zou3519

=================================== FAILURES ===================================
________________________ test_mean_and_variance[Normal] ________________________
[gw50] linux -- Python 3.6.3 /opt/conda/envs/pytorch-py3.6/bin/python

dist = <tests.distributions.dist_fixture.Fixture object at 0x7f3d68010fd0>

    def test_mean_and_variance(dist):
        for idx in dist.get_test_data_indices():
            num_samples = dist.get_num_samples(idx)
            dist_params = dist.get_dist_params(idx)

zou3519

----------------------------- Captured stderr call -----------------------------
2017-11-09 22:23:15,113 - INFO - allennlp.common.checks - Pytorch version: 0.3.0b0+1f694e9
_________________________ EntropyTest.test_masked_case _________________________

self = <tests.training.metrics.entropy_test.EntropyTest testMethod=test_masked_case>

    def test_masked_case(self):
        metric = Entropy()
        # This would have non-zero entropy without the mask.
        logits = torch.Tensor([[1, 1, 1, 1],

zou3519

import numpy as np
import torch
from torch.autograd import Variable

def set_seed(seed):
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

def main():

zou3519

import numpy as np
import torch
from torch.autograd import Variable

def set_seed(seed):
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

def main():

zou3519

import re
import subprocess
import sys

PY3 = sys.version_info >= (3, 0)

reinforce_cmd = 'python examples/reinforcement_learning/reinforce.py'
actor_critic_cmd = 'python examples/reinforcement_learning/actor_critic.py'

zou3519

import torch
from torch.autograd import Variable
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F


v_in = Variable(torch.Tensor([0.1, 0.1]).view(1, 2, 1, 1), requires_grad=True)
f1 = lambda x: x * 2
f2 = nn.Conv2d(2, 1, 1, 1)

zou3519

     # add this to TestAutograd in test/test_autograd.py
     def test_convbackwardbackward(self):
         v_in = Variable(torch.Tensor([0.1, 0.1]).view(1, 2, 1, 1), requires_grad=True)

         def forward(v_in):
             f1 = lambda x: x * 0.0001
             f2 = torch.nn.Conv2d(2, 1, 1, 1)
             grad_out = Variable(torch.ones(1, 1, 1, 1))
             gradient = torch.autograd.grad(outputs=f2(f1(v_in)), inputs=v_in,
                                      grad_outputs=grad_out,

zou3519

import torch
from torch.autograd import Variable
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F

v_in = Variable(torch.Tensor([0.1, 0.1]).view(2, 1), requires_grad=True)

def forward(v_in):
    f1 = lambda x: x * 2

zou3519

import torch

tensor = torch.randn(100).cuda()
%timeit tensor.var(0); torch.cuda.synchronize()

tensor = torch.randn(10000).cuda()
%timeit tensor.var(0); torch.cuda.synchronize()

tensor = torch.randn(1000, 2, 10).cuda()
%timeit tensor.var(2); torch.cuda.synchronize()

zou3519

n people: 0, 1, ..., n-1

Each person i will receive, from Enlightened Santa Alter bot, a person j.
Encode this as an n-digit binary number, such that the jth element is 1 and everything else is 0.
For example, if the person i receives person 3, their 'secret' number is (zero-indexed) 000100...0

Now, person i has some n-digit binary 'secret'. Call this x_i.
Construct n numbers (call them 'pieces'), {x_{i, 0}, x_{i, 1}, ..., x_{i, n-1}} 
such that the XOR of all of them is equal to x_i.