Preliminary Updates and Installations
(http://markus.com/install-theano-on-aws/)
sudo apt-get update
sudo apt-get -y dist-upgrade
#!/usr/bin/env bash | |
# bash 4.1.5(1) Linux Ubuntu 10.04 Date : 2019-01-02 | |
# | |
# _______________| noise : ambient Brown noise generator (cf. white noise). | |
# | |
# Usage: noise [minutes=59] [band-pass freq center=1786] [wave] | |
# ^minutes can be any positive integer. | |
# Command "noise 1" will display peak-level meter. | |
# | |
# Dependencies: play (from sox package) |
#!/usr/bin/env bash | |
# bash 4.1.5(1) Linux Ubuntu 10.04 Date : 2011-10-04 | |
# | |
# _______________| noise : ambient Brown noise generator (cf. white noise). | |
# | |
# Usage: noise [minutes=59] [band-pass freq center=1786] [wave] | |
# ^minutes can be any positive integer. | |
# Command "noise 1" will display peak-level meter. | |
# | |
# Dependencies: play (from sox package) |
Preliminary Updates and Installations
(http://markus.com/install-theano-on-aws/)
sudo apt-get update
sudo apt-get -y dist-upgrade
From N1256: (See http://port70.net/~nsz/c/c99/n1256.html#J.2)
main
using one of the specified forms (5.1.2.2.1).Simply put, Leisure is a document-oriented, exploratory computing environment for what we call Illuminated Programming. Leisure documents are:
Illuminated Programming is our term for application, source code, and data, all rolled up into one interactive, collboratively editable
/** | |
* This source file is used to print out a stack-trace when your program | |
* segfaults. It is relatively reliable and spot-on accurate. | |
* | |
* This code is in the public domain. Use it as you see fit, some credit | |
* would be appreciated, but is not a prerequisite for usage. Feedback | |
* on it's use would encourage further development and maintenance. | |
* | |
* Due to a bug in gcc-4.x.x you currently have to compile as C++ if you want | |
* demangling to work. |
''' | |
Created on Jul 13, 2015 | |
@author: kashefy | |
''' | |
import numpy as np | |
from scipy import signal | |
if __name__ == '__main__': | |
const I = x => x | |
const K = x => y => x | |
const A = f => x => f (x) | |
const T = x => f => f (x) | |
const W = f => x => f (x) (x) | |
const C = f => y => x => f (x) (y) | |
const B = f => g => x => f (g (x)) | |
const S = f => g => x => f (x) (g (x)) | |
const S_ = f => g => x => f (g (x)) (x) | |
const S2 = f => g => h => x => f (g (x)) (h (x)) |
Picking the right architecture = Picking the right battles + Managing trade-offs
import tensorflow as tf | |
from tensorflow.python.framework import ops | |
import numpy as np | |
# Define custom py_func which takes also a grad op as argument: | |
def py_func(func, inp, Tout, stateful=True, name=None, grad=None): | |
# Need to generate a unique name to avoid duplicates: | |
rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8)) | |