rreece

#!/usr/bin/env python

# Set up ROOT and RootCore
import ROOT
ROOT.gROOT.Macro('$ROOTCOREDIR/scripts/load_packages.C')

# Initialize the xAOD infrastructure
ROOT.xAOD.Init()

# Set up the input files (PDSF)

rreece

"""
Helper module for displaying ROOT canvases in ipython notebooks

Usage example:
    # Save this file as rootnotes.py to your working directory.
    
    import rootnotes
    c1 = rootnotes.default_canvas()
    fun1 = TF1( 'fun1', 'abs(sin(x)/x)', 0, 10)
    c1.SetGridx()

rreece

import numpy as np
import tensorflow as tf

__author__ = "Sangwoong Yoon"

def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
    """
    Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
    For supervised learning, feed training inputs to X and training labels to Y.
    For unsupervised learning, only feed training inputs to X, and feed None to Y.

rreece

#!/usr/bin/env bash
log=mv_files.log
spare=/spare/ryan
dest=/cb/data/imagenet1k2/new/
while true; do
    if test -n "$(shopt -s nullglob; echo $spare/*)"
    then
        echo "Moving files to $dest"
        echo "Moving files to $dest" >> $log
        ls -rt1 $spare | head -n -10 | xargs -n 1 echo

rreece

# Installing python3 on Mac OSX

brew install python3
# Error: An unexpected error occurred during the `brew link` step
# The formula built, but is not symlinked into /usr/local
# Permission denied @ dir_s_mkdir - /usr/local/Frameworks
# Error: Permission denied @ dir_s_mkdir - /usr/local/Frameworks
#
# See: https://github.com/Homebrew/homebrew-core/issues/19286

rreece

#!/usr/bin/env python2
"""
FROM: http://code.activestate.com/recipes/251871/

latin1_to_ascii -- The UNICODE Hammer -- AKA "The Stupid American"

This takes a UNICODE string and replaces Latin-1 characters with
something equivalent in 7-bit ASCII. This returns a plain ASCII string. 
This function makes a best effort to convert Latin-1 characters into 
ASCII equivalents. It does not just strip out the Latin1 characters.

rreece

#!/usr/bin/env python3
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read()  # should be simple plain text file
chars = list(set(data))

rreece

#!/usr/bin/env sh
# This scripts downloads the mnist data and unzips it.

DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"

echo "Downloading..."

for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
do

rreece

import tensorflow as tf
from google.protobuf import text_format

##______________________________________________________________________________
def model_fn(features, labels, mode, params, pbtxt):

    ...

    optimizer = tf.train.AdamOptimizer(learning_rate=params['lr'])
    train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())

rreece

import argparse
import psutil
import tensorflow as tf
from typing import Dict, Any, Callable, Tuple

## Data Input Function
def data_input_fn(data_param,
                  batch_size:int=None,
                  shuffle=False) -> Callable[[], Tuple]:
    """Return the input function to get the test data.