beginner/chatbot_tutorial
Run in Google Colab
Colab
Download Notebook
Notebook
View on GitHub
GitHub
Note
Go to the end to download the full example code.
Chatbot Tutorial#
Created On: Aug 14, 2018 | Last Updated: Jan 24, 2025 | Last Verified: Nov 05, 2024
Author: Matthew Inkawhich
In this tutorial, we explore a fun and interesting use-case of recurrent sequence-to-sequence models. We will train a simple chatbot using movie scripts from the Cornell Movie-Dialogs Corpus.
Conversational models are a hot topic in artificial intelligence research. Chatbots can be found in a variety of settings, including customer service applications and online helpdesks. These bots are often powered by retrieval-based models, which output predefined responses to questions of certain forms. In a highly restricted domain like a company’s IT helpdesk, these models may be sufficient, however, they are not robust enough for more general use-cases. Teaching a machine to carry out a meaningful conversation with a human in multiple domains is a research question that is far from solved. Recently, the deep learning boom has allowed for powerful generative models like Google’s Neural Conversational Model, which marks a large step towards multi-domain generative conversational models. In this tutorial, we will implement this kind of model in PyTorch.
> hello?
Bot: hello .
> where am I?
Bot: you re in a hospital .
> who are you?
Bot: i m a lawyer .
> how are you doing?
Bot: i m fine .
> are you my friend?
Bot: no .
> you're under arrest
Bot: i m trying to help you !
> i'm just kidding
Bot: i m sorry .
> where are you from?
Bot: san francisco .
> it's time for me to leave
Bot: i know .
> goodbye
Bot: goodbye .
Tutorial Highlights
Handle loading and preprocessing of Cornell Movie-Dialogs Corpus dataset
Implement a sequence-to-sequence model with Luong attention mechanism(s)
Jointly train encoder and decoder models using mini-batches
Implement greedy-search decoding module
Interact with trained chatbot
Acknowledgments
This tutorial borrows code from the following sources:
Yuan-Kuei Wu’s pytorch-chatbot implementation: ywk991112/pytorch-chatbot
Sean Robertson’s practical-pytorch seq2seq-translation example: spro/practical-pytorch
FloydHub Cornell Movie Corpus preprocessing code: floydhub/textutil-preprocess-cornell-movie-corpus
Preparations#
To get started, download the Movie-Dialogs Corpus zip file.
# and put in a ``data/`` directory under the current directory.
#
# After that, let’s import some necessities.
#
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
# If the current `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__ is available,
# we will use it. Otherwise, we use the CPU.
device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
print(f"Using {device} device")
Using cuda device
Load & Preprocess Data#
The next step is to reformat our data file and load the data into structures that we can work with.
The Cornell Movie-Dialogs Corpus is a rich dataset of movie character dialog:
220,579 conversational exchanges between 10,292 pairs of movie characters
9,035 characters from 617 movies
304,713 total utterances
This dataset is large and diverse, and there is a great variation of language formality, time periods, sentiment, etc. Our hope is that this diversity makes our model robust to many forms of inputs and queries.
First, we’ll take a look at some lines of our datafile to see the original format.
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'
Create formatted data file#
For convenience, we’ll create a nicely formatted data file in which each line contains a tab-separated query sentence and a response sentence pair.
The following functions facilitate the parsing of the raw
utterances.jsonl data file.
loadLinesAndConversationssplits each line of the file into a dictionary of lines with fields:lineID,characterID, and text and then groups them into conversations with fields:conversationID,movieID, and lines.extractSentencePairsextracts pairs of sentences from conversations
# Splits each line of the file to create lines and conversations
def loadLinesAndConversations(fileName):
lines = {}
conversations = {}
with open(fileName, 'r', encoding='iso-8859-1') as f:
for line in f:
lineJson = json.loads(line)
# Extract fields for line object
lineObj = {}
lineObj["lineID"] = lineJson["id"]
lineObj["characterID"] = lineJson["speaker"]
lineObj["text"] = lineJson["text"]
lines[lineObj['lineID']] = lineObj
# Extract fields for conversation object
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
# Extracts pairs of sentences from conversations
def extractSentencePairs(conversations):
qa_pairs = []
for conversation in conversations.values():
# Iterate over all the lines of the conversation
for i in range(len(conversation["lines"]) - 1): # We ignore the last line (no answer for it)
inputLine = conversation["lines"][i]["text"].strip()
targetLine = conversation["lines"][i+1]["text"].strip()
# Filter wrong samples (if one of the lists is empty)
if inputLine and targetLine:
qa_pairs.append([inputLine, targetLine])
return qa_pairs
Now we’ll call these functions and create the file. We’ll call it
formatted_movie_lines.txt.
# Define path to new file
datafile = os.path.join(corpus, "formatted_movie_lines.txt")
delimiter = '\t'
# Unescape the delimiter
delimiter = str(codecs.decode(delimiter, "unicode_escape"))
# Initialize lines dict and conversations dict
lines = {}
conversations = {}
# Load lines and conversations
print("\nProcessing corpus into lines and conversations...")
lines, conversations = loadLinesAndConversations(os.path.join(corpus, "utterances.jsonl"))
# Write new csv file
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)
# Print a sample of lines
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"
Load and trim data#
Our next order of business is to create a vocabulary and load query/response sentence pairs into memory.
Note that we are dealing with sequences of words, which do not have an implicit mapping to a discrete numerical space. Thus, we must create one by mapping each unique word that we encounter in our dataset to an index value.
For this we define a Voc class, which keeps a mapping from words to
indexes, a reverse mapping of indexes to words, a count of each word and
a total word count. The class provides methods for adding a word to the
vocabulary (addWord), adding all words in a sentence
(addSentence) and trimming infrequently seen words (trim). More
on trimming later.
# Default word tokens
PAD_token = 0 # Used for padding short sentences
SOS_token = 1 # Start-of-sentence token
EOS_token = 2 # End-of-sentence token
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 # Count SOS, EOS, PAD
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
# Remove words below a certain count threshold
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)
))
# Reinitialize dictionaries
self.word2index = {}
self.word2count = {}
self.index2word = {PAD_token: "PAD", SOS_token: "SOS", EOS_token: "EOS"}
self.num_words = 3 # Count default tokens
for word in keep_words:
self.addWord(word)
Now we can assemble our vocabulary and query/response sentence pairs. Before we are ready to use this data, we must perform some preprocessing.
First, we must convert the Unicode strings to ASCII using
unicodeToAscii. Next, we should convert all letters to lowercase and
trim all non-letter characters except for basic punctuation
(normalizeString). Finally, to aid in training convergence, we will
filter out sentences with length greater than the MAX_LENGTH
threshold (filterPairs).
MAX_LENGTH = 10 # Maximum sentence length to consider
# Turn a Unicode string to plain ASCII, thanks to
# https://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
return ''.join(
c for c in unicodedata.normalize('NFD', s)
if unicodedata.category(c) != 'Mn'
)
# Lowercase, trim, and remove non-letter characters
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
# Read query/response pairs and return a voc object
def readVocs(datafile, corpus_name):
print("Reading lines...")
# Read the file and split into lines
lines = open(datafile, encoding='utf-8').\
read().strip().split('\n')
# Split every line into pairs and normalize
pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]
voc = Voc(corpus_name)
return voc, pairs
# Returns True if both sentences in a pair 'p' are under the MAX_LENGTH threshold
def filterPair(p):
# Input sequences need to preserve the last word for EOS token
return len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH
# Filter pairs using the ``filterPair`` condition
def filterPairs(pairs):
return [pair for pair in pairs if filterPair(pair)]
# Using the functions defined above, return a populated voc object and pairs list
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
# Load/Assemble voc and pairs
save_dir = os.path.join("data", "save")
voc, pairs = loadPrepareData(corpus, corpus_name, datafile, save_dir)
# Print some pairs to validate
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']
Another tactic that is beneficial to achieving faster convergence during training is trimming rarely used words out of our vocabulary. Decreasing the feature space will also soften the difficulty of the function that the model must learn to approximate. We will do this as a two-step process:
Trim words used under
MIN_COUNTthreshold using thevoc.trimfunction.Filter out pairs with trimmed words.
MIN_COUNT = 3 # Minimum word count threshold for trimming
def trimRareWords(voc, pairs, MIN_COUNT):
# Trim words used under the MIN_COUNT from the voc
voc.trim(MIN_COUNT)
# Filter out pairs with trimmed words
keep_pairs = []
for pair in pairs:
input_sentence = pair[0]
output_sentence = pair[1]
keep_input = True
keep_output = True
# Check input sentence
for word in input_sentence.split(' '):
if word not in voc.word2index:
keep_input = False
break
# Check output sentence
for word in output_sentence.split(' '):
if word not in voc.word2index:
keep_output = False
break
# Only keep pairs that do not contain trimmed word(s) in their input or output sentence
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
# Trim voc and pairs
pairs = trimRareWords(voc, pairs, MIN_COUNT)
keep_words 7833 / 18079 = 0.4333
Trimmed from 64313 pairs to 53131, 0.8261 of total
Prepare Data for Models#
Although we have put a great deal of effort into preparing and massaging our data into a nice vocabulary object and list of sentence pairs, our models will ultimately expect numerical torch tensors as inputs. One way to prepare the processed data for the models can be found in the seq2seq translation tutorial. In that tutorial, we use a batch size of 1, meaning that all we have to do is convert the words in our sentence pairs to their corresponding indexes from the vocabulary and feed this to the models.
However, if you’re interested in speeding up training and/or would like to leverage GPU parallelization capabilities, you will need to train with mini-batches.
Using mini-batches also means that we must be mindful of the variation of sentence length in our batches. To accommodate sentences of different sizes in the same batch, we will make our batched input tensor of shape (max_length, batch_size), where sentences shorter than the max_length are zero padded after an EOS_token.
If we simply convert our English sentences to tensors by converting
words to their indexes(indexesFromSentence) and zero-pad, our
tensor would have shape (batch_size, max_length) and indexing the
first dimension would return a full sequence across all time-steps.
However, we need to be able to index our batch along time, and across
all sequences in the batch. Therefore, we transpose our input batch
shape to (max_length, batch_size), so that indexing across the first
dimension returns a time step across all sentences in the batch. We
handle this transpose implicitly in the zeroPadding function.
The inputVar function handles the process of converting sentences to
tensor, ultimately creating a correctly shaped zero-padded tensor. It
also returns a tensor of lengths for each of the sequences in the
batch which will be passed to our decoder later.
The outputVar function performs a similar function to inputVar,
but instead of returning a lengths tensor, it returns a binary mask
tensor and a maximum target sentence length. The binary mask tensor has
the same shape as the output target tensor, but every element that is a
PAD_token is 0 and all others are 1.
batch2TrainData simply takes a bunch of pairs and returns the input
and target tensors using the aforementioned functions.
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
# Returns padded input sequence tensor and lengths
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
# Returns padded target sequence tensor, padding mask, and max target length
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.BoolTensor(mask)
padVar = torch.LongTensor(padList)
return padVar, mask, max_target_len
# Returns all items for a given batch of pairs
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
# Example for validation
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([[ 33, 24, 11, 1218, 19],
[ 3, 571, 44, 75, 362],
[ 715, 5, 366, 10, 10],
[ 101, 4, 34, 2, 2],
[ 22, 365, 2161, 0, 0],
[1214, 84, 2, 0, 0],
[ 17, 85, 0, 0, 0],
[1805, 10, 0, 0, 0],
[ 14, 2, 0, 0, 0],
[ 2, 0, 0, 0, 0]])
lengths: tensor([10, 9, 6, 4, 4])
target_variable: tensor([[ 3, 128, 4, 1218, 62],
[ 77, 162, 85, 5632, 831],
[ 10, 14, 14, 14, 14],
[ 2, 11, 11, 2, 2],
[ 0, 531, 4370, 0, 0],
[ 0, 724, 566, 0, 0],
[ 0, 1747, 3356, 0, 0],
[ 0, 14, 24, 0, 0],
[ 0, 2, 14, 0, 0],
[ 0, 0, 2, 0, 0]])
mask: tensor([[ True, True, True, True, True],
[ True, True, True, True, True],
[ True, True, True, True, True],
[ True, True, True, True, True],
[False, True, True, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, True, True, False, False],
[False, False, True, False, False]])
max_target_len: 10
Define Models#
Seq2Seq Model#
The brains of our chatbot is a sequence-to-sequence (seq2seq) model. The goal of a seq2seq model is to take a variable-length sequence as an input, and return a variable-length sequence as an output using a fixed-sized model.
Sutskever et al. discovered that by using two separate recurrent neural nets together, we can accomplish this task. One RNN acts as an encoder, which encodes a variable length input sequence to a fixed-length context vector. In theory, this context vector (the final hidden layer of the RNN) will contain semantic information about the query sentence that is input to the bot. The second RNN is a decoder, which takes an input word and the context vector, and returns a guess for the next word in the sequence and a hidden state to use in the next iteration.
Image source: https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_intro/
Encoder#
The encoder RNN iterates through the input sentence one token (e.g. word) at a time, at each time step outputting an “output” vector and a “hidden state” vector. The hidden state vector is then passed to the next time step, while the output vector is recorded. The encoder transforms the context it saw at each point in the sequence into a set of points in a high-dimensional space, which the decoder will use to generate a meaningful output for the given task.
At the heart of our encoder is a multi-layered Gated Recurrent Unit, invented by Cho et al. in 2014. We will use a bidirectional variant of the GRU, meaning that there are essentially two independent RNNs: one that is fed the input sequence in normal sequential order, and one that is fed the input sequence in reverse order. The outputs of each network are summed at each time step. Using a bidirectional GRU will give us the advantage of encoding both past and future contexts.
Bidirectional RNN:
Image source: https://colah.github.io/posts/2015-09-NN-Types-FP/
Note that an embedding layer is used to encode our word indices in
an arbitrarily sized feature space. For our models, this layer will map
each word to a feature space of size hidden_size. When trained, these
values should encode semantic similarity between similar meaning words.
Finally, if passing a padded batch of sequences to an RNN module, we
must pack and unpack padding around the RNN pass using
nn.utils.rnn.pack_padded_sequence and
nn.utils.rnn.pad_packed_sequence respectively.
Computation Graph:
Convert word indexes to embeddings.
Pack padded batch of sequences for RNN module.
Forward pass through GRU.
Unpack padding.
Sum bidirectional GRU outputs.
Return output and final hidden state.
Inputs:
input_seq: batch of input sentences; shape=(max_length, batch_size)input_lengths: list of sentence lengths corresponding to each sentence in the batch; shape=(batch_size)hidden: hidden state; shape=(n_layers x num_directions, batch_size, hidden_size)
Outputs:
outputs: output features from the last hidden layer of the GRU (sum of bidirectional outputs); shape=(max_length, batch_size, hidden_size)hidden: updated hidden state from GRU; 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
# Initialize GRU; the input_size and hidden_size parameters are both set to 'hidden_size'
# because our input size is a word embedding with number of features == hidden_size
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):
# Convert word indexes to embeddings
embedded = self.embedding(input_seq)
# Pack padded batch of sequences for RNN module
packed = nn.utils.rnn.pack_padded_sequence(embedded, input_lengths)
# Forward pass through GRU
outputs, hidden = self.gru(packed, hidden)
# Unpack padding
outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs)
# Sum bidirectional GRU outputs
outputs = outputs[:, :, :self.hidden_size] + outputs[:, : ,self.hidden_size:]
# Return output and final hidden state
return outputs, hidden
Decoder#
The decoder RNN generates the response sentence in a token-by-token fashion. It uses the encoder’s context vectors, and internal hidden states to generate the next word in the sequence. It continues generating words until it outputs an EOS_token, representing the end of the sentence. A common problem with a vanilla seq2seq decoder is that if we rely solely on the context vector to encode the entire input sequence’s meaning, it is likely that we will have information loss. This is especially the case when dealing with long input sequences, greatly limiting the capability of our decoder.
To combat this, Bahdanau et al. created an “attention mechanism” that allows the decoder to pay attention to certain parts of the input sequence, rather than using the entire fixed context at every step.
At a high level, attention is calculated using the decoder’s current hidden state and the encoder’s outputs. The output attention weights have the same shape as the input sequence, allowing us to multiply them by the encoder outputs, giving us a weighted sum which indicates the parts of encoder output to pay attention to. Sean Robertson’s figure describes this very well:
Luong et al. improved upon Bahdanau et al.’s groundwork by creating “Global attention”. The key difference is that with “Global attention”, we consider all of the encoder’s hidden states, as opposed to Bahdanau et al.’s “Local attention”, which only considers the encoder’s hidden state from the current time step. Another difference is that with “Global attention”, we calculate attention weights, or energies, using the hidden state of the decoder from the current time step only. Bahdanau et al.’s attention calculation requires knowledge of the decoder’s state from the previous time step. Also, Luong et al. provides various methods to calculate the attention energies between the encoder output and decoder output which are called “score functions”:
where \(h_t\) = current target decoder state and \(\bar{h}_s\) = all encoder states.
Overall, the Global attention mechanism can be summarized by the
following figure. Note that we will implement the “Attention Layer” as a
separate nn.Module called Attn. The output of this module is a
softmax normalized weights tensor of shape (batch_size, 1,
max_length).
# Luong attention layer
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):
# Calculate the attention weights (energies) based on the given method
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)
# Transpose max_length and batch_size dimensions
attn_energies = attn_energies.t()
# Return the softmax normalized probability scores (with added dimension)
return F.softmax(attn_energies, dim=1).unsqueeze(1)
Now that we have defined our attention submodule, we can implement the actual decoder model. For the decoder, we will manually feed our batch one time step at a time. This means that our embedded word tensor and GRU output will both have shape (1, batch_size, hidden_size).
Computation Graph:
Get embedding of current input word.
Forward through unidirectional GRU.
Calculate attention weights from the current GRU output from (2).
Multiply attention weights to encoder outputs to get new “weighted sum” context vector.
Concatenate weighted context vector and GRU output using Luong eq. 5.
Predict next word using Luong eq. 6 (without softmax).
Return output and final hidden state.
Inputs:
input_step: one time step (one word) of input sequence batch; shape=(1, batch_size)last_hidden: final hidden layer of GRU; shape=(n_layers x num_directions, batch_size, hidden_size)encoder_outputs: encoder model’s output; shape=(max_length, batch_size, hidden_size)
Outputs:
output: softmax normalized tensor giving probabilities of each word being the correct next word in the decoded sequence; shape=(batch_size, voc.num_words)hidden: final hidden state of GRU; 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__()
# Keep for reference
self.attn_model = attn_model
self.hidden_size = hidden_size
self.output_size = output_size
self.n_layers = n_layers
self.dropout = dropout
# Define layers
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):
# Note: we run this one step (word) at a time
# Get embedding of current input word
embedded = self.embedding(input_step)
embedded = self.embedding_dropout(embedded)
# Forward through unidirectional GRU
rnn_output, hidden = self.gru(embedded, last_hidden)
# Calculate attention weights from the current GRU output
attn_weights = self.attn(rnn_output, encoder_outputs)
# Multiply attention weights to encoder outputs to get new "weighted sum" context vector
context = attn_weights.bmm(encoder_outputs.transpose(0, 1))
# Concatenate weighted context vector and GRU output using Luong eq. 5
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))
# Predict next word using Luong eq. 6
output = self.out(concat_output)
output = F.softmax(output, dim=1)
# Return output and final hidden state
return output, hidden
Define Training Procedure#
Masked loss#
Since we are dealing with batches of padded sequences, we cannot simply
consider all elements of the tensor when calculating loss. We define
maskNLLLoss to calculate our loss based on our decoder’s output
tensor, the target tensor, and a binary mask tensor describing the
padding of the target tensor. This loss function calculates the average
negative log likelihood of the elements that correspond to a 1 in the
mask tensor.
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()
Single training iteration#
The train function contains the algorithm for a single training
iteration (a single batch of inputs).
We will use a couple of clever tricks to aid in convergence:
The first trick is using teacher forcing. This means that at some probability, set by
teacher_forcing_ratio, we use the current target word as the decoder’s next input rather than using the decoder’s current guess. This technique acts as training wheels for the decoder, aiding in more efficient training. However, teacher forcing can lead to model instability during inference, as the decoder may not have a sufficient chance to truly craft its own output sequences during training. Thus, we must be mindful of how we are setting theteacher_forcing_ratio, and not be fooled by fast convergence.The second trick that we implement is gradient clipping. This is a commonly used technique for countering the “exploding gradient” problem. In essence, by clipping or thresholding gradients to a maximum value, we prevent the gradients from growing exponentially and either overflow (NaN), or overshoot steep cliffs in the cost function.
Image source: Goodfellow et al. Deep Learning. 2016. https://www.deeplearningbook.org/
Sequence of Operations:
Forward pass entire input batch through encoder.
Initialize decoder inputs as SOS_token, and hidden state as the encoder’s final hidden state.
Forward input batch sequence through decoder one time step at a time.
If teacher forcing: set next decoder input as the current target; else: set next decoder input as current decoder output.
Calculate and accumulate loss.
Perform backpropagation.
Clip gradients.
Update encoder and decoder model parameters.
Note
PyTorch’s RNN modules (RNN, LSTM, GRU) can be used like any
other non-recurrent layers by simply passing them the entire input
sequence (or batch of sequences). We use the GRU layer like this in
the encoder. The reality is that under the hood, there is an
iterative process looping over each time step calculating hidden states.
Alternatively, you can run these modules one time-step at a time. In
this case, we manually loop over the sequences during the training
process like we must do for the decoder model. As long as you
maintain the correct conceptual model of these modules, implementing
sequential models can be very straightforward.
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):
# Zero gradients
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
# Set device options
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")
# Initialize variables
loss = 0
print_losses = []
n_totals = 0
# Forward pass through encoder
encoder_outputs, encoder_hidden = encoder(input_variable, lengths)
# Create initial decoder input (start with SOS tokens for each sentence)
decoder_input = torch.LongTensor([[SOS_token for _ in range(batch_size)]])
decoder_input = decoder_input.to(device)
# Set initial decoder hidden state to the encoder's final hidden state
decoder_hidden = encoder_hidden[:decoder.n_layers]
# Determine if we are using teacher forcing this iteration
use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
# Forward batch of sequences through decoder one time step at a time
if use_teacher_forcing:
for t in range(max_target_len):
decoder_output, decoder_hidden = decoder(
decoder_input, decoder_hidden, encoder_outputs
)
# Teacher forcing: next input is current target
decoder_input = target_variable[t].view(1, -1)
# Calculate and accumulate loss
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
)
# No teacher forcing: next input is decoder's own current output
_, topi = decoder_output.topk(1)
decoder_input = torch.LongTensor([[topi[i][0] for i in range(batch_size)]])
decoder_input = decoder_input.to(device)
# Calculate and accumulate loss
mask_loss, nTotal = maskNLLLoss(decoder_output, target_variable[t], mask[t])
loss += mask_loss
print_losses.append(mask_loss.item() * nTotal)
n_totals += nTotal
# Perform backpropagation
loss.backward()
# Clip gradients: gradients are modified in place
_ = nn.utils.clip_grad_norm_(encoder.parameters(), clip)
_ = nn.utils.clip_grad_norm_(decoder.parameters(), clip)
# Adjust model weights
encoder_optimizer.step()
decoder_optimizer.step()
return sum(print_losses) / n_totals
Training iterations#
It is finally time to tie the full training procedure together with the
data. The trainIters function is responsible for running
n_iterations of training given the passed models, optimizers, data,
etc. This function is quite self explanatory, as we have done the heavy
lifting with the train function.
One thing to note is that when we save our model, we save a tarball
containing the encoder and decoder state_dicts (parameters), the
optimizers’ state_dicts, the loss, the iteration, etc. Saving the model
in this way will give us the ultimate flexibility with the checkpoint.
After loading a checkpoint, we will be able to use the model parameters
to run inference, or we can continue training right where we left off.
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):
# Load batches for each iteration
training_batches = [batch2TrainData(voc, [random.choice(pairs) for _ in range(batch_size)])
for _ in range(n_iteration)]
# Initializations
print('Initializing ...')
start_iteration = 1
print_loss = 0
if loadFilename:
start_iteration = checkpoint['iteration'] + 1
# Training loop
print("Training...")
for iteration in range(start_iteration, n_iteration + 1):
training_batch = training_batches[iteration - 1]
# Extract fields from batch
input_variable, lengths, target_variable, mask, max_target_len = training_batch
# Run a training iteration with batch
loss = train(input_variable, lengths, target_variable, mask, max_target_len, encoder,
decoder, embedding, encoder_optimizer, decoder_optimizer, batch_size, clip)
print_loss += loss
# Print progress
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
# Save checkpoint
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')))
Define Evaluation#
After training a model, we want to be able to talk to the bot ourselves. First, we must define how we want the model to decode the encoded input.
Greedy decoding#
Greedy decoding is the decoding method that we use during training when
we are NOT using teacher forcing. In other words, for each time
step, we simply choose the word from decoder_output with the highest
softmax value. This decoding method is optimal on a single time-step
level.
To facilitate the greedy decoding operation, we define a
GreedySearchDecoder class. When run, an object of this class takes
an input sequence (input_seq) of shape (input_seq length, 1), a
scalar input length (input_length) tensor, and a max_length to
bound the response sentence length. The input sentence is evaluated
using the following computational graph:
Computation Graph:
Forward input through encoder model.
Prepare encoder’s final hidden layer to be first hidden input to the decoder.
Initialize decoder’s first input as SOS_token.
Initialize tensors to append decoded words to.
- Iteratively decode one word token at a time:
Forward pass through decoder.
Obtain most likely word token and its softmax score.
Record token and score.
Prepare current token to be next decoder input.
Return collections of word tokens and scores.
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):
# Forward input through encoder model
encoder_outputs, encoder_hidden = self.encoder(input_seq, input_length)
# Prepare encoder's final hidden layer to be first hidden input to the decoder
decoder_hidden = encoder_hidden[:self.decoder.n_layers]
# Initialize decoder input with SOS_token
decoder_input = torch.ones(1, 1, device=device, dtype=torch.long) * SOS_token
# Initialize tensors to append decoded words to
all_tokens = torch.zeros([0], device=device, dtype=torch.long)
all_scores = torch.zeros([0], device=device)
# Iteratively decode one word token at a time
for _ in range(max_length):
# Forward pass through decoder
decoder_output, decoder_hidden = self.decoder(decoder_input, decoder_hidden, encoder_outputs)
# Obtain most likely word token and its softmax score
decoder_scores, decoder_input = torch.max(decoder_output, dim=1)
# Record token and score
all_tokens = torch.cat((all_tokens, decoder_input), dim=0)
all_scores = torch.cat((all_scores, decoder_scores), dim=0)
# Prepare current token to be next decoder input (add a dimension)
decoder_input = torch.unsqueeze(decoder_input, 0)
# Return collections of word tokens and scores
return all_tokens, all_scores
Evaluate my text#
Now that we have our decoding method defined, we can write functions for
evaluating a string input sentence. The evaluate function manages
the low-level process of handling the input sentence. We first format
the sentence as an input batch of word indexes with batch_size==1. We
do this by converting the words of the sentence to their corresponding
indexes, and transposing the dimensions to prepare the tensor for our
models. We also create a lengths tensor which contains the length of
our input sentence. In this case, lengths is scalar because we are
only evaluating one sentence at a time (batch_size==1). Next, we obtain
the decoded response sentence tensor using our GreedySearchDecoder
object (searcher). Finally, we convert the response’s indexes to
words and return the list of decoded words.
evaluateInput acts as the user interface for our chatbot. When
called, an input text field will spawn in which we can enter our query
sentence. After typing our input sentence and pressing Enter, our text
is normalized in the same way as our training data, and is ultimately
fed to the evaluate function to obtain a decoded output sentence. We
loop this process, so we can keep chatting with our bot until we enter
either “q” or “quit”.
Finally, if a sentence is entered that contains a word that is not in the vocabulary, we handle this gracefully by printing an error message and prompting the user to enter another sentence.
def evaluate(encoder, decoder, searcher, voc, sentence, max_length=MAX_LENGTH):
### Format input sentence as a batch
# words -> indexes
indexes_batch = [indexesFromSentence(voc, sentence)]
# Create lengths tensor
lengths = torch.tensor([len(indexes) for indexes in indexes_batch])
# Transpose dimensions of batch to match models' expectations
input_batch = torch.LongTensor(indexes_batch).transpose(0, 1)
# Use appropriate device
input_batch = input_batch.to(device)
lengths = lengths.to("cpu")
# Decode sentence with searcher
tokens, scores = searcher(input_batch, lengths, max_length)
# indexes -> words
decoded_words = [voc.index2word[token.item()] for token in tokens]
return decoded_words
def evaluateInput(encoder, decoder, searcher, voc):
input_sentence = ''
while(1):
try:
# Get input sentence
input_sentence = input('> ')
# Check if it is quit case
if input_sentence == 'q' or input_sentence == 'quit': break
# Normalize sentence
input_sentence = normalizeString(input_sentence)
# Evaluate sentence
output_words = evaluate(encoder, decoder, searcher, voc, input_sentence)
# Format and print response sentence
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.")
Run Model#
Finally, it is time to run our model!
Regardless of whether we want to train or test the chatbot model, we must initialize the individual encoder and decoder models. In the following block, we set our desired configurations, choose to start from scratch or set a checkpoint to load from, and build and initialize the models. Feel free to play with different model configurations to optimize performance.
# Configure models
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
# Set checkpoint to load from; set to None if starting from scratch
loadFilename = None
checkpoint_iter = 4000
Sample code to load from a checkpoint:
loadFilename = os.path.join(save_dir, model_name, corpus_name,
'{}-{}_{}'.format(encoder_n_layers, decoder_n_layers, hidden_size),
'{}_checkpoint.tar'.format(checkpoint_iter))
# Load model if a ``loadFilename`` is provided
if loadFilename:
# If loading on same machine the model was trained on
checkpoint = torch.load(loadFilename)
# If loading a model trained on GPU to CPU
#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 ...')
# Initialize word embeddings
embedding = nn.Embedding(voc.num_words, hidden_size)
if loadFilename:
embedding.load_state_dict(embedding_sd)
# Initialize encoder & decoder models
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)
# Use appropriate device
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!
Run Training#
Run the following block if you want to train the model.
First we set training parameters, then we initialize our optimizers, and
finally we call the trainIters function to run our training
iterations.
# Configure training/optimization
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
# Ensure dropout layers are in train mode
encoder.train()
decoder.train()
# Initialize optimizers
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)
# If you have an accelerator, configure it to call
for state in encoder_optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.to(device)
for state in decoder_optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.to(device)
# Run training iterations
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...
Iteration: 1; Percent complete: 0.0%; Average loss: 8.9774
Iteration: 2; Percent complete: 0.1%; Average loss: 8.8341
Iteration: 3; Percent complete: 0.1%; Average loss: 8.6424
Iteration: 4; Percent complete: 0.1%; Average loss: 8.4000
Iteration: 5; Percent complete: 0.1%; Average loss: 7.8370
Iteration: 6; Percent complete: 0.1%; Average loss: 7.3410
Iteration: 7; Percent complete: 0.2%; Average loss: 6.7115
Iteration: 8; Percent complete: 0.2%; Average loss: 6.6510
Iteration: 9; Percent complete: 0.2%; Average loss: 6.4709
Iteration: 10; Percent complete: 0.2%; Average loss: 6.1357
Iteration: 11; Percent complete: 0.3%; Average loss: 5.9387
Iteration: 12; Percent complete: 0.3%; Average loss: 5.8629
Iteration: 13; Percent complete: 0.3%; Average loss: 5.8387
Iteration: 14; Percent complete: 0.4%; Average loss: 5.7301
Iteration: 15; Percent complete: 0.4%; Average loss: 5.5168
Iteration: 16; Percent complete: 0.4%; Average loss: 5.3997
Iteration: 17; Percent complete: 0.4%; Average loss: 5.1055
Iteration: 18; Percent complete: 0.4%; Average loss: 5.1367
Iteration: 19; Percent complete: 0.5%; Average loss: 5.0545
Iteration: 20; Percent complete: 0.5%; Average loss: 5.0623
Iteration: 21; Percent complete: 0.5%; Average loss: 5.0641
Iteration: 22; Percent complete: 0.5%; Average loss: 5.0225
Iteration: 23; Percent complete: 0.6%; Average loss: 4.9509
Iteration: 24; Percent complete: 0.6%; Average loss: 4.9259
Iteration: 25; Percent complete: 0.6%; Average loss: 5.1651
Iteration: 26; Percent complete: 0.7%; Average loss: 4.8969
Iteration: 27; Percent complete: 0.7%; Average loss: 4.8292
Iteration: 28; Percent complete: 0.7%; Average loss: 4.8316
Iteration: 29; Percent complete: 0.7%; Average loss: 4.8094
Iteration: 30; Percent complete: 0.8%; Average loss: 4.5514
Iteration: 31; Percent complete: 0.8%; Average loss: 4.8685
Iteration: 32; Percent complete: 0.8%; Average loss: 4.7534
Iteration: 33; Percent complete: 0.8%; Average loss: 4.6621
Iteration: 34; Percent complete: 0.9%; Average loss: 4.6879
Iteration: 35; Percent complete: 0.9%; Average loss: 4.4965
Iteration: 36; Percent complete: 0.9%; Average loss: 5.0847
Iteration: 37; Percent complete: 0.9%; Average loss: 4.8580
Iteration: 38; Percent complete: 0.9%; Average loss: 4.7596
Iteration: 39; Percent complete: 1.0%; Average loss: 4.7197
Iteration: 40; Percent complete: 1.0%; Average loss: 4.7150
Iteration: 41; Percent complete: 1.0%; Average loss: 4.7271
Iteration: 42; Percent complete: 1.1%; Average loss: 4.7450
Iteration: 43; Percent complete: 1.1%; Average loss: 4.9174
Iteration: 44; Percent complete: 1.1%; Average loss: 4.9369
Iteration: 45; Percent complete: 1.1%; Average loss: 4.8105
Iteration: 46; Percent complete: 1.1%; Average loss: 4.7211
Iteration: 47; Percent complete: 1.2%; Average loss: 4.7754
Iteration: 48; Percent complete: 1.2%; Average loss: 4.7886
Iteration: 49; Percent complete: 1.2%; Average loss: 4.5945
Iteration: 50; Percent complete: 1.2%; Average loss: 4.4518
Iteration: 51; Percent complete: 1.3%; Average loss: 4.7952
Iteration: 52; Percent complete: 1.3%; Average loss: 4.5762
Iteration: 53; Percent complete: 1.3%; Average loss: 4.9399
Iteration: 54; Percent complete: 1.4%; Average loss: 4.6136
Iteration: 55; Percent complete: 1.4%; Average loss: 4.5536
Iteration: 56; Percent complete: 1.4%; Average loss: 4.5410
Iteration: 57; Percent complete: 1.4%; Average loss: 4.5608
Iteration: 58; Percent complete: 1.5%; Average loss: 4.7121
Iteration: 59; Percent complete: 1.5%; Average loss: 4.8474
Iteration: 60; Percent complete: 1.5%; Average loss: 4.9072
Iteration: 61; Percent complete: 1.5%; Average loss: 4.2951
Iteration: 62; Percent complete: 1.6%; Average loss: 4.5867
Iteration: 63; Percent complete: 1.6%; Average loss: 4.6514
Iteration: 64; Percent complete: 1.6%; Average loss: 4.5758
Iteration: 65; Percent complete: 1.6%; Average loss: 4.3930
Iteration: 66; Percent complete: 1.7%; Average loss: 4.4777
Iteration: 67; Percent complete: 1.7%; Average loss: 4.5889
Iteration: 68; Percent complete: 1.7%; Average loss: 4.4881
Iteration: 69; Percent complete: 1.7%; Average loss: 4.4183
Iteration: 70; Percent complete: 1.8%; Average loss: 4.4193
Iteration: 71; Percent complete: 1.8%; Average loss: 4.6319
Iteration: 72; Percent complete: 1.8%; Average loss: 4.4817
Iteration: 73; Percent complete: 1.8%; Average loss: 4.3544
Iteration: 74; Percent complete: 1.8%; Average loss: 4.3736
Iteration: 75; Percent complete: 1.9%; Average loss: 4.6423
Iteration: 76; Percent complete: 1.9%; Average loss: 4.5627
Iteration: 77; Percent complete: 1.9%; Average loss: 4.3339
Iteration: 78; Percent complete: 1.9%; Average loss: 4.4298
Iteration: 79; Percent complete: 2.0%; Average loss: 4.4196
Iteration: 80; Percent complete: 2.0%; Average loss: 4.4350
Iteration: 81; Percent complete: 2.0%; Average loss: 4.4973
Iteration: 82; Percent complete: 2.1%; Average loss: 4.5644
Iteration: 83; Percent complete: 2.1%; Average loss: 4.4175
Iteration: 84; Percent complete: 2.1%; Average loss: 4.5943
Iteration: 85; Percent complete: 2.1%; Average loss: 4.4169
Iteration: 86; Percent complete: 2.1%; Average loss: 4.3316
Iteration: 87; Percent complete: 2.2%; Average loss: 4.5137
Iteration: 88; Percent complete: 2.2%; Average loss: 4.4387
Iteration: 89; Percent complete: 2.2%; Average loss: 4.4143
Iteration: 90; Percent complete: 2.2%; Average loss: 4.3367
Iteration: 91; Percent complete: 2.3%; Average loss: 4.3701
Iteration: 92; Percent complete: 2.3%; Average loss: 4.2568
Iteration: 93; Percent complete: 2.3%; Average loss: 4.2104
Iteration: 94; Percent complete: 2.4%; Average loss: 4.4357
Iteration: 95; Percent complete: 2.4%; Average loss: 4.2585
Iteration: 96; Percent complete: 2.4%; Average loss: 4.6144
Iteration: 97; Percent complete: 2.4%; Average loss: 4.4214
Iteration: 98; Percent complete: 2.5%; Average loss: 4.5924
Iteration: 99; Percent complete: 2.5%; Average loss: 4.4449
Iteration: 100; Percent complete: 2.5%; Average loss: 4.4955
Iteration: 101; Percent complete: 2.5%; Average loss: 4.3739
Iteration: 102; Percent complete: 2.5%; Average loss: 4.4375
Iteration: 103; Percent complete: 2.6%; Average loss: 4.3417
Iteration: 104; Percent complete: 2.6%; Average loss: 4.7692
Iteration: 105; Percent complete: 2.6%; Average loss: 4.4539
Iteration: 106; Percent complete: 2.6%; Average loss: 4.4070
Iteration: 107; Percent complete: 2.7%; Average loss: 4.3732
Iteration: 108; Percent complete: 2.7%; Average loss: 4.4256
Iteration: 109; Percent complete: 2.7%; Average loss: 4.4653
Iteration: 110; Percent complete: 2.8%; Average loss: 4.2565
Iteration: 111; Percent complete: 2.8%; Average loss: 4.5187
Iteration: 112; Percent complete: 2.8%; Average loss: 4.3370
Iteration: 113; Percent complete: 2.8%; Average loss: 4.4431
Iteration: 114; Percent complete: 2.9%; Average loss: 3.9951
Iteration: 115; Percent complete: 2.9%; Average loss: 4.5769
Iteration: 116; Percent complete: 2.9%; Average loss: 4.3926
Iteration: 117; Percent complete: 2.9%; Average loss: 4.2465
Iteration: 118; Percent complete: 2.9%; Average loss: 4.4732
Iteration: 119; Percent complete: 3.0%; Average loss: 4.5370
Iteration: 120; Percent complete: 3.0%; Average loss: 4.2114
Iteration: 121; Percent complete: 3.0%; Average loss: 4.1668
Iteration: 122; Percent complete: 3.0%; Average loss: 4.5607
Iteration: 123; Percent complete: 3.1%; Average loss: 4.2399
Iteration: 124; Percent complete: 3.1%; Average loss: 4.5006
Iteration: 125; Percent complete: 3.1%; Average loss: 4.3249
Iteration: 126; Percent complete: 3.1%; Average loss: 4.2964
Iteration: 127; Percent complete: 3.2%; Average loss: 4.5457
Iteration: 128; Percent complete: 3.2%; Average loss: 4.4930
Iteration: 129; Percent complete: 3.2%; Average loss: 4.4733
Iteration: 130; Percent complete: 3.2%; Average loss: 4.0909
Iteration: 131; Percent complete: 3.3%; Average loss: 4.4972
Iteration: 132; Percent complete: 3.3%; Average loss: 4.5886
Iteration: 133; Percent complete: 3.3%; Average loss: 4.1414
Iteration: 134; Percent complete: 3.4%; Average loss: 4.1319
Iteration: 135; Percent complete: 3.4%; Average loss: 4.4457
Iteration: 136; Percent complete: 3.4%; Average loss: 4.3097
Iteration: 137; Percent complete: 3.4%; Average loss: 4.4191
Iteration: 138; Percent complete: 3.5%; Average loss: 4.2320
Iteration: 139; Percent complete: 3.5%; Average loss: 4.2024
Iteration: 140; Percent complete: 3.5%; Average loss: 4.3159
Iteration: 141; Percent complete: 3.5%; Average loss: 4.4711
Iteration: 142; Percent complete: 3.5%; Average loss: 4.5371
Iteration: 143; Percent complete: 3.6%; Average loss: 4.2810
Iteration: 144; Percent complete: 3.6%; Average loss: 4.3634
Iteration: 145; Percent complete: 3.6%; Average loss: 4.0201
Iteration: 146; Percent complete: 3.6%; Average loss: 4.3367
Iteration: 147; Percent complete: 3.7%; Average loss: 4.2590
Iteration: 148; Percent complete: 3.7%; Average loss: 4.3203
Iteration: 149; Percent complete: 3.7%; Average loss: 4.5749
Iteration: 150; Percent complete: 3.8%; Average loss: 4.4564
Iteration: 151; Percent complete: 3.8%; Average loss: 4.4065
Iteration: 152; Percent complete: 3.8%; Average loss: 4.3484
Iteration: 153; Percent complete: 3.8%; Average loss: 4.3979
Iteration: 154; Percent complete: 3.9%; Average loss: 4.3281
Iteration: 155; Percent complete: 3.9%; Average loss: 4.3227
Iteration: 156; Percent complete: 3.9%; Average loss: 4.3025
Iteration: 157; Percent complete: 3.9%; Average loss: 4.2773
Iteration: 158; Percent complete: 4.0%; Average loss: 4.1676
Iteration: 159; Percent complete: 4.0%; Average loss: 4.1787
Iteration: 160; Percent complete: 4.0%; Average loss: 4.2319
Iteration: 161; Percent complete: 4.0%; Average loss: 4.3672
Iteration: 162; Percent complete: 4.0%; Average loss: 4.1848
Iteration: 163; Percent complete: 4.1%; Average loss: 4.2817
Iteration: 164; Percent complete: 4.1%; Average loss: 4.3332
Iteration: 165; Percent complete: 4.1%; Average loss: 4.1286
Iteration: 166; Percent complete: 4.2%; Average loss: 4.0574
Iteration: 167; Percent complete: 4.2%; Average loss: 4.3139
Iteration: 168; Percent complete: 4.2%; Average loss: 4.4806
Iteration: 169; Percent complete: 4.2%; Average loss: 4.1551
Iteration: 170; Percent complete: 4.2%; Average loss: 4.1520
Iteration: 171; Percent complete: 4.3%; Average loss: 4.3593
Iteration: 172; Percent complete: 4.3%; Average loss: 4.3797
Iteration: 173; Percent complete: 4.3%; Average loss: 4.2606
Iteration: 174; Percent complete: 4.3%; Average loss: 4.2831
Iteration: 175; Percent complete: 4.4%; Average loss: 4.0315
Iteration: 176; Percent complete: 4.4%; Average loss: 4.1985
Iteration: 177; Percent complete: 4.4%; Average loss: 4.0929
Iteration: 178; Percent complete: 4.5%; Average loss: 4.2599
Iteration: 179; Percent complete: 4.5%; Average loss: 4.1789
Iteration: 180; Percent complete: 4.5%; Average loss: 4.1176
Iteration: 181; Percent complete: 4.5%; Average loss: 4.1162
Iteration: 182; Percent complete: 4.5%; Average loss: 4.1477
Iteration: 183; Percent complete: 4.6%; Average loss: 4.5203
Iteration: 184; Percent complete: 4.6%; Average loss: 4.0862
Iteration: 185; Percent complete: 4.6%; Average loss: 4.2179
Iteration: 186; Percent complete: 4.7%; Average loss: 4.3524
Iteration: 187; Percent complete: 4.7%; Average loss: 4.2183
Iteration: 188; Percent complete: 4.7%; Average loss: 4.1712
Iteration: 189; Percent complete: 4.7%; Average loss: 4.2476
Iteration: 190; Percent complete: 4.8%; Average loss: 4.0223
Iteration: 191; Percent complete: 4.8%; Average loss: 4.1565
Iteration: 192; Percent complete: 4.8%; Average loss: 3.9748
Iteration: 193; Percent complete: 4.8%; Average loss: 4.1380
Iteration: 194; Percent complete: 4.9%; Average loss: 4.0909
Iteration: 195; Percent complete: 4.9%; Average loss: 4.1286
Iteration: 196; Percent complete: 4.9%; Average loss: 4.0974
Iteration: 197; Percent complete: 4.9%; Average loss: 3.9570
Iteration: 198; Percent complete: 5.0%; Average loss: 3.9909
Iteration: 199; Percent complete: 5.0%; Average loss: 4.4069
Iteration: 200; Percent complete: 5.0%; Average loss: 4.2706
Iteration: 201; Percent complete: 5.0%; Average loss: 4.3914
Iteration: 202; Percent complete: 5.1%; Average loss: 4.2260
Iteration: 203; Percent complete: 5.1%; Average loss: 3.9397
Iteration: 204; Percent complete: 5.1%; Average loss: 4.2346
Iteration: 205; Percent complete: 5.1%; Average loss: 4.0522
Iteration: 206; Percent complete: 5.1%; Average loss: 4.0189
Iteration: 207; Percent complete: 5.2%; Average loss: 4.2732
Iteration: 208; Percent complete: 5.2%; Average loss: 3.9960
Iteration: 209; Percent complete: 5.2%; Average loss: 3.9509
Iteration: 210; Percent complete: 5.2%; Average loss: 3.9583
Iteration: 211; Percent complete: 5.3%; Average loss: 4.1972
Iteration: 212; Percent complete: 5.3%; Average loss: 4.0302
Iteration: 213; Percent complete: 5.3%; Average loss: 4.3014
Iteration: 214; Percent complete: 5.3%; Average loss: 4.0091
Iteration: 215; Percent complete: 5.4%; Average loss: 4.1480
Iteration: 216; Percent complete: 5.4%; Average loss: 4.1663
Iteration: 217; Percent complete: 5.4%; Average loss: 4.1042
Iteration: 218; Percent complete: 5.5%; Average loss: 4.1279
Iteration: 219; Percent complete: 5.5%; Average loss: 4.1407
Iteration: 220; Percent complete: 5.5%; Average loss: 4.2947
Iteration: 221; Percent complete: 5.5%; Average loss: 3.9882
Iteration: 222; Percent complete: 5.5%; Average loss: 4.1957
Iteration: 223; Percent complete: 5.6%; Average loss: 3.9240
Iteration: 224; Percent complete: 5.6%; Average loss: 4.0201
Iteration: 225; Percent complete: 5.6%; Average loss: 3.6809
Iteration: 226; Percent complete: 5.7%; Average loss: 3.8034
Iteration: 227; Percent complete: 5.7%; Average loss: 4.0501
Iteration: 228; Percent complete: 5.7%; Average loss: 4.2076
Iteration: 229; Percent complete: 5.7%; Average loss: 4.2732
Iteration: 230; Percent complete: 5.8%; Average loss: 4.0840
Iteration: 231; Percent complete: 5.8%; Average loss: 4.1673
Iteration: 232; Percent complete: 5.8%; Average loss: 4.0668
Iteration: 233; Percent complete: 5.8%; Average loss: 4.1467
Iteration: 234; Percent complete: 5.9%; Average loss: 4.1295
Iteration: 235; Percent complete: 5.9%; Average loss: 3.9382
Iteration: 236; Percent complete: 5.9%; Average loss: 4.1676
Iteration: 237; Percent complete: 5.9%; Average loss: 4.0237
Iteration: 238; Percent complete: 5.9%; Average loss: 4.0972
Iteration: 239; Percent complete: 6.0%; Average loss: 3.6052
Iteration: 240; Percent complete: 6.0%; Average loss: 3.9381
Iteration: 241; Percent complete: 6.0%; Average loss: 4.0765
Iteration: 242; Percent complete: 6.0%; Average loss: 3.7586
Iteration: 243; Percent complete: 6.1%; Average loss: 4.1045
Iteration: 244; Percent complete: 6.1%; Average loss: 3.9527
Iteration: 245; Percent complete: 6.1%; Average loss: 3.8940
Iteration: 246; Percent complete: 6.2%; Average loss: 4.0579
Iteration: 247; Percent complete: 6.2%; Average loss: 4.0859
Iteration: 248; Percent complete: 6.2%; Average loss: 3.9939
Iteration: 249; Percent complete: 6.2%; Average loss: 3.7664
Iteration: 250; Percent complete: 6.2%; Average loss: 4.1552
Iteration: 251; Percent complete: 6.3%; Average loss: 3.9978
Iteration: 252; Percent complete: 6.3%; Average loss: 3.9946
Iteration: 253; Percent complete: 6.3%; Average loss: 4.0670
Iteration: 254; Percent complete: 6.3%; Average loss: 3.9888
Iteration: 255; Percent complete: 6.4%; Average loss: 4.2486
Iteration: 256; Percent complete: 6.4%; Average loss: 3.9277
Iteration: 257; Percent complete: 6.4%; Average loss: 4.0895
Iteration: 258; Percent complete: 6.5%; Average loss: 4.0521
Iteration: 259; Percent complete: 6.5%; Average loss: 3.7197
Iteration: 260; Percent complete: 6.5%; Average loss: 3.8201
Iteration: 261; Percent complete: 6.5%; Average loss: 4.1185
Iteration: 262; Percent complete: 6.6%; Average loss: 3.9851
Iteration: 263; Percent complete: 6.6%; Average loss: 4.0255
Iteration: 264; Percent complete: 6.6%; Average loss: 4.1120
Iteration: 265; Percent complete: 6.6%; Average loss: 4.0935
Iteration: 266; Percent complete: 6.7%; Average loss: 4.0506
Iteration: 267; Percent complete: 6.7%; Average loss: 3.9810
Iteration: 268; Percent complete: 6.7%; Average loss: 3.9449
Iteration: 269; Percent complete: 6.7%; Average loss: 3.8249
Iteration: 270; Percent complete: 6.8%; Average loss: 3.9891
Iteration: 271; Percent complete: 6.8%; Average loss: 3.8761
Iteration: 272; Percent complete: 6.8%; Average loss: 3.9596
Iteration: 273; Percent complete: 6.8%; Average loss: 4.0066
Iteration: 274; Percent complete: 6.9%; Average loss: 4.1745
Iteration: 275; Percent complete: 6.9%; Average loss: 4.1485
Iteration: 276; Percent complete: 6.9%; Average loss: 3.9863
Iteration: 277; Percent complete: 6.9%; Average loss: 3.7913
Iteration: 278; Percent complete: 7.0%; Average loss: 3.8825
Iteration: 279; Percent complete: 7.0%; Average loss: 4.0305
Iteration: 280; Percent complete: 7.0%; Average loss: 3.8658
Iteration: 281; Percent complete: 7.0%; Average loss: 4.0399
Iteration: 282; Percent complete: 7.0%; Average loss: 3.8028
Iteration: 283; Percent complete: 7.1%; Average loss: 4.1705
Iteration: 284; Percent complete: 7.1%; Average loss: 3.8909
Iteration: 285; Percent complete: 7.1%; Average loss: 3.9367
Iteration: 286; Percent complete: 7.1%; Average loss: 4.0616
Iteration: 287; Percent complete: 7.2%; Average loss: 3.7900
Iteration: 288; Percent complete: 7.2%; Average loss: 3.9441
Iteration: 289; Percent complete: 7.2%; Average loss: 3.8406
Iteration: 290; Percent complete: 7.2%; Average loss: 3.9133
Iteration: 291; Percent complete: 7.3%; Average loss: 3.7502
Iteration: 292; Percent complete: 7.3%; Average loss: 3.9511
Iteration: 293; Percent complete: 7.3%; Average loss: 3.8544
Iteration: 294; Percent complete: 7.3%; Average loss: 3.8057
Iteration: 295; Percent complete: 7.4%; Average loss: 3.6615
Iteration: 296; Percent complete: 7.4%; Average loss: 4.2288
Iteration: 297; Percent complete: 7.4%; Average loss: 4.1463
Iteration: 298; Percent complete: 7.4%; Average loss: 3.6676
Iteration: 299; Percent complete: 7.5%; Average loss: 3.8550
Iteration: 300; Percent complete: 7.5%; Average loss: 3.8352
Iteration: 301; Percent complete: 7.5%; Average loss: 3.7248
Iteration: 302; Percent complete: 7.5%; Average loss: 4.0196
Iteration: 303; Percent complete: 7.6%; Average loss: 3.7983
Iteration: 304; Percent complete: 7.6%; Average loss: 4.0993
Iteration: 305; Percent complete: 7.6%; Average loss: 3.8578
Iteration: 306; Percent complete: 7.6%; Average loss: 3.9257
Iteration: 307; Percent complete: 7.7%; Average loss: 4.0899
Iteration: 308; Percent complete: 7.7%; Average loss: 3.9323
Iteration: 309; Percent complete: 7.7%; Average loss: 3.7979
Iteration: 310; Percent complete: 7.8%; Average loss: 3.8843
Iteration: 311; Percent complete: 7.8%; Average loss: 3.6746
Iteration: 312; Percent complete: 7.8%; Average loss: 3.5942
Iteration: 313; Percent complete: 7.8%; Average loss: 4.0972
Iteration: 314; Percent complete: 7.8%; Average loss: 3.8513
Iteration: 315; Percent complete: 7.9%; Average loss: 3.7099
Iteration: 316; Percent complete: 7.9%; Average loss: 3.7944
Iteration: 317; Percent complete: 7.9%; Average loss: 4.1875
Iteration: 318; Percent complete: 8.0%; Average loss: 3.9934
Iteration: 319; Percent complete: 8.0%; Average loss: 3.8147
Iteration: 320; Percent complete: 8.0%; Average loss: 3.7863
Iteration: 321; Percent complete: 8.0%; Average loss: 3.7183
Iteration: 322; Percent complete: 8.1%; Average loss: 3.7562
Iteration: 323; Percent complete: 8.1%; Average loss: 4.0615
Iteration: 324; Percent complete: 8.1%; Average loss: 3.7415
Iteration: 325; Percent complete: 8.1%; Average loss: 3.9581
Iteration: 326; Percent complete: 8.2%; Average loss: 3.7860
Iteration: 327; Percent complete: 8.2%; Average loss: 3.9047
Iteration: 328; Percent complete: 8.2%; Average loss: 3.9304
Iteration: 329; Percent complete: 8.2%; Average loss: 4.0587
Iteration: 330; Percent complete: 8.2%; Average loss: 3.8308
Iteration: 331; Percent complete: 8.3%; Average loss: 3.8080
Iteration: 332; Percent complete: 8.3%; Average loss: 3.9158
Iteration: 333; Percent complete: 8.3%; Average loss: 4.2061
Iteration: 334; Percent complete: 8.3%; Average loss: 3.9483
Iteration: 335; Percent complete: 8.4%; Average loss: 3.8565
Iteration: 336; Percent complete: 8.4%; Average loss: 3.7117
Iteration: 337; Percent complete: 8.4%; Average loss: 3.8821
Iteration: 338; Percent complete: 8.5%; Average loss: 3.8086
Iteration: 339; Percent complete: 8.5%; Average loss: 3.5877
Iteration: 340; Percent complete: 8.5%; Average loss: 4.0235
Iteration: 341; Percent complete: 8.5%; Average loss: 3.7770
Iteration: 342; Percent complete: 8.6%; Average loss: 4.0173
Iteration: 343; Percent complete: 8.6%; Average loss: 3.8437
Iteration: 344; Percent complete: 8.6%; Average loss: 3.8603
Iteration: 345; Percent complete: 8.6%; Average loss: 3.9137
Iteration: 346; Percent complete: 8.6%; Average loss: 3.6766
Iteration: 347; Percent complete: 8.7%; Average loss: 3.9748
Iteration: 348; Percent complete: 8.7%; Average loss: 4.0174
Iteration: 349; Percent complete: 8.7%; Average loss: 3.9461
Iteration: 350; Percent complete: 8.8%; Average loss: 3.7814
Iteration: 351; Percent complete: 8.8%; Average loss: 3.9309
Iteration: 352; Percent complete: 8.8%; Average loss: 3.8099
Iteration: 353; Percent complete: 8.8%; Average loss: 3.6420
Iteration: 354; Percent complete: 8.8%; Average loss: 4.0143
Iteration: 355; Percent complete: 8.9%; Average loss: 3.8787
Iteration: 356; Percent complete: 8.9%; Average loss: 4.0478
Iteration: 357; Percent complete: 8.9%; Average loss: 3.7709
Iteration: 358; Percent complete: 8.9%; Average loss: 3.4679
Iteration: 359; Percent complete: 9.0%; Average loss: 3.8586
Iteration: 360; Percent complete: 9.0%; Average loss: 3.4937
Iteration: 361; Percent complete: 9.0%; Average loss: 3.7886
Iteration: 362; Percent complete: 9.0%; Average loss: 3.9889
Iteration: 363; Percent complete: 9.1%; Average loss: 3.8050
Iteration: 364; Percent complete: 9.1%; Average loss: 4.0870
Iteration: 365; Percent complete: 9.1%; Average loss: 3.5397
Iteration: 366; Percent complete: 9.2%; Average loss: 3.8140
Iteration: 367; Percent complete: 9.2%; Average loss: 3.9381
Iteration: 368; Percent complete: 9.2%; Average loss: 3.6615
Iteration: 369; Percent complete: 9.2%; Average loss: 3.7597
Iteration: 370; Percent complete: 9.2%; Average loss: 3.8337
Iteration: 371; Percent complete: 9.3%; Average loss: 3.7493
Iteration: 372; Percent complete: 9.3%; Average loss: 3.7787
Iteration: 373; Percent complete: 9.3%; Average loss: 3.9527
Iteration: 374; Percent complete: 9.3%; Average loss: 4.0038
Iteration: 375; Percent complete: 9.4%; Average loss: 3.8687
Iteration: 376; Percent complete: 9.4%; Average loss: 3.7955
Iteration: 377; Percent complete: 9.4%; Average loss: 3.8789
Iteration: 378; Percent complete: 9.4%; Average loss: 3.6909
Iteration: 379; Percent complete: 9.5%; Average loss: 4.2129
Iteration: 380; Percent complete: 9.5%; Average loss: 3.7253
Iteration: 381; Percent complete: 9.5%; Average loss: 4.0956
Iteration: 382; Percent complete: 9.6%; Average loss: 3.6451
Iteration: 383; Percent complete: 9.6%; Average loss: 3.9363
Iteration: 384; Percent complete: 9.6%; Average loss: 3.8725
Iteration: 385; Percent complete: 9.6%; Average loss: 3.7663
Iteration: 386; Percent complete: 9.7%; Average loss: 3.7795
Iteration: 387; Percent complete: 9.7%; Average loss: 3.9142
Iteration: 388; Percent complete: 9.7%; Average loss: 3.6829
Iteration: 389; Percent complete: 9.7%; Average loss: 4.0650
Iteration: 390; Percent complete: 9.8%; Average loss: 3.6440
Iteration: 391; Percent complete: 9.8%; Average loss: 3.8104
Iteration: 392; Percent complete: 9.8%; Average loss: 3.9740
Iteration: 393; Percent complete: 9.8%; Average loss: 3.8396
Iteration: 394; Percent complete: 9.8%; Average loss: 3.8413
Iteration: 395; Percent complete: 9.9%; Average loss: 3.8850
Iteration: 396; Percent complete: 9.9%; Average loss: 3.7562
Iteration: 397; Percent complete: 9.9%; Average loss: 4.0343
Iteration: 398; Percent complete: 10.0%; Average loss: 3.9964
Iteration: 399; Percent complete: 10.0%; Average loss: 4.2741
Iteration: 400; Percent complete: 10.0%; Average loss: 3.7761
Iteration: 401; Percent complete: 10.0%; Average loss: 3.7300
Iteration: 402; Percent complete: 10.1%; Average loss: 3.4028
Iteration: 403; Percent complete: 10.1%; Average loss: 3.7310
Iteration: 404; Percent complete: 10.1%; Average loss: 3.5680
Iteration: 405; Percent complete: 10.1%; Average loss: 3.7041
Iteration: 406; Percent complete: 10.2%; Average loss: 4.0281
Iteration: 407; Percent complete: 10.2%; Average loss: 3.8550
Iteration: 408; Percent complete: 10.2%; Average loss: 4.1204
Iteration: 409; Percent complete: 10.2%; Average loss: 3.8371
Iteration: 410; Percent complete: 10.2%; Average loss: 3.8427
Iteration: 411; Percent complete: 10.3%; Average loss: 4.0000
Iteration: 412; Percent complete: 10.3%; Average loss: 3.7375
Iteration: 413; Percent complete: 10.3%; Average loss: 3.8096
Iteration: 414; Percent complete: 10.3%; Average loss: 3.5430
Iteration: 415; Percent complete: 10.4%; Average loss: 3.9669
Iteration: 416; Percent complete: 10.4%; Average loss: 3.7678
Iteration: 417; Percent complete: 10.4%; Average loss: 3.7290
Iteration: 418; Percent complete: 10.4%; Average loss: 4.0015
Iteration: 419; Percent complete: 10.5%; Average loss: 3.9972
Iteration: 420; Percent complete: 10.5%; Average loss: 4.0789
Iteration: 421; Percent complete: 10.5%; Average loss: 3.8945
Iteration: 422; Percent complete: 10.5%; Average loss: 3.9370
Iteration: 423; Percent complete: 10.6%; Average loss: 3.8117
Iteration: 424; Percent complete: 10.6%; Average loss: 3.7476
Iteration: 425; Percent complete: 10.6%; Average loss: 3.8856
Iteration: 426; Percent complete: 10.7%; Average loss: 3.7297
Iteration: 427; Percent complete: 10.7%; Average loss: 3.9187
Iteration: 428; Percent complete: 10.7%; Average loss: 3.6619
Iteration: 429; Percent complete: 10.7%; Average loss: 3.9273
Iteration: 430; Percent complete: 10.8%; Average loss: 3.9567
Iteration: 431; Percent complete: 10.8%; Average loss: 3.6064
Iteration: 432; Percent complete: 10.8%; Average loss: 3.9097
Iteration: 433; Percent complete: 10.8%; Average loss: 3.7953
Iteration: 434; Percent complete: 10.8%; Average loss: 3.6856
Iteration: 435; Percent complete: 10.9%; Average loss: 3.8188
Iteration: 436; Percent complete: 10.9%; Average loss: 3.7880
Iteration: 437; Percent complete: 10.9%; Average loss: 3.7901
Iteration: 438; Percent complete: 10.9%; Average loss: 3.9689
Iteration: 439; Percent complete: 11.0%; Average loss: 3.6844
Iteration: 440; Percent complete: 11.0%; Average loss: 3.8698
Iteration: 441; Percent complete: 11.0%; Average loss: 4.0391
Iteration: 442; Percent complete: 11.1%; Average loss: 3.7011
Iteration: 443; Percent complete: 11.1%; Average loss: 4.0770
Iteration: 444; Percent complete: 11.1%; Average loss: 3.7424
Iteration: 445; Percent complete: 11.1%; Average loss: 3.7880
Iteration: 446; Percent complete: 11.2%; Average loss: 3.8233
Iteration: 447; Percent complete: 11.2%; Average loss: 3.8161
Iteration: 448; Percent complete: 11.2%; Average loss: 3.7585
Iteration: 449; Percent complete: 11.2%; Average loss: 3.7613
Iteration: 450; Percent complete: 11.2%; Average loss: 4.1095
Iteration: 451; Percent complete: 11.3%; Average loss: 3.9075
Iteration: 452; Percent complete: 11.3%; Average loss: 3.8693
Iteration: 453; Percent complete: 11.3%; Average loss: 3.8369
Iteration: 454; Percent complete: 11.3%; Average loss: 3.7249
Iteration: 455; Percent complete: 11.4%; Average loss: 3.9876
Iteration: 456; Percent complete: 11.4%; Average loss: 4.1515
Iteration: 457; Percent complete: 11.4%; Average loss: 3.8136
Iteration: 458; Percent complete: 11.5%; Average loss: 3.7436
Iteration: 459; Percent complete: 11.5%; Average loss: 3.7833
Iteration: 460; Percent complete: 11.5%; Average loss: 3.8381
Iteration: 461; Percent complete: 11.5%; Average loss: 3.7199
Iteration: 462; Percent complete: 11.6%; Average loss: 3.9419
Iteration: 463; Percent complete: 11.6%; Average loss: 3.7000
Iteration: 464; Percent complete: 11.6%; Average loss: 3.7493
Iteration: 465; Percent complete: 11.6%; Average loss: 3.6276
Iteration: 466; Percent complete: 11.7%; Average loss: 3.9489
Iteration: 467; Percent complete: 11.7%; Average loss: 3.8073
Iteration: 468; Percent complete: 11.7%; Average loss: 3.8828
Iteration: 469; Percent complete: 11.7%; Average loss: 3.8206
Iteration: 470; Percent complete: 11.8%; Average loss: 3.4769
Iteration: 471; Percent complete: 11.8%; Average loss: 3.6657
Iteration: 472; Percent complete: 11.8%; Average loss: 3.9094
Iteration: 473; Percent complete: 11.8%; Average loss: 4.0184
Iteration: 474; Percent complete: 11.8%; Average loss: 4.0407
Iteration: 475; Percent complete: 11.9%; Average loss: 3.8733
Iteration: 476; Percent complete: 11.9%; Average loss: 3.6695
Iteration: 477; Percent complete: 11.9%; Average loss: 4.1219
Iteration: 478; Percent complete: 11.9%; Average loss: 3.8258
Iteration: 479; Percent complete: 12.0%; Average loss: 3.9007
Iteration: 480; Percent complete: 12.0%; Average loss: 3.8357
Iteration: 481; Percent complete: 12.0%; Average loss: 3.7844
Iteration: 482; Percent complete: 12.0%; Average loss: 4.0915
Iteration: 483; Percent complete: 12.1%; Average loss: 3.9223
Iteration: 484; Percent complete: 12.1%; Average loss: 3.5757
Iteration: 485; Percent complete: 12.1%; Average loss: 3.7515
Iteration: 486; Percent complete: 12.2%; Average loss: 3.7553
Iteration: 487; Percent complete: 12.2%; Average loss: 3.6600
Iteration: 488; Percent complete: 12.2%; Average loss: 3.5675
Iteration: 489; Percent complete: 12.2%; Average loss: 3.7581
Iteration: 490; Percent complete: 12.2%; Average loss: 3.8928
Iteration: 491; Percent complete: 12.3%; Average loss: 3.9914
Iteration: 492; Percent complete: 12.3%; Average loss: 3.6362
Iteration: 493; Percent complete: 12.3%; Average loss: 3.7697
Iteration: 494; Percent complete: 12.3%; Average loss: 3.6675
Iteration: 495; Percent complete: 12.4%; Average loss: 3.7895
Iteration: 496; Percent complete: 12.4%; Average loss: 3.7090
Iteration: 497; Percent complete: 12.4%; Average loss: 3.7281
Iteration: 498; Percent complete: 12.4%; Average loss: 3.9135
Iteration: 499; Percent complete: 12.5%; Average loss: 3.5663
Iteration: 500; Percent complete: 12.5%; Average loss: 3.8709
Iteration: 501; Percent complete: 12.5%; Average loss: 3.4996
Iteration: 502; Percent complete: 12.6%; Average loss: 3.8744
Iteration: 503; Percent complete: 12.6%; Average loss: 3.6604
Iteration: 504; Percent complete: 12.6%; Average loss: 3.7498
Iteration: 505; Percent complete: 12.6%; Average loss: 3.8051
Iteration: 506; Percent complete: 12.7%; Average loss: 3.7615
Iteration: 507; Percent complete: 12.7%; Average loss: 3.7143
Iteration: 508; Percent complete: 12.7%; Average loss: 3.6441
Iteration: 509; Percent complete: 12.7%; Average loss: 3.7048
Iteration: 510; Percent complete: 12.8%; Average loss: 3.6571
Iteration: 511; Percent complete: 12.8%; Average loss: 3.6265
Iteration: 512; Percent complete: 12.8%; Average loss: 3.5712
Iteration: 513; Percent complete: 12.8%; Average loss: 3.5857
Iteration: 514; Percent complete: 12.8%; Average loss: 3.6698
Iteration: 515; Percent complete: 12.9%; Average loss: 3.6363
Iteration: 516; Percent complete: 12.9%; Average loss: 3.5335
Iteration: 517; Percent complete: 12.9%; Average loss: 4.1587
Iteration: 518; Percent complete: 13.0%; Average loss: 3.9864
Iteration: 519; Percent complete: 13.0%; Average loss: 3.7621
Iteration: 520; Percent complete: 13.0%; Average loss: 3.6127
Iteration: 521; Percent complete: 13.0%; Average loss: 3.4904
Iteration: 522; Percent complete: 13.1%; Average loss: 3.8848
Iteration: 523; Percent complete: 13.1%; Average loss: 3.7168
Iteration: 524; Percent complete: 13.1%; Average loss: 3.6663
Iteration: 525; Percent complete: 13.1%; Average loss: 3.6297
Iteration: 526; Percent complete: 13.2%; Average loss: 3.8703
Iteration: 527; Percent complete: 13.2%; Average loss: 3.7539
Iteration: 528; Percent complete: 13.2%; Average loss: 3.5932
Iteration: 529; Percent complete: 13.2%; Average loss: 3.8466
Iteration: 530; Percent complete: 13.2%; Average loss: 3.8648
Iteration: 531; Percent complete: 13.3%; Average loss: 3.6973
Iteration: 532; Percent complete: 13.3%; Average loss: 3.5713
Iteration: 533; Percent complete: 13.3%; Average loss: 3.5576
Iteration: 534; Percent complete: 13.4%; Average loss: 3.6879
Iteration: 535; Percent complete: 13.4%; Average loss: 3.8520
Iteration: 536; Percent complete: 13.4%; Average loss: 3.8550
Iteration: 537; Percent complete: 13.4%; Average loss: 4.0054
Iteration: 538; Percent complete: 13.5%; Average loss: 3.8481
Iteration: 539; Percent complete: 13.5%; Average loss: 3.9212
Iteration: 540; Percent complete: 13.5%; Average loss: 3.9976
Iteration: 541; Percent complete: 13.5%; Average loss: 3.7377
Iteration: 542; Percent complete: 13.6%; Average loss: 3.7885
Iteration: 543; Percent complete: 13.6%; Average loss: 3.9074
Iteration: 544; Percent complete: 13.6%; Average loss: 3.7886
Iteration: 545; Percent complete: 13.6%; Average loss: 3.6749
Iteration: 546; Percent complete: 13.7%; Average loss: 3.9693
Iteration: 547; Percent complete: 13.7%; Average loss: 3.6307
Iteration: 548; Percent complete: 13.7%; Average loss: 3.8458
Iteration: 549; Percent complete: 13.7%; Average loss: 3.9715
Iteration: 550; Percent complete: 13.8%; Average loss: 3.8438
Iteration: 551; Percent complete: 13.8%; Average loss: 3.7255
Iteration: 552; Percent complete: 13.8%; Average loss: 3.5699
Iteration: 553; Percent complete: 13.8%; Average loss: 3.9376
Iteration: 554; Percent complete: 13.9%; Average loss: 3.7849
Iteration: 555; Percent complete: 13.9%; Average loss: 3.7603
Iteration: 556; Percent complete: 13.9%; Average loss: 3.7673
Iteration: 557; Percent complete: 13.9%; Average loss: 4.0246
Iteration: 558; Percent complete: 14.0%; Average loss: 3.7990
Iteration: 559; Percent complete: 14.0%; Average loss: 3.8539
Iteration: 560; Percent complete: 14.0%; Average loss: 3.7443
Iteration: 561; Percent complete: 14.0%; Average loss: 3.6069
Iteration: 562; Percent complete: 14.1%; Average loss: 3.8579
Iteration: 563; Percent complete: 14.1%; Average loss: 3.4488
Iteration: 564; Percent complete: 14.1%; Average loss: 3.5905
Iteration: 565; Percent complete: 14.1%; Average loss: 3.7749
Iteration: 566; Percent complete: 14.1%; Average loss: 3.5171
Iteration: 567; Percent complete: 14.2%; Average loss: 3.4322
Iteration: 568; Percent complete: 14.2%; Average loss: 3.8855
Iteration: 569; Percent complete: 14.2%; Average loss: 3.6740
Iteration: 570; Percent complete: 14.2%; Average loss: 3.4766
Iteration: 571; Percent complete: 14.3%; Average loss: 3.6565
Iteration: 572; Percent complete: 14.3%; Average loss: 3.7812
Iteration: 573; Percent complete: 14.3%; Average loss: 3.8424
Iteration: 574; Percent complete: 14.3%; Average loss: 3.8310
Iteration: 575; Percent complete: 14.4%; Average loss: 3.5251
Iteration: 576; Percent complete: 14.4%; Average loss: 3.9010
Iteration: 577; Percent complete: 14.4%; Average loss: 3.8234
Iteration: 578; Percent complete: 14.4%; Average loss: 4.0216
Iteration: 579; Percent complete: 14.5%; Average loss: 3.8917
Iteration: 580; Percent complete: 14.5%; Average loss: 3.6715
Iteration: 581; Percent complete: 14.5%; Average loss: 3.7663
Iteration: 582; Percent complete: 14.5%; Average loss: 3.4688
Iteration: 583; Percent complete: 14.6%; Average loss: 3.6377
Iteration: 584; Percent complete: 14.6%; Average loss: 3.5546
Iteration: 585; Percent complete: 14.6%; Average loss: 3.9055
Iteration: 586; Percent complete: 14.6%; Average loss: 3.7439
Iteration: 587; Percent complete: 14.7%; Average loss: 3.7352
Iteration: 588; Percent complete: 14.7%; Average loss: 3.7822
Iteration: 589; Percent complete: 14.7%; Average loss: 3.6984
Iteration: 590; Percent complete: 14.8%; Average loss: 3.7198
Iteration: 591; Percent complete: 14.8%; Average loss: 3.7116
Iteration: 592; Percent complete: 14.8%; Average loss: 3.9682
Iteration: 593; Percent complete: 14.8%; Average loss: 3.3987
Iteration: 594; Percent complete: 14.8%; Average loss: 3.7377
Iteration: 595; Percent complete: 14.9%; Average loss: 3.6347
Iteration: 596; Percent complete: 14.9%; Average loss: 3.6227
Iteration: 597; Percent complete: 14.9%; Average loss: 3.6304
Iteration: 598; Percent complete: 14.9%; Average loss: 3.6554
Iteration: 599; Percent complete: 15.0%; Average loss: 3.6947
Iteration: 600; Percent complete: 15.0%; Average loss: 3.8280
Iteration: 601; Percent complete: 15.0%; Average loss: 3.7895
Iteration: 602; Percent complete: 15.0%; Average loss: 3.8436
Iteration: 603; Percent complete: 15.1%; Average loss: 3.7240
Iteration: 604; Percent complete: 15.1%; Average loss: 3.9049
Iteration: 605; Percent complete: 15.1%; Average loss: 3.5576
Iteration: 606; Percent complete: 15.2%; Average loss: 3.7539
Iteration: 607; Percent complete: 15.2%; Average loss: 3.4721
Iteration: 608; Percent complete: 15.2%; Average loss: 3.8342
Iteration: 609; Percent complete: 15.2%; Average loss: 3.7952
Iteration: 610; Percent complete: 15.2%; Average loss: 3.6704
Iteration: 611; Percent complete: 15.3%; Average loss: 3.5726
Iteration: 612; Percent complete: 15.3%; Average loss: 3.8462
Iteration: 613; Percent complete: 15.3%; Average loss: 3.3714
Iteration: 614; Percent complete: 15.3%; Average loss: 3.5399
Iteration: 615; Percent complete: 15.4%; Average loss: 3.7392
Iteration: 616; Percent complete: 15.4%; Average loss: 3.9093
Iteration: 617; Percent complete: 15.4%; Average loss: 3.6682
Iteration: 618; Percent complete: 15.4%; Average loss: 3.8397
Iteration: 619; Percent complete: 15.5%; Average loss: 3.7512
Iteration: 620; Percent complete: 15.5%; Average loss: 3.8365
Iteration: 621; Percent complete: 15.5%; Average loss: 3.6728
Iteration: 622; Percent complete: 15.6%; Average loss: 3.5929
Iteration: 623; Percent complete: 15.6%; Average loss: 3.5273
Iteration: 624; Percent complete: 15.6%; Average loss: 3.7806
Iteration: 625; Percent complete: 15.6%; Average loss: 3.7583
Iteration: 626; Percent complete: 15.7%; Average loss: 3.7494
Iteration: 627; Percent complete: 15.7%; Average loss: 3.5775
Iteration: 628; Percent complete: 15.7%; Average loss: 3.8614
Iteration: 629; Percent complete: 15.7%; Average loss: 3.6692
Iteration: 630; Percent complete: 15.8%; Average loss: 3.6846
Iteration: 631; Percent complete: 15.8%; Average loss: 3.7874
Iteration: 632; Percent complete: 15.8%; Average loss: 3.7121
Iteration: 633; Percent complete: 15.8%; Average loss: 3.6894
Iteration: 634; Percent complete: 15.8%; Average loss: 3.5731
Iteration: 635; Percent complete: 15.9%; Average loss: 3.5875
Iteration: 636; Percent complete: 15.9%; Average loss: 3.5986
Iteration: 637; Percent complete: 15.9%; Average loss: 3.5661
Iteration: 638; Percent complete: 16.0%; Average loss: 4.0403
Iteration: 639; Percent complete: 16.0%; Average loss: 3.7815
Iteration: 640; Percent complete: 16.0%; Average loss: 3.6503
Iteration: 641; Percent complete: 16.0%; Average loss: 3.8341
Iteration: 642; Percent complete: 16.1%; Average loss: 3.2904
Iteration: 643; Percent complete: 16.1%; Average loss: 3.9198
Iteration: 644; Percent complete: 16.1%; Average loss: 3.6503
Iteration: 645; Percent complete: 16.1%; Average loss: 3.8613
Iteration: 646; Percent complete: 16.2%; Average loss: 3.8690
Iteration: 647; Percent complete: 16.2%; Average loss: 3.5951
Iteration: 648; Percent complete: 16.2%; Average loss: 3.2740
Iteration: 649; Percent complete: 16.2%; Average loss: 3.7458
Iteration: 650; Percent complete: 16.2%; Average loss: 3.3906
Iteration: 651; Percent complete: 16.3%; Average loss: 3.6319
Iteration: 652; Percent complete: 16.3%; Average loss: 3.4819
Iteration: 653; Percent complete: 16.3%; Average loss: 3.7045
Iteration: 654; Percent complete: 16.4%; Average loss: 3.6341
Iteration: 655; Percent complete: 16.4%; Average loss: 3.6083
Iteration: 656; Percent complete: 16.4%; Average loss: 4.0598
Iteration: 657; Percent complete: 16.4%; Average loss: 3.6200
Iteration: 658; Percent complete: 16.4%; Average loss: 3.6312
Iteration: 659; Percent complete: 16.5%; Average loss: 3.6529
Iteration: 660; Percent complete: 16.5%; Average loss: 3.9418
Iteration: 661; Percent complete: 16.5%; Average loss: 3.8793
Iteration: 662; Percent complete: 16.6%; Average loss: 3.7104
Iteration: 663; Percent complete: 16.6%; Average loss: 3.6745
Iteration: 664; Percent complete: 16.6%; Average loss: 3.7064
Iteration: 665; Percent complete: 16.6%; Average loss: 3.5764
Iteration: 666; Percent complete: 16.7%; Average loss: 3.6187
Iteration: 667; Percent complete: 16.7%; Average loss: 3.6339
Iteration: 668; Percent complete: 16.7%; Average loss: 3.4572
Iteration: 669; Percent complete: 16.7%; Average loss: 3.6531
Iteration: 670; Percent complete: 16.8%; Average loss: 3.4740
Iteration: 671; Percent complete: 16.8%; Average loss: 3.3912
Iteration: 672; Percent complete: 16.8%; Average loss: 3.5917
Iteration: 673; Percent complete: 16.8%; Average loss: 3.6430
Iteration: 674; Percent complete: 16.9%; Average loss: 3.3908
Iteration: 675; Percent complete: 16.9%; Average loss: 3.9986
Iteration: 676; Percent complete: 16.9%; Average loss: 3.6313
Iteration: 677; Percent complete: 16.9%; Average loss: 3.5314
Iteration: 678; Percent complete: 17.0%; Average loss: 3.7102
Iteration: 679; Percent complete: 17.0%; Average loss: 3.5948
Iteration: 680; Percent complete: 17.0%; Average loss: 3.6158
Iteration: 681; Percent complete: 17.0%; Average loss: 3.4320
Iteration: 682; Percent complete: 17.1%; Average loss: 3.3348
Iteration: 683; Percent complete: 17.1%; Average loss: 3.7010
Iteration: 684; Percent complete: 17.1%; Average loss: 3.6288
Iteration: 685; Percent complete: 17.1%; Average loss: 3.7525
Iteration: 686; Percent complete: 17.2%; Average loss: 3.4491
Iteration: 687; Percent complete: 17.2%; Average loss: 3.9630
Iteration: 688; Percent complete: 17.2%; Average loss: 3.7010
Iteration: 689; Percent complete: 17.2%; Average loss: 3.5383
Iteration: 690; Percent complete: 17.2%; Average loss: 3.6507
Iteration: 691; Percent complete: 17.3%; Average loss: 3.7452
Iteration: 692; Percent complete: 17.3%; Average loss: 3.7512
Iteration: 693; Percent complete: 17.3%; Average loss: 3.8682
Iteration: 694; Percent complete: 17.3%; Average loss: 3.6906
Iteration: 695; Percent complete: 17.4%; Average loss: 3.7973
Iteration: 696; Percent complete: 17.4%; Average loss: 3.6488
Iteration: 697; Percent complete: 17.4%; Average loss: 3.5397
Iteration: 698; Percent complete: 17.4%; Average loss: 3.6024
Iteration: 699; Percent complete: 17.5%; Average loss: 3.6363
Iteration: 700; Percent complete: 17.5%; Average loss: 3.7712
Iteration: 701; Percent complete: 17.5%; Average loss: 3.4335
Iteration: 702; Percent complete: 17.5%; Average loss: 3.7727
Iteration: 703; Percent complete: 17.6%; Average loss: 3.7930
Iteration: 704; Percent complete: 17.6%; Average loss: 3.9912
Iteration: 705; Percent complete: 17.6%; Average loss: 3.5878
Iteration: 706; Percent complete: 17.6%; Average loss: 3.4163
Iteration: 707; Percent complete: 17.7%; Average loss: 3.4284
Iteration: 708; Percent complete: 17.7%; Average loss: 3.8961
Iteration: 709; Percent complete: 17.7%; Average loss: 3.5159
Iteration: 710; Percent complete: 17.8%; Average loss: 3.6597
Iteration: 711; Percent complete: 17.8%; Average loss: 3.7635
Iteration: 712; Percent complete: 17.8%; Average loss: 3.6307
Iteration: 713; Percent complete: 17.8%; Average loss: 3.7476
Iteration: 714; Percent complete: 17.8%; Average loss: 3.4434
Iteration: 715; Percent complete: 17.9%; Average loss: 3.3797
Iteration: 716; Percent complete: 17.9%; Average loss: 3.5829
Iteration: 717; Percent complete: 17.9%; Average loss: 3.6529
Iteration: 718; Percent complete: 17.9%; Average loss: 3.8037
Iteration: 719; Percent complete: 18.0%; Average loss: 3.6554
Iteration: 720; Percent complete: 18.0%; Average loss: 3.8265
Iteration: 721; Percent complete: 18.0%; Average loss: 3.7606
Iteration: 722; Percent complete: 18.1%; Average loss: 3.4747
Iteration: 723; Percent complete: 18.1%; Average loss: 3.9042
Iteration: 724; Percent complete: 18.1%; Average loss: 3.4165
Iteration: 725; Percent complete: 18.1%; Average loss: 3.4442
Iteration: 726; Percent complete: 18.1%; Average loss: 3.7950
Iteration: 727; Percent complete: 18.2%; Average loss: 3.8495
Iteration: 728; Percent complete: 18.2%; Average loss: 3.6325
Iteration: 729; Percent complete: 18.2%; Average loss: 3.4421
Iteration: 730; Percent complete: 18.2%; Average loss: 3.8074
Iteration: 731; Percent complete: 18.3%; Average loss: 3.5576
Iteration: 732; Percent complete: 18.3%; Average loss: 3.5799
Iteration: 733; Percent complete: 18.3%; Average loss: 3.4818
Iteration: 734; Percent complete: 18.4%; Average loss: 3.4869
Iteration: 735; Percent complete: 18.4%; Average loss: 3.5721
Iteration: 736; Percent complete: 18.4%; Average loss: 3.7077
Iteration: 737; Percent complete: 18.4%; Average loss: 3.6257
Iteration: 738; Percent complete: 18.4%; Average loss: 3.4302
Iteration: 739; Percent complete: 18.5%; Average loss: 3.7108
Iteration: 740; Percent complete: 18.5%; Average loss: 3.8392
Iteration: 741; Percent complete: 18.5%; Average loss: 3.5587
Iteration: 742; Percent complete: 18.6%; Average loss: 4.0052
Iteration: 743; Percent complete: 18.6%; Average loss: 3.4400
Iteration: 744; Percent complete: 18.6%; Average loss: 3.6445
Iteration: 745; Percent complete: 18.6%; Average loss: 3.6932
Iteration: 746; Percent complete: 18.6%; Average loss: 3.7690
Iteration: 747; Percent complete: 18.7%; Average loss: 3.7414
Iteration: 748; Percent complete: 18.7%; Average loss: 3.5909
Iteration: 749; Percent complete: 18.7%; Average loss: 3.6755
Iteration: 750; Percent complete: 18.8%; Average loss: 3.4724
Iteration: 751; Percent complete: 18.8%; Average loss: 3.6626
Iteration: 752; Percent complete: 18.8%; Average loss: 3.5494
Iteration: 753; Percent complete: 18.8%; Average loss: 3.5868
Iteration: 754; Percent complete: 18.9%; Average loss: 3.6241
Iteration: 755; Percent complete: 18.9%; Average loss: 3.4832
Iteration: 756; Percent complete: 18.9%; Average loss: 3.6502
Iteration: 757; Percent complete: 18.9%; Average loss: 3.5137
Iteration: 758; Percent complete: 18.9%; Average loss: 3.6282
Iteration: 759; Percent complete: 19.0%; Average loss: 3.3142
Iteration: 760; Percent complete: 19.0%; Average loss: 3.5324
Iteration: 761; Percent complete: 19.0%; Average loss: 3.4729
Iteration: 762; Percent complete: 19.1%; Average loss: 3.4733
Iteration: 763; Percent complete: 19.1%; Average loss: 3.7251
Iteration: 764; Percent complete: 19.1%; Average loss: 3.4712
Iteration: 765; Percent complete: 19.1%; Average loss: 3.6378
Iteration: 766; Percent complete: 19.1%; Average loss: 3.5074
Iteration: 767; Percent complete: 19.2%; Average loss: 3.4603
Iteration: 768; Percent complete: 19.2%; Average loss: 3.3878
Iteration: 769; Percent complete: 19.2%; Average loss: 3.3921
Iteration: 770; Percent complete: 19.2%; Average loss: 3.5729
Iteration: 771; Percent complete: 19.3%; Average loss: 3.6364
Iteration: 772; Percent complete: 19.3%; Average loss: 3.7510
Iteration: 773; Percent complete: 19.3%; Average loss: 3.4534
Iteration: 774; Percent complete: 19.4%; Average loss: 3.4404
Iteration: 775; Percent complete: 19.4%; Average loss: 3.5047
Iteration: 776; Percent complete: 19.4%; Average loss: 3.7557
Iteration: 777; Percent complete: 19.4%; Average loss: 3.6500
Iteration: 778; Percent complete: 19.4%; Average loss: 3.8440
Iteration: 779; Percent complete: 19.5%; Average loss: 3.6121
Iteration: 780; Percent complete: 19.5%; Average loss: 3.5119
Iteration: 781; Percent complete: 19.5%; Average loss: 3.5104
Iteration: 782; Percent complete: 19.6%; Average loss: 3.5370
Iteration: 783; Percent complete: 19.6%; Average loss: 3.6236
Iteration: 784; Percent complete: 19.6%; Average loss: 3.6444
Iteration: 785; Percent complete: 19.6%; Average loss: 3.3949
Iteration: 786; Percent complete: 19.7%; Average loss: 3.4686
Iteration: 787; Percent complete: 19.7%; Average loss: 3.5557
Iteration: 788; Percent complete: 19.7%; Average loss: 3.3246
Iteration: 789; Percent complete: 19.7%; Average loss: 3.6519
Iteration: 790; Percent complete: 19.8%; Average loss: 3.4225
Iteration: 791; Percent complete: 19.8%; Average loss: 3.3995
Iteration: 792; Percent complete: 19.8%; Average loss: 3.4530
Iteration: 793; Percent complete: 19.8%; Average loss: 3.5768
Iteration: 794; Percent complete: 19.9%; Average loss: 3.6721
Iteration: 795; Percent complete: 19.9%; Average loss: 3.7122
Iteration: 796; Percent complete: 19.9%; Average loss: 3.3205
Iteration: 797; Percent complete: 19.9%; Average loss: 3.7064
Iteration: 798; Percent complete: 20.0%; Average loss: 3.9664
Iteration: 799; Percent complete: 20.0%; Average loss: 3.6012
Iteration: 800; Percent complete: 20.0%; Average loss: 3.5735
Iteration: 801; Percent complete: 20.0%; Average loss: 3.7321
Iteration: 802; Percent complete: 20.1%; Average loss: 3.4735
Iteration: 803; Percent complete: 20.1%; Average loss: 3.7837
Iteration: 804; Percent complete: 20.1%; Average loss: 3.6015
Iteration: 805; Percent complete: 20.1%; Average loss: 3.5080
Iteration: 806; Percent complete: 20.2%; Average loss: 3.4528
Iteration: 807; Percent complete: 20.2%; Average loss: 3.7264
Iteration: 808; Percent complete: 20.2%; Average loss: 3.6328
Iteration: 809; Percent complete: 20.2%; Average loss: 3.5788
Iteration: 810; Percent complete: 20.2%; Average loss: 3.6228
Iteration: 811; Percent complete: 20.3%; Average loss: 3.4702
Iteration: 812; Percent complete: 20.3%; Average loss: 3.6827
Iteration: 813; Percent complete: 20.3%; Average loss: 3.6595
Iteration: 814; Percent complete: 20.3%; Average loss: 3.2881
Iteration: 815; Percent complete: 20.4%; Average loss: 3.5034
Iteration: 816; Percent complete: 20.4%; Average loss: 3.4617
Iteration: 817; Percent complete: 20.4%; Average loss: 3.4067
Iteration: 818; Percent complete: 20.4%; Average loss: 3.6662
Iteration: 819; Percent complete: 20.5%; Average loss: 3.6093
Iteration: 820; Percent complete: 20.5%; Average loss: 3.3896
Iteration: 821; Percent complete: 20.5%; Average loss: 3.6563
Iteration: 822; Percent complete: 20.5%; Average loss: 3.4166
Iteration: 823; Percent complete: 20.6%; Average loss: 3.6936
Iteration: 824; Percent complete: 20.6%; Average loss: 3.5420
Iteration: 825; Percent complete: 20.6%; Average loss: 3.3562
Iteration: 826; Percent complete: 20.6%; Average loss: 3.7252
Iteration: 827; Percent complete: 20.7%; Average loss: 3.7999
Iteration: 828; Percent complete: 20.7%; Average loss: 3.7592
Iteration: 829; Percent complete: 20.7%; Average loss: 3.5546
Iteration: 830; Percent complete: 20.8%; Average loss: 3.7949
Iteration: 831; Percent complete: 20.8%; Average loss: 3.6955
Iteration: 832; Percent complete: 20.8%; Average loss: 3.5407
Iteration: 833; Percent complete: 20.8%; Average loss: 3.7938
Iteration: 834; Percent complete: 20.8%; Average loss: 3.5820
Iteration: 835; Percent complete: 20.9%; Average loss: 3.4986
Iteration: 836; Percent complete: 20.9%; Average loss: 3.6512
Iteration: 837; Percent complete: 20.9%; Average loss: 3.2172
Iteration: 838; Percent complete: 20.9%; Average loss: 3.4543
Iteration: 839; Percent complete: 21.0%; Average loss: 3.4507
Iteration: 840; Percent complete: 21.0%; Average loss: 3.4999
Iteration: 841; Percent complete: 21.0%; Average loss: 3.4154
Iteration: 842; Percent complete: 21.1%; Average loss: 3.5017
Iteration: 843; Percent complete: 21.1%; Average loss: 3.3967
Iteration: 844; Percent complete: 21.1%; Average loss: 3.5816
Iteration: 845; Percent complete: 21.1%; Average loss: 3.4043
Iteration: 846; Percent complete: 21.1%; Average loss: 3.5479
Iteration: 847; Percent complete: 21.2%; Average loss: 3.4340
Iteration: 848; Percent complete: 21.2%; Average loss: 3.4145
Iteration: 849; Percent complete: 21.2%; Average loss: 3.3761
Iteration: 850; Percent complete: 21.2%; Average loss: 3.4727
Iteration: 851; Percent complete: 21.3%; Average loss: 3.5388
Iteration: 852; Percent complete: 21.3%; Average loss: 3.6617
Iteration: 853; Percent complete: 21.3%; Average loss: 3.5491
Iteration: 854; Percent complete: 21.3%; Average loss: 3.3417
Iteration: 855; Percent complete: 21.4%; Average loss: 3.5796
Iteration: 856; Percent complete: 21.4%; Average loss: 3.3125
Iteration: 857; Percent complete: 21.4%; Average loss: 3.6693
Iteration: 858; Percent complete: 21.4%; Average loss: 3.4870
Iteration: 859; Percent complete: 21.5%; Average loss: 3.5867
Iteration: 860; Percent complete: 21.5%; Average loss: 3.7575
Iteration: 861; Percent complete: 21.5%; Average loss: 3.4642
Iteration: 862; Percent complete: 21.6%; Average loss: 3.4826
Iteration: 863; Percent complete: 21.6%; Average loss: 3.6998
Iteration: 864; Percent complete: 21.6%; Average loss: 3.7080
Iteration: 865; Percent complete: 21.6%; Average loss: 3.8526
Iteration: 866; Percent complete: 21.6%; Average loss: 3.5804
Iteration: 867; Percent complete: 21.7%; Average loss: 3.4933
Iteration: 868; Percent complete: 21.7%; Average loss: 3.7992
Iteration: 869; Percent complete: 21.7%; Average loss: 3.4967
Iteration: 870; Percent complete: 21.8%; Average loss: 3.6186
Iteration: 871; Percent complete: 21.8%; Average loss: 3.4924
Iteration: 872; Percent complete: 21.8%; Average loss: 3.5167
Iteration: 873; Percent complete: 21.8%; Average loss: 3.5533
Iteration: 874; Percent complete: 21.9%; Average loss: 3.7625
Iteration: 875; Percent complete: 21.9%; Average loss: 3.8514
Iteration: 876; Percent complete: 21.9%; Average loss: 3.5535
Iteration: 877; Percent complete: 21.9%; Average loss: 3.4287
Iteration: 878; Percent complete: 21.9%; Average loss: 3.6620
Iteration: 879; Percent complete: 22.0%; Average loss: 3.6824
Iteration: 880; Percent complete: 22.0%; Average loss: 3.6533
Iteration: 881; Percent complete: 22.0%; Average loss: 3.4407
Iteration: 882; Percent complete: 22.1%; Average loss: 3.5598
Iteration: 883; Percent complete: 22.1%; Average loss: 3.1998
Iteration: 884; Percent complete: 22.1%; Average loss: 3.3258
Iteration: 885; Percent complete: 22.1%; Average loss: 3.4190
Iteration: 886; Percent complete: 22.1%; Average loss: 3.5934
Iteration: 887; Percent complete: 22.2%; Average loss: 3.4937
Iteration: 888; Percent complete: 22.2%; Average loss: 3.6003
Iteration: 889; Percent complete: 22.2%; Average loss: 3.5760
Iteration: 890; Percent complete: 22.2%; Average loss: 3.4425
Iteration: 891; Percent complete: 22.3%; Average loss: 3.3092
Iteration: 892; Percent complete: 22.3%; Average loss: 3.4257
Iteration: 893; Percent complete: 22.3%; Average loss: 3.7268
Iteration: 894; Percent complete: 22.4%; Average loss: 3.2613
Iteration: 895; Percent complete: 22.4%; Average loss: 3.3865
Iteration: 896; Percent complete: 22.4%; Average loss: 3.5190
Iteration: 897; Percent complete: 22.4%; Average loss: 3.4691
Iteration: 898; Percent complete: 22.4%; Average loss: 3.5352
Iteration: 899; Percent complete: 22.5%; Average loss: 3.4322
Iteration: 900; Percent complete: 22.5%; Average loss: 3.4275
Iteration: 901; Percent complete: 22.5%; Average loss: 3.2986
Iteration: 902; Percent complete: 22.6%; Average loss: 3.5990
Iteration: 903; Percent complete: 22.6%; Average loss: 3.6596
Iteration: 904; Percent complete: 22.6%; Average loss: 3.8139
Iteration: 905; Percent complete: 22.6%; Average loss: 3.3389
Iteration: 906; Percent complete: 22.7%; Average loss: 3.6719
Iteration: 907; Percent complete: 22.7%; Average loss: 3.6774
Iteration: 908; Percent complete: 22.7%; Average loss: 3.3793
Iteration: 909; Percent complete: 22.7%; Average loss: 3.5504
Iteration: 910; Percent complete: 22.8%; Average loss: 3.2889
Iteration: 911; Percent complete: 22.8%; Average loss: 3.3936
Iteration: 912; Percent complete: 22.8%; Average loss: 3.5792
Iteration: 913; Percent complete: 22.8%; Average loss: 3.6084
Iteration: 914; Percent complete: 22.9%; Average loss: 3.5973
Iteration: 915; Percent complete: 22.9%; Average loss: 3.4296
Iteration: 916; Percent complete: 22.9%; Average loss: 3.6362
Iteration: 917; Percent complete: 22.9%; Average loss: 3.4870
Iteration: 918; Percent complete: 22.9%; Average loss: 3.5839
Iteration: 919; Percent complete: 23.0%; Average loss: 3.5591
Iteration: 920; Percent complete: 23.0%; Average loss: 3.5407
Iteration: 921; Percent complete: 23.0%; Average loss: 3.6696
Iteration: 922; Percent complete: 23.1%; Average loss: 3.4221
Iteration: 923; Percent complete: 23.1%; Average loss: 3.3204
Iteration: 924; Percent complete: 23.1%; Average loss: 3.4986
Iteration: 925; Percent complete: 23.1%; Average loss: 3.2642
Iteration: 926; Percent complete: 23.2%; Average loss: 3.3531
Iteration: 927; Percent complete: 23.2%; Average loss: 3.4504
Iteration: 928; Percent complete: 23.2%; Average loss: 3.5306
Iteration: 929; Percent complete: 23.2%; Average loss: 3.3553
Iteration: 930; Percent complete: 23.2%; Average loss: 3.4939
Iteration: 931; Percent complete: 23.3%; Average loss: 3.7573
Iteration: 932; Percent complete: 23.3%; Average loss: 3.4207
Iteration: 933; Percent complete: 23.3%; Average loss: 3.6045
Iteration: 934; Percent complete: 23.4%; Average loss: 3.6785
Iteration: 935; Percent complete: 23.4%; Average loss: 3.4078
Iteration: 936; Percent complete: 23.4%; Average loss: 3.5247
Iteration: 937; Percent complete: 23.4%; Average loss: 3.5423
Iteration: 938; Percent complete: 23.4%; Average loss: 3.5259
Iteration: 939; Percent complete: 23.5%; Average loss: 3.4389
Iteration: 940; Percent complete: 23.5%; Average loss: 3.6027
Iteration: 941; Percent complete: 23.5%; Average loss: 3.6324
Iteration: 942; Percent complete: 23.5%; Average loss: 3.6113
Iteration: 943; Percent complete: 23.6%; Average loss: 3.4994
Iteration: 944; Percent complete: 23.6%; Average loss: 3.4387
Iteration: 945; Percent complete: 23.6%; Average loss: 3.4870
Iteration: 946; Percent complete: 23.6%; Average loss: 3.4920
Iteration: 947; Percent complete: 23.7%; Average loss: 3.3725
Iteration: 948; Percent complete: 23.7%; Average loss: 3.8203
Iteration: 949; Percent complete: 23.7%; Average loss: 3.5583
Iteration: 950; Percent complete: 23.8%; Average loss: 3.3289
Iteration: 951; Percent complete: 23.8%; Average loss: 3.5590
Iteration: 952; Percent complete: 23.8%; Average loss: 3.5027
Iteration: 953; Percent complete: 23.8%; Average loss: 3.4197
Iteration: 954; Percent complete: 23.8%; Average loss: 3.4595
Iteration: 955; Percent complete: 23.9%; Average loss: 3.4382
Iteration: 956; Percent complete: 23.9%; Average loss: 3.6818
Iteration: 957; Percent complete: 23.9%; Average loss: 3.4193
Iteration: 958; Percent complete: 23.9%; Average loss: 3.3730
Iteration: 959; Percent complete: 24.0%; Average loss: 3.2661
Iteration: 960; Percent complete: 24.0%; Average loss: 3.3806
Iteration: 961; Percent complete: 24.0%; Average loss: 3.5150
Iteration: 962; Percent complete: 24.1%; Average loss: 3.3033
Iteration: 963; Percent complete: 24.1%; Average loss: 3.5790
Iteration: 964; Percent complete: 24.1%; Average loss: 3.5449
Iteration: 965; Percent complete: 24.1%; Average loss: 3.4691
Iteration: 966; Percent complete: 24.1%; Average loss: 3.6469
Iteration: 967; Percent complete: 24.2%; Average loss: 3.3831
Iteration: 968; Percent complete: 24.2%; Average loss: 3.6579
Iteration: 969; Percent complete: 24.2%; Average loss: 2.9416
Iteration: 970; Percent complete: 24.2%; Average loss: 3.6938
Iteration: 971; Percent complete: 24.3%; Average loss: 3.4342
Iteration: 972; Percent complete: 24.3%; Average loss: 3.6014
Iteration: 973; Percent complete: 24.3%; Average loss: 3.3756
Iteration: 974; Percent complete: 24.3%; Average loss: 3.5449
Iteration: 975; Percent complete: 24.4%; Average loss: 3.4023
Iteration: 976; Percent complete: 24.4%; Average loss: 3.3236
Iteration: 977; Percent complete: 24.4%; Average loss: 3.6348
Iteration: 978; Percent complete: 24.4%; Average loss: 3.4095
Iteration: 979; Percent complete: 24.5%; Average loss: 3.6084
Iteration: 980; Percent complete: 24.5%; Average loss: 3.6497
Iteration: 981; Percent complete: 24.5%; Average loss: 3.3172
Iteration: 982; Percent complete: 24.6%; Average loss: 3.4132
Iteration: 983; Percent complete: 24.6%; Average loss: 3.6163
Iteration: 984; Percent complete: 24.6%; Average loss: 3.7436
Iteration: 985; Percent complete: 24.6%; Average loss: 3.4465
Iteration: 986; Percent complete: 24.6%; Average loss: 3.3825
Iteration: 987; Percent complete: 24.7%; Average loss: 3.3961
Iteration: 988; Percent complete: 24.7%; Average loss: 3.4228
Iteration: 989; Percent complete: 24.7%; Average loss: 3.5325
Iteration: 990; Percent complete: 24.8%; Average loss: 3.5066
Iteration: 991; Percent complete: 24.8%; Average loss: 3.2523
Iteration: 992; Percent complete: 24.8%; Average loss: 3.7495
Iteration: 993; Percent complete: 24.8%; Average loss: 3.4360
Iteration: 994; Percent complete: 24.9%; Average loss: 3.3185
Iteration: 995; Percent complete: 24.9%; Average loss: 3.5845
Iteration: 996; Percent complete: 24.9%; Average loss: 3.4768
Iteration: 997; Percent complete: 24.9%; Average loss: 3.4989
Iteration: 998; Percent complete: 24.9%; Average loss: 3.5811
Iteration: 999; Percent complete: 25.0%; Average loss: 3.2932
Iteration: 1000; Percent complete: 25.0%; Average loss: 3.4992
Iteration: 1001; Percent complete: 25.0%; Average loss: 3.3530
Iteration: 1002; Percent complete: 25.1%; Average loss: 3.5574
Iteration: 1003; Percent complete: 25.1%; Average loss: 3.4977
Iteration: 1004; Percent complete: 25.1%; Average loss: 3.5225
Iteration: 1005; Percent complete: 25.1%; Average loss: 3.5092
Iteration: 1006; Percent complete: 25.1%; Average loss: 3.2989
Iteration: 1007; Percent complete: 25.2%; Average loss: 3.8528
Iteration: 1008; Percent complete: 25.2%; Average loss: 3.6315
Iteration: 1009; Percent complete: 25.2%; Average loss: 3.5460
Iteration: 1010; Percent complete: 25.2%; Average loss: 3.4610
Iteration: 1011; Percent complete: 25.3%; Average loss: 3.5129
Iteration: 1012; Percent complete: 25.3%; Average loss: 3.3859
Iteration: 1013; Percent complete: 25.3%; Average loss: 3.7068
Iteration: 1014; Percent complete: 25.4%; Average loss: 3.3922
Iteration: 1015; Percent complete: 25.4%; Average loss: 3.4578
Iteration: 1016; Percent complete: 25.4%; Average loss: 3.5477
Iteration: 1017; Percent complete: 25.4%; Average loss: 3.7203
Iteration: 1018; Percent complete: 25.4%; Average loss: 3.7022
Iteration: 1019; Percent complete: 25.5%; Average loss: 3.3236
Iteration: 1020; Percent complete: 25.5%; Average loss: 3.8115
Iteration: 1021; Percent complete: 25.5%; Average loss: 3.2848
Iteration: 1022; Percent complete: 25.6%; Average loss: 3.5813
Iteration: 1023; Percent complete: 25.6%; Average loss: 3.4001
Iteration: 1024; Percent complete: 25.6%; Average loss: 3.3485
Iteration: 1025; Percent complete: 25.6%; Average loss: 3.3283
Iteration: 1026; Percent complete: 25.7%; Average loss: 3.2179
Iteration: 1027; Percent complete: 25.7%; Average loss: 3.5633
Iteration: 1028; Percent complete: 25.7%; Average loss: 3.2193
Iteration: 1029; Percent complete: 25.7%; Average loss: 3.4382
Iteration: 1030; Percent complete: 25.8%; Average loss: 3.3190
Iteration: 1031; Percent complete: 25.8%; Average loss: 3.4951
Iteration: 1032; Percent complete: 25.8%; Average loss: 3.5764
Iteration: 1033; Percent complete: 25.8%; Average loss: 3.5326
Iteration: 1034; Percent complete: 25.9%; Average loss: 3.2657
Iteration: 1035; Percent complete: 25.9%; Average loss: 3.1667
Iteration: 1036; Percent complete: 25.9%; Average loss: 3.3355
Iteration: 1037; Percent complete: 25.9%; Average loss: 3.4512
Iteration: 1038; Percent complete: 25.9%; Average loss: 3.4420
Iteration: 1039; Percent complete: 26.0%; Average loss: 3.4585
Iteration: 1040; Percent complete: 26.0%; Average loss: 3.6308
Iteration: 1041; Percent complete: 26.0%; Average loss: 3.3193
Iteration: 1042; Percent complete: 26.1%; Average loss: 3.4068
Iteration: 1043; Percent complete: 26.1%; Average loss: 3.1301
Iteration: 1044; Percent complete: 26.1%; Average loss: 3.4920
Iteration: 1045; Percent complete: 26.1%; Average loss: 3.2783
Iteration: 1046; Percent complete: 26.2%; Average loss: 3.7655
Iteration: 1047; Percent complete: 26.2%; Average loss: 3.4295
Iteration: 1048; Percent complete: 26.2%; Average loss: 3.6618
Iteration: 1049; Percent complete: 26.2%; Average loss: 3.3463
Iteration: 1050; Percent complete: 26.2%; Average loss: 3.0872
Iteration: 1051; Percent complete: 26.3%; Average loss: 3.3383
Iteration: 1052; Percent complete: 26.3%; Average loss: 3.2340
Iteration: 1053; Percent complete: 26.3%; Average loss: 3.7363
Iteration: 1054; Percent complete: 26.4%; Average loss: 3.2826
Iteration: 1055; Percent complete: 26.4%; Average loss: 3.3704
Iteration: 1056; Percent complete: 26.4%; Average loss: 3.7654
Iteration: 1057; Percent complete: 26.4%; Average loss: 3.4580
Iteration: 1058; Percent complete: 26.5%; Average loss: 3.3418
Iteration: 1059; Percent complete: 26.5%; Average loss: 3.5935
Iteration: 1060; Percent complete: 26.5%; Average loss: 3.7291
Iteration: 1061; Percent complete: 26.5%; Average loss: 3.3046
Iteration: 1062; Percent complete: 26.6%; Average loss: 3.1970
Iteration: 1063; Percent complete: 26.6%; Average loss: 3.2541
Iteration: 1064; Percent complete: 26.6%; Average loss: 3.5191
Iteration: 1065; Percent complete: 26.6%; Average loss: 3.5451
Iteration: 1066; Percent complete: 26.7%; Average loss: 3.3481
Iteration: 1067; Percent complete: 26.7%; Average loss: 3.7048
Iteration: 1068; Percent complete: 26.7%; Average loss: 3.5110
Iteration: 1069; Percent complete: 26.7%; Average loss: 3.4589
Iteration: 1070; Percent complete: 26.8%; Average loss: 3.5595
Iteration: 1071; Percent complete: 26.8%; Average loss: 3.5169
Iteration: 1072; Percent complete: 26.8%; Average loss: 3.2353
Iteration: 1073; Percent complete: 26.8%; Average loss: 3.4763
Iteration: 1074; Percent complete: 26.9%; Average loss: 3.4650
Iteration: 1075; Percent complete: 26.9%; Average loss: 3.5438
Iteration: 1076; Percent complete: 26.9%; Average loss: 3.5236
Iteration: 1077; Percent complete: 26.9%; Average loss: 3.2044
Iteration: 1078; Percent complete: 27.0%; Average loss: 3.4796
Iteration: 1079; Percent complete: 27.0%; Average loss: 3.5082
Iteration: 1080; Percent complete: 27.0%; Average loss: 3.3905
Iteration: 1081; Percent complete: 27.0%; Average loss: 3.7555
Iteration: 1082; Percent complete: 27.1%; Average loss: 3.5059
Iteration: 1083; Percent complete: 27.1%; Average loss: 3.2438
Iteration: 1084; Percent complete: 27.1%; Average loss: 3.5169
Iteration: 1085; Percent complete: 27.1%; Average loss: 3.5531
Iteration: 1086; Percent complete: 27.2%; Average loss: 3.5438
Iteration: 1087; Percent complete: 27.2%; Average loss: 3.6356
Iteration: 1088; Percent complete: 27.2%; Average loss: 3.6513
Iteration: 1089; Percent complete: 27.2%; Average loss: 3.3842
Iteration: 1090; Percent complete: 27.3%; Average loss: 3.5240
Iteration: 1091; Percent complete: 27.3%; Average loss: 3.4828
Iteration: 1092; Percent complete: 27.3%; Average loss: 3.7745
Iteration: 1093; Percent complete: 27.3%; Average loss: 3.4454
Iteration: 1094; Percent complete: 27.4%; Average loss: 3.4141
Iteration: 1095; Percent complete: 27.4%; Average loss: 3.4852
Iteration: 1096; Percent complete: 27.4%; Average loss: 3.2755
Iteration: 1097; Percent complete: 27.4%; Average loss: 3.4156
Iteration: 1098; Percent complete: 27.5%; Average loss: 3.3864
Iteration: 1099; Percent complete: 27.5%; Average loss: 3.4172
Iteration: 1100; Percent complete: 27.5%; Average loss: 3.7434
Iteration: 1101; Percent complete: 27.5%; Average loss: 3.5932
Iteration: 1102; Percent complete: 27.6%; Average loss: 3.3133
Iteration: 1103; Percent complete: 27.6%; Average loss: 3.7445
Iteration: 1104; Percent complete: 27.6%; Average loss: 3.3822
Iteration: 1105; Percent complete: 27.6%; Average loss: 3.3517
Iteration: 1106; Percent complete: 27.7%; Average loss: 3.3658
Iteration: 1107; Percent complete: 27.7%; Average loss: 3.4903
Iteration: 1108; Percent complete: 27.7%; Average loss: 3.3837
Iteration: 1109; Percent complete: 27.7%; Average loss: 3.7054
Iteration: 1110; Percent complete: 27.8%; Average loss: 3.7229
Iteration: 1111; Percent complete: 27.8%; Average loss: 3.3067
Iteration: 1112; Percent complete: 27.8%; Average loss: 3.4893
Iteration: 1113; Percent complete: 27.8%; Average loss: 3.3535
Iteration: 1114; Percent complete: 27.9%; Average loss: 3.3236
Iteration: 1115; Percent complete: 27.9%; Average loss: 3.3625
Iteration: 1116; Percent complete: 27.9%; Average loss: 3.2945
Iteration: 1117; Percent complete: 27.9%; Average loss: 3.3043
Iteration: 1118; Percent complete: 28.0%; Average loss: 3.1730
Iteration: 1119; Percent complete: 28.0%; Average loss: 3.3992
Iteration: 1120; Percent complete: 28.0%; Average loss: 3.7038
Iteration: 1121; Percent complete: 28.0%; Average loss: 3.3107
Iteration: 1122; Percent complete: 28.1%; Average loss: 3.3901
Iteration: 1123; Percent complete: 28.1%; Average loss: 3.3641
Iteration: 1124; Percent complete: 28.1%; Average loss: 3.4782
Iteration: 1125; Percent complete: 28.1%; Average loss: 3.4296
Iteration: 1126; Percent complete: 28.1%; Average loss: 3.3142
Iteration: 1127; Percent complete: 28.2%; Average loss: 3.2747
Iteration: 1128; Percent complete: 28.2%; Average loss: 3.5849
Iteration: 1129; Percent complete: 28.2%; Average loss: 3.1968
Iteration: 1130; Percent complete: 28.2%; Average loss: 3.6720
Iteration: 1131; Percent complete: 28.3%; Average loss: 3.3402
Iteration: 1132; Percent complete: 28.3%; Average loss: 3.5326
Iteration: 1133; Percent complete: 28.3%; Average loss: 3.4577
Iteration: 1134; Percent complete: 28.3%; Average loss: 3.4767
Iteration: 1135; Percent complete: 28.4%; Average loss: 3.1755
Iteration: 1136; Percent complete: 28.4%; Average loss: 3.3042
Iteration: 1137; Percent complete: 28.4%; Average loss: 3.7309
Iteration: 1138; Percent complete: 28.4%; Average loss: 3.6123
Iteration: 1139; Percent complete: 28.5%; Average loss: 3.2623
Iteration: 1140; Percent complete: 28.5%; Average loss: 3.3819
Iteration: 1141; Percent complete: 28.5%; Average loss: 3.7459
Iteration: 1142; Percent complete: 28.5%; Average loss: 3.4022
Iteration: 1143; Percent complete: 28.6%; Average loss: 3.4978
Iteration: 1144; Percent complete: 28.6%; Average loss: 3.5529
Iteration: 1145; Percent complete: 28.6%; Average loss: 3.5268
Iteration: 1146; Percent complete: 28.6%; Average loss: 3.4091
Iteration: 1147; Percent complete: 28.7%; Average loss: 3.6612
Iteration: 1148; Percent complete: 28.7%; Average loss: 3.4670
Iteration: 1149; Percent complete: 28.7%; Average loss: 3.1894
Iteration: 1150; Percent complete: 28.7%; Average loss: 3.5083
Iteration: 1151; Percent complete: 28.8%; Average loss: 3.6371
Iteration: 1152; Percent complete: 28.8%; Average loss: 3.4492
Iteration: 1153; Percent complete: 28.8%; Average loss: 3.4090
Iteration: 1154; Percent complete: 28.8%; Average loss: 3.3464
Iteration: 1155; Percent complete: 28.9%; Average loss: 3.5136
Iteration: 1156; Percent complete: 28.9%; Average loss: 3.2812
Iteration: 1157; Percent complete: 28.9%; Average loss: 3.6336
Iteration: 1158; Percent complete: 28.9%; Average loss: 3.5660
Iteration: 1159; Percent complete: 29.0%; Average loss: 3.4883
Iteration: 1160; Percent complete: 29.0%; Average loss: 3.5508
Iteration: 1161; Percent complete: 29.0%; Average loss: 3.3217
Iteration: 1162; Percent complete: 29.0%; Average loss: 3.4308
Iteration: 1163; Percent complete: 29.1%; Average loss: 3.3817
Iteration: 1164; Percent complete: 29.1%; Average loss: 3.4750
Iteration: 1165; Percent complete: 29.1%; Average loss: 3.4159
Iteration: 1166; Percent complete: 29.1%; Average loss: 3.4003
Iteration: 1167; Percent complete: 29.2%; Average loss: 3.4774
Iteration: 1168; Percent complete: 29.2%; Average loss: 3.4700
Iteration: 1169; Percent complete: 29.2%; Average loss: 3.3082
Iteration: 1170; Percent complete: 29.2%; Average loss: 3.3479
Iteration: 1171; Percent complete: 29.3%; Average loss: 3.4035
Iteration: 1172; Percent complete: 29.3%; Average loss: 3.5797
Iteration: 1173; Percent complete: 29.3%; Average loss: 3.3167
Iteration: 1174; Percent complete: 29.3%; Average loss: 3.3979
Iteration: 1175; Percent complete: 29.4%; Average loss: 3.5637
Iteration: 1176; Percent complete: 29.4%; Average loss: 3.3395
Iteration: 1177; Percent complete: 29.4%; Average loss: 3.5697
Iteration: 1178; Percent complete: 29.4%; Average loss: 3.1677
Iteration: 1179; Percent complete: 29.5%; Average loss: 3.5904
Iteration: 1180; Percent complete: 29.5%; Average loss: 3.2743
Iteration: 1181; Percent complete: 29.5%; Average loss: 3.4543
Iteration: 1182; Percent complete: 29.5%; Average loss: 3.5481
Iteration: 1183; Percent complete: 29.6%; Average loss: 3.3555
Iteration: 1184; Percent complete: 29.6%; Average loss: 3.1031
Iteration: 1185; Percent complete: 29.6%; Average loss: 3.1686
Iteration: 1186; Percent complete: 29.6%; Average loss: 3.1009
Iteration: 1187; Percent complete: 29.7%; Average loss: 3.1816
Iteration: 1188; Percent complete: 29.7%; Average loss: 3.5799
Iteration: 1189; Percent complete: 29.7%; Average loss: 3.6720
Iteration: 1190; Percent complete: 29.8%; Average loss: 3.3965
Iteration: 1191; Percent complete: 29.8%; Average loss: 3.0452
Iteration: 1192; Percent complete: 29.8%; Average loss: 3.5426
Iteration: 1193; Percent complete: 29.8%; Average loss: 3.3405
Iteration: 1194; Percent complete: 29.8%; Average loss: 3.4511
Iteration: 1195; Percent complete: 29.9%; Average loss: 3.5673
Iteration: 1196; Percent complete: 29.9%; Average loss: 3.1884
Iteration: 1197; Percent complete: 29.9%; Average loss: 3.1090
Iteration: 1198; Percent complete: 29.9%; Average loss: 3.5437
Iteration: 1199; Percent complete: 30.0%; Average loss: 3.5272
Iteration: 1200; Percent complete: 30.0%; Average loss: 3.5797
Iteration: 1201; Percent complete: 30.0%; Average loss: 3.4075
Iteration: 1202; Percent complete: 30.0%; Average loss: 3.4126
Iteration: 1203; Percent complete: 30.1%; Average loss: 3.1931
Iteration: 1204; Percent complete: 30.1%; Average loss: 3.3700
Iteration: 1205; Percent complete: 30.1%; Average loss: 3.5629
Iteration: 1206; Percent complete: 30.1%; Average loss: 3.5307
Iteration: 1207; Percent complete: 30.2%; Average loss: 3.4341
Iteration: 1208; Percent complete: 30.2%; Average loss: 3.4316
Iteration: 1209; Percent complete: 30.2%; Average loss: 3.3733
Iteration: 1210; Percent complete: 30.2%; Average loss: 3.3238
Iteration: 1211; Percent complete: 30.3%; Average loss: 3.1380
Iteration: 1212; Percent complete: 30.3%; Average loss: 3.4563
Iteration: 1213; Percent complete: 30.3%; Average loss: 3.4440
Iteration: 1214; Percent complete: 30.3%; Average loss: 3.5660
Iteration: 1215; Percent complete: 30.4%; Average loss: 3.2516
Iteration: 1216; Percent complete: 30.4%; Average loss: 3.4766
Iteration: 1217; Percent complete: 30.4%; Average loss: 3.4601
Iteration: 1218; Percent complete: 30.4%; Average loss: 3.4203
Iteration: 1219; Percent complete: 30.5%; Average loss: 3.5250
Iteration: 1220; Percent complete: 30.5%; Average loss: 3.3264
Iteration: 1221; Percent complete: 30.5%; Average loss: 3.2909
Iteration: 1222; Percent complete: 30.6%; Average loss: 3.3278
Iteration: 1223; Percent complete: 30.6%; Average loss: 3.7000
Iteration: 1224; Percent complete: 30.6%; Average loss: 3.5860
Iteration: 1225; Percent complete: 30.6%; Average loss: 3.7586
Iteration: 1226; Percent complete: 30.6%; Average loss: 3.6814
Iteration: 1227; Percent complete: 30.7%; Average loss: 3.3904
Iteration: 1228; Percent complete: 30.7%; Average loss: 3.3464
Iteration: 1229; Percent complete: 30.7%; Average loss: 3.3373
Iteration: 1230; Percent complete: 30.8%; Average loss: 3.6227
Iteration: 1231; Percent complete: 30.8%; Average loss: 3.7558
Iteration: 1232; Percent complete: 30.8%; Average loss: 3.3776
Iteration: 1233; Percent complete: 30.8%; Average loss: 3.3980
Iteration: 1234; Percent complete: 30.9%; Average loss: 3.6101
Iteration: 1235; Percent complete: 30.9%; Average loss: 3.3388
Iteration: 1236; Percent complete: 30.9%; Average loss: 3.8893
Iteration: 1237; Percent complete: 30.9%; Average loss: 3.2947
Iteration: 1238; Percent complete: 30.9%; Average loss: 3.5828
Iteration: 1239; Percent complete: 31.0%; Average loss: 3.4410
Iteration: 1240; Percent complete: 31.0%; Average loss: 3.4610
Iteration: 1241; Percent complete: 31.0%; Average loss: 3.4468
Iteration: 1242; Percent complete: 31.1%; Average loss: 3.3010
Iteration: 1243; Percent complete: 31.1%; Average loss: 3.5953
Iteration: 1244; Percent complete: 31.1%; Average loss: 3.4884
Iteration: 1245; Percent complete: 31.1%; Average loss: 3.4624
Iteration: 1246; Percent complete: 31.1%; Average loss: 3.2797
Iteration: 1247; Percent complete: 31.2%; Average loss: 3.2372
Iteration: 1248; Percent complete: 31.2%; Average loss: 3.7003
Iteration: 1249; Percent complete: 31.2%; Average loss: 3.3303
Iteration: 1250; Percent complete: 31.2%; Average loss: 3.5594
Iteration: 1251; Percent complete: 31.3%; Average loss: 3.2901
Iteration: 1252; Percent complete: 31.3%; Average loss: 3.4037
Iteration: 1253; Percent complete: 31.3%; Average loss: 3.4483
Iteration: 1254; Percent complete: 31.4%; Average loss: 3.1784
Iteration: 1255; Percent complete: 31.4%; Average loss: 3.0479
Iteration: 1256; Percent complete: 31.4%; Average loss: 3.6024
Iteration: 1257; Percent complete: 31.4%; Average loss: 3.6808
Iteration: 1258; Percent complete: 31.4%; Average loss: 3.5322
Iteration: 1259; Percent complete: 31.5%; Average loss: 3.6322
Iteration: 1260; Percent complete: 31.5%; Average loss: 3.5033
Iteration: 1261; Percent complete: 31.5%; Average loss: 3.3282
Iteration: 1262; Percent complete: 31.6%; Average loss: 3.4599
Iteration: 1263; Percent complete: 31.6%; Average loss: 3.5575
Iteration: 1264; Percent complete: 31.6%; Average loss: 3.4042
Iteration: 1265; Percent complete: 31.6%; Average loss: 3.6743
Iteration: 1266; Percent complete: 31.6%; Average loss: 3.2806
Iteration: 1267; Percent complete: 31.7%; Average loss: 3.3538
Iteration: 1268; Percent complete: 31.7%; Average loss: 3.5095
Iteration: 1269; Percent complete: 31.7%; Average loss: 3.3059
Iteration: 1270; Percent complete: 31.8%; Average loss: 3.1298
Iteration: 1271; Percent complete: 31.8%; Average loss: 3.5676
Iteration: 1272; Percent complete: 31.8%; Average loss: 3.3841
Iteration: 1273; Percent complete: 31.8%; Average loss: 3.4699
Iteration: 1274; Percent complete: 31.9%; Average loss: 3.2060
Iteration: 1275; Percent complete: 31.9%; Average loss: 3.4192
Iteration: 1276; Percent complete: 31.9%; Average loss: 3.6059
Iteration: 1277; Percent complete: 31.9%; Average loss: 3.3577
Iteration: 1278; Percent complete: 31.9%; Average loss: 3.4522
Iteration: 1279; Percent complete: 32.0%; Average loss: 3.4636
Iteration: 1280; Percent complete: 32.0%; Average loss: 3.5233
Iteration: 1281; Percent complete: 32.0%; Average loss: 3.4975
Iteration: 1282; Percent complete: 32.0%; Average loss: 3.2573
Iteration: 1283; Percent complete: 32.1%; Average loss: 3.2141
Iteration: 1284; Percent complete: 32.1%; Average loss: 3.2120
Iteration: 1285; Percent complete: 32.1%; Average loss: 3.3956
Iteration: 1286; Percent complete: 32.1%; Average loss: 3.3226
Iteration: 1287; Percent complete: 32.2%; Average loss: 3.3454
Iteration: 1288; Percent complete: 32.2%; Average loss: 3.5247
Iteration: 1289; Percent complete: 32.2%; Average loss: 3.4134
Iteration: 1290; Percent complete: 32.2%; Average loss: 3.3927
Iteration: 1291; Percent complete: 32.3%; Average loss: 3.4847
Iteration: 1292; Percent complete: 32.3%; Average loss: 3.1794
Iteration: 1293; Percent complete: 32.3%; Average loss: 3.4739
Iteration: 1294; Percent complete: 32.4%; Average loss: 3.5041
Iteration: 1295; Percent complete: 32.4%; Average loss: 3.5786
Iteration: 1296; Percent complete: 32.4%; Average loss: 3.4784
Iteration: 1297; Percent complete: 32.4%; Average loss: 3.2940
Iteration: 1298; Percent complete: 32.5%; Average loss: 3.4020
Iteration: 1299; Percent complete: 32.5%; Average loss: 3.1700
Iteration: 1300; Percent complete: 32.5%; Average loss: 3.2840
Iteration: 1301; Percent complete: 32.5%; Average loss: 3.2508
Iteration: 1302; Percent complete: 32.6%; Average loss: 3.2806
Iteration: 1303; Percent complete: 32.6%; Average loss: 3.7215
Iteration: 1304; Percent complete: 32.6%; Average loss: 3.1767
Iteration: 1305; Percent complete: 32.6%; Average loss: 3.3961
Iteration: 1306; Percent complete: 32.6%; Average loss: 3.3091
Iteration: 1307; Percent complete: 32.7%; Average loss: 3.4663
Iteration: 1308; Percent complete: 32.7%; Average loss: 3.1259
Iteration: 1309; Percent complete: 32.7%; Average loss: 3.5445
Iteration: 1310; Percent complete: 32.8%; Average loss: 3.7114
Iteration: 1311; Percent complete: 32.8%; Average loss: 3.4459
Iteration: 1312; Percent complete: 32.8%; Average loss: 3.3343
Iteration: 1313; Percent complete: 32.8%; Average loss: 3.4875
Iteration: 1314; Percent complete: 32.9%; Average loss: 3.4005
Iteration: 1315; Percent complete: 32.9%; Average loss: 3.5584
Iteration: 1316; Percent complete: 32.9%; Average loss: 3.5511
Iteration: 1317; Percent complete: 32.9%; Average loss: 3.5203
Iteration: 1318; Percent complete: 33.0%; Average loss: 3.2046
Iteration: 1319; Percent complete: 33.0%; Average loss: 3.5310
Iteration: 1320; Percent complete: 33.0%; Average loss: 3.3213
Iteration: 1321; Percent complete: 33.0%; Average loss: 3.6187
Iteration: 1322; Percent complete: 33.1%; Average loss: 3.3863
Iteration: 1323; Percent complete: 33.1%; Average loss: 3.5522
Iteration: 1324; Percent complete: 33.1%; Average loss: 3.4621
Iteration: 1325; Percent complete: 33.1%; Average loss: 3.3641
Iteration: 1326; Percent complete: 33.1%; Average loss: 3.3150
Iteration: 1327; Percent complete: 33.2%; Average loss: 3.3924
Iteration: 1328; Percent complete: 33.2%; Average loss: 3.1254
Iteration: 1329; Percent complete: 33.2%; Average loss: 3.3766
Iteration: 1330; Percent complete: 33.2%; Average loss: 3.0967
Iteration: 1331; Percent complete: 33.3%; Average loss: 3.2310
Iteration: 1332; Percent complete: 33.3%; Average loss: 3.5860
Iteration: 1333; Percent complete: 33.3%; Average loss: 3.4836
Iteration: 1334; Percent complete: 33.4%; Average loss: 3.4780
Iteration: 1335; Percent complete: 33.4%; Average loss: 3.2501
Iteration: 1336; Percent complete: 33.4%; Average loss: 3.2200
Iteration: 1337; Percent complete: 33.4%; Average loss: 3.3819
Iteration: 1338; Percent complete: 33.5%; Average loss: 3.4899
Iteration: 1339; Percent complete: 33.5%; Average loss: 3.4072
Iteration: 1340; Percent complete: 33.5%; Average loss: 3.4964
Iteration: 1341; Percent complete: 33.5%; Average loss: 3.4467
Iteration: 1342; Percent complete: 33.6%; Average loss: 3.2005
Iteration: 1343; Percent complete: 33.6%; Average loss: 3.3992
Iteration: 1344; Percent complete: 33.6%; Average loss: 3.4175
Iteration: 1345; Percent complete: 33.6%; Average loss: 3.3373
Iteration: 1346; Percent complete: 33.7%; Average loss: 3.5580
Iteration: 1347; Percent complete: 33.7%; Average loss: 3.1958
Iteration: 1348; Percent complete: 33.7%; Average loss: 3.3614
Iteration: 1349; Percent complete: 33.7%; Average loss: 3.4381
Iteration: 1350; Percent complete: 33.8%; Average loss: 3.2708
Iteration: 1351; Percent complete: 33.8%; Average loss: 3.6443
Iteration: 1352; Percent complete: 33.8%; Average loss: 3.5196
Iteration: 1353; Percent complete: 33.8%; Average loss: 3.4430
Iteration: 1354; Percent complete: 33.9%; Average loss: 3.3617
Iteration: 1355; Percent complete: 33.9%; Average loss: 3.5517
Iteration: 1356; Percent complete: 33.9%; Average loss: 3.2576
Iteration: 1357; Percent complete: 33.9%; Average loss: 3.5799
Iteration: 1358; Percent complete: 34.0%; Average loss: 3.4884
Iteration: 1359; Percent complete: 34.0%; Average loss: 3.4228
Iteration: 1360; Percent complete: 34.0%; Average loss: 3.3605
Iteration: 1361; Percent complete: 34.0%; Average loss: 3.5022
Iteration: 1362; Percent complete: 34.1%; Average loss: 2.9722
Iteration: 1363; Percent complete: 34.1%; Average loss: 3.5181
Iteration: 1364; Percent complete: 34.1%; Average loss: 3.3707
Iteration: 1365; Percent complete: 34.1%; Average loss: 3.3027
Iteration: 1366; Percent complete: 34.2%; Average loss: 3.6764
Iteration: 1367; Percent complete: 34.2%; Average loss: 3.3342
Iteration: 1368; Percent complete: 34.2%; Average loss: 3.6242
Iteration: 1369; Percent complete: 34.2%; Average loss: 3.4882
Iteration: 1370; Percent complete: 34.2%; Average loss: 3.2496
Iteration: 1371; Percent complete: 34.3%; Average loss: 3.4750
Iteration: 1372; Percent complete: 34.3%; Average loss: 3.3216
Iteration: 1373; Percent complete: 34.3%; Average loss: 3.3905
Iteration: 1374; Percent complete: 34.4%; Average loss: 3.3072
Iteration: 1375; Percent complete: 34.4%; Average loss: 3.3226
Iteration: 1376; Percent complete: 34.4%; Average loss: 3.1596
Iteration: 1377; Percent complete: 34.4%; Average loss: 3.3834
Iteration: 1378; Percent complete: 34.4%; Average loss: 3.3507
Iteration: 1379; Percent complete: 34.5%; Average loss: 3.5304
Iteration: 1380; Percent complete: 34.5%; Average loss: 3.1742
Iteration: 1381; Percent complete: 34.5%; Average loss: 3.4475
Iteration: 1382; Percent complete: 34.5%; Average loss: 3.5676
Iteration: 1383; Percent complete: 34.6%; Average loss: 3.2320
Iteration: 1384; Percent complete: 34.6%; Average loss: 3.3313
Iteration: 1385; Percent complete: 34.6%; Average loss: 3.4895
Iteration: 1386; Percent complete: 34.6%; Average loss: 3.1525
Iteration: 1387; Percent complete: 34.7%; Average loss: 3.2703
Iteration: 1388; Percent complete: 34.7%; Average loss: 3.2703
Iteration: 1389; Percent complete: 34.7%; Average loss: 3.2679
Iteration: 1390; Percent complete: 34.8%; Average loss: 3.3911
Iteration: 1391; Percent complete: 34.8%; Average loss: 3.1987
Iteration: 1392; Percent complete: 34.8%; Average loss: 3.5430
Iteration: 1393; Percent complete: 34.8%; Average loss: 3.2523
Iteration: 1394; Percent complete: 34.8%; Average loss: 3.4243
Iteration: 1395; Percent complete: 34.9%; Average loss: 3.5935
Iteration: 1396; Percent complete: 34.9%; Average loss: 3.4437
Iteration: 1397; Percent complete: 34.9%; Average loss: 3.3807
Iteration: 1398; Percent complete: 34.9%; Average loss: 3.1877
Iteration: 1399; Percent complete: 35.0%; Average loss: 3.2349
Iteration: 1400; Percent complete: 35.0%; Average loss: 3.1994
Iteration: 1401; Percent complete: 35.0%; Average loss: 3.6870
Iteration: 1402; Percent complete: 35.0%; Average loss: 3.2561
Iteration: 1403; Percent complete: 35.1%; Average loss: 3.2697
Iteration: 1404; Percent complete: 35.1%; Average loss: 3.1939
Iteration: 1405; Percent complete: 35.1%; Average loss: 3.3102
Iteration: 1406; Percent complete: 35.1%; Average loss: 3.5189
Iteration: 1407; Percent complete: 35.2%; Average loss: 3.2507
Iteration: 1408; Percent complete: 35.2%; Average loss: 3.1490
Iteration: 1409; Percent complete: 35.2%; Average loss: 3.8533
Iteration: 1410; Percent complete: 35.2%; Average loss: 3.2804
Iteration: 1411; Percent complete: 35.3%; Average loss: 3.4961
Iteration: 1412; Percent complete: 35.3%; Average loss: 3.0985
Iteration: 1413; Percent complete: 35.3%; Average loss: 3.2835
Iteration: 1414; Percent complete: 35.4%; Average loss: 3.2475
Iteration: 1415; Percent complete: 35.4%; Average loss: 3.4583
Iteration: 1416; Percent complete: 35.4%; Average loss: 3.1523
Iteration: 1417; Percent complete: 35.4%; Average loss: 3.3953
Iteration: 1418; Percent complete: 35.4%; Average loss: 3.1770
Iteration: 1419; Percent complete: 35.5%; Average loss: 2.9741
Iteration: 1420; Percent complete: 35.5%; Average loss: 3.1992
Iteration: 1421; Percent complete: 35.5%; Average loss: 3.4471
Iteration: 1422; Percent complete: 35.5%; Average loss: 3.4445
Iteration: 1423; Percent complete: 35.6%; Average loss: 3.2621
Iteration: 1424; Percent complete: 35.6%; Average loss: 3.8279
Iteration: 1425; Percent complete: 35.6%; Average loss: 3.3407
Iteration: 1426; Percent complete: 35.6%; Average loss: 3.3693
Iteration: 1427; Percent complete: 35.7%; Average loss: 3.2366
Iteration: 1428; Percent complete: 35.7%; Average loss: 3.2418
Iteration: 1429; Percent complete: 35.7%; Average loss: 3.3948
Iteration: 1430; Percent complete: 35.8%; Average loss: 3.4759
Iteration: 1431; Percent complete: 35.8%; Average loss: 3.3684
Iteration: 1432; Percent complete: 35.8%; Average loss: 3.3356
Iteration: 1433; Percent complete: 35.8%; Average loss: 3.5777
Iteration: 1434; Percent complete: 35.9%; Average loss: 3.2768
Iteration: 1435; Percent complete: 35.9%; Average loss: 3.2013
Iteration: 1436; Percent complete: 35.9%; Average loss: 3.0938
Iteration: 1437; Percent complete: 35.9%; Average loss: 3.3870
Iteration: 1438; Percent complete: 35.9%; Average loss: 3.3955
Iteration: 1439; Percent complete: 36.0%; Average loss: 3.2643
Iteration: 1440; Percent complete: 36.0%; Average loss: 3.4358
Iteration: 1441; Percent complete: 36.0%; Average loss: 3.4542
Iteration: 1442; Percent complete: 36.0%; Average loss: 3.0628
Iteration: 1443; Percent complete: 36.1%; Average loss: 3.4932
Iteration: 1444; Percent complete: 36.1%; Average loss: 3.1879
Iteration: 1445; Percent complete: 36.1%; Average loss: 3.4046
Iteration: 1446; Percent complete: 36.1%; Average loss: 3.2518
Iteration: 1447; Percent complete: 36.2%; Average loss: 3.4707
Iteration: 1448; Percent complete: 36.2%; Average loss: 3.4925
Iteration: 1449; Percent complete: 36.2%; Average loss: 3.4941
Iteration: 1450; Percent complete: 36.2%; Average loss: 3.4538
Iteration: 1451; Percent complete: 36.3%; Average loss: 3.0035
Iteration: 1452; Percent complete: 36.3%; Average loss: 3.5908
Iteration: 1453; Percent complete: 36.3%; Average loss: 3.2282
Iteration: 1454; Percent complete: 36.4%; Average loss: 3.5888
Iteration: 1455; Percent complete: 36.4%; Average loss: 3.4555
Iteration: 1456; Percent complete: 36.4%; Average loss: 3.3692
Iteration: 1457; Percent complete: 36.4%; Average loss: 3.0499
Iteration: 1458; Percent complete: 36.4%; Average loss: 3.3212
Iteration: 1459; Percent complete: 36.5%; Average loss: 3.4170
Iteration: 1460; Percent complete: 36.5%; Average loss: 3.4918
Iteration: 1461; Percent complete: 36.5%; Average loss: 3.1218
Iteration: 1462; Percent complete: 36.5%; Average loss: 3.4114
Iteration: 1463; Percent complete: 36.6%; Average loss: 3.2810
Iteration: 1464; Percent complete: 36.6%; Average loss: 3.4282
Iteration: 1465; Percent complete: 36.6%; Average loss: 3.0453
Iteration: 1466; Percent complete: 36.6%; Average loss: 3.3456
Iteration: 1467; Percent complete: 36.7%; Average loss: 3.3138
Iteration: 1468; Percent complete: 36.7%; Average loss: 3.4044
Iteration: 1469; Percent complete: 36.7%; Average loss: 3.5343
Iteration: 1470; Percent complete: 36.8%; Average loss: 3.4605
Iteration: 1471; Percent complete: 36.8%; Average loss: 3.3864
Iteration: 1472; Percent complete: 36.8%; Average loss: 3.4023
Iteration: 1473; Percent complete: 36.8%; Average loss: 3.4250
Iteration: 1474; Percent complete: 36.9%; Average loss: 3.2343
Iteration: 1475; Percent complete: 36.9%; Average loss: 3.2594
Iteration: 1476; Percent complete: 36.9%; Average loss: 3.3415
Iteration: 1477; Percent complete: 36.9%; Average loss: 3.2780
Iteration: 1478; Percent complete: 37.0%; Average loss: 3.0263
Iteration: 1479; Percent complete: 37.0%; Average loss: 3.1344
Iteration: 1480; Percent complete: 37.0%; Average loss: 3.3488
Iteration: 1481; Percent complete: 37.0%; Average loss: 3.3270
Iteration: 1482; Percent complete: 37.0%; Average loss: 3.4150
Iteration: 1483; Percent complete: 37.1%; Average loss: 2.9783
Iteration: 1484; Percent complete: 37.1%; Average loss: 3.3232
Iteration: 1485; Percent complete: 37.1%; Average loss: 3.3916
Iteration: 1486; Percent complete: 37.1%; Average loss: 3.4591
Iteration: 1487; Percent complete: 37.2%; Average loss: 3.3187
Iteration: 1488; Percent complete: 37.2%; Average loss: 3.3960
Iteration: 1489; Percent complete: 37.2%; Average loss: 3.3482
Iteration: 1490; Percent complete: 37.2%; Average loss: 3.3326
Iteration: 1491; Percent complete: 37.3%; Average loss: 3.1004
Iteration: 1492; Percent complete: 37.3%; Average loss: 3.3360
Iteration: 1493; Percent complete: 37.3%; Average loss: 3.3492
Iteration: 1494; Percent complete: 37.4%; Average loss: 3.6017
Iteration: 1495; Percent complete: 37.4%; Average loss: 3.0431
Iteration: 1496; Percent complete: 37.4%; Average loss: 3.3452
Iteration: 1497; Percent complete: 37.4%; Average loss: 3.2325
Iteration: 1498; Percent complete: 37.5%; Average loss: 3.1073
Iteration: 1499; Percent complete: 37.5%; Average loss: 3.3274
Iteration: 1500; Percent complete: 37.5%; Average loss: 3.2243
Iteration: 1501; Percent complete: 37.5%; Average loss: 3.2568
Iteration: 1502; Percent complete: 37.5%; Average loss: 3.4953
Iteration: 1503; Percent complete: 37.6%; Average loss: 2.9863
Iteration: 1504; Percent complete: 37.6%; Average loss: 3.3193
Iteration: 1505; Percent complete: 37.6%; Average loss: 3.4044
Iteration: 1506; Percent complete: 37.6%; Average loss: 3.4469
Iteration: 1507; Percent complete: 37.7%; Average loss: 3.2733
Iteration: 1508; Percent complete: 37.7%; Average loss: 3.3229
Iteration: 1509; Percent complete: 37.7%; Average loss: 3.3813
Iteration: 1510; Percent complete: 37.8%; Average loss: 3.1791
Iteration: 1511; Percent complete: 37.8%; Average loss: 3.2839
Iteration: 1512; Percent complete: 37.8%; Average loss: 3.2749
Iteration: 1513; Percent complete: 37.8%; Average loss: 3.6092
Iteration: 1514; Percent complete: 37.9%; Average loss: 3.1322
Iteration: 1515; Percent complete: 37.9%; Average loss: 3.3545
Iteration: 1516; Percent complete: 37.9%; Average loss: 3.3802
Iteration: 1517; Percent complete: 37.9%; Average loss: 3.3353
Iteration: 1518; Percent complete: 38.0%; Average loss: 3.3747
Iteration: 1519; Percent complete: 38.0%; Average loss: 3.4891
Iteration: 1520; Percent complete: 38.0%; Average loss: 3.2664
Iteration: 1521; Percent complete: 38.0%; Average loss: 3.3667
Iteration: 1522; Percent complete: 38.0%; Average loss: 3.4656
Iteration: 1523; Percent complete: 38.1%; Average loss: 3.2067
Iteration: 1524; Percent complete: 38.1%; Average loss: 3.6777
Iteration: 1525; Percent complete: 38.1%; Average loss: 3.1345
Iteration: 1526; Percent complete: 38.1%; Average loss: 3.2352
Iteration: 1527; Percent complete: 38.2%; Average loss: 3.3468
Iteration: 1528; Percent complete: 38.2%; Average loss: 3.3254
Iteration: 1529; Percent complete: 38.2%; Average loss: 3.2919
Iteration: 1530; Percent complete: 38.2%; Average loss: 3.3700
Iteration: 1531; Percent complete: 38.3%; Average loss: 3.4288
Iteration: 1532; Percent complete: 38.3%; Average loss: 3.2472
Iteration: 1533; Percent complete: 38.3%; Average loss: 3.3105
Iteration: 1534; Percent complete: 38.4%; Average loss: 3.0852
Iteration: 1535; Percent complete: 38.4%; Average loss: 3.3576
Iteration: 1536; Percent complete: 38.4%; Average loss: 3.4502
Iteration: 1537; Percent complete: 38.4%; Average loss: 3.5597
Iteration: 1538; Percent complete: 38.5%; Average loss: 3.4247
Iteration: 1539; Percent complete: 38.5%; Average loss: 3.3396
Iteration: 1540; Percent complete: 38.5%; Average loss: 3.4791
Iteration: 1541; Percent complete: 38.5%; Average loss: 3.2544
Iteration: 1542; Percent complete: 38.6%; Average loss: 3.3588
Iteration: 1543; Percent complete: 38.6%; Average loss: 3.1100
Iteration: 1544; Percent complete: 38.6%; Average loss: 3.5435
Iteration: 1545; Percent complete: 38.6%; Average loss: 3.2562
Iteration: 1546; Percent complete: 38.6%; Average loss: 3.4486
Iteration: 1547; Percent complete: 38.7%; Average loss: 3.3222
Iteration: 1548; Percent complete: 38.7%; Average loss: 3.4522
Iteration: 1549; Percent complete: 38.7%; Average loss: 3.2062
Iteration: 1550; Percent complete: 38.8%; Average loss: 3.4631
Iteration: 1551; Percent complete: 38.8%; Average loss: 3.2730
Iteration: 1552; Percent complete: 38.8%; Average loss: 3.2386
Iteration: 1553; Percent complete: 38.8%; Average loss: 3.2877
Iteration: 1554; Percent complete: 38.9%; Average loss: 3.1461
Iteration: 1555; Percent complete: 38.9%; Average loss: 3.3166
Iteration: 1556; Percent complete: 38.9%; Average loss: 3.3815
Iteration: 1557; Percent complete: 38.9%; Average loss: 3.6122
Iteration: 1558; Percent complete: 39.0%; Average loss: 3.6303
Iteration: 1559; Percent complete: 39.0%; Average loss: 3.1602
Iteration: 1560; Percent complete: 39.0%; Average loss: 3.0942
Iteration: 1561; Percent complete: 39.0%; Average loss: 3.4802
Iteration: 1562; Percent complete: 39.1%; Average loss: 3.3727
Iteration: 1563; Percent complete: 39.1%; Average loss: 3.4222
Iteration: 1564; Percent complete: 39.1%; Average loss: 3.1654
Iteration: 1565; Percent complete: 39.1%; Average loss: 3.3273
Iteration: 1566; Percent complete: 39.1%; Average loss: 3.4496
Iteration: 1567; Percent complete: 39.2%; Average loss: 3.0658
Iteration: 1568; Percent complete: 39.2%; Average loss: 3.5443
Iteration: 1569; Percent complete: 39.2%; Average loss: 3.1302
Iteration: 1570; Percent complete: 39.2%; Average loss: 3.1438
Iteration: 1571; Percent complete: 39.3%; Average loss: 3.3313
Iteration: 1572; Percent complete: 39.3%; Average loss: 3.0580
Iteration: 1573; Percent complete: 39.3%; Average loss: 3.2668
Iteration: 1574; Percent complete: 39.4%; Average loss: 3.0956
Iteration: 1575; Percent complete: 39.4%; Average loss: 3.3095
Iteration: 1576; Percent complete: 39.4%; Average loss: 3.0448
Iteration: 1577; Percent complete: 39.4%; Average loss: 3.3873
Iteration: 1578; Percent complete: 39.5%; Average loss: 3.3178
Iteration: 1579; Percent complete: 39.5%; Average loss: 3.2060
Iteration: 1580; Percent complete: 39.5%; Average loss: 3.3644
Iteration: 1581; Percent complete: 39.5%; Average loss: 3.2529
Iteration: 1582; Percent complete: 39.6%; Average loss: 3.1574
Iteration: 1583; Percent complete: 39.6%; Average loss: 3.4877
Iteration: 1584; Percent complete: 39.6%; Average loss: 3.6368
Iteration: 1585; Percent complete: 39.6%; Average loss: 3.4377
Iteration: 1586; Percent complete: 39.6%; Average loss: 3.2250
Iteration: 1587; Percent complete: 39.7%; Average loss: 3.3836
Iteration: 1588; Percent complete: 39.7%; Average loss: 3.2723
Iteration: 1589; Percent complete: 39.7%; Average loss: 3.4322
Iteration: 1590; Percent complete: 39.8%; Average loss: 3.3985
Iteration: 1591; Percent complete: 39.8%; Average loss: 3.6160
Iteration: 1592; Percent complete: 39.8%; Average loss: 3.3680
Iteration: 1593; Percent complete: 39.8%; Average loss: 3.1748
Iteration: 1594; Percent complete: 39.9%; Average loss: 3.2286
Iteration: 1595; Percent complete: 39.9%; Average loss: 3.1887
Iteration: 1596; Percent complete: 39.9%; Average loss: 3.0269
Iteration: 1597; Percent complete: 39.9%; Average loss: 3.3876
Iteration: 1598; Percent complete: 40.0%; Average loss: 3.2681
Iteration: 1599; Percent complete: 40.0%; Average loss: 3.5797
Iteration: 1600; Percent complete: 40.0%; Average loss: 3.2637
Iteration: 1601; Percent complete: 40.0%; Average loss: 3.1764
Iteration: 1602; Percent complete: 40.1%; Average loss: 3.3422
Iteration: 1603; Percent complete: 40.1%; Average loss: 3.2812
Iteration: 1604; Percent complete: 40.1%; Average loss: 3.2706
Iteration: 1605; Percent complete: 40.1%; Average loss: 3.3560
Iteration: 1606; Percent complete: 40.2%; Average loss: 3.0264
Iteration: 1607; Percent complete: 40.2%; Average loss: 3.3519
Iteration: 1608; Percent complete: 40.2%; Average loss: 3.2751
Iteration: 1609; Percent complete: 40.2%; Average loss: 3.2840
Iteration: 1610; Percent complete: 40.2%; Average loss: 3.3339
Iteration: 1611; Percent complete: 40.3%; Average loss: 3.2809
Iteration: 1612; Percent complete: 40.3%; Average loss: 3.3271
Iteration: 1613; Percent complete: 40.3%; Average loss: 3.5942
Iteration: 1614; Percent complete: 40.4%; Average loss: 3.2276
Iteration: 1615; Percent complete: 40.4%; Average loss: 3.1474
Iteration: 1616; Percent complete: 40.4%; Average loss: 3.4431
Iteration: 1617; Percent complete: 40.4%; Average loss: 3.3881
Iteration: 1618; Percent complete: 40.5%; Average loss: 3.1597
Iteration: 1619; Percent complete: 40.5%; Average loss: 3.0618
Iteration: 1620; Percent complete: 40.5%; Average loss: 3.2627
Iteration: 1621; Percent complete: 40.5%; Average loss: 3.2111
Iteration: 1622; Percent complete: 40.6%; Average loss: 2.8831
Iteration: 1623; Percent complete: 40.6%; Average loss: 3.3553
Iteration: 1624; Percent complete: 40.6%; Average loss: 3.2822
Iteration: 1625; Percent complete: 40.6%; Average loss: 3.2734
Iteration: 1626; Percent complete: 40.6%; Average loss: 3.3040
Iteration: 1627; Percent complete: 40.7%; Average loss: 3.4713
Iteration: 1628; Percent complete: 40.7%; Average loss: 3.3031
Iteration: 1629; Percent complete: 40.7%; Average loss: 3.3851
Iteration: 1630; Percent complete: 40.8%; Average loss: 3.4332
Iteration: 1631; Percent complete: 40.8%; Average loss: 3.1804
Iteration: 1632; Percent complete: 40.8%; Average loss: 3.5164
Iteration: 1633; Percent complete: 40.8%; Average loss: 3.0100
Iteration: 1634; Percent complete: 40.8%; Average loss: 3.2900
Iteration: 1635; Percent complete: 40.9%; Average loss: 3.3445
Iteration: 1636; Percent complete: 40.9%; Average loss: 3.2530
Iteration: 1637; Percent complete: 40.9%; Average loss: 3.1852
Iteration: 1638; Percent complete: 40.9%; Average loss: 3.3767
Iteration: 1639; Percent complete: 41.0%; Average loss: 3.3379
Iteration: 1640; Percent complete: 41.0%; Average loss: 3.2997
Iteration: 1641; Percent complete: 41.0%; Average loss: 3.4388
Iteration: 1642; Percent complete: 41.0%; Average loss: 3.2082
Iteration: 1643; Percent complete: 41.1%; Average loss: 3.2928
Iteration: 1644; Percent complete: 41.1%; Average loss: 3.3469
Iteration: 1645; Percent complete: 41.1%; Average loss: 3.1897
Iteration: 1646; Percent complete: 41.1%; Average loss: 3.2518
Iteration: 1647; Percent complete: 41.2%; Average loss: 3.3058
Iteration: 1648; Percent complete: 41.2%; Average loss: 3.1239
Iteration: 1649; Percent complete: 41.2%; Average loss: 2.8960
Iteration: 1650; Percent complete: 41.2%; Average loss: 3.1064
Iteration: 1651; Percent complete: 41.3%; Average loss: 2.9998
Iteration: 1652; Percent complete: 41.3%; Average loss: 3.0927
Iteration: 1653; Percent complete: 41.3%; Average loss: 3.1388
Iteration: 1654; Percent complete: 41.3%; Average loss: 3.3930
Iteration: 1655; Percent complete: 41.4%; Average loss: 2.9087
Iteration: 1656; Percent complete: 41.4%; Average loss: 3.2463
Iteration: 1657; Percent complete: 41.4%; Average loss: 3.3298
Iteration: 1658; Percent complete: 41.4%; Average loss: 3.2548
Iteration: 1659; Percent complete: 41.5%; Average loss: 3.2794
Iteration: 1660; Percent complete: 41.5%; Average loss: 3.1617
Iteration: 1661; Percent complete: 41.5%; Average loss: 3.1209
Iteration: 1662; Percent complete: 41.5%; Average loss: 3.1874
Iteration: 1663; Percent complete: 41.6%; Average loss: 3.2569
Iteration: 1664; Percent complete: 41.6%; Average loss: 3.3887
Iteration: 1665; Percent complete: 41.6%; Average loss: 3.0828
Iteration: 1666; Percent complete: 41.6%; Average loss: 3.4649
Iteration: 1667; Percent complete: 41.7%; Average loss: 3.5073
Iteration: 1668; Percent complete: 41.7%; Average loss: 3.2877
Iteration: 1669; Percent complete: 41.7%; Average loss: 3.2739
Iteration: 1670; Percent complete: 41.8%; Average loss: 3.5022
Iteration: 1671; Percent complete: 41.8%; Average loss: 3.1855
Iteration: 1672; Percent complete: 41.8%; Average loss: 3.1813
Iteration: 1673; Percent complete: 41.8%; Average loss: 3.5255
Iteration: 1674; Percent complete: 41.9%; Average loss: 3.1604
Iteration: 1675; Percent complete: 41.9%; Average loss: 3.3574
Iteration: 1676; Percent complete: 41.9%; Average loss: 3.3982
Iteration: 1677; Percent complete: 41.9%; Average loss: 3.2934
Iteration: 1678; Percent complete: 41.9%; Average loss: 3.2839
Iteration: 1679; Percent complete: 42.0%; Average loss: 3.1345
Iteration: 1680; Percent complete: 42.0%; Average loss: 3.1078
Iteration: 1681; Percent complete: 42.0%; Average loss: 3.2991
Iteration: 1682; Percent complete: 42.0%; Average loss: 3.1164
Iteration: 1683; Percent complete: 42.1%; Average loss: 3.3463
Iteration: 1684; Percent complete: 42.1%; Average loss: 3.4904
Iteration: 1685; Percent complete: 42.1%; Average loss: 3.5056
Iteration: 1686; Percent complete: 42.1%; Average loss: 3.2105
Iteration: 1687; Percent complete: 42.2%; Average loss: 3.0108
Iteration: 1688; Percent complete: 42.2%; Average loss: 3.3975
Iteration: 1689; Percent complete: 42.2%; Average loss: 3.4522
Iteration: 1690; Percent complete: 42.2%; Average loss: 3.3229
Iteration: 1691; Percent complete: 42.3%; Average loss: 3.0015
Iteration: 1692; Percent complete: 42.3%; Average loss: 3.2616
Iteration: 1693; Percent complete: 42.3%; Average loss: 3.3531
Iteration: 1694; Percent complete: 42.4%; Average loss: 3.2247
Iteration: 1695; Percent complete: 42.4%; Average loss: 3.0603
Iteration: 1696; Percent complete: 42.4%; Average loss: 3.0362
Iteration: 1697; Percent complete: 42.4%; Average loss: 3.3709
Iteration: 1698; Percent complete: 42.4%; Average loss: 3.1890
Iteration: 1699; Percent complete: 42.5%; Average loss: 3.3653
Iteration: 1700; Percent complete: 42.5%; Average loss: 3.3098
Iteration: 1701; Percent complete: 42.5%; Average loss: 3.1911
Iteration: 1702; Percent complete: 42.5%; Average loss: 3.2786
Iteration: 1703; Percent complete: 42.6%; Average loss: 3.1719
Iteration: 1704; Percent complete: 42.6%; Average loss: 3.2439
Iteration: 1705; Percent complete: 42.6%; Average loss: 3.2535
Iteration: 1706; Percent complete: 42.6%; Average loss: 3.1530
Iteration: 1707; Percent complete: 42.7%; Average loss: 3.1425
Iteration: 1708; Percent complete: 42.7%; Average loss: 3.1254
Iteration: 1709; Percent complete: 42.7%; Average loss: 3.1090
Iteration: 1710; Percent complete: 42.8%; Average loss: 3.4276
Iteration: 1711; Percent complete: 42.8%; Average loss: 3.1779
Iteration: 1712; Percent complete: 42.8%; Average loss: 3.3113
Iteration: 1713; Percent complete: 42.8%; Average loss: 3.0668
Iteration: 1714; Percent complete: 42.9%; Average loss: 3.2152
Iteration: 1715; Percent complete: 42.9%; Average loss: 3.2636
Iteration: 1716; Percent complete: 42.9%; Average loss: 3.3068
Iteration: 1717; Percent complete: 42.9%; Average loss: 3.4339
Iteration: 1718; Percent complete: 43.0%; Average loss: 3.2835
Iteration: 1719; Percent complete: 43.0%; Average loss: 3.3535
Iteration: 1720; Percent complete: 43.0%; Average loss: 3.3532
Iteration: 1721; Percent complete: 43.0%; Average loss: 3.2809
Iteration: 1722; Percent complete: 43.0%; Average loss: 3.2619
Iteration: 1723; Percent complete: 43.1%; Average loss: 3.4249
Iteration: 1724; Percent complete: 43.1%; Average loss: 3.2537
Iteration: 1725; Percent complete: 43.1%; Average loss: 3.4295
Iteration: 1726; Percent complete: 43.1%; Average loss: 3.2463
Iteration: 1727; Percent complete: 43.2%; Average loss: 3.2805
Iteration: 1728; Percent complete: 43.2%; Average loss: 3.4815
Iteration: 1729; Percent complete: 43.2%; Average loss: 3.1902
Iteration: 1730; Percent complete: 43.2%; Average loss: 3.1794
Iteration: 1731; Percent complete: 43.3%; Average loss: 3.2776
Iteration: 1732; Percent complete: 43.3%; Average loss: 3.4717
Iteration: 1733; Percent complete: 43.3%; Average loss: 3.2134
Iteration: 1734; Percent complete: 43.4%; Average loss: 3.4593
Iteration: 1735; Percent complete: 43.4%; Average loss: 3.1675
Iteration: 1736; Percent complete: 43.4%; Average loss: 3.3057
Iteration: 1737; Percent complete: 43.4%; Average loss: 3.1064
Iteration: 1738; Percent complete: 43.5%; Average loss: 3.2611
Iteration: 1739; Percent complete: 43.5%; Average loss: 3.2475
Iteration: 1740; Percent complete: 43.5%; Average loss: 3.3093
Iteration: 1741; Percent complete: 43.5%; Average loss: 2.9838
Iteration: 1742; Percent complete: 43.5%; Average loss: 3.3830
Iteration: 1743; Percent complete: 43.6%; Average loss: 3.2477
Iteration: 1744; Percent complete: 43.6%; Average loss: 3.2205
Iteration: 1745; Percent complete: 43.6%; Average loss: 3.4336
Iteration: 1746; Percent complete: 43.6%; Average loss: 3.2605
Iteration: 1747; Percent complete: 43.7%; Average loss: 3.0654
Iteration: 1748; Percent complete: 43.7%; Average loss: 3.1015
Iteration: 1749; Percent complete: 43.7%; Average loss: 3.2086
Iteration: 1750; Percent complete: 43.8%; Average loss: 3.1003
Iteration: 1751; Percent complete: 43.8%; Average loss: 3.2961
Iteration: 1752; Percent complete: 43.8%; Average loss: 3.1764
Iteration: 1753; Percent complete: 43.8%; Average loss: 3.3447
Iteration: 1754; Percent complete: 43.9%; Average loss: 3.2424
Iteration: 1755; Percent complete: 43.9%; Average loss: 3.1574
Iteration: 1756; Percent complete: 43.9%; Average loss: 3.5368
Iteration: 1757; Percent complete: 43.9%; Average loss: 3.1804
Iteration: 1758; Percent complete: 44.0%; Average loss: 3.3075
Iteration: 1759; Percent complete: 44.0%; Average loss: 3.1180
Iteration: 1760; Percent complete: 44.0%; Average loss: 3.2433
Iteration: 1761; Percent complete: 44.0%; Average loss: 3.3501
Iteration: 1762; Percent complete: 44.0%; Average loss: 3.2294
Iteration: 1763; Percent complete: 44.1%; Average loss: 3.4443
Iteration: 1764; Percent complete: 44.1%; Average loss: 3.4445
Iteration: 1765; Percent complete: 44.1%; Average loss: 3.2341
Iteration: 1766; Percent complete: 44.1%; Average loss: 3.1948
Iteration: 1767; Percent complete: 44.2%; Average loss: 3.1798
Iteration: 1768; Percent complete: 44.2%; Average loss: 3.3484
Iteration: 1769; Percent complete: 44.2%; Average loss: 3.2570
Iteration: 1770; Percent complete: 44.2%; Average loss: 3.3283
Iteration: 1771; Percent complete: 44.3%; Average loss: 3.4927
Iteration: 1772; Percent complete: 44.3%; Average loss: 3.5760
Iteration: 1773; Percent complete: 44.3%; Average loss: 3.1905
Iteration: 1774; Percent complete: 44.4%; Average loss: 3.3212
Iteration: 1775; Percent complete: 44.4%; Average loss: 3.2328
Iteration: 1776; Percent complete: 44.4%; Average loss: 2.9195
Iteration: 1777; Percent complete: 44.4%; Average loss: 3.1498
Iteration: 1778; Percent complete: 44.5%; Average loss: 3.0018
Iteration: 1779; Percent complete: 44.5%; Average loss: 3.5260
Iteration: 1780; Percent complete: 44.5%; Average loss: 3.3009
Iteration: 1781; Percent complete: 44.5%; Average loss: 3.1648
Iteration: 1782; Percent complete: 44.5%; Average loss: 3.2622
Iteration: 1783; Percent complete: 44.6%; Average loss: 3.1023
Iteration: 1784; Percent complete: 44.6%; Average loss: 3.1845
Iteration: 1785; Percent complete: 44.6%; Average loss: 3.0728
Iteration: 1786; Percent complete: 44.6%; Average loss: 3.3770
Iteration: 1787; Percent complete: 44.7%; Average loss: 3.3417
Iteration: 1788; Percent complete: 44.7%; Average loss: 3.2077
Iteration: 1789; Percent complete: 44.7%; Average loss: 3.1060
Iteration: 1790; Percent complete: 44.8%; Average loss: 3.1291
Iteration: 1791; Percent complete: 44.8%; Average loss: 3.3010
Iteration: 1792; Percent complete: 44.8%; Average loss: 3.1994
Iteration: 1793; Percent complete: 44.8%; Average loss: 3.5035
Iteration: 1794; Percent complete: 44.9%; Average loss: 3.1511
Iteration: 1795; Percent complete: 44.9%; Average loss: 3.3352
Iteration: 1796; Percent complete: 44.9%; Average loss: 3.3972
Iteration: 1797; Percent complete: 44.9%; Average loss: 3.2292
Iteration: 1798; Percent complete: 45.0%; Average loss: 3.3023
Iteration: 1799; Percent complete: 45.0%; Average loss: 3.3290
Iteration: 1800; Percent complete: 45.0%; Average loss: 3.2506
Iteration: 1801; Percent complete: 45.0%; Average loss: 3.3064
Iteration: 1802; Percent complete: 45.1%; Average loss: 3.1218
Iteration: 1803; Percent complete: 45.1%; Average loss: 3.1187
Iteration: 1804; Percent complete: 45.1%; Average loss: 3.4656
Iteration: 1805; Percent complete: 45.1%; Average loss: 2.9927
Iteration: 1806; Percent complete: 45.1%; Average loss: 3.3745
Iteration: 1807; Percent complete: 45.2%; Average loss: 3.2496
Iteration: 1808; Percent complete: 45.2%; Average loss: 3.0802
Iteration: 1809; Percent complete: 45.2%; Average loss: 3.2932
Iteration: 1810; Percent complete: 45.2%; Average loss: 3.1540
Iteration: 1811; Percent complete: 45.3%; Average loss: 3.1425
Iteration: 1812; Percent complete: 45.3%; Average loss: 3.3486
Iteration: 1813; Percent complete: 45.3%; Average loss: 3.3384
Iteration: 1814; Percent complete: 45.4%; Average loss: 3.2455
Iteration: 1815; Percent complete: 45.4%; Average loss: 3.4838
Iteration: 1816; Percent complete: 45.4%; Average loss: 3.2706
Iteration: 1817; Percent complete: 45.4%; Average loss: 3.3201
Iteration: 1818; Percent complete: 45.5%; Average loss: 3.1657
Iteration: 1819; Percent complete: 45.5%; Average loss: 3.1155
Iteration: 1820; Percent complete: 45.5%; Average loss: 3.2068
Iteration: 1821; Percent complete: 45.5%; Average loss: 3.0820
Iteration: 1822; Percent complete: 45.6%; Average loss: 3.2867
Iteration: 1823; Percent complete: 45.6%; Average loss: 3.2389
Iteration: 1824; Percent complete: 45.6%; Average loss: 3.0749
Iteration: 1825; Percent complete: 45.6%; Average loss: 3.2232
Iteration: 1826; Percent complete: 45.6%; Average loss: 3.0674
Iteration: 1827; Percent complete: 45.7%; Average loss: 3.2559
Iteration: 1828; Percent complete: 45.7%; Average loss: 3.5695
Iteration: 1829; Percent complete: 45.7%; Average loss: 3.2917
Iteration: 1830; Percent complete: 45.8%; Average loss: 3.4655
Iteration: 1831; Percent complete: 45.8%; Average loss: 3.4368
Iteration: 1832; Percent complete: 45.8%; Average loss: 3.1821
Iteration: 1833; Percent complete: 45.8%; Average loss: 3.2002
Iteration: 1834; Percent complete: 45.9%; Average loss: 3.2516
Iteration: 1835; Percent complete: 45.9%; Average loss: 3.1654
Iteration: 1836; Percent complete: 45.9%; Average loss: 3.1006
Iteration: 1837; Percent complete: 45.9%; Average loss: 3.3801
Iteration: 1838; Percent complete: 46.0%; Average loss: 3.4370
Iteration: 1839; Percent complete: 46.0%; Average loss: 3.1542
Iteration: 1840; Percent complete: 46.0%; Average loss: 3.0324
Iteration: 1841; Percent complete: 46.0%; Average loss: 3.2754
Iteration: 1842; Percent complete: 46.1%; Average loss: 3.3725
Iteration: 1843; Percent complete: 46.1%; Average loss: 3.6819
Iteration: 1844; Percent complete: 46.1%; Average loss: 3.2932
Iteration: 1845; Percent complete: 46.1%; Average loss: 3.2417
Iteration: 1846; Percent complete: 46.2%; Average loss: 3.0423
Iteration: 1847; Percent complete: 46.2%; Average loss: 3.3388
Iteration: 1848; Percent complete: 46.2%; Average loss: 2.9813
Iteration: 1849; Percent complete: 46.2%; Average loss: 3.2213
Iteration: 1850; Percent complete: 46.2%; Average loss: 2.9538
Iteration: 1851; Percent complete: 46.3%; Average loss: 3.2789
Iteration: 1852; Percent complete: 46.3%; Average loss: 3.1604
Iteration: 1853; Percent complete: 46.3%; Average loss: 3.3164
Iteration: 1854; Percent complete: 46.4%; Average loss: 3.3747
Iteration: 1855; Percent complete: 46.4%; Average loss: 3.1253
Iteration: 1856; Percent complete: 46.4%; Average loss: 3.3419
Iteration: 1857; Percent complete: 46.4%; Average loss: 3.2471
Iteration: 1858; Percent complete: 46.5%; Average loss: 3.1826
Iteration: 1859; Percent complete: 46.5%; Average loss: 3.0724
Iteration: 1860; Percent complete: 46.5%; Average loss: 3.0404
Iteration: 1861; Percent complete: 46.5%; Average loss: 3.2820
Iteration: 1862; Percent complete: 46.6%; Average loss: 3.2381
Iteration: 1863; Percent complete: 46.6%; Average loss: 3.1805
Iteration: 1864; Percent complete: 46.6%; Average loss: 3.1184
Iteration: 1865; Percent complete: 46.6%; Average loss: 3.3963
Iteration: 1866; Percent complete: 46.7%; Average loss: 3.4030
Iteration: 1867; Percent complete: 46.7%; Average loss: 3.2446
Iteration: 1868; Percent complete: 46.7%; Average loss: 3.0129
Iteration: 1869; Percent complete: 46.7%; Average loss: 2.8899
Iteration: 1870; Percent complete: 46.8%; Average loss: 3.3115
Iteration: 1871; Percent complete: 46.8%; Average loss: 3.0366
Iteration: 1872; Percent complete: 46.8%; Average loss: 3.1243
Iteration: 1873; Percent complete: 46.8%; Average loss: 3.2891
Iteration: 1874; Percent complete: 46.9%; Average loss: 3.1479
Iteration: 1875; Percent complete: 46.9%; Average loss: 3.2607
Iteration: 1876; Percent complete: 46.9%; Average loss: 3.0647
Iteration: 1877; Percent complete: 46.9%; Average loss: 3.0056
Iteration: 1878; Percent complete: 46.9%; Average loss: 3.3717
Iteration: 1879; Percent complete: 47.0%; Average loss: 3.3156
Iteration: 1880; Percent complete: 47.0%; Average loss: 3.0998
Iteration: 1881; Percent complete: 47.0%; Average loss: 3.1046
Iteration: 1882; Percent complete: 47.0%; Average loss: 3.1705
Iteration: 1883; Percent complete: 47.1%; Average loss: 3.1547
Iteration: 1884; Percent complete: 47.1%; Average loss: 3.2353
Iteration: 1885; Percent complete: 47.1%; Average loss: 3.5103
Iteration: 1886; Percent complete: 47.1%; Average loss: 2.9429
Iteration: 1887; Percent complete: 47.2%; Average loss: 3.2086
Iteration: 1888; Percent complete: 47.2%; Average loss: 3.2866
Iteration: 1889; Percent complete: 47.2%; Average loss: 3.3912
Iteration: 1890; Percent complete: 47.2%; Average loss: 3.3609
Iteration: 1891; Percent complete: 47.3%; Average loss: 3.0960
Iteration: 1892; Percent complete: 47.3%; Average loss: 3.1038
Iteration: 1893; Percent complete: 47.3%; Average loss: 3.1807
Iteration: 1894; Percent complete: 47.3%; Average loss: 3.1639
Iteration: 1895; Percent complete: 47.4%; Average loss: 3.0228
Iteration: 1896; Percent complete: 47.4%; Average loss: 3.1207
Iteration: 1897; Percent complete: 47.4%; Average loss: 3.3335
Iteration: 1898; Percent complete: 47.4%; Average loss: 3.3738
Iteration: 1899; Percent complete: 47.5%; Average loss: 3.0141
Iteration: 1900; Percent complete: 47.5%; Average loss: 3.1481
Iteration: 1901; Percent complete: 47.5%; Average loss: 3.4241
Iteration: 1902; Percent complete: 47.5%; Average loss: 3.1486
Iteration: 1903; Percent complete: 47.6%; Average loss: 2.9245
Iteration: 1904; Percent complete: 47.6%; Average loss: 3.0672
Iteration: 1905; Percent complete: 47.6%; Average loss: 3.1607
Iteration: 1906; Percent complete: 47.6%; Average loss: 3.2723
Iteration: 1907; Percent complete: 47.7%; Average loss: 3.0585
Iteration: 1908; Percent complete: 47.7%; Average loss: 2.9753
Iteration: 1909; Percent complete: 47.7%; Average loss: 3.1644
Iteration: 1910; Percent complete: 47.8%; Average loss: 3.3159
Iteration: 1911; Percent complete: 47.8%; Average loss: 3.1843
Iteration: 1912; Percent complete: 47.8%; Average loss: 3.1862
Iteration: 1913; Percent complete: 47.8%; Average loss: 2.9030
Iteration: 1914; Percent complete: 47.9%; Average loss: 3.2155
Iteration: 1915; Percent complete: 47.9%; Average loss: 3.3006
Iteration: 1916; Percent complete: 47.9%; Average loss: 3.2842
Iteration: 1917; Percent complete: 47.9%; Average loss: 3.2492
Iteration: 1918; Percent complete: 47.9%; Average loss: 3.1764
Iteration: 1919; Percent complete: 48.0%; Average loss: 3.4872
Iteration: 1920; Percent complete: 48.0%; Average loss: 3.3608
Iteration: 1921; Percent complete: 48.0%; Average loss: 3.0224
Iteration: 1922; Percent complete: 48.0%; Average loss: 3.0998
Iteration: 1923; Percent complete: 48.1%; Average loss: 3.3333
Iteration: 1924; Percent complete: 48.1%; Average loss: 3.1957
Iteration: 1925; Percent complete: 48.1%; Average loss: 3.1523
Iteration: 1926; Percent complete: 48.1%; Average loss: 3.2847
Iteration: 1927; Percent complete: 48.2%; Average loss: 3.3374
Iteration: 1928; Percent complete: 48.2%; Average loss: 3.4533
Iteration: 1929; Percent complete: 48.2%; Average loss: 3.3082
Iteration: 1930; Percent complete: 48.2%; Average loss: 2.9079
Iteration: 1931; Percent complete: 48.3%; Average loss: 3.0927
Iteration: 1932; Percent complete: 48.3%; Average loss: 3.1399
Iteration: 1933; Percent complete: 48.3%; Average loss: 3.1331
Iteration: 1934; Percent complete: 48.4%; Average loss: 3.0910
Iteration: 1935; Percent complete: 48.4%; Average loss: 3.0257
Iteration: 1936; Percent complete: 48.4%; Average loss: 3.3320
Iteration: 1937; Percent complete: 48.4%; Average loss: 3.1060
Iteration: 1938; Percent complete: 48.4%; Average loss: 3.1697
Iteration: 1939; Percent complete: 48.5%; Average loss: 3.2030
Iteration: 1940; Percent complete: 48.5%; Average loss: 3.0512
Iteration: 1941; Percent complete: 48.5%; Average loss: 3.0101
Iteration: 1942; Percent complete: 48.5%; Average loss: 3.1221
Iteration: 1943; Percent complete: 48.6%; Average loss: 3.3336
Iteration: 1944; Percent complete: 48.6%; Average loss: 3.4432
Iteration: 1945; Percent complete: 48.6%; Average loss: 3.2687
Iteration: 1946; Percent complete: 48.6%; Average loss: 3.1664
Iteration: 1947; Percent complete: 48.7%; Average loss: 3.1538
Iteration: 1948; Percent complete: 48.7%; Average loss: 3.2381
Iteration: 1949; Percent complete: 48.7%; Average loss: 2.9967
Iteration: 1950; Percent complete: 48.8%; Average loss: 3.2836
Iteration: 1951; Percent complete: 48.8%; Average loss: 3.1595
Iteration: 1952; Percent complete: 48.8%; Average loss: 3.2049
Iteration: 1953; Percent complete: 48.8%; Average loss: 3.2381
Iteration: 1954; Percent complete: 48.9%; Average loss: 3.3625
Iteration: 1955; Percent complete: 48.9%; Average loss: 3.0854
Iteration: 1956; Percent complete: 48.9%; Average loss: 3.4912
Iteration: 1957; Percent complete: 48.9%; Average loss: 3.1253
Iteration: 1958; Percent complete: 48.9%; Average loss: 2.9249
Iteration: 1959; Percent complete: 49.0%; Average loss: 3.2719
Iteration: 1960; Percent complete: 49.0%; Average loss: 2.8307
Iteration: 1961; Percent complete: 49.0%; Average loss: 3.0738
Iteration: 1962; Percent complete: 49.0%; Average loss: 3.0993
Iteration: 1963; Percent complete: 49.1%; Average loss: 2.8925
Iteration: 1964; Percent complete: 49.1%; Average loss: 3.1701
Iteration: 1965; Percent complete: 49.1%; Average loss: 3.0458
Iteration: 1966; Percent complete: 49.1%; Average loss: 2.9393
Iteration: 1967; Percent complete: 49.2%; Average loss: 2.9979
Iteration: 1968; Percent complete: 49.2%; Average loss: 3.2043
Iteration: 1969; Percent complete: 49.2%; Average loss: 3.0476
Iteration: 1970; Percent complete: 49.2%; Average loss: 3.1273
Iteration: 1971; Percent complete: 49.3%; Average loss: 3.0600
Iteration: 1972; Percent complete: 49.3%; Average loss: 3.0073
Iteration: 1973; Percent complete: 49.3%; Average loss: 3.1467
Iteration: 1974; Percent complete: 49.4%; Average loss: 3.2167
Iteration: 1975; Percent complete: 49.4%; Average loss: 2.8668
Iteration: 1976; Percent complete: 49.4%; Average loss: 3.1760
Iteration: 1977; Percent complete: 49.4%; Average loss: 2.9115
Iteration: 1978; Percent complete: 49.5%; Average loss: 2.9963
Iteration: 1979; Percent complete: 49.5%; Average loss: 3.2836
Iteration: 1980; Percent complete: 49.5%; Average loss: 2.9205
Iteration: 1981; Percent complete: 49.5%; Average loss: 3.2412
Iteration: 1982; Percent complete: 49.5%; Average loss: 3.4002
Iteration: 1983; Percent complete: 49.6%; Average loss: 3.2077
Iteration: 1984; Percent complete: 49.6%; Average loss: 3.1681
Iteration: 1985; Percent complete: 49.6%; Average loss: 3.3066
Iteration: 1986; Percent complete: 49.6%; Average loss: 3.1866
Iteration: 1987; Percent complete: 49.7%; Average loss: 3.2635
Iteration: 1988; Percent complete: 49.7%; Average loss: 3.2242
Iteration: 1989; Percent complete: 49.7%; Average loss: 3.1293
Iteration: 1990; Percent complete: 49.8%; Average loss: 3.2881
Iteration: 1991; Percent complete: 49.8%; Average loss: 3.2930
Iteration: 1992; Percent complete: 49.8%; Average loss: 3.2621
Iteration: 1993; Percent complete: 49.8%; Average loss: 3.2606
Iteration: 1994; Percent complete: 49.9%; Average loss: 3.2770
Iteration: 1995; Percent complete: 49.9%; Average loss: 3.1917
Iteration: 1996; Percent complete: 49.9%; Average loss: 2.9323
Iteration: 1997; Percent complete: 49.9%; Average loss: 3.3475
Iteration: 1998; Percent complete: 50.0%; Average loss: 2.9007
Iteration: 1999; Percent complete: 50.0%; Average loss: 3.3134
Iteration: 2000; Percent complete: 50.0%; Average loss: 3.2163
Iteration: 2001; Percent complete: 50.0%; Average loss: 3.2091
Iteration: 2002; Percent complete: 50.0%; Average loss: 3.4416
Iteration: 2003; Percent complete: 50.1%; Average loss: 3.2368
Iteration: 2004; Percent complete: 50.1%; Average loss: 3.2196
Iteration: 2005; Percent complete: 50.1%; Average loss: 3.0741
Iteration: 2006; Percent complete: 50.1%; Average loss: 3.3976
Iteration: 2007; Percent complete: 50.2%; Average loss: 3.0234
Iteration: 2008; Percent complete: 50.2%; Average loss: 2.9842
Iteration: 2009; Percent complete: 50.2%; Average loss: 3.2361
Iteration: 2010; Percent complete: 50.2%; Average loss: 3.2382
Iteration: 2011; Percent complete: 50.3%; Average loss: 3.0029
Iteration: 2012; Percent complete: 50.3%; Average loss: 3.1875
Iteration: 2013; Percent complete: 50.3%; Average loss: 3.3371
Iteration: 2014; Percent complete: 50.3%; Average loss: 3.2049
Iteration: 2015; Percent complete: 50.4%; Average loss: 3.0801
Iteration: 2016; Percent complete: 50.4%; Average loss: 3.3158
Iteration: 2017; Percent complete: 50.4%; Average loss: 3.4085
Iteration: 2018; Percent complete: 50.4%; Average loss: 3.1461
Iteration: 2019; Percent complete: 50.5%; Average loss: 3.2373
Iteration: 2020; Percent complete: 50.5%; Average loss: 3.4032
Iteration: 2021; Percent complete: 50.5%; Average loss: 3.2103
Iteration: 2022; Percent complete: 50.5%; Average loss: 3.1089
Iteration: 2023; Percent complete: 50.6%; Average loss: 3.0843
Iteration: 2024; Percent complete: 50.6%; Average loss: 3.2097
Iteration: 2025; Percent complete: 50.6%; Average loss: 3.2842
Iteration: 2026; Percent complete: 50.6%; Average loss: 3.2467
Iteration: 2027; Percent complete: 50.7%; Average loss: 3.2624
Iteration: 2028; Percent complete: 50.7%; Average loss: 3.4325
Iteration: 2029; Percent complete: 50.7%; Average loss: 3.1456
Iteration: 2030; Percent complete: 50.7%; Average loss: 3.1801
Iteration: 2031; Percent complete: 50.8%; Average loss: 3.0619
Iteration: 2032; Percent complete: 50.8%; Average loss: 3.2030
Iteration: 2033; Percent complete: 50.8%; Average loss: 3.1948
Iteration: 2034; Percent complete: 50.8%; Average loss: 3.0823
Iteration: 2035; Percent complete: 50.9%; Average loss: 3.1678
Iteration: 2036; Percent complete: 50.9%; Average loss: 3.2263
Iteration: 2037; Percent complete: 50.9%; Average loss: 3.1456
Iteration: 2038; Percent complete: 50.9%; Average loss: 3.2797
Iteration: 2039; Percent complete: 51.0%; Average loss: 3.1293
Iteration: 2040; Percent complete: 51.0%; Average loss: 3.1362
Iteration: 2041; Percent complete: 51.0%; Average loss: 3.3331
Iteration: 2042; Percent complete: 51.0%; Average loss: 3.1029
Iteration: 2043; Percent complete: 51.1%; Average loss: 3.1493
Iteration: 2044; Percent complete: 51.1%; Average loss: 3.3562
Iteration: 2045; Percent complete: 51.1%; Average loss: 3.3770
Iteration: 2046; Percent complete: 51.1%; Average loss: 3.2045
Iteration: 2047; Percent complete: 51.2%; Average loss: 3.3185
Iteration: 2048; Percent complete: 51.2%; Average loss: 3.1857
Iteration: 2049; Percent complete: 51.2%; Average loss: 3.2907
Iteration: 2050; Percent complete: 51.2%; Average loss: 3.0690
Iteration: 2051; Percent complete: 51.3%; Average loss: 3.3327
Iteration: 2052; Percent complete: 51.3%; Average loss: 3.3588
Iteration: 2053; Percent complete: 51.3%; Average loss: 3.3270
Iteration: 2054; Percent complete: 51.3%; Average loss: 3.2400
Iteration: 2055; Percent complete: 51.4%; Average loss: 3.3591
Iteration: 2056; Percent complete: 51.4%; Average loss: 3.1763
Iteration: 2057; Percent complete: 51.4%; Average loss: 3.0988
Iteration: 2058; Percent complete: 51.4%; Average loss: 3.0811
Iteration: 2059; Percent complete: 51.5%; Average loss: 3.0705
Iteration: 2060; Percent complete: 51.5%; Average loss: 3.1977
Iteration: 2061; Percent complete: 51.5%; Average loss: 3.2460
Iteration: 2062; Percent complete: 51.5%; Average loss: 3.1501
Iteration: 2063; Percent complete: 51.6%; Average loss: 3.5105
Iteration: 2064; Percent complete: 51.6%; Average loss: 3.2015
Iteration: 2065; Percent complete: 51.6%; Average loss: 3.3096
Iteration: 2066; Percent complete: 51.6%; Average loss: 3.0067
Iteration: 2067; Percent complete: 51.7%; Average loss: 3.3625
Iteration: 2068; Percent complete: 51.7%; Average loss: 3.2997
Iteration: 2069; Percent complete: 51.7%; Average loss: 3.0499
Iteration: 2070; Percent complete: 51.7%; Average loss: 3.1598
Iteration: 2071; Percent complete: 51.8%; Average loss: 3.0524
Iteration: 2072; Percent complete: 51.8%; Average loss: 2.8705
Iteration: 2073; Percent complete: 51.8%; Average loss: 3.1291
Iteration: 2074; Percent complete: 51.8%; Average loss: 3.3029
Iteration: 2075; Percent complete: 51.9%; Average loss: 3.1555
Iteration: 2076; Percent complete: 51.9%; Average loss: 2.8038
Iteration: 2077; Percent complete: 51.9%; Average loss: 3.0935
Iteration: 2078; Percent complete: 51.9%; Average loss: 3.1567
Iteration: 2079; Percent complete: 52.0%; Average loss: 3.1909
Iteration: 2080; Percent complete: 52.0%; Average loss: 3.1192
Iteration: 2081; Percent complete: 52.0%; Average loss: 3.1858
Iteration: 2082; Percent complete: 52.0%; Average loss: 3.0583
Iteration: 2083; Percent complete: 52.1%; Average loss: 3.1190
Iteration: 2084; Percent complete: 52.1%; Average loss: 3.0229
Iteration: 2085; Percent complete: 52.1%; Average loss: 2.9478
Iteration: 2086; Percent complete: 52.1%; Average loss: 3.1790
Iteration: 2087; Percent complete: 52.2%; Average loss: 3.1637
Iteration: 2088; Percent complete: 52.2%; Average loss: 3.0147
Iteration: 2089; Percent complete: 52.2%; Average loss: 3.0475
Iteration: 2090; Percent complete: 52.2%; Average loss: 3.1220
Iteration: 2091; Percent complete: 52.3%; Average loss: 3.1355
Iteration: 2092; Percent complete: 52.3%; Average loss: 3.1655
Iteration: 2093; Percent complete: 52.3%; Average loss: 3.3925
Iteration: 2094; Percent complete: 52.3%; Average loss: 3.1072
Iteration: 2095; Percent complete: 52.4%; Average loss: 3.1244
Iteration: 2096; Percent complete: 52.4%; Average loss: 3.2955
Iteration: 2097; Percent complete: 52.4%; Average loss: 3.0663
Iteration: 2098; Percent complete: 52.4%; Average loss: 3.0846
Iteration: 2099; Percent complete: 52.5%; Average loss: 3.4460
Iteration: 2100; Percent complete: 52.5%; Average loss: 2.9538
Iteration: 2101; Percent complete: 52.5%; Average loss: 3.1249
Iteration: 2102; Percent complete: 52.5%; Average loss: 3.0792
Iteration: 2103; Percent complete: 52.6%; Average loss: 3.2483
Iteration: 2104; Percent complete: 52.6%; Average loss: 3.0261
Iteration: 2105; Percent complete: 52.6%; Average loss: 3.2737
Iteration: 2106; Percent complete: 52.6%; Average loss: 3.3848
Iteration: 2107; Percent complete: 52.7%; Average loss: 3.2582
Iteration: 2108; Percent complete: 52.7%; Average loss: 3.1995
Iteration: 2109; Percent complete: 52.7%; Average loss: 2.9633
Iteration: 2110; Percent complete: 52.8%; Average loss: 3.3459
Iteration: 2111; Percent complete: 52.8%; Average loss: 3.1750
Iteration: 2112; Percent complete: 52.8%; Average loss: 2.9940
Iteration: 2113; Percent complete: 52.8%; Average loss: 3.2396
Iteration: 2114; Percent complete: 52.8%; Average loss: 3.0883
Iteration: 2115; Percent complete: 52.9%; Average loss: 2.8897
Iteration: 2116; Percent complete: 52.9%; Average loss: 3.1390
Iteration: 2117; Percent complete: 52.9%; Average loss: 2.9217
Iteration: 2118; Percent complete: 52.9%; Average loss: 3.1502
Iteration: 2119; Percent complete: 53.0%; Average loss: 2.8353
Iteration: 2120; Percent complete: 53.0%; Average loss: 2.9611
Iteration: 2121; Percent complete: 53.0%; Average loss: 2.9120
Iteration: 2122; Percent complete: 53.0%; Average loss: 3.4139
Iteration: 2123; Percent complete: 53.1%; Average loss: 3.5302
Iteration: 2124; Percent complete: 53.1%; Average loss: 3.1964
Iteration: 2125; Percent complete: 53.1%; Average loss: 3.4728
Iteration: 2126; Percent complete: 53.1%; Average loss: 3.0648
Iteration: 2127; Percent complete: 53.2%; Average loss: 2.9954
Iteration: 2128; Percent complete: 53.2%; Average loss: 3.3558
Iteration: 2129; Percent complete: 53.2%; Average loss: 3.0896
Iteration: 2130; Percent complete: 53.2%; Average loss: 2.6493
Iteration: 2131; Percent complete: 53.3%; Average loss: 3.0187
Iteration: 2132; Percent complete: 53.3%; Average loss: 2.9317
Iteration: 2133; Percent complete: 53.3%; Average loss: 3.2936
Iteration: 2134; Percent complete: 53.3%; Average loss: 3.2826
Iteration: 2135; Percent complete: 53.4%; Average loss: 3.1341
Iteration: 2136; Percent complete: 53.4%; Average loss: 2.9604
Iteration: 2137; Percent complete: 53.4%; Average loss: 3.2010
Iteration: 2138; Percent complete: 53.4%; Average loss: 3.0332
Iteration: 2139; Percent complete: 53.5%; Average loss: 2.9482
Iteration: 2140; Percent complete: 53.5%; Average loss: 3.1124
Iteration: 2141; Percent complete: 53.5%; Average loss: 3.1855
Iteration: 2142; Percent complete: 53.5%; Average loss: 3.1478
Iteration: 2143; Percent complete: 53.6%; Average loss: 3.5426
Iteration: 2144; Percent complete: 53.6%; Average loss: 3.1735
Iteration: 2145; Percent complete: 53.6%; Average loss: 3.0658
Iteration: 2146; Percent complete: 53.6%; Average loss: 3.1987
Iteration: 2147; Percent complete: 53.7%; Average loss: 3.3555
Iteration: 2148; Percent complete: 53.7%; Average loss: 3.3501
Iteration: 2149; Percent complete: 53.7%; Average loss: 2.9848
Iteration: 2150; Percent complete: 53.8%; Average loss: 3.0014
Iteration: 2151; Percent complete: 53.8%; Average loss: 3.2068
Iteration: 2152; Percent complete: 53.8%; Average loss: 3.0970
Iteration: 2153; Percent complete: 53.8%; Average loss: 2.9942
Iteration: 2154; Percent complete: 53.8%; Average loss: 2.9825
Iteration: 2155; Percent complete: 53.9%; Average loss: 3.2468
Iteration: 2156; Percent complete: 53.9%; Average loss: 3.2625
Iteration: 2157; Percent complete: 53.9%; Average loss: 2.9561
Iteration: 2158; Percent complete: 53.9%; Average loss: 3.0289
Iteration: 2159; Percent complete: 54.0%; Average loss: 2.9558
Iteration: 2160; Percent complete: 54.0%; Average loss: 3.1344
Iteration: 2161; Percent complete: 54.0%; Average loss: 3.1535
Iteration: 2162; Percent complete: 54.0%; Average loss: 2.9123
Iteration: 2163; Percent complete: 54.1%; Average loss: 3.4722
Iteration: 2164; Percent complete: 54.1%; Average loss: 3.1353
Iteration: 2165; Percent complete: 54.1%; Average loss: 3.2885
Iteration: 2166; Percent complete: 54.1%; Average loss: 3.1774
Iteration: 2167; Percent complete: 54.2%; Average loss: 3.1906
Iteration: 2168; Percent complete: 54.2%; Average loss: 3.1400
Iteration: 2169; Percent complete: 54.2%; Average loss: 3.0076
Iteration: 2170; Percent complete: 54.2%; Average loss: 3.2838
Iteration: 2171; Percent complete: 54.3%; Average loss: 3.2887
Iteration: 2172; Percent complete: 54.3%; Average loss: 2.8597
Iteration: 2173; Percent complete: 54.3%; Average loss: 3.1427
Iteration: 2174; Percent complete: 54.4%; Average loss: 3.2744
Iteration: 2175; Percent complete: 54.4%; Average loss: 3.0022
Iteration: 2176; Percent complete: 54.4%; Average loss: 3.0411
Iteration: 2177; Percent complete: 54.4%; Average loss: 3.0805
Iteration: 2178; Percent complete: 54.4%; Average loss: 3.0710
Iteration: 2179; Percent complete: 54.5%; Average loss: 3.2441
Iteration: 2180; Percent complete: 54.5%; Average loss: 3.1780
Iteration: 2181; Percent complete: 54.5%; Average loss: 3.3642
Iteration: 2182; Percent complete: 54.5%; Average loss: 3.1496
Iteration: 2183; Percent complete: 54.6%; Average loss: 3.3266
Iteration: 2184; Percent complete: 54.6%; Average loss: 3.1680
Iteration: 2185; Percent complete: 54.6%; Average loss: 3.2445
Iteration: 2186; Percent complete: 54.6%; Average loss: 3.1398
Iteration: 2187; Percent complete: 54.7%; Average loss: 3.2415
Iteration: 2188; Percent complete: 54.7%; Average loss: 3.0186
Iteration: 2189; Percent complete: 54.7%; Average loss: 3.2084
Iteration: 2190; Percent complete: 54.8%; Average loss: 3.0459
Iteration: 2191; Percent complete: 54.8%; Average loss: 3.1591
Iteration: 2192; Percent complete: 54.8%; Average loss: 3.1583
Iteration: 2193; Percent complete: 54.8%; Average loss: 3.4648
Iteration: 2194; Percent complete: 54.9%; Average loss: 3.0624
Iteration: 2195; Percent complete: 54.9%; Average loss: 2.9894
Iteration: 2196; Percent complete: 54.9%; Average loss: 3.1890
Iteration: 2197; Percent complete: 54.9%; Average loss: 3.0639
Iteration: 2198; Percent complete: 54.9%; Average loss: 2.8342
Iteration: 2199; Percent complete: 55.0%; Average loss: 3.0511
Iteration: 2200; Percent complete: 55.0%; Average loss: 3.1817
Iteration: 2201; Percent complete: 55.0%; Average loss: 3.0108
Iteration: 2202; Percent complete: 55.0%; Average loss: 3.0194
Iteration: 2203; Percent complete: 55.1%; Average loss: 3.0112
Iteration: 2204; Percent complete: 55.1%; Average loss: 3.1263
Iteration: 2205; Percent complete: 55.1%; Average loss: 3.4300
Iteration: 2206; Percent complete: 55.1%; Average loss: 3.3221
Iteration: 2207; Percent complete: 55.2%; Average loss: 3.2965
Iteration: 2208; Percent complete: 55.2%; Average loss: 3.1666
Iteration: 2209; Percent complete: 55.2%; Average loss: 3.2775
Iteration: 2210; Percent complete: 55.2%; Average loss: 3.2243
Iteration: 2211; Percent complete: 55.3%; Average loss: 3.1105
Iteration: 2212; Percent complete: 55.3%; Average loss: 2.9626
Iteration: 2213; Percent complete: 55.3%; Average loss: 3.2851
Iteration: 2214; Percent complete: 55.4%; Average loss: 3.2035
Iteration: 2215; Percent complete: 55.4%; Average loss: 3.0164
Iteration: 2216; Percent complete: 55.4%; Average loss: 2.9750
Iteration: 2217; Percent complete: 55.4%; Average loss: 3.0763
Iteration: 2218; Percent complete: 55.5%; Average loss: 3.0052
Iteration: 2219; Percent complete: 55.5%; Average loss: 3.0899
Iteration: 2220; Percent complete: 55.5%; Average loss: 3.0275
Iteration: 2221; Percent complete: 55.5%; Average loss: 3.1763
Iteration: 2222; Percent complete: 55.5%; Average loss: 3.0341
Iteration: 2223; Percent complete: 55.6%; Average loss: 3.3113
Iteration: 2224; Percent complete: 55.6%; Average loss: 3.1396
Iteration: 2225; Percent complete: 55.6%; Average loss: 3.0777
Iteration: 2226; Percent complete: 55.6%; Average loss: 3.1890
Iteration: 2227; Percent complete: 55.7%; Average loss: 2.8324
Iteration: 2228; Percent complete: 55.7%; Average loss: 3.1128
Iteration: 2229; Percent complete: 55.7%; Average loss: 3.3010
Iteration: 2230; Percent complete: 55.8%; Average loss: 3.1110
Iteration: 2231; Percent complete: 55.8%; Average loss: 3.0423
Iteration: 2232; Percent complete: 55.8%; Average loss: 2.9593
Iteration: 2233; Percent complete: 55.8%; Average loss: 3.0175
Iteration: 2234; Percent complete: 55.9%; Average loss: 3.1685
Iteration: 2235; Percent complete: 55.9%; Average loss: 3.3077
Iteration: 2236; Percent complete: 55.9%; Average loss: 3.0904
Iteration: 2237; Percent complete: 55.9%; Average loss: 3.2730
Iteration: 2238; Percent complete: 56.0%; Average loss: 3.4541
Iteration: 2239; Percent complete: 56.0%; Average loss: 2.8738
Iteration: 2240; Percent complete: 56.0%; Average loss: 3.1644
Iteration: 2241; Percent complete: 56.0%; Average loss: 3.3990
Iteration: 2242; Percent complete: 56.0%; Average loss: 2.9961
Iteration: 2243; Percent complete: 56.1%; Average loss: 2.9699
Iteration: 2244; Percent complete: 56.1%; Average loss: 3.2008
Iteration: 2245; Percent complete: 56.1%; Average loss: 3.1083
Iteration: 2246; Percent complete: 56.1%; Average loss: 3.1241
Iteration: 2247; Percent complete: 56.2%; Average loss: 2.9343
Iteration: 2248; Percent complete: 56.2%; Average loss: 3.1477
Iteration: 2249; Percent complete: 56.2%; Average loss: 3.2771
Iteration: 2250; Percent complete: 56.2%; Average loss: 2.9443
Iteration: 2251; Percent complete: 56.3%; Average loss: 3.3670
Iteration: 2252; Percent complete: 56.3%; Average loss: 3.1656
Iteration: 2253; Percent complete: 56.3%; Average loss: 2.8650
Iteration: 2254; Percent complete: 56.4%; Average loss: 3.1727
Iteration: 2255; Percent complete: 56.4%; Average loss: 3.0879
Iteration: 2256; Percent complete: 56.4%; Average loss: 3.1704
Iteration: 2257; Percent complete: 56.4%; Average loss: 3.1444
Iteration: 2258; Percent complete: 56.5%; Average loss: 3.0964
Iteration: 2259; Percent complete: 56.5%; Average loss: 3.2500
Iteration: 2260; Percent complete: 56.5%; Average loss: 3.0931
Iteration: 2261; Percent complete: 56.5%; Average loss: 3.4189
Iteration: 2262; Percent complete: 56.5%; Average loss: 3.3713
Iteration: 2263; Percent complete: 56.6%; Average loss: 3.1273
Iteration: 2264; Percent complete: 56.6%; Average loss: 3.1218
Iteration: 2265; Percent complete: 56.6%; Average loss: 2.9865
Iteration: 2266; Percent complete: 56.6%; Average loss: 3.0152
Iteration: 2267; Percent complete: 56.7%; Average loss: 3.1642
Iteration: 2268; Percent complete: 56.7%; Average loss: 3.1318
Iteration: 2269; Percent complete: 56.7%; Average loss: 2.8805
Iteration: 2270; Percent complete: 56.8%; Average loss: 3.1874
Iteration: 2271; Percent complete: 56.8%; Average loss: 3.1170
Iteration: 2272; Percent complete: 56.8%; Average loss: 3.1810
Iteration: 2273; Percent complete: 56.8%; Average loss: 3.0697
Iteration: 2274; Percent complete: 56.9%; Average loss: 3.3038
Iteration: 2275; Percent complete: 56.9%; Average loss: 2.9871
Iteration: 2276; Percent complete: 56.9%; Average loss: 3.1023
Iteration: 2277; Percent complete: 56.9%; Average loss: 3.3132
Iteration: 2278; Percent complete: 57.0%; Average loss: 2.8669
Iteration: 2279; Percent complete: 57.0%; Average loss: 3.1231
Iteration: 2280; Percent complete: 57.0%; Average loss: 2.9772
Iteration: 2281; Percent complete: 57.0%; Average loss: 3.0014
Iteration: 2282; Percent complete: 57.0%; Average loss: 3.2989
Iteration: 2283; Percent complete: 57.1%; Average loss: 3.1339
Iteration: 2284; Percent complete: 57.1%; Average loss: 3.0194
Iteration: 2285; Percent complete: 57.1%; Average loss: 2.9458
Iteration: 2286; Percent complete: 57.1%; Average loss: 3.2723
Iteration: 2287; Percent complete: 57.2%; Average loss: 3.0809
Iteration: 2288; Percent complete: 57.2%; Average loss: 3.2826
Iteration: 2289; Percent complete: 57.2%; Average loss: 3.1645
Iteration: 2290; Percent complete: 57.2%; Average loss: 2.9562
Iteration: 2291; Percent complete: 57.3%; Average loss: 3.1913
Iteration: 2292; Percent complete: 57.3%; Average loss: 3.1210
Iteration: 2293; Percent complete: 57.3%; Average loss: 3.3535
Iteration: 2294; Percent complete: 57.4%; Average loss: 2.8536
Iteration: 2295; Percent complete: 57.4%; Average loss: 2.9815
Iteration: 2296; Percent complete: 57.4%; Average loss: 3.3305
Iteration: 2297; Percent complete: 57.4%; Average loss: 3.1179
Iteration: 2298; Percent complete: 57.5%; Average loss: 3.1868
Iteration: 2299; Percent complete: 57.5%; Average loss: 3.2079
Iteration: 2300; Percent complete: 57.5%; Average loss: 3.1937
Iteration: 2301; Percent complete: 57.5%; Average loss: 3.2766
Iteration: 2302; Percent complete: 57.6%; Average loss: 3.0784
Iteration: 2303; Percent complete: 57.6%; Average loss: 3.0738
Iteration: 2304; Percent complete: 57.6%; Average loss: 2.9818
Iteration: 2305; Percent complete: 57.6%; Average loss: 3.0106
Iteration: 2306; Percent complete: 57.6%; Average loss: 3.1849
Iteration: 2307; Percent complete: 57.7%; Average loss: 3.2167
Iteration: 2308; Percent complete: 57.7%; Average loss: 2.9762
Iteration: 2309; Percent complete: 57.7%; Average loss: 3.0219
Iteration: 2310; Percent complete: 57.8%; Average loss: 3.2110
Iteration: 2311; Percent complete: 57.8%; Average loss: 2.9457
Iteration: 2312; Percent complete: 57.8%; Average loss: 2.8686
Iteration: 2313; Percent complete: 57.8%; Average loss: 3.3935
Iteration: 2314; Percent complete: 57.9%; Average loss: 3.3247
Iteration: 2315; Percent complete: 57.9%; Average loss: 2.8038
Iteration: 2316; Percent complete: 57.9%; Average loss: 3.0803
Iteration: 2317; Percent complete: 57.9%; Average loss: 3.3807
Iteration: 2318; Percent complete: 58.0%; Average loss: 3.2669
Iteration: 2319; Percent complete: 58.0%; Average loss: 3.4243
Iteration: 2320; Percent complete: 58.0%; Average loss: 2.7431
Iteration: 2321; Percent complete: 58.0%; Average loss: 2.8132
Iteration: 2322; Percent complete: 58.1%; Average loss: 3.1488
Iteration: 2323; Percent complete: 58.1%; Average loss: 3.1732
Iteration: 2324; Percent complete: 58.1%; Average loss: 2.7837
Iteration: 2325; Percent complete: 58.1%; Average loss: 2.9533
Iteration: 2326; Percent complete: 58.1%; Average loss: 3.3243
Iteration: 2327; Percent complete: 58.2%; Average loss: 3.0427
Iteration: 2328; Percent complete: 58.2%; Average loss: 2.9238
Iteration: 2329; Percent complete: 58.2%; Average loss: 3.0009
Iteration: 2330; Percent complete: 58.2%; Average loss: 3.1011
Iteration: 2331; Percent complete: 58.3%; Average loss: 3.0776
Iteration: 2332; Percent complete: 58.3%; Average loss: 2.9446
Iteration: 2333; Percent complete: 58.3%; Average loss: 3.3423
Iteration: 2334; Percent complete: 58.4%; Average loss: 3.0241
Iteration: 2335; Percent complete: 58.4%; Average loss: 3.2601
Iteration: 2336; Percent complete: 58.4%; Average loss: 2.9144
Iteration: 2337; Percent complete: 58.4%; Average loss: 2.9375
Iteration: 2338; Percent complete: 58.5%; Average loss: 3.0115
Iteration: 2339; Percent complete: 58.5%; Average loss: 3.2284
Iteration: 2340; Percent complete: 58.5%; Average loss: 3.1393
Iteration: 2341; Percent complete: 58.5%; Average loss: 3.1856
Iteration: 2342; Percent complete: 58.6%; Average loss: 3.0601
Iteration: 2343; Percent complete: 58.6%; Average loss: 3.3893
Iteration: 2344; Percent complete: 58.6%; Average loss: 2.9363
Iteration: 2345; Percent complete: 58.6%; Average loss: 2.8144
Iteration: 2346; Percent complete: 58.7%; Average loss: 3.1052
Iteration: 2347; Percent complete: 58.7%; Average loss: 3.1068
Iteration: 2348; Percent complete: 58.7%; Average loss: 3.0633
Iteration: 2349; Percent complete: 58.7%; Average loss: 3.0822
Iteration: 2350; Percent complete: 58.8%; Average loss: 3.3801
Iteration: 2351; Percent complete: 58.8%; Average loss: 3.1487
Iteration: 2352; Percent complete: 58.8%; Average loss: 3.3075
Iteration: 2353; Percent complete: 58.8%; Average loss: 3.2599
Iteration: 2354; Percent complete: 58.9%; Average loss: 3.2315
Iteration: 2355; Percent complete: 58.9%; Average loss: 3.0783
Iteration: 2356; Percent complete: 58.9%; Average loss: 3.1895
Iteration: 2357; Percent complete: 58.9%; Average loss: 3.0168
Iteration: 2358; Percent complete: 59.0%; Average loss: 3.0993
Iteration: 2359; Percent complete: 59.0%; Average loss: 2.8502
Iteration: 2360; Percent complete: 59.0%; Average loss: 3.1471
Iteration: 2361; Percent complete: 59.0%; Average loss: 3.0050
Iteration: 2362; Percent complete: 59.1%; Average loss: 2.9810
Iteration: 2363; Percent complete: 59.1%; Average loss: 3.0075
Iteration: 2364; Percent complete: 59.1%; Average loss: 3.1816
Iteration: 2365; Percent complete: 59.1%; Average loss: 3.0168
Iteration: 2366; Percent complete: 59.2%; Average loss: 2.8941
Iteration: 2367; Percent complete: 59.2%; Average loss: 3.1189
Iteration: 2368; Percent complete: 59.2%; Average loss: 3.0381
Iteration: 2369; Percent complete: 59.2%; Average loss: 3.0727
Iteration: 2370; Percent complete: 59.2%; Average loss: 3.0233
Iteration: 2371; Percent complete: 59.3%; Average loss: 3.1429
Iteration: 2372; Percent complete: 59.3%; Average loss: 3.0469
Iteration: 2373; Percent complete: 59.3%; Average loss: 3.4109
Iteration: 2374; Percent complete: 59.4%; Average loss: 2.9498
Iteration: 2375; Percent complete: 59.4%; Average loss: 3.1704
Iteration: 2376; Percent complete: 59.4%; Average loss: 3.4573
Iteration: 2377; Percent complete: 59.4%; Average loss: 3.0918
Iteration: 2378; Percent complete: 59.5%; Average loss: 3.0713
Iteration: 2379; Percent complete: 59.5%; Average loss: 3.2027
Iteration: 2380; Percent complete: 59.5%; Average loss: 3.0683
Iteration: 2381; Percent complete: 59.5%; Average loss: 2.8731
Iteration: 2382; Percent complete: 59.6%; Average loss: 3.0782
Iteration: 2383; Percent complete: 59.6%; Average loss: 2.9267
Iteration: 2384; Percent complete: 59.6%; Average loss: 3.1722
Iteration: 2385; Percent complete: 59.6%; Average loss: 3.2633
Iteration: 2386; Percent complete: 59.7%; Average loss: 3.0276
Iteration: 2387; Percent complete: 59.7%; Average loss: 3.0497
Iteration: 2388; Percent complete: 59.7%; Average loss: 2.8373
Iteration: 2389; Percent complete: 59.7%; Average loss: 3.0477
Iteration: 2390; Percent complete: 59.8%; Average loss: 3.1061
Iteration: 2391; Percent complete: 59.8%; Average loss: 3.1265
Iteration: 2392; Percent complete: 59.8%; Average loss: 3.0646
Iteration: 2393; Percent complete: 59.8%; Average loss: 2.9478
Iteration: 2394; Percent complete: 59.9%; Average loss: 3.0043
Iteration: 2395; Percent complete: 59.9%; Average loss: 3.1154
Iteration: 2396; Percent complete: 59.9%; Average loss: 2.8862
Iteration: 2397; Percent complete: 59.9%; Average loss: 3.2657
Iteration: 2398; Percent complete: 60.0%; Average loss: 2.9011
Iteration: 2399; Percent complete: 60.0%; Average loss: 2.6127
Iteration: 2400; Percent complete: 60.0%; Average loss: 3.3889
Iteration: 2401; Percent complete: 60.0%; Average loss: 3.1685
Iteration: 2402; Percent complete: 60.1%; Average loss: 3.2973
Iteration: 2403; Percent complete: 60.1%; Average loss: 3.0369
Iteration: 2404; Percent complete: 60.1%; Average loss: 3.2858
Iteration: 2405; Percent complete: 60.1%; Average loss: 2.9427
Iteration: 2406; Percent complete: 60.2%; Average loss: 3.1143
Iteration: 2407; Percent complete: 60.2%; Average loss: 3.2201
Iteration: 2408; Percent complete: 60.2%; Average loss: 3.2545
Iteration: 2409; Percent complete: 60.2%; Average loss: 3.0780
Iteration: 2410; Percent complete: 60.2%; Average loss: 3.3364
Iteration: 2411; Percent complete: 60.3%; Average loss: 2.8796
Iteration: 2412; Percent complete: 60.3%; Average loss: 3.0610
Iteration: 2413; Percent complete: 60.3%; Average loss: 3.2235
Iteration: 2414; Percent complete: 60.4%; Average loss: 3.0266
Iteration: 2415; Percent complete: 60.4%; Average loss: 3.1772
Iteration: 2416; Percent complete: 60.4%; Average loss: 3.0513
Iteration: 2417; Percent complete: 60.4%; Average loss: 3.1813
Iteration: 2418; Percent complete: 60.5%; Average loss: 2.8906
Iteration: 2419; Percent complete: 60.5%; Average loss: 3.0850
Iteration: 2420; Percent complete: 60.5%; Average loss: 3.1372
Iteration: 2421; Percent complete: 60.5%; Average loss: 3.0729
Iteration: 2422; Percent complete: 60.6%; Average loss: 2.9392
Iteration: 2423; Percent complete: 60.6%; Average loss: 3.2199
Iteration: 2424; Percent complete: 60.6%; Average loss: 2.9078
Iteration: 2425; Percent complete: 60.6%; Average loss: 2.7599
Iteration: 2426; Percent complete: 60.7%; Average loss: 3.0554
Iteration: 2427; Percent complete: 60.7%; Average loss: 2.9297
Iteration: 2428; Percent complete: 60.7%; Average loss: 3.2584
Iteration: 2429; Percent complete: 60.7%; Average loss: 2.9356
Iteration: 2430; Percent complete: 60.8%; Average loss: 3.0567
Iteration: 2431; Percent complete: 60.8%; Average loss: 3.0075
Iteration: 2432; Percent complete: 60.8%; Average loss: 3.0339
Iteration: 2433; Percent complete: 60.8%; Average loss: 3.1319
Iteration: 2434; Percent complete: 60.9%; Average loss: 3.1034
Iteration: 2435; Percent complete: 60.9%; Average loss: 3.0336
Iteration: 2436; Percent complete: 60.9%; Average loss: 3.1599
Iteration: 2437; Percent complete: 60.9%; Average loss: 3.0148
Iteration: 2438; Percent complete: 61.0%; Average loss: 3.1923
Iteration: 2439; Percent complete: 61.0%; Average loss: 3.0770
Iteration: 2440; Percent complete: 61.0%; Average loss: 3.0363
Iteration: 2441; Percent complete: 61.0%; Average loss: 3.0803
Iteration: 2442; Percent complete: 61.1%; Average loss: 3.0201
Iteration: 2443; Percent complete: 61.1%; Average loss: 3.1167
Iteration: 2444; Percent complete: 61.1%; Average loss: 3.2212
Iteration: 2445; Percent complete: 61.1%; Average loss: 3.0552
Iteration: 2446; Percent complete: 61.2%; Average loss: 3.1656
Iteration: 2447; Percent complete: 61.2%; Average loss: 3.0604
Iteration: 2448; Percent complete: 61.2%; Average loss: 3.3394
Iteration: 2449; Percent complete: 61.2%; Average loss: 2.8477
Iteration: 2450; Percent complete: 61.3%; Average loss: 2.9159
Iteration: 2451; Percent complete: 61.3%; Average loss: 3.0371
Iteration: 2452; Percent complete: 61.3%; Average loss: 3.1471
Iteration: 2453; Percent complete: 61.3%; Average loss: 3.0060
Iteration: 2454; Percent complete: 61.4%; Average loss: 3.2173
Iteration: 2455; Percent complete: 61.4%; Average loss: 2.9931
Iteration: 2456; Percent complete: 61.4%; Average loss: 3.1396
Iteration: 2457; Percent complete: 61.4%; Average loss: 2.8938
Iteration: 2458; Percent complete: 61.5%; Average loss: 3.2279
Iteration: 2459; Percent complete: 61.5%; Average loss: 3.2810
Iteration: 2460; Percent complete: 61.5%; Average loss: 3.0545
Iteration: 2461; Percent complete: 61.5%; Average loss: 2.8797
Iteration: 2462; Percent complete: 61.6%; Average loss: 3.0271
Iteration: 2463; Percent complete: 61.6%; Average loss: 3.0354
Iteration: 2464; Percent complete: 61.6%; Average loss: 3.1606
Iteration: 2465; Percent complete: 61.6%; Average loss: 3.1897
Iteration: 2466; Percent complete: 61.7%; Average loss: 3.1204
Iteration: 2467; Percent complete: 61.7%; Average loss: 2.8659
Iteration: 2468; Percent complete: 61.7%; Average loss: 3.4217
Iteration: 2469; Percent complete: 61.7%; Average loss: 3.1433
Iteration: 2470; Percent complete: 61.8%; Average loss: 2.8017
Iteration: 2471; Percent complete: 61.8%; Average loss: 3.1330
Iteration: 2472; Percent complete: 61.8%; Average loss: 2.9128
Iteration: 2473; Percent complete: 61.8%; Average loss: 3.0400
Iteration: 2474; Percent complete: 61.9%; Average loss: 3.1314
Iteration: 2475; Percent complete: 61.9%; Average loss: 2.9130
Iteration: 2476; Percent complete: 61.9%; Average loss: 3.1358
Iteration: 2477; Percent complete: 61.9%; Average loss: 2.7385
Iteration: 2478; Percent complete: 62.0%; Average loss: 2.9948
Iteration: 2479; Percent complete: 62.0%; Average loss: 3.2456
Iteration: 2480; Percent complete: 62.0%; Average loss: 2.8981
Iteration: 2481; Percent complete: 62.0%; Average loss: 2.9926
Iteration: 2482; Percent complete: 62.1%; Average loss: 2.8133
Iteration: 2483; Percent complete: 62.1%; Average loss: 3.2217
Iteration: 2484; Percent complete: 62.1%; Average loss: 3.0710
Iteration: 2485; Percent complete: 62.1%; Average loss: 3.0138
Iteration: 2486; Percent complete: 62.2%; Average loss: 3.2565
Iteration: 2487; Percent complete: 62.2%; Average loss: 2.9284
Iteration: 2488; Percent complete: 62.2%; Average loss: 3.1488
Iteration: 2489; Percent complete: 62.2%; Average loss: 2.8936
Iteration: 2490; Percent complete: 62.3%; Average loss: 3.4841
Iteration: 2491; Percent complete: 62.3%; Average loss: 3.0278
Iteration: 2492; Percent complete: 62.3%; Average loss: 2.9257
Iteration: 2493; Percent complete: 62.3%; Average loss: 3.1068
Iteration: 2494; Percent complete: 62.4%; Average loss: 3.1899
Iteration: 2495; Percent complete: 62.4%; Average loss: 3.1355
Iteration: 2496; Percent complete: 62.4%; Average loss: 2.8128
Iteration: 2497; Percent complete: 62.4%; Average loss: 3.0159
Iteration: 2498; Percent complete: 62.5%; Average loss: 3.1015
Iteration: 2499; Percent complete: 62.5%; Average loss: 2.9614
Iteration: 2500; Percent complete: 62.5%; Average loss: 3.0279
Iteration: 2501; Percent complete: 62.5%; Average loss: 2.9974
Iteration: 2502; Percent complete: 62.5%; Average loss: 3.0308
Iteration: 2503; Percent complete: 62.6%; Average loss: 3.1698
Iteration: 2504; Percent complete: 62.6%; Average loss: 3.0771
Iteration: 2505; Percent complete: 62.6%; Average loss: 3.2013
Iteration: 2506; Percent complete: 62.6%; Average loss: 3.0436
Iteration: 2507; Percent complete: 62.7%; Average loss: 2.9936
Iteration: 2508; Percent complete: 62.7%; Average loss: 3.1721
Iteration: 2509; Percent complete: 62.7%; Average loss: 2.9988
Iteration: 2510; Percent complete: 62.7%; Average loss: 3.0431
Iteration: 2511; Percent complete: 62.8%; Average loss: 3.1017
Iteration: 2512; Percent complete: 62.8%; Average loss: 3.0667
Iteration: 2513; Percent complete: 62.8%; Average loss: 3.0153
Iteration: 2514; Percent complete: 62.8%; Average loss: 2.8358
Iteration: 2515; Percent complete: 62.9%; Average loss: 3.0134
Iteration: 2516; Percent complete: 62.9%; Average loss: 3.2455
Iteration: 2517; Percent complete: 62.9%; Average loss: 2.9227
Iteration: 2518; Percent complete: 62.9%; Average loss: 3.2278
Iteration: 2519; Percent complete: 63.0%; Average loss: 3.0986
Iteration: 2520; Percent complete: 63.0%; Average loss: 2.9547
Iteration: 2521; Percent complete: 63.0%; Average loss: 2.9508
Iteration: 2522; Percent complete: 63.0%; Average loss: 3.1411
Iteration: 2523; Percent complete: 63.1%; Average loss: 2.9226
Iteration: 2524; Percent complete: 63.1%; Average loss: 2.7727
Iteration: 2525; Percent complete: 63.1%; Average loss: 3.3352
Iteration: 2526; Percent complete: 63.1%; Average loss: 2.9737
Iteration: 2527; Percent complete: 63.2%; Average loss: 3.2434
Iteration: 2528; Percent complete: 63.2%; Average loss: 3.1314
Iteration: 2529; Percent complete: 63.2%; Average loss: 2.9952
Iteration: 2530; Percent complete: 63.2%; Average loss: 3.1585
Iteration: 2531; Percent complete: 63.3%; Average loss: 2.9937
Iteration: 2532; Percent complete: 63.3%; Average loss: 3.1264
Iteration: 2533; Percent complete: 63.3%; Average loss: 3.0298
Iteration: 2534; Percent complete: 63.3%; Average loss: 3.1254
Iteration: 2535; Percent complete: 63.4%; Average loss: 3.3127
Iteration: 2536; Percent complete: 63.4%; Average loss: 3.0640
Iteration: 2537; Percent complete: 63.4%; Average loss: 3.0547
Iteration: 2538; Percent complete: 63.4%; Average loss: 2.7863
Iteration: 2539; Percent complete: 63.5%; Average loss: 3.2282
Iteration: 2540; Percent complete: 63.5%; Average loss: 2.9660
Iteration: 2541; Percent complete: 63.5%; Average loss: 3.0181
Iteration: 2542; Percent complete: 63.5%; Average loss: 2.9620
Iteration: 2543; Percent complete: 63.6%; Average loss: 2.9828
Iteration: 2544; Percent complete: 63.6%; Average loss: 2.9941
Iteration: 2545; Percent complete: 63.6%; Average loss: 3.1742
Iteration: 2546; Percent complete: 63.6%; Average loss: 3.1747
Iteration: 2547; Percent complete: 63.7%; Average loss: 2.8280
Iteration: 2548; Percent complete: 63.7%; Average loss: 3.0761
Iteration: 2549; Percent complete: 63.7%; Average loss: 2.9968
Iteration: 2550; Percent complete: 63.7%; Average loss: 2.9896
Iteration: 2551; Percent complete: 63.8%; Average loss: 3.1476
Iteration: 2552; Percent complete: 63.8%; Average loss: 2.8473
Iteration: 2553; Percent complete: 63.8%; Average loss: 3.1473
Iteration: 2554; Percent complete: 63.8%; Average loss: 2.6819
Iteration: 2555; Percent complete: 63.9%; Average loss: 3.0531
Iteration: 2556; Percent complete: 63.9%; Average loss: 3.1034
Iteration: 2557; Percent complete: 63.9%; Average loss: 2.7053
Iteration: 2558; Percent complete: 63.9%; Average loss: 2.8609
Iteration: 2559; Percent complete: 64.0%; Average loss: 3.0403
Iteration: 2560; Percent complete: 64.0%; Average loss: 2.9141
Iteration: 2561; Percent complete: 64.0%; Average loss: 2.9346
Iteration: 2562; Percent complete: 64.0%; Average loss: 3.0399
Iteration: 2563; Percent complete: 64.1%; Average loss: 2.9423
Iteration: 2564; Percent complete: 64.1%; Average loss: 2.9022
Iteration: 2565; Percent complete: 64.1%; Average loss: 3.0325
Iteration: 2566; Percent complete: 64.1%; Average loss: 3.0821
Iteration: 2567; Percent complete: 64.2%; Average loss: 3.1723
Iteration: 2568; Percent complete: 64.2%; Average loss: 3.1530
Iteration: 2569; Percent complete: 64.2%; Average loss: 3.1622
Iteration: 2570; Percent complete: 64.2%; Average loss: 3.2254
Iteration: 2571; Percent complete: 64.3%; Average loss: 3.0827
Iteration: 2572; Percent complete: 64.3%; Average loss: 3.2680
Iteration: 2573; Percent complete: 64.3%; Average loss: 3.0754
Iteration: 2574; Percent complete: 64.3%; Average loss: 2.9957
Iteration: 2575; Percent complete: 64.4%; Average loss: 2.9657
Iteration: 2576; Percent complete: 64.4%; Average loss: 2.9758
Iteration: 2577; Percent complete: 64.4%; Average loss: 3.0416
Iteration: 2578; Percent complete: 64.5%; Average loss: 2.9515
Iteration: 2579; Percent complete: 64.5%; Average loss: 3.2366
Iteration: 2580; Percent complete: 64.5%; Average loss: 3.3207
Iteration: 2581; Percent complete: 64.5%; Average loss: 2.9808
Iteration: 2582; Percent complete: 64.5%; Average loss: 2.9460
Iteration: 2583; Percent complete: 64.6%; Average loss: 3.1515
Iteration: 2584; Percent complete: 64.6%; Average loss: 3.0578
Iteration: 2585; Percent complete: 64.6%; Average loss: 3.1073
Iteration: 2586; Percent complete: 64.6%; Average loss: 3.0915
Iteration: 2587; Percent complete: 64.7%; Average loss: 2.9438
Iteration: 2588; Percent complete: 64.7%; Average loss: 2.9327
Iteration: 2589; Percent complete: 64.7%; Average loss: 3.1424
Iteration: 2590; Percent complete: 64.8%; Average loss: 3.1945
Iteration: 2591; Percent complete: 64.8%; Average loss: 3.0849
Iteration: 2592; Percent complete: 64.8%; Average loss: 2.9651
Iteration: 2593; Percent complete: 64.8%; Average loss: 2.8714
Iteration: 2594; Percent complete: 64.8%; Average loss: 3.0525
Iteration: 2595; Percent complete: 64.9%; Average loss: 3.3324
Iteration: 2596; Percent complete: 64.9%; Average loss: 2.9806
Iteration: 2597; Percent complete: 64.9%; Average loss: 3.0434
Iteration: 2598; Percent complete: 65.0%; Average loss: 3.0718
Iteration: 2599; Percent complete: 65.0%; Average loss: 2.6306
Iteration: 2600; Percent complete: 65.0%; Average loss: 2.9892
Iteration: 2601; Percent complete: 65.0%; Average loss: 2.8377
Iteration: 2602; Percent complete: 65.0%; Average loss: 2.8847
Iteration: 2603; Percent complete: 65.1%; Average loss: 2.9119
Iteration: 2604; Percent complete: 65.1%; Average loss: 2.9746
Iteration: 2605; Percent complete: 65.1%; Average loss: 3.2200
Iteration: 2606; Percent complete: 65.1%; Average loss: 2.9605
Iteration: 2607; Percent complete: 65.2%; Average loss: 2.9272
Iteration: 2608; Percent complete: 65.2%; Average loss: 2.8177
Iteration: 2609; Percent complete: 65.2%; Average loss: 3.0208
Iteration: 2610; Percent complete: 65.2%; Average loss: 3.2486
Iteration: 2611; Percent complete: 65.3%; Average loss: 3.0459
Iteration: 2612; Percent complete: 65.3%; Average loss: 2.9585
Iteration: 2613; Percent complete: 65.3%; Average loss: 3.0142
Iteration: 2614; Percent complete: 65.3%; Average loss: 2.8022
Iteration: 2615; Percent complete: 65.4%; Average loss: 2.8728
Iteration: 2616; Percent complete: 65.4%; Average loss: 2.9211
Iteration: 2617; Percent complete: 65.4%; Average loss: 3.0922
Iteration: 2618; Percent complete: 65.5%; Average loss: 2.9753
Iteration: 2619; Percent complete: 65.5%; Average loss: 2.8518
Iteration: 2620; Percent complete: 65.5%; Average loss: 3.1732
Iteration: 2621; Percent complete: 65.5%; Average loss: 2.9862
Iteration: 2622; Percent complete: 65.5%; Average loss: 3.0293
Iteration: 2623; Percent complete: 65.6%; Average loss: 3.0974
Iteration: 2624; Percent complete: 65.6%; Average loss: 2.9087
Iteration: 2625; Percent complete: 65.6%; Average loss: 3.1166
Iteration: 2626; Percent complete: 65.6%; Average loss: 3.1031
Iteration: 2627; Percent complete: 65.7%; Average loss: 3.1431
Iteration: 2628; Percent complete: 65.7%; Average loss: 3.2895
Iteration: 2629; Percent complete: 65.7%; Average loss: 2.9923
Iteration: 2630; Percent complete: 65.8%; Average loss: 3.4274
Iteration: 2631; Percent complete: 65.8%; Average loss: 2.8743
Iteration: 2632; Percent complete: 65.8%; Average loss: 2.8761
Iteration: 2633; Percent complete: 65.8%; Average loss: 2.7070
Iteration: 2634; Percent complete: 65.8%; Average loss: 2.8664
Iteration: 2635; Percent complete: 65.9%; Average loss: 2.9115
Iteration: 2636; Percent complete: 65.9%; Average loss: 2.8040
Iteration: 2637; Percent complete: 65.9%; Average loss: 2.8364
Iteration: 2638; Percent complete: 66.0%; Average loss: 3.1612
Iteration: 2639; Percent complete: 66.0%; Average loss: 2.9815
Iteration: 2640; Percent complete: 66.0%; Average loss: 2.9529
Iteration: 2641; Percent complete: 66.0%; Average loss: 3.1192
Iteration: 2642; Percent complete: 66.0%; Average loss: 3.0830
Iteration: 2643; Percent complete: 66.1%; Average loss: 2.8620
Iteration: 2644; Percent complete: 66.1%; Average loss: 3.1118
Iteration: 2645; Percent complete: 66.1%; Average loss: 2.9636
Iteration: 2646; Percent complete: 66.1%; Average loss: 2.8573
Iteration: 2647; Percent complete: 66.2%; Average loss: 2.9165
Iteration: 2648; Percent complete: 66.2%; Average loss: 3.2484
Iteration: 2649; Percent complete: 66.2%; Average loss: 2.8322
Iteration: 2650; Percent complete: 66.2%; Average loss: 2.9639
Iteration: 2651; Percent complete: 66.3%; Average loss: 3.1059
Iteration: 2652; Percent complete: 66.3%; Average loss: 3.2212
Iteration: 2653; Percent complete: 66.3%; Average loss: 3.0661
Iteration: 2654; Percent complete: 66.3%; Average loss: 3.1055
Iteration: 2655; Percent complete: 66.4%; Average loss: 3.0385
Iteration: 2656; Percent complete: 66.4%; Average loss: 3.1344
Iteration: 2657; Percent complete: 66.4%; Average loss: 2.9862
Iteration: 2658; Percent complete: 66.5%; Average loss: 2.9242
Iteration: 2659; Percent complete: 66.5%; Average loss: 3.0752
Iteration: 2660; Percent complete: 66.5%; Average loss: 3.1033
Iteration: 2661; Percent complete: 66.5%; Average loss: 3.2007
Iteration: 2662; Percent complete: 66.5%; Average loss: 3.1384
Iteration: 2663; Percent complete: 66.6%; Average loss: 2.9115
Iteration: 2664; Percent complete: 66.6%; Average loss: 3.2197
Iteration: 2665; Percent complete: 66.6%; Average loss: 3.2150
Iteration: 2666; Percent complete: 66.6%; Average loss: 2.9844
Iteration: 2667; Percent complete: 66.7%; Average loss: 3.1125
Iteration: 2668; Percent complete: 66.7%; Average loss: 3.0823
Iteration: 2669; Percent complete: 66.7%; Average loss: 3.0242
Iteration: 2670; Percent complete: 66.8%; Average loss: 2.9833
Iteration: 2671; Percent complete: 66.8%; Average loss: 2.6259
Iteration: 2672; Percent complete: 66.8%; Average loss: 3.0243
Iteration: 2673; Percent complete: 66.8%; Average loss: 3.0077
Iteration: 2674; Percent complete: 66.8%; Average loss: 2.7978
Iteration: 2675; Percent complete: 66.9%; Average loss: 3.1722
Iteration: 2676; Percent complete: 66.9%; Average loss: 3.0787
Iteration: 2677; Percent complete: 66.9%; Average loss: 2.8835
Iteration: 2678; Percent complete: 67.0%; Average loss: 2.7733
Iteration: 2679; Percent complete: 67.0%; Average loss: 3.0799
Iteration: 2680; Percent complete: 67.0%; Average loss: 3.0629
Iteration: 2681; Percent complete: 67.0%; Average loss: 2.8475
Iteration: 2682; Percent complete: 67.0%; Average loss: 2.7908
Iteration: 2683; Percent complete: 67.1%; Average loss: 3.1692
Iteration: 2684; Percent complete: 67.1%; Average loss: 2.9146
Iteration: 2685; Percent complete: 67.1%; Average loss: 3.3120
Iteration: 2686; Percent complete: 67.2%; Average loss: 2.9868
Iteration: 2687; Percent complete: 67.2%; Average loss: 2.7864
Iteration: 2688; Percent complete: 67.2%; Average loss: 3.1011
Iteration: 2689; Percent complete: 67.2%; Average loss: 3.1679
Iteration: 2690; Percent complete: 67.2%; Average loss: 2.8192
Iteration: 2691; Percent complete: 67.3%; Average loss: 2.9811
Iteration: 2692; Percent complete: 67.3%; Average loss: 2.8009
Iteration: 2693; Percent complete: 67.3%; Average loss: 2.9534
Iteration: 2694; Percent complete: 67.3%; Average loss: 2.8686
Iteration: 2695; Percent complete: 67.4%; Average loss: 2.8744
Iteration: 2696; Percent complete: 67.4%; Average loss: 3.2172
Iteration: 2697; Percent complete: 67.4%; Average loss: 3.0920
Iteration: 2698; Percent complete: 67.5%; Average loss: 2.8925
Iteration: 2699; Percent complete: 67.5%; Average loss: 2.8569
Iteration: 2700; Percent complete: 67.5%; Average loss: 2.7967
Iteration: 2701; Percent complete: 67.5%; Average loss: 2.8822
Iteration: 2702; Percent complete: 67.5%; Average loss: 2.9368
Iteration: 2703; Percent complete: 67.6%; Average loss: 2.8934
Iteration: 2704; Percent complete: 67.6%; Average loss: 2.7611
Iteration: 2705; Percent complete: 67.6%; Average loss: 2.9656
Iteration: 2706; Percent complete: 67.7%; Average loss: 3.1672
Iteration: 2707; Percent complete: 67.7%; Average loss: 3.0246
Iteration: 2708; Percent complete: 67.7%; Average loss: 2.7356
Iteration: 2709; Percent complete: 67.7%; Average loss: 3.1687
Iteration: 2710; Percent complete: 67.8%; Average loss: 3.0541
Iteration: 2711; Percent complete: 67.8%; Average loss: 2.9615
Iteration: 2712; Percent complete: 67.8%; Average loss: 2.9242
Iteration: 2713; Percent complete: 67.8%; Average loss: 3.1589
Iteration: 2714; Percent complete: 67.8%; Average loss: 2.9526
Iteration: 2715; Percent complete: 67.9%; Average loss: 2.9994
Iteration: 2716; Percent complete: 67.9%; Average loss: 2.9504
Iteration: 2717; Percent complete: 67.9%; Average loss: 3.0345
Iteration: 2718; Percent complete: 68.0%; Average loss: 3.0094
Iteration: 2719; Percent complete: 68.0%; Average loss: 2.8575
Iteration: 2720; Percent complete: 68.0%; Average loss: 3.1333
Iteration: 2721; Percent complete: 68.0%; Average loss: 2.9136
Iteration: 2722; Percent complete: 68.0%; Average loss: 2.6655
Iteration: 2723; Percent complete: 68.1%; Average loss: 3.0368
Iteration: 2724; Percent complete: 68.1%; Average loss: 2.8692
Iteration: 2725; Percent complete: 68.1%; Average loss: 2.8165
Iteration: 2726; Percent complete: 68.2%; Average loss: 2.8761
Iteration: 2727; Percent complete: 68.2%; Average loss: 3.0206
Iteration: 2728; Percent complete: 68.2%; Average loss: 2.8367
Iteration: 2729; Percent complete: 68.2%; Average loss: 2.9849
Iteration: 2730; Percent complete: 68.2%; Average loss: 2.9854
Iteration: 2731; Percent complete: 68.3%; Average loss: 3.1280
Iteration: 2732; Percent complete: 68.3%; Average loss: 2.9564
Iteration: 2733; Percent complete: 68.3%; Average loss: 3.0013
Iteration: 2734; Percent complete: 68.3%; Average loss: 3.0162
Iteration: 2735; Percent complete: 68.4%; Average loss: 3.0265
Iteration: 2736; Percent complete: 68.4%; Average loss: 2.9908
Iteration: 2737; Percent complete: 68.4%; Average loss: 3.2663
Iteration: 2738; Percent complete: 68.5%; Average loss: 2.8812
Iteration: 2739; Percent complete: 68.5%; Average loss: 3.1365
Iteration: 2740; Percent complete: 68.5%; Average loss: 2.9377
Iteration: 2741; Percent complete: 68.5%; Average loss: 3.0887
Iteration: 2742; Percent complete: 68.5%; Average loss: 3.1056
Iteration: 2743; Percent complete: 68.6%; Average loss: 3.0717
Iteration: 2744; Percent complete: 68.6%; Average loss: 2.9146
Iteration: 2745; Percent complete: 68.6%; Average loss: 3.1079
Iteration: 2746; Percent complete: 68.7%; Average loss: 3.0099
Iteration: 2747; Percent complete: 68.7%; Average loss: 3.1056
Iteration: 2748; Percent complete: 68.7%; Average loss: 3.0813
Iteration: 2749; Percent complete: 68.7%; Average loss: 2.7731
Iteration: 2750; Percent complete: 68.8%; Average loss: 3.0396
Iteration: 2751; Percent complete: 68.8%; Average loss: 3.0307
Iteration: 2752; Percent complete: 68.8%; Average loss: 3.0239
Iteration: 2753; Percent complete: 68.8%; Average loss: 3.1288
Iteration: 2754; Percent complete: 68.8%; Average loss: 2.9644
Iteration: 2755; Percent complete: 68.9%; Average loss: 2.9696
Iteration: 2756; Percent complete: 68.9%; Average loss: 2.9664
Iteration: 2757; Percent complete: 68.9%; Average loss: 2.9504
Iteration: 2758; Percent complete: 69.0%; Average loss: 2.8234
Iteration: 2759; Percent complete: 69.0%; Average loss: 2.9846
Iteration: 2760; Percent complete: 69.0%; Average loss: 2.9985
Iteration: 2761; Percent complete: 69.0%; Average loss: 2.6782
Iteration: 2762; Percent complete: 69.0%; Average loss: 2.7856
Iteration: 2763; Percent complete: 69.1%; Average loss: 3.0268
Iteration: 2764; Percent complete: 69.1%; Average loss: 3.0906
Iteration: 2765; Percent complete: 69.1%; Average loss: 2.9512
Iteration: 2766; Percent complete: 69.2%; Average loss: 2.8900
Iteration: 2767; Percent complete: 69.2%; Average loss: 3.1284
Iteration: 2768; Percent complete: 69.2%; Average loss: 2.8062
Iteration: 2769; Percent complete: 69.2%; Average loss: 3.0960
Iteration: 2770; Percent complete: 69.2%; Average loss: 2.7977
Iteration: 2771; Percent complete: 69.3%; Average loss: 3.0160
Iteration: 2772; Percent complete: 69.3%; Average loss: 2.9808
Iteration: 2773; Percent complete: 69.3%; Average loss: 2.9669
Iteration: 2774; Percent complete: 69.3%; Average loss: 3.1434
Iteration: 2775; Percent complete: 69.4%; Average loss: 3.1403
Iteration: 2776; Percent complete: 69.4%; Average loss: 2.6963
Iteration: 2777; Percent complete: 69.4%; Average loss: 3.0046
Iteration: 2778; Percent complete: 69.5%; Average loss: 3.1099
Iteration: 2779; Percent complete: 69.5%; Average loss: 3.1281
Iteration: 2780; Percent complete: 69.5%; Average loss: 3.2096
Iteration: 2781; Percent complete: 69.5%; Average loss: 3.0099
Iteration: 2782; Percent complete: 69.5%; Average loss: 3.0497
Iteration: 2783; Percent complete: 69.6%; Average loss: 2.8601
Iteration: 2784; Percent complete: 69.6%; Average loss: 3.1538
Iteration: 2785; Percent complete: 69.6%; Average loss: 2.9013
Iteration: 2786; Percent complete: 69.7%; Average loss: 3.1291
Iteration: 2787; Percent complete: 69.7%; Average loss: 2.8316
Iteration: 2788; Percent complete: 69.7%; Average loss: 2.8476
Iteration: 2789; Percent complete: 69.7%; Average loss: 3.1336
Iteration: 2790; Percent complete: 69.8%; Average loss: 3.0017
Iteration: 2791; Percent complete: 69.8%; Average loss: 3.0247
Iteration: 2792; Percent complete: 69.8%; Average loss: 3.0305
Iteration: 2793; Percent complete: 69.8%; Average loss: 2.8409
Iteration: 2794; Percent complete: 69.8%; Average loss: 2.9535
Iteration: 2795; Percent complete: 69.9%; Average loss: 2.9660
Iteration: 2796; Percent complete: 69.9%; Average loss: 3.0026
Iteration: 2797; Percent complete: 69.9%; Average loss: 2.8322
Iteration: 2798; Percent complete: 70.0%; Average loss: 2.9118
Iteration: 2799; Percent complete: 70.0%; Average loss: 2.9807
Iteration: 2800; Percent complete: 70.0%; Average loss: 3.0937
Iteration: 2801; Percent complete: 70.0%; Average loss: 3.0674
Iteration: 2802; Percent complete: 70.0%; Average loss: 2.8245
Iteration: 2803; Percent complete: 70.1%; Average loss: 3.0495
Iteration: 2804; Percent complete: 70.1%; Average loss: 2.8630
Iteration: 2805; Percent complete: 70.1%; Average loss: 3.0671
Iteration: 2806; Percent complete: 70.2%; Average loss: 2.9506
Iteration: 2807; Percent complete: 70.2%; Average loss: 2.7815
Iteration: 2808; Percent complete: 70.2%; Average loss: 2.9679
Iteration: 2809; Percent complete: 70.2%; Average loss: 3.0494
Iteration: 2810; Percent complete: 70.2%; Average loss: 3.0624
Iteration: 2811; Percent complete: 70.3%; Average loss: 2.9856
Iteration: 2812; Percent complete: 70.3%; Average loss: 2.9497
Iteration: 2813; Percent complete: 70.3%; Average loss: 2.9408
Iteration: 2814; Percent complete: 70.3%; Average loss: 3.0667
Iteration: 2815; Percent complete: 70.4%; Average loss: 3.0378
Iteration: 2816; Percent complete: 70.4%; Average loss: 2.8237
Iteration: 2817; Percent complete: 70.4%; Average loss: 2.9855
Iteration: 2818; Percent complete: 70.5%; Average loss: 2.9692
Iteration: 2819; Percent complete: 70.5%; Average loss: 2.9658
Iteration: 2820; Percent complete: 70.5%; Average loss: 3.0298
Iteration: 2821; Percent complete: 70.5%; Average loss: 2.8947
Iteration: 2822; Percent complete: 70.5%; Average loss: 2.8219
Iteration: 2823; Percent complete: 70.6%; Average loss: 2.8762
Iteration: 2824; Percent complete: 70.6%; Average loss: 2.9276
Iteration: 2825; Percent complete: 70.6%; Average loss: 2.8168
Iteration: 2826; Percent complete: 70.7%; Average loss: 3.0218
Iteration: 2827; Percent complete: 70.7%; Average loss: 3.0829
Iteration: 2828; Percent complete: 70.7%; Average loss: 3.0666
Iteration: 2829; Percent complete: 70.7%; Average loss: 2.9441
Iteration: 2830; Percent complete: 70.8%; Average loss: 3.0430
Iteration: 2831; Percent complete: 70.8%; Average loss: 2.5871
Iteration: 2832; Percent complete: 70.8%; Average loss: 2.9906
Iteration: 2833; Percent complete: 70.8%; Average loss: 2.7653
Iteration: 2834; Percent complete: 70.9%; Average loss: 3.0823
Iteration: 2835; Percent complete: 70.9%; Average loss: 2.9922
Iteration: 2836; Percent complete: 70.9%; Average loss: 2.8876
Iteration: 2837; Percent complete: 70.9%; Average loss: 3.0857
Iteration: 2838; Percent complete: 71.0%; Average loss: 2.8559
Iteration: 2839; Percent complete: 71.0%; Average loss: 3.0567
Iteration: 2840; Percent complete: 71.0%; Average loss: 2.8976
Iteration: 2841; Percent complete: 71.0%; Average loss: 3.0599
Iteration: 2842; Percent complete: 71.0%; Average loss: 3.0646
Iteration: 2843; Percent complete: 71.1%; Average loss: 2.9315
Iteration: 2844; Percent complete: 71.1%; Average loss: 2.8333
Iteration: 2845; Percent complete: 71.1%; Average loss: 2.9358
Iteration: 2846; Percent complete: 71.2%; Average loss: 2.7975
Iteration: 2847; Percent complete: 71.2%; Average loss: 2.8693
Iteration: 2848; Percent complete: 71.2%; Average loss: 2.8344
Iteration: 2849; Percent complete: 71.2%; Average loss: 2.8811
Iteration: 2850; Percent complete: 71.2%; Average loss: 2.8073
Iteration: 2851; Percent complete: 71.3%; Average loss: 2.9226
Iteration: 2852; Percent complete: 71.3%; Average loss: 2.7607
Iteration: 2853; Percent complete: 71.3%; Average loss: 3.0993
Iteration: 2854; Percent complete: 71.4%; Average loss: 3.1688
Iteration: 2855; Percent complete: 71.4%; Average loss: 2.9904
Iteration: 2856; Percent complete: 71.4%; Average loss: 3.0334
Iteration: 2857; Percent complete: 71.4%; Average loss: 2.8663
Iteration: 2858; Percent complete: 71.5%; Average loss: 2.8292
Iteration: 2859; Percent complete: 71.5%; Average loss: 2.8777
Iteration: 2860; Percent complete: 71.5%; Average loss: 2.9453
Iteration: 2861; Percent complete: 71.5%; Average loss: 2.9710
Iteration: 2862; Percent complete: 71.5%; Average loss: 2.9633
Iteration: 2863; Percent complete: 71.6%; Average loss: 2.9890
Iteration: 2864; Percent complete: 71.6%; Average loss: 3.0227
Iteration: 2865; Percent complete: 71.6%; Average loss: 2.9253
Iteration: 2866; Percent complete: 71.7%; Average loss: 3.0257
Iteration: 2867; Percent complete: 71.7%; Average loss: 2.7619
Iteration: 2868; Percent complete: 71.7%; Average loss: 2.9054
Iteration: 2869; Percent complete: 71.7%; Average loss: 3.0187
Iteration: 2870; Percent complete: 71.8%; Average loss: 3.0106
Iteration: 2871; Percent complete: 71.8%; Average loss: 3.0431
Iteration: 2872; Percent complete: 71.8%; Average loss: 3.0672
Iteration: 2873; Percent complete: 71.8%; Average loss: 2.9165
Iteration: 2874; Percent complete: 71.9%; Average loss: 2.9802
Iteration: 2875; Percent complete: 71.9%; Average loss: 2.8336
Iteration: 2876; Percent complete: 71.9%; Average loss: 2.6535
Iteration: 2877; Percent complete: 71.9%; Average loss: 3.1243
Iteration: 2878; Percent complete: 72.0%; Average loss: 2.9946
Iteration: 2879; Percent complete: 72.0%; Average loss: 3.0632
Iteration: 2880; Percent complete: 72.0%; Average loss: 2.9253
Iteration: 2881; Percent complete: 72.0%; Average loss: 2.9114
Iteration: 2882; Percent complete: 72.0%; Average loss: 2.9300
Iteration: 2883; Percent complete: 72.1%; Average loss: 2.9746
Iteration: 2884; Percent complete: 72.1%; Average loss: 2.8442
Iteration: 2885; Percent complete: 72.1%; Average loss: 2.6941
Iteration: 2886; Percent complete: 72.2%; Average loss: 2.7975
Iteration: 2887; Percent complete: 72.2%; Average loss: 3.0753
Iteration: 2888; Percent complete: 72.2%; Average loss: 3.0121
Iteration: 2889; Percent complete: 72.2%; Average loss: 2.7335
Iteration: 2890; Percent complete: 72.2%; Average loss: 2.7468
Iteration: 2891; Percent complete: 72.3%; Average loss: 3.1184
Iteration: 2892; Percent complete: 72.3%; Average loss: 2.8921
Iteration: 2893; Percent complete: 72.3%; Average loss: 3.0695
Iteration: 2894; Percent complete: 72.4%; Average loss: 2.8983
Iteration: 2895; Percent complete: 72.4%; Average loss: 3.0239
Iteration: 2896; Percent complete: 72.4%; Average loss: 2.8849
Iteration: 2897; Percent complete: 72.4%; Average loss: 2.7651
Iteration: 2898; Percent complete: 72.5%; Average loss: 2.9051
Iteration: 2899; Percent complete: 72.5%; Average loss: 2.9722
Iteration: 2900; Percent complete: 72.5%; Average loss: 3.0140
Iteration: 2901; Percent complete: 72.5%; Average loss: 3.0164
Iteration: 2902; Percent complete: 72.5%; Average loss: 2.9474
Iteration: 2903; Percent complete: 72.6%; Average loss: 3.0484
Iteration: 2904; Percent complete: 72.6%; Average loss: 2.6838
Iteration: 2905; Percent complete: 72.6%; Average loss: 2.5243
Iteration: 2906; Percent complete: 72.7%; Average loss: 2.9433
Iteration: 2907; Percent complete: 72.7%; Average loss: 2.7621
Iteration: 2908; Percent complete: 72.7%; Average loss: 2.9840
Iteration: 2909; Percent complete: 72.7%; Average loss: 2.8517
Iteration: 2910; Percent complete: 72.8%; Average loss: 2.9735
Iteration: 2911; Percent complete: 72.8%; Average loss: 3.1554
Iteration: 2912; Percent complete: 72.8%; Average loss: 3.0111
Iteration: 2913; Percent complete: 72.8%; Average loss: 2.9525
Iteration: 2914; Percent complete: 72.9%; Average loss: 2.8282
Iteration: 2915; Percent complete: 72.9%; Average loss: 3.0836
Iteration: 2916; Percent complete: 72.9%; Average loss: 3.0026
Iteration: 2917; Percent complete: 72.9%; Average loss: 2.9413
Iteration: 2918; Percent complete: 73.0%; Average loss: 3.2382
Iteration: 2919; Percent complete: 73.0%; Average loss: 2.7687
Iteration: 2920; Percent complete: 73.0%; Average loss: 3.1958
Iteration: 2921; Percent complete: 73.0%; Average loss: 3.0464
Iteration: 2922; Percent complete: 73.0%; Average loss: 2.9966
Iteration: 2923; Percent complete: 73.1%; Average loss: 2.9226
Iteration: 2924; Percent complete: 73.1%; Average loss: 2.8803
Iteration: 2925; Percent complete: 73.1%; Average loss: 3.0496
Iteration: 2926; Percent complete: 73.2%; Average loss: 2.9241
Iteration: 2927; Percent complete: 73.2%; Average loss: 2.7492
Iteration: 2928; Percent complete: 73.2%; Average loss: 3.1142
Iteration: 2929; Percent complete: 73.2%; Average loss: 3.1005
Iteration: 2930; Percent complete: 73.2%; Average loss: 3.1744
Iteration: 2931; Percent complete: 73.3%; Average loss: 3.1716
Iteration: 2932; Percent complete: 73.3%; Average loss: 2.9120
Iteration: 2933; Percent complete: 73.3%; Average loss: 2.8882
Iteration: 2934; Percent complete: 73.4%; Average loss: 2.8815
Iteration: 2935; Percent complete: 73.4%; Average loss: 2.8738
Iteration: 2936; Percent complete: 73.4%; Average loss: 2.8258
Iteration: 2937; Percent complete: 73.4%; Average loss: 3.1327
Iteration: 2938; Percent complete: 73.5%; Average loss: 3.0412
Iteration: 2939; Percent complete: 73.5%; Average loss: 2.9796
Iteration: 2940; Percent complete: 73.5%; Average loss: 2.8205
Iteration: 2941; Percent complete: 73.5%; Average loss: 2.8504
Iteration: 2942; Percent complete: 73.6%; Average loss: 3.0815
Iteration: 2943; Percent complete: 73.6%; Average loss: 2.8178
Iteration: 2944; Percent complete: 73.6%; Average loss: 3.2296
Iteration: 2945; Percent complete: 73.6%; Average loss: 3.1356
Iteration: 2946; Percent complete: 73.7%; Average loss: 2.9070
Iteration: 2947; Percent complete: 73.7%; Average loss: 3.0280
Iteration: 2948; Percent complete: 73.7%; Average loss: 2.8484
Iteration: 2949; Percent complete: 73.7%; Average loss: 3.0276
Iteration: 2950; Percent complete: 73.8%; Average loss: 3.1207
Iteration: 2951; Percent complete: 73.8%; Average loss: 3.2161
Iteration: 2952; Percent complete: 73.8%; Average loss: 2.9257
Iteration: 2953; Percent complete: 73.8%; Average loss: 2.9294
Iteration: 2954; Percent complete: 73.9%; Average loss: 3.0752
Iteration: 2955; Percent complete: 73.9%; Average loss: 3.1083
Iteration: 2956; Percent complete: 73.9%; Average loss: 3.0049
Iteration: 2957; Percent complete: 73.9%; Average loss: 3.0609
Iteration: 2958; Percent complete: 74.0%; Average loss: 2.8809
Iteration: 2959; Percent complete: 74.0%; Average loss: 2.9729
Iteration: 2960; Percent complete: 74.0%; Average loss: 2.8258
Iteration: 2961; Percent complete: 74.0%; Average loss: 2.9956
Iteration: 2962; Percent complete: 74.1%; Average loss: 3.0344
Iteration: 2963; Percent complete: 74.1%; Average loss: 2.6219
Iteration: 2964; Percent complete: 74.1%; Average loss: 2.9894
Iteration: 2965; Percent complete: 74.1%; Average loss: 2.9744
Iteration: 2966; Percent complete: 74.2%; Average loss: 2.8246
Iteration: 2967; Percent complete: 74.2%; Average loss: 2.9643
Iteration: 2968; Percent complete: 74.2%; Average loss: 2.9065
Iteration: 2969; Percent complete: 74.2%; Average loss: 3.0283
Iteration: 2970; Percent complete: 74.2%; Average loss: 3.0229
Iteration: 2971; Percent complete: 74.3%; Average loss: 2.8448
Iteration: 2972; Percent complete: 74.3%; Average loss: 2.8801
Iteration: 2973; Percent complete: 74.3%; Average loss: 2.7730
Iteration: 2974; Percent complete: 74.4%; Average loss: 3.0641
Iteration: 2975; Percent complete: 74.4%; Average loss: 2.9351
Iteration: 2976; Percent complete: 74.4%; Average loss: 3.0132
Iteration: 2977; Percent complete: 74.4%; Average loss: 2.9685
Iteration: 2978; Percent complete: 74.5%; Average loss: 2.9333
Iteration: 2979; Percent complete: 74.5%; Average loss: 2.9233
Iteration: 2980; Percent complete: 74.5%; Average loss: 3.2471
Iteration: 2981; Percent complete: 74.5%; Average loss: 2.8537
Iteration: 2982; Percent complete: 74.6%; Average loss: 2.6213
Iteration: 2983; Percent complete: 74.6%; Average loss: 2.8760
Iteration: 2984; Percent complete: 74.6%; Average loss: 2.8640
Iteration: 2985; Percent complete: 74.6%; Average loss: 3.0502
Iteration: 2986; Percent complete: 74.7%; Average loss: 3.1401
Iteration: 2987; Percent complete: 74.7%; Average loss: 3.0435
Iteration: 2988; Percent complete: 74.7%; Average loss: 3.1813
Iteration: 2989; Percent complete: 74.7%; Average loss: 2.7872
Iteration: 2990; Percent complete: 74.8%; Average loss: 2.8464
Iteration: 2991; Percent complete: 74.8%; Average loss: 2.9973
Iteration: 2992; Percent complete: 74.8%; Average loss: 2.9935
Iteration: 2993; Percent complete: 74.8%; Average loss: 2.6706
Iteration: 2994; Percent complete: 74.9%; Average loss: 2.9345
Iteration: 2995; Percent complete: 74.9%; Average loss: 2.9850
Iteration: 2996; Percent complete: 74.9%; Average loss: 2.7891
Iteration: 2997; Percent complete: 74.9%; Average loss: 2.8776
Iteration: 2998; Percent complete: 75.0%; Average loss: 2.8950
Iteration: 2999; Percent complete: 75.0%; Average loss: 2.7775
Iteration: 3000; Percent complete: 75.0%; Average loss: 2.7493
Iteration: 3001; Percent complete: 75.0%; Average loss: 2.7397
Iteration: 3002; Percent complete: 75.0%; Average loss: 3.3392
Iteration: 3003; Percent complete: 75.1%; Average loss: 2.6874
Iteration: 3004; Percent complete: 75.1%; Average loss: 2.8332
Iteration: 3005; Percent complete: 75.1%; Average loss: 2.7359
Iteration: 3006; Percent complete: 75.1%; Average loss: 2.9707
Iteration: 3007; Percent complete: 75.2%; Average loss: 2.6631
Iteration: 3008; Percent complete: 75.2%; Average loss: 2.8827
Iteration: 3009; Percent complete: 75.2%; Average loss: 3.0420
Iteration: 3010; Percent complete: 75.2%; Average loss: 3.0260
Iteration: 3011; Percent complete: 75.3%; Average loss: 2.6366
Iteration: 3012; Percent complete: 75.3%; Average loss: 2.8749
Iteration: 3013; Percent complete: 75.3%; Average loss: 2.9014
Iteration: 3014; Percent complete: 75.3%; Average loss: 2.9316
Iteration: 3015; Percent complete: 75.4%; Average loss: 3.1303
Iteration: 3016; Percent complete: 75.4%; Average loss: 3.0679
Iteration: 3017; Percent complete: 75.4%; Average loss: 3.1123
Iteration: 3018; Percent complete: 75.4%; Average loss: 2.9904
Iteration: 3019; Percent complete: 75.5%; Average loss: 2.9643
Iteration: 3020; Percent complete: 75.5%; Average loss: 3.0885
Iteration: 3021; Percent complete: 75.5%; Average loss: 3.1840
Iteration: 3022; Percent complete: 75.5%; Average loss: 2.9856
Iteration: 3023; Percent complete: 75.6%; Average loss: 2.8222
Iteration: 3024; Percent complete: 75.6%; Average loss: 2.9253
Iteration: 3025; Percent complete: 75.6%; Average loss: 2.8509
Iteration: 3026; Percent complete: 75.6%; Average loss: 3.0572
Iteration: 3027; Percent complete: 75.7%; Average loss: 2.9906
Iteration: 3028; Percent complete: 75.7%; Average loss: 2.8863
Iteration: 3029; Percent complete: 75.7%; Average loss: 2.7364
Iteration: 3030; Percent complete: 75.8%; Average loss: 3.1297
Iteration: 3031; Percent complete: 75.8%; Average loss: 2.7187
Iteration: 3032; Percent complete: 75.8%; Average loss: 2.9327
Iteration: 3033; Percent complete: 75.8%; Average loss: 3.0197
Iteration: 3034; Percent complete: 75.8%; Average loss: 2.8786
Iteration: 3035; Percent complete: 75.9%; Average loss: 3.0966
Iteration: 3036; Percent complete: 75.9%; Average loss: 3.0790
Iteration: 3037; Percent complete: 75.9%; Average loss: 2.9840
Iteration: 3038; Percent complete: 75.9%; Average loss: 2.9485
Iteration: 3039; Percent complete: 76.0%; Average loss: 2.8158
Iteration: 3040; Percent complete: 76.0%; Average loss: 3.0074
Iteration: 3041; Percent complete: 76.0%; Average loss: 2.8347
Iteration: 3042; Percent complete: 76.0%; Average loss: 2.9105
Iteration: 3043; Percent complete: 76.1%; Average loss: 2.9404
Iteration: 3044; Percent complete: 76.1%; Average loss: 2.8793
Iteration: 3045; Percent complete: 76.1%; Average loss: 2.9752
Iteration: 3046; Percent complete: 76.1%; Average loss: 2.9881
Iteration: 3047; Percent complete: 76.2%; Average loss: 3.0220
Iteration: 3048; Percent complete: 76.2%; Average loss: 2.7391
Iteration: 3049; Percent complete: 76.2%; Average loss: 3.0525
Iteration: 3050; Percent complete: 76.2%; Average loss: 3.0191
Iteration: 3051; Percent complete: 76.3%; Average loss: 2.7442
Iteration: 3052; Percent complete: 76.3%; Average loss: 2.9304
Iteration: 3053; Percent complete: 76.3%; Average loss: 2.8572
Iteration: 3054; Percent complete: 76.3%; Average loss: 2.8778
Iteration: 3055; Percent complete: 76.4%; Average loss: 2.7859
Iteration: 3056; Percent complete: 76.4%; Average loss: 2.7379
Iteration: 3057; Percent complete: 76.4%; Average loss: 2.8852
Iteration: 3058; Percent complete: 76.4%; Average loss: 3.0183
Iteration: 3059; Percent complete: 76.5%; Average loss: 2.9661
Iteration: 3060; Percent complete: 76.5%; Average loss: 3.0544
Iteration: 3061; Percent complete: 76.5%; Average loss: 3.0683
Iteration: 3062; Percent complete: 76.5%; Average loss: 3.2036
Iteration: 3063; Percent complete: 76.6%; Average loss: 3.1449
Iteration: 3064; Percent complete: 76.6%; Average loss: 2.8757
Iteration: 3065; Percent complete: 76.6%; Average loss: 2.6527
Iteration: 3066; Percent complete: 76.6%; Average loss: 3.0189
Iteration: 3067; Percent complete: 76.7%; Average loss: 2.8408
Iteration: 3068; Percent complete: 76.7%; Average loss: 2.9995
Iteration: 3069; Percent complete: 76.7%; Average loss: 2.8392
Iteration: 3070; Percent complete: 76.8%; Average loss: 2.8832
Iteration: 3071; Percent complete: 76.8%; Average loss: 2.6944
Iteration: 3072; Percent complete: 76.8%; Average loss: 2.8055
Iteration: 3073; Percent complete: 76.8%; Average loss: 2.8686
Iteration: 3074; Percent complete: 76.8%; Average loss: 2.8955
Iteration: 3075; Percent complete: 76.9%; Average loss: 2.9792
Iteration: 3076; Percent complete: 76.9%; Average loss: 2.8533
Iteration: 3077; Percent complete: 76.9%; Average loss: 3.1355
Iteration: 3078; Percent complete: 77.0%; Average loss: 2.9980
Iteration: 3079; Percent complete: 77.0%; Average loss: 2.7922
Iteration: 3080; Percent complete: 77.0%; Average loss: 3.0189
Iteration: 3081; Percent complete: 77.0%; Average loss: 2.7402
Iteration: 3082; Percent complete: 77.0%; Average loss: 2.9063
Iteration: 3083; Percent complete: 77.1%; Average loss: 2.9882
Iteration: 3084; Percent complete: 77.1%; Average loss: 2.5103
Iteration: 3085; Percent complete: 77.1%; Average loss: 3.1710
Iteration: 3086; Percent complete: 77.1%; Average loss: 2.7549
Iteration: 3087; Percent complete: 77.2%; Average loss: 2.8469
Iteration: 3088; Percent complete: 77.2%; Average loss: 2.8940
Iteration: 3089; Percent complete: 77.2%; Average loss: 2.9410
Iteration: 3090; Percent complete: 77.2%; Average loss: 3.0526
Iteration: 3091; Percent complete: 77.3%; Average loss: 3.0050
Iteration: 3092; Percent complete: 77.3%; Average loss: 2.8783
Iteration: 3093; Percent complete: 77.3%; Average loss: 2.7576
Iteration: 3094; Percent complete: 77.3%; Average loss: 2.8000
Iteration: 3095; Percent complete: 77.4%; Average loss: 2.9164
Iteration: 3096; Percent complete: 77.4%; Average loss: 2.7458
Iteration: 3097; Percent complete: 77.4%; Average loss: 2.8556
Iteration: 3098; Percent complete: 77.5%; Average loss: 2.6465
Iteration: 3099; Percent complete: 77.5%; Average loss: 3.0142
Iteration: 3100; Percent complete: 77.5%; Average loss: 2.8121
Iteration: 3101; Percent complete: 77.5%; Average loss: 2.7718
Iteration: 3102; Percent complete: 77.5%; Average loss: 2.9767
Iteration: 3103; Percent complete: 77.6%; Average loss: 2.7143
Iteration: 3104; Percent complete: 77.6%; Average loss: 2.7289
Iteration: 3105; Percent complete: 77.6%; Average loss: 2.7950
Iteration: 3106; Percent complete: 77.6%; Average loss: 2.8342
Iteration: 3107; Percent complete: 77.7%; Average loss: 2.7536
Iteration: 3108; Percent complete: 77.7%; Average loss: 2.8235
Iteration: 3109; Percent complete: 77.7%; Average loss: 2.5753
Iteration: 3110; Percent complete: 77.8%; Average loss: 2.6824
Iteration: 3111; Percent complete: 77.8%; Average loss: 2.9227
Iteration: 3112; Percent complete: 77.8%; Average loss: 3.0157
Iteration: 3113; Percent complete: 77.8%; Average loss: 2.8738
Iteration: 3114; Percent complete: 77.8%; Average loss: 2.9000
Iteration: 3115; Percent complete: 77.9%; Average loss: 2.7412
Iteration: 3116; Percent complete: 77.9%; Average loss: 2.7884
Iteration: 3117; Percent complete: 77.9%; Average loss: 2.9598
Iteration: 3118; Percent complete: 78.0%; Average loss: 2.8776
Iteration: 3119; Percent complete: 78.0%; Average loss: 2.6347
Iteration: 3120; Percent complete: 78.0%; Average loss: 2.7313
Iteration: 3121; Percent complete: 78.0%; Average loss: 2.7202
Iteration: 3122; Percent complete: 78.0%; Average loss: 2.7980
Iteration: 3123; Percent complete: 78.1%; Average loss: 2.8362
Iteration: 3124; Percent complete: 78.1%; Average loss: 2.6387
Iteration: 3125; Percent complete: 78.1%; Average loss: 2.9826
Iteration: 3126; Percent complete: 78.1%; Average loss: 2.8010
Iteration: 3127; Percent complete: 78.2%; Average loss: 2.8438
Iteration: 3128; Percent complete: 78.2%; Average loss: 2.7663
Iteration: 3129; Percent complete: 78.2%; Average loss: 2.9939
Iteration: 3130; Percent complete: 78.2%; Average loss: 2.9483
Iteration: 3131; Percent complete: 78.3%; Average loss: 2.7807
Iteration: 3132; Percent complete: 78.3%; Average loss: 2.7794
Iteration: 3133; Percent complete: 78.3%; Average loss: 2.8894
Iteration: 3134; Percent complete: 78.3%; Average loss: 2.9968
Iteration: 3135; Percent complete: 78.4%; Average loss: 2.7643
Iteration: 3136; Percent complete: 78.4%; Average loss: 2.8969
Iteration: 3137; Percent complete: 78.4%; Average loss: 3.0009
Iteration: 3138; Percent complete: 78.5%; Average loss: 2.6767
Iteration: 3139; Percent complete: 78.5%; Average loss: 2.7908
Iteration: 3140; Percent complete: 78.5%; Average loss: 2.9960
Iteration: 3141; Percent complete: 78.5%; Average loss: 2.8969
Iteration: 3142; Percent complete: 78.5%; Average loss: 3.0587
Iteration: 3143; Percent complete: 78.6%; Average loss: 2.9711
Iteration: 3144; Percent complete: 78.6%; Average loss: 2.9446
Iteration: 3145; Percent complete: 78.6%; Average loss: 2.8001
Iteration: 3146; Percent complete: 78.6%; Average loss: 2.7359
Iteration: 3147; Percent complete: 78.7%; Average loss: 2.9054
Iteration: 3148; Percent complete: 78.7%; Average loss: 3.1523
Iteration: 3149; Percent complete: 78.7%; Average loss: 2.8836
Iteration: 3150; Percent complete: 78.8%; Average loss: 2.7803
Iteration: 3151; Percent complete: 78.8%; Average loss: 2.7420
Iteration: 3152; Percent complete: 78.8%; Average loss: 2.9387
Iteration: 3153; Percent complete: 78.8%; Average loss: 2.8631
Iteration: 3154; Percent complete: 78.8%; Average loss: 2.7872
Iteration: 3155; Percent complete: 78.9%; Average loss: 2.7855
Iteration: 3156; Percent complete: 78.9%; Average loss: 2.8743
Iteration: 3157; Percent complete: 78.9%; Average loss: 2.9916
Iteration: 3158; Percent complete: 79.0%; Average loss: 2.8877
Iteration: 3159; Percent complete: 79.0%; Average loss: 2.7520
Iteration: 3160; Percent complete: 79.0%; Average loss: 2.7458
Iteration: 3161; Percent complete: 79.0%; Average loss: 2.7943
Iteration: 3162; Percent complete: 79.0%; Average loss: 2.9164
Iteration: 3163; Percent complete: 79.1%; Average loss: 3.0226
Iteration: 3164; Percent complete: 79.1%; Average loss: 2.5259
Iteration: 3165; Percent complete: 79.1%; Average loss: 2.7660
Iteration: 3166; Percent complete: 79.1%; Average loss: 3.0297
Iteration: 3167; Percent complete: 79.2%; Average loss: 2.8561
Iteration: 3168; Percent complete: 79.2%; Average loss: 2.6619
Iteration: 3169; Percent complete: 79.2%; Average loss: 2.8572
Iteration: 3170; Percent complete: 79.2%; Average loss: 2.7989
Iteration: 3171; Percent complete: 79.3%; Average loss: 2.8076
Iteration: 3172; Percent complete: 79.3%; Average loss: 2.6854
Iteration: 3173; Percent complete: 79.3%; Average loss: 2.7400
Iteration: 3174; Percent complete: 79.3%; Average loss: 2.8010
Iteration: 3175; Percent complete: 79.4%; Average loss: 3.1019
Iteration: 3176; Percent complete: 79.4%; Average loss: 2.7881
Iteration: 3177; Percent complete: 79.4%; Average loss: 3.0035
Iteration: 3178; Percent complete: 79.5%; Average loss: 2.7698
Iteration: 3179; Percent complete: 79.5%; Average loss: 2.6952
Iteration: 3180; Percent complete: 79.5%; Average loss: 2.7958
Iteration: 3181; Percent complete: 79.5%; Average loss: 3.1578
Iteration: 3182; Percent complete: 79.5%; Average loss: 2.7660
Iteration: 3183; Percent complete: 79.6%; Average loss: 2.8481
Iteration: 3184; Percent complete: 79.6%; Average loss: 2.7970
Iteration: 3185; Percent complete: 79.6%; Average loss: 2.9891
Iteration: 3186; Percent complete: 79.7%; Average loss: 3.0608
Iteration: 3187; Percent complete: 79.7%; Average loss: 2.6060
Iteration: 3188; Percent complete: 79.7%; Average loss: 2.7572
Iteration: 3189; Percent complete: 79.7%; Average loss: 3.0024
Iteration: 3190; Percent complete: 79.8%; Average loss: 2.6589
Iteration: 3191; Percent complete: 79.8%; Average loss: 2.9085
Iteration: 3192; Percent complete: 79.8%; Average loss: 2.5867
Iteration: 3193; Percent complete: 79.8%; Average loss: 2.7796
Iteration: 3194; Percent complete: 79.8%; Average loss: 3.0646
Iteration: 3195; Percent complete: 79.9%; Average loss: 2.6413
Iteration: 3196; Percent complete: 79.9%; Average loss: 3.0064
Iteration: 3197; Percent complete: 79.9%; Average loss: 2.7273
Iteration: 3198; Percent complete: 80.0%; Average loss: 2.9703
Iteration: 3199; Percent complete: 80.0%; Average loss: 2.7818
Iteration: 3200; Percent complete: 80.0%; Average loss: 2.8981
Iteration: 3201; Percent complete: 80.0%; Average loss: 2.8612
Iteration: 3202; Percent complete: 80.0%; Average loss: 2.9147
Iteration: 3203; Percent complete: 80.1%; Average loss: 2.8561
Iteration: 3204; Percent complete: 80.1%; Average loss: 2.9001
Iteration: 3205; Percent complete: 80.1%; Average loss: 2.9719
Iteration: 3206; Percent complete: 80.2%; Average loss: 2.7569
Iteration: 3207; Percent complete: 80.2%; Average loss: 2.7727
Iteration: 3208; Percent complete: 80.2%; Average loss: 2.7604
Iteration: 3209; Percent complete: 80.2%; Average loss: 2.8806
Iteration: 3210; Percent complete: 80.2%; Average loss: 2.7376
Iteration: 3211; Percent complete: 80.3%; Average loss: 3.0287
Iteration: 3212; Percent complete: 80.3%; Average loss: 2.9291
Iteration: 3213; Percent complete: 80.3%; Average loss: 2.8227
Iteration: 3214; Percent complete: 80.3%; Average loss: 2.9381
Iteration: 3215; Percent complete: 80.4%; Average loss: 2.5919
Iteration: 3216; Percent complete: 80.4%; Average loss: 2.7344
Iteration: 3217; Percent complete: 80.4%; Average loss: 2.6780
Iteration: 3218; Percent complete: 80.5%; Average loss: 3.1168
Iteration: 3219; Percent complete: 80.5%; Average loss: 3.0275
Iteration: 3220; Percent complete: 80.5%; Average loss: 2.8482
Iteration: 3221; Percent complete: 80.5%; Average loss: 2.6731
Iteration: 3222; Percent complete: 80.5%; Average loss: 3.1980
Iteration: 3223; Percent complete: 80.6%; Average loss: 2.7896
Iteration: 3224; Percent complete: 80.6%; Average loss: 2.9723
Iteration: 3225; Percent complete: 80.6%; Average loss: 3.0282
Iteration: 3226; Percent complete: 80.7%; Average loss: 3.0765
Iteration: 3227; Percent complete: 80.7%; Average loss: 2.9299
Iteration: 3228; Percent complete: 80.7%; Average loss: 3.0339
Iteration: 3229; Percent complete: 80.7%; Average loss: 2.8326
Iteration: 3230; Percent complete: 80.8%; Average loss: 2.7416
Iteration: 3231; Percent complete: 80.8%; Average loss: 2.8444
Iteration: 3232; Percent complete: 80.8%; Average loss: 2.9465
Iteration: 3233; Percent complete: 80.8%; Average loss: 2.8866
Iteration: 3234; Percent complete: 80.8%; Average loss: 2.7228
Iteration: 3235; Percent complete: 80.9%; Average loss: 2.9365
Iteration: 3236; Percent complete: 80.9%; Average loss: 2.7527
Iteration: 3237; Percent complete: 80.9%; Average loss: 3.0087
Iteration: 3238; Percent complete: 81.0%; Average loss: 2.8686
Iteration: 3239; Percent complete: 81.0%; Average loss: 3.0310
Iteration: 3240; Percent complete: 81.0%; Average loss: 2.6794
Iteration: 3241; Percent complete: 81.0%; Average loss: 2.7214
Iteration: 3242; Percent complete: 81.0%; Average loss: 2.8634
Iteration: 3243; Percent complete: 81.1%; Average loss: 2.7011
Iteration: 3244; Percent complete: 81.1%; Average loss: 2.8292
Iteration: 3245; Percent complete: 81.1%; Average loss: 2.9913
Iteration: 3246; Percent complete: 81.2%; Average loss: 2.6843
Iteration: 3247; Percent complete: 81.2%; Average loss: 2.6629
Iteration: 3248; Percent complete: 81.2%; Average loss: 2.8205
Iteration: 3249; Percent complete: 81.2%; Average loss: 2.6751
Iteration: 3250; Percent complete: 81.2%; Average loss: 2.8272
Iteration: 3251; Percent complete: 81.3%; Average loss: 2.9112
Iteration: 3252; Percent complete: 81.3%; Average loss: 2.8152
Iteration: 3253; Percent complete: 81.3%; Average loss: 2.9044
Iteration: 3254; Percent complete: 81.3%; Average loss: 2.8010
Iteration: 3255; Percent complete: 81.4%; Average loss: 2.7353
Iteration: 3256; Percent complete: 81.4%; Average loss: 2.9701
Iteration: 3257; Percent complete: 81.4%; Average loss: 2.7209
Iteration: 3258; Percent complete: 81.5%; Average loss: 2.7993
Iteration: 3259; Percent complete: 81.5%; Average loss: 2.7544
Iteration: 3260; Percent complete: 81.5%; Average loss: 2.9892
Iteration: 3261; Percent complete: 81.5%; Average loss: 2.9971
Iteration: 3262; Percent complete: 81.5%; Average loss: 2.9980
Iteration: 3263; Percent complete: 81.6%; Average loss: 2.8154
Iteration: 3264; Percent complete: 81.6%; Average loss: 2.5423
Iteration: 3265; Percent complete: 81.6%; Average loss: 2.8566
Iteration: 3266; Percent complete: 81.7%; Average loss: 2.8044
Iteration: 3267; Percent complete: 81.7%; Average loss: 2.8122
Iteration: 3268; Percent complete: 81.7%; Average loss: 2.5858
Iteration: 3269; Percent complete: 81.7%; Average loss: 2.5316
Iteration: 3270; Percent complete: 81.8%; Average loss: 2.9568
Iteration: 3271; Percent complete: 81.8%; Average loss: 2.9340
Iteration: 3272; Percent complete: 81.8%; Average loss: 2.8751
Iteration: 3273; Percent complete: 81.8%; Average loss: 3.0054
Iteration: 3274; Percent complete: 81.8%; Average loss: 2.9081
Iteration: 3275; Percent complete: 81.9%; Average loss: 2.9650
Iteration: 3276; Percent complete: 81.9%; Average loss: 2.7231
Iteration: 3277; Percent complete: 81.9%; Average loss: 2.9338
Iteration: 3278; Percent complete: 82.0%; Average loss: 2.9238
Iteration: 3279; Percent complete: 82.0%; Average loss: 2.7954
Iteration: 3280; Percent complete: 82.0%; Average loss: 3.0976
Iteration: 3281; Percent complete: 82.0%; Average loss: 2.8504
Iteration: 3282; Percent complete: 82.0%; Average loss: 2.5952
Iteration: 3283; Percent complete: 82.1%; Average loss: 2.8713
Iteration: 3284; Percent complete: 82.1%; Average loss: 2.8300
Iteration: 3285; Percent complete: 82.1%; Average loss: 2.7180
Iteration: 3286; Percent complete: 82.2%; Average loss: 2.8521
Iteration: 3287; Percent complete: 82.2%; Average loss: 2.8488
Iteration: 3288; Percent complete: 82.2%; Average loss: 2.6995
Iteration: 3289; Percent complete: 82.2%; Average loss: 2.8799
Iteration: 3290; Percent complete: 82.2%; Average loss: 2.7736
Iteration: 3291; Percent complete: 82.3%; Average loss: 2.7041
Iteration: 3292; Percent complete: 82.3%; Average loss: 3.0218
Iteration: 3293; Percent complete: 82.3%; Average loss: 2.9714
Iteration: 3294; Percent complete: 82.3%; Average loss: 3.0824
Iteration: 3295; Percent complete: 82.4%; Average loss: 3.0449
Iteration: 3296; Percent complete: 82.4%; Average loss: 3.0383
Iteration: 3297; Percent complete: 82.4%; Average loss: 2.5992
Iteration: 3298; Percent complete: 82.5%; Average loss: 2.9263
Iteration: 3299; Percent complete: 82.5%; Average loss: 2.8084
Iteration: 3300; Percent complete: 82.5%; Average loss: 2.9217
Iteration: 3301; Percent complete: 82.5%; Average loss: 3.0162
Iteration: 3302; Percent complete: 82.5%; Average loss: 2.9599
Iteration: 3303; Percent complete: 82.6%; Average loss: 2.9172
Iteration: 3304; Percent complete: 82.6%; Average loss: 2.7487
Iteration: 3305; Percent complete: 82.6%; Average loss: 2.8770
Iteration: 3306; Percent complete: 82.7%; Average loss: 2.7877
Iteration: 3307; Percent complete: 82.7%; Average loss: 2.7579
Iteration: 3308; Percent complete: 82.7%; Average loss: 2.8878
Iteration: 3309; Percent complete: 82.7%; Average loss: 2.8339
Iteration: 3310; Percent complete: 82.8%; Average loss: 2.5475
Iteration: 3311; Percent complete: 82.8%; Average loss: 2.8700
Iteration: 3312; Percent complete: 82.8%; Average loss: 2.7739
Iteration: 3313; Percent complete: 82.8%; Average loss: 2.5222
Iteration: 3314; Percent complete: 82.8%; Average loss: 2.8811
Iteration: 3315; Percent complete: 82.9%; Average loss: 2.8532
Iteration: 3316; Percent complete: 82.9%; Average loss: 2.8141
Iteration: 3317; Percent complete: 82.9%; Average loss: 2.5974
Iteration: 3318; Percent complete: 83.0%; Average loss: 2.8150
Iteration: 3319; Percent complete: 83.0%; Average loss: 2.7808
Iteration: 3320; Percent complete: 83.0%; Average loss: 2.8327
Iteration: 3321; Percent complete: 83.0%; Average loss: 2.8876
Iteration: 3322; Percent complete: 83.0%; Average loss: 2.7426
Iteration: 3323; Percent complete: 83.1%; Average loss: 2.8174
Iteration: 3324; Percent complete: 83.1%; Average loss: 2.9318
Iteration: 3325; Percent complete: 83.1%; Average loss: 2.9162
Iteration: 3326; Percent complete: 83.2%; Average loss: 2.8015
Iteration: 3327; Percent complete: 83.2%; Average loss: 2.7790
Iteration: 3328; Percent complete: 83.2%; Average loss: 2.7674
Iteration: 3329; Percent complete: 83.2%; Average loss: 2.9018
Iteration: 3330; Percent complete: 83.2%; Average loss: 2.7770
Iteration: 3331; Percent complete: 83.3%; Average loss: 2.6904
Iteration: 3332; Percent complete: 83.3%; Average loss: 2.7817
Iteration: 3333; Percent complete: 83.3%; Average loss: 2.9185
Iteration: 3334; Percent complete: 83.4%; Average loss: 2.7932
Iteration: 3335; Percent complete: 83.4%; Average loss: 2.7730
Iteration: 3336; Percent complete: 83.4%; Average loss: 2.6759
Iteration: 3337; Percent complete: 83.4%; Average loss: 2.7636
Iteration: 3338; Percent complete: 83.5%; Average loss: 2.8530
Iteration: 3339; Percent complete: 83.5%; Average loss: 2.8220
Iteration: 3340; Percent complete: 83.5%; Average loss: 3.1266
Iteration: 3341; Percent complete: 83.5%; Average loss: 2.7782
Iteration: 3342; Percent complete: 83.5%; Average loss: 2.9564
Iteration: 3343; Percent complete: 83.6%; Average loss: 2.7433
Iteration: 3344; Percent complete: 83.6%; Average loss: 2.6591
Iteration: 3345; Percent complete: 83.6%; Average loss: 2.8597
Iteration: 3346; Percent complete: 83.7%; Average loss: 2.8243
Iteration: 3347; Percent complete: 83.7%; Average loss: 2.8454
Iteration: 3348; Percent complete: 83.7%; Average loss: 2.7797
Iteration: 3349; Percent complete: 83.7%; Average loss: 2.7691
Iteration: 3350; Percent complete: 83.8%; Average loss: 2.8404
Iteration: 3351; Percent complete: 83.8%; Average loss: 2.7629
Iteration: 3352; Percent complete: 83.8%; Average loss: 2.9128
Iteration: 3353; Percent complete: 83.8%; Average loss: 2.8287
Iteration: 3354; Percent complete: 83.9%; Average loss: 2.8477
Iteration: 3355; Percent complete: 83.9%; Average loss: 2.7844
Iteration: 3356; Percent complete: 83.9%; Average loss: 2.9308
Iteration: 3357; Percent complete: 83.9%; Average loss: 2.8814
Iteration: 3358; Percent complete: 84.0%; Average loss: 2.6714
Iteration: 3359; Percent complete: 84.0%; Average loss: 2.9507
Iteration: 3360; Percent complete: 84.0%; Average loss: 2.9311
Iteration: 3361; Percent complete: 84.0%; Average loss: 2.4897
Iteration: 3362; Percent complete: 84.0%; Average loss: 2.8313
Iteration: 3363; Percent complete: 84.1%; Average loss: 2.8367
Iteration: 3364; Percent complete: 84.1%; Average loss: 2.8346
Iteration: 3365; Percent complete: 84.1%; Average loss: 2.6558
Iteration: 3366; Percent complete: 84.2%; Average loss: 2.7918
Iteration: 3367; Percent complete: 84.2%; Average loss: 2.7706
Iteration: 3368; Percent complete: 84.2%; Average loss: 2.7751
Iteration: 3369; Percent complete: 84.2%; Average loss: 2.6998
Iteration: 3370; Percent complete: 84.2%; Average loss: 2.7752
Iteration: 3371; Percent complete: 84.3%; Average loss: 2.7006
Iteration: 3372; Percent complete: 84.3%; Average loss: 2.9134
Iteration: 3373; Percent complete: 84.3%; Average loss: 3.0343
Iteration: 3374; Percent complete: 84.4%; Average loss: 2.8894
Iteration: 3375; Percent complete: 84.4%; Average loss: 2.8866
Iteration: 3376; Percent complete: 84.4%; Average loss: 2.7618
Iteration: 3377; Percent complete: 84.4%; Average loss: 2.8372
Iteration: 3378; Percent complete: 84.5%; Average loss: 2.9199
Iteration: 3379; Percent complete: 84.5%; Average loss: 2.8223
Iteration: 3380; Percent complete: 84.5%; Average loss: 2.9016
Iteration: 3381; Percent complete: 84.5%; Average loss: 2.7556
Iteration: 3382; Percent complete: 84.5%; Average loss: 2.8689
Iteration: 3383; Percent complete: 84.6%; Average loss: 2.8295
Iteration: 3384; Percent complete: 84.6%; Average loss: 2.7875
Iteration: 3385; Percent complete: 84.6%; Average loss: 2.6503
Iteration: 3386; Percent complete: 84.7%; Average loss: 2.9292
Iteration: 3387; Percent complete: 84.7%; Average loss: 3.1240
Iteration: 3388; Percent complete: 84.7%; Average loss: 2.7193
Iteration: 3389; Percent complete: 84.7%; Average loss: 2.6052
Iteration: 3390; Percent complete: 84.8%; Average loss: 2.7681
Iteration: 3391; Percent complete: 84.8%; Average loss: 2.8405
Iteration: 3392; Percent complete: 84.8%; Average loss: 3.0539
Iteration: 3393; Percent complete: 84.8%; Average loss: 2.8239
Iteration: 3394; Percent complete: 84.9%; Average loss: 2.9635
Iteration: 3395; Percent complete: 84.9%; Average loss: 2.6876
Iteration: 3396; Percent complete: 84.9%; Average loss: 2.7119
Iteration: 3397; Percent complete: 84.9%; Average loss: 2.8811
Iteration: 3398; Percent complete: 85.0%; Average loss: 2.9104
Iteration: 3399; Percent complete: 85.0%; Average loss: 2.7355
Iteration: 3400; Percent complete: 85.0%; Average loss: 2.7342
Iteration: 3401; Percent complete: 85.0%; Average loss: 2.7454
Iteration: 3402; Percent complete: 85.0%; Average loss: 2.6744
Iteration: 3403; Percent complete: 85.1%; Average loss: 2.7098
Iteration: 3404; Percent complete: 85.1%; Average loss: 2.8947
Iteration: 3405; Percent complete: 85.1%; Average loss: 2.7201
Iteration: 3406; Percent complete: 85.2%; Average loss: 3.0385
Iteration: 3407; Percent complete: 85.2%; Average loss: 2.7439
Iteration: 3408; Percent complete: 85.2%; Average loss: 2.9481
Iteration: 3409; Percent complete: 85.2%; Average loss: 2.7786
Iteration: 3410; Percent complete: 85.2%; Average loss: 2.7586
Iteration: 3411; Percent complete: 85.3%; Average loss: 2.5485
Iteration: 3412; Percent complete: 85.3%; Average loss: 2.9222
Iteration: 3413; Percent complete: 85.3%; Average loss: 2.8842
Iteration: 3414; Percent complete: 85.4%; Average loss: 2.6476
Iteration: 3415; Percent complete: 85.4%; Average loss: 3.1085
Iteration: 3416; Percent complete: 85.4%; Average loss: 3.0845
Iteration: 3417; Percent complete: 85.4%; Average loss: 2.7451
Iteration: 3418; Percent complete: 85.5%; Average loss: 2.8134
Iteration: 3419; Percent complete: 85.5%; Average loss: 2.8717
Iteration: 3420; Percent complete: 85.5%; Average loss: 3.0190
Iteration: 3421; Percent complete: 85.5%; Average loss: 2.6275
Iteration: 3422; Percent complete: 85.5%; Average loss: 2.8021
Iteration: 3423; Percent complete: 85.6%; Average loss: 2.7224
Iteration: 3424; Percent complete: 85.6%; Average loss: 2.9189
Iteration: 3425; Percent complete: 85.6%; Average loss: 2.8842
Iteration: 3426; Percent complete: 85.7%; Average loss: 2.7448
Iteration: 3427; Percent complete: 85.7%; Average loss: 2.9321
Iteration: 3428; Percent complete: 85.7%; Average loss: 2.8526
Iteration: 3429; Percent complete: 85.7%; Average loss: 2.8685
Iteration: 3430; Percent complete: 85.8%; Average loss: 2.9515
Iteration: 3431; Percent complete: 85.8%; Average loss: 3.0327
Iteration: 3432; Percent complete: 85.8%; Average loss: 2.5675
Iteration: 3433; Percent complete: 85.8%; Average loss: 2.9209
Iteration: 3434; Percent complete: 85.9%; Average loss: 2.9988
Iteration: 3435; Percent complete: 85.9%; Average loss: 2.9393
Iteration: 3436; Percent complete: 85.9%; Average loss: 2.8846
Iteration: 3437; Percent complete: 85.9%; Average loss: 2.6774
Iteration: 3438; Percent complete: 86.0%; Average loss: 2.8053
Iteration: 3439; Percent complete: 86.0%; Average loss: 2.7876
Iteration: 3440; Percent complete: 86.0%; Average loss: 2.9291
Iteration: 3441; Percent complete: 86.0%; Average loss: 2.8846
Iteration: 3442; Percent complete: 86.1%; Average loss: 2.6540
Iteration: 3443; Percent complete: 86.1%; Average loss: 2.8697
Iteration: 3444; Percent complete: 86.1%; Average loss: 2.7687
Iteration: 3445; Percent complete: 86.1%; Average loss: 2.7665
Iteration: 3446; Percent complete: 86.2%; Average loss: 2.6395
Iteration: 3447; Percent complete: 86.2%; Average loss: 2.7522
Iteration: 3448; Percent complete: 86.2%; Average loss: 2.7734
Iteration: 3449; Percent complete: 86.2%; Average loss: 3.0991
Iteration: 3450; Percent complete: 86.2%; Average loss: 3.1845
Iteration: 3451; Percent complete: 86.3%; Average loss: 2.9450
Iteration: 3452; Percent complete: 86.3%; Average loss: 2.8118
Iteration: 3453; Percent complete: 86.3%; Average loss: 2.9156
Iteration: 3454; Percent complete: 86.4%; Average loss: 2.9851
Iteration: 3455; Percent complete: 86.4%; Average loss: 3.0338
Iteration: 3456; Percent complete: 86.4%; Average loss: 2.8980
Iteration: 3457; Percent complete: 86.4%; Average loss: 2.8176
Iteration: 3458; Percent complete: 86.5%; Average loss: 2.7635
Iteration: 3459; Percent complete: 86.5%; Average loss: 2.4833
Iteration: 3460; Percent complete: 86.5%; Average loss: 2.5383
Iteration: 3461; Percent complete: 86.5%; Average loss: 2.8706
Iteration: 3462; Percent complete: 86.6%; Average loss: 2.9202
Iteration: 3463; Percent complete: 86.6%; Average loss: 2.7676
Iteration: 3464; Percent complete: 86.6%; Average loss: 2.7018
Iteration: 3465; Percent complete: 86.6%; Average loss: 2.7276
Iteration: 3466; Percent complete: 86.7%; Average loss: 2.7701
Iteration: 3467; Percent complete: 86.7%; Average loss: 2.8612
Iteration: 3468; Percent complete: 86.7%; Average loss: 2.8740
Iteration: 3469; Percent complete: 86.7%; Average loss: 2.6886
Iteration: 3470; Percent complete: 86.8%; Average loss: 2.8175
Iteration: 3471; Percent complete: 86.8%; Average loss: 2.8462
Iteration: 3472; Percent complete: 86.8%; Average loss: 2.8179
Iteration: 3473; Percent complete: 86.8%; Average loss: 2.8015
Iteration: 3474; Percent complete: 86.9%; Average loss: 2.6988
Iteration: 3475; Percent complete: 86.9%; Average loss: 2.7397
Iteration: 3476; Percent complete: 86.9%; Average loss: 2.7352
Iteration: 3477; Percent complete: 86.9%; Average loss: 2.6167
Iteration: 3478; Percent complete: 87.0%; Average loss: 2.9257
Iteration: 3479; Percent complete: 87.0%; Average loss: 2.7334
Iteration: 3480; Percent complete: 87.0%; Average loss: 2.5828
Iteration: 3481; Percent complete: 87.0%; Average loss: 2.7730
Iteration: 3482; Percent complete: 87.1%; Average loss: 2.9267
Iteration: 3483; Percent complete: 87.1%; Average loss: 2.9549
Iteration: 3484; Percent complete: 87.1%; Average loss: 2.6211
Iteration: 3485; Percent complete: 87.1%; Average loss: 2.7503
Iteration: 3486; Percent complete: 87.2%; Average loss: 2.9571
Iteration: 3487; Percent complete: 87.2%; Average loss: 2.6926
Iteration: 3488; Percent complete: 87.2%; Average loss: 2.9070
Iteration: 3489; Percent complete: 87.2%; Average loss: 2.8065
Iteration: 3490; Percent complete: 87.2%; Average loss: 2.8654
Iteration: 3491; Percent complete: 87.3%; Average loss: 3.0305
Iteration: 3492; Percent complete: 87.3%; Average loss: 2.5990
Iteration: 3493; Percent complete: 87.3%; Average loss: 2.6826
Iteration: 3494; Percent complete: 87.4%; Average loss: 2.8198
Iteration: 3495; Percent complete: 87.4%; Average loss: 2.7134
Iteration: 3496; Percent complete: 87.4%; Average loss: 2.8593
Iteration: 3497; Percent complete: 87.4%; Average loss: 2.8602
Iteration: 3498; Percent complete: 87.5%; Average loss: 2.6696
Iteration: 3499; Percent complete: 87.5%; Average loss: 2.7875
Iteration: 3500; Percent complete: 87.5%; Average loss: 2.7683
Iteration: 3501; Percent complete: 87.5%; Average loss: 3.0424
Iteration: 3502; Percent complete: 87.5%; Average loss: 2.8530
Iteration: 3503; Percent complete: 87.6%; Average loss: 2.9572
Iteration: 3504; Percent complete: 87.6%; Average loss: 2.7862
Iteration: 3505; Percent complete: 87.6%; Average loss: 2.6734
Iteration: 3506; Percent complete: 87.6%; Average loss: 2.6852
Iteration: 3507; Percent complete: 87.7%; Average loss: 2.6570
Iteration: 3508; Percent complete: 87.7%; Average loss: 2.8424
Iteration: 3509; Percent complete: 87.7%; Average loss: 2.7017
Iteration: 3510; Percent complete: 87.8%; Average loss: 2.6869
Iteration: 3511; Percent complete: 87.8%; Average loss: 2.8588
Iteration: 3512; Percent complete: 87.8%; Average loss: 2.7742
Iteration: 3513; Percent complete: 87.8%; Average loss: 2.7607
Iteration: 3514; Percent complete: 87.8%; Average loss: 2.9769
Iteration: 3515; Percent complete: 87.9%; Average loss: 2.9344
Iteration: 3516; Percent complete: 87.9%; Average loss: 2.8559
Iteration: 3517; Percent complete: 87.9%; Average loss: 2.6682
Iteration: 3518; Percent complete: 87.9%; Average loss: 2.7350
Iteration: 3519; Percent complete: 88.0%; Average loss: 2.8537
Iteration: 3520; Percent complete: 88.0%; Average loss: 2.6156
Iteration: 3521; Percent complete: 88.0%; Average loss: 2.9735
Iteration: 3522; Percent complete: 88.0%; Average loss: 3.0626
Iteration: 3523; Percent complete: 88.1%; Average loss: 2.7127
Iteration: 3524; Percent complete: 88.1%; Average loss: 2.6801
Iteration: 3525; Percent complete: 88.1%; Average loss: 2.8757
Iteration: 3526; Percent complete: 88.1%; Average loss: 2.6515
Iteration: 3527; Percent complete: 88.2%; Average loss: 2.6460
Iteration: 3528; Percent complete: 88.2%; Average loss: 2.6548
Iteration: 3529; Percent complete: 88.2%; Average loss: 2.7694
Iteration: 3530; Percent complete: 88.2%; Average loss: 2.8627
Iteration: 3531; Percent complete: 88.3%; Average loss: 2.8914
Iteration: 3532; Percent complete: 88.3%; Average loss: 2.7014
Iteration: 3533; Percent complete: 88.3%; Average loss: 2.7976
Iteration: 3534; Percent complete: 88.3%; Average loss: 2.8521
Iteration: 3535; Percent complete: 88.4%; Average loss: 2.6417
Iteration: 3536; Percent complete: 88.4%; Average loss: 2.7121
Iteration: 3537; Percent complete: 88.4%; Average loss: 3.0076
Iteration: 3538; Percent complete: 88.4%; Average loss: 2.7848
Iteration: 3539; Percent complete: 88.5%; Average loss: 2.8139
Iteration: 3540; Percent complete: 88.5%; Average loss: 2.4903
Iteration: 3541; Percent complete: 88.5%; Average loss: 2.8019
Iteration: 3542; Percent complete: 88.5%; Average loss: 2.8461
Iteration: 3543; Percent complete: 88.6%; Average loss: 2.8801
Iteration: 3544; Percent complete: 88.6%; Average loss: 3.0575
Iteration: 3545; Percent complete: 88.6%; Average loss: 2.6484
Iteration: 3546; Percent complete: 88.6%; Average loss: 2.9345
Iteration: 3547; Percent complete: 88.7%; Average loss: 2.5531
Iteration: 3548; Percent complete: 88.7%; Average loss: 2.8452
Iteration: 3549; Percent complete: 88.7%; Average loss: 2.8516
Iteration: 3550; Percent complete: 88.8%; Average loss: 2.6697
Iteration: 3551; Percent complete: 88.8%; Average loss: 2.6145
Iteration: 3552; Percent complete: 88.8%; Average loss: 2.8617
Iteration: 3553; Percent complete: 88.8%; Average loss: 2.8394
Iteration: 3554; Percent complete: 88.8%; Average loss: 2.9199
Iteration: 3555; Percent complete: 88.9%; Average loss: 2.5104
Iteration: 3556; Percent complete: 88.9%; Average loss: 2.9887
Iteration: 3557; Percent complete: 88.9%; Average loss: 2.8193
Iteration: 3558; Percent complete: 88.9%; Average loss: 2.8263
Iteration: 3559; Percent complete: 89.0%; Average loss: 2.7364
Iteration: 3560; Percent complete: 89.0%; Average loss: 2.7098
Iteration: 3561; Percent complete: 89.0%; Average loss: 2.7636
Iteration: 3562; Percent complete: 89.0%; Average loss: 2.6755
Iteration: 3563; Percent complete: 89.1%; Average loss: 2.7228
Iteration: 3564; Percent complete: 89.1%; Average loss: 2.5924
Iteration: 3565; Percent complete: 89.1%; Average loss: 2.6255
Iteration: 3566; Percent complete: 89.1%; Average loss: 2.5722
Iteration: 3567; Percent complete: 89.2%; Average loss: 2.9877
Iteration: 3568; Percent complete: 89.2%; Average loss: 2.7269
Iteration: 3569; Percent complete: 89.2%; Average loss: 2.7618
Iteration: 3570; Percent complete: 89.2%; Average loss: 2.7891
Iteration: 3571; Percent complete: 89.3%; Average loss: 2.8459
Iteration: 3572; Percent complete: 89.3%; Average loss: 2.7320
Iteration: 3573; Percent complete: 89.3%; Average loss: 2.6048
Iteration: 3574; Percent complete: 89.3%; Average loss: 2.8187
Iteration: 3575; Percent complete: 89.4%; Average loss: 2.7661
Iteration: 3576; Percent complete: 89.4%; Average loss: 2.8020
Iteration: 3577; Percent complete: 89.4%; Average loss: 2.9715
Iteration: 3578; Percent complete: 89.5%; Average loss: 2.8346
Iteration: 3579; Percent complete: 89.5%; Average loss: 2.9531
Iteration: 3580; Percent complete: 89.5%; Average loss: 2.7815
Iteration: 3581; Percent complete: 89.5%; Average loss: 2.6623
Iteration: 3582; Percent complete: 89.5%; Average loss: 2.7293
Iteration: 3583; Percent complete: 89.6%; Average loss: 2.8295
Iteration: 3584; Percent complete: 89.6%; Average loss: 2.9283
Iteration: 3585; Percent complete: 89.6%; Average loss: 2.5486
Iteration: 3586; Percent complete: 89.6%; Average loss: 2.8626
Iteration: 3587; Percent complete: 89.7%; Average loss: 2.6824
Iteration: 3588; Percent complete: 89.7%; Average loss: 2.7245
Iteration: 3589; Percent complete: 89.7%; Average loss: 2.4707
Iteration: 3590; Percent complete: 89.8%; Average loss: 2.9924
Iteration: 3591; Percent complete: 89.8%; Average loss: 2.9156
Iteration: 3592; Percent complete: 89.8%; Average loss: 2.6320
Iteration: 3593; Percent complete: 89.8%; Average loss: 2.6597
Iteration: 3594; Percent complete: 89.8%; Average loss: 2.7633
Iteration: 3595; Percent complete: 89.9%; Average loss: 2.8232
Iteration: 3596; Percent complete: 89.9%; Average loss: 2.5736
Iteration: 3597; Percent complete: 89.9%; Average loss: 2.6847
Iteration: 3598; Percent complete: 90.0%; Average loss: 2.7897
Iteration: 3599; Percent complete: 90.0%; Average loss: 2.6097
Iteration: 3600; Percent complete: 90.0%; Average loss: 2.7273
Iteration: 3601; Percent complete: 90.0%; Average loss: 2.7912
Iteration: 3602; Percent complete: 90.0%; Average loss: 2.9303
Iteration: 3603; Percent complete: 90.1%; Average loss: 2.8640
Iteration: 3604; Percent complete: 90.1%; Average loss: 2.9084
Iteration: 3605; Percent complete: 90.1%; Average loss: 2.7002
Iteration: 3606; Percent complete: 90.1%; Average loss: 2.8279
Iteration: 3607; Percent complete: 90.2%; Average loss: 2.7865
Iteration: 3608; Percent complete: 90.2%; Average loss: 2.6645
Iteration: 3609; Percent complete: 90.2%; Average loss: 2.9038
Iteration: 3610; Percent complete: 90.2%; Average loss: 2.7513
Iteration: 3611; Percent complete: 90.3%; Average loss: 2.6506
Iteration: 3612; Percent complete: 90.3%; Average loss: 2.5651
Iteration: 3613; Percent complete: 90.3%; Average loss: 2.8535
Iteration: 3614; Percent complete: 90.3%; Average loss: 3.1157
Iteration: 3615; Percent complete: 90.4%; Average loss: 2.8866
Iteration: 3616; Percent complete: 90.4%; Average loss: 2.5507
Iteration: 3617; Percent complete: 90.4%; Average loss: 2.7757
Iteration: 3618; Percent complete: 90.5%; Average loss: 2.5324
Iteration: 3619; Percent complete: 90.5%; Average loss: 2.7607
Iteration: 3620; Percent complete: 90.5%; Average loss: 2.9486
Iteration: 3621; Percent complete: 90.5%; Average loss: 2.5999
Iteration: 3622; Percent complete: 90.5%; Average loss: 2.7755
Iteration: 3623; Percent complete: 90.6%; Average loss: 2.5279
Iteration: 3624; Percent complete: 90.6%; Average loss: 2.7091
Iteration: 3625; Percent complete: 90.6%; Average loss: 2.7863
Iteration: 3626; Percent complete: 90.6%; Average loss: 2.7939
Iteration: 3627; Percent complete: 90.7%; Average loss: 2.7642
Iteration: 3628; Percent complete: 90.7%; Average loss: 2.7723
Iteration: 3629; Percent complete: 90.7%; Average loss: 2.9288
Iteration: 3630; Percent complete: 90.8%; Average loss: 2.8299
Iteration: 3631; Percent complete: 90.8%; Average loss: 2.9301
Iteration: 3632; Percent complete: 90.8%; Average loss: 2.6367
Iteration: 3633; Percent complete: 90.8%; Average loss: 2.8221
Iteration: 3634; Percent complete: 90.8%; Average loss: 2.7212
Iteration: 3635; Percent complete: 90.9%; Average loss: 2.5578
Iteration: 3636; Percent complete: 90.9%; Average loss: 2.8952
Iteration: 3637; Percent complete: 90.9%; Average loss: 2.7734
Iteration: 3638; Percent complete: 91.0%; Average loss: 2.6731
Iteration: 3639; Percent complete: 91.0%; Average loss: 2.4445
Iteration: 3640; Percent complete: 91.0%; Average loss: 2.6746
Iteration: 3641; Percent complete: 91.0%; Average loss: 2.7447
Iteration: 3642; Percent complete: 91.0%; Average loss: 2.5486
Iteration: 3643; Percent complete: 91.1%; Average loss: 2.6901
Iteration: 3644; Percent complete: 91.1%; Average loss: 2.8717
Iteration: 3645; Percent complete: 91.1%; Average loss: 2.8068
Iteration: 3646; Percent complete: 91.1%; Average loss: 2.7918
Iteration: 3647; Percent complete: 91.2%; Average loss: 2.8474
Iteration: 3648; Percent complete: 91.2%; Average loss: 2.6135
Iteration: 3649; Percent complete: 91.2%; Average loss: 2.8315
Iteration: 3650; Percent complete: 91.2%; Average loss: 2.8536
Iteration: 3651; Percent complete: 91.3%; Average loss: 2.8787
Iteration: 3652; Percent complete: 91.3%; Average loss: 2.9124
Iteration: 3653; Percent complete: 91.3%; Average loss: 3.0212
Iteration: 3654; Percent complete: 91.3%; Average loss: 2.6135
Iteration: 3655; Percent complete: 91.4%; Average loss: 2.5802
Iteration: 3656; Percent complete: 91.4%; Average loss: 2.8056
Iteration: 3657; Percent complete: 91.4%; Average loss: 2.6254
Iteration: 3658; Percent complete: 91.5%; Average loss: 2.5597
Iteration: 3659; Percent complete: 91.5%; Average loss: 2.5830
Iteration: 3660; Percent complete: 91.5%; Average loss: 2.6586
Iteration: 3661; Percent complete: 91.5%; Average loss: 2.8471
Iteration: 3662; Percent complete: 91.5%; Average loss: 2.8795
Iteration: 3663; Percent complete: 91.6%; Average loss: 2.8149
Iteration: 3664; Percent complete: 91.6%; Average loss: 2.8030
Iteration: 3665; Percent complete: 91.6%; Average loss: 2.8139
Iteration: 3666; Percent complete: 91.6%; Average loss: 2.7688
Iteration: 3667; Percent complete: 91.7%; Average loss: 2.6496
Iteration: 3668; Percent complete: 91.7%; Average loss: 2.7075
Iteration: 3669; Percent complete: 91.7%; Average loss: 2.7973
Iteration: 3670; Percent complete: 91.8%; Average loss: 2.6254
Iteration: 3671; Percent complete: 91.8%; Average loss: 2.8807
Iteration: 3672; Percent complete: 91.8%; Average loss: 2.9670
Iteration: 3673; Percent complete: 91.8%; Average loss: 2.8481
Iteration: 3674; Percent complete: 91.8%; Average loss: 2.7949
Iteration: 3675; Percent complete: 91.9%; Average loss: 2.7055
Iteration: 3676; Percent complete: 91.9%; Average loss: 2.5988
Iteration: 3677; Percent complete: 91.9%; Average loss: 2.8199
Iteration: 3678; Percent complete: 92.0%; Average loss: 2.7064
Iteration: 3679; Percent complete: 92.0%; Average loss: 2.5986
Iteration: 3680; Percent complete: 92.0%; Average loss: 2.6826
Iteration: 3681; Percent complete: 92.0%; Average loss: 2.6126
Iteration: 3682; Percent complete: 92.0%; Average loss: 2.7025
Iteration: 3683; Percent complete: 92.1%; Average loss: 3.0262
Iteration: 3684; Percent complete: 92.1%; Average loss: 2.6588
Iteration: 3685; Percent complete: 92.1%; Average loss: 2.7390
Iteration: 3686; Percent complete: 92.2%; Average loss: 2.5086
Iteration: 3687; Percent complete: 92.2%; Average loss: 2.7422
Iteration: 3688; Percent complete: 92.2%; Average loss: 2.5990
Iteration: 3689; Percent complete: 92.2%; Average loss: 2.6483
Iteration: 3690; Percent complete: 92.2%; Average loss: 2.8632
Iteration: 3691; Percent complete: 92.3%; Average loss: 2.9690
Iteration: 3692; Percent complete: 92.3%; Average loss: 2.5833
Iteration: 3693; Percent complete: 92.3%; Average loss: 2.6708
Iteration: 3694; Percent complete: 92.3%; Average loss: 2.6847
Iteration: 3695; Percent complete: 92.4%; Average loss: 2.8252
Iteration: 3696; Percent complete: 92.4%; Average loss: 2.7645
Iteration: 3697; Percent complete: 92.4%; Average loss: 2.9844
Iteration: 3698; Percent complete: 92.5%; Average loss: 2.7169
Iteration: 3699; Percent complete: 92.5%; Average loss: 2.4826
Iteration: 3700; Percent complete: 92.5%; Average loss: 2.9463
Iteration: 3701; Percent complete: 92.5%; Average loss: 2.6445
Iteration: 3702; Percent complete: 92.5%; Average loss: 2.7442
Iteration: 3703; Percent complete: 92.6%; Average loss: 2.5657
Iteration: 3704; Percent complete: 92.6%; Average loss: 2.9308
Iteration: 3705; Percent complete: 92.6%; Average loss: 2.5747
Iteration: 3706; Percent complete: 92.7%; Average loss: 2.9079
Iteration: 3707; Percent complete: 92.7%; Average loss: 2.8758
Iteration: 3708; Percent complete: 92.7%; Average loss: 2.4199
Iteration: 3709; Percent complete: 92.7%; Average loss: 2.5379
Iteration: 3710; Percent complete: 92.8%; Average loss: 2.6102
Iteration: 3711; Percent complete: 92.8%; Average loss: 2.9339
Iteration: 3712; Percent complete: 92.8%; Average loss: 2.7867
Iteration: 3713; Percent complete: 92.8%; Average loss: 2.3831
Iteration: 3714; Percent complete: 92.8%; Average loss: 2.6594
Iteration: 3715; Percent complete: 92.9%; Average loss: 2.6996
Iteration: 3716; Percent complete: 92.9%; Average loss: 2.6973
Iteration: 3717; Percent complete: 92.9%; Average loss: 2.7462
Iteration: 3718; Percent complete: 93.0%; Average loss: 2.7391
Iteration: 3719; Percent complete: 93.0%; Average loss: 2.4731
Iteration: 3720; Percent complete: 93.0%; Average loss: 2.5842
Iteration: 3721; Percent complete: 93.0%; Average loss: 2.5145
Iteration: 3722; Percent complete: 93.0%; Average loss: 2.8348
Iteration: 3723; Percent complete: 93.1%; Average loss: 2.5491
Iteration: 3724; Percent complete: 93.1%; Average loss: 2.7662
Iteration: 3725; Percent complete: 93.1%; Average loss: 2.6464
Iteration: 3726; Percent complete: 93.2%; Average loss: 2.7095
Iteration: 3727; Percent complete: 93.2%; Average loss: 2.7233
Iteration: 3728; Percent complete: 93.2%; Average loss: 2.9386
Iteration: 3729; Percent complete: 93.2%; Average loss: 2.9774
Iteration: 3730; Percent complete: 93.2%; Average loss: 2.7049
Iteration: 3731; Percent complete: 93.3%; Average loss: 2.7389
Iteration: 3732; Percent complete: 93.3%; Average loss: 2.6401
Iteration: 3733; Percent complete: 93.3%; Average loss: 2.6572
Iteration: 3734; Percent complete: 93.3%; Average loss: 2.6106
Iteration: 3735; Percent complete: 93.4%; Average loss: 2.6196
Iteration: 3736; Percent complete: 93.4%; Average loss: 2.5886
Iteration: 3737; Percent complete: 93.4%; Average loss: 2.7174
Iteration: 3738; Percent complete: 93.5%; Average loss: 2.6079
Iteration: 3739; Percent complete: 93.5%; Average loss: 2.5389
Iteration: 3740; Percent complete: 93.5%; Average loss: 2.7443
Iteration: 3741; Percent complete: 93.5%; Average loss: 2.6993
Iteration: 3742; Percent complete: 93.5%; Average loss: 2.6573
Iteration: 3743; Percent complete: 93.6%; Average loss: 2.9755
Iteration: 3744; Percent complete: 93.6%; Average loss: 2.5016
Iteration: 3745; Percent complete: 93.6%; Average loss: 2.7324
Iteration: 3746; Percent complete: 93.7%; Average loss: 2.8701
Iteration: 3747; Percent complete: 93.7%; Average loss: 2.6562
Iteration: 3748; Percent complete: 93.7%; Average loss: 2.6568
Iteration: 3749; Percent complete: 93.7%; Average loss: 2.6277
Iteration: 3750; Percent complete: 93.8%; Average loss: 2.6429
Iteration: 3751; Percent complete: 93.8%; Average loss: 2.6227
Iteration: 3752; Percent complete: 93.8%; Average loss: 2.9640
Iteration: 3753; Percent complete: 93.8%; Average loss: 2.8402
Iteration: 3754; Percent complete: 93.8%; Average loss: 2.7757
Iteration: 3755; Percent complete: 93.9%; Average loss: 2.4691
Iteration: 3756; Percent complete: 93.9%; Average loss: 2.5764
Iteration: 3757; Percent complete: 93.9%; Average loss: 2.8466
Iteration: 3758; Percent complete: 94.0%; Average loss: 2.8617
Iteration: 3759; Percent complete: 94.0%; Average loss: 2.6320
Iteration: 3760; Percent complete: 94.0%; Average loss: 2.8681
Iteration: 3761; Percent complete: 94.0%; Average loss: 2.6157
Iteration: 3762; Percent complete: 94.0%; Average loss: 2.5606
Iteration: 3763; Percent complete: 94.1%; Average loss: 2.4355
Iteration: 3764; Percent complete: 94.1%; Average loss: 2.5737
Iteration: 3765; Percent complete: 94.1%; Average loss: 2.7685
Iteration: 3766; Percent complete: 94.2%; Average loss: 2.5145
Iteration: 3767; Percent complete: 94.2%; Average loss: 2.7167
Iteration: 3768; Percent complete: 94.2%; Average loss: 2.8171
Iteration: 3769; Percent complete: 94.2%; Average loss: 2.5078
Iteration: 3770; Percent complete: 94.2%; Average loss: 2.6589
Iteration: 3771; Percent complete: 94.3%; Average loss: 2.6416
Iteration: 3772; Percent complete: 94.3%; Average loss: 2.7597
Iteration: 3773; Percent complete: 94.3%; Average loss: 2.6691
Iteration: 3774; Percent complete: 94.3%; Average loss: 2.8732
Iteration: 3775; Percent complete: 94.4%; Average loss: 2.8183
Iteration: 3776; Percent complete: 94.4%; Average loss: 2.6874
Iteration: 3777; Percent complete: 94.4%; Average loss: 2.9548
Iteration: 3778; Percent complete: 94.5%; Average loss: 2.6109
Iteration: 3779; Percent complete: 94.5%; Average loss: 2.5714
Iteration: 3780; Percent complete: 94.5%; Average loss: 2.6454
Iteration: 3781; Percent complete: 94.5%; Average loss: 2.5507
Iteration: 3782; Percent complete: 94.5%; Average loss: 2.6404
Iteration: 3783; Percent complete: 94.6%; Average loss: 2.8877
Iteration: 3784; Percent complete: 94.6%; Average loss: 2.4944
Iteration: 3785; Percent complete: 94.6%; Average loss: 2.6495
Iteration: 3786; Percent complete: 94.7%; Average loss: 2.7662
Iteration: 3787; Percent complete: 94.7%; Average loss: 2.8747
Iteration: 3788; Percent complete: 94.7%; Average loss: 2.9837
Iteration: 3789; Percent complete: 94.7%; Average loss: 2.7845
Iteration: 3790; Percent complete: 94.8%; Average loss: 2.5112
Iteration: 3791; Percent complete: 94.8%; Average loss: 2.7403
Iteration: 3792; Percent complete: 94.8%; Average loss: 2.8498
Iteration: 3793; Percent complete: 94.8%; Average loss: 2.7382
Iteration: 3794; Percent complete: 94.8%; Average loss: 2.5949
Iteration: 3795; Percent complete: 94.9%; Average loss: 2.6039
Iteration: 3796; Percent complete: 94.9%; Average loss: 2.6285
Iteration: 3797; Percent complete: 94.9%; Average loss: 2.7798
Iteration: 3798; Percent complete: 95.0%; Average loss: 2.6549
Iteration: 3799; Percent complete: 95.0%; Average loss: 2.6688
Iteration: 3800; Percent complete: 95.0%; Average loss: 2.6190
Iteration: 3801; Percent complete: 95.0%; Average loss: 2.7209
Iteration: 3802; Percent complete: 95.0%; Average loss: 2.6361
Iteration: 3803; Percent complete: 95.1%; Average loss: 2.7759
Iteration: 3804; Percent complete: 95.1%; Average loss: 2.5009
Iteration: 3805; Percent complete: 95.1%; Average loss: 2.8078
Iteration: 3806; Percent complete: 95.2%; Average loss: 2.6646
Iteration: 3807; Percent complete: 95.2%; Average loss: 2.7721
Iteration: 3808; Percent complete: 95.2%; Average loss: 2.7316
Iteration: 3809; Percent complete: 95.2%; Average loss: 2.5602
Iteration: 3810; Percent complete: 95.2%; Average loss: 2.6655
Iteration: 3811; Percent complete: 95.3%; Average loss: 2.8261
Iteration: 3812; Percent complete: 95.3%; Average loss: 2.5790
Iteration: 3813; Percent complete: 95.3%; Average loss: 2.6399
Iteration: 3814; Percent complete: 95.3%; Average loss: 2.5284
Iteration: 3815; Percent complete: 95.4%; Average loss: 2.4607
Iteration: 3816; Percent complete: 95.4%; Average loss: 2.6960
Iteration: 3817; Percent complete: 95.4%; Average loss: 2.6637
Iteration: 3818; Percent complete: 95.5%; Average loss: 2.5656
Iteration: 3819; Percent complete: 95.5%; Average loss: 2.6427
Iteration: 3820; Percent complete: 95.5%; Average loss: 2.8437
Iteration: 3821; Percent complete: 95.5%; Average loss: 2.4584
Iteration: 3822; Percent complete: 95.5%; Average loss: 2.7035
Iteration: 3823; Percent complete: 95.6%; Average loss: 2.7229
Iteration: 3824; Percent complete: 95.6%; Average loss: 2.7574
Iteration: 3825; Percent complete: 95.6%; Average loss: 2.7742
Iteration: 3826; Percent complete: 95.7%; Average loss: 2.5568
Iteration: 3827; Percent complete: 95.7%; Average loss: 2.7118
Iteration: 3828; Percent complete: 95.7%; Average loss: 2.8061
Iteration: 3829; Percent complete: 95.7%; Average loss: 2.6779
Iteration: 3830; Percent complete: 95.8%; Average loss: 2.8125
Iteration: 3831; Percent complete: 95.8%; Average loss: 2.4562
Iteration: 3832; Percent complete: 95.8%; Average loss: 2.6053
Iteration: 3833; Percent complete: 95.8%; Average loss: 2.3043
Iteration: 3834; Percent complete: 95.9%; Average loss: 2.6268
Iteration: 3835; Percent complete: 95.9%; Average loss: 2.7206
Iteration: 3836; Percent complete: 95.9%; Average loss: 2.7404
Iteration: 3837; Percent complete: 95.9%; Average loss: 2.5205
Iteration: 3838; Percent complete: 96.0%; Average loss: 2.8459
Iteration: 3839; Percent complete: 96.0%; Average loss: 2.8603
Iteration: 3840; Percent complete: 96.0%; Average loss: 2.7370
Iteration: 3841; Percent complete: 96.0%; Average loss: 2.7251
Iteration: 3842; Percent complete: 96.0%; Average loss: 2.7116
Iteration: 3843; Percent complete: 96.1%; Average loss: 2.7422
Iteration: 3844; Percent complete: 96.1%; Average loss: 2.6841
Iteration: 3845; Percent complete: 96.1%; Average loss: 2.6561
Iteration: 3846; Percent complete: 96.2%; Average loss: 2.6195
Iteration: 3847; Percent complete: 96.2%; Average loss: 2.6565
Iteration: 3848; Percent complete: 96.2%; Average loss: 2.8486
Iteration: 3849; Percent complete: 96.2%; Average loss: 2.5008
Iteration: 3850; Percent complete: 96.2%; Average loss: 3.0639
Iteration: 3851; Percent complete: 96.3%; Average loss: 2.5158
Iteration: 3852; Percent complete: 96.3%; Average loss: 2.7182
Iteration: 3853; Percent complete: 96.3%; Average loss: 2.6343
Iteration: 3854; Percent complete: 96.4%; Average loss: 2.6931
Iteration: 3855; Percent complete: 96.4%; Average loss: 2.7174
Iteration: 3856; Percent complete: 96.4%; Average loss: 2.7724
Iteration: 3857; Percent complete: 96.4%; Average loss: 2.6876
Iteration: 3858; Percent complete: 96.5%; Average loss: 2.7885
Iteration: 3859; Percent complete: 96.5%; Average loss: 2.8335
Iteration: 3860; Percent complete: 96.5%; Average loss: 2.7944
Iteration: 3861; Percent complete: 96.5%; Average loss: 2.6779
Iteration: 3862; Percent complete: 96.5%; Average loss: 2.6990
Iteration: 3863; Percent complete: 96.6%; Average loss: 2.7111
Iteration: 3864; Percent complete: 96.6%; Average loss: 2.8331
Iteration: 3865; Percent complete: 96.6%; Average loss: 2.6566
Iteration: 3866; Percent complete: 96.7%; Average loss: 2.4972
Iteration: 3867; Percent complete: 96.7%; Average loss: 2.7796
Iteration: 3868; Percent complete: 96.7%; Average loss: 2.5610
Iteration: 3869; Percent complete: 96.7%; Average loss: 2.6655
Iteration: 3870; Percent complete: 96.8%; Average loss: 2.6723
Iteration: 3871; Percent complete: 96.8%; Average loss: 2.6558
Iteration: 3872; Percent complete: 96.8%; Average loss: 2.5573
Iteration: 3873; Percent complete: 96.8%; Average loss: 2.5266
Iteration: 3874; Percent complete: 96.9%; Average loss: 2.5521
Iteration: 3875; Percent complete: 96.9%; Average loss: 2.8360
Iteration: 3876; Percent complete: 96.9%; Average loss: 2.7345
Iteration: 3877; Percent complete: 96.9%; Average loss: 2.7385
Iteration: 3878; Percent complete: 97.0%; Average loss: 2.8492
Iteration: 3879; Percent complete: 97.0%; Average loss: 2.6256
Iteration: 3880; Percent complete: 97.0%; Average loss: 2.7069
Iteration: 3881; Percent complete: 97.0%; Average loss: 2.7422
Iteration: 3882; Percent complete: 97.0%; Average loss: 2.7414
Iteration: 3883; Percent complete: 97.1%; Average loss: 2.5743
Iteration: 3884; Percent complete: 97.1%; Average loss: 2.4871
Iteration: 3885; Percent complete: 97.1%; Average loss: 2.8165
Iteration: 3886; Percent complete: 97.2%; Average loss: 2.6807
Iteration: 3887; Percent complete: 97.2%; Average loss: 2.6111
Iteration: 3888; Percent complete: 97.2%; Average loss: 2.6021
Iteration: 3889; Percent complete: 97.2%; Average loss: 2.5970
Iteration: 3890; Percent complete: 97.2%; Average loss: 2.8956
Iteration: 3891; Percent complete: 97.3%; Average loss: 2.5498
Iteration: 3892; Percent complete: 97.3%; Average loss: 2.5910
Iteration: 3893; Percent complete: 97.3%; Average loss: 2.6430
Iteration: 3894; Percent complete: 97.4%; Average loss: 2.7680
Iteration: 3895; Percent complete: 97.4%; Average loss: 2.7170
Iteration: 3896; Percent complete: 97.4%; Average loss: 2.3405
Iteration: 3897; Percent complete: 97.4%; Average loss: 2.7482
Iteration: 3898; Percent complete: 97.5%; Average loss: 2.7100
Iteration: 3899; Percent complete: 97.5%; Average loss: 2.6536
Iteration: 3900; Percent complete: 97.5%; Average loss: 2.8853
Iteration: 3901; Percent complete: 97.5%; Average loss: 2.7450
Iteration: 3902; Percent complete: 97.5%; Average loss: 2.6541
Iteration: 3903; Percent complete: 97.6%; Average loss: 2.8753
Iteration: 3904; Percent complete: 97.6%; Average loss: 2.5130
Iteration: 3905; Percent complete: 97.6%; Average loss: 2.7300
Iteration: 3906; Percent complete: 97.7%; Average loss: 2.7158
Iteration: 3907; Percent complete: 97.7%; Average loss: 2.8951
Iteration: 3908; Percent complete: 97.7%; Average loss: 2.5556
Iteration: 3909; Percent complete: 97.7%; Average loss: 2.6767
Iteration: 3910; Percent complete: 97.8%; Average loss: 2.6715
Iteration: 3911; Percent complete: 97.8%; Average loss: 2.7672
Iteration: 3912; Percent complete: 97.8%; Average loss: 3.0198
Iteration: 3913; Percent complete: 97.8%; Average loss: 2.4271
Iteration: 3914; Percent complete: 97.9%; Average loss: 2.8076
Iteration: 3915; Percent complete: 97.9%; Average loss: 2.6116
Iteration: 3916; Percent complete: 97.9%; Average loss: 2.7703
Iteration: 3917; Percent complete: 97.9%; Average loss: 2.5974
Iteration: 3918; Percent complete: 98.0%; Average loss: 2.6378
Iteration: 3919; Percent complete: 98.0%; Average loss: 2.6573
Iteration: 3920; Percent complete: 98.0%; Average loss: 2.5835
Iteration: 3921; Percent complete: 98.0%; Average loss: 2.6818
Iteration: 3922; Percent complete: 98.0%; Average loss: 2.5500
Iteration: 3923; Percent complete: 98.1%; Average loss: 2.5221
Iteration: 3924; Percent complete: 98.1%; Average loss: 2.5741
Iteration: 3925; Percent complete: 98.1%; Average loss: 2.7943
Iteration: 3926; Percent complete: 98.2%; Average loss: 2.6735
Iteration: 3927; Percent complete: 98.2%; Average loss: 2.7042
Iteration: 3928; Percent complete: 98.2%; Average loss: 2.6074
Iteration: 3929; Percent complete: 98.2%; Average loss: 2.8457
Iteration: 3930; Percent complete: 98.2%; Average loss: 2.6647
Iteration: 3931; Percent complete: 98.3%; Average loss: 2.9727
Iteration: 3932; Percent complete: 98.3%; Average loss: 2.8483
Iteration: 3933; Percent complete: 98.3%; Average loss: 2.6451
Iteration: 3934; Percent complete: 98.4%; Average loss: 2.7004
Iteration: 3935; Percent complete: 98.4%; Average loss: 2.7002
Iteration: 3936; Percent complete: 98.4%; Average loss: 2.5117
Iteration: 3937; Percent complete: 98.4%; Average loss: 2.4554
Iteration: 3938; Percent complete: 98.5%; Average loss: 2.5206
Iteration: 3939; Percent complete: 98.5%; Average loss: 2.6814
Iteration: 3940; Percent complete: 98.5%; Average loss: 2.5204
Iteration: 3941; Percent complete: 98.5%; Average loss: 2.5594
Iteration: 3942; Percent complete: 98.6%; Average loss: 2.5858
Iteration: 3943; Percent complete: 98.6%; Average loss: 2.7395
Iteration: 3944; Percent complete: 98.6%; Average loss: 2.6083
Iteration: 3945; Percent complete: 98.6%; Average loss: 2.5960
Iteration: 3946; Percent complete: 98.7%; Average loss: 2.6962
Iteration: 3947; Percent complete: 98.7%; Average loss: 2.6422
Iteration: 3948; Percent complete: 98.7%; Average loss: 2.5564
Iteration: 3949; Percent complete: 98.7%; Average loss: 2.7407
Iteration: 3950; Percent complete: 98.8%; Average loss: 2.5231
Iteration: 3951; Percent complete: 98.8%; Average loss: 2.6287
Iteration: 3952; Percent complete: 98.8%; Average loss: 2.7067
Iteration: 3953; Percent complete: 98.8%; Average loss: 2.6248
Iteration: 3954; Percent complete: 98.9%; Average loss: 2.8082
Iteration: 3955; Percent complete: 98.9%; Average loss: 2.6305
Iteration: 3956; Percent complete: 98.9%; Average loss: 2.5132
Iteration: 3957; Percent complete: 98.9%; Average loss: 2.7062
Iteration: 3958; Percent complete: 99.0%; Average loss: 2.2956
Iteration: 3959; Percent complete: 99.0%; Average loss: 2.7265
Iteration: 3960; Percent complete: 99.0%; Average loss: 2.7822
Iteration: 3961; Percent complete: 99.0%; Average loss: 2.6493
Iteration: 3962; Percent complete: 99.1%; Average loss: 2.8993
Iteration: 3963; Percent complete: 99.1%; Average loss: 2.3845
Iteration: 3964; Percent complete: 99.1%; Average loss: 2.6043
Iteration: 3965; Percent complete: 99.1%; Average loss: 2.5707
Iteration: 3966; Percent complete: 99.2%; Average loss: 2.6528
Iteration: 3967; Percent complete: 99.2%; Average loss: 2.8622
Iteration: 3968; Percent complete: 99.2%; Average loss: 2.7044
Iteration: 3969; Percent complete: 99.2%; Average loss: 2.8174
Iteration: 3970; Percent complete: 99.2%; Average loss: 2.5725
Iteration: 3971; Percent complete: 99.3%; Average loss: 2.7344
Iteration: 3972; Percent complete: 99.3%; Average loss: 2.6321
Iteration: 3973; Percent complete: 99.3%; Average loss: 2.4158
Iteration: 3974; Percent complete: 99.4%; Average loss: 2.8477
Iteration: 3975; Percent complete: 99.4%; Average loss: 2.6930
Iteration: 3976; Percent complete: 99.4%; Average loss: 2.6678
Iteration: 3977; Percent complete: 99.4%; Average loss: 2.7311
Iteration: 3978; Percent complete: 99.5%; Average loss: 2.5414
Iteration: 3979; Percent complete: 99.5%; Average loss: 2.9337
Iteration: 3980; Percent complete: 99.5%; Average loss: 2.6800
Iteration: 3981; Percent complete: 99.5%; Average loss: 2.7297
Iteration: 3982; Percent complete: 99.6%; Average loss: 2.5608
Iteration: 3983; Percent complete: 99.6%; Average loss: 2.8523
Iteration: 3984; Percent complete: 99.6%; Average loss: 2.8647
Iteration: 3985; Percent complete: 99.6%; Average loss: 2.5557
Iteration: 3986; Percent complete: 99.7%; Average loss: 2.6155
Iteration: 3987; Percent complete: 99.7%; Average loss: 2.6691
Iteration: 3988; Percent complete: 99.7%; Average loss: 2.6968
Iteration: 3989; Percent complete: 99.7%; Average loss: 2.6979
Iteration: 3990; Percent complete: 99.8%; Average loss: 2.5588
Iteration: 3991; Percent complete: 99.8%; Average loss: 2.6935
Iteration: 3992; Percent complete: 99.8%; Average loss: 2.6058
Iteration: 3993; Percent complete: 99.8%; Average loss: 2.6191
Iteration: 3994; Percent complete: 99.9%; Average loss: 2.6086
Iteration: 3995; Percent complete: 99.9%; Average loss: 2.8629
Iteration: 3996; Percent complete: 99.9%; Average loss: 2.3897
Iteration: 3997; Percent complete: 99.9%; Average loss: 2.7701
Iteration: 3998; Percent complete: 100.0%; Average loss: 2.6439
Iteration: 3999; Percent complete: 100.0%; Average loss: 2.6649
Iteration: 4000; Percent complete: 100.0%; Average loss: 2.5970
Run Evaluation#
To chat with your model, run the following block.
# Set dropout layers to ``eval`` mode
encoder.eval()
decoder.eval()
# Initialize search module
searcher = GreedySearchDecoder(encoder, decoder)
# Begin chatting (uncomment and run the following line to begin)
# evaluateInput(encoder, decoder, searcher, voc)
Conclusion#
That’s all for this one, folks. Congratulations, you now know the fundamentals to building a generative chatbot model! If you’re interested, you can try tailoring the chatbot’s behavior by tweaking the model and training parameters and customizing the data that you train the model on.
Check out the other tutorials for more cool deep learning applications in PyTorch!
Total running time of the script: (2 minutes 17.966 seconds)
