speedcell4

import numpy as np
import tensorflow as tf
from numpy.random import RandomState
from sklearn.model_selection import StratifiedShuffleSplit
from tensorflow.contrib.layers import fully_connected

tf.logging.set_verbosity(tf.logging.INFO)

random_state = RandomState()

speedcell4

import tensorflow as tf

tf.app.flags.DEFINE_string('ps_hosts', '', 'Comma-separated list of hostname:port pairs')
tf.app.flags.DEFINE_string('worker_hosts', '', 'Comma-separated list of hostname:port pairs')
tf.app.flags.DEFINE_string('job_name', '', 'One of ps or worker')
tf.app.flags.DEFINE_integer('task_index', 0, 'Index of task within the job')
tf.app.flags.DEFINE_string('logdir', 'logdir', 'Directory to log')

FLAGS = tf.app.flags.FLAGS

speedcell4

from functools import reduce
from typing import List
from typing import Set

from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver import Chrome

URLs = [
    r'https://scholar.google.com/scholar?cites=17690265189765140803&as_sdt=2005&sciodt=0,5&hl=ja',
    r'https://scholar.google.com/scholar?cites=14180674818440394404&as_sdt=2005&sciodt=0,5&hl=ja',

speedcell4

#include <algorithm>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <iostream>
#include <numeric>

#include <deque>
#include <map>
#include <queue>

speedcell4

import chainer.functions as F
import chainer.links as L
import numpy as np
from chainer import Chain
from chainer import cuda, training, Variable
from chainer import datasets, iterators, optimizers, initializers
from chainer.training import extensions
from sklearn.datasets import load_iris

speedcell4

#ifndef DYNET_HELPER_H
#define DYNET_HELPER_H

#include <ctime>
#include <fstream>
#include <iomanip>
#include <iostream>

#include "dynet/dynet.h"
#include "dynet/expr.h"

speedcell4

import chainer.functions as F
from chainer import Variable, cuda


def masked_softmax(x, mask, axis=1):
    xp = cuda.get_array_module(x, mask)
    with cuda.get_device_from_array(x, mask):
        exp_x = F.exp(x)
        zeros = xp.zeros_like(x.data)
        sum_exp_x = F.sum(F.where(mask, exp_x, zeros), axis=axis, keepdims=True)

speedcell4

from chainer import Chain, report


class MultiTaskClassifier(Chain):
    def __init__(self, task1, task2, loss1, loss2, acc1, acc2, lambda_=1.0):
        """
        :param task1: `Chain` of task 1
        :param task2: `Chain` of task 2
        :param loss1: loss function of task1
        :param loss2: loss function of task2

speedcell4

import tensorflow as tf


class Summary(object):
    def __init__(self, logdir: str):
        self._placeholders = {}
        self._observations = {}

        self._session = tf.Session()
        self._writer = tf.summary.FileWriter(logdir=logdir)

speedcell4

#include <iostream>

using namespace std;

int binary_search(int *a, int value, int left, int right) {
  if (left >= right) return -1;
  else {
    int mid = (left + right) / 2;
    if (a[mid] == value) return mid;
    else if (a[mid] > value) return binary_search(a, value, left, mid);