Vikas Raunak vyraun

A Deep Siamese Network for Scene Detection

This is a model from the paper:

A Deep Siamese Network for Scene Detection in Broadcast Videos
Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
Proceedings of the 23rd ACM International Conference on Multimedia, 2015

Please cite the paper if you use the models.

Transforming Code into Beautiful, Idiomatic Python

Notes from Raymond Hettinger's talk at pycon US 2013 video, slides.

The code examples and direct quotes are all from Raymond's talk. I've reproduced them here for my own edification and the hopes that others will find them as handy as I have!

Looping over a range of numbers

for i in [0, 1, 2, 3, 4, 5]:

##Model

This is an implementation of Facebook's baseline GRU/LSTM model on the bAbI dataset Weston et al. 2015. It includes an interactive demo.

The bAbI dataset contains 20 different question answering tasks.

Model script

The model training script train.py and demo script demo.py are included below.

	# Tiny example of 3-layer nerual network with dropout in 2nd hidden layer
	# Output layer is linear with L2 cost (regression model)
	# Hidden layer activation is tanh

	import numpy as np

	n_epochs = 100
	n_samples = 100
	n_in = 10
	n_hidden = 5

	# 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

	import types
	import tensorflow as tf
	import numpy as np

	# Expressions are represented as lists of lists,
	# in lisp style -- the symbol name is the head (first element)
	# of the list, and the arguments follow.

	# add an expression to an expression list, recursively if necessary.
	def add_expr_to_list(exprlist, expr):

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	'''Trains a memory network on the bAbI dataset.
	References:
	- Jason Weston, Antoine Bordes, Sumit Chopra, Tomas Mikolov, Alexander M. Rush,
	"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
	http://arxiv.org/abs/1502.05698
	- Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus,
	"End-To-End Memory Networks",
	http://arxiv.org/abs/1503.08895
	Reaches 98.6% accuracy on task 'single_supporting_fact_10k' after 120 epochs.
	Time per epoch: 3s on CPU (core i7).

	// Node.js CheatSheet.
	// Download the Node.js source code or a pre-built installer for your platform, and start developing today.
	// Download: http://nodejs.org/download/
	// More: http://nodejs.org/api/all.html


	// 0. Synopsis.
	// http://nodejs.org/api/synopsis.html

	wget https://repo.continuum.io/archive/Anaconda2-4.2.0-Linux-x86_64.sh
	bash Anaconda2-4.2.0-Linux-x86_64.sh
	export PATH=/home/viraun/anaconda2/bin:$PATH
	pip install tensorflow keras

Vikas Raunak vyraun

A Deep Siamese Network for Scene Detection

Transforming Code into Beautiful, Idiomatic Python

Looping over a range of numbers

Model script

Instructions