##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
| 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' |
| library(shiny) | |
| library(spdep) | |
| library(leaflet) | |
| library(RColorBrewer) | |
| atx <- readRDS('travis.rds') | |
| atx$id <- 1:nrow(atx) | |
| atx2 <- atx[!is.na(atx$income), ] |
| 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 |