ir5

import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
import numpy as np


class MLP(chainer.Chain):
    def __init__(self):
        super(MLP, self).__init__(

ir5

import chainer
import chainer.computational_graph as c
import chainer.functions as F
import chainer.links as L
import numpy as np


# Network definition
class MLP(chainer.Chain):

ir5

import chainer
cupy = chainer.cuda.cupy


def profile(target):
    target()  # sometime first call is slow.

    start = cupy.cuda.Event()
    end = cupy.cuda.Event()
    start.record()

ir5

import time

import chainer
import numpy as np
cupy = chainer.cuda.cupy


def performance_check(n_runs, fortran=False, cudnn=True):
    ts = []
    for i in range(n_runs + 1):

ir5

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import time
import json
import pycurl
import StringIO
import urllib
import codecs

ir5

# coding: utf-8
from flask import Flask
from flask.ext.sqlalchemy import SQLAlchemy

app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///a.db'
db = SQLAlchemy(app)

''' Model Classes '''

ir5

import std.stdio, std.string, std.conv;
import std.algorithm, std.array, std.bigint, std.math, std.range, std.typecons;
import core.stdc.stdlib;

string readToken() {
  static string[] tokens;
  static int tokenId = 0;
  for (; tokenId == tokens.length; ) {
    if (stdin.eof) exit(0);
    tokens = readln.split, tokenId = 0;

ir5

#include <cstdio>
#include <cstring>
#include <cstdlib>
#include <cmath>
#include <string>
#include <vector>
#include <queue>
#include <map>
#include <set>
#include <algorithm>

ir5

#include <cstdio>
#include <cstring>
#include <cstdlib>
#include <cmath>
#include <string>
#include <vector>
#include <queue>
#include <map>
#include <set>
#include <algorithm>

ir5

#include <cstdio>
#include <cstring>
#include <cstdlib>
#include <cmath>
#include <iostream>
#include <algorithm>
using namespace std;
typedef long long ll;
const double PI = acos(-1.0), EPS = 1e-8;