+++ /dev/null
-
-This is an implementation of a deep residual network for predicting
-the dynamics of 2D shapes as described in
-
- F. Fleuret. Predicting the dynamics of 2d objects with a deep
- residual network. CoRR, abs/1610.04032, 2016.
-
- 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
-residual network 'dyncnn' written in the Lua/Torch7 framework.
-
-You can run the reference experiment by executing the run.sh shell
-script.
-
-It will
-
- (1) Generate the data-set of 40k triplets of images,
-
- (2) Train the deep network, and output validation results every 100
- epochs. This takes ~30h on a GTX 1080 with cuda 8.0, cudnn 5.1,
- and recent torch.
-
- (3) Generate two pictures of the internal activations.
-
- (4) Generate a graph with the loss curves if gnuplot is installed.
-
---
-Francois Fleuret
-Nov 24, 2016
-Martigny