Question

您好我下载了cifar-10数据集。

在我的代码中，它加载数据集如下。

import cv2
import numpy as np

from keras.datasets import cifar10
from keras import backend as K
from keras.utils import np_utils

nb_train_samples = 3000 # 3000 training samples
nb_valid_samples = 100 # 100 validation samples
num_classes = 10

def load_cifar10_data(img_rows, img_cols):

    # Load cifar10 training and validation sets
    (X_train, Y_train), (X_valid, Y_valid) = cifar10.load_data()

    # Resize trainging images
    if K.image_dim_ordering() == 'th':
        X_train = np.array([cv2.resize(img.transpose(1,2,0), (img_rows,img_cols)).transpose(2,0,1) for img in X_train[:nb_train_samples,:,:,:]])
        X_valid = np.array([cv2.resize(img.transpose(1,2,0), (img_rows,img_cols)).transpose(2,0,1) for img in X_valid[:nb_valid_samples,:,:,:]])
    else:
        X_train = np.array([cv2.resize(img, (img_rows,img_cols)) for img in X_train[:nb_train_samples,:,:,:]])
        X_valid = np.array([cv2.resize(img, (img_rows,img_cols)) for img in X_valid[:nb_valid_samples,:,:,:]])

    # Transform targets to keras compatible format
    Y_train = np_utils.to_categorical(Y_train[:nb_train_samples], num_classes)
    Y_valid = np_utils.to_categorical(Y_valid[:nb_valid_samples], num_classes)

    return X_train, Y_train, X_valid, Y_valid

但是下载数据集需要很长时间。相反，我下载了＃cifar-10-python.tar.gz＆＃39;手动。那么如何将其加载到变量（X_train，Y_train），（X_valid，Y_valid）而不是使用cifar10.load_data（）？

Answer 1

请原谅我的英语。我也试图手动加载cifar-10数据集。在以下代码中，我将cifar-10-python.tar.gz解压缩到一个文件夹，并将文件夹中的文件data_batch_1加载到4个数组中：x_train，y_train，x_test，{{ 1}}。 20％的y_test用于data_batch_1和x_test进行验证，其余用于y_test和x_train的培训。

y_train

Answer 2

此处的代码从dataset website中所述的各个批处理文件中读取训练和测试图像，对this post进行了修改并给出了很好的解释。

import pickle
import numpy as np

for i in range(1,6):
    path = 'data_batch_' + str(i)
    with open(path, mode='rb') as file:
        # note the encoding type is 'latin1'
        batch = pickle.load(file, encoding='latin1')
    if i == 1:  
        x_train = (batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)).astype('float32')
        y_train = batch['labels']
    else:
        x_train_temp = (batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)).astype('float32')
        y_train_temp = batch['labels']
        x_train = np.concatenate((x_train,x_train_temp),axis = 0)
        y_train = np.concatenate((y_train,y_train_temp),axis=0)

path = 'test_batch'
with open(path,'rb') as file:
    # note the encoding type is 'latin1'
    batch = pickle.load(file, encoding='latin1')
    x_test = (batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)).astype('float32')
    y_test = batch['labels']

我们可以将读取的数据可视化如下：

import matplotlib.pyplot as plt

x_train=x_train.astype(np.uint8)
y_train = np.expand_dims(y_train, axis = 1)

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
           'dog', 'frog', 'horse', 'ship', 'truck']

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(np.squeeze(x_train[i]), cmap=plt.cm.binary)
    # The CIFAR labels happen to be arrays, 
    # which is why you need the extra index
    plt.xlabel(class_names[y_train[i][0]])
plt.show()

另外，see here可能是您唯一的问题，如果您仍然需要下载时间，则仍然可以使用load_data()。

cifar10.load_data（）需要很长时间才能下载数据

2 个答案: