qq_36777103 2019-12-19 22:31 采纳率: 0%
浏览 2229

knn猫狗识别算法中样本为0报错

错误如下:
ValueError: With n_samples=0, test_size=0.25 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.

代码如下:
# import the necessary packages
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from pyimagesearch.preprocessing import SimplePreprocessor
from pyimagesearch.datasets import SimpleDatasetLoader
from imutils import paths
import argparse

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--datasets", type=str, default="‪H:\111\try1\pyimagesearch\datasets\animals\",
help="path to input dataset")
ap.add_argument("-k", "--neighbors", type=int, default=1,
help="# of nearest neighbors for classification")
ap.add_argument("-j", "--jobs", type=int, default=-1,
help="# of jobs for k-NN distance (-1 uses all available cores)")
args = vars(ap.parse_args())

# grab the list of images that we’ll be describing
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["datasets"]))
# initialize the image preprocessor, load the dataset from disk,
# and reshape the data matrix
sp = SimplePreprocessor.SimplePreprocessor(32, 32)
sdl = SimpleDatasetLoader.SimpleDatasetLoader(preprocessors=[sp])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.reshape((data.shape[0], 3072))
# show some information on memory consumption of the images
print("[INFO] features matrix: {:.1f}MB".format(
data.nbytes / (1024 * 1000.0)))

encode the labels as integers

le = LabelEncoder()
labels = le.fit_transform(labels)

partition the data into training and testing splits using 75% of

the data for training and the remaining 25% for testing

(trainX, testX, trainY, testY) = train_test_split(data, labels,
test_size=0.25, random_state=42)

# train and evaluate a k-NN classifier on the raw pixel intensities
print("[INFO] evaluating k-NN classifier...")
model = KNeighborsClassifier(n_neighbors=args["neighbors"],
n_jobs=args["jobs"])
model.fit(trainX, trainY)
print(classification_report(testY, model.predict(testX),
target_names=le.classes_))


  • 写回答

1条回答 默认 最新

  • 去吧---皮卡丘 2020-03-19 15:31
    关注

    sklearn的版本高,降一下,sklearn==0.19.1你试试

    评论

报告相同问题?

悬赏问题

  • ¥15 CNVcaller安装后无法找到文件
  • ¥15 visual studio2022中文乱码无法解决
  • ¥15 关于华为5g模块mh5000-31接线问题
  • ¥15 keil L6007U报错
  • ¥15 webapi 发布到iis后无法访问
  • ¥15 初学者如何快速上手学习stm32?
  • ¥15 如何自动更换布娃娃图片上的衣服
  • ¥15 心理学eprime编程
  • ¥15 arduino esp8266开发
  • ¥15 stm32单片机通过485发送命令给驱动器控制电机转动,同样的代码f103可以控制电机转动,换到f407不能动了,但是用串口助手调试407显示发送的命令都是正确的,卡了好久了这是发送规则