Python 3.x 批量读取Cifar10数据集

我正在尝试读取CIFAR10数据集，这些数据集是从>分批提供的。我试图用pickle把它放在一个数据框中，并读取其中的“数据”部分。但我得到了这个错误

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

试试这个

def取消勾选（文件）：
进口包装
打开（文件“rb”）作为fo时：
数据=cPickle.load（fo）
返回数据

我知道原因！我也有同样的问题，我解决了！ 关键问题在于编码方法，将代码从

dict = pickle.load(fo, encoding ='bytes')

到

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您可以通过下面给出的代码读取cifar 10数据集，但只需确保您提供了批次所在的写入目录

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)

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我过去也经历过类似的问题

---

我想对未来的读者说，您可以找到一个python包装器，用于自动下载、提取和解析cifar10数据集

添加打印dict.keys（）并查看inside@lejlotdict_键（[b'batch_label'，b'data'，b'labels'，b'filenames']）…试着做

X=dict[b'data']

thenyeah..刚刚做了，结果证明它是有效的：）…无论如何，谢谢@lejlot为什么OP要“尝试这个”？一个好的答案总是会有一个解释，说明做了什么以及为什么这样做，不仅是为了OP，而且是为了SO的未来访客。这对我很有用。非常感谢。

dict = pickle.load(fo, encoding ='latin1')

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)