akaptur

>>> import sys
>>> def stuff():
...     print("calling stuff!")
... 
>>> def printer(frame, event, arg):
...     print(frame, event, arg)
...     return printer  # return itself to keep tracing
... 
>>> sys.settrace(printer)  # register the tracing function
>>> stuff()

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

DanDiplo

// JS array equivalents to C# LINQ methods - by Dan B.

// First: This version using older JavaScript notation for universal browser support (scroll down for ES6 version):

// Here's a simple array of "person" objects
var people = [ 
	{ name: "John", age: 20 }, 
	{ name: "Mary", age: 35 }, 
	{ name: "Arthur", age: 78 }, 
	{ name: "Mike", age: 27 }, 

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
"""