bartvm

from theano import tensor

from blocks.bricks import Linear, Rectifier, Softmax
from blocks.bricks.cost import CategoricalCrossEntropy
from blocks.roles import WEIGHT
from blocks.graph import ComputationGraph
from blocks.filter import VariableFilter
from blocks.initialization import IsotropicGaussian, Constant
from blocks.algorithms import GradientDescent, Scale
from blocks.log.log import TrainingLog

bartvm

import theano
from theano import tensor
import numpy as np

def main():
    x = tensor.vector('x')
    y = tensor.alloc(np.float32(0), *x.shape)

    def step(x_elem, t, prev_y):
        new_y = tensor.set_subtensor(prev_y[t], x_elem)

bartvm

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

function loop(p, y, idxs)
  -- Only works if h is a derivable value as well
  x = p.x
  h = p.h
  for i = 1, x:size(1) do
    h[idxs[i]] = x[i]
  end

bartvm

import sys

import theano
from theano import tensor
import numpy as np


def main(length):
    # Sequential input
    x = tensor.vector('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)

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

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

local ffi = require 'ffi'

ffi.cdef([[
typedef long time_t;

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

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 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}]]