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I am having difficulty trying to load 18k of training data for training with tensorflow. The files are npy files named as such: 0.npy, 1.npy...18000.npy.

I was looking around the web and came up with a simple code to first read the files in the correct sequence and trying to concatenate the training data together but it takes forever.. 

import numpy as np
import glob
import re
import tensorflow as tf

print("TensorFlow version: {}".format(tf.__version__))

files = glob.glob('D:/project/train/*.npy')
files.sort(key=lambda var:[int(x) if x.isdigit() else x for x in 
           re.findall(r'[^0-9]|[0-9]+', var)])
# print(files)

final_dataset = []
i = 0
for file in files:    
    dataset = np.load(file, mmap_mode='r')
    print(i)
    #print("Size of dataset: {} ".format(dataset.shape))
    if (i==0):
      final_dataset = dataset
    else: 
      final_dataset = np.concatenate((final_dataset, dataset), axis = 0)
    i = i + 1

print("Size of final_dataset: {} ".format(final_dataset.shape))
np.save('combined_train.npy', final_dataset)
OverLordGoldDragon
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Eugene
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1 Answers1

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'Combining' arrays in any way involves (1), creating an array with the two arrays' total size; (2), copying their contents into the array. If you do this each time you load in an array, it repeats 18000 times - with time per iteration growing for each iteration (due to larger final_dataset).

A simple workaround is to append the arrays to a list - and then combine them all once at the end:

dataset = []
for file in files:
    data = np.load(file, mmap_mode='r')
    dataset.append(data)

final_dataset = np.concatenate(dataset, axis=0)

But beware: be sure final_dataset can actually fit in your RAM, else the program will crash. You can find out via ram_required = size_per_file * number_of_files. Relevant SO. (To speed things up even further, you can look into multiprocessing - but not simple to get working)

OverLordGoldDragon
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