Python深度學習筆記(三)：使用Keras載入常見的電腦視覺資料集

Yanwei Liu

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5 min read

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May 7, 2019

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用Keras載入四種常見的資料:

• MNIST: 分類手寫數字 (10 classes)
• Fashion-MNIST:分類服裝 (10 classes)
• CIFAR-10: 分類小圖片物件 (10 classes)
• CIFAR-100: 分類常見物件 (100 classes)

MNIST

from keras.datasets import mnistfrom matplotlib import pyplot# load dataset(trainX, trainy), (testX, testy) = mnist.load_data()# summarize loaded datasetprint('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))print('Test: X=%s, y=%s' % (testX.shape, testy.shape))# plot first few imagesfor i in range(9):    #define subplot
    pyplot.subplot(330 + 1 + i)
    #plot raw pixel data
    pyplot.imshow(trainX[i], cmap=pyplot.get_cmap('gray'))# show the figurepyplot.show()

Fashion-MNIST

# example of loading the fashion mnist datasetfrom matplotlib import pyplotfrom keras.datasets import fashion_mnist# load dataset(trainX, trainy), (testX, testy) = fashion_mnist.load_data()# summarize loaded datasetprint('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))print('Test: X=%s, y=%s' % (testX.shape, testy.shape))# plot first few imagesfor i in range(9):
    #define subplot
    pyplot.subplot(330 + 1 + i)
    #plot raw pixel data
    pyplot.imshow(trainX[i], cmap=pyplot.get_cmap('gray'))# show the figurepyplot.show()

CIFAR-10 Dataset

# example of loading the cifar10 dataset
from matplotlib import pyplot
from keras.datasets import cifar10
# load dataset
(trainX, trainy), (testX, testy) = cifar10.load_data()
# summarize loaded dataset
print(‘Train: X=%s, y=%s’ % (trainX.shape, trainy.shape))
print(‘Test: X=%s, y=%s’ % (testX.shape, testy.shape))
# plot first few images
for i in range(9):
    #define subplot
    pyplot.subplot(330 + 1 + i)
    #plot raw pixel data
     pyplot.imshow(trainX[i])
# show the figure
pyplot.show()

CIFAR-100 Dataset

# example of loading the cifar100 dataset
from matplotlib import pyplot
from keras.datasets import cifar100
# load dataset
(trainX, trainy), (testX, testy) = cifar100.load_data()
# summarize loaded dataset
print('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))
print('Test: X=%s, y=%s' % (testX.shape, testy.shape))
# plot first few images
for i in range(9):
    #define subplot
    pyplot.subplot(330 + 1 + i)
    #plot raw pixel data
    pyplot.imshow(trainX[i])
# show the figure
pyplot.show()