utils.py
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	import torch
	from torchvision import datasets, transforms
	from sklearn.preprocessing import normalize

	def load_data(dataset='EMNIST', batch_size=100000):
	if dataset=='EMNIST':
	trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
	data = datasets.EMNIST('./datasets/', 'bymerge', transform=trans, download = True)
	data_loader = torch.utils.data.DataLoader(
	dataset=data,
	batch_size=batch_size,
	shuffle=False,
	drop_last=True)

	return data_loader

	def getbigdatamatrix(data_loader):
	# Needed for ICA and SC and stuff...
	# loop over data_loader and concatenation
	return X

	def normalise_weightmatrix(W):
	return normalize(W)