##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
##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
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| library(shiny) | |
| library(spdep) | |
| library(leaflet) | |
| library(RColorBrewer) | |
| atx <- readRDS('travis.rds') | |
| atx$id <- 1:nrow(atx) | |
| atx2 <- atx[!is.na(atx$income), ] |
| --- | |
| title: "Introduction à R" | |
| author: "Herimanitra R." | |
| date: "17 octobre 2014" | |
| output: html_document | |
| --- | |
| <h1>Installation et presentation de l'interface</h1> | |
| R est le logiciel de programmation statistique la plus complète et la plus fournie au monde. | |
| Il contient des milliers de librairie ou package capable d’exécuter des taches spécifiques liées au domaine du calcul. De la bioinformatique à la Statistique en passant par l’économétrie, l’analyse numérique, la cartographie et le Data Mining. |
| import pandas | |
| import rpy2.robjects as robjects | |
| from rpy2.robjects.packages import importr | |
| from rpy2.robjects.lib import grid | |
| from rpy2.robjects.lib import ggplot2 | |
| ## read in the distances to railroad (we calculated) | |
| neardist = pandas.read_csv('data/NearDistance.csv') | |
| ## convert to R dataframe, via Python Dictionary data type |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta name="viewport" content="width=device-width, initial-scale=1 " charset="UTF-8" > | |
| <link rel="stylesheet" href="shared/jquery.mobile-1.4.2.min.css" /> | |
| <script src="shared/jquery.js" type="text/javascript"></script> | |
| <script src="shared/jquery.mobile-1.4.2.min.js" type="text/javascript" > </script> | |
| <script src="shared/shiny.js" type="text/javascript"></script> | |
| <script src="shared/highcharts.js" type="text/javascript"></script> | |
| <script src="shared/data.js" type="text/javascript"></script> |
| library(shiny) | |
| # Define server logic for random distribution application | |
| shinyServer(function(input, output,session) { | |
| data <- reactive({ | |
| dist <- switch(input$dist, | |
| norm = rnorm, | |
| unif = runif, | |
| lnorm = rlnorm, | |
| exp = rexp, |
| #include <Rcpp.h> | |
| using namespace Rcpp; | |
| // [[Rcpp::export]] | |
| List modiframe2(DataFrame& df ) { | |
| //nrow and ncol of the dataframe: | |
| int nrow = df.nrows(), ncol= df.size() ; | |
| double moy(0); | |
| //define an empty list~dataframe | |
| //List output(ncol) |