Andrews Cordolino Sobral andrewssobral

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

Vision & Language

Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images
- Mateusz Malinowski, Marcus Rohrbach, Mario Fritz
Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
- Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
Learning Query and Image Similarities With Ranking Canonical Correlation Analysis
Wah Ngo

	"""
	The MIT License (MIT)

	Copyright (c) 2015 Alec Radford

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is

	import theano
	from keras.models import Sequential
	from keras.layers.core import Dense, Activation

	X_train, y_train = ... # load some training data
	X_batch = ... # a batch of test data

	# this is your initial model
	model = Sequential()
	model.add(Dense(20, 64))

	# run with a custom --n
	# python run_luigi.py SquaredNumbers --local-scheduler --n 20

	import luigi

	class PrintNumbers(luigi.Task):
	n = luigi.IntParameter(default=10)

	def requires(self):
	return []

	'''Functional Keras is a more functional replacement for the Graph API.
	'''

	###################
	# 2 LSTM branches #
	###################
	a = Input(input_shape=(10, 32)) # output is a TF/TH placeholder, augmented with Keras attributes
	b = Input(input_shape=(10, 32))
	encoded_a = LSTM(32)(a) # output is a TF/TH tensor
	encoded_b = LSTM(32)(b)

	""" 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

	'''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