@@ -4,7 +4,7 @@ We study these binary activations with two datasets: [Part1: MNIST](#part1-mnist
This repository uses Pytorch library.
Colaboratory notebooks for part1 et part2 contains some differents visualization tools to compare networks with binary weights and network with no binary weights, e.g.:
Colaboratory notebooks in Parts 1 and 2 contain visualization tools to compare binary and no binary networks, such as:
- visualization activations values for a specific data
- visualization filters trained by network
- visualization heatmap for prediction data
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@@ -12,7 +12,7 @@ Colaboratory notebooks for part1 et part2 contains some differents visualization
- visualization generated images for activation maximization with gradient a ascent
- visualization 1 nearest neighbor classification with different global representation of data
Learning networks code use Pytorch Ignite (in "experiments/MNIST_binary_Run_Notebook.ipynb).
The code to train networks is located in "experiments/MNIST_binary_Run_Notebook.ipynb and uses Pytorch Ignite.