c0nn3r

(setv train_loader (torch.utils.data.DataLoader
  (datasets.MNIST "./data" :train True :download True
    :transform (transforms.Compose (list
      ((transforms.ToTensor)
      (transforms.Normalize :std 0.1307 :mean 0.3081))
  )))))

c0nn3r

def shape_list(x):
    """
    deal with dynamic shape in tensorflow cleanly
    """
    ps = x.get_shape().as_list()
    ts = tf.shape(x)
    return [ts[i] if ps[i] is None else ps[i] for i in range(len(ps))]

c0nn3r

c0nn3r

import torch
import itertools


def summed_permutations(matrix):
    columnwise_sum = torch.sum(matrix, dim=1)
    length = int(columnwise_sum.shape[0])

    result = torch.cat([
        torch.index_select(columnwise_sum, dim=0, index=torch.LongTensor(each))