Cool video!
I didn’t know it was possible to make NIFs in Cuda like this, so this was an interesting point.
However could you explain a bit more what happens when you run Test.sgd/2? That part I couldn’t really understand.
amarandon
sym_num
Thank you for your reply.
I will add a explanation.
SGD stands for Stochastic Gradient Descent.
The argument of Test.sgd /2 is sgd (size, epoc).
size is the size of the data for mini-batch learning.
epoc is the number of times to repeat mini-batch learning.
In the video, it is Test.sgd (100,100).
DeepPipe2 randomly extracts 100 from MNIST training data.
Then, learn the MNIST data. Repeat it 100 times. After the learning is over, check if the learning was successful with the confirmation data.
As a result, approximately 86% of correct answers can be obtained.
There is a test module in the test.ex file. There is the description code of the neural network. And there is also description of sgd/2.
sgd/2 calls Cumatrix, a matrix calculation library that uses a GPU. Learning requires a large amount of matrix computation. Thanks to the GPU, Cumatrix can perform calculations faster.
7
sym_num
I’m writing CNN features in CUDA and debugging. It now works partially.
e.g.
#for CNN test
defnetwork init_network4(_x) do
_x |> f(5,5) |> full
|> w(576,300) |> b(300) |> relu
|> w(300,100) |> b(100) |> relu
|> w(100,10) |> b(10) |> softmax
end
iex(1)> Test.cnn(100,100)
preparing data
ready
-2.313786029815674
-2.193302869796753
-2.1156041622161865
-2.0593271255493164
…
-0.3924008309841156
accuracy rate = 0.821
end
:ok
3
sym_num
tiagodavi
Great Job!!! I’ve been thinking why not use Distributed Erlang power to handle big matrices. Something like Tensor Flow or Spark but using Elixir. I work with Artificial Intelligence, pm if you need some help.
3
sym_num
Thank you very much.
Currently, processing large data can cause a segmentation fault. I am investigating the cause.
2
sym_num
sym_num
I have added basic CNN functionality. Now I am testing. To handle large matrices, at least 16GB of memory is required. My GPU is an old GTX960. For this reason, the mini-batch size cannot be increased. GTX1660 is recommended.
2
sym_num
I have added the garbage collection to run manually when learning on CNN. Segmentation fault was avoided. Thanks Mr. NobbZ.
0
sym_num
Please give me some advice.
I’m adding CNN functionality and repeating tests on the CIFAR10 data set. But it’s not working. It will help me if I can see how the image is transformed by CNN. Please let me know if you have any graphics tools for Elixir.
0