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eval.py
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eval.py
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import json
import glob
import argparse
import matplotlib.pylab as plt
def plot_results(list_log, to_plot="losses"):
list_color = [u'#E24A33',
u'#348ABD',
u'#FBC15E',
u'#777777',
u'#988ED5',
u'#8EBA42',
u'#FFB5B8']
plt.figure()
for idx, log in enumerate(list_log):
with open(log, "r") as f:
d = json.load(f)
experiment_name = d["experiment_name"]
color = list_color[idx]
plt.plot(d["train_%s" % to_plot],
color=color,
linewidth=3,
label=experiment_name)
#label="Train %s" % experiment_name)
plt.plot(d["val_%s" % to_plot],
color=color,
linestyle="--",
linewidth=3,)
plt.ylabel(to_plot, fontsize=20)
if to_plot == "losses":
plt.yscale("log")
if to_plot == "accs":
plt.ylim([0, 1.1])
plt.xlabel("Number of epochs", fontsize=20)
plt.title("%s experiment" % dataset, fontsize=22)
plt.legend(loc="best")
plt.tight_layout()
plt.savefig("./figures/%s_results_%s.eps" % (dataset, to_plot),format='eps',dpi=1000)
plt.savefig("./figures/%s_results_%s.png" % (dataset, to_plot))
plt.show()
if __name__ == '__main__':
list_log = glob.glob("./log/*.json")
parser = argparse.ArgumentParser(description='Plot results of experiments')
parser.add_argument('--dataset', type=str,
help='name of the dataset: cifar10, cifar100 or mnist')
parser.add_argument('--to_plot', type=str, default="losses",
help='metric to plot: losses (log loss) or accs (accuracies)')
args = parser.parse_args()
dataset = args.dataset
list_log = [l for l in list_log if dataset in l]
plot_results(list_log, to_plot=args.to_plot)