F. Fleuret. Predicting the dynamics of 2d objects with a deep
residual network. CoRR, abs/1610.04032, 2016.
- https://arxiv.org/pdf/1610.04032v1
+ https://arxiv.org/abs/1610.04032
This package is composed of a simple 2d physics simulator called
'flatland' written in C++, to generate the data-set, and a deep
It will
- (1) generate the data-set of 50k triplets of images,
+ (1) Generate the data-set of 40k triplets of images,
- (2) train the deep network, and output validation results every 100
- epochs. This take ~30h on a GTX 1080.
+ (2) Train the deep network, and output validation results every 100
+ epochs. This takes 15h on a GTX 1080 with cuda 8.0, cudnn 5.1,
+ and recent torch.
- (3) generate two pictures of the internal activations.
+ (3) Generate two pictures of the internal activations.
- (4) generate a graph with the loss curves if gnuplot is installed.
+ (4) Generate a graph with the loss curves if gnuplot is installed.
--
Francois Fleuret
-Oct 21, 2016
+Nov 6, 2016
Martigny