def convert2tensor(self, dataset, batch_size, limit):
b_data = dataset['X']
b_data = b_data[:limit]
print("normalizing images...")
b_data = common.normalize(b_data)
print("done")
target = dataset['y']
target = target.reshape((len(target)))
target = target[:limit]
"""SVHN dataset is between 1 to 10: shift this to 0 to 9 to fit with neural network"""
target = target - 1
data = []
for i in range(len(target)):
data.append(b_data[:,:,:,i])
data = np.asarray(data)
tensor_data = torch.from_numpy(data)
tensor_data = tensor_data.float()
tensor_target = torch.from_numpy(target)
loader = data_utils.TensorDataset(tensor_data, tensor_target)
loader_dataset = data_utils.DataLoader(loader, batch_size=batch_size, shuffle = True)
return loader_dataset
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