Split data into Train data and Test data for Cross-validation【Python】 - Qiita

from PIL import Image import os, glob import numpy as np from sklearn import model_selection classes = ["apple", "banana", "grape"] num_classes = len(classes) image_size = 50 X = [] Y = [] for i, classlabel in enumerate(classes): photos_dir = "./" + classlabel files = glob.glob(photos_dir + "/*.jpg") for i, file in enumerate(files): if i >= 200 : break image = Image.open(file) image = image.conver