Make unnecessary computations optional

[gheinrich]

Original (20 epochs LeNet on MNIST): 187s
Now: 142s

Helps with bug #339

[lukeyeager]

Dang your machine is slow. My results:

Original (20 epochs LeNet on MNIST): 30s
Now: 30s

And now I'm seeing extra output in the log. I thought this PR turned OFF confusion matrices?

2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: ConfusionMatrix:
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [[     242       0       0       0       2       0       1       0       0       0]   98.776%   [class: 0]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       0     280       0       0       1       0       0       1       2       0]   98.592%    [class: 1]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       1       0     254       0       1       0       0       2       0       0]   98.450%    [class: 2]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       0       1       2     243       0       3       0       2       0       1]   96.429%    [class: 3]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       0       1       0       0     236       0       1       1       0       6]   96.327%    [class: 4]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       1       2       0       2       0     213       4       1       0       0]   95.516%    [class: 5]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       1       1       0       0       0       0     237       0       0       0]   99.163%    [class: 6]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       0       1       4       1       2       0       0     247       0       2]   96.109%    [class: 7]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       4       0       1       1       1       4       1       2     226       3]   93.004%    [class: 8]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: [       0       1       0       1       4       2       1       1       0     242]]  96.032%    [class: 9]
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: + average row correct: 96.839545369148%
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: + average rowUcol correct (VOC measure): 93.902345299721%
2015-10-15 11:17:51 [20151015-111721-72f4] [WARNING] Train Torch Model unrecognized output: + global correct: 96.877502001601%

[gheinrich]

Perhaps our datasets don't have the same size? My MNIST dataset has 45k training samples and 15k validation samples. So you're saying the patch doesn't provide any speedup? I'll double check on my end.
Before the patch, accuracy and confusion used to be computed during both training and validation. This patch makes both optional in the Lua wrapper and the caller in torch_train.py turns it ON for validation (so we can draw the validation accuracy curve + we get validation confusion for free).

[lukeyeager]

Oh, that was careless of me. I misnamed my dataset and didn't notice. Whoops!

	20 epochs	5 epochs
Original	202s	56s
Now	204s	52s

I'm definitely picking up the new code because I'm seeing the confusion matrix in my log.

[gheinrich]

It looks like I need to review my patch!

[gheinrich]

Closing as patch needs to be revisited. May re-open later.

[gheinrich]

Re-opening with a new patch. These are the numbers I get:

	LeNet (MNIST, 30 epochs)	Alexnet (CIFAR10, 2 epochs)	GoogLeNet (reduced CIFAR10, 1 epoch)
original	1m54s	5m14s	2m4s
now	1m40s	4m38s	2ms2

[lukeyeager]

I've verified similar results on my machine. LGTM!