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git branch -m old_branch new_branch # Rename branch locally | |
git push origin :old_branch # Delete the old branch | |
git push --set-upstream origin new_branch # Push the new branch, set local branch to track the new remote |
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class ThreadPoolExecutor(concurrent.futures.ThreadPoolExecutor): | |
""" | |
A version of :class:`concurrent.futures.ThreadPoolExecutor` that | |
always uses native threads, even when threading is monkey-patched. | |
.. versionadded:: 1.2a1 | |
""" | |
def __init__(self, max_workers): | |
super(ThreadPoolExecutor, self).__init__(max_workers) |
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import numpy as np | |
__author__ = 'Fariz Rahman' | |
def eq(x, y): | |
return x.lower().replace(" ", "") == y.lower().replace(" ", "") | |
def get_words(x): | |
x = x.replace(" ", " ") |
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{ | |
"metadata": { | |
"name": "" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ |
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from time import sleep | |
from tornado.httpserver import HTTPServer | |
from tornado.ioloop import IOLoop | |
from tornado.web import Application, asynchronous, RequestHandler | |
from multiprocessing.pool import ThreadPool | |
_workers = ThreadPool(10) | |
def run_background(func, callback, args=(), kwds={}): | |
def _callback(result): |
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"""Asynchronous requests in Flask with gevent""" | |
from time import time | |
from flask import Flask, Response | |
from gevent.pywsgi import WSGIServer | |
from gevent import monkey | |
import requests |
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#!/usr/bin/env python | |
# see http://matpalm.com/blog/2015/03/28/theano_word_embeddings/ | |
import theano | |
import theano.tensor as T | |
import numpy as np | |
import random | |
E = np.asarray(np.random.randn(6, 2), dtype='float32') | |
t_E = theano.shared(E) | |
t_idxs = T.ivector() |
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#!/usr/bin/python | |
# load glove | |
import codecs | |
import array | |
import collections | |
import io | |
try: | |
# Python 2 compat | |
import cPickle as pickle | |
except ImportError: |
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""" | |
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)) | |
data_size, vocab_size = len(data), len(chars) |
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__author__ = 'Conan' | |
import numpy as np | |
import random | |
from keras.models import Sequential | |
from keras.layers.recurrent import SimpleRNN | |
# Toy dictionary of 1000 indices to random length 10 vectors |