Reading Cifar10 dataset in batches

Question

i am trying to read the CIFAR10 datasets, given in batches from https://www.cs.toronto.edu/~kriz/cifar.html>. i am trying to put it in a data frame using pickle and read 'data' part of it. But i am getting this error .

KeyError                                  Traceback (most recent call last)
<ipython-input-24-8758b7a31925> in <module>()
----> 1 unpickle('datasets/cifar-10-batches-py/test_batch')

<ipython-input-23-04002b89d842> in unpickle(file)
      3     fo = open(file, 'rb')
      4     dict = pickle.load(fo, encoding ='bytes')
----> 5     X = dict['data']
      6     fo.close()
      7     return dict

KeyError: 'data'.

i am using ipython and here is my code :

def unpickle(file):

 fo = open(file, 'rb')
 dict = pickle.load(fo, encoding ='bytes')
 X = dict['data']
 fo.close()
 return dict

unpickle('datasets/cifar-10-batches-py/test_batch')

Answer 1

you can read cifar 10 datasets by the code given below only make sure that you are giving write directory where the batches are placed

import tensorflow as tf
import pandas as pd
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
from six.moves import cPickle as pickle
import os
import platform
from subprocess import check_output
classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

%matplotlib inline


img_rows, img_cols = 32, 32
input_shape = (img_rows, img_cols, 3)
def load_pickle(f):
    version = platform.python_version_tuple()
    if version[0] == '2':
        return  pickle.load(f)
    elif version[0] == '3':
        return  pickle.load(f, encoding='latin1')
    raise ValueError("invalid python version: {}".format(version))

def load_CIFAR_batch(filename):
    """ load single batch of cifar """
    with open(filename, 'rb') as f:
        datadict = load_pickle(f)
        X = datadict['data']
        Y = datadict['labels']
        X = X.reshape(10000,3072)
        Y = np.array(Y)
        return X, Y

def load_CIFAR10(ROOT):
    """ load all of cifar """
    xs = []
    ys = []
    for b in range(1,6):
        f = os.path.join(ROOT, 'data_batch_%d' % (b, ))
        X, Y = load_CIFAR_batch(f)
        xs.append(X)
        ys.append(Y)
    Xtr = np.concatenate(xs)
    Ytr = np.concatenate(ys)
    del X, Y
    Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))
    return Xtr, Ytr, Xte, Yte
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
    # Load the raw CIFAR-10 data
    cifar10_dir = '../input/cifar-10-batches-py/'
    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)

    # Subsample the data
    mask = range(num_training, num_training + num_validation)
    X_val = X_train[mask]
    y_val = y_train[mask]
    mask = range(num_training)
    X_train = X_train[mask]
    y_train = y_train[mask]
    mask = range(num_test)
    X_test = X_test[mask]
    y_test = y_test[mask]

    x_train = X_train.astype('float32')
    x_test = X_test.astype('float32')

    x_train /= 255
    x_test /= 255

    return x_train, y_train, X_val, y_val, x_test, y_test


# Invoke the above function to get our data.
x_train, y_train, x_val, y_val, x_test, y_test = get_CIFAR10_data()


print('Train data shape: ', x_train.shape)
print('Train labels shape: ', y_train.shape)
print('Validation data shape: ', x_val.shape)
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', x_test.shape)
print('Test labels shape: ', y_test.shape)