johnhw

## np_to_tfrecords.py
import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.

## pytorch-glumpy.md

      
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              19 stars
            
          
                victor-shepardson
                / pytorch-glumpy.md
            
            
              Last active
              September 10, 2022 16:09
            
              
                using pycuda and glumpy to draw pytorch GPU tensors to the screen without copying to host memory
              
          
    Code

https://gist.github.com/victor-shepardson/5b3d3087dc2b4817b9bffdb8e87a57c4
Setup

I'm using Ubuntu 16.04 with a GTX 1060
install CUDA


## pytorch-glumpy.py
from contextlib import contextmanager
import numpy as np
import torch
from torch import Tensor, ByteTensor
import torch.nn.functional as F
from torch.autograd import Variable
import pycuda.driver
from pycuda.gl import graphics_map_flags
from glumpy import app, gloo, gl

## tutorial.md

      
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                janoliver
                / tutorial.md
            
            
              Created
              August 10, 2016 08:03
            
              
                Create scientific presentations with Inkscape, InkSlides and InkTex
              
          
    In this short article, I show you how to create nice scientific presentations using a combination of Inkscape and LaTex, made possible by two small tools I wrote.

The Tools


Inkscape: Our main vector graphics editor to draw
the presentation.
InkSlides: The python
script to convert an Inkscape SVG file to a PDF presentation. Heavily inspired and partly copied from inkscapeslide.


## pi_digits.py
"""
Run the algorithm below using CPython, Cython, PyPy and Numba and compare
their performance. (This is implementing a spigot algorithm by A. Sale,
D. Saada, S. Rabinowitz, mentioned on
http://mail.python.org/pipermail/edu-sig/2012-December/010721.html).
"""

def pi_digits(n):
    "Generate n digits of Pi."
    k, a, b, a1, b1 = 2, 4, 1, 12, 4
	import numpy as np
	import tensorflow as tf

	__author__ = "Sangwoong Yoon"

	def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
	"""
	Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
	For supervised learning, feed training inputs to X and training labels to Y.
	For unsupervised learning, only feed training inputs to X, and feed None to Y.
	from contextlib import contextmanager
	import numpy as np
	import torch
	from torch import Tensor, ByteTensor
	import torch.nn.functional as F
	from torch.autograd import Variable
	import pycuda.driver
	from pycuda.gl import graphics_map_flags
	from glumpy import app, gloo, gl
	"""
	Run the algorithm below using CPython, Cython, PyPy and Numba and compare
	their performance. (This is implementing a spigot algorithm by A. Sale,
	D. Saada, S. Rabinowitz, mentioned on
	http://mail.python.org/pipermail/edu-sig/2012-December/010721.html).
	"""

	def pi_digits(n):
	"Generate n digits of Pi."
	k, a, b, a1, b1 = 2, 4, 1, 12, 4