我正在尝试读取CIFAR10数据集,该数据集是从https://www.cs.toronto.edu/~kriz/cifar.html >分批给出的。我正在尝试使用泡菜将其放在数据框中,并读取其中的“数据”部分。但是我遇到了这个错误。
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
KeyError:“数据”。
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')
您可以通过下面给出的代码读取cifar 10数据集,但请确保已给批处理所在的写入目录
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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我来说两句