spicycode

# 0 is too far from ` ;)
set -g base-index 1

# Automatically set window title
set-window-option -g automatic-rename on
set-option -g set-titles on

#set -g default-terminal screen-256color
set -g status-keys vi
set -g history-limit 10000

msiemens

import os
import inspect
from functools import wraps


_file_cache = {}


def cache_file(path):
    def cache_is_fresh(name):

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

dulacp

# Builds a Libpng framework for the iPhone and the iPhone Simulator.
# Creates a set of universal libraries that can be used on an iPhone and in the
# iPhone simulator. Then creates a pseudo-framework to make using libpng in Xcode
# less painful.
#
# To configure the script, define:
#    IPHONE_SDKVERSION: iPhone SDK version (e.g. 8.1)
#
# Then go get the source tar.bz of the libpng you want to build, shove it in the
# same directory as this script, and run "./libpng.sh". Grab a cuppa. And voila.

benjbaron

#include <QtGui>
#include <QGraphicsRectItem>
#include <QGraphicsView>
#include <QApplication>
#include <QGraphicsSceneMouseEvent>

class CustomItem : public QGraphicsEllipseItem
{
protected:
    void mousePressEvent(QGraphicsSceneMouseEvent *event)

beci

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-5 g++-5

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 60 --slave /usr/bin/g++ g++ /usr/bin/g++-5

ducha-aiki

# The train/test net protocol buffer definition
net: "examples/cifar10/cifar10_full_sigmoid_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 1000 training iterations.
test_interval: 1000
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.001

mstankie

# A yaml constructor is for loading from a yaml node.
# This is taken from @misha 's answer: http://stackoverflow.com/a/15942429
def opencv_matrix_constructor(loader, node):
    mapping = loader.construct_mapping(node, deep=True)
    mat = np.array(mapping["data"])
    mat.resize(mapping["rows"], mapping["cols"])
    return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor)

# A yaml representer is for dumping structs into a yaml node.

ottokart

# coding: utf-8

# A little demo illustrating the effect of momentum in neural network training.
# Try using different values for MOMENTUM constant below (e.g. compare 0.0 with 0.9).
# This neural network is actually more like logistic regression, but I have used
# squared error to make the error surface more interesting.

import numpy as np
import pylab

rafaspadilha

How to create a custom Caffe layer in Python?

This tutorial will guide through the steps to create a simple custom layer for Caffe using python. By the end of it, there are some examples of custom layers.

- Why would I want to do that?

Usually you would create a custom layer to implement a funcionality that isn't available in Caffe, tuning it for your requirements.

- What will I need?

Probably just Python and Caffe installed.

- Is there any downside?