3)
plt.tight_layout()
fig_name = "c10-sln-mo-lc-train-loss.png"
plt.savefig(os.path.join(results_path, fig_name))
# cifar100
# c100 ce test acc
dataset_name, model, noise_mode, if_train = "cifar100", "ce", "symmetric", False
c100_ce_sym_acc, c100_ce_sym_acc_mean, c100_ce_sym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar100", "ce", "asymmetric", False
c100_ce_asym_acc, c100_ce_asym_acc_mean, c100_ce_asym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar100", "ce", "dependent", False
c100_ce_dep_acc, c100_ce_dep_acc_mean, c100_ce_dep_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
plt.figure(figsize=(6, 4))
plt.plot(c100_ce_sym_acc*100, color="blue", label="sym")
plt.plot(c100_ce_asym_acc*100, color="orange", label="asym")
plt.plot(c100_ce_dep_acc*100, color="green", label="dep")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (CE) on CIFAR-100")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(10, 72)
plt.tight_layout()
fig_name = "c100-ce-test-acc.png"
plt.savefig(os.path.join(results_path, fig_name))
# c100 sln test acc
dataset_name, model, noise_mode, if_train = "cifar100", "sln", "symmetric", False
c100_sln_sym_acc, c100_sln_sym_acc_mean, c100_sln_sym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar100", "sln", "asymmetric", False
c100_sln_asym_acc, c100_sln_asym_acc_mean, c100_sln_asym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar100", "sln", "dependent", False
c100_sln_dep_acc, c100_sln_dep_acc_mean, c100_sln_dep_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
plt.figure(figsize=(6, 4))
plt.plot(c100_sln_sym_acc*100, color="blue", label="sym")
plt.plot(c100_sln_asym_acc*100, color="orange", label="asym")
plt.plot(c100_sln_dep_acc*100, color="green", label="dep")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (SLN) on CIFAR-100")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(10, 72)
plt.tight_layout()
fig_name = "c100-sln-test-acc.png"
plt.savefig(os.path.join(results_path, fig_name))
# c100 sln-mo test acc
dataset_name, model, noise_mode, if_train = "cifar100", "sln-mo", "symmetric", False
c100_sln_mo_sym_acc, c100_sln_mo_sym_acc_mean, c100_sln_mo_sym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar100", "sln-mo", "asymmetric", False
c100_sln_mo_asym_acc, c100_sln_mo_asym_acc_mean, c100_sln_mo_asym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar100", "sln-mo", "dependent", False
c100_sln_mo_dep_acc, c100_sln_mo_dep_acc_mean, c100_sln_mo_dep_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
plt.figure(figsize=(6, 4))
plt.plot(c100_sln_mo_sym_acc*100, color="blue", label="sym")
plt.plot(c100_sln_mo_asym_acc*100, color="orange", label="asym")
plt.plot(c100_sln_mo_dep_acc*100, color="green", label="dep")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (SLN-MO) on CIFAR-100")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(10, 72)
plt.tight_layout()
fig_name = "c100-sln-mo-test-acc.png"
plt.savefig(os.path.join(results_path, fig_name))
# c100 sln-mo-lc test acc
dataset_name, model, noise_mode, if_train = "cifar100", "sln-mo-lc", "symmetric", False
c100_sln_mo_lc_sym_acc, c100_sln_mo_lc_sym_acc_mean, c100_sln_mo_lc_sym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar100", "sln-mo-lc", "asymmetric", False
c100_sln_mo_lc_asym_acc, c100_sln_mo_lc_asym_acc_mean, c100_sln_mo_lc_asym_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
dataset_name, model, noise_mode, if_train = "cifar100", "sln-mo-lc", "dependent", False
c100_sln_mo_lc_dep_acc, c100_sln_mo_lc_dep_acc_mean, c100_sln_mo_lc_dep_acc_std = get_accuracy(dataset_name, model, noise_mode, if_train)
plt.figure(figsize=(6, 4))
plt.plot(c100_sln_mo_lc_sym_acc*100, color="blue", label="sym")
plt.plot(c100_sln_mo_lc_asym_acc*100, color="orange", label="asym")
plt.plot(c100_sln_mo_lc_dep_acc*100, color="green", label="dep")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (SLN-MO-LC) on CIFAR-100")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(10, 72)
plt.tight_layout()
fig_name = "c100-sln-mo-lc-test-acc.png"
plt.savefig(os.path.join(results_path, fig_name))
plt.figure(figsize=(6, 4))
plt.plot(c100_sln_mo_lc_sym_acc*100, color="blue", label="sym")
plt.plot(c100_sln_mo_lc_asym_acc*100, color="orange", label="asym")
plt.plot(c100_sln_mo_lc_dep_acc*100, color="green", label="dep")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.title("Test Accuracy (SLN-MO-LC) on CIFAR-100")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(220, 300)
plt.ylim(40, 72)
plt.tight_layout()
fig_name = "c100-sln-mo-lc-test-acc-zoom.png"
plt.savefig(os.path.join(results_path, fig_name))
# c100 test acc table
data_cifar100 = {
"model": ["ce", "sln", "sln-mo", "sln-mo-lc"],
"symmetric": [
str(round(c100_ce_sym_acc_mean*100,2))+u"\u00B1"+str(round(c100_ce_sym_acc_std*100,2)),
str(round(c100_sln_sym_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_sym_acc_std*100,2)),
str(round(c100_sln_mo_sym_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_mo_sym_acc_std*100,2)),
str(round(c100_sln_mo_lc_sym_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_mo_lc_sym_acc_std*100,2)),
],
"asymmetric": [
str(round(c100_ce_asym_acc_mean*100,2))+u"\u00B1"+str(round(c100_ce_asym_acc_std*100,2)),
str(round(c100_sln_asym_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_asym_acc_std*100,2)),
str(round(c100_sln_mo_asym_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_mo_asym_acc_std*100,2)),
str(round(c100_sln_mo_lc_asym_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_mo_lc_asym_acc_std*100,2)),
],
"dependent": [
str(round(c100_ce_dep_acc_mean*100,2))+u"\u00B1"+str(round(c100_ce_dep_acc_std*100,2)),
str(round(c100_sln_dep_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_dep_acc_std*100,2)),
str(round(c100_sln_mo_dep_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_mo_dep_acc_std*100,2)),
str(round(c100_sln_mo_lc_dep_acc_mean*100,2))+u"\u00B1"+str(round(c100_sln_mo_lc_dep_acc_std*100,2)),
],
}
df_table_cifar100 = pd.DataFrame(data=data_cifar100)
table_name = "c100-test-acc-table.csv"
df_table_cifar100.to_csv(os.path.join(results_path, table_name))
# c100 ce train losses
dataset_name, model, noise_mode, loss_type = "cifar100", "ce", "symmetric", "clean"
c100_ce_sym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "ce", "symmetric", "noisy"
c100_ce_sym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "ce", "asymmetric", "clean"
c100_ce_asym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "ce", "asymmetric", "noisy"
c100_ce_asym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "ce", "dependent", "clean"
c100_ce_dep_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "ce", "dependent", "noisy"
c100_ce_dep_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
plt.figure(figsize=(6, 4))
plt.plot(c100_ce_sym_loss_train_clean, color="blue", label="sym clean")
plt.plot(c100_ce_sym_loss_train_noisy, color="blue", linestyle='dashed', label="sym noisy")
plt.plot(c100_ce_asym_loss_train_clean, color="orange", label="asym clean")
plt.plot(c100_ce_asym_loss_train_noisy, color="orange", linestyle='dashed', label="asym noisy")
plt.plot(c100_ce_dep_loss_train_clean, color="green", label="dep clean")
plt.plot(c100_ce_dep_loss_train_noisy, color="green", linestyle='dashed', label="dep noisy")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("CE Training Loss (CE) on CIFAR-100")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(0, 5.5)
plt.tight_layout()
fig_name = "c100-ce-train-loss.png"
plt.savefig(os.path.join(results_path, fig_name))
# c100 sln train losses
dataset_name, model, noise_mode, loss_type = "cifar100", "sln", "symmetric", "clean"
c100_sln_sym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln", "symmetric", "noisy"
c100_sln_sym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln", "asymmetric", "clean"
c100_sln_asym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln", "asymmetric", "noisy"
c100_sln_asym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln", "dependent", "clean"
c100_sln_dep_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln", "dependent", "noisy"
c100_sln_dep_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
plt.figure(figsize=(6, 4))
plt.plot(c100_sln_sym_loss_train_clean, color="blue", label="sym clean")
plt.plot(c100_sln_sym_loss_train_noisy, color="blue", linestyle='dashed', label="sym noisy")
plt.plot(c100_sln_asym_loss_train_clean, color="orange", label="asym clean")
plt.plot(c100_sln_asym_loss_train_noisy, color="orange", linestyle='dashed', label="asym noisy")
plt.plot(c100_sln_dep_loss_train_clean, color="green", label="dep clean")
plt.plot(c100_sln_dep_loss_train_noisy, color="green", linestyle='dashed', label="dep noisy")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("CE Training Loss (SLN) on CIFAR-100")
plt.legend(bbox_to_anchor=(1.04, 1), borderaxespad=0)
plt.xlim(0, 300)
plt.ylim(0, 5.5)
plt.tight_layout()
fig_name = "c100-sln-train-loss.png"
plt.savefig(os.path.join(results_path, fig_name))
# c100 sln-mo train losses
dataset_name, model, noise_mode, loss_type = "cifar100", "sln-mo", "symmetric", "clean"
c100_sln_mo_sym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln-mo", "symmetric", "noisy"
c100_sln_mo_sym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln-mo", "asymmetric", "clean"
c100_sln_mo_asym_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln-mo", "asymmetric", "noisy"
c100_sln_mo_asym_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln-mo", "dependent", "clean"
c100_sln_mo_dep_loss_train_clean = get_loss_train(dataset_name, model, noise_mode, loss_type)
dataset_name, model, noise_mode, loss_type = "cifar100", "sln-mo", "dependent", "noisy"
c100_sln_mo_dep_loss_train_noisy = get_loss_train(dataset_name, model, noise_mode, loss_type)
plt.figure(figsize=(6, 4))
plt.plot(c100_sln_mo_sym_loss_train_clean, color="blue", label="sym clean")
plt.plot(c100_sln_mo_sym_loss_train_noisy, color="blue", linestyle='dashed', label="sym noisy")