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import numpy as np

import os

datasets_dir = './'

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