jordanspooner

#=================================
# Sentence-level QE -- BiRNN model
#=================================
#
## Inputs:
# 1. Sentences in src language (shape: (mini_batch_size, line_words))
# 2. Parallel machine-translated documents (shape: (mini_batch_size, line_words))
#
## Output:
# 1. Sentence quality scores (shape: (mini_batch_size,))

guilhermebkel

TypeScript  25 hrs 24 mins ████████████████▋░░░░  79.7%
JavaScript  2 hrs 45 mins  █▊░░░░░░░░░░░░░░░░░░░   8.7%
Docker      1 hr 28 mins   ▉░░░░░░░░░░░░░░░░░░░░   4.6%
Markdown    36 mins        ▍░░░░░░░░░░░░░░░░░░░░   1.9%
Other       36 mins        ▍░░░░░░░░░░░░░░░░░░░░   1.9%

srafay

# Generates request data payload to be sent as 'form-data'

REQUEST_FORM_DATA_BOUNDARY = "REQUEST_FORM_DATA_BOUNDARY"
FORM_DATA_STARTING_PAYLOAD = '--{0}\r\nContent-Disposition: form-data; name=\\"'.format(REQUEST_FORM_DATA_BOUNDARY)
FORM_DATA_MIDDLE_PAYLOAD = '\"\r\n\r\n'
FORM_DATA_ENDING_PAYLOAD = '--{0}--'.format(REQUEST_FORM_DATA_BOUNDARY)
REQUEST_CUSTOM_HEADER = {
    'content-type': "multipart/form-data; boundary={}".format(REQUEST_FORM_DATA_BOUNDARY),
    'Content-Type': "",
    'cache-control': "no-cache"

nikhilkumarsingh

import requests
from bs4 import BeautifulSoup

headers = {
    'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'
}

login_data = {
    'name': '<username>',
    'pass': '<password>',

whyboris

import matplotlib.pyplot as plt

# Plot model history more easily

# when plotting, smooth out the points by some factor (0.5 = rough, 0.99 = smooth)
# method taken from `Deep Learning with Python` by François Chollet

def smooth_curve(points, factor=0.75):
    smoothed_points = []
    for point in points:

aparrish

joelouismarino

from __future__ import print_function
import imageio
from PIL import Image
import numpy as np
import keras

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation
from keras.models import Model
from keras.regularizers import l2
from keras.optimizers import SGD

rdeavila

#!/bin/bash
# Adicione um novo remote; pode chamá-lo de "upstream":

git remote add upstream https://github.com/usuario/projeto.git

# Obtenha todos os branches deste novo remote,
# como o upstream/master por exemplo:

git fetch upstream