如何使用tensorflow在X_train、y_train、X_test、y_test中分割图像数据集

Question

如何将图像数据拆分为X_train、Y_train、X_test和Y_test？

我使用keras与tensorflow后端

谢谢。

Answer 1

例如，您有这样的文件夹

full_dataset
|--horse (40 images)
|--donkey (30 images)
|--cow ((50 images)
|--zebra (70 images)

第一路

import glob
horse = glob.glob('full_dataset/horse/*.*')
donkey = glob.glob('full_dataset/donkey/*.*')
cow = glob.glob('full_dataset/cow/*.*')
zebra = glob.glob('full_dataset/zebra/*.*')
data = []
labels = []
for i in horse:   
image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
target_size= (280,280))
image=np.array(image)
data.append(image)
labels.append(0)
for i in donkey:   
image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
target_size= (280,280))
image=np.array(image)
data.append(image)
labels.append(1)
for i in cow:   
image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
target_size= (280,280))
image=np.array(image)
data.append(image)
labels.append(2)
for i in zebra:   
image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
target_size= (280,280))
image=np.array(image)
data.append(image)
labels.append(3)
data = np.array(data)
labels = np.array(labels)
from sklearn.model_selection import train_test_split
X_train, X_test, ytrain, ytest = train_test_split(data, labels, test_size=0.2,
random_state=42)

第二路

image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2)    
train_dataset = image_generator.flow_from_directory(batch_size=32,
directory='full_dataset',
shuffle=True,
target_size=(280, 280), 
subset="training",
class_mode='categorical')
validation_dataset = image_generator.flow_from_directory(batch_size=32,
directory='full_dataset',
shuffle=True,
target_size=(280, 280), 
subset="validation",
class_mode='categorical')

第二种方式的主要缺点是，你不能用来显示图片。如果您写入validation_dataset[1]，它将出错。但如果我使用第一种方式：X_test[1]，它就起作用了

Answer 2

您不必使用tensorflow或keras来划分数据集。如果你安装了sklearn软件包，那么你可以简单地使用它：

from sklearn.model_selection import train_test_split
X = ...
Y = ...
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)

您也可以将numpy用于相同的目的：

import numpy
X = ...
Y = ...
test_size = 0.2
train_nsamples = (1-test_size) * len(Y)
x_train, x_test, y_train, y_test = X[:train_nsamples,:], X[train_nsamples:, :], Y[:train_nsamples, ], Y[train_nsamples:,]

祝你好运！