RanaivosonHerimanitra

##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

RanaivosonHerimanitra

##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

RanaivosonHerimanitra

SELECT
  cont.sample_repo_name,
  repo.watch_count
FROM
  [bigquery-public-data:github_repos.sample_contents] as cont
JOIN [bigquery-public-data:github_repos.sample_repos] as repo
  ON cont.sample_repo_name = repo.repo_name  
WHERE
  cont.content CONTAINS 'findbugs-maven-plugin</artifactId>'
  AND cont.sample_path LIKE 'pom.xml'

RanaivosonHerimanitra

library(shiny)
library(spdep)
library(leaflet)
library(RColorBrewer)

atx <- readRDS('travis.rds')

atx$id <- 1:nrow(atx)

atx2 <- atx[!is.na(atx$income), ]

RanaivosonHerimanitra

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