baraldilorenzo

##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

genekogan

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

baraldilorenzo

##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

marekrei

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

karpathy

"""
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):

NatalieWolfe

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.

stober

#! /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

thorikawa

#include <opencv2/opencv.hpp>

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

using namespace std;
using namespace cv;