@@ -4,7 +4,7 @@ We study these binary activations with two datasets: [Part1: MNIST](#part1-mnist
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@@ -4,7 +4,7 @@ We study these binary activations with two datasets: [Part1: MNIST](#part1-mnist
This repository uses Pytorch library.
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 activations values for a specific data
- visualization filters trained by network
- visualization filters trained by network
- visualization heatmap for prediction data
- visualization heatmap for prediction data
...
@@ -12,7 +12,7 @@ Colaboratory notebooks for part1 et part2 contains some differents visualization
...
@@ -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 generated images for activation maximization with gradient a ascent
- visualization 1 nearest neighbor classification with different global representation of data
- 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.
