BasPH

#!/usr/bin/env bash

set -x

black=$1
input_file=$2
start_line=$3
end_line=$4

# Read selected lines and write to tmpfile

ptrblck

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F

import matplotlib.pyplot as plt

import numpy as np

JinhaiZ

from sshtunnel import SSHTunnelForwarder
import pymongo

MONGO_HOST = "IP_ADDRESS"
MONGO_USER = "USERNAME"
MONGO_PASS = "PASSWORD"
MONGO_DB = "DATABASE_NAME"
MONGO_COLLECTION = "COLLECTION_NAME"

# define ssh tunnel

georgepar

#!/usr/bin/env python3
"""
Credits https://github.com/ivanychev/learning/blob/master/Python/ipynb2pdf/ipynb2pdf.py

Current version of Jupyter doesn't support pdf exporting when it comes to 
greek language in the document. To fix this, current script has born.
It requires nbconvert as long as jupyter to be installed.


Author:     Sergey Ivanychev

njellinas

import keras.backend as K
from keras.layers import Layer
from keras.legacy import interfaces
from keras.engine import InputSpec
from keras import activations, initializers, regularizers, constraints


class DenseTransposeTied(Layer):

    @interfaces.legacy_dense_support

fchollet

import keras
import numpy as np

timesteps = 60
input_dim = 64
samples = 10000
batch_size = 128
output_dim = 64

# Test data.

MatthieuBizien

# coding: utf-8

import logging
import re
from collections import Counter

import numpy as np
import torch
from sklearn.datasets import fetch_20newsgroups
from torch.autograd import Variable

jihunchoi

def _sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.range(0, max_len - 1).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = (sequence_length.unsqueeze(1)

ameasure

from keras.engine.topology import Layer
from keras import initializations
from keras import backend as K

class Attention(Layer):
    '''Attention operation for temporal data.
    # Input shape
        3D tensor with shape: `(samples, steps, features)`.
    # Output shape
        2D tensor with shape: `(samples, features)`.

MaximumEntropy

import subprocess
import sys

tokenizer_path = sys.argv[1] # Path to the moses tokenizer mosesdecoder/scripts/tokenizer.perl
text = sys.argv[2] # Text to be tokenized
lang = sys.argv[3] # Input language ex: en, fr, de

pipe = subprocess.Popen(["perl", tokenizer_path, '-l', lang, text], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
pipe.stdin.write(text.encode('utf-8'))
pipe.stdin.close()