Facebookが作っているニューラルネットワークのツールがオープンソース化されたそうで。
Torch | Scientific computing for LuaJIT.
環境はUbuntu14.04
ホームページに公開されている方法の通りでインストールできる。
$ curl -sk https://raw.githubusercontent.com/torch/ezinstall/master/install-deps | bash $ curl -sk https://raw.githubusercontent.com/torch/ezinstall/master/install-luajit+torch | PREFIX=~/torch bash $ echo "export PATH=~/torch/bin:\$PATH; export LD_LIBRARY_PATH=~/torch/lib:\$LD_LIBRARY_PATH; " >>~/.bashrc && source ~/.bashrc $ ~/torch/bin/luarocks install nn
nn/training.md at master · torch/nn · GitHub
にある通りにXORのテストをしてみる。
起動。
ubuntu@ip-172-31-27-81:~$ th
______ __ | Torch7
/_ __/__ ________/ / | Scientific computing for Lua.
/ / / _ \/ __/ __/ _ \ |
/_/ \___/_/ \__/_//_/ | https://github.com/torch
| http://torch.ch
教師データの作成
th> dataset={};
[0.0000s]
th> function dataset:size() return 100 end -- 100 examples
[0.0001s]
th> for i=1,dataset:size() do
..> local input = torch.randn(2); -- normally distributed example in 2d
..> local output = torch.Tensor(1);
..> if input[1]*input[2]>0 then -- calculate label for XOR function
..> output[1] = -1;
..> else
..> output[1] = 1
..> end
..> dataset[i] = {input, output}
..> end
[0.0008s]
ネットワークの作成
th> require "nn"
true
[0.0107s]
th> mlp = nn.Sequential(); -- make a multi-layer perceptron
[0.0001s]
th> inputs = 2; outputs = 1; HUs = 20; -- parameters
[0.0000s]
th> mlp:add(nn.Linear(inputs, HUs))
nn.Sequential {
[input -> (1) -> output]
(1): nn.Linear(2 -> 20)
}
[0.0001s]
th> mlp:add(nn.Tanh())
nn.Sequential {
[input -> (1) -> (2) -> output]
(1): nn.Linear(2 -> 20)
(2): nn.Tanh
}
[0.0001s]
th> mlp:add(nn.Linear(HUs, outputs))
nn.Sequential {
[input -> (1) -> (2) -> (3) -> output]
(1): nn.Linear(2 -> 20)
(2): nn.Tanh
(3): nn.Linear(20 -> 1)
}
[0.0001s]
学習。
th> criterion = nn.MSECriterion()
[0.0001s]
th> trainer = nn.StochasticGradient(mlp, criterion)
[0.0001s]
th> trainer.learningRate = 0.01
[0.0000s]
th> trainer:train(dataset)
# StochasticGradient: training
# current error = 0.94303028750855
# current error = 0.81505805303687
# current error = 0.69978081138196
# current error = 0.60082425903428
# current error = 0.52480236545174
# current error = 0.47201262208585
# current error = 0.43779499334206
# current error = 0.41628235012982
# current error = 0.40265411888491
# current error = 0.39364373859055
# current error = 0.38724902974158
# current error = 0.38230491009015
# current error = 0.37814883526018
# current error = 0.37440484332827
# current error = 0.3708558692019
# current error = 0.36737167831577
# current error = 0.36386944118989
# current error = 0.36029291775182
# current error = 0.35660217366475
# current error = 0.352769265749
# current error = 0.34877731247394
# current error = 0.34462141986871
# current error = 0.34031036603419
# current error = 0.33586796009716
# current error = 0.33133289503198
# StochasticGradient: you have reached the maximum number of iterations
# training error = 0.33133289503198
[0.1410s]
テスト
th> x = torch.Tensor(2)
[0.0001s]
th> x[1] = 0.5; x[2] = 0.5; print(mlp:forward(x))
-0.8682
[torch.DoubleTensor of dimension 1]
[0.0002s]
th> x[1] = 0.5; x[2] = -0.5; print(mlp:forward(x))
0.2253
[torch.DoubleTensor of dimension 1]
[0.0001s]
th> x[1] = -0.5; x[2] = 0.5; print(mlp:forward(x))
0.2538
[torch.DoubleTensor of dimension 1]
[0.0001s]
th> x[1] = -0.5; x[2] = -0.5; print(mlp:forward(x))
-0.9561
[torch.DoubleTensor of dimension 1]
[0.0002s]
ちゃんとできてるし、それなりに速そう。Luaになれないが、FANNより使いやすそうだ。
