Shortcuts

챗봇 튜토리얼

Author: Matthew Inkawhich

번역: 김진현

이 튜토리얼에서는 순환(recurrent) 시퀀스 투 시퀀스(sequence-to-sequence) 모델의 재미있고 흥미로운 사용 예를 살펴보려 합니다. 간단한 챗봇을 학습해 볼 텐데, 사용할 데이터는 영화 대본으로 구성된 Cornell Movie-Dialogs(코넬 대학교의 영화 속 대화 말뭉치 데이터 입니다.

대화형 모델은 많은 사람들이 관심을 갖는 인공지능 분야의 연구 주제입니다. 고객 서비스와 관련된 활용, 온라인 헬프데스크 등 여러 상황에서 챗봇을 활용할 수 있습니다. 많은 챗봇이 검색 기반(retrieval-based) 모델을 사용하는데, 이는 특정한 형식을 갖춘 질문에 대해 미리 정해진 반응을 출력하는 방식입니다. 분야를 특정 회사의 IT 헬프데스크처럼 한정짓는다면 이러한 모델을 사용해도 충분합니다. 그러나 이런 모델은 좀 더 일반적인 상황에 활용할 수 있을만큼 강력하진 않습니다. 기계를 학습시켜서 사람과 여러 주제에 대해 의미 있는 대화를 하게끔 하는 것은 아직 해결되지 않은 연구 주제입니다. 그러나 최근에 딥러닝이 유행하면서 여러 가지의 강력한 생성 모델이 등장했습니다. 그러한 모델의 한 예인 구글의 신경 대화 모델(Neural Conversational Model) 은 다중 도메인 대화 생성 모델(multi-domain generative conversational models) 분야에 있어서 큰 진전을 이루었습니다. 우리는 이 튜토리얼을 통해 이러한 모델을 PyTorch로 구현해보려 합니다.

bot
> hello? (안녕하세요?)
Bot: hello . (안녕하세요.)
> where am I? (여긴 어디죠?)
Bot: you re in a hospital . (병원입니다.)
> who are you? (당신은 누구시죠?)
Bot: i m a lawyer . (변호사입니다.)
> how are you doing? (어떻게 지내세요?)
Bot: i m fine . (잘 지냅니다.)
> are you my friend? (당신은 제 친구인가요?)
Bot: no . (아뇨.)
> you're under arrest (당신을 체포하겠습니다)
Bot: i m trying to help you ! (난 당신을 도우려 하는 겁니다!)
> i'm just kidding (농담이었어요)
Bot: i m sorry . (미안하네요.)
> where are you from? (어디서 오셨어요?)
Bot: san francisco . (샌프란시스코요.)
> it's time for me to leave (전 이제 가봐야겠네요)
Bot: i know . (알겠습니다.)
> goodbye (안녕히 계세요)
Bot: goodbye . (안녕히 가세요.)

이 튜토리얼의 핵심 내용

감사의 글

이 튜토리얼은 다음 자료의 도움을 받아 작성하였습니다.

  1. Yuan-Kuei Wu의 pytorch-chatbot 구현체: https://github.com/ywk991112/pytorch-chatbot

  2. Sean Robertson의 practical-pytorch seq2seq-translation 예제: https://github.com/spro/practical-pytorch/tree/master/seq2seq-translation

  3. FloydHub의 코넬 대학교의 영화 말뭉치 데이터 전처리 코드: https://github.com/floydhub/textutil-preprocess-cornell-movie-corpus

준비 단계

시작에 앞서, 여기 에서 ZIP 파일 형태의 데이터를 내려받고, 현재 디렉토리 아래에 data/ 라는 디렉토리를 만들어서 내려받은 데이터를 옮겨두시기 바랍니다.

그 다음에는, 몇 가지 필요한 도구들을 import 하겠습니다.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import torch
from torch.jit import script, trace
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import csv
import random
import re
import os
import unicodedata
import codecs
from io import open
import itertools
import math


USE_CUDA = torch.cuda.is_available()
device = torch.device("cuda" if USE_CUDA else "cpu")

데이터 읽기 & 전처리하기

다음 단계는 데이터 파일의 형식을 재조정한 후, 우리가 작업하기 편한 구조로 읽어들이는 것입니다.

코넬 대학교의 영화 속 대화 말뭉치 데이터셋 은 영화 속 등장 인물의 대화가 풍부하게 포함된 데이터셋입니다.

  • 영화 속 등장 인물 10,292 쌍이 대화를 220,579번 주고받습니다

  • 영화 617개의 등장 인물 9,035명이 나옵니다

  • 총 발화(utterance) 수는 304,713번입니다

이 데이터셋은 규모도 크고 내용도 다양하며, 격식체와 비격식체, 여러 시간대, 여러 감정 상태 등이 두루 포함되어 있습니다. 우리의 바람은 이러한 다양성으로 인해 모델이 견고해지는, 즉 모델이 여러 종류의 입력 및 질의에 잘 대응할 수 있게 되는 것입니다.

우선은 원본 데이터 파일을 몇 줄 살펴보면서 형식이 어떻게 되어있는지 살펴 보겠습니다.

corpus_name = "cornell movie-dialogs corpus"
corpus = os.path.join("data", corpus_name)

def printLines(file, n=10):
    with open(file, 'rb') as datafile:
        lines = datafile.readlines()
    for line in lines[:n]:
        print(line)

printLines(os.path.join(corpus, "movie_lines.txt"))

Out:

b'L1045 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ They do not!\n'
b'L1044 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++$+++ They do to!\n'
b'L985 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ I hope so.\n'
b'L984 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++$+++ She okay?\n'
b"L925 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ Let's go.\n"
b'L924 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++$+++ Wow\n'
b"L872 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ Okay -- you're gonna need to learn how to lie.\n"
b'L871 +++$+++ u2 +++$+++ m0 +++$+++ CAMERON +++$+++ No\n'
b'L870 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ I\'m kidding.  You know how sometimes you just become this "persona"?  And you don\'t know how to quit?\n'
b'L869 +++$+++ u0 +++$+++ m0 +++$+++ BIANCA +++$+++ Like my fear of wearing pastels?\n'

원하는 형식의 데이터 파일로 만들기

편의를 위해 데이터의 형식을 원하는 형태로 만들려고 합니다. 각 줄에 질의 문장응답 문장 의 쌍이 탭으로 구분되어 있게끔 하는 것입니다.

다음의 함수를 통해 movie_lines.txt 원본 데이터 파일을 파싱하려 합니다.

  • loadLines 는 파일에 포함된 대사를 변환하여 항목(대사 ID lineID, 인물 ID characterID, 영화 ID movieID, 인물 character, 대사 내용 text)에 대한 사전 형태로 변환합니다

  • loadConversationsloadLines 를 통해 읽어들인 대사(lines)의 항목(fields)를 movie_conversations.txt 에 나와 있는 내용에 맞춰 대화 형태로 묶습니다

  • extractSentencePairs 는 대화(conversations)에서 문장 쌍을 추출합니다

# 파일에 포함된 대사를 쪼개서 항목에 대한 사전(``dict``) 형태로 변환합니다
def loadLines(fileName, fields):
    lines = {}
    with open(fileName, 'r', encoding='iso-8859-1') as f:
        for line in f:
            values = line.split(" +++$+++ ")
            # 항목을 추출합니다
            lineObj = {}
            for i, field in enumerate(fields):
                lineObj[field] = values[i]
            lines[lineObj['lineID']] = lineObj
    return lines


# 대사의 항목을 *movie_conversations.txt* 를 참고하여 대화 형태로 묶습니다
def loadConversations(fileName, lines, fields):
    conversations = []
    with open(fileName, 'r', encoding='iso-8859-1') as f:
        for line in f:
            values = line.split(" +++$+++ ")
            # 항목을 추출합니다
            convObj = {}
            for i, field in enumerate(fields):
                convObj[field] = values[i]
            # 문자열을 리스트로 변환합니다(convObj["utteranceIDs"] == "['L598485', 'L598486', ...]")
            utterance_id_pattern = re.compile('L[0-9]+')
            lineIds = utterance_id_pattern.findall(convObj["utteranceIDs"])
            # 대사를 재구성합니다
            convObj["lines"] = []
            for lineId in lineIds:
                convObj["lines"].append(lines[lineId])
            conversations.append(convObj)
    return conversations


# conversations에서 문장 쌍을 추출합니다
def extractSentencePairs(conversations):
    qa_pairs = []
    for conversation in conversations:
        # 대화를 이루는 각 대사에 대해 반복문을 수행합니다
        # 대화의 마지막 대사는 (그에 대한 응답이 없으므로) 무시합니다
        for i in range(len(conversation["lines"]) - 1):
            inputLine = conversation["lines"][i]["text"].strip()
            targetLine = conversation["lines"][i+1]["text"].strip()
            # 잘못된 샘플은 제거합니다(리스트가 하나라도 비어 있는 경우)
            if inputLine and targetLine:
                qa_pairs.append([inputLine, targetLine])
    return qa_pairs

이제 이 함수들을 호출하여 새로운 파일인 formatted_movie_lines.txt 를 만듭니다.

# 새 파일에 대한 경로를 정의합니다
datafile = os.path.join(corpus, "formatted_movie_lines.txt")

delimiter = '\t'
# 구분자에 대해 unescape 함수를 호출합니다
delimiter = str(codecs.decode(delimiter, "unicode_escape"))

# 대사 사전(dict), 대화 리스트(list), 그리고 각 항목의 이름을 초기화합니다
lines = {}
conversations = []
MOVIE_LINES_FIELDS = ["lineID", "characterID", "movieID", "character", "text"]
MOVIE_CONVERSATIONS_FIELDS = ["character1ID", "character2ID", "movieID", "utteranceIDs"]

# 대사(lines)를 읽어들여 대화(conversations)로 재구성합니다
print("\nProcessing corpus...")
lines = loadLines(os.path.join(corpus, "movie_lines.txt"), MOVIE_LINES_FIELDS)
print("\nLoading conversations...")
conversations = loadConversations(os.path.join(corpus, "movie_conversations.txt"),
                                  lines, MOVIE_CONVERSATIONS_FIELDS)

# 결과를 새로운 csv 파일로 저장합니다
print("\nWriting newly formatted file...")
with open(datafile, 'w', encoding='utf-8') as outputfile:
    writer = csv.writer(outputfile, delimiter=delimiter, lineterminator='\n')
    for pair in extractSentencePairs(conversations):
        writer.writerow(pair)

# 몇 줄을 예제 삼아 출력해 봅니다
print("\nSample lines from file:")
printLines(datafile)

Out:

Processing corpus...

Loading conversations...

Writing newly formatted file...

Sample lines from file:
b"Can we make this quick?  Roxanne Korrine and Andrew Barrett are having an incredibly horrendous public break- up on the quad.  Again.\tWell, I thought we'd start with pronunciation, if that's okay with you.\n"
b"Well, I thought we'd start with pronunciation, if that's okay with you.\tNot the hacking and gagging and spitting part.  Please.\n"
b"Not the hacking and gagging and spitting part.  Please.\tOkay... then how 'bout we try out some French cuisine.  Saturday?  Night?\n"
b"You're asking me out.  That's so cute. What's your name again?\tForget it.\n"
b"No, no, it's my fault -- we didn't have a proper introduction ---\tCameron.\n"
b"Cameron.\tThe thing is, Cameron -- I'm at the mercy of a particularly hideous breed of loser.  My sister.  I can't date until she does.\n"
b"The thing is, Cameron -- I'm at the mercy of a particularly hideous breed of loser.  My sister.  I can't date until she does.\tSeems like she could get a date easy enough...\n"
b'Why?\tUnsolved mystery.  She used to be really popular when she started high school, then it was just like she got sick of it or something.\n'
b"Unsolved mystery.  She used to be really popular when she started high school, then it was just like she got sick of it or something.\tThat's a shame.\n"
b'Gosh, if only we could find Kat a boyfriend...\tLet me see what I can do.\n'

데이터 읽고 정리하기

다음에 해야 할 일은 어휘집을 만들고, 질의/응답 문장 쌍을 메모리로 읽어들이는 것입니다.

우리가 다루는 대상은 일련의 단어 들이며, 따라서 이들을 이산 공간 상의 수치(discrete numerical space)로 자연스럽게 대응시키기 어렵다는 점에 유의하시기 바랍니다. 따라서 우리는 데이터셋 안에 들어 있는 단어를 인덱스 값으로 변환하는 매핑을 따로 만들어야 합니다.

이를 위해 우리는 Voc 라는 클래스를 만들어 단어에서 인덱스로의 매핑, 인덱스에서 단어로의 역 매핑, 각 단어의 등장 횟수, 전체 단어 수 등을 관리하려 합니다. 이 클래스는 어휘집에 새로운 단어를 추가하는 메서드(addWord), 문장에 등장하는 모든 단어를 추가하는 메서드(addSentence), 그리고 자주 등장하지 않는 단어를 정리하는 메서드(trim)를 제공합니다. 단어를 정리하는 내용에 대해서는 뒤에서 좀 더 자세히 살펴보겠습니다.

# 기본 단어 토큰 값
PAD_token = 0  # 짧은 문장을 채울(패딩, PADding) 때 사용할 제로 토큰
SOS_token = 1  # 문장의 시작(SOS, Start Of Sentence)을 나타내는 토큰
EOS_token = 2  # 문장의 끝(EOS, End Of Sentence)을 나태는 토큰

class Voc:
    def __init__(self, name):
        self.name = name
        self.trimmed = False
        self.word2index = {}
        self.word2count = {}
        self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
        self.num_words = 3  # SOS, EOS, PAD를 센 것

    def addSentence(self, sentence):
        for word in sentence.split(' '):
            self.addWord(word)

    def addWord(self, word):
        if word not in self.word2index:
            self.word2index[word] = self.num_words
            self.word2count[word] = 1
            self.index2word[self.num_words] = word
            self.num_words += 1
        else:
            self.word2count[word] += 1

    # 등장 횟수가 기준 이하인 단어를 정리합니다
    def trim(self, min_count):
        if self.trimmed:
            return
        self.trimmed = True

        keep_words = []

        for k, v in self.word2count.items():
            if v >= min_count:
                keep_words.append(k)

        print('keep_words {} / {} = {:.4f}'.format(
            len(keep_words), len(self.word2index), len(keep_words) / len(self.word2index)
        ))

        # 사전을 다시 초기화힙니다
        self.word2index = {}
        self.word2count = {}
        self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
        self.num_words = 3 # 기본 토큰을 센 것

        for word in keep_words:
            self.addWord(word)

이제 어휘집과 질의/응답 문장 쌍을 재구성하려 합니다. 그러한 데이터를 사용하려면 그 전에 약간의 전처리 작업을 수행해야 합니다.

우선, unicodeToAscii 를 이용하여 유니코드 문자열을 아스키로 변환해야 합니다. 다음에는 모든 글자를 소문자로 변환하고, 알파벳도 아니고 기본적인 문장 부호도 아닌 글자는 제거합니다(정규화, normalizeString). 마지막으로는 학습할 때의 편의성을 위해서, 길이가 일정 기준을 초과하는, 즉 MAX_LENGTH 보다 긴 문장을 제거합니다(filterPairs).

MAX_LENGTH = 10  # 고려할 문장의 최대 길이

# 유니코드 문자열을 아스키로 변환합니다
# https://stackoverflow.com/a/518232/2809427 참고
def unicodeToAscii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
    )

# 소문자로 만들고, 공백을 넣고, 알파벳 외의 글자를 제거합니다
def normalizeString(s):
    s = unicodeToAscii(s.lower().strip())
    s = re.sub(r"([.!?])", r" \1", s)
    s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
    s = re.sub(r"\s+", r" ", s).strip()
    return s

# 질의/응답 쌍을 읽어서 voc 객체를 반환합니다
def readVocs(datafile, corpus_name):
    print("Reading lines...")
    # 파일을 읽고, 쪼개어 lines에 저장합니다
    lines = open(datafile, encoding='utf-8').\
        read().strip().split('\n')
    # 각 줄을 쪼개어 pairs에 저장하고 정규화합니다
    pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]
    voc = Voc(corpus_name)
    return voc, pairs

# 문장의 쌍 'p'에 포함된 두 문장이 모두 MAX_LENGTH라는 기준보다 짧은지를 반환합니다
def filterPair(p):
    # EOS 토큰을 위해 입력 시퀀스의 마지막 단어를 보존해야 합니다
    return len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH

# 조건식 filterPair에 따라 pairs를 필터링합니다
def filterPairs(pairs):
    return [pair for pair in pairs if filterPair(pair)]

# 앞에서 정의한 함수를 이용하여 만든 voc 객체와 리스트 pairs를 반환합니다
def loadPrepareData(corpus, corpus_name, datafile, save_dir):
    print("Start preparing training data ...")
    voc, pairs = readVocs(datafile, corpus_name)
    print("Read {!s} sentence pairs".format(len(pairs)))
    pairs = filterPairs(pairs)
    print("Trimmed to {!s} sentence pairs".format(len(pairs)))
    print("Counting words...")
    for pair in pairs:
        voc.addSentence(pair[0])
        voc.addSentence(pair[1])
    print("Counted words:", voc.num_words)
    return voc, pairs


# voc와 pairs를 읽고 재구성합니다
save_dir = os.path.join("data", "save")
voc, pairs = loadPrepareData(corpus, corpus_name, datafile, save_dir)
# 검증을 위해 pairs의 일부 내용을 출력해 봅니다
print("\npairs:")
for pair in pairs[:10]:
    print(pair)

Out:

Start preparing training data ...
Reading lines...
Read 221282 sentence pairs
Trimmed to 64271 sentence pairs
Counting words...
Counted words: 18008

pairs:
['there .', 'where ?']
['you have my word . as a gentleman', 'you re sweet .']
['hi .', 'looks like things worked out tonight huh ?']
['you know chastity ?', 'i believe we share an art instructor']
['have fun tonight ?', 'tons']
['well no . . .', 'then that s all you had to say .']
['then that s all you had to say .', 'but']
['but', 'you always been this selfish ?']
['do you listen to this crap ?', 'what crap ?']
['what good stuff ?', 'the real you .']

학습 단계가 빨리 수렴하도록 하는 또 다른 전략은 자주 쓰이지 않는 단어를 어휘집에서 제거하는 것입니다. 피처 공간의 크기를 줄이면 모델이 학습을 통해 근사하려는 함수의 난이도를 낮추는 효과도 있습니다. 우리는 이를 두 단계로 나눠 진행하려 합니다.

  1. voc.trim 함수를 이용하여 MIN_COUNT 라는 기준 이하의 단어를 제거합니다.

  2. 제거하기로 한 단어를 포함하는 경우를 pairs에서 제외합니다.

MIN_COUNT = 3    # 제외할 단어의 기준이 되는 등장 횟수

def trimRareWords(voc, pairs, MIN_COUNT):
    # MIN_COUNT 미만으로 사용된 단어는 voc에서 제외합니다
    voc.trim(MIN_COUNT)
    # 제외할 단어가 포함된 경우를 pairs에서도 제외합니다
    keep_pairs = []
    for pair in pairs:
        input_sentence = pair[0]
        output_sentence = pair[1]
        keep_input = True
        keep_output = True
        # 입력 문장을 검사합니다
        for word in input_sentence.split(' '):
            if word not in voc.word2index:
                keep_input = False
                break
        # 출력 문장을 검사합니다
        for word in output_sentence.split(' '):
            if word not in voc.word2index:
                keep_output = False
                break

        # 입출력 문장에 제외하기로 한 단어를 포함하지 않는 경우만을 남겨둡니다
        if keep_input and keep_output:
            keep_pairs.append(pair)

    print("Trimmed from {} pairs to {}, {:.4f} of total".format(len(pairs), len(keep_pairs), len(keep_pairs) / len(pairs)))
    return keep_pairs


# voc와 pairs를 정돈합니다
pairs = trimRareWords(voc, pairs, MIN_COUNT)

Out:

keep_words 7823 / 18005 = 0.4345
Trimmed from 64271 pairs to 53165, 0.8272 of total

모델을 위한 데이터 준비하기

상당한 노력을 기울여 데이터를 전처리하고, 잘 정리하여 어휘집 객체와 문장 쌍의 리스트 형태로 만들어두긴 했지만, 결국 우리가 만들 모델에서 사용하는 입력은 수치 값으로 이루어진 torch 텐서입니다. 처리한 데이터를 모델에 맞는 형태로 준비하는 방법의 하나가 seq2seq 변환 튜토리얼 에 나와 있습니다. 이 튜토리얼에서는 배치 크기로 1을 사용하며, 이는 즉 문장에 등장하는 단어를 어휘집에서의 인덱스로 변환하여 모델에 제공하기만 하면 된다는 의미입니다.

그래도 여러분이 학습 속도나 GPU 병렬 처리 용량을 향상하고 싶다면 미니배치를 이용하여 학습해야 할 것입니다.

미니배치를 사용한다는 것은 배치에 포함된 문장 길이가 달라질 수 있다는 점에 유의해야 한다는 것을 뜻합니다. 같은 배치 안에서 크기가 다른 문장을 처리하기 위해서는 배치용 입력 텐서의 모양을 (max_length, batch_size) 로 맞춰야 합니다. 이때 max_length 보다 짧은 문장에 대해서는 EOS 토큰 뒤에 제로 토큰을 덧붙이면 됩니다.

영어로 된 문장을 텐서로 변환하기 위해 단순히 그에 대응하는 인덱스를 사용하고(indexesFromSentence) 제로 토큰을 패딩한다고 해봅시다. 그러면 텐서의 모양이 (batch_size, max_length) 이 되고, 첫 번째 차원에 대해 인덱싱을 수행하면 모든 시간대별 문장이 전부 반환될 것입니다. 그러나 우리는 배치를 시간에 따라, 그리고 배치에 포함된 모든 문장에 대해 인덱싱할 수도 있어야 합니다. 따라서 우리는 입력 배치의 모양을 뒤집어서 (max_length, batch_size) 형태로 만들 것입니다. 그러고 난 후에 첫 번째 차원에 대해 인덱싱하면 배치에 포함된 모든 문장을 시간에 대해 인덱싱한 결과를 반환하게 됩니다. 우리는 이 뒤집기 작업을 zeroPadding 함수를 이용하여 묵시적으로 수행할 것입니다.

batches

inputVar 함수는 문장을 텐서로 변환하는, 그리고 궁극적으로는 제로 패딩하여 올바른 모양으로 맞춘 텐서를 만드는 작업을 수행합니다. 이 함수는 각 배치에 포함된 시퀀스의 길이(lengths)로 구성된 텐서도 같이 반환합니다. 그리고 우리는 이를 나중에 디코더로 넘겨줄 것입니다.

outputVar 함수는 inputVar 와 비슷한 작업을 수행하지만, lengths 텐서를 반환하는 대신에 이진 마스크로 구성된 텐서와 목표 문장의 최대 길이를 같이 반환합니다. 이진 마스크 텐서는 출력에 해당하는 목표 텐서와 그 모양이 같지만, 패딩 토큰(PAD_token)에 해당하는 경우에는 값이 0이며 나머지 경우의 값은 1입니다.

batch2TrainData 는 단순히 여러 쌍을 입력으로 받아서, 앞서 설명한 함수를 이용하여 입력 및 목표 텐서를 구하여 반환합니다.

def indexesFromSentence(voc, sentence):
    return [voc.word2index[word] for word in sentence.split(' ')] + [EOS_token]


def zeroPadding(l, fillvalue=PAD_token):
    return list(itertools.zip_longest(*l, fillvalue=fillvalue))

def binaryMatrix(l, value=PAD_token):
    m = []
    for i, seq in enumerate(l):
        m.append([])
        for token in seq:
            if token == PAD_token:
                m[i].append(0)
            else:
                m[i].append(1)
    return m

# 입력 시퀀스 텐서에 패딩한 결과와 lengths를 반환합니다
def inputVar(l, voc):
    indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]
    lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
    padList = zeroPadding(indexes_batch)
    padVar = torch.LongTensor(padList)
    return padVar, lengths

# 패딩한 목표 시퀀스 텐서, 패딩 마스크, 그리고 최대 목표 길이를 반환합니다
def outputVar(l, voc):
    indexes_batch = [indexesFromSentence(voc, sentence) for sentence in l]
    max_target_len = max([len(indexes) for indexes in indexes_batch])
    padList = zeroPadding(indexes_batch)
    mask = binaryMatrix(padList)
    mask = torch.ByteTensor(mask)
    padVar = torch.LongTensor(padList)
    return padVar, mask, max_target_len

# 입력 배치를 이루는 쌍에 대한 모든 아이템을 반환합니다
def batch2TrainData(voc, pair_batch):
    pair_batch.sort(key=lambda x: len(x[0].split(" ")), reverse=True)
    input_batch, output_batch = [], []
    for pair in pair_batch:
        input_batch.append(pair[0])
        output_batch.append(pair[1])
    inp, lengths = inputVar(input_batch, voc)
    output, mask, max_target_len = outputVar(output_batch, voc)
    return inp, lengths, output, mask, max_target_len


# 검증용 예시
small_batch_size = 5
batches = batch2TrainData(voc, [random.choice(pairs) for _ in range(small_batch_size)])
input_variable, lengths, target_variable, mask, max_target_len = batches

print("input_variable:", input_variable)
print("lengths:", lengths)
print("target_variable:", target_variable)
print("mask:", mask)
print("max_target_len:", max_target_len)

Out:

input_variable: tensor([[ 226,   50,   45,  197,  318],
        [  76,  115,  317,  117,    4],
        [   4,  774,  895, 3796,    2],
        [ 226,  169, 7460,   83,    0],
        [  76,    7,    4,    4,    0],
        [   4,    6,    2,    2,    0],
        [   2,    2,    0,    0,    0]])
lengths: tensor([7, 7, 6, 6, 3])
target_variable: tensor([[  34,  147,  318,   50, 4317],
        [ 141,  575,  842,   47,    4],
        [  83,  227,  387,    7,    4],
        [   4,   47,    4,   47, 1753],
        [   4,    7,    4,  169,    6],
        [   2,    8,    4,   70,   66],
        [   0,    6,    2,  227,   66],
        [   0,    2,    0,    6,   66],
        [   0,    0,    0,    2,    2]])
mask: tensor([[1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [1, 1, 1, 1, 1],
        [0, 1, 1, 1, 1],
        [0, 1, 0, 1, 1],
        [0, 0, 0, 1, 1]], dtype=torch.uint8)
max_target_len: 9

모델 정의하기

Seq2Seq 모델

우리 챗봇의 두뇌에 해당하는 모델은 sequence-to-sequence (seq2seq) 모델입니다. seq2seq 모델의 목표는 가변 길이 시퀀스를 입력으로 받고, 크기가 고정된 모델을 이용하여, 가변 길이 시퀀스를 출력으로 반환하는 것입니다.

Sutskever 등 은 두 개의 독립된 순환 신경망을 같이 이용하여 이러한 목적을 달성할 수 있음을 발견했습니다. RNN 하나는 인코더 로, 가변 길이 입력 시퀀스를 고정된 길이의 문맥 벡터(context vector)로 인코딩합니다. 이론상 문맥 벡터(RNN의 마지막 은닉 레이어)는 봇에게 입력으로 주어지는 질의 문장에 대한 의미론적 정보를 담고 있을 것입니다. 두 번째 RNN은 디코더 입니다. 디코더는 단어 하나와 문맥 벡터를 입력으로 받고, 시퀀스의 다음 단어가 무엇일지를 추론하여 반환하며, 다음 단계에서 사용할 은닉 상태도 같이 반환합니다.

model

그림 출처: https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_intro/

인코더

인코더 RNN은 입력 시퀀스를 토큰 단위로(예를 들어, 단어 단위로) 한번에 하나씩 살펴보며 진행합니다. 그리고 각 단계마다 “출력” 벡터와 “은닉 상태” 벡터를 반환합니다. 은닉 상태 벡터는 다음 단계를 진행할 때 같이 사용되며, 출력 벡터는 차례대로 기록됩니다. 인코더는 시퀀스의 각 지점에 대해 파악한 문맥을 고차원 공간에 있는 점들의 집합으로 변환합니다. 나중에 디코더는 이를 이용하여 주어진 문제에 대해 의미 있는 출력을 구할 것입니다.

인코더의 핵심 부분에는 다중 레이어 게이트 순환 유닛(multi-layered Gated Recurrent Unit)이 있습니다. 이는 Cho 등 이 2014년에 고안한 것입니다. 우리는 GRU를 양방향으로 변환한 형태를 사용할 것이며, 이는 본질적으로 두 개의 독립된 RNN이 존재한다는 의미입니다. 하나는 입력 시퀀스를 원래 시퀀스에서의 순서로 처리하며, 다른 하나는 입력 시퀀스를 역순으로 처리합니다. 단계마다 각 네트워크의 출력을 합산합니다. 양방향 GRU를 사용하면 과거와 미래의 문맥을 함께 인코딩할 수 있다는 장점이 있습니다.

양방향 RNN:

rnn_bidir

그림 출처: https://colah.github.io/posts/2015-09-NN-Types-FP/

embedding 레이어가 단어 인덱스를 임의 크기의 피처 공간으로 인코딩하는 데 사용되었음에 유의하기 바랍니다. 우리의 모델에서는 이 레이어가 각 단어를 크기가 hidden_size 인 피처 공간으로 매핑할 것입니다. 학습을 거치면 서로 뜻이 유사한 단어는 의미적으로 유사하게 인코딩될 것입니다.

마지막으로, RNN 모듈에 패딩된 배치를 보내려면 RNN과 연결된 부분에서 패킹 및 언패킹하는 작업을 수행해야 합니다. 각각은 nn.utils.rnn.pack_padded_sequencenn.utils.rnn.pad_packed_sequence 를 통해 수행할 수 있습니다.

계산 그래프:

  1. 단어 인덱스를 임베딩으로 변환합니다.

  2. RNN 모듈을 위한 패딩된 배치 시퀀스를 패킹합니다.

  3. GRU로 포워드 패스를 수행합니다.

  4. 패딩을 언패킹합니다.

  5. 양방향 GRU의 출력을 합산합니다.

  6. 출력과 마지막 은닉 상태를 반환합니다.

입력:

  • input_seq: 입력 시퀀스 배치. shape=(max_length, batch_size)

  • input_lengths: 배치에 포함된 각 문장의 길이로 구성된 리스트. shape=(batch_size)

  • hidden: 은닉 상태. shape=(n_layers x num_directions, batch_size, hidden_size)

출력:

  • outputs: GRU의 마지막 은닉 레이어에 대한 출력 피처 값(양방향 (출력을 합산한 것). shape=(max_length, batch_size, hidden_size)

  • hidden: GRU의 최종 은닉 상태. shape=(n_layers x num_directions, batch_size, hidden_size)

class EncoderRNN(nn.Module):
    def __init__(self, hidden_size, embedding, n_layers=1, dropout=0):
        super(EncoderRNN, self).__init__()
        self.n_layers = n_layers
        self.hidden_size = hidden_size
        self.embedding = embedding

        # GRU를 초기화합니다. input_size와 hidden_size 매개변수는 둘 다 'hidden_size'로
        # 둡니다. 이는 우리 입력의 크기가 hideen_size 만큼의 피처를 갖는 단어 임베딩이기
        # 때문입니다.
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers,
                          dropout=(0 if n_layers == 1 else dropout), bidirectional=True)

    def forward(self, input_seq, input_lengths, hidden=None):
        # 단어 인덱스를 임베딩으로 변환합니다
        embedded = self.embedding(input_seq)
        # RNN 모듈을 위한 패딩된 배치 시퀀스를 패킹합니다
        packed = nn.utils.rnn.pack_padded_sequence(embedded, input_lengths)
        # GRU로 포워드 패스를 수행합니다
        outputs, hidden = self.gru(packed, hidden)
        # 패딩을 언패킹합니다
        outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs)
        # 양방향 GRU의 출력을 합산합니다
        outputs = outputs[:, :, :self.hidden_size] + outputs[:, : ,self.hidden_size:]
        # 출력과 마지막 은닉 상태를 반환합니다
        return outputs, hidden

디코더

디코더 RNN은 토큰 단위로 응답 문장을 생성하는 역할을 수행합니다. 이때 인코더의 문맥 벡터를 사용하며, 내부 은닉 상태에 따라 시퀀스의 다음 단어를 생성하게 됩니다. 디코더는 EOS_token, 즉 문장의 끝을 나타내는 토큰을 출력할 때까지 계속 단어를 생성합니다. 원래의 seq2seq 디코더에는 알려진 문제점이 있습니다. 만약 우리가 입력 시퀀스의 의미를 인코딩할 때 문맥 벡터에만 전적으로 의존한다면, 그 과정 중에 정보 손실이 일어날 가능성이 높다는 것입니다. 이는 특히 입력 시퀀스의 길이가 길 때 그러하며, 이 때문에 디코더의 기능이 크게 제한될 수 있습니다.

이를 해결하기 위한 방편으로, Bahdanau 등 은 ‘어텐션 메커니즘’을 고안했습니다. 이는 디코더가 매 단계에 대해 고정된 문맥을 계속 사용하는 것이 아니라, 입력 시퀀스의 특정 부분에 집중하게 하는 방식입니다.

높은 차원에서 이야기 하자면, 어텐션은 디코더의 현재 은닉 상태와 인코더의 출력을 바탕으로 계산됩니다. 출력되는 어텐션 가중치는 입력 시퀀스와 동일한 모양을 가집니다. 따라서 이를 인코더의 출력과 곱할 수 있고, 그 결과로 얻게 되는 가중치 합은 인코더의 출력에서 어느 부분에 집중해야 할지를 알려줍니다. Sean Robertson 의 그림에 이러한 내용이 잘 설명되어 있습니다.

attn2

Luong 등 은 Bahdanau의 기초 연구를 더욱 발전시킨 ‘전역(global) 어텐션’을 제안했습니다. ‘전역 어텐션’의 핵심적인 차이점은 인코더의 은닉 상태를 모두 고려한다는 점입니다. 이는 Bahdanau 등의 ‘지역(local) 어텐션’ 방식이 현재 시점에 대한 인코더의 은닉 상태만을 고려한다는 점과 다른 부분입니다. ‘전역 어텐션’의 또 다른 차이점은 어텐션에 대한 가중치, 혹은 에너지를 계산할 때 현재 시점에 대한 디코더의 은닉 상태만을 사용한다는 점입니다. Bahdanau 등은 어텐션을 계산할 때 디코더의 이전 단계 상태에 대한 정보를 활용합니다. 또한 Luong 등의 방법에서는 인코더의 출력과 디코더의 출력에 대한 어텐션 에너지를 계산하는 방법을 제공하며, 이를 ‘점수 함수(score function)’라 부릅니다.

scores

이때 \(h_t\) 는 목표 디코더의 현재 상태를, \(\bar{h}_s\) 는 인코더의 모든 상태를 뜻합니다.

종합해 보면, 전역 어텐션 메커니즘을 다음 그림과 같이 요약할 수 있을 것입니다. 우리가 ‘어텐션 레이어’를 Attn 라는 독립적인 nn.Module 로 구현할 것임에 유의하기 바랍니다. 이 모듈의 출력은 모양이 (batch_size, 1, max_length) 인 정규화된 softmax 가중치 텐서입니다.

global_attn
# Luong 어텐션 레이어
class Attn(nn.Module):
    def __init__(self, method, hidden_size):
        super(Attn, self).__init__()
        self.method = method
        if self.method not in ['dot', 'general', 'concat']:
            raise ValueError(self.method, "is not an appropriate attention method.")
        self.hidden_size = hidden_size
        if self.method == 'general':
            self.attn = nn.Linear(self.hidden_size, hidden_size)
        elif self.method == 'concat':
            self.attn = nn.Linear(self.hidden_size * 2, hidden_size)
            self.v = nn.Parameter(torch.FloatTensor(hidden_size))

    def dot_score(self, hidden, encoder_output):
        return torch.sum(hidden * encoder_output, dim=2)

    def general_score(self, hidden, encoder_output):
        energy = self.attn(encoder_output)
        return torch.sum(hidden * energy, dim=2)

    def concat_score(self, hidden, encoder_output):
        energy = self.attn(torch.cat((hidden.expand(encoder_output.size(0), -1, -1), encoder_output), 2)).tanh()
        return torch.sum(self.v * energy, dim=2)

    def forward(self, hidden, encoder_outputs):
        # Attention 가중치(에너지)를 제안된 방법에 따라 계산합니다
        if self.method == 'general':
            attn_energies = self.general_score(hidden, encoder_outputs)
        elif self.method == 'concat':
            attn_energies = self.concat_score(hidden, encoder_outputs)
        elif self.method == 'dot':
            attn_energies = self.dot_score(hidden, encoder_outputs)

        # max_length와 batch_size의 차원을 뒤집습니다
        attn_energies = attn_energies.t()

        # 정규화된 softmax 확률 점수를 반환합니다 (차원을 늘려서)
        return F.softmax(attn_energies, dim=1).unsqueeze(1)

이처럼 어텐션 서브모듈을 정의하고 나면 실제 디코더 모델을 구현할 수 있게 됩니다. 디코더에 대해서는 매 시간마다 배치를 하나씩 수동으로 제공하려 합니다. 이는 임베딩된 단어 텐서와 GRU 출력의 모양이 둘 다 (1, batch_size, hidden_size) 라는 의미입니다.

계산 그래프:

  1. 현재의 입력 단어에 대한 임베딩을 구합니다.

  2. 무방향 GRU로 포워드 패스를 수행합니다.

  3. (2)에서 구한 현재의 GRU 출력을 바탕으로 어텐션 가중치를 계산합니다.

  4. 인코더 출력에 어텐션을 곱하여 새로운 “가중치 합” 문맥 벡터를 구합니다.

  5. Luong의 논문에 나온 식 5를 이용하여 가중치 문맥 벡터와 GRU 출력을 결합합니다.

  6. Luong의 논문에 나온 식 6을 이용하여(softmax 없이) 다음 단어를 예측합니다.

  7. 출력과 마지막 은닉 상태를 반환합니다.

입력:

  • input_step: 입력 시퀀스 배치에 대한 한 단위 시간(한 단어). shape=(1, batch_size)

  • last_hidden: GRU의 마지막 은닉 레이어. shape=(n_layers x num_directions, batch_size, hidden_size)

  • encoder_outputs: 인코더 모델의 출력. shape=(max_length, batch_size, hidden_size)

출력:

  • output: 각 단어가 디코딩된 시퀀스에서 다음 단어로 사용되었을 때 적합할 확률을 나타내는 정규화된 softmax 텐서. shape=(batch_size, voc.num_words)

  • hidden: GRU의 마지막 은닉 상태. shape=(n_layers x num_directions, batch_size, hidden_size)

class LuongAttnDecoderRNN(nn.Module):
    def __init__(self, attn_model, embedding, hidden_size, output_size, n_layers=1, dropout=0.1):
        super(LuongAttnDecoderRNN, self).__init__()

        # 참조를 보존해 둡니다
        self.attn_model = attn_model
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.n_layers = n_layers
        self.dropout = dropout

        # 레이어를 정의합니다
        self.embedding = embedding
        self.embedding_dropout = nn.Dropout(dropout)
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers, dropout=(0 if n_layers == 1 else dropout))
        self.concat = nn.Linear(hidden_size * 2, hidden_size)
        self.out = nn.Linear(hidden_size, output_size)

        self.attn = Attn(attn_model, hidden_size)

    def forward(self, input_step, last_hidden, encoder_outputs):
        # 주의: 한 단위 시간에 대해 한 단계(단어)만을 수행합니다
        # 현재의 입력 단어에 대한 임베딩을 구합니다
        embedded = self.embedding(input_step)
        embedded = self.embedding_dropout(embedded)
        # 무방향 GRU로 포워드 패스를 수행합니다
        rnn_output, hidden = self.gru(embedded, last_hidden)
        # 현재의 GRU 출력을 바탕으로 어텐션 가중치를 계산합니다
        attn_weights = self.attn(rnn_output, encoder_outputs)
        # 인코더 출력에 어텐션을 곱하여 새로운 "가중치 합" 문맥 벡터를 구합니다
        context = attn_weights.bmm(encoder_outputs.transpose(0, 1))
        # Luong의 논문에 나온 식 5를 이용하여 가중치 문맥 벡터와 GRU 출력을 결합합니다
        rnn_output = rnn_output.squeeze(0)
        context = context.squeeze(1)
        concat_input = torch.cat((rnn_output, context), 1)
        concat_output = torch.tanh(self.concat(concat_input))
        # Luong의 논문에 나온 식 6을 이용하여 다음 단어를 예측합니다
        output = self.out(concat_output)
        output = F.softmax(output, dim=1)
        # 출력과 마지막 은닉 상태를 반환합니다
        return output, hidden

학습 프로시저 정의하기

Masked loss

우리는 패딩된 시퀀스 배치를 다루기 때문에 손실을 계산할 때 단순히 텐서의 모든 원소를 고려할 수는 없습니다. 우리는 maskNLLLoss 를 정의하여 디코더의 출력 텐서, 목표 텐서, 이진 마스크 텐서를 바탕으로 손실을 계산하려 합니다. 이 손실 함수에서는 마스크 텐서의 1 에 대응하는 원소에 대한 음의 로그 우도 값의 평균을 계산합니다.

def maskNLLLoss(inp, target, mask):
    nTotal = mask.sum()
    crossEntropy = -torch.log(torch.gather(inp, 1, target.view(-1, 1)).squeeze(1))
    loss = crossEntropy.masked_select(mask).mean()
    loss = loss.to(device)
    return loss, nTotal.item()

한 번의 학습 단계

train 함수에 학습을 한 단계(입력 배치 한 개에 대한) 진행하는 알고리즘이 나와 있습니다.

우리는 수렴이 잘 되도록 몇 가지 영리한 전략을 사용해보려 합니다.

  • 첫 번째 전략은 teacher forcing 을 사용하는 것입니다. 이는 teacher_forcing_ratio 로 정의된 확률에 따라, 디코더의 이번 단계 예측값 대신에 현재의 목표 단어를 디코더의 다음 입력 값으로 활용하는 것입니다. 이 기법은 디코더의 보조 바퀴처럼 작용하여 효율적으로 학습될 수 있게 도와 줍니다. 하지만 teacher forcing 기법은 추론 과정에서 모델이 불안정 해지도록 할 수도 있는데, 이는 디코더가 학습 과정에서 자신의 출력 시퀀스를 직접 만들어 볼 기회를 충분히 제공받지 못할 수 있기 때문입니다. 따라서 우리는 teacher_forcing_ratio 를 어떻게 설정해 두었는지에 주의를 기울여야 하며, 수렴이 빨리 되었다고 속아 넘어가서는 안 됩니다.

  • 우리가 구현한 두 번째 전략은 gradient clipping 입니다. 이는 소위 ‘그라디언트 폭발’ 문제를 해결하기 위해 널리 사용되는 기법입니다. 핵심은 그라디언트를 클리핑 하거나 임계값을 둠으로써, 그라디언트가 지수 함수적으로 증가하거나 오버플로를 일으키는(NaN) 경우를 막고, 비용 함수의 급격한 경사를 피하겠다는 것입니다.

grad_clip

그림 출처: Goodfellow 등 저. Deep Learning. 2016. https://www.deeplearningbook.org/

작업 절차:

  1. 전체 입력 배치에 대하여 인코더로 포워드 패스를 수행합니다.

  2. 디코더의 입력을 SOS_token로, 은닉 상태를 인코더의 마지막 은닉 상태로 초기화합니다.

  3. 입력 배치 시퀀스를 한 번에 하나씩 디코더로 포워드 패스합니다.

  4. Teacher forcing을 사용하는 경우, 디코더의 다음 입력을 현재의 목표로 둡니다. 그렇지 않으면 디코더의 다음 입력을 현재 디코더의 출력으로 둡니다.

  5. 손실을 계산하고 누적합니다.

  6. 역전파를 수행합니다.

  7. 그라디언트를 클리핑 합니다.

  8. 인코더 및 디코더 모델의 매개변수를 갱신합니다.

Warning

PyTorch의 RNN 모듈(RNN, LSTM, GRU)은 전체 입력 시퀀스(또는 시퀀스의 배치)를 단순히 넣어주기만 하면 다른 비순환 레이어처럼 사용할 수 있습니다. 우리는 encoder 에서 GRU 레이어를 이런 식으로 사용합니다. 그 안이 실제로 어떻게 되어 있는지를 살펴보면, 매 시간 단계마다 은닉 상태를 계산하는 반복 프로세스가 존재합니다. 또 다른 방법은, 이 모듈을 매번 한 단위 시간만큼 수행할 수도 있습니다. 그 경우에는 우리가 decoder 모델을 다룰 때처럼, 학습 과정에서 수동으로 시퀀스에 대해 반복 작업을 수행해 주어야 합니다. 이 모듈에 대해 모델의 개념을 확실히 갖고만 있다면, 순차 모델을 구현하는 것도 매우 단순할 것입니다.

def train(input_variable, lengths, target_variable, mask, max_target_len, encoder, decoder, embedding,
          encoder_optimizer, decoder_optimizer, batch_size, clip, max_length=MAX_LENGTH):

    # 제로 그라디언트
    encoder_optimizer.zero_grad()
    decoder_optimizer.zero_grad()

    # device 옵션을 설정합니다
    input_variable = input_variable.to(device)
    target_variable = target_variable.to(device)
    mask = mask.to(device)
    # Lengths for rnn packing should always be on the cpu
    lengths = lengths.to("cpu")

    # 변수를 초기화합니다
    loss = 0
    print_losses = []
    n_totals = 0

    # 인코더로 포워드 패스를 수행합니다
    encoder_outputs, encoder_hidden = encoder(input_variable, lengths)

    # 초기 디코더 입력을 생성합니다(각 문장을 SOS 토큰으로 시작합니다)
    decoder_input = torch.LongTensor([[SOS_token for _ in range(batch_size)]])
    decoder_input = decoder_input.to(device)

    # 디코더의 초기 은닉 상태를 인코더의 마지막 은닉 상태로 둡니다
    decoder_hidden = encoder_hidden[:decoder.n_layers]

    # 이번 반복에서 teacher forcing을 사용할지를 결정합니다
    use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False

    # 배치 시퀀스를 한 번에 하나씩 디코더로 포워드 패스합니다
    if use_teacher_forcing:
        for t in range(max_target_len):
            decoder_output, decoder_hidden = decoder(
                decoder_input, decoder_hidden, encoder_outputs
            )
            # Teacher forcing 사용: 다음 입력을 현재의 목표로 둡니다
            decoder_input = target_variable[t].view(1, -1)
            # 손실을 계산하고 누적합니다
            mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
            loss += mask_loss
            print_losses.append(mask_loss.item() * nTotal)
            n_totals += nTotal
    else:
        for t in range(max_target_len):
            decoder_output, decoder_hidden = decoder(
                decoder_input, decoder_hidden, encoder_outputs
            )
            # Teacher forcing 미사용: 다음 입력을 디코더의 출력으로 둡니다
            _, topi = decoder_output.topk(1)
            decoder_input = torch.LongTensor([[topi[i][0] for i in range(batch_size)]])
            decoder_input = decoder_input.to(device)
            # 손실을 계산하고 누적합니다
            mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
            loss += mask_loss
            print_losses.append(mask_loss.item() * nTotal)
            n_totals += nTotal

    # 역전파를 수행합니다
    loss.backward()

    # 그라디언트 클리핑: 그라디언트를 제자리에서 수정합니다
    _ = nn.utils.clip_grad_norm_(encoder.parameters(), clip)
    _ = nn.utils.clip_grad_norm_(decoder.parameters(), clip)

    # 모델의 가중치를 수정합니다
    encoder_optimizer.step()
    decoder_optimizer.step()

    return sum(print_losses) / n_totals

학습 단계

이제 마지막으로 전체 학습 프로시저와 데이터를 하나로 엮을 때가 되었습니다. trainIters 함수는 주어진 모델, optimizer, 데이터 등을 토대로 학습을 n_iterations 번의 단계만큼 진행하는 역할을 담당합니다. 이 함수는 자기 자신을 살 설명하고 있는 편인데, 무거운 작업을 train 함수에 옮겨 놓았기 때문입니다.

한 가지 주의할 점은 우리가 모델을 저장하려 할 때, 인코더와 디코더의 state_dicts (매개변수), optimizer의 state_dicts, 손실, 진행 단계 수 등을 tarball로 만들어 저장한다는 점입니다. 모델을 이러한 방식으로 저장하면 checkpoint에 대해 아주 높은 수준의 유연성을 확보할 수 있게 됩니다. Checkpoint를 불러오고 나면, 우리는 모델 매개변수를 이용하여 예측을 진행할 수도 있고, 이전에 멈췄던 부분부터 학습을 계속 진행할 수도 있게 됩니다.

def trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer, embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size, print_every, save_every, clip, corpus_name, loadFilename):

    # 각 단계에 대한 배치를 읽어옵니다
    training_batches = [batch2TrainData(voc, [random.choice(pairs) for _ in range(batch_size)])
                      for _ in range(n_iteration)]

    # 초기화
    print('Initializing ...')
    start_iteration = 1
    print_loss = 0
    if loadFilename:
        start_iteration = checkpoint['iteration'] + 1

    # 학습 루프
    print("Training...")
    for iteration in range(start_iteration, n_iteration + 1):
        training_batch = training_batches[iteration - 1]
        # 배치에서 각 필드를 읽어옵니다
        input_variable, lengths, target_variable, mask, max_target_len = training_batch

        # 배치에 대해 학습을 한 단계 진행합니다
        loss = train(input_variable, lengths, target_variable, mask, max_target_len, encoder,
                     decoder, embedding, encoder_optimizer, decoder_optimizer, batch_size, clip)
        print_loss += loss

        # 경과를 출력합니다
        if iteration % print_every == 0:
            print_loss_avg = print_loss / print_every
            print("Iteration: {}; Percent complete: {:.1f}%; Average loss: {:.4f}".format(iteration, iteration / n_iteration * 100, print_loss_avg))
            print_loss = 0

        # Checkpoint를 저장합니다
        if (iteration % save_every == 0):
            directory = os.path.join(save_dir, model_name, corpus_name, '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size))
            if not os.path.exists(directory):
                os.makedirs(directory)
            torch.save({
                'iteration': iteration,
                'en': encoder.state_dict(),
                'de': decoder.state_dict(),
                'en_opt': encoder_optimizer.state_dict(),
                'de_opt': decoder_optimizer.state_dict(),
                'loss': loss,
                'voc_dict': voc.__dict__,
                'embedding': embedding.state_dict()
            }, os.path.join(directory, '{}_{}.tar'.format(iteration, 'checkpoint')))

평가 정의하기

모델을 학습시키고 나면 직접 봇과 대화를 나눠보고 싶어질 것입니다. 그러려면 먼저 모델이 인코딩된 입력을 어떻게 디코딩할지를 정의해줘야 합니다.

탐욕적 디코딩

탐욕적 디코딩(Greedy decoding)은 우리가 학습 단계에서 teacher forcing을 적용하지 않았을 때 사용한 디코딩 방법입니다. 달리 말하면, 각 단계에 대해 단순히 decoder_output 에서 가장 높은 softmax값을 갖는 단어를 선택하는 방식입니다. 이 디코딩 방법은 한 번의 단계에 대해서는 최적입니다.

우리는 탐욕적 디코딩 연산을 수행할 수 있도록 GreedySearchDecoder 클래스를 만들었습니다. 수행 과정에서 이 클래스의 인스턴스는 모양이 (input_seq length, 1) 인 입력 시퀀스(input_seq), 조종할 입력 길이(input_length) 텐서, 그리고 응답 문장 길이의 제한을 나타내는 max_length 를 입력으로 받습니다. 입력 시퀀서는 다음과 같은 계산 그래프에 의해 평가됩니다.

계산 그래프:

  1. 인코더 모델로 입력을 포워드 패스합니다.

  2. 인코더의 마지막 은닉 레이어가 디코더의 첫 번째 은닉 레이어의 입력이 되도록 준비합니다.

  3. 디코더의 첫 번째 입력을 SOS_token으로 초기화합니다.

  4. 디코더가 단어를 덧붙여 나갈 텐서를 초기화합니다.

  5. 반복적으로 각 단계마다 하나의 단어 토큰을 디코딩합니다.
    1. 디코더로의 포워드 패스를 수행합니다.

    2. 가장 가능성 높은 단어 토큰과 그 softmax 점수를 구합니다.

    3. 토큰과 점수를 기록합니다.

    4. 현재의 토큰을 디코더의 다음 입력으로 준비시킵니다.

  6. 단어 토큰과 점수를 모아서 반환합니다.

class GreedySearchDecoder(nn.Module):
    def __init__(self, encoder, decoder):
        super(GreedySearchDecoder, self).__init__()
        self.encoder = encoder
        self.decoder = decoder

    def forward(self, input_seq, input_length, max_length):
        # 인코더 모델로 입력을 포워드 패스합니다
        encoder_outputs, encoder_hidden = self.encoder(input_seq, input_length)
        # 인코더의 마지막 은닉 레이어가 디코더의 첫 번째 은닉 레이어의 입력이 되도록 준비합니다
        decoder_hidden = encoder_hidden[:decoder.n_layers]
        # 디코더의 첫 번째 입력을 SOS_token으로 초기화합니다
        decoder_input = torch.ones(1, 1, device=device, dtype=torch.long) * SOS_token
        # 디코더가 단어를 덧붙여 나갈 텐서를 초기화합니다
        all_tokens = torch.zeros([0], device=device, dtype=torch.long)
        all_scores = torch.zeros([0], device=device)
        # 반복적으로 각 단계마다 하나의 단어 토큰을 디코딩합니다
        for _ in range(max_length):
            # 디코더로의 포워드 패스를 수행합니다
            decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs)
            # 가장 가능성 높은 단어 토큰과 그 softmax 점수를 구합니다
            decoder_scores, decoder_input = torch.max(decoder_output, dim=1)
            # 토큰과 점수를 기록합니다
            all_tokens = torch.cat((all_tokens, decoder_input), dim=0)
            all_scores = torch.cat((all_scores, decoder_scores), dim=0)
            # 현재의 토큰을 디코더의 다음 입력으로 준비시킵니다(차원을 증가시켜서)
            decoder_input = torch.unsqueeze(decoder_input, 0)
        # 단어 토큰과 점수를 모아서 반환합니다
        return all_tokens, all_scores

내 텍스트 평가하기

이제 디코딩 모델을 정의했으니, 문자열로 된 입력 시퀀스를 평가하는 함수를 작성해볼 수 있을 것입니다. evaluate 함수에 입력 시퀀스를 낮은 레벨에서 어떻게 처리할지가 나와 있습니다. 우리는 먼저 문장을 batch_size==1 이고 단어 인덱스로 구성된 입력 배치 형태로 만듭니다. 이를 위해 문장의 각 단어를 그에 대응하는 인덱스로 변환하고, 차원을 뒤집어서 모델에 맞는 입력 형태로 변환합니다. 우리는 입력 시퀀스의 길이를 저장하고 있는 lengths 텐서도 만듭니다. 이 경우에는 lengths 가 스칼라 값이 되는데, 우리는 한 번에 한 문장만 평가하기 때문입니다(batch_size==1). 다음으로는 GreedySearchDecoder 의 객체(searcher)를 이용하여 응답 문장 텐서를 디코딩합니다. 마지막으로, 응답 인덱스를 단어로 변환하고 디코딩된 단어의 리스트를 반환합니다.

evaluateInput 은 우리의 챗봇에 대한 인터페이스 역할을 수행합니다. 이를 호출하면 입력 텍스트 필드가 생성되는데, 거기에 우리의 질의 문장을 입력해볼 수 있습니다. 입력 문장을 타이핑하고 엔터 를 누르면, 입력한 텍스트가 학습 데이터와 같은 방식으로 정규화되고, 최종적으로는 evaluate 함수에 입력으로 제공되어 디코딩된 출력 문장을 구하게 됩니다. 우리는 이러한 과정을 계속 반복하며, 이를 통해 ‘q’나 ‘quit’를 입력하기 전까지는 계속 채팅할 수 있습니다.

마지막으로, 만약 어휘집에 포함되어 있지 않은 단어를 포함하고 있는 문장이 입력되더라도 이를 예의 바르게 처리합니다. 즉 에러 메시지를 출력하고 사용자에게 새로운 문장을 입력해달라고 요청합니다.

def evaluate(encoder, decoder, searcher, voc, sentence, max_length=MAX_LENGTH):
    ### 입력 시퀀스를 배치 형태로 만듭니다
    # 단어 -> 인덱스
    indexes_batch = [indexesFromSentence(voc, sentence)]
    # lengths 텐서를 만듭니다
    lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
    # 배치의 차원을 뒤집어서 모델이 사용하는 형태로 만듭니다
    input_batch = torch.LongTensor(indexes_batch).transpose(0, 1)
    # 적절한 디바이스를 사용합니다
    input_batch = input_batch.to(device)
    lengths = lengths.to("cpu")
    # searcher를 이용하여 문장을 디코딩합니다
    tokens, scores = searcher(input_batch, lengths, max_length)
    # 인덱스 -> 단어
    decoded_words = [voc.index2word[token.item()] for token in tokens]
    return decoded_words


def evaluateInput(encoder, decoder, searcher, voc):
    input_sentence = ''
    while(1):
        try:
            # 입력 문장을 받아옵니다
            input_sentence = input('> ')
            # 종료 조건인지 검사합니다
            if input_sentence == 'q' or input_sentence == 'quit': break
            # 문장을 정규화합니다
            input_sentence = normalizeString(input_sentence)
            # 문장을 평가합니다
            output_words = evaluate(encoder, decoder, searcher, voc, input_sentence)
            # 응답 문장을 형식에 맞춰 출력합니다
            output_words[:] = [x for x in output_words if not (x == 'EOS' or x == 'PAD')]
            print('Bot:', ' '.join(output_words))

        except KeyError:
            print("Error: Encountered unknown word.")

모델 수행하기

마지막으로, 우리의 모델을 수행해 볼 시간입니다!

우리가 챗봇 모델을 학습할 때든 테스트할 때든, 우리는 각각의 인코더 및 디코더 모델을 초기화해줘야 합니다. 다음 블록에서는 우리가 원하는대로 설정을 맞추고, 처음부터 시작할지, 아니면 checkpoint를 불러올지 정하고, 모델을 빌드하고 초기화합니다. 성능을 최적화하기 위해서는 모델 설정을 여러가지로 바꿔 보면서 테스트해보기 바랍니다.

# 모델을 설정합니다
model_name = 'cb_model'
attn_model = 'dot'
#attn_model = 'general'
#attn_model = 'concat'
hidden_size = 500
encoder_n_layers = 2
decoder_n_layers = 2
dropout = 0.1
batch_size = 64

# 불러올 checkpoint를 설정합니다. 처음부터 시작할 때는 None으로 둡니다.
loadFilename = None
checkpoint_iter = 4000
#loadFilename = os.path.join(save_dir, model_name, corpus_name,
#                            '{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
#                            '{}_checkpoint.tar'.format(checkpoint_iter))


# loadFilename이 제공되는 경우에는 모델을 불러옵니다
if loadFilename:
    # 모델을 학습할 때와 같은 기기에서 불러오는 경우
    checkpoint = torch.load(loadFilename)
    # GPU에서 학습한 모델을 CPU로 불러오는 경우
    #checkpoint = torch.load(loadFilename, map_location=torch.device('cpu'))
    encoder_sd = checkpoint['en']
    decoder_sd = checkpoint['de']
    encoder_optimizer_sd = checkpoint['en_opt']
    decoder_optimizer_sd = checkpoint['de_opt']
    embedding_sd = checkpoint['embedding']
    voc.__dict__ = checkpoint['voc_dict']


print('Building encoder and decoder ...')
# 단어 임베딩을 초기화합니다
embedding = nn.Embedding(voc.num_words, hidden_size)
if loadFilename:
    embedding.load_state_dict(embedding_sd)
# 인코더 및 디코더 모델을 초기화합니다
encoder = EncoderRNN(hidden_size, embedding, encoder_n_layers, dropout)
decoder = LuongAttnDecoderRNN(attn_model, embedding, hidden_size, voc.num_words, decoder_n_layers, dropout)
if loadFilename:
    encoder.load_state_dict(encoder_sd)
    decoder.load_state_dict(decoder_sd)
# 적절한 디바이스를 사용합니다
encoder = encoder.to(device)
decoder = decoder.to(device)
print('Models built and ready to go!')

Out:

Building encoder and decoder ...
Models built and ready to go!

학습 수행하기

모델을 학습해보고 싶다면 다음 블록을 수행하면 됩니다.

먼저 학습 매개변수를 설정하고, optimizer를 초기화한 뒤, 마지막으로 trainIters 함수를 호출하여 학습 단계를 진행합니다.

# 학습 및 최적화 설정
clip = 50.0
teacher_forcing_ratio = 1.0
learning_rate = 0.0001
decoder_learning_ratio = 5.0
n_iteration = 4000
print_every = 1
save_every = 500

# Dropout 레이어를 학습 모드로 둡니다
encoder.train()
decoder.train()

# Optimizer를 초기화합니다
print('Building optimizers ...')
encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)
if loadFilename:
    encoder_optimizer.load_state_dict(encoder_optimizer_sd)
    decoder_optimizer.load_state_dict(decoder_optimizer_sd)

# cuda가 있다면 cuda를 설정합니다
for state in encoder_optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.cuda()

for state in decoder_optimizer.state.values():
    for k, v in state.items():
        if isinstance(v, torch.Tensor):
            state[k] = v.cuda()

# 학습 단계를 수행합니다
print("Starting Training!")
trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer,
           embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size,
           print_every, save_every, clip, corpus_name, loadFilename)

Out:

Building optimizers ...
Starting Training!
Initializing ...
Training...
Iteration: 1; Percent complete: 0.0%; Average loss: 8.9737
Iteration: 2; Percent complete: 0.1%; Average loss: 8.8673
Iteration: 3; Percent complete: 0.1%; Average loss: 8.6810
Iteration: 4; Percent complete: 0.1%; Average loss: 8.4011
Iteration: 5; Percent complete: 0.1%; Average loss: 7.9552
Iteration: 6; Percent complete: 0.1%; Average loss: 7.4187
Iteration: 7; Percent complete: 0.2%; Average loss: 6.9239
Iteration: 8; Percent complete: 0.2%; Average loss: 7.1065
Iteration: 9; Percent complete: 0.2%; Average loss: 6.9982
Iteration: 10; Percent complete: 0.2%; Average loss: 6.9554
Iteration: 11; Percent complete: 0.3%; Average loss: 6.3128
Iteration: 12; Percent complete: 0.3%; Average loss: 5.9696
Iteration: 13; Percent complete: 0.3%; Average loss: 5.5926
Iteration: 14; Percent complete: 0.4%; Average loss: 5.7310
Iteration: 15; Percent complete: 0.4%; Average loss: 5.4320
Iteration: 16; Percent complete: 0.4%; Average loss: 5.5156
Iteration: 17; Percent complete: 0.4%; Average loss: 5.3931
Iteration: 18; Percent complete: 0.4%; Average loss: 5.0606
Iteration: 19; Percent complete: 0.5%; Average loss: 5.0519
Iteration: 20; Percent complete: 0.5%; Average loss: 4.7496
Iteration: 21; Percent complete: 0.5%; Average loss: 5.0695
Iteration: 22; Percent complete: 0.5%; Average loss: 4.9381
Iteration: 23; Percent complete: 0.6%; Average loss: 5.1066
Iteration: 24; Percent complete: 0.6%; Average loss: 5.0894
Iteration: 25; Percent complete: 0.6%; Average loss: 4.8798
Iteration: 26; Percent complete: 0.7%; Average loss: 4.9417
Iteration: 27; Percent complete: 0.7%; Average loss: 4.4995
Iteration: 28; Percent complete: 0.7%; Average loss: 4.7790
Iteration: 29; Percent complete: 0.7%; Average loss: 4.6578
Iteration: 30; Percent complete: 0.8%; Average loss: 5.0551
Iteration: 31; Percent complete: 0.8%; Average loss: 4.7747
Iteration: 32; Percent complete: 0.8%; Average loss: 4.6342
Iteration: 33; Percent complete: 0.8%; Average loss: 4.7962
Iteration: 34; Percent complete: 0.9%; Average loss: 4.7265
Iteration: 35; Percent complete: 0.9%; Average loss: 4.8134
Iteration: 36; Percent complete: 0.9%; Average loss: 4.7185
Iteration: 37; Percent complete: 0.9%; Average loss: 4.5926
Iteration: 38; Percent complete: 0.9%; Average loss: 4.7517
Iteration: 39; Percent complete: 1.0%; Average loss: 4.8251
Iteration: 40; Percent complete: 1.0%; Average loss: 4.9999
Iteration: 41; Percent complete: 1.0%; Average loss: 4.8705
Iteration: 42; Percent complete: 1.1%; Average loss: 4.9100
Iteration: 43; Percent complete: 1.1%; Average loss: 4.7628
Iteration: 44; Percent complete: 1.1%; Average loss: 4.6817
Iteration: 45; Percent complete: 1.1%; Average loss: 4.6846
Iteration: 46; Percent complete: 1.1%; Average loss: 4.7614
Iteration: 47; Percent complete: 1.2%; Average loss: 4.7635
Iteration: 48; Percent complete: 1.2%; Average loss: 4.5883
Iteration: 49; Percent complete: 1.2%; Average loss: 4.5317
Iteration: 50; Percent complete: 1.2%; Average loss: 4.6446
Iteration: 51; Percent complete: 1.3%; Average loss: 4.6256
Iteration: 52; Percent complete: 1.3%; Average loss: 4.4765
Iteration: 53; Percent complete: 1.3%; Average loss: 4.8705
Iteration: 54; Percent complete: 1.4%; Average loss: 4.5982
Iteration: 55; Percent complete: 1.4%; Average loss: 4.4787
Iteration: 56; Percent complete: 1.4%; Average loss: 4.8892
Iteration: 57; Percent complete: 1.4%; Average loss: 4.4237
Iteration: 58; Percent complete: 1.5%; Average loss: 4.8299
Iteration: 59; Percent complete: 1.5%; Average loss: 4.5990
Iteration: 60; Percent complete: 1.5%; Average loss: 4.5264
Iteration: 61; Percent complete: 1.5%; Average loss: 4.3679
Iteration: 62; Percent complete: 1.6%; Average loss: 4.5781
Iteration: 63; Percent complete: 1.6%; Average loss: 4.7083
Iteration: 64; Percent complete: 1.6%; Average loss: 4.4035
Iteration: 65; Percent complete: 1.6%; Average loss: 4.7643
Iteration: 66; Percent complete: 1.7%; Average loss: 4.4613
Iteration: 67; Percent complete: 1.7%; Average loss: 4.4284
Iteration: 68; Percent complete: 1.7%; Average loss: 4.4709
Iteration: 69; Percent complete: 1.7%; Average loss: 4.6659
Iteration: 70; Percent complete: 1.8%; Average loss: 4.5568
Iteration: 71; Percent complete: 1.8%; Average loss: 4.5185
Iteration: 72; Percent complete: 1.8%; Average loss: 4.5413
Iteration: 73; Percent complete: 1.8%; Average loss: 4.3374
Iteration: 74; Percent complete: 1.8%; Average loss: 4.6712
Iteration: 75; Percent complete: 1.9%; Average loss: 4.3031
Iteration: 76; Percent complete: 1.9%; Average loss: 4.4676
Iteration: 77; Percent complete: 1.9%; Average loss: 4.4898
Iteration: 78; Percent complete: 1.9%; Average loss: 4.3486
Iteration: 79; Percent complete: 2.0%; Average loss: 4.6832
Iteration: 80; Percent complete: 2.0%; Average loss: 4.5412
Iteration: 81; Percent complete: 2.0%; Average loss: 4.6749
Iteration: 82; Percent complete: 2.1%; Average loss: 4.5433
Iteration: 83; Percent complete: 2.1%; Average loss: 4.7092
Iteration: 84; Percent complete: 2.1%; Average loss: 4.5800
Iteration: 85; Percent complete: 2.1%; Average loss: 4.3350
Iteration: 86; Percent complete: 2.1%; Average loss: 4.3834
Iteration: 87; Percent complete: 2.2%; Average loss: 4.4165
Iteration: 88; Percent complete: 2.2%; Average loss: 4.4210
Iteration: 89; Percent complete: 2.2%; Average loss: 4.3114
Iteration: 90; Percent complete: 2.2%; Average loss: 4.5028
Iteration: 91; Percent complete: 2.3%; Average loss: 4.4593
Iteration: 92; Percent complete: 2.3%; Average loss: 4.1643
Iteration: 93; Percent complete: 2.3%; Average loss: 4.0996
Iteration: 94; Percent complete: 2.4%; Average loss: 4.2956
Iteration: 95; Percent complete: 2.4%; Average loss: 4.3231
Iteration: 96; Percent complete: 2.4%; Average loss: 4.4285
Iteration: 97; Percent complete: 2.4%; Average loss: 4.5587
Iteration: 98; Percent complete: 2.5%; Average loss: 4.6190
Iteration: 99; Percent complete: 2.5%; Average loss: 4.4508
Iteration: 100; Percent complete: 2.5%; Average loss: 4.4356
Iteration: 101; Percent complete: 2.5%; Average loss: 4.3594
Iteration: 102; Percent complete: 2.5%; Average loss: 4.3493
Iteration: 103; Percent complete: 2.6%; Average loss: 4.3376
Iteration: 104; Percent complete: 2.6%; Average loss: 4.3482
Iteration: 105; Percent complete: 2.6%; Average loss: 4.2394
Iteration: 106; Percent complete: 2.6%; Average loss: 4.2560
Iteration: 107; Percent complete: 2.7%; Average loss: 4.2658
Iteration: 108; Percent complete: 2.7%; Average loss: 4.1064
Iteration: 109; Percent complete: 2.7%; Average loss: 4.5319
Iteration: 110; Percent complete: 2.8%; Average loss: 4.4615
Iteration: 111; Percent complete: 2.8%; Average loss: 4.2501
Iteration: 112; Percent complete: 2.8%; Average loss: 4.1815
Iteration: 113; Percent complete: 2.8%; Average loss: 4.2652
Iteration: 114; Percent complete: 2.9%; Average loss: 4.3106
Iteration: 115; Percent complete: 2.9%; Average loss: 4.2659
Iteration: 116; Percent complete: 2.9%; Average loss: 4.5049
Iteration: 117; Percent complete: 2.9%; Average loss: 4.2592
Iteration: 118; Percent complete: 2.9%; Average loss: 4.4252
Iteration: 119; Percent complete: 3.0%; Average loss: 4.3087
Iteration: 120; Percent complete: 3.0%; Average loss: 4.4417
Iteration: 121; Percent complete: 3.0%; Average loss: 4.4674
Iteration: 122; Percent complete: 3.0%; Average loss: 4.1846
Iteration: 123; Percent complete: 3.1%; Average loss: 4.2095
Iteration: 124; Percent complete: 3.1%; Average loss: 4.3761
Iteration: 125; Percent complete: 3.1%; Average loss: 4.3495
Iteration: 126; Percent complete: 3.1%; Average loss: 4.2939
Iteration: 127; Percent complete: 3.2%; Average loss: 4.0598
Iteration: 128; Percent complete: 3.2%; Average loss: 4.4701
Iteration: 129; Percent complete: 3.2%; Average loss: 4.4787
Iteration: 130; Percent complete: 3.2%; Average loss: 4.2535
Iteration: 131; Percent complete: 3.3%; Average loss: 4.2722
Iteration: 132; Percent complete: 3.3%; Average loss: 4.3506
Iteration: 133; Percent complete: 3.3%; Average loss: 4.4878
Iteration: 134; Percent complete: 3.4%; Average loss: 4.3600
Iteration: 135; Percent complete: 3.4%; Average loss: 4.5171
Iteration: 136; Percent complete: 3.4%; Average loss: 4.1676
Iteration: 137; Percent complete: 3.4%; Average loss: 4.3628
Iteration: 138; Percent complete: 3.5%; Average loss: 4.4563
Iteration: 139; Percent complete: 3.5%; Average loss: 4.1642
Iteration: 140; Percent complete: 3.5%; Average loss: 4.2459
Iteration: 141; Percent complete: 3.5%; Average loss: 4.4302
Iteration: 142; Percent complete: 3.5%; Average loss: 4.2525
Iteration: 143; Percent complete: 3.6%; Average loss: 4.2342
Iteration: 144; Percent complete: 3.6%; Average loss: 4.2963
Iteration: 145; Percent complete: 3.6%; Average loss: 4.2749
Iteration: 146; Percent complete: 3.6%; Average loss: 4.2276
Iteration: 147; Percent complete: 3.7%; Average loss: 4.1824
Iteration: 148; Percent complete: 3.7%; Average loss: 4.1563
Iteration: 149; Percent complete: 3.7%; Average loss: 4.3782
Iteration: 150; Percent complete: 3.8%; Average loss: 4.3061
Iteration: 151; Percent complete: 3.8%; Average loss: 4.1691
Iteration: 152; Percent complete: 3.8%; Average loss: 4.0246
Iteration: 153; Percent complete: 3.8%; Average loss: 4.0315
Iteration: 154; Percent complete: 3.9%; Average loss: 4.0944
Iteration: 155; Percent complete: 3.9%; Average loss: 4.2209
Iteration: 156; Percent complete: 3.9%; Average loss: 4.2086
Iteration: 157; Percent complete: 3.9%; Average loss: 4.3841
Iteration: 158; Percent complete: 4.0%; Average loss: 4.4596
Iteration: 159; Percent complete: 4.0%; Average loss: 4.3154
Iteration: 160; Percent complete: 4.0%; Average loss: 4.3466
Iteration: 161; Percent complete: 4.0%; Average loss: 4.3043
Iteration: 162; Percent complete: 4.0%; Average loss: 4.2723
Iteration: 163; Percent complete: 4.1%; Average loss: 3.9427
Iteration: 164; Percent complete: 4.1%; Average loss: 4.0423
Iteration: 165; Percent complete: 4.1%; Average loss: 4.0284
Iteration: 166; Percent complete: 4.2%; Average loss: 4.1643
Iteration: 167; Percent complete: 4.2%; Average loss: 4.1175
Iteration: 168; Percent complete: 4.2%; Average loss: 4.0829
Iteration: 169; Percent complete: 4.2%; Average loss: 4.2451
Iteration: 170; Percent complete: 4.2%; Average loss: 3.9222
Iteration: 171; Percent complete: 4.3%; Average loss: 4.1608
Iteration: 172; Percent complete: 4.3%; Average loss: 3.8177
Iteration: 173; Percent complete: 4.3%; Average loss: 4.1224
Iteration: 174; Percent complete: 4.3%; Average loss: 4.3169
Iteration: 175; Percent complete: 4.4%; Average loss: 3.9370
Iteration: 176; Percent complete: 4.4%; Average loss: 4.2740
Iteration: 177; Percent complete: 4.4%; Average loss: 4.2788
Iteration: 178; Percent complete: 4.5%; Average loss: 4.0936
Iteration: 179; Percent complete: 4.5%; Average loss: 4.1466
Iteration: 180; Percent complete: 4.5%; Average loss: 4.0580
Iteration: 181; Percent complete: 4.5%; Average loss: 4.1868
Iteration: 182; Percent complete: 4.5%; Average loss: 4.3886
Iteration: 183; Percent complete: 4.6%; Average loss: 4.0977
Iteration: 184; Percent complete: 4.6%; Average loss: 4.0601
Iteration: 185; Percent complete: 4.6%; Average loss: 4.1175
Iteration: 186; Percent complete: 4.7%; Average loss: 4.3283
Iteration: 187; Percent complete: 4.7%; Average loss: 4.2227
Iteration: 188; Percent complete: 4.7%; Average loss: 3.9215
Iteration: 189; Percent complete: 4.7%; Average loss: 4.1261
Iteration: 190; Percent complete: 4.8%; Average loss: 3.7764
Iteration: 191; Percent complete: 4.8%; Average loss: 4.2330
Iteration: 192; Percent complete: 4.8%; Average loss: 4.2131
Iteration: 193; Percent complete: 4.8%; Average loss: 3.9905
Iteration: 194; Percent complete: 4.9%; Average loss: 4.1496
Iteration: 195; Percent complete: 4.9%; Average loss: 4.0952
Iteration: 196; Percent complete: 4.9%; Average loss: 3.9434
Iteration: 197; Percent complete: 4.9%; Average loss: 4.1104
Iteration: 198; Percent complete: 5.0%; Average loss: 4.0752
Iteration: 199; Percent complete: 5.0%; Average loss: 4.0201
Iteration: 200; Percent complete: 5.0%; Average loss: 3.9738
Iteration: 201; Percent complete: 5.0%; Average loss: 3.9750
Iteration: 202; Percent complete: 5.1%; Average loss: 3.8455
Iteration: 203; Percent complete: 5.1%; Average loss: 4.0790
Iteration: 204; Percent complete: 5.1%; Average loss: 3.9733
Iteration: 205; Percent complete: 5.1%; Average loss: 3.8263
Iteration: 206; Percent complete: 5.1%; Average loss: 4.1906
Iteration: 207; Percent complete: 5.2%; Average loss: 3.8916
Iteration: 208; Percent complete: 5.2%; Average loss: 3.9742
Iteration: 209; Percent complete: 5.2%; Average loss: 4.3342
Iteration: 210; Percent complete: 5.2%; Average loss: 3.9570
Iteration: 211; Percent complete: 5.3%; Average loss: 3.7830
Iteration: 212; Percent complete: 5.3%; Average loss: 3.9368
Iteration: 213; Percent complete: 5.3%; Average loss: 4.2228
Iteration: 214; Percent complete: 5.3%; Average loss: 4.0014
Iteration: 215; Percent complete: 5.4%; Average loss: 4.0227
Iteration: 216; Percent complete: 5.4%; Average loss: 4.1534
Iteration: 217; Percent complete: 5.4%; Average loss: 3.7611
Iteration: 218; Percent complete: 5.5%; Average loss: 4.0762
Iteration: 219; Percent complete: 5.5%; Average loss: 4.1298
Iteration: 220; Percent complete: 5.5%; Average loss: 4.1369
Iteration: 221; Percent complete: 5.5%; Average loss: 4.1143
Iteration: 222; Percent complete: 5.5%; Average loss: 4.0448
Iteration: 223; Percent complete: 5.6%; Average loss: 3.7447
Iteration: 224; Percent complete: 5.6%; Average loss: 3.9067
Iteration: 225; Percent complete: 5.6%; Average loss: 3.9972
Iteration: 226; Percent complete: 5.7%; Average loss: 3.9533
Iteration: 227; Percent complete: 5.7%; Average loss: 4.1046
Iteration: 228; Percent complete: 5.7%; Average loss: 4.0807
Iteration: 229; Percent complete: 5.7%; Average loss: 3.9304
Iteration: 230; Percent complete: 5.8%; Average loss: 4.0103
Iteration: 231; Percent complete: 5.8%; Average loss: 4.0770
Iteration: 232; Percent complete: 5.8%; Average loss: 4.2485
Iteration: 233; Percent complete: 5.8%; Average loss: 4.2171
Iteration: 234; Percent complete: 5.9%; Average loss: 3.9985
Iteration: 235; Percent complete: 5.9%; Average loss: 3.9684
Iteration: 236; Percent complete: 5.9%; Average loss: 4.0905
Iteration: 237; Percent complete: 5.9%; Average loss: 4.1071
Iteration: 238; Percent complete: 5.9%; Average loss: 3.9447
Iteration: 239; Percent complete: 6.0%; Average loss: 4.1767
Iteration: 240; Percent complete: 6.0%; Average loss: 3.8881
Iteration: 241; Percent complete: 6.0%; Average loss: 3.8708
Iteration: 242; Percent complete: 6.0%; Average loss: 4.1508
Iteration: 243; Percent complete: 6.1%; Average loss: 3.9013
Iteration: 244; Percent complete: 6.1%; Average loss: 3.7822
Iteration: 245; Percent complete: 6.1%; Average loss: 4.0532
Iteration: 246; Percent complete: 6.2%; Average loss: 3.9519
Iteration: 247; Percent complete: 6.2%; Average loss: 3.8335
Iteration: 248; Percent complete: 6.2%; Average loss: 3.9305
Iteration: 249; Percent complete: 6.2%; Average loss: 3.9857
Iteration: 250; Percent complete: 6.2%; Average loss: 3.9554
Iteration: 251; Percent complete: 6.3%; Average loss: 3.8040
Iteration: 252; Percent complete: 6.3%; Average loss: 4.0057
Iteration: 253; Percent complete: 6.3%; Average loss: 4.0379
Iteration: 254; Percent complete: 6.3%; Average loss: 4.0957
Iteration: 255; Percent complete: 6.4%; Average loss: 4.0088
Iteration: 256; Percent complete: 6.4%; Average loss: 4.2446
Iteration: 257; Percent complete: 6.4%; Average loss: 3.7290
Iteration: 258; Percent complete: 6.5%; Average loss: 3.8795
Iteration: 259; Percent complete: 6.5%; Average loss: 3.7926
Iteration: 260; Percent complete: 6.5%; Average loss: 3.6178
Iteration: 261; Percent complete: 6.5%; Average loss: 4.1187
Iteration: 262; Percent complete: 6.6%; Average loss: 4.1617
Iteration: 263; Percent complete: 6.6%; Average loss: 4.2088
Iteration: 264; Percent complete: 6.6%; Average loss: 4.2380
Iteration: 265; Percent complete: 6.6%; Average loss: 4.1630
Iteration: 266; Percent complete: 6.7%; Average loss: 3.8610
Iteration: 267; Percent complete: 6.7%; Average loss: 3.8488
Iteration: 268; Percent complete: 6.7%; Average loss: 3.8217
Iteration: 269; Percent complete: 6.7%; Average loss: 3.6758
Iteration: 270; Percent complete: 6.8%; Average loss: 3.9422
Iteration: 271; Percent complete: 6.8%; Average loss: 3.9914
Iteration: 272; Percent complete: 6.8%; Average loss: 4.0119
Iteration: 273; Percent complete: 6.8%; Average loss: 4.0700
Iteration: 274; Percent complete: 6.9%; Average loss: 3.9317
Iteration: 275; Percent complete: 6.9%; Average loss: 3.9444
Iteration: 276; Percent complete: 6.9%; Average loss: 4.2534
Iteration: 277; Percent complete: 6.9%; Average loss: 3.9929
Iteration: 278; Percent complete: 7.0%; Average loss: 3.8031
Iteration: 279; Percent complete: 7.0%; Average loss: 3.8558
Iteration: 280; Percent complete: 7.0%; Average loss: 3.7938
Iteration: 281; Percent complete: 7.0%; Average loss: 3.8171
Iteration: 282; Percent complete: 7.0%; Average loss: 3.7492
Iteration: 283; Percent complete: 7.1%; Average loss: 3.9851
Iteration: 284; Percent complete: 7.1%; Average loss: 3.7887
Iteration: 285; Percent complete: 7.1%; Average loss: 3.8864
Iteration: 286; Percent complete: 7.1%; Average loss: 3.9598
Iteration: 287; Percent complete: 7.2%; Average loss: 4.2087
Iteration: 288; Percent complete: 7.2%; Average loss: 4.0230
Iteration: 289; Percent complete: 7.2%; Average loss: 3.9150
Iteration: 290; Percent complete: 7.2%; Average loss: 4.0368
Iteration: 291; Percent complete: 7.3%; Average loss: 4.0521
Iteration: 292; Percent complete: 7.3%; Average loss: 3.8037
Iteration: 293; Percent complete: 7.3%; Average loss: 3.9889
Iteration: 294; Percent complete: 7.3%; Average loss: 3.8680
Iteration: 295; Percent complete: 7.4%; Average loss: 4.0805
Iteration: 296; Percent complete: 7.4%; Average loss: 4.0559
Iteration: 297; Percent complete: 7.4%; Average loss: 3.9397
Iteration: 298; Percent complete: 7.4%; Average loss: 4.1014
Iteration: 299; Percent complete: 7.5%; Average loss: 3.9497
Iteration: 300; Percent complete: 7.5%; Average loss: 3.8116
Iteration: 301; Percent complete: 7.5%; Average loss: 4.0169
Iteration: 302; Percent complete: 7.5%; Average loss: 3.8865
Iteration: 303; Percent complete: 7.6%; Average loss: 3.7474
Iteration: 304; Percent complete: 7.6%; Average loss: 3.8725
Iteration: 305; Percent complete: 7.6%; Average loss: 3.5592
Iteration: 306; Percent complete: 7.6%; Average loss: 3.8295
Iteration: 307; Percent complete: 7.7%; Average loss: 3.6803
Iteration: 308; Percent complete: 7.7%; Average loss: 3.9396
Iteration: 309; Percent complete: 7.7%; Average loss: 3.8598
Iteration: 310; Percent complete: 7.8%; Average loss: 3.9100
Iteration: 311; Percent complete: 7.8%; Average loss: 3.6464
Iteration: 312; Percent complete: 7.8%; Average loss: 3.8471
Iteration: 313; Percent complete: 7.8%; Average loss: 3.8088
Iteration: 314; Percent complete: 7.8%; Average loss: 3.8744
Iteration: 315; Percent complete: 7.9%; Average loss: 3.6323
Iteration: 316; Percent complete: 7.9%; Average loss: 3.6448
Iteration: 317; Percent complete: 7.9%; Average loss: 4.2493
Iteration: 318; Percent complete: 8.0%; Average loss: 3.9690
Iteration: 319; Percent complete: 8.0%; Average loss: 4.2154
Iteration: 320; Percent complete: 8.0%; Average loss: 3.8925
Iteration: 321; Percent complete: 8.0%; Average loss: 3.9033
Iteration: 322; Percent complete: 8.1%; Average loss: 3.8921
Iteration: 323; Percent complete: 8.1%; Average loss: 3.6261
Iteration: 324; Percent complete: 8.1%; Average loss: 3.6951
Iteration: 325; Percent complete: 8.1%; Average loss: 4.0316
Iteration: 326; Percent complete: 8.2%; Average loss: 3.7637
Iteration: 327; Percent complete: 8.2%; Average loss: 3.9556
Iteration: 328; Percent complete: 8.2%; Average loss: 4.0502
Iteration: 329; Percent complete: 8.2%; Average loss: 4.0905
Iteration: 330; Percent complete: 8.2%; Average loss: 4.0792
Iteration: 331; Percent complete: 8.3%; Average loss: 3.9418
Iteration: 332; Percent complete: 8.3%; Average loss: 3.8893
Iteration: 333; Percent complete: 8.3%; Average loss: 3.6412
Iteration: 334; Percent complete: 8.3%; Average loss: 3.8016
Iteration: 335; Percent complete: 8.4%; Average loss: 3.8597
Iteration: 336; Percent complete: 8.4%; Average loss: 3.8334
Iteration: 337; Percent complete: 8.4%; Average loss: 4.1943
Iteration: 338; Percent complete: 8.5%; Average loss: 3.9858
Iteration: 339; Percent complete: 8.5%; Average loss: 3.8073
Iteration: 340; Percent complete: 8.5%; Average loss: 3.8507
Iteration: 341; Percent complete: 8.5%; Average loss: 3.8126
Iteration: 342; Percent complete: 8.6%; Average loss: 4.1060
Iteration: 343; Percent complete: 8.6%; Average loss: 3.9153
Iteration: 344; Percent complete: 8.6%; Average loss: 3.9922
Iteration: 345; Percent complete: 8.6%; Average loss: 3.9789
Iteration: 346; Percent complete: 8.6%; Average loss: 3.6619
Iteration: 347; Percent complete: 8.7%; Average loss: 3.8688
Iteration: 348; Percent complete: 8.7%; Average loss: 4.0918
Iteration: 349; Percent complete: 8.7%; Average loss: 3.7240
Iteration: 350; Percent complete: 8.8%; Average loss: 3.9265
Iteration: 351; Percent complete: 8.8%; Average loss: 3.8449
Iteration: 352; Percent complete: 8.8%; Average loss: 3.8172
Iteration: 353; Percent complete: 8.8%; Average loss: 3.7919
Iteration: 354; Percent complete: 8.8%; Average loss: 3.7504
Iteration: 355; Percent complete: 8.9%; Average loss: 3.5988
Iteration: 356; Percent complete: 8.9%; Average loss: 4.0263
Iteration: 357; Percent complete: 8.9%; Average loss: 3.8532
Iteration: 358; Percent complete: 8.9%; Average loss: 4.1539
Iteration: 359; Percent complete: 9.0%; Average loss: 4.0223
Iteration: 360; Percent complete: 9.0%; Average loss: 3.7551
Iteration: 361; Percent complete: 9.0%; Average loss: 3.9228
Iteration: 362; Percent complete: 9.0%; Average loss: 3.6681
Iteration: 363; Percent complete: 9.1%; Average loss: 3.8701
Iteration: 364; Percent complete: 9.1%; Average loss: 3.7559
Iteration: 365; Percent complete: 9.1%; Average loss: 3.6295
Iteration: 366; Percent complete: 9.2%; Average loss: 3.8517
Iteration: 367; Percent complete: 9.2%; Average loss: 3.7587
Iteration: 368; Percent complete: 9.2%; Average loss: 3.8322
Iteration: 369; Percent complete: 9.2%; Average loss: 3.9057
Iteration: 370; Percent complete: 9.2%; Average loss: 3.6765
Iteration: 371; Percent complete: 9.3%; Average loss: 4.0171
Iteration: 372; Percent complete: 9.3%; Average loss: 3.7793
Iteration: 373; Percent complete: 9.3%; Average loss: 3.6282
Iteration: 374; Percent complete: 9.3%; Average loss: 3.8052
Iteration: 375; Percent complete: 9.4%; Average loss: 3.9073
Iteration: 376; Percent complete: 9.4%; Average loss: 3.8264
Iteration: 377; Percent complete: 9.4%; Average loss: 3.7442
Iteration: 378; Percent complete: 9.4%; Average loss: 4.1263
Iteration: 379; Percent complete: 9.5%; Average loss: 3.7135
Iteration: 380; Percent complete: 9.5%; Average loss: 3.7444
Iteration: 381; Percent complete: 9.5%; Average loss: 3.9339
Iteration: 382; Percent complete: 9.6%; Average loss: 3.9229
Iteration: 383; Percent complete: 9.6%; Average loss: 3.6978
Iteration: 384; Percent complete: 9.6%; Average loss: 3.9488
Iteration: 385; Percent complete: 9.6%; Average loss: 3.8531
Iteration: 386; Percent complete: 9.7%; Average loss: 3.5359
Iteration: 387; Percent complete: 9.7%; Average loss: 3.9117
Iteration: 388; Percent complete: 9.7%; Average loss: 3.6748
Iteration: 389; Percent complete: 9.7%; Average loss: 3.5401
Iteration: 390; Percent complete: 9.8%; Average loss: 3.8233
Iteration: 391; Percent complete: 9.8%; Average loss: 3.5644
Iteration: 392; Percent complete: 9.8%; Average loss: 3.8145
Iteration: 393; Percent complete: 9.8%; Average loss: 3.8402
Iteration: 394; Percent complete: 9.8%; Average loss: 3.4992
Iteration: 395; Percent complete: 9.9%; Average loss: 3.5618
Iteration: 396; Percent complete: 9.9%; Average loss: 3.8549
Iteration: 397; Percent complete: 9.9%; Average loss: 3.7143
Iteration: 398; Percent complete: 10.0%; Average loss: 3.9569
Iteration: 399; Percent complete: 10.0%; Average loss: 3.7268
Iteration: 400; Percent complete: 10.0%; Average loss: 4.0859
Iteration: 401; Percent complete: 10.0%; Average loss: 3.9611
Iteration: 402; Percent complete: 10.1%; Average loss: 3.9643
Iteration: 403; Percent complete: 10.1%; Average loss: 3.6830
Iteration: 404; Percent complete: 10.1%; Average loss: 3.7017
Iteration: 405; Percent complete: 10.1%; Average loss: 3.7024
Iteration: 406; Percent complete: 10.2%; Average loss: 3.9419
Iteration: 407; Percent complete: 10.2%; Average loss: 3.9892
Iteration: 408; Percent complete: 10.2%; Average loss: 3.9413
Iteration: 409; Percent complete: 10.2%; Average loss: 3.6552
Iteration: 410; Percent complete: 10.2%; Average loss: 3.8477
Iteration: 411; Percent complete: 10.3%; Average loss: 3.9564
Iteration: 412; Percent complete: 10.3%; Average loss: 3.7107
Iteration: 413; Percent complete: 10.3%; Average loss: 3.9743
Iteration: 414; Percent complete: 10.3%; Average loss: 3.8855
Iteration: 415; Percent complete: 10.4%; Average loss: 3.7899
Iteration: 416; Percent complete: 10.4%; Average loss: 3.5652
Iteration: 417; Percent complete: 10.4%; Average loss: 4.0925
Iteration: 418; Percent complete: 10.4%; Average loss: 3.5287
Iteration: 419; Percent complete: 10.5%; Average loss: 3.4022
Iteration: 420; Percent complete: 10.5%; Average loss: 3.9666
Iteration: 421; Percent complete: 10.5%; Average loss: 3.3373
Iteration: 422; Percent complete: 10.5%; Average loss: 3.6384
Iteration: 423; Percent complete: 10.6%; Average loss: 3.6187
Iteration: 424; Percent complete: 10.6%; Average loss: 3.7535
Iteration: 425; Percent complete: 10.6%; Average loss: 3.8088
Iteration: 426; Percent complete: 10.7%; Average loss: 3.5767
Iteration: 427; Percent complete: 10.7%; Average loss: 3.8246
Iteration: 428; Percent complete: 10.7%; Average loss: 3.8612
Iteration: 429; Percent complete: 10.7%; Average loss: 3.5421
Iteration: 430; Percent complete: 10.8%; Average loss: 3.6512
Iteration: 431; Percent complete: 10.8%; Average loss: 3.7708
Iteration: 432; Percent complete: 10.8%; Average loss: 3.7262
Iteration: 433; Percent complete: 10.8%; Average loss: 3.7543
Iteration: 434; Percent complete: 10.8%; Average loss: 3.6661
Iteration: 435; Percent complete: 10.9%; Average loss: 4.0206
Iteration: 436; Percent complete: 10.9%; Average loss: 3.8165
Iteration: 437; Percent complete: 10.9%; Average loss: 3.7865
Iteration: 438; Percent complete: 10.9%; Average loss: 3.9692
Iteration: 439; Percent complete: 11.0%; Average loss: 3.5041
Iteration: 440; Percent complete: 11.0%; Average loss: 3.6874
Iteration: 441; Percent complete: 11.0%; Average loss: 3.6642
Iteration: 442; Percent complete: 11.1%; Average loss: 3.7394
Iteration: 443; Percent complete: 11.1%; Average loss: 3.6857
Iteration: 444; Percent complete: 11.1%; Average loss: 3.9535
Iteration: 445; Percent complete: 11.1%; Average loss: 3.5845
Iteration: 446; Percent complete: 11.2%; Average loss: 3.6096
Iteration: 447; Percent complete: 11.2%; Average loss: 3.4255
Iteration: 448; Percent complete: 11.2%; Average loss: 3.5355
Iteration: 449; Percent complete: 11.2%; Average loss: 3.6974
Iteration: 450; Percent complete: 11.2%; Average loss: 3.9604
Iteration: 451; Percent complete: 11.3%; Average loss: 3.7499
Iteration: 452; Percent complete: 11.3%; Average loss: 3.6254
Iteration: 453; Percent complete: 11.3%; Average loss: 3.9487
Iteration: 454; Percent complete: 11.3%; Average loss: 3.9164
Iteration: 455; Percent complete: 11.4%; Average loss: 3.9095
Iteration: 456; Percent complete: 11.4%; Average loss: 3.4513
Iteration: 457; Percent complete: 11.4%; Average loss: 3.5377
Iteration: 458; Percent complete: 11.5%; Average loss: 3.9788
Iteration: 459; Percent complete: 11.5%; Average loss: 3.7175
Iteration: 460; Percent complete: 11.5%; Average loss: 3.5960
Iteration: 461; Percent complete: 11.5%; Average loss: 3.5093
Iteration: 462; Percent complete: 11.6%; Average loss: 3.5741
Iteration: 463; Percent complete: 11.6%; Average loss: 3.8480
Iteration: 464; Percent complete: 11.6%; Average loss: 3.5054
Iteration: 465; Percent complete: 11.6%; Average loss: 3.6262
Iteration: 466; Percent complete: 11.7%; Average loss: 3.7244
Iteration: 467; Percent complete: 11.7%; Average loss: 3.5093
Iteration: 468; Percent complete: 11.7%; Average loss: 3.5458
Iteration: 469; Percent complete: 11.7%; Average loss: 3.5359
Iteration: 470; Percent complete: 11.8%; Average loss: 3.5192
Iteration: 471; Percent complete: 11.8%; Average loss: 3.8314
Iteration: 472; Percent complete: 11.8%; Average loss: 3.7568
Iteration: 473; Percent complete: 11.8%; Average loss: 3.7386
Iteration: 474; Percent complete: 11.8%; Average loss: 3.9433
Iteration: 475; Percent complete: 11.9%; Average loss: 3.6931
Iteration: 476; Percent complete: 11.9%; Average loss: 4.0960
Iteration: 477; Percent complete: 11.9%; Average loss: 3.5205
Iteration: 478; Percent complete: 11.9%; Average loss: 3.4776
Iteration: 479; Percent complete: 12.0%; Average loss: 3.4989
Iteration: 480; Percent complete: 12.0%; Average loss: 3.5301
Iteration: 481; Percent complete: 12.0%; Average loss: 3.8389
Iteration: 482; Percent complete: 12.0%; Average loss: 3.7615
Iteration: 483; Percent complete: 12.1%; Average loss: 3.7006
Iteration: 484; Percent complete: 12.1%; Average loss: 3.6211
Iteration: 485; Percent complete: 12.1%; Average loss: 3.8844
Iteration: 486; Percent complete: 12.2%; Average loss: 3.8763
Iteration: 487; Percent complete: 12.2%; Average loss: 3.6853
Iteration: 488; Percent complete: 12.2%; Average loss: 3.5132
Iteration: 489; Percent complete: 12.2%; Average loss: 3.8169
Iteration: 490; Percent complete: 12.2%; Average loss: 4.0523
Iteration: 491; Percent complete: 12.3%; Average loss: 3.5673
Iteration: 492; Percent complete: 12.3%; Average loss: 3.7270
Iteration: 493; Percent complete: 12.3%; Average loss: 3.7258
Iteration: 494; Percent complete: 12.3%; Average loss: 3.6396
Iteration: 495; Percent complete: 12.4%; Average loss: 3.7643
Iteration: 496; Percent complete: 12.4%; Average loss: 3.8689
Iteration: 497; Percent complete: 12.4%; Average loss: 3.7360
Iteration: 498; Percent complete: 12.4%; Average loss: 3.5351
Iteration: 499; Percent complete: 12.5%; Average loss: 3.7243
Iteration: 500; Percent complete: 12.5%; Average loss: 3.5552
Iteration: 501; Percent complete: 12.5%; Average loss: 4.0549
Iteration: 502; Percent complete: 12.6%; Average loss: 3.8621
Iteration: 503; Percent complete: 12.6%; Average loss: 3.7367
Iteration: 504; Percent complete: 12.6%; Average loss: 3.6989
Iteration: 505; Percent complete: 12.6%; Average loss: 3.6126
Iteration: 506; Percent complete: 12.7%; Average loss: 3.8028
Iteration: 507; Percent complete: 12.7%; Average loss: 3.6851
Iteration: 508; Percent complete: 12.7%; Average loss: 3.5298
Iteration: 509; Percent complete: 12.7%; Average loss: 3.8134
Iteration: 510; Percent complete: 12.8%; Average loss: 3.4922
Iteration: 511; Percent complete: 12.8%; Average loss: 3.4571
Iteration: 512; Percent complete: 12.8%; Average loss: 3.7928
Iteration: 513; Percent complete: 12.8%; Average loss: 3.8090
Iteration: 514; Percent complete: 12.8%; Average loss: 3.6974
Iteration: 515; Percent complete: 12.9%; Average loss: 3.7293
Iteration: 516; Percent complete: 12.9%; Average loss: 3.8223
Iteration: 517; Percent complete: 12.9%; Average loss: 3.8564
Iteration: 518; Percent complete: 13.0%; Average loss: 3.9219
Iteration: 519; Percent complete: 13.0%; Average loss: 3.9109
Iteration: 520; Percent complete: 13.0%; Average loss: 3.7380
Iteration: 521; Percent complete: 13.0%; Average loss: 4.0598
Iteration: 522; Percent complete: 13.1%; Average loss: 3.8958
Iteration: 523; Percent complete: 13.1%; Average loss: 3.7444
Iteration: 524; Percent complete: 13.1%; Average loss: 3.9455
Iteration: 525; Percent complete: 13.1%; Average loss: 3.7539
Iteration: 526; Percent complete: 13.2%; Average loss: 3.7995
Iteration: 527; Percent complete: 13.2%; Average loss: 3.5973
Iteration: 528; Percent complete: 13.2%; Average loss: 3.5058
Iteration: 529; Percent complete: 13.2%; Average loss: 3.6771
Iteration: 530; Percent complete: 13.2%; Average loss: 3.4446
Iteration: 531; Percent complete: 13.3%; Average loss: 3.7384
Iteration: 532; Percent complete: 13.3%; Average loss: 3.6570
Iteration: 533; Percent complete: 13.3%; Average loss: 3.8192
Iteration: 534; Percent complete: 13.4%; Average loss: 3.8472
Iteration: 535; Percent complete: 13.4%; Average loss: 3.6009
Iteration: 536; Percent complete: 13.4%; Average loss: 3.4873
Iteration: 537; Percent complete: 13.4%; Average loss: 3.7903
Iteration: 538; Percent complete: 13.5%; Average loss: 3.7091
Iteration: 539; Percent complete: 13.5%; Average loss: 3.6403
Iteration: 540; Percent complete: 13.5%; Average loss: 3.8621
Iteration: 541; Percent complete: 13.5%; Average loss: 3.4838
Iteration: 542; Percent complete: 13.6%; Average loss: 3.6105
Iteration: 543; Percent complete: 13.6%; Average loss: 3.7062
Iteration: 544; Percent complete: 13.6%; Average loss: 3.8190
Iteration: 545; Percent complete: 13.6%; Average loss: 3.6254
Iteration: 546; Percent complete: 13.7%; Average loss: 3.6688
Iteration: 547; Percent complete: 13.7%; Average loss: 3.6881
Iteration: 548; Percent complete: 13.7%; Average loss: 3.9502
Iteration: 549; Percent complete: 13.7%; Average loss: 3.8390
Iteration: 550; Percent complete: 13.8%; Average loss: 3.6025
Iteration: 551; Percent complete: 13.8%; Average loss: 3.7708
Iteration: 552; Percent complete: 13.8%; Average loss: 3.4452
Iteration: 553; Percent complete: 13.8%; Average loss: 3.7938
Iteration: 554; Percent complete: 13.9%; Average loss: 3.7835
Iteration: 555; Percent complete: 13.9%; Average loss: 3.5817
Iteration: 556; Percent complete: 13.9%; Average loss: 3.7961
Iteration: 557; Percent complete: 13.9%; Average loss: 3.8169
Iteration: 558; Percent complete: 14.0%; Average loss: 3.7173
Iteration: 559; Percent complete: 14.0%; Average loss: 3.5854
Iteration: 560; Percent complete: 14.0%; Average loss: 3.6098
Iteration: 561; Percent complete: 14.0%; Average loss: 3.4718
Iteration: 562; Percent complete: 14.1%; Average loss: 3.7295
Iteration: 563; Percent complete: 14.1%; Average loss: 3.6648
Iteration: 564; Percent complete: 14.1%; Average loss: 3.8989
Iteration: 565; Percent complete: 14.1%; Average loss: 3.7249
Iteration: 566; Percent complete: 14.1%; Average loss: 3.5628
Iteration: 567; Percent complete: 14.2%; Average loss: 3.8465
Iteration: 568; Percent complete: 14.2%; Average loss: 3.6319
Iteration: 569; Percent complete: 14.2%; Average loss: 3.8552
Iteration: 570; Percent complete: 14.2%; Average loss: 3.7622
Iteration: 571; Percent complete: 14.3%; Average loss: 3.8431
Iteration: 572; Percent complete: 14.3%; Average loss: 3.7473
Iteration: 573; Percent complete: 14.3%; Average loss: 3.7096
Iteration: 574; Percent complete: 14.3%; Average loss: 3.6356
Iteration: 575; Percent complete: 14.4%; Average loss: 3.5758
Iteration: 576; Percent complete: 14.4%; Average loss: 3.3848
Iteration: 577; Percent complete: 14.4%; Average loss: 3.6799
Iteration: 578; Percent complete: 14.4%; Average loss: 3.7251
Iteration: 579; Percent complete: 14.5%; Average loss: 3.7031
Iteration: 580; Percent complete: 14.5%; Average loss: 3.9211
Iteration: 581; Percent complete: 14.5%; Average loss: 3.4621
Iteration: 582; Percent complete: 14.5%; Average loss: 3.6944
Iteration: 583; Percent complete: 14.6%; Average loss: 3.6076
Iteration: 584; Percent complete: 14.6%; Average loss: 3.7954
Iteration: 585; Percent complete: 14.6%; Average loss: 3.4948
Iteration: 586; Percent complete: 14.6%; Average loss: 3.4918
Iteration: 587; Percent complete: 14.7%; Average loss: 3.5928
Iteration: 588; Percent complete: 14.7%; Average loss: 3.6459
Iteration: 589; Percent complete: 14.7%; Average loss: 3.6793
Iteration: 590; Percent complete: 14.8%; Average loss: 3.6383
Iteration: 591; Percent complete: 14.8%; Average loss: 3.8619
Iteration: 592; Percent complete: 14.8%; Average loss: 3.7323
Iteration: 593; Percent complete: 14.8%; Average loss: 3.6814
Iteration: 594; Percent complete: 14.8%; Average loss: 3.7244
Iteration: 595; Percent complete: 14.9%; Average loss: 3.7449
Iteration: 596; Percent complete: 14.9%; Average loss: 3.7038
Iteration: 597; Percent complete: 14.9%; Average loss: 3.7008
Iteration: 598; Percent complete: 14.9%; Average loss: 3.4583
Iteration: 599; Percent complete: 15.0%; Average loss: 3.8939
Iteration: 600; Percent complete: 15.0%; Average loss: 3.5927
Iteration: 601; Percent complete: 15.0%; Average loss: 3.7271
Iteration: 602; Percent complete: 15.0%; Average loss: 3.8803
Iteration: 603; Percent complete: 15.1%; Average loss: 3.6234
Iteration: 604; Percent complete: 15.1%; Average loss: 3.5816
Iteration: 605; Percent complete: 15.1%; Average loss: 3.6899
Iteration: 606; Percent complete: 15.2%; Average loss: 4.0253
Iteration: 607; Percent complete: 15.2%; Average loss: 3.5119
Iteration: 608; Percent complete: 15.2%; Average loss: 3.4473
Iteration: 609; Percent complete: 15.2%; Average loss: 3.4517
Iteration: 610; Percent complete: 15.2%; Average loss: 3.4873
Iteration: 611; Percent complete: 15.3%; Average loss: 3.6854
Iteration: 612; Percent complete: 15.3%; Average loss: 3.7445
Iteration: 613; Percent complete: 15.3%; Average loss: 3.5610
Iteration: 614; Percent complete: 15.3%; Average loss: 3.8844
Iteration: 615; Percent complete: 15.4%; Average loss: 3.6542
Iteration: 616; Percent complete: 15.4%; Average loss: 3.5921
Iteration: 617; Percent complete: 15.4%; Average loss: 3.4920
Iteration: 618; Percent complete: 15.4%; Average loss: 3.4082
Iteration: 619; Percent complete: 15.5%; Average loss: 3.9784
Iteration: 620; Percent complete: 15.5%; Average loss: 3.4578
Iteration: 621; Percent complete: 15.5%; Average loss: 3.8031
Iteration: 622; Percent complete: 15.6%; Average loss: 3.4616
Iteration: 623; Percent complete: 15.6%; Average loss: 3.7800
Iteration: 624; Percent complete: 15.6%; Average loss: 3.6683
Iteration: 625; Percent complete: 15.6%; Average loss: 3.7022
Iteration: 626; Percent complete: 15.7%; Average loss: 3.6001
Iteration: 627; Percent complete: 15.7%; Average loss: 3.5005
Iteration: 628; Percent complete: 15.7%; Average loss: 3.4143
Iteration: 629; Percent complete: 15.7%; Average loss: 3.9020
Iteration: 630; Percent complete: 15.8%; Average loss: 3.6409
Iteration: 631; Percent complete: 15.8%; Average loss: 3.5107
Iteration: 632; Percent complete: 15.8%; Average loss: 3.7519
Iteration: 633; Percent complete: 15.8%; Average loss: 3.3289
Iteration: 634; Percent complete: 15.8%; Average loss: 3.6433
Iteration: 635; Percent complete: 15.9%; Average loss: 3.4649
Iteration: 636; Percent complete: 15.9%; Average loss: 3.6308
Iteration: 637; Percent complete: 15.9%; Average loss: 3.7147
Iteration: 638; Percent complete: 16.0%; Average loss: 3.8363
Iteration: 639; Percent complete: 16.0%; Average loss: 3.3411
Iteration: 640; Percent complete: 16.0%; Average loss: 3.6597
Iteration: 641; Percent complete: 16.0%; Average loss: 3.7226
Iteration: 642; Percent complete: 16.1%; Average loss: 3.6121
Iteration: 643; Percent complete: 16.1%; Average loss: 3.4453
Iteration: 644; Percent complete: 16.1%; Average loss: 3.4418
Iteration: 645; Percent complete: 16.1%; Average loss: 3.4897
Iteration: 646; Percent complete: 16.2%; Average loss: 3.5837
Iteration: 647; Percent complete: 16.2%; Average loss: 3.6356
Iteration: 648; Percent complete: 16.2%; Average loss: 3.8515
Iteration: 649; Percent complete: 16.2%; Average loss: 3.6069
Iteration: 650; Percent complete: 16.2%; Average loss: 3.6876
Iteration: 651; Percent complete: 16.3%; Average loss: 3.6932
Iteration: 652; Percent complete: 16.3%; Average loss: 3.7753
Iteration: 653; Percent complete: 16.3%; Average loss: 3.6518
Iteration: 654; Percent complete: 16.4%; Average loss: 3.3906
Iteration: 655; Percent complete: 16.4%; Average loss: 3.7468
Iteration: 656; Percent complete: 16.4%; Average loss: 3.3799
Iteration: 657; Percent complete: 16.4%; Average loss: 3.6546
Iteration: 658; Percent complete: 16.4%; Average loss: 3.8113
Iteration: 659; Percent complete: 16.5%; Average loss: 3.8049
Iteration: 660; Percent complete: 16.5%; Average loss: 3.7757
Iteration: 661; Percent complete: 16.5%; Average loss: 3.6203
Iteration: 662; Percent complete: 16.6%; Average loss: 3.5654
Iteration: 663; Percent complete: 16.6%; Average loss: 3.5881
Iteration: 664; Percent complete: 16.6%; Average loss: 3.6338
Iteration: 665; Percent complete: 16.6%; Average loss: 3.6839
Iteration: 666; Percent complete: 16.7%; Average loss: 3.6477
Iteration: 667; Percent complete: 16.7%; Average loss: 3.6873
Iteration: 668; Percent complete: 16.7%; Average loss: 3.5615
Iteration: 669; Percent complete: 16.7%; Average loss: 3.7905
Iteration: 670; Percent complete: 16.8%; Average loss: 3.5872
Iteration: 671; Percent complete: 16.8%; Average loss: 3.9050
Iteration: 672; Percent complete: 16.8%; Average loss: 3.6917
Iteration: 673; Percent complete: 16.8%; Average loss: 3.5201
Iteration: 674; Percent complete: 16.9%; Average loss: 3.4531
Iteration: 675; Percent complete: 16.9%; Average loss: 3.7132
Iteration: 676; Percent complete: 16.9%; Average loss: 3.4866
Iteration: 677; Percent complete: 16.9%; Average loss: 3.6307
Iteration: 678; Percent complete: 17.0%; Average loss: 3.7402
Iteration: 679; Percent complete: 17.0%; Average loss: 3.7082
Iteration: 680; Percent complete: 17.0%; Average loss: 3.6507
Iteration: 681; Percent complete: 17.0%; Average loss: 3.7779
Iteration: 682; Percent complete: 17.1%; Average loss: 3.4272
Iteration: 683; Percent complete: 17.1%; Average loss: 3.6553
Iteration: 684; Percent complete: 17.1%; Average loss: 3.6617
Iteration: 685; Percent complete: 17.1%; Average loss: 3.5282
Iteration: 686; Percent complete: 17.2%; Average loss: 3.4357
Iteration: 687; Percent complete: 17.2%; Average loss: 3.4967
Iteration: 688; Percent complete: 17.2%; Average loss: 3.9656
Iteration: 689; Percent complete: 17.2%; Average loss: 3.6309
Iteration: 690; Percent complete: 17.2%; Average loss: 3.8145
Iteration: 691; Percent complete: 17.3%; Average loss: 3.2959
Iteration: 692; Percent complete: 17.3%; Average loss: 3.8402
Iteration: 693; Percent complete: 17.3%; Average loss: 3.6583
Iteration: 694; Percent complete: 17.3%; Average loss: 3.7763
Iteration: 695; Percent complete: 17.4%; Average loss: 3.6174
Iteration: 696; Percent complete: 17.4%; Average loss: 3.5015
Iteration: 697; Percent complete: 17.4%; Average loss: 3.6084
Iteration: 698; Percent complete: 17.4%; Average loss: 3.8095
Iteration: 699; Percent complete: 17.5%; Average loss: 3.5717
Iteration: 700; Percent complete: 17.5%; Average loss: 3.5306
Iteration: 701; Percent complete: 17.5%; Average loss: 3.4129
Iteration: 702; Percent complete: 17.5%; Average loss: 3.5577
Iteration: 703; Percent complete: 17.6%; Average loss: 3.5874
Iteration: 704; Percent complete: 17.6%; Average loss: 3.5670
Iteration: 705; Percent complete: 17.6%; Average loss: 3.5861
Iteration: 706; Percent complete: 17.6%; Average loss: 3.6446
Iteration: 707; Percent complete: 17.7%; Average loss: 3.8355
Iteration: 708; Percent complete: 17.7%; Average loss: 3.4100
Iteration: 709; Percent complete: 17.7%; Average loss: 3.5738
Iteration: 710; Percent complete: 17.8%; Average loss: 3.8534
Iteration: 711; Percent complete: 17.8%; Average loss: 3.4819
Iteration: 712; Percent complete: 17.8%; Average loss: 3.5541
Iteration: 713; Percent complete: 17.8%; Average loss: 3.9776
Iteration: 714; Percent complete: 17.8%; Average loss: 3.5135
Iteration: 715; Percent complete: 17.9%; Average loss: 3.5283
Iteration: 716; Percent complete: 17.9%; Average loss: 3.7488
Iteration: 717; Percent complete: 17.9%; Average loss: 3.6236
Iteration: 718; Percent complete: 17.9%; Average loss: 3.3142
Iteration: 719; Percent complete: 18.0%; Average loss: 3.5925
Iteration: 720; Percent complete: 18.0%; Average loss: 3.4735
Iteration: 721; Percent complete: 18.0%; Average loss: 3.6584
Iteration: 722; Percent complete: 18.1%; Average loss: 3.7184
Iteration: 723; Percent complete: 18.1%; Average loss: 3.6306
Iteration: 724; Percent complete: 18.1%; Average loss: 3.5544
Iteration: 725; Percent complete: 18.1%; Average loss: 3.9823
Iteration: 726; Percent complete: 18.1%; Average loss: 3.5494
Iteration: 727; Percent complete: 18.2%; Average loss: 3.7180
Iteration: 728; Percent complete: 18.2%; Average loss: 3.6454
Iteration: 729; Percent complete: 18.2%; Average loss: 3.7005
Iteration: 730; Percent complete: 18.2%; Average loss: 3.9664
Iteration: 731; Percent complete: 18.3%; Average loss: 3.3562
Iteration: 732; Percent complete: 18.3%; Average loss: 3.3953
Iteration: 733; Percent complete: 18.3%; Average loss: 3.6503
Iteration: 734; Percent complete: 18.4%; Average loss: 3.7031
Iteration: 735; Percent complete: 18.4%; Average loss: 3.4579
Iteration: 736; Percent complete: 18.4%; Average loss: 3.5752
Iteration: 737; Percent complete: 18.4%; Average loss: 3.8109
Iteration: 738; Percent complete: 18.4%; Average loss: 3.4742
Iteration: 739; Percent complete: 18.5%; Average loss: 3.4080
Iteration: 740; Percent complete: 18.5%; Average loss: 3.4817
Iteration: 741; Percent complete: 18.5%; Average loss: 3.5251
Iteration: 742; Percent complete: 18.6%; Average loss: 3.3512
Iteration: 743; Percent complete: 18.6%; Average loss: 3.7857
Iteration: 744; Percent complete: 18.6%; Average loss: 3.5608
Iteration: 745; Percent complete: 18.6%; Average loss: 3.5131
Iteration: 746; Percent complete: 18.6%; Average loss: 3.5614
Iteration: 747; Percent complete: 18.7%; Average loss: 3.7453
Iteration: 748; Percent complete: 18.7%; Average loss: 3.6591
Iteration: 749; Percent complete: 18.7%; Average loss: 3.4662
Iteration: 750; Percent complete: 18.8%; Average loss: 3.4838
Iteration: 751; Percent complete: 18.8%; Average loss: 3.7825
Iteration: 752; Percent complete: 18.8%; Average loss: 3.6830
Iteration: 753; Percent complete: 18.8%; Average loss: 3.7610
Iteration: 754; Percent complete: 18.9%; Average loss: 3.3067
Iteration: 755; Percent complete: 18.9%; Average loss: 3.4994
Iteration: 756; Percent complete: 18.9%; Average loss: 3.6106
Iteration: 757; Percent complete: 18.9%; Average loss: 3.5400
Iteration: 758; Percent complete: 18.9%; Average loss: 3.9419
Iteration: 759; Percent complete: 19.0%; Average loss: 3.6179
Iteration: 760; Percent complete: 19.0%; Average loss: 3.5333
Iteration: 761; Percent complete: 19.0%; Average loss: 3.9086
Iteration: 762; Percent complete: 19.1%; Average loss: 3.7670
Iteration: 763; Percent complete: 19.1%; Average loss: 3.5589
Iteration: 764; Percent complete: 19.1%; Average loss: 3.5350
Iteration: 765; Percent complete: 19.1%; Average loss: 3.6170
Iteration: 766; Percent complete: 19.1%; Average loss: 3.7666
Iteration: 767; Percent complete: 19.2%; Average loss: 3.3942
Iteration: 768; Percent complete: 19.2%; Average loss: 3.6587
Iteration: 769; Percent complete: 19.2%; Average loss: 3.4393
Iteration: 770; Percent complete: 19.2%; Average loss: 3.4062
Iteration: 771; Percent complete: 19.3%; Average loss: 3.5900
Iteration: 772; Percent complete: 19.3%; Average loss: 3.4731
Iteration: 773; Percent complete: 19.3%; Average loss: 3.6513
Iteration: 774; Percent complete: 19.4%; Average loss: 3.5051
Iteration: 775; Percent complete: 19.4%; Average loss: 3.5127
Iteration: 776; Percent complete: 19.4%; Average loss: 3.4454
Iteration: 777; Percent complete: 19.4%; Average loss: 3.5779
Iteration: 778; Percent complete: 19.4%; Average loss: 3.4462
Iteration: 779; Percent complete: 19.5%; Average loss: 3.3064
Iteration: 780; Percent complete: 19.5%; Average loss: 3.4716
Iteration: 781; Percent complete: 19.5%; Average loss: 3.4269
Iteration: 782; Percent complete: 19.6%; Average loss: 3.3987
Iteration: 783; Percent complete: 19.6%; Average loss: 3.7984
Iteration: 784; Percent complete: 19.6%; Average loss: 3.8021
Iteration: 785; Percent complete: 19.6%; Average loss: 3.4114
Iteration: 786; Percent complete: 19.7%; Average loss: 3.5895
Iteration: 787; Percent complete: 19.7%; Average loss: 3.7530
Iteration: 788; Percent complete: 19.7%; Average loss: 3.5712
Iteration: 789; Percent complete: 19.7%; Average loss: 3.7473
Iteration: 790; Percent complete: 19.8%; Average loss: 3.6193
Iteration: 791; Percent complete: 19.8%; Average loss: 3.3930
Iteration: 792; Percent complete: 19.8%; Average loss: 3.6316
Iteration: 793; Percent complete: 19.8%; Average loss: 3.4250
Iteration: 794; Percent complete: 19.9%; Average loss: 3.5273
Iteration: 795; Percent complete: 19.9%; Average loss: 3.5850
Iteration: 796; Percent complete: 19.9%; Average loss: 3.7406
Iteration: 797; Percent complete: 19.9%; Average loss: 3.3689
Iteration: 798; Percent complete: 20.0%; Average loss: 3.6073
Iteration: 799; Percent complete: 20.0%; Average loss: 3.7134
Iteration: 800; Percent complete: 20.0%; Average loss: 3.5130
Iteration: 801; Percent complete: 20.0%; Average loss: 3.7380
Iteration: 802; Percent complete: 20.1%; Average loss: 3.5508
Iteration: 803; Percent complete: 20.1%; Average loss: 3.2482
Iteration: 804; Percent complete: 20.1%; Average loss: 3.7246
Iteration: 805; Percent complete: 20.1%; Average loss: 3.7912
Iteration: 806; Percent complete: 20.2%; Average loss: 3.3375
Iteration: 807; Percent complete: 20.2%; Average loss: 3.6858
Iteration: 808; Percent complete: 20.2%; Average loss: 3.4028
Iteration: 809; Percent complete: 20.2%; Average loss: 3.5799
Iteration: 810; Percent complete: 20.2%; Average loss: 3.5985
Iteration: 811; Percent complete: 20.3%; Average loss: 3.3569
Iteration: 812; Percent complete: 20.3%; Average loss: 3.4215
Iteration: 813; Percent complete: 20.3%; Average loss: 3.5614
Iteration: 814; Percent complete: 20.3%; Average loss: 3.4546
Iteration: 815; Percent complete: 20.4%; Average loss: 3.5449
Iteration: 816; Percent complete: 20.4%; Average loss: 3.4858
Iteration: 817; Percent complete: 20.4%; Average loss: 3.5957
Iteration: 818; Percent complete: 20.4%; Average loss: 3.4483
Iteration: 819; Percent complete: 20.5%; Average loss: 3.5267
Iteration: 820; Percent complete: 20.5%; Average loss: 3.5309
Iteration: 821; Percent complete: 20.5%; Average loss: 3.2477
Iteration: 822; Percent complete: 20.5%; Average loss: 3.5324
Iteration: 823; Percent complete: 20.6%; Average loss: 3.6691
Iteration: 824; Percent complete: 20.6%; Average loss: 3.5335
Iteration: 825; Percent complete: 20.6%; Average loss: 3.6681
Iteration: 826; Percent complete: 20.6%; Average loss: 3.6577
Iteration: 827; Percent complete: 20.7%; Average loss: 3.7434
Iteration: 828; Percent complete: 20.7%; Average loss: 3.5765
Iteration: 829; Percent complete: 20.7%; Average loss: 3.6430
Iteration: 830; Percent complete: 20.8%; Average loss: 3.5543
Iteration: 831; Percent complete: 20.8%; Average loss: 3.8071
Iteration: 832; Percent complete: 20.8%; Average loss: 3.4969
Iteration: 833; Percent complete: 20.8%; Average loss: 3.4315
Iteration: 834; Percent complete: 20.8%; Average loss: 3.7138
Iteration: 835; Percent complete: 20.9%; Average loss: 3.4299
Iteration: 836; Percent complete: 20.9%; Average loss: 3.6974
Iteration: 837; Percent complete: 20.9%; Average loss: 3.5909
Iteration: 838; Percent complete: 20.9%; Average loss: 3.4437
Iteration: 839; Percent complete: 21.0%; Average loss: 3.4974
Iteration: 840; Percent complete: 21.0%; Average loss: 3.5033
Iteration: 841; Percent complete: 21.0%; Average loss: 3.5593
Iteration: 842; Percent complete: 21.1%; Average loss: 3.5487
Iteration: 843; Percent complete: 21.1%; Average loss: 3.6503
Iteration: 844; Percent complete: 21.1%; Average loss: 3.8021
Iteration: 845; Percent complete: 21.1%; Average loss: 3.4401
Iteration: 846; Percent complete: 21.1%; Average loss: 3.7439
Iteration: 847; Percent complete: 21.2%; Average loss: 3.6791
Iteration: 848; Percent complete: 21.2%; Average loss: 3.4666
Iteration: 849; Percent complete: 21.2%; Average loss: 3.3676
Iteration: 850; Percent complete: 21.2%; Average loss: 3.5400
Iteration: 851; Percent complete: 21.3%; Average loss: 3.7637
Iteration: 852; Percent complete: 21.3%; Average loss: 3.5948
Iteration: 853; Percent complete: 21.3%; Average loss: 3.4547
Iteration: 854; Percent complete: 21.3%; Average loss: 3.5169
Iteration: 855; Percent complete: 21.4%; Average loss: 3.4449
Iteration: 856; Percent complete: 21.4%; Average loss: 3.6133
Iteration: 857; Percent complete: 21.4%; Average loss: 3.7155
Iteration: 858; Percent complete: 21.4%; Average loss: 3.4305
Iteration: 859; Percent complete: 21.5%; Average loss: 3.6209
Iteration: 860; Percent complete: 21.5%; Average loss: 3.6211
Iteration: 861; Percent complete: 21.5%; Average loss: 3.6711
Iteration: 862; Percent complete: 21.6%; Average loss: 3.3873
Iteration: 863; Percent complete: 21.6%; Average loss: 3.5331
Iteration: 864; Percent complete: 21.6%; Average loss: 3.6505
Iteration: 865; Percent complete: 21.6%; Average loss: 3.4900
Iteration: 866; Percent complete: 21.6%; Average loss: 3.3768
Iteration: 867; Percent complete: 21.7%; Average loss: 3.5131
Iteration: 868; Percent complete: 21.7%; Average loss: 3.6255
Iteration: 869; Percent complete: 21.7%; Average loss: 3.5872
Iteration: 870; Percent complete: 21.8%; Average loss: 3.5605
Iteration: 871; Percent complete: 21.8%; Average loss: 3.3322
Iteration: 872; Percent complete: 21.8%; Average loss: 3.7774
Iteration: 873; Percent complete: 21.8%; Average loss: 3.2446
Iteration: 874; Percent complete: 21.9%; Average loss: 3.5184
Iteration: 875; Percent complete: 21.9%; Average loss: 3.3918
Iteration: 876; Percent complete: 21.9%; Average loss: 3.4670
Iteration: 877; Percent complete: 21.9%; Average loss: 3.6394
Iteration: 878; Percent complete: 21.9%; Average loss: 3.3435
Iteration: 879; Percent complete: 22.0%; Average loss: 3.4311
Iteration: 880; Percent complete: 22.0%; Average loss: 3.6016
Iteration: 881; Percent complete: 22.0%; Average loss: 3.5275
Iteration: 882; Percent complete: 22.1%; Average loss: 3.5003
Iteration: 883; Percent complete: 22.1%; Average loss: 3.4622
Iteration: 884; Percent complete: 22.1%; Average loss: 3.7206
Iteration: 885; Percent complete: 22.1%; Average loss: 3.4165
Iteration: 886; Percent complete: 22.1%; Average loss: 3.4862
Iteration: 887; Percent complete: 22.2%; Average loss: 3.5317
Iteration: 888; Percent complete: 22.2%; Average loss: 3.6169
Iteration: 889; Percent complete: 22.2%; Average loss: 3.5889
Iteration: 890; Percent complete: 22.2%; Average loss: 3.5395
Iteration: 891; Percent complete: 22.3%; Average loss: 3.6121
Iteration: 892; Percent complete: 22.3%; Average loss: 3.7147
Iteration: 893; Percent complete: 22.3%; Average loss: 3.4280
Iteration: 894; Percent complete: 22.4%; Average loss: 3.3447
Iteration: 895; Percent complete: 22.4%; Average loss: 3.6573
Iteration: 896; Percent complete: 22.4%; Average loss: 3.4093
Iteration: 897; Percent complete: 22.4%; Average loss: 3.3207
Iteration: 898; Percent complete: 22.4%; Average loss: 3.3284
Iteration: 899; Percent complete: 22.5%; Average loss: 3.3417
Iteration: 900; Percent complete: 22.5%; Average loss: 3.6755
Iteration: 901; Percent complete: 22.5%; Average loss: 3.5929
Iteration: 902; Percent complete: 22.6%; Average loss: 3.4219
Iteration: 903; Percent complete: 22.6%; Average loss: 3.5738
Iteration: 904; Percent complete: 22.6%; Average loss: 3.5533
Iteration: 905; Percent complete: 22.6%; Average loss: 3.5792
Iteration: 906; Percent complete: 22.7%; Average loss: 3.8013
Iteration: 907; Percent complete: 22.7%; Average loss: 3.5469
Iteration: 908; Percent complete: 22.7%; Average loss: 3.4432
Iteration: 909; Percent complete: 22.7%; Average loss: 3.4361
Iteration: 910; Percent complete: 22.8%; Average loss: 3.6516
Iteration: 911; Percent complete: 22.8%; Average loss: 3.3225
Iteration: 912; Percent complete: 22.8%; Average loss: 3.3835
Iteration: 913; Percent complete: 22.8%; Average loss: 3.5031
Iteration: 914; Percent complete: 22.9%; Average loss: 3.5304
Iteration: 915; Percent complete: 22.9%; Average loss: 3.5325
Iteration: 916; Percent complete: 22.9%; Average loss: 3.4603
Iteration: 917; Percent complete: 22.9%; Average loss: 3.5568
Iteration: 918; Percent complete: 22.9%; Average loss: 3.7773
Iteration: 919; Percent complete: 23.0%; Average loss: 3.6102
Iteration: 920; Percent complete: 23.0%; Average loss: 3.6249
Iteration: 921; Percent complete: 23.0%; Average loss: 3.5106
Iteration: 922; Percent complete: 23.1%; Average loss: 3.4402
Iteration: 923; Percent complete: 23.1%; Average loss: 3.3754
Iteration: 924; Percent complete: 23.1%; Average loss: 3.3816
Iteration: 925; Percent complete: 23.1%; Average loss: 3.4631
Iteration: 926; Percent complete: 23.2%; Average loss: 3.5207
Iteration: 927; Percent complete: 23.2%; Average loss: 3.3886
Iteration: 928; Percent complete: 23.2%; Average loss: 3.4626
Iteration: 929; Percent complete: 23.2%; Average loss: 3.2952
Iteration: 930; Percent complete: 23.2%; Average loss: 3.5087
Iteration: 931; Percent complete: 23.3%; Average loss: 3.5789
Iteration: 932; Percent complete: 23.3%; Average loss: 3.6082
Iteration: 933; Percent complete: 23.3%; Average loss: 3.3852
Iteration: 934; Percent complete: 23.4%; Average loss: 3.4046
Iteration: 935; Percent complete: 23.4%; Average loss: 3.3841
Iteration: 936; Percent complete: 23.4%; Average loss: 3.4835
Iteration: 937; Percent complete: 23.4%; Average loss: 3.4598
Iteration: 938; Percent complete: 23.4%; Average loss: 3.4964
Iteration: 939; Percent complete: 23.5%; Average loss: 3.3560
Iteration: 940; Percent complete: 23.5%; Average loss: 3.4684
Iteration: 941; Percent complete: 23.5%; Average loss: 3.4266
Iteration: 942; Percent complete: 23.5%; Average loss: 3.5096
Iteration: 943; Percent complete: 23.6%; Average loss: 3.4089
Iteration: 944; Percent complete: 23.6%; Average loss: 3.7585
Iteration: 945; Percent complete: 23.6%; Average loss: 3.4558
Iteration: 946; Percent complete: 23.6%; Average loss: 3.5417
Iteration: 947; Percent complete: 23.7%; Average loss: 3.2698
Iteration: 948; Percent complete: 23.7%; Average loss: 3.6761
Iteration: 949; Percent complete: 23.7%; Average loss: 3.1061
Iteration: 950; Percent complete: 23.8%; Average loss: 3.4107
Iteration: 951; Percent complete: 23.8%; Average loss: 3.4955
Iteration: 952; Percent complete: 23.8%; Average loss: 3.5062
Iteration: 953; Percent complete: 23.8%; Average loss: 3.5866
Iteration: 954; Percent complete: 23.8%; Average loss: 3.5862
Iteration: 955; Percent complete: 23.9%; Average loss: 3.5674
Iteration: 956; Percent complete: 23.9%; Average loss: 3.5768
Iteration: 957; Percent complete: 23.9%; Average loss: 3.4869
Iteration: 958; Percent complete: 23.9%; Average loss: 3.7425
Iteration: 959; Percent complete: 24.0%; Average loss: 3.4166
Iteration: 960; Percent complete: 24.0%; Average loss: 3.3738
Iteration: 961; Percent complete: 24.0%; Average loss: 3.5948
Iteration: 962; Percent complete: 24.1%; Average loss: 3.5207
Iteration: 963; Percent complete: 24.1%; Average loss: 3.2754
Iteration: 964; Percent complete: 24.1%; Average loss: 3.5046
Iteration: 965; Percent complete: 24.1%; Average loss: 3.4958
Iteration: 966; Percent complete: 24.1%; Average loss: 3.5989
Iteration: 967; Percent complete: 24.2%; Average loss: 3.4203
Iteration: 968; Percent complete: 24.2%; Average loss: 3.5643
Iteration: 969; Percent complete: 24.2%; Average loss: 3.5584
Iteration: 970; Percent complete: 24.2%; Average loss: 3.4061
Iteration: 971; Percent complete: 24.3%; Average loss: 3.5679
Iteration: 972; Percent complete: 24.3%; Average loss: 3.6366
Iteration: 973; Percent complete: 24.3%; Average loss: 3.5251
Iteration: 974; Percent complete: 24.3%; Average loss: 3.1258
Iteration: 975; Percent complete: 24.4%; Average loss: 3.4927
Iteration: 976; Percent complete: 24.4%; Average loss: 3.4510
Iteration: 977; Percent complete: 24.4%; Average loss: 3.2716
Iteration: 978; Percent complete: 24.4%; Average loss: 3.3740
Iteration: 979; Percent complete: 24.5%; Average loss: 3.3897
Iteration: 980; Percent complete: 24.5%; Average loss: 3.5899
Iteration: 981; Percent complete: 24.5%; Average loss: 3.5413
Iteration: 982; Percent complete: 24.6%; Average loss: 3.4782
Iteration: 983; Percent complete: 24.6%; Average loss: 3.3439
Iteration: 984; Percent complete: 24.6%; Average loss: 3.4382
Iteration: 985; Percent complete: 24.6%; Average loss: 3.4920
Iteration: 986; Percent complete: 24.6%; Average loss: 3.5425
Iteration: 987; Percent complete: 24.7%; Average loss: 3.4099
Iteration: 988; Percent complete: 24.7%; Average loss: 3.5452
Iteration: 989; Percent complete: 24.7%; Average loss: 3.4742
Iteration: 990; Percent complete: 24.8%; Average loss: 3.3972
Iteration: 991; Percent complete: 24.8%; Average loss: 3.4692
Iteration: 992; Percent complete: 24.8%; Average loss: 3.5015
Iteration: 993; Percent complete: 24.8%; Average loss: 3.4593
Iteration: 994; Percent complete: 24.9%; Average loss: 3.2600
Iteration: 995; Percent complete: 24.9%; Average loss: 3.4401
Iteration: 996; Percent complete: 24.9%; Average loss: 3.4148
Iteration: 997; Percent complete: 24.9%; Average loss: 3.4236
Iteration: 998; Percent complete: 24.9%; Average loss: 3.6351
Iteration: 999; Percent complete: 25.0%; Average loss: 3.4895
Iteration: 1000; Percent complete: 25.0%; Average loss: 3.1991
Iteration: 1001; Percent complete: 25.0%; Average loss: 3.5161
Iteration: 1002; Percent complete: 25.1%; Average loss: 3.4609
Iteration: 1003; Percent complete: 25.1%; Average loss: 3.1782
Iteration: 1004; Percent complete: 25.1%; Average loss: 3.4600
Iteration: 1005; Percent complete: 25.1%; Average loss: 3.4286
Iteration: 1006; Percent complete: 25.1%; Average loss: 3.2785
Iteration: 1007; Percent complete: 25.2%; Average loss: 3.1804
Iteration: 1008; Percent complete: 25.2%; Average loss: 3.5109
Iteration: 1009; Percent complete: 25.2%; Average loss: 3.5229
Iteration: 1010; Percent complete: 25.2%; Average loss: 3.3922
Iteration: 1011; Percent complete: 25.3%; Average loss: 3.2608
Iteration: 1012; Percent complete: 25.3%; Average loss: 3.2854
Iteration: 1013; Percent complete: 25.3%; Average loss: 3.1059
Iteration: 1014; Percent complete: 25.4%; Average loss: 3.5768
Iteration: 1015; Percent complete: 25.4%; Average loss: 3.2839
Iteration: 1016; Percent complete: 25.4%; Average loss: 3.4251
Iteration: 1017; Percent complete: 25.4%; Average loss: 3.4839
Iteration: 1018; Percent complete: 25.4%; Average loss: 3.3743
Iteration: 1019; Percent complete: 25.5%; Average loss: 3.3832
Iteration: 1020; Percent complete: 25.5%; Average loss: 3.5308
Iteration: 1021; Percent complete: 25.5%; Average loss: 3.5318
Iteration: 1022; Percent complete: 25.6%; Average loss: 3.2761
Iteration: 1023; Percent complete: 25.6%; Average loss: 3.1474
Iteration: 1024; Percent complete: 25.6%; Average loss: 3.4693
Iteration: 1025; Percent complete: 25.6%; Average loss: 3.6005
Iteration: 1026; Percent complete: 25.7%; Average loss: 3.3618
Iteration: 1027; Percent complete: 25.7%; Average loss: 3.5079
Iteration: 1028; Percent complete: 25.7%; Average loss: 3.4867
Iteration: 1029; Percent complete: 25.7%; Average loss: 3.4788
Iteration: 1030; Percent complete: 25.8%; Average loss: 3.2307
Iteration: 1031; Percent complete: 25.8%; Average loss: 3.4060
Iteration: 1032; Percent complete: 25.8%; Average loss: 3.3300
Iteration: 1033; Percent complete: 25.8%; Average loss: 3.4681
Iteration: 1034; Percent complete: 25.9%; Average loss: 3.2542
Iteration: 1035; Percent complete: 25.9%; Average loss: 3.5067
Iteration: 1036; Percent complete: 25.9%; Average loss: 3.4297
Iteration: 1037; Percent complete: 25.9%; Average loss: 3.2952
Iteration: 1038; Percent complete: 25.9%; Average loss: 3.2415
Iteration: 1039; Percent complete: 26.0%; Average loss: 3.7286
Iteration: 1040; Percent complete: 26.0%; Average loss: 3.4387
Iteration: 1041; Percent complete: 26.0%; Average loss: 3.5638
Iteration: 1042; Percent complete: 26.1%; Average loss: 3.4020
Iteration: 1043; Percent complete: 26.1%; Average loss: 3.3959
Iteration: 1044; Percent complete: 26.1%; Average loss: 3.5169
Iteration: 1045; Percent complete: 26.1%; Average loss: 3.5376
Iteration: 1046; Percent complete: 26.2%; Average loss: 3.5072
Iteration: 1047; Percent complete: 26.2%; Average loss: 3.5607
Iteration: 1048; Percent complete: 26.2%; Average loss: 3.4127
Iteration: 1049; Percent complete: 26.2%; Average loss: 3.2870
Iteration: 1050; Percent complete: 26.2%; Average loss: 3.2427
Iteration: 1051; Percent complete: 26.3%; Average loss: 3.4669
Iteration: 1052; Percent complete: 26.3%; Average loss: 3.3380
Iteration: 1053; Percent complete: 26.3%; Average loss: 3.3557
Iteration: 1054; Percent complete: 26.4%; Average loss: 3.6286
Iteration: 1055; Percent complete: 26.4%; Average loss: 3.4782
Iteration: 1056; Percent complete: 26.4%; Average loss: 3.4185
Iteration: 1057; Percent complete: 26.4%; Average loss: 3.2591
Iteration: 1058; Percent complete: 26.5%; Average loss: 3.3755
Iteration: 1059; Percent complete: 26.5%; Average loss: 3.4187
Iteration: 1060; Percent complete: 26.5%; Average loss: 3.1194
Iteration: 1061; Percent complete: 26.5%; Average loss: 3.5556
Iteration: 1062; Percent complete: 26.6%; Average loss: 3.6294
Iteration: 1063; Percent complete: 26.6%; Average loss: 3.7251
Iteration: 1064; Percent complete: 26.6%; Average loss: 3.5064
Iteration: 1065; Percent complete: 26.6%; Average loss: 3.3924
Iteration: 1066; Percent complete: 26.7%; Average loss: 3.3687
Iteration: 1067; Percent complete: 26.7%; Average loss: 3.4693
Iteration: 1068; Percent complete: 26.7%; Average loss: 3.4340
Iteration: 1069; Percent complete: 26.7%; Average loss: 3.6246
Iteration: 1070; Percent complete: 26.8%; Average loss: 3.1260
Iteration: 1071; Percent complete: 26.8%; Average loss: 3.5033
Iteration: 1072; Percent complete: 26.8%; Average loss: 3.3712
Iteration: 1073; Percent complete: 26.8%; Average loss: 3.4579
Iteration: 1074; Percent complete: 26.9%; Average loss: 3.5859
Iteration: 1075; Percent complete: 26.9%; Average loss: 3.5738
Iteration: 1076; Percent complete: 26.9%; Average loss: 3.4656
Iteration: 1077; Percent complete: 26.9%; Average loss: 3.5932
Iteration: 1078; Percent complete: 27.0%; Average loss: 3.3251
Iteration: 1079; Percent complete: 27.0%; Average loss: 3.4441
Iteration: 1080; Percent complete: 27.0%; Average loss: 3.3477
Iteration: 1081; Percent complete: 27.0%; Average loss: 3.3676
Iteration: 1082; Percent complete: 27.1%; Average loss: 3.6204
Iteration: 1083; Percent complete: 27.1%; Average loss: 3.3889
Iteration: 1084; Percent complete: 27.1%; Average loss: 3.3779
Iteration: 1085; Percent complete: 27.1%; Average loss: 3.5247
Iteration: 1086; Percent complete: 27.2%; Average loss: 3.3439
Iteration: 1087; Percent complete: 27.2%; Average loss: 3.5445
Iteration: 1088; Percent complete: 27.2%; Average loss: 3.7407
Iteration: 1089; Percent complete: 27.2%; Average loss: 3.3262
Iteration: 1090; Percent complete: 27.3%; Average loss: 3.5439
Iteration: 1091; Percent complete: 27.3%; Average loss: 3.2888
Iteration: 1092; Percent complete: 27.3%; Average loss: 3.7115
Iteration: 1093; Percent complete: 27.3%; Average loss: 3.2854
Iteration: 1094; Percent complete: 27.4%; Average loss: 3.6889
Iteration: 1095; Percent complete: 27.4%; Average loss: 3.2983
Iteration: 1096; Percent complete: 27.4%; Average loss: 3.4961
Iteration: 1097; Percent complete: 27.4%; Average loss: 3.1057
Iteration: 1098; Percent complete: 27.5%; Average loss: 3.3149
Iteration: 1099; Percent complete: 27.5%; Average loss: 3.4050
Iteration: 1100; Percent complete: 27.5%; Average loss: 3.5886
Iteration: 1101; Percent complete: 27.5%; Average loss: 3.2606
Iteration: 1102; Percent complete: 27.6%; Average loss: 3.4447
Iteration: 1103; Percent complete: 27.6%; Average loss: 3.3734
Iteration: 1104; Percent complete: 27.6%; Average loss: 3.3548
Iteration: 1105; Percent complete: 27.6%; Average loss: 3.6204
Iteration: 1106; Percent complete: 27.7%; Average loss: 3.2586
Iteration: 1107; Percent complete: 27.7%; Average loss: 3.3166
Iteration: 1108; Percent complete: 27.7%; Average loss: 3.3736
Iteration: 1109; Percent complete: 27.7%; Average loss: 3.4870
Iteration: 1110; Percent complete: 27.8%; Average loss: 3.3854
Iteration: 1111; Percent complete: 27.8%; Average loss: 3.3804
Iteration: 1112; Percent complete: 27.8%; Average loss: 3.7800
Iteration: 1113; Percent complete: 27.8%; Average loss: 3.4931
Iteration: 1114; Percent complete: 27.9%; Average loss: 3.3792
Iteration: 1115; Percent complete: 27.9%; Average loss: 3.4771
Iteration: 1116; Percent complete: 27.9%; Average loss: 3.4759
Iteration: 1117; Percent complete: 27.9%; Average loss: 3.5050
Iteration: 1118; Percent complete: 28.0%; Average loss: 3.6040
Iteration: 1119; Percent complete: 28.0%; Average loss: 3.0617
Iteration: 1120; Percent complete: 28.0%; Average loss: 3.8208
Iteration: 1121; Percent complete: 28.0%; Average loss: 3.4759
Iteration: 1122; Percent complete: 28.1%; Average loss: 3.6628
Iteration: 1123; Percent complete: 28.1%; Average loss: 3.1694
Iteration: 1124; Percent complete: 28.1%; Average loss: 3.5450
Iteration: 1125; Percent complete: 28.1%; Average loss: 3.3060
Iteration: 1126; Percent complete: 28.1%; Average loss: 3.4075
Iteration: 1127; Percent complete: 28.2%; Average loss: 3.5335
Iteration: 1128; Percent complete: 28.2%; Average loss: 3.4984
Iteration: 1129; Percent complete: 28.2%; Average loss: 3.4106
Iteration: 1130; Percent complete: 28.2%; Average loss: 3.4828
Iteration: 1131; Percent complete: 28.3%; Average loss: 3.2384
Iteration: 1132; Percent complete: 28.3%; Average loss: 3.6697
Iteration: 1133; Percent complete: 28.3%; Average loss: 3.4468
Iteration: 1134; Percent complete: 28.3%; Average loss: 3.4032
Iteration: 1135; Percent complete: 28.4%; Average loss: 3.3932
Iteration: 1136; Percent complete: 28.4%; Average loss: 3.3660
Iteration: 1137; Percent complete: 28.4%; Average loss: 3.0889
Iteration: 1138; Percent complete: 28.4%; Average loss: 3.3223
Iteration: 1139; Percent complete: 28.5%; Average loss: 3.4646
Iteration: 1140; Percent complete: 28.5%; Average loss: 3.5292
Iteration: 1141; Percent complete: 28.5%; Average loss: 3.3266
Iteration: 1142; Percent complete: 28.5%; Average loss: 3.4963
Iteration: 1143; Percent complete: 28.6%; Average loss: 3.4978
Iteration: 1144; Percent complete: 28.6%; Average loss: 3.6814
Iteration: 1145; Percent complete: 28.6%; Average loss: 3.3864
Iteration: 1146; Percent complete: 28.6%; Average loss: 3.6419
Iteration: 1147; Percent complete: 28.7%; Average loss: 3.4089
Iteration: 1148; Percent complete: 28.7%; Average loss: 3.3886
Iteration: 1149; Percent complete: 28.7%; Average loss: 3.6465
Iteration: 1150; Percent complete: 28.7%; Average loss: 3.2462
Iteration: 1151; Percent complete: 28.8%; Average loss: 3.2142
Iteration: 1152; Percent complete: 28.8%; Average loss: 3.4169
Iteration: 1153; Percent complete: 28.8%; Average loss: 3.5120
Iteration: 1154; Percent complete: 28.8%; Average loss: 3.2541
Iteration: 1155; Percent complete: 28.9%; Average loss: 3.4479
Iteration: 1156; Percent complete: 28.9%; Average loss: 3.3619
Iteration: 1157; Percent complete: 28.9%; Average loss: 3.5093
Iteration: 1158; Percent complete: 28.9%; Average loss: 3.4310
Iteration: 1159; Percent complete: 29.0%; Average loss: 3.1883
Iteration: 1160; Percent complete: 29.0%; Average loss: 3.3430
Iteration: 1161; Percent complete: 29.0%; Average loss: 3.4974
Iteration: 1162; Percent complete: 29.0%; Average loss: 3.4545
Iteration: 1163; Percent complete: 29.1%; Average loss: 3.6778
Iteration: 1164; Percent complete: 29.1%; Average loss: 3.5685
Iteration: 1165; Percent complete: 29.1%; Average loss: 3.4069
Iteration: 1166; Percent complete: 29.1%; Average loss: 3.3106
Iteration: 1167; Percent complete: 29.2%; Average loss: 3.4179
Iteration: 1168; Percent complete: 29.2%; Average loss: 3.3682
Iteration: 1169; Percent complete: 29.2%; Average loss: 3.3254
Iteration: 1170; Percent complete: 29.2%; Average loss: 3.7250
Iteration: 1171; Percent complete: 29.3%; Average loss: 3.3661
Iteration: 1172; Percent complete: 29.3%; Average loss: 3.5267
Iteration: 1173; Percent complete: 29.3%; Average loss: 3.3053
Iteration: 1174; Percent complete: 29.3%; Average loss: 3.1228
Iteration: 1175; Percent complete: 29.4%; Average loss: 3.6463
Iteration: 1176; Percent complete: 29.4%; Average loss: 3.1985
Iteration: 1177; Percent complete: 29.4%; Average loss: 3.3325
Iteration: 1178; Percent complete: 29.4%; Average loss: 3.1705
Iteration: 1179; Percent complete: 29.5%; Average loss: 3.1474
Iteration: 1180; Percent complete: 29.5%; Average loss: 3.2707
Iteration: 1181; Percent complete: 29.5%; Average loss: 3.5833
Iteration: 1182; Percent complete: 29.5%; Average loss: 3.4114
Iteration: 1183; Percent complete: 29.6%; Average loss: 3.4694
Iteration: 1184; Percent complete: 29.6%; Average loss: 3.1680
Iteration: 1185; Percent complete: 29.6%; Average loss: 3.5813
Iteration: 1186; Percent complete: 29.6%; Average loss: 3.3847
Iteration: 1187; Percent complete: 29.7%; Average loss: 3.7014
Iteration: 1188; Percent complete: 29.7%; Average loss: 3.2199
Iteration: 1189; Percent complete: 29.7%; Average loss: 3.4206
Iteration: 1190; Percent complete: 29.8%; Average loss: 3.5494
Iteration: 1191; Percent complete: 29.8%; Average loss: 3.2778
Iteration: 1192; Percent complete: 29.8%; Average loss: 3.3822
Iteration: 1193; Percent complete: 29.8%; Average loss: 3.3789
Iteration: 1194; Percent complete: 29.8%; Average loss: 3.3071
Iteration: 1195; Percent complete: 29.9%; Average loss: 3.7269
Iteration: 1196; Percent complete: 29.9%; Average loss: 3.3336
Iteration: 1197; Percent complete: 29.9%; Average loss: 3.4966
Iteration: 1198; Percent complete: 29.9%; Average loss: 3.3592
Iteration: 1199; Percent complete: 30.0%; Average loss: 3.3833
Iteration: 1200; Percent complete: 30.0%; Average loss: 3.3507
Iteration: 1201; Percent complete: 30.0%; Average loss: 3.3743
Iteration: 1202; Percent complete: 30.0%; Average loss: 3.2714
Iteration: 1203; Percent complete: 30.1%; Average loss: 3.1875
Iteration: 1204; Percent complete: 30.1%; Average loss: 3.3518
Iteration: 1205; Percent complete: 30.1%; Average loss: 3.0256
Iteration: 1206; Percent complete: 30.1%; Average loss: 3.4375
Iteration: 1207; Percent complete: 30.2%; Average loss: 3.3762
Iteration: 1208; Percent complete: 30.2%; Average loss: 3.3724
Iteration: 1209; Percent complete: 30.2%; Average loss: 3.8093
Iteration: 1210; Percent complete: 30.2%; Average loss: 3.3024
Iteration: 1211; Percent complete: 30.3%; Average loss: 3.1077
Iteration: 1212; Percent complete: 30.3%; Average loss: 3.3865
Iteration: 1213; Percent complete: 30.3%; Average loss: 3.5115
Iteration: 1214; Percent complete: 30.3%; Average loss: 3.2422
Iteration: 1215; Percent complete: 30.4%; Average loss: 3.8208
Iteration: 1216; Percent complete: 30.4%; Average loss: 3.2154
Iteration: 1217; Percent complete: 30.4%; Average loss: 3.3981
Iteration: 1218; Percent complete: 30.4%; Average loss: 3.3635
Iteration: 1219; Percent complete: 30.5%; Average loss: 3.2617
Iteration: 1220; Percent complete: 30.5%; Average loss: 3.4470
Iteration: 1221; Percent complete: 30.5%; Average loss: 3.3757
Iteration: 1222; Percent complete: 30.6%; Average loss: 3.1581
Iteration: 1223; Percent complete: 30.6%; Average loss: 3.2789
Iteration: 1224; Percent complete: 30.6%; Average loss: 3.5364
Iteration: 1225; Percent complete: 30.6%; Average loss: 3.2119
Iteration: 1226; Percent complete: 30.6%; Average loss: 3.2151
Iteration: 1227; Percent complete: 30.7%; Average loss: 3.3469
Iteration: 1228; Percent complete: 30.7%; Average loss: 3.5220
Iteration: 1229; Percent complete: 30.7%; Average loss: 3.1531
Iteration: 1230; Percent complete: 30.8%; Average loss: 3.3826
Iteration: 1231; Percent complete: 30.8%; Average loss: 3.3457
Iteration: 1232; Percent complete: 30.8%; Average loss: 3.5095
Iteration: 1233; Percent complete: 30.8%; Average loss: 3.3398
Iteration: 1234; Percent complete: 30.9%; Average loss: 3.5274
Iteration: 1235; Percent complete: 30.9%; Average loss: 3.4084
Iteration: 1236; Percent complete: 30.9%; Average loss: 3.7830
Iteration: 1237; Percent complete: 30.9%; Average loss: 3.2181
Iteration: 1238; Percent complete: 30.9%; Average loss: 3.2089
Iteration: 1239; Percent complete: 31.0%; Average loss: 3.2392
Iteration: 1240; Percent complete: 31.0%; Average loss: 3.2349
Iteration: 1241; Percent complete: 31.0%; Average loss: 3.3132
Iteration: 1242; Percent complete: 31.1%; Average loss: 3.2199
Iteration: 1243; Percent complete: 31.1%; Average loss: 3.2809
Iteration: 1244; Percent complete: 31.1%; Average loss: 3.3852
Iteration: 1245; Percent complete: 31.1%; Average loss: 3.3712
Iteration: 1246; Percent complete: 31.1%; Average loss: 3.2326
Iteration: 1247; Percent complete: 31.2%; Average loss: 3.3803
Iteration: 1248; Percent complete: 31.2%; Average loss: 3.5027
Iteration: 1249; Percent complete: 31.2%; Average loss: 3.6290
Iteration: 1250; Percent complete: 31.2%; Average loss: 3.4113
Iteration: 1251; Percent complete: 31.3%; Average loss: 3.3427
Iteration: 1252; Percent complete: 31.3%; Average loss: 3.2205
Iteration: 1253; Percent complete: 31.3%; Average loss: 3.4896
Iteration: 1254; Percent complete: 31.4%; Average loss: 3.2242
Iteration: 1255; Percent complete: 31.4%; Average loss: 3.5621
Iteration: 1256; Percent complete: 31.4%; Average loss: 3.3455
Iteration: 1257; Percent complete: 31.4%; Average loss: 3.3126
Iteration: 1258; Percent complete: 31.4%; Average loss: 3.2511
Iteration: 1259; Percent complete: 31.5%; Average loss: 3.2193
Iteration: 1260; Percent complete: 31.5%; Average loss: 3.2094
Iteration: 1261; Percent complete: 31.5%; Average loss: 3.3626
Iteration: 1262; Percent complete: 31.6%; Average loss: 3.4926
Iteration: 1263; Percent complete: 31.6%; Average loss: 3.4489
Iteration: 1264; Percent complete: 31.6%; Average loss: 3.4484
Iteration: 1265; Percent complete: 31.6%; Average loss: 3.3015
Iteration: 1266; Percent complete: 31.6%; Average loss: 3.1370
Iteration: 1267; Percent complete: 31.7%; Average loss: 3.5611
Iteration: 1268; Percent complete: 31.7%; Average loss: 3.4063
Iteration: 1269; Percent complete: 31.7%; Average loss: 3.5394
Iteration: 1270; Percent complete: 31.8%; Average loss: 3.0440
Iteration: 1271; Percent complete: 31.8%; Average loss: 3.3414
Iteration: 1272; Percent complete: 31.8%; Average loss: 3.3900
Iteration: 1273; Percent complete: 31.8%; Average loss: 3.2258
Iteration: 1274; Percent complete: 31.9%; Average loss: 3.4383
Iteration: 1275; Percent complete: 31.9%; Average loss: 3.4819
Iteration: 1276; Percent complete: 31.9%; Average loss: 3.4400
Iteration: 1277; Percent complete: 31.9%; Average loss: 3.3488
Iteration: 1278; Percent complete: 31.9%; Average loss: 3.3397
Iteration: 1279; Percent complete: 32.0%; Average loss: 3.2860
Iteration: 1280; Percent complete: 32.0%; Average loss: 3.4968
Iteration: 1281; Percent complete: 32.0%; Average loss: 3.4581
Iteration: 1282; Percent complete: 32.0%; Average loss: 3.3282
Iteration: 1283; Percent complete: 32.1%; Average loss: 3.4199
Iteration: 1284; Percent complete: 32.1%; Average loss: 3.4207
Iteration: 1285; Percent complete: 32.1%; Average loss: 3.3863
Iteration: 1286; Percent complete: 32.1%; Average loss: 3.6147
Iteration: 1287; Percent complete: 32.2%; Average loss: 3.5023
Iteration: 1288; Percent complete: 32.2%; Average loss: 3.5169
Iteration: 1289; Percent complete: 32.2%; Average loss: 3.2370
Iteration: 1290; Percent complete: 32.2%; Average loss: 2.9544
Iteration: 1291; Percent complete: 32.3%; Average loss: 3.5199
Iteration: 1292; Percent complete: 32.3%; Average loss: 3.4186
Iteration: 1293; Percent complete: 32.3%; Average loss: 3.6149
Iteration: 1294; Percent complete: 32.4%; Average loss: 3.6267
Iteration: 1295; Percent complete: 32.4%; Average loss: 3.3085
Iteration: 1296; Percent complete: 32.4%; Average loss: 3.2279
Iteration: 1297; Percent complete: 32.4%; Average loss: 3.2641
Iteration: 1298; Percent complete: 32.5%; Average loss: 3.6408
Iteration: 1299; Percent complete: 32.5%; Average loss: 3.3762
Iteration: 1300; Percent complete: 32.5%; Average loss: 3.1914
Iteration: 1301; Percent complete: 32.5%; Average loss: 3.6315
Iteration: 1302; Percent complete: 32.6%; Average loss: 3.1211
Iteration: 1303; Percent complete: 32.6%; Average loss: 3.4914
Iteration: 1304; Percent complete: 32.6%; Average loss: 3.2984
Iteration: 1305; Percent complete: 32.6%; Average loss: 3.2670
Iteration: 1306; Percent complete: 32.6%; Average loss: 3.3238
Iteration: 1307; Percent complete: 32.7%; Average loss: 3.3330
Iteration: 1308; Percent complete: 32.7%; Average loss: 3.4387
Iteration: 1309; Percent complete: 32.7%; Average loss: 3.4471
Iteration: 1310; Percent complete: 32.8%; Average loss: 3.2587
Iteration: 1311; Percent complete: 32.8%; Average loss: 3.3583
Iteration: 1312; Percent complete: 32.8%; Average loss: 3.2792
Iteration: 1313; Percent complete: 32.8%; Average loss: 3.1519
Iteration: 1314; Percent complete: 32.9%; Average loss: 3.3907
Iteration: 1315; Percent complete: 32.9%; Average loss: 3.4850
Iteration: 1316; Percent complete: 32.9%; Average loss: 3.3821
Iteration: 1317; Percent complete: 32.9%; Average loss: 3.5306
Iteration: 1318; Percent complete: 33.0%; Average loss: 3.6481
Iteration: 1319; Percent complete: 33.0%; Average loss: 3.1007
Iteration: 1320; Percent complete: 33.0%; Average loss: 3.4287
Iteration: 1321; Percent complete: 33.0%; Average loss: 3.1809
Iteration: 1322; Percent complete: 33.1%; Average loss: 3.4572
Iteration: 1323; Percent complete: 33.1%; Average loss: 3.4255
Iteration: 1324; Percent complete: 33.1%; Average loss: 3.0761
Iteration: 1325; Percent complete: 33.1%; Average loss: 3.2617
Iteration: 1326; Percent complete: 33.1%; Average loss: 3.2055
Iteration: 1327; Percent complete: 33.2%; Average loss: 3.2477
Iteration: 1328; Percent complete: 33.2%; Average loss: 3.3229
Iteration: 1329; Percent complete: 33.2%; Average loss: 3.1640
Iteration: 1330; Percent complete: 33.2%; Average loss: 3.3439
Iteration: 1331; Percent complete: 33.3%; Average loss: 3.3832
Iteration: 1332; Percent complete: 33.3%; Average loss: 3.1278
Iteration: 1333; Percent complete: 33.3%; Average loss: 3.3940
Iteration: 1334; Percent complete: 33.4%; Average loss: 3.2999
Iteration: 1335; Percent complete: 33.4%; Average loss: 3.4838
Iteration: 1336; Percent complete: 33.4%; Average loss: 3.1661
Iteration: 1337; Percent complete: 33.4%; Average loss: 3.2800
Iteration: 1338; Percent complete: 33.5%; Average loss: 3.3053
Iteration: 1339; Percent complete: 33.5%; Average loss: 3.1609
Iteration: 1340; Percent complete: 33.5%; Average loss: 3.0523
Iteration: 1341; Percent complete: 33.5%; Average loss: 3.4790
Iteration: 1342; Percent complete: 33.6%; Average loss: 3.2056
Iteration: 1343; Percent complete: 33.6%; Average loss: 3.5402
Iteration: 1344; Percent complete: 33.6%; Average loss: 3.2213
Iteration: 1345; Percent complete: 33.6%; Average loss: 3.1946
Iteration: 1346; Percent complete: 33.7%; Average loss: 3.4279
Iteration: 1347; Percent complete: 33.7%; Average loss: 3.7618
Iteration: 1348; Percent complete: 33.7%; Average loss: 3.2997
Iteration: 1349; Percent complete: 33.7%; Average loss: 3.3132
Iteration: 1350; Percent complete: 33.8%; Average loss: 3.6779
Iteration: 1351; Percent complete: 33.8%; Average loss: 3.2672
Iteration: 1352; Percent complete: 33.8%; Average loss: 3.1873
Iteration: 1353; Percent complete: 33.8%; Average loss: 3.2474
Iteration: 1354; Percent complete: 33.9%; Average loss: 3.5053
Iteration: 1355; Percent complete: 33.9%; Average loss: 3.3077
Iteration: 1356; Percent complete: 33.9%; Average loss: 3.3365
Iteration: 1357; Percent complete: 33.9%; Average loss: 3.3469
Iteration: 1358; Percent complete: 34.0%; Average loss: 3.4529
Iteration: 1359; Percent complete: 34.0%; Average loss: 3.4070
Iteration: 1360; Percent complete: 34.0%; Average loss: 3.2082
Iteration: 1361; Percent complete: 34.0%; Average loss: 3.6062
Iteration: 1362; Percent complete: 34.1%; Average loss: 3.2452
Iteration: 1363; Percent complete: 34.1%; Average loss: 3.5128
Iteration: 1364; Percent complete: 34.1%; Average loss: 3.3976
Iteration: 1365; Percent complete: 34.1%; Average loss: 3.5504
Iteration: 1366; Percent complete: 34.2%; Average loss: 3.1844
Iteration: 1367; Percent complete: 34.2%; Average loss: 3.1014
Iteration: 1368; Percent complete: 34.2%; Average loss: 3.1121
Iteration: 1369; Percent complete: 34.2%; Average loss: 3.4288
Iteration: 1370; Percent complete: 34.2%; Average loss: 3.2998
Iteration: 1371; Percent complete: 34.3%; Average loss: 3.5346
Iteration: 1372; Percent complete: 34.3%; Average loss: 3.1531
Iteration: 1373; Percent complete: 34.3%; Average loss: 3.3715
Iteration: 1374; Percent complete: 34.4%; Average loss: 3.3251
Iteration: 1375; Percent complete: 34.4%; Average loss: 3.1727
Iteration: 1376; Percent complete: 34.4%; Average loss: 3.3374
Iteration: 1377; Percent complete: 34.4%; Average loss: 3.6246
Iteration: 1378; Percent complete: 34.4%; Average loss: 3.1488
Iteration: 1379; Percent complete: 34.5%; Average loss: 3.2771
Iteration: 1380; Percent complete: 34.5%; Average loss: 3.4404
Iteration: 1381; Percent complete: 34.5%; Average loss: 3.5377
Iteration: 1382; Percent complete: 34.5%; Average loss: 3.1987
Iteration: 1383; Percent complete: 34.6%; Average loss: 3.3381
Iteration: 1384; Percent complete: 34.6%; Average loss: 3.0863
Iteration: 1385; Percent complete: 34.6%; Average loss: 3.1838
Iteration: 1386; Percent complete: 34.6%; Average loss: 3.3097
Iteration: 1387; Percent complete: 34.7%; Average loss: 3.4983
Iteration: 1388; Percent complete: 34.7%; Average loss: 3.2360
Iteration: 1389; Percent complete: 34.7%; Average loss: 3.3601
Iteration: 1390; Percent complete: 34.8%; Average loss: 3.3311
Iteration: 1391; Percent complete: 34.8%; Average loss: 3.3217
Iteration: 1392; Percent complete: 34.8%; Average loss: 3.2498
Iteration: 1393; Percent complete: 34.8%; Average loss: 3.6482
Iteration: 1394; Percent complete: 34.8%; Average loss: 3.2764
Iteration: 1395; Percent complete: 34.9%; Average loss: 3.1801
Iteration: 1396; Percent complete: 34.9%; Average loss: 3.3125
Iteration: 1397; Percent complete: 34.9%; Average loss: 3.1293
Iteration: 1398; Percent complete: 34.9%; Average loss: 3.4192
Iteration: 1399; Percent complete: 35.0%; Average loss: 3.1674
Iteration: 1400; Percent complete: 35.0%; Average loss: 3.3524
Iteration: 1401; Percent complete: 35.0%; Average loss: 3.2289
Iteration: 1402; Percent complete: 35.0%; Average loss: 3.2880
Iteration: 1403; Percent complete: 35.1%; Average loss: 3.6098
Iteration: 1404; Percent complete: 35.1%; Average loss: 3.1480
Iteration: 1405; Percent complete: 35.1%; Average loss: 3.3062
Iteration: 1406; Percent complete: 35.1%; Average loss: 3.6173
Iteration: 1407; Percent complete: 35.2%; Average loss: 3.4162
Iteration: 1408; Percent complete: 35.2%; Average loss: 3.2148
Iteration: 1409; Percent complete: 35.2%; Average loss: 3.0370
Iteration: 1410; Percent complete: 35.2%; Average loss: 3.3598
Iteration: 1411; Percent complete: 35.3%; Average loss: 3.3228
Iteration: 1412; Percent complete: 35.3%; Average loss: 3.4404
Iteration: 1413; Percent complete: 35.3%; Average loss: 3.3387
Iteration: 1414; Percent complete: 35.4%; Average loss: 3.3234
Iteration: 1415; Percent complete: 35.4%; Average loss: 3.5419
Iteration: 1416; Percent complete: 35.4%; Average loss: 3.5134
Iteration: 1417; Percent complete: 35.4%; Average loss: 3.4476
Iteration: 1418; Percent complete: 35.4%; Average loss: 3.3789
Iteration: 1419; Percent complete: 35.5%; Average loss: 3.1535
Iteration: 1420; Percent complete: 35.5%; Average loss: 3.3931
Iteration: 1421; Percent complete: 35.5%; Average loss: 3.2837
Iteration: 1422; Percent complete: 35.5%; Average loss: 3.2819
Iteration: 1423; Percent complete: 35.6%; Average loss: 3.4363
Iteration: 1424; Percent complete: 35.6%; Average loss: 3.3077
Iteration: 1425; Percent complete: 35.6%; Average loss: 3.5211
Iteration: 1426; Percent complete: 35.6%; Average loss: 3.4322
Iteration: 1427; Percent complete: 35.7%; Average loss: 3.5157
Iteration: 1428; Percent complete: 35.7%; Average loss: 3.3830
Iteration: 1429; Percent complete: 35.7%; Average loss: 3.2627
Iteration: 1430; Percent complete: 35.8%; Average loss: 3.3548
Iteration: 1431; Percent complete: 35.8%; Average loss: 3.2914
Iteration: 1432; Percent complete: 35.8%; Average loss: 3.2652
Iteration: 1433; Percent complete: 35.8%; Average loss: 3.3370
Iteration: 1434; Percent complete: 35.9%; Average loss: 3.2470
Iteration: 1435; Percent complete: 35.9%; Average loss: 3.2941
Iteration: 1436; Percent complete: 35.9%; Average loss: 3.4759
Iteration: 1437; Percent complete: 35.9%; Average loss: 3.3512
Iteration: 1438; Percent complete: 35.9%; Average loss: 3.2534
Iteration: 1439; Percent complete: 36.0%; Average loss: 3.0918
Iteration: 1440; Percent complete: 36.0%; Average loss: 3.1422
Iteration: 1441; Percent complete: 36.0%; Average loss: 3.4424
Iteration: 1442; Percent complete: 36.0%; Average loss: 3.1943
Iteration: 1443; Percent complete: 36.1%; Average loss: 3.1223
Iteration: 1444; Percent complete: 36.1%; Average loss: 3.4846
Iteration: 1445; Percent complete: 36.1%; Average loss: 3.2640
Iteration: 1446; Percent complete: 36.1%; Average loss: 3.1548
Iteration: 1447; Percent complete: 36.2%; Average loss: 3.6280
Iteration: 1448; Percent complete: 36.2%; Average loss: 3.3905
Iteration: 1449; Percent complete: 36.2%; Average loss: 3.5281
Iteration: 1450; Percent complete: 36.2%; Average loss: 3.4641
Iteration: 1451; Percent complete: 36.3%; Average loss: 3.1925
Iteration: 1452; Percent complete: 36.3%; Average loss: 3.1958
Iteration: 1453; Percent complete: 36.3%; Average loss: 3.4768
Iteration: 1454; Percent complete: 36.4%; Average loss: 3.4186
Iteration: 1455; Percent complete: 36.4%; Average loss: 3.1326
Iteration: 1456; Percent complete: 36.4%; Average loss: 3.2408
Iteration: 1457; Percent complete: 36.4%; Average loss: 3.2774
Iteration: 1458; Percent complete: 36.4%; Average loss: 3.1801
Iteration: 1459; Percent complete: 36.5%; Average loss: 3.0974
Iteration: 1460; Percent complete: 36.5%; Average loss: 3.3340
Iteration: 1461; Percent complete: 36.5%; Average loss: 3.0310
Iteration: 1462; Percent complete: 36.5%; Average loss: 3.3993
Iteration: 1463; Percent complete: 36.6%; Average loss: 3.3435
Iteration: 1464; Percent complete: 36.6%; Average loss: 3.4853
Iteration: 1465; Percent complete: 36.6%; Average loss: 3.2279
Iteration: 1466; Percent complete: 36.6%; Average loss: 3.0320
Iteration: 1467; Percent complete: 36.7%; Average loss: 3.4078
Iteration: 1468; Percent complete: 36.7%; Average loss: 3.4742
Iteration: 1469; Percent complete: 36.7%; Average loss: 3.1290
Iteration: 1470; Percent complete: 36.8%; Average loss: 3.2764
Iteration: 1471; Percent complete: 36.8%; Average loss: 3.1581
Iteration: 1472; Percent complete: 36.8%; Average loss: 3.5213
Iteration: 1473; Percent complete: 36.8%; Average loss: 3.5796
Iteration: 1474; Percent complete: 36.9%; Average loss: 3.3803
Iteration: 1475; Percent complete: 36.9%; Average loss: 3.3901
Iteration: 1476; Percent complete: 36.9%; Average loss: 3.5393
Iteration: 1477; Percent complete: 36.9%; Average loss: 3.0053
Iteration: 1478; Percent complete: 37.0%; Average loss: 3.3070
Iteration: 1479; Percent complete: 37.0%; Average loss: 3.3752
Iteration: 1480; Percent complete: 37.0%; Average loss: 3.2411
Iteration: 1481; Percent complete: 37.0%; Average loss: 3.4195
Iteration: 1482; Percent complete: 37.0%; Average loss: 3.1379
Iteration: 1483; Percent complete: 37.1%; Average loss: 3.3801
Iteration: 1484; Percent complete: 37.1%; Average loss: 3.3214
Iteration: 1485; Percent complete: 37.1%; Average loss: 3.2734
Iteration: 1486; Percent complete: 37.1%; Average loss: 3.3772
Iteration: 1487; Percent complete: 37.2%; Average loss: 3.1363
Iteration: 1488; Percent complete: 37.2%; Average loss: 3.3342
Iteration: 1489; Percent complete: 37.2%; Average loss: 3.3760
Iteration: 1490; Percent complete: 37.2%; Average loss: 3.1774
Iteration: 1491; Percent complete: 37.3%; Average loss: 3.1499
Iteration: 1492; Percent complete: 37.3%; Average loss: 3.4535
Iteration: 1493; Percent complete: 37.3%; Average loss: 3.4060
Iteration: 1494; Percent complete: 37.4%; Average loss: 3.1564
Iteration: 1495; Percent complete: 37.4%; Average loss: 3.3251
Iteration: 1496; Percent complete: 37.4%; Average loss: 3.1622
Iteration: 1497; Percent complete: 37.4%; Average loss: 3.1604
Iteration: 1498; Percent complete: 37.5%; Average loss: 3.4184
Iteration: 1499; Percent complete: 37.5%; Average loss: 3.1332
Iteration: 1500; Percent complete: 37.5%; Average loss: 3.3614
Iteration: 1501; Percent complete: 37.5%; Average loss: 3.4618
Iteration: 1502; Percent complete: 37.5%; Average loss: 3.2000
Iteration: 1503; Percent complete: 37.6%; Average loss: 3.0464
Iteration: 1504; Percent complete: 37.6%; Average loss: 3.3029
Iteration: 1505; Percent complete: 37.6%; Average loss: 3.1847
Iteration: 1506; Percent complete: 37.6%; Average loss: 3.1377
Iteration: 1507; Percent complete: 37.7%; Average loss: 3.4105
Iteration: 1508; Percent complete: 37.7%; Average loss: 3.2233
Iteration: 1509; Percent complete: 37.7%; Average loss: 3.4861
Iteration: 1510; Percent complete: 37.8%; Average loss: 3.2687
Iteration: 1511; Percent complete: 37.8%; Average loss: 3.2093
Iteration: 1512; Percent complete: 37.8%; Average loss: 3.1496
Iteration: 1513; Percent complete: 37.8%; Average loss: 3.4150
Iteration: 1514; Percent complete: 37.9%; Average loss: 3.4511
Iteration: 1515; Percent complete: 37.9%; Average loss: 3.3146
Iteration: 1516; Percent complete: 37.9%; Average loss: 3.3223
Iteration: 1517; Percent complete: 37.9%; Average loss: 3.2308
Iteration: 1518; Percent complete: 38.0%; Average loss: 3.4881
Iteration: 1519; Percent complete: 38.0%; Average loss: 3.4908
Iteration: 1520; Percent complete: 38.0%; Average loss: 3.2441
Iteration: 1521; Percent complete: 38.0%; Average loss: 3.2092
Iteration: 1522; Percent complete: 38.0%; Average loss: 3.2122
Iteration: 1523; Percent complete: 38.1%; Average loss: 3.2293
Iteration: 1524; Percent complete: 38.1%; Average loss: 3.3526
Iteration: 1525; Percent complete: 38.1%; Average loss: 3.4152
Iteration: 1526; Percent complete: 38.1%; Average loss: 3.3717
Iteration: 1527; Percent complete: 38.2%; Average loss: 3.3750
Iteration: 1528; Percent complete: 38.2%; Average loss: 3.2400
Iteration: 1529; Percent complete: 38.2%; Average loss: 3.5633
Iteration: 1530; Percent complete: 38.2%; Average loss: 3.2741
Iteration: 1531; Percent complete: 38.3%; Average loss: 3.5545
Iteration: 1532; Percent complete: 38.3%; Average loss: 3.3292
Iteration: 1533; Percent complete: 38.3%; Average loss: 3.2625
Iteration: 1534; Percent complete: 38.4%; Average loss: 3.1406
Iteration: 1535; Percent complete: 38.4%; Average loss: 3.1892
Iteration: 1536; Percent complete: 38.4%; Average loss: 3.5774
Iteration: 1537; Percent complete: 38.4%; Average loss: 3.1479
Iteration: 1538; Percent complete: 38.5%; Average loss: 3.4136
Iteration: 1539; Percent complete: 38.5%; Average loss: 3.5285
Iteration: 1540; Percent complete: 38.5%; Average loss: 3.2993
Iteration: 1541; Percent complete: 38.5%; Average loss: 3.0526
Iteration: 1542; Percent complete: 38.6%; Average loss: 3.1295
Iteration: 1543; Percent complete: 38.6%; Average loss: 3.4066
Iteration: 1544; Percent complete: 38.6%; Average loss: 3.2655
Iteration: 1545; Percent complete: 38.6%; Average loss: 3.5714
Iteration: 1546; Percent complete: 38.6%; Average loss: 3.2140
Iteration: 1547; Percent complete: 38.7%; Average loss: 3.1734
Iteration: 1548; Percent complete: 38.7%; Average loss: 3.3115
Iteration: 1549; Percent complete: 38.7%; Average loss: 3.4249
Iteration: 1550; Percent complete: 38.8%; Average loss: 3.0799
Iteration: 1551; Percent complete: 38.8%; Average loss: 3.2590
Iteration: 1552; Percent complete: 38.8%; Average loss: 3.2460
Iteration: 1553; Percent complete: 38.8%; Average loss: 3.3641
Iteration: 1554; Percent complete: 38.9%; Average loss: 3.2367
Iteration: 1555; Percent complete: 38.9%; Average loss: 3.1907
Iteration: 1556; Percent complete: 38.9%; Average loss: 3.2595
Iteration: 1557; Percent complete: 38.9%; Average loss: 3.3070
Iteration: 1558; Percent complete: 39.0%; Average loss: 3.3947
Iteration: 1559; Percent complete: 39.0%; Average loss: 3.3875
Iteration: 1560; Percent complete: 39.0%; Average loss: 3.3586
Iteration: 1561; Percent complete: 39.0%; Average loss: 3.3489
Iteration: 1562; Percent complete: 39.1%; Average loss: 3.2362
Iteration: 1563; Percent complete: 39.1%; Average loss: 3.3614
Iteration: 1564; Percent complete: 39.1%; Average loss: 3.0895
Iteration: 1565; Percent complete: 39.1%; Average loss: 3.5528
Iteration: 1566; Percent complete: 39.1%; Average loss: 3.3138
Iteration: 1567; Percent complete: 39.2%; Average loss: 3.1968
Iteration: 1568; Percent complete: 39.2%; Average loss: 3.2305
Iteration: 1569; Percent complete: 39.2%; Average loss: 3.3790
Iteration: 1570; Percent complete: 39.2%; Average loss: 3.4557
Iteration: 1571; Percent complete: 39.3%; Average loss: 3.2613
Iteration: 1572; Percent complete: 39.3%; Average loss: 3.0733
Iteration: 1573; Percent complete: 39.3%; Average loss: 3.1630
Iteration: 1574; Percent complete: 39.4%; Average loss: 3.0674
Iteration: 1575; Percent complete: 39.4%; Average loss: 3.5901
Iteration: 1576; Percent complete: 39.4%; Average loss: 3.1868
Iteration: 1577; Percent complete: 39.4%; Average loss: 3.0544
Iteration: 1578; Percent complete: 39.5%; Average loss: 3.1950
Iteration: 1579; Percent complete: 39.5%; Average loss: 3.4525
Iteration: 1580; Percent complete: 39.5%; Average loss: 3.4219
Iteration: 1581; Percent complete: 39.5%; Average loss: 2.8932
Iteration: 1582; Percent complete: 39.6%; Average loss: 3.3934
Iteration: 1583; Percent complete: 39.6%; Average loss: 3.2018
Iteration: 1584; Percent complete: 39.6%; Average loss: 2.9949
Iteration: 1585; Percent complete: 39.6%; Average loss: 3.3270
Iteration: 1586; Percent complete: 39.6%; Average loss: 3.4183
Iteration: 1587; Percent complete: 39.7%; Average loss: 3.2588
Iteration: 1588; Percent complete: 39.7%; Average loss: 3.6624
Iteration: 1589; Percent complete: 39.7%; Average loss: 3.3002
Iteration: 1590; Percent complete: 39.8%; Average loss: 3.4348
Iteration: 1591; Percent complete: 39.8%; Average loss: 3.2343
Iteration: 1592; Percent complete: 39.8%; Average loss: 3.1815
Iteration: 1593; Percent complete: 39.8%; Average loss: 3.4366
Iteration: 1594; Percent complete: 39.9%; Average loss: 3.0372
Iteration: 1595; Percent complete: 39.9%; Average loss: 3.2364
Iteration: 1596; Percent complete: 39.9%; Average loss: 3.3116
Iteration: 1597; Percent complete: 39.9%; Average loss: 3.3275
Iteration: 1598; Percent complete: 40.0%; Average loss: 3.5495
Iteration: 1599; Percent complete: 40.0%; Average loss: 3.1202
Iteration: 1600; Percent complete: 40.0%; Average loss: 3.4759
Iteration: 1601; Percent complete: 40.0%; Average loss: 3.1513
Iteration: 1602; Percent complete: 40.1%; Average loss: 3.1936
Iteration: 1603; Percent complete: 40.1%; Average loss: 3.1712
Iteration: 1604; Percent complete: 40.1%; Average loss: 3.1328
Iteration: 1605; Percent complete: 40.1%; Average loss: 3.4303
Iteration: 1606; Percent complete: 40.2%; Average loss: 3.1910
Iteration: 1607; Percent complete: 40.2%; Average loss: 3.3106
Iteration: 1608; Percent complete: 40.2%; Average loss: 3.3821
Iteration: 1609; Percent complete: 40.2%; Average loss: 3.4753
Iteration: 1610; Percent complete: 40.2%; Average loss: 3.1239
Iteration: 1611; Percent complete: 40.3%; Average loss: 3.3336
Iteration: 1612; Percent complete: 40.3%; Average loss: 3.3510
Iteration: 1613; Percent complete: 40.3%; Average loss: 3.3663
Iteration: 1614; Percent complete: 40.4%; Average loss: 3.3505
Iteration: 1615; Percent complete: 40.4%; Average loss: 3.1140
Iteration: 1616; Percent complete: 40.4%; Average loss: 3.3521
Iteration: 1617; Percent complete: 40.4%; Average loss: 3.3489
Iteration: 1618; Percent complete: 40.5%; Average loss: 3.2163
Iteration: 1619; Percent complete: 40.5%; Average loss: 3.5185
Iteration: 1620; Percent complete: 40.5%; Average loss: 3.2696
Iteration: 1621; Percent complete: 40.5%; Average loss: 3.2874
Iteration: 1622; Percent complete: 40.6%; Average loss: 3.2782
Iteration: 1623; Percent complete: 40.6%; Average loss: 3.1104
Iteration: 1624; Percent complete: 40.6%; Average loss: 2.9973
Iteration: 1625; Percent complete: 40.6%; Average loss: 3.4892
Iteration: 1626; Percent complete: 40.6%; Average loss: 3.1261
Iteration: 1627; Percent complete: 40.7%; Average loss: 3.1890
Iteration: 1628; Percent complete: 40.7%; Average loss: 3.4112
Iteration: 1629; Percent complete: 40.7%; Average loss: 3.0066
Iteration: 1630; Percent complete: 40.8%; Average loss: 3.0742
Iteration: 1631; Percent complete: 40.8%; Average loss: 3.1593
Iteration: 1632; Percent complete: 40.8%; Average loss: 3.4729
Iteration: 1633; Percent complete: 40.8%; Average loss: 3.2127
Iteration: 1634; Percent complete: 40.8%; Average loss: 3.3625
Iteration: 1635; Percent complete: 40.9%; Average loss: 3.1243
Iteration: 1636; Percent complete: 40.9%; Average loss: 3.3681
Iteration: 1637; Percent complete: 40.9%; Average loss: 3.2066
Iteration: 1638; Percent complete: 40.9%; Average loss: 3.4520
Iteration: 1639; Percent complete: 41.0%; Average loss: 3.2573
Iteration: 1640; Percent complete: 41.0%; Average loss: 3.2147
Iteration: 1641; Percent complete: 41.0%; Average loss: 3.3018
Iteration: 1642; Percent complete: 41.0%; Average loss: 3.5712
Iteration: 1643; Percent complete: 41.1%; Average loss: 3.2865
Iteration: 1644; Percent complete: 41.1%; Average loss: 3.1525
Iteration: 1645; Percent complete: 41.1%; Average loss: 3.1137
Iteration: 1646; Percent complete: 41.1%; Average loss: 3.0443
Iteration: 1647; Percent complete: 41.2%; Average loss: 3.3592
Iteration: 1648; Percent complete: 41.2%; Average loss: 3.1437
Iteration: 1649; Percent complete: 41.2%; Average loss: 3.2468
Iteration: 1650; Percent complete: 41.2%; Average loss: 3.2950
Iteration: 1651; Percent complete: 41.3%; Average loss: 3.3519
Iteration: 1652; Percent complete: 41.3%; Average loss: 3.0891
Iteration: 1653; Percent complete: 41.3%; Average loss: 3.1324
Iteration: 1654; Percent complete: 41.3%; Average loss: 3.2678
Iteration: 1655; Percent complete: 41.4%; Average loss: 3.0931
Iteration: 1656; Percent complete: 41.4%; Average loss: 3.2085
Iteration: 1657; Percent complete: 41.4%; Average loss: 3.2125
Iteration: 1658; Percent complete: 41.4%; Average loss: 3.2569
Iteration: 1659; Percent complete: 41.5%; Average loss: 3.2823
Iteration: 1660; Percent complete: 41.5%; Average loss: 3.1748
Iteration: 1661; Percent complete: 41.5%; Average loss: 3.3248
Iteration: 1662; Percent complete: 41.5%; Average loss: 3.0548
Iteration: 1663; Percent complete: 41.6%; Average loss: 3.2991
Iteration: 1664; Percent complete: 41.6%; Average loss: 3.3576
Iteration: 1665; Percent complete: 41.6%; Average loss: 3.3247
Iteration: 1666; Percent complete: 41.6%; Average loss: 3.2293
Iteration: 1667; Percent complete: 41.7%; Average loss: 3.2070
Iteration: 1668; Percent complete: 41.7%; Average loss: 3.1033
Iteration: 1669; Percent complete: 41.7%; Average loss: 3.2839
Iteration: 1670; Percent complete: 41.8%; Average loss: 3.3398
Iteration: 1671; Percent complete: 41.8%; Average loss: 3.3653
Iteration: 1672; Percent complete: 41.8%; Average loss: 3.0685
Iteration: 1673; Percent complete: 41.8%; Average loss: 3.1507
Iteration: 1674; Percent complete: 41.9%; Average loss: 3.1327
Iteration: 1675; Percent complete: 41.9%; Average loss: 3.3596
Iteration: 1676; Percent complete: 41.9%; Average loss: 3.2734
Iteration: 1677; Percent complete: 41.9%; Average loss: 3.1678
Iteration: 1678; Percent complete: 41.9%; Average loss: 3.2181
Iteration: 1679; Percent complete: 42.0%; Average loss: 3.1603
Iteration: 1680; Percent complete: 42.0%; Average loss: 3.1588
Iteration: 1681; Percent complete: 42.0%; Average loss: 3.3545
Iteration: 1682; Percent complete: 42.0%; Average loss: 3.2299
Iteration: 1683; Percent complete: 42.1%; Average loss: 3.2262
Iteration: 1684; Percent complete: 42.1%; Average loss: 3.1723
Iteration: 1685; Percent complete: 42.1%; Average loss: 3.4892
Iteration: 1686; Percent complete: 42.1%; Average loss: 3.1076
Iteration: 1687; Percent complete: 42.2%; Average loss: 3.1639
Iteration: 1688; Percent complete: 42.2%; Average loss: 3.3337
Iteration: 1689; Percent complete: 42.2%; Average loss: 3.1632
Iteration: 1690; Percent complete: 42.2%; Average loss: 2.9114
Iteration: 1691; Percent complete: 42.3%; Average loss: 3.2783
Iteration: 1692; Percent complete: 42.3%; Average loss: 3.1564
Iteration: 1693; Percent complete: 42.3%; Average loss: 3.4554
Iteration: 1694; Percent complete: 42.4%; Average loss: 3.1882
Iteration: 1695; Percent complete: 42.4%; Average loss: 3.2046
Iteration: 1696; Percent complete: 42.4%; Average loss: 3.3639
Iteration: 1697; Percent complete: 42.4%; Average loss: 3.1662
Iteration: 1698; Percent complete: 42.4%; Average loss: 3.5438
Iteration: 1699; Percent complete: 42.5%; Average loss: 3.1406
Iteration: 1700; Percent complete: 42.5%; Average loss: 3.0701
Iteration: 1701; Percent complete: 42.5%; Average loss: 2.9544
Iteration: 1702; Percent complete: 42.5%; Average loss: 3.2207
Iteration: 1703; Percent complete: 42.6%; Average loss: 3.2627
Iteration: 1704; Percent complete: 42.6%; Average loss: 3.4479
Iteration: 1705; Percent complete: 42.6%; Average loss: 3.2635
Iteration: 1706; Percent complete: 42.6%; Average loss: 3.3328
Iteration: 1707; Percent complete: 42.7%; Average loss: 3.2124
Iteration: 1708; Percent complete: 42.7%; Average loss: 3.1241
Iteration: 1709; Percent complete: 42.7%; Average loss: 3.1724
Iteration: 1710; Percent complete: 42.8%; Average loss: 3.1446
Iteration: 1711; Percent complete: 42.8%; Average loss: 3.1352
Iteration: 1712; Percent complete: 42.8%; Average loss: 3.4151
Iteration: 1713; Percent complete: 42.8%; Average loss: 3.1564
Iteration: 1714; Percent complete: 42.9%; Average loss: 3.2228
Iteration: 1715; Percent complete: 42.9%; Average loss: 3.0369
Iteration: 1716; Percent complete: 42.9%; Average loss: 3.2066
Iteration: 1717; Percent complete: 42.9%; Average loss: 3.2868
Iteration: 1718; Percent complete: 43.0%; Average loss: 3.2359
Iteration: 1719; Percent complete: 43.0%; Average loss: 3.2155
Iteration: 1720; Percent complete: 43.0%; Average loss: 3.1257
Iteration: 1721; Percent complete: 43.0%; Average loss: 2.8635
Iteration: 1722; Percent complete: 43.0%; Average loss: 3.0811
Iteration: 1723; Percent complete: 43.1%; Average loss: 3.0279
Iteration: 1724; Percent complete: 43.1%; Average loss: 3.4243
Iteration: 1725; Percent complete: 43.1%; Average loss: 3.0247
Iteration: 1726; Percent complete: 43.1%; Average loss: 3.3080
Iteration: 1727; Percent complete: 43.2%; Average loss: 3.1090
Iteration: 1728; Percent complete: 43.2%; Average loss: 3.2918
Iteration: 1729; Percent complete: 43.2%; Average loss: 3.3564
Iteration: 1730; Percent complete: 43.2%; Average loss: 3.0412
Iteration: 1731; Percent complete: 43.3%; Average loss: 3.0345
Iteration: 1732; Percent complete: 43.3%; Average loss: 3.1543
Iteration: 1733; Percent complete: 43.3%; Average loss: 3.2196
Iteration: 1734; Percent complete: 43.4%; Average loss: 3.3736
Iteration: 1735; Percent complete: 43.4%; Average loss: 3.2761
Iteration: 1736; Percent complete: 43.4%; Average loss: 3.1488
Iteration: 1737; Percent complete: 43.4%; Average loss: 3.4228
Iteration: 1738; Percent complete: 43.5%; Average loss: 3.0039
Iteration: 1739; Percent complete: 43.5%; Average loss: 3.1610
Iteration: 1740; Percent complete: 43.5%; Average loss: 3.2472
Iteration: 1741; Percent complete: 43.5%; Average loss: 3.4047
Iteration: 1742; Percent complete: 43.5%; Average loss: 3.3848
Iteration: 1743; Percent complete: 43.6%; Average loss: 3.5208
Iteration: 1744; Percent complete: 43.6%; Average loss: 3.4942
Iteration: 1745; Percent complete: 43.6%; Average loss: 3.4376
Iteration: 1746; Percent complete: 43.6%; Average loss: 3.2502
Iteration: 1747; Percent complete: 43.7%; Average loss: 3.2615
Iteration: 1748; Percent complete: 43.7%; Average loss: 3.3243
Iteration: 1749; Percent complete: 43.7%; Average loss: 3.2072
Iteration: 1750; Percent complete: 43.8%; Average loss: 3.4496
Iteration: 1751; Percent complete: 43.8%; Average loss: 3.3187
Iteration: 1752; Percent complete: 43.8%; Average loss: 2.9315
Iteration: 1753; Percent complete: 43.8%; Average loss: 3.2130
Iteration: 1754; Percent complete: 43.9%; Average loss: 3.1077
Iteration: 1755; Percent complete: 43.9%; Average loss: 3.3208
Iteration: 1756; Percent complete: 43.9%; Average loss: 3.3526
Iteration: 1757; Percent complete: 43.9%; Average loss: 3.2057
Iteration: 1758; Percent complete: 44.0%; Average loss: 3.4340
Iteration: 1759; Percent complete: 44.0%; Average loss: 3.3638
Iteration: 1760; Percent complete: 44.0%; Average loss: 3.2330
Iteration: 1761; Percent complete: 44.0%; Average loss: 3.1710
Iteration: 1762; Percent complete: 44.0%; Average loss: 2.8293
Iteration: 1763; Percent complete: 44.1%; Average loss: 3.1570
Iteration: 1764; Percent complete: 44.1%; Average loss: 3.4112
Iteration: 1765; Percent complete: 44.1%; Average loss: 3.4190
Iteration: 1766; Percent complete: 44.1%; Average loss: 3.3056
Iteration: 1767; Percent complete: 44.2%; Average loss: 3.4745
Iteration: 1768; Percent complete: 44.2%; Average loss: 3.2031
Iteration: 1769; Percent complete: 44.2%; Average loss: 3.2184
Iteration: 1770; Percent complete: 44.2%; Average loss: 3.3363
Iteration: 1771; Percent complete: 44.3%; Average loss: 3.1877
Iteration: 1772; Percent complete: 44.3%; Average loss: 3.2321
Iteration: 1773; Percent complete: 44.3%; Average loss: 3.0663
Iteration: 1774; Percent complete: 44.4%; Average loss: 3.2288
Iteration: 1775; Percent complete: 44.4%; Average loss: 3.4850
Iteration: 1776; Percent complete: 44.4%; Average loss: 3.1235
Iteration: 1777; Percent complete: 44.4%; Average loss: 3.1958
Iteration: 1778; Percent complete: 44.5%; Average loss: 3.2291
Iteration: 1779; Percent complete: 44.5%; Average loss: 3.1819
Iteration: 1780; Percent complete: 44.5%; Average loss: 3.1708
Iteration: 1781; Percent complete: 44.5%; Average loss: 3.2588
Iteration: 1782; Percent complete: 44.5%; Average loss: 2.9484
Iteration: 1783; Percent complete: 44.6%; Average loss: 2.9172
Iteration: 1784; Percent complete: 44.6%; Average loss: 3.2290
Iteration: 1785; Percent complete: 44.6%; Average loss: 3.2047
Iteration: 1786; Percent complete: 44.6%; Average loss: 3.1745
Iteration: 1787; Percent complete: 44.7%; Average loss: 3.2466
Iteration: 1788; Percent complete: 44.7%; Average loss: 3.0289
Iteration: 1789; Percent complete: 44.7%; Average loss: 3.3698
Iteration: 1790; Percent complete: 44.8%; Average loss: 3.2132
Iteration: 1791; Percent complete: 44.8%; Average loss: 2.9457
Iteration: 1792; Percent complete: 44.8%; Average loss: 3.0607
Iteration: 1793; Percent complete: 44.8%; Average loss: 3.2943
Iteration: 1794; Percent complete: 44.9%; Average loss: 3.4496
Iteration: 1795; Percent complete: 44.9%; Average loss: 3.1458
Iteration: 1796; Percent complete: 44.9%; Average loss: 3.2395
Iteration: 1797; Percent complete: 44.9%; Average loss: 2.9533
Iteration: 1798; Percent complete: 45.0%; Average loss: 2.9923
Iteration: 1799; Percent complete: 45.0%; Average loss: 3.4398
Iteration: 1800; Percent complete: 45.0%; Average loss: 3.2898
Iteration: 1801; Percent complete: 45.0%; Average loss: 3.0851
Iteration: 1802; Percent complete: 45.1%; Average loss: 3.2055
Iteration: 1803; Percent complete: 45.1%; Average loss: 3.0212
Iteration: 1804; Percent complete: 45.1%; Average loss: 2.8935
Iteration: 1805; Percent complete: 45.1%; Average loss: 3.0435
Iteration: 1806; Percent complete: 45.1%; Average loss: 3.2955
Iteration: 1807; Percent complete: 45.2%; Average loss: 3.2853
Iteration: 1808; Percent complete: 45.2%; Average loss: 3.2867
Iteration: 1809; Percent complete: 45.2%; Average loss: 3.4450
Iteration: 1810; Percent complete: 45.2%; Average loss: 3.1815
Iteration: 1811; Percent complete: 45.3%; Average loss: 3.0391
Iteration: 1812; Percent complete: 45.3%; Average loss: 3.2866
Iteration: 1813; Percent complete: 45.3%; Average loss: 2.9299
Iteration: 1814; Percent complete: 45.4%; Average loss: 3.2719
Iteration: 1815; Percent complete: 45.4%; Average loss: 3.5211
Iteration: 1816; Percent complete: 45.4%; Average loss: 3.1031
Iteration: 1817; Percent complete: 45.4%; Average loss: 3.2879
Iteration: 1818; Percent complete: 45.5%; Average loss: 3.2505
Iteration: 1819; Percent complete: 45.5%; Average loss: 3.2096
Iteration: 1820; Percent complete: 45.5%; Average loss: 3.1196
Iteration: 1821; Percent complete: 45.5%; Average loss: 3.4109
Iteration: 1822; Percent complete: 45.6%; Average loss: 3.2322
Iteration: 1823; Percent complete: 45.6%; Average loss: 3.1173
Iteration: 1824; Percent complete: 45.6%; Average loss: 3.2982
Iteration: 1825; Percent complete: 45.6%; Average loss: 3.3687
Iteration: 1826; Percent complete: 45.6%; Average loss: 3.3586
Iteration: 1827; Percent complete: 45.7%; Average loss: 3.2695
Iteration: 1828; Percent complete: 45.7%; Average loss: 3.0837
Iteration: 1829; Percent complete: 45.7%; Average loss: 2.8314
Iteration: 1830; Percent complete: 45.8%; Average loss: 3.3055
Iteration: 1831; Percent complete: 45.8%; Average loss: 3.2041
Iteration: 1832; Percent complete: 45.8%; Average loss: 3.4231
Iteration: 1833; Percent complete: 45.8%; Average loss: 3.3695
Iteration: 1834; Percent complete: 45.9%; Average loss: 3.4898
Iteration: 1835; Percent complete: 45.9%; Average loss: 3.3277
Iteration: 1836; Percent complete: 45.9%; Average loss: 3.2013
Iteration: 1837; Percent complete: 45.9%; Average loss: 3.2657
Iteration: 1838; Percent complete: 46.0%; Average loss: 3.1701
Iteration: 1839; Percent complete: 46.0%; Average loss: 3.0539
Iteration: 1840; Percent complete: 46.0%; Average loss: 3.2720
Iteration: 1841; Percent complete: 46.0%; Average loss: 3.1000
Iteration: 1842; Percent complete: 46.1%; Average loss: 3.1129
Iteration: 1843; Percent complete: 46.1%; Average loss: 3.2834
Iteration: 1844; Percent complete: 46.1%; Average loss: 3.3791
Iteration: 1845; Percent complete: 46.1%; Average loss: 3.2676
Iteration: 1846; Percent complete: 46.2%; Average loss: 3.0101
Iteration: 1847; Percent complete: 46.2%; Average loss: 3.4314
Iteration: 1848; Percent complete: 46.2%; Average loss: 3.0316
Iteration: 1849; Percent complete: 46.2%; Average loss: 3.2280
Iteration: 1850; Percent complete: 46.2%; Average loss: 3.2174
Iteration: 1851; Percent complete: 46.3%; Average loss: 3.2406
Iteration: 1852; Percent complete: 46.3%; Average loss: 3.2473
Iteration: 1853; Percent complete: 46.3%; Average loss: 2.9555
Iteration: 1854; Percent complete: 46.4%; Average loss: 3.1006
Iteration: 1855; Percent complete: 46.4%; Average loss: 3.3014
Iteration: 1856; Percent complete: 46.4%; Average loss: 3.1607
Iteration: 1857; Percent complete: 46.4%; Average loss: 3.2646
Iteration: 1858; Percent complete: 46.5%; Average loss: 3.3465
Iteration: 1859; Percent complete: 46.5%; Average loss: 3.4704
Iteration: 1860; Percent complete: 46.5%; Average loss: 3.2318
Iteration: 1861; Percent complete: 46.5%; Average loss: 3.0174
Iteration: 1862; Percent complete: 46.6%; Average loss: 3.1948
Iteration: 1863; Percent complete: 46.6%; Average loss: 3.2264
Iteration: 1864; Percent complete: 46.6%; Average loss: 3.2419
Iteration: 1865; Percent complete: 46.6%; Average loss: 2.9243
Iteration: 1866; Percent complete: 46.7%; Average loss: 3.1581
Iteration: 1867; Percent complete: 46.7%; Average loss: 3.0562
Iteration: 1868; Percent complete: 46.7%; Average loss: 3.2365
Iteration: 1869; Percent complete: 46.7%; Average loss: 3.0872
Iteration: 1870; Percent complete: 46.8%; Average loss: 3.2305
Iteration: 1871; Percent complete: 46.8%; Average loss: 3.2479
Iteration: 1872; Percent complete: 46.8%; Average loss: 3.2006
Iteration: 1873; Percent complete: 46.8%; Average loss: 3.0240
Iteration: 1874; Percent complete: 46.9%; Average loss: 2.8595
Iteration: 1875; Percent complete: 46.9%; Average loss: 2.9139
Iteration: 1876; Percent complete: 46.9%; Average loss: 3.3916
Iteration: 1877; Percent complete: 46.9%; Average loss: 3.4556
Iteration: 1878; Percent complete: 46.9%; Average loss: 3.1362
Iteration: 1879; Percent complete: 47.0%; Average loss: 3.1423
Iteration: 1880; Percent complete: 47.0%; Average loss: 3.1587
Iteration: 1881; Percent complete: 47.0%; Average loss: 3.2888
Iteration: 1882; Percent complete: 47.0%; Average loss: 3.0256
Iteration: 1883; Percent complete: 47.1%; Average loss: 3.0682
Iteration: 1884; Percent complete: 47.1%; Average loss: 3.0158
Iteration: 1885; Percent complete: 47.1%; Average loss: 3.1362
Iteration: 1886; Percent complete: 47.1%; Average loss: 3.0691
Iteration: 1887; Percent complete: 47.2%; Average loss: 2.9133
Iteration: 1888; Percent complete: 47.2%; Average loss: 3.0142
Iteration: 1889; Percent complete: 47.2%; Average loss: 3.2717
Iteration: 1890; Percent complete: 47.2%; Average loss: 3.4621
Iteration: 1891; Percent complete: 47.3%; Average loss: 3.4521
Iteration: 1892; Percent complete: 47.3%; Average loss: 3.0634
Iteration: 1893; Percent complete: 47.3%; Average loss: 3.3619
Iteration: 1894; Percent complete: 47.3%; Average loss: 3.3584
Iteration: 1895; Percent complete: 47.4%; Average loss: 3.2222
Iteration: 1896; Percent complete: 47.4%; Average loss: 3.3381
Iteration: 1897; Percent complete: 47.4%; Average loss: 3.4746
Iteration: 1898; Percent complete: 47.4%; Average loss: 2.8931
Iteration: 1899; Percent complete: 47.5%; Average loss: 3.1736
Iteration: 1900; Percent complete: 47.5%; Average loss: 3.2633
Iteration: 1901; Percent complete: 47.5%; Average loss: 3.1305
Iteration: 1902; Percent complete: 47.5%; Average loss: 3.0664
Iteration: 1903; Percent complete: 47.6%; Average loss: 3.4965
Iteration: 1904; Percent complete: 47.6%; Average loss: 3.1645
Iteration: 1905; Percent complete: 47.6%; Average loss: 2.9056
Iteration: 1906; Percent complete: 47.6%; Average loss: 3.1983
Iteration: 1907; Percent complete: 47.7%; Average loss: 3.1205
Iteration: 1908; Percent complete: 47.7%; Average loss: 3.4052
Iteration: 1909; Percent complete: 47.7%; Average loss: 3.2131
Iteration: 1910; Percent complete: 47.8%; Average loss: 3.1548
Iteration: 1911; Percent complete: 47.8%; Average loss: 2.8645
Iteration: 1912; Percent complete: 47.8%; Average loss: 3.2906
Iteration: 1913; Percent complete: 47.8%; Average loss: 3.0970
Iteration: 1914; Percent complete: 47.9%; Average loss: 3.1459
Iteration: 1915; Percent complete: 47.9%; Average loss: 3.4884
Iteration: 1916; Percent complete: 47.9%; Average loss: 3.3238
Iteration: 1917; Percent complete: 47.9%; Average loss: 3.3817
Iteration: 1918; Percent complete: 47.9%; Average loss: 3.2999
Iteration: 1919; Percent complete: 48.0%; Average loss: 3.1047
Iteration: 1920; Percent complete: 48.0%; Average loss: 3.1369
Iteration: 1921; Percent complete: 48.0%; Average loss: 3.4650
Iteration: 1922; Percent complete: 48.0%; Average loss: 3.3304
Iteration: 1923; Percent complete: 48.1%; Average loss: 3.2140
Iteration: 1924; Percent complete: 48.1%; Average loss: 2.9475
Iteration: 1925; Percent complete: 48.1%; Average loss: 3.1567
Iteration: 1926; Percent complete: 48.1%; Average loss: 3.3046
Iteration: 1927; Percent complete: 48.2%; Average loss: 3.0341
Iteration: 1928; Percent complete: 48.2%; Average loss: 3.2080
Iteration: 1929; Percent complete: 48.2%; Average loss: 3.0441
Iteration: 1930; Percent complete: 48.2%; Average loss: 3.3464
Iteration: 1931; Percent complete: 48.3%; Average loss: 3.0008
Iteration: 1932; Percent complete: 48.3%; Average loss: 3.3250
Iteration: 1933; Percent complete: 48.3%; Average loss: 3.3087
Iteration: 1934; Percent complete: 48.4%; Average loss: 3.3408
Iteration: 1935; Percent complete: 48.4%; Average loss: 3.2571
Iteration: 1936; Percent complete: 48.4%; Average loss: 3.2714
Iteration: 1937; Percent complete: 48.4%; Average loss: 3.1883
Iteration: 1938; Percent complete: 48.4%; Average loss: 3.1007
Iteration: 1939; Percent complete: 48.5%; Average loss: 2.9746
Iteration: 1940; Percent complete: 48.5%; Average loss: 3.0965
Iteration: 1941; Percent complete: 48.5%; Average loss: 3.1623
Iteration: 1942; Percent complete: 48.5%; Average loss: 2.9710
Iteration: 1943; Percent complete: 48.6%; Average loss: 3.1372
Iteration: 1944; Percent complete: 48.6%; Average loss: 3.0437
Iteration: 1945; Percent complete: 48.6%; Average loss: 3.1834
Iteration: 1946; Percent complete: 48.6%; Average loss: 3.2853
Iteration: 1947; Percent complete: 48.7%; Average loss: 3.3917
Iteration: 1948; Percent complete: 48.7%; Average loss: 3.1702
Iteration: 1949; Percent complete: 48.7%; Average loss: 3.2480
Iteration: 1950; Percent complete: 48.8%; Average loss: 3.0506
Iteration: 1951; Percent complete: 48.8%; Average loss: 3.0394
Iteration: 1952; Percent complete: 48.8%; Average loss: 3.3827
Iteration: 1953; Percent complete: 48.8%; Average loss: 3.3062
Iteration: 1954; Percent complete: 48.9%; Average loss: 3.1369
Iteration: 1955; Percent complete: 48.9%; Average loss: 3.1333
Iteration: 1956; Percent complete: 48.9%; Average loss: 3.1127
Iteration: 1957; Percent complete: 48.9%; Average loss: 3.0622
Iteration: 1958; Percent complete: 48.9%; Average loss: 3.1862
Iteration: 1959; Percent complete: 49.0%; Average loss: 3.1256
Iteration: 1960; Percent complete: 49.0%; Average loss: 3.3481
Iteration: 1961; Percent complete: 49.0%; Average loss: 3.4376
Iteration: 1962; Percent complete: 49.0%; Average loss: 3.1987
Iteration: 1963; Percent complete: 49.1%; Average loss: 3.1679
Iteration: 1964; Percent complete: 49.1%; Average loss: 3.3046
Iteration: 1965; Percent complete: 49.1%; Average loss: 2.9047
Iteration: 1966; Percent complete: 49.1%; Average loss: 3.0463
Iteration: 1967; Percent complete: 49.2%; Average loss: 3.1419
Iteration: 1968; Percent complete: 49.2%; Average loss: 3.2081
Iteration: 1969; Percent complete: 49.2%; Average loss: 3.1535
Iteration: 1970; Percent complete: 49.2%; Average loss: 3.1219
Iteration: 1971; Percent complete: 49.3%; Average loss: 2.8410
Iteration: 1972; Percent complete: 49.3%; Average loss: 2.9852
Iteration: 1973; Percent complete: 49.3%; Average loss: 3.0542
Iteration: 1974; Percent complete: 49.4%; Average loss: 3.1434
Iteration: 1975; Percent complete: 49.4%; Average loss: 3.1439
Iteration: 1976; Percent complete: 49.4%; Average loss: 3.1188
Iteration: 1977; Percent complete: 49.4%; Average loss: 3.1763
Iteration: 1978; Percent complete: 49.5%; Average loss: 2.9411
Iteration: 1979; Percent complete: 49.5%; Average loss: 2.8561
Iteration: 1980; Percent complete: 49.5%; Average loss: 3.1136
Iteration: 1981; Percent complete: 49.5%; Average loss: 3.0249
Iteration: 1982; Percent complete: 49.5%; Average loss: 3.1069
Iteration: 1983; Percent complete: 49.6%; Average loss: 3.2657
Iteration: 1984; Percent complete: 49.6%; Average loss: 2.8910
Iteration: 1985; Percent complete: 49.6%; Average loss: 3.2468
Iteration: 1986; Percent complete: 49.6%; Average loss: 3.1986
Iteration: 1987; Percent complete: 49.7%; Average loss: 3.3448
Iteration: 1988; Percent complete: 49.7%; Average loss: 3.2473
Iteration: 1989; Percent complete: 49.7%; Average loss: 3.2414
Iteration: 1990; Percent complete: 49.8%; Average loss: 3.2130
Iteration: 1991; Percent complete: 49.8%; Average loss: 3.3277
Iteration: 1992; Percent complete: 49.8%; Average loss: 3.2007
Iteration: 1993; Percent complete: 49.8%; Average loss: 3.3771
Iteration: 1994; Percent complete: 49.9%; Average loss: 3.2190
Iteration: 1995; Percent complete: 49.9%; Average loss: 3.3538
Iteration: 1996; Percent complete: 49.9%; Average loss: 3.0968
Iteration: 1997; Percent complete: 49.9%; Average loss: 3.1438
Iteration: 1998; Percent complete: 50.0%; Average loss: 3.1403
Iteration: 1999; Percent complete: 50.0%; Average loss: 2.9946
Iteration: 2000; Percent complete: 50.0%; Average loss: 3.1570
Iteration: 2001; Percent complete: 50.0%; Average loss: 3.5324
Iteration: 2002; Percent complete: 50.0%; Average loss: 3.1699
Iteration: 2003; Percent complete: 50.1%; Average loss: 3.2789
Iteration: 2004; Percent complete: 50.1%; Average loss: 3.2060
Iteration: 2005; Percent complete: 50.1%; Average loss: 3.2143
Iteration: 2006; Percent complete: 50.1%; Average loss: 3.3390
Iteration: 2007; Percent complete: 50.2%; Average loss: 3.1899
Iteration: 2008; Percent complete: 50.2%; Average loss: 2.9362
Iteration: 2009; Percent complete: 50.2%; Average loss: 2.9869
Iteration: 2010; Percent complete: 50.2%; Average loss: 3.2653
Iteration: 2011; Percent complete: 50.3%; Average loss: 3.1640
Iteration: 2012; Percent complete: 50.3%; Average loss: 3.3757
Iteration: 2013; Percent complete: 50.3%; Average loss: 2.7656
Iteration: 2014; Percent complete: 50.3%; Average loss: 3.2018
Iteration: 2015; Percent complete: 50.4%; Average loss: 3.1406
Iteration: 2016; Percent complete: 50.4%; Average loss: 2.9054
Iteration: 2017; Percent complete: 50.4%; Average loss: 2.9902
Iteration: 2018; Percent complete: 50.4%; Average loss: 3.1396
Iteration: 2019; Percent complete: 50.5%; Average loss: 3.0250
Iteration: 2020; Percent complete: 50.5%; Average loss: 3.1179
Iteration: 2021; Percent complete: 50.5%; Average loss: 3.1231
Iteration: 2022; Percent complete: 50.5%; Average loss: 3.0955
Iteration: 2023; Percent complete: 50.6%; Average loss: 3.2463
Iteration: 2024; Percent complete: 50.6%; Average loss: 3.2256
Iteration: 2025; Percent complete: 50.6%; Average loss: 3.1164
Iteration: 2026; Percent complete: 50.6%; Average loss: 2.9884
Iteration: 2027; Percent complete: 50.7%; Average loss: 3.0151
Iteration: 2028; Percent complete: 50.7%; Average loss: 3.1076
Iteration: 2029; Percent complete: 50.7%; Average loss: 3.1132
Iteration: 2030; Percent complete: 50.7%; Average loss: 3.0238
Iteration: 2031; Percent complete: 50.8%; Average loss: 2.9807
Iteration: 2032; Percent complete: 50.8%; Average loss: 3.1590
Iteration: 2033; Percent complete: 50.8%; Average loss: 3.1620
Iteration: 2034; Percent complete: 50.8%; Average loss: 3.5284
Iteration: 2035; Percent complete: 50.9%; Average loss: 3.0248
Iteration: 2036; Percent complete: 50.9%; Average loss: 3.1160
Iteration: 2037; Percent complete: 50.9%; Average loss: 3.0285
Iteration: 2038; Percent complete: 50.9%; Average loss: 3.1356
Iteration: 2039; Percent complete: 51.0%; Average loss: 3.0562
Iteration: 2040; Percent complete: 51.0%; Average loss: 3.0035
Iteration: 2041; Percent complete: 51.0%; Average loss: 3.4537
Iteration: 2042; Percent complete: 51.0%; Average loss: 3.2150
Iteration: 2043; Percent complete: 51.1%; Average loss: 3.0826
Iteration: 2044; Percent complete: 51.1%; Average loss: 3.2388
Iteration: 2045; Percent complete: 51.1%; Average loss: 3.3385
Iteration: 2046; Percent complete: 51.1%; Average loss: 2.9966
Iteration: 2047; Percent complete: 51.2%; Average loss: 3.0764
Iteration: 2048; Percent complete: 51.2%; Average loss: 3.0668
Iteration: 2049; Percent complete: 51.2%; Average loss: 3.1613
Iteration: 2050; Percent complete: 51.2%; Average loss: 3.2974
Iteration: 2051; Percent complete: 51.3%; Average loss: 3.0532
Iteration: 2052; Percent complete: 51.3%; Average loss: 3.5089
Iteration: 2053; Percent complete: 51.3%; Average loss: 2.9867
Iteration: 2054; Percent complete: 51.3%; Average loss: 3.0313
Iteration: 2055; Percent complete: 51.4%; Average loss: 3.2223
Iteration: 2056; Percent complete: 51.4%; Average loss: 3.4768
Iteration: 2057; Percent complete: 51.4%; Average loss: 3.0290
Iteration: 2058; Percent complete: 51.4%; Average loss: 3.1287
Iteration: 2059; Percent complete: 51.5%; Average loss: 3.2906
Iteration: 2060; Percent complete: 51.5%; Average loss: 3.3685
Iteration: 2061; Percent complete: 51.5%; Average loss: 3.3024
Iteration: 2062; Percent complete: 51.5%; Average loss: 3.1738
Iteration: 2063; Percent complete: 51.6%; Average loss: 3.3294
Iteration: 2064; Percent complete: 51.6%; Average loss: 3.1508
Iteration: 2065; Percent complete: 51.6%; Average loss: 3.2568
Iteration: 2066; Percent complete: 51.6%; Average loss: 3.1928
Iteration: 2067; Percent complete: 51.7%; Average loss: 3.1088
Iteration: 2068; Percent complete: 51.7%; Average loss: 3.0799
Iteration: 2069; Percent complete: 51.7%; Average loss: 3.2321
Iteration: 2070; Percent complete: 51.7%; Average loss: 3.1222
Iteration: 2071; Percent complete: 51.8%; Average loss: 3.1766
Iteration: 2072; Percent complete: 51.8%; Average loss: 2.8639
Iteration: 2073; Percent complete: 51.8%; Average loss: 2.9887
Iteration: 2074; Percent complete: 51.8%; Average loss: 3.1434
Iteration: 2075; Percent complete: 51.9%; Average loss: 2.8605
Iteration: 2076; Percent complete: 51.9%; Average loss: 3.3801
Iteration: 2077; Percent complete: 51.9%; Average loss: 3.3674
Iteration: 2078; Percent complete: 51.9%; Average loss: 3.3512
Iteration: 2079; Percent complete: 52.0%; Average loss: 2.9221
Iteration: 2080; Percent complete: 52.0%; Average loss: 3.1980
Iteration: 2081; Percent complete: 52.0%; Average loss: 3.1244
Iteration: 2082; Percent complete: 52.0%; Average loss: 3.1810
Iteration: 2083; Percent complete: 52.1%; Average loss: 3.0831
Iteration: 2084; Percent complete: 52.1%; Average loss: 3.1509
Iteration: 2085; Percent complete: 52.1%; Average loss: 3.3368
Iteration: 2086; Percent complete: 52.1%; Average loss: 3.1308
Iteration: 2087; Percent complete: 52.2%; Average loss: 3.2327
Iteration: 2088; Percent complete: 52.2%; Average loss: 3.1297
Iteration: 2089; Percent complete: 52.2%; Average loss: 3.2937
Iteration: 2090; Percent complete: 52.2%; Average loss: 3.0654
Iteration: 2091; Percent complete: 52.3%; Average loss: 3.1590
Iteration: 2092; Percent complete: 52.3%; Average loss: 3.2923
Iteration: 2093; Percent complete: 52.3%; Average loss: 3.2329
Iteration: 2094; Percent complete: 52.3%; Average loss: 2.8993
Iteration: 2095; Percent complete: 52.4%; Average loss: 3.1578
Iteration: 2096; Percent complete: 52.4%; Average loss: 3.1435
Iteration: 2097; Percent complete: 52.4%; Average loss: 3.1706
Iteration: 2098; Percent complete: 52.4%; Average loss: 3.0318
Iteration: 2099; Percent complete: 52.5%; Average loss: 2.9086
Iteration: 2100; Percent complete: 52.5%; Average loss: 2.9898
Iteration: 2101; Percent complete: 52.5%; Average loss: 3.0771
Iteration: 2102; Percent complete: 52.5%; Average loss: 3.0253
Iteration: 2103; Percent complete: 52.6%; Average loss: 3.2950
Iteration: 2104; Percent complete: 52.6%; Average loss: 3.1950
Iteration: 2105; Percent complete: 52.6%; Average loss: 3.1549
Iteration: 2106; Percent complete: 52.6%; Average loss: 3.1398
Iteration: 2107; Percent complete: 52.7%; Average loss: 3.0043
Iteration: 2108; Percent complete: 52.7%; Average loss: 3.0174
Iteration: 2109; Percent complete: 52.7%; Average loss: 3.1092
Iteration: 2110; Percent complete: 52.8%; Average loss: 3.3703
Iteration: 2111; Percent complete: 52.8%; Average loss: 3.1257
Iteration: 2112; Percent complete: 52.8%; Average loss: 2.9829
Iteration: 2113; Percent complete: 52.8%; Average loss: 3.2566
Iteration: 2114; Percent complete: 52.8%; Average loss: 2.9887
Iteration: 2115; Percent complete: 52.9%; Average loss: 3.0735
Iteration: 2116; Percent complete: 52.9%; Average loss: 3.1807
Iteration: 2117; Percent complete: 52.9%; Average loss: 3.1158
Iteration: 2118; Percent complete: 52.9%; Average loss: 3.2301
Iteration: 2119; Percent complete: 53.0%; Average loss: 2.8546
Iteration: 2120; Percent complete: 53.0%; Average loss: 3.1455
Iteration: 2121; Percent complete: 53.0%; Average loss: 3.0920
Iteration: 2122; Percent complete: 53.0%; Average loss: 3.0728
Iteration: 2123; Percent complete: 53.1%; Average loss: 2.9591
Iteration: 2124; Percent complete: 53.1%; Average loss: 3.1752
Iteration: 2125; Percent complete: 53.1%; Average loss: 3.1728
Iteration: 2126; Percent complete: 53.1%; Average loss: 3.2888
Iteration: 2127; Percent complete: 53.2%; Average loss: 3.0835
Iteration: 2128; Percent complete: 53.2%; Average loss: 3.1277
Iteration: 2129; Percent complete: 53.2%; Average loss: 2.9822
Iteration: 2130; Percent complete: 53.2%; Average loss: 3.0346
Iteration: 2131; Percent complete: 53.3%; Average loss: 3.0104
Iteration: 2132; Percent complete: 53.3%; Average loss: 3.2763
Iteration: 2133; Percent complete: 53.3%; Average loss: 2.9692
Iteration: 2134; Percent complete: 53.3%; Average loss: 3.0738
Iteration: 2135; Percent complete: 53.4%; Average loss: 2.9931
Iteration: 2136; Percent complete: 53.4%; Average loss: 3.1312
Iteration: 2137; Percent complete: 53.4%; Average loss: 3.0631
Iteration: 2138; Percent complete: 53.4%; Average loss: 3.1574
Iteration: 2139; Percent complete: 53.5%; Average loss: 3.0803
Iteration: 2140; Percent complete: 53.5%; Average loss: 3.1131
Iteration: 2141; Percent complete: 53.5%; Average loss: 3.0281
Iteration: 2142; Percent complete: 53.5%; Average loss: 2.8867
Iteration: 2143; Percent complete: 53.6%; Average loss: 3.0348
Iteration: 2144; Percent complete: 53.6%; Average loss: 3.2739
Iteration: 2145; Percent complete: 53.6%; Average loss: 3.1502
Iteration: 2146; Percent complete: 53.6%; Average loss: 2.9659
Iteration: 2147; Percent complete: 53.7%; Average loss: 3.1951
Iteration: 2148; Percent complete: 53.7%; Average loss: 2.8088
Iteration: 2149; Percent complete: 53.7%; Average loss: 3.0493
Iteration: 2150; Percent complete: 53.8%; Average loss: 3.1219
Iteration: 2151; Percent complete: 53.8%; Average loss: 3.0706
Iteration: 2152; Percent complete: 53.8%; Average loss: 2.9435
Iteration: 2153; Percent complete: 53.8%; Average loss: 3.1692
Iteration: 2154; Percent complete: 53.8%; Average loss: 3.4778
Iteration: 2155; Percent complete: 53.9%; Average loss: 3.1606
Iteration: 2156; Percent complete: 53.9%; Average loss: 3.0788
Iteration: 2157; Percent complete: 53.9%; Average loss: 3.2583
Iteration: 2158; Percent complete: 53.9%; Average loss: 2.9821
Iteration: 2159; Percent complete: 54.0%; Average loss: 3.4152
Iteration: 2160; Percent complete: 54.0%; Average loss: 3.0923
Iteration: 2161; Percent complete: 54.0%; Average loss: 3.1159
Iteration: 2162; Percent complete: 54.0%; Average loss: 2.9306
Iteration: 2163; Percent complete: 54.1%; Average loss: 3.4404
Iteration: 2164; Percent complete: 54.1%; Average loss: 3.0712
Iteration: 2165; Percent complete: 54.1%; Average loss: 3.0418
Iteration: 2166; Percent complete: 54.1%; Average loss: 3.0740
Iteration: 2167; Percent complete: 54.2%; Average loss: 2.8415
Iteration: 2168; Percent complete: 54.2%; Average loss: 3.1661
Iteration: 2169; Percent complete: 54.2%; Average loss: 3.1787
Iteration: 2170; Percent complete: 54.2%; Average loss: 3.4127
Iteration: 2171; Percent complete: 54.3%; Average loss: 3.0994
Iteration: 2172; Percent complete: 54.3%; Average loss: 3.0558
Iteration: 2173; Percent complete: 54.3%; Average loss: 3.1825
Iteration: 2174; Percent complete: 54.4%; Average loss: 3.1856
Iteration: 2175; Percent complete: 54.4%; Average loss: 3.1383
Iteration: 2176; Percent complete: 54.4%; Average loss: 3.4604
Iteration: 2177; Percent complete: 54.4%; Average loss: 3.0737
Iteration: 2178; Percent complete: 54.4%; Average loss: 3.2938
Iteration: 2179; Percent complete: 54.5%; Average loss: 3.0376
Iteration: 2180; Percent complete: 54.5%; Average loss: 2.9471
Iteration: 2181; Percent complete: 54.5%; Average loss: 2.9422
Iteration: 2182; Percent complete: 54.5%; Average loss: 3.0584
Iteration: 2183; Percent complete: 54.6%; Average loss: 3.1692
Iteration: 2184; Percent complete: 54.6%; Average loss: 3.0989
Iteration: 2185; Percent complete: 54.6%; Average loss: 2.9087
Iteration: 2186; Percent complete: 54.6%; Average loss: 2.8393
Iteration: 2187; Percent complete: 54.7%; Average loss: 3.1036
Iteration: 2188; Percent complete: 54.7%; Average loss: 3.1629
Iteration: 2189; Percent complete: 54.7%; Average loss: 2.9313
Iteration: 2190; Percent complete: 54.8%; Average loss: 2.8576
Iteration: 2191; Percent complete: 54.8%; Average loss: 3.1232
Iteration: 2192; Percent complete: 54.8%; Average loss: 3.2127
Iteration: 2193; Percent complete: 54.8%; Average loss: 3.2774
Iteration: 2194; Percent complete: 54.9%; Average loss: 3.3025
Iteration: 2195; Percent complete: 54.9%; Average loss: 3.3100
Iteration: 2196; Percent complete: 54.9%; Average loss: 2.9285
Iteration: 2197; Percent complete: 54.9%; Average loss: 2.9345
Iteration: 2198; Percent complete: 54.9%; Average loss: 3.0009
Iteration: 2199; Percent complete: 55.0%; Average loss: 3.1659
Iteration: 2200; Percent complete: 55.0%; Average loss: 2.8263
Iteration: 2201; Percent complete: 55.0%; Average loss: 2.9404
Iteration: 2202; Percent complete: 55.0%; Average loss: 3.1492
Iteration: 2203; Percent complete: 55.1%; Average loss: 2.9161
Iteration: 2204; Percent complete: 55.1%; Average loss: 3.2744
Iteration: 2205; Percent complete: 55.1%; Average loss: 3.2437
Iteration: 2206; Percent complete: 55.1%; Average loss: 3.0559
Iteration: 2207; Percent complete: 55.2%; Average loss: 3.0619
Iteration: 2208; Percent complete: 55.2%; Average loss: 3.0631
Iteration: 2209; Percent complete: 55.2%; Average loss: 3.1273
Iteration: 2210; Percent complete: 55.2%; Average loss: 2.8888
Iteration: 2211; Percent complete: 55.3%; Average loss: 3.1202
Iteration: 2212; Percent complete: 55.3%; Average loss: 2.8751
Iteration: 2213; Percent complete: 55.3%; Average loss: 2.9121
Iteration: 2214; Percent complete: 55.4%; Average loss: 3.0409
Iteration: 2215; Percent complete: 55.4%; Average loss: 3.0973
Iteration: 2216; Percent complete: 55.4%; Average loss: 3.0395
Iteration: 2217; Percent complete: 55.4%; Average loss: 3.2066
Iteration: 2218; Percent complete: 55.5%; Average loss: 3.2460
Iteration: 2219; Percent complete: 55.5%; Average loss: 3.0768
Iteration: 2220; Percent complete: 55.5%; Average loss: 3.3571
Iteration: 2221; Percent complete: 55.5%; Average loss: 3.0678
Iteration: 2222; Percent complete: 55.5%; Average loss: 3.1947
Iteration: 2223; Percent complete: 55.6%; Average loss: 2.9302
Iteration: 2224; Percent complete: 55.6%; Average loss: 3.1491
Iteration: 2225; Percent complete: 55.6%; Average loss: 3.2945
Iteration: 2226; Percent complete: 55.6%; Average loss: 3.2302
Iteration: 2227; Percent complete: 55.7%; Average loss: 2.9889
Iteration: 2228; Percent complete: 55.7%; Average loss: 3.0764
Iteration: 2229; Percent complete: 55.7%; Average loss: 3.1587
Iteration: 2230; Percent complete: 55.8%; Average loss: 3.0102
Iteration: 2231; Percent complete: 55.8%; Average loss: 3.2175
Iteration: 2232; Percent complete: 55.8%; Average loss: 2.8616
Iteration: 2233; Percent complete: 55.8%; Average loss: 2.8776
Iteration: 2234; Percent complete: 55.9%; Average loss: 3.1057
Iteration: 2235; Percent complete: 55.9%; Average loss: 3.2271
Iteration: 2236; Percent complete: 55.9%; Average loss: 3.0305
Iteration: 2237; Percent complete: 55.9%; Average loss: 3.2244
Iteration: 2238; Percent complete: 56.0%; Average loss: 3.3615
Iteration: 2239; Percent complete: 56.0%; Average loss: 2.9668
Iteration: 2240; Percent complete: 56.0%; Average loss: 2.9979
Iteration: 2241; Percent complete: 56.0%; Average loss: 2.9358
Iteration: 2242; Percent complete: 56.0%; Average loss: 3.2683
Iteration: 2243; Percent complete: 56.1%; Average loss: 3.2160
Iteration: 2244; Percent complete: 56.1%; Average loss: 3.1017
Iteration: 2245; Percent complete: 56.1%; Average loss: 3.0896
Iteration: 2246; Percent complete: 56.1%; Average loss: 3.2038
Iteration: 2247; Percent complete: 56.2%; Average loss: 3.0480
Iteration: 2248; Percent complete: 56.2%; Average loss: 3.2398
Iteration: 2249; Percent complete: 56.2%; Average loss: 2.9928
Iteration: 2250; Percent complete: 56.2%; Average loss: 2.8458
Iteration: 2251; Percent complete: 56.3%; Average loss: 3.0444
Iteration: 2252; Percent complete: 56.3%; Average loss: 2.9626
Iteration: 2253; Percent complete: 56.3%; Average loss: 3.1113
Iteration: 2254; Percent complete: 56.4%; Average loss: 2.8893
Iteration: 2255; Percent complete: 56.4%; Average loss: 3.1248
Iteration: 2256; Percent complete: 56.4%; Average loss: 3.0132
Iteration: 2257; Percent complete: 56.4%; Average loss: 3.2951
Iteration: 2258; Percent complete: 56.5%; Average loss: 3.1601
Iteration: 2259; Percent complete: 56.5%; Average loss: 3.1706
Iteration: 2260; Percent complete: 56.5%; Average loss: 3.1743
Iteration: 2261; Percent complete: 56.5%; Average loss: 2.8073
Iteration: 2262; Percent complete: 56.5%; Average loss: 3.0524
Iteration: 2263; Percent complete: 56.6%; Average loss: 3.1819
Iteration: 2264; Percent complete: 56.6%; Average loss: 3.1504
Iteration: 2265; Percent complete: 56.6%; Average loss: 3.0559
Iteration: 2266; Percent complete: 56.6%; Average loss: 3.1770
Iteration: 2267; Percent complete: 56.7%; Average loss: 2.9828
Iteration: 2268; Percent complete: 56.7%; Average loss: 3.2022
Iteration: 2269; Percent complete: 56.7%; Average loss: 3.3100
Iteration: 2270; Percent complete: 56.8%; Average loss: 2.9747
Iteration: 2271; Percent complete: 56.8%; Average loss: 2.7644
Iteration: 2272; Percent complete: 56.8%; Average loss: 3.0305
Iteration: 2273; Percent complete: 56.8%; Average loss: 3.1432
Iteration: 2274; Percent complete: 56.9%; Average loss: 2.9470
Iteration: 2275; Percent complete: 56.9%; Average loss: 2.8520
Iteration: 2276; Percent complete: 56.9%; Average loss: 2.8293
Iteration: 2277; Percent complete: 56.9%; Average loss: 2.6704
Iteration: 2278; Percent complete: 57.0%; Average loss: 2.8304
Iteration: 2279; Percent complete: 57.0%; Average loss: 3.1542
Iteration: 2280; Percent complete: 57.0%; Average loss: 3.1195
Iteration: 2281; Percent complete: 57.0%; Average loss: 3.1075
Iteration: 2282; Percent complete: 57.0%; Average loss: 3.0042
Iteration: 2283; Percent complete: 57.1%; Average loss: 3.3124
Iteration: 2284; Percent complete: 57.1%; Average loss: 3.2819
Iteration: 2285; Percent complete: 57.1%; Average loss: 3.1268
Iteration: 2286; Percent complete: 57.1%; Average loss: 2.9412
Iteration: 2287; Percent complete: 57.2%; Average loss: 3.2231
Iteration: 2288; Percent complete: 57.2%; Average loss: 2.9629
Iteration: 2289; Percent complete: 57.2%; Average loss: 2.8212
Iteration: 2290; Percent complete: 57.2%; Average loss: 3.1837
Iteration: 2291; Percent complete: 57.3%; Average loss: 3.1595
Iteration: 2292; Percent complete: 57.3%; Average loss: 3.3352
Iteration: 2293; Percent complete: 57.3%; Average loss: 3.1604
Iteration: 2294; Percent complete: 57.4%; Average loss: 3.0194
Iteration: 2295; Percent complete: 57.4%; Average loss: 3.0027
Iteration: 2296; Percent complete: 57.4%; Average loss: 3.1560
Iteration: 2297; Percent complete: 57.4%; Average loss: 2.9663
Iteration: 2298; Percent complete: 57.5%; Average loss: 3.0208
Iteration: 2299; Percent complete: 57.5%; Average loss: 2.9517
Iteration: 2300; Percent complete: 57.5%; Average loss: 2.9927
Iteration: 2301; Percent complete: 57.5%; Average loss: 3.1915
Iteration: 2302; Percent complete: 57.6%; Average loss: 2.9265
Iteration: 2303; Percent complete: 57.6%; Average loss: 3.1558
Iteration: 2304; Percent complete: 57.6%; Average loss: 2.8471
Iteration: 2305; Percent complete: 57.6%; Average loss: 2.8977
Iteration: 2306; Percent complete: 57.6%; Average loss: 3.1724
Iteration: 2307; Percent complete: 57.7%; Average loss: 2.9728
Iteration: 2308; Percent complete: 57.7%; Average loss: 3.1356
Iteration: 2309; Percent complete: 57.7%; Average loss: 3.1878
Iteration: 2310; Percent complete: 57.8%; Average loss: 3.1096
Iteration: 2311; Percent complete: 57.8%; Average loss: 2.7326
Iteration: 2312; Percent complete: 57.8%; Average loss: 2.9089
Iteration: 2313; Percent complete: 57.8%; Average loss: 2.9633
Iteration: 2314; Percent complete: 57.9%; Average loss: 3.2703
Iteration: 2315; Percent complete: 57.9%; Average loss: 3.1002
Iteration: 2316; Percent complete: 57.9%; Average loss: 3.0240
Iteration: 2317; Percent complete: 57.9%; Average loss: 3.1669
Iteration: 2318; Percent complete: 58.0%; Average loss: 3.0886
Iteration: 2319; Percent complete: 58.0%; Average loss: 3.0759
Iteration: 2320; Percent complete: 58.0%; Average loss: 2.7977
Iteration: 2321; Percent complete: 58.0%; Average loss: 2.9533
Iteration: 2322; Percent complete: 58.1%; Average loss: 3.0490
Iteration: 2323; Percent complete: 58.1%; Average loss: 3.1150
Iteration: 2324; Percent complete: 58.1%; Average loss: 3.1059
Iteration: 2325; Percent complete: 58.1%; Average loss: 3.0184
Iteration: 2326; Percent complete: 58.1%; Average loss: 2.9843
Iteration: 2327; Percent complete: 58.2%; Average loss: 3.1379
Iteration: 2328; Percent complete: 58.2%; Average loss: 2.8938
Iteration: 2329; Percent complete: 58.2%; Average loss: 2.9595
Iteration: 2330; Percent complete: 58.2%; Average loss: 3.1822
Iteration: 2331; Percent complete: 58.3%; Average loss: 3.0487
Iteration: 2332; Percent complete: 58.3%; Average loss: 2.9754
Iteration: 2333; Percent complete: 58.3%; Average loss: 3.0372
Iteration: 2334; Percent complete: 58.4%; Average loss: 3.4157
Iteration: 2335; Percent complete: 58.4%; Average loss: 3.2564
Iteration: 2336; Percent complete: 58.4%; Average loss: 3.2058
Iteration: 2337; Percent complete: 58.4%; Average loss: 3.2440
Iteration: 2338; Percent complete: 58.5%; Average loss: 2.8506
Iteration: 2339; Percent complete: 58.5%; Average loss: 3.3591
Iteration: 2340; Percent complete: 58.5%; Average loss: 3.1201
Iteration: 2341; Percent complete: 58.5%; Average loss: 2.8817
Iteration: 2342; Percent complete: 58.6%; Average loss: 3.1742
Iteration: 2343; Percent complete: 58.6%; Average loss: 3.0344
Iteration: 2344; Percent complete: 58.6%; Average loss: 3.0904
Iteration: 2345; Percent complete: 58.6%; Average loss: 3.1231
Iteration: 2346; Percent complete: 58.7%; Average loss: 3.0795
Iteration: 2347; Percent complete: 58.7%; Average loss: 2.9845
Iteration: 2348; Percent complete: 58.7%; Average loss: 3.0243
Iteration: 2349; Percent complete: 58.7%; Average loss: 2.8026
Iteration: 2350; Percent complete: 58.8%; Average loss: 2.9229
Iteration: 2351; Percent complete: 58.8%; Average loss: 3.0746
Iteration: 2352; Percent complete: 58.8%; Average loss: 3.0341
Iteration: 2353; Percent complete: 58.8%; Average loss: 3.2013
Iteration: 2354; Percent complete: 58.9%; Average loss: 3.2879
Iteration: 2355; Percent complete: 58.9%; Average loss: 3.0687
Iteration: 2356; Percent complete: 58.9%; Average loss: 2.8874
Iteration: 2357; Percent complete: 58.9%; Average loss: 2.9027
Iteration: 2358; Percent complete: 59.0%; Average loss: 3.1824
Iteration: 2359; Percent complete: 59.0%; Average loss: 2.9022
Iteration: 2360; Percent complete: 59.0%; Average loss: 3.0406
Iteration: 2361; Percent complete: 59.0%; Average loss: 3.1081
Iteration: 2362; Percent complete: 59.1%; Average loss: 3.0197
Iteration: 2363; Percent complete: 59.1%; Average loss: 3.1985
Iteration: 2364; Percent complete: 59.1%; Average loss: 2.9741
Iteration: 2365; Percent complete: 59.1%; Average loss: 3.1594
Iteration: 2366; Percent complete: 59.2%; Average loss: 2.9737
Iteration: 2367; Percent complete: 59.2%; Average loss: 3.0419
Iteration: 2368; Percent complete: 59.2%; Average loss: 2.9368
Iteration: 2369; Percent complete: 59.2%; Average loss: 2.8313
Iteration: 2370; Percent complete: 59.2%; Average loss: 3.0719
Iteration: 2371; Percent complete: 59.3%; Average loss: 3.0050
Iteration: 2372; Percent complete: 59.3%; Average loss: 2.6662
Iteration: 2373; Percent complete: 59.3%; Average loss: 2.9691
Iteration: 2374; Percent complete: 59.4%; Average loss: 2.8346
Iteration: 2375; Percent complete: 59.4%; Average loss: 3.0911
Iteration: 2376; Percent complete: 59.4%; Average loss: 3.0231
Iteration: 2377; Percent complete: 59.4%; Average loss: 3.1623
Iteration: 2378; Percent complete: 59.5%; Average loss: 2.8986
Iteration: 2379; Percent complete: 59.5%; Average loss: 2.9846
Iteration: 2380; Percent complete: 59.5%; Average loss: 3.3247
Iteration: 2381; Percent complete: 59.5%; Average loss: 3.1315
Iteration: 2382; Percent complete: 59.6%; Average loss: 3.0436
Iteration: 2383; Percent complete: 59.6%; Average loss: 3.0833
Iteration: 2384; Percent complete: 59.6%; Average loss: 3.1271
Iteration: 2385; Percent complete: 59.6%; Average loss: 2.9008
Iteration: 2386; Percent complete: 59.7%; Average loss: 3.1297
Iteration: 2387; Percent complete: 59.7%; Average loss: 2.8533
Iteration: 2388; Percent complete: 59.7%; Average loss: 2.8953
Iteration: 2389; Percent complete: 59.7%; Average loss: 3.0297
Iteration: 2390; Percent complete: 59.8%; Average loss: 3.1615
Iteration: 2391; Percent complete: 59.8%; Average loss: 3.3329
Iteration: 2392; Percent complete: 59.8%; Average loss: 3.0473
Iteration: 2393; Percent complete: 59.8%; Average loss: 3.1557
Iteration: 2394; Percent complete: 59.9%; Average loss: 3.0545
Iteration: 2395; Percent complete: 59.9%; Average loss: 2.9594
Iteration: 2396; Percent complete: 59.9%; Average loss: 2.8805
Iteration: 2397; Percent complete: 59.9%; Average loss: 3.0746
Iteration: 2398; Percent complete: 60.0%; Average loss: 3.0767
Iteration: 2399; Percent complete: 60.0%; Average loss: 3.0489
Iteration: 2400; Percent complete: 60.0%; Average loss: 3.0194
Iteration: 2401; Percent complete: 60.0%; Average loss: 3.1032
Iteration: 2402; Percent complete: 60.1%; Average loss: 3.0225
Iteration: 2403; Percent complete: 60.1%; Average loss: 3.1612
Iteration: 2404; Percent complete: 60.1%; Average loss: 2.9323
Iteration: 2405; Percent complete: 60.1%; Average loss: 2.9504
Iteration: 2406; Percent complete: 60.2%; Average loss: 3.0687
Iteration: 2407; Percent complete: 60.2%; Average loss: 2.7987
Iteration: 2408; Percent complete: 60.2%; Average loss: 2.9299
Iteration: 2409; Percent complete: 60.2%; Average loss: 3.3009
Iteration: 2410; Percent complete: 60.2%; Average loss: 3.2572
Iteration: 2411; Percent complete: 60.3%; Average loss: 3.1003
Iteration: 2412; Percent complete: 60.3%; Average loss: 2.8880
Iteration: 2413; Percent complete: 60.3%; Average loss: 2.8401
Iteration: 2414; Percent complete: 60.4%; Average loss: 3.0449
Iteration: 2415; Percent complete: 60.4%; Average loss: 2.9381
Iteration: 2416; Percent complete: 60.4%; Average loss: 2.9970
Iteration: 2417; Percent complete: 60.4%; Average loss: 2.9740
Iteration: 2418; Percent complete: 60.5%; Average loss: 2.8287
Iteration: 2419; Percent complete: 60.5%; Average loss: 3.4079
Iteration: 2420; Percent complete: 60.5%; Average loss: 3.0881
Iteration: 2421; Percent complete: 60.5%; Average loss: 3.1259
Iteration: 2422; Percent complete: 60.6%; Average loss: 3.0142
Iteration: 2423; Percent complete: 60.6%; Average loss: 2.9558
Iteration: 2424; Percent complete: 60.6%; Average loss: 2.9089
Iteration: 2425; Percent complete: 60.6%; Average loss: 3.1328
Iteration: 2426; Percent complete: 60.7%; Average loss: 3.2773
Iteration: 2427; Percent complete: 60.7%; Average loss: 3.1286
Iteration: 2428; Percent complete: 60.7%; Average loss: 2.9959
Iteration: 2429; Percent complete: 60.7%; Average loss: 3.0557
Iteration: 2430; Percent complete: 60.8%; Average loss: 2.9926
Iteration: 2431; Percent complete: 60.8%; Average loss: 3.0156
Iteration: 2432; Percent complete: 60.8%; Average loss: 3.2945
Iteration: 2433; Percent complete: 60.8%; Average loss: 3.0398
Iteration: 2434; Percent complete: 60.9%; Average loss: 3.1675
Iteration: 2435; Percent complete: 60.9%; Average loss: 2.9539
Iteration: 2436; Percent complete: 60.9%; Average loss: 2.8177
Iteration: 2437; Percent complete: 60.9%; Average loss: 2.9248
Iteration: 2438; Percent complete: 61.0%; Average loss: 2.9298
Iteration: 2439; Percent complete: 61.0%; Average loss: 2.8373
Iteration: 2440; Percent complete: 61.0%; Average loss: 3.1828
Iteration: 2441; Percent complete: 61.0%; Average loss: 3.0327
Iteration: 2442; Percent complete: 61.1%; Average loss: 3.1395
Iteration: 2443; Percent complete: 61.1%; Average loss: 2.9737
Iteration: 2444; Percent complete: 61.1%; Average loss: 2.9726
Iteration: 2445; Percent complete: 61.1%; Average loss: 2.9638
Iteration: 2446; Percent complete: 61.2%; Average loss: 3.1045
Iteration: 2447; Percent complete: 61.2%; Average loss: 3.2512
Iteration: 2448; Percent complete: 61.2%; Average loss: 3.0837
Iteration: 2449; Percent complete: 61.2%; Average loss: 3.1246
Iteration: 2450; Percent complete: 61.3%; Average loss: 2.9558
Iteration: 2451; Percent complete: 61.3%; Average loss: 2.9705
Iteration: 2452; Percent complete: 61.3%; Average loss: 2.9132
Iteration: 2453; Percent complete: 61.3%; Average loss: 3.0361
Iteration: 2454; Percent complete: 61.4%; Average loss: 2.9146
Iteration: 2455; Percent complete: 61.4%; Average loss: 2.9727
Iteration: 2456; Percent complete: 61.4%; Average loss: 3.0056
Iteration: 2457; Percent complete: 61.4%; Average loss: 3.1380
Iteration: 2458; Percent complete: 61.5%; Average loss: 3.2513
Iteration: 2459; Percent complete: 61.5%; Average loss: 3.2073
Iteration: 2460; Percent complete: 61.5%; Average loss: 2.9823
Iteration: 2461; Percent complete: 61.5%; Average loss: 3.0501
Iteration: 2462; Percent complete: 61.6%; Average loss: 3.0562
Iteration: 2463; Percent complete: 61.6%; Average loss: 2.9238
Iteration: 2464; Percent complete: 61.6%; Average loss: 2.9540
Iteration: 2465; Percent complete: 61.6%; Average loss: 3.1654
Iteration: 2466; Percent complete: 61.7%; Average loss: 2.9105
Iteration: 2467; Percent complete: 61.7%; Average loss: 2.9037
Iteration: 2468; Percent complete: 61.7%; Average loss: 2.9661
Iteration: 2469; Percent complete: 61.7%; Average loss: 3.1303
Iteration: 2470; Percent complete: 61.8%; Average loss: 3.2168
Iteration: 2471; Percent complete: 61.8%; Average loss: 2.8617
Iteration: 2472; Percent complete: 61.8%; Average loss: 2.9940
Iteration: 2473; Percent complete: 61.8%; Average loss: 3.0194
Iteration: 2474; Percent complete: 61.9%; Average loss: 2.7617
Iteration: 2475; Percent complete: 61.9%; Average loss: 2.9790
Iteration: 2476; Percent complete: 61.9%; Average loss: 3.0352
Iteration: 2477; Percent complete: 61.9%; Average loss: 2.9848
Iteration: 2478; Percent complete: 62.0%; Average loss: 2.8526
Iteration: 2479; Percent complete: 62.0%; Average loss: 3.1041
Iteration: 2480; Percent complete: 62.0%; Average loss: 2.9482
Iteration: 2481; Percent complete: 62.0%; Average loss: 3.1413
Iteration: 2482; Percent complete: 62.1%; Average loss: 2.9102
Iteration: 2483; Percent complete: 62.1%; Average loss: 3.0561
Iteration: 2484; Percent complete: 62.1%; Average loss: 2.8896
Iteration: 2485; Percent complete: 62.1%; Average loss: 3.0608
Iteration: 2486; Percent complete: 62.2%; Average loss: 2.9052
Iteration: 2487; Percent complete: 62.2%; Average loss: 3.0498
Iteration: 2488; Percent complete: 62.2%; Average loss: 3.0826
Iteration: 2489; Percent complete: 62.2%; Average loss: 2.8115
Iteration: 2490; Percent complete: 62.3%; Average loss: 3.3833
Iteration: 2491; Percent complete: 62.3%; Average loss: 2.8753
Iteration: 2492; Percent complete: 62.3%; Average loss: 3.0102
Iteration: 2493; Percent complete: 62.3%; Average loss: 2.9438
Iteration: 2494; Percent complete: 62.4%; Average loss: 3.0186
Iteration: 2495; Percent complete: 62.4%; Average loss: 3.0045
Iteration: 2496; Percent complete: 62.4%; Average loss: 3.1321
Iteration: 2497; Percent complete: 62.4%; Average loss: 2.9889
Iteration: 2498; Percent complete: 62.5%; Average loss: 3.2202
Iteration: 2499; Percent complete: 62.5%; Average loss: 3.1497
Iteration: 2500; Percent complete: 62.5%; Average loss: 3.4035
Iteration: 2501; Percent complete: 62.5%; Average loss: 2.8906
Iteration: 2502; Percent complete: 62.5%; Average loss: 3.0487
Iteration: 2503; Percent complete: 62.6%; Average loss: 2.7493
Iteration: 2504; Percent complete: 62.6%; Average loss: 3.0027
Iteration: 2505; Percent complete: 62.6%; Average loss: 3.0274
Iteration: 2506; Percent complete: 62.6%; Average loss: 3.0998
Iteration: 2507; Percent complete: 62.7%; Average loss: 2.9323
Iteration: 2508; Percent complete: 62.7%; Average loss: 3.1363
Iteration: 2509; Percent complete: 62.7%; Average loss: 3.2860
Iteration: 2510; Percent complete: 62.7%; Average loss: 3.0966
Iteration: 2511; Percent complete: 62.8%; Average loss: 3.0257
Iteration: 2512; Percent complete: 62.8%; Average loss: 2.7406
Iteration: 2513; Percent complete: 62.8%; Average loss: 2.9440
Iteration: 2514; Percent complete: 62.8%; Average loss: 2.9851
Iteration: 2515; Percent complete: 62.9%; Average loss: 3.1804
Iteration: 2516; Percent complete: 62.9%; Average loss: 3.0006
Iteration: 2517; Percent complete: 62.9%; Average loss: 2.9825
Iteration: 2518; Percent complete: 62.9%; Average loss: 3.0920
Iteration: 2519; Percent complete: 63.0%; Average loss: 3.2354
Iteration: 2520; Percent complete: 63.0%; Average loss: 2.9770
Iteration: 2521; Percent complete: 63.0%; Average loss: 2.8396
Iteration: 2522; Percent complete: 63.0%; Average loss: 3.1383
Iteration: 2523; Percent complete: 63.1%; Average loss: 2.9116
Iteration: 2524; Percent complete: 63.1%; Average loss: 2.9493
Iteration: 2525; Percent complete: 63.1%; Average loss: 3.3190
Iteration: 2526; Percent complete: 63.1%; Average loss: 3.0351
Iteration: 2527; Percent complete: 63.2%; Average loss: 3.0052
Iteration: 2528; Percent complete: 63.2%; Average loss: 3.3510
Iteration: 2529; Percent complete: 63.2%; Average loss: 2.9380
Iteration: 2530; Percent complete: 63.2%; Average loss: 3.0833
Iteration: 2531; Percent complete: 63.3%; Average loss: 2.9896
Iteration: 2532; Percent complete: 63.3%; Average loss: 2.8527
Iteration: 2533; Percent complete: 63.3%; Average loss: 2.9321
Iteration: 2534; Percent complete: 63.3%; Average loss: 3.0420
Iteration: 2535; Percent complete: 63.4%; Average loss: 3.1144
Iteration: 2536; Percent complete: 63.4%; Average loss: 3.0370
Iteration: 2537; Percent complete: 63.4%; Average loss: 2.9773
Iteration: 2538; Percent complete: 63.4%; Average loss: 2.5066
Iteration: 2539; Percent complete: 63.5%; Average loss: 2.9593
Iteration: 2540; Percent complete: 63.5%; Average loss: 3.0580
Iteration: 2541; Percent complete: 63.5%; Average loss: 3.0693
Iteration: 2542; Percent complete: 63.5%; Average loss: 3.1463
Iteration: 2543; Percent complete: 63.6%; Average loss: 2.8826
Iteration: 2544; Percent complete: 63.6%; Average loss: 2.9614
Iteration: 2545; Percent complete: 63.6%; Average loss: 3.1323
Iteration: 2546; Percent complete: 63.6%; Average loss: 3.0087
Iteration: 2547; Percent complete: 63.7%; Average loss: 2.9742
Iteration: 2548; Percent complete: 63.7%; Average loss: 2.8951
Iteration: 2549; Percent complete: 63.7%; Average loss: 2.9762
Iteration: 2550; Percent complete: 63.7%; Average loss: 3.0753
Iteration: 2551; Percent complete: 63.8%; Average loss: 2.9692
Iteration: 2552; Percent complete: 63.8%; Average loss: 3.1166
Iteration: 2553; Percent complete: 63.8%; Average loss: 2.8418
Iteration: 2554; Percent complete: 63.8%; Average loss: 3.0746
Iteration: 2555; Percent complete: 63.9%; Average loss: 3.1776
Iteration: 2556; Percent complete: 63.9%; Average loss: 2.9344
Iteration: 2557; Percent complete: 63.9%; Average loss: 2.9443
Iteration: 2558; Percent complete: 63.9%; Average loss: 2.8101
Iteration: 2559; Percent complete: 64.0%; Average loss: 3.0284
Iteration: 2560; Percent complete: 64.0%; Average loss: 2.8990
Iteration: 2561; Percent complete: 64.0%; Average loss: 3.1460
Iteration: 2562; Percent complete: 64.0%; Average loss: 2.8156
Iteration: 2563; Percent complete: 64.1%; Average loss: 3.0734
Iteration: 2564; Percent complete: 64.1%; Average loss: 2.8373
Iteration: 2565; Percent complete: 64.1%; Average loss: 2.6433
Iteration: 2566; Percent complete: 64.1%; Average loss: 2.8402
Iteration: 2567; Percent complete: 64.2%; Average loss: 2.9585
Iteration: 2568; Percent complete: 64.2%; Average loss: 3.1208
Iteration: 2569; Percent complete: 64.2%; Average loss: 2.9828
Iteration: 2570; Percent complete: 64.2%; Average loss: 2.9667
Iteration: 2571; Percent complete: 64.3%; Average loss: 3.0825
Iteration: 2572; Percent complete: 64.3%; Average loss: 2.9577
Iteration: 2573; Percent complete: 64.3%; Average loss: 2.9278
Iteration: 2574; Percent complete: 64.3%; Average loss: 3.1239
Iteration: 2575; Percent complete: 64.4%; Average loss: 2.7332
Iteration: 2576; Percent complete: 64.4%; Average loss: 2.8875
Iteration: 2577; Percent complete: 64.4%; Average loss: 3.1292
Iteration: 2578; Percent complete: 64.5%; Average loss: 2.8647
Iteration: 2579; Percent complete: 64.5%; Average loss: 2.8935
Iteration: 2580; Percent complete: 64.5%; Average loss: 2.8960
Iteration: 2581; Percent complete: 64.5%; Average loss: 3.0516
Iteration: 2582; Percent complete: 64.5%; Average loss: 2.8134
Iteration: 2583; Percent complete: 64.6%; Average loss: 3.0083
Iteration: 2584; Percent complete: 64.6%; Average loss: 2.8318
Iteration: 2585; Percent complete: 64.6%; Average loss: 3.2043
Iteration: 2586; Percent complete: 64.6%; Average loss: 2.9528
Iteration: 2587; Percent complete: 64.7%; Average loss: 3.0834
Iteration: 2588; Percent complete: 64.7%; Average loss: 2.9821
Iteration: 2589; Percent complete: 64.7%; Average loss: 3.0513
Iteration: 2590; Percent complete: 64.8%; Average loss: 2.8930
Iteration: 2591; Percent complete: 64.8%; Average loss: 3.0340
Iteration: 2592; Percent complete: 64.8%; Average loss: 2.9833
Iteration: 2593; Percent complete: 64.8%; Average loss: 2.8504
Iteration: 2594; Percent complete: 64.8%; Average loss: 3.0638
Iteration: 2595; Percent complete: 64.9%; Average loss: 2.9324
Iteration: 2596; Percent complete: 64.9%; Average loss: 3.1650
Iteration: 2597; Percent complete: 64.9%; Average loss: 2.7738
Iteration: 2598; Percent complete: 65.0%; Average loss: 3.1282
Iteration: 2599; Percent complete: 65.0%; Average loss: 2.8935
Iteration: 2600; Percent complete: 65.0%; Average loss: 2.9735
Iteration: 2601; Percent complete: 65.0%; Average loss: 2.9488
Iteration: 2602; Percent complete: 65.0%; Average loss: 2.9395
Iteration: 2603; Percent complete: 65.1%; Average loss: 3.0925
Iteration: 2604; Percent complete: 65.1%; Average loss: 3.3804
Iteration: 2605; Percent complete: 65.1%; Average loss: 3.0513
Iteration: 2606; Percent complete: 65.1%; Average loss: 2.9367
Iteration: 2607; Percent complete: 65.2%; Average loss: 2.8870
Iteration: 2608; Percent complete: 65.2%; Average loss: 2.9640
Iteration: 2609; Percent complete: 65.2%; Average loss: 2.9390
Iteration: 2610; Percent complete: 65.2%; Average loss: 2.8579
Iteration: 2611; Percent complete: 65.3%; Average loss: 2.7349
Iteration: 2612; Percent complete: 65.3%; Average loss: 3.0857
Iteration: 2613; Percent complete: 65.3%; Average loss: 2.9091
Iteration: 2614; Percent complete: 65.3%; Average loss: 2.8172
Iteration: 2615; Percent complete: 65.4%; Average loss: 3.0257
Iteration: 2616; Percent complete: 65.4%; Average loss: 3.0368
Iteration: 2617; Percent complete: 65.4%; Average loss: 2.8132
Iteration: 2618; Percent complete: 65.5%; Average loss: 2.7311
Iteration: 2619; Percent complete: 65.5%; Average loss: 3.1343
Iteration: 2620; Percent complete: 65.5%; Average loss: 3.1410
Iteration: 2621; Percent complete: 65.5%; Average loss: 3.1070
Iteration: 2622; Percent complete: 65.5%; Average loss: 2.6985
Iteration: 2623; Percent complete: 65.6%; Average loss: 3.0086
Iteration: 2624; Percent complete: 65.6%; Average loss: 3.0780
Iteration: 2625; Percent complete: 65.6%; Average loss: 3.0440
Iteration: 2626; Percent complete: 65.6%; Average loss: 2.8134
Iteration: 2627; Percent complete: 65.7%; Average loss: 3.0313
Iteration: 2628; Percent complete: 65.7%; Average loss: 3.0497
Iteration: 2629; Percent complete: 65.7%; Average loss: 3.0947
Iteration: 2630; Percent complete: 65.8%; Average loss: 2.9209
Iteration: 2631; Percent complete: 65.8%; Average loss: 3.0296
Iteration: 2632; Percent complete: 65.8%; Average loss: 3.0647
Iteration: 2633; Percent complete: 65.8%; Average loss: 2.9341
Iteration: 2634; Percent complete: 65.8%; Average loss: 2.7563
Iteration: 2635; Percent complete: 65.9%; Average loss: 2.9971
Iteration: 2636; Percent complete: 65.9%; Average loss: 3.2164
Iteration: 2637; Percent complete: 65.9%; Average loss: 2.8215
Iteration: 2638; Percent complete: 66.0%; Average loss: 2.7911
Iteration: 2639; Percent complete: 66.0%; Average loss: 2.9633
Iteration: 2640; Percent complete: 66.0%; Average loss: 3.0977
Iteration: 2641; Percent complete: 66.0%; Average loss: 2.7454
Iteration: 2642; Percent complete: 66.0%; Average loss: 2.8990
Iteration: 2643; Percent complete: 66.1%; Average loss: 3.2114
Iteration: 2644; Percent complete: 66.1%; Average loss: 3.0958
Iteration: 2645; Percent complete: 66.1%; Average loss: 2.8414
Iteration: 2646; Percent complete: 66.1%; Average loss: 2.8345
Iteration: 2647; Percent complete: 66.2%; Average loss: 2.6616
Iteration: 2648; Percent complete: 66.2%; Average loss: 2.8403
Iteration: 2649; Percent complete: 66.2%; Average loss: 2.9910
Iteration: 2650; Percent complete: 66.2%; Average loss: 3.1376
Iteration: 2651; Percent complete: 66.3%; Average loss: 2.8221
Iteration: 2652; Percent complete: 66.3%; Average loss: 2.8092
Iteration: 2653; Percent complete: 66.3%; Average loss: 2.8902
Iteration: 2654; Percent complete: 66.3%; Average loss: 2.7770
Iteration: 2655; Percent complete: 66.4%; Average loss: 2.8977
Iteration: 2656; Percent complete: 66.4%; Average loss: 3.0085
Iteration: 2657; Percent complete: 66.4%; Average loss: 3.0273
Iteration: 2658; Percent complete: 66.5%; Average loss: 2.9860
Iteration: 2659; Percent complete: 66.5%; Average loss: 2.9850
Iteration: 2660; Percent complete: 66.5%; Average loss: 2.8862
Iteration: 2661; Percent complete: 66.5%; Average loss: 2.9132
Iteration: 2662; Percent complete: 66.5%; Average loss: 3.0384
Iteration: 2663; Percent complete: 66.6%; Average loss: 3.1264
Iteration: 2664; Percent complete: 66.6%; Average loss: 3.0436
Iteration: 2665; Percent complete: 66.6%; Average loss: 2.7367
Iteration: 2666; Percent complete: 66.6%; Average loss: 3.0342
Iteration: 2667; Percent complete: 66.7%; Average loss: 2.9856
Iteration: 2668; Percent complete: 66.7%; Average loss: 2.9091
Iteration: 2669; Percent complete: 66.7%; Average loss: 2.9160
Iteration: 2670; Percent complete: 66.8%; Average loss: 2.9724
Iteration: 2671; Percent complete: 66.8%; Average loss: 2.8174
Iteration: 2672; Percent complete: 66.8%; Average loss: 2.9558
Iteration: 2673; Percent complete: 66.8%; Average loss: 3.1032
Iteration: 2674; Percent complete: 66.8%; Average loss: 2.7043
Iteration: 2675; Percent complete: 66.9%; Average loss: 3.0826
Iteration: 2676; Percent complete: 66.9%; Average loss: 2.9787
Iteration: 2677; Percent complete: 66.9%; Average loss: 2.8004
Iteration: 2678; Percent complete: 67.0%; Average loss: 2.8878
Iteration: 2679; Percent complete: 67.0%; Average loss: 2.6935
Iteration: 2680; Percent complete: 67.0%; Average loss: 3.0529
Iteration: 2681; Percent complete: 67.0%; Average loss: 2.8321
Iteration: 2682; Percent complete: 67.0%; Average loss: 2.8798
Iteration: 2683; Percent complete: 67.1%; Average loss: 3.0110
Iteration: 2684; Percent complete: 67.1%; Average loss: 3.0556
Iteration: 2685; Percent complete: 67.1%; Average loss: 3.0391
Iteration: 2686; Percent complete: 67.2%; Average loss: 2.6736
Iteration: 2687; Percent complete: 67.2%; Average loss: 3.0683
Iteration: 2688; Percent complete: 67.2%; Average loss: 2.8718
Iteration: 2689; Percent complete: 67.2%; Average loss: 3.0719
Iteration: 2690; Percent complete: 67.2%; Average loss: 2.7546
Iteration: 2691; Percent complete: 67.3%; Average loss: 2.8785
Iteration: 2692; Percent complete: 67.3%; Average loss: 3.1076
Iteration: 2693; Percent complete: 67.3%; Average loss: 2.9560
Iteration: 2694; Percent complete: 67.3%; Average loss: 2.7533
Iteration: 2695; Percent complete: 67.4%; Average loss: 2.9832
Iteration: 2696; Percent complete: 67.4%; Average loss: 2.9343
Iteration: 2697; Percent complete: 67.4%; Average loss: 2.9986
Iteration: 2698; Percent complete: 67.5%; Average loss: 2.9466
Iteration: 2699; Percent complete: 67.5%; Average loss: 3.0100
Iteration: 2700; Percent complete: 67.5%; Average loss: 2.8979
Iteration: 2701; Percent complete: 67.5%; Average loss: 2.8906
Iteration: 2702; Percent complete: 67.5%; Average loss: 3.0279
Iteration: 2703; Percent complete: 67.6%; Average loss: 2.9814
Iteration: 2704; Percent complete: 67.6%; Average loss: 3.0487
Iteration: 2705; Percent complete: 67.6%; Average loss: 3.0424
Iteration: 2706; Percent complete: 67.7%; Average loss: 3.1632
Iteration: 2707; Percent complete: 67.7%; Average loss: 2.8477
Iteration: 2708; Percent complete: 67.7%; Average loss: 2.8004
Iteration: 2709; Percent complete: 67.7%; Average loss: 3.0256
Iteration: 2710; Percent complete: 67.8%; Average loss: 2.8719
Iteration: 2711; Percent complete: 67.8%; Average loss: 2.9777
Iteration: 2712; Percent complete: 67.8%; Average loss: 2.8910
Iteration: 2713; Percent complete: 67.8%; Average loss: 2.9887
Iteration: 2714; Percent complete: 67.8%; Average loss: 3.0302
Iteration: 2715; Percent complete: 67.9%; Average loss: 2.9693
Iteration: 2716; Percent complete: 67.9%; Average loss: 2.7711
Iteration: 2717; Percent complete: 67.9%; Average loss: 3.1276
Iteration: 2718; Percent complete: 68.0%; Average loss: 3.0542
Iteration: 2719; Percent complete: 68.0%; Average loss: 2.9575
Iteration: 2720; Percent complete: 68.0%; Average loss: 2.8322
Iteration: 2721; Percent complete: 68.0%; Average loss: 2.7592
Iteration: 2722; Percent complete: 68.0%; Average loss: 2.7889
Iteration: 2723; Percent complete: 68.1%; Average loss: 2.9844
Iteration: 2724; Percent complete: 68.1%; Average loss: 2.9345
Iteration: 2725; Percent complete: 68.1%; Average loss: 2.9164
Iteration: 2726; Percent complete: 68.2%; Average loss: 2.8585
Iteration: 2727; Percent complete: 68.2%; Average loss: 2.8445
Iteration: 2728; Percent complete: 68.2%; Average loss: 2.8218
Iteration: 2729; Percent complete: 68.2%; Average loss: 3.1264
Iteration: 2730; Percent complete: 68.2%; Average loss: 2.8390
Iteration: 2731; Percent complete: 68.3%; Average loss: 3.1239
Iteration: 2732; Percent complete: 68.3%; Average loss: 2.7123
Iteration: 2733; Percent complete: 68.3%; Average loss: 2.8516
Iteration: 2734; Percent complete: 68.3%; Average loss: 3.0002
Iteration: 2735; Percent complete: 68.4%; Average loss: 2.8823
Iteration: 2736; Percent complete: 68.4%; Average loss: 2.9535
Iteration: 2737; Percent complete: 68.4%; Average loss: 2.7034
Iteration: 2738; Percent complete: 68.5%; Average loss: 2.7936
Iteration: 2739; Percent complete: 68.5%; Average loss: 2.9707
Iteration: 2740; Percent complete: 68.5%; Average loss: 2.9312
Iteration: 2741; Percent complete: 68.5%; Average loss: 2.8153
Iteration: 2742; Percent complete: 68.5%; Average loss: 2.8690
Iteration: 2743; Percent complete: 68.6%; Average loss: 3.1494
Iteration: 2744; Percent complete: 68.6%; Average loss: 2.8971
Iteration: 2745; Percent complete: 68.6%; Average loss: 2.8586
Iteration: 2746; Percent complete: 68.7%; Average loss: 2.9201
Iteration: 2747; Percent complete: 68.7%; Average loss: 3.1435
Iteration: 2748; Percent complete: 68.7%; Average loss: 3.0559
Iteration: 2749; Percent complete: 68.7%; Average loss: 3.0281
Iteration: 2750; Percent complete: 68.8%; Average loss: 2.8994
Iteration: 2751; Percent complete: 68.8%; Average loss: 2.8376
Iteration: 2752; Percent complete: 68.8%; Average loss: 2.9469
Iteration: 2753; Percent complete: 68.8%; Average loss: 2.8410
Iteration: 2754; Percent complete: 68.8%; Average loss: 2.7900
Iteration: 2755; Percent complete: 68.9%; Average loss: 2.9216
Iteration: 2756; Percent complete: 68.9%; Average loss: 2.6813
Iteration: 2757; Percent complete: 68.9%; Average loss: 3.0170
Iteration: 2758; Percent complete: 69.0%; Average loss: 2.8257
Iteration: 2759; Percent complete: 69.0%; Average loss: 3.0115
Iteration: 2760; Percent complete: 69.0%; Average loss: 3.1456
Iteration: 2761; Percent complete: 69.0%; Average loss: 2.8858
Iteration: 2762; Percent complete: 69.0%; Average loss: 3.1047
Iteration: 2763; Percent complete: 69.1%; Average loss: 3.0095
Iteration: 2764; Percent complete: 69.1%; Average loss: 2.9524
Iteration: 2765; Percent complete: 69.1%; Average loss: 2.7513
Iteration: 2766; Percent complete: 69.2%; Average loss: 2.8337
Iteration: 2767; Percent complete: 69.2%; Average loss: 3.0124
Iteration: 2768; Percent complete: 69.2%; Average loss: 2.8310
Iteration: 2769; Percent complete: 69.2%; Average loss: 2.8158
Iteration: 2770; Percent complete: 69.2%; Average loss: 2.8150
Iteration: 2771; Percent complete: 69.3%; Average loss: 2.9025
Iteration: 2772; Percent complete: 69.3%; Average loss: 3.0717
Iteration: 2773; Percent complete: 69.3%; Average loss: 2.7976
Iteration: 2774; Percent complete: 69.3%; Average loss: 2.8129
Iteration: 2775; Percent complete: 69.4%; Average loss: 2.8629
Iteration: 2776; Percent complete: 69.4%; Average loss: 3.0547
Iteration: 2777; Percent complete: 69.4%; Average loss: 2.9804
Iteration: 2778; Percent complete: 69.5%; Average loss: 2.7584
Iteration: 2779; Percent complete: 69.5%; Average loss: 2.8980
Iteration: 2780; Percent complete: 69.5%; Average loss: 3.2313
Iteration: 2781; Percent complete: 69.5%; Average loss: 3.1849
Iteration: 2782; Percent complete: 69.5%; Average loss: 3.0787
Iteration: 2783; Percent complete: 69.6%; Average loss: 2.8921
Iteration: 2784; Percent complete: 69.6%; Average loss: 2.7729
Iteration: 2785; Percent complete: 69.6%; Average loss: 2.8323
Iteration: 2786; Percent complete: 69.7%; Average loss: 2.7138
Iteration: 2787; Percent complete: 69.7%; Average loss: 2.8061
Iteration: 2788; Percent complete: 69.7%; Average loss: 2.9348
Iteration: 2789; Percent complete: 69.7%; Average loss: 2.9572
Iteration: 2790; Percent complete: 69.8%; Average loss: 2.9102
Iteration: 2791; Percent complete: 69.8%; Average loss: 2.7405
Iteration: 2792; Percent complete: 69.8%; Average loss: 2.9374
Iteration: 2793; Percent complete: 69.8%; Average loss: 2.9228
Iteration: 2794; Percent complete: 69.8%; Average loss: 2.9829
Iteration: 2795; Percent complete: 69.9%; Average loss: 2.7195
Iteration: 2796; Percent complete: 69.9%; Average loss: 2.9360
Iteration: 2797; Percent complete: 69.9%; Average loss: 2.9550
Iteration: 2798; Percent complete: 70.0%; Average loss: 3.0633
Iteration: 2799; Percent complete: 70.0%; Average loss: 2.6800
Iteration: 2800; Percent complete: 70.0%; Average loss: 3.1799
Iteration: 2801; Percent complete: 70.0%; Average loss: 2.9294
Iteration: 2802; Percent complete: 70.0%; Average loss: 2.7814
Iteration: 2803; Percent complete: 70.1%; Average loss: 2.9884
Iteration: 2804; Percent complete: 70.1%; Average loss: 3.0291
Iteration: 2805; Percent complete: 70.1%; Average loss: 2.7717
Iteration: 2806; Percent complete: 70.2%; Average loss: 2.8775
Iteration: 2807; Percent complete: 70.2%; Average loss: 2.8643
Iteration: 2808; Percent complete: 70.2%; Average loss: 3.1025
Iteration: 2809; Percent complete: 70.2%; Average loss: 3.0938
Iteration: 2810; Percent complete: 70.2%; Average loss: 2.8646
Iteration: 2811; Percent complete: 70.3%; Average loss: 2.7263
Iteration: 2812; Percent complete: 70.3%; Average loss: 3.0500
Iteration: 2813; Percent complete: 70.3%; Average loss: 2.9340
Iteration: 2814; Percent complete: 70.3%; Average loss: 2.6685
Iteration: 2815; Percent complete: 70.4%; Average loss: 2.8652
Iteration: 2816; Percent complete: 70.4%; Average loss: 2.8752
Iteration: 2817; Percent complete: 70.4%; Average loss: 3.0406
Iteration: 2818; Percent complete: 70.5%; Average loss: 3.0191
Iteration: 2819; Percent complete: 70.5%; Average loss: 2.6496
Iteration: 2820; Percent complete: 70.5%; Average loss: 2.7198
Iteration: 2821; Percent complete: 70.5%; Average loss: 2.9720
Iteration: 2822; Percent complete: 70.5%; Average loss: 3.1247
Iteration: 2823; Percent complete: 70.6%; Average loss: 2.9722
Iteration: 2824; Percent complete: 70.6%; Average loss: 2.6199
Iteration: 2825; Percent complete: 70.6%; Average loss: 2.9400
Iteration: 2826; Percent complete: 70.7%; Average loss: 2.8704
Iteration: 2827; Percent complete: 70.7%; Average loss: 2.7781
Iteration: 2828; Percent complete: 70.7%; Average loss: 3.0080
Iteration: 2829; Percent complete: 70.7%; Average loss: 2.9429
Iteration: 2830; Percent complete: 70.8%; Average loss: 3.1290
Iteration: 2831; Percent complete: 70.8%; Average loss: 3.1029
Iteration: 2832; Percent complete: 70.8%; Average loss: 3.0907
Iteration: 2833; Percent complete: 70.8%; Average loss: 2.9109
Iteration: 2834; Percent complete: 70.9%; Average loss: 3.1531
Iteration: 2835; Percent complete: 70.9%; Average loss: 2.8010
Iteration: 2836; Percent complete: 70.9%; Average loss: 2.6398
Iteration: 2837; Percent complete: 70.9%; Average loss: 2.9365
Iteration: 2838; Percent complete: 71.0%; Average loss: 2.7245
Iteration: 2839; Percent complete: 71.0%; Average loss: 2.8366
Iteration: 2840; Percent complete: 71.0%; Average loss: 2.9463
Iteration: 2841; Percent complete: 71.0%; Average loss: 2.8624
Iteration: 2842; Percent complete: 71.0%; Average loss: 2.7370
Iteration: 2843; Percent complete: 71.1%; Average loss: 2.7956
Iteration: 2844; Percent complete: 71.1%; Average loss: 2.9019
Iteration: 2845; Percent complete: 71.1%; Average loss: 2.9879
Iteration: 2846; Percent complete: 71.2%; Average loss: 3.0947
Iteration: 2847; Percent complete: 71.2%; Average loss: 3.1663
Iteration: 2848; Percent complete: 71.2%; Average loss: 2.9139
Iteration: 2849; Percent complete: 71.2%; Average loss: 2.8927
Iteration: 2850; Percent complete: 71.2%; Average loss: 2.9163
Iteration: 2851; Percent complete: 71.3%; Average loss: 2.8440
Iteration: 2852; Percent complete: 71.3%; Average loss: 2.8388
Iteration: 2853; Percent complete: 71.3%; Average loss: 2.9484
Iteration: 2854; Percent complete: 71.4%; Average loss: 3.1529
Iteration: 2855; Percent complete: 71.4%; Average loss: 2.9596
Iteration: 2856; Percent complete: 71.4%; Average loss: 3.1530
Iteration: 2857; Percent complete: 71.4%; Average loss: 3.3048
Iteration: 2858; Percent complete: 71.5%; Average loss: 2.7153
Iteration: 2859; Percent complete: 71.5%; Average loss: 2.6925
Iteration: 2860; Percent complete: 71.5%; Average loss: 3.0682
Iteration: 2861; Percent complete: 71.5%; Average loss: 3.0024
Iteration: 2862; Percent complete: 71.5%; Average loss: 2.9144
Iteration: 2863; Percent complete: 71.6%; Average loss: 3.0007
Iteration: 2864; Percent complete: 71.6%; Average loss: 2.9867
Iteration: 2865; Percent complete: 71.6%; Average loss: 3.0511
Iteration: 2866; Percent complete: 71.7%; Average loss: 3.0022
Iteration: 2867; Percent complete: 71.7%; Average loss: 2.8382
Iteration: 2868; Percent complete: 71.7%; Average loss: 2.7944
Iteration: 2869; Percent complete: 71.7%; Average loss: 2.8752
Iteration: 2870; Percent complete: 71.8%; Average loss: 3.0232
Iteration: 2871; Percent complete: 71.8%; Average loss: 2.9726
Iteration: 2872; Percent complete: 71.8%; Average loss: 3.2391
Iteration: 2873; Percent complete: 71.8%; Average loss: 3.0235
Iteration: 2874; Percent complete: 71.9%; Average loss: 2.9074
Iteration: 2875; Percent complete: 71.9%; Average loss: 2.7986
Iteration: 2876; Percent complete: 71.9%; Average loss: 2.6427
Iteration: 2877; Percent complete: 71.9%; Average loss: 2.9351
Iteration: 2878; Percent complete: 72.0%; Average loss: 2.8914
Iteration: 2879; Percent complete: 72.0%; Average loss: 2.9362
Iteration: 2880; Percent complete: 72.0%; Average loss: 3.1171
Iteration: 2881; Percent complete: 72.0%; Average loss: 3.1384
Iteration: 2882; Percent complete: 72.0%; Average loss: 2.6803
Iteration: 2883; Percent complete: 72.1%; Average loss: 2.9768
Iteration: 2884; Percent complete: 72.1%; Average loss: 2.7451
Iteration: 2885; Percent complete: 72.1%; Average loss: 2.9752
Iteration: 2886; Percent complete: 72.2%; Average loss: 2.7298
Iteration: 2887; Percent complete: 72.2%; Average loss: 3.1216
Iteration: 2888; Percent complete: 72.2%; Average loss: 3.0891
Iteration: 2889; Percent complete: 72.2%; Average loss: 2.7752
Iteration: 2890; Percent complete: 72.2%; Average loss: 2.9939
Iteration: 2891; Percent complete: 72.3%; Average loss: 3.1195
Iteration: 2892; Percent complete: 72.3%; Average loss: 3.1905
Iteration: 2893; Percent complete: 72.3%; Average loss: 3.1429
Iteration: 2894; Percent complete: 72.4%; Average loss: 2.7827
Iteration: 2895; Percent complete: 72.4%; Average loss: 3.0860
Iteration: 2896; Percent complete: 72.4%; Average loss: 3.0000
Iteration: 2897; Percent complete: 72.4%; Average loss: 2.9245
Iteration: 2898; Percent complete: 72.5%; Average loss: 2.6882
Iteration: 2899; Percent complete: 72.5%; Average loss: 2.7992
Iteration: 2900; Percent complete: 72.5%; Average loss: 3.0603
Iteration: 2901; Percent complete: 72.5%; Average loss: 2.9160
Iteration: 2902; Percent complete: 72.5%; Average loss: 3.0924
Iteration: 2903; Percent complete: 72.6%; Average loss: 2.7921
Iteration: 2904; Percent complete: 72.6%; Average loss: 2.9718
Iteration: 2905; Percent complete: 72.6%; Average loss: 2.8624
Iteration: 2906; Percent complete: 72.7%; Average loss: 3.1603
Iteration: 2907; Percent complete: 72.7%; Average loss: 2.6938
Iteration: 2908; Percent complete: 72.7%; Average loss: 3.0082
Iteration: 2909; Percent complete: 72.7%; Average loss: 2.9422
Iteration: 2910; Percent complete: 72.8%; Average loss: 2.9291
Iteration: 2911; Percent complete: 72.8%; Average loss: 2.8774
Iteration: 2912; Percent complete: 72.8%; Average loss: 3.0255
Iteration: 2913; Percent complete: 72.8%; Average loss: 2.8076
Iteration: 2914; Percent complete: 72.9%; Average loss: 2.7979
Iteration: 2915; Percent complete: 72.9%; Average loss: 2.8854
Iteration: 2916; Percent complete: 72.9%; Average loss: 2.9102
Iteration: 2917; Percent complete: 72.9%; Average loss: 2.7644
Iteration: 2918; Percent complete: 73.0%; Average loss: 2.7796
Iteration: 2919; Percent complete: 73.0%; Average loss: 2.8839
Iteration: 2920; Percent complete: 73.0%; Average loss: 3.0510
Iteration: 2921; Percent complete: 73.0%; Average loss: 3.1162
Iteration: 2922; Percent complete: 73.0%; Average loss: 2.6576
Iteration: 2923; Percent complete: 73.1%; Average loss: 2.7890
Iteration: 2924; Percent complete: 73.1%; Average loss: 2.8052
Iteration: 2925; Percent complete: 73.1%; Average loss: 2.8178
Iteration: 2926; Percent complete: 73.2%; Average loss: 2.9040
Iteration: 2927; Percent complete: 73.2%; Average loss: 2.9708
Iteration: 2928; Percent complete: 73.2%; Average loss: 2.8730
Iteration: 2929; Percent complete: 73.2%; Average loss: 2.8783
Iteration: 2930; Percent complete: 73.2%; Average loss: 2.7564
Iteration: 2931; Percent complete: 73.3%; Average loss: 2.9212
Iteration: 2932; Percent complete: 73.3%; Average loss: 2.7831
Iteration: 2933; Percent complete: 73.3%; Average loss: 2.4173
Iteration: 2934; Percent complete: 73.4%; Average loss: 3.1082
Iteration: 2935; Percent complete: 73.4%; Average loss: 2.9749
Iteration: 2936; Percent complete: 73.4%; Average loss: 2.9479
Iteration: 2937; Percent complete: 73.4%; Average loss: 2.9122
Iteration: 2938; Percent complete: 73.5%; Average loss: 3.0905
Iteration: 2939; Percent complete: 73.5%; Average loss: 2.8161
Iteration: 2940; Percent complete: 73.5%; Average loss: 3.2034
Iteration: 2941; Percent complete: 73.5%; Average loss: 2.7320
Iteration: 2942; Percent complete: 73.6%; Average loss: 3.0316
Iteration: 2943; Percent complete: 73.6%; Average loss: 2.9839
Iteration: 2944; Percent complete: 73.6%; Average loss: 2.9779
Iteration: 2945; Percent complete: 73.6%; Average loss: 2.8150
Iteration: 2946; Percent complete: 73.7%; Average loss: 2.8116
Iteration: 2947; Percent complete: 73.7%; Average loss: 2.8375
Iteration: 2948; Percent complete: 73.7%; Average loss: 3.0658
Iteration: 2949; Percent complete: 73.7%; Average loss: 2.7760
Iteration: 2950; Percent complete: 73.8%; Average loss: 2.8314
Iteration: 2951; Percent complete: 73.8%; Average loss: 2.7302
Iteration: 2952; Percent complete: 73.8%; Average loss: 2.7489
Iteration: 2953; Percent complete: 73.8%; Average loss: 2.8003
Iteration: 2954; Percent complete: 73.9%; Average loss: 2.6514
Iteration: 2955; Percent complete: 73.9%; Average loss: 2.8129
Iteration: 2956; Percent complete: 73.9%; Average loss: 2.8720
Iteration: 2957; Percent complete: 73.9%; Average loss: 2.9088
Iteration: 2958; Percent complete: 74.0%; Average loss: 3.1309
Iteration: 2959; Percent complete: 74.0%; Average loss: 2.9219
Iteration: 2960; Percent complete: 74.0%; Average loss: 3.1955
Iteration: 2961; Percent complete: 74.0%; Average loss: 3.1219
Iteration: 2962; Percent complete: 74.1%; Average loss: 2.8395
Iteration: 2963; Percent complete: 74.1%; Average loss: 2.9457
Iteration: 2964; Percent complete: 74.1%; Average loss: 2.8563
Iteration: 2965; Percent complete: 74.1%; Average loss: 2.8992
Iteration: 2966; Percent complete: 74.2%; Average loss: 2.9192
Iteration: 2967; Percent complete: 74.2%; Average loss: 2.8198
Iteration: 2968; Percent complete: 74.2%; Average loss: 3.0925
Iteration: 2969; Percent complete: 74.2%; Average loss: 2.8812
Iteration: 2970; Percent complete: 74.2%; Average loss: 2.8820
Iteration: 2971; Percent complete: 74.3%; Average loss: 3.1652
Iteration: 2972; Percent complete: 74.3%; Average loss: 2.9098
Iteration: 2973; Percent complete: 74.3%; Average loss: 2.8347
Iteration: 2974; Percent complete: 74.4%; Average loss: 2.8428
Iteration: 2975; Percent complete: 74.4%; Average loss: 2.9480
Iteration: 2976; Percent complete: 74.4%; Average loss: 3.0231
Iteration: 2977; Percent complete: 74.4%; Average loss: 2.8567
Iteration: 2978; Percent complete: 74.5%; Average loss: 2.9714
Iteration: 2979; Percent complete: 74.5%; Average loss: 3.0676
Iteration: 2980; Percent complete: 74.5%; Average loss: 2.7823
Iteration: 2981; Percent complete: 74.5%; Average loss: 2.9183
Iteration: 2982; Percent complete: 74.6%; Average loss: 2.8034
Iteration: 2983; Percent complete: 74.6%; Average loss: 2.8716
Iteration: 2984; Percent complete: 74.6%; Average loss: 3.1111
Iteration: 2985; Percent complete: 74.6%; Average loss: 3.0037
Iteration: 2986; Percent complete: 74.7%; Average loss: 2.8499
Iteration: 2987; Percent complete: 74.7%; Average loss: 2.9372
Iteration: 2988; Percent complete: 74.7%; Average loss: 2.6869
Iteration: 2989; Percent complete: 74.7%; Average loss: 2.7415
Iteration: 2990; Percent complete: 74.8%; Average loss: 3.0126
Iteration: 2991; Percent complete: 74.8%; Average loss: 2.7099
Iteration: 2992; Percent complete: 74.8%; Average loss: 2.9132
Iteration: 2993; Percent complete: 74.8%; Average loss: 2.9401
Iteration: 2994; Percent complete: 74.9%; Average loss: 2.7900
Iteration: 2995; Percent complete: 74.9%; Average loss: 2.9864
Iteration: 2996; Percent complete: 74.9%; Average loss: 2.7834
Iteration: 2997; Percent complete: 74.9%; Average loss: 2.7755
Iteration: 2998; Percent complete: 75.0%; Average loss: 3.0325
Iteration: 2999; Percent complete: 75.0%; Average loss: 2.8679
Iteration: 3000; Percent complete: 75.0%; Average loss: 2.7763
Iteration: 3001; Percent complete: 75.0%; Average loss: 2.8924
Iteration: 3002; Percent complete: 75.0%; Average loss: 2.5339
Iteration: 3003; Percent complete: 75.1%; Average loss: 2.9155
Iteration: 3004; Percent complete: 75.1%; Average loss: 3.2776
Iteration: 3005; Percent complete: 75.1%; Average loss: 2.7945
Iteration: 3006; Percent complete: 75.1%; Average loss: 3.1221
Iteration: 3007; Percent complete: 75.2%; Average loss: 2.6958
Iteration: 3008; Percent complete: 75.2%; Average loss: 2.8534
Iteration: 3009; Percent complete: 75.2%; Average loss: 2.8258
Iteration: 3010; Percent complete: 75.2%; Average loss: 2.9095
Iteration: 3011; Percent complete: 75.3%; Average loss: 2.9505
Iteration: 3012; Percent complete: 75.3%; Average loss: 2.8529
Iteration: 3013; Percent complete: 75.3%; Average loss: 2.8068
Iteration: 3014; Percent complete: 75.3%; Average loss: 2.7879
Iteration: 3015; Percent complete: 75.4%; Average loss: 2.7559
Iteration: 3016; Percent complete: 75.4%; Average loss: 2.6401
Iteration: 3017; Percent complete: 75.4%; Average loss: 2.6306
Iteration: 3018; Percent complete: 75.4%; Average loss: 2.8556
Iteration: 3019; Percent complete: 75.5%; Average loss: 3.1147
Iteration: 3020; Percent complete: 75.5%; Average loss: 2.9723
Iteration: 3021; Percent complete: 75.5%; Average loss: 2.7176
Iteration: 3022; Percent complete: 75.5%; Average loss: 2.8970
Iteration: 3023; Percent complete: 75.6%; Average loss: 2.7123
Iteration: 3024; Percent complete: 75.6%; Average loss: 3.0614
Iteration: 3025; Percent complete: 75.6%; Average loss: 3.1085
Iteration: 3026; Percent complete: 75.6%; Average loss: 2.7295
Iteration: 3027; Percent complete: 75.7%; Average loss: 2.7645
Iteration: 3028; Percent complete: 75.7%; Average loss: 2.9567
Iteration: 3029; Percent complete: 75.7%; Average loss: 2.6664
Iteration: 3030; Percent complete: 75.8%; Average loss: 2.9023
Iteration: 3031; Percent complete: 75.8%; Average loss: 2.9166
Iteration: 3032; Percent complete: 75.8%; Average loss: 2.7386
Iteration: 3033; Percent complete: 75.8%; Average loss: 3.1610
Iteration: 3034; Percent complete: 75.8%; Average loss: 2.6394
Iteration: 3035; Percent complete: 75.9%; Average loss: 2.8616
Iteration: 3036; Percent complete: 75.9%; Average loss: 2.6794
Iteration: 3037; Percent complete: 75.9%; Average loss: 3.0877
Iteration: 3038; Percent complete: 75.9%; Average loss: 2.9931
Iteration: 3039; Percent complete: 76.0%; Average loss: 2.7142
Iteration: 3040; Percent complete: 76.0%; Average loss: 2.8303
Iteration: 3041; Percent complete: 76.0%; Average loss: 2.7506
Iteration: 3042; Percent complete: 76.0%; Average loss: 3.1466
Iteration: 3043; Percent complete: 76.1%; Average loss: 2.9037
Iteration: 3044; Percent complete: 76.1%; Average loss: 2.4777
Iteration: 3045; Percent complete: 76.1%; Average loss: 3.0244
Iteration: 3046; Percent complete: 76.1%; Average loss: 2.9049
Iteration: 3047; Percent complete: 76.2%; Average loss: 2.9151
Iteration: 3048; Percent complete: 76.2%; Average loss: 2.8133
Iteration: 3049; Percent complete: 76.2%; Average loss: 2.7823
Iteration: 3050; Percent complete: 76.2%; Average loss: 2.8415
Iteration: 3051; Percent complete: 76.3%; Average loss: 3.1206
Iteration: 3052; Percent complete: 76.3%; Average loss: 2.6698
Iteration: 3053; Percent complete: 76.3%; Average loss: 2.9419
Iteration: 3054; Percent complete: 76.3%; Average loss: 3.0377
Iteration: 3055; Percent complete: 76.4%; Average loss: 2.9887
Iteration: 3056; Percent complete: 76.4%; Average loss: 2.7823
Iteration: 3057; Percent complete: 76.4%; Average loss: 2.9183
Iteration: 3058; Percent complete: 76.4%; Average loss: 2.8419
Iteration: 3059; Percent complete: 76.5%; Average loss: 3.0344
Iteration: 3060; Percent complete: 76.5%; Average loss: 2.8073
Iteration: 3061; Percent complete: 76.5%; Average loss: 2.9453
Iteration: 3062; Percent complete: 76.5%; Average loss: 2.7683
Iteration: 3063; Percent complete: 76.6%; Average loss: 2.7715
Iteration: 3064; Percent complete: 76.6%; Average loss: 2.9831
Iteration: 3065; Percent complete: 76.6%; Average loss: 2.8163
Iteration: 3066; Percent complete: 76.6%; Average loss: 2.8827
Iteration: 3067; Percent complete: 76.7%; Average loss: 2.9258
Iteration: 3068; Percent complete: 76.7%; Average loss: 2.9446
Iteration: 3069; Percent complete: 76.7%; Average loss: 2.8519
Iteration: 3070; Percent complete: 76.8%; Average loss: 3.0308
Iteration: 3071; Percent complete: 76.8%; Average loss: 2.7257
Iteration: 3072; Percent complete: 76.8%; Average loss: 2.7506
Iteration: 3073; Percent complete: 76.8%; Average loss: 2.7544
Iteration: 3074; Percent complete: 76.8%; Average loss: 2.6566
Iteration: 3075; Percent complete: 76.9%; Average loss: 2.8152
Iteration: 3076; Percent complete: 76.9%; Average loss: 2.8036
Iteration: 3077; Percent complete: 76.9%; Average loss: 2.9157
Iteration: 3078; Percent complete: 77.0%; Average loss: 2.8436
Iteration: 3079; Percent complete: 77.0%; Average loss: 2.7057
Iteration: 3080; Percent complete: 77.0%; Average loss: 3.0228
Iteration: 3081; Percent complete: 77.0%; Average loss: 2.9637
Iteration: 3082; Percent complete: 77.0%; Average loss: 2.8482
Iteration: 3083; Percent complete: 77.1%; Average loss: 2.7813
Iteration: 3084; Percent complete: 77.1%; Average loss: 3.0234
Iteration: 3085; Percent complete: 77.1%; Average loss: 2.8226
Iteration: 3086; Percent complete: 77.1%; Average loss: 2.6918
Iteration: 3087; Percent complete: 77.2%; Average loss: 2.7964
Iteration: 3088; Percent complete: 77.2%; Average loss: 2.9496
Iteration: 3089; Percent complete: 77.2%; Average loss: 2.7870
Iteration: 3090; Percent complete: 77.2%; Average loss: 2.8789
Iteration: 3091; Percent complete: 77.3%; Average loss: 2.7385
Iteration: 3092; Percent complete: 77.3%; Average loss: 2.8139
Iteration: 3093; Percent complete: 77.3%; Average loss: 2.9023
Iteration: 3094; Percent complete: 77.3%; Average loss: 2.8267
Iteration: 3095; Percent complete: 77.4%; Average loss: 2.9409
Iteration: 3096; Percent complete: 77.4%; Average loss: 2.7531
Iteration: 3097; Percent complete: 77.4%; Average loss: 2.7193
Iteration: 3098; Percent complete: 77.5%; Average loss: 2.9434
Iteration: 3099; Percent complete: 77.5%; Average loss: 2.5966
Iteration: 3100; Percent complete: 77.5%; Average loss: 2.7954
Iteration: 3101; Percent complete: 77.5%; Average loss: 2.6866
Iteration: 3102; Percent complete: 77.5%; Average loss: 2.8174
Iteration: 3103; Percent complete: 77.6%; Average loss: 2.9347
Iteration: 3104; Percent complete: 77.6%; Average loss: 2.7928
Iteration: 3105; Percent complete: 77.6%; Average loss: 3.1118
Iteration: 3106; Percent complete: 77.6%; Average loss: 2.9209
Iteration: 3107; Percent complete: 77.7%; Average loss: 2.9531
Iteration: 3108; Percent complete: 77.7%; Average loss: 2.8199
Iteration: 3109; Percent complete: 77.7%; Average loss: 3.0698
Iteration: 3110; Percent complete: 77.8%; Average loss: 2.8377
Iteration: 3111; Percent complete: 77.8%; Average loss: 2.9232
Iteration: 3112; Percent complete: 77.8%; Average loss: 2.8116
Iteration: 3113; Percent complete: 77.8%; Average loss: 2.7189
Iteration: 3114; Percent complete: 77.8%; Average loss: 2.8747
Iteration: 3115; Percent complete: 77.9%; Average loss: 2.9777
Iteration: 3116; Percent complete: 77.9%; Average loss: 2.9268
Iteration: 3117; Percent complete: 77.9%; Average loss: 2.8620
Iteration: 3118; Percent complete: 78.0%; Average loss: 2.7047
Iteration: 3119; Percent complete: 78.0%; Average loss: 2.8396
Iteration: 3120; Percent complete: 78.0%; Average loss: 3.0007
Iteration: 3121; Percent complete: 78.0%; Average loss: 2.7478
Iteration: 3122; Percent complete: 78.0%; Average loss: 2.9327
Iteration: 3123; Percent complete: 78.1%; Average loss: 3.0689
Iteration: 3124; Percent complete: 78.1%; Average loss: 2.8705
Iteration: 3125; Percent complete: 78.1%; Average loss: 2.7441
Iteration: 3126; Percent complete: 78.1%; Average loss: 2.7796
Iteration: 3127; Percent complete: 78.2%; Average loss: 2.8897
Iteration: 3128; Percent complete: 78.2%; Average loss: 3.0260
Iteration: 3129; Percent complete: 78.2%; Average loss: 2.7853
Iteration: 3130; Percent complete: 78.2%; Average loss: 2.7110
Iteration: 3131; Percent complete: 78.3%; Average loss: 2.9494
Iteration: 3132; Percent complete: 78.3%; Average loss: 2.7970
Iteration: 3133; Percent complete: 78.3%; Average loss: 2.7412
Iteration: 3134; Percent complete: 78.3%; Average loss: 2.8381
Iteration: 3135; Percent complete: 78.4%; Average loss: 2.9950
Iteration: 3136; Percent complete: 78.4%; Average loss: 3.0575
Iteration: 3137; Percent complete: 78.4%; Average loss: 2.7848
Iteration: 3138; Percent complete: 78.5%; Average loss: 2.7042
Iteration: 3139; Percent complete: 78.5%; Average loss: 2.8117
Iteration: 3140; Percent complete: 78.5%; Average loss: 2.8163
Iteration: 3141; Percent complete: 78.5%; Average loss: 2.8089
Iteration: 3142; Percent complete: 78.5%; Average loss: 2.7885
Iteration: 3143; Percent complete: 78.6%; Average loss: 2.8735
Iteration: 3144; Percent complete: 78.6%; Average loss: 2.8143
Iteration: 3145; Percent complete: 78.6%; Average loss: 3.1065
Iteration: 3146; Percent complete: 78.6%; Average loss: 2.7918
Iteration: 3147; Percent complete: 78.7%; Average loss: 2.7431
Iteration: 3148; Percent complete: 78.7%; Average loss: 3.0739
Iteration: 3149; Percent complete: 78.7%; Average loss: 2.7540
Iteration: 3150; Percent complete: 78.8%; Average loss: 2.8474
Iteration: 3151; Percent complete: 78.8%; Average loss: 3.0025
Iteration: 3152; Percent complete: 78.8%; Average loss: 2.8719
Iteration: 3153; Percent complete: 78.8%; Average loss: 2.8161
Iteration: 3154; Percent complete: 78.8%; Average loss: 3.0377
Iteration: 3155; Percent complete: 78.9%; Average loss: 2.8338
Iteration: 3156; Percent complete: 78.9%; Average loss: 2.8190
Iteration: 3157; Percent complete: 78.9%; Average loss: 2.9694
Iteration: 3158; Percent complete: 79.0%; Average loss: 2.9429
Iteration: 3159; Percent complete: 79.0%; Average loss: 2.7067
Iteration: 3160; Percent complete: 79.0%; Average loss: 2.6686
Iteration: 3161; Percent complete: 79.0%; Average loss: 2.9499
Iteration: 3162; Percent complete: 79.0%; Average loss: 2.8701
Iteration: 3163; Percent complete: 79.1%; Average loss: 2.7641
Iteration: 3164; Percent complete: 79.1%; Average loss: 2.7798
Iteration: 3165; Percent complete: 79.1%; Average loss: 2.7054
Iteration: 3166; Percent complete: 79.1%; Average loss: 3.0236
Iteration: 3167; Percent complete: 79.2%; Average loss: 2.8158
Iteration: 3168; Percent complete: 79.2%; Average loss: 2.9354
Iteration: 3169; Percent complete: 79.2%; Average loss: 2.6661
Iteration: 3170; Percent complete: 79.2%; Average loss: 3.1510
Iteration: 3171; Percent complete: 79.3%; Average loss: 2.8134
Iteration: 3172; Percent complete: 79.3%; Average loss: 2.7623
Iteration: 3173; Percent complete: 79.3%; Average loss: 2.9630
Iteration: 3174; Percent complete: 79.3%; Average loss: 2.7328
Iteration: 3175; Percent complete: 79.4%; Average loss: 2.8830
Iteration: 3176; Percent complete: 79.4%; Average loss: 3.1075
Iteration: 3177; Percent complete: 79.4%; Average loss: 2.9695
Iteration: 3178; Percent complete: 79.5%; Average loss: 2.9044
Iteration: 3179; Percent complete: 79.5%; Average loss: 2.9350
Iteration: 3180; Percent complete: 79.5%; Average loss: 2.8182
Iteration: 3181; Percent complete: 79.5%; Average loss: 2.7452
Iteration: 3182; Percent complete: 79.5%; Average loss: 2.6490
Iteration: 3183; Percent complete: 79.6%; Average loss: 2.8096
Iteration: 3184; Percent complete: 79.6%; Average loss: 2.6823
Iteration: 3185; Percent complete: 79.6%; Average loss: 2.9367
Iteration: 3186; Percent complete: 79.7%; Average loss: 2.8500
Iteration: 3187; Percent complete: 79.7%; Average loss: 2.6638
Iteration: 3188; Percent complete: 79.7%; Average loss: 2.7566
Iteration: 3189; Percent complete: 79.7%; Average loss: 2.6147
Iteration: 3190; Percent complete: 79.8%; Average loss: 2.9727
Iteration: 3191; Percent complete: 79.8%; Average loss: 2.7727
Iteration: 3192; Percent complete: 79.8%; Average loss: 2.6364
Iteration: 3193; Percent complete: 79.8%; Average loss: 2.7665
Iteration: 3194; Percent complete: 79.8%; Average loss: 2.7727
Iteration: 3195; Percent complete: 79.9%; Average loss: 2.5969
Iteration: 3196; Percent complete: 79.9%; Average loss: 2.8027
Iteration: 3197; Percent complete: 79.9%; Average loss: 2.7135
Iteration: 3198; Percent complete: 80.0%; Average loss: 2.7215
Iteration: 3199; Percent complete: 80.0%; Average loss: 2.5977
Iteration: 3200; Percent complete: 80.0%; Average loss: 2.7387
Iteration: 3201; Percent complete: 80.0%; Average loss: 2.9698
Iteration: 3202; Percent complete: 80.0%; Average loss: 2.7629
Iteration: 3203; Percent complete: 80.1%; Average loss: 2.9980
Iteration: 3204; Percent complete: 80.1%; Average loss: 2.8306
Iteration: 3205; Percent complete: 80.1%; Average loss: 2.7930
Iteration: 3206; Percent complete: 80.2%; Average loss: 2.7341
Iteration: 3207; Percent complete: 80.2%; Average loss: 2.8681
Iteration: 3208; Percent complete: 80.2%; Average loss: 2.9520
Iteration: 3209; Percent complete: 80.2%; Average loss: 2.5164
Iteration: 3210; Percent complete: 80.2%; Average loss: 3.0385
Iteration: 3211; Percent complete: 80.3%; Average loss: 2.8436
Iteration: 3212; Percent complete: 80.3%; Average loss: 2.8587
Iteration: 3213; Percent complete: 80.3%; Average loss: 3.0582
Iteration: 3214; Percent complete: 80.3%; Average loss: 2.6145
Iteration: 3215; Percent complete: 80.4%; Average loss: 2.8273
Iteration: 3216; Percent complete: 80.4%; Average loss: 2.8323
Iteration: 3217; Percent complete: 80.4%; Average loss: 2.6843
Iteration: 3218; Percent complete: 80.5%; Average loss: 2.9377
Iteration: 3219; Percent complete: 80.5%; Average loss: 2.7548
Iteration: 3220; Percent complete: 80.5%; Average loss: 2.8504
Iteration: 3221; Percent complete: 80.5%; Average loss: 2.6366
Iteration: 3222; Percent complete: 80.5%; Average loss: 2.7460
Iteration: 3223; Percent complete: 80.6%; Average loss: 2.9011
Iteration: 3224; Percent complete: 80.6%; Average loss: 2.8300
Iteration: 3225; Percent complete: 80.6%; Average loss: 2.6733
Iteration: 3226; Percent complete: 80.7%; Average loss: 2.5848
Iteration: 3227; Percent complete: 80.7%; Average loss: 2.8308
Iteration: 3228; Percent complete: 80.7%; Average loss: 2.8314
Iteration: 3229; Percent complete: 80.7%; Average loss: 2.8071
Iteration: 3230; Percent complete: 80.8%; Average loss: 2.8127
Iteration: 3231; Percent complete: 80.8%; Average loss: 2.9303
Iteration: 3232; Percent complete: 80.8%; Average loss: 2.8557
Iteration: 3233; Percent complete: 80.8%; Average loss: 2.7842
Iteration: 3234; Percent complete: 80.8%; Average loss: 2.8395
Iteration: 3235; Percent complete: 80.9%; Average loss: 2.9420
Iteration: 3236; Percent complete: 80.9%; Average loss: 2.9109
Iteration: 3237; Percent complete: 80.9%; Average loss: 2.8647
Iteration: 3238; Percent complete: 81.0%; Average loss: 2.7299
Iteration: 3239; Percent complete: 81.0%; Average loss: 2.7139
Iteration: 3240; Percent complete: 81.0%; Average loss: 3.1912
Iteration: 3241; Percent complete: 81.0%; Average loss: 2.8426
Iteration: 3242; Percent complete: 81.0%; Average loss: 2.7687
Iteration: 3243; Percent complete: 81.1%; Average loss: 2.9588
Iteration: 3244; Percent complete: 81.1%; Average loss: 3.0167
Iteration: 3245; Percent complete: 81.1%; Average loss: 2.8369
Iteration: 3246; Percent complete: 81.2%; Average loss: 3.0661
Iteration: 3247; Percent complete: 81.2%; Average loss: 3.0669
Iteration: 3248; Percent complete: 81.2%; Average loss: 2.6413
Iteration: 3249; Percent complete: 81.2%; Average loss: 2.5953
Iteration: 3250; Percent complete: 81.2%; Average loss: 2.7445
Iteration: 3251; Percent complete: 81.3%; Average loss: 2.8041
Iteration: 3252; Percent complete: 81.3%; Average loss: 2.6986
Iteration: 3253; Percent complete: 81.3%; Average loss: 2.6706
Iteration: 3254; Percent complete: 81.3%; Average loss: 2.9763
Iteration: 3255; Percent complete: 81.4%; Average loss: 2.8778
Iteration: 3256; Percent complete: 81.4%; Average loss: 2.6329
Iteration: 3257; Percent complete: 81.4%; Average loss: 2.7307
Iteration: 3258; Percent complete: 81.5%; Average loss: 2.8690
Iteration: 3259; Percent complete: 81.5%; Average loss: 2.9317
Iteration: 3260; Percent complete: 81.5%; Average loss: 2.8515
Iteration: 3261; Percent complete: 81.5%; Average loss: 2.6238
Iteration: 3262; Percent complete: 81.5%; Average loss: 2.8677
Iteration: 3263; Percent complete: 81.6%; Average loss: 2.7861
Iteration: 3264; Percent complete: 81.6%; Average loss: 2.8027
Iteration: 3265; Percent complete: 81.6%; Average loss: 2.7209
Iteration: 3266; Percent complete: 81.7%; Average loss: 2.9622
Iteration: 3267; Percent complete: 81.7%; Average loss: 2.9503
Iteration: 3268; Percent complete: 81.7%; Average loss: 2.8481
Iteration: 3269; Percent complete: 81.7%; Average loss: 2.6690
Iteration: 3270; Percent complete: 81.8%; Average loss: 2.6923
Iteration: 3271; Percent complete: 81.8%; Average loss: 2.6658
Iteration: 3272; Percent complete: 81.8%; Average loss: 2.7535
Iteration: 3273; Percent complete: 81.8%; Average loss: 2.7157
Iteration: 3274; Percent complete: 81.8%; Average loss: 2.7606
Iteration: 3275; Percent complete: 81.9%; Average loss: 2.7672
Iteration: 3276; Percent complete: 81.9%; Average loss: 2.8319
Iteration: 3277; Percent complete: 81.9%; Average loss: 2.7426
Iteration: 3278; Percent complete: 82.0%; Average loss: 2.7892
Iteration: 3279; Percent complete: 82.0%; Average loss: 3.0576
Iteration: 3280; Percent complete: 82.0%; Average loss: 2.4740
Iteration: 3281; Percent complete: 82.0%; Average loss: 2.8931
Iteration: 3282; Percent complete: 82.0%; Average loss: 2.9426
Iteration: 3283; Percent complete: 82.1%; Average loss: 2.7815
Iteration: 3284; Percent complete: 82.1%; Average loss: 2.7496
Iteration: 3285; Percent complete: 82.1%; Average loss: 2.6631
Iteration: 3286; Percent complete: 82.2%; Average loss: 2.8374
Iteration: 3287; Percent complete: 82.2%; Average loss: 2.9043
Iteration: 3288; Percent complete: 82.2%; Average loss: 2.8357
Iteration: 3289; Percent complete: 82.2%; Average loss: 2.8673
Iteration: 3290; Percent complete: 82.2%; Average loss: 2.6333
Iteration: 3291; Percent complete: 82.3%; Average loss: 2.6468
Iteration: 3292; Percent complete: 82.3%; Average loss: 2.7714
Iteration: 3293; Percent complete: 82.3%; Average loss: 2.8986
Iteration: 3294; Percent complete: 82.3%; Average loss: 2.8788
Iteration: 3295; Percent complete: 82.4%; Average loss: 2.5269
Iteration: 3296; Percent complete: 82.4%; Average loss: 2.6996
Iteration: 3297; Percent complete: 82.4%; Average loss: 2.7476
Iteration: 3298; Percent complete: 82.5%; Average loss: 2.8391
Iteration: 3299; Percent complete: 82.5%; Average loss: 2.8558
Iteration: 3300; Percent complete: 82.5%; Average loss: 2.9840
Iteration: 3301; Percent complete: 82.5%; Average loss: 2.7325
Iteration: 3302; Percent complete: 82.5%; Average loss: 2.7483
Iteration: 3303; Percent complete: 82.6%; Average loss: 2.6604
Iteration: 3304; Percent complete: 82.6%; Average loss: 2.7956
Iteration: 3305; Percent complete: 82.6%; Average loss: 2.6339
Iteration: 3306; Percent complete: 82.7%; Average loss: 2.6100
Iteration: 3307; Percent complete: 82.7%; Average loss: 2.7610
Iteration: 3308; Percent complete: 82.7%; Average loss: 2.7033
Iteration: 3309; Percent complete: 82.7%; Average loss: 2.6698
Iteration: 3310; Percent complete: 82.8%; Average loss: 2.6921
Iteration: 3311; Percent complete: 82.8%; Average loss: 2.9616
Iteration: 3312; Percent complete: 82.8%; Average loss: 2.6803
Iteration: 3313; Percent complete: 82.8%; Average loss: 2.5914
Iteration: 3314; Percent complete: 82.8%; Average loss: 2.9630
Iteration: 3315; Percent complete: 82.9%; Average loss: 2.6423
Iteration: 3316; Percent complete: 82.9%; Average loss: 2.7052
Iteration: 3317; Percent complete: 82.9%; Average loss: 2.5851
Iteration: 3318; Percent complete: 83.0%; Average loss: 2.8188
Iteration: 3319; Percent complete: 83.0%; Average loss: 2.6655
Iteration: 3320; Percent complete: 83.0%; Average loss: 2.8188
Iteration: 3321; Percent complete: 83.0%; Average loss: 2.8586
Iteration: 3322; Percent complete: 83.0%; Average loss: 2.7286
Iteration: 3323; Percent complete: 83.1%; Average loss: 2.8695
Iteration: 3324; Percent complete: 83.1%; Average loss: 2.6434
Iteration: 3325; Percent complete: 83.1%; Average loss: 2.9062
Iteration: 3326; Percent complete: 83.2%; Average loss: 2.7866
Iteration: 3327; Percent complete: 83.2%; Average loss: 2.6879
Iteration: 3328; Percent complete: 83.2%; Average loss: 2.8786
Iteration: 3329; Percent complete: 83.2%; Average loss: 2.8678
Iteration: 3330; Percent complete: 83.2%; Average loss: 2.7344
Iteration: 3331; Percent complete: 83.3%; Average loss: 2.7485
Iteration: 3332; Percent complete: 83.3%; Average loss: 2.5515
Iteration: 3333; Percent complete: 83.3%; Average loss: 2.7261
Iteration: 3334; Percent complete: 83.4%; Average loss: 2.6828
Iteration: 3335; Percent complete: 83.4%; Average loss: 2.8095
Iteration: 3336; Percent complete: 83.4%; Average loss: 2.7963
Iteration: 3337; Percent complete: 83.4%; Average loss: 2.9992
Iteration: 3338; Percent complete: 83.5%; Average loss: 2.8466
Iteration: 3339; Percent complete: 83.5%; Average loss: 2.9230
Iteration: 3340; Percent complete: 83.5%; Average loss: 2.5736
Iteration: 3341; Percent complete: 83.5%; Average loss: 2.6422
Iteration: 3342; Percent complete: 83.5%; Average loss: 2.8368
Iteration: 3343; Percent complete: 83.6%; Average loss: 2.8208
Iteration: 3344; Percent complete: 83.6%; Average loss: 2.7414
Iteration: 3345; Percent complete: 83.6%; Average loss: 2.6262
Iteration: 3346; Percent complete: 83.7%; Average loss: 2.8260
Iteration: 3347; Percent complete: 83.7%; Average loss: 2.6435
Iteration: 3348; Percent complete: 83.7%; Average loss: 2.6476
Iteration: 3349; Percent complete: 83.7%; Average loss: 2.6884
Iteration: 3350; Percent complete: 83.8%; Average loss: 2.8001
Iteration: 3351; Percent complete: 83.8%; Average loss: 2.7348
Iteration: 3352; Percent complete: 83.8%; Average loss: 2.7456
Iteration: 3353; Percent complete: 83.8%; Average loss: 2.7444
Iteration: 3354; Percent complete: 83.9%; Average loss: 2.5898
Iteration: 3355; Percent complete: 83.9%; Average loss: 2.7740
Iteration: 3356; Percent complete: 83.9%; Average loss: 2.6410
Iteration: 3357; Percent complete: 83.9%; Average loss: 2.6606
Iteration: 3358; Percent complete: 84.0%; Average loss: 2.7267
Iteration: 3359; Percent complete: 84.0%; Average loss: 2.9525
Iteration: 3360; Percent complete: 84.0%; Average loss: 2.8940
Iteration: 3361; Percent complete: 84.0%; Average loss: 2.5814
Iteration: 3362; Percent complete: 84.0%; Average loss: 2.7008
Iteration: 3363; Percent complete: 84.1%; Average loss: 2.7358
Iteration: 3364; Percent complete: 84.1%; Average loss: 2.7021
Iteration: 3365; Percent complete: 84.1%; Average loss: 2.7883
Iteration: 3366; Percent complete: 84.2%; Average loss: 2.7843
Iteration: 3367; Percent complete: 84.2%; Average loss: 2.6173
Iteration: 3368; Percent complete: 84.2%; Average loss: 3.1359
Iteration: 3369; Percent complete: 84.2%; Average loss: 2.7787
Iteration: 3370; Percent complete: 84.2%; Average loss: 2.8708
Iteration: 3371; Percent complete: 84.3%; Average loss: 2.7410
Iteration: 3372; Percent complete: 84.3%; Average loss: 2.8681
Iteration: 3373; Percent complete: 84.3%; Average loss: 2.7064
Iteration: 3374; Percent complete: 84.4%; Average loss: 2.7814
Iteration: 3375; Percent complete: 84.4%; Average loss: 2.7461
Iteration: 3376; Percent complete: 84.4%; Average loss: 2.5207
Iteration: 3377; Percent complete: 84.4%; Average loss: 2.8533
Iteration: 3378; Percent complete: 84.5%; Average loss: 2.7284
Iteration: 3379; Percent complete: 84.5%; Average loss: 2.6605
Iteration: 3380; Percent complete: 84.5%; Average loss: 2.8449
Iteration: 3381; Percent complete: 84.5%; Average loss: 2.6639
Iteration: 3382; Percent complete: 84.5%; Average loss: 2.7771
Iteration: 3383; Percent complete: 84.6%; Average loss: 3.0664
Iteration: 3384; Percent complete: 84.6%; Average loss: 3.0626
Iteration: 3385; Percent complete: 84.6%; Average loss: 3.0542
Iteration: 3386; Percent complete: 84.7%; Average loss: 3.0386
Iteration: 3387; Percent complete: 84.7%; Average loss: 2.7994
Iteration: 3388; Percent complete: 84.7%; Average loss: 2.8219
Iteration: 3389; Percent complete: 84.7%; Average loss: 2.5718
Iteration: 3390; Percent complete: 84.8%; Average loss: 2.7572
Iteration: 3391; Percent complete: 84.8%; Average loss: 2.8357
Iteration: 3392; Percent complete: 84.8%; Average loss: 2.6933
Iteration: 3393; Percent complete: 84.8%; Average loss: 2.6116
Iteration: 3394; Percent complete: 84.9%; Average loss: 2.7502
Iteration: 3395; Percent complete: 84.9%; Average loss: 2.9136
Iteration: 3396; Percent complete: 84.9%; Average loss: 2.9234
Iteration: 3397; Percent complete: 84.9%; Average loss: 2.5320
Iteration: 3398; Percent complete: 85.0%; Average loss: 2.7133
Iteration: 3399; Percent complete: 85.0%; Average loss: 2.4029
Iteration: 3400; Percent complete: 85.0%; Average loss: 2.9716
Iteration: 3401; Percent complete: 85.0%; Average loss: 2.8232
Iteration: 3402; Percent complete: 85.0%; Average loss: 3.1328
Iteration: 3403; Percent complete: 85.1%; Average loss: 2.8723
Iteration: 3404; Percent complete: 85.1%; Average loss: 2.6732
Iteration: 3405; Percent complete: 85.1%; Average loss: 2.7056
Iteration: 3406; Percent complete: 85.2%; Average loss: 2.5090
Iteration: 3407; Percent complete: 85.2%; Average loss: 2.6355
Iteration: 3408; Percent complete: 85.2%; Average loss: 2.5470
Iteration: 3409; Percent complete: 85.2%; Average loss: 2.6875
Iteration: 3410; Percent complete: 85.2%; Average loss: 2.5159
Iteration: 3411; Percent complete: 85.3%; Average loss: 2.8232
Iteration: 3412; Percent complete: 85.3%; Average loss: 2.7146
Iteration: 3413; Percent complete: 85.3%; Average loss: 2.8771
Iteration: 3414; Percent complete: 85.4%; Average loss: 2.7984
Iteration: 3415; Percent complete: 85.4%; Average loss: 2.7340
Iteration: 3416; Percent complete: 85.4%; Average loss: 2.6380
Iteration: 3417; Percent complete: 85.4%; Average loss: 2.7293
Iteration: 3418; Percent complete: 85.5%; Average loss: 3.0802
Iteration: 3419; Percent complete: 85.5%; Average loss: 2.7973
Iteration: 3420; Percent complete: 85.5%; Average loss: 2.7697
Iteration: 3421; Percent complete: 85.5%; Average loss: 2.9307
Iteration: 3422; Percent complete: 85.5%; Average loss: 2.7232
Iteration: 3423; Percent complete: 85.6%; Average loss: 2.9668
Iteration: 3424; Percent complete: 85.6%; Average loss: 2.7918
Iteration: 3425; Percent complete: 85.6%; Average loss: 2.7747
Iteration: 3426; Percent complete: 85.7%; Average loss: 2.9154
Iteration: 3427; Percent complete: 85.7%; Average loss: 2.7359
Iteration: 3428; Percent complete: 85.7%; Average loss: 2.8266
Iteration: 3429; Percent complete: 85.7%; Average loss: 2.8396
Iteration: 3430; Percent complete: 85.8%; Average loss: 2.6076
Iteration: 3431; Percent complete: 85.8%; Average loss: 2.8065
Iteration: 3432; Percent complete: 85.8%; Average loss: 2.8576
Iteration: 3433; Percent complete: 85.8%; Average loss: 2.7450
Iteration: 3434; Percent complete: 85.9%; Average loss: 2.7502
Iteration: 3435; Percent complete: 85.9%; Average loss: 2.7590
Iteration: 3436; Percent complete: 85.9%; Average loss: 2.5417
Iteration: 3437; Percent complete: 85.9%; Average loss: 2.6977
Iteration: 3438; Percent complete: 86.0%; Average loss: 2.9218
Iteration: 3439; Percent complete: 86.0%; Average loss: 2.5235
Iteration: 3440; Percent complete: 86.0%; Average loss: 2.6858
Iteration: 3441; Percent complete: 86.0%; Average loss: 2.7657
Iteration: 3442; Percent complete: 86.1%; Average loss: 2.4701
Iteration: 3443; Percent complete: 86.1%; Average loss: 2.7584
Iteration: 3444; Percent complete: 86.1%; Average loss: 2.7668
Iteration: 3445; Percent complete: 86.1%; Average loss: 2.8299
Iteration: 3446; Percent complete: 86.2%; Average loss: 2.7420
Iteration: 3447; Percent complete: 86.2%; Average loss: 2.7643
Iteration: 3448; Percent complete: 86.2%; Average loss: 2.6611
Iteration: 3449; Percent complete: 86.2%; Average loss: 2.6262
Iteration: 3450; Percent complete: 86.2%; Average loss: 2.6740
Iteration: 3451; Percent complete: 86.3%; Average loss: 2.8052
Iteration: 3452; Percent complete: 86.3%; Average loss: 2.7549
Iteration: 3453; Percent complete: 86.3%; Average loss: 2.8105
Iteration: 3454; Percent complete: 86.4%; Average loss: 3.0228
Iteration: 3455; Percent complete: 86.4%; Average loss: 2.7610
Iteration: 3456; Percent complete: 86.4%; Average loss: 2.8310
Iteration: 3457; Percent complete: 86.4%; Average loss: 2.8229
Iteration: 3458; Percent complete: 86.5%; Average loss: 2.5127
Iteration: 3459; Percent complete: 86.5%; Average loss: 2.5884
Iteration: 3460; Percent complete: 86.5%; Average loss: 2.5632
Iteration: 3461; Percent complete: 86.5%; Average loss: 2.9187
Iteration: 3462; Percent complete: 86.6%; Average loss: 2.5962
Iteration: 3463; Percent complete: 86.6%; Average loss: 2.8677
Iteration: 3464; Percent complete: 86.6%; Average loss: 2.5974
Iteration: 3465; Percent complete: 86.6%; Average loss: 2.7895
Iteration: 3466; Percent complete: 86.7%; Average loss: 2.9541
Iteration: 3467; Percent complete: 86.7%; Average loss: 2.8609
Iteration: 3468; Percent complete: 86.7%; Average loss: 2.7465
Iteration: 3469; Percent complete: 86.7%; Average loss: 2.4573
Iteration: 3470; Percent complete: 86.8%; Average loss: 3.0317
Iteration: 3471; Percent complete: 86.8%; Average loss: 2.4607
Iteration: 3472; Percent complete: 86.8%; Average loss: 2.7366
Iteration: 3473; Percent complete: 86.8%; Average loss: 3.0014
Iteration: 3474; Percent complete: 86.9%; Average loss: 2.7380
Iteration: 3475; Percent complete: 86.9%; Average loss: 2.9703
Iteration: 3476; Percent complete: 86.9%; Average loss: 3.0055
Iteration: 3477; Percent complete: 86.9%; Average loss: 2.7923
Iteration: 3478; Percent complete: 87.0%; Average loss: 2.8064
Iteration: 3479; Percent complete: 87.0%; Average loss: 2.7790
Iteration: 3480; Percent complete: 87.0%; Average loss: 2.7550
Iteration: 3481; Percent complete: 87.0%; Average loss: 2.6137
Iteration: 3482; Percent complete: 87.1%; Average loss: 2.6338
Iteration: 3483; Percent complete: 87.1%; Average loss: 2.5911
Iteration: 3484; Percent complete: 87.1%; Average loss: 2.8331
Iteration: 3485; Percent complete: 87.1%; Average loss: 2.5263
Iteration: 3486; Percent complete: 87.2%; Average loss: 2.7023
Iteration: 3487; Percent complete: 87.2%; Average loss: 2.8818
Iteration: 3488; Percent complete: 87.2%; Average loss: 2.8791
Iteration: 3489; Percent complete: 87.2%; Average loss: 2.5605
Iteration: 3490; Percent complete: 87.2%; Average loss: 2.7946
Iteration: 3491; Percent complete: 87.3%; Average loss: 2.5475
Iteration: 3492; Percent complete: 87.3%; Average loss: 2.4224
Iteration: 3493; Percent complete: 87.3%; Average loss: 2.6569
Iteration: 3494; Percent complete: 87.4%; Average loss: 2.9138
Iteration: 3495; Percent complete: 87.4%; Average loss: 2.6399
Iteration: 3496; Percent complete: 87.4%; Average loss: 2.8050
Iteration: 3497; Percent complete: 87.4%; Average loss: 2.7922
Iteration: 3498; Percent complete: 87.5%; Average loss: 2.7564
Iteration: 3499; Percent complete: 87.5%; Average loss: 2.8386
Iteration: 3500; Percent complete: 87.5%; Average loss: 2.5651
Iteration: 3501; Percent complete: 87.5%; Average loss: 2.6385
Iteration: 3502; Percent complete: 87.5%; Average loss: 2.8098
Iteration: 3503; Percent complete: 87.6%; Average loss: 2.5499
Iteration: 3504; Percent complete: 87.6%; Average loss: 2.8049
Iteration: 3505; Percent complete: 87.6%; Average loss: 2.6417
Iteration: 3506; Percent complete: 87.6%; Average loss: 2.6224
Iteration: 3507; Percent complete: 87.7%; Average loss: 2.7383
Iteration: 3508; Percent complete: 87.7%; Average loss: 2.6862
Iteration: 3509; Percent complete: 87.7%; Average loss: 2.4890
Iteration: 3510; Percent complete: 87.8%; Average loss: 2.7264
Iteration: 3511; Percent complete: 87.8%; Average loss: 2.7368
Iteration: 3512; Percent complete: 87.8%; Average loss: 2.9179
Iteration: 3513; Percent complete: 87.8%; Average loss: 2.7336
Iteration: 3514; Percent complete: 87.8%; Average loss: 2.5882
Iteration: 3515; Percent complete: 87.9%; Average loss: 2.6519
Iteration: 3516; Percent complete: 87.9%; Average loss: 2.7437
Iteration: 3517; Percent complete: 87.9%; Average loss: 2.7551
Iteration: 3518; Percent complete: 87.9%; Average loss: 2.7071
Iteration: 3519; Percent complete: 88.0%; Average loss: 2.8176
Iteration: 3520; Percent complete: 88.0%; Average loss: 2.7175
Iteration: 3521; Percent complete: 88.0%; Average loss: 2.7986
Iteration: 3522; Percent complete: 88.0%; Average loss: 2.6929
Iteration: 3523; Percent complete: 88.1%; Average loss: 2.9022
Iteration: 3524; Percent complete: 88.1%; Average loss: 2.7846
Iteration: 3525; Percent complete: 88.1%; Average loss: 2.6710
Iteration: 3526; Percent complete: 88.1%; Average loss: 2.6921
Iteration: 3527; Percent complete: 88.2%; Average loss: 2.6961
Iteration: 3528; Percent complete: 88.2%; Average loss: 2.4774
Iteration: 3529; Percent complete: 88.2%; Average loss: 2.7842
Iteration: 3530; Percent complete: 88.2%; Average loss: 2.6047
Iteration: 3531; Percent complete: 88.3%; Average loss: 2.7466
Iteration: 3532; Percent complete: 88.3%; Average loss: 2.4994
Iteration: 3533; Percent complete: 88.3%; Average loss: 2.7582
Iteration: 3534; Percent complete: 88.3%; Average loss: 2.7525
Iteration: 3535; Percent complete: 88.4%; Average loss: 2.7434
Iteration: 3536; Percent complete: 88.4%; Average loss: 2.8167
Iteration: 3537; Percent complete: 88.4%; Average loss: 2.7499
Iteration: 3538; Percent complete: 88.4%; Average loss: 2.6877
Iteration: 3539; Percent complete: 88.5%; Average loss: 2.7840
Iteration: 3540; Percent complete: 88.5%; Average loss: 2.6046
Iteration: 3541; Percent complete: 88.5%; Average loss: 2.9266
Iteration: 3542; Percent complete: 88.5%; Average loss: 2.7080
Iteration: 3543; Percent complete: 88.6%; Average loss: 3.0548
Iteration: 3544; Percent complete: 88.6%; Average loss: 2.6643
Iteration: 3545; Percent complete: 88.6%; Average loss: 2.7383
Iteration: 3546; Percent complete: 88.6%; Average loss: 2.6546
Iteration: 3547; Percent complete: 88.7%; Average loss: 2.7716
Iteration: 3548; Percent complete: 88.7%; Average loss: 2.7810
Iteration: 3549; Percent complete: 88.7%; Average loss: 2.7534
Iteration: 3550; Percent complete: 88.8%; Average loss: 2.7363
Iteration: 3551; Percent complete: 88.8%; Average loss: 2.8701
Iteration: 3552; Percent complete: 88.8%; Average loss: 2.8094
Iteration: 3553; Percent complete: 88.8%; Average loss: 2.6736
Iteration: 3554; Percent complete: 88.8%; Average loss: 2.5906
Iteration: 3555; Percent complete: 88.9%; Average loss: 2.5301
Iteration: 3556; Percent complete: 88.9%; Average loss: 2.7936
Iteration: 3557; Percent complete: 88.9%; Average loss: 2.5647
Iteration: 3558; Percent complete: 88.9%; Average loss: 2.6089
Iteration: 3559; Percent complete: 89.0%; Average loss: 2.6545
Iteration: 3560; Percent complete: 89.0%; Average loss: 2.7893
Iteration: 3561; Percent complete: 89.0%; Average loss: 2.9047
Iteration: 3562; Percent complete: 89.0%; Average loss: 2.5768
Iteration: 3563; Percent complete: 89.1%; Average loss: 2.8110
Iteration: 3564; Percent complete: 89.1%; Average loss: 2.6357
Iteration: 3565; Percent complete: 89.1%; Average loss: 2.8292
Iteration: 3566; Percent complete: 89.1%; Average loss: 2.7374
Iteration: 3567; Percent complete: 89.2%; Average loss: 2.8303
Iteration: 3568; Percent complete: 89.2%; Average loss: 2.7284
Iteration: 3569; Percent complete: 89.2%; Average loss: 2.7133
Iteration: 3570; Percent complete: 89.2%; Average loss: 2.6583
Iteration: 3571; Percent complete: 89.3%; Average loss: 2.4754
Iteration: 3572; Percent complete: 89.3%; Average loss: 2.5365
Iteration: 3573; Percent complete: 89.3%; Average loss: 2.6312
Iteration: 3574; Percent complete: 89.3%; Average loss: 2.9503
Iteration: 3575; Percent complete: 89.4%; Average loss: 2.7658
Iteration: 3576; Percent complete: 89.4%; Average loss: 2.8076
Iteration: 3577; Percent complete: 89.4%; Average loss: 2.6617
Iteration: 3578; Percent complete: 89.5%; Average loss: 2.7611
Iteration: 3579; Percent complete: 89.5%; Average loss: 2.7365
Iteration: 3580; Percent complete: 89.5%; Average loss: 2.6291
Iteration: 3581; Percent complete: 89.5%; Average loss: 2.7058
Iteration: 3582; Percent complete: 89.5%; Average loss: 2.6340
Iteration: 3583; Percent complete: 89.6%; Average loss: 2.8538
Iteration: 3584; Percent complete: 89.6%; Average loss: 2.6633
Iteration: 3585; Percent complete: 89.6%; Average loss: 2.9659
Iteration: 3586; Percent complete: 89.6%; Average loss: 2.8093
Iteration: 3587; Percent complete: 89.7%; Average loss: 2.7977
Iteration: 3588; Percent complete: 89.7%; Average loss: 2.7641
Iteration: 3589; Percent complete: 89.7%; Average loss: 2.5712
Iteration: 3590; Percent complete: 89.8%; Average loss: 2.7980
Iteration: 3591; Percent complete: 89.8%; Average loss: 2.6346
Iteration: 3592; Percent complete: 89.8%; Average loss: 2.7053
Iteration: 3593; Percent complete: 89.8%; Average loss: 2.4371
Iteration: 3594; Percent complete: 89.8%; Average loss: 2.8026
Iteration: 3595; Percent complete: 89.9%; Average loss: 2.8180
Iteration: 3596; Percent complete: 89.9%; Average loss: 2.8125
Iteration: 3597; Percent complete: 89.9%; Average loss: 2.6828
Iteration: 3598; Percent complete: 90.0%; Average loss: 2.3899
Iteration: 3599; Percent complete: 90.0%; Average loss: 2.6957
Iteration: 3600; Percent complete: 90.0%; Average loss: 2.4739
Iteration: 3601; Percent complete: 90.0%; Average loss: 2.6485
Iteration: 3602; Percent complete: 90.0%; Average loss: 2.8636
Iteration: 3603; Percent complete: 90.1%; Average loss: 2.8352
Iteration: 3604; Percent complete: 90.1%; Average loss: 2.5670
Iteration: 3605; Percent complete: 90.1%; Average loss: 2.8576
Iteration: 3606; Percent complete: 90.1%; Average loss: 2.6184
Iteration: 3607; Percent complete: 90.2%; Average loss: 2.7410
Iteration: 3608; Percent complete: 90.2%; Average loss: 2.6275
Iteration: 3609; Percent complete: 90.2%; Average loss: 2.9007
Iteration: 3610; Percent complete: 90.2%; Average loss: 2.6943
Iteration: 3611; Percent complete: 90.3%; Average loss: 2.7183
Iteration: 3612; Percent complete: 90.3%; Average loss: 2.4513
Iteration: 3613; Percent complete: 90.3%; Average loss: 2.6610
Iteration: 3614; Percent complete: 90.3%; Average loss: 2.6533
Iteration: 3615; Percent complete: 90.4%; Average loss: 2.7341
Iteration: 3616; Percent complete: 90.4%; Average loss: 2.5691
Iteration: 3617; Percent complete: 90.4%; Average loss: 2.7268
Iteration: 3618; Percent complete: 90.5%; Average loss: 2.5343
Iteration: 3619; Percent complete: 90.5%; Average loss: 2.5453
Iteration: 3620; Percent complete: 90.5%; Average loss: 2.6214
Iteration: 3621; Percent complete: 90.5%; Average loss: 2.7005
Iteration: 3622; Percent complete: 90.5%; Average loss: 2.9369
Iteration: 3623; Percent complete: 90.6%; Average loss: 2.7028
Iteration: 3624; Percent complete: 90.6%; Average loss: 2.7125
Iteration: 3625; Percent complete: 90.6%; Average loss: 2.9458
Iteration: 3626; Percent complete: 90.6%; Average loss: 2.7539
Iteration: 3627; Percent complete: 90.7%; Average loss: 2.7018
Iteration: 3628; Percent complete: 90.7%; Average loss: 2.6961
Iteration: 3629; Percent complete: 90.7%; Average loss: 2.6971
Iteration: 3630; Percent complete: 90.8%; Average loss: 2.6951
Iteration: 3631; Percent complete: 90.8%; Average loss: 2.4988
Iteration: 3632; Percent complete: 90.8%; Average loss: 2.7011
Iteration: 3633; Percent complete: 90.8%; Average loss: 2.8141
Iteration: 3634; Percent complete: 90.8%; Average loss: 2.6901
Iteration: 3635; Percent complete: 90.9%; Average loss: 2.7521
Iteration: 3636; Percent complete: 90.9%; Average loss: 2.7566
Iteration: 3637; Percent complete: 90.9%; Average loss: 2.7268
Iteration: 3638; Percent complete: 91.0%; Average loss: 2.5463
Iteration: 3639; Percent complete: 91.0%; Average loss: 2.6671
Iteration: 3640; Percent complete: 91.0%; Average loss: 2.9633
Iteration: 3641; Percent complete: 91.0%; Average loss: 2.6944
Iteration: 3642; Percent complete: 91.0%; Average loss: 3.0312
Iteration: 3643; Percent complete: 91.1%; Average loss: 2.7419
Iteration: 3644; Percent complete: 91.1%; Average loss: 2.6674
Iteration: 3645; Percent complete: 91.1%; Average loss: 2.5598
Iteration: 3646; Percent complete: 91.1%; Average loss: 2.6562
Iteration: 3647; Percent complete: 91.2%; Average loss: 2.6373
Iteration: 3648; Percent complete: 91.2%; Average loss: 2.9750
Iteration: 3649; Percent complete: 91.2%; Average loss: 2.9014
Iteration: 3650; Percent complete: 91.2%; Average loss: 2.5997
Iteration: 3651; Percent complete: 91.3%; Average loss: 2.8122
Iteration: 3652; Percent complete: 91.3%; Average loss: 2.7794
Iteration: 3653; Percent complete: 91.3%; Average loss: 2.6625
Iteration: 3654; Percent complete: 91.3%; Average loss: 2.6071
Iteration: 3655; Percent complete: 91.4%; Average loss: 2.6762
Iteration: 3656; Percent complete: 91.4%; Average loss: 2.5123
Iteration: 3657; Percent complete: 91.4%; Average loss: 2.5574
Iteration: 3658; Percent complete: 91.5%; Average loss: 2.5843
Iteration: 3659; Percent complete: 91.5%; Average loss: 2.8204
Iteration: 3660; Percent complete: 91.5%; Average loss: 2.4228
Iteration: 3661; Percent complete: 91.5%; Average loss: 2.8045
Iteration: 3662; Percent complete: 91.5%; Average loss: 2.7603
Iteration: 3663; Percent complete: 91.6%; Average loss: 2.6875
Iteration: 3664; Percent complete: 91.6%; Average loss: 2.6880
Iteration: 3665; Percent complete: 91.6%; Average loss: 2.6458
Iteration: 3666; Percent complete: 91.6%; Average loss: 2.7569
Iteration: 3667; Percent complete: 91.7%; Average loss: 2.6908
Iteration: 3668; Percent complete: 91.7%; Average loss: 2.8002
Iteration: 3669; Percent complete: 91.7%; Average loss: 2.9018
Iteration: 3670; Percent complete: 91.8%; Average loss: 2.6981
Iteration: 3671; Percent complete: 91.8%; Average loss: 2.2976
Iteration: 3672; Percent complete: 91.8%; Average loss: 2.4781
Iteration: 3673; Percent complete: 91.8%; Average loss: 2.5014
Iteration: 3674; Percent complete: 91.8%; Average loss: 2.8122
Iteration: 3675; Percent complete: 91.9%; Average loss: 2.8042
Iteration: 3676; Percent complete: 91.9%; Average loss: 2.6112
Iteration: 3677; Percent complete: 91.9%; Average loss: 2.6734
Iteration: 3678; Percent complete: 92.0%; Average loss: 2.7774
Iteration: 3679; Percent complete: 92.0%; Average loss: 2.5438
Iteration: 3680; Percent complete: 92.0%; Average loss: 2.5830
Iteration: 3681; Percent complete: 92.0%; Average loss: 2.9983
Iteration: 3682; Percent complete: 92.0%; Average loss: 2.7525
Iteration: 3683; Percent complete: 92.1%; Average loss: 2.4943
Iteration: 3684; Percent complete: 92.1%; Average loss: 2.6358
Iteration: 3685; Percent complete: 92.1%; Average loss: 2.6530
Iteration: 3686; Percent complete: 92.2%; Average loss: 2.4156
Iteration: 3687; Percent complete: 92.2%; Average loss: 2.9255
Iteration: 3688; Percent complete: 92.2%; Average loss: 2.6274
Iteration: 3689; Percent complete: 92.2%; Average loss: 2.7300
Iteration: 3690; Percent complete: 92.2%; Average loss: 2.7159
Iteration: 3691; Percent complete: 92.3%; Average loss: 2.8169
Iteration: 3692; Percent complete: 92.3%; Average loss: 2.8709
Iteration: 3693; Percent complete: 92.3%; Average loss: 2.7033
Iteration: 3694; Percent complete: 92.3%; Average loss: 2.7714
Iteration: 3695; Percent complete: 92.4%; Average loss: 2.8289
Iteration: 3696; Percent complete: 92.4%; Average loss: 2.5615
Iteration: 3697; Percent complete: 92.4%; Average loss: 2.6468
Iteration: 3698; Percent complete: 92.5%; Average loss: 2.4936
Iteration: 3699; Percent complete: 92.5%; Average loss: 2.8588
Iteration: 3700; Percent complete: 92.5%; Average loss: 2.6611
Iteration: 3701; Percent complete: 92.5%; Average loss: 2.5707
Iteration: 3702; Percent complete: 92.5%; Average loss: 2.6605
Iteration: 3703; Percent complete: 92.6%; Average loss: 2.7718
Iteration: 3704; Percent complete: 92.6%; Average loss: 2.6498
Iteration: 3705; Percent complete: 92.6%; Average loss: 2.4599
Iteration: 3706; Percent complete: 92.7%; Average loss: 2.6439
Iteration: 3707; Percent complete: 92.7%; Average loss: 2.6099
Iteration: 3708; Percent complete: 92.7%; Average loss: 2.5936
Iteration: 3709; Percent complete: 92.7%; Average loss: 2.6986
Iteration: 3710; Percent complete: 92.8%; Average loss: 2.9044
Iteration: 3711; Percent complete: 92.8%; Average loss: 2.6494
Iteration: 3712; Percent complete: 92.8%; Average loss: 2.6778
Iteration: 3713; Percent complete: 92.8%; Average loss: 2.6166
Iteration: 3714; Percent complete: 92.8%; Average loss: 2.6833
Iteration: 3715; Percent complete: 92.9%; Average loss: 2.7578
Iteration: 3716; Percent complete: 92.9%; Average loss: 2.6699
Iteration: 3717; Percent complete: 92.9%; Average loss: 2.6790
Iteration: 3718; Percent complete: 93.0%; Average loss: 2.8376
Iteration: 3719; Percent complete: 93.0%; Average loss: 2.5495
Iteration: 3720; Percent complete: 93.0%; Average loss: 2.6381
Iteration: 3721; Percent complete: 93.0%; Average loss: 2.5150
Iteration: 3722; Percent complete: 93.0%; Average loss: 2.9778
Iteration: 3723; Percent complete: 93.1%; Average loss: 2.5181
Iteration: 3724; Percent complete: 93.1%; Average loss: 2.5684
Iteration: 3725; Percent complete: 93.1%; Average loss: 2.9438
Iteration: 3726; Percent complete: 93.2%; Average loss: 2.8238
Iteration: 3727; Percent complete: 93.2%; Average loss: 2.7405
Iteration: 3728; Percent complete: 93.2%; Average loss: 2.7187
Iteration: 3729; Percent complete: 93.2%; Average loss: 2.7007
Iteration: 3730; Percent complete: 93.2%; Average loss: 2.6903
Iteration: 3731; Percent complete: 93.3%; Average loss: 2.5325
Iteration: 3732; Percent complete: 93.3%; Average loss: 2.6318
Iteration: 3733; Percent complete: 93.3%; Average loss: 2.6275
Iteration: 3734; Percent complete: 93.3%; Average loss: 2.5812
Iteration: 3735; Percent complete: 93.4%; Average loss: 2.4929
Iteration: 3736; Percent complete: 93.4%; Average loss: 3.0084
Iteration: 3737; Percent complete: 93.4%; Average loss: 2.8053
Iteration: 3738; Percent complete: 93.5%; Average loss: 2.5284
Iteration: 3739; Percent complete: 93.5%; Average loss: 2.6100
Iteration: 3740; Percent complete: 93.5%; Average loss: 2.8044
Iteration: 3741; Percent complete: 93.5%; Average loss: 2.5155
Iteration: 3742; Percent complete: 93.5%; Average loss: 2.7324
Iteration: 3743; Percent complete: 93.6%; Average loss: 2.7341
Iteration: 3744; Percent complete: 93.6%; Average loss: 2.8747
Iteration: 3745; Percent complete: 93.6%; Average loss: 2.8691
Iteration: 3746; Percent complete: 93.7%; Average loss: 2.6269
Iteration: 3747; Percent complete: 93.7%; Average loss: 2.7078
Iteration: 3748; Percent complete: 93.7%; Average loss: 2.6731
Iteration: 3749; Percent complete: 93.7%; Average loss: 2.5647
Iteration: 3750; Percent complete: 93.8%; Average loss: 2.6649
Iteration: 3751; Percent complete: 93.8%; Average loss: 2.6313
Iteration: 3752; Percent complete: 93.8%; Average loss: 2.9025
Iteration: 3753; Percent complete: 93.8%; Average loss: 2.7506
Iteration: 3754; Percent complete: 93.8%; Average loss: 2.7635
Iteration: 3755; Percent complete: 93.9%; Average loss: 2.6598
Iteration: 3756; Percent complete: 93.9%; Average loss: 2.7049
Iteration: 3757; Percent complete: 93.9%; Average loss: 2.9731
Iteration: 3758; Percent complete: 94.0%; Average loss: 2.6780
Iteration: 3759; Percent complete: 94.0%; Average loss: 2.5445
Iteration: 3760; Percent complete: 94.0%; Average loss: 2.6036
Iteration: 3761; Percent complete: 94.0%; Average loss: 2.7429
Iteration: 3762; Percent complete: 94.0%; Average loss: 2.8791
Iteration: 3763; Percent complete: 94.1%; Average loss: 2.5310
Iteration: 3764; Percent complete: 94.1%; Average loss: 2.5199
Iteration: 3765; Percent complete: 94.1%; Average loss: 2.4117
Iteration: 3766; Percent complete: 94.2%; Average loss: 2.7689
Iteration: 3767; Percent complete: 94.2%; Average loss: 2.6482
Iteration: 3768; Percent complete: 94.2%; Average loss: 2.5675
Iteration: 3769; Percent complete: 94.2%; Average loss: 2.5398
Iteration: 3770; Percent complete: 94.2%; Average loss: 2.6158
Iteration: 3771; Percent complete: 94.3%; Average loss: 2.8238
Iteration: 3772; Percent complete: 94.3%; Average loss: 2.5630
Iteration: 3773; Percent complete: 94.3%; Average loss: 2.7351
Iteration: 3774; Percent complete: 94.3%; Average loss: 2.6729
Iteration: 3775; Percent complete: 94.4%; Average loss: 2.5877
Iteration: 3776; Percent complete: 94.4%; Average loss: 2.6902
Iteration: 3777; Percent complete: 94.4%; Average loss: 2.6975
Iteration: 3778; Percent complete: 94.5%; Average loss: 2.5215
Iteration: 3779; Percent complete: 94.5%; Average loss: 2.6400
Iteration: 3780; Percent complete: 94.5%; Average loss: 2.4996
Iteration: 3781; Percent complete: 94.5%; Average loss: 2.5131
Iteration: 3782; Percent complete: 94.5%; Average loss: 2.6543
Iteration: 3783; Percent complete: 94.6%; Average loss: 2.7008
Iteration: 3784; Percent complete: 94.6%; Average loss: 2.6857
Iteration: 3785; Percent complete: 94.6%; Average loss: 2.8602
Iteration: 3786; Percent complete: 94.7%; Average loss: 2.9395
Iteration: 3787; Percent complete: 94.7%; Average loss: 2.5986
Iteration: 3788; Percent complete: 94.7%; Average loss: 2.5962
Iteration: 3789; Percent complete: 94.7%; Average loss: 2.6092
Iteration: 3790; Percent complete: 94.8%; Average loss: 2.6616
Iteration: 3791; Percent complete: 94.8%; Average loss: 2.3104
Iteration: 3792; Percent complete: 94.8%; Average loss: 2.9125
Iteration: 3793; Percent complete: 94.8%; Average loss: 2.6520
Iteration: 3794; Percent complete: 94.8%; Average loss: 2.5798
Iteration: 3795; Percent complete: 94.9%; Average loss: 2.7488
Iteration: 3796; Percent complete: 94.9%; Average loss: 2.6829
Iteration: 3797; Percent complete: 94.9%; Average loss: 2.7034
Iteration: 3798; Percent complete: 95.0%; Average loss: 2.5951
Iteration: 3799; Percent complete: 95.0%; Average loss: 2.5479
Iteration: 3800; Percent complete: 95.0%; Average loss: 2.6198
Iteration: 3801; Percent complete: 95.0%; Average loss: 2.6682
Iteration: 3802; Percent complete: 95.0%; Average loss: 2.7656
Iteration: 3803; Percent complete: 95.1%; Average loss: 2.5611
Iteration: 3804; Percent complete: 95.1%; Average loss: 2.8472
Iteration: 3805; Percent complete: 95.1%; Average loss: 2.7280
Iteration: 3806; Percent complete: 95.2%; Average loss: 2.6759
Iteration: 3807; Percent complete: 95.2%; Average loss: 2.5489
Iteration: 3808; Percent complete: 95.2%; Average loss: 2.7059
Iteration: 3809; Percent complete: 95.2%; Average loss: 2.5785
Iteration: 3810; Percent complete: 95.2%; Average loss: 2.3011
Iteration: 3811; Percent complete: 95.3%; Average loss: 2.5996
Iteration: 3812; Percent complete: 95.3%; Average loss: 2.7448
Iteration: 3813; Percent complete: 95.3%; Average loss: 2.5261
Iteration: 3814; Percent complete: 95.3%; Average loss: 2.5058
Iteration: 3815; Percent complete: 95.4%; Average loss: 2.7710
Iteration: 3816; Percent complete: 95.4%; Average loss: 2.8966
Iteration: 3817; Percent complete: 95.4%; Average loss: 2.4969
Iteration: 3818; Percent complete: 95.5%; Average loss: 2.7513
Iteration: 3819; Percent complete: 95.5%; Average loss: 2.6119
Iteration: 3820; Percent complete: 95.5%; Average loss: 2.8560
Iteration: 3821; Percent complete: 95.5%; Average loss: 2.9608
Iteration: 3822; Percent complete: 95.5%; Average loss: 2.6126
Iteration: 3823; Percent complete: 95.6%; Average loss: 2.8152
Iteration: 3824; Percent complete: 95.6%; Average loss: 2.4968
Iteration: 3825; Percent complete: 95.6%; Average loss: 2.8463
Iteration: 3826; Percent complete: 95.7%; Average loss: 2.9512
Iteration: 3827; Percent complete: 95.7%; Average loss: 2.6965
Iteration: 3828; Percent complete: 95.7%; Average loss: 2.4667
Iteration: 3829; Percent complete: 95.7%; Average loss: 2.6264
Iteration: 3830; Percent complete: 95.8%; Average loss: 2.6754
Iteration: 3831; Percent complete: 95.8%; Average loss: 2.5881
Iteration: 3832; Percent complete: 95.8%; Average loss: 2.7766
Iteration: 3833; Percent complete: 95.8%; Average loss: 2.6383
Iteration: 3834; Percent complete: 95.9%; Average loss: 2.7531
Iteration: 3835; Percent complete: 95.9%; Average loss: 2.2840
Iteration: 3836; Percent complete: 95.9%; Average loss: 2.7073
Iteration: 3837; Percent complete: 95.9%; Average loss: 2.6272
Iteration: 3838; Percent complete: 96.0%; Average loss: 2.5989
Iteration: 3839; Percent complete: 96.0%; Average loss: 2.6813
Iteration: 3840; Percent complete: 96.0%; Average loss: 2.5778
Iteration: 3841; Percent complete: 96.0%; Average loss: 2.5751
Iteration: 3842; Percent complete: 96.0%; Average loss: 2.6312
Iteration: 3843; Percent complete: 96.1%; Average loss: 2.4143
Iteration: 3844; Percent complete: 96.1%; Average loss: 2.5690
Iteration: 3845; Percent complete: 96.1%; Average loss: 2.7539
Iteration: 3846; Percent complete: 96.2%; Average loss: 2.8772
Iteration: 3847; Percent complete: 96.2%; Average loss: 2.6626
Iteration: 3848; Percent complete: 96.2%; Average loss: 2.6008
Iteration: 3849; Percent complete: 96.2%; Average loss: 2.5440
Iteration: 3850; Percent complete: 96.2%; Average loss: 2.6881
Iteration: 3851; Percent complete: 96.3%; Average loss: 2.7717
Iteration: 3852; Percent complete: 96.3%; Average loss: 2.5394
Iteration: 3853; Percent complete: 96.3%; Average loss: 2.6024
Iteration: 3854; Percent complete: 96.4%; Average loss: 2.7909
Iteration: 3855; Percent complete: 96.4%; Average loss: 2.7098
Iteration: 3856; Percent complete: 96.4%; Average loss: 2.5148
Iteration: 3857; Percent complete: 96.4%; Average loss: 2.7881
Iteration: 3858; Percent complete: 96.5%; Average loss: 2.5069
Iteration: 3859; Percent complete: 96.5%; Average loss: 2.6076
Iteration: 3860; Percent complete: 96.5%; Average loss: 2.5238
Iteration: 3861; Percent complete: 96.5%; Average loss: 2.4877
Iteration: 3862; Percent complete: 96.5%; Average loss: 2.8029
Iteration: 3863; Percent complete: 96.6%; Average loss: 2.6933
Iteration: 3864; Percent complete: 96.6%; Average loss: 2.7186
Iteration: 3865; Percent complete: 96.6%; Average loss: 2.6342
Iteration: 3866; Percent complete: 96.7%; Average loss: 2.4857
Iteration: 3867; Percent complete: 96.7%; Average loss: 2.4699
Iteration: 3868; Percent complete: 96.7%; Average loss: 2.2797
Iteration: 3869; Percent complete: 96.7%; Average loss: 2.5453
Iteration: 3870; Percent complete: 96.8%; Average loss: 2.5696
Iteration: 3871; Percent complete: 96.8%; Average loss: 2.6652
Iteration: 3872; Percent complete: 96.8%; Average loss: 2.4961
Iteration: 3873; Percent complete: 96.8%; Average loss: 2.7841
Iteration: 3874; Percent complete: 96.9%; Average loss: 2.6331
Iteration: 3875; Percent complete: 96.9%; Average loss: 2.6781
Iteration: 3876; Percent complete: 96.9%; Average loss: 2.7354
Iteration: 3877; Percent complete: 96.9%; Average loss: 2.6605
Iteration: 3878; Percent complete: 97.0%; Average loss: 2.6431
Iteration: 3879; Percent complete: 97.0%; Average loss: 2.4936
Iteration: 3880; Percent complete: 97.0%; Average loss: 2.6185
Iteration: 3881; Percent complete: 97.0%; Average loss: 2.5512
Iteration: 3882; Percent complete: 97.0%; Average loss: 2.3814
Iteration: 3883; Percent complete: 97.1%; Average loss: 2.6407
Iteration: 3884; Percent complete: 97.1%; Average loss: 2.8483
Iteration: 3885; Percent complete: 97.1%; Average loss: 2.8817
Iteration: 3886; Percent complete: 97.2%; Average loss: 2.6324
Iteration: 3887; Percent complete: 97.2%; Average loss: 2.5198
Iteration: 3888; Percent complete: 97.2%; Average loss: 2.6275
Iteration: 3889; Percent complete: 97.2%; Average loss: 2.6738
Iteration: 3890; Percent complete: 97.2%; Average loss: 2.5952
Iteration: 3891; Percent complete: 97.3%; Average loss: 2.6138
Iteration: 3892; Percent complete: 97.3%; Average loss: 2.3384
Iteration: 3893; Percent complete: 97.3%; Average loss: 2.4474
Iteration: 3894; Percent complete: 97.4%; Average loss: 2.6437
Iteration: 3895; Percent complete: 97.4%; Average loss: 2.5661
Iteration: 3896; Percent complete: 97.4%; Average loss: 2.6745
Iteration: 3897; Percent complete: 97.4%; Average loss: 2.8353
Iteration: 3898; Percent complete: 97.5%; Average loss: 2.5571
Iteration: 3899; Percent complete: 97.5%; Average loss: 2.7100
Iteration: 3900; Percent complete: 97.5%; Average loss: 2.6802
Iteration: 3901; Percent complete: 97.5%; Average loss: 2.7347
Iteration: 3902; Percent complete: 97.5%; Average loss: 2.7713
Iteration: 3903; Percent complete: 97.6%; Average loss: 2.7915
Iteration: 3904; Percent complete: 97.6%; Average loss: 2.6873
Iteration: 3905; Percent complete: 97.6%; Average loss: 2.5491
Iteration: 3906; Percent complete: 97.7%; Average loss: 2.4947
Iteration: 3907; Percent complete: 97.7%; Average loss: 2.8905
Iteration: 3908; Percent complete: 97.7%; Average loss: 2.6304
Iteration: 3909; Percent complete: 97.7%; Average loss: 2.5164
Iteration: 3910; Percent complete: 97.8%; Average loss: 2.7969
Iteration: 3911; Percent complete: 97.8%; Average loss: 2.6548
Iteration: 3912; Percent complete: 97.8%; Average loss: 2.5854
Iteration: 3913; Percent complete: 97.8%; Average loss: 2.6446
Iteration: 3914; Percent complete: 97.9%; Average loss: 2.5819
Iteration: 3915; Percent complete: 97.9%; Average loss: 2.5381
Iteration: 3916; Percent complete: 97.9%; Average loss: 2.6742
Iteration: 3917; Percent complete: 97.9%; Average loss: 2.7410
Iteration: 3918; Percent complete: 98.0%; Average loss: 2.7106
Iteration: 3919; Percent complete: 98.0%; Average loss: 2.6828
Iteration: 3920; Percent complete: 98.0%; Average loss: 2.5430
Iteration: 3921; Percent complete: 98.0%; Average loss: 2.4549
Iteration: 3922; Percent complete: 98.0%; Average loss: 2.3888
Iteration: 3923; Percent complete: 98.1%; Average loss: 2.4892
Iteration: 3924; Percent complete: 98.1%; Average loss: 2.6432
Iteration: 3925; Percent complete: 98.1%; Average loss: 2.5475
Iteration: 3926; Percent complete: 98.2%; Average loss: 2.6357
Iteration: 3927; Percent complete: 98.2%; Average loss: 2.6811
Iteration: 3928; Percent complete: 98.2%; Average loss: 2.6616
Iteration: 3929; Percent complete: 98.2%; Average loss: 2.6268
Iteration: 3930; Percent complete: 98.2%; Average loss: 2.5999
Iteration: 3931; Percent complete: 98.3%; Average loss: 2.7848
Iteration: 3932; Percent complete: 98.3%; Average loss: 2.4806
Iteration: 3933; Percent complete: 98.3%; Average loss: 2.7151
Iteration: 3934; Percent complete: 98.4%; Average loss: 2.6007
Iteration: 3935; Percent complete: 98.4%; Average loss: 2.5633
Iteration: 3936; Percent complete: 98.4%; Average loss: 2.7825
Iteration: 3937; Percent complete: 98.4%; Average loss: 2.5495
Iteration: 3938; Percent complete: 98.5%; Average loss: 2.5664
Iteration: 3939; Percent complete: 98.5%; Average loss: 2.5823
Iteration: 3940; Percent complete: 98.5%; Average loss: 2.6620
Iteration: 3941; Percent complete: 98.5%; Average loss: 2.7173
Iteration: 3942; Percent complete: 98.6%; Average loss: 2.7156
Iteration: 3943; Percent complete: 98.6%; Average loss: 2.7155
Iteration: 3944; Percent complete: 98.6%; Average loss: 2.4041
Iteration: 3945; Percent complete: 98.6%; Average loss: 2.3459
Iteration: 3946; Percent complete: 98.7%; Average loss: 2.5007
Iteration: 3947; Percent complete: 98.7%; Average loss: 2.6550
Iteration: 3948; Percent complete: 98.7%; Average loss: 2.5909
Iteration: 3949; Percent complete: 98.7%; Average loss: 2.7058
Iteration: 3950; Percent complete: 98.8%; Average loss: 2.8398
Iteration: 3951; Percent complete: 98.8%; Average loss: 2.6626
Iteration: 3952; Percent complete: 98.8%; Average loss: 2.4536
Iteration: 3953; Percent complete: 98.8%; Average loss: 2.7015
Iteration: 3954; Percent complete: 98.9%; Average loss: 2.5521
Iteration: 3955; Percent complete: 98.9%; Average loss: 2.7805
Iteration: 3956; Percent complete: 98.9%; Average loss: 2.4502
Iteration: 3957; Percent complete: 98.9%; Average loss: 2.8086
Iteration: 3958; Percent complete: 99.0%; Average loss: 2.3437
Iteration: 3959; Percent complete: 99.0%; Average loss: 2.4871
Iteration: 3960; Percent complete: 99.0%; Average loss: 2.8065
Iteration: 3961; Percent complete: 99.0%; Average loss: 2.5811
Iteration: 3962; Percent complete: 99.1%; Average loss: 2.8113
Iteration: 3963; Percent complete: 99.1%; Average loss: 2.6603
Iteration: 3964; Percent complete: 99.1%; Average loss: 2.6296
Iteration: 3965; Percent complete: 99.1%; Average loss: 2.4536
Iteration: 3966; Percent complete: 99.2%; Average loss: 2.7118
Iteration: 3967; Percent complete: 99.2%; Average loss: 2.6689
Iteration: 3968; Percent complete: 99.2%; Average loss: 2.9764
Iteration: 3969; Percent complete: 99.2%; Average loss: 2.6777
Iteration: 3970; Percent complete: 99.2%; Average loss: 2.4727
Iteration: 3971; Percent complete: 99.3%; Average loss: 2.7145
Iteration: 3972; Percent complete: 99.3%; Average loss: 2.7384
Iteration: 3973; Percent complete: 99.3%; Average loss: 2.7348
Iteration: 3974; Percent complete: 99.4%; Average loss: 2.5515
Iteration: 3975; Percent complete: 99.4%; Average loss: 2.7308
Iteration: 3976; Percent complete: 99.4%; Average loss: 2.6790
Iteration: 3977; Percent complete: 99.4%; Average loss: 2.8302
Iteration: 3978; Percent complete: 99.5%; Average loss: 2.5369
Iteration: 3979; Percent complete: 99.5%; Average loss: 2.6195
Iteration: 3980; Percent complete: 99.5%; Average loss: 2.5253
Iteration: 3981; Percent complete: 99.5%; Average loss: 2.4806
Iteration: 3982; Percent complete: 99.6%; Average loss: 2.3531
Iteration: 3983; Percent complete: 99.6%; Average loss: 2.6379
Iteration: 3984; Percent complete: 99.6%; Average loss: 2.3504
Iteration: 3985; Percent complete: 99.6%; Average loss: 2.4711
Iteration: 3986; Percent complete: 99.7%; Average loss: 2.8084
Iteration: 3987; Percent complete: 99.7%; Average loss: 2.6769
Iteration: 3988; Percent complete: 99.7%; Average loss: 2.7176
Iteration: 3989; Percent complete: 99.7%; Average loss: 2.6277
Iteration: 3990; Percent complete: 99.8%; Average loss: 2.8573
Iteration: 3991; Percent complete: 99.8%; Average loss: 2.5058
Iteration: 3992; Percent complete: 99.8%; Average loss: 2.4711
Iteration: 3993; Percent complete: 99.8%; Average loss: 2.4371
Iteration: 3994; Percent complete: 99.9%; Average loss: 2.5930
Iteration: 3995; Percent complete: 99.9%; Average loss: 2.2600
Iteration: 3996; Percent complete: 99.9%; Average loss: 2.6623
Iteration: 3997; Percent complete: 99.9%; Average loss: 2.6919
Iteration: 3998; Percent complete: 100.0%; Average loss: 2.2651
Iteration: 3999; Percent complete: 100.0%; Average loss: 2.5989
Iteration: 4000; Percent complete: 100.0%; Average loss: 2.7574

평가 수행하기

여러분의 모델과 채팅을 해보고 싶다면 다음 블록을 수행하면 됩니다.

# Dropout 레이어를 평가 모드로 설정합니다
encoder.eval()
decoder.eval()

# 탐색 모듈을 초기화합니다
searcher = GreedySearchDecoder(encoder, decoder)

# 채팅을 시작합니다 (다음 줄의 주석을 제거하면 시작해볼 수 있습니다)
# evaluateInput(encoder, decoder, searcher, voc)

맺음말

이번 튜토리얼을 이것으로 마무리하겠습니다. 축하합니다! 여러분은 이제 생성 챗봇 모델을 만들기 위한 기초 지식을 습득했습니다. 만약 좀 더 관심이 있다면 모델이나 학습 매개변수를 수정해 보면서, 혹은 모델을 학습할 데이터를 바꿔 보면서 챗봇의 행동을 수정해볼 수 있을 것입니다.

그 외에도 딥러닝의 멋진 활용 예에 대한 PyTorch 튜토리얼이 있으니 한 번 확인해 보기 바랍니다!

Total running time of the script: ( 5 minutes 5.130 seconds)

Gallery generated by Sphinx-Gallery


이 튜토리얼이 어떠셨나요?

© Copyright 2022, PyTorch & 파이토치 한국 사용자 모임(PyTorch Korea User Group).

Built with Sphinx using a theme provided by Read the Docs.

PyTorchKorea @ GitHub

파이토치 한국 사용자 모임을 GitHub에서 만나보세요.

GitHub로 이동

한국어 튜토리얼

한국어로 번역 중인 PyTorch 튜토리얼입니다.

튜토리얼로 이동

커뮤니티

다른 사용자들과 의견을 나누고, 도와주세요!

커뮤니티로 이동