load.py

import numpy as np
import os

datasets_dir = '/media/datasets/'

def one_hot(x,n):
	if type(x) == list:
		x = np.array(x)
	x = x.flatten()
	o_h = np.zeros((len(x),n))
	o_h[np.arange(len(x)),x] = 1
	return o_h

def mnist(ntrain=60000,ntest=10000,onehot=True):
	data_dir = os.path.join(datasets_dir,'mnist/')
	fd = open(os.path.join(data_dir,'train-images-idx3-ubyte'))
	loaded = np.fromfile(file=fd,dtype=np.uint8)
	trX = loaded[16:].reshape((60000,28*28)).astype(float)

	fd = open(os.path.join(data_dir,'train-labels-idx1-ubyte'))
	loaded = np.fromfile(file=fd,dtype=np.uint8)
	trY = loaded[8:].reshape((60000))

	fd = open(os.path.join(data_dir,'t10k-images-idx3-ubyte'))
	loaded = np.fromfile(file=fd,dtype=np.uint8)
	teX = loaded[16:].reshape((10000,28*28)).astype(float)

	fd = open(os.path.join(data_dir,'t10k-labels-idx1-ubyte'))
	loaded = np.fromfile(file=fd,dtype=np.uint8)
	teY = loaded[8:].reshape((10000))

	trX = trX/255.
	teX = teX/255.

	trX = trX[:ntrain]
	trY = trY[:ntrain]

	teX = teX[:ntest]
	teY = teY[:ntest]

	if onehot:
		trY = one_hot(trY, 10)
		teY = one_hot(teY, 10)
	else:
		trY = np.asarray(trY)
		teY = np.asarray(teY)

	return trX,teX,trY,teY