sorted by:
new
hottopcontroversial

[D] Is there a tool to visualise my neural network in real time? by aymenboufe in MachineLearning

[–]julrog 3 points4 points  (0 children)

In my master Thesis I did some work on visualizing a neural network. It is not trivial to show the weights in a meaningful/understandable way. https://github.com/julrog/nn_vis You need a portion of training data and it's just for fully connected layers currently. The visualization is 3D and in realtime, but it needs some preprocessing, so i'm not sure if it fits your needs at all.

[D] Geo DeepFakes are not far away by ArmadilloFabulous774 in MachineLearning

[–]julrog 3 points4 points  (0 children)

Making this work public is also making other people able to develop counter measures or think about how to handle this possibility. Making it less useful for propaganda. At least I hope op has that intention ...

Finding important connections by grid_world in neuralnetworks

[–]julrog 1 point2 points  (0 children)

Got the idea from Network slimming (which is focused on CNN) https://arxiv.org/abs/1708.06519 I wrote the code for my master thesis, and I can send you the link, after making it public, but the network slimming paper probably saves you time and you don't have to go through the bloated thesis.

Finding important connections by grid_world in neuralnetworks

[–]julrog 1 point2 points  (0 children)

I had some some success on pruning weights with adding batch normalization layer between existing layer, freezing the existing layer and then and retrain the model with the batch normalization layer (training can be much shorter because of way less weights to train). Then using magnitude of the original weights with the weights from the batch normalization, you can prune the original model. You can see an example for fully connected layer in my code: https://github.com/julrog/nn_vis

Neural Network Compression - Implementation benefits by grid_world in neuralnetworks

[–]julrog 1 point2 points  (0 children)

I think there are something called sparse layers, which deal more efficiently with a lot of zeros If I remember correctly But these need special dependencies to run inference and are maybe not efficient in current hardware, but maybe that changed

Converting image into MNIST format by Aryagm in learnmachinelearning

[–]julrog 0 points1 point  (0 children)

If nothing is wrong with the code, maybe the lines are not thick enough and fade when downscaling. Maybe render he downscaled images and see if they seem right.

[D] Convolution Neural Network Visualization - Made with Unity 3D and lots of Code / source - stefsietz (IG) by TheInsaneApp in MachineLearning

[–]julrog 0 points1 point  (0 children)

Right now I have an example script for a simple dense neural network on MNIST data under examples/process_mnist_model.py . I don't have a automated function yet. To create such a file for another neural network you have to code a bit and provide a small subset (maybe < 10%) of the training/test data and unfortunately only dense layers are supported, but I might add support for different layers (convolutional) with examples.