Graph about deepcoral HOT 3 CLOSED

import pickle
import matplotlib.pyplot as plt

with open('training_statistic.pkl', 'rb') as data:
  training = pickle.load(data)
  ...

import pickle
import matplotlib.pyplot as plt

training = []
testing_s = []
testing_t = []
testing_so = []
testing_to = []


with open('w-coral-256/training_statistic.pkl', 'rb') as data:
  training = pickle.load(data)

with open('w-coral-256/testing_s_statistic.pkl', 'rb') as data:
  testing_s = pickle.load(data)

with open('w-coral-256/testing_t_statistic.pkl', 'rb') as data:
  testing_t = pickle.load(data)

with open('wo-coral-256/testing_s_statistic.pkl', 'rb') as data:
  testing_so = pickle.load(data)

with open('wo-coral-256/testing_t_statistic.pkl', 'rb') as data:
  testing_to = pickle.load(data)

summary = {}
summary['training_acc'] = [ep['accuracy'] for ep in testing_s]
summary['testing_acc'] = [ep['accuracy'] for ep in testing_t]
summary['training_acco'] = [ep['accuracy'] for ep in testing_so]
summary['testing_acco'] = [ep['accuracy'] for ep in testing_to]

plt.figure(figsize=(8,5))
plt.plot(summary['testing_acc'], 'b', marker='*', fillstyle='none', label='testing acc. w/ coral loss')
plt.plot(summary['testing_acco'], 'g', marker='o', fillstyle='none', label='testing acc. w/o coral loss')
plt.plot(summary['training_acc'], 'r', marker='|', fillstyle='none', label='training acc. w/ coral loss')
plt.plot(summary['training_acco'], 'pink', marker='s', fillstyle='none', label='training acc. w/o coral loss')

plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.title('Testing Accuracy (Batch = [200, 56])')
plt.legend()
plt.savefig('accuracy.png')
# plt.show()


print(len(training[0]))
loss = {}
loss['coral_loss'] = [x['coral_loss'] for ep in training for x in ep]
loss['classification_loss'] = [x['classification_loss'] for ep in training for x in ep]
plt.figure(figsize=(6.84,5.44))
plt.plot(loss['coral_loss'], 'b', label='CORAL loss')
plt.plot(loss['classification_loss'], 'r', label='classification loss')
plt.title('Loss')
plt.legend()
plt.xlabel('iterations')
plt.ylabel('loss')
plt.savefig('loss.png')
# plt.show()