mehdidc

def minibatcher(fn, batchsize=1000):
  """
  fn : a function that takes an input and returns an output
  batchsize : divide the total input into divisions of size batchsize at most
  
  iterate through all the divisions, call fn, get the results, 
  then concatenate all the results.
  """
  def f(X):
      results = []

mehdidc

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

mehdidc

class EnsembleRegressor(object):
    def __init__(self, regs=None):
        self.regs = regs

    def fit(self, X, y):
        return self

    def predict(self, X, return_std=False):
        if return_std:
            means = []

mehdidc

import collections

def flatten_dict(l):
    d = {}
    for k, v in l.items():
        if isinstance(v, collections.Mapping):
            d.update(flatten_dict(v))
        elif isinstance(v, list) or isinstance(v, tuple):
            for i, l in enumerate(v):
                d[k+'_{}'.format(i)] = l

mehdidc

Latency Comparison Numbers
--------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

mehdidc

# Implementation of a simple character RNN (using LSTM units), based on:
# https://github.com/karpathy/char-rnn
# Source : https://github.com/fchollet/keras/pull/2137
import numpy as np

from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, TimeDistributedDense
from keras.layers.recurrent import LSTM

text = open('input.txt', 'r').read()

mehdidc

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

mehdidc

Code Complete by Steve McConnell

Jeff Atwood (Coding Horror)
https://blog.codinghorror.com/code-reviews-just-do-it/

Measuring Defect Potentials and Defect Removal Efficiency
http://rbcs-us.com/site/assets/files/1337/measuring-defect-potentials-and-defect-removal-efficiency.pdf

Expectations, Outcomes, and Challenges Of Modern Code Review
https://www.microsoft.com/en-us/research/publication/expectations-outcomes-and-challenges-of-modern-code-review/

mehdidc

Code Complete by Steve McConnell

Jeff Atwood (Coding Horror)
https://blog.codinghorror.com/code-reviews-just-do-it/

Measuring Defect Potentials and Defect Removal Efficiency
http://rbcs-us.com/site/assets/files/1337/measuring-defect-potentials-and-defect-removal-efficiency.pdf

Expectations, Outcomes, and Challenges Of Modern Code Review
https://www.microsoft.com/en-us/research/publication/expectations-outcomes-and-challenges-of-modern-code-review/

mehdidc

import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from clize import run
from subprocess import call
import time
import pandas as pd