tensorflow框架的猫狗大战代码及详解.zip下载_course

这是利用卷积神经网络进行猫和狗的图像分类的相关代码，由于训练和测试的数据集较大，所以没有放上来，训练数据集需要自行下载 相关下载链接：//download.csdn.net/download/ttjb

knn猫狗识别算法中样本为0报错_course

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_)) ``` ```

猫狗大战全部代码 keras 深度学习案例下载_course

博客地址资源：https://blog.csdn.net/x1131230123/article/details/100900137 相关下载链接：//download.csdn.net/downlo

kaggle猫狗大战数据集下载_course

官网不开梯子没速度，分享一下，链接: https://pan.baidu.com/s/1o9yfRCI 密码: mvge 觉得好可以给两分 相关下载链接：//download.csdn.net/dow

手把手教你如何在Kaggle猫狗大战冲到Top2%-------运行成功python3.5------py源代码下载_course

手把手教你如何在Kaggle猫狗大战冲到Top2%-------运行成功python3.5------py源代码 相关下载链接：//download.csdn.net/download/yuwenle