dataset

来源： https://stackoverflow.com/questions/2912723/dataset-writexml-does-not-write-an-empty-tag-if-the-field-was-empty-c-sharp

来源： https://stackoverflow.com/questions/54590363/create-tensorflow-dataset-from-image-local-directory

来源： https://stackoverflow.com/questions/34907801/in-ssis-add-the-text-file-name-as-a-column-in-the-results-dataset

问题 I am new to Synthia dataset. I would like to read the label file from this datset. I expect to have one channel matrix with size of my RGB image, but when I load the data I got 3x760x1280 and it is full of zeros. I tried to read as belows: label = np.asarray(imread(label_path)) Can anyone help to read these labels file correctly? 回答1: I found the right way to read it as below: label = np.asarray(imageio.imread(label_path, format='PNG-FI'))[:,:,0] 来源： https://stackoverflow.com/questions

问题 I am using TF 1.8 with eager mode enabled. I cannot print the example inside the mapfunc. It when I run tf.executing_eagerly() from within the mapfunc I get "False" import os import tensorflow as tf tf.logging.set_verbosity(tf.logging.ERROR) tfe = tf.contrib.eager tf.enable_eager_execution() x = tf.random_uniform([16,10], -10, 0, tf.int64) print(x) DS = tf.data.Dataset.from_tensor_slices((x)) def mapfunc(ex, con): import pdb; pdb.set_trace() new_ex = ex + con print(new_ex) return new_ex DS =

问题 I'm trying to split the iris dataset into a training set and a test set. I used createDataPartition() like this: library(caret) createDataPartition(iris$Species, p=0.1) # [1] 12 22 26 41 42 57 63 79 89 93 114 117 134 137 142 createDataPartition(iris$Sepal.Length, p=0.1) # [1] 1 27 44 46 54 68 72 77 83 84 93 99 104 109 117 132 134 I understand the first query. I have a vector of 0.1*150 elements (150 is the number of samples in the dataset). However, I should have the same vector on the second

问题 I'm trying to split the iris dataset into a training set and a test set. I used createDataPartition() like this: library(caret) createDataPartition(iris$Species, p=0.1) # [1] 12 22 26 41 42 57 63 79 89 93 114 117 134 137 142 createDataPartition(iris$Sepal.Length, p=0.1) # [1] 1 27 44 46 54 68 72 77 83 84 93 99 104 109 117 132 134 I understand the first query. I have a vector of 0.1*150 elements (150 is the number of samples in the dataset). However, I should have the same vector on the second

问题 I'm trying to split the iris dataset into a training set and a test set. I used createDataPartition() like this: library(caret) createDataPartition(iris$Species, p=0.1) # [1] 12 22 26 41 42 57 63 79 89 93 114 117 134 137 142 createDataPartition(iris$Sepal.Length, p=0.1) # [1] 1 27 44 46 54 68 72 77 83 84 93 99 104 109 117 132 134 I understand the first query. I have a vector of 0.1*150 elements (150 is the number of samples in the dataset). However, I should have the same vector on the second

问题 Issue with Unix Split command for splitting large data : split -l 1000 file.json myfile . Want to split this file into multiple files of 1000 records each. But Im getting the output as single file - no change. P.S. File is created converting Pandas Dataframe to JSON. Edit: It turn outs that my JSON is formatted in a way that it contains only one row. wc -l file.json is returning 0 Here is the sample: file.json [ {"id":683156,"overall_rating":5.0,"hotel_id":220216,"hotel_name":"Beacon Hill

问题 Issue with Unix Split command for splitting large data : split -l 1000 file.json myfile . Want to split this file into multiple files of 1000 records each. But Im getting the output as single file - no change. P.S. File is created converting Pandas Dataframe to JSON. Edit: It turn outs that my JSON is formatted in a way that it contains only one row. wc -l file.json is returning 0 Here is the sample: file.json [ {"id":683156,"overall_rating":5.0,"hotel_id":220216,"hotel_name":"Beacon Hill