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Commit 6ed0414c authored by Picorba's avatar Picorba
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import pickle
import numpy as np
import glob
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
def read_cifar_batch(path_to_batch_file):
dict=unpickle(path_to_batch_file)
data=np.array(dict[b'data'],dtype=np.float32)/255
labels=np.array(dict[b'labels'],dtype=np.int64)
return data,labels
def read_cifar(path_to_batches_files):
files=glob.glob(path_to_batches_files)
data,labels=read_cifar_batch(files[0])
for i in range(1,len(files)):
data_temp,labels_temp=read_cifar_batch(files[i])
data=np.concatenate((data,data_temp),axis=0)
labels=np.concatenate((labels,labels_temp),axis=0)
return data,labels
if __name__ == "__main__":
path="image-classification/data/cifar-10-batches-py/data_batch_1"
read_cifar_batch(path)
#plot the 9 first image of the batch
import matplotlib.pyplot as plt
data,labels=read_cifar_batch(path)
fig, axes = plt.subplots(3, 3)
fig.subplots_adjust(hspace=0.6, wspace=0.3)
for i, ax in enumerate(axes.flat):
ax.imshow(data[i].reshape(3, 32, 32).transpose([1, 2, 0]))
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlabel(labels[i])
plt.show()
test.ipynb 0 → 100644
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