beginner/chatbot_tutorial

Run in Google Colab

Colab

Download Notebook

Notebook

View on GitHub

GitHub

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:

1. Yuan-Kuei Wu’s pytorch-chatbot implementation: ywk991112/pytorch-chatbot

2. Sean Robertson’s practical-pytorch seq2seq-translation example: spro/practical-pytorch

3. 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.

• loadLinesAndConversations splits 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.

• extractSentencePairs extracts 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:

1. Trim words used under MIN_COUNT threshold using the voc.trim function.

2. 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([[  24, 1517,   24, 2073,   86],
        [ 121,   25,  265, 2073, 4347],
        [1054,   62,   36,   14,   14],
        [ 753,  724,   10,    2,    2],
        [  58, 1992,    2,    0,    0],
        [  24,   14,    0,    0,    0],
        [2704,    2,    0,    0,    0],
        [   6,    0,    0,    0,    0],
        [   2,    0,    0,    0,    0]])
lengths: tensor([9, 7, 5, 4, 4])
target_variable: tensor([[ 829,   85,   19,   75,  175],
        [  31,   17,   10,   17,  109],
        [   6,   22,    2,   66,   24],
        [   2, 2465,    0,   10, 1117],
        [   0, 1149,    0,    2,   10],
        [   0,  530,    0,    0,    2],
        [   0,   14,    0,    0,    0],
        [   0,    2,    0,    0,    0]])
mask: tensor([[ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True, False,  True,  True],
        [False,  True, False,  True,  True],
        [False,  True, False, False,  True],
        [False,  True, False, False, False],
        [False,  True, False, False, False]])
max_target_len: 8

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:

1. Convert word indexes to embeddings.

2. Pack padded batch of sequences for RNN module.

3. Forward pass through GRU.

4. Unpack padding.

5. Sum bidirectional GRU outputs.

6. 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:

1. Get embedding of current input word.

2. Forward through unidirectional GRU.

3. Calculate attention weights from the current GRU output from (2).

4. Multiply attention weights to encoder outputs to get new “weighted sum” context vector.

5. Concatenate weighted context vector and GRU output using Luong eq. 5.

6. Predict next word using Luong eq. 6 (without softmax).

7. 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 the teacher_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:

1. Forward pass entire input batch through encoder.

2. Initialize decoder inputs as SOS_token, and hidden state as the encoder’s final hidden state.

3. Forward input batch sequence through decoder one time step at a time.

4. If teacher forcing: set next decoder input as the current target; else: set next decoder input as current decoder output.

5. Calculate and accumulate loss.

6. Perform backpropagation.

7. Clip gradients.

8. 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:

1. Forward input through encoder model.

2. Prepare encoder’s final hidden layer to be first hidden input to the decoder.

3. Initialize decoder’s first input as SOS_token.

4. Initialize tensors to append decoded words to.

5. Iteratively decode one word token at a time:

   1. Forward pass through decoder.

   2. Obtain most likely word token and its softmax score.

   3. Record token and score.

   4. Prepare current token to be next decoder input.

6. 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: 8.9677
Iteration: 2; Percent complete: 0.1%; Average loss: 8.8705
Iteration: 3; Percent complete: 0.1%; Average loss: 8.6915
Iteration: 4; Percent complete: 0.1%; Average loss: 8.3946
Iteration: 5; Percent complete: 0.1%; Average loss: 8.0786
Iteration: 6; Percent complete: 0.1%; Average loss: 7.5290
Iteration: 7; Percent complete: 0.2%; Average loss: 6.9596
Iteration: 8; Percent complete: 0.2%; Average loss: 6.8918
Iteration: 9; Percent complete: 0.2%; Average loss: 7.0967
Iteration: 10; Percent complete: 0.2%; Average loss: 6.9182
Iteration: 11; Percent complete: 0.3%; Average loss: 6.2335
Iteration: 12; Percent complete: 0.3%; Average loss: 5.9922
Iteration: 13; Percent complete: 0.3%; Average loss: 5.9575
Iteration: 14; Percent complete: 0.4%; Average loss: 5.7769
Iteration: 15; Percent complete: 0.4%; Average loss: 5.8727
Iteration: 16; Percent complete: 0.4%; Average loss: 5.4138
Iteration: 17; Percent complete: 0.4%; Average loss: 5.4526
Iteration: 18; Percent complete: 0.4%; Average loss: 5.2040
Iteration: 19; Percent complete: 0.5%; Average loss: 5.2360
Iteration: 20; Percent complete: 0.5%; Average loss: 5.0180
Iteration: 21; Percent complete: 0.5%; Average loss: 5.1657
Iteration: 22; Percent complete: 0.5%; Average loss: 4.9537
Iteration: 23; Percent complete: 0.6%; Average loss: 4.8166
Iteration: 24; Percent complete: 0.6%; Average loss: 4.6014
Iteration: 25; Percent complete: 0.6%; Average loss: 5.0502
Iteration: 26; Percent complete: 0.7%; Average loss: 4.9530
Iteration: 27; Percent complete: 0.7%; Average loss: 4.8794
Iteration: 28; Percent complete: 0.7%; Average loss: 4.8572
Iteration: 29; Percent complete: 0.7%; Average loss: 4.6835
Iteration: 30; Percent complete: 0.8%; Average loss: 4.7996
Iteration: 31; Percent complete: 0.8%; Average loss: 4.5679
Iteration: 32; Percent complete: 0.8%; Average loss: 4.7573
Iteration: 33; Percent complete: 0.8%; Average loss: 4.7096
Iteration: 34; Percent complete: 0.9%; Average loss: 4.5968
Iteration: 35; Percent complete: 0.9%; Average loss: 4.8331
Iteration: 36; Percent complete: 0.9%; Average loss: 4.6691
Iteration: 37; Percent complete: 0.9%; Average loss: 4.9375
Iteration: 38; Percent complete: 0.9%; Average loss: 4.8929
Iteration: 39; Percent complete: 1.0%; Average loss: 4.8079
Iteration: 40; Percent complete: 1.0%; Average loss: 4.6847
Iteration: 41; Percent complete: 1.0%; Average loss: 4.6192
Iteration: 42; Percent complete: 1.1%; Average loss: 4.7220
Iteration: 43; Percent complete: 1.1%; Average loss: 4.8197
Iteration: 44; Percent complete: 1.1%; Average loss: 4.7117
Iteration: 45; Percent complete: 1.1%; Average loss: 4.7313
Iteration: 46; Percent complete: 1.1%; Average loss: 4.5942
Iteration: 47; Percent complete: 1.2%; Average loss: 4.4567
Iteration: 48; Percent complete: 1.2%; Average loss: 4.6072
Iteration: 49; Percent complete: 1.2%; Average loss: 4.4187
Iteration: 50; Percent complete: 1.2%; Average loss: 4.7157
Iteration: 51; Percent complete: 1.3%; Average loss: 4.5554
Iteration: 52; Percent complete: 1.3%; Average loss: 4.7024
Iteration: 53; Percent complete: 1.3%; Average loss: 4.7338
Iteration: 54; Percent complete: 1.4%; Average loss: 4.6223
Iteration: 55; Percent complete: 1.4%; Average loss: 5.1401
Iteration: 56; Percent complete: 1.4%; Average loss: 4.4667
Iteration: 57; Percent complete: 1.4%; Average loss: 4.5908
Iteration: 58; Percent complete: 1.5%; Average loss: 4.6999
Iteration: 59; Percent complete: 1.5%; Average loss: 4.3722
Iteration: 60; Percent complete: 1.5%; Average loss: 4.5234
Iteration: 61; Percent complete: 1.5%; Average loss: 4.3098
Iteration: 62; Percent complete: 1.6%; Average loss: 4.6007
Iteration: 63; Percent complete: 1.6%; Average loss: 4.6746
Iteration: 64; Percent complete: 1.6%; Average loss: 4.3204
Iteration: 65; Percent complete: 1.6%; Average loss: 4.5007
Iteration: 66; Percent complete: 1.7%; Average loss: 4.5372
Iteration: 67; Percent complete: 1.7%; Average loss: 4.4228
Iteration: 68; Percent complete: 1.7%; Average loss: 4.4358
Iteration: 69; Percent complete: 1.7%; Average loss: 4.4286
Iteration: 70; Percent complete: 1.8%; Average loss: 4.4205
Iteration: 71; Percent complete: 1.8%; Average loss: 4.4163
Iteration: 72; Percent complete: 1.8%; Average loss: 4.8601
Iteration: 73; Percent complete: 1.8%; Average loss: 4.6114
Iteration: 74; Percent complete: 1.8%; Average loss: 4.6393
Iteration: 75; Percent complete: 1.9%; Average loss: 4.4327
Iteration: 76; Percent complete: 1.9%; Average loss: 4.2813
Iteration: 77; Percent complete: 1.9%; Average loss: 4.5355
Iteration: 78; Percent complete: 1.9%; Average loss: 4.3146
Iteration: 79; Percent complete: 2.0%; Average loss: 4.3956
Iteration: 80; Percent complete: 2.0%; Average loss: 4.5060
Iteration: 81; Percent complete: 2.0%; Average loss: 4.6030
Iteration: 82; Percent complete: 2.1%; Average loss: 4.1618
Iteration: 83; Percent complete: 2.1%; Average loss: 4.4502
Iteration: 84; Percent complete: 2.1%; Average loss: 4.5003
Iteration: 85; Percent complete: 2.1%; Average loss: 4.4968
Iteration: 86; Percent complete: 2.1%; Average loss: 4.3352
Iteration: 87; Percent complete: 2.2%; Average loss: 4.3198
Iteration: 88; Percent complete: 2.2%; Average loss: 4.5416
Iteration: 89; Percent complete: 2.2%; Average loss: 4.4808
Iteration: 90; Percent complete: 2.2%; Average loss: 4.5137
Iteration: 91; Percent complete: 2.3%; Average loss: 4.2658
Iteration: 92; Percent complete: 2.3%; Average loss: 4.2156
Iteration: 93; Percent complete: 2.3%; Average loss: 4.3090
Iteration: 94; Percent complete: 2.4%; Average loss: 4.1702
Iteration: 95; Percent complete: 2.4%; Average loss: 4.3019
Iteration: 96; Percent complete: 2.4%; Average loss: 4.4017
Iteration: 97; Percent complete: 2.4%; Average loss: 4.2240
Iteration: 98; Percent complete: 2.5%; Average loss: 4.5000
Iteration: 99; Percent complete: 2.5%; Average loss: 4.5180
Iteration: 100; Percent complete: 2.5%; Average loss: 4.3381
Iteration: 101; Percent complete: 2.5%; Average loss: 4.3769
Iteration: 102; Percent complete: 2.5%; Average loss: 4.4059
Iteration: 103; Percent complete: 2.6%; Average loss: 4.5871
Iteration: 104; Percent complete: 2.6%; Average loss: 4.7251
Iteration: 105; Percent complete: 2.6%; Average loss: 4.6331
Iteration: 106; Percent complete: 2.6%; Average loss: 4.2226
Iteration: 107; Percent complete: 2.7%; Average loss: 4.4470
Iteration: 108; Percent complete: 2.7%; Average loss: 4.4275
Iteration: 109; Percent complete: 2.7%; Average loss: 4.3664
Iteration: 110; Percent complete: 2.8%; Average loss: 4.2541
Iteration: 111; Percent complete: 2.8%; Average loss: 4.2906
Iteration: 112; Percent complete: 2.8%; Average loss: 4.2122
Iteration: 113; Percent complete: 2.8%; Average loss: 4.4387
Iteration: 114; Percent complete: 2.9%; Average loss: 4.4753
Iteration: 115; Percent complete: 2.9%; Average loss: 4.2110
Iteration: 116; Percent complete: 2.9%; Average loss: 4.2592
Iteration: 117; Percent complete: 2.9%; Average loss: 4.1893
Iteration: 118; Percent complete: 2.9%; Average loss: 4.3081
Iteration: 119; Percent complete: 3.0%; Average loss: 4.7046
Iteration: 120; Percent complete: 3.0%; Average loss: 4.5140
Iteration: 121; Percent complete: 3.0%; Average loss: 4.4618
Iteration: 122; Percent complete: 3.0%; Average loss: 4.2816
Iteration: 123; Percent complete: 3.1%; Average loss: 4.0214
Iteration: 124; Percent complete: 3.1%; Average loss: 4.4331
Iteration: 125; Percent complete: 3.1%; Average loss: 4.1495
Iteration: 126; Percent complete: 3.1%; Average loss: 4.4038
Iteration: 127; Percent complete: 3.2%; Average loss: 4.2612
Iteration: 128; Percent complete: 3.2%; Average loss: 4.4306
Iteration: 129; Percent complete: 3.2%; Average loss: 4.3784
Iteration: 130; Percent complete: 3.2%; Average loss: 4.5732
Iteration: 131; Percent complete: 3.3%; Average loss: 4.1610
Iteration: 132; Percent complete: 3.3%; Average loss: 4.2700
Iteration: 133; Percent complete: 3.3%; Average loss: 4.2409
Iteration: 134; Percent complete: 3.4%; Average loss: 4.1458
Iteration: 135; Percent complete: 3.4%; Average loss: 4.3567
Iteration: 136; Percent complete: 3.4%; Average loss: 4.3398
Iteration: 137; Percent complete: 3.4%; Average loss: 4.3157
Iteration: 138; Percent complete: 3.5%; Average loss: 4.2732
Iteration: 139; Percent complete: 3.5%; Average loss: 4.1883
Iteration: 140; Percent complete: 3.5%; Average loss: 4.0387
Iteration: 141; Percent complete: 3.5%; Average loss: 4.3508
Iteration: 142; Percent complete: 3.5%; Average loss: 3.8638
Iteration: 143; Percent complete: 3.6%; Average loss: 4.3799
Iteration: 144; Percent complete: 3.6%; Average loss: 4.3619
Iteration: 145; Percent complete: 3.6%; Average loss: 4.1675
Iteration: 146; Percent complete: 3.6%; Average loss: 4.0760
Iteration: 147; Percent complete: 3.7%; Average loss: 4.3155
Iteration: 148; Percent complete: 3.7%; Average loss: 4.5455
Iteration: 149; Percent complete: 3.7%; Average loss: 4.1889
Iteration: 150; Percent complete: 3.8%; Average loss: 4.2476
Iteration: 151; Percent complete: 3.8%; Average loss: 4.3571
Iteration: 152; Percent complete: 3.8%; Average loss: 4.2170
Iteration: 153; Percent complete: 3.8%; Average loss: 4.2223
Iteration: 154; Percent complete: 3.9%; Average loss: 4.1320
Iteration: 155; Percent complete: 3.9%; Average loss: 4.2193
Iteration: 156; Percent complete: 3.9%; Average loss: 3.9767
Iteration: 157; Percent complete: 3.9%; Average loss: 4.5271
Iteration: 158; Percent complete: 4.0%; Average loss: 4.3869
Iteration: 159; Percent complete: 4.0%; Average loss: 4.2835
Iteration: 160; Percent complete: 4.0%; Average loss: 4.0933
Iteration: 161; Percent complete: 4.0%; Average loss: 4.1530
Iteration: 162; Percent complete: 4.0%; Average loss: 4.0703
Iteration: 163; Percent complete: 4.1%; Average loss: 4.3369
Iteration: 164; Percent complete: 4.1%; Average loss: 4.1837
Iteration: 165; Percent complete: 4.1%; Average loss: 4.2411
Iteration: 166; Percent complete: 4.2%; Average loss: 4.3908
Iteration: 167; Percent complete: 4.2%; Average loss: 4.0009
Iteration: 168; Percent complete: 4.2%; Average loss: 4.0718
Iteration: 169; Percent complete: 4.2%; Average loss: 4.2127
Iteration: 170; Percent complete: 4.2%; Average loss: 4.1959
Iteration: 171; Percent complete: 4.3%; Average loss: 4.1693
Iteration: 172; Percent complete: 4.3%; Average loss: 4.2583
Iteration: 173; Percent complete: 4.3%; Average loss: 4.2352
Iteration: 174; Percent complete: 4.3%; Average loss: 4.0708
Iteration: 175; Percent complete: 4.4%; Average loss: 4.4572
Iteration: 176; Percent complete: 4.4%; Average loss: 4.3734
Iteration: 177; Percent complete: 4.4%; Average loss: 4.0852
Iteration: 178; Percent complete: 4.5%; Average loss: 4.5555
Iteration: 179; Percent complete: 4.5%; Average loss: 4.0977
Iteration: 180; Percent complete: 4.5%; Average loss: 4.1962
Iteration: 181; Percent complete: 4.5%; Average loss: 3.8334
Iteration: 182; Percent complete: 4.5%; Average loss: 4.0134
Iteration: 183; Percent complete: 4.6%; Average loss: 4.1332
Iteration: 184; Percent complete: 4.6%; Average loss: 4.0599
Iteration: 185; Percent complete: 4.6%; Average loss: 3.9880
Iteration: 186; Percent complete: 4.7%; Average loss: 3.7876
Iteration: 187; Percent complete: 4.7%; Average loss: 3.9976
Iteration: 188; Percent complete: 4.7%; Average loss: 4.0704
Iteration: 189; Percent complete: 4.7%; Average loss: 4.2102
Iteration: 190; Percent complete: 4.8%; Average loss: 4.1514
Iteration: 191; Percent complete: 4.8%; Average loss: 4.1505
Iteration: 192; Percent complete: 4.8%; Average loss: 4.3854
Iteration: 193; Percent complete: 4.8%; Average loss: 4.1182
Iteration: 194; Percent complete: 4.9%; Average loss: 4.1263
Iteration: 195; Percent complete: 4.9%; Average loss: 4.0725
Iteration: 196; Percent complete: 4.9%; Average loss: 4.0103
Iteration: 197; Percent complete: 4.9%; Average loss: 3.8889
Iteration: 198; Percent complete: 5.0%; Average loss: 4.1195
Iteration: 199; Percent complete: 5.0%; Average loss: 3.9795
Iteration: 200; Percent complete: 5.0%; Average loss: 4.1511
Iteration: 201; Percent complete: 5.0%; Average loss: 4.2263
Iteration: 202; Percent complete: 5.1%; Average loss: 4.2590
Iteration: 203; Percent complete: 5.1%; Average loss: 4.1389
Iteration: 204; Percent complete: 5.1%; Average loss: 4.0521
Iteration: 205; Percent complete: 5.1%; Average loss: 3.8440
Iteration: 206; Percent complete: 5.1%; Average loss: 3.9323
Iteration: 207; Percent complete: 5.2%; Average loss: 4.1231
Iteration: 208; Percent complete: 5.2%; Average loss: 4.0443
Iteration: 209; Percent complete: 5.2%; Average loss: 3.9878
Iteration: 210; Percent complete: 5.2%; Average loss: 3.6769
Iteration: 211; Percent complete: 5.3%; Average loss: 3.7915
Iteration: 212; Percent complete: 5.3%; Average loss: 3.8560
Iteration: 213; Percent complete: 5.3%; Average loss: 4.0251
Iteration: 214; Percent complete: 5.3%; Average loss: 3.9072
Iteration: 215; Percent complete: 5.4%; Average loss: 4.0490
Iteration: 216; Percent complete: 5.4%; Average loss: 4.1229
Iteration: 217; Percent complete: 5.4%; Average loss: 4.0644
Iteration: 218; Percent complete: 5.5%; Average loss: 3.9714
Iteration: 219; Percent complete: 5.5%; Average loss: 3.8476
Iteration: 220; Percent complete: 5.5%; Average loss: 3.9497
Iteration: 221; Percent complete: 5.5%; Average loss: 3.9768
Iteration: 222; Percent complete: 5.5%; Average loss: 4.1166
Iteration: 223; Percent complete: 5.6%; Average loss: 3.6661
Iteration: 224; Percent complete: 5.6%; Average loss: 3.9984
Iteration: 225; Percent complete: 5.6%; Average loss: 3.8503
Iteration: 226; Percent complete: 5.7%; Average loss: 3.8691
Iteration: 227; Percent complete: 5.7%; Average loss: 4.0720
Iteration: 228; Percent complete: 5.7%; Average loss: 3.9013
Iteration: 229; Percent complete: 5.7%; Average loss: 3.9309
Iteration: 230; Percent complete: 5.8%; Average loss: 3.9064
Iteration: 231; Percent complete: 5.8%; Average loss: 4.1521
Iteration: 232; Percent complete: 5.8%; Average loss: 4.1354
Iteration: 233; Percent complete: 5.8%; Average loss: 4.0493
Iteration: 234; Percent complete: 5.9%; Average loss: 3.7621
Iteration: 235; Percent complete: 5.9%; Average loss: 3.9524
Iteration: 236; Percent complete: 5.9%; Average loss: 4.0941
Iteration: 237; Percent complete: 5.9%; Average loss: 4.1073
Iteration: 238; Percent complete: 5.9%; Average loss: 4.1557
Iteration: 239; Percent complete: 6.0%; Average loss: 3.9897
Iteration: 240; Percent complete: 6.0%; Average loss: 3.8460
Iteration: 241; Percent complete: 6.0%; Average loss: 3.9463
Iteration: 242; Percent complete: 6.0%; Average loss: 3.8885
Iteration: 243; Percent complete: 6.1%; Average loss: 3.7836
Iteration: 244; Percent complete: 6.1%; Average loss: 4.1193
Iteration: 245; Percent complete: 6.1%; Average loss: 4.0169
Iteration: 246; Percent complete: 6.2%; Average loss: 4.1657
Iteration: 247; Percent complete: 6.2%; Average loss: 3.8835
Iteration: 248; Percent complete: 6.2%; Average loss: 4.0318
Iteration: 249; Percent complete: 6.2%; Average loss: 3.8886
Iteration: 250; Percent complete: 6.2%; Average loss: 4.1645
Iteration: 251; Percent complete: 6.3%; Average loss: 4.0943
Iteration: 252; Percent complete: 6.3%; Average loss: 4.1899
Iteration: 253; Percent complete: 6.3%; Average loss: 3.9863
Iteration: 254; Percent complete: 6.3%; Average loss: 3.7877
Iteration: 255; Percent complete: 6.4%; Average loss: 3.8230
Iteration: 256; Percent complete: 6.4%; Average loss: 3.8255
Iteration: 257; Percent complete: 6.4%; Average loss: 3.9467
Iteration: 258; Percent complete: 6.5%; Average loss: 3.9358
Iteration: 259; Percent complete: 6.5%; Average loss: 4.0972
Iteration: 260; Percent complete: 6.5%; Average loss: 4.0612
Iteration: 261; Percent complete: 6.5%; Average loss: 3.7319
Iteration: 262; Percent complete: 6.6%; Average loss: 3.9457
Iteration: 263; Percent complete: 6.6%; Average loss: 4.0850
Iteration: 264; Percent complete: 6.6%; Average loss: 4.0332
Iteration: 265; Percent complete: 6.6%; Average loss: 3.9077
Iteration: 266; Percent complete: 6.7%; Average loss: 3.8685
Iteration: 267; Percent complete: 6.7%; Average loss: 4.1578
Iteration: 268; Percent complete: 6.7%; Average loss: 4.0176
Iteration: 269; Percent complete: 6.7%; Average loss: 4.1531
Iteration: 270; Percent complete: 6.8%; Average loss: 3.8095
Iteration: 271; Percent complete: 6.8%; Average loss: 3.8137
Iteration: 272; Percent complete: 6.8%; Average loss: 3.7409
Iteration: 273; Percent complete: 6.8%; Average loss: 3.9861
Iteration: 274; Percent complete: 6.9%; Average loss: 3.8547
Iteration: 275; Percent complete: 6.9%; Average loss: 3.8434
Iteration: 276; Percent complete: 6.9%; Average loss: 3.8801
Iteration: 277; Percent complete: 6.9%; Average loss: 3.9888
Iteration: 278; Percent complete: 7.0%; Average loss: 4.0775
Iteration: 279; Percent complete: 7.0%; Average loss: 3.7415
Iteration: 280; Percent complete: 7.0%; Average loss: 3.7934
Iteration: 281; Percent complete: 7.0%; Average loss: 3.8949
Iteration: 282; Percent complete: 7.0%; Average loss: 3.9175
Iteration: 283; Percent complete: 7.1%; Average loss: 4.0591
Iteration: 284; Percent complete: 7.1%; Average loss: 3.7719
Iteration: 285; Percent complete: 7.1%; Average loss: 3.7876
Iteration: 286; Percent complete: 7.1%; Average loss: 3.9086
Iteration: 287; Percent complete: 7.2%; Average loss: 3.7639
Iteration: 288; Percent complete: 7.2%; Average loss: 3.9739
Iteration: 289; Percent complete: 7.2%; Average loss: 4.0146
Iteration: 290; Percent complete: 7.2%; Average loss: 4.1106
Iteration: 291; Percent complete: 7.3%; Average loss: 3.8569
Iteration: 292; Percent complete: 7.3%; Average loss: 3.9867
Iteration: 293; Percent complete: 7.3%; Average loss: 3.8287
Iteration: 294; Percent complete: 7.3%; Average loss: 3.8990
Iteration: 295; Percent complete: 7.4%; Average loss: 3.9168
Iteration: 296; Percent complete: 7.4%; Average loss: 3.9323
Iteration: 297; Percent complete: 7.4%; Average loss: 3.7323
Iteration: 298; Percent complete: 7.4%; Average loss: 4.1489
Iteration: 299; Percent complete: 7.5%; Average loss: 3.9443
Iteration: 300; Percent complete: 7.5%; Average loss: 3.7498
Iteration: 301; Percent complete: 7.5%; Average loss: 3.8679
Iteration: 302; Percent complete: 7.5%; Average loss: 3.7547
Iteration: 303; Percent complete: 7.6%; Average loss: 3.7950
Iteration: 304; Percent complete: 7.6%; Average loss: 3.8685
Iteration: 305; Percent complete: 7.6%; Average loss: 3.9529
Iteration: 306; Percent complete: 7.6%; Average loss: 3.8496
Iteration: 307; Percent complete: 7.7%; Average loss: 4.0164
Iteration: 308; Percent complete: 7.7%; Average loss: 4.0451
Iteration: 309; Percent complete: 7.7%; Average loss: 3.8872
Iteration: 310; Percent complete: 7.8%; Average loss: 3.7366
Iteration: 311; Percent complete: 7.8%; Average loss: 3.8241
Iteration: 312; Percent complete: 7.8%; Average loss: 4.0270
Iteration: 313; Percent complete: 7.8%; Average loss: 3.9867
Iteration: 314; Percent complete: 7.8%; Average loss: 3.9284
Iteration: 315; Percent complete: 7.9%; Average loss: 4.0577
Iteration: 316; Percent complete: 7.9%; Average loss: 4.0282
Iteration: 317; Percent complete: 7.9%; Average loss: 3.9486
Iteration: 318; Percent complete: 8.0%; Average loss: 3.6939
Iteration: 319; Percent complete: 8.0%; Average loss: 4.0618
Iteration: 320; Percent complete: 8.0%; Average loss: 3.9465
Iteration: 321; Percent complete: 8.0%; Average loss: 3.6670
Iteration: 322; Percent complete: 8.1%; Average loss: 4.0462
Iteration: 323; Percent complete: 8.1%; Average loss: 3.7516
Iteration: 324; Percent complete: 8.1%; Average loss: 3.7642
Iteration: 325; Percent complete: 8.1%; Average loss: 3.7861
Iteration: 326; Percent complete: 8.2%; Average loss: 3.8714
Iteration: 327; Percent complete: 8.2%; Average loss: 3.6140
Iteration: 328; Percent complete: 8.2%; Average loss: 3.5582
Iteration: 329; Percent complete: 8.2%; Average loss: 3.8820
Iteration: 330; Percent complete: 8.2%; Average loss: 3.8020
Iteration: 331; Percent complete: 8.3%; Average loss: 3.8910
Iteration: 332; Percent complete: 8.3%; Average loss: 4.0062
Iteration: 333; Percent complete: 8.3%; Average loss: 3.8335
Iteration: 334; Percent complete: 8.3%; Average loss: 3.8664
Iteration: 335; Percent complete: 8.4%; Average loss: 3.8506
Iteration: 336; Percent complete: 8.4%; Average loss: 4.1062
Iteration: 337; Percent complete: 8.4%; Average loss: 3.7387
Iteration: 338; Percent complete: 8.5%; Average loss: 3.9607
Iteration: 339; Percent complete: 8.5%; Average loss: 4.1128
Iteration: 340; Percent complete: 8.5%; Average loss: 3.5956
Iteration: 341; Percent complete: 8.5%; Average loss: 4.0037
Iteration: 342; Percent complete: 8.6%; Average loss: 3.5186
Iteration: 343; Percent complete: 8.6%; Average loss: 3.7467
Iteration: 344; Percent complete: 8.6%; Average loss: 3.8029
Iteration: 345; Percent complete: 8.6%; Average loss: 3.9015
Iteration: 346; Percent complete: 8.6%; Average loss: 3.9633
Iteration: 347; Percent complete: 8.7%; Average loss: 3.9212
Iteration: 348; Percent complete: 8.7%; Average loss: 3.5816
Iteration: 349; Percent complete: 8.7%; Average loss: 3.7129
Iteration: 350; Percent complete: 8.8%; Average loss: 3.9972
Iteration: 351; Percent complete: 8.8%; Average loss: 3.9879
Iteration: 352; Percent complete: 8.8%; Average loss: 4.0997
Iteration: 353; Percent complete: 8.8%; Average loss: 3.7927
Iteration: 354; Percent complete: 8.8%; Average loss: 3.8564
Iteration: 355; Percent complete: 8.9%; Average loss: 4.0561
Iteration: 356; Percent complete: 8.9%; Average loss: 3.9525
Iteration: 357; Percent complete: 8.9%; Average loss: 4.0556
Iteration: 358; Percent complete: 8.9%; Average loss: 3.8212
Iteration: 359; Percent complete: 9.0%; Average loss: 3.7158
Iteration: 360; Percent complete: 9.0%; Average loss: 3.7562
Iteration: 361; Percent complete: 9.0%; Average loss: 3.8762
Iteration: 362; Percent complete: 9.0%; Average loss: 3.4818
Iteration: 363; Percent complete: 9.1%; Average loss: 3.7528
Iteration: 364; Percent complete: 9.1%; Average loss: 3.6197
Iteration: 365; Percent complete: 9.1%; Average loss: 3.7673
Iteration: 366; Percent complete: 9.2%; Average loss: 4.1348
Iteration: 367; Percent complete: 9.2%; Average loss: 3.8661
Iteration: 368; Percent complete: 9.2%; Average loss: 3.8529
Iteration: 369; Percent complete: 9.2%; Average loss: 3.9748
Iteration: 370; Percent complete: 9.2%; Average loss: 3.8690
Iteration: 371; Percent complete: 9.3%; Average loss: 3.4633
Iteration: 372; Percent complete: 9.3%; Average loss: 3.5533
Iteration: 373; Percent complete: 9.3%; Average loss: 3.9351
Iteration: 374; Percent complete: 9.3%; Average loss: 4.0074
Iteration: 375; Percent complete: 9.4%; Average loss: 4.1575
Iteration: 376; Percent complete: 9.4%; Average loss: 3.8768
Iteration: 377; Percent complete: 9.4%; Average loss: 3.8885
Iteration: 378; Percent complete: 9.4%; Average loss: 4.1592
Iteration: 379; Percent complete: 9.5%; Average loss: 4.0826
Iteration: 380; Percent complete: 9.5%; Average loss: 4.1046
Iteration: 381; Percent complete: 9.5%; Average loss: 3.7827
Iteration: 382; Percent complete: 9.6%; Average loss: 4.1647
Iteration: 383; Percent complete: 9.6%; Average loss: 3.9726
Iteration: 384; Percent complete: 9.6%; Average loss: 3.8548
Iteration: 385; Percent complete: 9.6%; Average loss: 3.9728
Iteration: 386; Percent complete: 9.7%; Average loss: 3.8165
Iteration: 387; Percent complete: 9.7%; Average loss: 3.7708
Iteration: 388; Percent complete: 9.7%; Average loss: 3.8249
Iteration: 389; Percent complete: 9.7%; Average loss: 3.8004
Iteration: 390; Percent complete: 9.8%; Average loss: 3.8880
Iteration: 391; Percent complete: 9.8%; Average loss: 3.9858
Iteration: 392; Percent complete: 9.8%; Average loss: 3.9314
Iteration: 393; Percent complete: 9.8%; Average loss: 3.7353
Iteration: 394; Percent complete: 9.8%; Average loss: 3.8226
Iteration: 395; Percent complete: 9.9%; Average loss: 3.8166
Iteration: 396; Percent complete: 9.9%; Average loss: 3.3898
Iteration: 397; Percent complete: 9.9%; Average loss: 3.4800
Iteration: 398; Percent complete: 10.0%; Average loss: 3.6068
Iteration: 399; Percent complete: 10.0%; Average loss: 3.6059
Iteration: 400; Percent complete: 10.0%; Average loss: 3.7529
Iteration: 401; Percent complete: 10.0%; Average loss: 3.7431
Iteration: 402; Percent complete: 10.1%; Average loss: 3.8344
Iteration: 403; Percent complete: 10.1%; Average loss: 3.8505
Iteration: 404; Percent complete: 10.1%; Average loss: 3.8410
Iteration: 405; Percent complete: 10.1%; Average loss: 3.5948
Iteration: 406; Percent complete: 10.2%; Average loss: 3.9427
Iteration: 407; Percent complete: 10.2%; Average loss: 3.7378
Iteration: 408; Percent complete: 10.2%; Average loss: 3.7276
Iteration: 409; Percent complete: 10.2%; Average loss: 3.6399
Iteration: 410; Percent complete: 10.2%; Average loss: 3.7160
Iteration: 411; Percent complete: 10.3%; Average loss: 3.5355
Iteration: 412; Percent complete: 10.3%; Average loss: 3.7879
Iteration: 413; Percent complete: 10.3%; Average loss: 3.7960
Iteration: 414; Percent complete: 10.3%; Average loss: 3.8112
Iteration: 415; Percent complete: 10.4%; Average loss: 3.8777
Iteration: 416; Percent complete: 10.4%; Average loss: 3.9848
Iteration: 417; Percent complete: 10.4%; Average loss: 3.8618
Iteration: 418; Percent complete: 10.4%; Average loss: 3.7210
Iteration: 419; Percent complete: 10.5%; Average loss: 3.6392
Iteration: 420; Percent complete: 10.5%; Average loss: 4.0068
Iteration: 421; Percent complete: 10.5%; Average loss: 3.8917
Iteration: 422; Percent complete: 10.5%; Average loss: 3.8730
Iteration: 423; Percent complete: 10.6%; Average loss: 3.8079
Iteration: 424; Percent complete: 10.6%; Average loss: 3.6992
Iteration: 425; Percent complete: 10.6%; Average loss: 3.8271
Iteration: 426; Percent complete: 10.7%; Average loss: 3.6779
Iteration: 427; Percent complete: 10.7%; Average loss: 3.7989
Iteration: 428; Percent complete: 10.7%; Average loss: 3.6879
Iteration: 429; Percent complete: 10.7%; Average loss: 3.8516
Iteration: 430; Percent complete: 10.8%; Average loss: 3.4869
Iteration: 431; Percent complete: 10.8%; Average loss: 3.7664
Iteration: 432; Percent complete: 10.8%; Average loss: 3.9735
Iteration: 433; Percent complete: 10.8%; Average loss: 3.5731
Iteration: 434; Percent complete: 10.8%; Average loss: 3.8909
Iteration: 435; Percent complete: 10.9%; Average loss: 3.7208
Iteration: 436; Percent complete: 10.9%; Average loss: 3.8615
Iteration: 437; Percent complete: 10.9%; Average loss: 3.9555
Iteration: 438; Percent complete: 10.9%; Average loss: 3.7919
Iteration: 439; Percent complete: 11.0%; Average loss: 3.9106
Iteration: 440; Percent complete: 11.0%; Average loss: 3.8531
Iteration: 441; Percent complete: 11.0%; Average loss: 3.9365
Iteration: 442; Percent complete: 11.1%; Average loss: 3.8743
Iteration: 443; Percent complete: 11.1%; Average loss: 3.9855
Iteration: 444; Percent complete: 11.1%; Average loss: 3.8250
Iteration: 445; Percent complete: 11.1%; Average loss: 3.6623
Iteration: 446; Percent complete: 11.2%; Average loss: 3.8613
Iteration: 447; Percent complete: 11.2%; Average loss: 3.6446
Iteration: 448; Percent complete: 11.2%; Average loss: 4.0008
Iteration: 449; Percent complete: 11.2%; Average loss: 3.8126
Iteration: 450; Percent complete: 11.2%; Average loss: 3.5980
Iteration: 451; Percent complete: 11.3%; Average loss: 3.7817
Iteration: 452; Percent complete: 11.3%; Average loss: 3.8334
Iteration: 453; Percent complete: 11.3%; Average loss: 3.8282
Iteration: 454; Percent complete: 11.3%; Average loss: 3.7079
Iteration: 455; Percent complete: 11.4%; Average loss: 3.5277
Iteration: 456; Percent complete: 11.4%; Average loss: 3.8297
Iteration: 457; Percent complete: 11.4%; Average loss: 3.7111
Iteration: 458; Percent complete: 11.5%; Average loss: 3.9243
Iteration: 459; Percent complete: 11.5%; Average loss: 3.6290
Iteration: 460; Percent complete: 11.5%; Average loss: 3.7345
Iteration: 461; Percent complete: 11.5%; Average loss: 3.7123
Iteration: 462; Percent complete: 11.6%; Average loss: 3.8002
Iteration: 463; Percent complete: 11.6%; Average loss: 3.6910
Iteration: 464; Percent complete: 11.6%; Average loss: 3.8296
Iteration: 465; Percent complete: 11.6%; Average loss: 3.8757
Iteration: 466; Percent complete: 11.7%; Average loss: 3.8946
Iteration: 467; Percent complete: 11.7%; Average loss: 3.6542
Iteration: 468; Percent complete: 11.7%; Average loss: 3.4195
Iteration: 469; Percent complete: 11.7%; Average loss: 3.6769
Iteration: 470; Percent complete: 11.8%; Average loss: 3.7423
Iteration: 471; Percent complete: 11.8%; Average loss: 3.4244
Iteration: 472; Percent complete: 11.8%; Average loss: 3.6751
Iteration: 473; Percent complete: 11.8%; Average loss: 3.5773
Iteration: 474; Percent complete: 11.8%; Average loss: 3.6421
Iteration: 475; Percent complete: 11.9%; Average loss: 3.8838
Iteration: 476; Percent complete: 11.9%; Average loss: 3.5999
Iteration: 477; Percent complete: 11.9%; Average loss: 3.7459
Iteration: 478; Percent complete: 11.9%; Average loss: 3.6991
Iteration: 479; Percent complete: 12.0%; Average loss: 3.5102
Iteration: 480; Percent complete: 12.0%; Average loss: 3.6191
Iteration: 481; Percent complete: 12.0%; Average loss: 3.4671
Iteration: 482; Percent complete: 12.0%; Average loss: 3.8281
Iteration: 483; Percent complete: 12.1%; Average loss: 3.9516
Iteration: 484; Percent complete: 12.1%; Average loss: 3.7715
Iteration: 485; Percent complete: 12.1%; Average loss: 3.5811
Iteration: 486; Percent complete: 12.2%; Average loss: 3.9896
Iteration: 487; Percent complete: 12.2%; Average loss: 3.7278
Iteration: 488; Percent complete: 12.2%; Average loss: 3.6315
Iteration: 489; Percent complete: 12.2%; Average loss: 3.6388
Iteration: 490; Percent complete: 12.2%; Average loss: 3.8059
Iteration: 491; Percent complete: 12.3%; Average loss: 3.6705
Iteration: 492; Percent complete: 12.3%; Average loss: 3.7838
Iteration: 493; Percent complete: 12.3%; Average loss: 3.5776
Iteration: 494; Percent complete: 12.3%; Average loss: 3.7879
Iteration: 495; Percent complete: 12.4%; Average loss: 3.7613
Iteration: 496; Percent complete: 12.4%; Average loss: 3.8589
Iteration: 497; Percent complete: 12.4%; Average loss: 3.7466
Iteration: 498; Percent complete: 12.4%; Average loss: 3.9617
Iteration: 499; Percent complete: 12.5%; Average loss: 3.8837
Iteration: 500; Percent complete: 12.5%; Average loss: 3.6081
Iteration: 501; Percent complete: 12.5%; Average loss: 3.8174
Iteration: 502; Percent complete: 12.6%; Average loss: 3.4294
Iteration: 503; Percent complete: 12.6%; Average loss: 3.5071
Iteration: 504; Percent complete: 12.6%; Average loss: 3.8716
Iteration: 505; Percent complete: 12.6%; Average loss: 3.9025
Iteration: 506; Percent complete: 12.7%; Average loss: 3.9672
Iteration: 507; Percent complete: 12.7%; Average loss: 3.8023
Iteration: 508; Percent complete: 12.7%; Average loss: 3.4492
Iteration: 509; Percent complete: 12.7%; Average loss: 3.8042
Iteration: 510; Percent complete: 12.8%; Average loss: 3.6965
Iteration: 511; Percent complete: 12.8%; Average loss: 3.4900
Iteration: 512; Percent complete: 12.8%; Average loss: 3.7381
Iteration: 513; Percent complete: 12.8%; Average loss: 3.9456
Iteration: 514; Percent complete: 12.8%; Average loss: 3.7070
Iteration: 515; Percent complete: 12.9%; Average loss: 3.7998
Iteration: 516; Percent complete: 12.9%; Average loss: 3.6391
Iteration: 517; Percent complete: 12.9%; Average loss: 3.6193
Iteration: 518; Percent complete: 13.0%; Average loss: 3.7321
Iteration: 519; Percent complete: 13.0%; Average loss: 3.6586
Iteration: 520; Percent complete: 13.0%; Average loss: 3.5855
Iteration: 521; Percent complete: 13.0%; Average loss: 3.5904
Iteration: 522; Percent complete: 13.1%; Average loss: 3.6533
Iteration: 523; Percent complete: 13.1%; Average loss: 3.7862
Iteration: 524; Percent complete: 13.1%; Average loss: 3.6102
Iteration: 525; Percent complete: 13.1%; Average loss: 3.9416
Iteration: 526; Percent complete: 13.2%; Average loss: 3.4226
Iteration: 527; Percent complete: 13.2%; Average loss: 3.5697
Iteration: 528; Percent complete: 13.2%; Average loss: 3.7298
Iteration: 529; Percent complete: 13.2%; Average loss: 3.9010
Iteration: 530; Percent complete: 13.2%; Average loss: 3.5623
Iteration: 531; Percent complete: 13.3%; Average loss: 3.7227
Iteration: 532; Percent complete: 13.3%; Average loss: 3.7632
Iteration: 533; Percent complete: 13.3%; Average loss: 3.4954
Iteration: 534; Percent complete: 13.4%; Average loss: 3.6127
Iteration: 535; Percent complete: 13.4%; Average loss: 3.9475
Iteration: 536; Percent complete: 13.4%; Average loss: 3.5743
Iteration: 537; Percent complete: 13.4%; Average loss: 3.8358
Iteration: 538; Percent complete: 13.5%; Average loss: 3.6938
Iteration: 539; Percent complete: 13.5%; Average loss: 3.5665
Iteration: 540; Percent complete: 13.5%; Average loss: 3.3926
Iteration: 541; Percent complete: 13.5%; Average loss: 3.6174
Iteration: 542; Percent complete: 13.6%; Average loss: 3.3924
Iteration: 543; Percent complete: 13.6%; Average loss: 3.6192
Iteration: 544; Percent complete: 13.6%; Average loss: 3.7191
Iteration: 545; Percent complete: 13.6%; Average loss: 3.7148
Iteration: 546; Percent complete: 13.7%; Average loss: 3.7823
Iteration: 547; Percent complete: 13.7%; Average loss: 3.9853
Iteration: 548; Percent complete: 13.7%; Average loss: 3.6698
Iteration: 549; Percent complete: 13.7%; Average loss: 3.5961
Iteration: 550; Percent complete: 13.8%; Average loss: 3.7223
Iteration: 551; Percent complete: 13.8%; Average loss: 3.5553
Iteration: 552; Percent complete: 13.8%; Average loss: 3.7162
Iteration: 553; Percent complete: 13.8%; Average loss: 3.7678
Iteration: 554; Percent complete: 13.9%; Average loss: 4.0025
Iteration: 555; Percent complete: 13.9%; Average loss: 3.8247
Iteration: 556; Percent complete: 13.9%; Average loss: 3.8986
Iteration: 557; Percent complete: 13.9%; Average loss: 3.5517
Iteration: 558; Percent complete: 14.0%; Average loss: 3.5307
Iteration: 559; Percent complete: 14.0%; Average loss: 3.8604
Iteration: 560; Percent complete: 14.0%; Average loss: 3.3584
Iteration: 561; Percent complete: 14.0%; Average loss: 3.6097
Iteration: 562; Percent complete: 14.1%; Average loss: 3.9043
Iteration: 563; Percent complete: 14.1%; Average loss: 3.6024
Iteration: 564; Percent complete: 14.1%; Average loss: 3.7176
Iteration: 565; Percent complete: 14.1%; Average loss: 3.5486
Iteration: 566; Percent complete: 14.1%; Average loss: 3.7084
Iteration: 567; Percent complete: 14.2%; Average loss: 3.6799
Iteration: 568; Percent complete: 14.2%; Average loss: 3.4513
Iteration: 569; Percent complete: 14.2%; Average loss: 3.6300
Iteration: 570; Percent complete: 14.2%; Average loss: 3.5313
Iteration: 571; Percent complete: 14.3%; Average loss: 3.3316
Iteration: 572; Percent complete: 14.3%; Average loss: 3.6319
Iteration: 573; Percent complete: 14.3%; Average loss: 3.6362
Iteration: 574; Percent complete: 14.3%; Average loss: 3.5233
Iteration: 575; Percent complete: 14.4%; Average loss: 3.5025
Iteration: 576; Percent complete: 14.4%; Average loss: 3.7848
Iteration: 577; Percent complete: 14.4%; Average loss: 3.7592
Iteration: 578; Percent complete: 14.4%; Average loss: 3.6311
Iteration: 579; Percent complete: 14.5%; Average loss: 3.3657
Iteration: 580; Percent complete: 14.5%; Average loss: 3.4366
Iteration: 581; Percent complete: 14.5%; Average loss: 3.7745
Iteration: 582; Percent complete: 14.5%; Average loss: 3.5445
Iteration: 583; Percent complete: 14.6%; Average loss: 3.9105
Iteration: 584; Percent complete: 14.6%; Average loss: 3.7859
Iteration: 585; Percent complete: 14.6%; Average loss: 3.8347
Iteration: 586; Percent complete: 14.6%; Average loss: 3.8552
Iteration: 587; Percent complete: 14.7%; Average loss: 3.9578
Iteration: 588; Percent complete: 14.7%; Average loss: 3.5519
Iteration: 589; Percent complete: 14.7%; Average loss: 3.7549
Iteration: 590; Percent complete: 14.8%; Average loss: 3.5980
Iteration: 591; Percent complete: 14.8%; Average loss: 3.7627
Iteration: 592; Percent complete: 14.8%; Average loss: 3.8635
Iteration: 593; Percent complete: 14.8%; Average loss: 3.7228
Iteration: 594; Percent complete: 14.8%; Average loss: 3.6659
Iteration: 595; Percent complete: 14.9%; Average loss: 3.6264
Iteration: 596; Percent complete: 14.9%; Average loss: 3.5541
Iteration: 597; Percent complete: 14.9%; Average loss: 3.6990
Iteration: 598; Percent complete: 14.9%; Average loss: 3.6985
Iteration: 599; Percent complete: 15.0%; Average loss: 3.5620
Iteration: 600; Percent complete: 15.0%; Average loss: 4.0934
Iteration: 601; Percent complete: 15.0%; Average loss: 3.6635
Iteration: 602; Percent complete: 15.0%; Average loss: 3.5905
Iteration: 603; Percent complete: 15.1%; Average loss: 3.6693
Iteration: 604; Percent complete: 15.1%; Average loss: 3.6971
Iteration: 605; Percent complete: 15.1%; Average loss: 3.5871
Iteration: 606; Percent complete: 15.2%; Average loss: 3.4899
Iteration: 607; Percent complete: 15.2%; Average loss: 3.7504
Iteration: 608; Percent complete: 15.2%; Average loss: 3.5134
Iteration: 609; Percent complete: 15.2%; Average loss: 3.9219
Iteration: 610; Percent complete: 15.2%; Average loss: 3.3345
Iteration: 611; Percent complete: 15.3%; Average loss: 3.6911
Iteration: 612; Percent complete: 15.3%; Average loss: 3.7441
Iteration: 613; Percent complete: 15.3%; Average loss: 3.6900
Iteration: 614; Percent complete: 15.3%; Average loss: 3.5385
Iteration: 615; Percent complete: 15.4%; Average loss: 3.5219
Iteration: 616; Percent complete: 15.4%; Average loss: 3.6812
Iteration: 617; Percent complete: 15.4%; Average loss: 3.6031
Iteration: 618; Percent complete: 15.4%; Average loss: 3.6311
Iteration: 619; Percent complete: 15.5%; Average loss: 3.9241
Iteration: 620; Percent complete: 15.5%; Average loss: 3.6730
Iteration: 621; Percent complete: 15.5%; Average loss: 3.6363
Iteration: 622; Percent complete: 15.6%; Average loss: 3.5024
Iteration: 623; Percent complete: 15.6%; Average loss: 3.6311
Iteration: 624; Percent complete: 15.6%; Average loss: 3.9683
Iteration: 625; Percent complete: 15.6%; Average loss: 3.7113
Iteration: 626; Percent complete: 15.7%; Average loss: 3.6007
Iteration: 627; Percent complete: 15.7%; Average loss: 3.7522
Iteration: 628; Percent complete: 15.7%; Average loss: 3.9367
Iteration: 629; Percent complete: 15.7%; Average loss: 3.4290
Iteration: 630; Percent complete: 15.8%; Average loss: 3.8728
Iteration: 631; Percent complete: 15.8%; Average loss: 3.4707
Iteration: 632; Percent complete: 15.8%; Average loss: 3.6688
Iteration: 633; Percent complete: 15.8%; Average loss: 3.6616
Iteration: 634; Percent complete: 15.8%; Average loss: 3.8982
Iteration: 635; Percent complete: 15.9%; Average loss: 3.4392
Iteration: 636; Percent complete: 15.9%; Average loss: 3.7199
Iteration: 637; Percent complete: 15.9%; Average loss: 3.5899
Iteration: 638; Percent complete: 16.0%; Average loss: 3.6469
Iteration: 639; Percent complete: 16.0%; Average loss: 3.4339
Iteration: 640; Percent complete: 16.0%; Average loss: 3.7076
Iteration: 641; Percent complete: 16.0%; Average loss: 3.5920
Iteration: 642; Percent complete: 16.1%; Average loss: 3.3818
Iteration: 643; Percent complete: 16.1%; Average loss: 3.3972
Iteration: 644; Percent complete: 16.1%; Average loss: 3.7669
Iteration: 645; Percent complete: 16.1%; Average loss: 3.5809
Iteration: 646; Percent complete: 16.2%; Average loss: 3.4642
Iteration: 647; Percent complete: 16.2%; Average loss: 3.6118
Iteration: 648; Percent complete: 16.2%; Average loss: 3.3545
Iteration: 649; Percent complete: 16.2%; Average loss: 3.7126
Iteration: 650; Percent complete: 16.2%; Average loss: 3.6789
Iteration: 651; Percent complete: 16.3%; Average loss: 3.3888
Iteration: 652; Percent complete: 16.3%; Average loss: 3.4407
Iteration: 653; Percent complete: 16.3%; Average loss: 3.6318
Iteration: 654; Percent complete: 16.4%; Average loss: 3.8419
Iteration: 655; Percent complete: 16.4%; Average loss: 3.6282
Iteration: 656; Percent complete: 16.4%; Average loss: 3.5083
Iteration: 657; Percent complete: 16.4%; Average loss: 3.6976
Iteration: 658; Percent complete: 16.4%; Average loss: 3.7868
Iteration: 659; Percent complete: 16.5%; Average loss: 3.4776
Iteration: 660; Percent complete: 16.5%; Average loss: 3.7958
Iteration: 661; Percent complete: 16.5%; Average loss: 3.7507
Iteration: 662; Percent complete: 16.6%; Average loss: 3.5960
Iteration: 663; Percent complete: 16.6%; Average loss: 3.7080
Iteration: 664; Percent complete: 16.6%; Average loss: 3.6706
Iteration: 665; Percent complete: 16.6%; Average loss: 3.4410
Iteration: 666; Percent complete: 16.7%; Average loss: 3.7139
Iteration: 667; Percent complete: 16.7%; Average loss: 3.6621
Iteration: 668; Percent complete: 16.7%; Average loss: 3.7198
Iteration: 669; Percent complete: 16.7%; Average loss: 3.6644
Iteration: 670; Percent complete: 16.8%; Average loss: 3.4227
Iteration: 671; Percent complete: 16.8%; Average loss: 3.4459
Iteration: 672; Percent complete: 16.8%; Average loss: 3.5718
Iteration: 673; Percent complete: 16.8%; Average loss: 3.5257
Iteration: 674; Percent complete: 16.9%; Average loss: 3.8251
Iteration: 675; Percent complete: 16.9%; Average loss: 3.7606
Iteration: 676; Percent complete: 16.9%; Average loss: 3.6569
Iteration: 677; Percent complete: 16.9%; Average loss: 3.6202
Iteration: 678; Percent complete: 17.0%; Average loss: 3.5270
Iteration: 679; Percent complete: 17.0%; Average loss: 3.7465
Iteration: 680; Percent complete: 17.0%; Average loss: 3.7499
Iteration: 681; Percent complete: 17.0%; Average loss: 3.8541
Iteration: 682; Percent complete: 17.1%; Average loss: 3.7128
Iteration: 683; Percent complete: 17.1%; Average loss: 3.3632
Iteration: 684; Percent complete: 17.1%; Average loss: 3.5653
Iteration: 685; Percent complete: 17.1%; Average loss: 3.4145
Iteration: 686; Percent complete: 17.2%; Average loss: 3.5389
Iteration: 687; Percent complete: 17.2%; Average loss: 3.6127
Iteration: 688; Percent complete: 17.2%; Average loss: 3.7963
Iteration: 689; Percent complete: 17.2%; Average loss: 3.8151
Iteration: 690; Percent complete: 17.2%; Average loss: 3.5779
Iteration: 691; Percent complete: 17.3%; Average loss: 3.6405
Iteration: 692; Percent complete: 17.3%; Average loss: 4.0708
Iteration: 693; Percent complete: 17.3%; Average loss: 3.5478
Iteration: 694; Percent complete: 17.3%; Average loss: 3.5675
Iteration: 695; Percent complete: 17.4%; Average loss: 3.6912
Iteration: 696; Percent complete: 17.4%; Average loss: 3.5366
Iteration: 697; Percent complete: 17.4%; Average loss: 3.4753
Iteration: 698; Percent complete: 17.4%; Average loss: 3.5185
Iteration: 699; Percent complete: 17.5%; Average loss: 3.6846
Iteration: 700; Percent complete: 17.5%; Average loss: 3.5324
Iteration: 701; Percent complete: 17.5%; Average loss: 3.7312
Iteration: 702; Percent complete: 17.5%; Average loss: 3.5423
Iteration: 703; Percent complete: 17.6%; Average loss: 3.6797
Iteration: 704; Percent complete: 17.6%; Average loss: 3.8088
Iteration: 705; Percent complete: 17.6%; Average loss: 3.6049
Iteration: 706; Percent complete: 17.6%; Average loss: 3.7916
Iteration: 707; Percent complete: 17.7%; Average loss: 3.5747
Iteration: 708; Percent complete: 17.7%; Average loss: 3.6267
Iteration: 709; Percent complete: 17.7%; Average loss: 3.4919
Iteration: 710; Percent complete: 17.8%; Average loss: 3.6224
Iteration: 711; Percent complete: 17.8%; Average loss: 3.5643
Iteration: 712; Percent complete: 17.8%; Average loss: 3.4504
Iteration: 713; Percent complete: 17.8%; Average loss: 3.7856
Iteration: 714; Percent complete: 17.8%; Average loss: 3.3478
Iteration: 715; Percent complete: 17.9%; Average loss: 3.4567
Iteration: 716; Percent complete: 17.9%; Average loss: 3.6610
Iteration: 717; Percent complete: 17.9%; Average loss: 3.5844
Iteration: 718; Percent complete: 17.9%; Average loss: 3.5864
Iteration: 719; Percent complete: 18.0%; Average loss: 3.8428
Iteration: 720; Percent complete: 18.0%; Average loss: 3.6146
Iteration: 721; Percent complete: 18.0%; Average loss: 3.6270
Iteration: 722; Percent complete: 18.1%; Average loss: 3.7817
Iteration: 723; Percent complete: 18.1%; Average loss: 3.3504
Iteration: 724; Percent complete: 18.1%; Average loss: 3.7619
Iteration: 725; Percent complete: 18.1%; Average loss: 3.7376
Iteration: 726; Percent complete: 18.1%; Average loss: 3.5453
Iteration: 727; Percent complete: 18.2%; Average loss: 3.4679
Iteration: 728; Percent complete: 18.2%; Average loss: 3.5436
Iteration: 729; Percent complete: 18.2%; Average loss: 3.8049
Iteration: 730; Percent complete: 18.2%; Average loss: 3.4375
Iteration: 731; Percent complete: 18.3%; Average loss: 3.5845
Iteration: 732; Percent complete: 18.3%; Average loss: 3.5647
Iteration: 733; Percent complete: 18.3%; Average loss: 3.4437
Iteration: 734; Percent complete: 18.4%; Average loss: 3.8804
Iteration: 735; Percent complete: 18.4%; Average loss: 3.7188
Iteration: 736; Percent complete: 18.4%; Average loss: 3.3616
Iteration: 737; Percent complete: 18.4%; Average loss: 3.4421
Iteration: 738; Percent complete: 18.4%; Average loss: 3.3276
Iteration: 739; Percent complete: 18.5%; Average loss: 3.5352
Iteration: 740; Percent complete: 18.5%; Average loss: 3.4031
Iteration: 741; Percent complete: 18.5%; Average loss: 3.6769
Iteration: 742; Percent complete: 18.6%; Average loss: 3.4493
Iteration: 743; Percent complete: 18.6%; Average loss: 3.6523
Iteration: 744; Percent complete: 18.6%; Average loss: 3.4110
Iteration: 745; Percent complete: 18.6%; Average loss: 3.4252
Iteration: 746; Percent complete: 18.6%; Average loss: 3.3961
Iteration: 747; Percent complete: 18.7%; Average loss: 3.6555
Iteration: 748; Percent complete: 18.7%; Average loss: 3.3075
Iteration: 749; Percent complete: 18.7%; Average loss: 3.4988
Iteration: 750; Percent complete: 18.8%; Average loss: 3.4055
Iteration: 751; Percent complete: 18.8%; Average loss: 3.4401
Iteration: 752; Percent complete: 18.8%; Average loss: 3.6985
Iteration: 753; Percent complete: 18.8%; Average loss: 3.4901
Iteration: 754; Percent complete: 18.9%; Average loss: 3.5567
Iteration: 755; Percent complete: 18.9%; Average loss: 3.6019
Iteration: 756; Percent complete: 18.9%; Average loss: 3.4985
Iteration: 757; Percent complete: 18.9%; Average loss: 3.5910
Iteration: 758; Percent complete: 18.9%; Average loss: 3.7413
Iteration: 759; Percent complete: 19.0%; Average loss: 3.5280
Iteration: 760; Percent complete: 19.0%; Average loss: 3.7501
Iteration: 761; Percent complete: 19.0%; Average loss: 3.3833
Iteration: 762; Percent complete: 19.1%; Average loss: 3.6356
Iteration: 763; Percent complete: 19.1%; Average loss: 3.7361
Iteration: 764; Percent complete: 19.1%; Average loss: 3.4951
Iteration: 765; Percent complete: 19.1%; Average loss: 3.4049
Iteration: 766; Percent complete: 19.1%; Average loss: 3.4771
Iteration: 767; Percent complete: 19.2%; Average loss: 3.7635
Iteration: 768; Percent complete: 19.2%; Average loss: 3.1530
Iteration: 769; Percent complete: 19.2%; Average loss: 3.5561
Iteration: 770; Percent complete: 19.2%; Average loss: 3.4933
Iteration: 771; Percent complete: 19.3%; Average loss: 3.3998
Iteration: 772; Percent complete: 19.3%; Average loss: 3.4563
Iteration: 773; Percent complete: 19.3%; Average loss: 3.4772
Iteration: 774; Percent complete: 19.4%; Average loss: 3.7417
Iteration: 775; Percent complete: 19.4%; Average loss: 3.4413
Iteration: 776; Percent complete: 19.4%; Average loss: 3.4711
Iteration: 777; Percent complete: 19.4%; Average loss: 3.5219
Iteration: 778; Percent complete: 19.4%; Average loss: 3.4235
Iteration: 779; Percent complete: 19.5%; Average loss: 3.5387
Iteration: 780; Percent complete: 19.5%; Average loss: 3.4132
Iteration: 781; Percent complete: 19.5%; Average loss: 3.5259
Iteration: 782; Percent complete: 19.6%; Average loss: 3.7497
Iteration: 783; Percent complete: 19.6%; Average loss: 3.5302
Iteration: 784; Percent complete: 19.6%; Average loss: 3.9174
Iteration: 785; Percent complete: 19.6%; Average loss: 3.8921
Iteration: 786; Percent complete: 19.7%; Average loss: 3.3553
Iteration: 787; Percent complete: 19.7%; Average loss: 3.6839
Iteration: 788; Percent complete: 19.7%; Average loss: 3.4674
Iteration: 789; Percent complete: 19.7%; Average loss: 3.5733
Iteration: 790; Percent complete: 19.8%; Average loss: 3.4200
Iteration: 791; Percent complete: 19.8%; Average loss: 3.3761
Iteration: 792; Percent complete: 19.8%; Average loss: 3.4481
Iteration: 793; Percent complete: 19.8%; Average loss: 3.5051
Iteration: 794; Percent complete: 19.9%; Average loss: 3.5194
Iteration: 795; Percent complete: 19.9%; Average loss: 3.3912
Iteration: 796; Percent complete: 19.9%; Average loss: 3.5205
Iteration: 797; Percent complete: 19.9%; Average loss: 3.5305
Iteration: 798; Percent complete: 20.0%; Average loss: 3.6814
Iteration: 799; Percent complete: 20.0%; Average loss: 3.6379
Iteration: 800; Percent complete: 20.0%; Average loss: 3.6761
Iteration: 801; Percent complete: 20.0%; Average loss: 3.4871
Iteration: 802; Percent complete: 20.1%; Average loss: 3.6121
Iteration: 803; Percent complete: 20.1%; Average loss: 3.4495
Iteration: 804; Percent complete: 20.1%; Average loss: 3.5719
Iteration: 805; Percent complete: 20.1%; Average loss: 3.5343
Iteration: 806; Percent complete: 20.2%; Average loss: 3.4312
Iteration: 807; Percent complete: 20.2%; Average loss: 3.5957
Iteration: 808; Percent complete: 20.2%; Average loss: 3.3752
Iteration: 809; Percent complete: 20.2%; Average loss: 3.2365
Iteration: 810; Percent complete: 20.2%; Average loss: 3.5048
Iteration: 811; Percent complete: 20.3%; Average loss: 3.7529
Iteration: 812; Percent complete: 20.3%; Average loss: 3.3963
Iteration: 813; Percent complete: 20.3%; Average loss: 3.2827
Iteration: 814; Percent complete: 20.3%; Average loss: 3.6496
Iteration: 815; Percent complete: 20.4%; Average loss: 3.5005
Iteration: 816; Percent complete: 20.4%; Average loss: 3.7206
Iteration: 817; Percent complete: 20.4%; Average loss: 3.6463
Iteration: 818; Percent complete: 20.4%; Average loss: 3.6117
Iteration: 819; Percent complete: 20.5%; Average loss: 3.4910
Iteration: 820; Percent complete: 20.5%; Average loss: 3.5760
Iteration: 821; Percent complete: 20.5%; Average loss: 3.5679
Iteration: 822; Percent complete: 20.5%; Average loss: 3.8028
Iteration: 823; Percent complete: 20.6%; Average loss: 3.6567
Iteration: 824; Percent complete: 20.6%; Average loss: 3.4464
Iteration: 825; Percent complete: 20.6%; Average loss: 3.3388
Iteration: 826; Percent complete: 20.6%; Average loss: 3.6734
Iteration: 827; Percent complete: 20.7%; Average loss: 3.3215
Iteration: 828; Percent complete: 20.7%; Average loss: 3.7350
Iteration: 829; Percent complete: 20.7%; Average loss: 3.4605
Iteration: 830; Percent complete: 20.8%; Average loss: 3.7198
Iteration: 831; Percent complete: 20.8%; Average loss: 3.4232
Iteration: 832; Percent complete: 20.8%; Average loss: 3.4761
Iteration: 833; Percent complete: 20.8%; Average loss: 3.5433
Iteration: 834; Percent complete: 20.8%; Average loss: 3.3562
Iteration: 835; Percent complete: 20.9%; Average loss: 3.2379
Iteration: 836; Percent complete: 20.9%; Average loss: 3.3975
Iteration: 837; Percent complete: 20.9%; Average loss: 3.3833
Iteration: 838; Percent complete: 20.9%; Average loss: 3.5508
Iteration: 839; Percent complete: 21.0%; Average loss: 3.7102
Iteration: 840; Percent complete: 21.0%; Average loss: 3.3904
Iteration: 841; Percent complete: 21.0%; Average loss: 3.4200
Iteration: 842; Percent complete: 21.1%; Average loss: 3.7652
Iteration: 843; Percent complete: 21.1%; Average loss: 3.4246
Iteration: 844; Percent complete: 21.1%; Average loss: 3.2599
Iteration: 845; Percent complete: 21.1%; Average loss: 3.3561
Iteration: 846; Percent complete: 21.1%; Average loss: 3.7766
Iteration: 847; Percent complete: 21.2%; Average loss: 3.7518
Iteration: 848; Percent complete: 21.2%; Average loss: 3.4819
Iteration: 849; Percent complete: 21.2%; Average loss: 3.3728
Iteration: 850; Percent complete: 21.2%; Average loss: 3.3376
Iteration: 851; Percent complete: 21.3%; Average loss: 3.6096
Iteration: 852; Percent complete: 21.3%; Average loss: 3.5049
Iteration: 853; Percent complete: 21.3%; Average loss: 3.5220
Iteration: 854; Percent complete: 21.3%; Average loss: 3.7195
Iteration: 855; Percent complete: 21.4%; Average loss: 3.5452
Iteration: 856; Percent complete: 21.4%; Average loss: 3.4889
Iteration: 857; Percent complete: 21.4%; Average loss: 3.5575
Iteration: 858; Percent complete: 21.4%; Average loss: 3.5509
Iteration: 859; Percent complete: 21.5%; Average loss: 3.4113
Iteration: 860; Percent complete: 21.5%; Average loss: 3.5326
Iteration: 861; Percent complete: 21.5%; Average loss: 3.4089
Iteration: 862; Percent complete: 21.6%; Average loss: 3.3541
Iteration: 863; Percent complete: 21.6%; Average loss: 3.4156
Iteration: 864; Percent complete: 21.6%; Average loss: 3.6084
Iteration: 865; Percent complete: 21.6%; Average loss: 3.6190
Iteration: 866; Percent complete: 21.6%; Average loss: 3.3266
Iteration: 867; Percent complete: 21.7%; Average loss: 3.5303
Iteration: 868; Percent complete: 21.7%; Average loss: 3.5010
Iteration: 869; Percent complete: 21.7%; Average loss: 3.3878
Iteration: 870; Percent complete: 21.8%; Average loss: 3.1048
Iteration: 871; Percent complete: 21.8%; Average loss: 3.7402
Iteration: 872; Percent complete: 21.8%; Average loss: 3.6111
Iteration: 873; Percent complete: 21.8%; Average loss: 3.3800
Iteration: 874; Percent complete: 21.9%; Average loss: 3.5106
Iteration: 875; Percent complete: 21.9%; Average loss: 3.4926
Iteration: 876; Percent complete: 21.9%; Average loss: 3.4804
Iteration: 877; Percent complete: 21.9%; Average loss: 3.4488
Iteration: 878; Percent complete: 21.9%; Average loss: 3.3409
Iteration: 879; Percent complete: 22.0%; Average loss: 3.5705
Iteration: 880; Percent complete: 22.0%; Average loss: 3.8444
Iteration: 881; Percent complete: 22.0%; Average loss: 3.2860
Iteration: 882; Percent complete: 22.1%; Average loss: 3.5967
Iteration: 883; Percent complete: 22.1%; Average loss: 3.4793
Iteration: 884; Percent complete: 22.1%; Average loss: 3.7051
Iteration: 885; Percent complete: 22.1%; Average loss: 3.5781
Iteration: 886; Percent complete: 22.1%; Average loss: 3.5029
Iteration: 887; Percent complete: 22.2%; Average loss: 3.4405
Iteration: 888; Percent complete: 22.2%; Average loss: 3.4573
Iteration: 889; Percent complete: 22.2%; Average loss: 3.6307
Iteration: 890; Percent complete: 22.2%; Average loss: 3.4553
Iteration: 891; Percent complete: 22.3%; Average loss: 3.7180
Iteration: 892; Percent complete: 22.3%; Average loss: 3.4251
Iteration: 893; Percent complete: 22.3%; Average loss: 3.6178
Iteration: 894; Percent complete: 22.4%; Average loss: 3.4503
Iteration: 895; Percent complete: 22.4%; Average loss: 3.8410
Iteration: 896; Percent complete: 22.4%; Average loss: 3.4756
Iteration: 897; Percent complete: 22.4%; Average loss: 3.2717
Iteration: 898; Percent complete: 22.4%; Average loss: 3.5942
Iteration: 899; Percent complete: 22.5%; Average loss: 3.8686
Iteration: 900; Percent complete: 22.5%; Average loss: 3.5300
Iteration: 901; Percent complete: 22.5%; Average loss: 3.6393
Iteration: 902; Percent complete: 22.6%; Average loss: 3.6192
Iteration: 903; Percent complete: 22.6%; Average loss: 3.6625
Iteration: 904; Percent complete: 22.6%; Average loss: 3.5839
Iteration: 905; Percent complete: 22.6%; Average loss: 3.5569
Iteration: 906; Percent complete: 22.7%; Average loss: 3.6053
Iteration: 907; Percent complete: 22.7%; Average loss: 3.1495
Iteration: 908; Percent complete: 22.7%; Average loss: 3.3684
Iteration: 909; Percent complete: 22.7%; Average loss: 3.1249
Iteration: 910; Percent complete: 22.8%; Average loss: 3.5749
Iteration: 911; Percent complete: 22.8%; Average loss: 3.3884
Iteration: 912; Percent complete: 22.8%; Average loss: 3.5308
Iteration: 913; Percent complete: 22.8%; Average loss: 3.6873
Iteration: 914; Percent complete: 22.9%; Average loss: 3.5690
Iteration: 915; Percent complete: 22.9%; Average loss: 3.4925
Iteration: 916; Percent complete: 22.9%; Average loss: 3.5282
Iteration: 917; Percent complete: 22.9%; Average loss: 3.6410
Iteration: 918; Percent complete: 22.9%; Average loss: 3.3064
Iteration: 919; Percent complete: 23.0%; Average loss: 3.3183
Iteration: 920; Percent complete: 23.0%; Average loss: 3.6246
Iteration: 921; Percent complete: 23.0%; Average loss: 3.7155
Iteration: 922; Percent complete: 23.1%; Average loss: 3.4651
Iteration: 923; Percent complete: 23.1%; Average loss: 3.4402
Iteration: 924; Percent complete: 23.1%; Average loss: 3.5521
Iteration: 925; Percent complete: 23.1%; Average loss: 3.4110
Iteration: 926; Percent complete: 23.2%; Average loss: 3.6807
Iteration: 927; Percent complete: 23.2%; Average loss: 3.2436
Iteration: 928; Percent complete: 23.2%; Average loss: 3.5876
Iteration: 929; Percent complete: 23.2%; Average loss: 3.5683
Iteration: 930; Percent complete: 23.2%; Average loss: 3.5178
Iteration: 931; Percent complete: 23.3%; Average loss: 3.5568
Iteration: 932; Percent complete: 23.3%; Average loss: 3.6111
Iteration: 933; Percent complete: 23.3%; Average loss: 3.2649
Iteration: 934; Percent complete: 23.4%; Average loss: 3.3478
Iteration: 935; Percent complete: 23.4%; Average loss: 3.5124
Iteration: 936; Percent complete: 23.4%; Average loss: 3.7318
Iteration: 937; Percent complete: 23.4%; Average loss: 3.7330
Iteration: 938; Percent complete: 23.4%; Average loss: 3.5331
Iteration: 939; Percent complete: 23.5%; Average loss: 3.6429
Iteration: 940; Percent complete: 23.5%; Average loss: 3.4339
Iteration: 941; Percent complete: 23.5%; Average loss: 3.4488
Iteration: 942; Percent complete: 23.5%; Average loss: 3.5322
Iteration: 943; Percent complete: 23.6%; Average loss: 3.3059
Iteration: 944; Percent complete: 23.6%; Average loss: 3.8428
Iteration: 945; Percent complete: 23.6%; Average loss: 3.4885
Iteration: 946; Percent complete: 23.6%; Average loss: 3.2782
Iteration: 947; Percent complete: 23.7%; Average loss: 3.4055
Iteration: 948; Percent complete: 23.7%; Average loss: 3.2753
Iteration: 949; Percent complete: 23.7%; Average loss: 3.4670
Iteration: 950; Percent complete: 23.8%; Average loss: 3.2783
Iteration: 951; Percent complete: 23.8%; Average loss: 3.4884
Iteration: 952; Percent complete: 23.8%; Average loss: 3.4237
Iteration: 953; Percent complete: 23.8%; Average loss: 3.5219
Iteration: 954; Percent complete: 23.8%; Average loss: 3.6677
Iteration: 955; Percent complete: 23.9%; Average loss: 2.9449
Iteration: 956; Percent complete: 23.9%; Average loss: 3.4900
Iteration: 957; Percent complete: 23.9%; Average loss: 3.3844
Iteration: 958; Percent complete: 23.9%; Average loss: 3.5010
Iteration: 959; Percent complete: 24.0%; Average loss: 3.4273
Iteration: 960; Percent complete: 24.0%; Average loss: 3.6300
Iteration: 961; Percent complete: 24.0%; Average loss: 3.3821
Iteration: 962; Percent complete: 24.1%; Average loss: 3.7445
Iteration: 963; Percent complete: 24.1%; Average loss: 3.4383
Iteration: 964; Percent complete: 24.1%; Average loss: 3.5681
Iteration: 965; Percent complete: 24.1%; Average loss: 3.6428
Iteration: 966; Percent complete: 24.1%; Average loss: 3.5348
Iteration: 967; Percent complete: 24.2%; Average loss: 3.4057
Iteration: 968; Percent complete: 24.2%; Average loss: 3.4160
Iteration: 969; Percent complete: 24.2%; Average loss: 3.4956
Iteration: 970; Percent complete: 24.2%; Average loss: 3.4019
Iteration: 971; Percent complete: 24.3%; Average loss: 3.2986
Iteration: 972; Percent complete: 24.3%; Average loss: 3.8125
Iteration: 973; Percent complete: 24.3%; Average loss: 3.6183
Iteration: 974; Percent complete: 24.3%; Average loss: 3.4514
Iteration: 975; Percent complete: 24.4%; Average loss: 3.5251
Iteration: 976; Percent complete: 24.4%; Average loss: 3.4007
Iteration: 977; Percent complete: 24.4%; Average loss: 3.5726
Iteration: 978; Percent complete: 24.4%; Average loss: 3.6999
Iteration: 979; Percent complete: 24.5%; Average loss: 3.3466
Iteration: 980; Percent complete: 24.5%; Average loss: 3.4244
Iteration: 981; Percent complete: 24.5%; Average loss: 3.5131
Iteration: 982; Percent complete: 24.6%; Average loss: 3.4732
Iteration: 983; Percent complete: 24.6%; Average loss: 3.6582
Iteration: 984; Percent complete: 24.6%; Average loss: 3.4252
Iteration: 985; Percent complete: 24.6%; Average loss: 3.6687
Iteration: 986; Percent complete: 24.6%; Average loss: 3.4721
Iteration: 987; Percent complete: 24.7%; Average loss: 3.5966
Iteration: 988; Percent complete: 24.7%; Average loss: 3.2842
Iteration: 989; Percent complete: 24.7%; Average loss: 3.2792
Iteration: 990; Percent complete: 24.8%; Average loss: 3.1227
Iteration: 991; Percent complete: 24.8%; Average loss: 3.5238
Iteration: 992; Percent complete: 24.8%; Average loss: 3.5197
Iteration: 993; Percent complete: 24.8%; Average loss: 3.6510
Iteration: 994; Percent complete: 24.9%; Average loss: 3.6462
Iteration: 995; Percent complete: 24.9%; Average loss: 3.4858
Iteration: 996; Percent complete: 24.9%; Average loss: 3.2577
Iteration: 997; Percent complete: 24.9%; Average loss: 3.4033
Iteration: 998; Percent complete: 24.9%; Average loss: 3.3220
Iteration: 999; Percent complete: 25.0%; Average loss: 3.6034
Iteration: 1000; Percent complete: 25.0%; Average loss: 3.3368
Iteration: 1001; Percent complete: 25.0%; Average loss: 3.5709
Iteration: 1002; Percent complete: 25.1%; Average loss: 3.6236
Iteration: 1003; Percent complete: 25.1%; Average loss: 3.5363
Iteration: 1004; Percent complete: 25.1%; Average loss: 3.3350
Iteration: 1005; Percent complete: 25.1%; Average loss: 3.1066
Iteration: 1006; Percent complete: 25.1%; Average loss: 3.6030
Iteration: 1007; Percent complete: 25.2%; Average loss: 3.3585
Iteration: 1008; Percent complete: 25.2%; Average loss: 3.3894
Iteration: 1009; Percent complete: 25.2%; Average loss: 3.4915
Iteration: 1010; Percent complete: 25.2%; Average loss: 3.4931
Iteration: 1011; Percent complete: 25.3%; Average loss: 3.5134
Iteration: 1012; Percent complete: 25.3%; Average loss: 3.1530
Iteration: 1013; Percent complete: 25.3%; Average loss: 3.4320
Iteration: 1014; Percent complete: 25.4%; Average loss: 3.6081
Iteration: 1015; Percent complete: 25.4%; Average loss: 3.4084
Iteration: 1016; Percent complete: 25.4%; Average loss: 3.4174
Iteration: 1017; Percent complete: 25.4%; Average loss: 3.5118
Iteration: 1018; Percent complete: 25.4%; Average loss: 3.3684
Iteration: 1019; Percent complete: 25.5%; Average loss: 3.6329
Iteration: 1020; Percent complete: 25.5%; Average loss: 3.4593
Iteration: 1021; Percent complete: 25.5%; Average loss: 3.3772
Iteration: 1022; Percent complete: 25.6%; Average loss: 3.3903
Iteration: 1023; Percent complete: 25.6%; Average loss: 3.2530
Iteration: 1024; Percent complete: 25.6%; Average loss: 3.0957
Iteration: 1025; Percent complete: 25.6%; Average loss: 3.4705
Iteration: 1026; Percent complete: 25.7%; Average loss: 3.6478
Iteration: 1027; Percent complete: 25.7%; Average loss: 3.7062
Iteration: 1028; Percent complete: 25.7%; Average loss: 3.1056
Iteration: 1029; Percent complete: 25.7%; Average loss: 3.4170
Iteration: 1030; Percent complete: 25.8%; Average loss: 3.4585
Iteration: 1031; Percent complete: 25.8%; Average loss: 3.2704
Iteration: 1032; Percent complete: 25.8%; Average loss: 3.4088
Iteration: 1033; Percent complete: 25.8%; Average loss: 3.2611
Iteration: 1034; Percent complete: 25.9%; Average loss: 3.2400
Iteration: 1035; Percent complete: 25.9%; Average loss: 3.4629
Iteration: 1036; Percent complete: 25.9%; Average loss: 3.6547
Iteration: 1037; Percent complete: 25.9%; Average loss: 3.5723
Iteration: 1038; Percent complete: 25.9%; Average loss: 3.3853
Iteration: 1039; Percent complete: 26.0%; Average loss: 3.6465
Iteration: 1040; Percent complete: 26.0%; Average loss: 3.4676
Iteration: 1041; Percent complete: 26.0%; Average loss: 3.6816
Iteration: 1042; Percent complete: 26.1%; Average loss: 3.5921
Iteration: 1043; Percent complete: 26.1%; Average loss: 3.4205
Iteration: 1044; Percent complete: 26.1%; Average loss: 3.4771
Iteration: 1045; Percent complete: 26.1%; Average loss: 3.5897
Iteration: 1046; Percent complete: 26.2%; Average loss: 3.3415
Iteration: 1047; Percent complete: 26.2%; Average loss: 3.1738
Iteration: 1048; Percent complete: 26.2%; Average loss: 3.5096
Iteration: 1049; Percent complete: 26.2%; Average loss: 3.4668
Iteration: 1050; Percent complete: 26.2%; Average loss: 3.7379
Iteration: 1051; Percent complete: 26.3%; Average loss: 3.4255
Iteration: 1052; Percent complete: 26.3%; Average loss: 3.4777
Iteration: 1053; Percent complete: 26.3%; Average loss: 3.4556
Iteration: 1054; Percent complete: 26.4%; Average loss: 3.3218
Iteration: 1055; Percent complete: 26.4%; Average loss: 3.4673
Iteration: 1056; Percent complete: 26.4%; Average loss: 3.4612
Iteration: 1057; Percent complete: 26.4%; Average loss: 3.3810
Iteration: 1058; Percent complete: 26.5%; Average loss: 3.0225
Iteration: 1059; Percent complete: 26.5%; Average loss: 3.3506
Iteration: 1060; Percent complete: 26.5%; Average loss: 3.3430
Iteration: 1061; Percent complete: 26.5%; Average loss: 3.1516
Iteration: 1062; Percent complete: 26.6%; Average loss: 3.5996
Iteration: 1063; Percent complete: 26.6%; Average loss: 3.3799
Iteration: 1064; Percent complete: 26.6%; Average loss: 3.4277
Iteration: 1065; Percent complete: 26.6%; Average loss: 3.7654
Iteration: 1066; Percent complete: 26.7%; Average loss: 3.6191
Iteration: 1067; Percent complete: 26.7%; Average loss: 3.3727
Iteration: 1068; Percent complete: 26.7%; Average loss: 3.5367
Iteration: 1069; Percent complete: 26.7%; Average loss: 3.2039
Iteration: 1070; Percent complete: 26.8%; Average loss: 3.2377
Iteration: 1071; Percent complete: 26.8%; Average loss: 3.2657
Iteration: 1072; Percent complete: 26.8%; Average loss: 3.2987
Iteration: 1073; Percent complete: 26.8%; Average loss: 3.5917
Iteration: 1074; Percent complete: 26.9%; Average loss: 3.0964
Iteration: 1075; Percent complete: 26.9%; Average loss: 3.3910
Iteration: 1076; Percent complete: 26.9%; Average loss: 3.3928
Iteration: 1077; Percent complete: 26.9%; Average loss: 3.4420
Iteration: 1078; Percent complete: 27.0%; Average loss: 3.5992
Iteration: 1079; Percent complete: 27.0%; Average loss: 3.3338
Iteration: 1080; Percent complete: 27.0%; Average loss: 3.3511
Iteration: 1081; Percent complete: 27.0%; Average loss: 3.5167
Iteration: 1082; Percent complete: 27.1%; Average loss: 3.5390
Iteration: 1083; Percent complete: 27.1%; Average loss: 3.5129
Iteration: 1084; Percent complete: 27.1%; Average loss: 3.4364
Iteration: 1085; Percent complete: 27.1%; Average loss: 3.4829
Iteration: 1086; Percent complete: 27.2%; Average loss: 3.3213
Iteration: 1087; Percent complete: 27.2%; Average loss: 3.5564
Iteration: 1088; Percent complete: 27.2%; Average loss: 3.4972
Iteration: 1089; Percent complete: 27.2%; Average loss: 3.6405
Iteration: 1090; Percent complete: 27.3%; Average loss: 3.5204
Iteration: 1091; Percent complete: 27.3%; Average loss: 3.2441
Iteration: 1092; Percent complete: 27.3%; Average loss: 3.4460
Iteration: 1093; Percent complete: 27.3%; Average loss: 3.4446
Iteration: 1094; Percent complete: 27.4%; Average loss: 3.5398
Iteration: 1095; Percent complete: 27.4%; Average loss: 3.4340
Iteration: 1096; Percent complete: 27.4%; Average loss: 3.4178
Iteration: 1097; Percent complete: 27.4%; Average loss: 3.4050
Iteration: 1098; Percent complete: 27.5%; Average loss: 3.3724
Iteration: 1099; Percent complete: 27.5%; Average loss: 3.3858
Iteration: 1100; Percent complete: 27.5%; Average loss: 3.5558
Iteration: 1101; Percent complete: 27.5%; Average loss: 3.1908
Iteration: 1102; Percent complete: 27.6%; Average loss: 3.4775
Iteration: 1103; Percent complete: 27.6%; Average loss: 3.5429
Iteration: 1104; Percent complete: 27.6%; Average loss: 3.4871
Iteration: 1105; Percent complete: 27.6%; Average loss: 3.0571
Iteration: 1106; Percent complete: 27.7%; Average loss: 3.3462
Iteration: 1107; Percent complete: 27.7%; Average loss: 3.3478
Iteration: 1108; Percent complete: 27.7%; Average loss: 3.6119
Iteration: 1109; Percent complete: 27.7%; Average loss: 3.2902
Iteration: 1110; Percent complete: 27.8%; Average loss: 3.4577
Iteration: 1111; Percent complete: 27.8%; Average loss: 3.4604
Iteration: 1112; Percent complete: 27.8%; Average loss: 3.4484
Iteration: 1113; Percent complete: 27.8%; Average loss: 3.6411
Iteration: 1114; Percent complete: 27.9%; Average loss: 3.3542
Iteration: 1115; Percent complete: 27.9%; Average loss: 3.4832
Iteration: 1116; Percent complete: 27.9%; Average loss: 3.3015
Iteration: 1117; Percent complete: 27.9%; Average loss: 3.2490
Iteration: 1118; Percent complete: 28.0%; Average loss: 3.7680
Iteration: 1119; Percent complete: 28.0%; Average loss: 3.3619
Iteration: 1120; Percent complete: 28.0%; Average loss: 3.3580
Iteration: 1121; Percent complete: 28.0%; Average loss: 3.5941
Iteration: 1122; Percent complete: 28.1%; Average loss: 3.3509
Iteration: 1123; Percent complete: 28.1%; Average loss: 3.2749
Iteration: 1124; Percent complete: 28.1%; Average loss: 3.4586
Iteration: 1125; Percent complete: 28.1%; Average loss: 3.6180
Iteration: 1126; Percent complete: 28.1%; Average loss: 3.4833
Iteration: 1127; Percent complete: 28.2%; Average loss: 3.4612
Iteration: 1128; Percent complete: 28.2%; Average loss: 3.1187
Iteration: 1129; Percent complete: 28.2%; Average loss: 3.2614
Iteration: 1130; Percent complete: 28.2%; Average loss: 3.6712
Iteration: 1131; Percent complete: 28.3%; Average loss: 3.6165
Iteration: 1132; Percent complete: 28.3%; Average loss: 3.5052
Iteration: 1133; Percent complete: 28.3%; Average loss: 3.3924
Iteration: 1134; Percent complete: 28.3%; Average loss: 3.6026
Iteration: 1135; Percent complete: 28.4%; Average loss: 3.8882
Iteration: 1136; Percent complete: 28.4%; Average loss: 3.2184
Iteration: 1137; Percent complete: 28.4%; Average loss: 3.3276
Iteration: 1138; Percent complete: 28.4%; Average loss: 3.3220
Iteration: 1139; Percent complete: 28.5%; Average loss: 3.1828
Iteration: 1140; Percent complete: 28.5%; Average loss: 3.3561
Iteration: 1141; Percent complete: 28.5%; Average loss: 3.1903
Iteration: 1142; Percent complete: 28.5%; Average loss: 3.6533
Iteration: 1143; Percent complete: 28.6%; Average loss: 3.4080
Iteration: 1144; Percent complete: 28.6%; Average loss: 3.4830
Iteration: 1145; Percent complete: 28.6%; Average loss: 3.5856
Iteration: 1146; Percent complete: 28.6%; Average loss: 3.3909
Iteration: 1147; Percent complete: 28.7%; Average loss: 3.4728
Iteration: 1148; Percent complete: 28.7%; Average loss: 3.2640
Iteration: 1149; Percent complete: 28.7%; Average loss: 3.5943
Iteration: 1150; Percent complete: 28.7%; Average loss: 3.7953
Iteration: 1151; Percent complete: 28.8%; Average loss: 3.5096
Iteration: 1152; Percent complete: 28.8%; Average loss: 3.4847
Iteration: 1153; Percent complete: 28.8%; Average loss: 3.2606
Iteration: 1154; Percent complete: 28.8%; Average loss: 3.2650
Iteration: 1155; Percent complete: 28.9%; Average loss: 3.4589
Iteration: 1156; Percent complete: 28.9%; Average loss: 3.3990
Iteration: 1157; Percent complete: 28.9%; Average loss: 3.5077
Iteration: 1158; Percent complete: 28.9%; Average loss: 3.4530
Iteration: 1159; Percent complete: 29.0%; Average loss: 3.2869
Iteration: 1160; Percent complete: 29.0%; Average loss: 3.3091
Iteration: 1161; Percent complete: 29.0%; Average loss: 3.2439
Iteration: 1162; Percent complete: 29.0%; Average loss: 3.4448
Iteration: 1163; Percent complete: 29.1%; Average loss: 3.5133
Iteration: 1164; Percent complete: 29.1%; Average loss: 3.4233
Iteration: 1165; Percent complete: 29.1%; Average loss: 3.1288
Iteration: 1166; Percent complete: 29.1%; Average loss: 3.4686
Iteration: 1167; Percent complete: 29.2%; Average loss: 3.3653
Iteration: 1168; Percent complete: 29.2%; Average loss: 3.2403
Iteration: 1169; Percent complete: 29.2%; Average loss: 3.6802
Iteration: 1170; Percent complete: 29.2%; Average loss: 3.4464
Iteration: 1171; Percent complete: 29.3%; Average loss: 3.3463
Iteration: 1172; Percent complete: 29.3%; Average loss: 3.4071
Iteration: 1173; Percent complete: 29.3%; Average loss: 3.1945
Iteration: 1174; Percent complete: 29.3%; Average loss: 3.3138
Iteration: 1175; Percent complete: 29.4%; Average loss: 3.2039
Iteration: 1176; Percent complete: 29.4%; Average loss: 3.3954
Iteration: 1177; Percent complete: 29.4%; Average loss: 3.3257
Iteration: 1178; Percent complete: 29.4%; Average loss: 3.4930
Iteration: 1179; Percent complete: 29.5%; Average loss: 3.5491
Iteration: 1180; Percent complete: 29.5%; Average loss: 3.5128
Iteration: 1181; Percent complete: 29.5%; Average loss: 3.5458
Iteration: 1182; Percent complete: 29.5%; Average loss: 3.3272
Iteration: 1183; Percent complete: 29.6%; Average loss: 3.1943
Iteration: 1184; Percent complete: 29.6%; Average loss: 3.1975
Iteration: 1185; Percent complete: 29.6%; Average loss: 3.5850
Iteration: 1186; Percent complete: 29.6%; Average loss: 3.2572
Iteration: 1187; Percent complete: 29.7%; Average loss: 3.2601
Iteration: 1188; Percent complete: 29.7%; Average loss: 3.3810
Iteration: 1189; Percent complete: 29.7%; Average loss: 3.3945
Iteration: 1190; Percent complete: 29.8%; Average loss: 3.3685
Iteration: 1191; Percent complete: 29.8%; Average loss: 3.4522
Iteration: 1192; Percent complete: 29.8%; Average loss: 3.6273
Iteration: 1193; Percent complete: 29.8%; Average loss: 3.5867
Iteration: 1194; Percent complete: 29.8%; Average loss: 3.1931
Iteration: 1195; Percent complete: 29.9%; Average loss: 3.5373
Iteration: 1196; Percent complete: 29.9%; Average loss: 3.1713
Iteration: 1197; Percent complete: 29.9%; Average loss: 3.4537
Iteration: 1198; Percent complete: 29.9%; Average loss: 3.5321
Iteration: 1199; Percent complete: 30.0%; Average loss: 3.2817
Iteration: 1200; Percent complete: 30.0%; Average loss: 3.3665
Iteration: 1201; Percent complete: 30.0%; Average loss: 3.4615
Iteration: 1202; Percent complete: 30.0%; Average loss: 3.5887
Iteration: 1203; Percent complete: 30.1%; Average loss: 3.4501
Iteration: 1204; Percent complete: 30.1%; Average loss: 3.4998
Iteration: 1205; Percent complete: 30.1%; Average loss: 3.4856
Iteration: 1206; Percent complete: 30.1%; Average loss: 3.6396
Iteration: 1207; Percent complete: 30.2%; Average loss: 3.2850
Iteration: 1208; Percent complete: 30.2%; Average loss: 3.2112
Iteration: 1209; Percent complete: 30.2%; Average loss: 3.2769
Iteration: 1210; Percent complete: 30.2%; Average loss: 3.3418
Iteration: 1211; Percent complete: 30.3%; Average loss: 3.3096
Iteration: 1212; Percent complete: 30.3%; Average loss: 3.1662
Iteration: 1213; Percent complete: 30.3%; Average loss: 3.3057
Iteration: 1214; Percent complete: 30.3%; Average loss: 3.0844
Iteration: 1215; Percent complete: 30.4%; Average loss: 3.7828
Iteration: 1216; Percent complete: 30.4%; Average loss: 3.1821
Iteration: 1217; Percent complete: 30.4%; Average loss: 3.4870
Iteration: 1218; Percent complete: 30.4%; Average loss: 3.3352
Iteration: 1219; Percent complete: 30.5%; Average loss: 3.4706
Iteration: 1220; Percent complete: 30.5%; Average loss: 3.1342
Iteration: 1221; Percent complete: 30.5%; Average loss: 3.5202
Iteration: 1222; Percent complete: 30.6%; Average loss: 3.3665
Iteration: 1223; Percent complete: 30.6%; Average loss: 3.4243
Iteration: 1224; Percent complete: 30.6%; Average loss: 3.3875
Iteration: 1225; Percent complete: 30.6%; Average loss: 3.4198
Iteration: 1226; Percent complete: 30.6%; Average loss: 3.4930
Iteration: 1227; Percent complete: 30.7%; Average loss: 3.3312
Iteration: 1228; Percent complete: 30.7%; Average loss: 3.5107
Iteration: 1229; Percent complete: 30.7%; Average loss: 3.4819
Iteration: 1230; Percent complete: 30.8%; Average loss: 3.5430
Iteration: 1231; Percent complete: 30.8%; Average loss: 3.2605
Iteration: 1232; Percent complete: 30.8%; Average loss: 3.1755
Iteration: 1233; Percent complete: 30.8%; Average loss: 3.3303
Iteration: 1234; Percent complete: 30.9%; Average loss: 3.2128
Iteration: 1235; Percent complete: 30.9%; Average loss: 3.3656
Iteration: 1236; Percent complete: 30.9%; Average loss: 3.3274
Iteration: 1237; Percent complete: 30.9%; Average loss: 3.4217
Iteration: 1238; Percent complete: 30.9%; Average loss: 3.1414
Iteration: 1239; Percent complete: 31.0%; Average loss: 3.4878
Iteration: 1240; Percent complete: 31.0%; Average loss: 3.4686
Iteration: 1241; Percent complete: 31.0%; Average loss: 3.6367
Iteration: 1242; Percent complete: 31.1%; Average loss: 3.4255
Iteration: 1243; Percent complete: 31.1%; Average loss: 3.2479
Iteration: 1244; Percent complete: 31.1%; Average loss: 3.3381
Iteration: 1245; Percent complete: 31.1%; Average loss: 3.3356
Iteration: 1246; Percent complete: 31.1%; Average loss: 3.2768
Iteration: 1247; Percent complete: 31.2%; Average loss: 3.4093
Iteration: 1248; Percent complete: 31.2%; Average loss: 3.5390
Iteration: 1249; Percent complete: 31.2%; Average loss: 3.1830
Iteration: 1250; Percent complete: 31.2%; Average loss: 3.4913
Iteration: 1251; Percent complete: 31.3%; Average loss: 3.7212
Iteration: 1252; Percent complete: 31.3%; Average loss: 3.1772
Iteration: 1253; Percent complete: 31.3%; Average loss: 3.4189
Iteration: 1254; Percent complete: 31.4%; Average loss: 3.2099
Iteration: 1255; Percent complete: 31.4%; Average loss: 3.2862
Iteration: 1256; Percent complete: 31.4%; Average loss: 3.2378
Iteration: 1257; Percent complete: 31.4%; Average loss: 3.4769
Iteration: 1258; Percent complete: 31.4%; Average loss: 3.4945
Iteration: 1259; Percent complete: 31.5%; Average loss: 3.4523
Iteration: 1260; Percent complete: 31.5%; Average loss: 3.2292
Iteration: 1261; Percent complete: 31.5%; Average loss: 3.5124
Iteration: 1262; Percent complete: 31.6%; Average loss: 3.3913
Iteration: 1263; Percent complete: 31.6%; Average loss: 3.2628
Iteration: 1264; Percent complete: 31.6%; Average loss: 3.2793
Iteration: 1265; Percent complete: 31.6%; Average loss: 3.5312
Iteration: 1266; Percent complete: 31.6%; Average loss: 3.1634
Iteration: 1267; Percent complete: 31.7%; Average loss: 3.4867
Iteration: 1268; Percent complete: 31.7%; Average loss: 3.3444
Iteration: 1269; Percent complete: 31.7%; Average loss: 3.3797
Iteration: 1270; Percent complete: 31.8%; Average loss: 3.2880
Iteration: 1271; Percent complete: 31.8%; Average loss: 3.2535
Iteration: 1272; Percent complete: 31.8%; Average loss: 3.2782
Iteration: 1273; Percent complete: 31.8%; Average loss: 3.3799
Iteration: 1274; Percent complete: 31.9%; Average loss: 3.7475
Iteration: 1275; Percent complete: 31.9%; Average loss: 3.3681
Iteration: 1276; Percent complete: 31.9%; Average loss: 3.2603
Iteration: 1277; Percent complete: 31.9%; Average loss: 3.3161
Iteration: 1278; Percent complete: 31.9%; Average loss: 3.4648
Iteration: 1279; Percent complete: 32.0%; Average loss: 3.4773
Iteration: 1280; Percent complete: 32.0%; Average loss: 3.5219
Iteration: 1281; Percent complete: 32.0%; Average loss: 3.6911
Iteration: 1282; Percent complete: 32.0%; Average loss: 3.5941
Iteration: 1283; Percent complete: 32.1%; Average loss: 3.1477
Iteration: 1284; Percent complete: 32.1%; Average loss: 3.5284
Iteration: 1285; Percent complete: 32.1%; Average loss: 3.2346
Iteration: 1286; Percent complete: 32.1%; Average loss: 3.5060
Iteration: 1287; Percent complete: 32.2%; Average loss: 3.5023
Iteration: 1288; Percent complete: 32.2%; Average loss: 3.3827
Iteration: 1289; Percent complete: 32.2%; Average loss: 3.0998
Iteration: 1290; Percent complete: 32.2%; Average loss: 3.3521
Iteration: 1291; Percent complete: 32.3%; Average loss: 3.3937
Iteration: 1292; Percent complete: 32.3%; Average loss: 3.5548
Iteration: 1293; Percent complete: 32.3%; Average loss: 3.7436
Iteration: 1294; Percent complete: 32.4%; Average loss: 3.1549
Iteration: 1295; Percent complete: 32.4%; Average loss: 3.3617
Iteration: 1296; Percent complete: 32.4%; Average loss: 3.2157
Iteration: 1297; Percent complete: 32.4%; Average loss: 3.6514
Iteration: 1298; Percent complete: 32.5%; Average loss: 3.4641
Iteration: 1299; Percent complete: 32.5%; Average loss: 3.6216
Iteration: 1300; Percent complete: 32.5%; Average loss: 3.3636
Iteration: 1301; Percent complete: 32.5%; Average loss: 3.3502
Iteration: 1302; Percent complete: 32.6%; Average loss: 3.3616
Iteration: 1303; Percent complete: 32.6%; Average loss: 3.5673
Iteration: 1304; Percent complete: 32.6%; Average loss: 3.4794
Iteration: 1305; Percent complete: 32.6%; Average loss: 3.6178
Iteration: 1306; Percent complete: 32.6%; Average loss: 3.2145
Iteration: 1307; Percent complete: 32.7%; Average loss: 3.5963
Iteration: 1308; Percent complete: 32.7%; Average loss: 3.4788
Iteration: 1309; Percent complete: 32.7%; Average loss: 3.2959
Iteration: 1310; Percent complete: 32.8%; Average loss: 3.4759
Iteration: 1311; Percent complete: 32.8%; Average loss: 3.5300
Iteration: 1312; Percent complete: 32.8%; Average loss: 3.1450
Iteration: 1313; Percent complete: 32.8%; Average loss: 3.5493
Iteration: 1314; Percent complete: 32.9%; Average loss: 3.1280
Iteration: 1315; Percent complete: 32.9%; Average loss: 3.4290
Iteration: 1316; Percent complete: 32.9%; Average loss: 3.4433
Iteration: 1317; Percent complete: 32.9%; Average loss: 3.4668
Iteration: 1318; Percent complete: 33.0%; Average loss: 3.3281
Iteration: 1319; Percent complete: 33.0%; Average loss: 3.4312
Iteration: 1320; Percent complete: 33.0%; Average loss: 3.5647
Iteration: 1321; Percent complete: 33.0%; Average loss: 3.1956
Iteration: 1322; Percent complete: 33.1%; Average loss: 3.4230
Iteration: 1323; Percent complete: 33.1%; Average loss: 3.3089
Iteration: 1324; Percent complete: 33.1%; Average loss: 3.4138
Iteration: 1325; Percent complete: 33.1%; Average loss: 3.1418
Iteration: 1326; Percent complete: 33.1%; Average loss: 3.5566
Iteration: 1327; Percent complete: 33.2%; Average loss: 3.5872
Iteration: 1328; Percent complete: 33.2%; Average loss: 3.4020
Iteration: 1329; Percent complete: 33.2%; Average loss: 3.2250
Iteration: 1330; Percent complete: 33.2%; Average loss: 3.2538
Iteration: 1331; Percent complete: 33.3%; Average loss: 3.3920
Iteration: 1332; Percent complete: 33.3%; Average loss: 3.1850
Iteration: 1333; Percent complete: 33.3%; Average loss: 3.3896
Iteration: 1334; Percent complete: 33.4%; Average loss: 3.3446
Iteration: 1335; Percent complete: 33.4%; Average loss: 3.4586
Iteration: 1336; Percent complete: 33.4%; Average loss: 3.3059
Iteration: 1337; Percent complete: 33.4%; Average loss: 3.3136
Iteration: 1338; Percent complete: 33.5%; Average loss: 3.2252
Iteration: 1339; Percent complete: 33.5%; Average loss: 3.3740
Iteration: 1340; Percent complete: 33.5%; Average loss: 3.4929
Iteration: 1341; Percent complete: 33.5%; Average loss: 3.2895
Iteration: 1342; Percent complete: 33.6%; Average loss: 3.1323
Iteration: 1343; Percent complete: 33.6%; Average loss: 3.6888
Iteration: 1344; Percent complete: 33.6%; Average loss: 3.0705
Iteration: 1345; Percent complete: 33.6%; Average loss: 2.9786
Iteration: 1346; Percent complete: 33.7%; Average loss: 3.4132
Iteration: 1347; Percent complete: 33.7%; Average loss: 3.1532
Iteration: 1348; Percent complete: 33.7%; Average loss: 3.3585
Iteration: 1349; Percent complete: 33.7%; Average loss: 3.3561
Iteration: 1350; Percent complete: 33.8%; Average loss: 3.4649
Iteration: 1351; Percent complete: 33.8%; Average loss: 3.1969
Iteration: 1352; Percent complete: 33.8%; Average loss: 3.3699
Iteration: 1353; Percent complete: 33.8%; Average loss: 3.1850
Iteration: 1354; Percent complete: 33.9%; Average loss: 3.2470
Iteration: 1355; Percent complete: 33.9%; Average loss: 3.2194
Iteration: 1356; Percent complete: 33.9%; Average loss: 3.3898
Iteration: 1357; Percent complete: 33.9%; Average loss: 3.2343
Iteration: 1358; Percent complete: 34.0%; Average loss: 3.0051
Iteration: 1359; Percent complete: 34.0%; Average loss: 3.4035
Iteration: 1360; Percent complete: 34.0%; Average loss: 3.5099
Iteration: 1361; Percent complete: 34.0%; Average loss: 3.0360
Iteration: 1362; Percent complete: 34.1%; Average loss: 3.4988
Iteration: 1363; Percent complete: 34.1%; Average loss: 3.5953
Iteration: 1364; Percent complete: 34.1%; Average loss: 3.0512
Iteration: 1365; Percent complete: 34.1%; Average loss: 3.3555
Iteration: 1366; Percent complete: 34.2%; Average loss: 3.3093
Iteration: 1367; Percent complete: 34.2%; Average loss: 2.9699
Iteration: 1368; Percent complete: 34.2%; Average loss: 3.1125
Iteration: 1369; Percent complete: 34.2%; Average loss: 3.4421
Iteration: 1370; Percent complete: 34.2%; Average loss: 3.4628
Iteration: 1371; Percent complete: 34.3%; Average loss: 3.2974
Iteration: 1372; Percent complete: 34.3%; Average loss: 3.5034
Iteration: 1373; Percent complete: 34.3%; Average loss: 3.3668
Iteration: 1374; Percent complete: 34.4%; Average loss: 3.1323
Iteration: 1375; Percent complete: 34.4%; Average loss: 3.2977
Iteration: 1376; Percent complete: 34.4%; Average loss: 3.1011
Iteration: 1377; Percent complete: 34.4%; Average loss: 3.5885
Iteration: 1378; Percent complete: 34.4%; Average loss: 3.2581
Iteration: 1379; Percent complete: 34.5%; Average loss: 3.0738
Iteration: 1380; Percent complete: 34.5%; Average loss: 3.6665
Iteration: 1381; Percent complete: 34.5%; Average loss: 3.2497
Iteration: 1382; Percent complete: 34.5%; Average loss: 3.2061
Iteration: 1383; Percent complete: 34.6%; Average loss: 3.6039
Iteration: 1384; Percent complete: 34.6%; Average loss: 3.3342
Iteration: 1385; Percent complete: 34.6%; Average loss: 3.3672
Iteration: 1386; Percent complete: 34.6%; Average loss: 3.4397
Iteration: 1387; Percent complete: 34.7%; Average loss: 3.3109
Iteration: 1388; Percent complete: 34.7%; Average loss: 3.2760
Iteration: 1389; Percent complete: 34.7%; Average loss: 3.3207
Iteration: 1390; Percent complete: 34.8%; Average loss: 3.2600
Iteration: 1391; Percent complete: 34.8%; Average loss: 3.4326
Iteration: 1392; Percent complete: 34.8%; Average loss: 3.4384
Iteration: 1393; Percent complete: 34.8%; Average loss: 3.3268
Iteration: 1394; Percent complete: 34.8%; Average loss: 3.3110
Iteration: 1395; Percent complete: 34.9%; Average loss: 3.3934
Iteration: 1396; Percent complete: 34.9%; Average loss: 3.4177
Iteration: 1397; Percent complete: 34.9%; Average loss: 3.1849
Iteration: 1398; Percent complete: 34.9%; Average loss: 3.2303
Iteration: 1399; Percent complete: 35.0%; Average loss: 3.6541
Iteration: 1400; Percent complete: 35.0%; Average loss: 3.3757
Iteration: 1401; Percent complete: 35.0%; Average loss: 3.3646
Iteration: 1402; Percent complete: 35.0%; Average loss: 3.2989
Iteration: 1403; Percent complete: 35.1%; Average loss: 3.2527
Iteration: 1404; Percent complete: 35.1%; Average loss: 3.5421
Iteration: 1405; Percent complete: 35.1%; Average loss: 3.3472
Iteration: 1406; Percent complete: 35.1%; Average loss: 3.3718
Iteration: 1407; Percent complete: 35.2%; Average loss: 3.3352
Iteration: 1408; Percent complete: 35.2%; Average loss: 3.2101
Iteration: 1409; Percent complete: 35.2%; Average loss: 3.1271
Iteration: 1410; Percent complete: 35.2%; Average loss: 3.2670
Iteration: 1411; Percent complete: 35.3%; Average loss: 3.1480
Iteration: 1412; Percent complete: 35.3%; Average loss: 3.7454
Iteration: 1413; Percent complete: 35.3%; Average loss: 2.8046
Iteration: 1414; Percent complete: 35.4%; Average loss: 3.3565
Iteration: 1415; Percent complete: 35.4%; Average loss: 3.1915
Iteration: 1416; Percent complete: 35.4%; Average loss: 3.3594
Iteration: 1417; Percent complete: 35.4%; Average loss: 3.2881
Iteration: 1418; Percent complete: 35.4%; Average loss: 3.4745
Iteration: 1419; Percent complete: 35.5%; Average loss: 3.2407
Iteration: 1420; Percent complete: 35.5%; Average loss: 3.4232
Iteration: 1421; Percent complete: 35.5%; Average loss: 3.2260
Iteration: 1422; Percent complete: 35.5%; Average loss: 3.2481
Iteration: 1423; Percent complete: 35.6%; Average loss: 3.1998
Iteration: 1424; Percent complete: 35.6%; Average loss: 3.3684
Iteration: 1425; Percent complete: 35.6%; Average loss: 3.7565
Iteration: 1426; Percent complete: 35.6%; Average loss: 3.4072
Iteration: 1427; Percent complete: 35.7%; Average loss: 3.2123
Iteration: 1428; Percent complete: 35.7%; Average loss: 3.1962
Iteration: 1429; Percent complete: 35.7%; Average loss: 3.3628
Iteration: 1430; Percent complete: 35.8%; Average loss: 3.3721
Iteration: 1431; Percent complete: 35.8%; Average loss: 3.3671
Iteration: 1432; Percent complete: 35.8%; Average loss: 3.3447
Iteration: 1433; Percent complete: 35.8%; Average loss: 3.4383
Iteration: 1434; Percent complete: 35.9%; Average loss: 3.4965
Iteration: 1435; Percent complete: 35.9%; Average loss: 3.3372
Iteration: 1436; Percent complete: 35.9%; Average loss: 3.4321
Iteration: 1437; Percent complete: 35.9%; Average loss: 3.2840
Iteration: 1438; Percent complete: 35.9%; Average loss: 3.6135
Iteration: 1439; Percent complete: 36.0%; Average loss: 3.1130
Iteration: 1440; Percent complete: 36.0%; Average loss: 3.2741
Iteration: 1441; Percent complete: 36.0%; Average loss: 3.2532
Iteration: 1442; Percent complete: 36.0%; Average loss: 3.0650
Iteration: 1443; Percent complete: 36.1%; Average loss: 3.3987
Iteration: 1444; Percent complete: 36.1%; Average loss: 3.5027
Iteration: 1445; Percent complete: 36.1%; Average loss: 3.2964
Iteration: 1446; Percent complete: 36.1%; Average loss: 3.0235
Iteration: 1447; Percent complete: 36.2%; Average loss: 3.4173
Iteration: 1448; Percent complete: 36.2%; Average loss: 3.4562
Iteration: 1449; Percent complete: 36.2%; Average loss: 3.3338
Iteration: 1450; Percent complete: 36.2%; Average loss: 3.3168
Iteration: 1451; Percent complete: 36.3%; Average loss: 3.3657
Iteration: 1452; Percent complete: 36.3%; Average loss: 3.1191
Iteration: 1453; Percent complete: 36.3%; Average loss: 3.3432
Iteration: 1454; Percent complete: 36.4%; Average loss: 3.5665
Iteration: 1455; Percent complete: 36.4%; Average loss: 3.5060
Iteration: 1456; Percent complete: 36.4%; Average loss: 3.2822
Iteration: 1457; Percent complete: 36.4%; Average loss: 3.5774
Iteration: 1458; Percent complete: 36.4%; Average loss: 3.1718
Iteration: 1459; Percent complete: 36.5%; Average loss: 2.9860
Iteration: 1460; Percent complete: 36.5%; Average loss: 3.3942
Iteration: 1461; Percent complete: 36.5%; Average loss: 3.4447
Iteration: 1462; Percent complete: 36.5%; Average loss: 3.4138
Iteration: 1463; Percent complete: 36.6%; Average loss: 3.1978
Iteration: 1464; Percent complete: 36.6%; Average loss: 3.1624
Iteration: 1465; Percent complete: 36.6%; Average loss: 3.2562
Iteration: 1466; Percent complete: 36.6%; Average loss: 3.3585
Iteration: 1467; Percent complete: 36.7%; Average loss: 3.3470
Iteration: 1468; Percent complete: 36.7%; Average loss: 3.2913
Iteration: 1469; Percent complete: 36.7%; Average loss: 3.1441
Iteration: 1470; Percent complete: 36.8%; Average loss: 3.2250
Iteration: 1471; Percent complete: 36.8%; Average loss: 3.1722
Iteration: 1472; Percent complete: 36.8%; Average loss: 3.2659
Iteration: 1473; Percent complete: 36.8%; Average loss: 3.4904
Iteration: 1474; Percent complete: 36.9%; Average loss: 3.3496
Iteration: 1475; Percent complete: 36.9%; Average loss: 3.0815
Iteration: 1476; Percent complete: 36.9%; Average loss: 2.9860
Iteration: 1477; Percent complete: 36.9%; Average loss: 3.3469
Iteration: 1478; Percent complete: 37.0%; Average loss: 3.4645
Iteration: 1479; Percent complete: 37.0%; Average loss: 3.5434
Iteration: 1480; Percent complete: 37.0%; Average loss: 3.1389
Iteration: 1481; Percent complete: 37.0%; Average loss: 3.2364
Iteration: 1482; Percent complete: 37.0%; Average loss: 3.3602
Iteration: 1483; Percent complete: 37.1%; Average loss: 3.2757
Iteration: 1484; Percent complete: 37.1%; Average loss: 3.4301
Iteration: 1485; Percent complete: 37.1%; Average loss: 3.3591
Iteration: 1486; Percent complete: 37.1%; Average loss: 3.2775
Iteration: 1487; Percent complete: 37.2%; Average loss: 3.1674
Iteration: 1488; Percent complete: 37.2%; Average loss: 3.4241
Iteration: 1489; Percent complete: 37.2%; Average loss: 3.3987
Iteration: 1490; Percent complete: 37.2%; Average loss: 3.1601
Iteration: 1491; Percent complete: 37.3%; Average loss: 3.3634
Iteration: 1492; Percent complete: 37.3%; Average loss: 3.1772
Iteration: 1493; Percent complete: 37.3%; Average loss: 3.4150
Iteration: 1494; Percent complete: 37.4%; Average loss: 3.1581
Iteration: 1495; Percent complete: 37.4%; Average loss: 3.5234
Iteration: 1496; Percent complete: 37.4%; Average loss: 3.3586
Iteration: 1497; Percent complete: 37.4%; Average loss: 3.2897
Iteration: 1498; Percent complete: 37.5%; Average loss: 3.2879
Iteration: 1499; Percent complete: 37.5%; Average loss: 3.1476
Iteration: 1500; Percent complete: 37.5%; Average loss: 3.2959
Iteration: 1501; Percent complete: 37.5%; Average loss: 3.3119
Iteration: 1502; Percent complete: 37.5%; Average loss: 3.4385
Iteration: 1503; Percent complete: 37.6%; Average loss: 3.2354
Iteration: 1504; Percent complete: 37.6%; Average loss: 3.1755
Iteration: 1505; Percent complete: 37.6%; Average loss: 3.2959
Iteration: 1506; Percent complete: 37.6%; Average loss: 3.3841
Iteration: 1507; Percent complete: 37.7%; Average loss: 3.2730
Iteration: 1508; Percent complete: 37.7%; Average loss: 3.3016
Iteration: 1509; Percent complete: 37.7%; Average loss: 3.4686
Iteration: 1510; Percent complete: 37.8%; Average loss: 3.3700
Iteration: 1511; Percent complete: 37.8%; Average loss: 3.3369
Iteration: 1512; Percent complete: 37.8%; Average loss: 3.3081
Iteration: 1513; Percent complete: 37.8%; Average loss: 3.2150
Iteration: 1514; Percent complete: 37.9%; Average loss: 3.3529
Iteration: 1515; Percent complete: 37.9%; Average loss: 3.4862
Iteration: 1516; Percent complete: 37.9%; Average loss: 3.1583
Iteration: 1517; Percent complete: 37.9%; Average loss: 3.2379
Iteration: 1518; Percent complete: 38.0%; Average loss: 3.1179
Iteration: 1519; Percent complete: 38.0%; Average loss: 3.3442
Iteration: 1520; Percent complete: 38.0%; Average loss: 3.4251
Iteration: 1521; Percent complete: 38.0%; Average loss: 3.1989
Iteration: 1522; Percent complete: 38.0%; Average loss: 3.5173
Iteration: 1523; Percent complete: 38.1%; Average loss: 3.5838
Iteration: 1524; Percent complete: 38.1%; Average loss: 3.5045
Iteration: 1525; Percent complete: 38.1%; Average loss: 3.0811
Iteration: 1526; Percent complete: 38.1%; Average loss: 3.4206
Iteration: 1527; Percent complete: 38.2%; Average loss: 3.2803
Iteration: 1528; Percent complete: 38.2%; Average loss: 3.2088
Iteration: 1529; Percent complete: 38.2%; Average loss: 3.0632
Iteration: 1530; Percent complete: 38.2%; Average loss: 3.5339
Iteration: 1531; Percent complete: 38.3%; Average loss: 3.5080
Iteration: 1532; Percent complete: 38.3%; Average loss: 3.3471
Iteration: 1533; Percent complete: 38.3%; Average loss: 3.2459
Iteration: 1534; Percent complete: 38.4%; Average loss: 3.4135
Iteration: 1535; Percent complete: 38.4%; Average loss: 3.2665
Iteration: 1536; Percent complete: 38.4%; Average loss: 3.2815
Iteration: 1537; Percent complete: 38.4%; Average loss: 3.2428
Iteration: 1538; Percent complete: 38.5%; Average loss: 3.5494
Iteration: 1539; Percent complete: 38.5%; Average loss: 3.1921
Iteration: 1540; Percent complete: 38.5%; Average loss: 3.2811
Iteration: 1541; Percent complete: 38.5%; Average loss: 3.0139
Iteration: 1542; Percent complete: 38.6%; Average loss: 3.1489
Iteration: 1543; Percent complete: 38.6%; Average loss: 3.3532
Iteration: 1544; Percent complete: 38.6%; Average loss: 3.3221
Iteration: 1545; Percent complete: 38.6%; Average loss: 3.2102
Iteration: 1546; Percent complete: 38.6%; Average loss: 3.2253
Iteration: 1547; Percent complete: 38.7%; Average loss: 3.2342
Iteration: 1548; Percent complete: 38.7%; Average loss: 3.0814
Iteration: 1549; Percent complete: 38.7%; Average loss: 3.3196
Iteration: 1550; Percent complete: 38.8%; Average loss: 3.5431
Iteration: 1551; Percent complete: 38.8%; Average loss: 3.1291
Iteration: 1552; Percent complete: 38.8%; Average loss: 3.2070
Iteration: 1553; Percent complete: 38.8%; Average loss: 3.1917
Iteration: 1554; Percent complete: 38.9%; Average loss: 3.4988
Iteration: 1555; Percent complete: 38.9%; Average loss: 3.2829
Iteration: 1556; Percent complete: 38.9%; Average loss: 3.2727
Iteration: 1557; Percent complete: 38.9%; Average loss: 3.4253
Iteration: 1558; Percent complete: 39.0%; Average loss: 3.3929
Iteration: 1559; Percent complete: 39.0%; Average loss: 3.2370
Iteration: 1560; Percent complete: 39.0%; Average loss: 3.1805
Iteration: 1561; Percent complete: 39.0%; Average loss: 3.1206
Iteration: 1562; Percent complete: 39.1%; Average loss: 3.1491
Iteration: 1563; Percent complete: 39.1%; Average loss: 3.1516
Iteration: 1564; Percent complete: 39.1%; Average loss: 3.2250
Iteration: 1565; Percent complete: 39.1%; Average loss: 3.3191
Iteration: 1566; Percent complete: 39.1%; Average loss: 3.3274
Iteration: 1567; Percent complete: 39.2%; Average loss: 3.3322
Iteration: 1568; Percent complete: 39.2%; Average loss: 3.2639
Iteration: 1569; Percent complete: 39.2%; Average loss: 3.1817
Iteration: 1570; Percent complete: 39.2%; Average loss: 3.4532
Iteration: 1571; Percent complete: 39.3%; Average loss: 3.4003
Iteration: 1572; Percent complete: 39.3%; Average loss: 3.1107
Iteration: 1573; Percent complete: 39.3%; Average loss: 3.3153
Iteration: 1574; Percent complete: 39.4%; Average loss: 3.3088
Iteration: 1575; Percent complete: 39.4%; Average loss: 3.2841
Iteration: 1576; Percent complete: 39.4%; Average loss: 3.2072
Iteration: 1577; Percent complete: 39.4%; Average loss: 3.4998
Iteration: 1578; Percent complete: 39.5%; Average loss: 3.3335
Iteration: 1579; Percent complete: 39.5%; Average loss: 3.0057
Iteration: 1580; Percent complete: 39.5%; Average loss: 3.5928
Iteration: 1581; Percent complete: 39.5%; Average loss: 3.1745
Iteration: 1582; Percent complete: 39.6%; Average loss: 3.3965
Iteration: 1583; Percent complete: 39.6%; Average loss: 3.1876
Iteration: 1584; Percent complete: 39.6%; Average loss: 3.3650
Iteration: 1585; Percent complete: 39.6%; Average loss: 3.2790
Iteration: 1586; Percent complete: 39.6%; Average loss: 3.2316
Iteration: 1587; Percent complete: 39.7%; Average loss: 3.2461
Iteration: 1588; Percent complete: 39.7%; Average loss: 3.3172
Iteration: 1589; Percent complete: 39.7%; Average loss: 3.2167
Iteration: 1590; Percent complete: 39.8%; Average loss: 3.2078
Iteration: 1591; Percent complete: 39.8%; Average loss: 3.1533
Iteration: 1592; Percent complete: 39.8%; Average loss: 3.2209
Iteration: 1593; Percent complete: 39.8%; Average loss: 3.1156
Iteration: 1594; Percent complete: 39.9%; Average loss: 3.2425
Iteration: 1595; Percent complete: 39.9%; Average loss: 3.3077
Iteration: 1596; Percent complete: 39.9%; Average loss: 3.6607
Iteration: 1597; Percent complete: 39.9%; Average loss: 3.3765
Iteration: 1598; Percent complete: 40.0%; Average loss: 3.1285
Iteration: 1599; Percent complete: 40.0%; Average loss: 3.1177
Iteration: 1600; Percent complete: 40.0%; Average loss: 3.3523
Iteration: 1601; Percent complete: 40.0%; Average loss: 3.3947
Iteration: 1602; Percent complete: 40.1%; Average loss: 3.2587
Iteration: 1603; Percent complete: 40.1%; Average loss: 3.3561
Iteration: 1604; Percent complete: 40.1%; Average loss: 3.1920
Iteration: 1605; Percent complete: 40.1%; Average loss: 3.0634
Iteration: 1606; Percent complete: 40.2%; Average loss: 3.4555
Iteration: 1607; Percent complete: 40.2%; Average loss: 3.0726
Iteration: 1608; Percent complete: 40.2%; Average loss: 3.1049
Iteration: 1609; Percent complete: 40.2%; Average loss: 3.2377
Iteration: 1610; Percent complete: 40.2%; Average loss: 3.1513
Iteration: 1611; Percent complete: 40.3%; Average loss: 3.1327
Iteration: 1612; Percent complete: 40.3%; Average loss: 3.4880
Iteration: 1613; Percent complete: 40.3%; Average loss: 3.0986
Iteration: 1614; Percent complete: 40.4%; Average loss: 3.0146
Iteration: 1615; Percent complete: 40.4%; Average loss: 3.0186
Iteration: 1616; Percent complete: 40.4%; Average loss: 3.0520
Iteration: 1617; Percent complete: 40.4%; Average loss: 3.1422
Iteration: 1618; Percent complete: 40.5%; Average loss: 3.0589
Iteration: 1619; Percent complete: 40.5%; Average loss: 3.0967
Iteration: 1620; Percent complete: 40.5%; Average loss: 3.3281
Iteration: 1621; Percent complete: 40.5%; Average loss: 3.3164
Iteration: 1622; Percent complete: 40.6%; Average loss: 3.1545
Iteration: 1623; Percent complete: 40.6%; Average loss: 3.2375
Iteration: 1624; Percent complete: 40.6%; Average loss: 3.3503
Iteration: 1625; Percent complete: 40.6%; Average loss: 3.4284
Iteration: 1626; Percent complete: 40.6%; Average loss: 3.1673
Iteration: 1627; Percent complete: 40.7%; Average loss: 3.4322
Iteration: 1628; Percent complete: 40.7%; Average loss: 3.4369
Iteration: 1629; Percent complete: 40.7%; Average loss: 3.4072
Iteration: 1630; Percent complete: 40.8%; Average loss: 3.2620
Iteration: 1631; Percent complete: 40.8%; Average loss: 3.0974
Iteration: 1632; Percent complete: 40.8%; Average loss: 2.9991
Iteration: 1633; Percent complete: 40.8%; Average loss: 3.2109
Iteration: 1634; Percent complete: 40.8%; Average loss: 3.3529
Iteration: 1635; Percent complete: 40.9%; Average loss: 3.3598
Iteration: 1636; Percent complete: 40.9%; Average loss: 3.4504
Iteration: 1637; Percent complete: 40.9%; Average loss: 3.2404
Iteration: 1638; Percent complete: 40.9%; Average loss: 3.2726
Iteration: 1639; Percent complete: 41.0%; Average loss: 3.2413
Iteration: 1640; Percent complete: 41.0%; Average loss: 3.1682
Iteration: 1641; Percent complete: 41.0%; Average loss: 3.2956
Iteration: 1642; Percent complete: 41.0%; Average loss: 3.0516
Iteration: 1643; Percent complete: 41.1%; Average loss: 3.3138
Iteration: 1644; Percent complete: 41.1%; Average loss: 3.3419
Iteration: 1645; Percent complete: 41.1%; Average loss: 3.4631
Iteration: 1646; Percent complete: 41.1%; Average loss: 3.3523
Iteration: 1647; Percent complete: 41.2%; Average loss: 3.1983
Iteration: 1648; Percent complete: 41.2%; Average loss: 3.2673
Iteration: 1649; Percent complete: 41.2%; Average loss: 3.1796
Iteration: 1650; Percent complete: 41.2%; Average loss: 3.0281
Iteration: 1651; Percent complete: 41.3%; Average loss: 3.4568
Iteration: 1652; Percent complete: 41.3%; Average loss: 3.5283
Iteration: 1653; Percent complete: 41.3%; Average loss: 3.2501
Iteration: 1654; Percent complete: 41.3%; Average loss: 3.1979
Iteration: 1655; Percent complete: 41.4%; Average loss: 3.2867
Iteration: 1656; Percent complete: 41.4%; Average loss: 3.2397
Iteration: 1657; Percent complete: 41.4%; Average loss: 3.4587
Iteration: 1658; Percent complete: 41.4%; Average loss: 3.2989
Iteration: 1659; Percent complete: 41.5%; Average loss: 3.1831
Iteration: 1660; Percent complete: 41.5%; Average loss: 2.9067
Iteration: 1661; Percent complete: 41.5%; Average loss: 2.9742
Iteration: 1662; Percent complete: 41.5%; Average loss: 3.1754
Iteration: 1663; Percent complete: 41.6%; Average loss: 3.1019
Iteration: 1664; Percent complete: 41.6%; Average loss: 3.4643
Iteration: 1665; Percent complete: 41.6%; Average loss: 3.4258
Iteration: 1666; Percent complete: 41.6%; Average loss: 3.1614
Iteration: 1667; Percent complete: 41.7%; Average loss: 3.3265
Iteration: 1668; Percent complete: 41.7%; Average loss: 3.2173
Iteration: 1669; Percent complete: 41.7%; Average loss: 3.1567
Iteration: 1670; Percent complete: 41.8%; Average loss: 3.4575
Iteration: 1671; Percent complete: 41.8%; Average loss: 3.3126
Iteration: 1672; Percent complete: 41.8%; Average loss: 3.3694
Iteration: 1673; Percent complete: 41.8%; Average loss: 3.2089
Iteration: 1674; Percent complete: 41.9%; Average loss: 3.2749
Iteration: 1675; Percent complete: 41.9%; Average loss: 3.2133
Iteration: 1676; Percent complete: 41.9%; Average loss: 3.2675
Iteration: 1677; Percent complete: 41.9%; Average loss: 3.5695
Iteration: 1678; Percent complete: 41.9%; Average loss: 3.1907
Iteration: 1679; Percent complete: 42.0%; Average loss: 3.2500
Iteration: 1680; Percent complete: 42.0%; Average loss: 3.3853
Iteration: 1681; Percent complete: 42.0%; Average loss: 3.1659
Iteration: 1682; Percent complete: 42.0%; Average loss: 3.3196
Iteration: 1683; Percent complete: 42.1%; Average loss: 3.2177
Iteration: 1684; Percent complete: 42.1%; Average loss: 3.3182
Iteration: 1685; Percent complete: 42.1%; Average loss: 3.4254
Iteration: 1686; Percent complete: 42.1%; Average loss: 3.1936
Iteration: 1687; Percent complete: 42.2%; Average loss: 3.3854
Iteration: 1688; Percent complete: 42.2%; Average loss: 3.2972
Iteration: 1689; Percent complete: 42.2%; Average loss: 3.3007
Iteration: 1690; Percent complete: 42.2%; Average loss: 3.1742
Iteration: 1691; Percent complete: 42.3%; Average loss: 3.2923
Iteration: 1692; Percent complete: 42.3%; Average loss: 3.3959
Iteration: 1693; Percent complete: 42.3%; Average loss: 3.2639
Iteration: 1694; Percent complete: 42.4%; Average loss: 3.3954
Iteration: 1695; Percent complete: 42.4%; Average loss: 3.2037
Iteration: 1696; Percent complete: 42.4%; Average loss: 3.3319
Iteration: 1697; Percent complete: 42.4%; Average loss: 3.1224
Iteration: 1698; Percent complete: 42.4%; Average loss: 3.1697
Iteration: 1699; Percent complete: 42.5%; Average loss: 3.0264
Iteration: 1700; Percent complete: 42.5%; Average loss: 3.1522
Iteration: 1701; Percent complete: 42.5%; Average loss: 3.2652
Iteration: 1702; Percent complete: 42.5%; Average loss: 3.1182
Iteration: 1703; Percent complete: 42.6%; Average loss: 3.2377
Iteration: 1704; Percent complete: 42.6%; Average loss: 3.4184
Iteration: 1705; Percent complete: 42.6%; Average loss: 3.2435
Iteration: 1706; Percent complete: 42.6%; Average loss: 3.3315
Iteration: 1707; Percent complete: 42.7%; Average loss: 3.0372
Iteration: 1708; Percent complete: 42.7%; Average loss: 3.2897
Iteration: 1709; Percent complete: 42.7%; Average loss: 3.3168
Iteration: 1710; Percent complete: 42.8%; Average loss: 3.0326
Iteration: 1711; Percent complete: 42.8%; Average loss: 3.2646
Iteration: 1712; Percent complete: 42.8%; Average loss: 3.1763
Iteration: 1713; Percent complete: 42.8%; Average loss: 3.3270
Iteration: 1714; Percent complete: 42.9%; Average loss: 3.2060
Iteration: 1715; Percent complete: 42.9%; Average loss: 3.2366
Iteration: 1716; Percent complete: 42.9%; Average loss: 2.9795
Iteration: 1717; Percent complete: 42.9%; Average loss: 3.1079
Iteration: 1718; Percent complete: 43.0%; Average loss: 3.2392
Iteration: 1719; Percent complete: 43.0%; Average loss: 3.2521
Iteration: 1720; Percent complete: 43.0%; Average loss: 2.9962
Iteration: 1721; Percent complete: 43.0%; Average loss: 3.1187
Iteration: 1722; Percent complete: 43.0%; Average loss: 3.5036
Iteration: 1723; Percent complete: 43.1%; Average loss: 3.3675
Iteration: 1724; Percent complete: 43.1%; Average loss: 3.0272
Iteration: 1725; Percent complete: 43.1%; Average loss: 3.2598
Iteration: 1726; Percent complete: 43.1%; Average loss: 3.1904
Iteration: 1727; Percent complete: 43.2%; Average loss: 3.2251
Iteration: 1728; Percent complete: 43.2%; Average loss: 3.2519
Iteration: 1729; Percent complete: 43.2%; Average loss: 3.3358
Iteration: 1730; Percent complete: 43.2%; Average loss: 3.0024
Iteration: 1731; Percent complete: 43.3%; Average loss: 3.1092
Iteration: 1732; Percent complete: 43.3%; Average loss: 3.3408
Iteration: 1733; Percent complete: 43.3%; Average loss: 3.2109
Iteration: 1734; Percent complete: 43.4%; Average loss: 3.1875
Iteration: 1735; Percent complete: 43.4%; Average loss: 3.5403
Iteration: 1736; Percent complete: 43.4%; Average loss: 3.2555
Iteration: 1737; Percent complete: 43.4%; Average loss: 3.2672
Iteration: 1738; Percent complete: 43.5%; Average loss: 3.0651
Iteration: 1739; Percent complete: 43.5%; Average loss: 3.1696
Iteration: 1740; Percent complete: 43.5%; Average loss: 3.4781
Iteration: 1741; Percent complete: 43.5%; Average loss: 3.1408
Iteration: 1742; Percent complete: 43.5%; Average loss: 3.2923
Iteration: 1743; Percent complete: 43.6%; Average loss: 3.3744
Iteration: 1744; Percent complete: 43.6%; Average loss: 3.4926
Iteration: 1745; Percent complete: 43.6%; Average loss: 3.3567
Iteration: 1746; Percent complete: 43.6%; Average loss: 3.3058
Iteration: 1747; Percent complete: 43.7%; Average loss: 3.2616
Iteration: 1748; Percent complete: 43.7%; Average loss: 3.2087
Iteration: 1749; Percent complete: 43.7%; Average loss: 3.3977
Iteration: 1750; Percent complete: 43.8%; Average loss: 3.1475
Iteration: 1751; Percent complete: 43.8%; Average loss: 3.2528
Iteration: 1752; Percent complete: 43.8%; Average loss: 3.1068
Iteration: 1753; Percent complete: 43.8%; Average loss: 3.2876
Iteration: 1754; Percent complete: 43.9%; Average loss: 3.0754
Iteration: 1755; Percent complete: 43.9%; Average loss: 3.1289
Iteration: 1756; Percent complete: 43.9%; Average loss: 3.0288
Iteration: 1757; Percent complete: 43.9%; Average loss: 3.2199
Iteration: 1758; Percent complete: 44.0%; Average loss: 3.3561
Iteration: 1759; Percent complete: 44.0%; Average loss: 3.0666
Iteration: 1760; Percent complete: 44.0%; Average loss: 3.0834
Iteration: 1761; Percent complete: 44.0%; Average loss: 3.2765
Iteration: 1762; Percent complete: 44.0%; Average loss: 3.2050
Iteration: 1763; Percent complete: 44.1%; Average loss: 3.1996
Iteration: 1764; Percent complete: 44.1%; Average loss: 3.2851
Iteration: 1765; Percent complete: 44.1%; Average loss: 3.4105
Iteration: 1766; Percent complete: 44.1%; Average loss: 3.0607
Iteration: 1767; Percent complete: 44.2%; Average loss: 3.2244
Iteration: 1768; Percent complete: 44.2%; Average loss: 3.0578
Iteration: 1769; Percent complete: 44.2%; Average loss: 3.2024
Iteration: 1770; Percent complete: 44.2%; Average loss: 3.0518
Iteration: 1771; Percent complete: 44.3%; Average loss: 3.1315
Iteration: 1772; Percent complete: 44.3%; Average loss: 3.0516
Iteration: 1773; Percent complete: 44.3%; Average loss: 3.3325
Iteration: 1774; Percent complete: 44.4%; Average loss: 3.2676
Iteration: 1775; Percent complete: 44.4%; Average loss: 3.1259
Iteration: 1776; Percent complete: 44.4%; Average loss: 3.0089
Iteration: 1777; Percent complete: 44.4%; Average loss: 3.5113
Iteration: 1778; Percent complete: 44.5%; Average loss: 2.9910
Iteration: 1779; Percent complete: 44.5%; Average loss: 2.8776
Iteration: 1780; Percent complete: 44.5%; Average loss: 3.2695
Iteration: 1781; Percent complete: 44.5%; Average loss: 3.2313
Iteration: 1782; Percent complete: 44.5%; Average loss: 3.2053
Iteration: 1783; Percent complete: 44.6%; Average loss: 3.1423
Iteration: 1784; Percent complete: 44.6%; Average loss: 3.1115
Iteration: 1785; Percent complete: 44.6%; Average loss: 3.3493
Iteration: 1786; Percent complete: 44.6%; Average loss: 3.1293
Iteration: 1787; Percent complete: 44.7%; Average loss: 3.2869
Iteration: 1788; Percent complete: 44.7%; Average loss: 3.1803
Iteration: 1789; Percent complete: 44.7%; Average loss: 3.2245
Iteration: 1790; Percent complete: 44.8%; Average loss: 3.1964
Iteration: 1791; Percent complete: 44.8%; Average loss: 3.0892
Iteration: 1792; Percent complete: 44.8%; Average loss: 3.2206
Iteration: 1793; Percent complete: 44.8%; Average loss: 3.0965
Iteration: 1794; Percent complete: 44.9%; Average loss: 3.4037
Iteration: 1795; Percent complete: 44.9%; Average loss: 3.0945
Iteration: 1796; Percent complete: 44.9%; Average loss: 2.9081
Iteration: 1797; Percent complete: 44.9%; Average loss: 3.2712
Iteration: 1798; Percent complete: 45.0%; Average loss: 3.2782
Iteration: 1799; Percent complete: 45.0%; Average loss: 3.2018
Iteration: 1800; Percent complete: 45.0%; Average loss: 3.0645
Iteration: 1801; Percent complete: 45.0%; Average loss: 3.1078
Iteration: 1802; Percent complete: 45.1%; Average loss: 3.0181
Iteration: 1803; Percent complete: 45.1%; Average loss: 3.4297
Iteration: 1804; Percent complete: 45.1%; Average loss: 3.3111
Iteration: 1805; Percent complete: 45.1%; Average loss: 3.3268
Iteration: 1806; Percent complete: 45.1%; Average loss: 3.2924
Iteration: 1807; Percent complete: 45.2%; Average loss: 3.2317
Iteration: 1808; Percent complete: 45.2%; Average loss: 3.0487
Iteration: 1809; Percent complete: 45.2%; Average loss: 3.1981
Iteration: 1810; Percent complete: 45.2%; Average loss: 3.0530
Iteration: 1811; Percent complete: 45.3%; Average loss: 2.9769
Iteration: 1812; Percent complete: 45.3%; Average loss: 3.0328
Iteration: 1813; Percent complete: 45.3%; Average loss: 3.1365
Iteration: 1814; Percent complete: 45.4%; Average loss: 3.5464
Iteration: 1815; Percent complete: 45.4%; Average loss: 3.1222
Iteration: 1816; Percent complete: 45.4%; Average loss: 3.1106
Iteration: 1817; Percent complete: 45.4%; Average loss: 3.1127
Iteration: 1818; Percent complete: 45.5%; Average loss: 3.2652
Iteration: 1819; Percent complete: 45.5%; Average loss: 3.2508
Iteration: 1820; Percent complete: 45.5%; Average loss: 3.1481
Iteration: 1821; Percent complete: 45.5%; Average loss: 3.1723
Iteration: 1822; Percent complete: 45.6%; Average loss: 3.2188
Iteration: 1823; Percent complete: 45.6%; Average loss: 3.3353
Iteration: 1824; Percent complete: 45.6%; Average loss: 3.0618
Iteration: 1825; Percent complete: 45.6%; Average loss: 3.3867
Iteration: 1826; Percent complete: 45.6%; Average loss: 3.1179
Iteration: 1827; Percent complete: 45.7%; Average loss: 2.9692
Iteration: 1828; Percent complete: 45.7%; Average loss: 2.9969
Iteration: 1829; Percent complete: 45.7%; Average loss: 3.1044
Iteration: 1830; Percent complete: 45.8%; Average loss: 3.2875
Iteration: 1831; Percent complete: 45.8%; Average loss: 3.2814
Iteration: 1832; Percent complete: 45.8%; Average loss: 2.8611
Iteration: 1833; Percent complete: 45.8%; Average loss: 3.3629
Iteration: 1834; Percent complete: 45.9%; Average loss: 3.2159
Iteration: 1835; Percent complete: 45.9%; Average loss: 3.1002
Iteration: 1836; Percent complete: 45.9%; Average loss: 3.5071
Iteration: 1837; Percent complete: 45.9%; Average loss: 3.2976
Iteration: 1838; Percent complete: 46.0%; Average loss: 2.8921
Iteration: 1839; Percent complete: 46.0%; Average loss: 3.1909
Iteration: 1840; Percent complete: 46.0%; Average loss: 3.4447
Iteration: 1841; Percent complete: 46.0%; Average loss: 3.4435
Iteration: 1842; Percent complete: 46.1%; Average loss: 3.0395
Iteration: 1843; Percent complete: 46.1%; Average loss: 3.3712
Iteration: 1844; Percent complete: 46.1%; Average loss: 3.4133
Iteration: 1845; Percent complete: 46.1%; Average loss: 3.1976
Iteration: 1846; Percent complete: 46.2%; Average loss: 3.1616
Iteration: 1847; Percent complete: 46.2%; Average loss: 3.1673
Iteration: 1848; Percent complete: 46.2%; Average loss: 3.3924
Iteration: 1849; Percent complete: 46.2%; Average loss: 3.1996
Iteration: 1850; Percent complete: 46.2%; Average loss: 3.1051
Iteration: 1851; Percent complete: 46.3%; Average loss: 3.0454
Iteration: 1852; Percent complete: 46.3%; Average loss: 3.1170
Iteration: 1853; Percent complete: 46.3%; Average loss: 3.2376
Iteration: 1854; Percent complete: 46.4%; Average loss: 3.2933
Iteration: 1855; Percent complete: 46.4%; Average loss: 3.4311
Iteration: 1856; Percent complete: 46.4%; Average loss: 3.1439
Iteration: 1857; Percent complete: 46.4%; Average loss: 3.1973
Iteration: 1858; Percent complete: 46.5%; Average loss: 3.0934
Iteration: 1859; Percent complete: 46.5%; Average loss: 3.3640
Iteration: 1860; Percent complete: 46.5%; Average loss: 3.3194
Iteration: 1861; Percent complete: 46.5%; Average loss: 3.2805
Iteration: 1862; Percent complete: 46.6%; Average loss: 3.0877
Iteration: 1863; Percent complete: 46.6%; Average loss: 3.2497
Iteration: 1864; Percent complete: 46.6%; Average loss: 3.0034
Iteration: 1865; Percent complete: 46.6%; Average loss: 3.1900
Iteration: 1866; Percent complete: 46.7%; Average loss: 3.1575
Iteration: 1867; Percent complete: 46.7%; Average loss: 3.1673
Iteration: 1868; Percent complete: 46.7%; Average loss: 3.1315
Iteration: 1869; Percent complete: 46.7%; Average loss: 3.1793
Iteration: 1870; Percent complete: 46.8%; Average loss: 3.3892
Iteration: 1871; Percent complete: 46.8%; Average loss: 3.3964
Iteration: 1872; Percent complete: 46.8%; Average loss: 3.1686
Iteration: 1873; Percent complete: 46.8%; Average loss: 3.3189
Iteration: 1874; Percent complete: 46.9%; Average loss: 3.3056
Iteration: 1875; Percent complete: 46.9%; Average loss: 3.3562
Iteration: 1876; Percent complete: 46.9%; Average loss: 3.0182
Iteration: 1877; Percent complete: 46.9%; Average loss: 3.0885
Iteration: 1878; Percent complete: 46.9%; Average loss: 3.1977
Iteration: 1879; Percent complete: 47.0%; Average loss: 3.2960
Iteration: 1880; Percent complete: 47.0%; Average loss: 3.2011
Iteration: 1881; Percent complete: 47.0%; Average loss: 3.2989
Iteration: 1882; Percent complete: 47.0%; Average loss: 3.1519
Iteration: 1883; Percent complete: 47.1%; Average loss: 2.8017
Iteration: 1884; Percent complete: 47.1%; Average loss: 3.1497
Iteration: 1885; Percent complete: 47.1%; Average loss: 3.1983
Iteration: 1886; Percent complete: 47.1%; Average loss: 3.1714
Iteration: 1887; Percent complete: 47.2%; Average loss: 3.1527
Iteration: 1888; Percent complete: 47.2%; Average loss: 3.0922
Iteration: 1889; Percent complete: 47.2%; Average loss: 3.3320
Iteration: 1890; Percent complete: 47.2%; Average loss: 3.0397
Iteration: 1891; Percent complete: 47.3%; Average loss: 3.0107
Iteration: 1892; Percent complete: 47.3%; Average loss: 3.2004
Iteration: 1893; Percent complete: 47.3%; Average loss: 3.0820
Iteration: 1894; Percent complete: 47.3%; Average loss: 3.0710
Iteration: 1895; Percent complete: 47.4%; Average loss: 3.1748
Iteration: 1896; Percent complete: 47.4%; Average loss: 2.9233
Iteration: 1897; Percent complete: 47.4%; Average loss: 3.2263
Iteration: 1898; Percent complete: 47.4%; Average loss: 3.2178
Iteration: 1899; Percent complete: 47.5%; Average loss: 3.3972
Iteration: 1900; Percent complete: 47.5%; Average loss: 3.0908
Iteration: 1901; Percent complete: 47.5%; Average loss: 3.0515
Iteration: 1902; Percent complete: 47.5%; Average loss: 3.1440
Iteration: 1903; Percent complete: 47.6%; Average loss: 3.3376
Iteration: 1904; Percent complete: 47.6%; Average loss: 2.9618
Iteration: 1905; Percent complete: 47.6%; Average loss: 3.4533
Iteration: 1906; Percent complete: 47.6%; Average loss: 3.0707
Iteration: 1907; Percent complete: 47.7%; Average loss: 3.1044
Iteration: 1908; Percent complete: 47.7%; Average loss: 3.2142
Iteration: 1909; Percent complete: 47.7%; Average loss: 2.8546
Iteration: 1910; Percent complete: 47.8%; Average loss: 3.1315
Iteration: 1911; Percent complete: 47.8%; Average loss: 3.4859
Iteration: 1912; Percent complete: 47.8%; Average loss: 3.0872
Iteration: 1913; Percent complete: 47.8%; Average loss: 3.0695
Iteration: 1914; Percent complete: 47.9%; Average loss: 3.2761
Iteration: 1915; Percent complete: 47.9%; Average loss: 3.3370
Iteration: 1916; Percent complete: 47.9%; Average loss: 3.0408
Iteration: 1917; Percent complete: 47.9%; Average loss: 3.0752
Iteration: 1918; Percent complete: 47.9%; Average loss: 3.1130
Iteration: 1919; Percent complete: 48.0%; Average loss: 2.9892
Iteration: 1920; Percent complete: 48.0%; Average loss: 2.9422
Iteration: 1921; Percent complete: 48.0%; Average loss: 3.1843
Iteration: 1922; Percent complete: 48.0%; Average loss: 3.1860
Iteration: 1923; Percent complete: 48.1%; Average loss: 3.0252
Iteration: 1924; Percent complete: 48.1%; Average loss: 3.4118
Iteration: 1925; Percent complete: 48.1%; Average loss: 3.1766
Iteration: 1926; Percent complete: 48.1%; Average loss: 3.4214
Iteration: 1927; Percent complete: 48.2%; Average loss: 3.1568
Iteration: 1928; Percent complete: 48.2%; Average loss: 3.1243
Iteration: 1929; Percent complete: 48.2%; Average loss: 3.0988
Iteration: 1930; Percent complete: 48.2%; Average loss: 3.1179
Iteration: 1931; Percent complete: 48.3%; Average loss: 2.9517
Iteration: 1932; Percent complete: 48.3%; Average loss: 3.3800
Iteration: 1933; Percent complete: 48.3%; Average loss: 3.1345
Iteration: 1934; Percent complete: 48.4%; Average loss: 3.2601
Iteration: 1935; Percent complete: 48.4%; Average loss: 2.8710
Iteration: 1936; Percent complete: 48.4%; Average loss: 3.0422
Iteration: 1937; Percent complete: 48.4%; Average loss: 3.2833
Iteration: 1938; Percent complete: 48.4%; Average loss: 3.4653
Iteration: 1939; Percent complete: 48.5%; Average loss: 3.0300
Iteration: 1940; Percent complete: 48.5%; Average loss: 3.3360
Iteration: 1941; Percent complete: 48.5%; Average loss: 3.2830
Iteration: 1942; Percent complete: 48.5%; Average loss: 3.2434
Iteration: 1943; Percent complete: 48.6%; Average loss: 3.2556
Iteration: 1944; Percent complete: 48.6%; Average loss: 3.0974
Iteration: 1945; Percent complete: 48.6%; Average loss: 3.3846
Iteration: 1946; Percent complete: 48.6%; Average loss: 3.2572
Iteration: 1947; Percent complete: 48.7%; Average loss: 3.2232
Iteration: 1948; Percent complete: 48.7%; Average loss: 3.0141
Iteration: 1949; Percent complete: 48.7%; Average loss: 3.0368
Iteration: 1950; Percent complete: 48.8%; Average loss: 3.1364
Iteration: 1951; Percent complete: 48.8%; Average loss: 3.0467
Iteration: 1952; Percent complete: 48.8%; Average loss: 3.1505
Iteration: 1953; Percent complete: 48.8%; Average loss: 2.9190
Iteration: 1954; Percent complete: 48.9%; Average loss: 3.1872
Iteration: 1955; Percent complete: 48.9%; Average loss: 3.3112
Iteration: 1956; Percent complete: 48.9%; Average loss: 3.1352
Iteration: 1957; Percent complete: 48.9%; Average loss: 3.1970
Iteration: 1958; Percent complete: 48.9%; Average loss: 3.1771
Iteration: 1959; Percent complete: 49.0%; Average loss: 3.0740
Iteration: 1960; Percent complete: 49.0%; Average loss: 3.2024
Iteration: 1961; Percent complete: 49.0%; Average loss: 3.2273
Iteration: 1962; Percent complete: 49.0%; Average loss: 3.3654
Iteration: 1963; Percent complete: 49.1%; Average loss: 3.2153
Iteration: 1964; Percent complete: 49.1%; Average loss: 3.2246
Iteration: 1965; Percent complete: 49.1%; Average loss: 3.2724
Iteration: 1966; Percent complete: 49.1%; Average loss: 3.2488
Iteration: 1967; Percent complete: 49.2%; Average loss: 3.4145
Iteration: 1968; Percent complete: 49.2%; Average loss: 3.3014
Iteration: 1969; Percent complete: 49.2%; Average loss: 2.9949
Iteration: 1970; Percent complete: 49.2%; Average loss: 3.1646
Iteration: 1971; Percent complete: 49.3%; Average loss: 3.1349
Iteration: 1972; Percent complete: 49.3%; Average loss: 3.0506
Iteration: 1973; Percent complete: 49.3%; Average loss: 3.4305
Iteration: 1974; Percent complete: 49.4%; Average loss: 3.1745
Iteration: 1975; Percent complete: 49.4%; Average loss: 3.1919
Iteration: 1976; Percent complete: 49.4%; Average loss: 3.0647
Iteration: 1977; Percent complete: 49.4%; Average loss: 3.1154
Iteration: 1978; Percent complete: 49.5%; Average loss: 3.0916
Iteration: 1979; Percent complete: 49.5%; Average loss: 3.4704
Iteration: 1980; Percent complete: 49.5%; Average loss: 3.1664
Iteration: 1981; Percent complete: 49.5%; Average loss: 3.2168
Iteration: 1982; Percent complete: 49.5%; Average loss: 3.1234
Iteration: 1983; Percent complete: 49.6%; Average loss: 3.2940
Iteration: 1984; Percent complete: 49.6%; Average loss: 2.7460
Iteration: 1985; Percent complete: 49.6%; Average loss: 3.1085
Iteration: 1986; Percent complete: 49.6%; Average loss: 2.9968
Iteration: 1987; Percent complete: 49.7%; Average loss: 3.0358
Iteration: 1988; Percent complete: 49.7%; Average loss: 3.1642
Iteration: 1989; Percent complete: 49.7%; Average loss: 3.0547
Iteration: 1990; Percent complete: 49.8%; Average loss: 3.0976
Iteration: 1991; Percent complete: 49.8%; Average loss: 2.9510
Iteration: 1992; Percent complete: 49.8%; Average loss: 2.9161
Iteration: 1993; Percent complete: 49.8%; Average loss: 3.3401
Iteration: 1994; Percent complete: 49.9%; Average loss: 3.3479
Iteration: 1995; Percent complete: 49.9%; Average loss: 2.9526
Iteration: 1996; Percent complete: 49.9%; Average loss: 3.1498
Iteration: 1997; Percent complete: 49.9%; Average loss: 3.2739
Iteration: 1998; Percent complete: 50.0%; Average loss: 3.0241
Iteration: 1999; Percent complete: 50.0%; Average loss: 3.2295
Iteration: 2000; Percent complete: 50.0%; Average loss: 3.2543
Iteration: 2001; Percent complete: 50.0%; Average loss: 3.2470
Iteration: 2002; Percent complete: 50.0%; Average loss: 3.4560
Iteration: 2003; Percent complete: 50.1%; Average loss: 3.0480
Iteration: 2004; Percent complete: 50.1%; Average loss: 2.9863
Iteration: 2005; Percent complete: 50.1%; Average loss: 3.0640
Iteration: 2006; Percent complete: 50.1%; Average loss: 3.0149
Iteration: 2007; Percent complete: 50.2%; Average loss: 3.2029
Iteration: 2008; Percent complete: 50.2%; Average loss: 2.9275
Iteration: 2009; Percent complete: 50.2%; Average loss: 3.1108
Iteration: 2010; Percent complete: 50.2%; Average loss: 2.9112
Iteration: 2011; Percent complete: 50.3%; Average loss: 3.1644
Iteration: 2012; Percent complete: 50.3%; Average loss: 3.0702
Iteration: 2013; Percent complete: 50.3%; Average loss: 3.1591
Iteration: 2014; Percent complete: 50.3%; Average loss: 3.3209
Iteration: 2015; Percent complete: 50.4%; Average loss: 3.1436
Iteration: 2016; Percent complete: 50.4%; Average loss: 3.1069
Iteration: 2017; Percent complete: 50.4%; Average loss: 3.1275
Iteration: 2018; Percent complete: 50.4%; Average loss: 2.9898
Iteration: 2019; Percent complete: 50.5%; Average loss: 3.0044
Iteration: 2020; Percent complete: 50.5%; Average loss: 3.1300
Iteration: 2021; Percent complete: 50.5%; Average loss: 3.1320
Iteration: 2022; Percent complete: 50.5%; Average loss: 3.1145
Iteration: 2023; Percent complete: 50.6%; Average loss: 3.3863
Iteration: 2024; Percent complete: 50.6%; Average loss: 3.3677
Iteration: 2025; Percent complete: 50.6%; Average loss: 2.8903
Iteration: 2026; Percent complete: 50.6%; Average loss: 3.0747
Iteration: 2027; Percent complete: 50.7%; Average loss: 3.0999
Iteration: 2028; Percent complete: 50.7%; Average loss: 3.3058
Iteration: 2029; Percent complete: 50.7%; Average loss: 3.0369
Iteration: 2030; Percent complete: 50.7%; Average loss: 3.4023
Iteration: 2031; Percent complete: 50.8%; Average loss: 3.1651
Iteration: 2032; Percent complete: 50.8%; Average loss: 3.1877
Iteration: 2033; Percent complete: 50.8%; Average loss: 3.1909
Iteration: 2034; Percent complete: 50.8%; Average loss: 3.1890
Iteration: 2035; Percent complete: 50.9%; Average loss: 3.1013
Iteration: 2036; Percent complete: 50.9%; Average loss: 3.1264
Iteration: 2037; Percent complete: 50.9%; Average loss: 3.2753
Iteration: 2038; Percent complete: 50.9%; Average loss: 3.3223
Iteration: 2039; Percent complete: 51.0%; Average loss: 3.3862
Iteration: 2040; Percent complete: 51.0%; Average loss: 2.9500
Iteration: 2041; Percent complete: 51.0%; Average loss: 2.9720
Iteration: 2042; Percent complete: 51.0%; Average loss: 2.8449
Iteration: 2043; Percent complete: 51.1%; Average loss: 3.1199
Iteration: 2044; Percent complete: 51.1%; Average loss: 3.1510
Iteration: 2045; Percent complete: 51.1%; Average loss: 2.8382
Iteration: 2046; Percent complete: 51.1%; Average loss: 3.2377
Iteration: 2047; Percent complete: 51.2%; Average loss: 3.0163
Iteration: 2048; Percent complete: 51.2%; Average loss: 2.9245
Iteration: 2049; Percent complete: 51.2%; Average loss: 3.4361
Iteration: 2050; Percent complete: 51.2%; Average loss: 3.1095
Iteration: 2051; Percent complete: 51.3%; Average loss: 3.0159
Iteration: 2052; Percent complete: 51.3%; Average loss: 3.2097
Iteration: 2053; Percent complete: 51.3%; Average loss: 3.0445
Iteration: 2054; Percent complete: 51.3%; Average loss: 3.2602
Iteration: 2055; Percent complete: 51.4%; Average loss: 3.2023
Iteration: 2056; Percent complete: 51.4%; Average loss: 3.2113
Iteration: 2057; Percent complete: 51.4%; Average loss: 3.0625
Iteration: 2058; Percent complete: 51.4%; Average loss: 3.0197
Iteration: 2059; Percent complete: 51.5%; Average loss: 2.9008
Iteration: 2060; Percent complete: 51.5%; Average loss: 3.0475
Iteration: 2061; Percent complete: 51.5%; Average loss: 3.1717
Iteration: 2062; Percent complete: 51.5%; Average loss: 3.2413
Iteration: 2063; Percent complete: 51.6%; Average loss: 3.1493
Iteration: 2064; Percent complete: 51.6%; Average loss: 2.9013
Iteration: 2065; Percent complete: 51.6%; Average loss: 3.1792
Iteration: 2066; Percent complete: 51.6%; Average loss: 3.2543
Iteration: 2067; Percent complete: 51.7%; Average loss: 3.3640
Iteration: 2068; Percent complete: 51.7%; Average loss: 3.1644
Iteration: 2069; Percent complete: 51.7%; Average loss: 3.1852
Iteration: 2070; Percent complete: 51.7%; Average loss: 3.1603
Iteration: 2071; Percent complete: 51.8%; Average loss: 3.0990
Iteration: 2072; Percent complete: 51.8%; Average loss: 3.0215
Iteration: 2073; Percent complete: 51.8%; Average loss: 2.9707
Iteration: 2074; Percent complete: 51.8%; Average loss: 3.0786
Iteration: 2075; Percent complete: 51.9%; Average loss: 3.1033
Iteration: 2076; Percent complete: 51.9%; Average loss: 3.2224
Iteration: 2077; Percent complete: 51.9%; Average loss: 3.2214
Iteration: 2078; Percent complete: 51.9%; Average loss: 3.1874
Iteration: 2079; Percent complete: 52.0%; Average loss: 3.2390
Iteration: 2080; Percent complete: 52.0%; Average loss: 2.9399
Iteration: 2081; Percent complete: 52.0%; Average loss: 3.1459
Iteration: 2082; Percent complete: 52.0%; Average loss: 2.9536
Iteration: 2083; Percent complete: 52.1%; Average loss: 3.1406
Iteration: 2084; Percent complete: 52.1%; Average loss: 3.0843
Iteration: 2085; Percent complete: 52.1%; Average loss: 3.1794
Iteration: 2086; Percent complete: 52.1%; Average loss: 3.2340
Iteration: 2087; Percent complete: 52.2%; Average loss: 3.3657
Iteration: 2088; Percent complete: 52.2%; Average loss: 3.2493
Iteration: 2089; Percent complete: 52.2%; Average loss: 2.8922
Iteration: 2090; Percent complete: 52.2%; Average loss: 3.1653
Iteration: 2091; Percent complete: 52.3%; Average loss: 3.2244
Iteration: 2092; Percent complete: 52.3%; Average loss: 3.1911
Iteration: 2093; Percent complete: 52.3%; Average loss: 3.3413
Iteration: 2094; Percent complete: 52.3%; Average loss: 3.1929
Iteration: 2095; Percent complete: 52.4%; Average loss: 3.3614
Iteration: 2096; Percent complete: 52.4%; Average loss: 2.9652
Iteration: 2097; Percent complete: 52.4%; Average loss: 3.0866
Iteration: 2098; Percent complete: 52.4%; Average loss: 3.2483
Iteration: 2099; Percent complete: 52.5%; Average loss: 3.1217
Iteration: 2100; Percent complete: 52.5%; Average loss: 3.1029
Iteration: 2101; Percent complete: 52.5%; Average loss: 2.8791
Iteration: 2102; Percent complete: 52.5%; Average loss: 3.2168
Iteration: 2103; Percent complete: 52.6%; Average loss: 3.0973
Iteration: 2104; Percent complete: 52.6%; Average loss: 2.8509
Iteration: 2105; Percent complete: 52.6%; Average loss: 2.9591
Iteration: 2106; Percent complete: 52.6%; Average loss: 3.5344
Iteration: 2107; Percent complete: 52.7%; Average loss: 3.1987
Iteration: 2108; Percent complete: 52.7%; Average loss: 2.9657
Iteration: 2109; Percent complete: 52.7%; Average loss: 3.3554
Iteration: 2110; Percent complete: 52.8%; Average loss: 3.2218
Iteration: 2111; Percent complete: 52.8%; Average loss: 2.9754
Iteration: 2112; Percent complete: 52.8%; Average loss: 3.2532
Iteration: 2113; Percent complete: 52.8%; Average loss: 3.0746
Iteration: 2114; Percent complete: 52.8%; Average loss: 2.9869
Iteration: 2115; Percent complete: 52.9%; Average loss: 3.0187
Iteration: 2116; Percent complete: 52.9%; Average loss: 3.2278
Iteration: 2117; Percent complete: 52.9%; Average loss: 3.3271
Iteration: 2118; Percent complete: 52.9%; Average loss: 3.3101
Iteration: 2119; Percent complete: 53.0%; Average loss: 3.2430
Iteration: 2120; Percent complete: 53.0%; Average loss: 3.0086
Iteration: 2121; Percent complete: 53.0%; Average loss: 3.0356
Iteration: 2122; Percent complete: 53.0%; Average loss: 3.4074
Iteration: 2123; Percent complete: 53.1%; Average loss: 3.1610
Iteration: 2124; Percent complete: 53.1%; Average loss: 3.1610
Iteration: 2125; Percent complete: 53.1%; Average loss: 3.1046
Iteration: 2126; Percent complete: 53.1%; Average loss: 3.1445
Iteration: 2127; Percent complete: 53.2%; Average loss: 3.1196
Iteration: 2128; Percent complete: 53.2%; Average loss: 3.0924
Iteration: 2129; Percent complete: 53.2%; Average loss: 3.3731
Iteration: 2130; Percent complete: 53.2%; Average loss: 3.0182
Iteration: 2131; Percent complete: 53.3%; Average loss: 3.1774
Iteration: 2132; Percent complete: 53.3%; Average loss: 3.1144
Iteration: 2133; Percent complete: 53.3%; Average loss: 2.9695
Iteration: 2134; Percent complete: 53.3%; Average loss: 3.0169
Iteration: 2135; Percent complete: 53.4%; Average loss: 2.9126
Iteration: 2136; Percent complete: 53.4%; Average loss: 3.2499
Iteration: 2137; Percent complete: 53.4%; Average loss: 2.9456
Iteration: 2138; Percent complete: 53.4%; Average loss: 3.2947
Iteration: 2139; Percent complete: 53.5%; Average loss: 3.3642
Iteration: 2140; Percent complete: 53.5%; Average loss: 3.5005
Iteration: 2141; Percent complete: 53.5%; Average loss: 3.0651
Iteration: 2142; Percent complete: 53.5%; Average loss: 2.9033
Iteration: 2143; Percent complete: 53.6%; Average loss: 3.2101
Iteration: 2144; Percent complete: 53.6%; Average loss: 3.1705
Iteration: 2145; Percent complete: 53.6%; Average loss: 3.2089
Iteration: 2146; Percent complete: 53.6%; Average loss: 3.1988
Iteration: 2147; Percent complete: 53.7%; Average loss: 2.9531
Iteration: 2148; Percent complete: 53.7%; Average loss: 3.0278
Iteration: 2149; Percent complete: 53.7%; Average loss: 3.1744
Iteration: 2150; Percent complete: 53.8%; Average loss: 3.0580
Iteration: 2151; Percent complete: 53.8%; Average loss: 2.9967
Iteration: 2152; Percent complete: 53.8%; Average loss: 3.2327
Iteration: 2153; Percent complete: 53.8%; Average loss: 3.0083
Iteration: 2154; Percent complete: 53.8%; Average loss: 3.0084
Iteration: 2155; Percent complete: 53.9%; Average loss: 3.1428
Iteration: 2156; Percent complete: 53.9%; Average loss: 3.0689
Iteration: 2157; Percent complete: 53.9%; Average loss: 2.9626
Iteration: 2158; Percent complete: 53.9%; Average loss: 2.8398
Iteration: 2159; Percent complete: 54.0%; Average loss: 3.1323
Iteration: 2160; Percent complete: 54.0%; Average loss: 2.9264
Iteration: 2161; Percent complete: 54.0%; Average loss: 3.2388
Iteration: 2162; Percent complete: 54.0%; Average loss: 3.0748
Iteration: 2163; Percent complete: 54.1%; Average loss: 2.8793
Iteration: 2164; Percent complete: 54.1%; Average loss: 3.0598
Iteration: 2165; Percent complete: 54.1%; Average loss: 3.2705
Iteration: 2166; Percent complete: 54.1%; Average loss: 3.0999
Iteration: 2167; Percent complete: 54.2%; Average loss: 3.0768
Iteration: 2168; Percent complete: 54.2%; Average loss: 3.0062
Iteration: 2169; Percent complete: 54.2%; Average loss: 3.1212
Iteration: 2170; Percent complete: 54.2%; Average loss: 3.1239
Iteration: 2171; Percent complete: 54.3%; Average loss: 3.1393
Iteration: 2172; Percent complete: 54.3%; Average loss: 3.2011
Iteration: 2173; Percent complete: 54.3%; Average loss: 3.1039
Iteration: 2174; Percent complete: 54.4%; Average loss: 3.1800
Iteration: 2175; Percent complete: 54.4%; Average loss: 3.1467
Iteration: 2176; Percent complete: 54.4%; Average loss: 3.1564
Iteration: 2177; Percent complete: 54.4%; Average loss: 3.2688
Iteration: 2178; Percent complete: 54.4%; Average loss: 3.1057
Iteration: 2179; Percent complete: 54.5%; Average loss: 3.1082
Iteration: 2180; Percent complete: 54.5%; Average loss: 3.1637
Iteration: 2181; Percent complete: 54.5%; Average loss: 3.1497
Iteration: 2182; Percent complete: 54.5%; Average loss: 3.2167
Iteration: 2183; Percent complete: 54.6%; Average loss: 3.0753
Iteration: 2184; Percent complete: 54.6%; Average loss: 3.3835
Iteration: 2185; Percent complete: 54.6%; Average loss: 2.9732
Iteration: 2186; Percent complete: 54.6%; Average loss: 2.9860
Iteration: 2187; Percent complete: 54.7%; Average loss: 3.0479
Iteration: 2188; Percent complete: 54.7%; Average loss: 3.0963
Iteration: 2189; Percent complete: 54.7%; Average loss: 3.2908
Iteration: 2190; Percent complete: 54.8%; Average loss: 2.8950
Iteration: 2191; Percent complete: 54.8%; Average loss: 3.3274
Iteration: 2192; Percent complete: 54.8%; Average loss: 3.2044
Iteration: 2193; Percent complete: 54.8%; Average loss: 2.9228
Iteration: 2194; Percent complete: 54.9%; Average loss: 3.2040
Iteration: 2195; Percent complete: 54.9%; Average loss: 3.1232
Iteration: 2196; Percent complete: 54.9%; Average loss: 3.1563
Iteration: 2197; Percent complete: 54.9%; Average loss: 3.0140
Iteration: 2198; Percent complete: 54.9%; Average loss: 3.3409
Iteration: 2199; Percent complete: 55.0%; Average loss: 2.9911
Iteration: 2200; Percent complete: 55.0%; Average loss: 3.0736
Iteration: 2201; Percent complete: 55.0%; Average loss: 3.3165
Iteration: 2202; Percent complete: 55.0%; Average loss: 3.1211
Iteration: 2203; Percent complete: 55.1%; Average loss: 3.1982
Iteration: 2204; Percent complete: 55.1%; Average loss: 3.1253
Iteration: 2205; Percent complete: 55.1%; Average loss: 3.0529
Iteration: 2206; Percent complete: 55.1%; Average loss: 3.0823
Iteration: 2207; Percent complete: 55.2%; Average loss: 2.9908
Iteration: 2208; Percent complete: 55.2%; Average loss: 3.1688
Iteration: 2209; Percent complete: 55.2%; Average loss: 3.0747
Iteration: 2210; Percent complete: 55.2%; Average loss: 3.0763
Iteration: 2211; Percent complete: 55.3%; Average loss: 3.2017
Iteration: 2212; Percent complete: 55.3%; Average loss: 2.9656
Iteration: 2213; Percent complete: 55.3%; Average loss: 3.1567
Iteration: 2214; Percent complete: 55.4%; Average loss: 3.0320
Iteration: 2215; Percent complete: 55.4%; Average loss: 2.7090
Iteration: 2216; Percent complete: 55.4%; Average loss: 3.1125
Iteration: 2217; Percent complete: 55.4%; Average loss: 2.8272
Iteration: 2218; Percent complete: 55.5%; Average loss: 3.0643
Iteration: 2219; Percent complete: 55.5%; Average loss: 3.0543
Iteration: 2220; Percent complete: 55.5%; Average loss: 3.0761
Iteration: 2221; Percent complete: 55.5%; Average loss: 3.3399
Iteration: 2222; Percent complete: 55.5%; Average loss: 3.0809
Iteration: 2223; Percent complete: 55.6%; Average loss: 3.0226
Iteration: 2224; Percent complete: 55.6%; Average loss: 3.2363
Iteration: 2225; Percent complete: 55.6%; Average loss: 3.0432
Iteration: 2226; Percent complete: 55.6%; Average loss: 3.1046
Iteration: 2227; Percent complete: 55.7%; Average loss: 3.0454
Iteration: 2228; Percent complete: 55.7%; Average loss: 2.6455
Iteration: 2229; Percent complete: 55.7%; Average loss: 3.0005
Iteration: 2230; Percent complete: 55.8%; Average loss: 3.1865
Iteration: 2231; Percent complete: 55.8%; Average loss: 2.7690
Iteration: 2232; Percent complete: 55.8%; Average loss: 3.0113
Iteration: 2233; Percent complete: 55.8%; Average loss: 3.3090
Iteration: 2234; Percent complete: 55.9%; Average loss: 2.9135
Iteration: 2235; Percent complete: 55.9%; Average loss: 3.1582
Iteration: 2236; Percent complete: 55.9%; Average loss: 2.9762
Iteration: 2237; Percent complete: 55.9%; Average loss: 3.1111
Iteration: 2238; Percent complete: 56.0%; Average loss: 3.0916
Iteration: 2239; Percent complete: 56.0%; Average loss: 3.0760
Iteration: 2240; Percent complete: 56.0%; Average loss: 2.9286
Iteration: 2241; Percent complete: 56.0%; Average loss: 2.9861
Iteration: 2242; Percent complete: 56.0%; Average loss: 3.1293
Iteration: 2243; Percent complete: 56.1%; Average loss: 3.1274
Iteration: 2244; Percent complete: 56.1%; Average loss: 3.1286
Iteration: 2245; Percent complete: 56.1%; Average loss: 3.1356
Iteration: 2246; Percent complete: 56.1%; Average loss: 3.0851
Iteration: 2247; Percent complete: 56.2%; Average loss: 3.4120
Iteration: 2248; Percent complete: 56.2%; Average loss: 2.8365
Iteration: 2249; Percent complete: 56.2%; Average loss: 3.0582
Iteration: 2250; Percent complete: 56.2%; Average loss: 2.8823
Iteration: 2251; Percent complete: 56.3%; Average loss: 2.9945
Iteration: 2252; Percent complete: 56.3%; Average loss: 3.1266
Iteration: 2253; Percent complete: 56.3%; Average loss: 3.0998
Iteration: 2254; Percent complete: 56.4%; Average loss: 2.9777
Iteration: 2255; Percent complete: 56.4%; Average loss: 2.7800
Iteration: 2256; Percent complete: 56.4%; Average loss: 3.2294
Iteration: 2257; Percent complete: 56.4%; Average loss: 2.9532
Iteration: 2258; Percent complete: 56.5%; Average loss: 3.3892
Iteration: 2259; Percent complete: 56.5%; Average loss: 3.3728
Iteration: 2260; Percent complete: 56.5%; Average loss: 3.0942
Iteration: 2261; Percent complete: 56.5%; Average loss: 3.1189
Iteration: 2262; Percent complete: 56.5%; Average loss: 3.0742
Iteration: 2263; Percent complete: 56.6%; Average loss: 3.0056
Iteration: 2264; Percent complete: 56.6%; Average loss: 3.1694
Iteration: 2265; Percent complete: 56.6%; Average loss: 3.0673
Iteration: 2266; Percent complete: 56.6%; Average loss: 3.2105
Iteration: 2267; Percent complete: 56.7%; Average loss: 3.1138
Iteration: 2268; Percent complete: 56.7%; Average loss: 3.3186
Iteration: 2269; Percent complete: 56.7%; Average loss: 3.0062
Iteration: 2270; Percent complete: 56.8%; Average loss: 3.2340
Iteration: 2271; Percent complete: 56.8%; Average loss: 3.0892
Iteration: 2272; Percent complete: 56.8%; Average loss: 3.1201
Iteration: 2273; Percent complete: 56.8%; Average loss: 2.8975
Iteration: 2274; Percent complete: 56.9%; Average loss: 3.2360
Iteration: 2275; Percent complete: 56.9%; Average loss: 3.1153
Iteration: 2276; Percent complete: 56.9%; Average loss: 2.9881
Iteration: 2277; Percent complete: 56.9%; Average loss: 2.9832
Iteration: 2278; Percent complete: 57.0%; Average loss: 3.2173
Iteration: 2279; Percent complete: 57.0%; Average loss: 3.1739
Iteration: 2280; Percent complete: 57.0%; Average loss: 3.4142
Iteration: 2281; Percent complete: 57.0%; Average loss: 2.9385
Iteration: 2282; Percent complete: 57.0%; Average loss: 3.1953
Iteration: 2283; Percent complete: 57.1%; Average loss: 3.2067
Iteration: 2284; Percent complete: 57.1%; Average loss: 2.9240
Iteration: 2285; Percent complete: 57.1%; Average loss: 2.7670
Iteration: 2286; Percent complete: 57.1%; Average loss: 3.0961
Iteration: 2287; Percent complete: 57.2%; Average loss: 3.0606
Iteration: 2288; Percent complete: 57.2%; Average loss: 2.9135
Iteration: 2289; Percent complete: 57.2%; Average loss: 3.1736
Iteration: 2290; Percent complete: 57.2%; Average loss: 3.0481
Iteration: 2291; Percent complete: 57.3%; Average loss: 3.0142
Iteration: 2292; Percent complete: 57.3%; Average loss: 3.3452
Iteration: 2293; Percent complete: 57.3%; Average loss: 2.8280
Iteration: 2294; Percent complete: 57.4%; Average loss: 3.0649
Iteration: 2295; Percent complete: 57.4%; Average loss: 3.0517
Iteration: 2296; Percent complete: 57.4%; Average loss: 3.1070
Iteration: 2297; Percent complete: 57.4%; Average loss: 3.1255
Iteration: 2298; Percent complete: 57.5%; Average loss: 3.0166
Iteration: 2299; Percent complete: 57.5%; Average loss: 3.1086
Iteration: 2300; Percent complete: 57.5%; Average loss: 3.1679
Iteration: 2301; Percent complete: 57.5%; Average loss: 2.8877
Iteration: 2302; Percent complete: 57.6%; Average loss: 2.9381
Iteration: 2303; Percent complete: 57.6%; Average loss: 2.9845
Iteration: 2304; Percent complete: 57.6%; Average loss: 3.0442
Iteration: 2305; Percent complete: 57.6%; Average loss: 3.0721
Iteration: 2306; Percent complete: 57.6%; Average loss: 3.0307
Iteration: 2307; Percent complete: 57.7%; Average loss: 2.8844
Iteration: 2308; Percent complete: 57.7%; Average loss: 2.9521
Iteration: 2309; Percent complete: 57.7%; Average loss: 3.0445
Iteration: 2310; Percent complete: 57.8%; Average loss: 3.0382
Iteration: 2311; Percent complete: 57.8%; Average loss: 3.1092
Iteration: 2312; Percent complete: 57.8%; Average loss: 3.0258
Iteration: 2313; Percent complete: 57.8%; Average loss: 3.1023
Iteration: 2314; Percent complete: 57.9%; Average loss: 3.0442
Iteration: 2315; Percent complete: 57.9%; Average loss: 2.9748
Iteration: 2316; Percent complete: 57.9%; Average loss: 2.8589
Iteration: 2317; Percent complete: 57.9%; Average loss: 2.9897
Iteration: 2318; Percent complete: 58.0%; Average loss: 3.3384
Iteration: 2319; Percent complete: 58.0%; Average loss: 2.9643
Iteration: 2320; Percent complete: 58.0%; Average loss: 3.0037
Iteration: 2321; Percent complete: 58.0%; Average loss: 3.2324
Iteration: 2322; Percent complete: 58.1%; Average loss: 3.2478
Iteration: 2323; Percent complete: 58.1%; Average loss: 3.0617
Iteration: 2324; Percent complete: 58.1%; Average loss: 3.1730
Iteration: 2325; Percent complete: 58.1%; Average loss: 3.1176
Iteration: 2326; Percent complete: 58.1%; Average loss: 2.9565
Iteration: 2327; Percent complete: 58.2%; Average loss: 2.9505
Iteration: 2328; Percent complete: 58.2%; Average loss: 3.0041
Iteration: 2329; Percent complete: 58.2%; Average loss: 2.9855
Iteration: 2330; Percent complete: 58.2%; Average loss: 2.9093
Iteration: 2331; Percent complete: 58.3%; Average loss: 2.9454
Iteration: 2332; Percent complete: 58.3%; Average loss: 3.3897
Iteration: 2333; Percent complete: 58.3%; Average loss: 2.9976
Iteration: 2334; Percent complete: 58.4%; Average loss: 3.2373
Iteration: 2335; Percent complete: 58.4%; Average loss: 3.1873
Iteration: 2336; Percent complete: 58.4%; Average loss: 3.0559
Iteration: 2337; Percent complete: 58.4%; Average loss: 3.2714
Iteration: 2338; Percent complete: 58.5%; Average loss: 3.0217
Iteration: 2339; Percent complete: 58.5%; Average loss: 3.0540
Iteration: 2340; Percent complete: 58.5%; Average loss: 2.9649
Iteration: 2341; Percent complete: 58.5%; Average loss: 3.2057
Iteration: 2342; Percent complete: 58.6%; Average loss: 3.1141
Iteration: 2343; Percent complete: 58.6%; Average loss: 3.0000
Iteration: 2344; Percent complete: 58.6%; Average loss: 3.0689
Iteration: 2345; Percent complete: 58.6%; Average loss: 3.2656
Iteration: 2346; Percent complete: 58.7%; Average loss: 2.8518
Iteration: 2347; Percent complete: 58.7%; Average loss: 2.8680
Iteration: 2348; Percent complete: 58.7%; Average loss: 3.0927
Iteration: 2349; Percent complete: 58.7%; Average loss: 2.9071
Iteration: 2350; Percent complete: 58.8%; Average loss: 3.3419
Iteration: 2351; Percent complete: 58.8%; Average loss: 3.1563
Iteration: 2352; Percent complete: 58.8%; Average loss: 2.9309
Iteration: 2353; Percent complete: 58.8%; Average loss: 3.2710
Iteration: 2354; Percent complete: 58.9%; Average loss: 2.8658
Iteration: 2355; Percent complete: 58.9%; Average loss: 2.9889
Iteration: 2356; Percent complete: 58.9%; Average loss: 2.9337
Iteration: 2357; Percent complete: 58.9%; Average loss: 3.2435
Iteration: 2358; Percent complete: 59.0%; Average loss: 3.0487
Iteration: 2359; Percent complete: 59.0%; Average loss: 2.9458
Iteration: 2360; Percent complete: 59.0%; Average loss: 2.9656
Iteration: 2361; Percent complete: 59.0%; Average loss: 3.0236
Iteration: 2362; Percent complete: 59.1%; Average loss: 2.8445
Iteration: 2363; Percent complete: 59.1%; Average loss: 3.4365
Iteration: 2364; Percent complete: 59.1%; Average loss: 2.9370
Iteration: 2365; Percent complete: 59.1%; Average loss: 3.0087
Iteration: 2366; Percent complete: 59.2%; Average loss: 3.1326
Iteration: 2367; Percent complete: 59.2%; Average loss: 3.2316
Iteration: 2368; Percent complete: 59.2%; Average loss: 2.9799
Iteration: 2369; Percent complete: 59.2%; Average loss: 3.2893
Iteration: 2370; Percent complete: 59.2%; Average loss: 3.1812
Iteration: 2371; Percent complete: 59.3%; Average loss: 3.2739
Iteration: 2372; Percent complete: 59.3%; Average loss: 3.0859
Iteration: 2373; Percent complete: 59.3%; Average loss: 3.0632
Iteration: 2374; Percent complete: 59.4%; Average loss: 3.3129
Iteration: 2375; Percent complete: 59.4%; Average loss: 3.2648
Iteration: 2376; Percent complete: 59.4%; Average loss: 3.1113
Iteration: 2377; Percent complete: 59.4%; Average loss: 2.9630
Iteration: 2378; Percent complete: 59.5%; Average loss: 2.9199
Iteration: 2379; Percent complete: 59.5%; Average loss: 3.0508
Iteration: 2380; Percent complete: 59.5%; Average loss: 3.2639
Iteration: 2381; Percent complete: 59.5%; Average loss: 2.9154
Iteration: 2382; Percent complete: 59.6%; Average loss: 3.2650
Iteration: 2383; Percent complete: 59.6%; Average loss: 2.9757
Iteration: 2384; Percent complete: 59.6%; Average loss: 3.2794
Iteration: 2385; Percent complete: 59.6%; Average loss: 2.8603
Iteration: 2386; Percent complete: 59.7%; Average loss: 2.9618
Iteration: 2387; Percent complete: 59.7%; Average loss: 3.0594
Iteration: 2388; Percent complete: 59.7%; Average loss: 2.9097
Iteration: 2389; Percent complete: 59.7%; Average loss: 3.0183
Iteration: 2390; Percent complete: 59.8%; Average loss: 3.0312
Iteration: 2391; Percent complete: 59.8%; Average loss: 2.9823
Iteration: 2392; Percent complete: 59.8%; Average loss: 3.2607
Iteration: 2393; Percent complete: 59.8%; Average loss: 2.9036
Iteration: 2394; Percent complete: 59.9%; Average loss: 3.2327
Iteration: 2395; Percent complete: 59.9%; Average loss: 3.1395
Iteration: 2396; Percent complete: 59.9%; Average loss: 2.9999
Iteration: 2397; Percent complete: 59.9%; Average loss: 2.9594
Iteration: 2398; Percent complete: 60.0%; Average loss: 2.7853
Iteration: 2399; Percent complete: 60.0%; Average loss: 2.8987
Iteration: 2400; Percent complete: 60.0%; Average loss: 3.2212
Iteration: 2401; Percent complete: 60.0%; Average loss: 2.9649
Iteration: 2402; Percent complete: 60.1%; Average loss: 2.9649
Iteration: 2403; Percent complete: 60.1%; Average loss: 3.2145
Iteration: 2404; Percent complete: 60.1%; Average loss: 3.1458
Iteration: 2405; Percent complete: 60.1%; Average loss: 2.7132
Iteration: 2406; Percent complete: 60.2%; Average loss: 3.2532
Iteration: 2407; Percent complete: 60.2%; Average loss: 2.7336
Iteration: 2408; Percent complete: 60.2%; Average loss: 2.8753
Iteration: 2409; Percent complete: 60.2%; Average loss: 3.2200
Iteration: 2410; Percent complete: 60.2%; Average loss: 3.0139
Iteration: 2411; Percent complete: 60.3%; Average loss: 2.9891
Iteration: 2412; Percent complete: 60.3%; Average loss: 3.1803
Iteration: 2413; Percent complete: 60.3%; Average loss: 3.0797
Iteration: 2414; Percent complete: 60.4%; Average loss: 3.0555
Iteration: 2415; Percent complete: 60.4%; Average loss: 3.0255
Iteration: 2416; Percent complete: 60.4%; Average loss: 2.7635
Iteration: 2417; Percent complete: 60.4%; Average loss: 2.9469
Iteration: 2418; Percent complete: 60.5%; Average loss: 3.2703
Iteration: 2419; Percent complete: 60.5%; Average loss: 2.8416
Iteration: 2420; Percent complete: 60.5%; Average loss: 3.0330
Iteration: 2421; Percent complete: 60.5%; Average loss: 2.9800
Iteration: 2422; Percent complete: 60.6%; Average loss: 2.6311
Iteration: 2423; Percent complete: 60.6%; Average loss: 3.0937
Iteration: 2424; Percent complete: 60.6%; Average loss: 3.1763
Iteration: 2425; Percent complete: 60.6%; Average loss: 3.0798
Iteration: 2426; Percent complete: 60.7%; Average loss: 3.0349
Iteration: 2427; Percent complete: 60.7%; Average loss: 2.9849
Iteration: 2428; Percent complete: 60.7%; Average loss: 3.2812
Iteration: 2429; Percent complete: 60.7%; Average loss: 3.2838
Iteration: 2430; Percent complete: 60.8%; Average loss: 3.1679
Iteration: 2431; Percent complete: 60.8%; Average loss: 3.0123
Iteration: 2432; Percent complete: 60.8%; Average loss: 2.9398
Iteration: 2433; Percent complete: 60.8%; Average loss: 2.8379
Iteration: 2434; Percent complete: 60.9%; Average loss: 3.1590
Iteration: 2435; Percent complete: 60.9%; Average loss: 2.8497
Iteration: 2436; Percent complete: 60.9%; Average loss: 3.0768
Iteration: 2437; Percent complete: 60.9%; Average loss: 2.9702
Iteration: 2438; Percent complete: 61.0%; Average loss: 2.9699
Iteration: 2439; Percent complete: 61.0%; Average loss: 3.1308
Iteration: 2440; Percent complete: 61.0%; Average loss: 3.1539
Iteration: 2441; Percent complete: 61.0%; Average loss: 3.0463
Iteration: 2442; Percent complete: 61.1%; Average loss: 3.0787
Iteration: 2443; Percent complete: 61.1%; Average loss: 2.8388
Iteration: 2444; Percent complete: 61.1%; Average loss: 3.0957
Iteration: 2445; Percent complete: 61.1%; Average loss: 3.1643
Iteration: 2446; Percent complete: 61.2%; Average loss: 2.9286
Iteration: 2447; Percent complete: 61.2%; Average loss: 3.0022
Iteration: 2448; Percent complete: 61.2%; Average loss: 3.1173
Iteration: 2449; Percent complete: 61.2%; Average loss: 3.3333
Iteration: 2450; Percent complete: 61.3%; Average loss: 3.3129
Iteration: 2451; Percent complete: 61.3%; Average loss: 3.1316
Iteration: 2452; Percent complete: 61.3%; Average loss: 2.9484
Iteration: 2453; Percent complete: 61.3%; Average loss: 3.1213
Iteration: 2454; Percent complete: 61.4%; Average loss: 2.9636
Iteration: 2455; Percent complete: 61.4%; Average loss: 3.2347
Iteration: 2456; Percent complete: 61.4%; Average loss: 2.9898
Iteration: 2457; Percent complete: 61.4%; Average loss: 2.8764
Iteration: 2458; Percent complete: 61.5%; Average loss: 2.9881
Iteration: 2459; Percent complete: 61.5%; Average loss: 3.2338
Iteration: 2460; Percent complete: 61.5%; Average loss: 3.0196
Iteration: 2461; Percent complete: 61.5%; Average loss: 3.1578
Iteration: 2462; Percent complete: 61.6%; Average loss: 3.0828
Iteration: 2463; Percent complete: 61.6%; Average loss: 3.1060
Iteration: 2464; Percent complete: 61.6%; Average loss: 3.0334
Iteration: 2465; Percent complete: 61.6%; Average loss: 2.9437
Iteration: 2466; Percent complete: 61.7%; Average loss: 2.9606
Iteration: 2467; Percent complete: 61.7%; Average loss: 2.9623
Iteration: 2468; Percent complete: 61.7%; Average loss: 2.6970
Iteration: 2469; Percent complete: 61.7%; Average loss: 3.1081
Iteration: 2470; Percent complete: 61.8%; Average loss: 2.9960
Iteration: 2471; Percent complete: 61.8%; Average loss: 3.0460
Iteration: 2472; Percent complete: 61.8%; Average loss: 2.8783
Iteration: 2473; Percent complete: 61.8%; Average loss: 2.9784
Iteration: 2474; Percent complete: 61.9%; Average loss: 2.9414
Iteration: 2475; Percent complete: 61.9%; Average loss: 2.8960
Iteration: 2476; Percent complete: 61.9%; Average loss: 3.1354
Iteration: 2477; Percent complete: 61.9%; Average loss: 2.9935
Iteration: 2478; Percent complete: 62.0%; Average loss: 3.1065
Iteration: 2479; Percent complete: 62.0%; Average loss: 2.9395
Iteration: 2480; Percent complete: 62.0%; Average loss: 2.9044
Iteration: 2481; Percent complete: 62.0%; Average loss: 2.9140
Iteration: 2482; Percent complete: 62.1%; Average loss: 2.8699
Iteration: 2483; Percent complete: 62.1%; Average loss: 2.9858
Iteration: 2484; Percent complete: 62.1%; Average loss: 2.8850
Iteration: 2485; Percent complete: 62.1%; Average loss: 2.9645
Iteration: 2486; Percent complete: 62.2%; Average loss: 2.8758
Iteration: 2487; Percent complete: 62.2%; Average loss: 2.8308
Iteration: 2488; Percent complete: 62.2%; Average loss: 3.1720
Iteration: 2489; Percent complete: 62.2%; Average loss: 3.0060
Iteration: 2490; Percent complete: 62.3%; Average loss: 3.1093
Iteration: 2491; Percent complete: 62.3%; Average loss: 3.1868
Iteration: 2492; Percent complete: 62.3%; Average loss: 2.8817
Iteration: 2493; Percent complete: 62.3%; Average loss: 3.0230
Iteration: 2494; Percent complete: 62.4%; Average loss: 2.7958
Iteration: 2495; Percent complete: 62.4%; Average loss: 3.3077
Iteration: 2496; Percent complete: 62.4%; Average loss: 3.0690
Iteration: 2497; Percent complete: 62.4%; Average loss: 3.1491
Iteration: 2498; Percent complete: 62.5%; Average loss: 3.1316
Iteration: 2499; Percent complete: 62.5%; Average loss: 3.0315
Iteration: 2500; Percent complete: 62.5%; Average loss: 2.9499
Iteration: 2501; Percent complete: 62.5%; Average loss: 2.8527
Iteration: 2502; Percent complete: 62.5%; Average loss: 2.9991
Iteration: 2503; Percent complete: 62.6%; Average loss: 2.8540
Iteration: 2504; Percent complete: 62.6%; Average loss: 3.1220
Iteration: 2505; Percent complete: 62.6%; Average loss: 3.0353
Iteration: 2506; Percent complete: 62.6%; Average loss: 2.6359
Iteration: 2507; Percent complete: 62.7%; Average loss: 3.2061
Iteration: 2508; Percent complete: 62.7%; Average loss: 2.9524
Iteration: 2509; Percent complete: 62.7%; Average loss: 3.1085
Iteration: 2510; Percent complete: 62.7%; Average loss: 3.1858
Iteration: 2511; Percent complete: 62.8%; Average loss: 2.9993
Iteration: 2512; Percent complete: 62.8%; Average loss: 3.0057
Iteration: 2513; Percent complete: 62.8%; Average loss: 2.8245
Iteration: 2514; Percent complete: 62.8%; Average loss: 2.8452
Iteration: 2515; Percent complete: 62.9%; Average loss: 2.9228
Iteration: 2516; Percent complete: 62.9%; Average loss: 3.2551
Iteration: 2517; Percent complete: 62.9%; Average loss: 3.0565
Iteration: 2518; Percent complete: 62.9%; Average loss: 3.1051
Iteration: 2519; Percent complete: 63.0%; Average loss: 3.0454
Iteration: 2520; Percent complete: 63.0%; Average loss: 3.1576
Iteration: 2521; Percent complete: 63.0%; Average loss: 3.1484
Iteration: 2522; Percent complete: 63.0%; Average loss: 2.9883
Iteration: 2523; Percent complete: 63.1%; Average loss: 3.1075
Iteration: 2524; Percent complete: 63.1%; Average loss: 2.9643
Iteration: 2525; Percent complete: 63.1%; Average loss: 2.8538
Iteration: 2526; Percent complete: 63.1%; Average loss: 3.0980
Iteration: 2527; Percent complete: 63.2%; Average loss: 3.0561
Iteration: 2528; Percent complete: 63.2%; Average loss: 3.0371
Iteration: 2529; Percent complete: 63.2%; Average loss: 3.0356
Iteration: 2530; Percent complete: 63.2%; Average loss: 3.1436
Iteration: 2531; Percent complete: 63.3%; Average loss: 3.0566
Iteration: 2532; Percent complete: 63.3%; Average loss: 2.7497
Iteration: 2533; Percent complete: 63.3%; Average loss: 3.1932
Iteration: 2534; Percent complete: 63.3%; Average loss: 2.7545
Iteration: 2535; Percent complete: 63.4%; Average loss: 3.0112
Iteration: 2536; Percent complete: 63.4%; Average loss: 2.9050
Iteration: 2537; Percent complete: 63.4%; Average loss: 3.2039
Iteration: 2538; Percent complete: 63.4%; Average loss: 3.2011
Iteration: 2539; Percent complete: 63.5%; Average loss: 2.8784
Iteration: 2540; Percent complete: 63.5%; Average loss: 3.0531
Iteration: 2541; Percent complete: 63.5%; Average loss: 2.9909
Iteration: 2542; Percent complete: 63.5%; Average loss: 3.1782
Iteration: 2543; Percent complete: 63.6%; Average loss: 2.9434
Iteration: 2544; Percent complete: 63.6%; Average loss: 2.7431
Iteration: 2545; Percent complete: 63.6%; Average loss: 2.8105
Iteration: 2546; Percent complete: 63.6%; Average loss: 2.9913
Iteration: 2547; Percent complete: 63.7%; Average loss: 2.9447
Iteration: 2548; Percent complete: 63.7%; Average loss: 3.1020
Iteration: 2549; Percent complete: 63.7%; Average loss: 3.1180
Iteration: 2550; Percent complete: 63.7%; Average loss: 3.1196
Iteration: 2551; Percent complete: 63.8%; Average loss: 2.7668
Iteration: 2552; Percent complete: 63.8%; Average loss: 2.9619
Iteration: 2553; Percent complete: 63.8%; Average loss: 3.1555
Iteration: 2554; Percent complete: 63.8%; Average loss: 2.9934
Iteration: 2555; Percent complete: 63.9%; Average loss: 3.0762
Iteration: 2556; Percent complete: 63.9%; Average loss: 3.0106
Iteration: 2557; Percent complete: 63.9%; Average loss: 2.8468
Iteration: 2558; Percent complete: 63.9%; Average loss: 3.1518
Iteration: 2559; Percent complete: 64.0%; Average loss: 2.9248
Iteration: 2560; Percent complete: 64.0%; Average loss: 3.0370
Iteration: 2561; Percent complete: 64.0%; Average loss: 2.8422
Iteration: 2562; Percent complete: 64.0%; Average loss: 2.8987
Iteration: 2563; Percent complete: 64.1%; Average loss: 2.9993
Iteration: 2564; Percent complete: 64.1%; Average loss: 3.0234
Iteration: 2565; Percent complete: 64.1%; Average loss: 2.8371
Iteration: 2566; Percent complete: 64.1%; Average loss: 2.9831
Iteration: 2567; Percent complete: 64.2%; Average loss: 3.1728
Iteration: 2568; Percent complete: 64.2%; Average loss: 3.0018
Iteration: 2569; Percent complete: 64.2%; Average loss: 2.9316
Iteration: 2570; Percent complete: 64.2%; Average loss: 2.9266
Iteration: 2571; Percent complete: 64.3%; Average loss: 2.9247
Iteration: 2572; Percent complete: 64.3%; Average loss: 2.9081
Iteration: 2573; Percent complete: 64.3%; Average loss: 2.9552
Iteration: 2574; Percent complete: 64.3%; Average loss: 2.8706
Iteration: 2575; Percent complete: 64.4%; Average loss: 3.0060
Iteration: 2576; Percent complete: 64.4%; Average loss: 2.9460
Iteration: 2577; Percent complete: 64.4%; Average loss: 2.8894
Iteration: 2578; Percent complete: 64.5%; Average loss: 2.8695
Iteration: 2579; Percent complete: 64.5%; Average loss: 2.9760
Iteration: 2580; Percent complete: 64.5%; Average loss: 2.9977
Iteration: 2581; Percent complete: 64.5%; Average loss: 3.1329
Iteration: 2582; Percent complete: 64.5%; Average loss: 2.9121
Iteration: 2583; Percent complete: 64.6%; Average loss: 3.0633
Iteration: 2584; Percent complete: 64.6%; Average loss: 2.9591
Iteration: 2585; Percent complete: 64.6%; Average loss: 2.9102
Iteration: 2586; Percent complete: 64.6%; Average loss: 2.8617
Iteration: 2587; Percent complete: 64.7%; Average loss: 3.0220
Iteration: 2588; Percent complete: 64.7%; Average loss: 3.1344
Iteration: 2589; Percent complete: 64.7%; Average loss: 3.0491
Iteration: 2590; Percent complete: 64.8%; Average loss: 3.0830
Iteration: 2591; Percent complete: 64.8%; Average loss: 2.9283
Iteration: 2592; Percent complete: 64.8%; Average loss: 3.0855
Iteration: 2593; Percent complete: 64.8%; Average loss: 3.0120
Iteration: 2594; Percent complete: 64.8%; Average loss: 2.8553
Iteration: 2595; Percent complete: 64.9%; Average loss: 2.9999
Iteration: 2596; Percent complete: 64.9%; Average loss: 2.9768
Iteration: 2597; Percent complete: 64.9%; Average loss: 2.9375
Iteration: 2598; Percent complete: 65.0%; Average loss: 3.1226
Iteration: 2599; Percent complete: 65.0%; Average loss: 2.9683
Iteration: 2600; Percent complete: 65.0%; Average loss: 3.0993
Iteration: 2601; Percent complete: 65.0%; Average loss: 2.9495
Iteration: 2602; Percent complete: 65.0%; Average loss: 2.9810
Iteration: 2603; Percent complete: 65.1%; Average loss: 3.0733
Iteration: 2604; Percent complete: 65.1%; Average loss: 2.9228
Iteration: 2605; Percent complete: 65.1%; Average loss: 2.8572
Iteration: 2606; Percent complete: 65.1%; Average loss: 3.0866
Iteration: 2607; Percent complete: 65.2%; Average loss: 2.9257
Iteration: 2608; Percent complete: 65.2%; Average loss: 2.8400
Iteration: 2609; Percent complete: 65.2%; Average loss: 3.1422
Iteration: 2610; Percent complete: 65.2%; Average loss: 3.0612
Iteration: 2611; Percent complete: 65.3%; Average loss: 2.8913
Iteration: 2612; Percent complete: 65.3%; Average loss: 3.0336
Iteration: 2613; Percent complete: 65.3%; Average loss: 2.6778
Iteration: 2614; Percent complete: 65.3%; Average loss: 3.1690
Iteration: 2615; Percent complete: 65.4%; Average loss: 2.9029
Iteration: 2616; Percent complete: 65.4%; Average loss: 3.1663
Iteration: 2617; Percent complete: 65.4%; Average loss: 2.8108
Iteration: 2618; Percent complete: 65.5%; Average loss: 2.7272
Iteration: 2619; Percent complete: 65.5%; Average loss: 2.9994
Iteration: 2620; Percent complete: 65.5%; Average loss: 2.8249
Iteration: 2621; Percent complete: 65.5%; Average loss: 3.0437
Iteration: 2622; Percent complete: 65.5%; Average loss: 2.7949
Iteration: 2623; Percent complete: 65.6%; Average loss: 3.2499
Iteration: 2624; Percent complete: 65.6%; Average loss: 2.9994
Iteration: 2625; Percent complete: 65.6%; Average loss: 3.0687
Iteration: 2626; Percent complete: 65.6%; Average loss: 3.0726
Iteration: 2627; Percent complete: 65.7%; Average loss: 2.9189
Iteration: 2628; Percent complete: 65.7%; Average loss: 3.0344
Iteration: 2629; Percent complete: 65.7%; Average loss: 3.0005
Iteration: 2630; Percent complete: 65.8%; Average loss: 2.8101
Iteration: 2631; Percent complete: 65.8%; Average loss: 3.2348
Iteration: 2632; Percent complete: 65.8%; Average loss: 3.2193
Iteration: 2633; Percent complete: 65.8%; Average loss: 2.9373
Iteration: 2634; Percent complete: 65.8%; Average loss: 3.1275
Iteration: 2635; Percent complete: 65.9%; Average loss: 3.0558
Iteration: 2636; Percent complete: 65.9%; Average loss: 3.0240
Iteration: 2637; Percent complete: 65.9%; Average loss: 2.8696
Iteration: 2638; Percent complete: 66.0%; Average loss: 2.8506
Iteration: 2639; Percent complete: 66.0%; Average loss: 3.4178
Iteration: 2640; Percent complete: 66.0%; Average loss: 2.8914
Iteration: 2641; Percent complete: 66.0%; Average loss: 3.0360
Iteration: 2642; Percent complete: 66.0%; Average loss: 2.9893
Iteration: 2643; Percent complete: 66.1%; Average loss: 3.1118
Iteration: 2644; Percent complete: 66.1%; Average loss: 3.0839
Iteration: 2645; Percent complete: 66.1%; Average loss: 3.0125
Iteration: 2646; Percent complete: 66.1%; Average loss: 3.0499
Iteration: 2647; Percent complete: 66.2%; Average loss: 3.1074
Iteration: 2648; Percent complete: 66.2%; Average loss: 2.9311
Iteration: 2649; Percent complete: 66.2%; Average loss: 3.0570
Iteration: 2650; Percent complete: 66.2%; Average loss: 3.2022
Iteration: 2651; Percent complete: 66.3%; Average loss: 2.7811
Iteration: 2652; Percent complete: 66.3%; Average loss: 2.5761
Iteration: 2653; Percent complete: 66.3%; Average loss: 2.9609
Iteration: 2654; Percent complete: 66.3%; Average loss: 3.1681
Iteration: 2655; Percent complete: 66.4%; Average loss: 2.9106
Iteration: 2656; Percent complete: 66.4%; Average loss: 2.7486
Iteration: 2657; Percent complete: 66.4%; Average loss: 2.9448
Iteration: 2658; Percent complete: 66.5%; Average loss: 2.9617
Iteration: 2659; Percent complete: 66.5%; Average loss: 3.1875
Iteration: 2660; Percent complete: 66.5%; Average loss: 3.2040
Iteration: 2661; Percent complete: 66.5%; Average loss: 3.1268
Iteration: 2662; Percent complete: 66.5%; Average loss: 3.3047
Iteration: 2663; Percent complete: 66.6%; Average loss: 2.9719
Iteration: 2664; Percent complete: 66.6%; Average loss: 2.9874
Iteration: 2665; Percent complete: 66.6%; Average loss: 2.8270
Iteration: 2666; Percent complete: 66.6%; Average loss: 3.0135
Iteration: 2667; Percent complete: 66.7%; Average loss: 3.0396
Iteration: 2668; Percent complete: 66.7%; Average loss: 3.2200
Iteration: 2669; Percent complete: 66.7%; Average loss: 2.8353
Iteration: 2670; Percent complete: 66.8%; Average loss: 3.0831
Iteration: 2671; Percent complete: 66.8%; Average loss: 2.9654
Iteration: 2672; Percent complete: 66.8%; Average loss: 3.0191
Iteration: 2673; Percent complete: 66.8%; Average loss: 2.8529
Iteration: 2674; Percent complete: 66.8%; Average loss: 3.0602
Iteration: 2675; Percent complete: 66.9%; Average loss: 3.0879
Iteration: 2676; Percent complete: 66.9%; Average loss: 3.0887
Iteration: 2677; Percent complete: 66.9%; Average loss: 3.1156
Iteration: 2678; Percent complete: 67.0%; Average loss: 3.1680
Iteration: 2679; Percent complete: 67.0%; Average loss: 2.7849
Iteration: 2680; Percent complete: 67.0%; Average loss: 3.0446
Iteration: 2681; Percent complete: 67.0%; Average loss: 2.9740
Iteration: 2682; Percent complete: 67.0%; Average loss: 3.0085
Iteration: 2683; Percent complete: 67.1%; Average loss: 2.8403
Iteration: 2684; Percent complete: 67.1%; Average loss: 2.8035
Iteration: 2685; Percent complete: 67.1%; Average loss: 2.8082
Iteration: 2686; Percent complete: 67.2%; Average loss: 2.7859
Iteration: 2687; Percent complete: 67.2%; Average loss: 2.9959
Iteration: 2688; Percent complete: 67.2%; Average loss: 2.8527
Iteration: 2689; Percent complete: 67.2%; Average loss: 2.9628
Iteration: 2690; Percent complete: 67.2%; Average loss: 2.9570
Iteration: 2691; Percent complete: 67.3%; Average loss: 2.9099
Iteration: 2692; Percent complete: 67.3%; Average loss: 2.9348
Iteration: 2693; Percent complete: 67.3%; Average loss: 2.7703
Iteration: 2694; Percent complete: 67.3%; Average loss: 2.9767
Iteration: 2695; Percent complete: 67.4%; Average loss: 2.9301
Iteration: 2696; Percent complete: 67.4%; Average loss: 2.7889
Iteration: 2697; Percent complete: 67.4%; Average loss: 2.9391
Iteration: 2698; Percent complete: 67.5%; Average loss: 3.0775
Iteration: 2699; Percent complete: 67.5%; Average loss: 2.8138
Iteration: 2700; Percent complete: 67.5%; Average loss: 2.8801
Iteration: 2701; Percent complete: 67.5%; Average loss: 3.1514
Iteration: 2702; Percent complete: 67.5%; Average loss: 2.9306
Iteration: 2703; Percent complete: 67.6%; Average loss: 3.0105
Iteration: 2704; Percent complete: 67.6%; Average loss: 3.0908
Iteration: 2705; Percent complete: 67.6%; Average loss: 2.7400
Iteration: 2706; Percent complete: 67.7%; Average loss: 2.8347
Iteration: 2707; Percent complete: 67.7%; Average loss: 2.8110
Iteration: 2708; Percent complete: 67.7%; Average loss: 2.8505
Iteration: 2709; Percent complete: 67.7%; Average loss: 2.8220
Iteration: 2710; Percent complete: 67.8%; Average loss: 2.5242
Iteration: 2711; Percent complete: 67.8%; Average loss: 2.8330
Iteration: 2712; Percent complete: 67.8%; Average loss: 3.0211
Iteration: 2713; Percent complete: 67.8%; Average loss: 2.8576
Iteration: 2714; Percent complete: 67.8%; Average loss: 3.1456
Iteration: 2715; Percent complete: 67.9%; Average loss: 3.1727
Iteration: 2716; Percent complete: 67.9%; Average loss: 2.9707
Iteration: 2717; Percent complete: 67.9%; Average loss: 3.0503
Iteration: 2718; Percent complete: 68.0%; Average loss: 2.9980
Iteration: 2719; Percent complete: 68.0%; Average loss: 3.1468
Iteration: 2720; Percent complete: 68.0%; Average loss: 3.1023
Iteration: 2721; Percent complete: 68.0%; Average loss: 3.1878
Iteration: 2722; Percent complete: 68.0%; Average loss: 2.8261
Iteration: 2723; Percent complete: 68.1%; Average loss: 3.0055
Iteration: 2724; Percent complete: 68.1%; Average loss: 3.0336
Iteration: 2725; Percent complete: 68.1%; Average loss: 2.9246
Iteration: 2726; Percent complete: 68.2%; Average loss: 3.2190
Iteration: 2727; Percent complete: 68.2%; Average loss: 2.8545
Iteration: 2728; Percent complete: 68.2%; Average loss: 3.0234
Iteration: 2729; Percent complete: 68.2%; Average loss: 2.9845
Iteration: 2730; Percent complete: 68.2%; Average loss: 3.2773
Iteration: 2731; Percent complete: 68.3%; Average loss: 3.0363
Iteration: 2732; Percent complete: 68.3%; Average loss: 2.9636
Iteration: 2733; Percent complete: 68.3%; Average loss: 3.0326
Iteration: 2734; Percent complete: 68.3%; Average loss: 3.0859
Iteration: 2735; Percent complete: 68.4%; Average loss: 2.8599
Iteration: 2736; Percent complete: 68.4%; Average loss: 2.9481
Iteration: 2737; Percent complete: 68.4%; Average loss: 3.0054
Iteration: 2738; Percent complete: 68.5%; Average loss: 2.6884
Iteration: 2739; Percent complete: 68.5%; Average loss: 2.7947
Iteration: 2740; Percent complete: 68.5%; Average loss: 3.0286
Iteration: 2741; Percent complete: 68.5%; Average loss: 2.7925
Iteration: 2742; Percent complete: 68.5%; Average loss: 3.0289
Iteration: 2743; Percent complete: 68.6%; Average loss: 2.7467
Iteration: 2744; Percent complete: 68.6%; Average loss: 2.7703
Iteration: 2745; Percent complete: 68.6%; Average loss: 2.9899
Iteration: 2746; Percent complete: 68.7%; Average loss: 2.9935
Iteration: 2747; Percent complete: 68.7%; Average loss: 3.0869
Iteration: 2748; Percent complete: 68.7%; Average loss: 2.8332
Iteration: 2749; Percent complete: 68.7%; Average loss: 3.1310
Iteration: 2750; Percent complete: 68.8%; Average loss: 3.2751
Iteration: 2751; Percent complete: 68.8%; Average loss: 2.8671
Iteration: 2752; Percent complete: 68.8%; Average loss: 2.8028
Iteration: 2753; Percent complete: 68.8%; Average loss: 3.1278
Iteration: 2754; Percent complete: 68.8%; Average loss: 2.9763
Iteration: 2755; Percent complete: 68.9%; Average loss: 3.0514
Iteration: 2756; Percent complete: 68.9%; Average loss: 2.9789
Iteration: 2757; Percent complete: 68.9%; Average loss: 3.0624
Iteration: 2758; Percent complete: 69.0%; Average loss: 2.9437
Iteration: 2759; Percent complete: 69.0%; Average loss: 3.0869
Iteration: 2760; Percent complete: 69.0%; Average loss: 2.7765
Iteration: 2761; Percent complete: 69.0%; Average loss: 2.9755
Iteration: 2762; Percent complete: 69.0%; Average loss: 2.8749
Iteration: 2763; Percent complete: 69.1%; Average loss: 2.9621
Iteration: 2764; Percent complete: 69.1%; Average loss: 3.3681
Iteration: 2765; Percent complete: 69.1%; Average loss: 2.8824
Iteration: 2766; Percent complete: 69.2%; Average loss: 2.9051
Iteration: 2767; Percent complete: 69.2%; Average loss: 2.9358
Iteration: 2768; Percent complete: 69.2%; Average loss: 3.1219
Iteration: 2769; Percent complete: 69.2%; Average loss: 2.7431
Iteration: 2770; Percent complete: 69.2%; Average loss: 3.0025
Iteration: 2771; Percent complete: 69.3%; Average loss: 3.1026
Iteration: 2772; Percent complete: 69.3%; Average loss: 2.8307
Iteration: 2773; Percent complete: 69.3%; Average loss: 2.8539
Iteration: 2774; Percent complete: 69.3%; Average loss: 2.8616
Iteration: 2775; Percent complete: 69.4%; Average loss: 2.8849
Iteration: 2776; Percent complete: 69.4%; Average loss: 3.0897
Iteration: 2777; Percent complete: 69.4%; Average loss: 2.7391
Iteration: 2778; Percent complete: 69.5%; Average loss: 3.0612
Iteration: 2779; Percent complete: 69.5%; Average loss: 2.8973
Iteration: 2780; Percent complete: 69.5%; Average loss: 2.9709
Iteration: 2781; Percent complete: 69.5%; Average loss: 3.0014
Iteration: 2782; Percent complete: 69.5%; Average loss: 2.9508
Iteration: 2783; Percent complete: 69.6%; Average loss: 2.9012
Iteration: 2784; Percent complete: 69.6%; Average loss: 2.9531
Iteration: 2785; Percent complete: 69.6%; Average loss: 2.8463
Iteration: 2786; Percent complete: 69.7%; Average loss: 3.2515
Iteration: 2787; Percent complete: 69.7%; Average loss: 2.7153
Iteration: 2788; Percent complete: 69.7%; Average loss: 2.8462
Iteration: 2789; Percent complete: 69.7%; Average loss: 2.9029
Iteration: 2790; Percent complete: 69.8%; Average loss: 2.9081
Iteration: 2791; Percent complete: 69.8%; Average loss: 3.0011
Iteration: 2792; Percent complete: 69.8%; Average loss: 3.0356
Iteration: 2793; Percent complete: 69.8%; Average loss: 2.9782
Iteration: 2794; Percent complete: 69.8%; Average loss: 3.0007
Iteration: 2795; Percent complete: 69.9%; Average loss: 3.1119
Iteration: 2796; Percent complete: 69.9%; Average loss: 2.9450
Iteration: 2797; Percent complete: 69.9%; Average loss: 2.7681
Iteration: 2798; Percent complete: 70.0%; Average loss: 3.1053
Iteration: 2799; Percent complete: 70.0%; Average loss: 2.7863
Iteration: 2800; Percent complete: 70.0%; Average loss: 3.0100
Iteration: 2801; Percent complete: 70.0%; Average loss: 3.0154
Iteration: 2802; Percent complete: 70.0%; Average loss: 2.9204
Iteration: 2803; Percent complete: 70.1%; Average loss: 2.9955
Iteration: 2804; Percent complete: 70.1%; Average loss: 3.1066
Iteration: 2805; Percent complete: 70.1%; Average loss: 3.1127
Iteration: 2806; Percent complete: 70.2%; Average loss: 2.8799
Iteration: 2807; Percent complete: 70.2%; Average loss: 2.9217
Iteration: 2808; Percent complete: 70.2%; Average loss: 2.7801
Iteration: 2809; Percent complete: 70.2%; Average loss: 3.0431
Iteration: 2810; Percent complete: 70.2%; Average loss: 3.1746
Iteration: 2811; Percent complete: 70.3%; Average loss: 3.0403
Iteration: 2812; Percent complete: 70.3%; Average loss: 2.9497
Iteration: 2813; Percent complete: 70.3%; Average loss: 2.8270
Iteration: 2814; Percent complete: 70.3%; Average loss: 3.2227
Iteration: 2815; Percent complete: 70.4%; Average loss: 3.1341
Iteration: 2816; Percent complete: 70.4%; Average loss: 2.9801
Iteration: 2817; Percent complete: 70.4%; Average loss: 2.8918
Iteration: 2818; Percent complete: 70.5%; Average loss: 2.7774
Iteration: 2819; Percent complete: 70.5%; Average loss: 2.8428
Iteration: 2820; Percent complete: 70.5%; Average loss: 2.8629
Iteration: 2821; Percent complete: 70.5%; Average loss: 3.2091
Iteration: 2822; Percent complete: 70.5%; Average loss: 2.8169
Iteration: 2823; Percent complete: 70.6%; Average loss: 2.8677
Iteration: 2824; Percent complete: 70.6%; Average loss: 2.8208
Iteration: 2825; Percent complete: 70.6%; Average loss: 2.7192
Iteration: 2826; Percent complete: 70.7%; Average loss: 2.7599
Iteration: 2827; Percent complete: 70.7%; Average loss: 2.7364
Iteration: 2828; Percent complete: 70.7%; Average loss: 2.5443
Iteration: 2829; Percent complete: 70.7%; Average loss: 2.7902
Iteration: 2830; Percent complete: 70.8%; Average loss: 2.9173
Iteration: 2831; Percent complete: 70.8%; Average loss: 2.9133
Iteration: 2832; Percent complete: 70.8%; Average loss: 2.9094
Iteration: 2833; Percent complete: 70.8%; Average loss: 2.6337
Iteration: 2834; Percent complete: 70.9%; Average loss: 2.6725
Iteration: 2835; Percent complete: 70.9%; Average loss: 2.9779
Iteration: 2836; Percent complete: 70.9%; Average loss: 2.8470
Iteration: 2837; Percent complete: 70.9%; Average loss: 2.8784
Iteration: 2838; Percent complete: 71.0%; Average loss: 3.0524
Iteration: 2839; Percent complete: 71.0%; Average loss: 3.0639
Iteration: 2840; Percent complete: 71.0%; Average loss: 2.8765
Iteration: 2841; Percent complete: 71.0%; Average loss: 2.8488
Iteration: 2842; Percent complete: 71.0%; Average loss: 3.0331
Iteration: 2843; Percent complete: 71.1%; Average loss: 3.1165
Iteration: 2844; Percent complete: 71.1%; Average loss: 2.8903
Iteration: 2845; Percent complete: 71.1%; Average loss: 2.6078
Iteration: 2846; Percent complete: 71.2%; Average loss: 2.9089
Iteration: 2847; Percent complete: 71.2%; Average loss: 3.0554
Iteration: 2848; Percent complete: 71.2%; Average loss: 2.7172
Iteration: 2849; Percent complete: 71.2%; Average loss: 2.8813
Iteration: 2850; Percent complete: 71.2%; Average loss: 2.8268
Iteration: 2851; Percent complete: 71.3%; Average loss: 2.6477
Iteration: 2852; Percent complete: 71.3%; Average loss: 3.0634
Iteration: 2853; Percent complete: 71.3%; Average loss: 3.0400
Iteration: 2854; Percent complete: 71.4%; Average loss: 3.1126
Iteration: 2855; Percent complete: 71.4%; Average loss: 3.0129
Iteration: 2856; Percent complete: 71.4%; Average loss: 2.8769
Iteration: 2857; Percent complete: 71.4%; Average loss: 2.7065
Iteration: 2858; Percent complete: 71.5%; Average loss: 2.7909
Iteration: 2859; Percent complete: 71.5%; Average loss: 3.1944
Iteration: 2860; Percent complete: 71.5%; Average loss: 2.9173
Iteration: 2861; Percent complete: 71.5%; Average loss: 2.6841
Iteration: 2862; Percent complete: 71.5%; Average loss: 2.9682
Iteration: 2863; Percent complete: 71.6%; Average loss: 2.9292
Iteration: 2864; Percent complete: 71.6%; Average loss: 2.9103
Iteration: 2865; Percent complete: 71.6%; Average loss: 2.8220
Iteration: 2866; Percent complete: 71.7%; Average loss: 2.8040
Iteration: 2867; Percent complete: 71.7%; Average loss: 3.1294
Iteration: 2868; Percent complete: 71.7%; Average loss: 2.8584
Iteration: 2869; Percent complete: 71.7%; Average loss: 2.7421
Iteration: 2870; Percent complete: 71.8%; Average loss: 3.0993
Iteration: 2871; Percent complete: 71.8%; Average loss: 3.1537
Iteration: 2872; Percent complete: 71.8%; Average loss: 3.0146
Iteration: 2873; Percent complete: 71.8%; Average loss: 2.9420
Iteration: 2874; Percent complete: 71.9%; Average loss: 2.7853
Iteration: 2875; Percent complete: 71.9%; Average loss: 2.9151
Iteration: 2876; Percent complete: 71.9%; Average loss: 2.7556
Iteration: 2877; Percent complete: 71.9%; Average loss: 2.8366
Iteration: 2878; Percent complete: 72.0%; Average loss: 2.6649
Iteration: 2879; Percent complete: 72.0%; Average loss: 2.6029
Iteration: 2880; Percent complete: 72.0%; Average loss: 2.6849
Iteration: 2881; Percent complete: 72.0%; Average loss: 2.9045
Iteration: 2882; Percent complete: 72.0%; Average loss: 2.9978
Iteration: 2883; Percent complete: 72.1%; Average loss: 2.7497
Iteration: 2884; Percent complete: 72.1%; Average loss: 3.0003
Iteration: 2885; Percent complete: 72.1%; Average loss: 2.7141
Iteration: 2886; Percent complete: 72.2%; Average loss: 2.9069
Iteration: 2887; Percent complete: 72.2%; Average loss: 3.0552
Iteration: 2888; Percent complete: 72.2%; Average loss: 3.1381
Iteration: 2889; Percent complete: 72.2%; Average loss: 3.0756
Iteration: 2890; Percent complete: 72.2%; Average loss: 2.9691
Iteration: 2891; Percent complete: 72.3%; Average loss: 2.9128
Iteration: 2892; Percent complete: 72.3%; Average loss: 2.8532
Iteration: 2893; Percent complete: 72.3%; Average loss: 2.9001
Iteration: 2894; Percent complete: 72.4%; Average loss: 2.8562
Iteration: 2895; Percent complete: 72.4%; Average loss: 2.9910
Iteration: 2896; Percent complete: 72.4%; Average loss: 2.9267
Iteration: 2897; Percent complete: 72.4%; Average loss: 2.7979
Iteration: 2898; Percent complete: 72.5%; Average loss: 2.9114
Iteration: 2899; Percent complete: 72.5%; Average loss: 2.9982
Iteration: 2900; Percent complete: 72.5%; Average loss: 2.9907
Iteration: 2901; Percent complete: 72.5%; Average loss: 2.7211
Iteration: 2902; Percent complete: 72.5%; Average loss: 2.6766
Iteration: 2903; Percent complete: 72.6%; Average loss: 3.1342
Iteration: 2904; Percent complete: 72.6%; Average loss: 2.8953
Iteration: 2905; Percent complete: 72.6%; Average loss: 2.9200
Iteration: 2906; Percent complete: 72.7%; Average loss: 2.9076
Iteration: 2907; Percent complete: 72.7%; Average loss: 2.8108
Iteration: 2908; Percent complete: 72.7%; Average loss: 2.7904
Iteration: 2909; Percent complete: 72.7%; Average loss: 2.9361
Iteration: 2910; Percent complete: 72.8%; Average loss: 3.0107
Iteration: 2911; Percent complete: 72.8%; Average loss: 2.8008
Iteration: 2912; Percent complete: 72.8%; Average loss: 2.8409
Iteration: 2913; Percent complete: 72.8%; Average loss: 2.8680
Iteration: 2914; Percent complete: 72.9%; Average loss: 2.8945
Iteration: 2915; Percent complete: 72.9%; Average loss: 3.0071
Iteration: 2916; Percent complete: 72.9%; Average loss: 2.9965
Iteration: 2917; Percent complete: 72.9%; Average loss: 3.0691
Iteration: 2918; Percent complete: 73.0%; Average loss: 2.9273
Iteration: 2919; Percent complete: 73.0%; Average loss: 3.1355
Iteration: 2920; Percent complete: 73.0%; Average loss: 2.8526
Iteration: 2921; Percent complete: 73.0%; Average loss: 2.9525
Iteration: 2922; Percent complete: 73.0%; Average loss: 2.7657
Iteration: 2923; Percent complete: 73.1%; Average loss: 2.7550
Iteration: 2924; Percent complete: 73.1%; Average loss: 2.9328
Iteration: 2925; Percent complete: 73.1%; Average loss: 2.7979
Iteration: 2926; Percent complete: 73.2%; Average loss: 2.7995
Iteration: 2927; Percent complete: 73.2%; Average loss: 3.0378
Iteration: 2928; Percent complete: 73.2%; Average loss: 2.7091
Iteration: 2929; Percent complete: 73.2%; Average loss: 2.9650
Iteration: 2930; Percent complete: 73.2%; Average loss: 2.8043
Iteration: 2931; Percent complete: 73.3%; Average loss: 3.0395
Iteration: 2932; Percent complete: 73.3%; Average loss: 2.8780
Iteration: 2933; Percent complete: 73.3%; Average loss: 3.2191
Iteration: 2934; Percent complete: 73.4%; Average loss: 3.0108
Iteration: 2935; Percent complete: 73.4%; Average loss: 2.8314
Iteration: 2936; Percent complete: 73.4%; Average loss: 2.9071
Iteration: 2937; Percent complete: 73.4%; Average loss: 2.9207
Iteration: 2938; Percent complete: 73.5%; Average loss: 2.8430
Iteration: 2939; Percent complete: 73.5%; Average loss: 2.8981
Iteration: 2940; Percent complete: 73.5%; Average loss: 3.0089
Iteration: 2941; Percent complete: 73.5%; Average loss: 2.8242
Iteration: 2942; Percent complete: 73.6%; Average loss: 2.7969
Iteration: 2943; Percent complete: 73.6%; Average loss: 2.8821
Iteration: 2944; Percent complete: 73.6%; Average loss: 2.8676
Iteration: 2945; Percent complete: 73.6%; Average loss: 2.7943
Iteration: 2946; Percent complete: 73.7%; Average loss: 2.9787
Iteration: 2947; Percent complete: 73.7%; Average loss: 2.7783
Iteration: 2948; Percent complete: 73.7%; Average loss: 2.9476
Iteration: 2949; Percent complete: 73.7%; Average loss: 3.1912
Iteration: 2950; Percent complete: 73.8%; Average loss: 2.8664
Iteration: 2951; Percent complete: 73.8%; Average loss: 2.9330
Iteration: 2952; Percent complete: 73.8%; Average loss: 2.8719
Iteration: 2953; Percent complete: 73.8%; Average loss: 2.7759
Iteration: 2954; Percent complete: 73.9%; Average loss: 2.7794
Iteration: 2955; Percent complete: 73.9%; Average loss: 3.1661
Iteration: 2956; Percent complete: 73.9%; Average loss: 3.1258
Iteration: 2957; Percent complete: 73.9%; Average loss: 2.8493
Iteration: 2958; Percent complete: 74.0%; Average loss: 2.9916
Iteration: 2959; Percent complete: 74.0%; Average loss: 2.7682
Iteration: 2960; Percent complete: 74.0%; Average loss: 2.9721
Iteration: 2961; Percent complete: 74.0%; Average loss: 2.9582
Iteration: 2962; Percent complete: 74.1%; Average loss: 3.0555
Iteration: 2963; Percent complete: 74.1%; Average loss: 3.1694
Iteration: 2964; Percent complete: 74.1%; Average loss: 3.0374
Iteration: 2965; Percent complete: 74.1%; Average loss: 2.9760
Iteration: 2966; Percent complete: 74.2%; Average loss: 2.6881
Iteration: 2967; Percent complete: 74.2%; Average loss: 3.0884
Iteration: 2968; Percent complete: 74.2%; Average loss: 2.9942
Iteration: 2969; Percent complete: 74.2%; Average loss: 2.7773
Iteration: 2970; Percent complete: 74.2%; Average loss: 2.7306
Iteration: 2971; Percent complete: 74.3%; Average loss: 2.8109
Iteration: 2972; Percent complete: 74.3%; Average loss: 2.7654
Iteration: 2973; Percent complete: 74.3%; Average loss: 3.0806
Iteration: 2974; Percent complete: 74.4%; Average loss: 3.1162
Iteration: 2975; Percent complete: 74.4%; Average loss: 2.8658
Iteration: 2976; Percent complete: 74.4%; Average loss: 2.9019
Iteration: 2977; Percent complete: 74.4%; Average loss: 2.9815
Iteration: 2978; Percent complete: 74.5%; Average loss: 3.0906
Iteration: 2979; Percent complete: 74.5%; Average loss: 2.6463
Iteration: 2980; Percent complete: 74.5%; Average loss: 2.8153
Iteration: 2981; Percent complete: 74.5%; Average loss: 3.0980
Iteration: 2982; Percent complete: 74.6%; Average loss: 2.7108
Iteration: 2983; Percent complete: 74.6%; Average loss: 2.8753
Iteration: 2984; Percent complete: 74.6%; Average loss: 2.7769
Iteration: 2985; Percent complete: 74.6%; Average loss: 3.1888
Iteration: 2986; Percent complete: 74.7%; Average loss: 2.8904
Iteration: 2987; Percent complete: 74.7%; Average loss: 2.8290
Iteration: 2988; Percent complete: 74.7%; Average loss: 2.8074
Iteration: 2989; Percent complete: 74.7%; Average loss: 2.9517
Iteration: 2990; Percent complete: 74.8%; Average loss: 2.9856
Iteration: 2991; Percent complete: 74.8%; Average loss: 2.7519
Iteration: 2992; Percent complete: 74.8%; Average loss: 2.7666
Iteration: 2993; Percent complete: 74.8%; Average loss: 3.0083
Iteration: 2994; Percent complete: 74.9%; Average loss: 2.9187
Iteration: 2995; Percent complete: 74.9%; Average loss: 2.9117
Iteration: 2996; Percent complete: 74.9%; Average loss: 2.7579
Iteration: 2997; Percent complete: 74.9%; Average loss: 2.9429
Iteration: 2998; Percent complete: 75.0%; Average loss: 2.9555
Iteration: 2999; Percent complete: 75.0%; Average loss: 2.6645
Iteration: 3000; Percent complete: 75.0%; Average loss: 2.8453
Iteration: 3001; Percent complete: 75.0%; Average loss: 2.8798
Iteration: 3002; Percent complete: 75.0%; Average loss: 2.7890
Iteration: 3003; Percent complete: 75.1%; Average loss: 3.0283
Iteration: 3004; Percent complete: 75.1%; Average loss: 2.7600
Iteration: 3005; Percent complete: 75.1%; Average loss: 2.8172
Iteration: 3006; Percent complete: 75.1%; Average loss: 2.6767
Iteration: 3007; Percent complete: 75.2%; Average loss: 2.9963
Iteration: 3008; Percent complete: 75.2%; Average loss: 2.7598
Iteration: 3009; Percent complete: 75.2%; Average loss: 2.9159
Iteration: 3010; Percent complete: 75.2%; Average loss: 2.7003
Iteration: 3011; Percent complete: 75.3%; Average loss: 3.1274
Iteration: 3012; Percent complete: 75.3%; Average loss: 2.8794
Iteration: 3013; Percent complete: 75.3%; Average loss: 3.0740
Iteration: 3014; Percent complete: 75.3%; Average loss: 2.9182
Iteration: 3015; Percent complete: 75.4%; Average loss: 2.9808
Iteration: 3016; Percent complete: 75.4%; Average loss: 2.8996
Iteration: 3017; Percent complete: 75.4%; Average loss: 2.7808
Iteration: 3018; Percent complete: 75.4%; Average loss: 2.8520
Iteration: 3019; Percent complete: 75.5%; Average loss: 2.6411
Iteration: 3020; Percent complete: 75.5%; Average loss: 2.8330
Iteration: 3021; Percent complete: 75.5%; Average loss: 2.6357
Iteration: 3022; Percent complete: 75.5%; Average loss: 3.0764
Iteration: 3023; Percent complete: 75.6%; Average loss: 2.9264
Iteration: 3024; Percent complete: 75.6%; Average loss: 2.8672
Iteration: 3025; Percent complete: 75.6%; Average loss: 3.0992
Iteration: 3026; Percent complete: 75.6%; Average loss: 3.0697
Iteration: 3027; Percent complete: 75.7%; Average loss: 2.7169
Iteration: 3028; Percent complete: 75.7%; Average loss: 2.6840
Iteration: 3029; Percent complete: 75.7%; Average loss: 2.6499
Iteration: 3030; Percent complete: 75.8%; Average loss: 2.8962
Iteration: 3031; Percent complete: 75.8%; Average loss: 2.7781
Iteration: 3032; Percent complete: 75.8%; Average loss: 2.9241
Iteration: 3033; Percent complete: 75.8%; Average loss: 3.0582
Iteration: 3034; Percent complete: 75.8%; Average loss: 3.0576
Iteration: 3035; Percent complete: 75.9%; Average loss: 3.1624
Iteration: 3036; Percent complete: 75.9%; Average loss: 3.0976
Iteration: 3037; Percent complete: 75.9%; Average loss: 2.6986
Iteration: 3038; Percent complete: 75.9%; Average loss: 2.7848
Iteration: 3039; Percent complete: 76.0%; Average loss: 2.8992
Iteration: 3040; Percent complete: 76.0%; Average loss: 2.7406
Iteration: 3041; Percent complete: 76.0%; Average loss: 2.9903
Iteration: 3042; Percent complete: 76.0%; Average loss: 3.0909
Iteration: 3043; Percent complete: 76.1%; Average loss: 2.8658
Iteration: 3044; Percent complete: 76.1%; Average loss: 2.6684
Iteration: 3045; Percent complete: 76.1%; Average loss: 2.9134
Iteration: 3046; Percent complete: 76.1%; Average loss: 3.0932
Iteration: 3047; Percent complete: 76.2%; Average loss: 2.7923
Iteration: 3048; Percent complete: 76.2%; Average loss: 2.7497
Iteration: 3049; Percent complete: 76.2%; Average loss: 2.9353
Iteration: 3050; Percent complete: 76.2%; Average loss: 2.7993
Iteration: 3051; Percent complete: 76.3%; Average loss: 2.9765
Iteration: 3052; Percent complete: 76.3%; Average loss: 3.1052
Iteration: 3053; Percent complete: 76.3%; Average loss: 2.9115
Iteration: 3054; Percent complete: 76.3%; Average loss: 3.1250
Iteration: 3055; Percent complete: 76.4%; Average loss: 2.8385
Iteration: 3056; Percent complete: 76.4%; Average loss: 2.8784
Iteration: 3057; Percent complete: 76.4%; Average loss: 2.7897
Iteration: 3058; Percent complete: 76.4%; Average loss: 2.8238
Iteration: 3059; Percent complete: 76.5%; Average loss: 3.0127
Iteration: 3060; Percent complete: 76.5%; Average loss: 2.7604
Iteration: 3061; Percent complete: 76.5%; Average loss: 2.7495
Iteration: 3062; Percent complete: 76.5%; Average loss: 2.8311
Iteration: 3063; Percent complete: 76.6%; Average loss: 2.8808
Iteration: 3064; Percent complete: 76.6%; Average loss: 2.9123
Iteration: 3065; Percent complete: 76.6%; Average loss: 2.7126
Iteration: 3066; Percent complete: 76.6%; Average loss: 2.9008
Iteration: 3067; Percent complete: 76.7%; Average loss: 3.0157
Iteration: 3068; Percent complete: 76.7%; Average loss: 2.9679
Iteration: 3069; Percent complete: 76.7%; Average loss: 2.6375
Iteration: 3070; Percent complete: 76.8%; Average loss: 2.7847
Iteration: 3071; Percent complete: 76.8%; Average loss: 3.0364
Iteration: 3072; Percent complete: 76.8%; Average loss: 2.7015
Iteration: 3073; Percent complete: 76.8%; Average loss: 2.8083
Iteration: 3074; Percent complete: 76.8%; Average loss: 2.8363
Iteration: 3075; Percent complete: 76.9%; Average loss: 2.7575
Iteration: 3076; Percent complete: 76.9%; Average loss: 2.8892
Iteration: 3077; Percent complete: 76.9%; Average loss: 2.7574
Iteration: 3078; Percent complete: 77.0%; Average loss: 2.7611
Iteration: 3079; Percent complete: 77.0%; Average loss: 2.5348
Iteration: 3080; Percent complete: 77.0%; Average loss: 3.1280
Iteration: 3081; Percent complete: 77.0%; Average loss: 2.7508
Iteration: 3082; Percent complete: 77.0%; Average loss: 2.8738
Iteration: 3083; Percent complete: 77.1%; Average loss: 2.7906
Iteration: 3084; Percent complete: 77.1%; Average loss: 2.6746
Iteration: 3085; Percent complete: 77.1%; Average loss: 2.7099
Iteration: 3086; Percent complete: 77.1%; Average loss: 2.9837
Iteration: 3087; Percent complete: 77.2%; Average loss: 2.7574
Iteration: 3088; Percent complete: 77.2%; Average loss: 2.8790
Iteration: 3089; Percent complete: 77.2%; Average loss: 3.0399
Iteration: 3090; Percent complete: 77.2%; Average loss: 2.9377
Iteration: 3091; Percent complete: 77.3%; Average loss: 2.8178
Iteration: 3092; Percent complete: 77.3%; Average loss: 2.8832
Iteration: 3093; Percent complete: 77.3%; Average loss: 2.9735
Iteration: 3094; Percent complete: 77.3%; Average loss: 2.7778
Iteration: 3095; Percent complete: 77.4%; Average loss: 2.8008
Iteration: 3096; Percent complete: 77.4%; Average loss: 3.0904
Iteration: 3097; Percent complete: 77.4%; Average loss: 2.5804
Iteration: 3098; Percent complete: 77.5%; Average loss: 2.8410
Iteration: 3099; Percent complete: 77.5%; Average loss: 2.9578
Iteration: 3100; Percent complete: 77.5%; Average loss: 2.7130
Iteration: 3101; Percent complete: 77.5%; Average loss: 2.6150
Iteration: 3102; Percent complete: 77.5%; Average loss: 2.8398
Iteration: 3103; Percent complete: 77.6%; Average loss: 2.8835
Iteration: 3104; Percent complete: 77.6%; Average loss: 2.8192
Iteration: 3105; Percent complete: 77.6%; Average loss: 2.7284
Iteration: 3106; Percent complete: 77.6%; Average loss: 2.7701
Iteration: 3107; Percent complete: 77.7%; Average loss: 2.8095
Iteration: 3108; Percent complete: 77.7%; Average loss: 2.7637
Iteration: 3109; Percent complete: 77.7%; Average loss: 2.9412
Iteration: 3110; Percent complete: 77.8%; Average loss: 2.9188
Iteration: 3111; Percent complete: 77.8%; Average loss: 2.6038
Iteration: 3112; Percent complete: 77.8%; Average loss: 2.7956
Iteration: 3113; Percent complete: 77.8%; Average loss: 2.5747
Iteration: 3114; Percent complete: 77.8%; Average loss: 2.7551
Iteration: 3115; Percent complete: 77.9%; Average loss: 3.0064
Iteration: 3116; Percent complete: 77.9%; Average loss: 2.9113
Iteration: 3117; Percent complete: 77.9%; Average loss: 2.9289
Iteration: 3118; Percent complete: 78.0%; Average loss: 2.9733
Iteration: 3119; Percent complete: 78.0%; Average loss: 2.7256
Iteration: 3120; Percent complete: 78.0%; Average loss: 2.6150
Iteration: 3121; Percent complete: 78.0%; Average loss: 3.1338
Iteration: 3122; Percent complete: 78.0%; Average loss: 2.9925
Iteration: 3123; Percent complete: 78.1%; Average loss: 2.8156
Iteration: 3124; Percent complete: 78.1%; Average loss: 2.8754
Iteration: 3125; Percent complete: 78.1%; Average loss: 2.9098
Iteration: 3126; Percent complete: 78.1%; Average loss: 2.9711
Iteration: 3127; Percent complete: 78.2%; Average loss: 2.7191
Iteration: 3128; Percent complete: 78.2%; Average loss: 2.8103
Iteration: 3129; Percent complete: 78.2%; Average loss: 3.0886
Iteration: 3130; Percent complete: 78.2%; Average loss: 2.8162
Iteration: 3131; Percent complete: 78.3%; Average loss: 2.8998
Iteration: 3132; Percent complete: 78.3%; Average loss: 2.9077
Iteration: 3133; Percent complete: 78.3%; Average loss: 2.7949
Iteration: 3134; Percent complete: 78.3%; Average loss: 2.6917
Iteration: 3135; Percent complete: 78.4%; Average loss: 2.8604
Iteration: 3136; Percent complete: 78.4%; Average loss: 3.0708
Iteration: 3137; Percent complete: 78.4%; Average loss: 2.7532
Iteration: 3138; Percent complete: 78.5%; Average loss: 2.9629
Iteration: 3139; Percent complete: 78.5%; Average loss: 2.9492
Iteration: 3140; Percent complete: 78.5%; Average loss: 2.6932
Iteration: 3141; Percent complete: 78.5%; Average loss: 2.7894
Iteration: 3142; Percent complete: 78.5%; Average loss: 2.9128
Iteration: 3143; Percent complete: 78.6%; Average loss: 2.8393
Iteration: 3144; Percent complete: 78.6%; Average loss: 3.1461
Iteration: 3145; Percent complete: 78.6%; Average loss: 2.6505
Iteration: 3146; Percent complete: 78.6%; Average loss: 2.8780
Iteration: 3147; Percent complete: 78.7%; Average loss: 2.6040
Iteration: 3148; Percent complete: 78.7%; Average loss: 2.8426
Iteration: 3149; Percent complete: 78.7%; Average loss: 2.9195
Iteration: 3150; Percent complete: 78.8%; Average loss: 2.8183
Iteration: 3151; Percent complete: 78.8%; Average loss: 2.8561
Iteration: 3152; Percent complete: 78.8%; Average loss: 2.7821
Iteration: 3153; Percent complete: 78.8%; Average loss: 2.8166
Iteration: 3154; Percent complete: 78.8%; Average loss: 2.8347
Iteration: 3155; Percent complete: 78.9%; Average loss: 3.2945
Iteration: 3156; Percent complete: 78.9%; Average loss: 2.8858
Iteration: 3157; Percent complete: 78.9%; Average loss: 2.7861
Iteration: 3158; Percent complete: 79.0%; Average loss: 2.8201
Iteration: 3159; Percent complete: 79.0%; Average loss: 2.6612
Iteration: 3160; Percent complete: 79.0%; Average loss: 2.7961
Iteration: 3161; Percent complete: 79.0%; Average loss: 2.8769
Iteration: 3162; Percent complete: 79.0%; Average loss: 2.8443
Iteration: 3163; Percent complete: 79.1%; Average loss: 2.8098
Iteration: 3164; Percent complete: 79.1%; Average loss: 2.6879
Iteration: 3165; Percent complete: 79.1%; Average loss: 2.7082
Iteration: 3166; Percent complete: 79.1%; Average loss: 2.6637
Iteration: 3167; Percent complete: 79.2%; Average loss: 2.6023
Iteration: 3168; Percent complete: 79.2%; Average loss: 2.8780
Iteration: 3169; Percent complete: 79.2%; Average loss: 2.8611
Iteration: 3170; Percent complete: 79.2%; Average loss: 3.0529
Iteration: 3171; Percent complete: 79.3%; Average loss: 3.0041
Iteration: 3172; Percent complete: 79.3%; Average loss: 3.0713
Iteration: 3173; Percent complete: 79.3%; Average loss: 2.8848
Iteration: 3174; Percent complete: 79.3%; Average loss: 2.4720
Iteration: 3175; Percent complete: 79.4%; Average loss: 2.8039
Iteration: 3176; Percent complete: 79.4%; Average loss: 2.7433
Iteration: 3177; Percent complete: 79.4%; Average loss: 2.8665
Iteration: 3178; Percent complete: 79.5%; Average loss: 2.8534
Iteration: 3179; Percent complete: 79.5%; Average loss: 2.7445
Iteration: 3180; Percent complete: 79.5%; Average loss: 2.7902
Iteration: 3181; Percent complete: 79.5%; Average loss: 3.0349
Iteration: 3182; Percent complete: 79.5%; Average loss: 2.9031
Iteration: 3183; Percent complete: 79.6%; Average loss: 3.1377
Iteration: 3184; Percent complete: 79.6%; Average loss: 2.8677
Iteration: 3185; Percent complete: 79.6%; Average loss: 2.7315
Iteration: 3186; Percent complete: 79.7%; Average loss: 2.9020
Iteration: 3187; Percent complete: 79.7%; Average loss: 2.9549
Iteration: 3188; Percent complete: 79.7%; Average loss: 2.8396
Iteration: 3189; Percent complete: 79.7%; Average loss: 2.7507
Iteration: 3190; Percent complete: 79.8%; Average loss: 2.6328
Iteration: 3191; Percent complete: 79.8%; Average loss: 2.6199
Iteration: 3192; Percent complete: 79.8%; Average loss: 3.0320
Iteration: 3193; Percent complete: 79.8%; Average loss: 2.8072
Iteration: 3194; Percent complete: 79.8%; Average loss: 2.7435
Iteration: 3195; Percent complete: 79.9%; Average loss: 2.8493
Iteration: 3196; Percent complete: 79.9%; Average loss: 2.9433
Iteration: 3197; Percent complete: 79.9%; Average loss: 2.6334
Iteration: 3198; Percent complete: 80.0%; Average loss: 2.9101
Iteration: 3199; Percent complete: 80.0%; Average loss: 2.9393
Iteration: 3200; Percent complete: 80.0%; Average loss: 2.7562
Iteration: 3201; Percent complete: 80.0%; Average loss: 2.8210
Iteration: 3202; Percent complete: 80.0%; Average loss: 2.8735
Iteration: 3203; Percent complete: 80.1%; Average loss: 2.8777
Iteration: 3204; Percent complete: 80.1%; Average loss: 2.7351
Iteration: 3205; Percent complete: 80.1%; Average loss: 2.9607
Iteration: 3206; Percent complete: 80.2%; Average loss: 2.6159
Iteration: 3207; Percent complete: 80.2%; Average loss: 2.9453
Iteration: 3208; Percent complete: 80.2%; Average loss: 3.0179
Iteration: 3209; Percent complete: 80.2%; Average loss: 2.6859
Iteration: 3210; Percent complete: 80.2%; Average loss: 3.0607
Iteration: 3211; Percent complete: 80.3%; Average loss: 2.7217
Iteration: 3212; Percent complete: 80.3%; Average loss: 2.8844
Iteration: 3213; Percent complete: 80.3%; Average loss: 2.5928
Iteration: 3214; Percent complete: 80.3%; Average loss: 2.9948
Iteration: 3215; Percent complete: 80.4%; Average loss: 2.8099
Iteration: 3216; Percent complete: 80.4%; Average loss: 2.7119
Iteration: 3217; Percent complete: 80.4%; Average loss: 2.8638
Iteration: 3218; Percent complete: 80.5%; Average loss: 2.7358
Iteration: 3219; Percent complete: 80.5%; Average loss: 2.9789
Iteration: 3220; Percent complete: 80.5%; Average loss: 2.8209
Iteration: 3221; Percent complete: 80.5%; Average loss: 2.8982
Iteration: 3222; Percent complete: 80.5%; Average loss: 2.8071
Iteration: 3223; Percent complete: 80.6%; Average loss: 3.1343
Iteration: 3224; Percent complete: 80.6%; Average loss: 2.7328
Iteration: 3225; Percent complete: 80.6%; Average loss: 2.9034
Iteration: 3226; Percent complete: 80.7%; Average loss: 2.8717
Iteration: 3227; Percent complete: 80.7%; Average loss: 3.1340
Iteration: 3228; Percent complete: 80.7%; Average loss: 2.7620
Iteration: 3229; Percent complete: 80.7%; Average loss: 2.8676
Iteration: 3230; Percent complete: 80.8%; Average loss: 3.0203
Iteration: 3231; Percent complete: 80.8%; Average loss: 2.7740
Iteration: 3232; Percent complete: 80.8%; Average loss: 2.6893
Iteration: 3233; Percent complete: 80.8%; Average loss: 2.9369
Iteration: 3234; Percent complete: 80.8%; Average loss: 2.7491
Iteration: 3235; Percent complete: 80.9%; Average loss: 2.7258
Iteration: 3236; Percent complete: 80.9%; Average loss: 2.7200
Iteration: 3237; Percent complete: 80.9%; Average loss: 2.9130
Iteration: 3238; Percent complete: 81.0%; Average loss: 2.7932
Iteration: 3239; Percent complete: 81.0%; Average loss: 2.8262
Iteration: 3240; Percent complete: 81.0%; Average loss: 2.6917
Iteration: 3241; Percent complete: 81.0%; Average loss: 2.8425
Iteration: 3242; Percent complete: 81.0%; Average loss: 2.7806
Iteration: 3243; Percent complete: 81.1%; Average loss: 3.0988
Iteration: 3244; Percent complete: 81.1%; Average loss: 2.9198
Iteration: 3245; Percent complete: 81.1%; Average loss: 2.8057
Iteration: 3246; Percent complete: 81.2%; Average loss: 2.9866
Iteration: 3247; Percent complete: 81.2%; Average loss: 2.7699
Iteration: 3248; Percent complete: 81.2%; Average loss: 2.7306
Iteration: 3249; Percent complete: 81.2%; Average loss: 2.7507
Iteration: 3250; Percent complete: 81.2%; Average loss: 2.8410
Iteration: 3251; Percent complete: 81.3%; Average loss: 2.7494
Iteration: 3252; Percent complete: 81.3%; Average loss: 2.7512
Iteration: 3253; Percent complete: 81.3%; Average loss: 2.9329
Iteration: 3254; Percent complete: 81.3%; Average loss: 2.8543
Iteration: 3255; Percent complete: 81.4%; Average loss: 2.8392
Iteration: 3256; Percent complete: 81.4%; Average loss: 2.7531
Iteration: 3257; Percent complete: 81.4%; Average loss: 2.6358
Iteration: 3258; Percent complete: 81.5%; Average loss: 2.9322
Iteration: 3259; Percent complete: 81.5%; Average loss: 2.8967
Iteration: 3260; Percent complete: 81.5%; Average loss: 2.6951
Iteration: 3261; Percent complete: 81.5%; Average loss: 2.9081
Iteration: 3262; Percent complete: 81.5%; Average loss: 2.7402
Iteration: 3263; Percent complete: 81.6%; Average loss: 2.6537
Iteration: 3264; Percent complete: 81.6%; Average loss: 2.5335
Iteration: 3265; Percent complete: 81.6%; Average loss: 2.5957
Iteration: 3266; Percent complete: 81.7%; Average loss: 2.6445
Iteration: 3267; Percent complete: 81.7%; Average loss: 2.9293
Iteration: 3268; Percent complete: 81.7%; Average loss: 3.0143
Iteration: 3269; Percent complete: 81.7%; Average loss: 2.8572
Iteration: 3270; Percent complete: 81.8%; Average loss: 2.9444
Iteration: 3271; Percent complete: 81.8%; Average loss: 2.6444
Iteration: 3272; Percent complete: 81.8%; Average loss: 2.6120
Iteration: 3273; Percent complete: 81.8%; Average loss: 2.8245
Iteration: 3274; Percent complete: 81.8%; Average loss: 2.7316
Iteration: 3275; Percent complete: 81.9%; Average loss: 2.9034
Iteration: 3276; Percent complete: 81.9%; Average loss: 2.8683
Iteration: 3277; Percent complete: 81.9%; Average loss: 2.8939
Iteration: 3278; Percent complete: 82.0%; Average loss: 2.9172
Iteration: 3279; Percent complete: 82.0%; Average loss: 2.8130
Iteration: 3280; Percent complete: 82.0%; Average loss: 2.6581
Iteration: 3281; Percent complete: 82.0%; Average loss: 2.7703
Iteration: 3282; Percent complete: 82.0%; Average loss: 2.7716
Iteration: 3283; Percent complete: 82.1%; Average loss: 2.7206
Iteration: 3284; Percent complete: 82.1%; Average loss: 2.8840
Iteration: 3285; Percent complete: 82.1%; Average loss: 2.6083
Iteration: 3286; Percent complete: 82.2%; Average loss: 2.8608
Iteration: 3287; Percent complete: 82.2%; Average loss: 2.7899
Iteration: 3288; Percent complete: 82.2%; Average loss: 2.9054
Iteration: 3289; Percent complete: 82.2%; Average loss: 2.7359
Iteration: 3290; Percent complete: 82.2%; Average loss: 2.8280
Iteration: 3291; Percent complete: 82.3%; Average loss: 2.8218
Iteration: 3292; Percent complete: 82.3%; Average loss: 2.6937
Iteration: 3293; Percent complete: 82.3%; Average loss: 2.7855
Iteration: 3294; Percent complete: 82.3%; Average loss: 3.0421
Iteration: 3295; Percent complete: 82.4%; Average loss: 2.9310
Iteration: 3296; Percent complete: 82.4%; Average loss: 2.7790
Iteration: 3297; Percent complete: 82.4%; Average loss: 2.8483
Iteration: 3298; Percent complete: 82.5%; Average loss: 2.6655
Iteration: 3299; Percent complete: 82.5%; Average loss: 2.9214
Iteration: 3300; Percent complete: 82.5%; Average loss: 2.7335
Iteration: 3301; Percent complete: 82.5%; Average loss: 3.0115
Iteration: 3302; Percent complete: 82.5%; Average loss: 3.0500
Iteration: 3303; Percent complete: 82.6%; Average loss: 3.0792
Iteration: 3304; Percent complete: 82.6%; Average loss: 2.7860
Iteration: 3305; Percent complete: 82.6%; Average loss: 2.7748
Iteration: 3306; Percent complete: 82.7%; Average loss: 2.8029
Iteration: 3307; Percent complete: 82.7%; Average loss: 2.8957
Iteration: 3308; Percent complete: 82.7%; Average loss: 2.7579
Iteration: 3309; Percent complete: 82.7%; Average loss: 2.7953
Iteration: 3310; Percent complete: 82.8%; Average loss: 2.7348
Iteration: 3311; Percent complete: 82.8%; Average loss: 2.8192
Iteration: 3312; Percent complete: 82.8%; Average loss: 2.6997
Iteration: 3313; Percent complete: 82.8%; Average loss: 2.8442
Iteration: 3314; Percent complete: 82.8%; Average loss: 2.7378
Iteration: 3315; Percent complete: 82.9%; Average loss: 2.6642
Iteration: 3316; Percent complete: 82.9%; Average loss: 2.6946
Iteration: 3317; Percent complete: 82.9%; Average loss: 2.6862
Iteration: 3318; Percent complete: 83.0%; Average loss: 2.8559
Iteration: 3319; Percent complete: 83.0%; Average loss: 2.7361
Iteration: 3320; Percent complete: 83.0%; Average loss: 2.8552
Iteration: 3321; Percent complete: 83.0%; Average loss: 2.7130
Iteration: 3322; Percent complete: 83.0%; Average loss: 2.7525
Iteration: 3323; Percent complete: 83.1%; Average loss: 2.9151
Iteration: 3324; Percent complete: 83.1%; Average loss: 2.7052
Iteration: 3325; Percent complete: 83.1%; Average loss: 2.5333
Iteration: 3326; Percent complete: 83.2%; Average loss: 2.6969
Iteration: 3327; Percent complete: 83.2%; Average loss: 2.6678
Iteration: 3328; Percent complete: 83.2%; Average loss: 2.6840
Iteration: 3329; Percent complete: 83.2%; Average loss: 2.7426
Iteration: 3330; Percent complete: 83.2%; Average loss: 2.9360
Iteration: 3331; Percent complete: 83.3%; Average loss: 2.7462
Iteration: 3332; Percent complete: 83.3%; Average loss: 2.7382
Iteration: 3333; Percent complete: 83.3%; Average loss: 2.6073
Iteration: 3334; Percent complete: 83.4%; Average loss: 2.7748
Iteration: 3335; Percent complete: 83.4%; Average loss: 2.6582
Iteration: 3336; Percent complete: 83.4%; Average loss: 3.0145
Iteration: 3337; Percent complete: 83.4%; Average loss: 2.6671
Iteration: 3338; Percent complete: 83.5%; Average loss: 2.7364
Iteration: 3339; Percent complete: 83.5%; Average loss: 2.6717
Iteration: 3340; Percent complete: 83.5%; Average loss: 2.9387
Iteration: 3341; Percent complete: 83.5%; Average loss: 2.9086
Iteration: 3342; Percent complete: 83.5%; Average loss: 2.4824
Iteration: 3343; Percent complete: 83.6%; Average loss: 2.5103
Iteration: 3344; Percent complete: 83.6%; Average loss: 2.9211
Iteration: 3345; Percent complete: 83.6%; Average loss: 2.9173
Iteration: 3346; Percent complete: 83.7%; Average loss: 2.6403
Iteration: 3347; Percent complete: 83.7%; Average loss: 2.8332
Iteration: 3348; Percent complete: 83.7%; Average loss: 2.5942
Iteration: 3349; Percent complete: 83.7%; Average loss: 2.8042
Iteration: 3350; Percent complete: 83.8%; Average loss: 2.8257
Iteration: 3351; Percent complete: 83.8%; Average loss: 2.8604
Iteration: 3352; Percent complete: 83.8%; Average loss: 2.6781
Iteration: 3353; Percent complete: 83.8%; Average loss: 2.9329
Iteration: 3354; Percent complete: 83.9%; Average loss: 2.5509
Iteration: 3355; Percent complete: 83.9%; Average loss: 2.6654
Iteration: 3356; Percent complete: 83.9%; Average loss: 2.7079
Iteration: 3357; Percent complete: 83.9%; Average loss: 2.7119
Iteration: 3358; Percent complete: 84.0%; Average loss: 2.9940
Iteration: 3359; Percent complete: 84.0%; Average loss: 2.9163
Iteration: 3360; Percent complete: 84.0%; Average loss: 2.7204
Iteration: 3361; Percent complete: 84.0%; Average loss: 2.7907
Iteration: 3362; Percent complete: 84.0%; Average loss: 2.5500
Iteration: 3363; Percent complete: 84.1%; Average loss: 2.8780
Iteration: 3364; Percent complete: 84.1%; Average loss: 2.5985
Iteration: 3365; Percent complete: 84.1%; Average loss: 3.0182
Iteration: 3366; Percent complete: 84.2%; Average loss: 2.7733
Iteration: 3367; Percent complete: 84.2%; Average loss: 2.9159
Iteration: 3368; Percent complete: 84.2%; Average loss: 2.7852
Iteration: 3369; Percent complete: 84.2%; Average loss: 2.8224
Iteration: 3370; Percent complete: 84.2%; Average loss: 2.6406
Iteration: 3371; Percent complete: 84.3%; Average loss: 2.6293
Iteration: 3372; Percent complete: 84.3%; Average loss: 2.4587
Iteration: 3373; Percent complete: 84.3%; Average loss: 2.8391
Iteration: 3374; Percent complete: 84.4%; Average loss: 2.5802
Iteration: 3375; Percent complete: 84.4%; Average loss: 2.8839
Iteration: 3376; Percent complete: 84.4%; Average loss: 2.8782
Iteration: 3377; Percent complete: 84.4%; Average loss: 3.0845
Iteration: 3378; Percent complete: 84.5%; Average loss: 3.0549
Iteration: 3379; Percent complete: 84.5%; Average loss: 2.9178
Iteration: 3380; Percent complete: 84.5%; Average loss: 2.6388
Iteration: 3381; Percent complete: 84.5%; Average loss: 2.7824
Iteration: 3382; Percent complete: 84.5%; Average loss: 2.9017
Iteration: 3383; Percent complete: 84.6%; Average loss: 2.6622
Iteration: 3384; Percent complete: 84.6%; Average loss: 2.8911
Iteration: 3385; Percent complete: 84.6%; Average loss: 2.8645
Iteration: 3386; Percent complete: 84.7%; Average loss: 2.8197
Iteration: 3387; Percent complete: 84.7%; Average loss: 2.8605
Iteration: 3388; Percent complete: 84.7%; Average loss: 2.7145
Iteration: 3389; Percent complete: 84.7%; Average loss: 2.7015
Iteration: 3390; Percent complete: 84.8%; Average loss: 2.8924
Iteration: 3391; Percent complete: 84.8%; Average loss: 2.7611
Iteration: 3392; Percent complete: 84.8%; Average loss: 2.6624
Iteration: 3393; Percent complete: 84.8%; Average loss: 2.8783
Iteration: 3394; Percent complete: 84.9%; Average loss: 2.9368
Iteration: 3395; Percent complete: 84.9%; Average loss: 2.8806
Iteration: 3396; Percent complete: 84.9%; Average loss: 3.0179
Iteration: 3397; Percent complete: 84.9%; Average loss: 2.7189
Iteration: 3398; Percent complete: 85.0%; Average loss: 2.6739
Iteration: 3399; Percent complete: 85.0%; Average loss: 2.8433
Iteration: 3400; Percent complete: 85.0%; Average loss: 2.8416
Iteration: 3401; Percent complete: 85.0%; Average loss: 2.5234
Iteration: 3402; Percent complete: 85.0%; Average loss: 2.6861
Iteration: 3403; Percent complete: 85.1%; Average loss: 2.7784
Iteration: 3404; Percent complete: 85.1%; Average loss: 3.0522
Iteration: 3405; Percent complete: 85.1%; Average loss: 2.6975
Iteration: 3406; Percent complete: 85.2%; Average loss: 2.6625
Iteration: 3407; Percent complete: 85.2%; Average loss: 2.8792
Iteration: 3408; Percent complete: 85.2%; Average loss: 2.6055
Iteration: 3409; Percent complete: 85.2%; Average loss: 2.9143
Iteration: 3410; Percent complete: 85.2%; Average loss: 2.6472
Iteration: 3411; Percent complete: 85.3%; Average loss: 2.6239
Iteration: 3412; Percent complete: 85.3%; Average loss: 2.6746
Iteration: 3413; Percent complete: 85.3%; Average loss: 3.0343
Iteration: 3414; Percent complete: 85.4%; Average loss: 2.6277
Iteration: 3415; Percent complete: 85.4%; Average loss: 2.8810
Iteration: 3416; Percent complete: 85.4%; Average loss: 2.6760
Iteration: 3417; Percent complete: 85.4%; Average loss: 2.8519
Iteration: 3418; Percent complete: 85.5%; Average loss: 2.7330
Iteration: 3419; Percent complete: 85.5%; Average loss: 2.6078
Iteration: 3420; Percent complete: 85.5%; Average loss: 2.8984
Iteration: 3421; Percent complete: 85.5%; Average loss: 2.6109
Iteration: 3422; Percent complete: 85.5%; Average loss: 2.7490
Iteration: 3423; Percent complete: 85.6%; Average loss: 2.6896
Iteration: 3424; Percent complete: 85.6%; Average loss: 2.4995
Iteration: 3425; Percent complete: 85.6%; Average loss: 2.8749
Iteration: 3426; Percent complete: 85.7%; Average loss: 2.7982
Iteration: 3427; Percent complete: 85.7%; Average loss: 2.9906
Iteration: 3428; Percent complete: 85.7%; Average loss: 2.6440
Iteration: 3429; Percent complete: 85.7%; Average loss: 2.5698
Iteration: 3430; Percent complete: 85.8%; Average loss: 2.8016
Iteration: 3431; Percent complete: 85.8%; Average loss: 2.7405
Iteration: 3432; Percent complete: 85.8%; Average loss: 2.5026
Iteration: 3433; Percent complete: 85.8%; Average loss: 2.9379
Iteration: 3434; Percent complete: 85.9%; Average loss: 2.6102
Iteration: 3435; Percent complete: 85.9%; Average loss: 2.7628
Iteration: 3436; Percent complete: 85.9%; Average loss: 2.6952
Iteration: 3437; Percent complete: 85.9%; Average loss: 2.8600
Iteration: 3438; Percent complete: 86.0%; Average loss: 2.5278
Iteration: 3439; Percent complete: 86.0%; Average loss: 2.7798
Iteration: 3440; Percent complete: 86.0%; Average loss: 2.5623
Iteration: 3441; Percent complete: 86.0%; Average loss: 2.8872
Iteration: 3442; Percent complete: 86.1%; Average loss: 2.8657
Iteration: 3443; Percent complete: 86.1%; Average loss: 2.8330
Iteration: 3444; Percent complete: 86.1%; Average loss: 2.8035
Iteration: 3445; Percent complete: 86.1%; Average loss: 2.8260
Iteration: 3446; Percent complete: 86.2%; Average loss: 2.5535
Iteration: 3447; Percent complete: 86.2%; Average loss: 2.9481
Iteration: 3448; Percent complete: 86.2%; Average loss: 2.8186
Iteration: 3449; Percent complete: 86.2%; Average loss: 2.7270
Iteration: 3450; Percent complete: 86.2%; Average loss: 2.7110
Iteration: 3451; Percent complete: 86.3%; Average loss: 2.7643
Iteration: 3452; Percent complete: 86.3%; Average loss: 2.8873
Iteration: 3453; Percent complete: 86.3%; Average loss: 2.5780
Iteration: 3454; Percent complete: 86.4%; Average loss: 2.9092
Iteration: 3455; Percent complete: 86.4%; Average loss: 2.7288
Iteration: 3456; Percent complete: 86.4%; Average loss: 2.7770
Iteration: 3457; Percent complete: 86.4%; Average loss: 2.9182
Iteration: 3458; Percent complete: 86.5%; Average loss: 2.6121
Iteration: 3459; Percent complete: 86.5%; Average loss: 2.6750
Iteration: 3460; Percent complete: 86.5%; Average loss: 2.7825
Iteration: 3461; Percent complete: 86.5%; Average loss: 2.7371
Iteration: 3462; Percent complete: 86.6%; Average loss: 2.6938
Iteration: 3463; Percent complete: 86.6%; Average loss: 2.8753
Iteration: 3464; Percent complete: 86.6%; Average loss: 2.7973
Iteration: 3465; Percent complete: 86.6%; Average loss: 2.7761
Iteration: 3466; Percent complete: 86.7%; Average loss: 2.9202
Iteration: 3467; Percent complete: 86.7%; Average loss: 2.7107
Iteration: 3468; Percent complete: 86.7%; Average loss: 2.7730
Iteration: 3469; Percent complete: 86.7%; Average loss: 2.7050
Iteration: 3470; Percent complete: 86.8%; Average loss: 2.9098
Iteration: 3471; Percent complete: 86.8%; Average loss: 2.8292
Iteration: 3472; Percent complete: 86.8%; Average loss: 2.5048
Iteration: 3473; Percent complete: 86.8%; Average loss: 2.6533
Iteration: 3474; Percent complete: 86.9%; Average loss: 2.8426
Iteration: 3475; Percent complete: 86.9%; Average loss: 2.8071
Iteration: 3476; Percent complete: 86.9%; Average loss: 2.7571
Iteration: 3477; Percent complete: 86.9%; Average loss: 2.6789
Iteration: 3478; Percent complete: 87.0%; Average loss: 2.6737
Iteration: 3479; Percent complete: 87.0%; Average loss: 2.7442
Iteration: 3480; Percent complete: 87.0%; Average loss: 2.7941
Iteration: 3481; Percent complete: 87.0%; Average loss: 2.7989
Iteration: 3482; Percent complete: 87.1%; Average loss: 2.7874
Iteration: 3483; Percent complete: 87.1%; Average loss: 2.5893
Iteration: 3484; Percent complete: 87.1%; Average loss: 2.8803
Iteration: 3485; Percent complete: 87.1%; Average loss: 3.0495
Iteration: 3486; Percent complete: 87.2%; Average loss: 2.7631
Iteration: 3487; Percent complete: 87.2%; Average loss: 2.7817
Iteration: 3488; Percent complete: 87.2%; Average loss: 2.7667
Iteration: 3489; Percent complete: 87.2%; Average loss: 2.6783
Iteration: 3490; Percent complete: 87.2%; Average loss: 2.8652
Iteration: 3491; Percent complete: 87.3%; Average loss: 2.6914
Iteration: 3492; Percent complete: 87.3%; Average loss: 2.7663
Iteration: 3493; Percent complete: 87.3%; Average loss: 2.7826
Iteration: 3494; Percent complete: 87.4%; Average loss: 2.5802
Iteration: 3495; Percent complete: 87.4%; Average loss: 2.7460
Iteration: 3496; Percent complete: 87.4%; Average loss: 2.9836
Iteration: 3497; Percent complete: 87.4%; Average loss: 2.6449
Iteration: 3498; Percent complete: 87.5%; Average loss: 2.6691
Iteration: 3499; Percent complete: 87.5%; Average loss: 2.7443
Iteration: 3500; Percent complete: 87.5%; Average loss: 2.6531
Iteration: 3501; Percent complete: 87.5%; Average loss: 2.6968
Iteration: 3502; Percent complete: 87.5%; Average loss: 2.8882
Iteration: 3503; Percent complete: 87.6%; Average loss: 2.5856
Iteration: 3504; Percent complete: 87.6%; Average loss: 2.9074
Iteration: 3505; Percent complete: 87.6%; Average loss: 3.0104
Iteration: 3506; Percent complete: 87.6%; Average loss: 2.6674
Iteration: 3507; Percent complete: 87.7%; Average loss: 2.7618
Iteration: 3508; Percent complete: 87.7%; Average loss: 2.6460
Iteration: 3509; Percent complete: 87.7%; Average loss: 2.9135
Iteration: 3510; Percent complete: 87.8%; Average loss: 2.8101
Iteration: 3511; Percent complete: 87.8%; Average loss: 2.8104
Iteration: 3512; Percent complete: 87.8%; Average loss: 2.7414
Iteration: 3513; Percent complete: 87.8%; Average loss: 2.5670
Iteration: 3514; Percent complete: 87.8%; Average loss: 2.8252
Iteration: 3515; Percent complete: 87.9%; Average loss: 2.6518
Iteration: 3516; Percent complete: 87.9%; Average loss: 2.6722
Iteration: 3517; Percent complete: 87.9%; Average loss: 2.6326
Iteration: 3518; Percent complete: 87.9%; Average loss: 2.7523
Iteration: 3519; Percent complete: 88.0%; Average loss: 2.8893
Iteration: 3520; Percent complete: 88.0%; Average loss: 2.5777
Iteration: 3521; Percent complete: 88.0%; Average loss: 2.5754
Iteration: 3522; Percent complete: 88.0%; Average loss: 3.0170
Iteration: 3523; Percent complete: 88.1%; Average loss: 2.6920
Iteration: 3524; Percent complete: 88.1%; Average loss: 2.6752
Iteration: 3525; Percent complete: 88.1%; Average loss: 2.4280
Iteration: 3526; Percent complete: 88.1%; Average loss: 2.4397
Iteration: 3527; Percent complete: 88.2%; Average loss: 2.9032
Iteration: 3528; Percent complete: 88.2%; Average loss: 2.7286
Iteration: 3529; Percent complete: 88.2%; Average loss: 2.6492
Iteration: 3530; Percent complete: 88.2%; Average loss: 2.7741
Iteration: 3531; Percent complete: 88.3%; Average loss: 2.6857
Iteration: 3532; Percent complete: 88.3%; Average loss: 2.6501
Iteration: 3533; Percent complete: 88.3%; Average loss: 2.6950
Iteration: 3534; Percent complete: 88.3%; Average loss: 2.7250
Iteration: 3535; Percent complete: 88.4%; Average loss: 2.7844
Iteration: 3536; Percent complete: 88.4%; Average loss: 2.7302
Iteration: 3537; Percent complete: 88.4%; Average loss: 2.6598
Iteration: 3538; Percent complete: 88.4%; Average loss: 2.7709
Iteration: 3539; Percent complete: 88.5%; Average loss: 2.7890
Iteration: 3540; Percent complete: 88.5%; Average loss: 2.8066
Iteration: 3541; Percent complete: 88.5%; Average loss: 2.8239
Iteration: 3542; Percent complete: 88.5%; Average loss: 2.6824
Iteration: 3543; Percent complete: 88.6%; Average loss: 2.8947
Iteration: 3544; Percent complete: 88.6%; Average loss: 2.8361
Iteration: 3545; Percent complete: 88.6%; Average loss: 2.5072
Iteration: 3546; Percent complete: 88.6%; Average loss: 2.7896
Iteration: 3547; Percent complete: 88.7%; Average loss: 2.7343
Iteration: 3548; Percent complete: 88.7%; Average loss: 2.7431
Iteration: 3549; Percent complete: 88.7%; Average loss: 2.6203
Iteration: 3550; Percent complete: 88.8%; Average loss: 2.8537
Iteration: 3551; Percent complete: 88.8%; Average loss: 2.9926
Iteration: 3552; Percent complete: 88.8%; Average loss: 2.6783
Iteration: 3553; Percent complete: 88.8%; Average loss: 2.6350
Iteration: 3554; Percent complete: 88.8%; Average loss: 2.7018
Iteration: 3555; Percent complete: 88.9%; Average loss: 2.6892
Iteration: 3556; Percent complete: 88.9%; Average loss: 2.7054
Iteration: 3557; Percent complete: 88.9%; Average loss: 2.5839
Iteration: 3558; Percent complete: 88.9%; Average loss: 2.6708
Iteration: 3559; Percent complete: 89.0%; Average loss: 2.7404
Iteration: 3560; Percent complete: 89.0%; Average loss: 2.6081
Iteration: 3561; Percent complete: 89.0%; Average loss: 2.7376
Iteration: 3562; Percent complete: 89.0%; Average loss: 2.5966
Iteration: 3563; Percent complete: 89.1%; Average loss: 2.5593
Iteration: 3564; Percent complete: 89.1%; Average loss: 2.4864
Iteration: 3565; Percent complete: 89.1%; Average loss: 2.6306
Iteration: 3566; Percent complete: 89.1%; Average loss: 2.7002
Iteration: 3567; Percent complete: 89.2%; Average loss: 2.9146
Iteration: 3568; Percent complete: 89.2%; Average loss: 2.5392
Iteration: 3569; Percent complete: 89.2%; Average loss: 2.9325
Iteration: 3570; Percent complete: 89.2%; Average loss: 2.5743
Iteration: 3571; Percent complete: 89.3%; Average loss: 2.7931
Iteration: 3572; Percent complete: 89.3%; Average loss: 2.6380
Iteration: 3573; Percent complete: 89.3%; Average loss: 2.5844
Iteration: 3574; Percent complete: 89.3%; Average loss: 2.6205
Iteration: 3575; Percent complete: 89.4%; Average loss: 2.7201
Iteration: 3576; Percent complete: 89.4%; Average loss: 2.9070
Iteration: 3577; Percent complete: 89.4%; Average loss: 2.7343
Iteration: 3578; Percent complete: 89.5%; Average loss: 2.6057
Iteration: 3579; Percent complete: 89.5%; Average loss: 2.7014
Iteration: 3580; Percent complete: 89.5%; Average loss: 2.8550
Iteration: 3581; Percent complete: 89.5%; Average loss: 2.9837
Iteration: 3582; Percent complete: 89.5%; Average loss: 2.9186
Iteration: 3583; Percent complete: 89.6%; Average loss: 2.4531
Iteration: 3584; Percent complete: 89.6%; Average loss: 2.6695
Iteration: 3585; Percent complete: 89.6%; Average loss: 2.6680
Iteration: 3586; Percent complete: 89.6%; Average loss: 2.8140
Iteration: 3587; Percent complete: 89.7%; Average loss: 2.8797
Iteration: 3588; Percent complete: 89.7%; Average loss: 2.7178
Iteration: 3589; Percent complete: 89.7%; Average loss: 2.5616
Iteration: 3590; Percent complete: 89.8%; Average loss: 2.8023
Iteration: 3591; Percent complete: 89.8%; Average loss: 2.6211
Iteration: 3592; Percent complete: 89.8%; Average loss: 2.7324
Iteration: 3593; Percent complete: 89.8%; Average loss: 2.5945
Iteration: 3594; Percent complete: 89.8%; Average loss: 2.8419
Iteration: 3595; Percent complete: 89.9%; Average loss: 2.8031
Iteration: 3596; Percent complete: 89.9%; Average loss: 2.6630
Iteration: 3597; Percent complete: 89.9%; Average loss: 2.7685
Iteration: 3598; Percent complete: 90.0%; Average loss: 2.8287
Iteration: 3599; Percent complete: 90.0%; Average loss: 2.6559
Iteration: 3600; Percent complete: 90.0%; Average loss: 2.5740
Iteration: 3601; Percent complete: 90.0%; Average loss: 2.6477
Iteration: 3602; Percent complete: 90.0%; Average loss: 2.6956
Iteration: 3603; Percent complete: 90.1%; Average loss: 2.5637
Iteration: 3604; Percent complete: 90.1%; Average loss: 2.8558
Iteration: 3605; Percent complete: 90.1%; Average loss: 2.4954
Iteration: 3606; Percent complete: 90.1%; Average loss: 2.4849
Iteration: 3607; Percent complete: 90.2%; Average loss: 2.6602
Iteration: 3608; Percent complete: 90.2%; Average loss: 2.8446
Iteration: 3609; Percent complete: 90.2%; Average loss: 2.5853
Iteration: 3610; Percent complete: 90.2%; Average loss: 2.4455
Iteration: 3611; Percent complete: 90.3%; Average loss: 2.6134
Iteration: 3612; Percent complete: 90.3%; Average loss: 2.6230
Iteration: 3613; Percent complete: 90.3%; Average loss: 2.9976
Iteration: 3614; Percent complete: 90.3%; Average loss: 2.7221
Iteration: 3615; Percent complete: 90.4%; Average loss: 2.7213
Iteration: 3616; Percent complete: 90.4%; Average loss: 2.5405
Iteration: 3617; Percent complete: 90.4%; Average loss: 2.7805
Iteration: 3618; Percent complete: 90.5%; Average loss: 2.6301
Iteration: 3619; Percent complete: 90.5%; Average loss: 2.6079
Iteration: 3620; Percent complete: 90.5%; Average loss: 2.5641
Iteration: 3621; Percent complete: 90.5%; Average loss: 2.9120
Iteration: 3622; Percent complete: 90.5%; Average loss: 2.9556
Iteration: 3623; Percent complete: 90.6%; Average loss: 2.6377
Iteration: 3624; Percent complete: 90.6%; Average loss: 2.7236
Iteration: 3625; Percent complete: 90.6%; Average loss: 2.4848
Iteration: 3626; Percent complete: 90.6%; Average loss: 2.3395
Iteration: 3627; Percent complete: 90.7%; Average loss: 2.8974
Iteration: 3628; Percent complete: 90.7%; Average loss: 2.8283
Iteration: 3629; Percent complete: 90.7%; Average loss: 2.8597
Iteration: 3630; Percent complete: 90.8%; Average loss: 2.6707
Iteration: 3631; Percent complete: 90.8%; Average loss: 2.7485
Iteration: 3632; Percent complete: 90.8%; Average loss: 2.7777
Iteration: 3633; Percent complete: 90.8%; Average loss: 2.8831
Iteration: 3634; Percent complete: 90.8%; Average loss: 2.6551
Iteration: 3635; Percent complete: 90.9%; Average loss: 2.6940
Iteration: 3636; Percent complete: 90.9%; Average loss: 2.4925
Iteration: 3637; Percent complete: 90.9%; Average loss: 2.6022
Iteration: 3638; Percent complete: 91.0%; Average loss: 2.6820
Iteration: 3639; Percent complete: 91.0%; Average loss: 2.6383
Iteration: 3640; Percent complete: 91.0%; Average loss: 2.5131
Iteration: 3641; Percent complete: 91.0%; Average loss: 2.9181
Iteration: 3642; Percent complete: 91.0%; Average loss: 2.5125
Iteration: 3643; Percent complete: 91.1%; Average loss: 2.7097
Iteration: 3644; Percent complete: 91.1%; Average loss: 2.6571
Iteration: 3645; Percent complete: 91.1%; Average loss: 2.8912
Iteration: 3646; Percent complete: 91.1%; Average loss: 3.0244
Iteration: 3647; Percent complete: 91.2%; Average loss: 2.7462
Iteration: 3648; Percent complete: 91.2%; Average loss: 2.7933
Iteration: 3649; Percent complete: 91.2%; Average loss: 2.6719
Iteration: 3650; Percent complete: 91.2%; Average loss: 2.7611
Iteration: 3651; Percent complete: 91.3%; Average loss: 2.3485
Iteration: 3652; Percent complete: 91.3%; Average loss: 2.6582
Iteration: 3653; Percent complete: 91.3%; Average loss: 2.7467
Iteration: 3654; Percent complete: 91.3%; Average loss: 2.5934
Iteration: 3655; Percent complete: 91.4%; Average loss: 2.7518
Iteration: 3656; Percent complete: 91.4%; Average loss: 2.6539
Iteration: 3657; Percent complete: 91.4%; Average loss: 2.6896
Iteration: 3658; Percent complete: 91.5%; Average loss: 2.9346
Iteration: 3659; Percent complete: 91.5%; Average loss: 2.6903
Iteration: 3660; Percent complete: 91.5%; Average loss: 2.4097
Iteration: 3661; Percent complete: 91.5%; Average loss: 2.9863
Iteration: 3662; Percent complete: 91.5%; Average loss: 2.8337
Iteration: 3663; Percent complete: 91.6%; Average loss: 2.5759
Iteration: 3664; Percent complete: 91.6%; Average loss: 2.7153
Iteration: 3665; Percent complete: 91.6%; Average loss: 2.7481
Iteration: 3666; Percent complete: 91.6%; Average loss: 2.8343
Iteration: 3667; Percent complete: 91.7%; Average loss: 2.9005
Iteration: 3668; Percent complete: 91.7%; Average loss: 2.3344
Iteration: 3669; Percent complete: 91.7%; Average loss: 2.4418
Iteration: 3670; Percent complete: 91.8%; Average loss: 2.9036
Iteration: 3671; Percent complete: 91.8%; Average loss: 2.5246
Iteration: 3672; Percent complete: 91.8%; Average loss: 2.6286
Iteration: 3673; Percent complete: 91.8%; Average loss: 2.7356
Iteration: 3674; Percent complete: 91.8%; Average loss: 2.6149
Iteration: 3675; Percent complete: 91.9%; Average loss: 2.8337
Iteration: 3676; Percent complete: 91.9%; Average loss: 2.4619
Iteration: 3677; Percent complete: 91.9%; Average loss: 2.7108
Iteration: 3678; Percent complete: 92.0%; Average loss: 2.5886
Iteration: 3679; Percent complete: 92.0%; Average loss: 2.5895
Iteration: 3680; Percent complete: 92.0%; Average loss: 2.5346
Iteration: 3681; Percent complete: 92.0%; Average loss: 2.7611
Iteration: 3682; Percent complete: 92.0%; Average loss: 2.5964
Iteration: 3683; Percent complete: 92.1%; Average loss: 2.5843
Iteration: 3684; Percent complete: 92.1%; Average loss: 2.7802
Iteration: 3685; Percent complete: 92.1%; Average loss: 2.6287
Iteration: 3686; Percent complete: 92.2%; Average loss: 2.7009
Iteration: 3687; Percent complete: 92.2%; Average loss: 2.6051
Iteration: 3688; Percent complete: 92.2%; Average loss: 2.7767
Iteration: 3689; Percent complete: 92.2%; Average loss: 2.7351
Iteration: 3690; Percent complete: 92.2%; Average loss: 2.6533
Iteration: 3691; Percent complete: 92.3%; Average loss: 2.6076
Iteration: 3692; Percent complete: 92.3%; Average loss: 2.7976
Iteration: 3693; Percent complete: 92.3%; Average loss: 2.5894
Iteration: 3694; Percent complete: 92.3%; Average loss: 2.7365
Iteration: 3695; Percent complete: 92.4%; Average loss: 2.4747
Iteration: 3696; Percent complete: 92.4%; Average loss: 2.6848
Iteration: 3697; Percent complete: 92.4%; Average loss: 2.8352
Iteration: 3698; Percent complete: 92.5%; Average loss: 2.6135
Iteration: 3699; Percent complete: 92.5%; Average loss: 2.7830
Iteration: 3700; Percent complete: 92.5%; Average loss: 2.7456
Iteration: 3701; Percent complete: 92.5%; Average loss: 2.6803
Iteration: 3702; Percent complete: 92.5%; Average loss: 2.8419
Iteration: 3703; Percent complete: 92.6%; Average loss: 2.6897
Iteration: 3704; Percent complete: 92.6%; Average loss: 2.6523
Iteration: 3705; Percent complete: 92.6%; Average loss: 2.6061
Iteration: 3706; Percent complete: 92.7%; Average loss: 2.5663
Iteration: 3707; Percent complete: 92.7%; Average loss: 2.7608
Iteration: 3708; Percent complete: 92.7%; Average loss: 2.5845
Iteration: 3709; Percent complete: 92.7%; Average loss: 2.6349
Iteration: 3710; Percent complete: 92.8%; Average loss: 2.6977
Iteration: 3711; Percent complete: 92.8%; Average loss: 2.8066
Iteration: 3712; Percent complete: 92.8%; Average loss: 2.6410
Iteration: 3713; Percent complete: 92.8%; Average loss: 2.9014
Iteration: 3714; Percent complete: 92.8%; Average loss: 2.3559
Iteration: 3715; Percent complete: 92.9%; Average loss: 2.9388
Iteration: 3716; Percent complete: 92.9%; Average loss: 2.7386
Iteration: 3717; Percent complete: 92.9%; Average loss: 2.8803
Iteration: 3718; Percent complete: 93.0%; Average loss: 2.5511
Iteration: 3719; Percent complete: 93.0%; Average loss: 2.6047
Iteration: 3720; Percent complete: 93.0%; Average loss: 2.7612
Iteration: 3721; Percent complete: 93.0%; Average loss: 2.6927
Iteration: 3722; Percent complete: 93.0%; Average loss: 2.4676
Iteration: 3723; Percent complete: 93.1%; Average loss: 2.6919
Iteration: 3724; Percent complete: 93.1%; Average loss: 2.4447
Iteration: 3725; Percent complete: 93.1%; Average loss: 2.5825
Iteration: 3726; Percent complete: 93.2%; Average loss: 2.7552
Iteration: 3727; Percent complete: 93.2%; Average loss: 2.6727
Iteration: 3728; Percent complete: 93.2%; Average loss: 2.7467
Iteration: 3729; Percent complete: 93.2%; Average loss: 2.8835
Iteration: 3730; Percent complete: 93.2%; Average loss: 2.6882
Iteration: 3731; Percent complete: 93.3%; Average loss: 2.6655
Iteration: 3732; Percent complete: 93.3%; Average loss: 2.5727
Iteration: 3733; Percent complete: 93.3%; Average loss: 2.7933
Iteration: 3734; Percent complete: 93.3%; Average loss: 2.5134
Iteration: 3735; Percent complete: 93.4%; Average loss: 2.7144
Iteration: 3736; Percent complete: 93.4%; Average loss: 2.6109
Iteration: 3737; Percent complete: 93.4%; Average loss: 2.4968
Iteration: 3738; Percent complete: 93.5%; Average loss: 2.7538
Iteration: 3739; Percent complete: 93.5%; Average loss: 2.7184
Iteration: 3740; Percent complete: 93.5%; Average loss: 2.5931
Iteration: 3741; Percent complete: 93.5%; Average loss: 2.6055
Iteration: 3742; Percent complete: 93.5%; Average loss: 2.7135
Iteration: 3743; Percent complete: 93.6%; Average loss: 2.6205
Iteration: 3744; Percent complete: 93.6%; Average loss: 2.5740
Iteration: 3745; Percent complete: 93.6%; Average loss: 2.6429
Iteration: 3746; Percent complete: 93.7%; Average loss: 2.6895
Iteration: 3747; Percent complete: 93.7%; Average loss: 2.5423
Iteration: 3748; Percent complete: 93.7%; Average loss: 2.5825
Iteration: 3749; Percent complete: 93.7%; Average loss: 2.7077
Iteration: 3750; Percent complete: 93.8%; Average loss: 2.9651
Iteration: 3751; Percent complete: 93.8%; Average loss: 2.7086
Iteration: 3752; Percent complete: 93.8%; Average loss: 2.8856
Iteration: 3753; Percent complete: 93.8%; Average loss: 2.5619
Iteration: 3754; Percent complete: 93.8%; Average loss: 2.5866
Iteration: 3755; Percent complete: 93.9%; Average loss: 2.7132
Iteration: 3756; Percent complete: 93.9%; Average loss: 2.7633
Iteration: 3757; Percent complete: 93.9%; Average loss: 2.6127
Iteration: 3758; Percent complete: 94.0%; Average loss: 2.7197
Iteration: 3759; Percent complete: 94.0%; Average loss: 2.6720
Iteration: 3760; Percent complete: 94.0%; Average loss: 2.7782
Iteration: 3761; Percent complete: 94.0%; Average loss: 2.7905
Iteration: 3762; Percent complete: 94.0%; Average loss: 3.0147
Iteration: 3763; Percent complete: 94.1%; Average loss: 2.7168
Iteration: 3764; Percent complete: 94.1%; Average loss: 2.8647
Iteration: 3765; Percent complete: 94.1%; Average loss: 2.9033
Iteration: 3766; Percent complete: 94.2%; Average loss: 2.5805
Iteration: 3767; Percent complete: 94.2%; Average loss: 2.5001
Iteration: 3768; Percent complete: 94.2%; Average loss: 3.0495
Iteration: 3769; Percent complete: 94.2%; Average loss: 2.5357
Iteration: 3770; Percent complete: 94.2%; Average loss: 2.7966
Iteration: 3771; Percent complete: 94.3%; Average loss: 2.6805
Iteration: 3772; Percent complete: 94.3%; Average loss: 2.5035
Iteration: 3773; Percent complete: 94.3%; Average loss: 2.7443
Iteration: 3774; Percent complete: 94.3%; Average loss: 2.6739
Iteration: 3775; Percent complete: 94.4%; Average loss: 3.1555
Iteration: 3776; Percent complete: 94.4%; Average loss: 2.5219
Iteration: 3777; Percent complete: 94.4%; Average loss: 2.5353
Iteration: 3778; Percent complete: 94.5%; Average loss: 2.7799
Iteration: 3779; Percent complete: 94.5%; Average loss: 2.4825
Iteration: 3780; Percent complete: 94.5%; Average loss: 2.5200
Iteration: 3781; Percent complete: 94.5%; Average loss: 2.8455
Iteration: 3782; Percent complete: 94.5%; Average loss: 2.4922
Iteration: 3783; Percent complete: 94.6%; Average loss: 2.9627
Iteration: 3784; Percent complete: 94.6%; Average loss: 2.8066
Iteration: 3785; Percent complete: 94.6%; Average loss: 2.8224
Iteration: 3786; Percent complete: 94.7%; Average loss: 2.9062
Iteration: 3787; Percent complete: 94.7%; Average loss: 2.6777
Iteration: 3788; Percent complete: 94.7%; Average loss: 2.8929
Iteration: 3789; Percent complete: 94.7%; Average loss: 2.5173
Iteration: 3790; Percent complete: 94.8%; Average loss: 2.5955
Iteration: 3791; Percent complete: 94.8%; Average loss: 2.7263
Iteration: 3792; Percent complete: 94.8%; Average loss: 2.6929
Iteration: 3793; Percent complete: 94.8%; Average loss: 2.5077
Iteration: 3794; Percent complete: 94.8%; Average loss: 2.9645
Iteration: 3795; Percent complete: 94.9%; Average loss: 2.7725
Iteration: 3796; Percent complete: 94.9%; Average loss: 2.7661
Iteration: 3797; Percent complete: 94.9%; Average loss: 2.8079
Iteration: 3798; Percent complete: 95.0%; Average loss: 2.7052
Iteration: 3799; Percent complete: 95.0%; Average loss: 2.6015
Iteration: 3800; Percent complete: 95.0%; Average loss: 2.6956
Iteration: 3801; Percent complete: 95.0%; Average loss: 2.7424
Iteration: 3802; Percent complete: 95.0%; Average loss: 2.6214
Iteration: 3803; Percent complete: 95.1%; Average loss: 2.5622
Iteration: 3804; Percent complete: 95.1%; Average loss: 2.7182
Iteration: 3805; Percent complete: 95.1%; Average loss: 2.5868
Iteration: 3806; Percent complete: 95.2%; Average loss: 2.5583
Iteration: 3807; Percent complete: 95.2%; Average loss: 2.4575
Iteration: 3808; Percent complete: 95.2%; Average loss: 2.6537
Iteration: 3809; Percent complete: 95.2%; Average loss: 2.4749
Iteration: 3810; Percent complete: 95.2%; Average loss: 2.3702
Iteration: 3811; Percent complete: 95.3%; Average loss: 2.7148
Iteration: 3812; Percent complete: 95.3%; Average loss: 2.4493
Iteration: 3813; Percent complete: 95.3%; Average loss: 2.7417
Iteration: 3814; Percent complete: 95.3%; Average loss: 2.6270
Iteration: 3815; Percent complete: 95.4%; Average loss: 2.6387
Iteration: 3816; Percent complete: 95.4%; Average loss: 2.6071
Iteration: 3817; Percent complete: 95.4%; Average loss: 2.5443
Iteration: 3818; Percent complete: 95.5%; Average loss: 2.7009
Iteration: 3819; Percent complete: 95.5%; Average loss: 2.5512
Iteration: 3820; Percent complete: 95.5%; Average loss: 2.5252
Iteration: 3821; Percent complete: 95.5%; Average loss: 2.6551
Iteration: 3822; Percent complete: 95.5%; Average loss: 2.7030
Iteration: 3823; Percent complete: 95.6%; Average loss: 2.6199
Iteration: 3824; Percent complete: 95.6%; Average loss: 2.6427
Iteration: 3825; Percent complete: 95.6%; Average loss: 2.6884
Iteration: 3826; Percent complete: 95.7%; Average loss: 2.6165
Iteration: 3827; Percent complete: 95.7%; Average loss: 2.6220
Iteration: 3828; Percent complete: 95.7%; Average loss: 2.5926
Iteration: 3829; Percent complete: 95.7%; Average loss: 2.6808
Iteration: 3830; Percent complete: 95.8%; Average loss: 2.6425
Iteration: 3831; Percent complete: 95.8%; Average loss: 2.4733
Iteration: 3832; Percent complete: 95.8%; Average loss: 2.5232
Iteration: 3833; Percent complete: 95.8%; Average loss: 2.7341
Iteration: 3834; Percent complete: 95.9%; Average loss: 2.5395
Iteration: 3835; Percent complete: 95.9%; Average loss: 2.4199
Iteration: 3836; Percent complete: 95.9%; Average loss: 2.7970
Iteration: 3837; Percent complete: 95.9%; Average loss: 2.6187
Iteration: 3838; Percent complete: 96.0%; Average loss: 2.5129
Iteration: 3839; Percent complete: 96.0%; Average loss: 2.9316
Iteration: 3840; Percent complete: 96.0%; Average loss: 2.7576
Iteration: 3841; Percent complete: 96.0%; Average loss: 2.5042
Iteration: 3842; Percent complete: 96.0%; Average loss: 2.5001
Iteration: 3843; Percent complete: 96.1%; Average loss: 2.8141
Iteration: 3844; Percent complete: 96.1%; Average loss: 2.6730
Iteration: 3845; Percent complete: 96.1%; Average loss: 2.5281
Iteration: 3846; Percent complete: 96.2%; Average loss: 2.7058
Iteration: 3847; Percent complete: 96.2%; Average loss: 2.7670
Iteration: 3848; Percent complete: 96.2%; Average loss: 2.6466
Iteration: 3849; Percent complete: 96.2%; Average loss: 2.6616
Iteration: 3850; Percent complete: 96.2%; Average loss: 2.5560
Iteration: 3851; Percent complete: 96.3%; Average loss: 2.7158
Iteration: 3852; Percent complete: 96.3%; Average loss: 2.6137
Iteration: 3853; Percent complete: 96.3%; Average loss: 2.5463
Iteration: 3854; Percent complete: 96.4%; Average loss: 2.8368
Iteration: 3855; Percent complete: 96.4%; Average loss: 2.7996
Iteration: 3856; Percent complete: 96.4%; Average loss: 2.6292
Iteration: 3857; Percent complete: 96.4%; Average loss: 2.8106
Iteration: 3858; Percent complete: 96.5%; Average loss: 2.5090
Iteration: 3859; Percent complete: 96.5%; Average loss: 2.5682
Iteration: 3860; Percent complete: 96.5%; Average loss: 2.5286
Iteration: 3861; Percent complete: 96.5%; Average loss: 2.6955
Iteration: 3862; Percent complete: 96.5%; Average loss: 2.6270
Iteration: 3863; Percent complete: 96.6%; Average loss: 2.6034
Iteration: 3864; Percent complete: 96.6%; Average loss: 2.4265
Iteration: 3865; Percent complete: 96.6%; Average loss: 3.0877
Iteration: 3866; Percent complete: 96.7%; Average loss: 2.6545
Iteration: 3867; Percent complete: 96.7%; Average loss: 2.5035
Iteration: 3868; Percent complete: 96.7%; Average loss: 2.7115
Iteration: 3869; Percent complete: 96.7%; Average loss: 2.5902
Iteration: 3870; Percent complete: 96.8%; Average loss: 2.7981
Iteration: 3871; Percent complete: 96.8%; Average loss: 2.7269
Iteration: 3872; Percent complete: 96.8%; Average loss: 2.6070
Iteration: 3873; Percent complete: 96.8%; Average loss: 2.4934
Iteration: 3874; Percent complete: 96.9%; Average loss: 2.7861
Iteration: 3875; Percent complete: 96.9%; Average loss: 2.7175
Iteration: 3876; Percent complete: 96.9%; Average loss: 2.6880
Iteration: 3877; Percent complete: 96.9%; Average loss: 2.5210
Iteration: 3878; Percent complete: 97.0%; Average loss: 2.5993
Iteration: 3879; Percent complete: 97.0%; Average loss: 2.5457
Iteration: 3880; Percent complete: 97.0%; Average loss: 2.7541
Iteration: 3881; Percent complete: 97.0%; Average loss: 2.9485
Iteration: 3882; Percent complete: 97.0%; Average loss: 2.6507
Iteration: 3883; Percent complete: 97.1%; Average loss: 2.3900
Iteration: 3884; Percent complete: 97.1%; Average loss: 2.7810
Iteration: 3885; Percent complete: 97.1%; Average loss: 2.7499
Iteration: 3886; Percent complete: 97.2%; Average loss: 2.5146
Iteration: 3887; Percent complete: 97.2%; Average loss: 2.6741
Iteration: 3888; Percent complete: 97.2%; Average loss: 2.5315
Iteration: 3889; Percent complete: 97.2%; Average loss: 2.6796
Iteration: 3890; Percent complete: 97.2%; Average loss: 2.7340
Iteration: 3891; Percent complete: 97.3%; Average loss: 2.5594
Iteration: 3892; Percent complete: 97.3%; Average loss: 2.6983
Iteration: 3893; Percent complete: 97.3%; Average loss: 2.5056
Iteration: 3894; Percent complete: 97.4%; Average loss: 2.7607
Iteration: 3895; Percent complete: 97.4%; Average loss: 2.6053
Iteration: 3896; Percent complete: 97.4%; Average loss: 2.5810
Iteration: 3897; Percent complete: 97.4%; Average loss: 2.5472
Iteration: 3898; Percent complete: 97.5%; Average loss: 2.6967
Iteration: 3899; Percent complete: 97.5%; Average loss: 2.9481
Iteration: 3900; Percent complete: 97.5%; Average loss: 2.6363
Iteration: 3901; Percent complete: 97.5%; Average loss: 2.6336
Iteration: 3902; Percent complete: 97.5%; Average loss: 2.9051
Iteration: 3903; Percent complete: 97.6%; Average loss: 2.6036
Iteration: 3904; Percent complete: 97.6%; Average loss: 2.4988
Iteration: 3905; Percent complete: 97.6%; Average loss: 2.6653
Iteration: 3906; Percent complete: 97.7%; Average loss: 2.5935
Iteration: 3907; Percent complete: 97.7%; Average loss: 2.6864
Iteration: 3908; Percent complete: 97.7%; Average loss: 2.6210
Iteration: 3909; Percent complete: 97.7%; Average loss: 2.8411
Iteration: 3910; Percent complete: 97.8%; Average loss: 2.7763
Iteration: 3911; Percent complete: 97.8%; Average loss: 2.9240
Iteration: 3912; Percent complete: 97.8%; Average loss: 2.4750
Iteration: 3913; Percent complete: 97.8%; Average loss: 2.6410
Iteration: 3914; Percent complete: 97.9%; Average loss: 2.5918
Iteration: 3915; Percent complete: 97.9%; Average loss: 2.4828
Iteration: 3916; Percent complete: 97.9%; Average loss: 2.5953
Iteration: 3917; Percent complete: 97.9%; Average loss: 2.5963
Iteration: 3918; Percent complete: 98.0%; Average loss: 2.5839
Iteration: 3919; Percent complete: 98.0%; Average loss: 2.8232
Iteration: 3920; Percent complete: 98.0%; Average loss: 2.6896
Iteration: 3921; Percent complete: 98.0%; Average loss: 2.2800
Iteration: 3922; Percent complete: 98.0%; Average loss: 2.5928
Iteration: 3923; Percent complete: 98.1%; Average loss: 2.5659
Iteration: 3924; Percent complete: 98.1%; Average loss: 2.4370
Iteration: 3925; Percent complete: 98.1%; Average loss: 2.5022
Iteration: 3926; Percent complete: 98.2%; Average loss: 2.5138
Iteration: 3927; Percent complete: 98.2%; Average loss: 2.6562
Iteration: 3928; Percent complete: 98.2%; Average loss: 2.6976
Iteration: 3929; Percent complete: 98.2%; Average loss: 2.8020
Iteration: 3930; Percent complete: 98.2%; Average loss: 2.5954
Iteration: 3931; Percent complete: 98.3%; Average loss: 2.4311
Iteration: 3932; Percent complete: 98.3%; Average loss: 2.8779
Iteration: 3933; Percent complete: 98.3%; Average loss: 2.6109
Iteration: 3934; Percent complete: 98.4%; Average loss: 2.5123
Iteration: 3935; Percent complete: 98.4%; Average loss: 2.5012
Iteration: 3936; Percent complete: 98.4%; Average loss: 2.6405
Iteration: 3937; Percent complete: 98.4%; Average loss: 2.5509
Iteration: 3938; Percent complete: 98.5%; Average loss: 2.5171
Iteration: 3939; Percent complete: 98.5%; Average loss: 2.5987
Iteration: 3940; Percent complete: 98.5%; Average loss: 2.6960
Iteration: 3941; Percent complete: 98.5%; Average loss: 2.6804
Iteration: 3942; Percent complete: 98.6%; Average loss: 2.7086
Iteration: 3943; Percent complete: 98.6%; Average loss: 2.7911
Iteration: 3944; Percent complete: 98.6%; Average loss: 2.7552
Iteration: 3945; Percent complete: 98.6%; Average loss: 2.6105
Iteration: 3946; Percent complete: 98.7%; Average loss: 2.6550
Iteration: 3947; Percent complete: 98.7%; Average loss: 2.5916
Iteration: 3948; Percent complete: 98.7%; Average loss: 2.4206
Iteration: 3949; Percent complete: 98.7%; Average loss: 2.7644
Iteration: 3950; Percent complete: 98.8%; Average loss: 2.6005
Iteration: 3951; Percent complete: 98.8%; Average loss: 2.5401
Iteration: 3952; Percent complete: 98.8%; Average loss: 2.6580
Iteration: 3953; Percent complete: 98.8%; Average loss: 2.9679
Iteration: 3954; Percent complete: 98.9%; Average loss: 2.6734
Iteration: 3955; Percent complete: 98.9%; Average loss: 2.5677
Iteration: 3956; Percent complete: 98.9%; Average loss: 2.8076
Iteration: 3957; Percent complete: 98.9%; Average loss: 2.7855
Iteration: 3958; Percent complete: 99.0%; Average loss: 2.5441
Iteration: 3959; Percent complete: 99.0%; Average loss: 2.4578
Iteration: 3960; Percent complete: 99.0%; Average loss: 2.5692
Iteration: 3961; Percent complete: 99.0%; Average loss: 2.7824
Iteration: 3962; Percent complete: 99.1%; Average loss: 2.6620
Iteration: 3963; Percent complete: 99.1%; Average loss: 2.5763
Iteration: 3964; Percent complete: 99.1%; Average loss: 2.4827
Iteration: 3965; Percent complete: 99.1%; Average loss: 2.6408
Iteration: 3966; Percent complete: 99.2%; Average loss: 2.5224
Iteration: 3967; Percent complete: 99.2%; Average loss: 2.6190
Iteration: 3968; Percent complete: 99.2%; Average loss: 2.6845
Iteration: 3969; Percent complete: 99.2%; Average loss: 2.7299
Iteration: 3970; Percent complete: 99.2%; Average loss: 2.7630
Iteration: 3971; Percent complete: 99.3%; Average loss: 2.6139
Iteration: 3972; Percent complete: 99.3%; Average loss: 2.6714
Iteration: 3973; Percent complete: 99.3%; Average loss: 2.4657
Iteration: 3974; Percent complete: 99.4%; Average loss: 2.8743
Iteration: 3975; Percent complete: 99.4%; Average loss: 2.8182
Iteration: 3976; Percent complete: 99.4%; Average loss: 2.4866
Iteration: 3977; Percent complete: 99.4%; Average loss: 2.6388
Iteration: 3978; Percent complete: 99.5%; Average loss: 2.7096
Iteration: 3979; Percent complete: 99.5%; Average loss: 2.6666
Iteration: 3980; Percent complete: 99.5%; Average loss: 2.4384
Iteration: 3981; Percent complete: 99.5%; Average loss: 2.4116
Iteration: 3982; Percent complete: 99.6%; Average loss: 2.7096
Iteration: 3983; Percent complete: 99.6%; Average loss: 2.5381
Iteration: 3984; Percent complete: 99.6%; Average loss: 2.6455
Iteration: 3985; Percent complete: 99.6%; Average loss: 2.8500
Iteration: 3986; Percent complete: 99.7%; Average loss: 2.5403
Iteration: 3987; Percent complete: 99.7%; Average loss: 2.6687
Iteration: 3988; Percent complete: 99.7%; Average loss: 2.4606
Iteration: 3989; Percent complete: 99.7%; Average loss: 2.6758
Iteration: 3990; Percent complete: 99.8%; Average loss: 2.4751
Iteration: 3991; Percent complete: 99.8%; Average loss: 2.8013
Iteration: 3992; Percent complete: 99.8%; Average loss: 2.7350
Iteration: 3993; Percent complete: 99.8%; Average loss: 2.6582
Iteration: 3994; Percent complete: 99.9%; Average loss: 2.6402
Iteration: 3995; Percent complete: 99.9%; Average loss: 2.4707
Iteration: 3996; Percent complete: 99.9%; Average loss: 2.5528
Iteration: 3997; Percent complete: 99.9%; Average loss: 2.5349
Iteration: 3998; Percent complete: 100.0%; Average loss: 2.4000
Iteration: 3999; Percent complete: 100.0%; Average loss: 2.6669
Iteration: 4000; Percent complete: 100.0%; Average loss: 2.5963

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!