In [7]:

    

import numpy as np
import cPickle
import matplotlib.pyplot as plt


def extractImagesAndLabels(path, file):
    f = open(path+file, 'rb')
    dict = cPickle.load(f)
    images = dict['data']
    images = np.reshape(images, (10000, 3, 32, 32))
    labels = dict['labels']
    return images, labels

def extractCategories(path, file):
    f = open(path+file, 'rb')
    dict = cPickle.load(f)
    return dict['label_names']

images, labels = extractImagesAndLabels("data/CIFAR-10/cifar-10-batches-py/", "test_batch")
categories = extractCategories("data/CIFAR-10/cifar-10-batches-py/", "batches.meta")

def getImage(images, id):
    image = images[id]
    image = image.transpose([1, 2, 0])
    image = image.astype('float32')
    image /= 255
    return image

imgid=35
image = getImage(images, imgid)
%matplotlib inline
imgplot = plt.imshow(image)
categoryid = labels[imgid]
print(categories[categoryid])


    

    




bird






    

In [8]:

    

from keras.datasets import cifar10
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
print(X_test.shape[0], 'testing samples')
image=X_test[0]
image = image.astype('float32')
image /= 255
%matplotlib inline
imgplot = plt.imshow(image)


    

    




('X_train shape:', (50000, 32, 32, 3))
('X_test shape:', (10000, 32, 32, 3))
(10000, 'testing samples')






    

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