viksit

"""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

viksit

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):

viksit

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

viksit

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("  ", " ")

viksit

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)

viksit

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

viksit

"""
    Demo of json_required decorator for API input validation/error handling
"""

import inspect
import functools
import json
from traceback import format_exception
from flask import jsonify, request
import sys

viksit

Deprecating transferPropsTo

It's a common pattern in React to wrap a component in an abstraction. The outer component exposes a simple property to do something that might have more complex implementation details.

We used to have a helper function called transferPropsTo. We no longer support this method. Instead you're expected to use a generic object helper to merge props.

render() {
 return Component(Object.assign({}, this.props, { more: 'values' }));

viksit

class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape

viksit

import tensorflow as tf
import numpy as np

class ConvolutionalAttentionNLI(object):
    
    def __init__(self, embeddings_shape, target_classes=2, conv_filter_size=3, conv_projection_size=300, attention_output_size=200, comparison_output_size=100, learning_rate=0.05):
        self._embeddings_shape = embeddings_shape
        self._target_classes = target_classes
        self._conv_filter_size = conv_filter_size
        self._conv_projection_size = conv_projection_size