Wie konvertiert man eine CSV-Datei in TFrecord TensorFlow-Format?

Question

Hallo zusammen, ich muss eine CSV-Datei in TFrecord für TensorFlow konvertieren. Ich schätze deine Hilfe sehr. Ein Beispiel für CSV-Datei, die ich umwandeln müssen, ist:

Col1 Col2 Col3 Col4 Ziel

2,56 0,98 0,45 7,8 0,189

3,10 5,78 4,78 9,0 0,78

....

Vielen Dank !!!

Answer 1

Der folgende Code wird eine TFRecords-Datei aus mehreren CSV-Dateien erstellen ... einfach konnte die Daten noch nicht auslesen.

import pandas as pd 
import numpy as np 
import os 
import tensorflow as tf 
from tqdm import tqdm 


def _int64_feature(value): 
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 


def _float_feature(value): 
    return tf.train.Feature(float_list=tf.train.FloatList(value=value)) 


def _bytes_feature(value): 
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 


def make_q_list(filepathlist, filetype): 
    filepathlist = filepathlist 
    filepaths = [] 
    labels = [] 
    for path in filepathlist: 
     data_files = os.listdir(path) 
     for data in data_files: 
      if data.endswith(filetype): 
       data_file = os.path.join(path, data) 
       data_file = data_file 
       data_label = os.path.basename(os.path.normpath(path)) 
       filepaths.append(data_file) 
       labels.append(data_label) 

    return filepaths, labels 

def tables_to_TF(queue_list, tf_filename, file_type='csv'): 
    # Target variable needs to be the last column of data 
    filepath = os.path.join(tf_filename) 
    print('Writing', filepath) 
    writer = tf.python_io.TFRecordWriter(tf_filename) 
    for file in tqdm(queue_list): 
     if file_type == 'csv': 
      data = pd.read_csv(file).values 
     elif file_type == 'hdf': 
      data = pd.read_hdf(file).values 
     else: 
      print(file_type, 'is not supported at this time...') 
      break 
     for row in data: 
      # file formate : feature 1…..feature n, label 
      features, label = row[:-1], row[-1] 
      example = tf.train.Example() 
      example.features.feature["features"].float_list.value.extend(features) 
      example.features.feature["label"].float_list.value.append(label) 
      writer.write(example.SerializeToString() 

#Generate data 
for i in range(10): 
    filename = './Data/random_csv' + str(i) + '.csv' 
    pd.DataFrame(np.random.uniform(0,100,size=(100, 50))).to_csv(filename) 

filepathlist = ['./Data'] 
q, _ = make_q_list(filepathlist, '.csv') 
tffilename = 'Demo_TFR.tfrecords' 
tables_to_TF(q, tffilename, file_type='csv')