Tensorflow Csv Dataset

Download_MNIST_CSV. I read it a bit but looks like you can only train it with their given MNIST data. Dataset in Tensorflow - Python. keras for training. line_terminator str, optional. Create a source dataset using one of the factory functions like Dataset. csv where index is the index of the example (running from 1 to 10000) and channel is the index of the channel (running from 1 to 5). End-to-End Deep Learning using Python and Cancer Dataset: An Application of Tensorflow and Keras by WACAMLDS Buy for $25 End-to-End Deep Learning using Python and Cancer Dataset: An Application of Tensorflow and Keras. Your data in CSV format. For a csv file, the dataset method reads one line at a time. The easiest way to start working with DataFrames is to use an example Databricks dataset available in the /databricks-datasetsfolder accessible within the Databricks workspace. A collection of datasets ready to use with TensorFlow - tensorflow/datasets. annotations_csv: csv file containing annotations for a single image. The mnist_test. Calls the decode_csv function with each element in the. Any additional features are not provided in the datasets, just the raw images are provided in ‘. jl does not support this file type, so in order to follow the exercise, we need to extract the data from the tfrecord-dataset. Multi-class classification is simply classifying objects into any one of multiple categories. A collection of datasets ready to use with TensorFlow - tensorflow/datasets. The code exposed will allow you to build a regression model, specify the categorical features and build your own activation function with Tensorflow. TensorFlow数据读取方式:Dataset API Datasets:一种为TensorFlow 模型创建输入管道的新方式。把数组、元组、张量等转换成DatasetV1Adapter格式. Dataset表示一系列元素,其中每个元素包含一个或多个Tensor对象。. Multi-class classification is simply classifying objects into any one of multiple categories. It is quite easy data from a directory using Python with or without tensorflow. Here are some examples of the digits included in the dataset: Let's create a Python program to work with this dataset. This document introduces the API by walking through two simple examples: Reading lines from a csv file. In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. DNNClassifier. For example, using the Dataset API, you can easily read in records from a large collection of files in parallel and join them into a single stream. I've been really struggling since 2-3 days and unfortunately I haven't made any real progress. We look into how to create TFRecords to and handle images from a custom dataset. This blog will cover the different Arrow datasets available and how they can be used to feed common TensorFlow workloads. You'll utilize ResNet-50 (pre-trained on ImageNet) to extract features from a large image dataset, and then use incremental learning to train a classifier on top of the extracted features. read_files() Read a dataset from a set of files. The new Open Images dataset gives us everything we need to train computer vision models, and just happens to be perfect for a demo!Tensorflow's Object Detection API and its ability to handle large volumes of data make it a perfect choice, so let's jump right in…. One such application is. Datasets Quick Start. In order to get started with Convolutional Neural Network in Tensorflow, I used the official tutorial as reference. Training a model from a CSV dataset. • Shredded train-dev-test. The API detects objects using ResNet-50 and ResNet-101 feature extractors trained on the iNaturalist Species Detection Dataset for 4 million iterations. MovieLens 1B Synthetic Dataset MovieLens 1B is a synthetic dataset that is expanded from the 20 million real-world ratings from ML-20M, distributed in support of MLPerf. py_func 能较完美地实现数据读取. I first list the names of my columns and how many I have: numerical_feature_names = ["N1", "N2"] categorical_feature_. It addresses the problem of MNIST being too easy for modern neural networks, along with some other issues. To feed the model, you need to separate the features from the label. Posted by Bryan Cutler Apache Arrow enables the means for high-performance data exchange with TensorFlow that is both standardized and optimized for analytics and machine learning. You must be able to load your data before you can start your machine learning project. Instead, you provide a function that returns inputs and labels. Now that you have the idea behind a convolutional neural network, you’ll code one in Tensorflow. data中的,从Tensor…. A collection of datasets ready to use with TensorFlow - tensorflow/datasets. Saturday, May 13, 2017 csv, csv extract row, delete python row csv, large csv split, mnist csv row to pil image, mnist grey scale dataset to black and white, python, reader, writer Friday, May 12, 2017. I find a lot of documents/forums telling how to convert a csv to a Tensorflow dataset, but not a single one saying how to convert a dataset to a csv. Download the TensorFlow models repository. In this dataset, we have two classes. So kann man die von uns erhobenen Daten direkt und ohne Aufwand selbst in TensorFlow-basierten Projekten verwenden und eigene Vorhersagemodelle erstellen. Run download_and_prepare locally; 3. repeat(repeat_count) # Repeats dataset this # times dataset = dataset. Distributed Computing with TensorFlow TensorFlow supports reading larger datasets, specifically so that the data is never all kept in memory at once (it wouldn’t be very useful if it had this limitation). It covered a big part of our. Here are some examples of the digits included in the dataset: Let's create a Python program to work with this dataset. data then use the tf. There will be parts that are revised and elaborated for better understanding, however, I hereby acknowledge that the following post is based on TensorFlow tutorial provided in: For more detailed…. The dataset we will be using in this tutorial is called the MNIST dataset, and it is a classic in the machine learning community. learn TensorFlow’s high-level machine learning API Easy to configure, train, and evaluate a variety of machine learning models Datasets available in tf. Outcome or class labels are converted to One Hot encoding. My dataset is more than 1TB big and I need to have the field 'Persona' on Mongo to query it fast. The training set consists of 6206 images of both cats and dogs of different breeds. It supports column type inference and many other features, like batching and shuffling, to make usage simple. Get batch size back from input dataset in Python using TensorFlow. It includes three iris species with 50 samples each as well as some properties about each flower. This blog will cover the different Arrow datasets available and how they can be used to feed common TensorFlow workloads. All the scripts mentioned in this section receive arguments from the command line and have help messages through the -h/--help flags. Retrieved from "http://ufldl. Tensorflow: working with tensorboard, CSV, and saving results - tensorFlowIrisCSV. TensorFlow also has support …. The clinician’s motorUPDRS score has to be predicted from the set of features. Now let’s build the random forest classifier using the train_x and train_y datasets. Before creating our neural net, it is best to explore the data to get an idea of the general form, properties, etc. You can vote up the examples you like or vote down the ones you don't like. The sample app is based on Pima Indians Diabetes dataset (available on Kaggle). Contrast this with a classification problem, where we aim to predict a discrete label (for…. Outputs will not be saved. map(lambda *items: tf. This question come up somewhere between lab1 and lab2 in week2 of Course3. Pre-requisites. The Apache Spark Dataset API provides a type-safe, object-oriented programming interface. It is trained on Wikipedia and the Book Corpus dataset. So kann man die von uns erhobenen Daten direkt und ohne Aufwand selbst in TensorFlow-basierten Projekten verwenden und eigene Vorhersagemodelle erstellen. will be intentionally skipped. CSV Splitter: The number of row per package is the number of rows for each CSV. I’ve been installing tensorflow by pip3 install ‘tensorflow==1. How to use TFRecords with the Dataset and Estimator APIs in TensorFlow. Datasets Quick Start. The time cost for just read 1000 iteration needs roughly 12s. For example, using the Dataset API, you can easily read in records from a large collection of files in parallel and join them into a single stream. I managed to extract MNIST as png images and CSV files but I really don't know what I am doing. https://github. Now for the dataset, we are going to use Youtube spam collection dataset provided by UCI Machine Learning Repository. Maps documents to sequences of word ids. A working, new version of TensorFlow installed. The dataset is divided in three disjoint sets: a balanced evaluation set, a balanced training set, and an unbalanced training set. 3的Datasets和Estimator,你了解多少? Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). This tutorial will walk through all the steps for building a custom object classification model using TensorFlow’s API. So if you're interested in creating a new, unique and impressive project then take a look at these blog posts where I explain the mindset you'll need to adopt to start your project and how you can go through and collect data from any website:. To feed the model, you need to separate the features from the label. 2- Feed data step-by-step. Then we apply decode_csvto each of the lines. 转载注明原文:tensorflow – 解析csv时升级到tf. …I've already opened it up…here in my spreadsheet. csv) and we are in business. The TensorFlow Dataset API provides various facilities for creating scalable input pipelines for TensorFlow models, including: Reading data from a variety of formats including CSV files and TFRecords files (the standard binary format for TensorFlow training data). 0, just a week after the Release Candidate release. This article will explain the basic mechanics of the Dataset,. This is a toy example, using quite small dataset and network, but it shows the potential of this models. This is a binary format specific to Python (WARNING: if you attempt to read this data in Python 3, you need to set encoding='latin1' when you call np. 이 발표는 2018년 4월 14일 서울에서 열린 TensorFlow Dev Summit Extended Seoul '18 에서 TensorFlow Dev Summit 2018의 발표 내용 중 TensorFlow. Here's the train set and test set. datasets; load_csv_test. The folder contains two files and one folder: imdb_word_index. Here's a preview of the dataset in Kaggle: First, we'll import the libraries we'll be using to build this model: import numpy as np import pandas as pd import tensorflow as tf import tensorflow_hub as hub from sklearn. So when we show our batch, we can see that our dataset uses only those columns from the CSV and ignores the rest. It requires the least amount of code. We use cookies for various purposes including analytics. At start-up, the script reads all the CSV files in the “train” and “eval” folders into arrays of data for use throughout the training process. 3, the Dataset API is now the standard method for loading data into TensorFlow models. Everyone wants to see the pipeline on the go. In this post, we will be using the UCI mushroom database as training data for us to determine if a given mushroom with specific attributes is either edible or poisonous. With this tool users can create DNN and avoid common code issues, creating stable and clean code based on TensorFlow estimators. Hub에 관한 발표들을 정리한 내용입니다. load or your process. Dataset表示一系列元素,其中每个元素包含一个或多个Tensor对象。. If you need to scale up to a large set of files, or need a loader that integrates with TensorFlow and tf. If you'd like to share your work with the community, you can check in your dataset implementation to tensorflow/datasets. 1, the Estimator API is now at tf. Name the testing dataset CSV files as test. The features dictionary maps feature column names to Tensors containing the corresponding feature data, and labels is a Tensor containing the batch's label data. #Splitting the data set into learning and test sets #Fitting the Linear Regression model to the training set regressor LinearRegression on black friday data set with anaconda - Stack Linear Regression with TensorFlow [Examples] - Guru99 Data Sets - Cengage Import Data. Mar 05 2019- POSTED BY Brijesh Comments Off on How to use TensorFlow Dataset API for NLP input pipeline Spread the love NLP models use text to produce a basic form of natural language understanding. How to use the Tensorflow Dataset API to read files with different names without evaluating the filename string Say I received csv dataset files with filenames of the form index_channel. Pre-requisites. Datasets provide compile-time type safety—which means that production applications can be checked for errors before they are run—and they allow direct operations over user-defined classes. # 35,126 examples. import os import glob import pandas as pd import xml. import csv: import os: import re: from absl import logging: import numpy as np: import tensorflow. In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to predict a discrete label (for…. This dataset is made up of images of handwritten digits, 28x28 pixels in size. A collection of datasets ready to use with TensorFlow - tensorflow/datasets. Init module for TensorFlow Data Validation. The lack of a standard means that subtle differences often exist in the data produced and consumed by different applications. Welcome to a tutorial where we'll be discussing how to load in our own outside datasets, which comes with all sorts of challenges! First, we need a dataset. We will use the dataset named “Malaria Cell Images Dataset” available on Kaggle. PyTorch全盛の今ですが、TensorFlowが2. The features dictionary maps feature column names to tensors containing the corresponding feature data, and labels is a tensor containing the batch's label data. Hub에 관한 발표들을 정리한 내용입니다. This tutorial illustrates one way to train a feed forward neural network based on a CSV file using TensorFlow. Creates a dataset of a step-separated range of values. Machine learning is cool, but we can't really do much without data. csv and test. read_csv('report_exec_times. (Python) The Python version of the dataset is distributed as a NumPy npz file. record_defaults: A list of default values for the CSV fields. quotechar str, default ‘”’ String of length 1. But, in real-life use cases like SONAR, you will be provided with the raw data set which you need to read and pre-process so that you can train your model around it. keras for training. Convert CSV Files to TensorFlow Format. 0 comes with Keras packaged inside, so there is no need to import Keras as a separate module (although you can do this if. Depending on the storage format specified, this dataset can be used for Caffe or TensorFlow models. Ich habe eine Sammlung von CSV-Dateien, die im LZO-Format komprimiert sind und in TensorFlow importiert werden sollen. Now let’s build the random forest classifier using the train_x and train_y datasets. Build predictive deep learning models using Keras and Tensorflow| R Studio 3. Run download_and_prepare locally; 3. 实践YJango:免费上机:TensorFlow 通用框架 Estimator目录前言机器学习两大模块:数据、模型三个阶段:训练、评估、预测优势实现数据集:TFRecord+Dataset定义input_fn定义model_fn正向传播CNN:二维卷积层RNN:…. The lack of a standard means that subtle differences often exist in the data produced and consumed by different applications. sh it says fle does not exist though it’s present Am i doing it right?. Whenever working with a new dataset, this is always the first thing to do (after importing, of course). To put the guide into concrete practice, we will use the standard Flowers dataset from TensorFlow. TensorFlow Object Detection Model Training. Dataset Usage MNIST in CSV. pip3 install --upgrade tensorflow-gpu. png’ format. Any additional features are not provided in the datasets, just the raw images are provided in ‘. Here's the train set and test set. Multi-class classification is simply classifying objects into any one of multiple categories. csv, and pass the arrays to TensorFlow. The following are code examples for showing how to use tensorflow. Install TensorFlow. In big organizations the datasets are large and training deep learning text classification models from scratch is a feasible solution but for the majority of real-life problems your dataset is small and if you want to build your machine learning model you need to be smart. Dataset APIを使うとき、CSVライクにデータを読める。 圧縮やバイナリ格納、型保持機能などがあり、便利。 分散処理やクラウドからの処理でIOを節約したいケースとかではこういうフォーマットを使うと. This MNIST data is hosted on Yann LeCun's websit. To build the dataset, you need to use the object TextLineDataset. record files (TFRecord format). Special Database 1 and Special Database 3 consist of digits written by high school students and employees of the United States Census Bureau, respectively. The training set consists of 6206 images of both cats and dogs of different breeds. # Read the local file and get a record batch iterator batch_iter = read_and_process(filename). TensorFlow data API: tf. Import the necessary python modules to get started including the Driverless AI client. 128-dimensional audio features extracted at 1Hz. data中的,从Tensor…. (Python) The Python version of the dataset is distributed as a NumPy npz file. keras for training. 第一种方法使用tensorflow的dataset API。因为条目不等长,我们不能使用CsvDataset,而是使用TextLineDataset。 假设文件的每一行的格式是"label,item0,value0,item1,value1,", 那么可以用下面的来代码来解析. csv) and we are in business. Tensorflow怎样读取本地txt,csv数据集文件? 用 tf 的 dataset,结合 tf. Let's run through the problem we are going to solve. The time cost for just read 1000 iteration needs roughly 12s. Later we load these records into a model and do some predictions. This motivated us to release Google-Landmarks, the largest worldwide dataset to date, to foster progress in this problem. Estimators: TensorFlow 모델을 만드는 상위 수준(high-level)의 방식입니다. All datasets are exposed as tf. csv file to train our classifier to categorize a given image as either the image of a cat or a dog. titanic_batches = tf. Gone are those…. Stock prices come in several different flavours. If you'd like to share your work with the community, you can check in your dataset implementation to tensorflow/datasets. And, more importantly, how to actually use the trained network daemon-style in practice on the raspberrry pie. MNIST classification with TensorFlow's Dataset API. Vocabulary Processor. For the encoder part it depends on what kind of non-image data you have. map (parse_csv, num_parallel_calls = 10) dataset = dataset. pytorch的torch. decode_csv: 각 줄을 필드로 분할하고 필요한. If you need to scale up to a large set of files, or need a loader that integrates with TensorFlow and tfdatasets then use the make_csv_dataset function:. Learn how to load a data set into memory and scale the data so it can be used with TensorFlow. 实践YJango:免费上机:TensorFlow 通用框架 Estimator目录前言机器学习两大模块:数据、模型三个阶段:训练、评估、预测优势实现数据集:TFRecord+Dataset定义input_fn定义model_fn正向传播CNN:二维卷积层RNN:…. Such as classifying just into either a dog or cat from the dataset above. 저도 학습하는 입장에서 정확하지 않을수도 있습니다. We can use one or more numpy arrays and pass it to TensorFlow for creating the dataset. The n_classes argument lets TensorFlow known that there are two possible classes to select between; namely benign (0) or malignant (1). record_defaults = [999,999,999,999] dataset = tf. read_files() Read a dataset from a set of files. # Read the local file and get a record batch iterator batch_iter = read_and_process(filename). I can't have it on the csv file, because it will become huge (There are many rows with the same author and each author has a Persona with more than 100 sentences each). For this Image Recognition I would like to train my own image dataset and test that dataset. Extracted audio features that are stored as TensorFlow Record files. These two names contain a series of powerful algorithms that share a common challenge—to allow a computer to learn how to automatically spot complex patterns and/or to make best possible decisions. csv contains 10,000 test examples and labels. Spread the love. The data contains the number of total passengers every month, from January 1949 to December 1960. Flower species are stored as integers, with 0 denoting Iris setosa, 1 denoting Iris versicolor, and 2 denoting Iris virginica. Interestingly, in “How to use Dataset in Tensorflow”, Download the Dataset. data that will work with existing input pipelines and tf. 167,21,0 0,137,40,35,168,43. There might be times when you have your data only in a one huge CSV file and you need to feed it into Tensorflow and at the same time, you need to split it into two sets: training and testing. Usually, data would come from CSV, there is a helper TensorFlow. The numbers in the dataset refer to the amount in thousands. 5。 ロジスティック回帰 import csv import numpy as np from sklearn. I assume you are using Python with Tensorflow. Precision Medicine Modeling using Deep Learning (TensorFlow) Yupeng Wang, Ph. Learn how to load a data set into memory and scale the data so it can be used with TensorFlow. com/Hvass-Labs/TensorFlow-Tutorials. Reads CSV files into a dataset, where each element is a (features, labels) tuple that corresponds to a batch of CSV rows. It can be run directly in Google's Colaboratory Platform without installing Python. In this tutorial, we will know how to load CSV data using tf. Creates a dataset of a step-separated range of values. Tensors / Creation We have utility functions for common cases like Scalar, 1D, 2D, 3D and 4D tensors, as well a number of functions to initialize tensors in ways useful for machine learning. Saturday, May 13, 2017 csv, csv extract row, delete python row csv, large csv split, mnist csv row to pil image, mnist grey scale dataset to black and white, python, reader, writer Friday, May 12, 2017. Dataset annotation yang kita buat diatas menggunakan aplikasi labelImg perlu dikonversi dari format. TensorFlow is an open-source software library for dataflow programming across a range of tasks. Basically, this dataset is comprised of digit and the correponding label. It covered a big part of our. if documents are longer, they will be trimmed, if shorter - padded. The GTSRB dataset is pre-split into training/testing splits for us. Other than that- I am completely new to TF, so I would need be walked through how to best train on my dataset. The tidyverse is an opinionated collection of R packages designed for data science. 잘못된부분에 대해서는 지적 부탁드. Datasets and Estimators are two key TensorFlow features you should use: Datasets: The best practice way of creating input pipelines (that is, reading data into your program). csv datasets progressively using BigQuery • Used tf. A Dataset is a sequence of elements, which are themselves composed of tf. 3的Datasets和Estimator,你了解多少? Calls the decode_csv function with each element in the dataset as an argument (since we are using TextLineDataset, each element will be a line of CSV text). We need to implement a function for model_fn to build our custom model, loss function, evaluation matrices. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license. TensorFlow applications can be written in a few languages: Python, Go, Java and C. Outcome or class labels are converted to One Hot encoding. I plan to read the feature data from multi csv files. TensorFlow allows us to build custom models for estimators. We will first prepare our datasets for the experiment. A number of "canned estimators" are at tf. Labs are all similar, they ask you to load some dataset (usually from some file. The features dictionary maps feature column names to tensors containing the corresponding feature data, and labels is a tensor containing the batch's label data. Let's grab the Dogs vs Cats dataset. preprocessing import MultiLabelBinarizer I've made the CSV file from this dataset available in a public Cloud Storage. data API supports a variety of file formats (including csv) so that you can process datasets that do not fit in memory. Object Detection on Custom Dataset with TensorFlow 2 and Keras using Python. Create a CSV file dataset Create a dataset from CSV files. will be intentionally skipped. Where they walk us through estimate API. And, more importantly, how to actually use the trained network daemon-style in practice on the raspberrry pie. Laurence Moroney (@lmoroney) gives you the quick breakdown on using Comma Separated Values (CSVs), with Keras. Usually, data would come from CSV, there is a helper TensorFlow. Iris Data Set, along with the MNIST dataset, is probably one of the best-known datasets to be found in the pattern recognition literature. Pre-requisites. You must be able to load your data before you can start your machine learning project. Check your code style; 6. For a dataset created with the csv_dataset() function, the passed. Tensors are the core datastructure of TensorFlow. Convert the CSV file into tf_record format, understood by the network, with this script. 5。 ロジスティック回帰 import csv import numpy as np from sklearn. It will be faster if 5000 is chosen but it will generate a lot more CSV files. 阅读CSV数据的TensorFlow教程中的示例非常分散,只能帮助您在CSV数据上进行训训练。 这是我编写的基于CSV教程的代码: from __future__ import print_function import tensorflow as tf def file_l. Datasets provide compile-time type safety—which means that production applications can be checked for errors before they are run—and they allow direct operations over user-defined classes. These features will be used to predict the output of another column. A number of "canned estimators" are at tf. TFRecord is a flexible and efficient binary format usually containing protocol buffers (an open source binary format). make_csv_dataset() Reads CSV files into a batched dataset. Multi-class classification is simply classifying objects into any one of multiple categories. They are from open source Python projects. Have to follow the HTTP protocol and how framework dealing with files over net. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. Ex: mnist_train-000. batch(32) # Batch size to use. The data used in this tutorial are taken from the Titanic passenger list. From Placeholder - If the data inside the dataset needs to be dynamically changed, we should use a. data then use the tf. GitHub Gist: instantly share code, notes, and snippets. layers import Dense, Conv2D, MaxPooling2D, Flatten. One per column of CSV data, with either a scalar Tensor default value for the column if it is optional, or DType or empty Tensor if required. Introduced in TensorFlow 1. This MNIST data is hosted on Yann LeCun's websit. This part shows you how to install the TensorFlow model code on a development system and run the model on the MovieLens dataset. TensorFlow talk at Google I/O. Use the sample datasets in Azure Machine Learning Studio (classic) 01/19/2018; 14 minutes to read +7; In this article. PyTorch全盛の今ですが、TensorFlowが2. The features dictionary maps feature column names to tensors containing the corresponding feature data, and labels is a tensor containing the batch's label data. Where they walk us through estimate API. Let's grab the Dogs vs Cats dataset. I am just starting off with TensorFlow and trying to implement an RNN for a dataset which consists of 11 Features (all numeric). csv and test. Before creating our neural net, it is best to explore the data to get an idea of the general form, properties, etc. Sign up import csv: import os: import tensorflow. Nearly all datasets can utilize these two data structures. To build the dataset, you need to use the object TextLineDataset. As you can see, the columns in the CSV are named. QUOTE_NONNUMERIC will treat them as non-numeric. As I am new to TensorFlow, I would like to do image recognition in TensorFlow using Python. A MIDI-only archive of the dataset is also available. The test dataset also has the actual value for the output, which helps us in understanding how efficient the model is. 将TensorFlow以及代码都打包到Docker镜像中,就可以在Docker容器中运行TensorFlow。 这样,开发者仅需要安装Docker,而不需要安装TensorFlow;同时,Docker保证了代码一定可以在任何Docker主机上正确执行,因为它所依赖的运行环境全部打包在Docker镜像中。. Run download_and_prepare locally; 3. Given one or more filenames and a list of defaults, a CsvDataset will produce a tuple of elements whose types correspond to the types of the defaults provided, per CSV record. TextLineDataset(csv_path). Skip to content. Run trainTestSplit to divide labeledData. Complete Guide to Deep Learning. csv') dataset = raw. It is often used with classification task. jl does not support this file type, so in order to follow the exercise, we need to extract the data from the tfrecord-dataset. make_csv_dataset function:. Using train_test_split function of Scikit-Learn cannot be proper because of using a TextLineReader of Tensorflow Data API so the data is now a tensor. csv where index is the index of the example (running from 1 to 10000) and channel is the index of the channel (running from 1 to 5). A feature column. Convert XML ke CSV.