참고
Click here to download the full example code
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.

> 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
코넬 대학교의 영화 속 대화 말뭉치 데이터셋 을 읽어오고 전처리합니다
Luong의 어텐션(attention) 메커니즘 을 이용하여 sequence-to-sequence 모델을 구현합니다
미니배치를 이용하여 인코더와 디코더를 함께 학습합니다
탐욕적 탐색 기법(greedy-search)을 사용하는 디코더 모듈을 구현합니다
학습한 챗봇과 대화를 나눠 봅니다
감사의 글
이 튜토리얼은 다음 자료의 도움을 받아 작성하였습니다.
Yuan-Kuei Wu의 pytorch-chatbot 구현체: https://github.com/ywk991112/pytorch-chatbot
Sean Robertson의 practical-pytorch seq2seq-translation 예제: https://github.com/spro/practical-pytorch/tree/master/seq2seq-translation
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
import json
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 = "movie-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, "utterances.jsonl"))
b'{"id": "L1045", "conversation_id": "L1044", "text": "They do not!", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 1, "toks": [{"tok": "They", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "do", "tag": "VBP", "dep": "ROOT", "dn": [0, 2, 3]}, {"tok": "not", "tag": "RB", "dep": "neg", "up": 1, "dn": []}, {"tok": "!", "tag": ".", "dep": "punct", "up": 1, "dn": []}]}]}, "reply-to": "L1044", "timestamp": null, "vectors": []}\n'
b'{"id": "L1044", "conversation_id": "L1044", "text": "They do to!", "speaker": "u2", "meta": {"movie_id": "m0", "parsed": [{"rt": 1, "toks": [{"tok": "They", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "do", "tag": "VBP", "dep": "ROOT", "dn": [0, 2, 3]}, {"tok": "to", "tag": "TO", "dep": "dobj", "up": 1, "dn": []}, {"tok": "!", "tag": ".", "dep": "punct", "up": 1, "dn": []}]}]}, "reply-to": null, "timestamp": null, "vectors": []}\n'
b'{"id": "L985", "conversation_id": "L984", "text": "I hope so.", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 1, "toks": [{"tok": "I", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "hope", "tag": "VBP", "dep": "ROOT", "dn": [0, 2, 3]}, {"tok": "so", "tag": "RB", "dep": "advmod", "up": 1, "dn": []}, {"tok": ".", "tag": ".", "dep": "punct", "up": 1, "dn": []}]}]}, "reply-to": "L984", "timestamp": null, "vectors": []}\n'
b'{"id": "L984", "conversation_id": "L984", "text": "She okay?", "speaker": "u2", "meta": {"movie_id": "m0", "parsed": [{"rt": 1, "toks": [{"tok": "She", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "okay", "tag": "RB", "dep": "ROOT", "dn": [0, 2]}, {"tok": "?", "tag": ".", "dep": "punct", "up": 1, "dn": []}]}]}, "reply-to": null, "timestamp": null, "vectors": []}\n'
b'{"id": "L925", "conversation_id": "L924", "text": "Let\'s go.", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 0, "toks": [{"tok": "Let", "tag": "VB", "dep": "ROOT", "dn": [2, 3]}, {"tok": "\'s", "tag": "PRP", "dep": "nsubj", "up": 2, "dn": []}, {"tok": "go", "tag": "VB", "dep": "ccomp", "up": 0, "dn": [1]}, {"tok": ".", "tag": ".", "dep": "punct", "up": 0, "dn": []}]}]}, "reply-to": "L924", "timestamp": null, "vectors": []}\n'
b'{"id": "L924", "conversation_id": "L924", "text": "Wow", "speaker": "u2", "meta": {"movie_id": "m0", "parsed": [{"rt": 0, "toks": [{"tok": "Wow", "tag": "UH", "dep": "ROOT", "dn": []}]}]}, "reply-to": null, "timestamp": null, "vectors": []}\n'
b'{"id": "L872", "conversation_id": "L870", "text": "Okay -- you\'re gonna need to learn how to lie.", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 4, "toks": [{"tok": "Okay", "tag": "UH", "dep": "intj", "up": 4, "dn": []}, {"tok": "--", "tag": ":", "dep": "punct", "up": 4, "dn": []}, {"tok": "you", "tag": "PRP", "dep": "nsubj", "up": 4, "dn": []}, {"tok": "\'re", "tag": "VBP", "dep": "aux", "up": 4, "dn": []}, {"tok": "gon", "tag": "VBG", "dep": "ROOT", "dn": [0, 1, 2, 3, 6, 12]}, {"tok": "na", "tag": "TO", "dep": "aux", "up": 6, "dn": []}, {"tok": "need", "tag": "VB", "dep": "xcomp", "up": 4, "dn": [5, 8]}, {"tok": "to", "tag": "TO", "dep": "aux", "up": 8, "dn": []}, {"tok": "learn", "tag": "VB", "dep": "xcomp", "up": 6, "dn": [7, 11]}, {"tok": "how", "tag": "WRB", "dep": "advmod", "up": 11, "dn": []}, {"tok": "to", "tag": "TO", "dep": "aux", "up": 11, "dn": []}, {"tok": "lie", "tag": "VB", "dep": "xcomp", "up": 8, "dn": [9, 10]}, {"tok": ".", "tag": ".", "dep": "punct", "up": 4, "dn": []}]}]}, "reply-to": "L871", "timestamp": null, "vectors": []}\n'
b'{"id": "L871", "conversation_id": "L870", "text": "No", "speaker": "u2", "meta": {"movie_id": "m0", "parsed": [{"rt": 0, "toks": [{"tok": "No", "tag": "UH", "dep": "ROOT", "dn": []}]}]}, "reply-to": "L870", "timestamp": null, "vectors": []}\n'
b'{"id": "L870", "conversation_id": "L870", "text": "I\'m kidding. You know how sometimes you just become this \\"persona\\"? And you don\'t know how to quit?", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 2, "toks": [{"tok": "I", "tag": "PRP", "dep": "nsubj", "up": 2, "dn": []}, {"tok": "\'m", "tag": "VBP", "dep": "aux", "up": 2, "dn": []}, {"tok": "kidding", "tag": "VBG", "dep": "ROOT", "dn": [0, 1, 3]}, {"tok": ".", "tag": ".", "dep": "punct", "up": 2, "dn": [4]}, {"tok": " ", "tag": "_SP", "dep": "", "up": 3, "dn": []}]}, {"rt": 1, "toks": [{"tok": "You", "tag": "PRP", "dep": "nsubj", "up": 1, "dn": []}, {"tok": "know", "tag": "VBP", "dep": "ROOT", "dn": [0, 6, 11]}, {"tok": "how", "tag": "WRB", "dep": "advmod", "up": 3, "dn": []}, {"tok": "sometimes", "tag": "RB", "dep": "advmod", "up": 6, "dn": [2]}, {"tok": "you", "tag": "PRP", "dep": "nsubj", "up": 6, "dn": []}, {"tok": "just", "tag": "RB", "dep": "advmod", "up": 6, "dn": []}, {"tok": "become", "tag": "VBP", "dep": "ccomp", "up": 1, "dn": [3, 4, 5, 9]}, {"tok": "this", "tag": "DT", "dep": "det", "up": 9, "dn": []}, {"tok": "\\"", "tag": "``", "dep": "punct", "up": 9, "dn": []}, {"tok": "persona", "tag": "NN", "dep": "attr", "up": 6, "dn": [7, 8, 10]}, {"tok": "\\"", "tag": "\'\'", "dep": "punct", "up": 9, "dn": []}, {"tok": "?", "tag": ".", "dep": "punct", "up": 1, "dn": [12]}, {"tok": " ", "tag": "_SP", "dep": "", "up": 11, "dn": []}]}, {"rt": 4, "toks": [{"tok": "And", "tag": "CC", "dep": "cc", "up": 4, "dn": []}, {"tok": "you", "tag": "PRP", "dep": "nsubj", "up": 4, "dn": []}, {"tok": "do", "tag": "VBP", "dep": "aux", "up": 4, "dn": []}, {"tok": "n\'t", "tag": "RB", "dep": "neg", "up": 4, "dn": []}, {"tok": "know", "tag": "VB", "dep": "ROOT", "dn": [0, 1, 2, 3, 7, 8]}, {"tok": "how", "tag": "WRB", "dep": "advmod", "up": 7, "dn": []}, {"tok": "to", "tag": "TO", "dep": "aux", "up": 7, "dn": []}, {"tok": "quit", "tag": "VB", "dep": "xcomp", "up": 4, "dn": [5, 6]}, {"tok": "?", "tag": ".", "dep": "punct", "up": 4, "dn": []}]}]}, "reply-to": null, "timestamp": null, "vectors": []}\n'
b'{"id": "L869", "conversation_id": "L866", "text": "Like my fear of wearing pastels?", "speaker": "u0", "meta": {"movie_id": "m0", "parsed": [{"rt": 0, "toks": [{"tok": "Like", "tag": "IN", "dep": "ROOT", "dn": [2, 6]}, {"tok": "my", "tag": "PRP$", "dep": "poss", "up": 2, "dn": []}, {"tok": "fear", "tag": "NN", "dep": "pobj", "up": 0, "dn": [1, 3]}, {"tok": "of", "tag": "IN", "dep": "prep", "up": 2, "dn": [4]}, {"tok": "wearing", "tag": "VBG", "dep": "pcomp", "up": 3, "dn": [5]}, {"tok": "pastels", "tag": "NNS", "dep": "dobj", "up": 4, "dn": []}, {"tok": "?", "tag": ".", "dep": "punct", "up": 0, "dn": []}]}]}, "reply-to": "L868", "timestamp": null, "vectors": []}\n'
원하는 형식의 데이터 파일로 만들기¶
편의를 위해 데이터의 형식을 원하는 형태로 만들려고 합니다. 각 줄에 질의 문장 과 응답 문장 의 쌍이 탭으로 구분되어 있게끔 하는 것입니다.
다음의 함수를 통해 utterances.jsonl 원본 데이터 파일을 파싱하려 합니다.
loadLines
는 파일에 포함된 대사를 변환하여 항목(대사 IDlineID
, 인물 IDcharacterID
, 영화 IDmovieID
, 인물character
, 대사 내용text
)에 대한 사전 형태로 변환합니다loadConversations
는loadLines
를 통해 읽어들인 대사(lines
)의 항목(fields
)를 movie_conversations.txt 에 나와 있는 내용에 맞춰 대화 형태로 묶습니다extractSentencePairs
는 대화(conversations
)에서 문장 쌍을 추출합니다
# 파일에 포함된 각 줄을 쪼개서 대사(line)와 대화(conversation)를 생성합니다.
def loadLinesAndConversations(fileName):
lines = {}
conversations = {}
with open(fileName, 'r', encoding='iso-8859-1') as f:
for line in f:
lineJson = json.loads(line)
# 필드를 추출하여 line 객체를 구성합니다
lineObj = {}
lineObj["lineID"] = lineJson["id"]
lineObj["characterID"] = lineJson["speaker"]
lineObj["text"] = lineJson["text"]
lines[lineObj['lineID']] = lineObj
# 필드를 추출하여 conversation 객체를 구성합니다
if lineJson["conversation_id"] not in conversations:
convObj = {}
convObj["conversationID"] = lineJson["conversation_id"]
convObj["movieID"] = lineJson["meta"]["movie_id"]
convObj["lines"] = [lineObj]
else:
convObj = conversations[lineJson["conversation_id"]]
convObj["lines"].insert(0, lineObj)
conversations[convObj["conversationID"]] = convObj
return lines, conversations
# conversation들에서 문장 쌍을 추출합니다
def extractSentencePairs(conversations):
qa_pairs = []
for conversation in conversations.values():
# 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_utterances.jsonl 를 만듭니다.
# 새 파일에 대한 경로를 정의합니다
datafile = os.path.join(corpus, "formatted_utterances.jsonl")
delimiter = '\t'
# 구분자에 대해 unescape 함수를 호출합니다
delimiter = str(codecs.decode(delimiter, "unicode_escape"))
# 대사 사전(lines dict)과 대화 사전(conversations dict)을 초기화합니다
lines = {}
conversations = {}
# 대사와 대화를 불러옵니다
print("\nProcessing corpus into lines and conversations...")
lines, conversations = loadLinesAndConversations(os.path.join(corpus, "utterances.jsonl"))
# 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)
Processing corpus into lines and conversations...
Writing newly formatted file...
Sample lines from file:
b'They do to!\tThey do not!\n'
b'She okay?\tI hope so.\n'
b"Wow\tLet's go.\n"
b'"I\'m kidding. You know how sometimes you just become this ""persona""? And you don\'t know how to quit?"\tNo\n'
b"No\tOkay -- you're gonna need to learn how to lie.\n"
b"I figured you'd get to the good stuff eventually.\tWhat good stuff?\n"
b'What good stuff?\t"The ""real you""."\n'
b'"The ""real you""."\tLike my fear of wearing pastels?\n'
b'do you listen to this crap?\tWhat crap?\n'
b"What crap?\tMe. This endless ...blonde babble. I'm like, boring myself.\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 초기화합니다
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)
Start preparing training data ...
Reading lines...
Read 221282 sentence pairs
Trimmed to 64313 sentence pairs
Counting words...
Counted words: 18082
pairs:
['they do to !', 'they do not !']
['she okay ?', 'i hope so .']
['wow', 'let s go .']
['what good stuff ?', 'the real you .']
['the real you .', 'like my fear of wearing pastels ?']
['do you listen to this crap ?', 'what crap ?']
['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 ?']
['have fun tonight ?', 'tons']
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.
voc.trim
함수를 이용하여MIN_COUNT
라는 기준 이하의 단어를 제거합니다.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)
keep_words 7833 / 18079 = 0.4333
Trimmed from 64313 pairs to 53131, 0.8261 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.

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)
input_variable: tensor([[ 317, 280, 33, 11, 44],
[ 24, 223, 85, 176, 261],
[ 228, 24, 17, 251, 14],
[ 365, 57, 639, 186, 2],
[ 93, 61, 28, 52, 0],
[ 14, 22, 1894, 2424, 0],
[ 14, 1501, 14, 14, 0],
[ 14, 14, 2, 2, 0],
[ 2, 2, 0, 0, 0]])
lengths: tensor([9, 9, 8, 8, 4])
target_variable: tensor([[ 93, 280, 50, 103, 11],
[ 194, 14, 208, 7, 990],
[2603, 2, 135, 10, 135],
[ 11, 0, 1895, 2, 14],
[ 200, 0, 187, 0, 2],
[ 727, 0, 14, 0, 0],
[ 14, 0, 2, 0, 0],
[ 2, 0, 0, 0, 0]])
mask: tensor([[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, 1, 0, 0],
[1, 0, 1, 0, 0],
[1, 0, 0, 0, 0]], dtype=torch.uint8)
max_target_len: 8
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.

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:
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_sequence
와
nn.utils.rnn.pad_packed_sequence
를 통해 수행할 수 있습니다.
계산 그래프:
단어 인덱스를 임베딩으로 변환합니다.
RNN 모듈을 위한 패딩된 배치 시퀀스를 패킹합니다.
GRU로 포워드 패스를 수행합니다.
패딩을 언패킹합니다.
양방향 GRU의 출력을 합산합니다.
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.

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.
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.
# 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:
현재의 입력 단어에 대한 임베딩을 구합니다.
무방향 GRU로 포워드 패스를 수행합니다.
(2)에서 구한 현재의 GRU 출력을 바탕으로 어텐션 가중치를 계산합니다.
인코더 출력에 어텐션을 곱하여 새로운 《가중치 합》 문맥 벡터를 구합니다.
Luong의 논문에 나온 식 5를 이용하여 가중치 문맥 벡터와 GRU 출력을 결합합니다.
Luong의 논문에 나온 식 6을 이용하여(softmax 없이) 다음 단어를 예측합니다.
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 우리는teacher_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.
그림 출처: Goodfellow 등 저. Deep Learning. 2016. https://www.deeplearningbook.org/
작업 절차:
전체 입력 배치에 대하여 인코더로 포워드 패스를 수행합니다.
디코더의 입력을 SOS_token로, 은닉 상태를 인코더의 마지막 은닉 상태로 초기화합니다.
입력 배치 시퀀스를 한 번에 하나씩 디코더로 포워드 패스합니다.
Teacher forcing을 사용하는 경우, 디코더의 다음 입력을 현재의 목표로 둡니다. 그렇지 않으면 디코더의 다음 입력을 현재 디코더의 출력으로 둡니다.
손실을 계산하고 누적합니다.
역전파를 수행합니다.
그라디언트를 클리핑 합니다.
인코더 및 디코더 모델의 매개변수를 갱신합니다.
경고
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:
인코더 모델로 입력을 포워드 패스합니다.
인코더의 마지막 은닉 레이어가 디코더의 첫 번째 은닉 레이어의 입력이 되도록 준비합니다.
디코더의 첫 번째 입력을 SOS_token으로 초기화합니다.
디코더가 단어를 덧붙여 나갈 텐서를 초기화합니다.
- 반복적으로 각 단계마다 하나의 단어 토큰을 디코딩합니다.
디코더로의 포워드 패스를 수행합니다.
가장 가능성 높은 단어 토큰과 그 softmax 점수를 구합니다.
토큰과 점수를 기록합니다.
현재의 토큰을 디코더의 다음 입력으로 준비시킵니다.
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!')
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)
Building optimizers ...
Starting Training!
Initializing ...
Training...
/workspace/ko-latest/beginner_source/chatbot_tutorial.py:879: UserWarning:
indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (Triggered internally at ../aten/src/ATen/native/IndexingUtils.h:27.)
/root/.local/lib/python3.9/site-packages/torch/autograd/__init__.py:197: UserWarning:
masked_scatter_ received a mask with dtype torch.uint8, this behavior is now deprecated,please use a mask with dtype torch.bool instead. (Triggered internally at ../aten/src/ATen/native/cuda/IndexKernel.cpp:74.)
Iteration: 1; Percent complete: 0.0%; Average loss: 8.9657
Iteration: 2; Percent complete: 0.1%; Average loss: 8.8463
Iteration: 3; Percent complete: 0.1%; Average loss: 8.6700
Iteration: 4; Percent complete: 0.1%; Average loss: 8.3349
Iteration: 5; Percent complete: 0.1%; Average loss: 8.0310
Iteration: 6; Percent complete: 0.1%; Average loss: 7.4867
Iteration: 7; Percent complete: 0.2%; Average loss: 6.9311
Iteration: 8; Percent complete: 0.2%; Average loss: 6.7360
Iteration: 9; Percent complete: 0.2%; Average loss: 6.5087
Iteration: 10; Percent complete: 0.2%; Average loss: 6.4902
Iteration: 11; Percent complete: 0.3%; Average loss: 6.2772
Iteration: 12; Percent complete: 0.3%; Average loss: 6.0458
Iteration: 13; Percent complete: 0.3%; Average loss: 5.5728
Iteration: 14; Percent complete: 0.4%; Average loss: 5.5890
Iteration: 15; Percent complete: 0.4%; Average loss: 5.6384
Iteration: 16; Percent complete: 0.4%; Average loss: 5.4649
Iteration: 17; Percent complete: 0.4%; Average loss: 4.9964
Iteration: 18; Percent complete: 0.4%; Average loss: 4.9614
Iteration: 19; Percent complete: 0.5%; Average loss: 5.0357
Iteration: 20; Percent complete: 0.5%; Average loss: 5.0715
Iteration: 21; Percent complete: 0.5%; Average loss: 4.8974
Iteration: 22; Percent complete: 0.5%; Average loss: 5.0697
Iteration: 23; Percent complete: 0.6%; Average loss: 4.8416
Iteration: 24; Percent complete: 0.6%; Average loss: 4.7385
Iteration: 25; Percent complete: 0.6%; Average loss: 4.7006
Iteration: 26; Percent complete: 0.7%; Average loss: 4.6221
Iteration: 27; Percent complete: 0.7%; Average loss: 4.5848
Iteration: 28; Percent complete: 0.7%; Average loss: 4.8615
Iteration: 29; Percent complete: 0.7%; Average loss: 4.8653
Iteration: 30; Percent complete: 0.8%; Average loss: 4.6314
Iteration: 31; Percent complete: 0.8%; Average loss: 4.7552
Iteration: 32; Percent complete: 0.8%; Average loss: 4.6318
Iteration: 33; Percent complete: 0.8%; Average loss: 4.7634
Iteration: 34; Percent complete: 0.9%; Average loss: 4.7234
Iteration: 35; Percent complete: 0.9%; Average loss: 4.8369
Iteration: 36; Percent complete: 0.9%; Average loss: 4.8603
Iteration: 37; Percent complete: 0.9%; Average loss: 4.6633
Iteration: 38; Percent complete: 0.9%; Average loss: 4.6908
Iteration: 39; Percent complete: 1.0%; Average loss: 4.5208
Iteration: 40; Percent complete: 1.0%; Average loss: 4.4950
Iteration: 41; Percent complete: 1.0%; Average loss: 4.6704
Iteration: 42; Percent complete: 1.1%; Average loss: 4.6164
Iteration: 43; Percent complete: 1.1%; Average loss: 4.6346
Iteration: 44; Percent complete: 1.1%; Average loss: 4.4144
Iteration: 45; Percent complete: 1.1%; Average loss: 4.8993
Iteration: 46; Percent complete: 1.1%; Average loss: 4.7463
Iteration: 47; Percent complete: 1.2%; Average loss: 4.7941
Iteration: 48; Percent complete: 1.2%; Average loss: 4.7020
Iteration: 49; Percent complete: 1.2%; Average loss: 4.6326
Iteration: 50; Percent complete: 1.2%; Average loss: 4.5634
Iteration: 51; Percent complete: 1.3%; Average loss: 4.5897
Iteration: 52; Percent complete: 1.3%; Average loss: 4.7376
Iteration: 53; Percent complete: 1.3%; Average loss: 4.6357
Iteration: 54; Percent complete: 1.4%; Average loss: 4.8027
Iteration: 55; Percent complete: 1.4%; Average loss: 4.5699
Iteration: 56; Percent complete: 1.4%; Average loss: 4.6726
Iteration: 57; Percent complete: 1.4%; Average loss: 4.6730
Iteration: 58; Percent complete: 1.5%; Average loss: 4.6478
Iteration: 59; Percent complete: 1.5%; Average loss: 4.7109
Iteration: 60; Percent complete: 1.5%; Average loss: 4.5778
Iteration: 61; Percent complete: 1.5%; Average loss: 4.5551
Iteration: 62; Percent complete: 1.6%; Average loss: 4.3969
Iteration: 63; Percent complete: 1.6%; Average loss: 4.6311
Iteration: 64; Percent complete: 1.6%; Average loss: 4.5277
Iteration: 65; Percent complete: 1.6%; Average loss: 4.5423
Iteration: 66; Percent complete: 1.7%; Average loss: 4.4235
Iteration: 67; Percent complete: 1.7%; Average loss: 4.6389
Iteration: 68; Percent complete: 1.7%; Average loss: 4.6713
Iteration: 69; Percent complete: 1.7%; Average loss: 4.4748
Iteration: 70; Percent complete: 1.8%; Average loss: 4.4916
Iteration: 71; Percent complete: 1.8%; Average loss: 4.4203
Iteration: 72; Percent complete: 1.8%; Average loss: 4.6554
Iteration: 73; Percent complete: 1.8%; Average loss: 4.4590
Iteration: 74; Percent complete: 1.8%; Average loss: 4.2473
Iteration: 75; Percent complete: 1.9%; Average loss: 4.5166
Iteration: 76; Percent complete: 1.9%; Average loss: 4.5450
Iteration: 77; Percent complete: 1.9%; Average loss: 4.5027
Iteration: 78; Percent complete: 1.9%; Average loss: 4.5224
Iteration: 79; Percent complete: 2.0%; Average loss: 4.7163
Iteration: 80; Percent complete: 2.0%; Average loss: 4.5158
Iteration: 81; Percent complete: 2.0%; Average loss: 4.2448
Iteration: 82; Percent complete: 2.1%; Average loss: 4.3661
Iteration: 83; Percent complete: 2.1%; Average loss: 4.5708
Iteration: 84; Percent complete: 2.1%; Average loss: 4.3324
Iteration: 85; Percent complete: 2.1%; Average loss: 4.6890
Iteration: 86; Percent complete: 2.1%; Average loss: 4.4387
Iteration: 87; Percent complete: 2.2%; Average loss: 4.5854
Iteration: 88; Percent complete: 2.2%; Average loss: 4.2732
Iteration: 89; Percent complete: 2.2%; Average loss: 4.5047
Iteration: 90; Percent complete: 2.2%; Average loss: 4.3406
Iteration: 91; Percent complete: 2.3%; Average loss: 4.2896
Iteration: 92; Percent complete: 2.3%; Average loss: 4.3937
Iteration: 93; Percent complete: 2.3%; Average loss: 4.3468
Iteration: 94; Percent complete: 2.4%; Average loss: 4.1440
Iteration: 95; Percent complete: 2.4%; Average loss: 4.3547
Iteration: 96; Percent complete: 2.4%; Average loss: 4.4923
Iteration: 97; Percent complete: 2.4%; Average loss: 4.2711
Iteration: 98; Percent complete: 2.5%; Average loss: 4.3908
Iteration: 99; Percent complete: 2.5%; Average loss: 4.4556
Iteration: 100; Percent complete: 2.5%; Average loss: 4.5007
Iteration: 101; Percent complete: 2.5%; Average loss: 4.6873
Iteration: 102; Percent complete: 2.5%; Average loss: 4.3115
Iteration: 103; Percent complete: 2.6%; Average loss: 4.5270
Iteration: 104; Percent complete: 2.6%; Average loss: 4.6869
Iteration: 105; Percent complete: 2.6%; Average loss: 4.3205
Iteration: 106; Percent complete: 2.6%; Average loss: 4.2702
Iteration: 107; Percent complete: 2.7%; Average loss: 4.3069
Iteration: 108; Percent complete: 2.7%; Average loss: 4.2715
Iteration: 109; Percent complete: 2.7%; Average loss: 4.6359
Iteration: 110; Percent complete: 2.8%; Average loss: 4.1105
Iteration: 111; Percent complete: 2.8%; Average loss: 4.3740
Iteration: 112; Percent complete: 2.8%; Average loss: 4.2661
Iteration: 113; Percent complete: 2.8%; Average loss: 4.3733
Iteration: 114; Percent complete: 2.9%; Average loss: 4.3885
Iteration: 115; Percent complete: 2.9%; Average loss: 4.3712
Iteration: 116; Percent complete: 2.9%; Average loss: 4.3786
Iteration: 117; Percent complete: 2.9%; Average loss: 4.3534
Iteration: 118; Percent complete: 2.9%; Average loss: 4.4268
Iteration: 119; Percent complete: 3.0%; Average loss: 4.4975
Iteration: 120; Percent complete: 3.0%; Average loss: 4.5037
Iteration: 121; Percent complete: 3.0%; Average loss: 4.2676
Iteration: 122; Percent complete: 3.0%; Average loss: 4.1443
Iteration: 123; Percent complete: 3.1%; Average loss: 4.2163
Iteration: 124; Percent complete: 3.1%; Average loss: 4.3758
Iteration: 125; Percent complete: 3.1%; Average loss: 4.3905
Iteration: 126; Percent complete: 3.1%; Average loss: 4.3932
Iteration: 127; Percent complete: 3.2%; Average loss: 4.6480
Iteration: 128; Percent complete: 3.2%; Average loss: 4.2873
Iteration: 129; Percent complete: 3.2%; Average loss: 4.3417
Iteration: 130; Percent complete: 3.2%; Average loss: 4.0992
Iteration: 131; Percent complete: 3.3%; Average loss: 4.1908
Iteration: 132; Percent complete: 3.3%; Average loss: 4.2735
Iteration: 133; Percent complete: 3.3%; Average loss: 4.4015
Iteration: 134; Percent complete: 3.4%; Average loss: 4.3572
Iteration: 135; Percent complete: 3.4%; Average loss: 4.2948
Iteration: 136; Percent complete: 3.4%; Average loss: 4.1369
Iteration: 137; Percent complete: 3.4%; Average loss: 4.4547
Iteration: 138; Percent complete: 3.5%; Average loss: 4.4131
Iteration: 139; Percent complete: 3.5%; Average loss: 4.2647
Iteration: 140; Percent complete: 3.5%; Average loss: 4.5650
Iteration: 141; Percent complete: 3.5%; Average loss: 4.2351
Iteration: 142; Percent complete: 3.5%; Average loss: 4.5088
Iteration: 143; Percent complete: 3.6%; Average loss: 4.3850
Iteration: 144; Percent complete: 3.6%; Average loss: 4.3757
Iteration: 145; Percent complete: 3.6%; Average loss: 4.2623
Iteration: 146; Percent complete: 3.6%; Average loss: 4.1435
Iteration: 147; Percent complete: 3.7%; Average loss: 4.1411
Iteration: 148; Percent complete: 3.7%; Average loss: 4.2322
Iteration: 149; Percent complete: 3.7%; Average loss: 4.3807
Iteration: 150; Percent complete: 3.8%; Average loss: 4.5193
Iteration: 151; Percent complete: 3.8%; Average loss: 4.3010
Iteration: 152; Percent complete: 3.8%; Average loss: 4.0738
Iteration: 153; Percent complete: 3.8%; Average loss: 4.1253
Iteration: 154; Percent complete: 3.9%; Average loss: 4.4457
Iteration: 155; Percent complete: 3.9%; Average loss: 4.2288
Iteration: 156; Percent complete: 3.9%; Average loss: 4.5074
Iteration: 157; Percent complete: 3.9%; Average loss: 4.1063
Iteration: 158; Percent complete: 4.0%; Average loss: 3.8532
Iteration: 159; Percent complete: 4.0%; Average loss: 4.3723
Iteration: 160; Percent complete: 4.0%; Average loss: 4.4265
Iteration: 161; Percent complete: 4.0%; Average loss: 4.1598
Iteration: 162; Percent complete: 4.0%; Average loss: 3.9994
Iteration: 163; Percent complete: 4.1%; Average loss: 4.2402
Iteration: 164; Percent complete: 4.1%; Average loss: 4.2666
Iteration: 165; Percent complete: 4.1%; Average loss: 4.0939
Iteration: 166; Percent complete: 4.2%; Average loss: 4.2417
Iteration: 167; Percent complete: 4.2%; Average loss: 4.3552
Iteration: 168; Percent complete: 4.2%; Average loss: 4.0682
Iteration: 169; Percent complete: 4.2%; Average loss: 4.2711
Iteration: 170; Percent complete: 4.2%; Average loss: 4.1574
Iteration: 171; Percent complete: 4.3%; Average loss: 4.0947
Iteration: 172; Percent complete: 4.3%; Average loss: 4.1908
Iteration: 173; Percent complete: 4.3%; Average loss: 4.2836
Iteration: 174; Percent complete: 4.3%; Average loss: 4.0039
Iteration: 175; Percent complete: 4.4%; Average loss: 3.9831
Iteration: 176; Percent complete: 4.4%; Average loss: 3.9995
Iteration: 177; Percent complete: 4.4%; Average loss: 4.2403
Iteration: 178; Percent complete: 4.5%; Average loss: 4.1533
Iteration: 179; Percent complete: 4.5%; Average loss: 4.1926
Iteration: 180; Percent complete: 4.5%; Average loss: 4.2145
Iteration: 181; Percent complete: 4.5%; Average loss: 4.3625
Iteration: 182; Percent complete: 4.5%; Average loss: 4.0584
Iteration: 183; Percent complete: 4.6%; Average loss: 4.2972
Iteration: 184; Percent complete: 4.6%; Average loss: 4.2805
Iteration: 185; Percent complete: 4.6%; Average loss: 4.0263
Iteration: 186; Percent complete: 4.7%; Average loss: 4.1831
Iteration: 187; Percent complete: 4.7%; Average loss: 4.2487
Iteration: 188; Percent complete: 4.7%; Average loss: 4.1764
Iteration: 189; Percent complete: 4.7%; Average loss: 3.9825
Iteration: 190; Percent complete: 4.8%; Average loss: 4.1881
Iteration: 191; Percent complete: 4.8%; Average loss: 4.3451
Iteration: 192; Percent complete: 4.8%; Average loss: 3.7638
Iteration: 193; Percent complete: 4.8%; Average loss: 4.2249
Iteration: 194; Percent complete: 4.9%; Average loss: 4.3181
Iteration: 195; Percent complete: 4.9%; Average loss: 4.0401
Iteration: 196; Percent complete: 4.9%; Average loss: 3.8649
Iteration: 197; Percent complete: 4.9%; Average loss: 3.9804
Iteration: 198; Percent complete: 5.0%; Average loss: 4.0909
Iteration: 199; Percent complete: 5.0%; Average loss: 4.1585
Iteration: 200; Percent complete: 5.0%; Average loss: 3.8685
Iteration: 201; Percent complete: 5.0%; Average loss: 4.1544
Iteration: 202; Percent complete: 5.1%; Average loss: 4.2270
Iteration: 203; Percent complete: 5.1%; Average loss: 3.8711
Iteration: 204; Percent complete: 5.1%; Average loss: 4.0601
Iteration: 205; Percent complete: 5.1%; Average loss: 4.0838
Iteration: 206; Percent complete: 5.1%; Average loss: 4.0747
Iteration: 207; Percent complete: 5.2%; Average loss: 4.1331
Iteration: 208; Percent complete: 5.2%; Average loss: 4.1024
Iteration: 209; Percent complete: 5.2%; Average loss: 3.8964
Iteration: 210; Percent complete: 5.2%; Average loss: 4.2774
Iteration: 211; Percent complete: 5.3%; Average loss: 3.9300
Iteration: 212; Percent complete: 5.3%; Average loss: 4.0683
Iteration: 213; Percent complete: 5.3%; Average loss: 4.4879
Iteration: 214; Percent complete: 5.3%; Average loss: 3.9277
Iteration: 215; Percent complete: 5.4%; Average loss: 4.2778
Iteration: 216; Percent complete: 5.4%; Average loss: 3.9833
Iteration: 217; Percent complete: 5.4%; Average loss: 4.0215
Iteration: 218; Percent complete: 5.5%; Average loss: 4.2168
Iteration: 219; Percent complete: 5.5%; Average loss: 3.9616
Iteration: 220; Percent complete: 5.5%; Average loss: 3.8588
Iteration: 221; Percent complete: 5.5%; Average loss: 4.0774
Iteration: 222; Percent complete: 5.5%; Average loss: 4.0471
Iteration: 223; Percent complete: 5.6%; Average loss: 4.0545
Iteration: 224; Percent complete: 5.6%; Average loss: 4.4775
Iteration: 225; Percent complete: 5.6%; Average loss: 4.0790
Iteration: 226; Percent complete: 5.7%; Average loss: 3.8958
Iteration: 227; Percent complete: 5.7%; Average loss: 4.2053
Iteration: 228; Percent complete: 5.7%; Average loss: 4.0383
Iteration: 229; Percent complete: 5.7%; Average loss: 4.0910
Iteration: 230; Percent complete: 5.8%; Average loss: 4.1671
Iteration: 231; Percent complete: 5.8%; Average loss: 4.0960
Iteration: 232; Percent complete: 5.8%; Average loss: 4.0895
Iteration: 233; Percent complete: 5.8%; Average loss: 3.9873
Iteration: 234; Percent complete: 5.9%; Average loss: 4.1687
Iteration: 235; Percent complete: 5.9%; Average loss: 3.8214
Iteration: 236; Percent complete: 5.9%; Average loss: 4.4066
Iteration: 237; Percent complete: 5.9%; Average loss: 3.9549
Iteration: 238; Percent complete: 5.9%; Average loss: 4.1129
Iteration: 239; Percent complete: 6.0%; Average loss: 3.8933
Iteration: 240; Percent complete: 6.0%; Average loss: 4.1056
Iteration: 241; Percent complete: 6.0%; Average loss: 3.8226
Iteration: 242; Percent complete: 6.0%; Average loss: 4.1138
Iteration: 243; Percent complete: 6.1%; Average loss: 4.1897
Iteration: 244; Percent complete: 6.1%; Average loss: 3.9900
Iteration: 245; Percent complete: 6.1%; Average loss: 4.1779
Iteration: 246; Percent complete: 6.2%; Average loss: 3.7862
Iteration: 247; Percent complete: 6.2%; Average loss: 4.1578
Iteration: 248; Percent complete: 6.2%; Average loss: 3.9890
Iteration: 249; Percent complete: 6.2%; Average loss: 3.8810
Iteration: 250; Percent complete: 6.2%; Average loss: 3.8605
Iteration: 251; Percent complete: 6.3%; Average loss: 3.8102
Iteration: 252; Percent complete: 6.3%; Average loss: 4.1139
Iteration: 253; Percent complete: 6.3%; Average loss: 3.9321
Iteration: 254; Percent complete: 6.3%; Average loss: 3.9629
Iteration: 255; Percent complete: 6.4%; Average loss: 4.0791
Iteration: 256; Percent complete: 6.4%; Average loss: 4.0093
Iteration: 257; Percent complete: 6.4%; Average loss: 4.0538
Iteration: 258; Percent complete: 6.5%; Average loss: 4.1743
Iteration: 259; Percent complete: 6.5%; Average loss: 3.6451
Iteration: 260; Percent complete: 6.5%; Average loss: 3.9016
Iteration: 261; Percent complete: 6.5%; Average loss: 3.8605
Iteration: 262; Percent complete: 6.6%; Average loss: 3.8790
Iteration: 263; Percent complete: 6.6%; Average loss: 3.9192
Iteration: 264; Percent complete: 6.6%; Average loss: 3.8807
Iteration: 265; Percent complete: 6.6%; Average loss: 3.8248
Iteration: 266; Percent complete: 6.7%; Average loss: 4.1604
Iteration: 267; Percent complete: 6.7%; Average loss: 3.8220
Iteration: 268; Percent complete: 6.7%; Average loss: 3.7401
Iteration: 269; Percent complete: 6.7%; Average loss: 3.7823
Iteration: 270; Percent complete: 6.8%; Average loss: 4.0778
Iteration: 271; Percent complete: 6.8%; Average loss: 3.7018
Iteration: 272; Percent complete: 6.8%; Average loss: 3.6836
Iteration: 273; Percent complete: 6.8%; Average loss: 3.8709
Iteration: 274; Percent complete: 6.9%; Average loss: 3.8448
Iteration: 275; Percent complete: 6.9%; Average loss: 4.0915
Iteration: 276; Percent complete: 6.9%; Average loss: 3.9757
Iteration: 277; Percent complete: 6.9%; Average loss: 3.9276
Iteration: 278; Percent complete: 7.0%; Average loss: 3.9575
Iteration: 279; Percent complete: 7.0%; Average loss: 3.9840
Iteration: 280; Percent complete: 7.0%; Average loss: 3.7553
Iteration: 281; Percent complete: 7.0%; Average loss: 3.8771
Iteration: 282; Percent complete: 7.0%; Average loss: 3.7707
Iteration: 283; Percent complete: 7.1%; Average loss: 4.3057
Iteration: 284; Percent complete: 7.1%; Average loss: 4.0104
Iteration: 285; Percent complete: 7.1%; Average loss: 4.0553
Iteration: 286; Percent complete: 7.1%; Average loss: 3.9065
Iteration: 287; Percent complete: 7.2%; Average loss: 3.8357
Iteration: 288; Percent complete: 7.2%; Average loss: 3.9208
Iteration: 289; Percent complete: 7.2%; Average loss: 3.8219
Iteration: 290; Percent complete: 7.2%; Average loss: 3.9703
Iteration: 291; Percent complete: 7.3%; Average loss: 3.7480
Iteration: 292; Percent complete: 7.3%; Average loss: 4.2030
Iteration: 293; Percent complete: 7.3%; Average loss: 3.8525
Iteration: 294; Percent complete: 7.3%; Average loss: 3.9250
Iteration: 295; Percent complete: 7.4%; Average loss: 3.7604
Iteration: 296; Percent complete: 7.4%; Average loss: 3.9679
Iteration: 297; Percent complete: 7.4%; Average loss: 4.0952
Iteration: 298; Percent complete: 7.4%; Average loss: 3.8905
Iteration: 299; Percent complete: 7.5%; Average loss: 4.3958
Iteration: 300; Percent complete: 7.5%; Average loss: 3.9023
Iteration: 301; Percent complete: 7.5%; Average loss: 3.8665
Iteration: 302; Percent complete: 7.5%; Average loss: 3.8054
Iteration: 303; Percent complete: 7.6%; Average loss: 4.0701
Iteration: 304; Percent complete: 7.6%; Average loss: 4.0563
Iteration: 305; Percent complete: 7.6%; Average loss: 4.0035
Iteration: 306; Percent complete: 7.6%; Average loss: 3.7462
Iteration: 307; Percent complete: 7.7%; Average loss: 3.8435
Iteration: 308; Percent complete: 7.7%; Average loss: 4.0308
Iteration: 309; Percent complete: 7.7%; Average loss: 3.8374
Iteration: 310; Percent complete: 7.8%; Average loss: 4.0057
Iteration: 311; Percent complete: 7.8%; Average loss: 3.9204
Iteration: 312; Percent complete: 7.8%; Average loss: 3.8801
Iteration: 313; Percent complete: 7.8%; Average loss: 3.7964
Iteration: 314; Percent complete: 7.8%; Average loss: 3.8741
Iteration: 315; Percent complete: 7.9%; Average loss: 3.9673
Iteration: 316; Percent complete: 7.9%; Average loss: 3.8374
Iteration: 317; Percent complete: 7.9%; Average loss: 4.0041
Iteration: 318; Percent complete: 8.0%; Average loss: 3.5375
Iteration: 319; Percent complete: 8.0%; Average loss: 3.7707
Iteration: 320; Percent complete: 8.0%; Average loss: 3.7590
Iteration: 321; Percent complete: 8.0%; Average loss: 4.0125
Iteration: 322; Percent complete: 8.1%; Average loss: 4.0491
Iteration: 323; Percent complete: 8.1%; Average loss: 3.7088
Iteration: 324; Percent complete: 8.1%; Average loss: 3.8541
Iteration: 325; Percent complete: 8.1%; Average loss: 3.9058
Iteration: 326; Percent complete: 8.2%; Average loss: 3.7547
Iteration: 327; Percent complete: 8.2%; Average loss: 4.2893
Iteration: 328; Percent complete: 8.2%; Average loss: 3.9207
Iteration: 329; Percent complete: 8.2%; Average loss: 3.7060
Iteration: 330; Percent complete: 8.2%; Average loss: 3.9613
Iteration: 331; Percent complete: 8.3%; Average loss: 3.8682
Iteration: 332; Percent complete: 8.3%; Average loss: 3.7131
Iteration: 333; Percent complete: 8.3%; Average loss: 4.0999
Iteration: 334; Percent complete: 8.3%; Average loss: 3.6946
Iteration: 335; Percent complete: 8.4%; Average loss: 3.6462
Iteration: 336; Percent complete: 8.4%; Average loss: 3.7141
Iteration: 337; Percent complete: 8.4%; Average loss: 3.8503
Iteration: 338; Percent complete: 8.5%; Average loss: 3.8566
Iteration: 339; Percent complete: 8.5%; Average loss: 3.4939
Iteration: 340; Percent complete: 8.5%; Average loss: 3.9709
Iteration: 341; Percent complete: 8.5%; Average loss: 3.9189
Iteration: 342; Percent complete: 8.6%; Average loss: 3.8021
Iteration: 343; Percent complete: 8.6%; Average loss: 3.9156
Iteration: 344; Percent complete: 8.6%; Average loss: 3.9103
Iteration: 345; Percent complete: 8.6%; Average loss: 3.9195
Iteration: 346; Percent complete: 8.6%; Average loss: 3.7483
Iteration: 347; Percent complete: 8.7%; Average loss: 3.7985
Iteration: 348; Percent complete: 8.7%; Average loss: 3.9489
Iteration: 349; Percent complete: 8.7%; Average loss: 3.6688
Iteration: 350; Percent complete: 8.8%; Average loss: 3.9097
Iteration: 351; Percent complete: 8.8%; Average loss: 3.6503
Iteration: 352; Percent complete: 8.8%; Average loss: 3.8790
Iteration: 353; Percent complete: 8.8%; Average loss: 3.9323
Iteration: 354; Percent complete: 8.8%; Average loss: 3.9513
Iteration: 355; Percent complete: 8.9%; Average loss: 3.7927
Iteration: 356; Percent complete: 8.9%; Average loss: 3.8779
Iteration: 357; Percent complete: 8.9%; Average loss: 4.0918
Iteration: 358; Percent complete: 8.9%; Average loss: 3.8673
Iteration: 359; Percent complete: 9.0%; Average loss: 3.8024
Iteration: 360; Percent complete: 9.0%; Average loss: 3.6940
Iteration: 361; Percent complete: 9.0%; Average loss: 3.8129
Iteration: 362; Percent complete: 9.0%; Average loss: 3.5317
Iteration: 363; Percent complete: 9.1%; Average loss: 3.9412
Iteration: 364; Percent complete: 9.1%; Average loss: 3.9599
Iteration: 365; Percent complete: 9.1%; Average loss: 3.6883
Iteration: 366; Percent complete: 9.2%; Average loss: 3.7188
Iteration: 367; Percent complete: 9.2%; Average loss: 3.8928
Iteration: 368; Percent complete: 9.2%; Average loss: 3.7749
Iteration: 369; Percent complete: 9.2%; Average loss: 3.7660
Iteration: 370; Percent complete: 9.2%; Average loss: 3.6873
Iteration: 371; Percent complete: 9.3%; Average loss: 3.8924
Iteration: 372; Percent complete: 9.3%; Average loss: 3.7162
Iteration: 373; Percent complete: 9.3%; Average loss: 3.4000
Iteration: 374; Percent complete: 9.3%; Average loss: 3.8970
Iteration: 375; Percent complete: 9.4%; Average loss: 3.8254
Iteration: 376; Percent complete: 9.4%; Average loss: 3.9417
Iteration: 377; Percent complete: 9.4%; Average loss: 3.8268
Iteration: 378; Percent complete: 9.4%; Average loss: 3.7059
Iteration: 379; Percent complete: 9.5%; Average loss: 4.0375
Iteration: 380; Percent complete: 9.5%; Average loss: 3.7648
Iteration: 381; Percent complete: 9.5%; Average loss: 3.6712
Iteration: 382; Percent complete: 9.6%; Average loss: 3.7536
Iteration: 383; Percent complete: 9.6%; Average loss: 3.6954
Iteration: 384; Percent complete: 9.6%; Average loss: 3.7799
Iteration: 385; Percent complete: 9.6%; Average loss: 3.5754
Iteration: 386; Percent complete: 9.7%; Average loss: 3.6424
Iteration: 387; Percent complete: 9.7%; Average loss: 3.9024
Iteration: 388; Percent complete: 9.7%; Average loss: 4.0737
Iteration: 389; Percent complete: 9.7%; Average loss: 3.9061
Iteration: 390; Percent complete: 9.8%; Average loss: 3.8639
Iteration: 391; Percent complete: 9.8%; Average loss: 3.6485
Iteration: 392; Percent complete: 9.8%; Average loss: 3.8098
Iteration: 393; Percent complete: 9.8%; Average loss: 3.8036
Iteration: 394; Percent complete: 9.8%; Average loss: 3.8966
Iteration: 395; Percent complete: 9.9%; Average loss: 3.7996
Iteration: 396; Percent complete: 9.9%; Average loss: 3.9953
Iteration: 397; Percent complete: 9.9%; Average loss: 3.6608
Iteration: 398; Percent complete: 10.0%; Average loss: 3.7114
Iteration: 399; Percent complete: 10.0%; Average loss: 3.7378
Iteration: 400; Percent complete: 10.0%; Average loss: 4.0314
Iteration: 401; Percent complete: 10.0%; Average loss: 3.9431
Iteration: 402; Percent complete: 10.1%; Average loss: 3.6681
Iteration: 403; Percent complete: 10.1%; Average loss: 3.6075
Iteration: 404; Percent complete: 10.1%; Average loss: 4.0037
Iteration: 405; Percent complete: 10.1%; Average loss: 3.5799
Iteration: 406; Percent complete: 10.2%; Average loss: 3.6841
Iteration: 407; Percent complete: 10.2%; Average loss: 3.9009
Iteration: 408; Percent complete: 10.2%; Average loss: 3.6790
Iteration: 409; Percent complete: 10.2%; Average loss: 3.7048
Iteration: 410; Percent complete: 10.2%; Average loss: 3.7815
Iteration: 411; Percent complete: 10.3%; Average loss: 3.9993
Iteration: 412; Percent complete: 10.3%; Average loss: 3.8412
Iteration: 413; Percent complete: 10.3%; Average loss: 3.8345
Iteration: 414; Percent complete: 10.3%; Average loss: 3.6550
Iteration: 415; Percent complete: 10.4%; Average loss: 3.7340
Iteration: 416; Percent complete: 10.4%; Average loss: 3.7956
Iteration: 417; Percent complete: 10.4%; Average loss: 3.7204
Iteration: 418; Percent complete: 10.4%; Average loss: 3.7282
Iteration: 419; Percent complete: 10.5%; Average loss: 3.9655
Iteration: 420; Percent complete: 10.5%; Average loss: 3.8815
Iteration: 421; Percent complete: 10.5%; Average loss: 3.6088
Iteration: 422; Percent complete: 10.5%; Average loss: 4.0736
Iteration: 423; Percent complete: 10.6%; Average loss: 3.8203
Iteration: 424; Percent complete: 10.6%; Average loss: 3.8242
Iteration: 425; Percent complete: 10.6%; Average loss: 3.8786
Iteration: 426; Percent complete: 10.7%; Average loss: 3.8220
Iteration: 427; Percent complete: 10.7%; Average loss: 3.4561
Iteration: 428; Percent complete: 10.7%; Average loss: 3.7508
Iteration: 429; Percent complete: 10.7%; Average loss: 3.8654
Iteration: 430; Percent complete: 10.8%; Average loss: 3.8544
Iteration: 431; Percent complete: 10.8%; Average loss: 3.7085
Iteration: 432; Percent complete: 10.8%; Average loss: 3.8262
Iteration: 433; Percent complete: 10.8%; Average loss: 3.9486
Iteration: 434; Percent complete: 10.8%; Average loss: 3.8370
Iteration: 435; Percent complete: 10.9%; Average loss: 4.0843
Iteration: 436; Percent complete: 10.9%; Average loss: 3.8658
Iteration: 437; Percent complete: 10.9%; Average loss: 3.8468
Iteration: 438; Percent complete: 10.9%; Average loss: 3.7798
Iteration: 439; Percent complete: 11.0%; Average loss: 3.5708
Iteration: 440; Percent complete: 11.0%; Average loss: 3.9490
Iteration: 441; Percent complete: 11.0%; Average loss: 3.5364
Iteration: 442; Percent complete: 11.1%; Average loss: 3.8691
Iteration: 443; Percent complete: 11.1%; Average loss: 3.8428
Iteration: 444; Percent complete: 11.1%; Average loss: 3.5084
Iteration: 445; Percent complete: 11.1%; Average loss: 3.7419
Iteration: 446; Percent complete: 11.2%; Average loss: 3.8517
Iteration: 447; Percent complete: 11.2%; Average loss: 3.9052
Iteration: 448; Percent complete: 11.2%; Average loss: 4.0101
Iteration: 449; Percent complete: 11.2%; Average loss: 3.5160
Iteration: 450; Percent complete: 11.2%; Average loss: 3.6575
Iteration: 451; Percent complete: 11.3%; Average loss: 3.9337
Iteration: 452; Percent complete: 11.3%; Average loss: 3.8726
Iteration: 453; Percent complete: 11.3%; Average loss: 3.6350
Iteration: 454; Percent complete: 11.3%; Average loss: 3.5969
Iteration: 455; Percent complete: 11.4%; Average loss: 3.7119
Iteration: 456; Percent complete: 11.4%; Average loss: 3.8284
Iteration: 457; Percent complete: 11.4%; Average loss: 3.6785
Iteration: 458; Percent complete: 11.5%; Average loss: 3.6903
Iteration: 459; Percent complete: 11.5%; Average loss: 3.8591
Iteration: 460; Percent complete: 11.5%; Average loss: 3.5846
Iteration: 461; Percent complete: 11.5%; Average loss: 3.8098
Iteration: 462; Percent complete: 11.6%; Average loss: 3.7235
Iteration: 463; Percent complete: 11.6%; Average loss: 3.5635
Iteration: 464; Percent complete: 11.6%; Average loss: 3.7448
Iteration: 465; Percent complete: 11.6%; Average loss: 3.7097
Iteration: 466; Percent complete: 11.7%; Average loss: 4.0047
Iteration: 467; Percent complete: 11.7%; Average loss: 3.6847
Iteration: 468; Percent complete: 11.7%; Average loss: 3.6181
Iteration: 469; Percent complete: 11.7%; Average loss: 3.6815
Iteration: 470; Percent complete: 11.8%; Average loss: 3.6965
Iteration: 471; Percent complete: 11.8%; Average loss: 3.7293
Iteration: 472; Percent complete: 11.8%; Average loss: 3.8935
Iteration: 473; Percent complete: 11.8%; Average loss: 3.8712
Iteration: 474; Percent complete: 11.8%; Average loss: 3.9065
Iteration: 475; Percent complete: 11.9%; Average loss: 3.7622
Iteration: 476; Percent complete: 11.9%; Average loss: 3.7534
Iteration: 477; Percent complete: 11.9%; Average loss: 3.7524
Iteration: 478; Percent complete: 11.9%; Average loss: 3.8609
Iteration: 479; Percent complete: 12.0%; Average loss: 3.6305
Iteration: 480; Percent complete: 12.0%; Average loss: 3.7476
Iteration: 481; Percent complete: 12.0%; Average loss: 3.8070
Iteration: 482; Percent complete: 12.0%; Average loss: 3.8358
Iteration: 483; Percent complete: 12.1%; Average loss: 3.8508
Iteration: 484; Percent complete: 12.1%; Average loss: 3.7200
Iteration: 485; Percent complete: 12.1%; Average loss: 3.6231
Iteration: 486; Percent complete: 12.2%; Average loss: 3.8467
Iteration: 487; Percent complete: 12.2%; Average loss: 3.7055
Iteration: 488; Percent complete: 12.2%; Average loss: 3.8983
Iteration: 489; Percent complete: 12.2%; Average loss: 3.7810
Iteration: 490; Percent complete: 12.2%; Average loss: 3.4014
Iteration: 491; Percent complete: 12.3%; Average loss: 3.8732
Iteration: 492; Percent complete: 12.3%; Average loss: 3.7444
Iteration: 493; Percent complete: 12.3%; Average loss: 3.6024
Iteration: 494; Percent complete: 12.3%; Average loss: 3.8704
Iteration: 495; Percent complete: 12.4%; Average loss: 3.7017
Iteration: 496; Percent complete: 12.4%; Average loss: 3.6937
Iteration: 497; Percent complete: 12.4%; Average loss: 3.7663
Iteration: 498; Percent complete: 12.4%; Average loss: 3.6361
Iteration: 499; Percent complete: 12.5%; Average loss: 3.8452
Iteration: 500; Percent complete: 12.5%; Average loss: 3.8376
Iteration: 501; Percent complete: 12.5%; Average loss: 3.7494
Iteration: 502; Percent complete: 12.6%; Average loss: 3.7829
Iteration: 503; Percent complete: 12.6%; Average loss: 3.9986
Iteration: 504; Percent complete: 12.6%; Average loss: 3.8892
Iteration: 505; Percent complete: 12.6%; Average loss: 3.8723
Iteration: 506; Percent complete: 12.7%; Average loss: 3.5434
Iteration: 507; Percent complete: 12.7%; Average loss: 3.5130
Iteration: 508; Percent complete: 12.7%; Average loss: 3.7150
Iteration: 509; Percent complete: 12.7%; Average loss: 3.9332
Iteration: 510; Percent complete: 12.8%; Average loss: 3.6204
Iteration: 511; Percent complete: 12.8%; Average loss: 4.0127
Iteration: 512; Percent complete: 12.8%; Average loss: 3.6630
Iteration: 513; Percent complete: 12.8%; Average loss: 3.7141
Iteration: 514; Percent complete: 12.8%; Average loss: 3.5769
Iteration: 515; Percent complete: 12.9%; Average loss: 3.8023
Iteration: 516; Percent complete: 12.9%; Average loss: 3.8627
Iteration: 517; Percent complete: 12.9%; Average loss: 3.7419
Iteration: 518; Percent complete: 13.0%; Average loss: 3.7113
Iteration: 519; Percent complete: 13.0%; Average loss: 3.8317
Iteration: 520; Percent complete: 13.0%; Average loss: 3.5973
Iteration: 521; Percent complete: 13.0%; Average loss: 3.7576
Iteration: 522; Percent complete: 13.1%; Average loss: 3.7641
Iteration: 523; Percent complete: 13.1%; Average loss: 3.8165
Iteration: 524; Percent complete: 13.1%; Average loss: 3.5998
Iteration: 525; Percent complete: 13.1%; Average loss: 3.5956
Iteration: 526; Percent complete: 13.2%; Average loss: 3.9293
Iteration: 527; Percent complete: 13.2%; Average loss: 3.6252
Iteration: 528; Percent complete: 13.2%; Average loss: 3.7539
Iteration: 529; Percent complete: 13.2%; Average loss: 3.6398
Iteration: 530; Percent complete: 13.2%; Average loss: 3.6072
Iteration: 531; Percent complete: 13.3%; Average loss: 3.7737
Iteration: 532; Percent complete: 13.3%; Average loss: 3.5860
Iteration: 533; Percent complete: 13.3%; Average loss: 3.6540
Iteration: 534; Percent complete: 13.4%; Average loss: 3.6920
Iteration: 535; Percent complete: 13.4%; Average loss: 3.4645
Iteration: 536; Percent complete: 13.4%; Average loss: 4.0246
Iteration: 537; Percent complete: 13.4%; Average loss: 3.5430
Iteration: 538; Percent complete: 13.5%; Average loss: 3.6298
Iteration: 539; Percent complete: 13.5%; Average loss: 3.6536
Iteration: 540; Percent complete: 13.5%; Average loss: 3.6337
Iteration: 541; Percent complete: 13.5%; Average loss: 3.8490
Iteration: 542; Percent complete: 13.6%; Average loss: 3.5458
Iteration: 543; Percent complete: 13.6%; Average loss: 3.7389
Iteration: 544; Percent complete: 13.6%; Average loss: 3.8803
Iteration: 545; Percent complete: 13.6%; Average loss: 3.7353
Iteration: 546; Percent complete: 13.7%; Average loss: 3.6840
Iteration: 547; Percent complete: 13.7%; Average loss: 4.0084
Iteration: 548; Percent complete: 13.7%; Average loss: 3.4506
Iteration: 549; Percent complete: 13.7%; Average loss: 3.8671
Iteration: 550; Percent complete: 13.8%; Average loss: 3.7777
Iteration: 551; Percent complete: 13.8%; Average loss: 3.7008
Iteration: 552; Percent complete: 13.8%; Average loss: 3.6789
Iteration: 553; Percent complete: 13.8%; Average loss: 3.6634
Iteration: 554; Percent complete: 13.9%; Average loss: 3.3261
Iteration: 555; Percent complete: 13.9%; Average loss: 3.6311
Iteration: 556; Percent complete: 13.9%; Average loss: 3.8889
Iteration: 557; Percent complete: 13.9%; Average loss: 3.6161
Iteration: 558; Percent complete: 14.0%; Average loss: 3.5841
Iteration: 559; Percent complete: 14.0%; Average loss: 3.7317
Iteration: 560; Percent complete: 14.0%; Average loss: 4.0798
Iteration: 561; Percent complete: 14.0%; Average loss: 3.6985
Iteration: 562; Percent complete: 14.1%; Average loss: 3.8980
Iteration: 563; Percent complete: 14.1%; Average loss: 3.5294
Iteration: 564; Percent complete: 14.1%; Average loss: 3.7720
Iteration: 565; Percent complete: 14.1%; Average loss: 3.7590
Iteration: 566; Percent complete: 14.1%; Average loss: 3.7670
Iteration: 567; Percent complete: 14.2%; Average loss: 3.4977
Iteration: 568; Percent complete: 14.2%; Average loss: 3.7078
Iteration: 569; Percent complete: 14.2%; Average loss: 3.8789
Iteration: 570; Percent complete: 14.2%; Average loss: 3.6666
Iteration: 571; Percent complete: 14.3%; Average loss: 3.6262
Iteration: 572; Percent complete: 14.3%; Average loss: 3.5091
Iteration: 573; Percent complete: 14.3%; Average loss: 4.0233
Iteration: 574; Percent complete: 14.3%; Average loss: 3.7229
Iteration: 575; Percent complete: 14.4%; Average loss: 3.6971
Iteration: 576; Percent complete: 14.4%; Average loss: 3.6559
Iteration: 577; Percent complete: 14.4%; Average loss: 3.6975
Iteration: 578; Percent complete: 14.4%; Average loss: 3.7869
Iteration: 579; Percent complete: 14.5%; Average loss: 3.5721
Iteration: 580; Percent complete: 14.5%; Average loss: 3.7016
Iteration: 581; Percent complete: 14.5%; Average loss: 3.6963
Iteration: 582; Percent complete: 14.5%; Average loss: 3.6849
Iteration: 583; Percent complete: 14.6%; Average loss: 3.7623
Iteration: 584; Percent complete: 14.6%; Average loss: 3.6107
Iteration: 585; Percent complete: 14.6%; Average loss: 3.7808
Iteration: 586; Percent complete: 14.6%; Average loss: 3.5211
Iteration: 587; Percent complete: 14.7%; Average loss: 3.8553
Iteration: 588; Percent complete: 14.7%; Average loss: 3.9608
Iteration: 589; Percent complete: 14.7%; Average loss: 3.5735
Iteration: 590; Percent complete: 14.8%; Average loss: 3.8783
Iteration: 591; Percent complete: 14.8%; Average loss: 3.4992
Iteration: 592; Percent complete: 14.8%; Average loss: 3.8375
Iteration: 593; Percent complete: 14.8%; Average loss: 3.8577
Iteration: 594; Percent complete: 14.8%; Average loss: 3.6649
Iteration: 595; Percent complete: 14.9%; Average loss: 3.7553
Iteration: 596; Percent complete: 14.9%; Average loss: 3.7331
Iteration: 597; Percent complete: 14.9%; Average loss: 3.8484
Iteration: 598; Percent complete: 14.9%; Average loss: 3.6219
Iteration: 599; Percent complete: 15.0%; Average loss: 3.7943
Iteration: 600; Percent complete: 15.0%; Average loss: 3.4934
Iteration: 601; Percent complete: 15.0%; Average loss: 3.7253
Iteration: 602; Percent complete: 15.0%; Average loss: 3.6119
Iteration: 603; Percent complete: 15.1%; Average loss: 3.6553
Iteration: 604; Percent complete: 15.1%; Average loss: 3.5786
Iteration: 605; Percent complete: 15.1%; Average loss: 3.6746
Iteration: 606; Percent complete: 15.2%; Average loss: 3.7778
Iteration: 607; Percent complete: 15.2%; Average loss: 3.5296
Iteration: 608; Percent complete: 15.2%; Average loss: 3.3384
Iteration: 609; Percent complete: 15.2%; Average loss: 3.6337
Iteration: 610; Percent complete: 15.2%; Average loss: 3.9097
Iteration: 611; Percent complete: 15.3%; Average loss: 3.5343
Iteration: 612; Percent complete: 15.3%; Average loss: 3.4143
Iteration: 613; Percent complete: 15.3%; Average loss: 3.6186
Iteration: 614; Percent complete: 15.3%; Average loss: 3.6729
Iteration: 615; Percent complete: 15.4%; Average loss: 3.7350
Iteration: 616; Percent complete: 15.4%; Average loss: 3.5680
Iteration: 617; Percent complete: 15.4%; Average loss: 3.4874
Iteration: 618; Percent complete: 15.4%; Average loss: 3.7975
Iteration: 619; Percent complete: 15.5%; Average loss: 3.5392
Iteration: 620; Percent complete: 15.5%; Average loss: 3.7173
Iteration: 621; Percent complete: 15.5%; Average loss: 4.0011
Iteration: 622; Percent complete: 15.6%; Average loss: 3.8051
Iteration: 623; Percent complete: 15.6%; Average loss: 3.5894
Iteration: 624; Percent complete: 15.6%; Average loss: 3.8773
Iteration: 625; Percent complete: 15.6%; Average loss: 3.7835
Iteration: 626; Percent complete: 15.7%; Average loss: 3.6314
Iteration: 627; Percent complete: 15.7%; Average loss: 3.6902
Iteration: 628; Percent complete: 15.7%; Average loss: 3.6485
Iteration: 629; Percent complete: 15.7%; Average loss: 3.2811
Iteration: 630; Percent complete: 15.8%; Average loss: 3.8817
Iteration: 631; Percent complete: 15.8%; Average loss: 3.7145
Iteration: 632; Percent complete: 15.8%; Average loss: 3.7998
Iteration: 633; Percent complete: 15.8%; Average loss: 3.5560
Iteration: 634; Percent complete: 15.8%; Average loss: 3.7335
Iteration: 635; Percent complete: 15.9%; Average loss: 3.6088
Iteration: 636; Percent complete: 15.9%; Average loss: 3.6081
Iteration: 637; Percent complete: 15.9%; Average loss: 3.6630
Iteration: 638; Percent complete: 16.0%; Average loss: 3.4235
Iteration: 639; Percent complete: 16.0%; Average loss: 3.6376
Iteration: 640; Percent complete: 16.0%; Average loss: 3.6311
Iteration: 641; Percent complete: 16.0%; Average loss: 3.8205
Iteration: 642; Percent complete: 16.1%; Average loss: 3.7698
Iteration: 643; Percent complete: 16.1%; Average loss: 3.7128
Iteration: 644; Percent complete: 16.1%; Average loss: 3.8140
Iteration: 645; Percent complete: 16.1%; Average loss: 3.4052
Iteration: 646; Percent complete: 16.2%; Average loss: 3.7856
Iteration: 647; Percent complete: 16.2%; Average loss: 3.3565
Iteration: 648; Percent complete: 16.2%; Average loss: 3.9129
Iteration: 649; Percent complete: 16.2%; Average loss: 3.7488
Iteration: 650; Percent complete: 16.2%; Average loss: 3.6851
Iteration: 651; Percent complete: 16.3%; Average loss: 3.7916
Iteration: 652; Percent complete: 16.3%; Average loss: 3.7187
Iteration: 653; Percent complete: 16.3%; Average loss: 3.6373
Iteration: 654; Percent complete: 16.4%; Average loss: 3.5530
Iteration: 655; Percent complete: 16.4%; Average loss: 3.4593
Iteration: 656; Percent complete: 16.4%; Average loss: 3.4079
Iteration: 657; Percent complete: 16.4%; Average loss: 3.3916
Iteration: 658; Percent complete: 16.4%; Average loss: 3.6300
Iteration: 659; Percent complete: 16.5%; Average loss: 3.7890
Iteration: 660; Percent complete: 16.5%; Average loss: 3.3985
Iteration: 661; Percent complete: 16.5%; Average loss: 3.7793
Iteration: 662; Percent complete: 16.6%; Average loss: 3.7102
Iteration: 663; Percent complete: 16.6%; Average loss: 3.6765
Iteration: 664; Percent complete: 16.6%; Average loss: 3.4696
Iteration: 665; Percent complete: 16.6%; Average loss: 3.6110
Iteration: 666; Percent complete: 16.7%; Average loss: 3.5666
Iteration: 667; Percent complete: 16.7%; Average loss: 3.8297
Iteration: 668; Percent complete: 16.7%; Average loss: 3.5745
Iteration: 669; Percent complete: 16.7%; Average loss: 3.5673
Iteration: 670; Percent complete: 16.8%; Average loss: 3.4270
Iteration: 671; Percent complete: 16.8%; Average loss: 3.5943
Iteration: 672; Percent complete: 16.8%; Average loss: 4.0215
Iteration: 673; Percent complete: 16.8%; Average loss: 3.5426
Iteration: 674; Percent complete: 16.9%; Average loss: 3.5329
Iteration: 675; Percent complete: 16.9%; Average loss: 3.6302
Iteration: 676; Percent complete: 16.9%; Average loss: 3.5608
Iteration: 677; Percent complete: 16.9%; Average loss: 3.8552
Iteration: 678; Percent complete: 17.0%; Average loss: 3.5388
Iteration: 679; Percent complete: 17.0%; Average loss: 3.6217
Iteration: 680; Percent complete: 17.0%; Average loss: 3.3981
Iteration: 681; Percent complete: 17.0%; Average loss: 3.5235
Iteration: 682; Percent complete: 17.1%; Average loss: 3.9308
Iteration: 683; Percent complete: 17.1%; Average loss: 3.3826
Iteration: 684; Percent complete: 17.1%; Average loss: 3.7694
Iteration: 685; Percent complete: 17.1%; Average loss: 3.5213
Iteration: 686; Percent complete: 17.2%; Average loss: 3.7036
Iteration: 687; Percent complete: 17.2%; Average loss: 3.8115
Iteration: 688; Percent complete: 17.2%; Average loss: 3.5250
Iteration: 689; Percent complete: 17.2%; Average loss: 3.9486
Iteration: 690; Percent complete: 17.2%; Average loss: 3.4179
Iteration: 691; Percent complete: 17.3%; Average loss: 3.8717
Iteration: 692; Percent complete: 17.3%; Average loss: 3.5004
Iteration: 693; Percent complete: 17.3%; Average loss: 3.7514
Iteration: 694; Percent complete: 17.3%; Average loss: 3.5455
Iteration: 695; Percent complete: 17.4%; Average loss: 3.4269
Iteration: 696; Percent complete: 17.4%; Average loss: 3.4558
Iteration: 697; Percent complete: 17.4%; Average loss: 3.8136
Iteration: 698; Percent complete: 17.4%; Average loss: 3.9140
Iteration: 699; Percent complete: 17.5%; Average loss: 3.5867
Iteration: 700; Percent complete: 17.5%; Average loss: 3.3925
Iteration: 701; Percent complete: 17.5%; Average loss: 3.5288
Iteration: 702; Percent complete: 17.5%; Average loss: 3.6042
Iteration: 703; Percent complete: 17.6%; Average loss: 3.4459
Iteration: 704; Percent complete: 17.6%; Average loss: 3.8378
Iteration: 705; Percent complete: 17.6%; Average loss: 3.4372
Iteration: 706; Percent complete: 17.6%; Average loss: 3.5896
Iteration: 707; Percent complete: 17.7%; Average loss: 3.5881
Iteration: 708; Percent complete: 17.7%; Average loss: 3.5002
Iteration: 709; Percent complete: 17.7%; Average loss: 3.9176
Iteration: 710; Percent complete: 17.8%; Average loss: 3.4077
Iteration: 711; Percent complete: 17.8%; Average loss: 3.7703
Iteration: 712; Percent complete: 17.8%; Average loss: 3.5356
Iteration: 713; Percent complete: 17.8%; Average loss: 3.4306
Iteration: 714; Percent complete: 17.8%; Average loss: 3.4750
Iteration: 715; Percent complete: 17.9%; Average loss: 3.4762
Iteration: 716; Percent complete: 17.9%; Average loss: 3.8139
Iteration: 717; Percent complete: 17.9%; Average loss: 3.5617
Iteration: 718; Percent complete: 17.9%; Average loss: 3.3348
Iteration: 719; Percent complete: 18.0%; Average loss: 3.7124
Iteration: 720; Percent complete: 18.0%; Average loss: 3.8690
Iteration: 721; Percent complete: 18.0%; Average loss: 3.5351
Iteration: 722; Percent complete: 18.1%; Average loss: 3.7127
Iteration: 723; Percent complete: 18.1%; Average loss: 3.6513
Iteration: 724; Percent complete: 18.1%; Average loss: 3.8906
Iteration: 725; Percent complete: 18.1%; Average loss: 3.9215
Iteration: 726; Percent complete: 18.1%; Average loss: 3.7577
Iteration: 727; Percent complete: 18.2%; Average loss: 3.4719
Iteration: 728; Percent complete: 18.2%; Average loss: 3.7182
Iteration: 729; Percent complete: 18.2%; Average loss: 3.6805
Iteration: 730; Percent complete: 18.2%; Average loss: 3.9325
Iteration: 731; Percent complete: 18.3%; Average loss: 3.6287
Iteration: 732; Percent complete: 18.3%; Average loss: 3.8086
Iteration: 733; Percent complete: 18.3%; Average loss: 3.6961
Iteration: 734; Percent complete: 18.4%; Average loss: 3.3932
Iteration: 735; Percent complete: 18.4%; Average loss: 3.6963
Iteration: 736; Percent complete: 18.4%; Average loss: 3.7617
Iteration: 737; Percent complete: 18.4%; Average loss: 3.6691
Iteration: 738; Percent complete: 18.4%; Average loss: 3.7696
Iteration: 739; Percent complete: 18.5%; Average loss: 3.6544
Iteration: 740; Percent complete: 18.5%; Average loss: 3.7551
Iteration: 741; Percent complete: 18.5%; Average loss: 3.7191
Iteration: 742; Percent complete: 18.6%; Average loss: 3.6835
Iteration: 743; Percent complete: 18.6%; Average loss: 3.5042
Iteration: 744; Percent complete: 18.6%; Average loss: 3.6487
Iteration: 745; Percent complete: 18.6%; Average loss: 3.9013
Iteration: 746; Percent complete: 18.6%; Average loss: 3.3426
Iteration: 747; Percent complete: 18.7%; Average loss: 3.7567
Iteration: 748; Percent complete: 18.7%; Average loss: 3.5318
Iteration: 749; Percent complete: 18.7%; Average loss: 3.5173
Iteration: 750; Percent complete: 18.8%; Average loss: 3.8316
Iteration: 751; Percent complete: 18.8%; Average loss: 3.4949
Iteration: 752; Percent complete: 18.8%; Average loss: 3.6952
Iteration: 753; Percent complete: 18.8%; Average loss: 3.5112
Iteration: 754; Percent complete: 18.9%; Average loss: 3.4914
Iteration: 755; Percent complete: 18.9%; Average loss: 3.8271
Iteration: 756; Percent complete: 18.9%; Average loss: 3.6154
Iteration: 757; Percent complete: 18.9%; Average loss: 3.5199
Iteration: 758; Percent complete: 18.9%; Average loss: 3.6681
Iteration: 759; Percent complete: 19.0%; Average loss: 3.6830
Iteration: 760; Percent complete: 19.0%; Average loss: 3.6950
Iteration: 761; Percent complete: 19.0%; Average loss: 3.4367
Iteration: 762; Percent complete: 19.1%; Average loss: 3.7137
Iteration: 763; Percent complete: 19.1%; Average loss: 3.6745
Iteration: 764; Percent complete: 19.1%; Average loss: 3.6010
Iteration: 765; Percent complete: 19.1%; Average loss: 3.4704
Iteration: 766; Percent complete: 19.1%; Average loss: 3.2953
Iteration: 767; Percent complete: 19.2%; Average loss: 3.7929
Iteration: 768; Percent complete: 19.2%; Average loss: 3.7036
Iteration: 769; Percent complete: 19.2%; Average loss: 3.4612
Iteration: 770; Percent complete: 19.2%; Average loss: 3.6361
Iteration: 771; Percent complete: 19.3%; Average loss: 3.4969
Iteration: 772; Percent complete: 19.3%; Average loss: 3.6115
Iteration: 773; Percent complete: 19.3%; Average loss: 3.5864
Iteration: 774; Percent complete: 19.4%; Average loss: 3.6264
Iteration: 775; Percent complete: 19.4%; Average loss: 3.6619
Iteration: 776; Percent complete: 19.4%; Average loss: 3.6877
Iteration: 777; Percent complete: 19.4%; Average loss: 3.5693
Iteration: 778; Percent complete: 19.4%; Average loss: 3.7424
Iteration: 779; Percent complete: 19.5%; Average loss: 3.7008
Iteration: 780; Percent complete: 19.5%; Average loss: 3.6263
Iteration: 781; Percent complete: 19.5%; Average loss: 3.5493
Iteration: 782; Percent complete: 19.6%; Average loss: 3.4022
Iteration: 783; Percent complete: 19.6%; Average loss: 3.3734
Iteration: 784; Percent complete: 19.6%; Average loss: 3.3349
Iteration: 785; Percent complete: 19.6%; Average loss: 3.5145
Iteration: 786; Percent complete: 19.7%; Average loss: 3.5493
Iteration: 787; Percent complete: 19.7%; Average loss: 3.5405
Iteration: 788; Percent complete: 19.7%; Average loss: 3.7152
Iteration: 789; Percent complete: 19.7%; Average loss: 3.6775
Iteration: 790; Percent complete: 19.8%; Average loss: 3.7060
Iteration: 791; Percent complete: 19.8%; Average loss: 3.5516
Iteration: 792; Percent complete: 19.8%; Average loss: 3.5767
Iteration: 793; Percent complete: 19.8%; Average loss: 3.7264
Iteration: 794; Percent complete: 19.9%; Average loss: 4.0083
Iteration: 795; Percent complete: 19.9%; Average loss: 3.6404
Iteration: 796; Percent complete: 19.9%; Average loss: 3.3208
Iteration: 797; Percent complete: 19.9%; Average loss: 3.7385
Iteration: 798; Percent complete: 20.0%; Average loss: 3.3820
Iteration: 799; Percent complete: 20.0%; Average loss: 3.3342
Iteration: 800; Percent complete: 20.0%; Average loss: 3.4405
Iteration: 801; Percent complete: 20.0%; Average loss: 3.8508
Iteration: 802; Percent complete: 20.1%; Average loss: 3.5572
Iteration: 803; Percent complete: 20.1%; Average loss: 3.7378
Iteration: 804; Percent complete: 20.1%; Average loss: 3.6316
Iteration: 805; Percent complete: 20.1%; Average loss: 3.4211
Iteration: 806; Percent complete: 20.2%; Average loss: 3.3214
Iteration: 807; Percent complete: 20.2%; Average loss: 3.4377
Iteration: 808; Percent complete: 20.2%; Average loss: 3.3181
Iteration: 809; Percent complete: 20.2%; Average loss: 3.4958
Iteration: 810; Percent complete: 20.2%; Average loss: 3.7503
Iteration: 811; Percent complete: 20.3%; Average loss: 3.4705
Iteration: 812; Percent complete: 20.3%; Average loss: 3.6244
Iteration: 813; Percent complete: 20.3%; Average loss: 3.5095
Iteration: 814; Percent complete: 20.3%; Average loss: 3.7089
Iteration: 815; Percent complete: 20.4%; Average loss: 3.2269
Iteration: 816; Percent complete: 20.4%; Average loss: 3.4573
Iteration: 817; Percent complete: 20.4%; Average loss: 3.5587
Iteration: 818; Percent complete: 20.4%; Average loss: 3.4391
Iteration: 819; Percent complete: 20.5%; Average loss: 3.4987
Iteration: 820; Percent complete: 20.5%; Average loss: 3.5591
Iteration: 821; Percent complete: 20.5%; Average loss: 3.2835
Iteration: 822; Percent complete: 20.5%; Average loss: 3.4731
Iteration: 823; Percent complete: 20.6%; Average loss: 3.3648
Iteration: 824; Percent complete: 20.6%; Average loss: 3.5984
Iteration: 825; Percent complete: 20.6%; Average loss: 3.5026
Iteration: 826; Percent complete: 20.6%; Average loss: 3.6212
Iteration: 827; Percent complete: 20.7%; Average loss: 3.6795
Iteration: 828; Percent complete: 20.7%; Average loss: 3.5752
Iteration: 829; Percent complete: 20.7%; Average loss: 3.5146
Iteration: 830; Percent complete: 20.8%; Average loss: 3.6805
Iteration: 831; Percent complete: 20.8%; Average loss: 3.4367
Iteration: 832; Percent complete: 20.8%; Average loss: 3.5134
Iteration: 833; Percent complete: 20.8%; Average loss: 3.4511
Iteration: 834; Percent complete: 20.8%; Average loss: 3.5887
Iteration: 835; Percent complete: 20.9%; Average loss: 3.5246
Iteration: 836; Percent complete: 20.9%; Average loss: 3.5236
Iteration: 837; Percent complete: 20.9%; Average loss: 3.8183
Iteration: 838; Percent complete: 20.9%; Average loss: 3.6887
Iteration: 839; Percent complete: 21.0%; Average loss: 3.6171
Iteration: 840; Percent complete: 21.0%; Average loss: 3.5775
Iteration: 841; Percent complete: 21.0%; Average loss: 3.7538
Iteration: 842; Percent complete: 21.1%; Average loss: 3.4250
Iteration: 843; Percent complete: 21.1%; Average loss: 3.6270
Iteration: 844; Percent complete: 21.1%; Average loss: 3.7226
Iteration: 845; Percent complete: 21.1%; Average loss: 3.8047
Iteration: 846; Percent complete: 21.1%; Average loss: 3.4906
Iteration: 847; Percent complete: 21.2%; Average loss: 3.6278
Iteration: 848; Percent complete: 21.2%; Average loss: 3.4831
Iteration: 849; Percent complete: 21.2%; Average loss: 3.6440
Iteration: 850; Percent complete: 21.2%; Average loss: 3.3417
Iteration: 851; Percent complete: 21.3%; Average loss: 3.4619
Iteration: 852; Percent complete: 21.3%; Average loss: 3.5831
Iteration: 853; Percent complete: 21.3%; Average loss: 3.7263
Iteration: 854; Percent complete: 21.3%; Average loss: 3.4700
Iteration: 855; Percent complete: 21.4%; Average loss: 3.5816
Iteration: 856; Percent complete: 21.4%; Average loss: 3.5291
Iteration: 857; Percent complete: 21.4%; Average loss: 3.4034
Iteration: 858; Percent complete: 21.4%; Average loss: 3.7554
Iteration: 859; Percent complete: 21.5%; Average loss: 3.6025
Iteration: 860; Percent complete: 21.5%; Average loss: 3.4152
Iteration: 861; Percent complete: 21.5%; Average loss: 3.4925
Iteration: 862; Percent complete: 21.6%; Average loss: 3.4693
Iteration: 863; Percent complete: 21.6%; Average loss: 3.9193
Iteration: 864; Percent complete: 21.6%; Average loss: 3.5635
Iteration: 865; Percent complete: 21.6%; Average loss: 3.5186
Iteration: 866; Percent complete: 21.6%; Average loss: 3.4143
Iteration: 867; Percent complete: 21.7%; Average loss: 3.3849
Iteration: 868; Percent complete: 21.7%; Average loss: 3.5323
Iteration: 869; Percent complete: 21.7%; Average loss: 3.7797
Iteration: 870; Percent complete: 21.8%; Average loss: 3.5132
Iteration: 871; Percent complete: 21.8%; Average loss: 3.4729
Iteration: 872; Percent complete: 21.8%; Average loss: 3.3910
Iteration: 873; Percent complete: 21.8%; Average loss: 3.3633
Iteration: 874; Percent complete: 21.9%; Average loss: 3.6170
Iteration: 875; Percent complete: 21.9%; Average loss: 3.2350
Iteration: 876; Percent complete: 21.9%; Average loss: 3.4506
Iteration: 877; Percent complete: 21.9%; Average loss: 3.1744
Iteration: 878; Percent complete: 21.9%; Average loss: 3.5178
Iteration: 879; Percent complete: 22.0%; Average loss: 3.4732
Iteration: 880; Percent complete: 22.0%; Average loss: 3.3667
Iteration: 881; Percent complete: 22.0%; Average loss: 3.6152
Iteration: 882; Percent complete: 22.1%; Average loss: 3.5189
Iteration: 883; Percent complete: 22.1%; Average loss: 3.4150
Iteration: 884; Percent complete: 22.1%; Average loss: 3.5814
Iteration: 885; Percent complete: 22.1%; Average loss: 3.5341
Iteration: 886; Percent complete: 22.1%; Average loss: 3.0606
Iteration: 887; Percent complete: 22.2%; Average loss: 3.2574
Iteration: 888; Percent complete: 22.2%; Average loss: 3.4328
Iteration: 889; Percent complete: 22.2%; Average loss: 3.3521
Iteration: 890; Percent complete: 22.2%; Average loss: 3.3327
Iteration: 891; Percent complete: 22.3%; Average loss: 3.4810
Iteration: 892; Percent complete: 22.3%; Average loss: 3.6304
Iteration: 893; Percent complete: 22.3%; Average loss: 3.4821
Iteration: 894; Percent complete: 22.4%; Average loss: 3.5378
Iteration: 895; Percent complete: 22.4%; Average loss: 3.4583
Iteration: 896; Percent complete: 22.4%; Average loss: 3.3854
Iteration: 897; Percent complete: 22.4%; Average loss: 3.5790
Iteration: 898; Percent complete: 22.4%; Average loss: 3.3741
Iteration: 899; Percent complete: 22.5%; Average loss: 3.4818
Iteration: 900; Percent complete: 22.5%; Average loss: 3.5065
Iteration: 901; Percent complete: 22.5%; Average loss: 3.6677
Iteration: 902; Percent complete: 22.6%; Average loss: 3.5952
Iteration: 903; Percent complete: 22.6%; Average loss: 3.7617
Iteration: 904; Percent complete: 22.6%; Average loss: 3.4929
Iteration: 905; Percent complete: 22.6%; Average loss: 3.4285
Iteration: 906; Percent complete: 22.7%; Average loss: 3.4874
Iteration: 907; Percent complete: 22.7%; Average loss: 3.4589
Iteration: 908; Percent complete: 22.7%; Average loss: 3.3270
Iteration: 909; Percent complete: 22.7%; Average loss: 3.6365
Iteration: 910; Percent complete: 22.8%; Average loss: 3.5700
Iteration: 911; Percent complete: 22.8%; Average loss: 3.5541
Iteration: 912; Percent complete: 22.8%; Average loss: 3.4709
Iteration: 913; Percent complete: 22.8%; Average loss: 3.7980
Iteration: 914; Percent complete: 22.9%; Average loss: 3.5772
Iteration: 915; Percent complete: 22.9%; Average loss: 3.6565
Iteration: 916; Percent complete: 22.9%; Average loss: 3.5212
Iteration: 917; Percent complete: 22.9%; Average loss: 3.5613
Iteration: 918; Percent complete: 22.9%; Average loss: 3.5420
Iteration: 919; Percent complete: 23.0%; Average loss: 3.5574
Iteration: 920; Percent complete: 23.0%; Average loss: 3.5689
Iteration: 921; Percent complete: 23.0%; Average loss: 3.6374
Iteration: 922; Percent complete: 23.1%; Average loss: 3.5472
Iteration: 923; Percent complete: 23.1%; Average loss: 3.3856
Iteration: 924; Percent complete: 23.1%; Average loss: 3.6661
Iteration: 925; Percent complete: 23.1%; Average loss: 3.8136
Iteration: 926; Percent complete: 23.2%; Average loss: 3.5951
Iteration: 927; Percent complete: 23.2%; Average loss: 3.3420
Iteration: 928; Percent complete: 23.2%; Average loss: 3.5012
Iteration: 929; Percent complete: 23.2%; Average loss: 3.7526
Iteration: 930; Percent complete: 23.2%; Average loss: 3.7083
Iteration: 931; Percent complete: 23.3%; Average loss: 3.3448
Iteration: 932; Percent complete: 23.3%; Average loss: 3.5042
Iteration: 933; Percent complete: 23.3%; Average loss: 3.3933
Iteration: 934; Percent complete: 23.4%; Average loss: 3.7380
Iteration: 935; Percent complete: 23.4%; Average loss: 3.5468
Iteration: 936; Percent complete: 23.4%; Average loss: 3.4880
Iteration: 937; Percent complete: 23.4%; Average loss: 3.3620
Iteration: 938; Percent complete: 23.4%; Average loss: 3.2507
Iteration: 939; Percent complete: 23.5%; Average loss: 3.5143
Iteration: 940; Percent complete: 23.5%; Average loss: 3.5335
Iteration: 941; Percent complete: 23.5%; Average loss: 3.4047
Iteration: 942; Percent complete: 23.5%; Average loss: 3.8545
Iteration: 943; Percent complete: 23.6%; Average loss: 3.1377
Iteration: 944; Percent complete: 23.6%; Average loss: 3.5479
Iteration: 945; Percent complete: 23.6%; Average loss: 3.4285
Iteration: 946; Percent complete: 23.6%; Average loss: 3.2445
Iteration: 947; Percent complete: 23.7%; Average loss: 3.2062
Iteration: 948; Percent complete: 23.7%; Average loss: 3.3257
Iteration: 949; Percent complete: 23.7%; Average loss: 3.5820
Iteration: 950; Percent complete: 23.8%; Average loss: 3.3558
Iteration: 951; Percent complete: 23.8%; Average loss: 3.4447
Iteration: 952; Percent complete: 23.8%; Average loss: 3.3328
Iteration: 953; Percent complete: 23.8%; Average loss: 3.3758
Iteration: 954; Percent complete: 23.8%; Average loss: 3.1945
Iteration: 955; Percent complete: 23.9%; Average loss: 3.2336
Iteration: 956; Percent complete: 23.9%; Average loss: 3.6831
Iteration: 957; Percent complete: 23.9%; Average loss: 3.3611
Iteration: 958; Percent complete: 23.9%; Average loss: 3.7375
Iteration: 959; Percent complete: 24.0%; Average loss: 3.5712
Iteration: 960; Percent complete: 24.0%; Average loss: 3.5567
Iteration: 961; Percent complete: 24.0%; Average loss: 3.1443
Iteration: 962; Percent complete: 24.1%; Average loss: 3.2312
Iteration: 963; Percent complete: 24.1%; Average loss: 3.3557
Iteration: 964; Percent complete: 24.1%; Average loss: 3.3988
Iteration: 965; Percent complete: 24.1%; Average loss: 3.3572
Iteration: 966; Percent complete: 24.1%; Average loss: 3.4975
Iteration: 967; Percent complete: 24.2%; Average loss: 3.5702
Iteration: 968; Percent complete: 24.2%; Average loss: 3.5294
Iteration: 969; Percent complete: 24.2%; Average loss: 3.8527
Iteration: 970; Percent complete: 24.2%; Average loss: 3.6593
Iteration: 971; Percent complete: 24.3%; Average loss: 3.2328
Iteration: 972; Percent complete: 24.3%; Average loss: 3.8250
Iteration: 973; Percent complete: 24.3%; Average loss: 3.5717
Iteration: 974; Percent complete: 24.3%; Average loss: 3.7690
Iteration: 975; Percent complete: 24.4%; Average loss: 3.3847
Iteration: 976; Percent complete: 24.4%; Average loss: 3.5523
Iteration: 977; Percent complete: 24.4%; Average loss: 3.3119
Iteration: 978; Percent complete: 24.4%; Average loss: 3.4917
Iteration: 979; Percent complete: 24.5%; Average loss: 3.3402
Iteration: 980; Percent complete: 24.5%; Average loss: 3.3333
Iteration: 981; Percent complete: 24.5%; Average loss: 3.5962
Iteration: 982; Percent complete: 24.6%; Average loss: 3.5396
Iteration: 983; Percent complete: 24.6%; Average loss: 3.6214
Iteration: 984; Percent complete: 24.6%; Average loss: 3.3517
Iteration: 985; Percent complete: 24.6%; Average loss: 3.6834
Iteration: 986; Percent complete: 24.6%; Average loss: 3.3503
Iteration: 987; Percent complete: 24.7%; Average loss: 3.6817
Iteration: 988; Percent complete: 24.7%; Average loss: 3.6126
Iteration: 989; Percent complete: 24.7%; Average loss: 3.5665
Iteration: 990; Percent complete: 24.8%; Average loss: 3.7593
Iteration: 991; Percent complete: 24.8%; Average loss: 3.2907
Iteration: 992; Percent complete: 24.8%; Average loss: 3.5541
Iteration: 993; Percent complete: 24.8%; Average loss: 3.4734
Iteration: 994; Percent complete: 24.9%; Average loss: 3.5016
Iteration: 995; Percent complete: 24.9%; Average loss: 3.2738
Iteration: 996; Percent complete: 24.9%; Average loss: 3.6210
Iteration: 997; Percent complete: 24.9%; Average loss: 3.3927
Iteration: 998; Percent complete: 24.9%; Average loss: 3.3932
Iteration: 999; Percent complete: 25.0%; Average loss: 3.1865
Iteration: 1000; Percent complete: 25.0%; Average loss: 3.3136
Iteration: 1001; Percent complete: 25.0%; Average loss: 3.3687
Iteration: 1002; Percent complete: 25.1%; Average loss: 3.4640
Iteration: 1003; Percent complete: 25.1%; Average loss: 3.6725
Iteration: 1004; Percent complete: 25.1%; Average loss: 3.5340
Iteration: 1005; Percent complete: 25.1%; Average loss: 3.6228
Iteration: 1006; Percent complete: 25.1%; Average loss: 3.3268
Iteration: 1007; Percent complete: 25.2%; Average loss: 3.5239
Iteration: 1008; Percent complete: 25.2%; Average loss: 3.6865
Iteration: 1009; Percent complete: 25.2%; Average loss: 3.5562
Iteration: 1010; Percent complete: 25.2%; Average loss: 3.5120
Iteration: 1011; Percent complete: 25.3%; Average loss: 3.4725
Iteration: 1012; Percent complete: 25.3%; Average loss: 3.4763
Iteration: 1013; Percent complete: 25.3%; Average loss: 3.3275
Iteration: 1014; Percent complete: 25.4%; Average loss: 3.6247
Iteration: 1015; Percent complete: 25.4%; Average loss: 3.5819
Iteration: 1016; Percent complete: 25.4%; Average loss: 3.2898
Iteration: 1017; Percent complete: 25.4%; Average loss: 3.3743
Iteration: 1018; Percent complete: 25.4%; Average loss: 3.5667
Iteration: 1019; Percent complete: 25.5%; Average loss: 3.4187
Iteration: 1020; Percent complete: 25.5%; Average loss: 3.4711
Iteration: 1021; Percent complete: 25.5%; Average loss: 3.5039
Iteration: 1022; Percent complete: 25.6%; Average loss: 3.3064
Iteration: 1023; Percent complete: 25.6%; Average loss: 3.4670
Iteration: 1024; Percent complete: 25.6%; Average loss: 3.2626
Iteration: 1025; Percent complete: 25.6%; Average loss: 3.4795
Iteration: 1026; Percent complete: 25.7%; Average loss: 3.5297
Iteration: 1027; Percent complete: 25.7%; Average loss: 3.2582
Iteration: 1028; Percent complete: 25.7%; Average loss: 3.4465
Iteration: 1029; Percent complete: 25.7%; Average loss: 3.7386
Iteration: 1030; Percent complete: 25.8%; Average loss: 3.5491
Iteration: 1031; Percent complete: 25.8%; Average loss: 3.6151
Iteration: 1032; Percent complete: 25.8%; Average loss: 3.3153
Iteration: 1033; Percent complete: 25.8%; Average loss: 3.4764
Iteration: 1034; Percent complete: 25.9%; Average loss: 3.5068
Iteration: 1035; Percent complete: 25.9%; Average loss: 3.4050
Iteration: 1036; Percent complete: 25.9%; Average loss: 3.7214
Iteration: 1037; Percent complete: 25.9%; Average loss: 3.5323
Iteration: 1038; Percent complete: 25.9%; Average loss: 3.2972
Iteration: 1039; Percent complete: 26.0%; Average loss: 3.5990
Iteration: 1040; Percent complete: 26.0%; Average loss: 3.4302
Iteration: 1041; Percent complete: 26.0%; Average loss: 3.3688
Iteration: 1042; Percent complete: 26.1%; Average loss: 3.3142
Iteration: 1043; Percent complete: 26.1%; Average loss: 3.6500
Iteration: 1044; Percent complete: 26.1%; Average loss: 3.5637
Iteration: 1045; Percent complete: 26.1%; Average loss: 3.6175
Iteration: 1046; Percent complete: 26.2%; Average loss: 3.5011
Iteration: 1047; Percent complete: 26.2%; Average loss: 3.7644
Iteration: 1048; Percent complete: 26.2%; Average loss: 3.4068
Iteration: 1049; Percent complete: 26.2%; Average loss: 3.3903
Iteration: 1050; Percent complete: 26.2%; Average loss: 3.3020
Iteration: 1051; Percent complete: 26.3%; Average loss: 3.5358
Iteration: 1052; Percent complete: 26.3%; Average loss: 3.4457
Iteration: 1053; Percent complete: 26.3%; Average loss: 3.3085
Iteration: 1054; Percent complete: 26.4%; Average loss: 3.1782
Iteration: 1055; Percent complete: 26.4%; Average loss: 3.3138
Iteration: 1056; Percent complete: 26.4%; Average loss: 3.6401
Iteration: 1057; Percent complete: 26.4%; Average loss: 3.5205
Iteration: 1058; Percent complete: 26.5%; Average loss: 3.3168
Iteration: 1059; Percent complete: 26.5%; Average loss: 3.6332
Iteration: 1060; Percent complete: 26.5%; Average loss: 3.8052
Iteration: 1061; Percent complete: 26.5%; Average loss: 3.5121
Iteration: 1062; Percent complete: 26.6%; Average loss: 3.6272
Iteration: 1063; Percent complete: 26.6%; Average loss: 3.4883
Iteration: 1064; Percent complete: 26.6%; Average loss: 3.4812
Iteration: 1065; Percent complete: 26.6%; Average loss: 3.6263
Iteration: 1066; Percent complete: 26.7%; Average loss: 3.5569
Iteration: 1067; Percent complete: 26.7%; Average loss: 3.4835
Iteration: 1068; Percent complete: 26.7%; Average loss: 3.7630
Iteration: 1069; Percent complete: 26.7%; Average loss: 3.6842
Iteration: 1070; Percent complete: 26.8%; Average loss: 3.3756
Iteration: 1071; Percent complete: 26.8%; Average loss: 3.3746
Iteration: 1072; Percent complete: 26.8%; Average loss: 3.4236
Iteration: 1073; Percent complete: 26.8%; Average loss: 3.4660
Iteration: 1074; Percent complete: 26.9%; Average loss: 3.6819
Iteration: 1075; Percent complete: 26.9%; Average loss: 3.4846
Iteration: 1076; Percent complete: 26.9%; Average loss: 3.2766
Iteration: 1077; Percent complete: 26.9%; Average loss: 3.4281
Iteration: 1078; Percent complete: 27.0%; Average loss: 3.4841
Iteration: 1079; Percent complete: 27.0%; Average loss: 3.4478
Iteration: 1080; Percent complete: 27.0%; Average loss: 3.5704
Iteration: 1081; Percent complete: 27.0%; Average loss: 3.4192
Iteration: 1082; Percent complete: 27.1%; Average loss: 3.7105
Iteration: 1083; Percent complete: 27.1%; Average loss: 3.6266
Iteration: 1084; Percent complete: 27.1%; Average loss: 3.4417
Iteration: 1085; Percent complete: 27.1%; Average loss: 3.3004
Iteration: 1086; Percent complete: 27.2%; Average loss: 3.4389
Iteration: 1087; Percent complete: 27.2%; Average loss: 3.7696
Iteration: 1088; Percent complete: 27.2%; Average loss: 3.5996
Iteration: 1089; Percent complete: 27.2%; Average loss: 3.6028
Iteration: 1090; Percent complete: 27.3%; Average loss: 3.5113
Iteration: 1091; Percent complete: 27.3%; Average loss: 3.4250
Iteration: 1092; Percent complete: 27.3%; Average loss: 3.3918
Iteration: 1093; Percent complete: 27.3%; Average loss: 3.4998
Iteration: 1094; Percent complete: 27.4%; Average loss: 3.4294
Iteration: 1095; Percent complete: 27.4%; Average loss: 3.5461
Iteration: 1096; Percent complete: 27.4%; Average loss: 3.2988
Iteration: 1097; Percent complete: 27.4%; Average loss: 3.3130
Iteration: 1098; Percent complete: 27.5%; Average loss: 3.5912
Iteration: 1099; Percent complete: 27.5%; Average loss: 3.3252
Iteration: 1100; Percent complete: 27.5%; Average loss: 3.3298
Iteration: 1101; Percent complete: 27.5%; Average loss: 3.5055
Iteration: 1102; Percent complete: 27.6%; Average loss: 3.5767
Iteration: 1103; Percent complete: 27.6%; Average loss: 3.4297
Iteration: 1104; Percent complete: 27.6%; Average loss: 3.3641
Iteration: 1105; Percent complete: 27.6%; Average loss: 3.5328
Iteration: 1106; Percent complete: 27.7%; Average loss: 3.5640
Iteration: 1107; Percent complete: 27.7%; Average loss: 3.4920
Iteration: 1108; Percent complete: 27.7%; Average loss: 3.2650
Iteration: 1109; Percent complete: 27.7%; Average loss: 3.6386
Iteration: 1110; Percent complete: 27.8%; Average loss: 3.8141
Iteration: 1111; Percent complete: 27.8%; Average loss: 3.2882
Iteration: 1112; Percent complete: 27.8%; Average loss: 3.4487
Iteration: 1113; Percent complete: 27.8%; Average loss: 3.1517
Iteration: 1114; Percent complete: 27.9%; Average loss: 3.4542
Iteration: 1115; Percent complete: 27.9%; Average loss: 3.5352
Iteration: 1116; Percent complete: 27.9%; Average loss: 3.3446
Iteration: 1117; Percent complete: 27.9%; Average loss: 3.6004
Iteration: 1118; Percent complete: 28.0%; Average loss: 3.3335
Iteration: 1119; Percent complete: 28.0%; Average loss: 3.5156
Iteration: 1120; Percent complete: 28.0%; Average loss: 3.3309
Iteration: 1121; Percent complete: 28.0%; Average loss: 3.2410
Iteration: 1122; Percent complete: 28.1%; Average loss: 3.4091
Iteration: 1123; Percent complete: 28.1%; Average loss: 3.6634
Iteration: 1124; Percent complete: 28.1%; Average loss: 3.5051
Iteration: 1125; Percent complete: 28.1%; Average loss: 3.3132
Iteration: 1126; Percent complete: 28.1%; Average loss: 3.4377
Iteration: 1127; Percent complete: 28.2%; Average loss: 3.6899
Iteration: 1128; Percent complete: 28.2%; Average loss: 3.4887
Iteration: 1129; Percent complete: 28.2%; Average loss: 3.4432
Iteration: 1130; Percent complete: 28.2%; Average loss: 3.7370
Iteration: 1131; Percent complete: 28.3%; Average loss: 3.6277
Iteration: 1132; Percent complete: 28.3%; Average loss: 3.2655
Iteration: 1133; Percent complete: 28.3%; Average loss: 3.6771
Iteration: 1134; Percent complete: 28.3%; Average loss: 3.4362
Iteration: 1135; Percent complete: 28.4%; Average loss: 3.3833
Iteration: 1136; Percent complete: 28.4%; Average loss: 3.5088
Iteration: 1137; Percent complete: 28.4%; Average loss: 3.3629
Iteration: 1138; Percent complete: 28.4%; Average loss: 3.5553
Iteration: 1139; Percent complete: 28.5%; Average loss: 3.2862
Iteration: 1140; Percent complete: 28.5%; Average loss: 3.4826
Iteration: 1141; Percent complete: 28.5%; Average loss: 3.4882
Iteration: 1142; Percent complete: 28.5%; Average loss: 3.0701
Iteration: 1143; Percent complete: 28.6%; Average loss: 3.4919
Iteration: 1144; Percent complete: 28.6%; Average loss: 3.3664
Iteration: 1145; Percent complete: 28.6%; Average loss: 3.3044
Iteration: 1146; Percent complete: 28.6%; Average loss: 3.3079
Iteration: 1147; Percent complete: 28.7%; Average loss: 3.5026
Iteration: 1148; Percent complete: 28.7%; Average loss: 3.6228
Iteration: 1149; Percent complete: 28.7%; Average loss: 3.0159
Iteration: 1150; Percent complete: 28.7%; Average loss: 3.3031
Iteration: 1151; Percent complete: 28.8%; Average loss: 3.4530
Iteration: 1152; Percent complete: 28.8%; Average loss: 3.1734
Iteration: 1153; Percent complete: 28.8%; Average loss: 3.5433
Iteration: 1154; Percent complete: 28.8%; Average loss: 3.3992
Iteration: 1155; Percent complete: 28.9%; Average loss: 3.4980
Iteration: 1156; Percent complete: 28.9%; Average loss: 3.5495
Iteration: 1157; Percent complete: 28.9%; Average loss: 3.3580
Iteration: 1158; Percent complete: 28.9%; Average loss: 3.3391
Iteration: 1159; Percent complete: 29.0%; Average loss: 3.7042
Iteration: 1160; Percent complete: 29.0%; Average loss: 3.3206
Iteration: 1161; Percent complete: 29.0%; Average loss: 3.0679
Iteration: 1162; Percent complete: 29.0%; Average loss: 3.1326
Iteration: 1163; Percent complete: 29.1%; Average loss: 3.2475
Iteration: 1164; Percent complete: 29.1%; Average loss: 3.3235
Iteration: 1165; Percent complete: 29.1%; Average loss: 3.3933
Iteration: 1166; Percent complete: 29.1%; Average loss: 3.5452
Iteration: 1167; Percent complete: 29.2%; Average loss: 3.2949
Iteration: 1168; Percent complete: 29.2%; Average loss: 3.2881
Iteration: 1169; Percent complete: 29.2%; Average loss: 3.5395
Iteration: 1170; Percent complete: 29.2%; Average loss: 3.2835
Iteration: 1171; Percent complete: 29.3%; Average loss: 3.3281
Iteration: 1172; Percent complete: 29.3%; Average loss: 3.4616
Iteration: 1173; Percent complete: 29.3%; Average loss: 3.3588
Iteration: 1174; Percent complete: 29.3%; Average loss: 3.2520
Iteration: 1175; Percent complete: 29.4%; Average loss: 3.4493
Iteration: 1176; Percent complete: 29.4%; Average loss: 3.4964
Iteration: 1177; Percent complete: 29.4%; Average loss: 3.3231
Iteration: 1178; Percent complete: 29.4%; Average loss: 3.4737
Iteration: 1179; Percent complete: 29.5%; Average loss: 3.1206
Iteration: 1180; Percent complete: 29.5%; Average loss: 3.1886
Iteration: 1181; Percent complete: 29.5%; Average loss: 3.3124
Iteration: 1182; Percent complete: 29.5%; Average loss: 3.4186
Iteration: 1183; Percent complete: 29.6%; Average loss: 3.4443
Iteration: 1184; Percent complete: 29.6%; Average loss: 3.2607
Iteration: 1185; Percent complete: 29.6%; Average loss: 3.4911
Iteration: 1186; Percent complete: 29.6%; Average loss: 3.2551
Iteration: 1187; Percent complete: 29.7%; Average loss: 3.4364
Iteration: 1188; Percent complete: 29.7%; Average loss: 3.6460
Iteration: 1189; Percent complete: 29.7%; Average loss: 3.2413
Iteration: 1190; Percent complete: 29.8%; Average loss: 3.8726
Iteration: 1191; Percent complete: 29.8%; Average loss: 3.5101
Iteration: 1192; Percent complete: 29.8%; Average loss: 3.3380
Iteration: 1193; Percent complete: 29.8%; Average loss: 3.2900
Iteration: 1194; Percent complete: 29.8%; Average loss: 3.3273
Iteration: 1195; Percent complete: 29.9%; Average loss: 3.7185
Iteration: 1196; Percent complete: 29.9%; Average loss: 3.7347
Iteration: 1197; Percent complete: 29.9%; Average loss: 3.4081
Iteration: 1198; Percent complete: 29.9%; Average loss: 3.4549
Iteration: 1199; Percent complete: 30.0%; Average loss: 3.4499
Iteration: 1200; Percent complete: 30.0%; Average loss: 3.4832
Iteration: 1201; Percent complete: 30.0%; Average loss: 3.4311
Iteration: 1202; Percent complete: 30.0%; Average loss: 3.4005
Iteration: 1203; Percent complete: 30.1%; Average loss: 3.3583
Iteration: 1204; Percent complete: 30.1%; Average loss: 3.4450
Iteration: 1205; Percent complete: 30.1%; Average loss: 3.5308
Iteration: 1206; Percent complete: 30.1%; Average loss: 2.9791
Iteration: 1207; Percent complete: 30.2%; Average loss: 3.3443
Iteration: 1208; Percent complete: 30.2%; Average loss: 3.1393
Iteration: 1209; Percent complete: 30.2%; Average loss: 3.5747
Iteration: 1210; Percent complete: 30.2%; Average loss: 3.5552
Iteration: 1211; Percent complete: 30.3%; Average loss: 3.4360
Iteration: 1212; Percent complete: 30.3%; Average loss: 3.2439
Iteration: 1213; Percent complete: 30.3%; Average loss: 3.2192
Iteration: 1214; Percent complete: 30.3%; Average loss: 3.2098
Iteration: 1215; Percent complete: 30.4%; Average loss: 3.4286
Iteration: 1216; Percent complete: 30.4%; Average loss: 3.5231
Iteration: 1217; Percent complete: 30.4%; Average loss: 3.3451
Iteration: 1218; Percent complete: 30.4%; Average loss: 3.4251
Iteration: 1219; Percent complete: 30.5%; Average loss: 3.5369
Iteration: 1220; Percent complete: 30.5%; Average loss: 3.3253
Iteration: 1221; Percent complete: 30.5%; Average loss: 2.9800
Iteration: 1222; Percent complete: 30.6%; Average loss: 3.2734
Iteration: 1223; Percent complete: 30.6%; Average loss: 3.2733
Iteration: 1224; Percent complete: 30.6%; Average loss: 3.5021
Iteration: 1225; Percent complete: 30.6%; Average loss: 3.7015
Iteration: 1226; Percent complete: 30.6%; Average loss: 3.2390
Iteration: 1227; Percent complete: 30.7%; Average loss: 3.4708
Iteration: 1228; Percent complete: 30.7%; Average loss: 3.4282
Iteration: 1229; Percent complete: 30.7%; Average loss: 3.4168
Iteration: 1230; Percent complete: 30.8%; Average loss: 3.2688
Iteration: 1231; Percent complete: 30.8%; Average loss: 3.6284
Iteration: 1232; Percent complete: 30.8%; Average loss: 3.4763
Iteration: 1233; Percent complete: 30.8%; Average loss: 3.3164
Iteration: 1234; Percent complete: 30.9%; Average loss: 3.1673
Iteration: 1235; Percent complete: 30.9%; Average loss: 3.4385
Iteration: 1236; Percent complete: 30.9%; Average loss: 3.3503
Iteration: 1237; Percent complete: 30.9%; Average loss: 3.4668
Iteration: 1238; Percent complete: 30.9%; Average loss: 3.3083
Iteration: 1239; Percent complete: 31.0%; Average loss: 3.3048
Iteration: 1240; Percent complete: 31.0%; Average loss: 3.3595
Iteration: 1241; Percent complete: 31.0%; Average loss: 3.3141
Iteration: 1242; Percent complete: 31.1%; Average loss: 3.3112
Iteration: 1243; Percent complete: 31.1%; Average loss: 3.2999
Iteration: 1244; Percent complete: 31.1%; Average loss: 3.2121
Iteration: 1245; Percent complete: 31.1%; Average loss: 3.0463
Iteration: 1246; Percent complete: 31.1%; Average loss: 3.1751
Iteration: 1247; Percent complete: 31.2%; Average loss: 3.3778
Iteration: 1248; Percent complete: 31.2%; Average loss: 3.2489
Iteration: 1249; Percent complete: 31.2%; Average loss: 3.4141
Iteration: 1250; Percent complete: 31.2%; Average loss: 3.0283
Iteration: 1251; Percent complete: 31.3%; Average loss: 3.4165
Iteration: 1252; Percent complete: 31.3%; Average loss: 3.3142
Iteration: 1253; Percent complete: 31.3%; Average loss: 3.3545
Iteration: 1254; Percent complete: 31.4%; Average loss: 3.3276
Iteration: 1255; Percent complete: 31.4%; Average loss: 3.3641
Iteration: 1256; Percent complete: 31.4%; Average loss: 3.4444
Iteration: 1257; Percent complete: 31.4%; Average loss: 3.0321
Iteration: 1258; Percent complete: 31.4%; Average loss: 3.7035
Iteration: 1259; Percent complete: 31.5%; Average loss: 3.5317
Iteration: 1260; Percent complete: 31.5%; Average loss: 3.1581
Iteration: 1261; Percent complete: 31.5%; Average loss: 3.2307
Iteration: 1262; Percent complete: 31.6%; Average loss: 3.4492
Iteration: 1263; Percent complete: 31.6%; Average loss: 3.4151
Iteration: 1264; Percent complete: 31.6%; Average loss: 3.4218
Iteration: 1265; Percent complete: 31.6%; Average loss: 3.3193
Iteration: 1266; Percent complete: 31.6%; Average loss: 3.7352
Iteration: 1267; Percent complete: 31.7%; Average loss: 3.2661
Iteration: 1268; Percent complete: 31.7%; Average loss: 3.5251
Iteration: 1269; Percent complete: 31.7%; Average loss: 3.1668
Iteration: 1270; Percent complete: 31.8%; Average loss: 3.5015
Iteration: 1271; Percent complete: 31.8%; Average loss: 3.4041
Iteration: 1272; Percent complete: 31.8%; Average loss: 3.3924
Iteration: 1273; Percent complete: 31.8%; Average loss: 3.4902
Iteration: 1274; Percent complete: 31.9%; Average loss: 3.1900
Iteration: 1275; Percent complete: 31.9%; Average loss: 3.0244
Iteration: 1276; Percent complete: 31.9%; Average loss: 3.6555
Iteration: 1277; Percent complete: 31.9%; Average loss: 3.3056
Iteration: 1278; Percent complete: 31.9%; Average loss: 3.6375
Iteration: 1279; Percent complete: 32.0%; Average loss: 3.4899
Iteration: 1280; Percent complete: 32.0%; Average loss: 3.3920
Iteration: 1281; Percent complete: 32.0%; Average loss: 3.3833
Iteration: 1282; Percent complete: 32.0%; Average loss: 3.6302
Iteration: 1283; Percent complete: 32.1%; Average loss: 3.4365
Iteration: 1284; Percent complete: 32.1%; Average loss: 3.0696
Iteration: 1285; Percent complete: 32.1%; Average loss: 3.3128
Iteration: 1286; Percent complete: 32.1%; Average loss: 3.2947
Iteration: 1287; Percent complete: 32.2%; Average loss: 3.4684
Iteration: 1288; Percent complete: 32.2%; Average loss: 3.2623
Iteration: 1289; Percent complete: 32.2%; Average loss: 3.7216
Iteration: 1290; Percent complete: 32.2%; Average loss: 3.2878
Iteration: 1291; Percent complete: 32.3%; Average loss: 3.4609
Iteration: 1292; Percent complete: 32.3%; Average loss: 3.4624
Iteration: 1293; Percent complete: 32.3%; Average loss: 3.2873
Iteration: 1294; Percent complete: 32.4%; Average loss: 3.1789
Iteration: 1295; Percent complete: 32.4%; Average loss: 3.3356
Iteration: 1296; Percent complete: 32.4%; Average loss: 3.3331
Iteration: 1297; Percent complete: 32.4%; Average loss: 3.3119
Iteration: 1298; Percent complete: 32.5%; Average loss: 3.4365
Iteration: 1299; Percent complete: 32.5%; Average loss: 3.3133
Iteration: 1300; Percent complete: 32.5%; Average loss: 3.3386
Iteration: 1301; Percent complete: 32.5%; Average loss: 3.3348
Iteration: 1302; Percent complete: 32.6%; Average loss: 3.2804
Iteration: 1303; Percent complete: 32.6%; Average loss: 3.3670
Iteration: 1304; Percent complete: 32.6%; Average loss: 3.3582
Iteration: 1305; Percent complete: 32.6%; Average loss: 3.7527
Iteration: 1306; Percent complete: 32.6%; Average loss: 3.4256
Iteration: 1307; Percent complete: 32.7%; Average loss: 3.4509
Iteration: 1308; Percent complete: 32.7%; Average loss: 3.2469
Iteration: 1309; Percent complete: 32.7%; Average loss: 3.4079
Iteration: 1310; Percent complete: 32.8%; Average loss: 3.2962
Iteration: 1311; Percent complete: 32.8%; Average loss: 3.3760
Iteration: 1312; Percent complete: 32.8%; Average loss: 3.5242
Iteration: 1313; Percent complete: 32.8%; Average loss: 3.4110
Iteration: 1314; Percent complete: 32.9%; Average loss: 3.3878
Iteration: 1315; Percent complete: 32.9%; Average loss: 3.3454
Iteration: 1316; Percent complete: 32.9%; Average loss: 3.3425
Iteration: 1317; Percent complete: 32.9%; Average loss: 3.1830
Iteration: 1318; Percent complete: 33.0%; Average loss: 3.5460
Iteration: 1319; Percent complete: 33.0%; Average loss: 3.2933
Iteration: 1320; Percent complete: 33.0%; Average loss: 3.2004
Iteration: 1321; Percent complete: 33.0%; Average loss: 3.2919
Iteration: 1322; Percent complete: 33.1%; Average loss: 3.5772
Iteration: 1323; Percent complete: 33.1%; Average loss: 3.3254
Iteration: 1324; Percent complete: 33.1%; Average loss: 3.3537
Iteration: 1325; Percent complete: 33.1%; Average loss: 3.3766
Iteration: 1326; Percent complete: 33.1%; Average loss: 3.2197
Iteration: 1327; Percent complete: 33.2%; Average loss: 3.3624
Iteration: 1328; Percent complete: 33.2%; Average loss: 3.4357
Iteration: 1329; Percent complete: 33.2%; Average loss: 3.3885
Iteration: 1330; Percent complete: 33.2%; Average loss: 3.5434
Iteration: 1331; Percent complete: 33.3%; Average loss: 3.2437
Iteration: 1332; Percent complete: 33.3%; Average loss: 3.4158
Iteration: 1333; Percent complete: 33.3%; Average loss: 3.1663
Iteration: 1334; Percent complete: 33.4%; Average loss: 3.1345
Iteration: 1335; Percent complete: 33.4%; Average loss: 3.4153
Iteration: 1336; Percent complete: 33.4%; Average loss: 3.2542
Iteration: 1337; Percent complete: 33.4%; Average loss: 3.4062
Iteration: 1338; Percent complete: 33.5%; Average loss: 3.0532
Iteration: 1339; Percent complete: 33.5%; Average loss: 3.1101
Iteration: 1340; Percent complete: 33.5%; Average loss: 3.3140
Iteration: 1341; Percent complete: 33.5%; Average loss: 3.3134
Iteration: 1342; Percent complete: 33.6%; Average loss: 3.2509
Iteration: 1343; Percent complete: 33.6%; Average loss: 3.2923
Iteration: 1344; Percent complete: 33.6%; Average loss: 3.7070
Iteration: 1345; Percent complete: 33.6%; Average loss: 3.4475
Iteration: 1346; Percent complete: 33.7%; Average loss: 3.0339
Iteration: 1347; Percent complete: 33.7%; Average loss: 3.3038
Iteration: 1348; Percent complete: 33.7%; Average loss: 3.4449
Iteration: 1349; Percent complete: 33.7%; Average loss: 3.3419
Iteration: 1350; Percent complete: 33.8%; Average loss: 3.2634
Iteration: 1351; Percent complete: 33.8%; Average loss: 3.0782
Iteration: 1352; Percent complete: 33.8%; Average loss: 3.5133
Iteration: 1353; Percent complete: 33.8%; Average loss: 3.3710
Iteration: 1354; Percent complete: 33.9%; Average loss: 3.1756
Iteration: 1355; Percent complete: 33.9%; Average loss: 3.4078
Iteration: 1356; Percent complete: 33.9%; Average loss: 3.4148
Iteration: 1357; Percent complete: 33.9%; Average loss: 3.3705
Iteration: 1358; Percent complete: 34.0%; Average loss: 3.6418
Iteration: 1359; Percent complete: 34.0%; Average loss: 3.6480
Iteration: 1360; Percent complete: 34.0%; Average loss: 3.1203
Iteration: 1361; Percent complete: 34.0%; Average loss: 3.0480
Iteration: 1362; Percent complete: 34.1%; Average loss: 3.4259
Iteration: 1363; Percent complete: 34.1%; Average loss: 3.4624
Iteration: 1364; Percent complete: 34.1%; Average loss: 3.2765
Iteration: 1365; Percent complete: 34.1%; Average loss: 3.3777
Iteration: 1366; Percent complete: 34.2%; Average loss: 3.4561
Iteration: 1367; Percent complete: 34.2%; Average loss: 3.3388
Iteration: 1368; Percent complete: 34.2%; Average loss: 3.3898
Iteration: 1369; Percent complete: 34.2%; Average loss: 3.3264
Iteration: 1370; Percent complete: 34.2%; Average loss: 3.5263
Iteration: 1371; Percent complete: 34.3%; Average loss: 3.4320
Iteration: 1372; Percent complete: 34.3%; Average loss: 3.2922
Iteration: 1373; Percent complete: 34.3%; Average loss: 3.4908
Iteration: 1374; Percent complete: 34.4%; Average loss: 3.5035
Iteration: 1375; Percent complete: 34.4%; Average loss: 3.2043
Iteration: 1376; Percent complete: 34.4%; Average loss: 3.2141
Iteration: 1377; Percent complete: 34.4%; Average loss: 3.6864
Iteration: 1378; Percent complete: 34.4%; Average loss: 3.3310
Iteration: 1379; Percent complete: 34.5%; Average loss: 3.3331
Iteration: 1380; Percent complete: 34.5%; Average loss: 3.5633
Iteration: 1381; Percent complete: 34.5%; Average loss: 3.2947
Iteration: 1382; Percent complete: 34.5%; Average loss: 3.5884
Iteration: 1383; Percent complete: 34.6%; Average loss: 3.6747
Iteration: 1384; Percent complete: 34.6%; Average loss: 3.4213
Iteration: 1385; Percent complete: 34.6%; Average loss: 3.4355
Iteration: 1386; Percent complete: 34.6%; Average loss: 3.4311
Iteration: 1387; Percent complete: 34.7%; Average loss: 3.2700
Iteration: 1388; Percent complete: 34.7%; Average loss: 3.5603
Iteration: 1389; Percent complete: 34.7%; Average loss: 3.6155
Iteration: 1390; Percent complete: 34.8%; Average loss: 3.1868
Iteration: 1391; Percent complete: 34.8%; Average loss: 3.2999
Iteration: 1392; Percent complete: 34.8%; Average loss: 3.1786
Iteration: 1393; Percent complete: 34.8%; Average loss: 3.2998
Iteration: 1394; Percent complete: 34.8%; Average loss: 3.1770
Iteration: 1395; Percent complete: 34.9%; Average loss: 3.5128
Iteration: 1396; Percent complete: 34.9%; Average loss: 3.2233
Iteration: 1397; Percent complete: 34.9%; Average loss: 3.3434
Iteration: 1398; Percent complete: 34.9%; Average loss: 3.3606
Iteration: 1399; Percent complete: 35.0%; Average loss: 3.3770
Iteration: 1400; Percent complete: 35.0%; Average loss: 3.3059
Iteration: 1401; Percent complete: 35.0%; Average loss: 3.5145
Iteration: 1402; Percent complete: 35.0%; Average loss: 3.2057
Iteration: 1403; Percent complete: 35.1%; Average loss: 3.1161
Iteration: 1404; Percent complete: 35.1%; Average loss: 3.3825
Iteration: 1405; Percent complete: 35.1%; Average loss: 3.2273
Iteration: 1406; Percent complete: 35.1%; Average loss: 3.1134
Iteration: 1407; Percent complete: 35.2%; Average loss: 3.2905
Iteration: 1408; Percent complete: 35.2%; Average loss: 3.2933
Iteration: 1409; Percent complete: 35.2%; Average loss: 3.3545
Iteration: 1410; Percent complete: 35.2%; Average loss: 3.3579
Iteration: 1411; Percent complete: 35.3%; Average loss: 3.3047
Iteration: 1412; Percent complete: 35.3%; Average loss: 3.1943
Iteration: 1413; Percent complete: 35.3%; Average loss: 3.3169
Iteration: 1414; Percent complete: 35.4%; Average loss: 3.1836
Iteration: 1415; Percent complete: 35.4%; Average loss: 3.2220
Iteration: 1416; Percent complete: 35.4%; Average loss: 3.5348
Iteration: 1417; Percent complete: 35.4%; Average loss: 3.2238
Iteration: 1418; Percent complete: 35.4%; Average loss: 3.5151
Iteration: 1419; Percent complete: 35.5%; Average loss: 3.1379
Iteration: 1420; Percent complete: 35.5%; Average loss: 3.2291
Iteration: 1421; Percent complete: 35.5%; Average loss: 3.5999
Iteration: 1422; Percent complete: 35.5%; Average loss: 3.3401
Iteration: 1423; Percent complete: 35.6%; Average loss: 3.3606
Iteration: 1424; Percent complete: 35.6%; Average loss: 3.1676
Iteration: 1425; Percent complete: 35.6%; Average loss: 3.2270
Iteration: 1426; Percent complete: 35.6%; Average loss: 3.2404
Iteration: 1427; Percent complete: 35.7%; Average loss: 2.8353
Iteration: 1428; Percent complete: 35.7%; Average loss: 3.3797
Iteration: 1429; Percent complete: 35.7%; Average loss: 3.4737
Iteration: 1430; Percent complete: 35.8%; Average loss: 3.0969
Iteration: 1431; Percent complete: 35.8%; Average loss: 3.3826
Iteration: 1432; Percent complete: 35.8%; Average loss: 3.0919
Iteration: 1433; Percent complete: 35.8%; Average loss: 3.5288
Iteration: 1434; Percent complete: 35.9%; Average loss: 3.3991
Iteration: 1435; Percent complete: 35.9%; Average loss: 3.2397
Iteration: 1436; Percent complete: 35.9%; Average loss: 3.5306
Iteration: 1437; Percent complete: 35.9%; Average loss: 3.2600
Iteration: 1438; Percent complete: 35.9%; Average loss: 3.0256
Iteration: 1439; Percent complete: 36.0%; Average loss: 3.3275
Iteration: 1440; Percent complete: 36.0%; Average loss: 3.4346
Iteration: 1441; Percent complete: 36.0%; Average loss: 3.5101
Iteration: 1442; Percent complete: 36.0%; Average loss: 3.2794
Iteration: 1443; Percent complete: 36.1%; Average loss: 3.2692
Iteration: 1444; Percent complete: 36.1%; Average loss: 3.3764
Iteration: 1445; Percent complete: 36.1%; Average loss: 3.5009
Iteration: 1446; Percent complete: 36.1%; Average loss: 3.3231
Iteration: 1447; Percent complete: 36.2%; Average loss: 3.5356
Iteration: 1448; Percent complete: 36.2%; Average loss: 3.4477
Iteration: 1449; Percent complete: 36.2%; Average loss: 3.2861
Iteration: 1450; Percent complete: 36.2%; Average loss: 3.2281
Iteration: 1451; Percent complete: 36.3%; Average loss: 3.3333
Iteration: 1452; Percent complete: 36.3%; Average loss: 3.1605
Iteration: 1453; Percent complete: 36.3%; Average loss: 3.3103
Iteration: 1454; Percent complete: 36.4%; Average loss: 3.3696
Iteration: 1455; Percent complete: 36.4%; Average loss: 3.4089
Iteration: 1456; Percent complete: 36.4%; Average loss: 2.9508
Iteration: 1457; Percent complete: 36.4%; Average loss: 3.2891
Iteration: 1458; Percent complete: 36.4%; Average loss: 3.2968
Iteration: 1459; Percent complete: 36.5%; Average loss: 3.6862
Iteration: 1460; Percent complete: 36.5%; Average loss: 3.3476
Iteration: 1461; Percent complete: 36.5%; Average loss: 3.4273
Iteration: 1462; Percent complete: 36.5%; Average loss: 3.1514
Iteration: 1463; Percent complete: 36.6%; Average loss: 3.2289
Iteration: 1464; Percent complete: 36.6%; Average loss: 3.2872
Iteration: 1465; Percent complete: 36.6%; Average loss: 3.4310
Iteration: 1466; Percent complete: 36.6%; Average loss: 3.1251
Iteration: 1467; Percent complete: 36.7%; Average loss: 3.0533
Iteration: 1468; Percent complete: 36.7%; Average loss: 3.4658
Iteration: 1469; Percent complete: 36.7%; Average loss: 3.3324
Iteration: 1470; Percent complete: 36.8%; Average loss: 3.4632
Iteration: 1471; Percent complete: 36.8%; Average loss: 3.3988
Iteration: 1472; Percent complete: 36.8%; Average loss: 3.4599
Iteration: 1473; Percent complete: 36.8%; Average loss: 3.1897
Iteration: 1474; Percent complete: 36.9%; Average loss: 3.3505
Iteration: 1475; Percent complete: 36.9%; Average loss: 3.6373
Iteration: 1476; Percent complete: 36.9%; Average loss: 3.5391
Iteration: 1477; Percent complete: 36.9%; Average loss: 3.1825
Iteration: 1478; Percent complete: 37.0%; Average loss: 3.0619
Iteration: 1479; Percent complete: 37.0%; Average loss: 3.3597
Iteration: 1480; Percent complete: 37.0%; Average loss: 3.1474
Iteration: 1481; Percent complete: 37.0%; Average loss: 3.1731
Iteration: 1482; Percent complete: 37.0%; Average loss: 3.2613
Iteration: 1483; Percent complete: 37.1%; Average loss: 3.1450
Iteration: 1484; Percent complete: 37.1%; Average loss: 3.2622
Iteration: 1485; Percent complete: 37.1%; Average loss: 3.2978
Iteration: 1486; Percent complete: 37.1%; Average loss: 3.0726
Iteration: 1487; Percent complete: 37.2%; Average loss: 3.3099
Iteration: 1488; Percent complete: 37.2%; Average loss: 3.5739
Iteration: 1489; Percent complete: 37.2%; Average loss: 3.4930
Iteration: 1490; Percent complete: 37.2%; Average loss: 3.1666
Iteration: 1491; Percent complete: 37.3%; Average loss: 3.4734
Iteration: 1492; Percent complete: 37.3%; Average loss: 3.2043
Iteration: 1493; Percent complete: 37.3%; Average loss: 3.4342
Iteration: 1494; Percent complete: 37.4%; Average loss: 3.5538
Iteration: 1495; Percent complete: 37.4%; Average loss: 3.2648
Iteration: 1496; Percent complete: 37.4%; Average loss: 3.2717
Iteration: 1497; Percent complete: 37.4%; Average loss: 3.3492
Iteration: 1498; Percent complete: 37.5%; Average loss: 3.2876
Iteration: 1499; Percent complete: 37.5%; Average loss: 3.5183
Iteration: 1500; Percent complete: 37.5%; Average loss: 3.2547
Iteration: 1501; Percent complete: 37.5%; Average loss: 3.5273
Iteration: 1502; Percent complete: 37.5%; Average loss: 3.2633
Iteration: 1503; Percent complete: 37.6%; Average loss: 3.4259
Iteration: 1504; Percent complete: 37.6%; Average loss: 3.5753
Iteration: 1505; Percent complete: 37.6%; Average loss: 3.3024
Iteration: 1506; Percent complete: 37.6%; Average loss: 3.0482
Iteration: 1507; Percent complete: 37.7%; Average loss: 3.0574
Iteration: 1508; Percent complete: 37.7%; Average loss: 3.5070
Iteration: 1509; Percent complete: 37.7%; Average loss: 3.2776
Iteration: 1510; Percent complete: 37.8%; Average loss: 3.2560
Iteration: 1511; Percent complete: 37.8%; Average loss: 3.4614
Iteration: 1512; Percent complete: 37.8%; Average loss: 3.2828
Iteration: 1513; Percent complete: 37.8%; Average loss: 3.4100
Iteration: 1514; Percent complete: 37.9%; Average loss: 3.1783
Iteration: 1515; Percent complete: 37.9%; Average loss: 3.0816
Iteration: 1516; Percent complete: 37.9%; Average loss: 3.4453
Iteration: 1517; Percent complete: 37.9%; Average loss: 3.2349
Iteration: 1518; Percent complete: 38.0%; Average loss: 3.0663
Iteration: 1519; Percent complete: 38.0%; Average loss: 3.5304
Iteration: 1520; Percent complete: 38.0%; Average loss: 3.2128
Iteration: 1521; Percent complete: 38.0%; Average loss: 3.3065
Iteration: 1522; Percent complete: 38.0%; Average loss: 3.1439
Iteration: 1523; Percent complete: 38.1%; Average loss: 3.1505
Iteration: 1524; Percent complete: 38.1%; Average loss: 3.2469
Iteration: 1525; Percent complete: 38.1%; Average loss: 3.4386
Iteration: 1526; Percent complete: 38.1%; Average loss: 3.0337
Iteration: 1527; Percent complete: 38.2%; Average loss: 3.3538
Iteration: 1528; Percent complete: 38.2%; Average loss: 3.4594
Iteration: 1529; Percent complete: 38.2%; Average loss: 3.1715
Iteration: 1530; Percent complete: 38.2%; Average loss: 3.1160
Iteration: 1531; Percent complete: 38.3%; Average loss: 3.2217
Iteration: 1532; Percent complete: 38.3%; Average loss: 3.1423
Iteration: 1533; Percent complete: 38.3%; Average loss: 3.2296
Iteration: 1534; Percent complete: 38.4%; Average loss: 3.4443
Iteration: 1535; Percent complete: 38.4%; Average loss: 3.2890
Iteration: 1536; Percent complete: 38.4%; Average loss: 3.1745
Iteration: 1537; Percent complete: 38.4%; Average loss: 3.2789
Iteration: 1538; Percent complete: 38.5%; Average loss: 3.1533
Iteration: 1539; Percent complete: 38.5%; Average loss: 3.4691
Iteration: 1540; Percent complete: 38.5%; Average loss: 3.4507
Iteration: 1541; Percent complete: 38.5%; Average loss: 3.3482
Iteration: 1542; Percent complete: 38.6%; Average loss: 3.2382
Iteration: 1543; Percent complete: 38.6%; Average loss: 3.2440
Iteration: 1544; Percent complete: 38.6%; Average loss: 3.4150
Iteration: 1545; Percent complete: 38.6%; Average loss: 3.2287
Iteration: 1546; Percent complete: 38.6%; Average loss: 3.2277
Iteration: 1547; Percent complete: 38.7%; Average loss: 3.2224
Iteration: 1548; Percent complete: 38.7%; Average loss: 3.1983
Iteration: 1549; Percent complete: 38.7%; Average loss: 3.2872
Iteration: 1550; Percent complete: 38.8%; Average loss: 3.0761
Iteration: 1551; Percent complete: 38.8%; Average loss: 3.2000
Iteration: 1552; Percent complete: 38.8%; Average loss: 2.9796
Iteration: 1553; Percent complete: 38.8%; Average loss: 3.0954
Iteration: 1554; Percent complete: 38.9%; Average loss: 3.2295
Iteration: 1555; Percent complete: 38.9%; Average loss: 3.1663
Iteration: 1556; Percent complete: 38.9%; Average loss: 3.1989
Iteration: 1557; Percent complete: 38.9%; Average loss: 3.2716
Iteration: 1558; Percent complete: 39.0%; Average loss: 3.3627
Iteration: 1559; Percent complete: 39.0%; Average loss: 3.1355
Iteration: 1560; Percent complete: 39.0%; Average loss: 3.1921
Iteration: 1561; Percent complete: 39.0%; Average loss: 3.2555
Iteration: 1562; Percent complete: 39.1%; Average loss: 3.3150
Iteration: 1563; Percent complete: 39.1%; Average loss: 3.2837
Iteration: 1564; Percent complete: 39.1%; Average loss: 3.4164
Iteration: 1565; Percent complete: 39.1%; Average loss: 3.2709
Iteration: 1566; Percent complete: 39.1%; Average loss: 3.1653
Iteration: 1567; Percent complete: 39.2%; Average loss: 3.3883
Iteration: 1568; Percent complete: 39.2%; Average loss: 3.4294
Iteration: 1569; Percent complete: 39.2%; Average loss: 3.3451
Iteration: 1570; Percent complete: 39.2%; Average loss: 3.2185
Iteration: 1571; Percent complete: 39.3%; Average loss: 3.1571
Iteration: 1572; Percent complete: 39.3%; Average loss: 3.0690
Iteration: 1573; Percent complete: 39.3%; Average loss: 3.3480
Iteration: 1574; Percent complete: 39.4%; Average loss: 3.4055
Iteration: 1575; Percent complete: 39.4%; Average loss: 3.3448
Iteration: 1576; Percent complete: 39.4%; Average loss: 3.1790
Iteration: 1577; Percent complete: 39.4%; Average loss: 3.3016
Iteration: 1578; Percent complete: 39.5%; Average loss: 3.0529
Iteration: 1579; Percent complete: 39.5%; Average loss: 3.1351
Iteration: 1580; Percent complete: 39.5%; Average loss: 3.3887
Iteration: 1581; Percent complete: 39.5%; Average loss: 3.2858
Iteration: 1582; Percent complete: 39.6%; Average loss: 3.1154
Iteration: 1583; Percent complete: 39.6%; Average loss: 3.2075
Iteration: 1584; Percent complete: 39.6%; Average loss: 3.2351
Iteration: 1585; Percent complete: 39.6%; Average loss: 3.3915
Iteration: 1586; Percent complete: 39.6%; Average loss: 3.3405
Iteration: 1587; Percent complete: 39.7%; Average loss: 3.3234
Iteration: 1588; Percent complete: 39.7%; Average loss: 3.3075
Iteration: 1589; Percent complete: 39.7%; Average loss: 3.3098
Iteration: 1590; Percent complete: 39.8%; Average loss: 2.9653
Iteration: 1591; Percent complete: 39.8%; Average loss: 3.2451
Iteration: 1592; Percent complete: 39.8%; Average loss: 3.3592
Iteration: 1593; Percent complete: 39.8%; Average loss: 3.0656
Iteration: 1594; Percent complete: 39.9%; Average loss: 3.3268
Iteration: 1595; Percent complete: 39.9%; Average loss: 3.4601
Iteration: 1596; Percent complete: 39.9%; Average loss: 3.2018
Iteration: 1597; Percent complete: 39.9%; Average loss: 3.2714
Iteration: 1598; Percent complete: 40.0%; Average loss: 3.2532
Iteration: 1599; Percent complete: 40.0%; Average loss: 3.3601
Iteration: 1600; Percent complete: 40.0%; Average loss: 3.1622
Iteration: 1601; Percent complete: 40.0%; Average loss: 3.3437
Iteration: 1602; Percent complete: 40.1%; Average loss: 3.3878
Iteration: 1603; Percent complete: 40.1%; Average loss: 3.2717
Iteration: 1604; Percent complete: 40.1%; Average loss: 3.2596
Iteration: 1605; Percent complete: 40.1%; Average loss: 3.2370
Iteration: 1606; Percent complete: 40.2%; Average loss: 3.3769
Iteration: 1607; Percent complete: 40.2%; Average loss: 3.0836
Iteration: 1608; Percent complete: 40.2%; Average loss: 3.4567
Iteration: 1609; Percent complete: 40.2%; Average loss: 3.2845
Iteration: 1610; Percent complete: 40.2%; Average loss: 2.9888
Iteration: 1611; Percent complete: 40.3%; Average loss: 3.3205
Iteration: 1612; Percent complete: 40.3%; Average loss: 3.2521
Iteration: 1613; Percent complete: 40.3%; Average loss: 3.3273
Iteration: 1614; Percent complete: 40.4%; Average loss: 3.2182
Iteration: 1615; Percent complete: 40.4%; Average loss: 3.3037
Iteration: 1616; Percent complete: 40.4%; Average loss: 3.1197
Iteration: 1617; Percent complete: 40.4%; Average loss: 3.2567
Iteration: 1618; Percent complete: 40.5%; Average loss: 3.4274
Iteration: 1619; Percent complete: 40.5%; Average loss: 3.2481
Iteration: 1620; Percent complete: 40.5%; Average loss: 3.1426
Iteration: 1621; Percent complete: 40.5%; Average loss: 3.1560
Iteration: 1622; Percent complete: 40.6%; Average loss: 3.0369
Iteration: 1623; Percent complete: 40.6%; Average loss: 3.1065
Iteration: 1624; Percent complete: 40.6%; Average loss: 3.2938
Iteration: 1625; Percent complete: 40.6%; Average loss: 3.3616
Iteration: 1626; Percent complete: 40.6%; Average loss: 3.3729
Iteration: 1627; Percent complete: 40.7%; Average loss: 3.1981
Iteration: 1628; Percent complete: 40.7%; Average loss: 3.1606
Iteration: 1629; Percent complete: 40.7%; Average loss: 3.1118
Iteration: 1630; Percent complete: 40.8%; Average loss: 3.5525
Iteration: 1631; Percent complete: 40.8%; Average loss: 3.3014
Iteration: 1632; Percent complete: 40.8%; Average loss: 3.4024
Iteration: 1633; Percent complete: 40.8%; Average loss: 3.2005
Iteration: 1634; Percent complete: 40.8%; Average loss: 3.3526
Iteration: 1635; Percent complete: 40.9%; Average loss: 3.4502
Iteration: 1636; Percent complete: 40.9%; Average loss: 3.2905
Iteration: 1637; Percent complete: 40.9%; Average loss: 3.2522
Iteration: 1638; Percent complete: 40.9%; Average loss: 3.3654
Iteration: 1639; Percent complete: 41.0%; Average loss: 3.2487
Iteration: 1640; Percent complete: 41.0%; Average loss: 3.3282
Iteration: 1641; Percent complete: 41.0%; Average loss: 3.4348
Iteration: 1642; Percent complete: 41.0%; Average loss: 3.3791
Iteration: 1643; Percent complete: 41.1%; Average loss: 3.2643
Iteration: 1644; Percent complete: 41.1%; Average loss: 3.3373
Iteration: 1645; Percent complete: 41.1%; Average loss: 3.2314
Iteration: 1646; Percent complete: 41.1%; Average loss: 3.0583
Iteration: 1647; Percent complete: 41.2%; Average loss: 3.1918
Iteration: 1648; Percent complete: 41.2%; Average loss: 3.2533
Iteration: 1649; Percent complete: 41.2%; Average loss: 3.3558
Iteration: 1650; Percent complete: 41.2%; Average loss: 3.1572
Iteration: 1651; Percent complete: 41.3%; Average loss: 3.1964
Iteration: 1652; Percent complete: 41.3%; Average loss: 3.2926
Iteration: 1653; Percent complete: 41.3%; Average loss: 3.4081
Iteration: 1654; Percent complete: 41.3%; Average loss: 3.3472
Iteration: 1655; Percent complete: 41.4%; Average loss: 3.2144
Iteration: 1656; Percent complete: 41.4%; Average loss: 3.4598
Iteration: 1657; Percent complete: 41.4%; Average loss: 3.4100
Iteration: 1658; Percent complete: 41.4%; Average loss: 3.5350
Iteration: 1659; Percent complete: 41.5%; Average loss: 3.1346
Iteration: 1660; Percent complete: 41.5%; Average loss: 3.2760
Iteration: 1661; Percent complete: 41.5%; Average loss: 3.4369
Iteration: 1662; Percent complete: 41.5%; Average loss: 3.4500
Iteration: 1663; Percent complete: 41.6%; Average loss: 3.0910
Iteration: 1664; Percent complete: 41.6%; Average loss: 3.2232
Iteration: 1665; Percent complete: 41.6%; Average loss: 3.4246
Iteration: 1666; Percent complete: 41.6%; Average loss: 3.2888
Iteration: 1667; Percent complete: 41.7%; Average loss: 3.1894
Iteration: 1668; Percent complete: 41.7%; Average loss: 3.2530
Iteration: 1669; Percent complete: 41.7%; Average loss: 3.0486
Iteration: 1670; Percent complete: 41.8%; Average loss: 3.2542
Iteration: 1671; Percent complete: 41.8%; Average loss: 3.2876
Iteration: 1672; Percent complete: 41.8%; Average loss: 3.2047
Iteration: 1673; Percent complete: 41.8%; Average loss: 3.1228
Iteration: 1674; Percent complete: 41.9%; Average loss: 3.3068
Iteration: 1675; Percent complete: 41.9%; Average loss: 3.2613
Iteration: 1676; Percent complete: 41.9%; Average loss: 3.2313
Iteration: 1677; Percent complete: 41.9%; Average loss: 2.9422
Iteration: 1678; Percent complete: 41.9%; Average loss: 3.1982
Iteration: 1679; Percent complete: 42.0%; Average loss: 3.3886
Iteration: 1680; Percent complete: 42.0%; Average loss: 3.3368
Iteration: 1681; Percent complete: 42.0%; Average loss: 3.3145
Iteration: 1682; Percent complete: 42.0%; Average loss: 3.2358
Iteration: 1683; Percent complete: 42.1%; Average loss: 3.1503
Iteration: 1684; Percent complete: 42.1%; Average loss: 3.4931
Iteration: 1685; Percent complete: 42.1%; Average loss: 3.2884
Iteration: 1686; Percent complete: 42.1%; Average loss: 3.0853
Iteration: 1687; Percent complete: 42.2%; Average loss: 3.3242
Iteration: 1688; Percent complete: 42.2%; Average loss: 3.3104
Iteration: 1689; Percent complete: 42.2%; Average loss: 3.3109
Iteration: 1690; Percent complete: 42.2%; Average loss: 3.3169
Iteration: 1691; Percent complete: 42.3%; Average loss: 3.0719
Iteration: 1692; Percent complete: 42.3%; Average loss: 3.4615
Iteration: 1693; Percent complete: 42.3%; Average loss: 3.4011
Iteration: 1694; Percent complete: 42.4%; Average loss: 3.1081
Iteration: 1695; Percent complete: 42.4%; Average loss: 3.0353
Iteration: 1696; Percent complete: 42.4%; Average loss: 3.3310
Iteration: 1697; Percent complete: 42.4%; Average loss: 3.2536
Iteration: 1698; Percent complete: 42.4%; Average loss: 3.3360
Iteration: 1699; Percent complete: 42.5%; Average loss: 3.1247
Iteration: 1700; Percent complete: 42.5%; Average loss: 3.1261
Iteration: 1701; Percent complete: 42.5%; Average loss: 3.1517
Iteration: 1702; Percent complete: 42.5%; Average loss: 3.2439
Iteration: 1703; Percent complete: 42.6%; Average loss: 3.3569
Iteration: 1704; Percent complete: 42.6%; Average loss: 3.3175
Iteration: 1705; Percent complete: 42.6%; Average loss: 3.3216
Iteration: 1706; Percent complete: 42.6%; Average loss: 3.2292
Iteration: 1707; Percent complete: 42.7%; Average loss: 3.2927
Iteration: 1708; Percent complete: 42.7%; Average loss: 3.1422
Iteration: 1709; Percent complete: 42.7%; Average loss: 3.4055
Iteration: 1710; Percent complete: 42.8%; Average loss: 3.2058
Iteration: 1711; Percent complete: 42.8%; Average loss: 3.2884
Iteration: 1712; Percent complete: 42.8%; Average loss: 3.3480
Iteration: 1713; Percent complete: 42.8%; Average loss: 3.5666
Iteration: 1714; Percent complete: 42.9%; Average loss: 3.3376
Iteration: 1715; Percent complete: 42.9%; Average loss: 3.3564
Iteration: 1716; Percent complete: 42.9%; Average loss: 3.2986
Iteration: 1717; Percent complete: 42.9%; Average loss: 3.1677
Iteration: 1718; Percent complete: 43.0%; Average loss: 2.9218
Iteration: 1719; Percent complete: 43.0%; Average loss: 3.2434
Iteration: 1720; Percent complete: 43.0%; Average loss: 3.2402
Iteration: 1721; Percent complete: 43.0%; Average loss: 3.1883
Iteration: 1722; Percent complete: 43.0%; Average loss: 3.5128
Iteration: 1723; Percent complete: 43.1%; Average loss: 2.9702
Iteration: 1724; Percent complete: 43.1%; Average loss: 3.2223
Iteration: 1725; Percent complete: 43.1%; Average loss: 3.3221
Iteration: 1726; Percent complete: 43.1%; Average loss: 3.2170
Iteration: 1727; Percent complete: 43.2%; Average loss: 3.2869
Iteration: 1728; Percent complete: 43.2%; Average loss: 3.0622
Iteration: 1729; Percent complete: 43.2%; Average loss: 3.3154
Iteration: 1730; Percent complete: 43.2%; Average loss: 3.5027
Iteration: 1731; Percent complete: 43.3%; Average loss: 3.3489
Iteration: 1732; Percent complete: 43.3%; Average loss: 3.1136
Iteration: 1733; Percent complete: 43.3%; Average loss: 3.2671
Iteration: 1734; Percent complete: 43.4%; Average loss: 3.4319
Iteration: 1735; Percent complete: 43.4%; Average loss: 3.3356
Iteration: 1736; Percent complete: 43.4%; Average loss: 2.9689
Iteration: 1737; Percent complete: 43.4%; Average loss: 3.0905
Iteration: 1738; Percent complete: 43.5%; Average loss: 3.6552
Iteration: 1739; Percent complete: 43.5%; Average loss: 3.0043
Iteration: 1740; Percent complete: 43.5%; Average loss: 3.0163
Iteration: 1741; Percent complete: 43.5%; Average loss: 3.3814
Iteration: 1742; Percent complete: 43.5%; Average loss: 3.1121
Iteration: 1743; Percent complete: 43.6%; Average loss: 3.2563
Iteration: 1744; Percent complete: 43.6%; Average loss: 3.2327
Iteration: 1745; Percent complete: 43.6%; Average loss: 3.2337
Iteration: 1746; Percent complete: 43.6%; Average loss: 3.2578
Iteration: 1747; Percent complete: 43.7%; Average loss: 3.4321
Iteration: 1748; Percent complete: 43.7%; Average loss: 2.9909
Iteration: 1749; Percent complete: 43.7%; Average loss: 3.2494
Iteration: 1750; Percent complete: 43.8%; Average loss: 3.2327
Iteration: 1751; Percent complete: 43.8%; Average loss: 3.4958
Iteration: 1752; Percent complete: 43.8%; Average loss: 3.2657
Iteration: 1753; Percent complete: 43.8%; Average loss: 3.1449
Iteration: 1754; Percent complete: 43.9%; Average loss: 3.1893
Iteration: 1755; Percent complete: 43.9%; Average loss: 3.2391
Iteration: 1756; Percent complete: 43.9%; Average loss: 3.1594
Iteration: 1757; Percent complete: 43.9%; Average loss: 3.3037
Iteration: 1758; Percent complete: 44.0%; Average loss: 3.3235
Iteration: 1759; Percent complete: 44.0%; Average loss: 3.1517
Iteration: 1760; Percent complete: 44.0%; Average loss: 3.4500
Iteration: 1761; Percent complete: 44.0%; Average loss: 3.1750
Iteration: 1762; Percent complete: 44.0%; Average loss: 3.4464
Iteration: 1763; Percent complete: 44.1%; Average loss: 2.9352
Iteration: 1764; Percent complete: 44.1%; Average loss: 3.1601
Iteration: 1765; Percent complete: 44.1%; Average loss: 3.0936
Iteration: 1766; Percent complete: 44.1%; Average loss: 3.1782
Iteration: 1767; Percent complete: 44.2%; Average loss: 3.0179
Iteration: 1768; Percent complete: 44.2%; Average loss: 3.5377
Iteration: 1769; Percent complete: 44.2%; Average loss: 3.0372
Iteration: 1770; Percent complete: 44.2%; Average loss: 3.2297
Iteration: 1771; Percent complete: 44.3%; Average loss: 3.2610
Iteration: 1772; Percent complete: 44.3%; Average loss: 3.1433
Iteration: 1773; Percent complete: 44.3%; Average loss: 3.1318
Iteration: 1774; Percent complete: 44.4%; Average loss: 3.2718
Iteration: 1775; Percent complete: 44.4%; Average loss: 3.3789
Iteration: 1776; Percent complete: 44.4%; Average loss: 3.3457
Iteration: 1777; Percent complete: 44.4%; Average loss: 3.1310
Iteration: 1778; Percent complete: 44.5%; Average loss: 3.0885
Iteration: 1779; Percent complete: 44.5%; Average loss: 3.3254
Iteration: 1780; Percent complete: 44.5%; Average loss: 3.5338
Iteration: 1781; Percent complete: 44.5%; Average loss: 3.0271
Iteration: 1782; Percent complete: 44.5%; Average loss: 3.1607
Iteration: 1783; Percent complete: 44.6%; Average loss: 3.6050
Iteration: 1784; Percent complete: 44.6%; Average loss: 3.2760
Iteration: 1785; Percent complete: 44.6%; Average loss: 3.0577
Iteration: 1786; Percent complete: 44.6%; Average loss: 3.0797
Iteration: 1787; Percent complete: 44.7%; Average loss: 3.1566
Iteration: 1788; Percent complete: 44.7%; Average loss: 3.1683
Iteration: 1789; Percent complete: 44.7%; Average loss: 3.5128
Iteration: 1790; Percent complete: 44.8%; Average loss: 3.3177
Iteration: 1791; Percent complete: 44.8%; Average loss: 3.4610
Iteration: 1792; Percent complete: 44.8%; Average loss: 3.3368
Iteration: 1793; Percent complete: 44.8%; Average loss: 3.1034
Iteration: 1794; Percent complete: 44.9%; Average loss: 3.3129
Iteration: 1795; Percent complete: 44.9%; Average loss: 2.8660
Iteration: 1796; Percent complete: 44.9%; Average loss: 3.4375
Iteration: 1797; Percent complete: 44.9%; Average loss: 3.3151
Iteration: 1798; Percent complete: 45.0%; Average loss: 3.2182
Iteration: 1799; Percent complete: 45.0%; Average loss: 3.1562
Iteration: 1800; Percent complete: 45.0%; Average loss: 3.1257
Iteration: 1801; Percent complete: 45.0%; Average loss: 3.5474
Iteration: 1802; Percent complete: 45.1%; Average loss: 2.9848
Iteration: 1803; Percent complete: 45.1%; Average loss: 3.2787
Iteration: 1804; Percent complete: 45.1%; Average loss: 3.1808
Iteration: 1805; Percent complete: 45.1%; Average loss: 3.1448
Iteration: 1806; Percent complete: 45.1%; Average loss: 3.2684
Iteration: 1807; Percent complete: 45.2%; Average loss: 3.0258
Iteration: 1808; Percent complete: 45.2%; Average loss: 3.3496
Iteration: 1809; Percent complete: 45.2%; Average loss: 3.2794
Iteration: 1810; Percent complete: 45.2%; Average loss: 2.9651
Iteration: 1811; Percent complete: 45.3%; Average loss: 3.2458
Iteration: 1812; Percent complete: 45.3%; Average loss: 3.1783
Iteration: 1813; Percent complete: 45.3%; Average loss: 3.1338
Iteration: 1814; Percent complete: 45.4%; Average loss: 3.1685
Iteration: 1815; Percent complete: 45.4%; Average loss: 3.2736
Iteration: 1816; Percent complete: 45.4%; Average loss: 3.2458
Iteration: 1817; Percent complete: 45.4%; Average loss: 3.2337
Iteration: 1818; Percent complete: 45.5%; Average loss: 3.2469
Iteration: 1819; Percent complete: 45.5%; Average loss: 3.0296
Iteration: 1820; Percent complete: 45.5%; Average loss: 3.2809
Iteration: 1821; Percent complete: 45.5%; Average loss: 3.3724
Iteration: 1822; Percent complete: 45.6%; Average loss: 3.0605
Iteration: 1823; Percent complete: 45.6%; Average loss: 3.1463
Iteration: 1824; Percent complete: 45.6%; Average loss: 3.1096
Iteration: 1825; Percent complete: 45.6%; Average loss: 3.2243
Iteration: 1826; Percent complete: 45.6%; Average loss: 3.3433
Iteration: 1827; Percent complete: 45.7%; Average loss: 3.1284
Iteration: 1828; Percent complete: 45.7%; Average loss: 3.3194
Iteration: 1829; Percent complete: 45.7%; Average loss: 3.3586
Iteration: 1830; Percent complete: 45.8%; Average loss: 3.1275
Iteration: 1831; Percent complete: 45.8%; Average loss: 3.4014
Iteration: 1832; Percent complete: 45.8%; Average loss: 3.2517
Iteration: 1833; Percent complete: 45.8%; Average loss: 3.1637
Iteration: 1834; Percent complete: 45.9%; Average loss: 3.2548
Iteration: 1835; Percent complete: 45.9%; Average loss: 3.2678
Iteration: 1836; Percent complete: 45.9%; Average loss: 3.0676
Iteration: 1837; Percent complete: 45.9%; Average loss: 3.4197
Iteration: 1838; Percent complete: 46.0%; Average loss: 3.0762
Iteration: 1839; Percent complete: 46.0%; Average loss: 2.9444
Iteration: 1840; Percent complete: 46.0%; Average loss: 3.3632
Iteration: 1841; Percent complete: 46.0%; Average loss: 3.2326
Iteration: 1842; Percent complete: 46.1%; Average loss: 3.3512
Iteration: 1843; Percent complete: 46.1%; Average loss: 3.0339
Iteration: 1844; Percent complete: 46.1%; Average loss: 3.5156
Iteration: 1845; Percent complete: 46.1%; Average loss: 3.4373
Iteration: 1846; Percent complete: 46.2%; Average loss: 2.9553
Iteration: 1847; Percent complete: 46.2%; Average loss: 2.9594
Iteration: 1848; Percent complete: 46.2%; Average loss: 3.4335
Iteration: 1849; Percent complete: 46.2%; Average loss: 3.1741
Iteration: 1850; Percent complete: 46.2%; Average loss: 2.9172
Iteration: 1851; Percent complete: 46.3%; Average loss: 3.0995
Iteration: 1852; Percent complete: 46.3%; Average loss: 3.1754
Iteration: 1853; Percent complete: 46.3%; Average loss: 3.3703
Iteration: 1854; Percent complete: 46.4%; Average loss: 3.3092
Iteration: 1855; Percent complete: 46.4%; Average loss: 3.4266
Iteration: 1856; Percent complete: 46.4%; Average loss: 3.2539
Iteration: 1857; Percent complete: 46.4%; Average loss: 3.2650
Iteration: 1858; Percent complete: 46.5%; Average loss: 3.1807
Iteration: 1859; Percent complete: 46.5%; Average loss: 3.1588
Iteration: 1860; Percent complete: 46.5%; Average loss: 3.2129
Iteration: 1861; Percent complete: 46.5%; Average loss: 3.1481
Iteration: 1862; Percent complete: 46.6%; Average loss: 3.2959
Iteration: 1863; Percent complete: 46.6%; Average loss: 3.2086
Iteration: 1864; Percent complete: 46.6%; Average loss: 3.2225
Iteration: 1865; Percent complete: 46.6%; Average loss: 3.1955
Iteration: 1866; Percent complete: 46.7%; Average loss: 3.1875
Iteration: 1867; Percent complete: 46.7%; Average loss: 3.0982
Iteration: 1868; Percent complete: 46.7%; Average loss: 3.1861
Iteration: 1869; Percent complete: 46.7%; Average loss: 3.0029
Iteration: 1870; Percent complete: 46.8%; Average loss: 3.0085
Iteration: 1871; Percent complete: 46.8%; Average loss: 3.4368
Iteration: 1872; Percent complete: 46.8%; Average loss: 3.2990
Iteration: 1873; Percent complete: 46.8%; Average loss: 3.0507
Iteration: 1874; Percent complete: 46.9%; Average loss: 3.3902
Iteration: 1875; Percent complete: 46.9%; Average loss: 3.0148
Iteration: 1876; Percent complete: 46.9%; Average loss: 3.0575
Iteration: 1877; Percent complete: 46.9%; Average loss: 3.3337
Iteration: 1878; Percent complete: 46.9%; Average loss: 3.0393
Iteration: 1879; Percent complete: 47.0%; Average loss: 3.3501
Iteration: 1880; Percent complete: 47.0%; Average loss: 3.0604
Iteration: 1881; Percent complete: 47.0%; Average loss: 2.9745
Iteration: 1882; Percent complete: 47.0%; Average loss: 3.1986
Iteration: 1883; Percent complete: 47.1%; Average loss: 3.1868
Iteration: 1884; Percent complete: 47.1%; Average loss: 3.2004
Iteration: 1885; Percent complete: 47.1%; Average loss: 3.2607
Iteration: 1886; Percent complete: 47.1%; Average loss: 3.1468
Iteration: 1887; Percent complete: 47.2%; Average loss: 3.0905
Iteration: 1888; Percent complete: 47.2%; Average loss: 3.2259
Iteration: 1889; Percent complete: 47.2%; Average loss: 3.1627
Iteration: 1890; Percent complete: 47.2%; Average loss: 3.1306
Iteration: 1891; Percent complete: 47.3%; Average loss: 2.9883
Iteration: 1892; Percent complete: 47.3%; Average loss: 3.2084
Iteration: 1893; Percent complete: 47.3%; Average loss: 2.9483
Iteration: 1894; Percent complete: 47.3%; Average loss: 3.1505
Iteration: 1895; Percent complete: 47.4%; Average loss: 3.1444
Iteration: 1896; Percent complete: 47.4%; Average loss: 3.1188
Iteration: 1897; Percent complete: 47.4%; Average loss: 2.9788
Iteration: 1898; Percent complete: 47.4%; Average loss: 3.5105
Iteration: 1899; Percent complete: 47.5%; Average loss: 3.3895
Iteration: 1900; Percent complete: 47.5%; Average loss: 3.1778
Iteration: 1901; Percent complete: 47.5%; Average loss: 3.1914
Iteration: 1902; Percent complete: 47.5%; Average loss: 3.0725
Iteration: 1903; Percent complete: 47.6%; Average loss: 3.1924
Iteration: 1904; Percent complete: 47.6%; Average loss: 3.0526
Iteration: 1905; Percent complete: 47.6%; Average loss: 3.2352
Iteration: 1906; Percent complete: 47.6%; Average loss: 3.4449
Iteration: 1907; Percent complete: 47.7%; Average loss: 3.1466
Iteration: 1908; Percent complete: 47.7%; Average loss: 3.2288
Iteration: 1909; Percent complete: 47.7%; Average loss: 3.2263
Iteration: 1910; Percent complete: 47.8%; Average loss: 3.1310
Iteration: 1911; Percent complete: 47.8%; Average loss: 2.9701
Iteration: 1912; Percent complete: 47.8%; Average loss: 3.1661
Iteration: 1913; Percent complete: 47.8%; Average loss: 3.2789
Iteration: 1914; Percent complete: 47.9%; Average loss: 3.2583
Iteration: 1915; Percent complete: 47.9%; Average loss: 3.0264
Iteration: 1916; Percent complete: 47.9%; Average loss: 2.9158
Iteration: 1917; Percent complete: 47.9%; Average loss: 3.2804
Iteration: 1918; Percent complete: 47.9%; Average loss: 3.1355
Iteration: 1919; Percent complete: 48.0%; Average loss: 3.2659
Iteration: 1920; Percent complete: 48.0%; Average loss: 3.2846
Iteration: 1921; Percent complete: 48.0%; Average loss: 3.1785
Iteration: 1922; Percent complete: 48.0%; Average loss: 3.2270
Iteration: 1923; Percent complete: 48.1%; Average loss: 3.2942
Iteration: 1924; Percent complete: 48.1%; Average loss: 3.1939
Iteration: 1925; Percent complete: 48.1%; Average loss: 3.3737
Iteration: 1926; Percent complete: 48.1%; Average loss: 3.1723
Iteration: 1927; Percent complete: 48.2%; Average loss: 3.4889
Iteration: 1928; Percent complete: 48.2%; Average loss: 3.1493
Iteration: 1929; Percent complete: 48.2%; Average loss: 3.3250
Iteration: 1930; Percent complete: 48.2%; Average loss: 3.2851
Iteration: 1931; Percent complete: 48.3%; Average loss: 3.2735
Iteration: 1932; Percent complete: 48.3%; Average loss: 3.2469
Iteration: 1933; Percent complete: 48.3%; Average loss: 3.4941
Iteration: 1934; Percent complete: 48.4%; Average loss: 3.4364
Iteration: 1935; Percent complete: 48.4%; Average loss: 3.2219
Iteration: 1936; Percent complete: 48.4%; Average loss: 3.1048
Iteration: 1937; Percent complete: 48.4%; Average loss: 3.0707
Iteration: 1938; Percent complete: 48.4%; Average loss: 3.1143
Iteration: 1939; Percent complete: 48.5%; Average loss: 3.3828
Iteration: 1940; Percent complete: 48.5%; Average loss: 3.2538
Iteration: 1941; Percent complete: 48.5%; Average loss: 3.1528
Iteration: 1942; Percent complete: 48.5%; Average loss: 3.2419
Iteration: 1943; Percent complete: 48.6%; Average loss: 2.9779
Iteration: 1944; Percent complete: 48.6%; Average loss: 3.2918
Iteration: 1945; Percent complete: 48.6%; Average loss: 3.0030
Iteration: 1946; Percent complete: 48.6%; Average loss: 3.2547
Iteration: 1947; Percent complete: 48.7%; Average loss: 3.2896
Iteration: 1948; Percent complete: 48.7%; Average loss: 3.2147
Iteration: 1949; Percent complete: 48.7%; Average loss: 2.9448
Iteration: 1950; Percent complete: 48.8%; Average loss: 2.9273
Iteration: 1951; Percent complete: 48.8%; Average loss: 3.2484
Iteration: 1952; Percent complete: 48.8%; Average loss: 3.2647
Iteration: 1953; Percent complete: 48.8%; Average loss: 3.1451
Iteration: 1954; Percent complete: 48.9%; Average loss: 3.0875
Iteration: 1955; Percent complete: 48.9%; Average loss: 3.2920
Iteration: 1956; Percent complete: 48.9%; Average loss: 2.9840
Iteration: 1957; Percent complete: 48.9%; Average loss: 2.9325
Iteration: 1958; Percent complete: 48.9%; Average loss: 3.0176
Iteration: 1959; Percent complete: 49.0%; Average loss: 3.0811
Iteration: 1960; Percent complete: 49.0%; Average loss: 3.4061
Iteration: 1961; Percent complete: 49.0%; Average loss: 3.3615
Iteration: 1962; Percent complete: 49.0%; Average loss: 3.1880
Iteration: 1963; Percent complete: 49.1%; Average loss: 3.0662
Iteration: 1964; Percent complete: 49.1%; Average loss: 3.1240
Iteration: 1965; Percent complete: 49.1%; Average loss: 3.1176
Iteration: 1966; Percent complete: 49.1%; Average loss: 3.1429
Iteration: 1967; Percent complete: 49.2%; Average loss: 3.1587
Iteration: 1968; Percent complete: 49.2%; Average loss: 3.3988
Iteration: 1969; Percent complete: 49.2%; Average loss: 3.1291
Iteration: 1970; Percent complete: 49.2%; Average loss: 3.1376
Iteration: 1971; Percent complete: 49.3%; Average loss: 3.4674
Iteration: 1972; Percent complete: 49.3%; Average loss: 3.1420
Iteration: 1973; Percent complete: 49.3%; Average loss: 3.4394
Iteration: 1974; Percent complete: 49.4%; Average loss: 3.0184
Iteration: 1975; Percent complete: 49.4%; Average loss: 2.9232
Iteration: 1976; Percent complete: 49.4%; Average loss: 3.2766
Iteration: 1977; Percent complete: 49.4%; Average loss: 3.2733
Iteration: 1978; Percent complete: 49.5%; Average loss: 3.2112
Iteration: 1979; Percent complete: 49.5%; Average loss: 3.2411
Iteration: 1980; Percent complete: 49.5%; Average loss: 3.2093
Iteration: 1981; Percent complete: 49.5%; Average loss: 3.4414
Iteration: 1982; Percent complete: 49.5%; Average loss: 3.0754
Iteration: 1983; Percent complete: 49.6%; Average loss: 3.2653
Iteration: 1984; Percent complete: 49.6%; Average loss: 3.1127
Iteration: 1985; Percent complete: 49.6%; Average loss: 3.3187
Iteration: 1986; Percent complete: 49.6%; Average loss: 3.4128
Iteration: 1987; Percent complete: 49.7%; Average loss: 3.3687
Iteration: 1988; Percent complete: 49.7%; Average loss: 3.2303
Iteration: 1989; Percent complete: 49.7%; Average loss: 3.1228
Iteration: 1990; Percent complete: 49.8%; Average loss: 3.0710
Iteration: 1991; Percent complete: 49.8%; Average loss: 3.0836
Iteration: 1992; Percent complete: 49.8%; Average loss: 3.3306
Iteration: 1993; Percent complete: 49.8%; Average loss: 3.0419
Iteration: 1994; Percent complete: 49.9%; Average loss: 3.1574
Iteration: 1995; Percent complete: 49.9%; Average loss: 3.2135
Iteration: 1996; Percent complete: 49.9%; Average loss: 2.9156
Iteration: 1997; Percent complete: 49.9%; Average loss: 3.0200
Iteration: 1998; Percent complete: 50.0%; Average loss: 2.9869
Iteration: 1999; Percent complete: 50.0%; Average loss: 3.1364
Iteration: 2000; Percent complete: 50.0%; Average loss: 3.0280
Iteration: 2001; Percent complete: 50.0%; Average loss: 3.0027
Iteration: 2002; Percent complete: 50.0%; Average loss: 3.2693
Iteration: 2003; Percent complete: 50.1%; Average loss: 3.3254
Iteration: 2004; Percent complete: 50.1%; Average loss: 3.2816
Iteration: 2005; Percent complete: 50.1%; Average loss: 3.1392
Iteration: 2006; Percent complete: 50.1%; Average loss: 3.0744
Iteration: 2007; Percent complete: 50.2%; Average loss: 2.8491
Iteration: 2008; Percent complete: 50.2%; Average loss: 3.2037
Iteration: 2009; Percent complete: 50.2%; Average loss: 3.0913
Iteration: 2010; Percent complete: 50.2%; Average loss: 3.2489
Iteration: 2011; Percent complete: 50.3%; Average loss: 3.0197
Iteration: 2012; Percent complete: 50.3%; Average loss: 3.0208
Iteration: 2013; Percent complete: 50.3%; Average loss: 3.3135
Iteration: 2014; Percent complete: 50.3%; Average loss: 3.0292
Iteration: 2015; Percent complete: 50.4%; Average loss: 3.2463
Iteration: 2016; Percent complete: 50.4%; Average loss: 2.9617
Iteration: 2017; Percent complete: 50.4%; Average loss: 3.3493
Iteration: 2018; Percent complete: 50.4%; Average loss: 3.1490
Iteration: 2019; Percent complete: 50.5%; Average loss: 3.2516
Iteration: 2020; Percent complete: 50.5%; Average loss: 3.1917
Iteration: 2021; Percent complete: 50.5%; Average loss: 3.3739
Iteration: 2022; Percent complete: 50.5%; Average loss: 3.3059
Iteration: 2023; Percent complete: 50.6%; Average loss: 3.0862
Iteration: 2024; Percent complete: 50.6%; Average loss: 2.9008
Iteration: 2025; Percent complete: 50.6%; Average loss: 2.9282
Iteration: 2026; Percent complete: 50.6%; Average loss: 3.1835
Iteration: 2027; Percent complete: 50.7%; Average loss: 2.9798
Iteration: 2028; Percent complete: 50.7%; Average loss: 3.1187
Iteration: 2029; Percent complete: 50.7%; Average loss: 3.1805
Iteration: 2030; Percent complete: 50.7%; Average loss: 3.3238
Iteration: 2031; Percent complete: 50.8%; Average loss: 3.1858
Iteration: 2032; Percent complete: 50.8%; Average loss: 3.2379
Iteration: 2033; Percent complete: 50.8%; Average loss: 3.1521
Iteration: 2034; Percent complete: 50.8%; Average loss: 3.1925
Iteration: 2035; Percent complete: 50.9%; Average loss: 2.9148
Iteration: 2036; Percent complete: 50.9%; Average loss: 3.0308
Iteration: 2037; Percent complete: 50.9%; Average loss: 3.1501
Iteration: 2038; Percent complete: 50.9%; Average loss: 3.1998
Iteration: 2039; Percent complete: 51.0%; Average loss: 3.0575
Iteration: 2040; Percent complete: 51.0%; Average loss: 3.1976
Iteration: 2041; Percent complete: 51.0%; Average loss: 3.1937
Iteration: 2042; Percent complete: 51.0%; Average loss: 3.2309
Iteration: 2043; Percent complete: 51.1%; Average loss: 3.3624
Iteration: 2044; Percent complete: 51.1%; Average loss: 2.9932
Iteration: 2045; Percent complete: 51.1%; Average loss: 3.3985
Iteration: 2046; Percent complete: 51.1%; Average loss: 3.3832
Iteration: 2047; Percent complete: 51.2%; Average loss: 3.3627
Iteration: 2048; Percent complete: 51.2%; Average loss: 3.3454
Iteration: 2049; Percent complete: 51.2%; Average loss: 3.0116
Iteration: 2050; Percent complete: 51.2%; Average loss: 3.2577
Iteration: 2051; Percent complete: 51.3%; Average loss: 3.0401
Iteration: 2052; Percent complete: 51.3%; Average loss: 3.1676
Iteration: 2053; Percent complete: 51.3%; Average loss: 2.9942
Iteration: 2054; Percent complete: 51.3%; Average loss: 3.2092
Iteration: 2055; Percent complete: 51.4%; Average loss: 3.4259
Iteration: 2056; Percent complete: 51.4%; Average loss: 3.0703
Iteration: 2057; Percent complete: 51.4%; Average loss: 3.0433
Iteration: 2058; Percent complete: 51.4%; Average loss: 3.2436
Iteration: 2059; Percent complete: 51.5%; Average loss: 3.1543
Iteration: 2060; Percent complete: 51.5%; Average loss: 3.2561
Iteration: 2061; Percent complete: 51.5%; Average loss: 3.4174
Iteration: 2062; Percent complete: 51.5%; Average loss: 3.3072
Iteration: 2063; Percent complete: 51.6%; Average loss: 3.0370
Iteration: 2064; Percent complete: 51.6%; Average loss: 3.0658
Iteration: 2065; Percent complete: 51.6%; Average loss: 3.1104
Iteration: 2066; Percent complete: 51.6%; Average loss: 3.1514
Iteration: 2067; Percent complete: 51.7%; Average loss: 3.0982
Iteration: 2068; Percent complete: 51.7%; Average loss: 2.9878
Iteration: 2069; Percent complete: 51.7%; Average loss: 2.9439
Iteration: 2070; Percent complete: 51.7%; Average loss: 3.0262
Iteration: 2071; Percent complete: 51.8%; Average loss: 3.0079
Iteration: 2072; Percent complete: 51.8%; Average loss: 3.1200
Iteration: 2073; Percent complete: 51.8%; Average loss: 3.1327
Iteration: 2074; Percent complete: 51.8%; Average loss: 3.4385
Iteration: 2075; Percent complete: 51.9%; Average loss: 3.1973
Iteration: 2076; Percent complete: 51.9%; Average loss: 3.2300
Iteration: 2077; Percent complete: 51.9%; Average loss: 3.1913
Iteration: 2078; Percent complete: 51.9%; Average loss: 2.9042
Iteration: 2079; Percent complete: 52.0%; Average loss: 3.1188
Iteration: 2080; Percent complete: 52.0%; Average loss: 3.1488
Iteration: 2081; Percent complete: 52.0%; Average loss: 3.2415
Iteration: 2082; Percent complete: 52.0%; Average loss: 3.2070
Iteration: 2083; Percent complete: 52.1%; Average loss: 2.8610
Iteration: 2084; Percent complete: 52.1%; Average loss: 3.0160
Iteration: 2085; Percent complete: 52.1%; Average loss: 3.1402
Iteration: 2086; Percent complete: 52.1%; Average loss: 3.4313
Iteration: 2087; Percent complete: 52.2%; Average loss: 3.1742
Iteration: 2088; Percent complete: 52.2%; Average loss: 3.2499
Iteration: 2089; Percent complete: 52.2%; Average loss: 3.1101
Iteration: 2090; Percent complete: 52.2%; Average loss: 3.2420
Iteration: 2091; Percent complete: 52.3%; Average loss: 3.3563
Iteration: 2092; Percent complete: 52.3%; Average loss: 3.1702
Iteration: 2093; Percent complete: 52.3%; Average loss: 3.0494
Iteration: 2094; Percent complete: 52.3%; Average loss: 3.1911
Iteration: 2095; Percent complete: 52.4%; Average loss: 2.9485
Iteration: 2096; Percent complete: 52.4%; Average loss: 3.1210
Iteration: 2097; Percent complete: 52.4%; Average loss: 3.0920
Iteration: 2098; Percent complete: 52.4%; Average loss: 3.0718
Iteration: 2099; Percent complete: 52.5%; Average loss: 3.0357
Iteration: 2100; Percent complete: 52.5%; Average loss: 3.3627
Iteration: 2101; Percent complete: 52.5%; Average loss: 2.9587
Iteration: 2102; Percent complete: 52.5%; Average loss: 3.1517
Iteration: 2103; Percent complete: 52.6%; Average loss: 2.9797
Iteration: 2104; Percent complete: 52.6%; Average loss: 3.3461
Iteration: 2105; Percent complete: 52.6%; Average loss: 3.0691
Iteration: 2106; Percent complete: 52.6%; Average loss: 3.1455
Iteration: 2107; Percent complete: 52.7%; Average loss: 3.3411
Iteration: 2108; Percent complete: 52.7%; Average loss: 3.1681
Iteration: 2109; Percent complete: 52.7%; Average loss: 2.9534
Iteration: 2110; Percent complete: 52.8%; Average loss: 3.1801
Iteration: 2111; Percent complete: 52.8%; Average loss: 2.8234
Iteration: 2112; Percent complete: 52.8%; Average loss: 3.1311
Iteration: 2113; Percent complete: 52.8%; Average loss: 3.2175
Iteration: 2114; Percent complete: 52.8%; Average loss: 3.2492
Iteration: 2115; Percent complete: 52.9%; Average loss: 3.0574
Iteration: 2116; Percent complete: 52.9%; Average loss: 3.0161
Iteration: 2117; Percent complete: 52.9%; Average loss: 3.4025
Iteration: 2118; Percent complete: 52.9%; Average loss: 3.2399
Iteration: 2119; Percent complete: 53.0%; Average loss: 3.2270
Iteration: 2120; Percent complete: 53.0%; Average loss: 3.1477
Iteration: 2121; Percent complete: 53.0%; Average loss: 3.1336
Iteration: 2122; Percent complete: 53.0%; Average loss: 3.1473
Iteration: 2123; Percent complete: 53.1%; Average loss: 3.3168
Iteration: 2124; Percent complete: 53.1%; Average loss: 3.2168
Iteration: 2125; Percent complete: 53.1%; Average loss: 3.0798
Iteration: 2126; Percent complete: 53.1%; Average loss: 3.1736
Iteration: 2127; Percent complete: 53.2%; Average loss: 2.9987
Iteration: 2128; Percent complete: 53.2%; Average loss: 3.2419
Iteration: 2129; Percent complete: 53.2%; Average loss: 3.0731
Iteration: 2130; Percent complete: 53.2%; Average loss: 3.4057
Iteration: 2131; Percent complete: 53.3%; Average loss: 3.1509
Iteration: 2132; Percent complete: 53.3%; Average loss: 3.2206
Iteration: 2133; Percent complete: 53.3%; Average loss: 2.7739
Iteration: 2134; Percent complete: 53.3%; Average loss: 3.2508
Iteration: 2135; Percent complete: 53.4%; Average loss: 2.9496
Iteration: 2136; Percent complete: 53.4%; Average loss: 3.1022
Iteration: 2137; Percent complete: 53.4%; Average loss: 3.1140
Iteration: 2138; Percent complete: 53.4%; Average loss: 3.2637
Iteration: 2139; Percent complete: 53.5%; Average loss: 3.2776
Iteration: 2140; Percent complete: 53.5%; Average loss: 3.1447
Iteration: 2141; Percent complete: 53.5%; Average loss: 3.1327
Iteration: 2142; Percent complete: 53.5%; Average loss: 3.4275
Iteration: 2143; Percent complete: 53.6%; Average loss: 3.1822
Iteration: 2144; Percent complete: 53.6%; Average loss: 3.2452
Iteration: 2145; Percent complete: 53.6%; Average loss: 3.0647
Iteration: 2146; Percent complete: 53.6%; Average loss: 3.1135
Iteration: 2147; Percent complete: 53.7%; Average loss: 3.0163
Iteration: 2148; Percent complete: 53.7%; Average loss: 2.9661
Iteration: 2149; Percent complete: 53.7%; Average loss: 3.2190
Iteration: 2150; Percent complete: 53.8%; Average loss: 2.9528
Iteration: 2151; Percent complete: 53.8%; Average loss: 3.1599
Iteration: 2152; Percent complete: 53.8%; Average loss: 3.0302
Iteration: 2153; Percent complete: 53.8%; Average loss: 3.2090
Iteration: 2154; Percent complete: 53.8%; Average loss: 3.3358
Iteration: 2155; Percent complete: 53.9%; Average loss: 3.3397
Iteration: 2156; Percent complete: 53.9%; Average loss: 3.1430
Iteration: 2157; Percent complete: 53.9%; Average loss: 2.9804
Iteration: 2158; Percent complete: 53.9%; Average loss: 3.1879
Iteration: 2159; Percent complete: 54.0%; Average loss: 3.3313
Iteration: 2160; Percent complete: 54.0%; Average loss: 3.0763
Iteration: 2161; Percent complete: 54.0%; Average loss: 3.2557
Iteration: 2162; Percent complete: 54.0%; Average loss: 3.1668
Iteration: 2163; Percent complete: 54.1%; Average loss: 2.9628
Iteration: 2164; Percent complete: 54.1%; Average loss: 3.1918
Iteration: 2165; Percent complete: 54.1%; Average loss: 2.9684
Iteration: 2166; Percent complete: 54.1%; Average loss: 3.2916
Iteration: 2167; Percent complete: 54.2%; Average loss: 3.3406
Iteration: 2168; Percent complete: 54.2%; Average loss: 3.1086
Iteration: 2169; Percent complete: 54.2%; Average loss: 3.2492
Iteration: 2170; Percent complete: 54.2%; Average loss: 3.0045
Iteration: 2171; Percent complete: 54.3%; Average loss: 3.1281
Iteration: 2172; Percent complete: 54.3%; Average loss: 3.2992
Iteration: 2173; Percent complete: 54.3%; Average loss: 3.2712
Iteration: 2174; Percent complete: 54.4%; Average loss: 3.1606
Iteration: 2175; Percent complete: 54.4%; Average loss: 3.1562
Iteration: 2176; Percent complete: 54.4%; Average loss: 3.0154
Iteration: 2177; Percent complete: 54.4%; Average loss: 2.9702
Iteration: 2178; Percent complete: 54.4%; Average loss: 3.3098
Iteration: 2179; Percent complete: 54.5%; Average loss: 3.1180
Iteration: 2180; Percent complete: 54.5%; Average loss: 3.0942
Iteration: 2181; Percent complete: 54.5%; Average loss: 3.2064
Iteration: 2182; Percent complete: 54.5%; Average loss: 3.1625
Iteration: 2183; Percent complete: 54.6%; Average loss: 3.2719
Iteration: 2184; Percent complete: 54.6%; Average loss: 3.2848
Iteration: 2185; Percent complete: 54.6%; Average loss: 3.0781
Iteration: 2186; Percent complete: 54.6%; Average loss: 2.8919
Iteration: 2187; Percent complete: 54.7%; Average loss: 3.1750
Iteration: 2188; Percent complete: 54.7%; Average loss: 3.2122
Iteration: 2189; Percent complete: 54.7%; Average loss: 2.9251
Iteration: 2190; Percent complete: 54.8%; Average loss: 3.0934
Iteration: 2191; Percent complete: 54.8%; Average loss: 3.0329
Iteration: 2192; Percent complete: 54.8%; Average loss: 3.2610
Iteration: 2193; Percent complete: 54.8%; Average loss: 3.2837
Iteration: 2194; Percent complete: 54.9%; Average loss: 3.2152
Iteration: 2195; Percent complete: 54.9%; Average loss: 2.9849
Iteration: 2196; Percent complete: 54.9%; Average loss: 3.3973
Iteration: 2197; Percent complete: 54.9%; Average loss: 3.0060
Iteration: 2198; Percent complete: 54.9%; Average loss: 3.2489
Iteration: 2199; Percent complete: 55.0%; Average loss: 3.3534
Iteration: 2200; Percent complete: 55.0%; Average loss: 3.1607
Iteration: 2201; Percent complete: 55.0%; Average loss: 3.1632
Iteration: 2202; Percent complete: 55.0%; Average loss: 3.4960
Iteration: 2203; Percent complete: 55.1%; Average loss: 2.9035
Iteration: 2204; Percent complete: 55.1%; Average loss: 2.9290
Iteration: 2205; Percent complete: 55.1%; Average loss: 2.9121
Iteration: 2206; Percent complete: 55.1%; Average loss: 3.0441
Iteration: 2207; Percent complete: 55.2%; Average loss: 3.0576
Iteration: 2208; Percent complete: 55.2%; Average loss: 3.1535
Iteration: 2209; Percent complete: 55.2%; Average loss: 3.1605
Iteration: 2210; Percent complete: 55.2%; Average loss: 3.0389
Iteration: 2211; Percent complete: 55.3%; Average loss: 2.9307
Iteration: 2212; Percent complete: 55.3%; Average loss: 3.1548
Iteration: 2213; Percent complete: 55.3%; Average loss: 3.1642
Iteration: 2214; Percent complete: 55.4%; Average loss: 3.1371
Iteration: 2215; Percent complete: 55.4%; Average loss: 3.4341
Iteration: 2216; Percent complete: 55.4%; Average loss: 2.9766
Iteration: 2217; Percent complete: 55.4%; Average loss: 3.1890
Iteration: 2218; Percent complete: 55.5%; Average loss: 2.9229
Iteration: 2219; Percent complete: 55.5%; Average loss: 3.3319
Iteration: 2220; Percent complete: 55.5%; Average loss: 2.9541
Iteration: 2221; Percent complete: 55.5%; Average loss: 2.9796
Iteration: 2222; Percent complete: 55.5%; Average loss: 3.2616
Iteration: 2223; Percent complete: 55.6%; Average loss: 3.1804
Iteration: 2224; Percent complete: 55.6%; Average loss: 3.0984
Iteration: 2225; Percent complete: 55.6%; Average loss: 2.8898
Iteration: 2226; Percent complete: 55.6%; Average loss: 3.1104
Iteration: 2227; Percent complete: 55.7%; Average loss: 3.3046
Iteration: 2228; Percent complete: 55.7%; Average loss: 3.1323
Iteration: 2229; Percent complete: 55.7%; Average loss: 3.2352
Iteration: 2230; Percent complete: 55.8%; Average loss: 3.0725
Iteration: 2231; Percent complete: 55.8%; Average loss: 3.2447
Iteration: 2232; Percent complete: 55.8%; Average loss: 2.9024
Iteration: 2233; Percent complete: 55.8%; Average loss: 3.0428
Iteration: 2234; Percent complete: 55.9%; Average loss: 2.8335
Iteration: 2235; Percent complete: 55.9%; Average loss: 2.9825
Iteration: 2236; Percent complete: 55.9%; Average loss: 3.1945
Iteration: 2237; Percent complete: 55.9%; Average loss: 3.1189
Iteration: 2238; Percent complete: 56.0%; Average loss: 2.9762
Iteration: 2239; Percent complete: 56.0%; Average loss: 3.1466
Iteration: 2240; Percent complete: 56.0%; Average loss: 3.0691
Iteration: 2241; Percent complete: 56.0%; Average loss: 3.0448
Iteration: 2242; Percent complete: 56.0%; Average loss: 3.0311
Iteration: 2243; Percent complete: 56.1%; Average loss: 3.3388
Iteration: 2244; Percent complete: 56.1%; Average loss: 2.8586
Iteration: 2245; Percent complete: 56.1%; Average loss: 3.1152
Iteration: 2246; Percent complete: 56.1%; Average loss: 3.0282
Iteration: 2247; Percent complete: 56.2%; Average loss: 3.0662
Iteration: 2248; Percent complete: 56.2%; Average loss: 2.9475
Iteration: 2249; Percent complete: 56.2%; Average loss: 3.0249
Iteration: 2250; Percent complete: 56.2%; Average loss: 2.8962
Iteration: 2251; Percent complete: 56.3%; Average loss: 3.0451
Iteration: 2252; Percent complete: 56.3%; Average loss: 3.3195
Iteration: 2253; Percent complete: 56.3%; Average loss: 2.8902
Iteration: 2254; Percent complete: 56.4%; Average loss: 3.2925
Iteration: 2255; Percent complete: 56.4%; Average loss: 3.0965
Iteration: 2256; Percent complete: 56.4%; Average loss: 3.0711
Iteration: 2257; Percent complete: 56.4%; Average loss: 3.1548
Iteration: 2258; Percent complete: 56.5%; Average loss: 2.7184
Iteration: 2259; Percent complete: 56.5%; Average loss: 2.9248
Iteration: 2260; Percent complete: 56.5%; Average loss: 3.1891
Iteration: 2261; Percent complete: 56.5%; Average loss: 3.1140
Iteration: 2262; Percent complete: 56.5%; Average loss: 3.2078
Iteration: 2263; Percent complete: 56.6%; Average loss: 3.0589
Iteration: 2264; Percent complete: 56.6%; Average loss: 3.0528
Iteration: 2265; Percent complete: 56.6%; Average loss: 3.0728
Iteration: 2266; Percent complete: 56.6%; Average loss: 3.2826
Iteration: 2267; Percent complete: 56.7%; Average loss: 3.1912
Iteration: 2268; Percent complete: 56.7%; Average loss: 3.0833
Iteration: 2269; Percent complete: 56.7%; Average loss: 3.2232
Iteration: 2270; Percent complete: 56.8%; Average loss: 3.2577
Iteration: 2271; Percent complete: 56.8%; Average loss: 3.0132
Iteration: 2272; Percent complete: 56.8%; Average loss: 3.2298
Iteration: 2273; Percent complete: 56.8%; Average loss: 3.2801
Iteration: 2274; Percent complete: 56.9%; Average loss: 3.2777
Iteration: 2275; Percent complete: 56.9%; Average loss: 3.2178
Iteration: 2276; Percent complete: 56.9%; Average loss: 3.1152
Iteration: 2277; Percent complete: 56.9%; Average loss: 3.0548
Iteration: 2278; Percent complete: 57.0%; Average loss: 3.3997
Iteration: 2279; Percent complete: 57.0%; Average loss: 3.3175
Iteration: 2280; Percent complete: 57.0%; Average loss: 3.2206
Iteration: 2281; Percent complete: 57.0%; Average loss: 3.0592
Iteration: 2282; Percent complete: 57.0%; Average loss: 3.0365
Iteration: 2283; Percent complete: 57.1%; Average loss: 3.1050
Iteration: 2284; Percent complete: 57.1%; Average loss: 3.0909
Iteration: 2285; Percent complete: 57.1%; Average loss: 3.2166
Iteration: 2286; Percent complete: 57.1%; Average loss: 2.8822
Iteration: 2287; Percent complete: 57.2%; Average loss: 3.2318
Iteration: 2288; Percent complete: 57.2%; Average loss: 3.2857
Iteration: 2289; Percent complete: 57.2%; Average loss: 2.8363
Iteration: 2290; Percent complete: 57.2%; Average loss: 3.0412
Iteration: 2291; Percent complete: 57.3%; Average loss: 3.2621
Iteration: 2292; Percent complete: 57.3%; Average loss: 3.4483
Iteration: 2293; Percent complete: 57.3%; Average loss: 3.2006
Iteration: 2294; Percent complete: 57.4%; Average loss: 2.9428
Iteration: 2295; Percent complete: 57.4%; Average loss: 2.9695
Iteration: 2296; Percent complete: 57.4%; Average loss: 3.3301
Iteration: 2297; Percent complete: 57.4%; Average loss: 3.0592
Iteration: 2298; Percent complete: 57.5%; Average loss: 3.1062
Iteration: 2299; Percent complete: 57.5%; Average loss: 3.0751
Iteration: 2300; Percent complete: 57.5%; Average loss: 3.2807
Iteration: 2301; Percent complete: 57.5%; Average loss: 3.1352
Iteration: 2302; Percent complete: 57.6%; Average loss: 2.8921
Iteration: 2303; Percent complete: 57.6%; Average loss: 3.0306
Iteration: 2304; Percent complete: 57.6%; Average loss: 3.1343
Iteration: 2305; Percent complete: 57.6%; Average loss: 3.1059
Iteration: 2306; Percent complete: 57.6%; Average loss: 2.9054
Iteration: 2307; Percent complete: 57.7%; Average loss: 3.1971
Iteration: 2308; Percent complete: 57.7%; Average loss: 2.9234
Iteration: 2309; Percent complete: 57.7%; Average loss: 3.0698
Iteration: 2310; Percent complete: 57.8%; Average loss: 3.0534
Iteration: 2311; Percent complete: 57.8%; Average loss: 3.2655
Iteration: 2312; Percent complete: 57.8%; Average loss: 3.1205
Iteration: 2313; Percent complete: 57.8%; Average loss: 3.0028
Iteration: 2314; Percent complete: 57.9%; Average loss: 3.1166
Iteration: 2315; Percent complete: 57.9%; Average loss: 3.0459
Iteration: 2316; Percent complete: 57.9%; Average loss: 3.0637
Iteration: 2317; Percent complete: 57.9%; Average loss: 3.1083
Iteration: 2318; Percent complete: 58.0%; Average loss: 2.9408
Iteration: 2319; Percent complete: 58.0%; Average loss: 3.2025
Iteration: 2320; Percent complete: 58.0%; Average loss: 3.2403
Iteration: 2321; Percent complete: 58.0%; Average loss: 3.1884
Iteration: 2322; Percent complete: 58.1%; Average loss: 3.1783
Iteration: 2323; Percent complete: 58.1%; Average loss: 2.9874
Iteration: 2324; Percent complete: 58.1%; Average loss: 3.1343
Iteration: 2325; Percent complete: 58.1%; Average loss: 3.3337
Iteration: 2326; Percent complete: 58.1%; Average loss: 3.1503
Iteration: 2327; Percent complete: 58.2%; Average loss: 2.9673
Iteration: 2328; Percent complete: 58.2%; Average loss: 3.2949
Iteration: 2329; Percent complete: 58.2%; Average loss: 3.1567
Iteration: 2330; Percent complete: 58.2%; Average loss: 2.8888
Iteration: 2331; Percent complete: 58.3%; Average loss: 3.1560
Iteration: 2332; Percent complete: 58.3%; Average loss: 2.8947
Iteration: 2333; Percent complete: 58.3%; Average loss: 2.9621
Iteration: 2334; Percent complete: 58.4%; Average loss: 3.2231
Iteration: 2335; Percent complete: 58.4%; Average loss: 2.9345
Iteration: 2336; Percent complete: 58.4%; Average loss: 3.2495
Iteration: 2337; Percent complete: 58.4%; Average loss: 3.2440
Iteration: 2338; Percent complete: 58.5%; Average loss: 3.2411
Iteration: 2339; Percent complete: 58.5%; Average loss: 2.9436
Iteration: 2340; Percent complete: 58.5%; Average loss: 3.0994
Iteration: 2341; Percent complete: 58.5%; Average loss: 3.2585
Iteration: 2342; Percent complete: 58.6%; Average loss: 3.0794
Iteration: 2343; Percent complete: 58.6%; Average loss: 3.1262
Iteration: 2344; Percent complete: 58.6%; Average loss: 3.3436
Iteration: 2345; Percent complete: 58.6%; Average loss: 3.2105
Iteration: 2346; Percent complete: 58.7%; Average loss: 3.1514
Iteration: 2347; Percent complete: 58.7%; Average loss: 3.2097
Iteration: 2348; Percent complete: 58.7%; Average loss: 2.8915
Iteration: 2349; Percent complete: 58.7%; Average loss: 3.2105
Iteration: 2350; Percent complete: 58.8%; Average loss: 3.3089
Iteration: 2351; Percent complete: 58.8%; Average loss: 3.1208
Iteration: 2352; Percent complete: 58.8%; Average loss: 3.1295
Iteration: 2353; Percent complete: 58.8%; Average loss: 2.9720
Iteration: 2354; Percent complete: 58.9%; Average loss: 2.9314
Iteration: 2355; Percent complete: 58.9%; Average loss: 2.9594
Iteration: 2356; Percent complete: 58.9%; Average loss: 3.0198
Iteration: 2357; Percent complete: 58.9%; Average loss: 2.9782
Iteration: 2358; Percent complete: 59.0%; Average loss: 2.9579
Iteration: 2359; Percent complete: 59.0%; Average loss: 3.0261
Iteration: 2360; Percent complete: 59.0%; Average loss: 2.9368
Iteration: 2361; Percent complete: 59.0%; Average loss: 2.9434
Iteration: 2362; Percent complete: 59.1%; Average loss: 3.1233
Iteration: 2363; Percent complete: 59.1%; Average loss: 3.0206
Iteration: 2364; Percent complete: 59.1%; Average loss: 3.1354
Iteration: 2365; Percent complete: 59.1%; Average loss: 3.0099
Iteration: 2366; Percent complete: 59.2%; Average loss: 3.1055
Iteration: 2367; Percent complete: 59.2%; Average loss: 2.9164
Iteration: 2368; Percent complete: 59.2%; Average loss: 3.1236
Iteration: 2369; Percent complete: 59.2%; Average loss: 3.1805
Iteration: 2370; Percent complete: 59.2%; Average loss: 3.1678
Iteration: 2371; Percent complete: 59.3%; Average loss: 3.2744
Iteration: 2372; Percent complete: 59.3%; Average loss: 3.1446
Iteration: 2373; Percent complete: 59.3%; Average loss: 3.1094
Iteration: 2374; Percent complete: 59.4%; Average loss: 3.0704
Iteration: 2375; Percent complete: 59.4%; Average loss: 3.1194
Iteration: 2376; Percent complete: 59.4%; Average loss: 3.1354
Iteration: 2377; Percent complete: 59.4%; Average loss: 3.0774
Iteration: 2378; Percent complete: 59.5%; Average loss: 3.0265
Iteration: 2379; Percent complete: 59.5%; Average loss: 2.9836
Iteration: 2380; Percent complete: 59.5%; Average loss: 3.0516
Iteration: 2381; Percent complete: 59.5%; Average loss: 3.1426
Iteration: 2382; Percent complete: 59.6%; Average loss: 3.1988
Iteration: 2383; Percent complete: 59.6%; Average loss: 3.0276
Iteration: 2384; Percent complete: 59.6%; Average loss: 3.1485
Iteration: 2385; Percent complete: 59.6%; Average loss: 3.2447
Iteration: 2386; Percent complete: 59.7%; Average loss: 3.0621
Iteration: 2387; Percent complete: 59.7%; Average loss: 3.0435
Iteration: 2388; Percent complete: 59.7%; Average loss: 3.2737
Iteration: 2389; Percent complete: 59.7%; Average loss: 3.1132
Iteration: 2390; Percent complete: 59.8%; Average loss: 3.2558
Iteration: 2391; Percent complete: 59.8%; Average loss: 2.9865
Iteration: 2392; Percent complete: 59.8%; Average loss: 3.4095
Iteration: 2393; Percent complete: 59.8%; Average loss: 3.0445
Iteration: 2394; Percent complete: 59.9%; Average loss: 3.1898
Iteration: 2395; Percent complete: 59.9%; Average loss: 3.0841
Iteration: 2396; Percent complete: 59.9%; Average loss: 3.1407
Iteration: 2397; Percent complete: 59.9%; Average loss: 3.2321
Iteration: 2398; Percent complete: 60.0%; Average loss: 3.0528
Iteration: 2399; Percent complete: 60.0%; Average loss: 3.2247
Iteration: 2400; Percent complete: 60.0%; Average loss: 2.9461
Iteration: 2401; Percent complete: 60.0%; Average loss: 2.9792
Iteration: 2402; Percent complete: 60.1%; Average loss: 2.9324
Iteration: 2403; Percent complete: 60.1%; Average loss: 2.9486
Iteration: 2404; Percent complete: 60.1%; Average loss: 3.0523
Iteration: 2405; Percent complete: 60.1%; Average loss: 3.0630
Iteration: 2406; Percent complete: 60.2%; Average loss: 3.0284
Iteration: 2407; Percent complete: 60.2%; Average loss: 3.1089
Iteration: 2408; Percent complete: 60.2%; Average loss: 3.2010
Iteration: 2409; Percent complete: 60.2%; Average loss: 3.0991
Iteration: 2410; Percent complete: 60.2%; Average loss: 2.8840
Iteration: 2411; Percent complete: 60.3%; Average loss: 2.8690
Iteration: 2412; Percent complete: 60.3%; Average loss: 2.9966
Iteration: 2413; Percent complete: 60.3%; Average loss: 2.9629
Iteration: 2414; Percent complete: 60.4%; Average loss: 2.9213
Iteration: 2415; Percent complete: 60.4%; Average loss: 2.8397
Iteration: 2416; Percent complete: 60.4%; Average loss: 3.0276
Iteration: 2417; Percent complete: 60.4%; Average loss: 3.1769
Iteration: 2418; Percent complete: 60.5%; Average loss: 2.8821
Iteration: 2419; Percent complete: 60.5%; Average loss: 3.0097
Iteration: 2420; Percent complete: 60.5%; Average loss: 2.9223
Iteration: 2421; Percent complete: 60.5%; Average loss: 3.1042
Iteration: 2422; Percent complete: 60.6%; Average loss: 3.1541
Iteration: 2423; Percent complete: 60.6%; Average loss: 3.0003
Iteration: 2424; Percent complete: 60.6%; Average loss: 3.0334
Iteration: 2425; Percent complete: 60.6%; Average loss: 3.1150
Iteration: 2426; Percent complete: 60.7%; Average loss: 3.3533
Iteration: 2427; Percent complete: 60.7%; Average loss: 3.2612
Iteration: 2428; Percent complete: 60.7%; Average loss: 2.9355
Iteration: 2429; Percent complete: 60.7%; Average loss: 3.2858
Iteration: 2430; Percent complete: 60.8%; Average loss: 3.0094
Iteration: 2431; Percent complete: 60.8%; Average loss: 3.1291
Iteration: 2432; Percent complete: 60.8%; Average loss: 3.0964
Iteration: 2433; Percent complete: 60.8%; Average loss: 3.0858
Iteration: 2434; Percent complete: 60.9%; Average loss: 2.8501
Iteration: 2435; Percent complete: 60.9%; Average loss: 2.8739
Iteration: 2436; Percent complete: 60.9%; Average loss: 2.9707
Iteration: 2437; Percent complete: 60.9%; Average loss: 3.1851
Iteration: 2438; Percent complete: 61.0%; Average loss: 3.1555
Iteration: 2439; Percent complete: 61.0%; Average loss: 2.7534
Iteration: 2440; Percent complete: 61.0%; Average loss: 3.1413
Iteration: 2441; Percent complete: 61.0%; Average loss: 3.3048
Iteration: 2442; Percent complete: 61.1%; Average loss: 2.8923
Iteration: 2443; Percent complete: 61.1%; Average loss: 3.0267
Iteration: 2444; Percent complete: 61.1%; Average loss: 3.1659
Iteration: 2445; Percent complete: 61.1%; Average loss: 3.0294
Iteration: 2446; Percent complete: 61.2%; Average loss: 3.1431
Iteration: 2447; Percent complete: 61.2%; Average loss: 2.8712
Iteration: 2448; Percent complete: 61.2%; Average loss: 2.8859
Iteration: 2449; Percent complete: 61.2%; Average loss: 3.2629
Iteration: 2450; Percent complete: 61.3%; Average loss: 2.9785
Iteration: 2451; Percent complete: 61.3%; Average loss: 3.0558
Iteration: 2452; Percent complete: 61.3%; Average loss: 3.0041
Iteration: 2453; Percent complete: 61.3%; Average loss: 2.9857
Iteration: 2454; Percent complete: 61.4%; Average loss: 2.9888
Iteration: 2455; Percent complete: 61.4%; Average loss: 3.0132
Iteration: 2456; Percent complete: 61.4%; Average loss: 3.1223
Iteration: 2457; Percent complete: 61.4%; Average loss: 3.1140
Iteration: 2458; Percent complete: 61.5%; Average loss: 2.9506
Iteration: 2459; Percent complete: 61.5%; Average loss: 2.9575
Iteration: 2460; Percent complete: 61.5%; Average loss: 2.8677
Iteration: 2461; Percent complete: 61.5%; Average loss: 3.2334
Iteration: 2462; Percent complete: 61.6%; Average loss: 2.9112
Iteration: 2463; Percent complete: 61.6%; Average loss: 2.8523
Iteration: 2464; Percent complete: 61.6%; Average loss: 2.8775
Iteration: 2465; Percent complete: 61.6%; Average loss: 3.2586
Iteration: 2466; Percent complete: 61.7%; Average loss: 2.7900
Iteration: 2467; Percent complete: 61.7%; Average loss: 3.0348
Iteration: 2468; Percent complete: 61.7%; Average loss: 3.1728
Iteration: 2469; Percent complete: 61.7%; Average loss: 3.0882
Iteration: 2470; Percent complete: 61.8%; Average loss: 2.9822
Iteration: 2471; Percent complete: 61.8%; Average loss: 2.9242
Iteration: 2472; Percent complete: 61.8%; Average loss: 2.9421
Iteration: 2473; Percent complete: 61.8%; Average loss: 2.8849
Iteration: 2474; Percent complete: 61.9%; Average loss: 2.9752
Iteration: 2475; Percent complete: 61.9%; Average loss: 3.2130
Iteration: 2476; Percent complete: 61.9%; Average loss: 2.9930
Iteration: 2477; Percent complete: 61.9%; Average loss: 3.0530
Iteration: 2478; Percent complete: 62.0%; Average loss: 3.1664
Iteration: 2479; Percent complete: 62.0%; Average loss: 3.3823
Iteration: 2480; Percent complete: 62.0%; Average loss: 2.8370
Iteration: 2481; Percent complete: 62.0%; Average loss: 3.1419
Iteration: 2482; Percent complete: 62.1%; Average loss: 3.2941
Iteration: 2483; Percent complete: 62.1%; Average loss: 3.0628
Iteration: 2484; Percent complete: 62.1%; Average loss: 3.1455
Iteration: 2485; Percent complete: 62.1%; Average loss: 2.9991
Iteration: 2486; Percent complete: 62.2%; Average loss: 3.0147
Iteration: 2487; Percent complete: 62.2%; Average loss: 2.8701
Iteration: 2488; Percent complete: 62.2%; Average loss: 2.9340
Iteration: 2489; Percent complete: 62.2%; Average loss: 2.9468
Iteration: 2490; Percent complete: 62.3%; Average loss: 3.1832
Iteration: 2491; Percent complete: 62.3%; Average loss: 2.9595
Iteration: 2492; Percent complete: 62.3%; Average loss: 3.0628
Iteration: 2493; Percent complete: 62.3%; Average loss: 2.5838
Iteration: 2494; Percent complete: 62.4%; Average loss: 2.9875
Iteration: 2495; Percent complete: 62.4%; Average loss: 3.0123
Iteration: 2496; Percent complete: 62.4%; Average loss: 2.8821
Iteration: 2497; Percent complete: 62.4%; Average loss: 3.1803
Iteration: 2498; Percent complete: 62.5%; Average loss: 3.0839
Iteration: 2499; Percent complete: 62.5%; Average loss: 3.0329
Iteration: 2500; Percent complete: 62.5%; Average loss: 2.9443
Iteration: 2501; Percent complete: 62.5%; Average loss: 3.0609
Iteration: 2502; Percent complete: 62.5%; Average loss: 3.1991
Iteration: 2503; Percent complete: 62.6%; Average loss: 3.1210
Iteration: 2504; Percent complete: 62.6%; Average loss: 2.9707
Iteration: 2505; Percent complete: 62.6%; Average loss: 2.9130
Iteration: 2506; Percent complete: 62.6%; Average loss: 3.1052
Iteration: 2507; Percent complete: 62.7%; Average loss: 2.9818
Iteration: 2508; Percent complete: 62.7%; Average loss: 3.0109
Iteration: 2509; Percent complete: 62.7%; Average loss: 3.1208
Iteration: 2510; Percent complete: 62.7%; Average loss: 3.0526
Iteration: 2511; Percent complete: 62.8%; Average loss: 2.8242
Iteration: 2512; Percent complete: 62.8%; Average loss: 2.9299
Iteration: 2513; Percent complete: 62.8%; Average loss: 3.5364
Iteration: 2514; Percent complete: 62.8%; Average loss: 2.9169
Iteration: 2515; Percent complete: 62.9%; Average loss: 2.9967
Iteration: 2516; Percent complete: 62.9%; Average loss: 3.1448
Iteration: 2517; Percent complete: 62.9%; Average loss: 2.9203
Iteration: 2518; Percent complete: 62.9%; Average loss: 2.9064
Iteration: 2519; Percent complete: 63.0%; Average loss: 3.1009
Iteration: 2520; Percent complete: 63.0%; Average loss: 2.8425
Iteration: 2521; Percent complete: 63.0%; Average loss: 3.1065
Iteration: 2522; Percent complete: 63.0%; Average loss: 3.1380
Iteration: 2523; Percent complete: 63.1%; Average loss: 3.1332
Iteration: 2524; Percent complete: 63.1%; Average loss: 3.0863
Iteration: 2525; Percent complete: 63.1%; Average loss: 3.2582
Iteration: 2526; Percent complete: 63.1%; Average loss: 3.1393
Iteration: 2527; Percent complete: 63.2%; Average loss: 2.8142
Iteration: 2528; Percent complete: 63.2%; Average loss: 2.9278
Iteration: 2529; Percent complete: 63.2%; Average loss: 3.2220
Iteration: 2530; Percent complete: 63.2%; Average loss: 3.1263
Iteration: 2531; Percent complete: 63.3%; Average loss: 3.1003
Iteration: 2532; Percent complete: 63.3%; Average loss: 2.8295
Iteration: 2533; Percent complete: 63.3%; Average loss: 3.1039
Iteration: 2534; Percent complete: 63.3%; Average loss: 3.1022
Iteration: 2535; Percent complete: 63.4%; Average loss: 3.0440
Iteration: 2536; Percent complete: 63.4%; Average loss: 3.0560
Iteration: 2537; Percent complete: 63.4%; Average loss: 2.8964
Iteration: 2538; Percent complete: 63.4%; Average loss: 2.8208
Iteration: 2539; Percent complete: 63.5%; Average loss: 2.9958
Iteration: 2540; Percent complete: 63.5%; Average loss: 3.1789
Iteration: 2541; Percent complete: 63.5%; Average loss: 2.8928
Iteration: 2542; Percent complete: 63.5%; Average loss: 3.0626
Iteration: 2543; Percent complete: 63.6%; Average loss: 3.1873
Iteration: 2544; Percent complete: 63.6%; Average loss: 3.1674
Iteration: 2545; Percent complete: 63.6%; Average loss: 2.9550
Iteration: 2546; Percent complete: 63.6%; Average loss: 2.8719
Iteration: 2547; Percent complete: 63.7%; Average loss: 3.1114
Iteration: 2548; Percent complete: 63.7%; Average loss: 2.9373
Iteration: 2549; Percent complete: 63.7%; Average loss: 3.0065
Iteration: 2550; Percent complete: 63.7%; Average loss: 2.9007
Iteration: 2551; Percent complete: 63.8%; Average loss: 3.0177
Iteration: 2552; Percent complete: 63.8%; Average loss: 3.0218
Iteration: 2553; Percent complete: 63.8%; Average loss: 3.1938
Iteration: 2554; Percent complete: 63.8%; Average loss: 3.2398
Iteration: 2555; Percent complete: 63.9%; Average loss: 2.7515
Iteration: 2556; Percent complete: 63.9%; Average loss: 3.0738
Iteration: 2557; Percent complete: 63.9%; Average loss: 3.0856
Iteration: 2558; Percent complete: 63.9%; Average loss: 3.1682
Iteration: 2559; Percent complete: 64.0%; Average loss: 2.8381
Iteration: 2560; Percent complete: 64.0%; Average loss: 3.0370
Iteration: 2561; Percent complete: 64.0%; Average loss: 3.0883
Iteration: 2562; Percent complete: 64.0%; Average loss: 2.8757
Iteration: 2563; Percent complete: 64.1%; Average loss: 2.8961
Iteration: 2564; Percent complete: 64.1%; Average loss: 2.9722
Iteration: 2565; Percent complete: 64.1%; Average loss: 3.2644
Iteration: 2566; Percent complete: 64.1%; Average loss: 3.2901
Iteration: 2567; Percent complete: 64.2%; Average loss: 3.0173
Iteration: 2568; Percent complete: 64.2%; Average loss: 2.9923
Iteration: 2569; Percent complete: 64.2%; Average loss: 2.9120
Iteration: 2570; Percent complete: 64.2%; Average loss: 3.2105
Iteration: 2571; Percent complete: 64.3%; Average loss: 2.9908
Iteration: 2572; Percent complete: 64.3%; Average loss: 2.8347
Iteration: 2573; Percent complete: 64.3%; Average loss: 2.9693
Iteration: 2574; Percent complete: 64.3%; Average loss: 3.1612
Iteration: 2575; Percent complete: 64.4%; Average loss: 3.4388
Iteration: 2576; Percent complete: 64.4%; Average loss: 3.1231
Iteration: 2577; Percent complete: 64.4%; Average loss: 2.8581
Iteration: 2578; Percent complete: 64.5%; Average loss: 3.0105
Iteration: 2579; Percent complete: 64.5%; Average loss: 3.0254
Iteration: 2580; Percent complete: 64.5%; Average loss: 3.0408
Iteration: 2581; Percent complete: 64.5%; Average loss: 3.0451
Iteration: 2582; Percent complete: 64.5%; Average loss: 3.0587
Iteration: 2583; Percent complete: 64.6%; Average loss: 2.9195
Iteration: 2584; Percent complete: 64.6%; Average loss: 2.9112
Iteration: 2585; Percent complete: 64.6%; Average loss: 2.9740
Iteration: 2586; Percent complete: 64.6%; Average loss: 3.0602
Iteration: 2587; Percent complete: 64.7%; Average loss: 2.9329
Iteration: 2588; Percent complete: 64.7%; Average loss: 3.0172
Iteration: 2589; Percent complete: 64.7%; Average loss: 3.1310
Iteration: 2590; Percent complete: 64.8%; Average loss: 2.8028
Iteration: 2591; Percent complete: 64.8%; Average loss: 2.7629
Iteration: 2592; Percent complete: 64.8%; Average loss: 3.1730
Iteration: 2593; Percent complete: 64.8%; Average loss: 2.8508
Iteration: 2594; Percent complete: 64.8%; Average loss: 3.1867
Iteration: 2595; Percent complete: 64.9%; Average loss: 2.9273
Iteration: 2596; Percent complete: 64.9%; Average loss: 3.1439
Iteration: 2597; Percent complete: 64.9%; Average loss: 3.1594
Iteration: 2598; Percent complete: 65.0%; Average loss: 2.7941
Iteration: 2599; Percent complete: 65.0%; Average loss: 3.0966
Iteration: 2600; Percent complete: 65.0%; Average loss: 2.8281
Iteration: 2601; Percent complete: 65.0%; Average loss: 3.2003
Iteration: 2602; Percent complete: 65.0%; Average loss: 2.8567
Iteration: 2603; Percent complete: 65.1%; Average loss: 2.8813
Iteration: 2604; Percent complete: 65.1%; Average loss: 3.2839
Iteration: 2605; Percent complete: 65.1%; Average loss: 2.8639
Iteration: 2606; Percent complete: 65.1%; Average loss: 2.9574
Iteration: 2607; Percent complete: 65.2%; Average loss: 2.9591
Iteration: 2608; Percent complete: 65.2%; Average loss: 2.8390
Iteration: 2609; Percent complete: 65.2%; Average loss: 2.9743
Iteration: 2610; Percent complete: 65.2%; Average loss: 3.0924
Iteration: 2611; Percent complete: 65.3%; Average loss: 2.9926
Iteration: 2612; Percent complete: 65.3%; Average loss: 3.0874
Iteration: 2613; Percent complete: 65.3%; Average loss: 2.9743
Iteration: 2614; Percent complete: 65.3%; Average loss: 3.1961
Iteration: 2615; Percent complete: 65.4%; Average loss: 3.1667
Iteration: 2616; Percent complete: 65.4%; Average loss: 2.9477
Iteration: 2617; Percent complete: 65.4%; Average loss: 2.9011
Iteration: 2618; Percent complete: 65.5%; Average loss: 3.2331
Iteration: 2619; Percent complete: 65.5%; Average loss: 2.7532
Iteration: 2620; Percent complete: 65.5%; Average loss: 2.8117
Iteration: 2621; Percent complete: 65.5%; Average loss: 3.0764
Iteration: 2622; Percent complete: 65.5%; Average loss: 3.1233
Iteration: 2623; Percent complete: 65.6%; Average loss: 3.0311
Iteration: 2624; Percent complete: 65.6%; Average loss: 2.8314
Iteration: 2625; Percent complete: 65.6%; Average loss: 3.0272
Iteration: 2626; Percent complete: 65.6%; Average loss: 2.8218
Iteration: 2627; Percent complete: 65.7%; Average loss: 2.9292
Iteration: 2628; Percent complete: 65.7%; Average loss: 3.1540
Iteration: 2629; Percent complete: 65.7%; Average loss: 3.2318
Iteration: 2630; Percent complete: 65.8%; Average loss: 2.9340
Iteration: 2631; Percent complete: 65.8%; Average loss: 2.9134
Iteration: 2632; Percent complete: 65.8%; Average loss: 2.9699
Iteration: 2633; Percent complete: 65.8%; Average loss: 2.9573
Iteration: 2634; Percent complete: 65.8%; Average loss: 2.9273
Iteration: 2635; Percent complete: 65.9%; Average loss: 3.1744
Iteration: 2636; Percent complete: 65.9%; Average loss: 2.8706
Iteration: 2637; Percent complete: 65.9%; Average loss: 2.8221
Iteration: 2638; Percent complete: 66.0%; Average loss: 3.0809
Iteration: 2639; Percent complete: 66.0%; Average loss: 2.6323
Iteration: 2640; Percent complete: 66.0%; Average loss: 3.2562
Iteration: 2641; Percent complete: 66.0%; Average loss: 2.8193
Iteration: 2642; Percent complete: 66.0%; Average loss: 2.9846
Iteration: 2643; Percent complete: 66.1%; Average loss: 3.1477
Iteration: 2644; Percent complete: 66.1%; Average loss: 2.9878
Iteration: 2645; Percent complete: 66.1%; Average loss: 3.1941
Iteration: 2646; Percent complete: 66.1%; Average loss: 3.0270
Iteration: 2647; Percent complete: 66.2%; Average loss: 2.9208
Iteration: 2648; Percent complete: 66.2%; Average loss: 3.1194
Iteration: 2649; Percent complete: 66.2%; Average loss: 2.9688
Iteration: 2650; Percent complete: 66.2%; Average loss: 2.7779
Iteration: 2651; Percent complete: 66.3%; Average loss: 3.1247
Iteration: 2652; Percent complete: 66.3%; Average loss: 2.9277
Iteration: 2653; Percent complete: 66.3%; Average loss: 2.9705
Iteration: 2654; Percent complete: 66.3%; Average loss: 3.0171
Iteration: 2655; Percent complete: 66.4%; Average loss: 2.9473
Iteration: 2656; Percent complete: 66.4%; Average loss: 3.0336
Iteration: 2657; Percent complete: 66.4%; Average loss: 3.3301
Iteration: 2658; Percent complete: 66.5%; Average loss: 3.0319
Iteration: 2659; Percent complete: 66.5%; Average loss: 3.0822
Iteration: 2660; Percent complete: 66.5%; Average loss: 2.8983
Iteration: 2661; Percent complete: 66.5%; Average loss: 2.7649
Iteration: 2662; Percent complete: 66.5%; Average loss: 2.7934
Iteration: 2663; Percent complete: 66.6%; Average loss: 3.2290
Iteration: 2664; Percent complete: 66.6%; Average loss: 2.8663
Iteration: 2665; Percent complete: 66.6%; Average loss: 3.0092
Iteration: 2666; Percent complete: 66.6%; Average loss: 2.8011
Iteration: 2667; Percent complete: 66.7%; Average loss: 2.8905
Iteration: 2668; Percent complete: 66.7%; Average loss: 2.9987
Iteration: 2669; Percent complete: 66.7%; Average loss: 3.0646
Iteration: 2670; Percent complete: 66.8%; Average loss: 2.8338
Iteration: 2671; Percent complete: 66.8%; Average loss: 2.7555
Iteration: 2672; Percent complete: 66.8%; Average loss: 2.6924
Iteration: 2673; Percent complete: 66.8%; Average loss: 3.0165
Iteration: 2674; Percent complete: 66.8%; Average loss: 3.2798
Iteration: 2675; Percent complete: 66.9%; Average loss: 3.1171
Iteration: 2676; Percent complete: 66.9%; Average loss: 2.7833
Iteration: 2677; Percent complete: 66.9%; Average loss: 2.9770
Iteration: 2678; Percent complete: 67.0%; Average loss: 2.9397
Iteration: 2679; Percent complete: 67.0%; Average loss: 2.9469
Iteration: 2680; Percent complete: 67.0%; Average loss: 2.9677
Iteration: 2681; Percent complete: 67.0%; Average loss: 2.9055
Iteration: 2682; Percent complete: 67.0%; Average loss: 2.7231
Iteration: 2683; Percent complete: 67.1%; Average loss: 3.1210
Iteration: 2684; Percent complete: 67.1%; Average loss: 2.9613
Iteration: 2685; Percent complete: 67.1%; Average loss: 2.8761
Iteration: 2686; Percent complete: 67.2%; Average loss: 2.6840
Iteration: 2687; Percent complete: 67.2%; Average loss: 2.9711
Iteration: 2688; Percent complete: 67.2%; Average loss: 3.1379
Iteration: 2689; Percent complete: 67.2%; Average loss: 3.2012
Iteration: 2690; Percent complete: 67.2%; Average loss: 2.8963
Iteration: 2691; Percent complete: 67.3%; Average loss: 3.1428
Iteration: 2692; Percent complete: 67.3%; Average loss: 3.0349
Iteration: 2693; Percent complete: 67.3%; Average loss: 3.0278
Iteration: 2694; Percent complete: 67.3%; Average loss: 2.9175
Iteration: 2695; Percent complete: 67.4%; Average loss: 3.0082
Iteration: 2696; Percent complete: 67.4%; Average loss: 2.9737
Iteration: 2697; Percent complete: 67.4%; Average loss: 2.8733
Iteration: 2698; Percent complete: 67.5%; Average loss: 3.0758
Iteration: 2699; Percent complete: 67.5%; Average loss: 2.9987
Iteration: 2700; Percent complete: 67.5%; Average loss: 2.9768
Iteration: 2701; Percent complete: 67.5%; Average loss: 2.9228
Iteration: 2702; Percent complete: 67.5%; Average loss: 2.7600
Iteration: 2703; Percent complete: 67.6%; Average loss: 2.9874
Iteration: 2704; Percent complete: 67.6%; Average loss: 3.0251
Iteration: 2705; Percent complete: 67.6%; Average loss: 3.1835
Iteration: 2706; Percent complete: 67.7%; Average loss: 3.0944
Iteration: 2707; Percent complete: 67.7%; Average loss: 2.7598
Iteration: 2708; Percent complete: 67.7%; Average loss: 2.8380
Iteration: 2709; Percent complete: 67.7%; Average loss: 3.0498
Iteration: 2710; Percent complete: 67.8%; Average loss: 2.8290
Iteration: 2711; Percent complete: 67.8%; Average loss: 3.1700
Iteration: 2712; Percent complete: 67.8%; Average loss: 2.7459
Iteration: 2713; Percent complete: 67.8%; Average loss: 3.1760
Iteration: 2714; Percent complete: 67.8%; Average loss: 2.8737
Iteration: 2715; Percent complete: 67.9%; Average loss: 2.8036
Iteration: 2716; Percent complete: 67.9%; Average loss: 2.6323
Iteration: 2717; Percent complete: 67.9%; Average loss: 2.9118
Iteration: 2718; Percent complete: 68.0%; Average loss: 2.7747
Iteration: 2719; Percent complete: 68.0%; Average loss: 3.0112
Iteration: 2720; Percent complete: 68.0%; Average loss: 3.0178
Iteration: 2721; Percent complete: 68.0%; Average loss: 3.1680
Iteration: 2722; Percent complete: 68.0%; Average loss: 3.2113
Iteration: 2723; Percent complete: 68.1%; Average loss: 3.0224
Iteration: 2724; Percent complete: 68.1%; Average loss: 2.9610
Iteration: 2725; Percent complete: 68.1%; Average loss: 2.9646
Iteration: 2726; Percent complete: 68.2%; Average loss: 3.0485
Iteration: 2727; Percent complete: 68.2%; Average loss: 2.9393
Iteration: 2728; Percent complete: 68.2%; Average loss: 3.1421
Iteration: 2729; Percent complete: 68.2%; Average loss: 2.9177
Iteration: 2730; Percent complete: 68.2%; Average loss: 2.9482
Iteration: 2731; Percent complete: 68.3%; Average loss: 2.9243
Iteration: 2732; Percent complete: 68.3%; Average loss: 3.0112
Iteration: 2733; Percent complete: 68.3%; Average loss: 2.8543
Iteration: 2734; Percent complete: 68.3%; Average loss: 2.8611
Iteration: 2735; Percent complete: 68.4%; Average loss: 3.2603
Iteration: 2736; Percent complete: 68.4%; Average loss: 3.0612
Iteration: 2737; Percent complete: 68.4%; Average loss: 2.8715
Iteration: 2738; Percent complete: 68.5%; Average loss: 3.0220
Iteration: 2739; Percent complete: 68.5%; Average loss: 3.0019
Iteration: 2740; Percent complete: 68.5%; Average loss: 2.9504
Iteration: 2741; Percent complete: 68.5%; Average loss: 3.0531
Iteration: 2742; Percent complete: 68.5%; Average loss: 3.0527
Iteration: 2743; Percent complete: 68.6%; Average loss: 2.9349
Iteration: 2744; Percent complete: 68.6%; Average loss: 2.8801
Iteration: 2745; Percent complete: 68.6%; Average loss: 2.8969
Iteration: 2746; Percent complete: 68.7%; Average loss: 2.9377
Iteration: 2747; Percent complete: 68.7%; Average loss: 2.9198
Iteration: 2748; Percent complete: 68.7%; Average loss: 2.8529
Iteration: 2749; Percent complete: 68.7%; Average loss: 2.9018
Iteration: 2750; Percent complete: 68.8%; Average loss: 3.0277
Iteration: 2751; Percent complete: 68.8%; Average loss: 3.1529
Iteration: 2752; Percent complete: 68.8%; Average loss: 3.0351
Iteration: 2753; Percent complete: 68.8%; Average loss: 2.9583
Iteration: 2754; Percent complete: 68.8%; Average loss: 2.6827
Iteration: 2755; Percent complete: 68.9%; Average loss: 2.9085
Iteration: 2756; Percent complete: 68.9%; Average loss: 2.8498
Iteration: 2757; Percent complete: 68.9%; Average loss: 2.9369
Iteration: 2758; Percent complete: 69.0%; Average loss: 2.8640
Iteration: 2759; Percent complete: 69.0%; Average loss: 3.0769
Iteration: 2760; Percent complete: 69.0%; Average loss: 2.9987
Iteration: 2761; Percent complete: 69.0%; Average loss: 2.8405
Iteration: 2762; Percent complete: 69.0%; Average loss: 2.8704
Iteration: 2763; Percent complete: 69.1%; Average loss: 2.9425
Iteration: 2764; Percent complete: 69.1%; Average loss: 3.2087
Iteration: 2765; Percent complete: 69.1%; Average loss: 2.8228
Iteration: 2766; Percent complete: 69.2%; Average loss: 3.0665
Iteration: 2767; Percent complete: 69.2%; Average loss: 3.0346
Iteration: 2768; Percent complete: 69.2%; Average loss: 2.9389
Iteration: 2769; Percent complete: 69.2%; Average loss: 2.8305
Iteration: 2770; Percent complete: 69.2%; Average loss: 3.1069
Iteration: 2771; Percent complete: 69.3%; Average loss: 2.8528
Iteration: 2772; Percent complete: 69.3%; Average loss: 2.8521
Iteration: 2773; Percent complete: 69.3%; Average loss: 3.0383
Iteration: 2774; Percent complete: 69.3%; Average loss: 3.0396
Iteration: 2775; Percent complete: 69.4%; Average loss: 3.0824
Iteration: 2776; Percent complete: 69.4%; Average loss: 3.0907
Iteration: 2777; Percent complete: 69.4%; Average loss: 3.3444
Iteration: 2778; Percent complete: 69.5%; Average loss: 2.7319
Iteration: 2779; Percent complete: 69.5%; Average loss: 2.8626
Iteration: 2780; Percent complete: 69.5%; Average loss: 3.3156
Iteration: 2781; Percent complete: 69.5%; Average loss: 3.0284
Iteration: 2782; Percent complete: 69.5%; Average loss: 3.1556
Iteration: 2783; Percent complete: 69.6%; Average loss: 3.0236
Iteration: 2784; Percent complete: 69.6%; Average loss: 2.9507
Iteration: 2785; Percent complete: 69.6%; Average loss: 3.1169
Iteration: 2786; Percent complete: 69.7%; Average loss: 2.9773
Iteration: 2787; Percent complete: 69.7%; Average loss: 2.7635
Iteration: 2788; Percent complete: 69.7%; Average loss: 2.6837
Iteration: 2789; Percent complete: 69.7%; Average loss: 2.8723
Iteration: 2790; Percent complete: 69.8%; Average loss: 3.0189
Iteration: 2791; Percent complete: 69.8%; Average loss: 3.0320
Iteration: 2792; Percent complete: 69.8%; Average loss: 2.7468
Iteration: 2793; Percent complete: 69.8%; Average loss: 2.8799
Iteration: 2794; Percent complete: 69.8%; Average loss: 2.6431
Iteration: 2795; Percent complete: 69.9%; Average loss: 2.8711
Iteration: 2796; Percent complete: 69.9%; Average loss: 2.7601
Iteration: 2797; Percent complete: 69.9%; Average loss: 2.9221
Iteration: 2798; Percent complete: 70.0%; Average loss: 2.9329
Iteration: 2799; Percent complete: 70.0%; Average loss: 2.6764
Iteration: 2800; Percent complete: 70.0%; Average loss: 2.7508
Iteration: 2801; Percent complete: 70.0%; Average loss: 2.9807
Iteration: 2802; Percent complete: 70.0%; Average loss: 2.8395
Iteration: 2803; Percent complete: 70.1%; Average loss: 3.0872
Iteration: 2804; Percent complete: 70.1%; Average loss: 3.0623
Iteration: 2805; Percent complete: 70.1%; Average loss: 2.9260
Iteration: 2806; Percent complete: 70.2%; Average loss: 2.9938
Iteration: 2807; Percent complete: 70.2%; Average loss: 2.9124
Iteration: 2808; Percent complete: 70.2%; Average loss: 2.9047
Iteration: 2809; Percent complete: 70.2%; Average loss: 3.1132
Iteration: 2810; Percent complete: 70.2%; Average loss: 2.9573
Iteration: 2811; Percent complete: 70.3%; Average loss: 3.0857
Iteration: 2812; Percent complete: 70.3%; Average loss: 3.0119
Iteration: 2813; Percent complete: 70.3%; Average loss: 2.6641
Iteration: 2814; Percent complete: 70.3%; Average loss: 2.9955
Iteration: 2815; Percent complete: 70.4%; Average loss: 2.7934
Iteration: 2816; Percent complete: 70.4%; Average loss: 3.0083
Iteration: 2817; Percent complete: 70.4%; Average loss: 2.9851
Iteration: 2818; Percent complete: 70.5%; Average loss: 2.7228
Iteration: 2819; Percent complete: 70.5%; Average loss: 2.8221
Iteration: 2820; Percent complete: 70.5%; Average loss: 3.0838
Iteration: 2821; Percent complete: 70.5%; Average loss: 2.7397
Iteration: 2822; Percent complete: 70.5%; Average loss: 2.9684
Iteration: 2823; Percent complete: 70.6%; Average loss: 2.9679
Iteration: 2824; Percent complete: 70.6%; Average loss: 2.9677
Iteration: 2825; Percent complete: 70.6%; Average loss: 2.9945
Iteration: 2826; Percent complete: 70.7%; Average loss: 3.1092
Iteration: 2827; Percent complete: 70.7%; Average loss: 2.7499
Iteration: 2828; Percent complete: 70.7%; Average loss: 2.8127
Iteration: 2829; Percent complete: 70.7%; Average loss: 3.1256
Iteration: 2830; Percent complete: 70.8%; Average loss: 3.2830
Iteration: 2831; Percent complete: 70.8%; Average loss: 3.0519
Iteration: 2832; Percent complete: 70.8%; Average loss: 3.0045
Iteration: 2833; Percent complete: 70.8%; Average loss: 2.9836
Iteration: 2834; Percent complete: 70.9%; Average loss: 3.0966
Iteration: 2835; Percent complete: 70.9%; Average loss: 2.8792
Iteration: 2836; Percent complete: 70.9%; Average loss: 2.8265
Iteration: 2837; Percent complete: 70.9%; Average loss: 2.9977
Iteration: 2838; Percent complete: 71.0%; Average loss: 2.8758
Iteration: 2839; Percent complete: 71.0%; Average loss: 2.9886
Iteration: 2840; Percent complete: 71.0%; Average loss: 2.8132
Iteration: 2841; Percent complete: 71.0%; Average loss: 3.0187
Iteration: 2842; Percent complete: 71.0%; Average loss: 2.7627
Iteration: 2843; Percent complete: 71.1%; Average loss: 3.0002
Iteration: 2844; Percent complete: 71.1%; Average loss: 2.8375
Iteration: 2845; Percent complete: 71.1%; Average loss: 2.7303
Iteration: 2846; Percent complete: 71.2%; Average loss: 2.8693
Iteration: 2847; Percent complete: 71.2%; Average loss: 3.0817
Iteration: 2848; Percent complete: 71.2%; Average loss: 2.9517
Iteration: 2849; Percent complete: 71.2%; Average loss: 3.1063
Iteration: 2850; Percent complete: 71.2%; Average loss: 3.0050
Iteration: 2851; Percent complete: 71.3%; Average loss: 2.7699
Iteration: 2852; Percent complete: 71.3%; Average loss: 2.8731
Iteration: 2853; Percent complete: 71.3%; Average loss: 3.0645
Iteration: 2854; Percent complete: 71.4%; Average loss: 2.9038
Iteration: 2855; Percent complete: 71.4%; Average loss: 2.9953
Iteration: 2856; Percent complete: 71.4%; Average loss: 3.0234
Iteration: 2857; Percent complete: 71.4%; Average loss: 3.1030
Iteration: 2858; Percent complete: 71.5%; Average loss: 2.8177
Iteration: 2859; Percent complete: 71.5%; Average loss: 2.9438
Iteration: 2860; Percent complete: 71.5%; Average loss: 3.1730
Iteration: 2861; Percent complete: 71.5%; Average loss: 2.8083
Iteration: 2862; Percent complete: 71.5%; Average loss: 3.1182
Iteration: 2863; Percent complete: 71.6%; Average loss: 2.9281
Iteration: 2864; Percent complete: 71.6%; Average loss: 2.6934
Iteration: 2865; Percent complete: 71.6%; Average loss: 2.9598
Iteration: 2866; Percent complete: 71.7%; Average loss: 2.9411
Iteration: 2867; Percent complete: 71.7%; Average loss: 2.7955
Iteration: 2868; Percent complete: 71.7%; Average loss: 2.9601
Iteration: 2869; Percent complete: 71.7%; Average loss: 2.6748
Iteration: 2870; Percent complete: 71.8%; Average loss: 2.8243
Iteration: 2871; Percent complete: 71.8%; Average loss: 2.8416
Iteration: 2872; Percent complete: 71.8%; Average loss: 2.9099
Iteration: 2873; Percent complete: 71.8%; Average loss: 3.1273
Iteration: 2874; Percent complete: 71.9%; Average loss: 3.0627
Iteration: 2875; Percent complete: 71.9%; Average loss: 2.9748
Iteration: 2876; Percent complete: 71.9%; Average loss: 2.8101
Iteration: 2877; Percent complete: 71.9%; Average loss: 3.0045
Iteration: 2878; Percent complete: 72.0%; Average loss: 3.2551
Iteration: 2879; Percent complete: 72.0%; Average loss: 2.6658
Iteration: 2880; Percent complete: 72.0%; Average loss: 2.7028
Iteration: 2881; Percent complete: 72.0%; Average loss: 2.9999
Iteration: 2882; Percent complete: 72.0%; Average loss: 2.8165
Iteration: 2883; Percent complete: 72.1%; Average loss: 2.8136
Iteration: 2884; Percent complete: 72.1%; Average loss: 2.9561
Iteration: 2885; Percent complete: 72.1%; Average loss: 2.9803
Iteration: 2886; Percent complete: 72.2%; Average loss: 2.7117
Iteration: 2887; Percent complete: 72.2%; Average loss: 3.0633
Iteration: 2888; Percent complete: 72.2%; Average loss: 2.9110
Iteration: 2889; Percent complete: 72.2%; Average loss: 2.7776
Iteration: 2890; Percent complete: 72.2%; Average loss: 2.8830
Iteration: 2891; Percent complete: 72.3%; Average loss: 3.0290
Iteration: 2892; Percent complete: 72.3%; Average loss: 2.8488
Iteration: 2893; Percent complete: 72.3%; Average loss: 2.8951
Iteration: 2894; Percent complete: 72.4%; Average loss: 2.7446
Iteration: 2895; Percent complete: 72.4%; Average loss: 2.8286
Iteration: 2896; Percent complete: 72.4%; Average loss: 2.8033
Iteration: 2897; Percent complete: 72.4%; Average loss: 2.6596
Iteration: 2898; Percent complete: 72.5%; Average loss: 2.9679
Iteration: 2899; Percent complete: 72.5%; Average loss: 2.9378
Iteration: 2900; Percent complete: 72.5%; Average loss: 2.9660
Iteration: 2901; Percent complete: 72.5%; Average loss: 2.8103
Iteration: 2902; Percent complete: 72.5%; Average loss: 2.9945
Iteration: 2903; Percent complete: 72.6%; Average loss: 2.7087
Iteration: 2904; Percent complete: 72.6%; Average loss: 2.8357
Iteration: 2905; Percent complete: 72.6%; Average loss: 2.8355
Iteration: 2906; Percent complete: 72.7%; Average loss: 2.9164
Iteration: 2907; Percent complete: 72.7%; Average loss: 2.7946
Iteration: 2908; Percent complete: 72.7%; Average loss: 2.8697
Iteration: 2909; Percent complete: 72.7%; Average loss: 3.0143
Iteration: 2910; Percent complete: 72.8%; Average loss: 2.9934
Iteration: 2911; Percent complete: 72.8%; Average loss: 2.7578
Iteration: 2912; Percent complete: 72.8%; Average loss: 2.9362
Iteration: 2913; Percent complete: 72.8%; Average loss: 2.9552
Iteration: 2914; Percent complete: 72.9%; Average loss: 2.7675
Iteration: 2915; Percent complete: 72.9%; Average loss: 2.8689
Iteration: 2916; Percent complete: 72.9%; Average loss: 3.1621
Iteration: 2917; Percent complete: 72.9%; Average loss: 2.8435
Iteration: 2918; Percent complete: 73.0%; Average loss: 2.8220
Iteration: 2919; Percent complete: 73.0%; Average loss: 2.6873
Iteration: 2920; Percent complete: 73.0%; Average loss: 3.0001
Iteration: 2921; Percent complete: 73.0%; Average loss: 2.8204
Iteration: 2922; Percent complete: 73.0%; Average loss: 2.9470
Iteration: 2923; Percent complete: 73.1%; Average loss: 3.0011
Iteration: 2924; Percent complete: 73.1%; Average loss: 2.9485
Iteration: 2925; Percent complete: 73.1%; Average loss: 2.8466
Iteration: 2926; Percent complete: 73.2%; Average loss: 2.6774
Iteration: 2927; Percent complete: 73.2%; Average loss: 2.9707
Iteration: 2928; Percent complete: 73.2%; Average loss: 2.6638
Iteration: 2929; Percent complete: 73.2%; Average loss: 2.9559
Iteration: 2930; Percent complete: 73.2%; Average loss: 2.8218
Iteration: 2931; Percent complete: 73.3%; Average loss: 2.9951
Iteration: 2932; Percent complete: 73.3%; Average loss: 2.9555
Iteration: 2933; Percent complete: 73.3%; Average loss: 2.9357
Iteration: 2934; Percent complete: 73.4%; Average loss: 3.0486
Iteration: 2935; Percent complete: 73.4%; Average loss: 2.8835
Iteration: 2936; Percent complete: 73.4%; Average loss: 2.9315
Iteration: 2937; Percent complete: 73.4%; Average loss: 2.9071
Iteration: 2938; Percent complete: 73.5%; Average loss: 2.8929
Iteration: 2939; Percent complete: 73.5%; Average loss: 2.8282
Iteration: 2940; Percent complete: 73.5%; Average loss: 3.0280
Iteration: 2941; Percent complete: 73.5%; Average loss: 3.0133
Iteration: 2942; Percent complete: 73.6%; Average loss: 2.8846
Iteration: 2943; Percent complete: 73.6%; Average loss: 2.9819
Iteration: 2944; Percent complete: 73.6%; Average loss: 2.9751
Iteration: 2945; Percent complete: 73.6%; Average loss: 2.6137
Iteration: 2946; Percent complete: 73.7%; Average loss: 2.6685
Iteration: 2947; Percent complete: 73.7%; Average loss: 3.0565
Iteration: 2948; Percent complete: 73.7%; Average loss: 3.1756
Iteration: 2949; Percent complete: 73.7%; Average loss: 2.7518
Iteration: 2950; Percent complete: 73.8%; Average loss: 2.8410
Iteration: 2951; Percent complete: 73.8%; Average loss: 2.7142
Iteration: 2952; Percent complete: 73.8%; Average loss: 2.9840
Iteration: 2953; Percent complete: 73.8%; Average loss: 2.9769
Iteration: 2954; Percent complete: 73.9%; Average loss: 2.8466
Iteration: 2955; Percent complete: 73.9%; Average loss: 3.0011
Iteration: 2956; Percent complete: 73.9%; Average loss: 2.8315
Iteration: 2957; Percent complete: 73.9%; Average loss: 2.7113
Iteration: 2958; Percent complete: 74.0%; Average loss: 2.8680
Iteration: 2959; Percent complete: 74.0%; Average loss: 2.9814
Iteration: 2960; Percent complete: 74.0%; Average loss: 3.0087
Iteration: 2961; Percent complete: 74.0%; Average loss: 2.7345
Iteration: 2962; Percent complete: 74.1%; Average loss: 2.9282
Iteration: 2963; Percent complete: 74.1%; Average loss: 2.8201
Iteration: 2964; Percent complete: 74.1%; Average loss: 2.9237
Iteration: 2965; Percent complete: 74.1%; Average loss: 2.6653
Iteration: 2966; Percent complete: 74.2%; Average loss: 3.0634
Iteration: 2967; Percent complete: 74.2%; Average loss: 2.8987
Iteration: 2968; Percent complete: 74.2%; Average loss: 2.8424
Iteration: 2969; Percent complete: 74.2%; Average loss: 2.7150
Iteration: 2970; Percent complete: 74.2%; Average loss: 2.7509
Iteration: 2971; Percent complete: 74.3%; Average loss: 2.7952
Iteration: 2972; Percent complete: 74.3%; Average loss: 3.0056
Iteration: 2973; Percent complete: 74.3%; Average loss: 2.7306
Iteration: 2974; Percent complete: 74.4%; Average loss: 2.9304
Iteration: 2975; Percent complete: 74.4%; Average loss: 3.0600
Iteration: 2976; Percent complete: 74.4%; Average loss: 2.8578
Iteration: 2977; Percent complete: 74.4%; Average loss: 3.0665
Iteration: 2978; Percent complete: 74.5%; Average loss: 3.1404
Iteration: 2979; Percent complete: 74.5%; Average loss: 2.6647
Iteration: 2980; Percent complete: 74.5%; Average loss: 2.8593
Iteration: 2981; Percent complete: 74.5%; Average loss: 2.9158
Iteration: 2982; Percent complete: 74.6%; Average loss: 2.9132
Iteration: 2983; Percent complete: 74.6%; Average loss: 2.7758
Iteration: 2984; Percent complete: 74.6%; Average loss: 2.7854
Iteration: 2985; Percent complete: 74.6%; Average loss: 2.8594
Iteration: 2986; Percent complete: 74.7%; Average loss: 3.1313
Iteration: 2987; Percent complete: 74.7%; Average loss: 2.8872
Iteration: 2988; Percent complete: 74.7%; Average loss: 2.8901
Iteration: 2989; Percent complete: 74.7%; Average loss: 2.8069
Iteration: 2990; Percent complete: 74.8%; Average loss: 2.9414
Iteration: 2991; Percent complete: 74.8%; Average loss: 2.9761
Iteration: 2992; Percent complete: 74.8%; Average loss: 2.9362
Iteration: 2993; Percent complete: 74.8%; Average loss: 3.1281
Iteration: 2994; Percent complete: 74.9%; Average loss: 2.8524
Iteration: 2995; Percent complete: 74.9%; Average loss: 2.7378
Iteration: 2996; Percent complete: 74.9%; Average loss: 2.6569
Iteration: 2997; Percent complete: 74.9%; Average loss: 2.8532
Iteration: 2998; Percent complete: 75.0%; Average loss: 2.9236
Iteration: 2999; Percent complete: 75.0%; Average loss: 2.7350
Iteration: 3000; Percent complete: 75.0%; Average loss: 3.0461
Iteration: 3001; Percent complete: 75.0%; Average loss: 2.9514
Iteration: 3002; Percent complete: 75.0%; Average loss: 3.0748
Iteration: 3003; Percent complete: 75.1%; Average loss: 2.9561
Iteration: 3004; Percent complete: 75.1%; Average loss: 2.9497
Iteration: 3005; Percent complete: 75.1%; Average loss: 3.0533
Iteration: 3006; Percent complete: 75.1%; Average loss: 3.0052
Iteration: 3007; Percent complete: 75.2%; Average loss: 2.7560
Iteration: 3008; Percent complete: 75.2%; Average loss: 2.7156
Iteration: 3009; Percent complete: 75.2%; Average loss: 2.5941
Iteration: 3010; Percent complete: 75.2%; Average loss: 3.0676
Iteration: 3011; Percent complete: 75.3%; Average loss: 2.7836
Iteration: 3012; Percent complete: 75.3%; Average loss: 3.0900
Iteration: 3013; Percent complete: 75.3%; Average loss: 2.7825
Iteration: 3014; Percent complete: 75.3%; Average loss: 2.7961
Iteration: 3015; Percent complete: 75.4%; Average loss: 2.9118
Iteration: 3016; Percent complete: 75.4%; Average loss: 2.6749
Iteration: 3017; Percent complete: 75.4%; Average loss: 2.9623
Iteration: 3018; Percent complete: 75.4%; Average loss: 3.0370
Iteration: 3019; Percent complete: 75.5%; Average loss: 2.7479
Iteration: 3020; Percent complete: 75.5%; Average loss: 3.0931
Iteration: 3021; Percent complete: 75.5%; Average loss: 2.7482
Iteration: 3022; Percent complete: 75.5%; Average loss: 3.1210
Iteration: 3023; Percent complete: 75.6%; Average loss: 2.8391
Iteration: 3024; Percent complete: 75.6%; Average loss: 2.6178
Iteration: 3025; Percent complete: 75.6%; Average loss: 2.8650
Iteration: 3026; Percent complete: 75.6%; Average loss: 2.8067
Iteration: 3027; Percent complete: 75.7%; Average loss: 2.9466
Iteration: 3028; Percent complete: 75.7%; Average loss: 2.6622
Iteration: 3029; Percent complete: 75.7%; Average loss: 2.8813
Iteration: 3030; Percent complete: 75.8%; Average loss: 2.7907
Iteration: 3031; Percent complete: 75.8%; Average loss: 2.9332
Iteration: 3032; Percent complete: 75.8%; Average loss: 2.8671
Iteration: 3033; Percent complete: 75.8%; Average loss: 2.8404
Iteration: 3034; Percent complete: 75.8%; Average loss: 2.8443
Iteration: 3035; Percent complete: 75.9%; Average loss: 2.8621
Iteration: 3036; Percent complete: 75.9%; Average loss: 3.0639
Iteration: 3037; Percent complete: 75.9%; Average loss: 2.7060
Iteration: 3038; Percent complete: 75.9%; Average loss: 2.6728
Iteration: 3039; Percent complete: 76.0%; Average loss: 2.9002
Iteration: 3040; Percent complete: 76.0%; Average loss: 2.9113
Iteration: 3041; Percent complete: 76.0%; Average loss: 3.0060
Iteration: 3042; Percent complete: 76.0%; Average loss: 2.9017
Iteration: 3043; Percent complete: 76.1%; Average loss: 2.7876
Iteration: 3044; Percent complete: 76.1%; Average loss: 2.7330
Iteration: 3045; Percent complete: 76.1%; Average loss: 2.7272
Iteration: 3046; Percent complete: 76.1%; Average loss: 2.6029
Iteration: 3047; Percent complete: 76.2%; Average loss: 2.9358
Iteration: 3048; Percent complete: 76.2%; Average loss: 3.1272
Iteration: 3049; Percent complete: 76.2%; Average loss: 2.9298
Iteration: 3050; Percent complete: 76.2%; Average loss: 2.7981
Iteration: 3051; Percent complete: 76.3%; Average loss: 2.9772
Iteration: 3052; Percent complete: 76.3%; Average loss: 2.7544
Iteration: 3053; Percent complete: 76.3%; Average loss: 2.9157
Iteration: 3054; Percent complete: 76.3%; Average loss: 2.9591
Iteration: 3055; Percent complete: 76.4%; Average loss: 2.7974
Iteration: 3056; Percent complete: 76.4%; Average loss: 2.9601
Iteration: 3057; Percent complete: 76.4%; Average loss: 2.7008
Iteration: 3058; Percent complete: 76.4%; Average loss: 2.8368
Iteration: 3059; Percent complete: 76.5%; Average loss: 3.2072
Iteration: 3060; Percent complete: 76.5%; Average loss: 2.8745
Iteration: 3061; Percent complete: 76.5%; Average loss: 2.8660
Iteration: 3062; Percent complete: 76.5%; Average loss: 2.8567
Iteration: 3063; Percent complete: 76.6%; Average loss: 2.7794
Iteration: 3064; Percent complete: 76.6%; Average loss: 2.9313
Iteration: 3065; Percent complete: 76.6%; Average loss: 2.8773
Iteration: 3066; Percent complete: 76.6%; Average loss: 2.8114
Iteration: 3067; Percent complete: 76.7%; Average loss: 2.7984
Iteration: 3068; Percent complete: 76.7%; Average loss: 2.8504
Iteration: 3069; Percent complete: 76.7%; Average loss: 2.9869
Iteration: 3070; Percent complete: 76.8%; Average loss: 2.9947
Iteration: 3071; Percent complete: 76.8%; Average loss: 2.6140
Iteration: 3072; Percent complete: 76.8%; Average loss: 2.8269
Iteration: 3073; Percent complete: 76.8%; Average loss: 2.9737
Iteration: 3074; Percent complete: 76.8%; Average loss: 2.7850
Iteration: 3075; Percent complete: 76.9%; Average loss: 2.8044
Iteration: 3076; Percent complete: 76.9%; Average loss: 2.7693
Iteration: 3077; Percent complete: 76.9%; Average loss: 2.8386
Iteration: 3078; Percent complete: 77.0%; Average loss: 2.7679
Iteration: 3079; Percent complete: 77.0%; Average loss: 2.8719
Iteration: 3080; Percent complete: 77.0%; Average loss: 2.8305
Iteration: 3081; Percent complete: 77.0%; Average loss: 2.6769
Iteration: 3082; Percent complete: 77.0%; Average loss: 3.2034
Iteration: 3083; Percent complete: 77.1%; Average loss: 2.8817
Iteration: 3084; Percent complete: 77.1%; Average loss: 3.0407
Iteration: 3085; Percent complete: 77.1%; Average loss: 3.0328
Iteration: 3086; Percent complete: 77.1%; Average loss: 2.7557
Iteration: 3087; Percent complete: 77.2%; Average loss: 2.9435
Iteration: 3088; Percent complete: 77.2%; Average loss: 2.8590
Iteration: 3089; Percent complete: 77.2%; Average loss: 2.6743
Iteration: 3090; Percent complete: 77.2%; Average loss: 2.9657
Iteration: 3091; Percent complete: 77.3%; Average loss: 2.9865
Iteration: 3092; Percent complete: 77.3%; Average loss: 2.9190
Iteration: 3093; Percent complete: 77.3%; Average loss: 2.8566
Iteration: 3094; Percent complete: 77.3%; Average loss: 2.7062
Iteration: 3095; Percent complete: 77.4%; Average loss: 2.8840
Iteration: 3096; Percent complete: 77.4%; Average loss: 2.9234
Iteration: 3097; Percent complete: 77.4%; Average loss: 2.7972
Iteration: 3098; Percent complete: 77.5%; Average loss: 2.9673
Iteration: 3099; Percent complete: 77.5%; Average loss: 2.9003
Iteration: 3100; Percent complete: 77.5%; Average loss: 2.7710
Iteration: 3101; Percent complete: 77.5%; Average loss: 2.8932
Iteration: 3102; Percent complete: 77.5%; Average loss: 3.0703
Iteration: 3103; Percent complete: 77.6%; Average loss: 2.7866
Iteration: 3104; Percent complete: 77.6%; Average loss: 2.8606
Iteration: 3105; Percent complete: 77.6%; Average loss: 2.8668
Iteration: 3106; Percent complete: 77.6%; Average loss: 2.9974
Iteration: 3107; Percent complete: 77.7%; Average loss: 2.7143
Iteration: 3108; Percent complete: 77.7%; Average loss: 2.9293
Iteration: 3109; Percent complete: 77.7%; Average loss: 2.7443
Iteration: 3110; Percent complete: 77.8%; Average loss: 2.9945
Iteration: 3111; Percent complete: 77.8%; Average loss: 2.9451
Iteration: 3112; Percent complete: 77.8%; Average loss: 2.6839
Iteration: 3113; Percent complete: 77.8%; Average loss: 3.2674
Iteration: 3114; Percent complete: 77.8%; Average loss: 2.8591
Iteration: 3115; Percent complete: 77.9%; Average loss: 2.6743
Iteration: 3116; Percent complete: 77.9%; Average loss: 3.0746
Iteration: 3117; Percent complete: 77.9%; Average loss: 2.9257
Iteration: 3118; Percent complete: 78.0%; Average loss: 2.8611
Iteration: 3119; Percent complete: 78.0%; Average loss: 3.0455
Iteration: 3120; Percent complete: 78.0%; Average loss: 2.8313
Iteration: 3121; Percent complete: 78.0%; Average loss: 2.9652
Iteration: 3122; Percent complete: 78.0%; Average loss: 2.9064
Iteration: 3123; Percent complete: 78.1%; Average loss: 3.0036
Iteration: 3124; Percent complete: 78.1%; Average loss: 2.8729
Iteration: 3125; Percent complete: 78.1%; Average loss: 2.7017
Iteration: 3126; Percent complete: 78.1%; Average loss: 2.9404
Iteration: 3127; Percent complete: 78.2%; Average loss: 2.8024
Iteration: 3128; Percent complete: 78.2%; Average loss: 2.8290
Iteration: 3129; Percent complete: 78.2%; Average loss: 2.9617
Iteration: 3130; Percent complete: 78.2%; Average loss: 2.7417
Iteration: 3131; Percent complete: 78.3%; Average loss: 2.7952
Iteration: 3132; Percent complete: 78.3%; Average loss: 2.8247
Iteration: 3133; Percent complete: 78.3%; Average loss: 2.7670
Iteration: 3134; Percent complete: 78.3%; Average loss: 2.8868
Iteration: 3135; Percent complete: 78.4%; Average loss: 2.7258
Iteration: 3136; Percent complete: 78.4%; Average loss: 3.0654
Iteration: 3137; Percent complete: 78.4%; Average loss: 2.7540
Iteration: 3138; Percent complete: 78.5%; Average loss: 2.8136
Iteration: 3139; Percent complete: 78.5%; Average loss: 2.8839
Iteration: 3140; Percent complete: 78.5%; Average loss: 2.7914
Iteration: 3141; Percent complete: 78.5%; Average loss: 2.7487
Iteration: 3142; Percent complete: 78.5%; Average loss: 2.8766
Iteration: 3143; Percent complete: 78.6%; Average loss: 2.8213
Iteration: 3144; Percent complete: 78.6%; Average loss: 2.6648
Iteration: 3145; Percent complete: 78.6%; Average loss: 2.6037
Iteration: 3146; Percent complete: 78.6%; Average loss: 2.8891
Iteration: 3147; Percent complete: 78.7%; Average loss: 2.9031
Iteration: 3148; Percent complete: 78.7%; Average loss: 3.0725
Iteration: 3149; Percent complete: 78.7%; Average loss: 2.8242
Iteration: 3150; Percent complete: 78.8%; Average loss: 2.8755
Iteration: 3151; Percent complete: 78.8%; Average loss: 2.9147
Iteration: 3152; Percent complete: 78.8%; Average loss: 2.5646
Iteration: 3153; Percent complete: 78.8%; Average loss: 2.6263
Iteration: 3154; Percent complete: 78.8%; Average loss: 2.9336
Iteration: 3155; Percent complete: 78.9%; Average loss: 2.7877
Iteration: 3156; Percent complete: 78.9%; Average loss: 2.8902
Iteration: 3157; Percent complete: 78.9%; Average loss: 2.7546
Iteration: 3158; Percent complete: 79.0%; Average loss: 3.1943
Iteration: 3159; Percent complete: 79.0%; Average loss: 2.8774
Iteration: 3160; Percent complete: 79.0%; Average loss: 2.7620
Iteration: 3161; Percent complete: 79.0%; Average loss: 2.8728
Iteration: 3162; Percent complete: 79.0%; Average loss: 2.5542
Iteration: 3163; Percent complete: 79.1%; Average loss: 2.8584
Iteration: 3164; Percent complete: 79.1%; Average loss: 2.7911
Iteration: 3165; Percent complete: 79.1%; Average loss: 2.8860
Iteration: 3166; Percent complete: 79.1%; Average loss: 3.0919
Iteration: 3167; Percent complete: 79.2%; Average loss: 2.9605
Iteration: 3168; Percent complete: 79.2%; Average loss: 2.6872
Iteration: 3169; Percent complete: 79.2%; Average loss: 2.7716
Iteration: 3170; Percent complete: 79.2%; Average loss: 2.7139
Iteration: 3171; Percent complete: 79.3%; Average loss: 2.6870
Iteration: 3172; Percent complete: 79.3%; Average loss: 2.7934
Iteration: 3173; Percent complete: 79.3%; Average loss: 2.6762
Iteration: 3174; Percent complete: 79.3%; Average loss: 2.9188
Iteration: 3175; Percent complete: 79.4%; Average loss: 2.9462
Iteration: 3176; Percent complete: 79.4%; Average loss: 3.0500
Iteration: 3177; Percent complete: 79.4%; Average loss: 2.7132
Iteration: 3178; Percent complete: 79.5%; Average loss: 2.5325
Iteration: 3179; Percent complete: 79.5%; Average loss: 2.6096
Iteration: 3180; Percent complete: 79.5%; Average loss: 3.0108
Iteration: 3181; Percent complete: 79.5%; Average loss: 2.9933
Iteration: 3182; Percent complete: 79.5%; Average loss: 2.9583
Iteration: 3183; Percent complete: 79.6%; Average loss: 2.8072
Iteration: 3184; Percent complete: 79.6%; Average loss: 2.7978
Iteration: 3185; Percent complete: 79.6%; Average loss: 2.7875
Iteration: 3186; Percent complete: 79.7%; Average loss: 2.8550
Iteration: 3187; Percent complete: 79.7%; Average loss: 3.0604
Iteration: 3188; Percent complete: 79.7%; Average loss: 2.5748
Iteration: 3189; Percent complete: 79.7%; Average loss: 2.7082
Iteration: 3190; Percent complete: 79.8%; Average loss: 2.9443
Iteration: 3191; Percent complete: 79.8%; Average loss: 2.7768
Iteration: 3192; Percent complete: 79.8%; Average loss: 2.9687
Iteration: 3193; Percent complete: 79.8%; Average loss: 2.9612
Iteration: 3194; Percent complete: 79.8%; Average loss: 2.8542
Iteration: 3195; Percent complete: 79.9%; Average loss: 2.6784
Iteration: 3196; Percent complete: 79.9%; Average loss: 2.9378
Iteration: 3197; Percent complete: 79.9%; Average loss: 2.7807
Iteration: 3198; Percent complete: 80.0%; Average loss: 2.7677
Iteration: 3199; Percent complete: 80.0%; Average loss: 2.9701
Iteration: 3200; Percent complete: 80.0%; Average loss: 3.0685
Iteration: 3201; Percent complete: 80.0%; Average loss: 2.7350
Iteration: 3202; Percent complete: 80.0%; Average loss: 2.8016
Iteration: 3203; Percent complete: 80.1%; Average loss: 2.8969
Iteration: 3204; Percent complete: 80.1%; Average loss: 2.8190
Iteration: 3205; Percent complete: 80.1%; Average loss: 2.7865
Iteration: 3206; Percent complete: 80.2%; Average loss: 2.5256
Iteration: 3207; Percent complete: 80.2%; Average loss: 2.8503
Iteration: 3208; Percent complete: 80.2%; Average loss: 2.7760
Iteration: 3209; Percent complete: 80.2%; Average loss: 2.9586
Iteration: 3210; Percent complete: 80.2%; Average loss: 3.0037
Iteration: 3211; Percent complete: 80.3%; Average loss: 2.7333
Iteration: 3212; Percent complete: 80.3%; Average loss: 2.6731
Iteration: 3213; Percent complete: 80.3%; Average loss: 2.7455
Iteration: 3214; Percent complete: 80.3%; Average loss: 2.9548
Iteration: 3215; Percent complete: 80.4%; Average loss: 2.8795
Iteration: 3216; Percent complete: 80.4%; Average loss: 3.1304
Iteration: 3217; Percent complete: 80.4%; Average loss: 2.8980
Iteration: 3218; Percent complete: 80.5%; Average loss: 3.0011
Iteration: 3219; Percent complete: 80.5%; Average loss: 2.9086
Iteration: 3220; Percent complete: 80.5%; Average loss: 2.8703
Iteration: 3221; Percent complete: 80.5%; Average loss: 2.8340
Iteration: 3222; Percent complete: 80.5%; Average loss: 2.7695
Iteration: 3223; Percent complete: 80.6%; Average loss: 2.9286
Iteration: 3224; Percent complete: 80.6%; Average loss: 3.0352
Iteration: 3225; Percent complete: 80.6%; Average loss: 2.7676
Iteration: 3226; Percent complete: 80.7%; Average loss: 2.9830
Iteration: 3227; Percent complete: 80.7%; Average loss: 2.8992
Iteration: 3228; Percent complete: 80.7%; Average loss: 2.7327
Iteration: 3229; Percent complete: 80.7%; Average loss: 2.7441
Iteration: 3230; Percent complete: 80.8%; Average loss: 3.0318
Iteration: 3231; Percent complete: 80.8%; Average loss: 2.5785
Iteration: 3232; Percent complete: 80.8%; Average loss: 3.0985
Iteration: 3233; Percent complete: 80.8%; Average loss: 2.8065
Iteration: 3234; Percent complete: 80.8%; Average loss: 2.7052
Iteration: 3235; Percent complete: 80.9%; Average loss: 2.7505
Iteration: 3236; Percent complete: 80.9%; Average loss: 2.9852
Iteration: 3237; Percent complete: 80.9%; Average loss: 2.6799
Iteration: 3238; Percent complete: 81.0%; Average loss: 2.9769
Iteration: 3239; Percent complete: 81.0%; Average loss: 2.8437
Iteration: 3240; Percent complete: 81.0%; Average loss: 2.6562
Iteration: 3241; Percent complete: 81.0%; Average loss: 2.8007
Iteration: 3242; Percent complete: 81.0%; Average loss: 2.7568
Iteration: 3243; Percent complete: 81.1%; Average loss: 2.6897
Iteration: 3244; Percent complete: 81.1%; Average loss: 2.9992
Iteration: 3245; Percent complete: 81.1%; Average loss: 2.8504
Iteration: 3246; Percent complete: 81.2%; Average loss: 2.7677
Iteration: 3247; Percent complete: 81.2%; Average loss: 3.0071
Iteration: 3248; Percent complete: 81.2%; Average loss: 2.8937
Iteration: 3249; Percent complete: 81.2%; Average loss: 2.8081
Iteration: 3250; Percent complete: 81.2%; Average loss: 2.8869
Iteration: 3251; Percent complete: 81.3%; Average loss: 2.8103
Iteration: 3252; Percent complete: 81.3%; Average loss: 2.8112
Iteration: 3253; Percent complete: 81.3%; Average loss: 3.0038
Iteration: 3254; Percent complete: 81.3%; Average loss: 2.8265
Iteration: 3255; Percent complete: 81.4%; Average loss: 2.7632
Iteration: 3256; Percent complete: 81.4%; Average loss: 2.6257
Iteration: 3257; Percent complete: 81.4%; Average loss: 2.7664
Iteration: 3258; Percent complete: 81.5%; Average loss: 2.6516
Iteration: 3259; Percent complete: 81.5%; Average loss: 2.8683
Iteration: 3260; Percent complete: 81.5%; Average loss: 2.8612
Iteration: 3261; Percent complete: 81.5%; Average loss: 2.9193
Iteration: 3262; Percent complete: 81.5%; Average loss: 2.8254
Iteration: 3263; Percent complete: 81.6%; Average loss: 2.7405
Iteration: 3264; Percent complete: 81.6%; Average loss: 2.7210
Iteration: 3265; Percent complete: 81.6%; Average loss: 2.8934
Iteration: 3266; Percent complete: 81.7%; Average loss: 2.8140
Iteration: 3267; Percent complete: 81.7%; Average loss: 2.7855
Iteration: 3268; Percent complete: 81.7%; Average loss: 2.8850
Iteration: 3269; Percent complete: 81.7%; Average loss: 2.7846
Iteration: 3270; Percent complete: 81.8%; Average loss: 2.7924
Iteration: 3271; Percent complete: 81.8%; Average loss: 2.8396
Iteration: 3272; Percent complete: 81.8%; Average loss: 2.7655
Iteration: 3273; Percent complete: 81.8%; Average loss: 2.7654
Iteration: 3274; Percent complete: 81.8%; Average loss: 2.7954
Iteration: 3275; Percent complete: 81.9%; Average loss: 2.7928
Iteration: 3276; Percent complete: 81.9%; Average loss: 2.9356
Iteration: 3277; Percent complete: 81.9%; Average loss: 2.9862
Iteration: 3278; Percent complete: 82.0%; Average loss: 2.7969
Iteration: 3279; Percent complete: 82.0%; Average loss: 3.0406
Iteration: 3280; Percent complete: 82.0%; Average loss: 2.7548
Iteration: 3281; Percent complete: 82.0%; Average loss: 2.7574
Iteration: 3282; Percent complete: 82.0%; Average loss: 2.8022
Iteration: 3283; Percent complete: 82.1%; Average loss: 2.7902
Iteration: 3284; Percent complete: 82.1%; Average loss: 2.9308
Iteration: 3285; Percent complete: 82.1%; Average loss: 2.7873
Iteration: 3286; Percent complete: 82.2%; Average loss: 2.4638
Iteration: 3287; Percent complete: 82.2%; Average loss: 2.6817
Iteration: 3288; Percent complete: 82.2%; Average loss: 2.9326
Iteration: 3289; Percent complete: 82.2%; Average loss: 2.9619
Iteration: 3290; Percent complete: 82.2%; Average loss: 2.7564
Iteration: 3291; Percent complete: 82.3%; Average loss: 2.9361
Iteration: 3292; Percent complete: 82.3%; Average loss: 2.9545
Iteration: 3293; Percent complete: 82.3%; Average loss: 2.7231
Iteration: 3294; Percent complete: 82.3%; Average loss: 2.6682
Iteration: 3295; Percent complete: 82.4%; Average loss: 2.6265
Iteration: 3296; Percent complete: 82.4%; Average loss: 2.7281
Iteration: 3297; Percent complete: 82.4%; Average loss: 2.9460
Iteration: 3298; Percent complete: 82.5%; Average loss: 2.8143
Iteration: 3299; Percent complete: 82.5%; Average loss: 2.9536
Iteration: 3300; Percent complete: 82.5%; Average loss: 2.9056
Iteration: 3301; Percent complete: 82.5%; Average loss: 2.7589
Iteration: 3302; Percent complete: 82.5%; Average loss: 3.0455
Iteration: 3303; Percent complete: 82.6%; Average loss: 2.8779
Iteration: 3304; Percent complete: 82.6%; Average loss: 2.8115
Iteration: 3305; Percent complete: 82.6%; Average loss: 2.6812
Iteration: 3306; Percent complete: 82.7%; Average loss: 2.8265
Iteration: 3307; Percent complete: 82.7%; Average loss: 2.7356
Iteration: 3308; Percent complete: 82.7%; Average loss: 2.8804
Iteration: 3309; Percent complete: 82.7%; Average loss: 2.7883
Iteration: 3310; Percent complete: 82.8%; Average loss: 2.6543
Iteration: 3311; Percent complete: 82.8%; Average loss: 2.6686
Iteration: 3312; Percent complete: 82.8%; Average loss: 2.9270
Iteration: 3313; Percent complete: 82.8%; Average loss: 2.9599
Iteration: 3314; Percent complete: 82.8%; Average loss: 2.4579
Iteration: 3315; Percent complete: 82.9%; Average loss: 2.7864
Iteration: 3316; Percent complete: 82.9%; Average loss: 2.6703
Iteration: 3317; Percent complete: 82.9%; Average loss: 2.7834
Iteration: 3318; Percent complete: 83.0%; Average loss: 2.7423
Iteration: 3319; Percent complete: 83.0%; Average loss: 2.8086
Iteration: 3320; Percent complete: 83.0%; Average loss: 2.8172
Iteration: 3321; Percent complete: 83.0%; Average loss: 2.5955
Iteration: 3322; Percent complete: 83.0%; Average loss: 3.0481
Iteration: 3323; Percent complete: 83.1%; Average loss: 2.8823
Iteration: 3324; Percent complete: 83.1%; Average loss: 2.7212
Iteration: 3325; Percent complete: 83.1%; Average loss: 2.7745
Iteration: 3326; Percent complete: 83.2%; Average loss: 2.5303
Iteration: 3327; Percent complete: 83.2%; Average loss: 2.6571
Iteration: 3328; Percent complete: 83.2%; Average loss: 2.6556
Iteration: 3329; Percent complete: 83.2%; Average loss: 2.9842
Iteration: 3330; Percent complete: 83.2%; Average loss: 2.7672
Iteration: 3331; Percent complete: 83.3%; Average loss: 2.9716
Iteration: 3332; Percent complete: 83.3%; Average loss: 2.8782
Iteration: 3333; Percent complete: 83.3%; Average loss: 2.9178
Iteration: 3334; Percent complete: 83.4%; Average loss: 2.5178
Iteration: 3335; Percent complete: 83.4%; Average loss: 2.9247
Iteration: 3336; Percent complete: 83.4%; Average loss: 2.8087
Iteration: 3337; Percent complete: 83.4%; Average loss: 2.8809
Iteration: 3338; Percent complete: 83.5%; Average loss: 2.7982
Iteration: 3339; Percent complete: 83.5%; Average loss: 2.7787
Iteration: 3340; Percent complete: 83.5%; Average loss: 2.7916
Iteration: 3341; Percent complete: 83.5%; Average loss: 3.0480
Iteration: 3342; Percent complete: 83.5%; Average loss: 2.7336
Iteration: 3343; Percent complete: 83.6%; Average loss: 2.7286
Iteration: 3344; Percent complete: 83.6%; Average loss: 2.9943
Iteration: 3345; Percent complete: 83.6%; Average loss: 2.8740
Iteration: 3346; Percent complete: 83.7%; Average loss: 2.9468
Iteration: 3347; Percent complete: 83.7%; Average loss: 2.7970
Iteration: 3348; Percent complete: 83.7%; Average loss: 2.9257
Iteration: 3349; Percent complete: 83.7%; Average loss: 2.9923
Iteration: 3350; Percent complete: 83.8%; Average loss: 2.5972
Iteration: 3351; Percent complete: 83.8%; Average loss: 2.6951
Iteration: 3352; Percent complete: 83.8%; Average loss: 2.8877
Iteration: 3353; Percent complete: 83.8%; Average loss: 2.8607
Iteration: 3354; Percent complete: 83.9%; Average loss: 2.6028
Iteration: 3355; Percent complete: 83.9%; Average loss: 2.8886
Iteration: 3356; Percent complete: 83.9%; Average loss: 3.0234
Iteration: 3357; Percent complete: 83.9%; Average loss: 2.7876
Iteration: 3358; Percent complete: 84.0%; Average loss: 2.7288
Iteration: 3359; Percent complete: 84.0%; Average loss: 2.6526
Iteration: 3360; Percent complete: 84.0%; Average loss: 2.6736
Iteration: 3361; Percent complete: 84.0%; Average loss: 2.8360
Iteration: 3362; Percent complete: 84.0%; Average loss: 2.8474
Iteration: 3363; Percent complete: 84.1%; Average loss: 2.8389
Iteration: 3364; Percent complete: 84.1%; Average loss: 2.8352
Iteration: 3365; Percent complete: 84.1%; Average loss: 2.8278
Iteration: 3366; Percent complete: 84.2%; Average loss: 2.6363
Iteration: 3367; Percent complete: 84.2%; Average loss: 2.8360
Iteration: 3368; Percent complete: 84.2%; Average loss: 2.7662
Iteration: 3369; Percent complete: 84.2%; Average loss: 3.0368
Iteration: 3370; Percent complete: 84.2%; Average loss: 2.9320
Iteration: 3371; Percent complete: 84.3%; Average loss: 2.9734
Iteration: 3372; Percent complete: 84.3%; Average loss: 3.0058
Iteration: 3373; Percent complete: 84.3%; Average loss: 2.7719
Iteration: 3374; Percent complete: 84.4%; Average loss: 2.8855
Iteration: 3375; Percent complete: 84.4%; Average loss: 2.8289
Iteration: 3376; Percent complete: 84.4%; Average loss: 2.5928
Iteration: 3377; Percent complete: 84.4%; Average loss: 2.6162
Iteration: 3378; Percent complete: 84.5%; Average loss: 2.7504
Iteration: 3379; Percent complete: 84.5%; Average loss: 2.8831
Iteration: 3380; Percent complete: 84.5%; Average loss: 2.8006
Iteration: 3381; Percent complete: 84.5%; Average loss: 2.8636
Iteration: 3382; Percent complete: 84.5%; Average loss: 2.6685
Iteration: 3383; Percent complete: 84.6%; Average loss: 2.7489
Iteration: 3384; Percent complete: 84.6%; Average loss: 2.8260
Iteration: 3385; Percent complete: 84.6%; Average loss: 2.9073
Iteration: 3386; Percent complete: 84.7%; Average loss: 2.7793
Iteration: 3387; Percent complete: 84.7%; Average loss: 2.6780
Iteration: 3388; Percent complete: 84.7%; Average loss: 2.4904
Iteration: 3389; Percent complete: 84.7%; Average loss: 2.9182
Iteration: 3390; Percent complete: 84.8%; Average loss: 2.7081
Iteration: 3391; Percent complete: 84.8%; Average loss: 2.8765
Iteration: 3392; Percent complete: 84.8%; Average loss: 2.9088
Iteration: 3393; Percent complete: 84.8%; Average loss: 2.6613
Iteration: 3394; Percent complete: 84.9%; Average loss: 3.1247
Iteration: 3395; Percent complete: 84.9%; Average loss: 2.5699
Iteration: 3396; Percent complete: 84.9%; Average loss: 2.8205
Iteration: 3397; Percent complete: 84.9%; Average loss: 2.5444
Iteration: 3398; Percent complete: 85.0%; Average loss: 2.9280
Iteration: 3399; Percent complete: 85.0%; Average loss: 2.8964
Iteration: 3400; Percent complete: 85.0%; Average loss: 2.6413
Iteration: 3401; Percent complete: 85.0%; Average loss: 2.6981
Iteration: 3402; Percent complete: 85.0%; Average loss: 2.7196
Iteration: 3403; Percent complete: 85.1%; Average loss: 2.8629
Iteration: 3404; Percent complete: 85.1%; Average loss: 2.7346
Iteration: 3405; Percent complete: 85.1%; Average loss: 2.6789
Iteration: 3406; Percent complete: 85.2%; Average loss: 2.9829
Iteration: 3407; Percent complete: 85.2%; Average loss: 2.8236
Iteration: 3408; Percent complete: 85.2%; Average loss: 2.7190
Iteration: 3409; Percent complete: 85.2%; Average loss: 2.6382
Iteration: 3410; Percent complete: 85.2%; Average loss: 2.7296
Iteration: 3411; Percent complete: 85.3%; Average loss: 2.6966
Iteration: 3412; Percent complete: 85.3%; Average loss: 2.7739
Iteration: 3413; Percent complete: 85.3%; Average loss: 2.8523
Iteration: 3414; Percent complete: 85.4%; Average loss: 2.7064
Iteration: 3415; Percent complete: 85.4%; Average loss: 2.8318
Iteration: 3416; Percent complete: 85.4%; Average loss: 2.8826
Iteration: 3417; Percent complete: 85.4%; Average loss: 2.6192
Iteration: 3418; Percent complete: 85.5%; Average loss: 2.8208
Iteration: 3419; Percent complete: 85.5%; Average loss: 2.7770
Iteration: 3420; Percent complete: 85.5%; Average loss: 2.7946
Iteration: 3421; Percent complete: 85.5%; Average loss: 2.7829
Iteration: 3422; Percent complete: 85.5%; Average loss: 2.8868
Iteration: 3423; Percent complete: 85.6%; Average loss: 2.9152
Iteration: 3424; Percent complete: 85.6%; Average loss: 2.6420
Iteration: 3425; Percent complete: 85.6%; Average loss: 2.7938
Iteration: 3426; Percent complete: 85.7%; Average loss: 2.7072
Iteration: 3427; Percent complete: 85.7%; Average loss: 2.8132
Iteration: 3428; Percent complete: 85.7%; Average loss: 2.9613
Iteration: 3429; Percent complete: 85.7%; Average loss: 2.8762
Iteration: 3430; Percent complete: 85.8%; Average loss: 2.8326
Iteration: 3431; Percent complete: 85.8%; Average loss: 2.7935
Iteration: 3432; Percent complete: 85.8%; Average loss: 2.5820
Iteration: 3433; Percent complete: 85.8%; Average loss: 2.7628
Iteration: 3434; Percent complete: 85.9%; Average loss: 2.9727
Iteration: 3435; Percent complete: 85.9%; Average loss: 2.8352
Iteration: 3436; Percent complete: 85.9%; Average loss: 2.8306
Iteration: 3437; Percent complete: 85.9%; Average loss: 2.8947
Iteration: 3438; Percent complete: 86.0%; Average loss: 2.9057
Iteration: 3439; Percent complete: 86.0%; Average loss: 2.6971
Iteration: 3440; Percent complete: 86.0%; Average loss: 2.6279
Iteration: 3441; Percent complete: 86.0%; Average loss: 2.9780
Iteration: 3442; Percent complete: 86.1%; Average loss: 2.6939
Iteration: 3443; Percent complete: 86.1%; Average loss: 2.7526
Iteration: 3444; Percent complete: 86.1%; Average loss: 2.5988
Iteration: 3445; Percent complete: 86.1%; Average loss: 2.8382
Iteration: 3446; Percent complete: 86.2%; Average loss: 2.9945
Iteration: 3447; Percent complete: 86.2%; Average loss: 2.8006
Iteration: 3448; Percent complete: 86.2%; Average loss: 2.7636
Iteration: 3449; Percent complete: 86.2%; Average loss: 2.6577
Iteration: 3450; Percent complete: 86.2%; Average loss: 2.7565
Iteration: 3451; Percent complete: 86.3%; Average loss: 2.7263
Iteration: 3452; Percent complete: 86.3%; Average loss: 2.4852
Iteration: 3453; Percent complete: 86.3%; Average loss: 2.6552
Iteration: 3454; Percent complete: 86.4%; Average loss: 2.5797
Iteration: 3455; Percent complete: 86.4%; Average loss: 2.7966
Iteration: 3456; Percent complete: 86.4%; Average loss: 2.8034
Iteration: 3457; Percent complete: 86.4%; Average loss: 2.6125
Iteration: 3458; Percent complete: 86.5%; Average loss: 2.5128
Iteration: 3459; Percent complete: 86.5%; Average loss: 2.8678
Iteration: 3460; Percent complete: 86.5%; Average loss: 2.9363
Iteration: 3461; Percent complete: 86.5%; Average loss: 2.6282
Iteration: 3462; Percent complete: 86.6%; Average loss: 2.8725
Iteration: 3463; Percent complete: 86.6%; Average loss: 2.4925
Iteration: 3464; Percent complete: 86.6%; Average loss: 2.7546
Iteration: 3465; Percent complete: 86.6%; Average loss: 2.8224
Iteration: 3466; Percent complete: 86.7%; Average loss: 2.5846
Iteration: 3467; Percent complete: 86.7%; Average loss: 2.7183
Iteration: 3468; Percent complete: 86.7%; Average loss: 2.7448
Iteration: 3469; Percent complete: 86.7%; Average loss: 2.5341
Iteration: 3470; Percent complete: 86.8%; Average loss: 2.6129
Iteration: 3471; Percent complete: 86.8%; Average loss: 2.6167
Iteration: 3472; Percent complete: 86.8%; Average loss: 2.4472
Iteration: 3473; Percent complete: 86.8%; Average loss: 2.8480
Iteration: 3474; Percent complete: 86.9%; Average loss: 2.7905
Iteration: 3475; Percent complete: 86.9%; Average loss: 2.8354
Iteration: 3476; Percent complete: 86.9%; Average loss: 2.9326
Iteration: 3477; Percent complete: 86.9%; Average loss: 2.9919
Iteration: 3478; Percent complete: 87.0%; Average loss: 2.8495
Iteration: 3479; Percent complete: 87.0%; Average loss: 2.7328
Iteration: 3480; Percent complete: 87.0%; Average loss: 2.7148
Iteration: 3481; Percent complete: 87.0%; Average loss: 2.6997
Iteration: 3482; Percent complete: 87.1%; Average loss: 2.8215
Iteration: 3483; Percent complete: 87.1%; Average loss: 2.4480
Iteration: 3484; Percent complete: 87.1%; Average loss: 2.8014
Iteration: 3485; Percent complete: 87.1%; Average loss: 2.6185
Iteration: 3486; Percent complete: 87.2%; Average loss: 3.0070
Iteration: 3487; Percent complete: 87.2%; Average loss: 2.7128
Iteration: 3488; Percent complete: 87.2%; Average loss: 2.9003
Iteration: 3489; Percent complete: 87.2%; Average loss: 2.5697
Iteration: 3490; Percent complete: 87.2%; Average loss: 2.6938
Iteration: 3491; Percent complete: 87.3%; Average loss: 2.6125
Iteration: 3492; Percent complete: 87.3%; Average loss: 2.7871
Iteration: 3493; Percent complete: 87.3%; Average loss: 2.6497
Iteration: 3494; Percent complete: 87.4%; Average loss: 3.0193
Iteration: 3495; Percent complete: 87.4%; Average loss: 2.7091
Iteration: 3496; Percent complete: 87.4%; Average loss: 2.6599
Iteration: 3497; Percent complete: 87.4%; Average loss: 2.6969
Iteration: 3498; Percent complete: 87.5%; Average loss: 2.7667
Iteration: 3499; Percent complete: 87.5%; Average loss: 2.6772
Iteration: 3500; Percent complete: 87.5%; Average loss: 2.5038
Iteration: 3501; Percent complete: 87.5%; Average loss: 2.8604
Iteration: 3502; Percent complete: 87.5%; Average loss: 2.7169
Iteration: 3503; Percent complete: 87.6%; Average loss: 2.8159
Iteration: 3504; Percent complete: 87.6%; Average loss: 2.8219
Iteration: 3505; Percent complete: 87.6%; Average loss: 2.7778
Iteration: 3506; Percent complete: 87.6%; Average loss: 2.7346
Iteration: 3507; Percent complete: 87.7%; Average loss: 2.9558
Iteration: 3508; Percent complete: 87.7%; Average loss: 2.5107
Iteration: 3509; Percent complete: 87.7%; Average loss: 2.5760
Iteration: 3510; Percent complete: 87.8%; Average loss: 2.7620
Iteration: 3511; Percent complete: 87.8%; Average loss: 2.8030
Iteration: 3512; Percent complete: 87.8%; Average loss: 2.9248
Iteration: 3513; Percent complete: 87.8%; Average loss: 2.5120
Iteration: 3514; Percent complete: 87.8%; Average loss: 2.6609
Iteration: 3515; Percent complete: 87.9%; Average loss: 2.9924
Iteration: 3516; Percent complete: 87.9%; Average loss: 2.9117
Iteration: 3517; Percent complete: 87.9%; Average loss: 2.8854
Iteration: 3518; Percent complete: 87.9%; Average loss: 2.6412
Iteration: 3519; Percent complete: 88.0%; Average loss: 2.8752
Iteration: 3520; Percent complete: 88.0%; Average loss: 2.7088
Iteration: 3521; Percent complete: 88.0%; Average loss: 2.8992
Iteration: 3522; Percent complete: 88.0%; Average loss: 2.6387
Iteration: 3523; Percent complete: 88.1%; Average loss: 2.9260
Iteration: 3524; Percent complete: 88.1%; Average loss: 2.6286
Iteration: 3525; Percent complete: 88.1%; Average loss: 2.5923
Iteration: 3526; Percent complete: 88.1%; Average loss: 2.6651
Iteration: 3527; Percent complete: 88.2%; Average loss: 2.7807
Iteration: 3528; Percent complete: 88.2%; Average loss: 2.9218
Iteration: 3529; Percent complete: 88.2%; Average loss: 2.5115
Iteration: 3530; Percent complete: 88.2%; Average loss: 2.7224
Iteration: 3531; Percent complete: 88.3%; Average loss: 2.7881
Iteration: 3532; Percent complete: 88.3%; Average loss: 2.7686
Iteration: 3533; Percent complete: 88.3%; Average loss: 2.6168
Iteration: 3534; Percent complete: 88.3%; Average loss: 2.7440
Iteration: 3535; Percent complete: 88.4%; Average loss: 2.9372
Iteration: 3536; Percent complete: 88.4%; Average loss: 2.8988
Iteration: 3537; Percent complete: 88.4%; Average loss: 2.7528
Iteration: 3538; Percent complete: 88.4%; Average loss: 2.9038
Iteration: 3539; Percent complete: 88.5%; Average loss: 2.7003
Iteration: 3540; Percent complete: 88.5%; Average loss: 2.6503
Iteration: 3541; Percent complete: 88.5%; Average loss: 2.8113
Iteration: 3542; Percent complete: 88.5%; Average loss: 2.5898
Iteration: 3543; Percent complete: 88.6%; Average loss: 2.6662
Iteration: 3544; Percent complete: 88.6%; Average loss: 2.5419
Iteration: 3545; Percent complete: 88.6%; Average loss: 2.6221
Iteration: 3546; Percent complete: 88.6%; Average loss: 2.9322
Iteration: 3547; Percent complete: 88.7%; Average loss: 2.7269
Iteration: 3548; Percent complete: 88.7%; Average loss: 2.7463
Iteration: 3549; Percent complete: 88.7%; Average loss: 2.7462
Iteration: 3550; Percent complete: 88.8%; Average loss: 2.7736
Iteration: 3551; Percent complete: 88.8%; Average loss: 2.8560
Iteration: 3552; Percent complete: 88.8%; Average loss: 2.7787
Iteration: 3553; Percent complete: 88.8%; Average loss: 2.7632
Iteration: 3554; Percent complete: 88.8%; Average loss: 2.5391
Iteration: 3555; Percent complete: 88.9%; Average loss: 2.5577
Iteration: 3556; Percent complete: 88.9%; Average loss: 2.7962
Iteration: 3557; Percent complete: 88.9%; Average loss: 2.7391
Iteration: 3558; Percent complete: 88.9%; Average loss: 2.9519
Iteration: 3559; Percent complete: 89.0%; Average loss: 2.6909
Iteration: 3560; Percent complete: 89.0%; Average loss: 2.7490
Iteration: 3561; Percent complete: 89.0%; Average loss: 2.7956
Iteration: 3562; Percent complete: 89.0%; Average loss: 2.6075
Iteration: 3563; Percent complete: 89.1%; Average loss: 2.7627
Iteration: 3564; Percent complete: 89.1%; Average loss: 2.7197
Iteration: 3565; Percent complete: 89.1%; Average loss: 2.9433
Iteration: 3566; Percent complete: 89.1%; Average loss: 2.7195
Iteration: 3567; Percent complete: 89.2%; Average loss: 2.7068
Iteration: 3568; Percent complete: 89.2%; Average loss: 2.6592
Iteration: 3569; Percent complete: 89.2%; Average loss: 2.6402
Iteration: 3570; Percent complete: 89.2%; Average loss: 3.0071
Iteration: 3571; Percent complete: 89.3%; Average loss: 2.9444
Iteration: 3572; Percent complete: 89.3%; Average loss: 2.7886
Iteration: 3573; Percent complete: 89.3%; Average loss: 2.7337
Iteration: 3574; Percent complete: 89.3%; Average loss: 2.5798
Iteration: 3575; Percent complete: 89.4%; Average loss: 2.8144
Iteration: 3576; Percent complete: 89.4%; Average loss: 2.9512
Iteration: 3577; Percent complete: 89.4%; Average loss: 2.6589
Iteration: 3578; Percent complete: 89.5%; Average loss: 2.6421
Iteration: 3579; Percent complete: 89.5%; Average loss: 2.8280
Iteration: 3580; Percent complete: 89.5%; Average loss: 2.7538
Iteration: 3581; Percent complete: 89.5%; Average loss: 2.6780
Iteration: 3582; Percent complete: 89.5%; Average loss: 2.6433
Iteration: 3583; Percent complete: 89.6%; Average loss: 2.7350
Iteration: 3584; Percent complete: 89.6%; Average loss: 2.6723
Iteration: 3585; Percent complete: 89.6%; Average loss: 2.6306
Iteration: 3586; Percent complete: 89.6%; Average loss: 2.8765
Iteration: 3587; Percent complete: 89.7%; Average loss: 2.8240
Iteration: 3588; Percent complete: 89.7%; Average loss: 2.8207
Iteration: 3589; Percent complete: 89.7%; Average loss: 2.8264
Iteration: 3590; Percent complete: 89.8%; Average loss: 2.5697
Iteration: 3591; Percent complete: 89.8%; Average loss: 2.7111
Iteration: 3592; Percent complete: 89.8%; Average loss: 2.5576
Iteration: 3593; Percent complete: 89.8%; Average loss: 2.6476
Iteration: 3594; Percent complete: 89.8%; Average loss: 2.4661
Iteration: 3595; Percent complete: 89.9%; Average loss: 2.9426
Iteration: 3596; Percent complete: 89.9%; Average loss: 2.9243
Iteration: 3597; Percent complete: 89.9%; Average loss: 3.1154
Iteration: 3598; Percent complete: 90.0%; Average loss: 2.8488
Iteration: 3599; Percent complete: 90.0%; Average loss: 2.4982
Iteration: 3600; Percent complete: 90.0%; Average loss: 2.8884
Iteration: 3601; Percent complete: 90.0%; Average loss: 2.7865
Iteration: 3602; Percent complete: 90.0%; Average loss: 2.6005
Iteration: 3603; Percent complete: 90.1%; Average loss: 2.7351
Iteration: 3604; Percent complete: 90.1%; Average loss: 2.6252
Iteration: 3605; Percent complete: 90.1%; Average loss: 2.6117
Iteration: 3606; Percent complete: 90.1%; Average loss: 2.7937
Iteration: 3607; Percent complete: 90.2%; Average loss: 2.6996
Iteration: 3608; Percent complete: 90.2%; Average loss: 2.6575
Iteration: 3609; Percent complete: 90.2%; Average loss: 2.5570
Iteration: 3610; Percent complete: 90.2%; Average loss: 2.8406
Iteration: 3611; Percent complete: 90.3%; Average loss: 2.8255
Iteration: 3612; Percent complete: 90.3%; Average loss: 2.8590
Iteration: 3613; Percent complete: 90.3%; Average loss: 2.9398
Iteration: 3614; Percent complete: 90.3%; Average loss: 2.8885
Iteration: 3615; Percent complete: 90.4%; Average loss: 2.7285
Iteration: 3616; Percent complete: 90.4%; Average loss: 2.6497
Iteration: 3617; Percent complete: 90.4%; Average loss: 2.6599
Iteration: 3618; Percent complete: 90.5%; Average loss: 2.8325
Iteration: 3619; Percent complete: 90.5%; Average loss: 2.8012
Iteration: 3620; Percent complete: 90.5%; Average loss: 2.9344
Iteration: 3621; Percent complete: 90.5%; Average loss: 2.5977
Iteration: 3622; Percent complete: 90.5%; Average loss: 2.6180
Iteration: 3623; Percent complete: 90.6%; Average loss: 2.5864
Iteration: 3624; Percent complete: 90.6%; Average loss: 2.7046
Iteration: 3625; Percent complete: 90.6%; Average loss: 2.8245
Iteration: 3626; Percent complete: 90.6%; Average loss: 2.8744
Iteration: 3627; Percent complete: 90.7%; Average loss: 2.9190
Iteration: 3628; Percent complete: 90.7%; Average loss: 2.4929
Iteration: 3629; Percent complete: 90.7%; Average loss: 2.8536
Iteration: 3630; Percent complete: 90.8%; Average loss: 2.6195
Iteration: 3631; Percent complete: 90.8%; Average loss: 2.4546
Iteration: 3632; Percent complete: 90.8%; Average loss: 2.7728
Iteration: 3633; Percent complete: 90.8%; Average loss: 2.7494
Iteration: 3634; Percent complete: 90.8%; Average loss: 2.7881
Iteration: 3635; Percent complete: 90.9%; Average loss: 2.8615
Iteration: 3636; Percent complete: 90.9%; Average loss: 2.7964
Iteration: 3637; Percent complete: 90.9%; Average loss: 2.5806
Iteration: 3638; Percent complete: 91.0%; Average loss: 2.4287
Iteration: 3639; Percent complete: 91.0%; Average loss: 2.6133
Iteration: 3640; Percent complete: 91.0%; Average loss: 2.5738
Iteration: 3641; Percent complete: 91.0%; Average loss: 2.7453
Iteration: 3642; Percent complete: 91.0%; Average loss: 2.6772
Iteration: 3643; Percent complete: 91.1%; Average loss: 3.0138
Iteration: 3644; Percent complete: 91.1%; Average loss: 2.6451
Iteration: 3645; Percent complete: 91.1%; Average loss: 2.7277
Iteration: 3646; Percent complete: 91.1%; Average loss: 3.0041
Iteration: 3647; Percent complete: 91.2%; Average loss: 2.5159
Iteration: 3648; Percent complete: 91.2%; Average loss: 2.6460
Iteration: 3649; Percent complete: 91.2%; Average loss: 2.7281
Iteration: 3650; Percent complete: 91.2%; Average loss: 2.7310
Iteration: 3651; Percent complete: 91.3%; Average loss: 2.9503
Iteration: 3652; Percent complete: 91.3%; Average loss: 2.6898
Iteration: 3653; Percent complete: 91.3%; Average loss: 2.5186
Iteration: 3654; Percent complete: 91.3%; Average loss: 2.8087
Iteration: 3655; Percent complete: 91.4%; Average loss: 2.5191
Iteration: 3656; Percent complete: 91.4%; Average loss: 2.4937
Iteration: 3657; Percent complete: 91.4%; Average loss: 2.3907
Iteration: 3658; Percent complete: 91.5%; Average loss: 2.4815
Iteration: 3659; Percent complete: 91.5%; Average loss: 2.6115
Iteration: 3660; Percent complete: 91.5%; Average loss: 2.7933
Iteration: 3661; Percent complete: 91.5%; Average loss: 2.5922
Iteration: 3662; Percent complete: 91.5%; Average loss: 2.8307
Iteration: 3663; Percent complete: 91.6%; Average loss: 2.7030
Iteration: 3664; Percent complete: 91.6%; Average loss: 2.6730
Iteration: 3665; Percent complete: 91.6%; Average loss: 2.7112
Iteration: 3666; Percent complete: 91.6%; Average loss: 2.6331
Iteration: 3667; Percent complete: 91.7%; Average loss: 2.6930
Iteration: 3668; Percent complete: 91.7%; Average loss: 2.7464
Iteration: 3669; Percent complete: 91.7%; Average loss: 2.6951
Iteration: 3670; Percent complete: 91.8%; Average loss: 2.7615
Iteration: 3671; Percent complete: 91.8%; Average loss: 2.6430
Iteration: 3672; Percent complete: 91.8%; Average loss: 2.6405
Iteration: 3673; Percent complete: 91.8%; Average loss: 2.7638
Iteration: 3674; Percent complete: 91.8%; Average loss: 2.7520
Iteration: 3675; Percent complete: 91.9%; Average loss: 2.7438
Iteration: 3676; Percent complete: 91.9%; Average loss: 2.6439
Iteration: 3677; Percent complete: 91.9%; Average loss: 2.7340
Iteration: 3678; Percent complete: 92.0%; Average loss: 2.5531
Iteration: 3679; Percent complete: 92.0%; Average loss: 2.5511
Iteration: 3680; Percent complete: 92.0%; Average loss: 2.7155
Iteration: 3681; Percent complete: 92.0%; Average loss: 2.6471
Iteration: 3682; Percent complete: 92.0%; Average loss: 2.7007
Iteration: 3683; Percent complete: 92.1%; Average loss: 2.8350
Iteration: 3684; Percent complete: 92.1%; Average loss: 2.5288
Iteration: 3685; Percent complete: 92.1%; Average loss: 2.6809
Iteration: 3686; Percent complete: 92.2%; Average loss: 2.8709
Iteration: 3687; Percent complete: 92.2%; Average loss: 2.6571
Iteration: 3688; Percent complete: 92.2%; Average loss: 2.7018
Iteration: 3689; Percent complete: 92.2%; Average loss: 2.6927
Iteration: 3690; Percent complete: 92.2%; Average loss: 2.7733
Iteration: 3691; Percent complete: 92.3%; Average loss: 2.7159
Iteration: 3692; Percent complete: 92.3%; Average loss: 2.5628
Iteration: 3693; Percent complete: 92.3%; Average loss: 2.6375
Iteration: 3694; Percent complete: 92.3%; Average loss: 2.7262
Iteration: 3695; Percent complete: 92.4%; Average loss: 2.8467
Iteration: 3696; Percent complete: 92.4%; Average loss: 2.6414
Iteration: 3697; Percent complete: 92.4%; Average loss: 2.6479
Iteration: 3698; Percent complete: 92.5%; Average loss: 2.5849
Iteration: 3699; Percent complete: 92.5%; Average loss: 2.8669
Iteration: 3700; Percent complete: 92.5%; Average loss: 2.6994
Iteration: 3701; Percent complete: 92.5%; Average loss: 2.8283
Iteration: 3702; Percent complete: 92.5%; Average loss: 2.6938
Iteration: 3703; Percent complete: 92.6%; Average loss: 2.6214
Iteration: 3704; Percent complete: 92.6%; Average loss: 2.5451
Iteration: 3705; Percent complete: 92.6%; Average loss: 2.7033
Iteration: 3706; Percent complete: 92.7%; Average loss: 2.7410
Iteration: 3707; Percent complete: 92.7%; Average loss: 2.6628
Iteration: 3708; Percent complete: 92.7%; Average loss: 2.9374
Iteration: 3709; Percent complete: 92.7%; Average loss: 2.7069
Iteration: 3710; Percent complete: 92.8%; Average loss: 2.6469
Iteration: 3711; Percent complete: 92.8%; Average loss: 2.6651
Iteration: 3712; Percent complete: 92.8%; Average loss: 2.5920
Iteration: 3713; Percent complete: 92.8%; Average loss: 2.7968
Iteration: 3714; Percent complete: 92.8%; Average loss: 2.5073
Iteration: 3715; Percent complete: 92.9%; Average loss: 2.9195
Iteration: 3716; Percent complete: 92.9%; Average loss: 2.7327
Iteration: 3717; Percent complete: 92.9%; Average loss: 2.6719
Iteration: 3718; Percent complete: 93.0%; Average loss: 2.5964
Iteration: 3719; Percent complete: 93.0%; Average loss: 2.3882
Iteration: 3720; Percent complete: 93.0%; Average loss: 2.5177
Iteration: 3721; Percent complete: 93.0%; Average loss: 2.7254
Iteration: 3722; Percent complete: 93.0%; Average loss: 2.6562
Iteration: 3723; Percent complete: 93.1%; Average loss: 2.7228
Iteration: 3724; Percent complete: 93.1%; Average loss: 2.7025
Iteration: 3725; Percent complete: 93.1%; Average loss: 2.7250
Iteration: 3726; Percent complete: 93.2%; Average loss: 2.4278
Iteration: 3727; Percent complete: 93.2%; Average loss: 2.5831
Iteration: 3728; Percent complete: 93.2%; Average loss: 2.8284
Iteration: 3729; Percent complete: 93.2%; Average loss: 2.7217
Iteration: 3730; Percent complete: 93.2%; Average loss: 2.7412
Iteration: 3731; Percent complete: 93.3%; Average loss: 2.9394
Iteration: 3732; Percent complete: 93.3%; Average loss: 2.5128
Iteration: 3733; Percent complete: 93.3%; Average loss: 2.4632
Iteration: 3734; Percent complete: 93.3%; Average loss: 2.5889
Iteration: 3735; Percent complete: 93.4%; Average loss: 2.5730
Iteration: 3736; Percent complete: 93.4%; Average loss: 2.4513
Iteration: 3737; Percent complete: 93.4%; Average loss: 2.5723
Iteration: 3738; Percent complete: 93.5%; Average loss: 2.8907
Iteration: 3739; Percent complete: 93.5%; Average loss: 2.7002
Iteration: 3740; Percent complete: 93.5%; Average loss: 2.7710
Iteration: 3741; Percent complete: 93.5%; Average loss: 2.7012
Iteration: 3742; Percent complete: 93.5%; Average loss: 2.4761
Iteration: 3743; Percent complete: 93.6%; Average loss: 2.8754
Iteration: 3744; Percent complete: 93.6%; Average loss: 2.6352
Iteration: 3745; Percent complete: 93.6%; Average loss: 2.7332
Iteration: 3746; Percent complete: 93.7%; Average loss: 2.7035
Iteration: 3747; Percent complete: 93.7%; Average loss: 2.7709
Iteration: 3748; Percent complete: 93.7%; Average loss: 2.5524
Iteration: 3749; Percent complete: 93.7%; Average loss: 2.7041
Iteration: 3750; Percent complete: 93.8%; Average loss: 2.6494
Iteration: 3751; Percent complete: 93.8%; Average loss: 2.5585
Iteration: 3752; Percent complete: 93.8%; Average loss: 2.6520
Iteration: 3753; Percent complete: 93.8%; Average loss: 2.5042
Iteration: 3754; Percent complete: 93.8%; Average loss: 2.6460
Iteration: 3755; Percent complete: 93.9%; Average loss: 2.7511
Iteration: 3756; Percent complete: 93.9%; Average loss: 2.6776
Iteration: 3757; Percent complete: 93.9%; Average loss: 2.6980
Iteration: 3758; Percent complete: 94.0%; Average loss: 2.4735
Iteration: 3759; Percent complete: 94.0%; Average loss: 2.7461
Iteration: 3760; Percent complete: 94.0%; Average loss: 2.6203
Iteration: 3761; Percent complete: 94.0%; Average loss: 2.5473
Iteration: 3762; Percent complete: 94.0%; Average loss: 2.5772
Iteration: 3763; Percent complete: 94.1%; Average loss: 2.5833
Iteration: 3764; Percent complete: 94.1%; Average loss: 2.8299
Iteration: 3765; Percent complete: 94.1%; Average loss: 2.6956
Iteration: 3766; Percent complete: 94.2%; Average loss: 2.9765
Iteration: 3767; Percent complete: 94.2%; Average loss: 2.8938
Iteration: 3768; Percent complete: 94.2%; Average loss: 2.7748
Iteration: 3769; Percent complete: 94.2%; Average loss: 2.7623
Iteration: 3770; Percent complete: 94.2%; Average loss: 2.6998
Iteration: 3771; Percent complete: 94.3%; Average loss: 2.5124
Iteration: 3772; Percent complete: 94.3%; Average loss: 2.7566
Iteration: 3773; Percent complete: 94.3%; Average loss: 2.4981
Iteration: 3774; Percent complete: 94.3%; Average loss: 2.5357
Iteration: 3775; Percent complete: 94.4%; Average loss: 2.8411
Iteration: 3776; Percent complete: 94.4%; Average loss: 2.6484
Iteration: 3777; Percent complete: 94.4%; Average loss: 2.5266
Iteration: 3778; Percent complete: 94.5%; Average loss: 2.8810
Iteration: 3779; Percent complete: 94.5%; Average loss: 2.4221
Iteration: 3780; Percent complete: 94.5%; Average loss: 2.7456
Iteration: 3781; Percent complete: 94.5%; Average loss: 2.6444
Iteration: 3782; Percent complete: 94.5%; Average loss: 2.6986
Iteration: 3783; Percent complete: 94.6%; Average loss: 2.6325
Iteration: 3784; Percent complete: 94.6%; Average loss: 2.6349
Iteration: 3785; Percent complete: 94.6%; Average loss: 2.5695
Iteration: 3786; Percent complete: 94.7%; Average loss: 2.7042
Iteration: 3787; Percent complete: 94.7%; Average loss: 2.8492
Iteration: 3788; Percent complete: 94.7%; Average loss: 2.9690
Iteration: 3789; Percent complete: 94.7%; Average loss: 2.6735
Iteration: 3790; Percent complete: 94.8%; Average loss: 2.8112
Iteration: 3791; Percent complete: 94.8%; Average loss: 2.7418
Iteration: 3792; Percent complete: 94.8%; Average loss: 2.4199
Iteration: 3793; Percent complete: 94.8%; Average loss: 2.6982
Iteration: 3794; Percent complete: 94.8%; Average loss: 2.8058
Iteration: 3795; Percent complete: 94.9%; Average loss: 2.3143
Iteration: 3796; Percent complete: 94.9%; Average loss: 2.7521
Iteration: 3797; Percent complete: 94.9%; Average loss: 2.7612
Iteration: 3798; Percent complete: 95.0%; Average loss: 2.4852
Iteration: 3799; Percent complete: 95.0%; Average loss: 2.5469
Iteration: 3800; Percent complete: 95.0%; Average loss: 2.4360
Iteration: 3801; Percent complete: 95.0%; Average loss: 2.7439
Iteration: 3802; Percent complete: 95.0%; Average loss: 2.4833
Iteration: 3803; Percent complete: 95.1%; Average loss: 2.5624
Iteration: 3804; Percent complete: 95.1%; Average loss: 2.5659
Iteration: 3805; Percent complete: 95.1%; Average loss: 2.7136
Iteration: 3806; Percent complete: 95.2%; Average loss: 2.5677
Iteration: 3807; Percent complete: 95.2%; Average loss: 2.5319
Iteration: 3808; Percent complete: 95.2%; Average loss: 2.7946
Iteration: 3809; Percent complete: 95.2%; Average loss: 2.5611
Iteration: 3810; Percent complete: 95.2%; Average loss: 2.6257
Iteration: 3811; Percent complete: 95.3%; Average loss: 2.8043
Iteration: 3812; Percent complete: 95.3%; Average loss: 2.5616
Iteration: 3813; Percent complete: 95.3%; Average loss: 2.5922
Iteration: 3814; Percent complete: 95.3%; Average loss: 2.7864
Iteration: 3815; Percent complete: 95.4%; Average loss: 2.6325
Iteration: 3816; Percent complete: 95.4%; Average loss: 2.9058
Iteration: 3817; Percent complete: 95.4%; Average loss: 2.5351
Iteration: 3818; Percent complete: 95.5%; Average loss: 2.7380
Iteration: 3819; Percent complete: 95.5%; Average loss: 2.8337
Iteration: 3820; Percent complete: 95.5%; Average loss: 2.6480
Iteration: 3821; Percent complete: 95.5%; Average loss: 2.5485
Iteration: 3822; Percent complete: 95.5%; Average loss: 2.7339
Iteration: 3823; Percent complete: 95.6%; Average loss: 2.7992
Iteration: 3824; Percent complete: 95.6%; Average loss: 2.6813
Iteration: 3825; Percent complete: 95.6%; Average loss: 2.5431
Iteration: 3826; Percent complete: 95.7%; Average loss: 2.6872
Iteration: 3827; Percent complete: 95.7%; Average loss: 2.8353
Iteration: 3828; Percent complete: 95.7%; Average loss: 2.5401
Iteration: 3829; Percent complete: 95.7%; Average loss: 2.7583
Iteration: 3830; Percent complete: 95.8%; Average loss: 2.6580
Iteration: 3831; Percent complete: 95.8%; Average loss: 2.6560
Iteration: 3832; Percent complete: 95.8%; Average loss: 2.7611
Iteration: 3833; Percent complete: 95.8%; Average loss: 2.6549
Iteration: 3834; Percent complete: 95.9%; Average loss: 2.6624
Iteration: 3835; Percent complete: 95.9%; Average loss: 2.6816
Iteration: 3836; Percent complete: 95.9%; Average loss: 2.5962
Iteration: 3837; Percent complete: 95.9%; Average loss: 2.7502
Iteration: 3838; Percent complete: 96.0%; Average loss: 2.6080
Iteration: 3839; Percent complete: 96.0%; Average loss: 2.4704
Iteration: 3840; Percent complete: 96.0%; Average loss: 2.4508
Iteration: 3841; Percent complete: 96.0%; Average loss: 2.6648
Iteration: 3842; Percent complete: 96.0%; Average loss: 2.7381
Iteration: 3843; Percent complete: 96.1%; Average loss: 2.8563
Iteration: 3844; Percent complete: 96.1%; Average loss: 2.9807
Iteration: 3845; Percent complete: 96.1%; Average loss: 2.7434
Iteration: 3846; Percent complete: 96.2%; Average loss: 2.5351
Iteration: 3847; Percent complete: 96.2%; Average loss: 2.6818
Iteration: 3848; Percent complete: 96.2%; Average loss: 2.6991
Iteration: 3849; Percent complete: 96.2%; Average loss: 2.7869
Iteration: 3850; Percent complete: 96.2%; Average loss: 2.5365
Iteration: 3851; Percent complete: 96.3%; Average loss: 2.5949
Iteration: 3852; Percent complete: 96.3%; Average loss: 2.5410
Iteration: 3853; Percent complete: 96.3%; Average loss: 2.5917
Iteration: 3854; Percent complete: 96.4%; Average loss: 2.6998
Iteration: 3855; Percent complete: 96.4%; Average loss: 2.7670
Iteration: 3856; Percent complete: 96.4%; Average loss: 2.8561
Iteration: 3857; Percent complete: 96.4%; Average loss: 2.6378
Iteration: 3858; Percent complete: 96.5%; Average loss: 2.6074
Iteration: 3859; Percent complete: 96.5%; Average loss: 2.7332
Iteration: 3860; Percent complete: 96.5%; Average loss: 2.6347
Iteration: 3861; Percent complete: 96.5%; Average loss: 2.5888
Iteration: 3862; Percent complete: 96.5%; Average loss: 2.6708
Iteration: 3863; Percent complete: 96.6%; Average loss: 2.5016
Iteration: 3864; Percent complete: 96.6%; Average loss: 2.4632
Iteration: 3865; Percent complete: 96.6%; Average loss: 2.7590
Iteration: 3866; Percent complete: 96.7%; Average loss: 2.6368
Iteration: 3867; Percent complete: 96.7%; Average loss: 2.5048
Iteration: 3868; Percent complete: 96.7%; Average loss: 2.6718
Iteration: 3869; Percent complete: 96.7%; Average loss: 2.4734
Iteration: 3870; Percent complete: 96.8%; Average loss: 2.5832
Iteration: 3871; Percent complete: 96.8%; Average loss: 2.7252
Iteration: 3872; Percent complete: 96.8%; Average loss: 2.6860
Iteration: 3873; Percent complete: 96.8%; Average loss: 2.7136
Iteration: 3874; Percent complete: 96.9%; Average loss: 2.7842
Iteration: 3875; Percent complete: 96.9%; Average loss: 2.5447
Iteration: 3876; Percent complete: 96.9%; Average loss: 2.7010
Iteration: 3877; Percent complete: 96.9%; Average loss: 2.6208
Iteration: 3878; Percent complete: 97.0%; Average loss: 2.6068
Iteration: 3879; Percent complete: 97.0%; Average loss: 2.6438
Iteration: 3880; Percent complete: 97.0%; Average loss: 2.8785
Iteration: 3881; Percent complete: 97.0%; Average loss: 2.5360
Iteration: 3882; Percent complete: 97.0%; Average loss: 2.6758
Iteration: 3883; Percent complete: 97.1%; Average loss: 2.5650
Iteration: 3884; Percent complete: 97.1%; Average loss: 2.7287
Iteration: 3885; Percent complete: 97.1%; Average loss: 2.6350
Iteration: 3886; Percent complete: 97.2%; Average loss: 2.8066
Iteration: 3887; Percent complete: 97.2%; Average loss: 2.5393
Iteration: 3888; Percent complete: 97.2%; Average loss: 2.4945
Iteration: 3889; Percent complete: 97.2%; Average loss: 2.8686
Iteration: 3890; Percent complete: 97.2%; Average loss: 2.5861
Iteration: 3891; Percent complete: 97.3%; Average loss: 2.4523
Iteration: 3892; Percent complete: 97.3%; Average loss: 2.5687
Iteration: 3893; Percent complete: 97.3%; Average loss: 2.5362
Iteration: 3894; Percent complete: 97.4%; Average loss: 2.5231
Iteration: 3895; Percent complete: 97.4%; Average loss: 2.2312
Iteration: 3896; Percent complete: 97.4%; Average loss: 2.7177
Iteration: 3897; Percent complete: 97.4%; Average loss: 2.7032
Iteration: 3898; Percent complete: 97.5%; Average loss: 2.8950
Iteration: 3899; Percent complete: 97.5%; Average loss: 2.9076
Iteration: 3900; Percent complete: 97.5%; Average loss: 3.0132
Iteration: 3901; Percent complete: 97.5%; Average loss: 2.5274
Iteration: 3902; Percent complete: 97.5%; Average loss: 2.6200
Iteration: 3903; Percent complete: 97.6%; Average loss: 2.9569
Iteration: 3904; Percent complete: 97.6%; Average loss: 2.6903
Iteration: 3905; Percent complete: 97.6%; Average loss: 2.7775
Iteration: 3906; Percent complete: 97.7%; Average loss: 2.6597
Iteration: 3907; Percent complete: 97.7%; Average loss: 2.7180
Iteration: 3908; Percent complete: 97.7%; Average loss: 2.8119
Iteration: 3909; Percent complete: 97.7%; Average loss: 2.7190
Iteration: 3910; Percent complete: 97.8%; Average loss: 2.3948
Iteration: 3911; Percent complete: 97.8%; Average loss: 2.4414
Iteration: 3912; Percent complete: 97.8%; Average loss: 2.7128
Iteration: 3913; Percent complete: 97.8%; Average loss: 2.7636
Iteration: 3914; Percent complete: 97.9%; Average loss: 2.8030
Iteration: 3915; Percent complete: 97.9%; Average loss: 2.2765
Iteration: 3916; Percent complete: 97.9%; Average loss: 2.5561
Iteration: 3917; Percent complete: 97.9%; Average loss: 2.8036
Iteration: 3918; Percent complete: 98.0%; Average loss: 2.4946
Iteration: 3919; Percent complete: 98.0%; Average loss: 2.7953
Iteration: 3920; Percent complete: 98.0%; Average loss: 2.8283
Iteration: 3921; Percent complete: 98.0%; Average loss: 2.7684
Iteration: 3922; Percent complete: 98.0%; Average loss: 2.9522
Iteration: 3923; Percent complete: 98.1%; Average loss: 2.7094
Iteration: 3924; Percent complete: 98.1%; Average loss: 2.7753
Iteration: 3925; Percent complete: 98.1%; Average loss: 2.9516
Iteration: 3926; Percent complete: 98.2%; Average loss: 2.6586
Iteration: 3927; Percent complete: 98.2%; Average loss: 2.6051
Iteration: 3928; Percent complete: 98.2%; Average loss: 2.7139
Iteration: 3929; Percent complete: 98.2%; Average loss: 2.7905
Iteration: 3930; Percent complete: 98.2%; Average loss: 2.5185
Iteration: 3931; Percent complete: 98.3%; Average loss: 2.7053
Iteration: 3932; Percent complete: 98.3%; Average loss: 2.6090
Iteration: 3933; Percent complete: 98.3%; Average loss: 2.5121
Iteration: 3934; Percent complete: 98.4%; Average loss: 2.4863
Iteration: 3935; Percent complete: 98.4%; Average loss: 2.3510
Iteration: 3936; Percent complete: 98.4%; Average loss: 2.5478
Iteration: 3937; Percent complete: 98.4%; Average loss: 2.8876
Iteration: 3938; Percent complete: 98.5%; Average loss: 2.7442
Iteration: 3939; Percent complete: 98.5%; Average loss: 2.7344
Iteration: 3940; Percent complete: 98.5%; Average loss: 2.6686
Iteration: 3941; Percent complete: 98.5%; Average loss: 2.6384
Iteration: 3942; Percent complete: 98.6%; Average loss: 2.4656
Iteration: 3943; Percent complete: 98.6%; Average loss: 2.5861
Iteration: 3944; Percent complete: 98.6%; Average loss: 2.5496
Iteration: 3945; Percent complete: 98.6%; Average loss: 2.7331
Iteration: 3946; Percent complete: 98.7%; Average loss: 2.6745
Iteration: 3947; Percent complete: 98.7%; Average loss: 2.5792
Iteration: 3948; Percent complete: 98.7%; Average loss: 2.8632
Iteration: 3949; Percent complete: 98.7%; Average loss: 2.5834
Iteration: 3950; Percent complete: 98.8%; Average loss: 2.5679
Iteration: 3951; Percent complete: 98.8%; Average loss: 2.4275
Iteration: 3952; Percent complete: 98.8%; Average loss: 2.5733
Iteration: 3953; Percent complete: 98.8%; Average loss: 2.5492
Iteration: 3954; Percent complete: 98.9%; Average loss: 2.4786
Iteration: 3955; Percent complete: 98.9%; Average loss: 2.8575
Iteration: 3956; Percent complete: 98.9%; Average loss: 2.7579
Iteration: 3957; Percent complete: 98.9%; Average loss: 2.8071
Iteration: 3958; Percent complete: 99.0%; Average loss: 2.4894
Iteration: 3959; Percent complete: 99.0%; Average loss: 2.7110
Iteration: 3960; Percent complete: 99.0%; Average loss: 2.9522
Iteration: 3961; Percent complete: 99.0%; Average loss: 2.7411
Iteration: 3962; Percent complete: 99.1%; Average loss: 2.6986
Iteration: 3963; Percent complete: 99.1%; Average loss: 2.7611
Iteration: 3964; Percent complete: 99.1%; Average loss: 2.5650
Iteration: 3965; Percent complete: 99.1%; Average loss: 2.6625
Iteration: 3966; Percent complete: 99.2%; Average loss: 2.5677
Iteration: 3967; Percent complete: 99.2%; Average loss: 2.7082
Iteration: 3968; Percent complete: 99.2%; Average loss: 2.4884
Iteration: 3969; Percent complete: 99.2%; Average loss: 2.5601
Iteration: 3970; Percent complete: 99.2%; Average loss: 2.4393
Iteration: 3971; Percent complete: 99.3%; Average loss: 2.8024
Iteration: 3972; Percent complete: 99.3%; Average loss: 2.6185
Iteration: 3973; Percent complete: 99.3%; Average loss: 2.7097
Iteration: 3974; Percent complete: 99.4%; Average loss: 2.5809
Iteration: 3975; Percent complete: 99.4%; Average loss: 2.8033
Iteration: 3976; Percent complete: 99.4%; Average loss: 2.6931
Iteration: 3977; Percent complete: 99.4%; Average loss: 2.6621
Iteration: 3978; Percent complete: 99.5%; Average loss: 2.4925
Iteration: 3979; Percent complete: 99.5%; Average loss: 2.5439
Iteration: 3980; Percent complete: 99.5%; Average loss: 2.4796
Iteration: 3981; Percent complete: 99.5%; Average loss: 2.5088
Iteration: 3982; Percent complete: 99.6%; Average loss: 2.6998
Iteration: 3983; Percent complete: 99.6%; Average loss: 2.7644
Iteration: 3984; Percent complete: 99.6%; Average loss: 2.7679
Iteration: 3985; Percent complete: 99.6%; Average loss: 2.6910
Iteration: 3986; Percent complete: 99.7%; Average loss: 2.7545
Iteration: 3987; Percent complete: 99.7%; Average loss: 2.2075
Iteration: 3988; Percent complete: 99.7%; Average loss: 2.5333
Iteration: 3989; Percent complete: 99.7%; Average loss: 2.5561
Iteration: 3990; Percent complete: 99.8%; Average loss: 2.7196
Iteration: 3991; Percent complete: 99.8%; Average loss: 2.4498
Iteration: 3992; Percent complete: 99.8%; Average loss: 2.4679
Iteration: 3993; Percent complete: 99.8%; Average loss: 2.6390
Iteration: 3994; Percent complete: 99.9%; Average loss: 2.5349
Iteration: 3995; Percent complete: 99.9%; Average loss: 2.6800
Iteration: 3996; Percent complete: 99.9%; Average loss: 2.5346
Iteration: 3997; Percent complete: 99.9%; Average loss: 2.6866
Iteration: 3998; Percent complete: 100.0%; Average loss: 2.6170
Iteration: 3999; Percent complete: 100.0%; Average loss: 2.6433
Iteration: 4000; Percent complete: 100.0%; Average loss: 2.5451
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: ( 4 minutes 24.652 seconds)