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addnoise_SN.py
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addnoise_SN.py
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import pickle
import numpy as np
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='latin1')
return dict
# r is noise rate
r = 0.1
count = 0
# add symmetric noise
a = unpickle('./cifar-10-batches-py/data_batch_1')
for i in range(10000):
if np.random.random()< r:
a['labels'][i] = np.random.randint(0,10)
count += 1
with open('./cifar-10-batches-py/data_batch_1','wb') as file:
pickle.dump(a,file)
a = unpickle('./cifar-10-batches-py/data_batch_2')
for i in range(10000):
if np.random.random() < r:
a['labels'][i] = np.random.randint(0, 10)
count += 1
with open('./cifar-10-batches-py/data_batch_2', 'wb') as file:
pickle.dump(a, file)
a = unpickle('./cifar-10-batches-py/data_batch_3')
for i in range(10000):
if np.random.random() < r:
a['labels'][i] = np.random.randint(0, 10)
count += 1
with open('./cifar-10-batches-py/data_batch_3', 'wb') as file:
pickle.dump(a, file)
a = unpickle('./cifar-10-batches-py/data_batch_4')
for i in range(10000):
if np.random.random() < r:
a['labels'][i] = np.random.randint(0, 10)
count += 1
with open('./cifar-10-batches-py/data_batch_4', 'wb') as file:
pickle.dump(a, file)
a = unpickle('./cifar-10-batches-py/data_batch_5')
for i in range(5000):
if np.random.random() < r:
a['labels'][i] = np.random.randint(0, 10)
count += 1
with open('./cifar-10-batches-py/data_batch_5', 'wb') as file:
pickle.dump(a, file)
print(count)