How to read data into Tensorflow?

def func()
    return 1,2,3,4

b = func() 

print b #(1, 2, 3, 4)

print [num for num in b] # [1, 2, 3, 4]

Hi its nothing to do with tensorflow its simple python need not define 1000 variable. tf.decode_csv returns a tuple.

No idea on database handling, I think u can use python and just input the data in the form of array to the tensorflow.

Hope this is helpful

of course you can implement to directly read batch random sort trained data from mongo to feed to tensorflow. below is my way:

        for step in range(self.steps):


            pageNum=1;
            while(True):
                trainArray,trainLabelsArray = loadBatchTrainDataFromMongo(****)
                if len(trainArray)==0:
                    logging.info("train datas consume up!")
                    break;
                logging.info("started to train")
                sess.run([model.train_op],
                         feed_dict={self.input: trainArray,
                                    self.output: np.asarray(trainLabelsArray),
                                    self.keep_prob: params['dropout_rate']})

                pageNum=pageNum+1;

and also you need preprocess trained data in mongodb, such like: assign each trained data in mongodb a random sort value...

1. You definitely don't need to define col1, col2, to col1000...

   generally, you might do things like this:

   
   columns = tf.decode_csv(value, record_defaults=record_defaults)
   features = tf.pack(columns)
   do_whatever_you_want_to_play_with_features(features)
   

2. I do not know any off-the-shelf way to directly read data from MongoDB. Maybe you can just write a short script to convert data from MongoDB in a format that Tensorflow supports, I would recommend binary form TFRecord, which is much faster to read than csv record. This is a good blog post about this topic. Or you can choose to implement a customized data reader by yourself, see the official doc here.