批量读取Cifar10数据集

Question

我正在尝试读取 CIFAR10 数据集，这些数据集是从 https://www.cs.toronto.edu/~kriz/cifar.html> 中分批提供的。我正在尝试使用 pickle 将其放入数据框中并读取其中的 'data' 部分。但是我收到了这个错误。

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

按键错误：'data'.

我正在使用 ipython，这是我的代码：

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

试试这个

def unpickle(file): import cPickle with open(file, 'rb') as fo: data = cPickle.load(fo) return data

Answer 2

我知道原因了！我有同样的问题，我解决了！关键问题是关于编码方式，把代码改成

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

到

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

Answer 3

您可以通过下面给出的代码读取 cifar 10 数据集，只需确保您提供了放置批次的写入目录

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)

Answer 4

我过去也遇到过类似的问题。

我想提醒未来的读者，您可以找到 here 一个 python 包装器，用于自动下载、提取和解析 cifar10 数据集。

批量读取Cifar10数据集

Reading Cifar10 dataset in batches

machine-learning

computer-vision

batch-processing

python-3.x