Yili Zhao panovr

##VGG19 model for Keras

This is the Keras model of the 19-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

Instructions for making a Neural-Style movie

The following instructions are for creating your own animations using the style transfer technique described by Gatys, Ecker, and Bethge, and implemented by Justin Johnson. To see an example of such an animation, see this video of Alice in Wonderland re-styled by 17 paintings.

Setting up the environment

The easiest way to set up the environment is to simply load Samim's a pre-built Terminal.com snap or use another cloud service like Amazon EC2. Unfortunately the g2.2xlarge GPU instances cost $0.99 per hour, and depending on parameters selected, it may take 10-15 minutes to produce a 512px-wide image, so it can cost $2-3 to generate 1 sec of video at 12fps.

If you do load the

##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

	import numpy as np
	import os, sys, getopt

	# Main path to your caffe installation
	caffe_root = '/path/to/your/caffe/'

	# Model prototxt file
	model_prototxt = caffe_root + 'models/bvlc_googlenet/deploy.prototxt'

	# Model caffemodel file

	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):

	natalie@WorkBook:funcSpeed$ g++ -std=c++11 test.cpp main.cpp -o funcSpeed && ./funcSpeed
	--- Direct Call Tests ---
	testInline 9317ms.
	testExternal 9433ms.
	tester.testInlineMember 9252ms.
	tester.testExternalMember 9328ms.
	--- Pointer Call Tests ---
	(&testInline) 9401ms.
	(&testExternal) 9642ms.
	(tester.*(&Test::testInlineMember)) 9515ms.

	#! /usr/bin/env python
	"""
	Author: Jeremy M. Stober
	Program: SOFTMAX.PY
	Date: Wednesday, February 29 2012
	Description: Simple softmax function.
	"""

	import numpy as np
	npa = np.array

	#include <opencv2/opencv.hpp>

	#include <iostream>
	#include <vector>
	#include <cmath>
	#include <assert.h>

	using namespace std;
	using namespace cv;