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Re: why is octave 4.2.0 x86_64-w64-mingw32 so slow?


From: Ray Tayek
Subject: Re: why is octave 4.2.0 x86_64-w64-mingw32 so slow?
Date: Tue, 24 Oct 2017 17:43:51 -0700
User-agent: Mozilla/5.0 (Windows NT 6.3; WOW64; rv:52.0) Gecko/20100101 Thunderbird/52.4.0

On 10/24/2017 5:41 AM, Nicholas Jankowski wrote:
On Tue, Oct 24, 2017 at 8:38 AM, Nicholas Jankowski <address@hidden <mailto:address@hidden>> wrote:
...

    oh,wait, you posted two separate functions.  So, 'train' that you
    posted first calls 'fprop', correct? no other functions missing? If
    I run train(1) with both functions present, i should see what you're
    seeing?



i hope so.



nope, apparently there's at least a custom load_data function that we don't have.

so, it seems unless you can provide a minimal, complete example to test we're not going to be able to help much.

please see load data below. unfortunately the data.mat is 7 mb and in some binary format. i tried to look at it and it's a bunch of structures. indices that select training, validation, and test sets, and the data itself.

i downloaded the benchmark. but the configure and makefile have carriage returns in them and cywin complains.

maybe it's just a slow log.

thanks



function [train_input, train_target, valid_input, valid_target, test_input, test_target, vocab] = load_data(N)
% This method loads the training, validation and test set.
% It also divides the training set into mini-batches.
% Inputs:
%   N: Mini-batch size.
% Outputs:
%   train_input: An array of size D X N X M, where
%                 D: number of input dimensions (in this case, 3).
%                 N: size of each mini-batch (in this case, 100).
%                 M: number of minibatches.
%   train_target: An array of size 1 X N X M.
% valid_input: An array of size D X number of points in the validation set.
%   test: An array of size D X number of points in the test set.
%   vocab: Vocabulary containing index to word mapping.

load data.mat;
numdims = size(data.trainData, 1);
D = numdims - 1;
M = floor(size(data.trainData, 2) / N);
train_input = reshape(data.trainData(1:D, 1:N * M), D, N, M);
train_target = reshape(data.trainData(D + 1, 1:N * M), 1, N, M);
valid_input = data.validData(1:D, :);
valid_target = data.validData(D + 1, :);
test_input = data.testData(1:D, :);
test_target = data.testData(D + 1, :);
vocab = data.vocab;
end


--
Honesty is a very expensive gift. So, don't expect it from cheap people - Warren Buffett´╗┐
http://tayek.com/



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