bartvm

    if opt.hogwild > 1 then
      local ipc = require 'libipc'
      local q = ipc.workqueue('examples')
      local q2 = ipc.workqueue('done')
      local ids = ipc.workqueue('ids')
      for i = 1, opt.hogwild do
        ids:write(i)
      end

      -- Initialize the states

bartvm

-- Vectorized approach
local dict_size = probs:size(2)
local max_out_arcs = out_arcs:size(2)
local max_in_arcs = in_arcs:size(2)
local state_probs = torch.narrow(params.state_probs, 1, 1, seq_len + 1)
state_probs.value:fill(0)
for i = 2, seq_len + 1 do
  local starts = torch.ones(max_in_arcs):long() * i
  local origins = starts:add(-lengths:index(1, in_arcs[i]))
  local indices = ((origins - 1) * dict_size + 1):add(in_arcs[i] - 1)

bartvm

local dict_size = probs:size(2)
local max_out_arcs = out_arcs:size(2)
local max_in_arcs = in_arcs:size(2)
local state_probs = torch.narrow(params.state_probs, 1, 1, seq_len)
state_probs.value:fill(0)
for i = 2, seq_len do
  local starts = torch.ones(max_in_arcs):long() * i
  local origins = starts:add(-lengths:index(1, in_arcs[i]))
  local indices = ((origins - 1) * dict_size + 1):add(in_arcs[i])
  local in_arc_probs = torch.index(probs:view(seq_len * dict_size), 1, indices)

bartvm

1       1       2.2973299250376         2.2973299250376                                                                                                                                                                                                                       [644/1881]
1       2       2.0864643271487         2.2762433652487
1       3       2.1500752827293         2.2636265569968
1       4       1.9636232815994         2.2336262294571
1       5       2.0671982139671         2.2169834279081
1       6       2.1094124953551         2.2062263346528
1       7       1.9331442971793         2.1789181309054
1       8       4.0584201436111         2.366868332176
1       9       1.8639954152644         2.3165810404848
1       10      1.6428338575222         2.2492063221886

bartvm

local function nll(params, probs, out_arcs, out_mask, lengths)
  local seq_len = probs:size(1)
  local max_out_arcs = out_arcs:size(2)
  local state_nll = {0}
  for i = 1, seq_len do
    for j = 1, max_out_arcs do
        if out_mask[{i, j}] ~= 1 then
          break
        end
        local target = i + lengths[out_arcs[{i, j}]]

bartvm

• Use SSHFS (installed with Homebrew) to browse remote files

Atom packages

• atom-beautify
  • autopep8 through Anaconda
  • uncrustify through Homebrew
• Linter
  • linter-clang
• linter-cppcheck

bartvm

local ffi = require 'ffi'

ffi.cdef([[
typedef long time_t;

typedef struct timeval {
 time_t tv_sec;
 time_t tv_usec;
};

bartvm

import numpy
import theano
from theano import tensor, config

# The parameters
W = theano.shared(numpy.arange(9, dtype=config.floatX).reshape(3, 3))
storage = theano.shared(numpy.zeros((3, 3), dtype=config.floatX))

# The input
x = tensor.vector('x')

bartvm

grad = require 'autograd'
torch = require 'torch'

params={
  W=torch.range(0, 8):view(3, 3),
  storage=torch.zeros(3, 3)
}

function f(params, x)
  params.storage[2] = params.W * x

bartvm

t = require 'torch'
grad = require 'autograd'

function loop(x, h, y, idxs)
  for i = 1, x:size(1) do
    h[idxs[i]] = x[i]
    if i == 1 then
      cost = t.pow(y[idxs[i]] - h[idxs[i]], 2)
    else
      cost = cost + t.pow(y[idxs[i]] - h[idxs[i]], 2)