f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Demonstrates that cuDNN does not choose the fastest code path internally.

It first asks cuDNN to compute a particular full convolution as a forward
pass with sufficient zero padding of the input.
It then asks cuDNN to compute the same as a backward pass of a valid convolution
without zero padding of the input.

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Compares some algorithms for computing a sliding median of an array.

Results:
  scipy.signal.medfilt2d is a bit faster than scipy.ndimage.filter.median_filter
  and significantly faster than scipy.signal.medfilt.
  Maintaining a sorted list of the window becomes faster than that for a filter

f0k

# -*- coding: utf-8 -*-

"""
Preliminary implementation of batch normalization for Lasagne.
Does not include a way to properly compute the normalization factors over the
full training set for testing, but can be used as a drop-in for training and
validation.

Author: Jan Schlüter
"""

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Strips the pHYs chunk (resolution information) of a .png file.
Handy if pdflatex fails with "dimension too large" due to the
PNG resolution being set to 1.

For usage information, call without any parameters.

f0k

#include <stdio.h>
#include <cuda.h>
#include <cuda_runtime_api.h>

/* Outputs some information on CUDA-enabled devices on your computer,
 * including compute capability and current memory usage.
 *
 * On Linux, compile with: nvcc -o cuda_check cuda_check.c -lcuda
 * On Windows, compile with: nvcc -o cuda_check.exe cuda_check.c -lcuda
 *

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Example employing Lasagne for digit generation using the MNIST dataset and
Deep Convolutional Generative Adversarial Networks
(DCGANs, see http://arxiv.org/abs/1511.06434).

It is based on the MNIST example in Lasagne:
http://lasagne.readthedocs.org/en/latest/user/tutorial.html

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Example employing Lasagne for digit generation using the MNIST dataset and
Wasserstein Generative Adversarial Networks
(WGANs, see https://arxiv.org/abs/1701.07875 for the paper and
https://github.com/martinarjovsky/WassersteinGAN for the "official" code).

It is based on a DCGAN example:

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Example employing Lasagne for digit generation using the MNIST dataset and
Least Squares Generative Adversarial Networks
(LSGANs, see https://arxiv.org/abs/1611.04076 for the paper).

It is based on a WGAN example:
https://gist.github.com/f0k/f3190ebba6c53887d598d03119ca2066

f0k

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Outputs some information on CUDA-enabled devices on your computer,
including current memory usage.

It's a port of https://gist.github.com/f0k/0d6431e3faa60bffc788f8b4daa029b1
from C to Python with ctypes, so it can run without compiling anything. Note
that this is a direct translation with no attempt to make the code Pythonic.

f0k

MIT License

Copyright (c) 2017 Jan Schlüter

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions: