From a79fda5dc501909019e40b185760be3fbaa4d12d Mon Sep 17 00:00:00 2001 From: Francois Fleuret Date: Fri, 21 Oct 2016 08:01:31 +0200 Subject: [PATCH] Update with the arxiv reference. --- README.txt | 30 ++++++++++++++++++++++-------- 1 file changed, 22 insertions(+), 8 deletions(-) diff --git a/README.txt b/README.txt index 8a83250..85cf8ba 100644 --- a/README.txt +++ b/README.txt @@ -1,17 +1,31 @@ This is an implementation of a deep residual network for predicting -the dynamics of 2D shapes. +the dynamics of 2D shapes as described in -This package is composed of two main parts: 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. + F. Fleuret. Predicting the dynamics of 2d objects with a deep + residual network. CoRR, abs/1610.04032, 2016. + + https://arxiv.org/pdf/1610.04032v1 + +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 generate the data-set of 50k triplets of images, train -the deep network, and output validation results every 100 epochs. +script. + +It will + + (1) generate the data-set of 50k triplets of images, + + (2) train the deep network, and output validation results every 100 + epochs. This take ~30h on a GTX 1080. + + (3) generate two pictures of the internal activations. + + (4) generate a graph with the loss curves if gnuplot is installed. -- Francois Fleuret -Oct 7, 2016 +Oct 21, 2016 Martigny -- 2.39.5