soumith

#!/usr/bin/env torch
require 'nn'
require 'image'
require 'xlua'
require 'pl'


opt = lapp[[
		 -t,--threads            (default 8)           number of threads
   -p,--type               (default float)       float or cuda

soumith

-- multiple learning rates per network. Optimizes two copies of a model network and checks if the optimization steps (2) and (3) produce the same weights/parameters.

require 'torch'
require 'nn'
require 'optim'
torch.setdefaulttensortype('torch.FloatTensor')

-- (1) Define a model for this example.
local model = nn.Sequential()
model:add(nn.Linear(10,20))

soumith

import torch.multiprocessing as mp
from torch.multiprocessing import Semaphore
import sys


if sys.version_info[0] == 3:
    Barrier = mp.Barrier
else: # version 2
    # from http://stackoverflow.com/a/26703365/117844
    class Barrier:

soumith

import torch
import torch.nn as nn
import torch.nn.parallel

class DCGAN_D(nn.Container):
    def __init__(self, isize, nz, nc, ndf, ngpu, n_extra_layers=0):
        super(DCGAN_D, self).__init__()
        self.ngpu = ngpu
        assert isize % 16 == 0, "isize has to be a multiple of 16"

soumith

[WARNING]: No mapping options supplied. 'Naive' options will be used which might fail compilation
[WARNING]: Autotuning results won't be cached. 'cache' option is not specified
[WARNING]: Using naive options for autotuning
 
template<typename T> inline __device__ T floord(T n, T d) {
  return n < 0 ? - (-n + d - 1)/d : n / d;
}

// Halide type handling
typedef int int32;

soumith

Torch level 1

function	Symbolic_implemented
gather
equal
__and__, __iand__, __or__, __ior__, __xor__, __ixor__, __lshift__, __ilshift__, __rshift__, __irshift__
min, max
all
any
frac	yes