dataset

问题 If we have filled a DataSet using a select query in C#, how can we read the column values? I want to do something like this: string name = DataSetObj.rows[0].columns["name"] What would the correct syntax look like to achieve my goal? 回答1: foreach(var row in DataSetObj.Tables[0].Rows) { Console.WriteLine(row["column_name"]); } 回答2: If you already have a dataset, it's something like this; object value = dataSet.Tables["MyTable"].Rows[index]["MyColumn"] If you are using a DataReader: using

问题 When I load a dataset, I wonder if there is any quick way to find the number of samples or batches in that dataset. I know that if I load a dataset with with_info=True , I can see for example total_num_examples=6000, but this information is not available if I split a dataset. Currently, I count the number of samples as follows, but wondering if there is any better solution: train_subsplit_1, train_subsplit_2, train_subsplit_3 = tfds.Split.TRAIN.subsplit(3) cifar10_trainsub3 = tfds.load(

问题 I have a large dataset that I would like to use for training in Tensorflow. The data is stored in compressed numpy format (using numpy.savez_compressed ). There are variable numbers of images per file due to the way they are produced. Currently I use a Keras Sequence based generator object to train, but I'd like to move entirely to Tensorflow without Keras. I'm looking at the Dataset API on the TF website, but it is not obvious how I might use this to read numpy data. My first idea was this

问题 I have a large dataset that I would like to use for training in Tensorflow. The data is stored in compressed numpy format (using numpy.savez_compressed ). There are variable numbers of images per file due to the way they are produced. Currently I use a Keras Sequence based generator object to train, but I'd like to move entirely to Tensorflow without Keras. I'm looking at the Dataset API on the TF website, but it is not obvious how I might use this to read numpy data. My first idea was this

问题 Can someone please help how to get the list of built-in data sets and their dependency packages? 回答1: There are several ways to find the included datasets in R: 1: Using data() will give you a list of the datasets of all loaded packages (and not only the ones from the datasets package); the datasets are ordered by package 2: Using data(package = .packages(all.available = TRUE)) will give you a list of all datasets in the available packages on your computer (i.e. also the not-loaded ones) 3:

问题 Can someone please help how to get the list of built-in data sets and their dependency packages? 回答1: There are several ways to find the included datasets in R: 1: Using data() will give you a list of the datasets of all loaded packages (and not only the ones from the datasets package); the datasets are ordered by package 2: Using data(package = .packages(all.available = TRUE)) will give you a list of all datasets in the available packages on your computer (i.e. also the not-loaded ones) 3:

问题 I need to take exactly 10 files (images) in a dataset randomly, but this dataset is hierarchically structured. So I need that for each subdirectory that contains images hold just 10 of them randomly. Is there an easy way to do that or I should do it manually? def getListOfFiles(dirName): ### create a list of file and sub directories ### names in the given directory listOfFile = os.listdir(dirName) allFiles = list() ### Iterate over all the entries for entry in listOfFile: ### Create full path

问题 I need to take exactly 10 files (images) in a dataset randomly, but this dataset is hierarchically structured. So I need that for each subdirectory that contains images hold just 10 of them randomly. Is there an easy way to do that or I should do it manually? def getListOfFiles(dirName): ### create a list of file and sub directories ### names in the given directory listOfFile = os.listdir(dirName) allFiles = list() ### Iterate over all the entries for entry in listOfFile: ### Create full path

问题 I need to take exactly 10 files (images) in a dataset randomly, but this dataset is hierarchically structured. So I need that for each subdirectory that contains images hold just 10 of them randomly. Is there an easy way to do that or I should do it manually? def getListOfFiles(dirName): ### create a list of file and sub directories ### names in the given directory listOfFile = os.listdir(dirName) allFiles = list() ### Iterate over all the entries for entry in listOfFile: ### Create full path

问题 I am trying to infer tinyYOLO-V2 with INT8 weights and activation. I can convert the weights to INT8 with TFliteConverter. For INT8 activation, I have to give representative dataset to estimate the scaling factor. My method of creating such dataset seems wrong. What is the correct procedure ? def rep_data_gen(): a = [] for i in range(160): inst = anns[i] file_name = inst['filename'] img = cv2.imread(img_dir + file_name) img = cv2.resize(img, (NORM_H, NORM_W)) img = img / 255.0 img = img