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License: MIT License
Toolbox for doing deep learning with Theano
License: MIT License
Either by
Also interesting discussion in soumith/convnet-benchmarks#35.
But be careful:
os.fork() is problematic for CUDA if you fork a process where a CUDA context has already been initialized, even if you do not use the context in the forked process.
If the fork happens before the initialization of CUDA, it should be OK.
(Source)
See also: https://github.com/Theano/Theano/wiki/Using-Multiple-GPUs
Ideally by making a "MultiCost" cost layer which uses the weighted sum of multiple other cost layers as cost.
Extremely cool visualizations by Alec Radford.
The relevant comment by Nicolas Boulanger actually has two ways to implement it.
https://github.com/torch/nn/blob/master/doc/containers.md#nn.Containers
People (you know who you are =) really like them.
It was quite useful, esp. the saving and loading of the training.
Would speed-up augmented prediction tremendously if it fits into RAM.
Can be done either at the Augmenter
-level or at the Predictor
-level, not sure yet which is better.
This should actually be a straightforward but useful change, esp. for #1.
=)
It was very, very useful in loops of experiments for getting at variance.
Currently, there's no way to make them deterministic!
obviously.
i.e. as an weighted sums of feature maps.
Existing implementations:
The following verbose outputs would be useful:
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