Convert-own-data-to-MNIST-format

MNIST is a famous test data with deep learning on the web. This project is convert own data to MNIST format. It may help you to quickly test new DL model without modify too much code. This is update version of davidflanagan

Changes:

1. Not limit with 28 x 28 image resolution, it depends on first input image's resolution.
2. Not limit with 10 class, it depends on how many sub-folders in your target folder.
3. Not limit with gray images, it depend on on first input image's channels.
4. Cancel output file name, use MNIST original file name, instead of test, train or ratio number to decide output
5. Improve data number, if data number is 0 or none, it pack whole data in each class.  

Own data should use following strcture to put file, you must to transfer label to index in your code, our output labels is index of alphabetical order.

Instructions:

python convert_to_mnist_format.py target_folder test_train_or_ratio data_number

target_folder: must give minimal folder path to convert data
test_train_or_ratio: must define 'test' or 'train' about this data, if you want seperate total data to test and train automatically, you can input one integer for test ratio, e.q. if you input 2, it mean 2% data will become test data
data_number: if you input 0 or nothing, it convert total images under each label folder.

Samples:

1. python convert_to_mnist_format.py notMNIST_small test 0
   mean: use whole data to make test format from notMNIST_small folder
2. python convert_to_mnist_format.py notMNIST_small train
   mean: use whole data to make train format from notMNIST_small folder
3. python convert_to_mnist_format.py notMNIST_small 5
   mean: use whole data to make MNIST format, and 5% is test format, 95% is train format
4. python convert_to_mnist_format.py notMNIST_small 10 300
   mean: each class use 300 images to make MNIST format, and 10% is test, 90% is train

Important:
After create file, you must excute following code
gzip data/*ubyte.

Use it on your target DL model:

1. copy t10k-labels-idx1-ubyte, t10k-images-idx3-ubyte, train-labels-idx1-ubyte, train-images-idx3-ubyte to MNIST data path that some DL model follow MNIST

2. Fixed a little source code
   a. Copy mnist folder to root of DL code.
   b. And find related like following:      

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets(‘MNIST Path’, one_hot=True)

Fixed to

import mnist2.mnist as mn
mnist = mn.read_data_sets(‘MNIST Path’, one_hot=True, num_classes=18)
#num_classes is how many class you in your data

3. Change channels
   If your own data is RGB, then mnist2 will output [batch_size, heigh x width, channels].
   In normal case, yo still need to know how to change with RGB data, or you can reshape to [batch_size, heigh x width x channels]

Enjoy!