1.使用scikit-learn（GridSearchCV）进行网格搜索超参数调整（Python代码，包括数据集）

# USAGE # python train_model.py --dataset texture_dataset # import the necessary packages from pyimagesearch.localbinarypatterns import LocalBinaryPatterns from sklearn.model_selection import GridSearchCV from sklearn.metrics import classification_report from sklearn.svm import SVC from sklearn.model_selection import train_test_split from imutils import paths import argparse import time import cv2 import os # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-d", "--dataset", required=True, help="path to input dataset") args = vars(ap.parse_args()) # grab the image paths in the input dataset directory imagePaths = list(paths.list_images(args["dataset"])) # initialize the local binary patterns descriptor along with # the data and label lists print("[INFO] extracting features...") desc = LocalBinaryPatterns(24, 8) data = [] labels = [] # loop over the dataset of images for imagePath in imagePaths: # load the image, convert it to grayscale, and quantify it # using LBPs image = cv2.imread(imagePath) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) hist = desc.describe(gray) # extract the label from the image path, then update the # label and data lists labels.append(imagePath.split(os.path.sep)[-2]) data.append(hist) # partition the data into training and testing splits using 75% of # the data for training and the remaining 25% for testing print("[INFO] constructing training/testing split...") (trainX, testX, trainY, testY) = train_test_split(data, labels, random_state=22, test_size=0.25) # construct the set of hyperparameters to tune parameters = [ {"kernel": ["linear"], "C": [0.0001, 0.001, 0.1, 1, 10, 100, 1000]}, {"kernel": ["poly"], "degree": [2, 3, 4], "C": [0.0001, 0.001, 0.1, 1, 10, 100, 1000]}, {"kernel": ["rbf"], "gamma": ["auto", "scale"], "C": [0.0001, 0.001, 0.1, 1, 10, 100, 1000]} ] # tune the hyperparameters via a cross-validated grid search print("[INFO] tuning hyperparameters via grid search") grid = GridSearchCV(estimator=SVC(), param_grid=parameters, n_jobs=-1) start = time.time() grid.fit(trainX, trainY) end = time.time() # show the grid search information print("[INFO] grid search took {:.2f} seconds".format( end - start)) print("[INFO] grid search best score: {:.2f}%".format( grid.best_score_ * 100)) print("[INFO] grid search best parameters: {}".format( grid.best_params_)) # grab the best model and evaluate it print("[INFO] evaluating...") model = grid.best_estimator_ predictions = model.predict(testX) print(classification_report(testY, predictions))