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ONE_SENTENCE_DESCRIPTION
PARAGRAPH_DESCRIPTION
Parameters
----------
PARAM_NAME : DTYPE
              DESCRIBE PARAMETER
... 
Returns
-------
RETURN_NAME : DTYPE

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##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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# DEFINE ENVIRONMENT VARIABLES

apt-get update 
apt-get upgrade -y 


# INSTALL PACKAGES
apt-get install -y aria2

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##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

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import pandas as pd
from github import Github

g = Github("username", "password")

final = ({'url':r.html_url , 'name': r.name} for r in g.get_user().get_starred())
pd.DataFrame(final).to_excel('Github Stars 20160101.xlsx')

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starts_with <- function(vars, match, ignore.case = TRUE) {
  if (ignore.case) match <- tolower(match)
  n <- nchar(match)

  if (ignore.case) vars <- tolower(vars)
  substr(vars, 1, n) == match
}

ends_with <- function(vars, match, ignore.case = TRUE) {
  if (ignore.case) match <- tolower(match)

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mylist <- list(structure(list(Hit = "True", Project = "Blue", Year = "2011", 
Rating = "4", Launch = "26 Jan 2012", ID = "19", Dept = "1, 2, 4"), .Names = c("Hit", 
"Project", "Year", "Rating", "Launch", "ID", "Dept")), structure(list(
Hit = "False", Error = "Record not found"), .Names = c("Hit", 
"Error")), structure(list(Hit = "True", Project = "Green", Year = "2004", 
Rating = "8", Launch = "29 Feb 2004", ID = "183", Dept = "6, 8"), .Names = c("Hit", 
"Project", "Year", "Rating", "Launch", "ID", "Dept")))

dfs <- lapply(mylist, data.frame, stringsAsFactors = FALSE)
library(dplyr)

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def select(dataframe, columns, keep_others=True):
    ''' Re-order or select columns. If keep_others, then it is simply re-ordered else it will select columns'''
    cols = set(dataframe.columns)
    if keep_others:
        others = list(cols.difference(columns))
        reordered = columns + others
        return dataframe[reordered]
    else:
        return dataframe[columns]

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def search_item(dataframe, name, query, na=False, case=False, regex=True):

    idx = pd.Series([False]*len(dataframe))

    # For each item in the query look for the item and collect the documents ids it pertains to
    for q in query:
        matches = dataframe[text_column].str.contains(q, na=False, case=False, regex=True)

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