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([[  86,   19,   11,  631,   19],
        [7471,   10,  113,   40,   17],
        [   6,   67,  479,   34,   79],
        [ 312,   90,   36,  631,  708],
        [  92,    8, 5623,   14,   10],
        [   6,   10,   14,    2,    2],
        [   2,    2,    2,    0,    0]])
lengths: tensor([7, 7, 7, 6, 6])
target_variable: tensor([[ 162, 1563,  132, 1652,   26],
        [5877,   14,   19,   79, 2093],
        [  19,   14, 2277, 2656,  708],
        [  90,   14,  317,   10,   90],
        [  85,    2,   50,    2,  724],
        [  10,    0,   39,    0,  711],
        [   2,    0,   34,    0,   14],
        [   0,    0,   10,    0,    2],
        [   0,    0,    2,    0,    0]])
mask: tensor([[ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True, False,  True, False,  True],
        [ True, False,  True, False,  True],
        [False, False,  True, False,  True],
        [False, False,  True, False, False]])
max_target_len: 9

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: 8.9666
Iteration: 2; Percent complete: 0.1%; Average loss: 8.8479
Iteration: 3; Percent complete: 0.1%; Average loss: 8.6621
Iteration: 4; Percent complete: 0.1%; Average loss: 8.4430
Iteration: 5; Percent complete: 0.1%; Average loss: 8.0177
Iteration: 6; Percent complete: 0.1%; Average loss: 7.5270
Iteration: 7; Percent complete: 0.2%; Average loss: 6.8204
Iteration: 8; Percent complete: 0.2%; Average loss: 6.7056
Iteration: 9; Percent complete: 0.2%; Average loss: 6.9050
Iteration: 10; Percent complete: 0.2%; Average loss: 6.8790
Iteration: 11; Percent complete: 0.3%; Average loss: 6.3753
Iteration: 12; Percent complete: 0.3%; Average loss: 6.1323
Iteration: 13; Percent complete: 0.3%; Average loss: 5.5753
Iteration: 14; Percent complete: 0.4%; Average loss: 5.6969
Iteration: 15; Percent complete: 0.4%; Average loss: 5.6677
Iteration: 16; Percent complete: 0.4%; Average loss: 5.7981
Iteration: 17; Percent complete: 0.4%; Average loss: 5.4036
Iteration: 18; Percent complete: 0.4%; Average loss: 5.3300
Iteration: 19; Percent complete: 0.5%; Average loss: 5.0964
Iteration: 20; Percent complete: 0.5%; Average loss: 4.9325
Iteration: 21; Percent complete: 0.5%; Average loss: 4.8806
Iteration: 22; Percent complete: 0.5%; Average loss: 4.8807
Iteration: 23; Percent complete: 0.6%; Average loss: 4.9231
Iteration: 24; Percent complete: 0.6%; Average loss: 4.8740
Iteration: 25; Percent complete: 0.6%; Average loss: 4.9418
Iteration: 26; Percent complete: 0.7%; Average loss: 4.7800
Iteration: 27; Percent complete: 0.7%; Average loss: 4.9318
Iteration: 28; Percent complete: 0.7%; Average loss: 4.8057
Iteration: 29; Percent complete: 0.7%; Average loss: 4.9932
Iteration: 30; Percent complete: 0.8%; Average loss: 4.7240
Iteration: 31; Percent complete: 0.8%; Average loss: 4.9764
Iteration: 32; Percent complete: 0.8%; Average loss: 4.7430
Iteration: 33; Percent complete: 0.8%; Average loss: 4.9912
Iteration: 34; Percent complete: 0.9%; Average loss: 4.8192
Iteration: 35; Percent complete: 0.9%; Average loss: 4.5436
Iteration: 36; Percent complete: 0.9%; Average loss: 4.8759
Iteration: 37; Percent complete: 0.9%; Average loss: 4.7996
Iteration: 38; Percent complete: 0.9%; Average loss: 4.8449
Iteration: 39; Percent complete: 1.0%; Average loss: 4.6706
Iteration: 40; Percent complete: 1.0%; Average loss: 4.7597
Iteration: 41; Percent complete: 1.0%; Average loss: 4.7314
Iteration: 42; Percent complete: 1.1%; Average loss: 4.7361
Iteration: 43; Percent complete: 1.1%; Average loss: 4.7356
Iteration: 44; Percent complete: 1.1%; Average loss: 4.6513
Iteration: 45; Percent complete: 1.1%; Average loss: 4.8754
Iteration: 46; Percent complete: 1.1%; Average loss: 4.6556
Iteration: 47; Percent complete: 1.2%; Average loss: 4.3737
Iteration: 48; Percent complete: 1.2%; Average loss: 4.6250
Iteration: 49; Percent complete: 1.2%; Average loss: 4.7934
Iteration: 50; Percent complete: 1.2%; Average loss: 4.6910
Iteration: 51; Percent complete: 1.3%; Average loss: 4.5018
Iteration: 52; Percent complete: 1.3%; Average loss: 4.6043
Iteration: 53; Percent complete: 1.3%; Average loss: 4.4826
Iteration: 54; Percent complete: 1.4%; Average loss: 4.5703
Iteration: 55; Percent complete: 1.4%; Average loss: 4.7251
Iteration: 56; Percent complete: 1.4%; Average loss: 4.5184
Iteration: 57; Percent complete: 1.4%; Average loss: 4.7446
Iteration: 58; Percent complete: 1.5%; Average loss: 4.6486
Iteration: 59; Percent complete: 1.5%; Average loss: 4.7570
Iteration: 60; Percent complete: 1.5%; Average loss: 4.6050
Iteration: 61; Percent complete: 1.5%; Average loss: 4.6415
Iteration: 62; Percent complete: 1.6%; Average loss: 4.4729
Iteration: 63; Percent complete: 1.6%; Average loss: 4.8007
Iteration: 64; Percent complete: 1.6%; Average loss: 4.5336
Iteration: 65; Percent complete: 1.6%; Average loss: 4.5981
Iteration: 66; Percent complete: 1.7%; Average loss: 4.7704
Iteration: 67; Percent complete: 1.7%; Average loss: 4.3616
Iteration: 68; Percent complete: 1.7%; Average loss: 4.3866
Iteration: 69; Percent complete: 1.7%; Average loss: 4.6616
Iteration: 70; Percent complete: 1.8%; Average loss: 4.4386
Iteration: 71; Percent complete: 1.8%; Average loss: 4.5557
Iteration: 72; Percent complete: 1.8%; Average loss: 4.4083
Iteration: 73; Percent complete: 1.8%; Average loss: 4.3753
Iteration: 74; Percent complete: 1.8%; Average loss: 4.3717
Iteration: 75; Percent complete: 1.9%; Average loss: 5.0034
Iteration: 76; Percent complete: 1.9%; Average loss: 4.7998
Iteration: 77; Percent complete: 1.9%; Average loss: 4.5040
Iteration: 78; Percent complete: 1.9%; Average loss: 4.6390
Iteration: 79; Percent complete: 2.0%; Average loss: 4.5564
Iteration: 80; Percent complete: 2.0%; Average loss: 4.4464
Iteration: 81; Percent complete: 2.0%; Average loss: 4.3632
Iteration: 82; Percent complete: 2.1%; Average loss: 4.5027
Iteration: 83; Percent complete: 2.1%; Average loss: 4.5877
Iteration: 84; Percent complete: 2.1%; Average loss: 4.5941
Iteration: 85; Percent complete: 2.1%; Average loss: 4.5138
Iteration: 86; Percent complete: 2.1%; Average loss: 4.6006
Iteration: 87; Percent complete: 2.2%; Average loss: 4.7908
Iteration: 88; Percent complete: 2.2%; Average loss: 4.4437
Iteration: 89; Percent complete: 2.2%; Average loss: 4.5652
Iteration: 90; Percent complete: 2.2%; Average loss: 4.4943
Iteration: 91; Percent complete: 2.3%; Average loss: 4.4374
Iteration: 92; Percent complete: 2.3%; Average loss: 4.5598
Iteration: 93; Percent complete: 2.3%; Average loss: 4.3145
Iteration: 94; Percent complete: 2.4%; Average loss: 4.2264
Iteration: 95; Percent complete: 2.4%; Average loss: 4.6352
Iteration: 96; Percent complete: 2.4%; Average loss: 4.6726
Iteration: 97; Percent complete: 2.4%; Average loss: 4.6067
Iteration: 98; Percent complete: 2.5%; Average loss: 4.4110
Iteration: 99; Percent complete: 2.5%; Average loss: 4.4634
Iteration: 100; Percent complete: 2.5%; Average loss: 4.2848
Iteration: 101; Percent complete: 2.5%; Average loss: 4.2737
Iteration: 102; Percent complete: 2.5%; Average loss: 4.4409
Iteration: 103; Percent complete: 2.6%; Average loss: 4.6888
Iteration: 104; Percent complete: 2.6%; Average loss: 4.5546
Iteration: 105; Percent complete: 2.6%; Average loss: 4.3063
Iteration: 106; Percent complete: 2.6%; Average loss: 4.4777
Iteration: 107; Percent complete: 2.7%; Average loss: 4.2858
Iteration: 108; Percent complete: 2.7%; Average loss: 4.4629
Iteration: 109; Percent complete: 2.7%; Average loss: 4.4230
Iteration: 110; Percent complete: 2.8%; Average loss: 4.5404
Iteration: 111; Percent complete: 2.8%; Average loss: 4.6122
Iteration: 112; Percent complete: 2.8%; Average loss: 4.4554
Iteration: 113; Percent complete: 2.8%; Average loss: 4.2324
Iteration: 114; Percent complete: 2.9%; Average loss: 4.5465
Iteration: 115; Percent complete: 2.9%; Average loss: 4.4354
Iteration: 116; Percent complete: 2.9%; Average loss: 4.4870
Iteration: 117; Percent complete: 2.9%; Average loss: 4.4942
Iteration: 118; Percent complete: 2.9%; Average loss: 4.5002
Iteration: 119; Percent complete: 3.0%; Average loss: 4.2650
Iteration: 120; Percent complete: 3.0%; Average loss: 4.3312
Iteration: 121; Percent complete: 3.0%; Average loss: 4.4253
Iteration: 122; Percent complete: 3.0%; Average loss: 4.2654
Iteration: 123; Percent complete: 3.1%; Average loss: 4.4389
Iteration: 124; Percent complete: 3.1%; Average loss: 4.2412
Iteration: 125; Percent complete: 3.1%; Average loss: 4.4684
Iteration: 126; Percent complete: 3.1%; Average loss: 4.3946
Iteration: 127; Percent complete: 3.2%; Average loss: 4.3683
Iteration: 128; Percent complete: 3.2%; Average loss: 4.1397
Iteration: 129; Percent complete: 3.2%; Average loss: 4.2236
Iteration: 130; Percent complete: 3.2%; Average loss: 4.0977
Iteration: 131; Percent complete: 3.3%; Average loss: 4.1708
Iteration: 132; Percent complete: 3.3%; Average loss: 4.0860
Iteration: 133; Percent complete: 3.3%; Average loss: 4.3887
Iteration: 134; Percent complete: 3.4%; Average loss: 4.3182
Iteration: 135; Percent complete: 3.4%; Average loss: 4.3485
Iteration: 136; Percent complete: 3.4%; Average loss: 4.4091
Iteration: 137; Percent complete: 3.4%; Average loss: 4.1757
Iteration: 138; Percent complete: 3.5%; Average loss: 4.3162
Iteration: 139; Percent complete: 3.5%; Average loss: 4.3872
Iteration: 140; Percent complete: 3.5%; Average loss: 4.3838
Iteration: 141; Percent complete: 3.5%; Average loss: 4.5650
Iteration: 142; Percent complete: 3.5%; Average loss: 4.3404
Iteration: 143; Percent complete: 3.6%; Average loss: 3.9919
Iteration: 144; Percent complete: 3.6%; Average loss: 4.3775
Iteration: 145; Percent complete: 3.6%; Average loss: 4.2161
Iteration: 146; Percent complete: 3.6%; Average loss: 4.5946
Iteration: 147; Percent complete: 3.7%; Average loss: 4.3883
Iteration: 148; Percent complete: 3.7%; Average loss: 4.1866
Iteration: 149; Percent complete: 3.7%; Average loss: 4.3422
Iteration: 150; Percent complete: 3.8%; Average loss: 4.3170
Iteration: 151; Percent complete: 3.8%; Average loss: 4.1916
Iteration: 152; Percent complete: 3.8%; Average loss: 4.1621
Iteration: 153; Percent complete: 3.8%; Average loss: 4.2491
Iteration: 154; Percent complete: 3.9%; Average loss: 4.5571
Iteration: 155; Percent complete: 3.9%; Average loss: 4.4573
Iteration: 156; Percent complete: 3.9%; Average loss: 4.4096
Iteration: 157; Percent complete: 3.9%; Average loss: 4.3201
Iteration: 158; Percent complete: 4.0%; Average loss: 4.2150
Iteration: 159; Percent complete: 4.0%; Average loss: 3.9948
Iteration: 160; Percent complete: 4.0%; Average loss: 4.0997
Iteration: 161; Percent complete: 4.0%; Average loss: 4.1356
Iteration: 162; Percent complete: 4.0%; Average loss: 4.2860
Iteration: 163; Percent complete: 4.1%; Average loss: 3.9765
Iteration: 164; Percent complete: 4.1%; Average loss: 3.8972
Iteration: 165; Percent complete: 4.1%; Average loss: 4.3133
Iteration: 166; Percent complete: 4.2%; Average loss: 4.1744
Iteration: 167; Percent complete: 4.2%; Average loss: 4.0238
Iteration: 168; Percent complete: 4.2%; Average loss: 4.1336
Iteration: 169; Percent complete: 4.2%; Average loss: 4.1491
Iteration: 170; Percent complete: 4.2%; Average loss: 4.0231
Iteration: 171; Percent complete: 4.3%; Average loss: 3.9061
Iteration: 172; Percent complete: 4.3%; Average loss: 4.1885
Iteration: 173; Percent complete: 4.3%; Average loss: 4.0623
Iteration: 174; Percent complete: 4.3%; Average loss: 4.2164
Iteration: 175; Percent complete: 4.4%; Average loss: 4.0643
Iteration: 176; Percent complete: 4.4%; Average loss: 3.8962
Iteration: 177; Percent complete: 4.4%; Average loss: 4.4844
Iteration: 178; Percent complete: 4.5%; Average loss: 4.4525
Iteration: 179; Percent complete: 4.5%; Average loss: 4.2441
Iteration: 180; Percent complete: 4.5%; Average loss: 3.9858
Iteration: 181; Percent complete: 4.5%; Average loss: 4.0561
Iteration: 182; Percent complete: 4.5%; Average loss: 4.1148
Iteration: 183; Percent complete: 4.6%; Average loss: 4.2531
Iteration: 184; Percent complete: 4.6%; Average loss: 4.0750
Iteration: 185; Percent complete: 4.6%; Average loss: 4.3453
Iteration: 186; Percent complete: 4.7%; Average loss: 4.2351
Iteration: 187; Percent complete: 4.7%; Average loss: 4.0409
Iteration: 188; Percent complete: 4.7%; Average loss: 4.0924
Iteration: 189; Percent complete: 4.7%; Average loss: 4.1410
Iteration: 190; Percent complete: 4.8%; Average loss: 4.2376
Iteration: 191; Percent complete: 4.8%; Average loss: 4.1719
Iteration: 192; Percent complete: 4.8%; Average loss: 4.1547
Iteration: 193; Percent complete: 4.8%; Average loss: 4.1585
Iteration: 194; Percent complete: 4.9%; Average loss: 4.1650
Iteration: 195; Percent complete: 4.9%; Average loss: 3.9539
Iteration: 196; Percent complete: 4.9%; Average loss: 4.0660
Iteration: 197; Percent complete: 4.9%; Average loss: 4.3063
Iteration: 198; Percent complete: 5.0%; Average loss: 4.3738
Iteration: 199; Percent complete: 5.0%; Average loss: 4.2360
Iteration: 200; Percent complete: 5.0%; Average loss: 4.0881
Iteration: 201; Percent complete: 5.0%; Average loss: 3.8893
Iteration: 202; Percent complete: 5.1%; Average loss: 4.1264
Iteration: 203; Percent complete: 5.1%; Average loss: 4.0838
Iteration: 204; Percent complete: 5.1%; Average loss: 3.8958
Iteration: 205; Percent complete: 5.1%; Average loss: 4.1650
Iteration: 206; Percent complete: 5.1%; Average loss: 4.1490
Iteration: 207; Percent complete: 5.2%; Average loss: 4.1126
Iteration: 208; Percent complete: 5.2%; Average loss: 3.9959
Iteration: 209; Percent complete: 5.2%; Average loss: 4.1553
Iteration: 210; Percent complete: 5.2%; Average loss: 4.1622
Iteration: 211; Percent complete: 5.3%; Average loss: 4.0715
Iteration: 212; Percent complete: 5.3%; Average loss: 4.0905
Iteration: 213; Percent complete: 5.3%; Average loss: 4.0597
Iteration: 214; Percent complete: 5.3%; Average loss: 4.0665
Iteration: 215; Percent complete: 5.4%; Average loss: 3.9934
Iteration: 216; Percent complete: 5.4%; Average loss: 3.8673
Iteration: 217; Percent complete: 5.4%; Average loss: 3.8289
Iteration: 218; Percent complete: 5.5%; Average loss: 4.4361
Iteration: 219; Percent complete: 5.5%; Average loss: 4.0751
Iteration: 220; Percent complete: 5.5%; Average loss: 4.1097
Iteration: 221; Percent complete: 5.5%; Average loss: 4.2077
Iteration: 222; Percent complete: 5.5%; Average loss: 4.2092
Iteration: 223; Percent complete: 5.6%; Average loss: 4.1545
Iteration: 224; Percent complete: 5.6%; Average loss: 4.1416
Iteration: 225; Percent complete: 5.6%; Average loss: 4.1928
Iteration: 226; Percent complete: 5.7%; Average loss: 3.9792
Iteration: 227; Percent complete: 5.7%; Average loss: 4.2563
Iteration: 228; Percent complete: 5.7%; Average loss: 3.8573
Iteration: 229; Percent complete: 5.7%; Average loss: 4.1209
Iteration: 230; Percent complete: 5.8%; Average loss: 3.9499
Iteration: 231; Percent complete: 5.8%; Average loss: 3.8420
Iteration: 232; Percent complete: 5.8%; Average loss: 3.9815
Iteration: 233; Percent complete: 5.8%; Average loss: 4.3232
Iteration: 234; Percent complete: 5.9%; Average loss: 4.0894
Iteration: 235; Percent complete: 5.9%; Average loss: 3.8160
Iteration: 236; Percent complete: 5.9%; Average loss: 3.9425
Iteration: 237; Percent complete: 5.9%; Average loss: 3.9802
Iteration: 238; Percent complete: 5.9%; Average loss: 4.1489
Iteration: 239; Percent complete: 6.0%; Average loss: 4.0325
Iteration: 240; Percent complete: 6.0%; Average loss: 3.9266
Iteration: 241; Percent complete: 6.0%; Average loss: 4.0881
Iteration: 242; Percent complete: 6.0%; Average loss: 4.1025
Iteration: 243; Percent complete: 6.1%; Average loss: 4.2817
Iteration: 244; Percent complete: 6.1%; Average loss: 3.9286
Iteration: 245; Percent complete: 6.1%; Average loss: 3.8332
Iteration: 246; Percent complete: 6.2%; Average loss: 3.9454
Iteration: 247; Percent complete: 6.2%; Average loss: 3.6429
Iteration: 248; Percent complete: 6.2%; Average loss: 4.0342
Iteration: 249; Percent complete: 6.2%; Average loss: 3.8119
Iteration: 250; Percent complete: 6.2%; Average loss: 4.1033
Iteration: 251; Percent complete: 6.3%; Average loss: 4.1013
Iteration: 252; Percent complete: 6.3%; Average loss: 4.1590
Iteration: 253; Percent complete: 6.3%; Average loss: 4.2334
Iteration: 254; Percent complete: 6.3%; Average loss: 4.0434
Iteration: 255; Percent complete: 6.4%; Average loss: 4.3117
Iteration: 256; Percent complete: 6.4%; Average loss: 3.8882
Iteration: 257; Percent complete: 6.4%; Average loss: 4.1714
Iteration: 258; Percent complete: 6.5%; Average loss: 4.2814
Iteration: 259; Percent complete: 6.5%; Average loss: 3.9549
Iteration: 260; Percent complete: 6.5%; Average loss: 4.0823
Iteration: 261; Percent complete: 6.5%; Average loss: 3.7846
Iteration: 262; Percent complete: 6.6%; Average loss: 4.1967
Iteration: 263; Percent complete: 6.6%; Average loss: 3.8223
Iteration: 264; Percent complete: 6.6%; Average loss: 4.1136
Iteration: 265; Percent complete: 6.6%; Average loss: 4.1957
Iteration: 266; Percent complete: 6.7%; Average loss: 3.9977
Iteration: 267; Percent complete: 6.7%; Average loss: 3.9221
Iteration: 268; Percent complete: 6.7%; Average loss: 3.9892
Iteration: 269; Percent complete: 6.7%; Average loss: 4.2016
Iteration: 270; Percent complete: 6.8%; Average loss: 4.0049
Iteration: 271; Percent complete: 6.8%; Average loss: 4.1851
Iteration: 272; Percent complete: 6.8%; Average loss: 3.9993
Iteration: 273; Percent complete: 6.8%; Average loss: 3.9543
Iteration: 274; Percent complete: 6.9%; Average loss: 3.9481
Iteration: 275; Percent complete: 6.9%; Average loss: 3.9368
Iteration: 276; Percent complete: 6.9%; Average loss: 4.2964
Iteration: 277; Percent complete: 6.9%; Average loss: 3.9897
Iteration: 278; Percent complete: 7.0%; Average loss: 3.8091
Iteration: 279; Percent complete: 7.0%; Average loss: 3.7206
Iteration: 280; Percent complete: 7.0%; Average loss: 3.8881
Iteration: 281; Percent complete: 7.0%; Average loss: 3.9766
Iteration: 282; Percent complete: 7.0%; Average loss: 4.1154
Iteration: 283; Percent complete: 7.1%; Average loss: 3.8371
Iteration: 284; Percent complete: 7.1%; Average loss: 4.0124
Iteration: 285; Percent complete: 7.1%; Average loss: 4.0646
Iteration: 286; Percent complete: 7.1%; Average loss: 4.1267
Iteration: 287; Percent complete: 7.2%; Average loss: 3.8763
Iteration: 288; Percent complete: 7.2%; Average loss: 3.8785
Iteration: 289; Percent complete: 7.2%; Average loss: 3.6487
Iteration: 290; Percent complete: 7.2%; Average loss: 3.9347
Iteration: 291; Percent complete: 7.3%; Average loss: 3.7838
Iteration: 292; Percent complete: 7.3%; Average loss: 4.0048
Iteration: 293; Percent complete: 7.3%; Average loss: 3.8781
Iteration: 294; Percent complete: 7.3%; Average loss: 3.7997
Iteration: 295; Percent complete: 7.4%; Average loss: 3.6786
Iteration: 296; Percent complete: 7.4%; Average loss: 4.0864
Iteration: 297; Percent complete: 7.4%; Average loss: 4.0440
Iteration: 298; Percent complete: 7.4%; Average loss: 3.9112
Iteration: 299; Percent complete: 7.5%; Average loss: 3.9777
Iteration: 300; Percent complete: 7.5%; Average loss: 4.0828
Iteration: 301; Percent complete: 7.5%; Average loss: 3.9136
Iteration: 302; Percent complete: 7.5%; Average loss: 3.9647
Iteration: 303; Percent complete: 7.6%; Average loss: 3.7457
Iteration: 304; Percent complete: 7.6%; Average loss: 3.8520
Iteration: 305; Percent complete: 7.6%; Average loss: 3.7469
Iteration: 306; Percent complete: 7.6%; Average loss: 3.8876
Iteration: 307; Percent complete: 7.7%; Average loss: 4.0786
Iteration: 308; Percent complete: 7.7%; Average loss: 4.1026
Iteration: 309; Percent complete: 7.7%; Average loss: 3.8186
Iteration: 310; Percent complete: 7.8%; Average loss: 3.6751
Iteration: 311; Percent complete: 7.8%; Average loss: 4.1557
Iteration: 312; Percent complete: 7.8%; Average loss: 3.8001
Iteration: 313; Percent complete: 7.8%; Average loss: 3.7740
Iteration: 314; Percent complete: 7.8%; Average loss: 3.9567
Iteration: 315; Percent complete: 7.9%; Average loss: 4.0724
Iteration: 316; Percent complete: 7.9%; Average loss: 3.7730
Iteration: 317; Percent complete: 7.9%; Average loss: 3.8214
Iteration: 318; Percent complete: 8.0%; Average loss: 4.0005
Iteration: 319; Percent complete: 8.0%; Average loss: 3.8158
Iteration: 320; Percent complete: 8.0%; Average loss: 3.8700
Iteration: 321; Percent complete: 8.0%; Average loss: 3.8632
Iteration: 322; Percent complete: 8.1%; Average loss: 4.1100
Iteration: 323; Percent complete: 8.1%; Average loss: 3.9168
Iteration: 324; Percent complete: 8.1%; Average loss: 3.8729
Iteration: 325; Percent complete: 8.1%; Average loss: 3.8990
Iteration: 326; Percent complete: 8.2%; Average loss: 3.6726
Iteration: 327; Percent complete: 8.2%; Average loss: 3.9549
Iteration: 328; Percent complete: 8.2%; Average loss: 3.9177
Iteration: 329; Percent complete: 8.2%; Average loss: 4.0169
Iteration: 330; Percent complete: 8.2%; Average loss: 3.5635
Iteration: 331; Percent complete: 8.3%; Average loss: 4.2847
Iteration: 332; Percent complete: 8.3%; Average loss: 3.7688
Iteration: 333; Percent complete: 8.3%; Average loss: 3.5813
Iteration: 334; Percent complete: 8.3%; Average loss: 3.8633
Iteration: 335; Percent complete: 8.4%; Average loss: 4.0817
Iteration: 336; Percent complete: 8.4%; Average loss: 3.9683
Iteration: 337; Percent complete: 8.4%; Average loss: 3.8492
Iteration: 338; Percent complete: 8.5%; Average loss: 3.7847
Iteration: 339; Percent complete: 8.5%; Average loss: 3.8454
Iteration: 340; Percent complete: 8.5%; Average loss: 3.7504
Iteration: 341; Percent complete: 8.5%; Average loss: 3.4632
Iteration: 342; Percent complete: 8.6%; Average loss: 4.0036
Iteration: 343; Percent complete: 8.6%; Average loss: 3.8209
Iteration: 344; Percent complete: 8.6%; Average loss: 3.6772
Iteration: 345; Percent complete: 8.6%; Average loss: 3.9537
Iteration: 346; Percent complete: 8.6%; Average loss: 3.8451
Iteration: 347; Percent complete: 8.7%; Average loss: 3.5178
Iteration: 348; Percent complete: 8.7%; Average loss: 3.6912
Iteration: 349; Percent complete: 8.7%; Average loss: 3.9806
Iteration: 350; Percent complete: 8.8%; Average loss: 3.7679
Iteration: 351; Percent complete: 8.8%; Average loss: 3.9598
Iteration: 352; Percent complete: 8.8%; Average loss: 3.9043
Iteration: 353; Percent complete: 8.8%; Average loss: 3.7612
Iteration: 354; Percent complete: 8.8%; Average loss: 4.0019
Iteration: 355; Percent complete: 8.9%; Average loss: 3.8737
Iteration: 356; Percent complete: 8.9%; Average loss: 3.8994
Iteration: 357; Percent complete: 8.9%; Average loss: 3.7013
Iteration: 358; Percent complete: 8.9%; Average loss: 3.7841
Iteration: 359; Percent complete: 9.0%; Average loss: 4.0558
Iteration: 360; Percent complete: 9.0%; Average loss: 3.8457
Iteration: 361; Percent complete: 9.0%; Average loss: 3.9379
Iteration: 362; Percent complete: 9.0%; Average loss: 3.8589
Iteration: 363; Percent complete: 9.1%; Average loss: 4.0065
Iteration: 364; Percent complete: 9.1%; Average loss: 3.7372
Iteration: 365; Percent complete: 9.1%; Average loss: 3.9347
Iteration: 366; Percent complete: 9.2%; Average loss: 3.7272
Iteration: 367; Percent complete: 9.2%; Average loss: 3.9563
Iteration: 368; Percent complete: 9.2%; Average loss: 3.9425
Iteration: 369; Percent complete: 9.2%; Average loss: 4.0358
Iteration: 370; Percent complete: 9.2%; Average loss: 3.7613
Iteration: 371; Percent complete: 9.3%; Average loss: 4.2599
Iteration: 372; Percent complete: 9.3%; Average loss: 3.7785
Iteration: 373; Percent complete: 9.3%; Average loss: 3.6784
Iteration: 374; Percent complete: 9.3%; Average loss: 3.9326
Iteration: 375; Percent complete: 9.4%; Average loss: 3.6933
Iteration: 376; Percent complete: 9.4%; Average loss: 3.9797
Iteration: 377; Percent complete: 9.4%; Average loss: 3.6358
Iteration: 378; Percent complete: 9.4%; Average loss: 3.6632
Iteration: 379; Percent complete: 9.5%; Average loss: 3.9558
Iteration: 380; Percent complete: 9.5%; Average loss: 3.7491
Iteration: 381; Percent complete: 9.5%; Average loss: 3.9314
Iteration: 382; Percent complete: 9.6%; Average loss: 3.9219
Iteration: 383; Percent complete: 9.6%; Average loss: 3.7203
Iteration: 384; Percent complete: 9.6%; Average loss: 3.8907
Iteration: 385; Percent complete: 9.6%; Average loss: 3.9824
Iteration: 386; Percent complete: 9.7%; Average loss: 3.7411
Iteration: 387; Percent complete: 9.7%; Average loss: 4.0500
Iteration: 388; Percent complete: 9.7%; Average loss: 3.7787
Iteration: 389; Percent complete: 9.7%; Average loss: 4.0109
Iteration: 390; Percent complete: 9.8%; Average loss: 3.9011
Iteration: 391; Percent complete: 9.8%; Average loss: 3.7563
Iteration: 392; Percent complete: 9.8%; Average loss: 3.9205
Iteration: 393; Percent complete: 9.8%; Average loss: 3.6404
Iteration: 394; Percent complete: 9.8%; Average loss: 3.8888
Iteration: 395; Percent complete: 9.9%; Average loss: 3.3773
Iteration: 396; Percent complete: 9.9%; Average loss: 3.7637
Iteration: 397; Percent complete: 9.9%; Average loss: 4.0781
Iteration: 398; Percent complete: 10.0%; Average loss: 3.8895
Iteration: 399; Percent complete: 10.0%; Average loss: 3.7149
Iteration: 400; Percent complete: 10.0%; Average loss: 3.8218
Iteration: 401; Percent complete: 10.0%; Average loss: 3.9498
Iteration: 402; Percent complete: 10.1%; Average loss: 3.7941
Iteration: 403; Percent complete: 10.1%; Average loss: 3.8166
Iteration: 404; Percent complete: 10.1%; Average loss: 3.8533
Iteration: 405; Percent complete: 10.1%; Average loss: 3.9581
Iteration: 406; Percent complete: 10.2%; Average loss: 3.8497
Iteration: 407; Percent complete: 10.2%; Average loss: 3.7559
Iteration: 408; Percent complete: 10.2%; Average loss: 3.6973
Iteration: 409; Percent complete: 10.2%; Average loss: 3.9473
Iteration: 410; Percent complete: 10.2%; Average loss: 3.6072
Iteration: 411; Percent complete: 10.3%; Average loss: 3.6537
Iteration: 412; Percent complete: 10.3%; Average loss: 4.0640
Iteration: 413; Percent complete: 10.3%; Average loss: 3.5439
Iteration: 414; Percent complete: 10.3%; Average loss: 3.6621
Iteration: 415; Percent complete: 10.4%; Average loss: 3.8547
Iteration: 416; Percent complete: 10.4%; Average loss: 3.8657
Iteration: 417; Percent complete: 10.4%; Average loss: 3.8646
Iteration: 418; Percent complete: 10.4%; Average loss: 3.9120
Iteration: 419; Percent complete: 10.5%; Average loss: 3.8033
Iteration: 420; Percent complete: 10.5%; Average loss: 3.8529
Iteration: 421; Percent complete: 10.5%; Average loss: 3.6261
Iteration: 422; Percent complete: 10.5%; Average loss: 3.5464
Iteration: 423; Percent complete: 10.6%; Average loss: 3.7509
Iteration: 424; Percent complete: 10.6%; Average loss: 3.9796
Iteration: 425; Percent complete: 10.6%; Average loss: 3.5938
Iteration: 426; Percent complete: 10.7%; Average loss: 3.8382
Iteration: 427; Percent complete: 10.7%; Average loss: 3.9296
Iteration: 428; Percent complete: 10.7%; Average loss: 3.8460
Iteration: 429; Percent complete: 10.7%; Average loss: 3.9669
Iteration: 430; Percent complete: 10.8%; Average loss: 3.9962
Iteration: 431; Percent complete: 10.8%; Average loss: 3.7847
Iteration: 432; Percent complete: 10.8%; Average loss: 3.9350
Iteration: 433; Percent complete: 10.8%; Average loss: 3.9628
Iteration: 434; Percent complete: 10.8%; Average loss: 3.8060
Iteration: 435; Percent complete: 10.9%; Average loss: 4.0846
Iteration: 436; Percent complete: 10.9%; Average loss: 3.9507
Iteration: 437; Percent complete: 10.9%; Average loss: 3.5403
Iteration: 438; Percent complete: 10.9%; Average loss: 4.0912
Iteration: 439; Percent complete: 11.0%; Average loss: 3.5663
Iteration: 440; Percent complete: 11.0%; Average loss: 3.8707
Iteration: 441; Percent complete: 11.0%; Average loss: 3.8741
Iteration: 442; Percent complete: 11.1%; Average loss: 3.7232
Iteration: 443; Percent complete: 11.1%; Average loss: 3.8237
Iteration: 444; Percent complete: 11.1%; Average loss: 3.8807
Iteration: 445; Percent complete: 11.1%; Average loss: 3.9255
Iteration: 446; Percent complete: 11.2%; Average loss: 3.7899
Iteration: 447; Percent complete: 11.2%; Average loss: 3.8532
Iteration: 448; Percent complete: 11.2%; Average loss: 3.6919
Iteration: 449; Percent complete: 11.2%; Average loss: 3.9169
Iteration: 450; Percent complete: 11.2%; Average loss: 3.9859
Iteration: 451; Percent complete: 11.3%; Average loss: 3.8067
Iteration: 452; Percent complete: 11.3%; Average loss: 3.7149
Iteration: 453; Percent complete: 11.3%; Average loss: 3.8043
Iteration: 454; Percent complete: 11.3%; Average loss: 3.9554
Iteration: 455; Percent complete: 11.4%; Average loss: 3.5774
Iteration: 456; Percent complete: 11.4%; Average loss: 3.7593
Iteration: 457; Percent complete: 11.4%; Average loss: 3.8481
Iteration: 458; Percent complete: 11.5%; Average loss: 3.9458
Iteration: 459; Percent complete: 11.5%; Average loss: 3.7708
Iteration: 460; Percent complete: 11.5%; Average loss: 3.8217
Iteration: 461; Percent complete: 11.5%; Average loss: 3.7262
Iteration: 462; Percent complete: 11.6%; Average loss: 3.8242
Iteration: 463; Percent complete: 11.6%; Average loss: 3.8594
Iteration: 464; Percent complete: 11.6%; Average loss: 3.7610
Iteration: 465; Percent complete: 11.6%; Average loss: 3.7889
Iteration: 466; Percent complete: 11.7%; Average loss: 4.0095
Iteration: 467; Percent complete: 11.7%; Average loss: 3.5679
Iteration: 468; Percent complete: 11.7%; Average loss: 3.9321
Iteration: 469; Percent complete: 11.7%; Average loss: 3.6344
Iteration: 470; Percent complete: 11.8%; Average loss: 3.9713
Iteration: 471; Percent complete: 11.8%; Average loss: 3.4681
Iteration: 472; Percent complete: 11.8%; Average loss: 3.6284
Iteration: 473; Percent complete: 11.8%; Average loss: 3.7367
Iteration: 474; Percent complete: 11.8%; Average loss: 3.9257
Iteration: 475; Percent complete: 11.9%; Average loss: 3.7459
Iteration: 476; Percent complete: 11.9%; Average loss: 3.8017
Iteration: 477; Percent complete: 11.9%; Average loss: 3.8757
Iteration: 478; Percent complete: 11.9%; Average loss: 3.6395
Iteration: 479; Percent complete: 12.0%; Average loss: 3.8515
Iteration: 480; Percent complete: 12.0%; Average loss: 3.7395
Iteration: 481; Percent complete: 12.0%; Average loss: 3.7299
Iteration: 482; Percent complete: 12.0%; Average loss: 3.7645
Iteration: 483; Percent complete: 12.1%; Average loss: 3.7231
Iteration: 484; Percent complete: 12.1%; Average loss: 4.1627
Iteration: 485; Percent complete: 12.1%; Average loss: 3.6131
Iteration: 486; Percent complete: 12.2%; Average loss: 3.7303
Iteration: 487; Percent complete: 12.2%; Average loss: 3.5711
Iteration: 488; Percent complete: 12.2%; Average loss: 3.8294
Iteration: 489; Percent complete: 12.2%; Average loss: 3.8055
Iteration: 490; Percent complete: 12.2%; Average loss: 3.6134
Iteration: 491; Percent complete: 12.3%; Average loss: 3.7932
Iteration: 492; Percent complete: 12.3%; Average loss: 3.8362
Iteration: 493; Percent complete: 12.3%; Average loss: 3.9267
Iteration: 494; Percent complete: 12.3%; Average loss: 3.6283
Iteration: 495; Percent complete: 12.4%; Average loss: 3.7383
Iteration: 496; Percent complete: 12.4%; Average loss: 3.6617
Iteration: 497; Percent complete: 12.4%; Average loss: 3.7563
Iteration: 498; Percent complete: 12.4%; Average loss: 3.8389
Iteration: 499; Percent complete: 12.5%; Average loss: 3.8932
Iteration: 500; Percent complete: 12.5%; Average loss: 3.9226
Iteration: 501; Percent complete: 12.5%; Average loss: 3.7718
Iteration: 502; Percent complete: 12.6%; Average loss: 3.6079
Iteration: 503; Percent complete: 12.6%; Average loss: 3.6875
Iteration: 504; Percent complete: 12.6%; Average loss: 3.8985
Iteration: 505; Percent complete: 12.6%; Average loss: 3.9835
Iteration: 506; Percent complete: 12.7%; Average loss: 3.9319
Iteration: 507; Percent complete: 12.7%; Average loss: 3.9371
Iteration: 508; Percent complete: 12.7%; Average loss: 3.5873
Iteration: 509; Percent complete: 12.7%; Average loss: 3.7874
Iteration: 510; Percent complete: 12.8%; Average loss: 3.8358
Iteration: 511; Percent complete: 12.8%; Average loss: 3.6543
Iteration: 512; Percent complete: 12.8%; Average loss: 3.7400
Iteration: 513; Percent complete: 12.8%; Average loss: 3.5214
Iteration: 514; Percent complete: 12.8%; Average loss: 3.7848
Iteration: 515; Percent complete: 12.9%; Average loss: 3.8155
Iteration: 516; Percent complete: 12.9%; Average loss: 3.7929
Iteration: 517; Percent complete: 12.9%; Average loss: 3.7535
Iteration: 518; Percent complete: 13.0%; Average loss: 3.8067
Iteration: 519; Percent complete: 13.0%; Average loss: 3.7337
Iteration: 520; Percent complete: 13.0%; Average loss: 3.8585
Iteration: 521; Percent complete: 13.0%; Average loss: 3.7652
Iteration: 522; Percent complete: 13.1%; Average loss: 3.5882
Iteration: 523; Percent complete: 13.1%; Average loss: 3.6973
Iteration: 524; Percent complete: 13.1%; Average loss: 3.7286
Iteration: 525; Percent complete: 13.1%; Average loss: 3.6818
Iteration: 526; Percent complete: 13.2%; Average loss: 3.6055
Iteration: 527; Percent complete: 13.2%; Average loss: 3.9492
Iteration: 528; Percent complete: 13.2%; Average loss: 3.7744
Iteration: 529; Percent complete: 13.2%; Average loss: 3.7650
Iteration: 530; Percent complete: 13.2%; Average loss: 3.8221
Iteration: 531; Percent complete: 13.3%; Average loss: 3.8132
Iteration: 532; Percent complete: 13.3%; Average loss: 3.7057
Iteration: 533; Percent complete: 13.3%; Average loss: 3.3118
Iteration: 534; Percent complete: 13.4%; Average loss: 3.3968
Iteration: 535; Percent complete: 13.4%; Average loss: 3.8435
Iteration: 536; Percent complete: 13.4%; Average loss: 3.6614
Iteration: 537; Percent complete: 13.4%; Average loss: 3.5107
Iteration: 538; Percent complete: 13.5%; Average loss: 3.4803
Iteration: 539; Percent complete: 13.5%; Average loss: 3.7728
Iteration: 540; Percent complete: 13.5%; Average loss: 3.9178
Iteration: 541; Percent complete: 13.5%; Average loss: 3.9485
Iteration: 542; Percent complete: 13.6%; Average loss: 3.5537
Iteration: 543; Percent complete: 13.6%; Average loss: 3.7468
Iteration: 544; Percent complete: 13.6%; Average loss: 3.5230
Iteration: 545; Percent complete: 13.6%; Average loss: 3.5401
Iteration: 546; Percent complete: 13.7%; Average loss: 3.9489
Iteration: 547; Percent complete: 13.7%; Average loss: 3.7094
Iteration: 548; Percent complete: 13.7%; Average loss: 3.9461
Iteration: 549; Percent complete: 13.7%; Average loss: 3.9507
Iteration: 550; Percent complete: 13.8%; Average loss: 3.7760
Iteration: 551; Percent complete: 13.8%; Average loss: 3.7915
Iteration: 552; Percent complete: 13.8%; Average loss: 3.5329
Iteration: 553; Percent complete: 13.8%; Average loss: 3.5863
Iteration: 554; Percent complete: 13.9%; Average loss: 3.7345
Iteration: 555; Percent complete: 13.9%; Average loss: 3.6379
Iteration: 556; Percent complete: 13.9%; Average loss: 3.5480
Iteration: 557; Percent complete: 13.9%; Average loss: 3.6600
Iteration: 558; Percent complete: 14.0%; Average loss: 3.9149
Iteration: 559; Percent complete: 14.0%; Average loss: 3.8431
Iteration: 560; Percent complete: 14.0%; Average loss: 3.5920
Iteration: 561; Percent complete: 14.0%; Average loss: 3.5077
Iteration: 562; Percent complete: 14.1%; Average loss: 3.8915
Iteration: 563; Percent complete: 14.1%; Average loss: 3.6619
Iteration: 564; Percent complete: 14.1%; Average loss: 3.8666
Iteration: 565; Percent complete: 14.1%; Average loss: 3.6034
Iteration: 566; Percent complete: 14.1%; Average loss: 3.5229
Iteration: 567; Percent complete: 14.2%; Average loss: 3.7827
Iteration: 568; Percent complete: 14.2%; Average loss: 3.7121
Iteration: 569; Percent complete: 14.2%; Average loss: 3.9156
Iteration: 570; Percent complete: 14.2%; Average loss: 3.7964
Iteration: 571; Percent complete: 14.3%; Average loss: 3.6588
Iteration: 572; Percent complete: 14.3%; Average loss: 3.4504
Iteration: 573; Percent complete: 14.3%; Average loss: 3.5750
Iteration: 574; Percent complete: 14.3%; Average loss: 3.7854
Iteration: 575; Percent complete: 14.4%; Average loss: 3.7038
Iteration: 576; Percent complete: 14.4%; Average loss: 3.9221
Iteration: 577; Percent complete: 14.4%; Average loss: 3.9209
Iteration: 578; Percent complete: 14.4%; Average loss: 3.5046
Iteration: 579; Percent complete: 14.5%; Average loss: 3.7623
Iteration: 580; Percent complete: 14.5%; Average loss: 3.7891
Iteration: 581; Percent complete: 14.5%; Average loss: 3.6156
Iteration: 582; Percent complete: 14.5%; Average loss: 3.5280
Iteration: 583; Percent complete: 14.6%; Average loss: 3.6871
Iteration: 584; Percent complete: 14.6%; Average loss: 4.0510
Iteration: 585; Percent complete: 14.6%; Average loss: 3.6438
Iteration: 586; Percent complete: 14.6%; Average loss: 3.5769
Iteration: 587; Percent complete: 14.7%; Average loss: 3.8438
Iteration: 588; Percent complete: 14.7%; Average loss: 3.7349
Iteration: 589; Percent complete: 14.7%; Average loss: 4.0405
Iteration: 590; Percent complete: 14.8%; Average loss: 3.7619
Iteration: 591; Percent complete: 14.8%; Average loss: 3.4949
Iteration: 592; Percent complete: 14.8%; Average loss: 3.7449
Iteration: 593; Percent complete: 14.8%; Average loss: 3.5970
Iteration: 594; Percent complete: 14.8%; Average loss: 3.7375
Iteration: 595; Percent complete: 14.9%; Average loss: 3.7820
Iteration: 596; Percent complete: 14.9%; Average loss: 3.4231
Iteration: 597; Percent complete: 14.9%; Average loss: 3.8895
Iteration: 598; Percent complete: 14.9%; Average loss: 3.5398
Iteration: 599; Percent complete: 15.0%; Average loss: 3.6805
Iteration: 600; Percent complete: 15.0%; Average loss: 3.7654
Iteration: 601; Percent complete: 15.0%; Average loss: 3.5129
Iteration: 602; Percent complete: 15.0%; Average loss: 3.6822
Iteration: 603; Percent complete: 15.1%; Average loss: 3.7572
Iteration: 604; Percent complete: 15.1%; Average loss: 3.4499
Iteration: 605; Percent complete: 15.1%; Average loss: 3.8784
Iteration: 606; Percent complete: 15.2%; Average loss: 3.6638
Iteration: 607; Percent complete: 15.2%; Average loss: 3.4556
Iteration: 608; Percent complete: 15.2%; Average loss: 3.8029
Iteration: 609; Percent complete: 15.2%; Average loss: 3.5725
Iteration: 610; Percent complete: 15.2%; Average loss: 4.1415
Iteration: 611; Percent complete: 15.3%; Average loss: 3.4166
Iteration: 612; Percent complete: 15.3%; Average loss: 3.6932
Iteration: 613; Percent complete: 15.3%; Average loss: 3.6089
Iteration: 614; Percent complete: 15.3%; Average loss: 3.7806
Iteration: 615; Percent complete: 15.4%; Average loss: 3.6859
Iteration: 616; Percent complete: 15.4%; Average loss: 3.8517
Iteration: 617; Percent complete: 15.4%; Average loss: 3.6063
Iteration: 618; Percent complete: 15.4%; Average loss: 3.6470
Iteration: 619; Percent complete: 15.5%; Average loss: 3.5196
Iteration: 620; Percent complete: 15.5%; Average loss: 3.6247
Iteration: 621; Percent complete: 15.5%; Average loss: 3.9699
Iteration: 622; Percent complete: 15.6%; Average loss: 3.8702
Iteration: 623; Percent complete: 15.6%; Average loss: 3.7329
Iteration: 624; Percent complete: 15.6%; Average loss: 3.7473
Iteration: 625; Percent complete: 15.6%; Average loss: 3.5724
Iteration: 626; Percent complete: 15.7%; Average loss: 3.5080
Iteration: 627; Percent complete: 15.7%; Average loss: 3.6244
Iteration: 628; Percent complete: 15.7%; Average loss: 3.6946
Iteration: 629; Percent complete: 15.7%; Average loss: 3.6953
Iteration: 630; Percent complete: 15.8%; Average loss: 3.5127
Iteration: 631; Percent complete: 15.8%; Average loss: 3.5870
Iteration: 632; Percent complete: 15.8%; Average loss: 3.9421
Iteration: 633; Percent complete: 15.8%; Average loss: 3.5946
Iteration: 634; Percent complete: 15.8%; Average loss: 3.8046
Iteration: 635; Percent complete: 15.9%; Average loss: 3.7470
Iteration: 636; Percent complete: 15.9%; Average loss: 3.5957
Iteration: 637; Percent complete: 15.9%; Average loss: 3.8567
Iteration: 638; Percent complete: 16.0%; Average loss: 3.5726
Iteration: 639; Percent complete: 16.0%; Average loss: 3.6332
Iteration: 640; Percent complete: 16.0%; Average loss: 3.6686
Iteration: 641; Percent complete: 16.0%; Average loss: 3.8293
Iteration: 642; Percent complete: 16.1%; Average loss: 3.7686
Iteration: 643; Percent complete: 16.1%; Average loss: 3.6635
Iteration: 644; Percent complete: 16.1%; Average loss: 3.6652
Iteration: 645; Percent complete: 16.1%; Average loss: 3.5397
Iteration: 646; Percent complete: 16.2%; Average loss: 3.4374
Iteration: 647; Percent complete: 16.2%; Average loss: 3.5391
Iteration: 648; Percent complete: 16.2%; Average loss: 3.8290
Iteration: 649; Percent complete: 16.2%; Average loss: 3.6778
Iteration: 650; Percent complete: 16.2%; Average loss: 3.4320
Iteration: 651; Percent complete: 16.3%; Average loss: 3.5906
Iteration: 652; Percent complete: 16.3%; Average loss: 3.9495
Iteration: 653; Percent complete: 16.3%; Average loss: 3.5971
Iteration: 654; Percent complete: 16.4%; Average loss: 3.4697
Iteration: 655; Percent complete: 16.4%; Average loss: 3.8033
Iteration: 656; Percent complete: 16.4%; Average loss: 3.7098
Iteration: 657; Percent complete: 16.4%; Average loss: 3.6896
Iteration: 658; Percent complete: 16.4%; Average loss: 3.5436
Iteration: 659; Percent complete: 16.5%; Average loss: 3.5733
Iteration: 660; Percent complete: 16.5%; Average loss: 3.6031
Iteration: 661; Percent complete: 16.5%; Average loss: 3.4280
Iteration: 662; Percent complete: 16.6%; Average loss: 3.7352
Iteration: 663; Percent complete: 16.6%; Average loss: 3.5700
Iteration: 664; Percent complete: 16.6%; Average loss: 3.9872
Iteration: 665; Percent complete: 16.6%; Average loss: 3.8042
Iteration: 666; Percent complete: 16.7%; Average loss: 3.8189
Iteration: 667; Percent complete: 16.7%; Average loss: 3.5447
Iteration: 668; Percent complete: 16.7%; Average loss: 3.5144
Iteration: 669; Percent complete: 16.7%; Average loss: 3.4484
Iteration: 670; Percent complete: 16.8%; Average loss: 3.6758
Iteration: 671; Percent complete: 16.8%; Average loss: 3.8971
Iteration: 672; Percent complete: 16.8%; Average loss: 3.5349
Iteration: 673; Percent complete: 16.8%; Average loss: 3.7950
Iteration: 674; Percent complete: 16.9%; Average loss: 3.8565
Iteration: 675; Percent complete: 16.9%; Average loss: 3.3679
Iteration: 676; Percent complete: 16.9%; Average loss: 3.4667
Iteration: 677; Percent complete: 16.9%; Average loss: 3.7827
Iteration: 678; Percent complete: 17.0%; Average loss: 3.6532
Iteration: 679; Percent complete: 17.0%; Average loss: 3.7116
Iteration: 680; Percent complete: 17.0%; Average loss: 3.7174
Iteration: 681; Percent complete: 17.0%; Average loss: 3.6548
Iteration: 682; Percent complete: 17.1%; Average loss: 3.7184
Iteration: 683; Percent complete: 17.1%; Average loss: 3.6371
Iteration: 684; Percent complete: 17.1%; Average loss: 3.6987
Iteration: 685; Percent complete: 17.1%; Average loss: 3.6775
Iteration: 686; Percent complete: 17.2%; Average loss: 3.4639
Iteration: 687; Percent complete: 17.2%; Average loss: 3.4927
Iteration: 688; Percent complete: 17.2%; Average loss: 3.6514
Iteration: 689; Percent complete: 17.2%; Average loss: 3.6555
Iteration: 690; Percent complete: 17.2%; Average loss: 3.5225
Iteration: 691; Percent complete: 17.3%; Average loss: 3.7468
Iteration: 692; Percent complete: 17.3%; Average loss: 3.3916
Iteration: 693; Percent complete: 17.3%; Average loss: 3.5617
Iteration: 694; Percent complete: 17.3%; Average loss: 3.8038
Iteration: 695; Percent complete: 17.4%; Average loss: 3.7569
Iteration: 696; Percent complete: 17.4%; Average loss: 3.3154
Iteration: 697; Percent complete: 17.4%; Average loss: 3.3929
Iteration: 698; Percent complete: 17.4%; Average loss: 3.4117
Iteration: 699; Percent complete: 17.5%; Average loss: 3.7113
Iteration: 700; Percent complete: 17.5%; Average loss: 3.3231
Iteration: 701; Percent complete: 17.5%; Average loss: 3.6446
Iteration: 702; Percent complete: 17.5%; Average loss: 3.9322
Iteration: 703; Percent complete: 17.6%; Average loss: 3.7803
Iteration: 704; Percent complete: 17.6%; Average loss: 3.6342
Iteration: 705; Percent complete: 17.6%; Average loss: 3.1439
Iteration: 706; Percent complete: 17.6%; Average loss: 3.4717
Iteration: 707; Percent complete: 17.7%; Average loss: 3.6206
Iteration: 708; Percent complete: 17.7%; Average loss: 3.6993
Iteration: 709; Percent complete: 17.7%; Average loss: 3.5686
Iteration: 710; Percent complete: 17.8%; Average loss: 3.5512
Iteration: 711; Percent complete: 17.8%; Average loss: 3.7855
Iteration: 712; Percent complete: 17.8%; Average loss: 3.4039
Iteration: 713; Percent complete: 17.8%; Average loss: 3.4933
Iteration: 714; Percent complete: 17.8%; Average loss: 3.5936
Iteration: 715; Percent complete: 17.9%; Average loss: 3.6051
Iteration: 716; Percent complete: 17.9%; Average loss: 3.7475
Iteration: 717; Percent complete: 17.9%; Average loss: 3.5592
Iteration: 718; Percent complete: 17.9%; Average loss: 3.4894
Iteration: 719; Percent complete: 18.0%; Average loss: 3.4337
Iteration: 720; Percent complete: 18.0%; Average loss: 3.5198
Iteration: 721; Percent complete: 18.0%; Average loss: 3.6397
Iteration: 722; Percent complete: 18.1%; Average loss: 3.6636
Iteration: 723; Percent complete: 18.1%; Average loss: 3.8078
Iteration: 724; Percent complete: 18.1%; Average loss: 3.5325
Iteration: 725; Percent complete: 18.1%; Average loss: 3.5434
Iteration: 726; Percent complete: 18.1%; Average loss: 3.6856
Iteration: 727; Percent complete: 18.2%; Average loss: 3.8235
Iteration: 728; Percent complete: 18.2%; Average loss: 3.8683
Iteration: 729; Percent complete: 18.2%; Average loss: 3.6871
Iteration: 730; Percent complete: 18.2%; Average loss: 3.5617
Iteration: 731; Percent complete: 18.3%; Average loss: 3.4791
Iteration: 732; Percent complete: 18.3%; Average loss: 3.7939
Iteration: 733; Percent complete: 18.3%; Average loss: 3.8388
Iteration: 734; Percent complete: 18.4%; Average loss: 3.7315
Iteration: 735; Percent complete: 18.4%; Average loss: 3.7058
Iteration: 736; Percent complete: 18.4%; Average loss: 3.6344
Iteration: 737; Percent complete: 18.4%; Average loss: 3.8660
Iteration: 738; Percent complete: 18.4%; Average loss: 3.6991
Iteration: 739; Percent complete: 18.5%; Average loss: 3.4252
Iteration: 740; Percent complete: 18.5%; Average loss: 3.4822
Iteration: 741; Percent complete: 18.5%; Average loss: 3.4616
Iteration: 742; Percent complete: 18.6%; Average loss: 3.5571
Iteration: 743; Percent complete: 18.6%; Average loss: 3.6357
Iteration: 744; Percent complete: 18.6%; Average loss: 3.5276
Iteration: 745; Percent complete: 18.6%; Average loss: 3.8741
Iteration: 746; Percent complete: 18.6%; Average loss: 3.6448
Iteration: 747; Percent complete: 18.7%; Average loss: 3.7548
Iteration: 748; Percent complete: 18.7%; Average loss: 3.6781
Iteration: 749; Percent complete: 18.7%; Average loss: 3.7593
Iteration: 750; Percent complete: 18.8%; Average loss: 3.5313
Iteration: 751; Percent complete: 18.8%; Average loss: 3.5907
Iteration: 752; Percent complete: 18.8%; Average loss: 3.4354
Iteration: 753; Percent complete: 18.8%; Average loss: 3.6088
Iteration: 754; Percent complete: 18.9%; Average loss: 3.5746
Iteration: 755; Percent complete: 18.9%; Average loss: 3.5699
Iteration: 756; Percent complete: 18.9%; Average loss: 3.5246
Iteration: 757; Percent complete: 18.9%; Average loss: 3.5496
Iteration: 758; Percent complete: 18.9%; Average loss: 3.6855
Iteration: 759; Percent complete: 19.0%; Average loss: 3.5870
Iteration: 760; Percent complete: 19.0%; Average loss: 3.7606
Iteration: 761; Percent complete: 19.0%; Average loss: 3.7017
Iteration: 762; Percent complete: 19.1%; Average loss: 3.5185
Iteration: 763; Percent complete: 19.1%; Average loss: 3.3181
Iteration: 764; Percent complete: 19.1%; Average loss: 3.5759
Iteration: 765; Percent complete: 19.1%; Average loss: 3.6747
Iteration: 766; Percent complete: 19.1%; Average loss: 3.4417
Iteration: 767; Percent complete: 19.2%; Average loss: 3.3533
Iteration: 768; Percent complete: 19.2%; Average loss: 3.6321
Iteration: 769; Percent complete: 19.2%; Average loss: 3.5765
Iteration: 770; Percent complete: 19.2%; Average loss: 3.3584
Iteration: 771; Percent complete: 19.3%; Average loss: 3.7060
Iteration: 772; Percent complete: 19.3%; Average loss: 3.5387
Iteration: 773; Percent complete: 19.3%; Average loss: 3.4314
Iteration: 774; Percent complete: 19.4%; Average loss: 3.5862
Iteration: 775; Percent complete: 19.4%; Average loss: 3.5772
Iteration: 776; Percent complete: 19.4%; Average loss: 3.4341
Iteration: 777; Percent complete: 19.4%; Average loss: 3.6838
Iteration: 778; Percent complete: 19.4%; Average loss: 3.7042
Iteration: 779; Percent complete: 19.5%; Average loss: 3.5917
Iteration: 780; Percent complete: 19.5%; Average loss: 3.6237
Iteration: 781; Percent complete: 19.5%; Average loss: 3.7231
Iteration: 782; Percent complete: 19.6%; Average loss: 3.3352
Iteration: 783; Percent complete: 19.6%; Average loss: 3.6312
Iteration: 784; Percent complete: 19.6%; Average loss: 3.6490
Iteration: 785; Percent complete: 19.6%; Average loss: 3.6864
Iteration: 786; Percent complete: 19.7%; Average loss: 3.7442
Iteration: 787; Percent complete: 19.7%; Average loss: 3.2601
Iteration: 788; Percent complete: 19.7%; Average loss: 3.5501
Iteration: 789; Percent complete: 19.7%; Average loss: 3.5357
Iteration: 790; Percent complete: 19.8%; Average loss: 3.6438
Iteration: 791; Percent complete: 19.8%; Average loss: 3.4675
Iteration: 792; Percent complete: 19.8%; Average loss: 3.6738
Iteration: 793; Percent complete: 19.8%; Average loss: 3.8384
Iteration: 794; Percent complete: 19.9%; Average loss: 3.5892
Iteration: 795; Percent complete: 19.9%; Average loss: 3.3500
Iteration: 796; Percent complete: 19.9%; Average loss: 3.4725
Iteration: 797; Percent complete: 19.9%; Average loss: 3.5939
Iteration: 798; Percent complete: 20.0%; Average loss: 3.7069
Iteration: 799; Percent complete: 20.0%; Average loss: 3.7037
Iteration: 800; Percent complete: 20.0%; Average loss: 3.6850
Iteration: 801; Percent complete: 20.0%; Average loss: 3.5025
Iteration: 802; Percent complete: 20.1%; Average loss: 3.5847
Iteration: 803; Percent complete: 20.1%; Average loss: 3.6752
Iteration: 804; Percent complete: 20.1%; Average loss: 3.5172
Iteration: 805; Percent complete: 20.1%; Average loss: 3.5346
Iteration: 806; Percent complete: 20.2%; Average loss: 3.7442
Iteration: 807; Percent complete: 20.2%; Average loss: 3.8680
Iteration: 808; Percent complete: 20.2%; Average loss: 3.6233
Iteration: 809; Percent complete: 20.2%; Average loss: 3.2977
Iteration: 810; Percent complete: 20.2%; Average loss: 3.2758
Iteration: 811; Percent complete: 20.3%; Average loss: 3.3884
Iteration: 812; Percent complete: 20.3%; Average loss: 3.4590
Iteration: 813; Percent complete: 20.3%; Average loss: 3.8304
Iteration: 814; Percent complete: 20.3%; Average loss: 3.5286
Iteration: 815; Percent complete: 20.4%; Average loss: 3.5086
Iteration: 816; Percent complete: 20.4%; Average loss: 3.4911
Iteration: 817; Percent complete: 20.4%; Average loss: 3.3412
Iteration: 818; Percent complete: 20.4%; Average loss: 3.2577
Iteration: 819; Percent complete: 20.5%; Average loss: 3.6504
Iteration: 820; Percent complete: 20.5%; Average loss: 3.5955
Iteration: 821; Percent complete: 20.5%; Average loss: 3.1932
Iteration: 822; Percent complete: 20.5%; Average loss: 3.4230
Iteration: 823; Percent complete: 20.6%; Average loss: 3.6964
Iteration: 824; Percent complete: 20.6%; Average loss: 3.5229
Iteration: 825; Percent complete: 20.6%; Average loss: 3.5407
Iteration: 826; Percent complete: 20.6%; Average loss: 3.3394
Iteration: 827; Percent complete: 20.7%; Average loss: 3.4633
Iteration: 828; Percent complete: 20.7%; Average loss: 3.4149
Iteration: 829; Percent complete: 20.7%; Average loss: 3.6961
Iteration: 830; Percent complete: 20.8%; Average loss: 3.5099
Iteration: 831; Percent complete: 20.8%; Average loss: 3.4239
Iteration: 832; Percent complete: 20.8%; Average loss: 3.4929
Iteration: 833; Percent complete: 20.8%; Average loss: 3.5571
Iteration: 834; Percent complete: 20.8%; Average loss: 3.4072
Iteration: 835; Percent complete: 20.9%; Average loss: 3.4176
Iteration: 836; Percent complete: 20.9%; Average loss: 3.2004
Iteration: 837; Percent complete: 20.9%; Average loss: 3.7166
Iteration: 838; Percent complete: 20.9%; Average loss: 3.6533
Iteration: 839; Percent complete: 21.0%; Average loss: 3.5312
Iteration: 840; Percent complete: 21.0%; Average loss: 3.5365
Iteration: 841; Percent complete: 21.0%; Average loss: 3.4441
Iteration: 842; Percent complete: 21.1%; Average loss: 3.3727
Iteration: 843; Percent complete: 21.1%; Average loss: 3.5048
Iteration: 844; Percent complete: 21.1%; Average loss: 3.5661
Iteration: 845; Percent complete: 21.1%; Average loss: 3.4740
Iteration: 846; Percent complete: 21.1%; Average loss: 3.6022
Iteration: 847; Percent complete: 21.2%; Average loss: 3.2126
Iteration: 848; Percent complete: 21.2%; Average loss: 3.6734
Iteration: 849; Percent complete: 21.2%; Average loss: 3.8067
Iteration: 850; Percent complete: 21.2%; Average loss: 3.7979
Iteration: 851; Percent complete: 21.3%; Average loss: 3.5779
Iteration: 852; Percent complete: 21.3%; Average loss: 3.5585
Iteration: 853; Percent complete: 21.3%; Average loss: 3.4951
Iteration: 854; Percent complete: 21.3%; Average loss: 3.5752
Iteration: 855; Percent complete: 21.4%; Average loss: 3.4294
Iteration: 856; Percent complete: 21.4%; Average loss: 3.3502
Iteration: 857; Percent complete: 21.4%; Average loss: 3.7820
Iteration: 858; Percent complete: 21.4%; Average loss: 3.4047
Iteration: 859; Percent complete: 21.5%; Average loss: 3.6819
Iteration: 860; Percent complete: 21.5%; Average loss: 3.5851
Iteration: 861; Percent complete: 21.5%; Average loss: 3.7129
Iteration: 862; Percent complete: 21.6%; Average loss: 3.5245
Iteration: 863; Percent complete: 21.6%; Average loss: 3.6708
Iteration: 864; Percent complete: 21.6%; Average loss: 3.3896
Iteration: 865; Percent complete: 21.6%; Average loss: 3.5876
Iteration: 866; Percent complete: 21.6%; Average loss: 3.2886
Iteration: 867; Percent complete: 21.7%; Average loss: 3.7315
Iteration: 868; Percent complete: 21.7%; Average loss: 3.6438
Iteration: 869; Percent complete: 21.7%; Average loss: 3.6845
Iteration: 870; Percent complete: 21.8%; Average loss: 3.6568
Iteration: 871; Percent complete: 21.8%; Average loss: 3.4740
Iteration: 872; Percent complete: 21.8%; Average loss: 3.6282
Iteration: 873; Percent complete: 21.8%; Average loss: 3.5934
Iteration: 874; Percent complete: 21.9%; Average loss: 3.3505
Iteration: 875; Percent complete: 21.9%; Average loss: 3.5808
Iteration: 876; Percent complete: 21.9%; Average loss: 3.5762
Iteration: 877; Percent complete: 21.9%; Average loss: 3.3491
Iteration: 878; Percent complete: 21.9%; Average loss: 3.3511
Iteration: 879; Percent complete: 22.0%; Average loss: 3.6713
Iteration: 880; Percent complete: 22.0%; Average loss: 3.5947
Iteration: 881; Percent complete: 22.0%; Average loss: 3.4007
Iteration: 882; Percent complete: 22.1%; Average loss: 3.3845
Iteration: 883; Percent complete: 22.1%; Average loss: 3.8341
Iteration: 884; Percent complete: 22.1%; Average loss: 3.4680
Iteration: 885; Percent complete: 22.1%; Average loss: 3.7023
Iteration: 886; Percent complete: 22.1%; Average loss: 3.7007
Iteration: 887; Percent complete: 22.2%; Average loss: 3.4314
Iteration: 888; Percent complete: 22.2%; Average loss: 3.5344
Iteration: 889; Percent complete: 22.2%; Average loss: 3.4491
Iteration: 890; Percent complete: 22.2%; Average loss: 3.4142
Iteration: 891; Percent complete: 22.3%; Average loss: 3.4192
Iteration: 892; Percent complete: 22.3%; Average loss: 3.8210
Iteration: 893; Percent complete: 22.3%; Average loss: 3.4068
Iteration: 894; Percent complete: 22.4%; Average loss: 3.7920
Iteration: 895; Percent complete: 22.4%; Average loss: 3.5884
Iteration: 896; Percent complete: 22.4%; Average loss: 3.5319
Iteration: 897; Percent complete: 22.4%; Average loss: 3.6210
Iteration: 898; Percent complete: 22.4%; Average loss: 3.7416
Iteration: 899; Percent complete: 22.5%; Average loss: 3.4110
Iteration: 900; Percent complete: 22.5%; Average loss: 3.5818
Iteration: 901; Percent complete: 22.5%; Average loss: 3.6348
Iteration: 902; Percent complete: 22.6%; Average loss: 3.3964
Iteration: 903; Percent complete: 22.6%; Average loss: 3.1765
Iteration: 904; Percent complete: 22.6%; Average loss: 3.7222
Iteration: 905; Percent complete: 22.6%; Average loss: 3.5176
Iteration: 906; Percent complete: 22.7%; Average loss: 3.4310
Iteration: 907; Percent complete: 22.7%; Average loss: 3.6406
Iteration: 908; Percent complete: 22.7%; Average loss: 3.4411
Iteration: 909; Percent complete: 22.7%; Average loss: 3.3150
Iteration: 910; Percent complete: 22.8%; Average loss: 3.3771
Iteration: 911; Percent complete: 22.8%; Average loss: 3.6327
Iteration: 912; Percent complete: 22.8%; Average loss: 3.3969
Iteration: 913; Percent complete: 22.8%; Average loss: 3.3188
Iteration: 914; Percent complete: 22.9%; Average loss: 3.4487
Iteration: 915; Percent complete: 22.9%; Average loss: 3.4034
Iteration: 916; Percent complete: 22.9%; Average loss: 3.3536
Iteration: 917; Percent complete: 22.9%; Average loss: 3.3335
Iteration: 918; Percent complete: 22.9%; Average loss: 3.2451
Iteration: 919; Percent complete: 23.0%; Average loss: 3.4985
Iteration: 920; Percent complete: 23.0%; Average loss: 3.3771
Iteration: 921; Percent complete: 23.0%; Average loss: 3.4964
Iteration: 922; Percent complete: 23.1%; Average loss: 3.7720
Iteration: 923; Percent complete: 23.1%; Average loss: 3.5362
Iteration: 924; Percent complete: 23.1%; Average loss: 3.4222
Iteration: 925; Percent complete: 23.1%; Average loss: 3.4802
Iteration: 926; Percent complete: 23.2%; Average loss: 3.1474
Iteration: 927; Percent complete: 23.2%; Average loss: 3.4360
Iteration: 928; Percent complete: 23.2%; Average loss: 3.6061
Iteration: 929; Percent complete: 23.2%; Average loss: 3.6803
Iteration: 930; Percent complete: 23.2%; Average loss: 3.6016
Iteration: 931; Percent complete: 23.3%; Average loss: 3.5302
Iteration: 932; Percent complete: 23.3%; Average loss: 3.5333
Iteration: 933; Percent complete: 23.3%; Average loss: 3.3940
Iteration: 934; Percent complete: 23.4%; Average loss: 3.6692
Iteration: 935; Percent complete: 23.4%; Average loss: 3.5399
Iteration: 936; Percent complete: 23.4%; Average loss: 3.7961
Iteration: 937; Percent complete: 23.4%; Average loss: 3.3058
Iteration: 938; Percent complete: 23.4%; Average loss: 3.4929
Iteration: 939; Percent complete: 23.5%; Average loss: 3.4219
Iteration: 940; Percent complete: 23.5%; Average loss: 3.4546
Iteration: 941; Percent complete: 23.5%; Average loss: 3.6392
Iteration: 942; Percent complete: 23.5%; Average loss: 3.4436
Iteration: 943; Percent complete: 23.6%; Average loss: 3.8448
Iteration: 944; Percent complete: 23.6%; Average loss: 3.6372
Iteration: 945; Percent complete: 23.6%; Average loss: 3.7879
Iteration: 946; Percent complete: 23.6%; Average loss: 3.6685
Iteration: 947; Percent complete: 23.7%; Average loss: 3.8195
Iteration: 948; Percent complete: 23.7%; Average loss: 3.5728
Iteration: 949; Percent complete: 23.7%; Average loss: 3.5416
Iteration: 950; Percent complete: 23.8%; Average loss: 3.5198
Iteration: 951; Percent complete: 23.8%; Average loss: 3.4379
Iteration: 952; Percent complete: 23.8%; Average loss: 3.3608
Iteration: 953; Percent complete: 23.8%; Average loss: 3.5868
Iteration: 954; Percent complete: 23.8%; Average loss: 3.2593
Iteration: 955; Percent complete: 23.9%; Average loss: 3.5724
Iteration: 956; Percent complete: 23.9%; Average loss: 3.3387
Iteration: 957; Percent complete: 23.9%; Average loss: 3.4660
Iteration: 958; Percent complete: 23.9%; Average loss: 3.6876
Iteration: 959; Percent complete: 24.0%; Average loss: 3.5252
Iteration: 960; Percent complete: 24.0%; Average loss: 3.5065
Iteration: 961; Percent complete: 24.0%; Average loss: 3.6024
Iteration: 962; Percent complete: 24.1%; Average loss: 3.4975
Iteration: 963; Percent complete: 24.1%; Average loss: 3.6020
Iteration: 964; Percent complete: 24.1%; Average loss: 3.4071
Iteration: 965; Percent complete: 24.1%; Average loss: 3.2196
Iteration: 966; Percent complete: 24.1%; Average loss: 3.5724
Iteration: 967; Percent complete: 24.2%; Average loss: 3.6397
Iteration: 968; Percent complete: 24.2%; Average loss: 3.5983
Iteration: 969; Percent complete: 24.2%; Average loss: 3.7001
Iteration: 970; Percent complete: 24.2%; Average loss: 3.6001
Iteration: 971; Percent complete: 24.3%; Average loss: 3.4536
Iteration: 972; Percent complete: 24.3%; Average loss: 3.2838
Iteration: 973; Percent complete: 24.3%; Average loss: 3.7019
Iteration: 974; Percent complete: 24.3%; Average loss: 3.6679
Iteration: 975; Percent complete: 24.4%; Average loss: 3.4371
Iteration: 976; Percent complete: 24.4%; Average loss: 3.5069
Iteration: 977; Percent complete: 24.4%; Average loss: 3.2799
Iteration: 978; Percent complete: 24.4%; Average loss: 3.2872
Iteration: 979; Percent complete: 24.5%; Average loss: 3.7992
Iteration: 980; Percent complete: 24.5%; Average loss: 3.5744
Iteration: 981; Percent complete: 24.5%; Average loss: 3.4691
Iteration: 982; Percent complete: 24.6%; Average loss: 3.5518
Iteration: 983; Percent complete: 24.6%; Average loss: 3.4870
Iteration: 984; Percent complete: 24.6%; Average loss: 3.2071
Iteration: 985; Percent complete: 24.6%; Average loss: 3.2463
Iteration: 986; Percent complete: 24.6%; Average loss: 3.7042
Iteration: 987; Percent complete: 24.7%; Average loss: 3.3585
Iteration: 988; Percent complete: 24.7%; Average loss: 3.5212
Iteration: 989; Percent complete: 24.7%; Average loss: 3.5045
Iteration: 990; Percent complete: 24.8%; Average loss: 3.4182
Iteration: 991; Percent complete: 24.8%; Average loss: 3.5299
Iteration: 992; Percent complete: 24.8%; Average loss: 3.3513
Iteration: 993; Percent complete: 24.8%; Average loss: 3.2463
Iteration: 994; Percent complete: 24.9%; Average loss: 3.7042
Iteration: 995; Percent complete: 24.9%; Average loss: 3.2211
Iteration: 996; Percent complete: 24.9%; Average loss: 3.4346
Iteration: 997; Percent complete: 24.9%; Average loss: 3.7919
Iteration: 998; Percent complete: 24.9%; Average loss: 3.6878
Iteration: 999; Percent complete: 25.0%; Average loss: 3.5606
Iteration: 1000; Percent complete: 25.0%; Average loss: 3.6325
Iteration: 1001; Percent complete: 25.0%; Average loss: 3.6896
Iteration: 1002; Percent complete: 25.1%; Average loss: 3.4546
Iteration: 1003; Percent complete: 25.1%; Average loss: 3.4236
Iteration: 1004; Percent complete: 25.1%; Average loss: 3.2610
Iteration: 1005; Percent complete: 25.1%; Average loss: 3.3215
Iteration: 1006; Percent complete: 25.1%; Average loss: 3.5542
Iteration: 1007; Percent complete: 25.2%; Average loss: 3.7248
Iteration: 1008; Percent complete: 25.2%; Average loss: 3.6404
Iteration: 1009; Percent complete: 25.2%; Average loss: 3.2442
Iteration: 1010; Percent complete: 25.2%; Average loss: 3.3533
Iteration: 1011; Percent complete: 25.3%; Average loss: 3.6004
Iteration: 1012; Percent complete: 25.3%; Average loss: 3.6192
Iteration: 1013; Percent complete: 25.3%; Average loss: 3.2397
Iteration: 1014; Percent complete: 25.4%; Average loss: 3.5626
Iteration: 1015; Percent complete: 25.4%; Average loss: 3.5167
Iteration: 1016; Percent complete: 25.4%; Average loss: 3.5523
Iteration: 1017; Percent complete: 25.4%; Average loss: 3.4053
Iteration: 1018; Percent complete: 25.4%; Average loss: 3.4223
Iteration: 1019; Percent complete: 25.5%; Average loss: 3.2424
Iteration: 1020; Percent complete: 25.5%; Average loss: 3.4271
Iteration: 1021; Percent complete: 25.5%; Average loss: 3.4887
Iteration: 1022; Percent complete: 25.6%; Average loss: 3.5374
Iteration: 1023; Percent complete: 25.6%; Average loss: 3.4489
Iteration: 1024; Percent complete: 25.6%; Average loss: 3.5690
Iteration: 1025; Percent complete: 25.6%; Average loss: 3.5309
Iteration: 1026; Percent complete: 25.7%; Average loss: 3.5696
Iteration: 1027; Percent complete: 25.7%; Average loss: 3.6163
Iteration: 1028; Percent complete: 25.7%; Average loss: 3.6842
Iteration: 1029; Percent complete: 25.7%; Average loss: 3.7511
Iteration: 1030; Percent complete: 25.8%; Average loss: 3.4673
Iteration: 1031; Percent complete: 25.8%; Average loss: 3.5030
Iteration: 1032; Percent complete: 25.8%; Average loss: 3.7151
Iteration: 1033; Percent complete: 25.8%; Average loss: 3.4650
Iteration: 1034; Percent complete: 25.9%; Average loss: 3.4393
Iteration: 1035; Percent complete: 25.9%; Average loss: 3.3901
Iteration: 1036; Percent complete: 25.9%; Average loss: 3.4965
Iteration: 1037; Percent complete: 25.9%; Average loss: 3.3480
Iteration: 1038; Percent complete: 25.9%; Average loss: 3.2744
Iteration: 1039; Percent complete: 26.0%; Average loss: 3.5026
Iteration: 1040; Percent complete: 26.0%; Average loss: 3.2565
Iteration: 1041; Percent complete: 26.0%; Average loss: 3.4397
Iteration: 1042; Percent complete: 26.1%; Average loss: 3.3853
Iteration: 1043; Percent complete: 26.1%; Average loss: 3.6238
Iteration: 1044; Percent complete: 26.1%; Average loss: 3.3980
Iteration: 1045; Percent complete: 26.1%; Average loss: 3.5308
Iteration: 1046; Percent complete: 26.2%; Average loss: 3.5447
Iteration: 1047; Percent complete: 26.2%; Average loss: 3.3960
Iteration: 1048; Percent complete: 26.2%; Average loss: 3.5016
Iteration: 1049; Percent complete: 26.2%; Average loss: 3.4342
Iteration: 1050; Percent complete: 26.2%; Average loss: 3.4653
Iteration: 1051; Percent complete: 26.3%; Average loss: 3.6640
Iteration: 1052; Percent complete: 26.3%; Average loss: 3.6454
Iteration: 1053; Percent complete: 26.3%; Average loss: 3.4643
Iteration: 1054; Percent complete: 26.4%; Average loss: 3.6463
Iteration: 1055; Percent complete: 26.4%; Average loss: 3.2931
Iteration: 1056; Percent complete: 26.4%; Average loss: 3.5795
Iteration: 1057; Percent complete: 26.4%; Average loss: 3.1869
Iteration: 1058; Percent complete: 26.5%; Average loss: 3.4739
Iteration: 1059; Percent complete: 26.5%; Average loss: 3.1876
Iteration: 1060; Percent complete: 26.5%; Average loss: 3.2413
Iteration: 1061; Percent complete: 26.5%; Average loss: 3.4457
Iteration: 1062; Percent complete: 26.6%; Average loss: 3.2987
Iteration: 1063; Percent complete: 26.6%; Average loss: 3.4989
Iteration: 1064; Percent complete: 26.6%; Average loss: 3.5102
Iteration: 1065; Percent complete: 26.6%; Average loss: 3.3125
Iteration: 1066; Percent complete: 26.7%; Average loss: 3.4034
Iteration: 1067; Percent complete: 26.7%; Average loss: 3.3661
Iteration: 1068; Percent complete: 26.7%; Average loss: 3.6793
Iteration: 1069; Percent complete: 26.7%; Average loss: 3.3601
Iteration: 1070; Percent complete: 26.8%; Average loss: 3.3038
Iteration: 1071; Percent complete: 26.8%; Average loss: 3.4866
Iteration: 1072; Percent complete: 26.8%; Average loss: 3.4289
Iteration: 1073; Percent complete: 26.8%; Average loss: 3.3960
Iteration: 1074; Percent complete: 26.9%; Average loss: 3.4356
Iteration: 1075; Percent complete: 26.9%; Average loss: 3.2992
Iteration: 1076; Percent complete: 26.9%; Average loss: 3.5784
Iteration: 1077; Percent complete: 26.9%; Average loss: 3.1395
Iteration: 1078; Percent complete: 27.0%; Average loss: 3.4388
Iteration: 1079; Percent complete: 27.0%; Average loss: 3.4057
Iteration: 1080; Percent complete: 27.0%; Average loss: 3.3559
Iteration: 1081; Percent complete: 27.0%; Average loss: 3.2962
Iteration: 1082; Percent complete: 27.1%; Average loss: 3.5063
Iteration: 1083; Percent complete: 27.1%; Average loss: 3.2478
Iteration: 1084; Percent complete: 27.1%; Average loss: 3.4284
Iteration: 1085; Percent complete: 27.1%; Average loss: 3.6208
Iteration: 1086; Percent complete: 27.2%; Average loss: 3.4261
Iteration: 1087; Percent complete: 27.2%; Average loss: 3.4349
Iteration: 1088; Percent complete: 27.2%; Average loss: 3.6547
Iteration: 1089; Percent complete: 27.2%; Average loss: 3.4769
Iteration: 1090; Percent complete: 27.3%; Average loss: 3.6507
Iteration: 1091; Percent complete: 27.3%; Average loss: 3.4988
Iteration: 1092; Percent complete: 27.3%; Average loss: 3.5451
Iteration: 1093; Percent complete: 27.3%; Average loss: 3.4066
Iteration: 1094; Percent complete: 27.4%; Average loss: 3.4966
Iteration: 1095; Percent complete: 27.4%; Average loss: 3.3896
Iteration: 1096; Percent complete: 27.4%; Average loss: 3.5426
Iteration: 1097; Percent complete: 27.4%; Average loss: 3.4664
Iteration: 1098; Percent complete: 27.5%; Average loss: 3.5740
Iteration: 1099; Percent complete: 27.5%; Average loss: 3.3336
Iteration: 1100; Percent complete: 27.5%; Average loss: 3.4886
Iteration: 1101; Percent complete: 27.5%; Average loss: 3.6012
Iteration: 1102; Percent complete: 27.6%; Average loss: 3.2207
Iteration: 1103; Percent complete: 27.6%; Average loss: 3.3717
Iteration: 1104; Percent complete: 27.6%; Average loss: 3.5684
Iteration: 1105; Percent complete: 27.6%; Average loss: 3.5846
Iteration: 1106; Percent complete: 27.7%; Average loss: 3.3076
Iteration: 1107; Percent complete: 27.7%; Average loss: 3.4735
Iteration: 1108; Percent complete: 27.7%; Average loss: 3.2873
Iteration: 1109; Percent complete: 27.7%; Average loss: 3.4954
Iteration: 1110; Percent complete: 27.8%; Average loss: 3.2851
Iteration: 1111; Percent complete: 27.8%; Average loss: 3.5162
Iteration: 1112; Percent complete: 27.8%; Average loss: 3.6408
Iteration: 1113; Percent complete: 27.8%; Average loss: 3.3505
Iteration: 1114; Percent complete: 27.9%; Average loss: 3.5637
Iteration: 1115; Percent complete: 27.9%; Average loss: 3.1530
Iteration: 1116; Percent complete: 27.9%; Average loss: 3.6280
Iteration: 1117; Percent complete: 27.9%; Average loss: 3.4810
Iteration: 1118; Percent complete: 28.0%; Average loss: 3.5110
Iteration: 1119; Percent complete: 28.0%; Average loss: 3.4177
Iteration: 1120; Percent complete: 28.0%; Average loss: 3.6287
Iteration: 1121; Percent complete: 28.0%; Average loss: 3.5020
Iteration: 1122; Percent complete: 28.1%; Average loss: 3.5431
Iteration: 1123; Percent complete: 28.1%; Average loss: 3.4112
Iteration: 1124; Percent complete: 28.1%; Average loss: 3.5334
Iteration: 1125; Percent complete: 28.1%; Average loss: 3.4710
Iteration: 1126; Percent complete: 28.1%; Average loss: 3.4512
Iteration: 1127; Percent complete: 28.2%; Average loss: 3.3728
Iteration: 1128; Percent complete: 28.2%; Average loss: 3.3324
Iteration: 1129; Percent complete: 28.2%; Average loss: 3.4654
Iteration: 1130; Percent complete: 28.2%; Average loss: 3.6058
Iteration: 1131; Percent complete: 28.3%; Average loss: 3.2767
Iteration: 1132; Percent complete: 28.3%; Average loss: 3.6744
Iteration: 1133; Percent complete: 28.3%; Average loss: 3.5963
Iteration: 1134; Percent complete: 28.3%; Average loss: 3.5755
Iteration: 1135; Percent complete: 28.4%; Average loss: 3.2257
Iteration: 1136; Percent complete: 28.4%; Average loss: 3.4419
Iteration: 1137; Percent complete: 28.4%; Average loss: 3.5349
Iteration: 1138; Percent complete: 28.4%; Average loss: 3.3427
Iteration: 1139; Percent complete: 28.5%; Average loss: 3.3980
Iteration: 1140; Percent complete: 28.5%; Average loss: 3.3248
Iteration: 1141; Percent complete: 28.5%; Average loss: 3.4137
Iteration: 1142; Percent complete: 28.5%; Average loss: 3.3883
Iteration: 1143; Percent complete: 28.6%; Average loss: 3.1795
Iteration: 1144; Percent complete: 28.6%; Average loss: 3.3944
Iteration: 1145; Percent complete: 28.6%; Average loss: 3.3523
Iteration: 1146; Percent complete: 28.6%; Average loss: 3.5849
Iteration: 1147; Percent complete: 28.7%; Average loss: 3.4722
Iteration: 1148; Percent complete: 28.7%; Average loss: 3.2783
Iteration: 1149; Percent complete: 28.7%; Average loss: 3.5432
Iteration: 1150; Percent complete: 28.7%; Average loss: 3.4930
Iteration: 1151; Percent complete: 28.8%; Average loss: 3.4794
Iteration: 1152; Percent complete: 28.8%; Average loss: 3.4529
Iteration: 1153; Percent complete: 28.8%; Average loss: 3.2407
Iteration: 1154; Percent complete: 28.8%; Average loss: 3.6042
Iteration: 1155; Percent complete: 28.9%; Average loss: 3.5559
Iteration: 1156; Percent complete: 28.9%; Average loss: 3.4530
Iteration: 1157; Percent complete: 28.9%; Average loss: 3.3368
Iteration: 1158; Percent complete: 28.9%; Average loss: 3.5525
Iteration: 1159; Percent complete: 29.0%; Average loss: 3.3875
Iteration: 1160; Percent complete: 29.0%; Average loss: 3.4644
Iteration: 1161; Percent complete: 29.0%; Average loss: 3.4452
Iteration: 1162; Percent complete: 29.0%; Average loss: 3.6835
Iteration: 1163; Percent complete: 29.1%; Average loss: 3.1652
Iteration: 1164; Percent complete: 29.1%; Average loss: 3.2090
Iteration: 1165; Percent complete: 29.1%; Average loss: 3.4145
Iteration: 1166; Percent complete: 29.1%; Average loss: 3.3378
Iteration: 1167; Percent complete: 29.2%; Average loss: 3.3503
Iteration: 1168; Percent complete: 29.2%; Average loss: 3.1063
Iteration: 1169; Percent complete: 29.2%; Average loss: 3.4671
Iteration: 1170; Percent complete: 29.2%; Average loss: 3.4957
Iteration: 1171; Percent complete: 29.3%; Average loss: 3.3727
Iteration: 1172; Percent complete: 29.3%; Average loss: 3.5867
Iteration: 1173; Percent complete: 29.3%; Average loss: 3.1216
Iteration: 1174; Percent complete: 29.3%; Average loss: 3.6502
Iteration: 1175; Percent complete: 29.4%; Average loss: 3.3531
Iteration: 1176; Percent complete: 29.4%; Average loss: 3.5508
Iteration: 1177; Percent complete: 29.4%; Average loss: 3.4019
Iteration: 1178; Percent complete: 29.4%; Average loss: 3.3146
Iteration: 1179; Percent complete: 29.5%; Average loss: 3.2881
Iteration: 1180; Percent complete: 29.5%; Average loss: 3.4365
Iteration: 1181; Percent complete: 29.5%; Average loss: 3.5669
Iteration: 1182; Percent complete: 29.5%; Average loss: 3.5316
Iteration: 1183; Percent complete: 29.6%; Average loss: 3.2765
Iteration: 1184; Percent complete: 29.6%; Average loss: 3.1699
Iteration: 1185; Percent complete: 29.6%; Average loss: 3.4899
Iteration: 1186; Percent complete: 29.6%; Average loss: 3.1962
Iteration: 1187; Percent complete: 29.7%; Average loss: 3.5283
Iteration: 1188; Percent complete: 29.7%; Average loss: 3.4539
Iteration: 1189; Percent complete: 29.7%; Average loss: 3.1940
Iteration: 1190; Percent complete: 29.8%; Average loss: 3.2497
Iteration: 1191; Percent complete: 29.8%; Average loss: 2.9533
Iteration: 1192; Percent complete: 29.8%; Average loss: 3.2876
Iteration: 1193; Percent complete: 29.8%; Average loss: 3.6264
Iteration: 1194; Percent complete: 29.8%; Average loss: 3.3198
Iteration: 1195; Percent complete: 29.9%; Average loss: 3.4188
Iteration: 1196; Percent complete: 29.9%; Average loss: 3.7976
Iteration: 1197; Percent complete: 29.9%; Average loss: 3.5448
Iteration: 1198; Percent complete: 29.9%; Average loss: 3.3777
Iteration: 1199; Percent complete: 30.0%; Average loss: 3.7818
Iteration: 1200; Percent complete: 30.0%; Average loss: 3.4317
Iteration: 1201; Percent complete: 30.0%; Average loss: 3.5399
Iteration: 1202; Percent complete: 30.0%; Average loss: 3.2713
Iteration: 1203; Percent complete: 30.1%; Average loss: 3.3913
Iteration: 1204; Percent complete: 30.1%; Average loss: 3.2318
Iteration: 1205; Percent complete: 30.1%; Average loss: 3.3005
Iteration: 1206; Percent complete: 30.1%; Average loss: 3.4715
Iteration: 1207; Percent complete: 30.2%; Average loss: 3.6483
Iteration: 1208; Percent complete: 30.2%; Average loss: 3.3479
Iteration: 1209; Percent complete: 30.2%; Average loss: 3.5318
Iteration: 1210; Percent complete: 30.2%; Average loss: 3.4272
Iteration: 1211; Percent complete: 30.3%; Average loss: 3.3990
Iteration: 1212; Percent complete: 30.3%; Average loss: 3.4388
Iteration: 1213; Percent complete: 30.3%; Average loss: 3.4945
Iteration: 1214; Percent complete: 30.3%; Average loss: 3.3567
Iteration: 1215; Percent complete: 30.4%; Average loss: 3.1726
Iteration: 1216; Percent complete: 30.4%; Average loss: 3.2800
Iteration: 1217; Percent complete: 30.4%; Average loss: 3.2508
Iteration: 1218; Percent complete: 30.4%; Average loss: 3.5558
Iteration: 1219; Percent complete: 30.5%; Average loss: 3.5448
Iteration: 1220; Percent complete: 30.5%; Average loss: 3.4249
Iteration: 1221; Percent complete: 30.5%; Average loss: 3.4301
Iteration: 1222; Percent complete: 30.6%; Average loss: 3.3179
Iteration: 1223; Percent complete: 30.6%; Average loss: 3.2321
Iteration: 1224; Percent complete: 30.6%; Average loss: 3.4806
Iteration: 1225; Percent complete: 30.6%; Average loss: 3.7411
Iteration: 1226; Percent complete: 30.6%; Average loss: 3.1596
Iteration: 1227; Percent complete: 30.7%; Average loss: 3.3259
Iteration: 1228; Percent complete: 30.7%; Average loss: 3.5311
Iteration: 1229; Percent complete: 30.7%; Average loss: 3.1011
Iteration: 1230; Percent complete: 30.8%; Average loss: 3.1685
Iteration: 1231; Percent complete: 30.8%; Average loss: 3.2334
Iteration: 1232; Percent complete: 30.8%; Average loss: 3.4691
Iteration: 1233; Percent complete: 30.8%; Average loss: 3.4232
Iteration: 1234; Percent complete: 30.9%; Average loss: 3.3912
Iteration: 1235; Percent complete: 30.9%; Average loss: 3.5072
Iteration: 1236; Percent complete: 30.9%; Average loss: 3.0471
Iteration: 1237; Percent complete: 30.9%; Average loss: 3.5756
Iteration: 1238; Percent complete: 30.9%; Average loss: 3.5151
Iteration: 1239; Percent complete: 31.0%; Average loss: 3.5164
Iteration: 1240; Percent complete: 31.0%; Average loss: 3.3763
Iteration: 1241; Percent complete: 31.0%; Average loss: 3.3048
Iteration: 1242; Percent complete: 31.1%; Average loss: 3.2870
Iteration: 1243; Percent complete: 31.1%; Average loss: 3.1649
Iteration: 1244; Percent complete: 31.1%; Average loss: 3.3546
Iteration: 1245; Percent complete: 31.1%; Average loss: 3.5095
Iteration: 1246; Percent complete: 31.1%; Average loss: 3.3574
Iteration: 1247; Percent complete: 31.2%; Average loss: 3.4048
Iteration: 1248; Percent complete: 31.2%; Average loss: 3.4845
Iteration: 1249; Percent complete: 31.2%; Average loss: 3.3805
Iteration: 1250; Percent complete: 31.2%; Average loss: 3.5545
Iteration: 1251; Percent complete: 31.3%; Average loss: 3.5501
Iteration: 1252; Percent complete: 31.3%; Average loss: 3.0414
Iteration: 1253; Percent complete: 31.3%; Average loss: 3.3129
Iteration: 1254; Percent complete: 31.4%; Average loss: 3.3431
Iteration: 1255; Percent complete: 31.4%; Average loss: 3.2184
Iteration: 1256; Percent complete: 31.4%; Average loss: 3.3655
Iteration: 1257; Percent complete: 31.4%; Average loss: 3.5572
Iteration: 1258; Percent complete: 31.4%; Average loss: 3.5691
Iteration: 1259; Percent complete: 31.5%; Average loss: 3.3019
Iteration: 1260; Percent complete: 31.5%; Average loss: 3.3867
Iteration: 1261; Percent complete: 31.5%; Average loss: 3.5492
Iteration: 1262; Percent complete: 31.6%; Average loss: 3.3243
Iteration: 1263; Percent complete: 31.6%; Average loss: 3.5014
Iteration: 1264; Percent complete: 31.6%; Average loss: 3.2912
Iteration: 1265; Percent complete: 31.6%; Average loss: 3.3548
Iteration: 1266; Percent complete: 31.6%; Average loss: 3.4033
Iteration: 1267; Percent complete: 31.7%; Average loss: 3.5499
Iteration: 1268; Percent complete: 31.7%; Average loss: 3.2889
Iteration: 1269; Percent complete: 31.7%; Average loss: 3.2942
Iteration: 1270; Percent complete: 31.8%; Average loss: 3.1346
Iteration: 1271; Percent complete: 31.8%; Average loss: 3.1260
Iteration: 1272; Percent complete: 31.8%; Average loss: 3.2485
Iteration: 1273; Percent complete: 31.8%; Average loss: 3.4259
Iteration: 1274; Percent complete: 31.9%; Average loss: 3.6174
Iteration: 1275; Percent complete: 31.9%; Average loss: 3.1811
Iteration: 1276; Percent complete: 31.9%; Average loss: 3.4949
Iteration: 1277; Percent complete: 31.9%; Average loss: 3.4419
Iteration: 1278; Percent complete: 31.9%; Average loss: 3.5622
Iteration: 1279; Percent complete: 32.0%; Average loss: 3.3827
Iteration: 1280; Percent complete: 32.0%; Average loss: 3.1514
Iteration: 1281; Percent complete: 32.0%; Average loss: 3.3152
Iteration: 1282; Percent complete: 32.0%; Average loss: 3.3678
Iteration: 1283; Percent complete: 32.1%; Average loss: 3.4011
Iteration: 1284; Percent complete: 32.1%; Average loss: 3.3751
Iteration: 1285; Percent complete: 32.1%; Average loss: 3.5718
Iteration: 1286; Percent complete: 32.1%; Average loss: 3.6460
Iteration: 1287; Percent complete: 32.2%; Average loss: 3.6357
Iteration: 1288; Percent complete: 32.2%; Average loss: 3.4433
Iteration: 1289; Percent complete: 32.2%; Average loss: 3.4286
Iteration: 1290; Percent complete: 32.2%; Average loss: 3.2520
Iteration: 1291; Percent complete: 32.3%; Average loss: 3.4369
Iteration: 1292; Percent complete: 32.3%; Average loss: 3.3645
Iteration: 1293; Percent complete: 32.3%; Average loss: 3.5490
Iteration: 1294; Percent complete: 32.4%; Average loss: 3.3664
Iteration: 1295; Percent complete: 32.4%; Average loss: 3.4137
Iteration: 1296; Percent complete: 32.4%; Average loss: 3.5378
Iteration: 1297; Percent complete: 32.4%; Average loss: 3.4184
Iteration: 1298; Percent complete: 32.5%; Average loss: 3.5898
Iteration: 1299; Percent complete: 32.5%; Average loss: 3.3559
Iteration: 1300; Percent complete: 32.5%; Average loss: 3.3219
Iteration: 1301; Percent complete: 32.5%; Average loss: 3.2286
Iteration: 1302; Percent complete: 32.6%; Average loss: 3.3267
Iteration: 1303; Percent complete: 32.6%; Average loss: 3.5407
Iteration: 1304; Percent complete: 32.6%; Average loss: 3.2932
Iteration: 1305; Percent complete: 32.6%; Average loss: 3.4911
Iteration: 1306; Percent complete: 32.6%; Average loss: 3.2287
Iteration: 1307; Percent complete: 32.7%; Average loss: 3.2528
Iteration: 1308; Percent complete: 32.7%; Average loss: 3.4014
Iteration: 1309; Percent complete: 32.7%; Average loss: 3.1620
Iteration: 1310; Percent complete: 32.8%; Average loss: 3.3785
Iteration: 1311; Percent complete: 32.8%; Average loss: 3.2110
Iteration: 1312; Percent complete: 32.8%; Average loss: 3.2990
Iteration: 1313; Percent complete: 32.8%; Average loss: 3.7140
Iteration: 1314; Percent complete: 32.9%; Average loss: 3.1625
Iteration: 1315; Percent complete: 32.9%; Average loss: 3.5729
Iteration: 1316; Percent complete: 32.9%; Average loss: 3.0750
Iteration: 1317; Percent complete: 32.9%; Average loss: 3.2940
Iteration: 1318; Percent complete: 33.0%; Average loss: 3.2003
Iteration: 1319; Percent complete: 33.0%; Average loss: 3.4962
Iteration: 1320; Percent complete: 33.0%; Average loss: 3.3730
Iteration: 1321; Percent complete: 33.0%; Average loss: 3.3535
Iteration: 1322; Percent complete: 33.1%; Average loss: 3.2119
Iteration: 1323; Percent complete: 33.1%; Average loss: 3.6457
Iteration: 1324; Percent complete: 33.1%; Average loss: 3.3426
Iteration: 1325; Percent complete: 33.1%; Average loss: 3.5095
Iteration: 1326; Percent complete: 33.1%; Average loss: 3.2735
Iteration: 1327; Percent complete: 33.2%; Average loss: 3.5105
Iteration: 1328; Percent complete: 33.2%; Average loss: 3.4328
Iteration: 1329; Percent complete: 33.2%; Average loss: 3.3128
Iteration: 1330; Percent complete: 33.2%; Average loss: 3.3730
Iteration: 1331; Percent complete: 33.3%; Average loss: 3.3038
Iteration: 1332; Percent complete: 33.3%; Average loss: 3.2812
Iteration: 1333; Percent complete: 33.3%; Average loss: 3.3381
Iteration: 1334; Percent complete: 33.4%; Average loss: 3.2252
Iteration: 1335; Percent complete: 33.4%; Average loss: 3.4922
Iteration: 1336; Percent complete: 33.4%; Average loss: 3.2221
Iteration: 1337; Percent complete: 33.4%; Average loss: 3.3095
Iteration: 1338; Percent complete: 33.5%; Average loss: 3.4892
Iteration: 1339; Percent complete: 33.5%; Average loss: 3.1906
Iteration: 1340; Percent complete: 33.5%; Average loss: 3.2503
Iteration: 1341; Percent complete: 33.5%; Average loss: 3.4737
Iteration: 1342; Percent complete: 33.6%; Average loss: 3.2555
Iteration: 1343; Percent complete: 33.6%; Average loss: 3.4066
Iteration: 1344; Percent complete: 33.6%; Average loss: 3.4326
Iteration: 1345; Percent complete: 33.6%; Average loss: 3.4438
Iteration: 1346; Percent complete: 33.7%; Average loss: 3.2848
Iteration: 1347; Percent complete: 33.7%; Average loss: 3.3184
Iteration: 1348; Percent complete: 33.7%; Average loss: 3.3069
Iteration: 1349; Percent complete: 33.7%; Average loss: 3.4949
Iteration: 1350; Percent complete: 33.8%; Average loss: 3.3985
Iteration: 1351; Percent complete: 33.8%; Average loss: 3.3769
Iteration: 1352; Percent complete: 33.8%; Average loss: 3.1591
Iteration: 1353; Percent complete: 33.8%; Average loss: 3.4066
Iteration: 1354; Percent complete: 33.9%; Average loss: 3.3934
Iteration: 1355; Percent complete: 33.9%; Average loss: 3.6965
Iteration: 1356; Percent complete: 33.9%; Average loss: 3.4640
Iteration: 1357; Percent complete: 33.9%; Average loss: 3.3676
Iteration: 1358; Percent complete: 34.0%; Average loss: 2.9923
Iteration: 1359; Percent complete: 34.0%; Average loss: 3.3142
Iteration: 1360; Percent complete: 34.0%; Average loss: 3.3112
Iteration: 1361; Percent complete: 34.0%; Average loss: 3.6090
Iteration: 1362; Percent complete: 34.1%; Average loss: 3.2521
Iteration: 1363; Percent complete: 34.1%; Average loss: 3.5494
Iteration: 1364; Percent complete: 34.1%; Average loss: 3.1759
Iteration: 1365; Percent complete: 34.1%; Average loss: 3.5496
Iteration: 1366; Percent complete: 34.2%; Average loss: 3.2052
Iteration: 1367; Percent complete: 34.2%; Average loss: 3.4504
Iteration: 1368; Percent complete: 34.2%; Average loss: 3.0208
Iteration: 1369; Percent complete: 34.2%; Average loss: 3.0884
Iteration: 1370; Percent complete: 34.2%; Average loss: 3.5941
Iteration: 1371; Percent complete: 34.3%; Average loss: 3.3680
Iteration: 1372; Percent complete: 34.3%; Average loss: 3.6305
Iteration: 1373; Percent complete: 34.3%; Average loss: 3.3211
Iteration: 1374; Percent complete: 34.4%; Average loss: 3.4803
Iteration: 1375; Percent complete: 34.4%; Average loss: 3.3295
Iteration: 1376; Percent complete: 34.4%; Average loss: 3.3018
Iteration: 1377; Percent complete: 34.4%; Average loss: 3.3929
Iteration: 1378; Percent complete: 34.4%; Average loss: 3.5537
Iteration: 1379; Percent complete: 34.5%; Average loss: 3.3019
Iteration: 1380; Percent complete: 34.5%; Average loss: 3.5069
Iteration: 1381; Percent complete: 34.5%; Average loss: 3.4751
Iteration: 1382; Percent complete: 34.5%; Average loss: 3.1921
Iteration: 1383; Percent complete: 34.6%; Average loss: 3.1875
Iteration: 1384; Percent complete: 34.6%; Average loss: 3.2928
Iteration: 1385; Percent complete: 34.6%; Average loss: 3.4030
Iteration: 1386; Percent complete: 34.6%; Average loss: 3.1855
Iteration: 1387; Percent complete: 34.7%; Average loss: 3.2451
Iteration: 1388; Percent complete: 34.7%; Average loss: 3.4378
Iteration: 1389; Percent complete: 34.7%; Average loss: 3.2305
Iteration: 1390; Percent complete: 34.8%; Average loss: 3.6351
Iteration: 1391; Percent complete: 34.8%; Average loss: 3.3703
Iteration: 1392; Percent complete: 34.8%; Average loss: 3.2449
Iteration: 1393; Percent complete: 34.8%; Average loss: 3.3024
Iteration: 1394; Percent complete: 34.8%; Average loss: 3.1693
Iteration: 1395; Percent complete: 34.9%; Average loss: 3.4484
Iteration: 1396; Percent complete: 34.9%; Average loss: 3.2103
Iteration: 1397; Percent complete: 34.9%; Average loss: 3.2172
Iteration: 1398; Percent complete: 34.9%; Average loss: 3.2298
Iteration: 1399; Percent complete: 35.0%; Average loss: 3.3802
Iteration: 1400; Percent complete: 35.0%; Average loss: 3.3796
Iteration: 1401; Percent complete: 35.0%; Average loss: 3.1329
Iteration: 1402; Percent complete: 35.0%; Average loss: 3.4091
Iteration: 1403; Percent complete: 35.1%; Average loss: 3.0624
Iteration: 1404; Percent complete: 35.1%; Average loss: 3.3395
Iteration: 1405; Percent complete: 35.1%; Average loss: 3.3263
Iteration: 1406; Percent complete: 35.1%; Average loss: 3.5581
Iteration: 1407; Percent complete: 35.2%; Average loss: 3.1366
Iteration: 1408; Percent complete: 35.2%; Average loss: 3.3585
Iteration: 1409; Percent complete: 35.2%; Average loss: 3.3863
Iteration: 1410; Percent complete: 35.2%; Average loss: 3.4658
Iteration: 1411; Percent complete: 35.3%; Average loss: 3.3729
Iteration: 1412; Percent complete: 35.3%; Average loss: 3.1204
Iteration: 1413; Percent complete: 35.3%; Average loss: 3.2381
Iteration: 1414; Percent complete: 35.4%; Average loss: 3.4526
Iteration: 1415; Percent complete: 35.4%; Average loss: 3.2261
Iteration: 1416; Percent complete: 35.4%; Average loss: 3.1493
Iteration: 1417; Percent complete: 35.4%; Average loss: 3.2732
Iteration: 1418; Percent complete: 35.4%; Average loss: 3.1973
Iteration: 1419; Percent complete: 35.5%; Average loss: 3.2740
Iteration: 1420; Percent complete: 35.5%; Average loss: 3.2568
Iteration: 1421; Percent complete: 35.5%; Average loss: 3.3518
Iteration: 1422; Percent complete: 35.5%; Average loss: 3.3250
Iteration: 1423; Percent complete: 35.6%; Average loss: 3.4088
Iteration: 1424; Percent complete: 35.6%; Average loss: 3.3792
Iteration: 1425; Percent complete: 35.6%; Average loss: 3.2454
Iteration: 1426; Percent complete: 35.6%; Average loss: 3.3304
Iteration: 1427; Percent complete: 35.7%; Average loss: 3.1303
Iteration: 1428; Percent complete: 35.7%; Average loss: 3.3692
Iteration: 1429; Percent complete: 35.7%; Average loss: 3.4263
Iteration: 1430; Percent complete: 35.8%; Average loss: 3.1072
Iteration: 1431; Percent complete: 35.8%; Average loss: 3.2841
Iteration: 1432; Percent complete: 35.8%; Average loss: 3.3250
Iteration: 1433; Percent complete: 35.8%; Average loss: 3.2489
Iteration: 1434; Percent complete: 35.9%; Average loss: 3.3581
Iteration: 1435; Percent complete: 35.9%; Average loss: 3.4444
Iteration: 1436; Percent complete: 35.9%; Average loss: 3.1991
Iteration: 1437; Percent complete: 35.9%; Average loss: 3.3432
Iteration: 1438; Percent complete: 35.9%; Average loss: 3.2313
Iteration: 1439; Percent complete: 36.0%; Average loss: 3.4381
Iteration: 1440; Percent complete: 36.0%; Average loss: 3.2126
Iteration: 1441; Percent complete: 36.0%; Average loss: 3.5677
Iteration: 1442; Percent complete: 36.0%; Average loss: 3.0504
Iteration: 1443; Percent complete: 36.1%; Average loss: 3.3428
Iteration: 1444; Percent complete: 36.1%; Average loss: 3.3467
Iteration: 1445; Percent complete: 36.1%; Average loss: 3.2174
Iteration: 1446; Percent complete: 36.1%; Average loss: 3.5245
Iteration: 1447; Percent complete: 36.2%; Average loss: 3.1320
Iteration: 1448; Percent complete: 36.2%; Average loss: 3.3760
Iteration: 1449; Percent complete: 36.2%; Average loss: 3.2774
Iteration: 1450; Percent complete: 36.2%; Average loss: 3.2453
Iteration: 1451; Percent complete: 36.3%; Average loss: 3.3555
Iteration: 1452; Percent complete: 36.3%; Average loss: 3.3448
Iteration: 1453; Percent complete: 36.3%; Average loss: 3.5000
Iteration: 1454; Percent complete: 36.4%; Average loss: 3.2217
Iteration: 1455; Percent complete: 36.4%; Average loss: 3.2345
Iteration: 1456; Percent complete: 36.4%; Average loss: 3.2149
Iteration: 1457; Percent complete: 36.4%; Average loss: 3.5036
Iteration: 1458; Percent complete: 36.4%; Average loss: 3.2624
Iteration: 1459; Percent complete: 36.5%; Average loss: 2.9251
Iteration: 1460; Percent complete: 36.5%; Average loss: 3.3397
Iteration: 1461; Percent complete: 36.5%; Average loss: 3.4761
Iteration: 1462; Percent complete: 36.5%; Average loss: 3.3993
Iteration: 1463; Percent complete: 36.6%; Average loss: 3.4149
Iteration: 1464; Percent complete: 36.6%; Average loss: 3.0872
Iteration: 1465; Percent complete: 36.6%; Average loss: 3.3593
Iteration: 1466; Percent complete: 36.6%; Average loss: 3.4753
Iteration: 1467; Percent complete: 36.7%; Average loss: 3.3565
Iteration: 1468; Percent complete: 36.7%; Average loss: 3.2939
Iteration: 1469; Percent complete: 36.7%; Average loss: 3.0999
Iteration: 1470; Percent complete: 36.8%; Average loss: 3.2216
Iteration: 1471; Percent complete: 36.8%; Average loss: 3.4053
Iteration: 1472; Percent complete: 36.8%; Average loss: 3.3023
Iteration: 1473; Percent complete: 36.8%; Average loss: 3.1994
Iteration: 1474; Percent complete: 36.9%; Average loss: 3.2807
Iteration: 1475; Percent complete: 36.9%; Average loss: 3.2070
Iteration: 1476; Percent complete: 36.9%; Average loss: 3.5221
Iteration: 1477; Percent complete: 36.9%; Average loss: 3.1913
Iteration: 1478; Percent complete: 37.0%; Average loss: 3.5374
Iteration: 1479; Percent complete: 37.0%; Average loss: 3.4140
Iteration: 1480; Percent complete: 37.0%; Average loss: 3.5887
Iteration: 1481; Percent complete: 37.0%; Average loss: 3.2787
Iteration: 1482; Percent complete: 37.0%; Average loss: 3.4657
Iteration: 1483; Percent complete: 37.1%; Average loss: 3.3435
Iteration: 1484; Percent complete: 37.1%; Average loss: 3.1032
Iteration: 1485; Percent complete: 37.1%; Average loss: 3.3904
Iteration: 1486; Percent complete: 37.1%; Average loss: 3.3673
Iteration: 1487; Percent complete: 37.2%; Average loss: 3.2101
Iteration: 1488; Percent complete: 37.2%; Average loss: 3.1780
Iteration: 1489; Percent complete: 37.2%; Average loss: 3.3316
Iteration: 1490; Percent complete: 37.2%; Average loss: 3.2153
Iteration: 1491; Percent complete: 37.3%; Average loss: 3.3122
Iteration: 1492; Percent complete: 37.3%; Average loss: 3.2460
Iteration: 1493; Percent complete: 37.3%; Average loss: 3.0981
Iteration: 1494; Percent complete: 37.4%; Average loss: 3.1706
Iteration: 1495; Percent complete: 37.4%; Average loss: 3.3737
Iteration: 1496; Percent complete: 37.4%; Average loss: 3.4681
Iteration: 1497; Percent complete: 37.4%; Average loss: 3.1093
Iteration: 1498; Percent complete: 37.5%; Average loss: 3.3378
Iteration: 1499; Percent complete: 37.5%; Average loss: 3.2593
Iteration: 1500; Percent complete: 37.5%; Average loss: 3.2346
Iteration: 1501; Percent complete: 37.5%; Average loss: 3.1526
Iteration: 1502; Percent complete: 37.5%; Average loss: 3.1905
Iteration: 1503; Percent complete: 37.6%; Average loss: 3.3511
Iteration: 1504; Percent complete: 37.6%; Average loss: 3.2963
Iteration: 1505; Percent complete: 37.6%; Average loss: 3.1097
Iteration: 1506; Percent complete: 37.6%; Average loss: 3.3620
Iteration: 1507; Percent complete: 37.7%; Average loss: 3.4944
Iteration: 1508; Percent complete: 37.7%; Average loss: 3.1134
Iteration: 1509; Percent complete: 37.7%; Average loss: 3.1002
Iteration: 1510; Percent complete: 37.8%; Average loss: 3.5910
Iteration: 1511; Percent complete: 37.8%; Average loss: 3.2683
Iteration: 1512; Percent complete: 37.8%; Average loss: 3.2632
Iteration: 1513; Percent complete: 37.8%; Average loss: 3.0234
Iteration: 1514; Percent complete: 37.9%; Average loss: 3.1583
Iteration: 1515; Percent complete: 37.9%; Average loss: 3.1539
Iteration: 1516; Percent complete: 37.9%; Average loss: 3.1444
Iteration: 1517; Percent complete: 37.9%; Average loss: 3.0145
Iteration: 1518; Percent complete: 38.0%; Average loss: 3.3521
Iteration: 1519; Percent complete: 38.0%; Average loss: 3.1637
Iteration: 1520; Percent complete: 38.0%; Average loss: 3.0876
Iteration: 1521; Percent complete: 38.0%; Average loss: 3.3530
Iteration: 1522; Percent complete: 38.0%; Average loss: 3.1156
Iteration: 1523; Percent complete: 38.1%; Average loss: 3.3677
Iteration: 1524; Percent complete: 38.1%; Average loss: 3.1501
Iteration: 1525; Percent complete: 38.1%; Average loss: 3.5102
Iteration: 1526; Percent complete: 38.1%; Average loss: 3.3119
Iteration: 1527; Percent complete: 38.2%; Average loss: 3.5164
Iteration: 1528; Percent complete: 38.2%; Average loss: 3.2107
Iteration: 1529; Percent complete: 38.2%; Average loss: 3.3219
Iteration: 1530; Percent complete: 38.2%; Average loss: 3.5317
Iteration: 1531; Percent complete: 38.3%; Average loss: 3.2339
Iteration: 1532; Percent complete: 38.3%; Average loss: 3.2178
Iteration: 1533; Percent complete: 38.3%; Average loss: 3.3839
Iteration: 1534; Percent complete: 38.4%; Average loss: 3.3772
Iteration: 1535; Percent complete: 38.4%; Average loss: 3.3367
Iteration: 1536; Percent complete: 38.4%; Average loss: 3.4413
Iteration: 1537; Percent complete: 38.4%; Average loss: 3.2078
Iteration: 1538; Percent complete: 38.5%; Average loss: 3.1065
Iteration: 1539; Percent complete: 38.5%; Average loss: 3.3713
Iteration: 1540; Percent complete: 38.5%; Average loss: 3.3524
Iteration: 1541; Percent complete: 38.5%; Average loss: 2.9787
Iteration: 1542; Percent complete: 38.6%; Average loss: 3.2212
Iteration: 1543; Percent complete: 38.6%; Average loss: 3.1362
Iteration: 1544; Percent complete: 38.6%; Average loss: 3.2238
Iteration: 1545; Percent complete: 38.6%; Average loss: 3.5528
Iteration: 1546; Percent complete: 38.6%; Average loss: 3.5333
Iteration: 1547; Percent complete: 38.7%; Average loss: 3.1229
Iteration: 1548; Percent complete: 38.7%; Average loss: 3.3279
Iteration: 1549; Percent complete: 38.7%; Average loss: 3.3563
Iteration: 1550; Percent complete: 38.8%; Average loss: 3.2366
Iteration: 1551; Percent complete: 38.8%; Average loss: 3.5138
Iteration: 1552; Percent complete: 38.8%; Average loss: 3.4058
Iteration: 1553; Percent complete: 38.8%; Average loss: 3.2513
Iteration: 1554; Percent complete: 38.9%; Average loss: 3.2210
Iteration: 1555; Percent complete: 38.9%; Average loss: 3.3565
Iteration: 1556; Percent complete: 38.9%; Average loss: 3.3161
Iteration: 1557; Percent complete: 38.9%; Average loss: 3.4958
Iteration: 1558; Percent complete: 39.0%; Average loss: 3.1498
Iteration: 1559; Percent complete: 39.0%; Average loss: 3.3421
Iteration: 1560; Percent complete: 39.0%; Average loss: 3.4629
Iteration: 1561; Percent complete: 39.0%; Average loss: 3.0990
Iteration: 1562; Percent complete: 39.1%; Average loss: 3.2412
Iteration: 1563; Percent complete: 39.1%; Average loss: 3.3435
Iteration: 1564; Percent complete: 39.1%; Average loss: 3.1646
Iteration: 1565; Percent complete: 39.1%; Average loss: 3.3729
Iteration: 1566; Percent complete: 39.1%; Average loss: 3.2800
Iteration: 1567; Percent complete: 39.2%; Average loss: 3.4212
Iteration: 1568; Percent complete: 39.2%; Average loss: 3.4421
Iteration: 1569; Percent complete: 39.2%; Average loss: 3.2247
Iteration: 1570; Percent complete: 39.2%; Average loss: 3.3343
Iteration: 1571; Percent complete: 39.3%; Average loss: 3.2432
Iteration: 1572; Percent complete: 39.3%; Average loss: 3.1845
Iteration: 1573; Percent complete: 39.3%; Average loss: 3.4306
Iteration: 1574; Percent complete: 39.4%; Average loss: 3.2096
Iteration: 1575; Percent complete: 39.4%; Average loss: 3.4167
Iteration: 1576; Percent complete: 39.4%; Average loss: 3.4299
Iteration: 1577; Percent complete: 39.4%; Average loss: 3.2552
Iteration: 1578; Percent complete: 39.5%; Average loss: 2.9377
Iteration: 1579; Percent complete: 39.5%; Average loss: 3.0544
Iteration: 1580; Percent complete: 39.5%; Average loss: 3.6009
Iteration: 1581; Percent complete: 39.5%; Average loss: 3.3200
Iteration: 1582; Percent complete: 39.6%; Average loss: 3.2199
Iteration: 1583; Percent complete: 39.6%; Average loss: 3.1350
Iteration: 1584; Percent complete: 39.6%; Average loss: 3.3610
Iteration: 1585; Percent complete: 39.6%; Average loss: 3.1245
Iteration: 1586; Percent complete: 39.6%; Average loss: 3.4068
Iteration: 1587; Percent complete: 39.7%; Average loss: 3.0944
Iteration: 1588; Percent complete: 39.7%; Average loss: 3.3000
Iteration: 1589; Percent complete: 39.7%; Average loss: 3.2114
Iteration: 1590; Percent complete: 39.8%; Average loss: 3.3335
Iteration: 1591; Percent complete: 39.8%; Average loss: 3.1276
Iteration: 1592; Percent complete: 39.8%; Average loss: 3.2858
Iteration: 1593; Percent complete: 39.8%; Average loss: 2.9818
Iteration: 1594; Percent complete: 39.9%; Average loss: 3.4217
Iteration: 1595; Percent complete: 39.9%; Average loss: 3.3205
Iteration: 1596; Percent complete: 39.9%; Average loss: 3.3278
Iteration: 1597; Percent complete: 39.9%; Average loss: 3.3542
Iteration: 1598; Percent complete: 40.0%; Average loss: 3.2389
Iteration: 1599; Percent complete: 40.0%; Average loss: 3.4025
Iteration: 1600; Percent complete: 40.0%; Average loss: 3.2534
Iteration: 1601; Percent complete: 40.0%; Average loss: 3.4496
Iteration: 1602; Percent complete: 40.1%; Average loss: 3.3978
Iteration: 1603; Percent complete: 40.1%; Average loss: 3.2308
Iteration: 1604; Percent complete: 40.1%; Average loss: 3.4110
Iteration: 1605; Percent complete: 40.1%; Average loss: 3.4015
Iteration: 1606; Percent complete: 40.2%; Average loss: 3.2791
Iteration: 1607; Percent complete: 40.2%; Average loss: 3.2500
Iteration: 1608; Percent complete: 40.2%; Average loss: 3.1220
Iteration: 1609; Percent complete: 40.2%; Average loss: 3.2642
Iteration: 1610; Percent complete: 40.2%; Average loss: 3.3591
Iteration: 1611; Percent complete: 40.3%; Average loss: 3.3095
Iteration: 1612; Percent complete: 40.3%; Average loss: 3.1072
Iteration: 1613; Percent complete: 40.3%; Average loss: 3.2202
Iteration: 1614; Percent complete: 40.4%; Average loss: 3.0210
Iteration: 1615; Percent complete: 40.4%; Average loss: 3.2772
Iteration: 1616; Percent complete: 40.4%; Average loss: 3.1364
Iteration: 1617; Percent complete: 40.4%; Average loss: 3.2074
Iteration: 1618; Percent complete: 40.5%; Average loss: 3.2104
Iteration: 1619; Percent complete: 40.5%; Average loss: 3.2887
Iteration: 1620; Percent complete: 40.5%; Average loss: 3.4210
Iteration: 1621; Percent complete: 40.5%; Average loss: 3.3934
Iteration: 1622; Percent complete: 40.6%; Average loss: 3.2064
Iteration: 1623; Percent complete: 40.6%; Average loss: 3.3408
Iteration: 1624; Percent complete: 40.6%; Average loss: 3.0414
Iteration: 1625; Percent complete: 40.6%; Average loss: 3.3386
Iteration: 1626; Percent complete: 40.6%; Average loss: 3.2740
Iteration: 1627; Percent complete: 40.7%; Average loss: 3.1205
Iteration: 1628; Percent complete: 40.7%; Average loss: 3.1434
Iteration: 1629; Percent complete: 40.7%; Average loss: 3.3276
Iteration: 1630; Percent complete: 40.8%; Average loss: 3.3187
Iteration: 1631; Percent complete: 40.8%; Average loss: 3.2849
Iteration: 1632; Percent complete: 40.8%; Average loss: 3.3038
Iteration: 1633; Percent complete: 40.8%; Average loss: 3.2135
Iteration: 1634; Percent complete: 40.8%; Average loss: 3.1384
Iteration: 1635; Percent complete: 40.9%; Average loss: 3.6381
Iteration: 1636; Percent complete: 40.9%; Average loss: 3.2541
Iteration: 1637; Percent complete: 40.9%; Average loss: 3.1018
Iteration: 1638; Percent complete: 40.9%; Average loss: 3.1148
Iteration: 1639; Percent complete: 41.0%; Average loss: 3.0965
Iteration: 1640; Percent complete: 41.0%; Average loss: 3.3071
Iteration: 1641; Percent complete: 41.0%; Average loss: 3.2191
Iteration: 1642; Percent complete: 41.0%; Average loss: 3.3566
Iteration: 1643; Percent complete: 41.1%; Average loss: 3.1243
Iteration: 1644; Percent complete: 41.1%; Average loss: 3.0353
Iteration: 1645; Percent complete: 41.1%; Average loss: 3.0896
Iteration: 1646; Percent complete: 41.1%; Average loss: 3.1634
Iteration: 1647; Percent complete: 41.2%; Average loss: 3.1321
Iteration: 1648; Percent complete: 41.2%; Average loss: 3.2724
Iteration: 1649; Percent complete: 41.2%; Average loss: 2.9534
Iteration: 1650; Percent complete: 41.2%; Average loss: 3.3364
Iteration: 1651; Percent complete: 41.3%; Average loss: 3.5274
Iteration: 1652; Percent complete: 41.3%; Average loss: 3.4439
Iteration: 1653; Percent complete: 41.3%; Average loss: 3.4077
Iteration: 1654; Percent complete: 41.3%; Average loss: 3.3967
Iteration: 1655; Percent complete: 41.4%; Average loss: 2.8397
Iteration: 1656; Percent complete: 41.4%; Average loss: 2.9405
Iteration: 1657; Percent complete: 41.4%; Average loss: 3.3881
Iteration: 1658; Percent complete: 41.4%; Average loss: 3.2102
Iteration: 1659; Percent complete: 41.5%; Average loss: 3.5020
Iteration: 1660; Percent complete: 41.5%; Average loss: 3.4545
Iteration: 1661; Percent complete: 41.5%; Average loss: 3.4719
Iteration: 1662; Percent complete: 41.5%; Average loss: 3.5151
Iteration: 1663; Percent complete: 41.6%; Average loss: 3.0963
Iteration: 1664; Percent complete: 41.6%; Average loss: 3.4565
Iteration: 1665; Percent complete: 41.6%; Average loss: 3.1510
Iteration: 1666; Percent complete: 41.6%; Average loss: 3.1632
Iteration: 1667; Percent complete: 41.7%; Average loss: 3.0386
Iteration: 1668; Percent complete: 41.7%; Average loss: 3.2186
Iteration: 1669; Percent complete: 41.7%; Average loss: 3.2860
Iteration: 1670; Percent complete: 41.8%; Average loss: 3.2022
Iteration: 1671; Percent complete: 41.8%; Average loss: 3.0768
Iteration: 1672; Percent complete: 41.8%; Average loss: 3.3216
Iteration: 1673; Percent complete: 41.8%; Average loss: 3.1026
Iteration: 1674; Percent complete: 41.9%; Average loss: 3.2078
Iteration: 1675; Percent complete: 41.9%; Average loss: 3.3438
Iteration: 1676; Percent complete: 41.9%; Average loss: 3.3705
Iteration: 1677; Percent complete: 41.9%; Average loss: 3.1785
Iteration: 1678; Percent complete: 41.9%; Average loss: 3.3192
Iteration: 1679; Percent complete: 42.0%; Average loss: 3.3268
Iteration: 1680; Percent complete: 42.0%; Average loss: 3.1389
Iteration: 1681; Percent complete: 42.0%; Average loss: 3.2856
Iteration: 1682; Percent complete: 42.0%; Average loss: 3.3877
Iteration: 1683; Percent complete: 42.1%; Average loss: 3.2082
Iteration: 1684; Percent complete: 42.1%; Average loss: 3.4878
Iteration: 1685; Percent complete: 42.1%; Average loss: 3.2903
Iteration: 1686; Percent complete: 42.1%; Average loss: 3.2732
Iteration: 1687; Percent complete: 42.2%; Average loss: 3.1073
Iteration: 1688; Percent complete: 42.2%; Average loss: 3.1960
Iteration: 1689; Percent complete: 42.2%; Average loss: 3.2211
Iteration: 1690; Percent complete: 42.2%; Average loss: 3.0103
Iteration: 1691; Percent complete: 42.3%; Average loss: 3.3979
Iteration: 1692; Percent complete: 42.3%; Average loss: 3.3222
Iteration: 1693; Percent complete: 42.3%; Average loss: 3.2330
Iteration: 1694; Percent complete: 42.4%; Average loss: 3.3384
Iteration: 1695; Percent complete: 42.4%; Average loss: 3.2962
Iteration: 1696; Percent complete: 42.4%; Average loss: 3.1462
Iteration: 1697; Percent complete: 42.4%; Average loss: 3.3303
Iteration: 1698; Percent complete: 42.4%; Average loss: 3.2387
Iteration: 1699; Percent complete: 42.5%; Average loss: 3.3243
Iteration: 1700; Percent complete: 42.5%; Average loss: 3.1488
Iteration: 1701; Percent complete: 42.5%; Average loss: 3.4143
Iteration: 1702; Percent complete: 42.5%; Average loss: 3.0928
Iteration: 1703; Percent complete: 42.6%; Average loss: 3.3673
Iteration: 1704; Percent complete: 42.6%; Average loss: 3.0796
Iteration: 1705; Percent complete: 42.6%; Average loss: 3.3108
Iteration: 1706; Percent complete: 42.6%; Average loss: 3.1458
Iteration: 1707; Percent complete: 42.7%; Average loss: 3.1983
Iteration: 1708; Percent complete: 42.7%; Average loss: 3.2112
Iteration: 1709; Percent complete: 42.7%; Average loss: 3.2245
Iteration: 1710; Percent complete: 42.8%; Average loss: 3.4029
Iteration: 1711; Percent complete: 42.8%; Average loss: 3.1537
Iteration: 1712; Percent complete: 42.8%; Average loss: 3.1847
Iteration: 1713; Percent complete: 42.8%; Average loss: 3.1029
Iteration: 1714; Percent complete: 42.9%; Average loss: 3.2943
Iteration: 1715; Percent complete: 42.9%; Average loss: 3.2114
Iteration: 1716; Percent complete: 42.9%; Average loss: 3.2473
Iteration: 1717; Percent complete: 42.9%; Average loss: 3.3336
Iteration: 1718; Percent complete: 43.0%; Average loss: 3.2431
Iteration: 1719; Percent complete: 43.0%; Average loss: 3.3539
Iteration: 1720; Percent complete: 43.0%; Average loss: 3.2217
Iteration: 1721; Percent complete: 43.0%; Average loss: 3.3925
Iteration: 1722; Percent complete: 43.0%; Average loss: 2.9704
Iteration: 1723; Percent complete: 43.1%; Average loss: 3.0772
Iteration: 1724; Percent complete: 43.1%; Average loss: 3.1850
Iteration: 1725; Percent complete: 43.1%; Average loss: 3.1911
Iteration: 1726; Percent complete: 43.1%; Average loss: 3.4226
Iteration: 1727; Percent complete: 43.2%; Average loss: 3.1151
Iteration: 1728; Percent complete: 43.2%; Average loss: 3.0698
Iteration: 1729; Percent complete: 43.2%; Average loss: 3.1265
Iteration: 1730; Percent complete: 43.2%; Average loss: 3.3485
Iteration: 1731; Percent complete: 43.3%; Average loss: 3.3270
Iteration: 1732; Percent complete: 43.3%; Average loss: 3.1367
Iteration: 1733; Percent complete: 43.3%; Average loss: 3.0933
Iteration: 1734; Percent complete: 43.4%; Average loss: 3.2342
Iteration: 1735; Percent complete: 43.4%; Average loss: 3.1267
Iteration: 1736; Percent complete: 43.4%; Average loss: 3.1588
Iteration: 1737; Percent complete: 43.4%; Average loss: 3.2715
Iteration: 1738; Percent complete: 43.5%; Average loss: 3.1515
Iteration: 1739; Percent complete: 43.5%; Average loss: 3.0777
Iteration: 1740; Percent complete: 43.5%; Average loss: 2.9580
Iteration: 1741; Percent complete: 43.5%; Average loss: 3.2065
Iteration: 1742; Percent complete: 43.5%; Average loss: 3.4504
Iteration: 1743; Percent complete: 43.6%; Average loss: 2.9213
Iteration: 1744; Percent complete: 43.6%; Average loss: 3.1796
Iteration: 1745; Percent complete: 43.6%; Average loss: 3.4325
Iteration: 1746; Percent complete: 43.6%; Average loss: 3.3466
Iteration: 1747; Percent complete: 43.7%; Average loss: 2.8665
Iteration: 1748; Percent complete: 43.7%; Average loss: 3.3158
Iteration: 1749; Percent complete: 43.7%; Average loss: 3.3507
Iteration: 1750; Percent complete: 43.8%; Average loss: 3.0303
Iteration: 1751; Percent complete: 43.8%; Average loss: 3.2531
Iteration: 1752; Percent complete: 43.8%; Average loss: 3.3207
Iteration: 1753; Percent complete: 43.8%; Average loss: 2.9932
Iteration: 1754; Percent complete: 43.9%; Average loss: 3.3040
Iteration: 1755; Percent complete: 43.9%; Average loss: 3.3931
Iteration: 1756; Percent complete: 43.9%; Average loss: 3.3272
Iteration: 1757; Percent complete: 43.9%; Average loss: 2.9310
Iteration: 1758; Percent complete: 44.0%; Average loss: 3.2985
Iteration: 1759; Percent complete: 44.0%; Average loss: 3.1624
Iteration: 1760; Percent complete: 44.0%; Average loss: 3.1147
Iteration: 1761; Percent complete: 44.0%; Average loss: 3.2134
Iteration: 1762; Percent complete: 44.0%; Average loss: 3.2337
Iteration: 1763; Percent complete: 44.1%; Average loss: 3.0914
Iteration: 1764; Percent complete: 44.1%; Average loss: 3.2211
Iteration: 1765; Percent complete: 44.1%; Average loss: 3.0489
Iteration: 1766; Percent complete: 44.1%; Average loss: 3.1829
Iteration: 1767; Percent complete: 44.2%; Average loss: 3.1740
Iteration: 1768; Percent complete: 44.2%; Average loss: 3.1469
Iteration: 1769; Percent complete: 44.2%; Average loss: 3.2550
Iteration: 1770; Percent complete: 44.2%; Average loss: 3.4044
Iteration: 1771; Percent complete: 44.3%; Average loss: 3.4143
Iteration: 1772; Percent complete: 44.3%; Average loss: 3.2121
Iteration: 1773; Percent complete: 44.3%; Average loss: 3.2367
Iteration: 1774; Percent complete: 44.4%; Average loss: 3.0782
Iteration: 1775; Percent complete: 44.4%; Average loss: 3.2986
Iteration: 1776; Percent complete: 44.4%; Average loss: 3.2426
Iteration: 1777; Percent complete: 44.4%; Average loss: 3.1149
Iteration: 1778; Percent complete: 44.5%; Average loss: 3.2915
Iteration: 1779; Percent complete: 44.5%; Average loss: 3.2111
Iteration: 1780; Percent complete: 44.5%; Average loss: 3.3198
Iteration: 1781; Percent complete: 44.5%; Average loss: 3.3480
Iteration: 1782; Percent complete: 44.5%; Average loss: 3.1369
Iteration: 1783; Percent complete: 44.6%; Average loss: 3.1793
Iteration: 1784; Percent complete: 44.6%; Average loss: 3.2598
Iteration: 1785; Percent complete: 44.6%; Average loss: 3.2889
Iteration: 1786; Percent complete: 44.6%; Average loss: 3.1613
Iteration: 1787; Percent complete: 44.7%; Average loss: 3.1961
Iteration: 1788; Percent complete: 44.7%; Average loss: 3.1628
Iteration: 1789; Percent complete: 44.7%; Average loss: 3.2131
Iteration: 1790; Percent complete: 44.8%; Average loss: 3.3372
Iteration: 1791; Percent complete: 44.8%; Average loss: 3.4166
Iteration: 1792; Percent complete: 44.8%; Average loss: 3.5163
Iteration: 1793; Percent complete: 44.8%; Average loss: 3.1472
Iteration: 1794; Percent complete: 44.9%; Average loss: 3.2486
Iteration: 1795; Percent complete: 44.9%; Average loss: 3.0471
Iteration: 1796; Percent complete: 44.9%; Average loss: 3.2470
Iteration: 1797; Percent complete: 44.9%; Average loss: 3.2294
Iteration: 1798; Percent complete: 45.0%; Average loss: 3.0585
Iteration: 1799; Percent complete: 45.0%; Average loss: 3.1552
Iteration: 1800; Percent complete: 45.0%; Average loss: 3.1759
Iteration: 1801; Percent complete: 45.0%; Average loss: 3.0206
Iteration: 1802; Percent complete: 45.1%; Average loss: 3.4244
Iteration: 1803; Percent complete: 45.1%; Average loss: 3.1501
Iteration: 1804; Percent complete: 45.1%; Average loss: 3.0846
Iteration: 1805; Percent complete: 45.1%; Average loss: 3.2445
Iteration: 1806; Percent complete: 45.1%; Average loss: 3.2619
Iteration: 1807; Percent complete: 45.2%; Average loss: 3.3346
Iteration: 1808; Percent complete: 45.2%; Average loss: 3.3714
Iteration: 1809; Percent complete: 45.2%; Average loss: 2.9372
Iteration: 1810; Percent complete: 45.2%; Average loss: 3.0878
Iteration: 1811; Percent complete: 45.3%; Average loss: 3.2024
Iteration: 1812; Percent complete: 45.3%; Average loss: 3.1903
Iteration: 1813; Percent complete: 45.3%; Average loss: 3.1835
Iteration: 1814; Percent complete: 45.4%; Average loss: 3.1674
Iteration: 1815; Percent complete: 45.4%; Average loss: 3.0724
Iteration: 1816; Percent complete: 45.4%; Average loss: 3.1654
Iteration: 1817; Percent complete: 45.4%; Average loss: 3.5504
Iteration: 1818; Percent complete: 45.5%; Average loss: 3.5192
Iteration: 1819; Percent complete: 45.5%; Average loss: 3.2434
Iteration: 1820; Percent complete: 45.5%; Average loss: 3.2972
Iteration: 1821; Percent complete: 45.5%; Average loss: 3.3341
Iteration: 1822; Percent complete: 45.6%; Average loss: 3.3299
Iteration: 1823; Percent complete: 45.6%; Average loss: 3.2336
Iteration: 1824; Percent complete: 45.6%; Average loss: 3.2171
Iteration: 1825; Percent complete: 45.6%; Average loss: 3.2765
Iteration: 1826; Percent complete: 45.6%; Average loss: 3.1510
Iteration: 1827; Percent complete: 45.7%; Average loss: 3.0437
Iteration: 1828; Percent complete: 45.7%; Average loss: 3.0124
Iteration: 1829; Percent complete: 45.7%; Average loss: 3.3942
Iteration: 1830; Percent complete: 45.8%; Average loss: 3.3025
Iteration: 1831; Percent complete: 45.8%; Average loss: 3.2301
Iteration: 1832; Percent complete: 45.8%; Average loss: 3.0886
Iteration: 1833; Percent complete: 45.8%; Average loss: 3.1809
Iteration: 1834; Percent complete: 45.9%; Average loss: 3.1025
Iteration: 1835; Percent complete: 45.9%; Average loss: 3.1486
Iteration: 1836; Percent complete: 45.9%; Average loss: 3.2026
Iteration: 1837; Percent complete: 45.9%; Average loss: 3.3899
Iteration: 1838; Percent complete: 46.0%; Average loss: 3.2521
Iteration: 1839; Percent complete: 46.0%; Average loss: 3.2558
Iteration: 1840; Percent complete: 46.0%; Average loss: 3.2106
Iteration: 1841; Percent complete: 46.0%; Average loss: 3.1837
Iteration: 1842; Percent complete: 46.1%; Average loss: 3.0249
Iteration: 1843; Percent complete: 46.1%; Average loss: 3.4836
Iteration: 1844; Percent complete: 46.1%; Average loss: 3.4100
Iteration: 1845; Percent complete: 46.1%; Average loss: 3.3395
Iteration: 1846; Percent complete: 46.2%; Average loss: 3.1006
Iteration: 1847; Percent complete: 46.2%; Average loss: 3.4164
Iteration: 1848; Percent complete: 46.2%; Average loss: 3.2567
Iteration: 1849; Percent complete: 46.2%; Average loss: 3.0778
Iteration: 1850; Percent complete: 46.2%; Average loss: 3.2813
Iteration: 1851; Percent complete: 46.3%; Average loss: 3.1567
Iteration: 1852; Percent complete: 46.3%; Average loss: 3.3394
Iteration: 1853; Percent complete: 46.3%; Average loss: 3.1990
Iteration: 1854; Percent complete: 46.4%; Average loss: 3.3732
Iteration: 1855; Percent complete: 46.4%; Average loss: 3.1774
Iteration: 1856; Percent complete: 46.4%; Average loss: 2.9947
Iteration: 1857; Percent complete: 46.4%; Average loss: 2.9406
Iteration: 1858; Percent complete: 46.5%; Average loss: 3.5052
Iteration: 1859; Percent complete: 46.5%; Average loss: 2.9619
Iteration: 1860; Percent complete: 46.5%; Average loss: 3.1523
Iteration: 1861; Percent complete: 46.5%; Average loss: 3.2434
Iteration: 1862; Percent complete: 46.6%; Average loss: 3.3914
Iteration: 1863; Percent complete: 46.6%; Average loss: 3.0513
Iteration: 1864; Percent complete: 46.6%; Average loss: 3.3604
Iteration: 1865; Percent complete: 46.6%; Average loss: 3.2625
Iteration: 1866; Percent complete: 46.7%; Average loss: 3.0411
Iteration: 1867; Percent complete: 46.7%; Average loss: 3.2363
Iteration: 1868; Percent complete: 46.7%; Average loss: 3.3090
Iteration: 1869; Percent complete: 46.7%; Average loss: 3.1309
Iteration: 1870; Percent complete: 46.8%; Average loss: 3.1918
Iteration: 1871; Percent complete: 46.8%; Average loss: 3.6985
Iteration: 1872; Percent complete: 46.8%; Average loss: 3.3499
Iteration: 1873; Percent complete: 46.8%; Average loss: 3.1351
Iteration: 1874; Percent complete: 46.9%; Average loss: 3.1146
Iteration: 1875; Percent complete: 46.9%; Average loss: 3.2788
Iteration: 1876; Percent complete: 46.9%; Average loss: 3.2631
Iteration: 1877; Percent complete: 46.9%; Average loss: 3.3889
Iteration: 1878; Percent complete: 46.9%; Average loss: 3.1073
Iteration: 1879; Percent complete: 47.0%; Average loss: 3.2409
Iteration: 1880; Percent complete: 47.0%; Average loss: 3.1497
Iteration: 1881; Percent complete: 47.0%; Average loss: 3.6050
Iteration: 1882; Percent complete: 47.0%; Average loss: 3.3690
Iteration: 1883; Percent complete: 47.1%; Average loss: 3.2541
Iteration: 1884; Percent complete: 47.1%; Average loss: 3.0893
Iteration: 1885; Percent complete: 47.1%; Average loss: 3.1146
Iteration: 1886; Percent complete: 47.1%; Average loss: 2.9855
Iteration: 1887; Percent complete: 47.2%; Average loss: 3.4233
Iteration: 1888; Percent complete: 47.2%; Average loss: 2.9646
Iteration: 1889; Percent complete: 47.2%; Average loss: 3.0868
Iteration: 1890; Percent complete: 47.2%; Average loss: 3.0723
Iteration: 1891; Percent complete: 47.3%; Average loss: 3.2070
Iteration: 1892; Percent complete: 47.3%; Average loss: 3.1038
Iteration: 1893; Percent complete: 47.3%; Average loss: 3.2795
Iteration: 1894; Percent complete: 47.3%; Average loss: 3.3675
Iteration: 1895; Percent complete: 47.4%; Average loss: 3.1657
Iteration: 1896; Percent complete: 47.4%; Average loss: 3.2467
Iteration: 1897; Percent complete: 47.4%; Average loss: 3.0604
Iteration: 1898; Percent complete: 47.4%; Average loss: 3.0925
Iteration: 1899; Percent complete: 47.5%; Average loss: 3.0079
Iteration: 1900; Percent complete: 47.5%; Average loss: 2.9895
Iteration: 1901; Percent complete: 47.5%; Average loss: 3.2587
Iteration: 1902; Percent complete: 47.5%; Average loss: 3.0433
Iteration: 1903; Percent complete: 47.6%; Average loss: 3.2441
Iteration: 1904; Percent complete: 47.6%; Average loss: 3.0522
Iteration: 1905; Percent complete: 47.6%; Average loss: 3.1017
Iteration: 1906; Percent complete: 47.6%; Average loss: 2.9211
Iteration: 1907; Percent complete: 47.7%; Average loss: 2.8747
Iteration: 1908; Percent complete: 47.7%; Average loss: 3.0951
Iteration: 1909; Percent complete: 47.7%; Average loss: 2.9442
Iteration: 1910; Percent complete: 47.8%; Average loss: 3.0211
Iteration: 1911; Percent complete: 47.8%; Average loss: 2.9967
Iteration: 1912; Percent complete: 47.8%; Average loss: 3.2621
Iteration: 1913; Percent complete: 47.8%; Average loss: 3.0473
Iteration: 1914; Percent complete: 47.9%; Average loss: 2.9385
Iteration: 1915; Percent complete: 47.9%; Average loss: 3.0702
Iteration: 1916; Percent complete: 47.9%; Average loss: 3.1585
Iteration: 1917; Percent complete: 47.9%; Average loss: 3.1541
Iteration: 1918; Percent complete: 47.9%; Average loss: 3.2422
Iteration: 1919; Percent complete: 48.0%; Average loss: 3.2816
Iteration: 1920; Percent complete: 48.0%; Average loss: 3.2103
Iteration: 1921; Percent complete: 48.0%; Average loss: 3.1401
Iteration: 1922; Percent complete: 48.0%; Average loss: 3.2103
Iteration: 1923; Percent complete: 48.1%; Average loss: 3.3532
Iteration: 1924; Percent complete: 48.1%; Average loss: 3.0970
Iteration: 1925; Percent complete: 48.1%; Average loss: 3.3747
Iteration: 1926; Percent complete: 48.1%; Average loss: 3.1425
Iteration: 1927; Percent complete: 48.2%; Average loss: 2.9892
Iteration: 1928; Percent complete: 48.2%; Average loss: 3.7115
Iteration: 1929; Percent complete: 48.2%; Average loss: 3.3014
Iteration: 1930; Percent complete: 48.2%; Average loss: 3.2732
Iteration: 1931; Percent complete: 48.3%; Average loss: 3.1459
Iteration: 1932; Percent complete: 48.3%; Average loss: 3.2993
Iteration: 1933; Percent complete: 48.3%; Average loss: 3.2997
Iteration: 1934; Percent complete: 48.4%; Average loss: 2.9014
Iteration: 1935; Percent complete: 48.4%; Average loss: 3.3502
Iteration: 1936; Percent complete: 48.4%; Average loss: 3.0599
Iteration: 1937; Percent complete: 48.4%; Average loss: 3.1216
Iteration: 1938; Percent complete: 48.4%; Average loss: 3.1427
Iteration: 1939; Percent complete: 48.5%; Average loss: 3.1723
Iteration: 1940; Percent complete: 48.5%; Average loss: 3.1338
Iteration: 1941; Percent complete: 48.5%; Average loss: 3.1248
Iteration: 1942; Percent complete: 48.5%; Average loss: 3.1531
Iteration: 1943; Percent complete: 48.6%; Average loss: 3.0992
Iteration: 1944; Percent complete: 48.6%; Average loss: 3.2295
Iteration: 1945; Percent complete: 48.6%; Average loss: 3.1903
Iteration: 1946; Percent complete: 48.6%; Average loss: 3.2667
Iteration: 1947; Percent complete: 48.7%; Average loss: 3.3022
Iteration: 1948; Percent complete: 48.7%; Average loss: 3.1656
Iteration: 1949; Percent complete: 48.7%; Average loss: 2.8772
Iteration: 1950; Percent complete: 48.8%; Average loss: 3.0561
Iteration: 1951; Percent complete: 48.8%; Average loss: 3.3190
Iteration: 1952; Percent complete: 48.8%; Average loss: 3.1064
Iteration: 1953; Percent complete: 48.8%; Average loss: 3.6576
Iteration: 1954; Percent complete: 48.9%; Average loss: 2.9839
Iteration: 1955; Percent complete: 48.9%; Average loss: 3.2937
Iteration: 1956; Percent complete: 48.9%; Average loss: 3.1734
Iteration: 1957; Percent complete: 48.9%; Average loss: 3.4058
Iteration: 1958; Percent complete: 48.9%; Average loss: 3.2456
Iteration: 1959; Percent complete: 49.0%; Average loss: 2.9951
Iteration: 1960; Percent complete: 49.0%; Average loss: 2.8572
Iteration: 1961; Percent complete: 49.0%; Average loss: 3.1705
Iteration: 1962; Percent complete: 49.0%; Average loss: 3.0190
Iteration: 1963; Percent complete: 49.1%; Average loss: 3.0889
Iteration: 1964; Percent complete: 49.1%; Average loss: 3.1539
Iteration: 1965; Percent complete: 49.1%; Average loss: 2.8810
Iteration: 1966; Percent complete: 49.1%; Average loss: 3.3141
Iteration: 1967; Percent complete: 49.2%; Average loss: 3.1905
Iteration: 1968; Percent complete: 49.2%; Average loss: 3.1138
Iteration: 1969; Percent complete: 49.2%; Average loss: 3.0785
Iteration: 1970; Percent complete: 49.2%; Average loss: 3.1141
Iteration: 1971; Percent complete: 49.3%; Average loss: 3.0518
Iteration: 1972; Percent complete: 49.3%; Average loss: 3.3362
Iteration: 1973; Percent complete: 49.3%; Average loss: 2.8868
Iteration: 1974; Percent complete: 49.4%; Average loss: 2.8998
Iteration: 1975; Percent complete: 49.4%; Average loss: 3.2032
Iteration: 1976; Percent complete: 49.4%; Average loss: 2.8775
Iteration: 1977; Percent complete: 49.4%; Average loss: 2.9449
Iteration: 1978; Percent complete: 49.5%; Average loss: 3.2507
Iteration: 1979; Percent complete: 49.5%; Average loss: 3.1061
Iteration: 1980; Percent complete: 49.5%; Average loss: 3.2901
Iteration: 1981; Percent complete: 49.5%; Average loss: 3.1348
Iteration: 1982; Percent complete: 49.5%; Average loss: 3.2187
Iteration: 1983; Percent complete: 49.6%; Average loss: 3.0105
Iteration: 1984; Percent complete: 49.6%; Average loss: 3.2178
Iteration: 1985; Percent complete: 49.6%; Average loss: 3.3409
Iteration: 1986; Percent complete: 49.6%; Average loss: 3.1533
Iteration: 1987; Percent complete: 49.7%; Average loss: 3.2421
Iteration: 1988; Percent complete: 49.7%; Average loss: 3.3685
Iteration: 1989; Percent complete: 49.7%; Average loss: 3.0425
Iteration: 1990; Percent complete: 49.8%; Average loss: 3.1321
Iteration: 1991; Percent complete: 49.8%; Average loss: 3.0935
Iteration: 1992; Percent complete: 49.8%; Average loss: 3.2356
Iteration: 1993; Percent complete: 49.8%; Average loss: 3.1334
Iteration: 1994; Percent complete: 49.9%; Average loss: 3.1247
Iteration: 1995; Percent complete: 49.9%; Average loss: 3.1644
Iteration: 1996; Percent complete: 49.9%; Average loss: 3.0676
Iteration: 1997; Percent complete: 49.9%; Average loss: 3.1982
Iteration: 1998; Percent complete: 50.0%; Average loss: 3.2062
Iteration: 1999; Percent complete: 50.0%; Average loss: 3.3601
Iteration: 2000; Percent complete: 50.0%; Average loss: 2.9911
Iteration: 2001; Percent complete: 50.0%; Average loss: 3.1539
Iteration: 2002; Percent complete: 50.0%; Average loss: 3.3632
Iteration: 2003; Percent complete: 50.1%; Average loss: 3.3983
Iteration: 2004; Percent complete: 50.1%; Average loss: 2.8807
Iteration: 2005; Percent complete: 50.1%; Average loss: 3.0510
Iteration: 2006; Percent complete: 50.1%; Average loss: 2.6627
Iteration: 2007; Percent complete: 50.2%; Average loss: 3.4649
Iteration: 2008; Percent complete: 50.2%; Average loss: 3.1487
Iteration: 2009; Percent complete: 50.2%; Average loss: 3.1522
Iteration: 2010; Percent complete: 50.2%; Average loss: 3.0779
Iteration: 2011; Percent complete: 50.3%; Average loss: 3.1058
Iteration: 2012; Percent complete: 50.3%; Average loss: 3.1216
Iteration: 2013; Percent complete: 50.3%; Average loss: 2.9707
Iteration: 2014; Percent complete: 50.3%; Average loss: 2.9132
Iteration: 2015; Percent complete: 50.4%; Average loss: 3.2268
Iteration: 2016; Percent complete: 50.4%; Average loss: 3.0826
Iteration: 2017; Percent complete: 50.4%; Average loss: 2.9542
Iteration: 2018; Percent complete: 50.4%; Average loss: 3.0527
Iteration: 2019; Percent complete: 50.5%; Average loss: 3.2867
Iteration: 2020; Percent complete: 50.5%; Average loss: 3.1939
Iteration: 2021; Percent complete: 50.5%; Average loss: 3.0366
Iteration: 2022; Percent complete: 50.5%; Average loss: 3.1370
Iteration: 2023; Percent complete: 50.6%; Average loss: 3.0774
Iteration: 2024; Percent complete: 50.6%; Average loss: 3.3550
Iteration: 2025; Percent complete: 50.6%; Average loss: 3.3128
Iteration: 2026; Percent complete: 50.6%; Average loss: 3.2223
Iteration: 2027; Percent complete: 50.7%; Average loss: 3.3134
Iteration: 2028; Percent complete: 50.7%; Average loss: 3.2295
Iteration: 2029; Percent complete: 50.7%; Average loss: 3.0164
Iteration: 2030; Percent complete: 50.7%; Average loss: 3.0822
Iteration: 2031; Percent complete: 50.8%; Average loss: 3.1401
Iteration: 2032; Percent complete: 50.8%; Average loss: 3.3144
Iteration: 2033; Percent complete: 50.8%; Average loss: 3.2346
Iteration: 2034; Percent complete: 50.8%; Average loss: 3.1488
Iteration: 2035; Percent complete: 50.9%; Average loss: 3.0060
Iteration: 2036; Percent complete: 50.9%; Average loss: 3.0249
Iteration: 2037; Percent complete: 50.9%; Average loss: 3.2349
Iteration: 2038; Percent complete: 50.9%; Average loss: 3.1159
Iteration: 2039; Percent complete: 51.0%; Average loss: 3.1530
Iteration: 2040; Percent complete: 51.0%; Average loss: 3.2330
Iteration: 2041; Percent complete: 51.0%; Average loss: 3.1528
Iteration: 2042; Percent complete: 51.0%; Average loss: 3.1113
Iteration: 2043; Percent complete: 51.1%; Average loss: 3.0179
Iteration: 2044; Percent complete: 51.1%; Average loss: 3.0219
Iteration: 2045; Percent complete: 51.1%; Average loss: 3.1509
Iteration: 2046; Percent complete: 51.1%; Average loss: 3.1192
Iteration: 2047; Percent complete: 51.2%; Average loss: 3.1706
Iteration: 2048; Percent complete: 51.2%; Average loss: 2.9724
Iteration: 2049; Percent complete: 51.2%; Average loss: 3.0646
Iteration: 2050; Percent complete: 51.2%; Average loss: 3.0210
Iteration: 2051; Percent complete: 51.3%; Average loss: 3.2035
Iteration: 2052; Percent complete: 51.3%; Average loss: 3.3166
Iteration: 2053; Percent complete: 51.3%; Average loss: 3.1920
Iteration: 2054; Percent complete: 51.3%; Average loss: 3.1468
Iteration: 2055; Percent complete: 51.4%; Average loss: 3.0291
Iteration: 2056; Percent complete: 51.4%; Average loss: 3.1070
Iteration: 2057; Percent complete: 51.4%; Average loss: 3.0667
Iteration: 2058; Percent complete: 51.4%; Average loss: 3.2515
Iteration: 2059; Percent complete: 51.5%; Average loss: 3.2730
Iteration: 2060; Percent complete: 51.5%; Average loss: 2.9549
Iteration: 2061; Percent complete: 51.5%; Average loss: 3.1838
Iteration: 2062; Percent complete: 51.5%; Average loss: 3.0530
Iteration: 2063; Percent complete: 51.6%; Average loss: 2.9146
Iteration: 2064; Percent complete: 51.6%; Average loss: 3.1970
Iteration: 2065; Percent complete: 51.6%; Average loss: 3.0211
Iteration: 2066; Percent complete: 51.6%; Average loss: 3.1296
Iteration: 2067; Percent complete: 51.7%; Average loss: 3.1946
Iteration: 2068; Percent complete: 51.7%; Average loss: 3.0617
Iteration: 2069; Percent complete: 51.7%; Average loss: 3.3744
Iteration: 2070; Percent complete: 51.7%; Average loss: 3.1073
Iteration: 2071; Percent complete: 51.8%; Average loss: 3.1645
Iteration: 2072; Percent complete: 51.8%; Average loss: 3.0119
Iteration: 2073; Percent complete: 51.8%; Average loss: 3.0948
Iteration: 2074; Percent complete: 51.8%; Average loss: 3.1103
Iteration: 2075; Percent complete: 51.9%; Average loss: 3.1883
Iteration: 2076; Percent complete: 51.9%; Average loss: 3.2326
Iteration: 2077; Percent complete: 51.9%; Average loss: 3.2763
Iteration: 2078; Percent complete: 51.9%; Average loss: 3.0204
Iteration: 2079; Percent complete: 52.0%; Average loss: 3.4686
Iteration: 2080; Percent complete: 52.0%; Average loss: 3.0602
Iteration: 2081; Percent complete: 52.0%; Average loss: 3.1727
Iteration: 2082; Percent complete: 52.0%; Average loss: 3.1971
Iteration: 2083; Percent complete: 52.1%; Average loss: 2.7769
Iteration: 2084; Percent complete: 52.1%; Average loss: 3.0834
Iteration: 2085; Percent complete: 52.1%; Average loss: 3.0624
Iteration: 2086; Percent complete: 52.1%; Average loss: 3.1274
Iteration: 2087; Percent complete: 52.2%; Average loss: 3.0525
Iteration: 2088; Percent complete: 52.2%; Average loss: 3.0634
Iteration: 2089; Percent complete: 52.2%; Average loss: 2.9591
Iteration: 2090; Percent complete: 52.2%; Average loss: 3.0509
Iteration: 2091; Percent complete: 52.3%; Average loss: 3.2697
Iteration: 2092; Percent complete: 52.3%; Average loss: 2.9271
Iteration: 2093; Percent complete: 52.3%; Average loss: 3.0176
Iteration: 2094; Percent complete: 52.3%; Average loss: 2.9239
Iteration: 2095; Percent complete: 52.4%; Average loss: 3.2068
Iteration: 2096; Percent complete: 52.4%; Average loss: 3.1542
Iteration: 2097; Percent complete: 52.4%; Average loss: 3.0819
Iteration: 2098; Percent complete: 52.4%; Average loss: 3.3360
Iteration: 2099; Percent complete: 52.5%; Average loss: 3.4576
Iteration: 2100; Percent complete: 52.5%; Average loss: 3.0194
Iteration: 2101; Percent complete: 52.5%; Average loss: 3.1271
Iteration: 2102; Percent complete: 52.5%; Average loss: 2.8017
Iteration: 2103; Percent complete: 52.6%; Average loss: 3.3040
Iteration: 2104; Percent complete: 52.6%; Average loss: 3.1899
Iteration: 2105; Percent complete: 52.6%; Average loss: 3.2223
Iteration: 2106; Percent complete: 52.6%; Average loss: 3.0729
Iteration: 2107; Percent complete: 52.7%; Average loss: 3.1531
Iteration: 2108; Percent complete: 52.7%; Average loss: 2.8772
Iteration: 2109; Percent complete: 52.7%; Average loss: 3.0999
Iteration: 2110; Percent complete: 52.8%; Average loss: 3.3094
Iteration: 2111; Percent complete: 52.8%; Average loss: 3.2198
Iteration: 2112; Percent complete: 52.8%; Average loss: 3.1835
Iteration: 2113; Percent complete: 52.8%; Average loss: 2.9189
Iteration: 2114; Percent complete: 52.8%; Average loss: 3.2049
Iteration: 2115; Percent complete: 52.9%; Average loss: 2.7881
Iteration: 2116; Percent complete: 52.9%; Average loss: 3.0506
Iteration: 2117; Percent complete: 52.9%; Average loss: 3.3160
Iteration: 2118; Percent complete: 52.9%; Average loss: 3.3980
Iteration: 2119; Percent complete: 53.0%; Average loss: 2.8936
Iteration: 2120; Percent complete: 53.0%; Average loss: 3.1434
Iteration: 2121; Percent complete: 53.0%; Average loss: 2.9466
Iteration: 2122; Percent complete: 53.0%; Average loss: 3.0542
Iteration: 2123; Percent complete: 53.1%; Average loss: 3.1526
Iteration: 2124; Percent complete: 53.1%; Average loss: 3.2608
Iteration: 2125; Percent complete: 53.1%; Average loss: 3.1930
Iteration: 2126; Percent complete: 53.1%; Average loss: 2.9216
Iteration: 2127; Percent complete: 53.2%; Average loss: 3.1755
Iteration: 2128; Percent complete: 53.2%; Average loss: 2.9881
Iteration: 2129; Percent complete: 53.2%; Average loss: 2.9299
Iteration: 2130; Percent complete: 53.2%; Average loss: 3.1843
Iteration: 2131; Percent complete: 53.3%; Average loss: 3.0078
Iteration: 2132; Percent complete: 53.3%; Average loss: 2.9497
Iteration: 2133; Percent complete: 53.3%; Average loss: 3.0394
Iteration: 2134; Percent complete: 53.3%; Average loss: 3.1401
Iteration: 2135; Percent complete: 53.4%; Average loss: 3.0225
Iteration: 2136; Percent complete: 53.4%; Average loss: 3.1313
Iteration: 2137; Percent complete: 53.4%; Average loss: 3.1517
Iteration: 2138; Percent complete: 53.4%; Average loss: 3.0199
Iteration: 2139; Percent complete: 53.5%; Average loss: 2.9615
Iteration: 2140; Percent complete: 53.5%; Average loss: 3.1004
Iteration: 2141; Percent complete: 53.5%; Average loss: 3.3265
Iteration: 2142; Percent complete: 53.5%; Average loss: 2.8925
Iteration: 2143; Percent complete: 53.6%; Average loss: 2.9304
Iteration: 2144; Percent complete: 53.6%; Average loss: 2.8461
Iteration: 2145; Percent complete: 53.6%; Average loss: 2.7691
Iteration: 2146; Percent complete: 53.6%; Average loss: 2.9710
Iteration: 2147; Percent complete: 53.7%; Average loss: 3.1309
Iteration: 2148; Percent complete: 53.7%; Average loss: 3.1052
Iteration: 2149; Percent complete: 53.7%; Average loss: 3.1733
Iteration: 2150; Percent complete: 53.8%; Average loss: 3.2065
Iteration: 2151; Percent complete: 53.8%; Average loss: 3.0492
Iteration: 2152; Percent complete: 53.8%; Average loss: 3.0576
Iteration: 2153; Percent complete: 53.8%; Average loss: 3.0341
Iteration: 2154; Percent complete: 53.8%; Average loss: 2.9895
Iteration: 2155; Percent complete: 53.9%; Average loss: 2.9879
Iteration: 2156; Percent complete: 53.9%; Average loss: 3.1340
Iteration: 2157; Percent complete: 53.9%; Average loss: 3.0170
Iteration: 2158; Percent complete: 53.9%; Average loss: 3.0742
Iteration: 2159; Percent complete: 54.0%; Average loss: 3.2915
Iteration: 2160; Percent complete: 54.0%; Average loss: 2.8513
Iteration: 2161; Percent complete: 54.0%; Average loss: 2.9967
Iteration: 2162; Percent complete: 54.0%; Average loss: 3.0184
Iteration: 2163; Percent complete: 54.1%; Average loss: 3.1177
Iteration: 2164; Percent complete: 54.1%; Average loss: 3.1645
Iteration: 2165; Percent complete: 54.1%; Average loss: 3.1469
Iteration: 2166; Percent complete: 54.1%; Average loss: 2.8797
Iteration: 2167; Percent complete: 54.2%; Average loss: 3.1210
Iteration: 2168; Percent complete: 54.2%; Average loss: 3.1642
Iteration: 2169; Percent complete: 54.2%; Average loss: 3.0032
Iteration: 2170; Percent complete: 54.2%; Average loss: 3.0520
Iteration: 2171; Percent complete: 54.3%; Average loss: 3.0550
Iteration: 2172; Percent complete: 54.3%; Average loss: 3.1196
Iteration: 2173; Percent complete: 54.3%; Average loss: 2.9918
Iteration: 2174; Percent complete: 54.4%; Average loss: 3.1261
Iteration: 2175; Percent complete: 54.4%; Average loss: 3.1781
Iteration: 2176; Percent complete: 54.4%; Average loss: 3.1154
Iteration: 2177; Percent complete: 54.4%; Average loss: 3.4201
Iteration: 2178; Percent complete: 54.4%; Average loss: 3.1775
Iteration: 2179; Percent complete: 54.5%; Average loss: 3.4494
Iteration: 2180; Percent complete: 54.5%; Average loss: 3.0041
Iteration: 2181; Percent complete: 54.5%; Average loss: 3.1082
Iteration: 2182; Percent complete: 54.5%; Average loss: 3.2593
Iteration: 2183; Percent complete: 54.6%; Average loss: 3.0445
Iteration: 2184; Percent complete: 54.6%; Average loss: 2.9970
Iteration: 2185; Percent complete: 54.6%; Average loss: 3.2958
Iteration: 2186; Percent complete: 54.6%; Average loss: 3.1855
Iteration: 2187; Percent complete: 54.7%; Average loss: 2.8936
Iteration: 2188; Percent complete: 54.7%; Average loss: 3.0125
Iteration: 2189; Percent complete: 54.7%; Average loss: 3.0133
Iteration: 2190; Percent complete: 54.8%; Average loss: 3.1940
Iteration: 2191; Percent complete: 54.8%; Average loss: 2.9999
Iteration: 2192; Percent complete: 54.8%; Average loss: 2.9697
Iteration: 2193; Percent complete: 54.8%; Average loss: 3.1392
Iteration: 2194; Percent complete: 54.9%; Average loss: 2.9798
Iteration: 2195; Percent complete: 54.9%; Average loss: 2.8470
Iteration: 2196; Percent complete: 54.9%; Average loss: 3.1470
Iteration: 2197; Percent complete: 54.9%; Average loss: 3.0452
Iteration: 2198; Percent complete: 54.9%; Average loss: 3.1776
Iteration: 2199; Percent complete: 55.0%; Average loss: 3.1453
Iteration: 2200; Percent complete: 55.0%; Average loss: 3.1416
Iteration: 2201; Percent complete: 55.0%; Average loss: 3.2182
Iteration: 2202; Percent complete: 55.0%; Average loss: 3.1953
Iteration: 2203; Percent complete: 55.1%; Average loss: 2.9682
Iteration: 2204; Percent complete: 55.1%; Average loss: 3.0214
Iteration: 2205; Percent complete: 55.1%; Average loss: 3.0322
Iteration: 2206; Percent complete: 55.1%; Average loss: 2.7622
Iteration: 2207; Percent complete: 55.2%; Average loss: 3.2405
Iteration: 2208; Percent complete: 55.2%; Average loss: 3.3040
Iteration: 2209; Percent complete: 55.2%; Average loss: 3.1958
Iteration: 2210; Percent complete: 55.2%; Average loss: 2.9194
Iteration: 2211; Percent complete: 55.3%; Average loss: 2.8202
Iteration: 2212; Percent complete: 55.3%; Average loss: 3.1159
Iteration: 2213; Percent complete: 55.3%; Average loss: 3.3550
Iteration: 2214; Percent complete: 55.4%; Average loss: 3.1493
Iteration: 2215; Percent complete: 55.4%; Average loss: 2.7748
Iteration: 2216; Percent complete: 55.4%; Average loss: 3.2903
Iteration: 2217; Percent complete: 55.4%; Average loss: 3.2390
Iteration: 2218; Percent complete: 55.5%; Average loss: 3.1098
Iteration: 2219; Percent complete: 55.5%; Average loss: 2.9313
Iteration: 2220; Percent complete: 55.5%; Average loss: 3.1565
Iteration: 2221; Percent complete: 55.5%; Average loss: 3.1583
Iteration: 2222; Percent complete: 55.5%; Average loss: 3.1124
Iteration: 2223; Percent complete: 55.6%; Average loss: 3.0785
Iteration: 2224; Percent complete: 55.6%; Average loss: 3.2560
Iteration: 2225; Percent complete: 55.6%; Average loss: 3.2174
Iteration: 2226; Percent complete: 55.6%; Average loss: 2.8584
Iteration: 2227; Percent complete: 55.7%; Average loss: 3.1350
Iteration: 2228; Percent complete: 55.7%; Average loss: 3.1114
Iteration: 2229; Percent complete: 55.7%; Average loss: 3.1136
Iteration: 2230; Percent complete: 55.8%; Average loss: 2.9900
Iteration: 2231; Percent complete: 55.8%; Average loss: 2.8794
Iteration: 2232; Percent complete: 55.8%; Average loss: 3.2196
Iteration: 2233; Percent complete: 55.8%; Average loss: 2.9703
Iteration: 2234; Percent complete: 55.9%; Average loss: 3.1943
Iteration: 2235; Percent complete: 55.9%; Average loss: 3.0743
Iteration: 2236; Percent complete: 55.9%; Average loss: 2.7771
Iteration: 2237; Percent complete: 55.9%; Average loss: 2.9510
Iteration: 2238; Percent complete: 56.0%; Average loss: 2.9247
Iteration: 2239; Percent complete: 56.0%; Average loss: 3.0885
Iteration: 2240; Percent complete: 56.0%; Average loss: 3.1363
Iteration: 2241; Percent complete: 56.0%; Average loss: 3.3062
Iteration: 2242; Percent complete: 56.0%; Average loss: 3.1083
Iteration: 2243; Percent complete: 56.1%; Average loss: 3.0402
Iteration: 2244; Percent complete: 56.1%; Average loss: 2.9773
Iteration: 2245; Percent complete: 56.1%; Average loss: 2.7829
Iteration: 2246; Percent complete: 56.1%; Average loss: 3.1443
Iteration: 2247; Percent complete: 56.2%; Average loss: 3.0746
Iteration: 2248; Percent complete: 56.2%; Average loss: 3.1280
Iteration: 2249; Percent complete: 56.2%; Average loss: 3.0224
Iteration: 2250; Percent complete: 56.2%; Average loss: 3.0364
Iteration: 2251; Percent complete: 56.3%; Average loss: 2.9561
Iteration: 2252; Percent complete: 56.3%; Average loss: 3.0581
Iteration: 2253; Percent complete: 56.3%; Average loss: 3.0688
Iteration: 2254; Percent complete: 56.4%; Average loss: 3.0422
Iteration: 2255; Percent complete: 56.4%; Average loss: 3.2024
Iteration: 2256; Percent complete: 56.4%; Average loss: 2.8482
Iteration: 2257; Percent complete: 56.4%; Average loss: 3.0037
Iteration: 2258; Percent complete: 56.5%; Average loss: 3.0064
Iteration: 2259; Percent complete: 56.5%; Average loss: 2.9935
Iteration: 2260; Percent complete: 56.5%; Average loss: 3.3156
Iteration: 2261; Percent complete: 56.5%; Average loss: 2.9912
Iteration: 2262; Percent complete: 56.5%; Average loss: 3.1163
Iteration: 2263; Percent complete: 56.6%; Average loss: 3.3010
Iteration: 2264; Percent complete: 56.6%; Average loss: 3.1214
Iteration: 2265; Percent complete: 56.6%; Average loss: 3.5314
Iteration: 2266; Percent complete: 56.6%; Average loss: 3.0649
Iteration: 2267; Percent complete: 56.7%; Average loss: 2.9481
Iteration: 2268; Percent complete: 56.7%; Average loss: 3.1508
Iteration: 2269; Percent complete: 56.7%; Average loss: 3.1602
Iteration: 2270; Percent complete: 56.8%; Average loss: 3.2260
Iteration: 2271; Percent complete: 56.8%; Average loss: 3.0606
Iteration: 2272; Percent complete: 56.8%; Average loss: 3.0347
Iteration: 2273; Percent complete: 56.8%; Average loss: 2.8901
Iteration: 2274; Percent complete: 56.9%; Average loss: 3.1561
Iteration: 2275; Percent complete: 56.9%; Average loss: 3.2192
Iteration: 2276; Percent complete: 56.9%; Average loss: 2.9589
Iteration: 2277; Percent complete: 56.9%; Average loss: 3.0976
Iteration: 2278; Percent complete: 57.0%; Average loss: 2.8757
Iteration: 2279; Percent complete: 57.0%; Average loss: 3.0782
Iteration: 2280; Percent complete: 57.0%; Average loss: 2.9389
Iteration: 2281; Percent complete: 57.0%; Average loss: 2.9954
Iteration: 2282; Percent complete: 57.0%; Average loss: 3.3522
Iteration: 2283; Percent complete: 57.1%; Average loss: 3.2140
Iteration: 2284; Percent complete: 57.1%; Average loss: 3.2308
Iteration: 2285; Percent complete: 57.1%; Average loss: 3.1379
Iteration: 2286; Percent complete: 57.1%; Average loss: 3.1428
Iteration: 2287; Percent complete: 57.2%; Average loss: 3.1522
Iteration: 2288; Percent complete: 57.2%; Average loss: 3.2347
Iteration: 2289; Percent complete: 57.2%; Average loss: 2.9700
Iteration: 2290; Percent complete: 57.2%; Average loss: 3.2595
Iteration: 2291; Percent complete: 57.3%; Average loss: 2.9991
Iteration: 2292; Percent complete: 57.3%; Average loss: 2.9797
Iteration: 2293; Percent complete: 57.3%; Average loss: 3.0163
Iteration: 2294; Percent complete: 57.4%; Average loss: 2.9305
Iteration: 2295; Percent complete: 57.4%; Average loss: 2.8909
Iteration: 2296; Percent complete: 57.4%; Average loss: 3.0328
Iteration: 2297; Percent complete: 57.4%; Average loss: 3.1579
Iteration: 2298; Percent complete: 57.5%; Average loss: 3.0711
Iteration: 2299; Percent complete: 57.5%; Average loss: 2.8786
Iteration: 2300; Percent complete: 57.5%; Average loss: 3.0007
Iteration: 2301; Percent complete: 57.5%; Average loss: 3.2233
Iteration: 2302; Percent complete: 57.6%; Average loss: 3.3686
Iteration: 2303; Percent complete: 57.6%; Average loss: 3.0076
Iteration: 2304; Percent complete: 57.6%; Average loss: 3.2935
Iteration: 2305; Percent complete: 57.6%; Average loss: 3.3710
Iteration: 2306; Percent complete: 57.6%; Average loss: 3.0359
Iteration: 2307; Percent complete: 57.7%; Average loss: 3.1277
Iteration: 2308; Percent complete: 57.7%; Average loss: 2.9894
Iteration: 2309; Percent complete: 57.7%; Average loss: 2.8534
Iteration: 2310; Percent complete: 57.8%; Average loss: 3.0989
Iteration: 2311; Percent complete: 57.8%; Average loss: 3.0249
Iteration: 2312; Percent complete: 57.8%; Average loss: 2.9912
Iteration: 2313; Percent complete: 57.8%; Average loss: 3.1359
Iteration: 2314; Percent complete: 57.9%; Average loss: 3.1416
Iteration: 2315; Percent complete: 57.9%; Average loss: 3.0053
Iteration: 2316; Percent complete: 57.9%; Average loss: 3.1304
Iteration: 2317; Percent complete: 57.9%; Average loss: 3.1534
Iteration: 2318; Percent complete: 58.0%; Average loss: 3.2572
Iteration: 2319; Percent complete: 58.0%; Average loss: 3.2175
Iteration: 2320; Percent complete: 58.0%; Average loss: 2.8192
Iteration: 2321; Percent complete: 58.0%; Average loss: 2.9249
Iteration: 2322; Percent complete: 58.1%; Average loss: 3.2368
Iteration: 2323; Percent complete: 58.1%; Average loss: 3.1780
Iteration: 2324; Percent complete: 58.1%; Average loss: 2.9314
Iteration: 2325; Percent complete: 58.1%; Average loss: 2.7617
Iteration: 2326; Percent complete: 58.1%; Average loss: 3.1690
Iteration: 2327; Percent complete: 58.2%; Average loss: 2.9991
Iteration: 2328; Percent complete: 58.2%; Average loss: 3.1546
Iteration: 2329; Percent complete: 58.2%; Average loss: 3.0987
Iteration: 2330; Percent complete: 58.2%; Average loss: 3.0825
Iteration: 2331; Percent complete: 58.3%; Average loss: 3.1512
Iteration: 2332; Percent complete: 58.3%; Average loss: 2.9668
Iteration: 2333; Percent complete: 58.3%; Average loss: 2.8767
Iteration: 2334; Percent complete: 58.4%; Average loss: 3.0137
Iteration: 2335; Percent complete: 58.4%; Average loss: 2.9601
Iteration: 2336; Percent complete: 58.4%; Average loss: 2.8514
Iteration: 2337; Percent complete: 58.4%; Average loss: 2.9536
Iteration: 2338; Percent complete: 58.5%; Average loss: 2.9839
Iteration: 2339; Percent complete: 58.5%; Average loss: 3.1270
Iteration: 2340; Percent complete: 58.5%; Average loss: 2.9505
Iteration: 2341; Percent complete: 58.5%; Average loss: 2.8800
Iteration: 2342; Percent complete: 58.6%; Average loss: 3.0974
Iteration: 2343; Percent complete: 58.6%; Average loss: 2.9407
Iteration: 2344; Percent complete: 58.6%; Average loss: 3.0123
Iteration: 2345; Percent complete: 58.6%; Average loss: 2.9357
Iteration: 2346; Percent complete: 58.7%; Average loss: 2.9087
Iteration: 2347; Percent complete: 58.7%; Average loss: 2.9685
Iteration: 2348; Percent complete: 58.7%; Average loss: 2.9554
Iteration: 2349; Percent complete: 58.7%; Average loss: 3.0320
Iteration: 2350; Percent complete: 58.8%; Average loss: 3.2669
Iteration: 2351; Percent complete: 58.8%; Average loss: 3.1602
Iteration: 2352; Percent complete: 58.8%; Average loss: 2.7893
Iteration: 2353; Percent complete: 58.8%; Average loss: 2.9583
Iteration: 2354; Percent complete: 58.9%; Average loss: 3.0855
Iteration: 2355; Percent complete: 58.9%; Average loss: 3.0823
Iteration: 2356; Percent complete: 58.9%; Average loss: 3.1484
Iteration: 2357; Percent complete: 58.9%; Average loss: 3.2834
Iteration: 2358; Percent complete: 59.0%; Average loss: 3.1702
Iteration: 2359; Percent complete: 59.0%; Average loss: 3.1257
Iteration: 2360; Percent complete: 59.0%; Average loss: 3.1223
Iteration: 2361; Percent complete: 59.0%; Average loss: 3.1200
Iteration: 2362; Percent complete: 59.1%; Average loss: 3.3044
Iteration: 2363; Percent complete: 59.1%; Average loss: 3.1971
Iteration: 2364; Percent complete: 59.1%; Average loss: 3.0604
Iteration: 2365; Percent complete: 59.1%; Average loss: 2.9914
Iteration: 2366; Percent complete: 59.2%; Average loss: 3.0744
Iteration: 2367; Percent complete: 59.2%; Average loss: 2.9853
Iteration: 2368; Percent complete: 59.2%; Average loss: 3.2283
Iteration: 2369; Percent complete: 59.2%; Average loss: 2.9008
Iteration: 2370; Percent complete: 59.2%; Average loss: 2.7990
Iteration: 2371; Percent complete: 59.3%; Average loss: 2.8773
Iteration: 2372; Percent complete: 59.3%; Average loss: 3.0951
Iteration: 2373; Percent complete: 59.3%; Average loss: 3.0430
Iteration: 2374; Percent complete: 59.4%; Average loss: 3.1242
Iteration: 2375; Percent complete: 59.4%; Average loss: 3.1648
Iteration: 2376; Percent complete: 59.4%; Average loss: 2.8579
Iteration: 2377; Percent complete: 59.4%; Average loss: 2.9875
Iteration: 2378; Percent complete: 59.5%; Average loss: 2.8168
Iteration: 2379; Percent complete: 59.5%; Average loss: 3.1879
Iteration: 2380; Percent complete: 59.5%; Average loss: 2.9505
Iteration: 2381; Percent complete: 59.5%; Average loss: 2.9778
Iteration: 2382; Percent complete: 59.6%; Average loss: 3.0170
Iteration: 2383; Percent complete: 59.6%; Average loss: 3.0934
Iteration: 2384; Percent complete: 59.6%; Average loss: 2.8515
Iteration: 2385; Percent complete: 59.6%; Average loss: 2.9867
Iteration: 2386; Percent complete: 59.7%; Average loss: 3.0527
Iteration: 2387; Percent complete: 59.7%; Average loss: 2.6696
Iteration: 2388; Percent complete: 59.7%; Average loss: 3.0298
Iteration: 2389; Percent complete: 59.7%; Average loss: 2.9453
Iteration: 2390; Percent complete: 59.8%; Average loss: 3.0712
Iteration: 2391; Percent complete: 59.8%; Average loss: 3.0482
Iteration: 2392; Percent complete: 59.8%; Average loss: 3.0613
Iteration: 2393; Percent complete: 59.8%; Average loss: 3.0958
Iteration: 2394; Percent complete: 59.9%; Average loss: 3.1147
Iteration: 2395; Percent complete: 59.9%; Average loss: 3.4187
Iteration: 2396; Percent complete: 59.9%; Average loss: 3.1878
Iteration: 2397; Percent complete: 59.9%; Average loss: 3.1709
Iteration: 2398; Percent complete: 60.0%; Average loss: 3.0455
Iteration: 2399; Percent complete: 60.0%; Average loss: 3.1445
Iteration: 2400; Percent complete: 60.0%; Average loss: 3.1410
Iteration: 2401; Percent complete: 60.0%; Average loss: 3.1558
Iteration: 2402; Percent complete: 60.1%; Average loss: 2.9517
Iteration: 2403; Percent complete: 60.1%; Average loss: 2.9608
Iteration: 2404; Percent complete: 60.1%; Average loss: 3.0954
Iteration: 2405; Percent complete: 60.1%; Average loss: 3.1064
Iteration: 2406; Percent complete: 60.2%; Average loss: 3.1734
Iteration: 2407; Percent complete: 60.2%; Average loss: 3.1773
Iteration: 2408; Percent complete: 60.2%; Average loss: 3.0301
Iteration: 2409; Percent complete: 60.2%; Average loss: 3.2692
Iteration: 2410; Percent complete: 60.2%; Average loss: 2.8754
Iteration: 2411; Percent complete: 60.3%; Average loss: 2.8577
Iteration: 2412; Percent complete: 60.3%; Average loss: 3.0001
Iteration: 2413; Percent complete: 60.3%; Average loss: 3.1167
Iteration: 2414; Percent complete: 60.4%; Average loss: 2.8776
Iteration: 2415; Percent complete: 60.4%; Average loss: 3.2657
Iteration: 2416; Percent complete: 60.4%; Average loss: 3.0065
Iteration: 2417; Percent complete: 60.4%; Average loss: 3.0028
Iteration: 2418; Percent complete: 60.5%; Average loss: 3.0870
Iteration: 2419; Percent complete: 60.5%; Average loss: 2.8686
Iteration: 2420; Percent complete: 60.5%; Average loss: 3.2341
Iteration: 2421; Percent complete: 60.5%; Average loss: 2.9116
Iteration: 2422; Percent complete: 60.6%; Average loss: 3.0444
Iteration: 2423; Percent complete: 60.6%; Average loss: 2.8941
Iteration: 2424; Percent complete: 60.6%; Average loss: 2.7938
Iteration: 2425; Percent complete: 60.6%; Average loss: 3.1072
Iteration: 2426; Percent complete: 60.7%; Average loss: 2.8344
Iteration: 2427; Percent complete: 60.7%; Average loss: 2.9302
Iteration: 2428; Percent complete: 60.7%; Average loss: 2.7490
Iteration: 2429; Percent complete: 60.7%; Average loss: 3.0941
Iteration: 2430; Percent complete: 60.8%; Average loss: 3.2798
Iteration: 2431; Percent complete: 60.8%; Average loss: 2.6597
Iteration: 2432; Percent complete: 60.8%; Average loss: 3.0647
Iteration: 2433; Percent complete: 60.8%; Average loss: 3.0839
Iteration: 2434; Percent complete: 60.9%; Average loss: 3.1950
Iteration: 2435; Percent complete: 60.9%; Average loss: 2.9783
Iteration: 2436; Percent complete: 60.9%; Average loss: 3.0548
Iteration: 2437; Percent complete: 60.9%; Average loss: 2.8139
Iteration: 2438; Percent complete: 61.0%; Average loss: 3.0639
Iteration: 2439; Percent complete: 61.0%; Average loss: 3.0965
Iteration: 2440; Percent complete: 61.0%; Average loss: 3.0061
Iteration: 2441; Percent complete: 61.0%; Average loss: 2.9168
Iteration: 2442; Percent complete: 61.1%; Average loss: 3.2231
Iteration: 2443; Percent complete: 61.1%; Average loss: 3.0681
Iteration: 2444; Percent complete: 61.1%; Average loss: 3.1197
Iteration: 2445; Percent complete: 61.1%; Average loss: 2.9703
Iteration: 2446; Percent complete: 61.2%; Average loss: 2.9496
Iteration: 2447; Percent complete: 61.2%; Average loss: 2.8339
Iteration: 2448; Percent complete: 61.2%; Average loss: 3.3140
Iteration: 2449; Percent complete: 61.2%; Average loss: 2.8079
Iteration: 2450; Percent complete: 61.3%; Average loss: 3.0736
Iteration: 2451; Percent complete: 61.3%; Average loss: 2.9725
Iteration: 2452; Percent complete: 61.3%; Average loss: 2.9834
Iteration: 2453; Percent complete: 61.3%; Average loss: 2.8317
Iteration: 2454; Percent complete: 61.4%; Average loss: 3.0336
Iteration: 2455; Percent complete: 61.4%; Average loss: 3.1410
Iteration: 2456; Percent complete: 61.4%; Average loss: 3.1679
Iteration: 2457; Percent complete: 61.4%; Average loss: 2.9145
Iteration: 2458; Percent complete: 61.5%; Average loss: 2.8620
Iteration: 2459; Percent complete: 61.5%; Average loss: 2.9717
Iteration: 2460; Percent complete: 61.5%; Average loss: 2.9315
Iteration: 2461; Percent complete: 61.5%; Average loss: 2.8877
Iteration: 2462; Percent complete: 61.6%; Average loss: 2.8998
Iteration: 2463; Percent complete: 61.6%; Average loss: 2.9958
Iteration: 2464; Percent complete: 61.6%; Average loss: 2.9743
Iteration: 2465; Percent complete: 61.6%; Average loss: 3.0048
Iteration: 2466; Percent complete: 61.7%; Average loss: 2.9669
Iteration: 2467; Percent complete: 61.7%; Average loss: 3.1573
Iteration: 2468; Percent complete: 61.7%; Average loss: 3.1529
Iteration: 2469; Percent complete: 61.7%; Average loss: 3.1774
Iteration: 2470; Percent complete: 61.8%; Average loss: 3.0236
Iteration: 2471; Percent complete: 61.8%; Average loss: 3.0971
Iteration: 2472; Percent complete: 61.8%; Average loss: 2.9550
Iteration: 2473; Percent complete: 61.8%; Average loss: 3.0432
Iteration: 2474; Percent complete: 61.9%; Average loss: 3.1052
Iteration: 2475; Percent complete: 61.9%; Average loss: 3.2898
Iteration: 2476; Percent complete: 61.9%; Average loss: 3.1029
Iteration: 2477; Percent complete: 61.9%; Average loss: 3.1771
Iteration: 2478; Percent complete: 62.0%; Average loss: 3.1525
Iteration: 2479; Percent complete: 62.0%; Average loss: 3.1843
Iteration: 2480; Percent complete: 62.0%; Average loss: 2.8908
Iteration: 2481; Percent complete: 62.0%; Average loss: 3.0839
Iteration: 2482; Percent complete: 62.1%; Average loss: 2.7895
Iteration: 2483; Percent complete: 62.1%; Average loss: 3.2337
Iteration: 2484; Percent complete: 62.1%; Average loss: 2.7419
Iteration: 2485; Percent complete: 62.1%; Average loss: 2.9599
Iteration: 2486; Percent complete: 62.2%; Average loss: 3.1131
Iteration: 2487; Percent complete: 62.2%; Average loss: 2.9641
Iteration: 2488; Percent complete: 62.2%; Average loss: 3.0127
Iteration: 2489; Percent complete: 62.2%; Average loss: 3.1913
Iteration: 2490; Percent complete: 62.3%; Average loss: 2.9569
Iteration: 2491; Percent complete: 62.3%; Average loss: 3.2942
Iteration: 2492; Percent complete: 62.3%; Average loss: 2.9457
Iteration: 2493; Percent complete: 62.3%; Average loss: 3.0620
Iteration: 2494; Percent complete: 62.4%; Average loss: 2.9397
Iteration: 2495; Percent complete: 62.4%; Average loss: 3.0263
Iteration: 2496; Percent complete: 62.4%; Average loss: 3.1644
Iteration: 2497; Percent complete: 62.4%; Average loss: 3.0226
Iteration: 2498; Percent complete: 62.5%; Average loss: 2.5855
Iteration: 2499; Percent complete: 62.5%; Average loss: 2.9279
Iteration: 2500; Percent complete: 62.5%; Average loss: 2.8714
Iteration: 2501; Percent complete: 62.5%; Average loss: 2.9510
Iteration: 2502; Percent complete: 62.5%; Average loss: 2.8894
Iteration: 2503; Percent complete: 62.6%; Average loss: 2.9008
Iteration: 2504; Percent complete: 62.6%; Average loss: 3.1439
Iteration: 2505; Percent complete: 62.6%; Average loss: 3.1656
Iteration: 2506; Percent complete: 62.6%; Average loss: 2.8996
Iteration: 2507; Percent complete: 62.7%; Average loss: 3.0900
Iteration: 2508; Percent complete: 62.7%; Average loss: 2.9056
Iteration: 2509; Percent complete: 62.7%; Average loss: 3.1114
Iteration: 2510; Percent complete: 62.7%; Average loss: 3.1029
Iteration: 2511; Percent complete: 62.8%; Average loss: 3.0407
Iteration: 2512; Percent complete: 62.8%; Average loss: 3.0970
Iteration: 2513; Percent complete: 62.8%; Average loss: 3.0155
Iteration: 2514; Percent complete: 62.8%; Average loss: 3.0832
Iteration: 2515; Percent complete: 62.9%; Average loss: 3.2323
Iteration: 2516; Percent complete: 62.9%; Average loss: 2.7526
Iteration: 2517; Percent complete: 62.9%; Average loss: 2.9942
Iteration: 2518; Percent complete: 62.9%; Average loss: 2.9113
Iteration: 2519; Percent complete: 63.0%; Average loss: 3.0654
Iteration: 2520; Percent complete: 63.0%; Average loss: 3.2228
Iteration: 2521; Percent complete: 63.0%; Average loss: 2.9826
Iteration: 2522; Percent complete: 63.0%; Average loss: 3.1372
Iteration: 2523; Percent complete: 63.1%; Average loss: 2.9777
Iteration: 2524; Percent complete: 63.1%; Average loss: 3.1578
Iteration: 2525; Percent complete: 63.1%; Average loss: 2.7461
Iteration: 2526; Percent complete: 63.1%; Average loss: 3.2058
Iteration: 2527; Percent complete: 63.2%; Average loss: 3.2776
Iteration: 2528; Percent complete: 63.2%; Average loss: 2.9730
Iteration: 2529; Percent complete: 63.2%; Average loss: 3.2976
Iteration: 2530; Percent complete: 63.2%; Average loss: 3.1994
Iteration: 2531; Percent complete: 63.3%; Average loss: 2.8540
Iteration: 2532; Percent complete: 63.3%; Average loss: 2.9684
Iteration: 2533; Percent complete: 63.3%; Average loss: 2.9364
Iteration: 2534; Percent complete: 63.3%; Average loss: 3.1175
Iteration: 2535; Percent complete: 63.4%; Average loss: 2.8799
Iteration: 2536; Percent complete: 63.4%; Average loss: 3.1802
Iteration: 2537; Percent complete: 63.4%; Average loss: 2.7364
Iteration: 2538; Percent complete: 63.4%; Average loss: 3.3106
Iteration: 2539; Percent complete: 63.5%; Average loss: 3.1871
Iteration: 2540; Percent complete: 63.5%; Average loss: 3.0306
Iteration: 2541; Percent complete: 63.5%; Average loss: 3.0432
Iteration: 2542; Percent complete: 63.5%; Average loss: 3.0027
Iteration: 2543; Percent complete: 63.6%; Average loss: 3.0842
Iteration: 2544; Percent complete: 63.6%; Average loss: 2.9132
Iteration: 2545; Percent complete: 63.6%; Average loss: 2.9477
Iteration: 2546; Percent complete: 63.6%; Average loss: 3.1194
Iteration: 2547; Percent complete: 63.7%; Average loss: 3.1898
Iteration: 2548; Percent complete: 63.7%; Average loss: 3.0476
Iteration: 2549; Percent complete: 63.7%; Average loss: 3.0808
Iteration: 2550; Percent complete: 63.7%; Average loss: 2.9035
Iteration: 2551; Percent complete: 63.8%; Average loss: 3.3374
Iteration: 2552; Percent complete: 63.8%; Average loss: 3.1201
Iteration: 2553; Percent complete: 63.8%; Average loss: 2.9463
Iteration: 2554; Percent complete: 63.8%; Average loss: 3.1493
Iteration: 2555; Percent complete: 63.9%; Average loss: 3.0034
Iteration: 2556; Percent complete: 63.9%; Average loss: 2.7182
Iteration: 2557; Percent complete: 63.9%; Average loss: 2.9300
Iteration: 2558; Percent complete: 63.9%; Average loss: 3.3725
Iteration: 2559; Percent complete: 64.0%; Average loss: 2.6953
Iteration: 2560; Percent complete: 64.0%; Average loss: 2.9584
Iteration: 2561; Percent complete: 64.0%; Average loss: 2.9796
Iteration: 2562; Percent complete: 64.0%; Average loss: 2.7370
Iteration: 2563; Percent complete: 64.1%; Average loss: 2.7626
Iteration: 2564; Percent complete: 64.1%; Average loss: 2.8949
Iteration: 2565; Percent complete: 64.1%; Average loss: 3.0603
Iteration: 2566; Percent complete: 64.1%; Average loss: 2.9926
Iteration: 2567; Percent complete: 64.2%; Average loss: 3.0103
Iteration: 2568; Percent complete: 64.2%; Average loss: 2.8394
Iteration: 2569; Percent complete: 64.2%; Average loss: 3.0099
Iteration: 2570; Percent complete: 64.2%; Average loss: 3.1332
Iteration: 2571; Percent complete: 64.3%; Average loss: 2.9304
Iteration: 2572; Percent complete: 64.3%; Average loss: 2.8352
Iteration: 2573; Percent complete: 64.3%; Average loss: 3.2220
Iteration: 2574; Percent complete: 64.3%; Average loss: 3.0883
Iteration: 2575; Percent complete: 64.4%; Average loss: 2.7966
Iteration: 2576; Percent complete: 64.4%; Average loss: 2.8943
Iteration: 2577; Percent complete: 64.4%; Average loss: 3.0301
Iteration: 2578; Percent complete: 64.5%; Average loss: 2.9636
Iteration: 2579; Percent complete: 64.5%; Average loss: 3.1751
Iteration: 2580; Percent complete: 64.5%; Average loss: 2.9839
Iteration: 2581; Percent complete: 64.5%; Average loss: 2.9742
Iteration: 2582; Percent complete: 64.5%; Average loss: 2.9007
Iteration: 2583; Percent complete: 64.6%; Average loss: 3.1467
Iteration: 2584; Percent complete: 64.6%; Average loss: 3.3118
Iteration: 2585; Percent complete: 64.6%; Average loss: 2.9847
Iteration: 2586; Percent complete: 64.6%; Average loss: 2.8293
Iteration: 2587; Percent complete: 64.7%; Average loss: 3.0268
Iteration: 2588; Percent complete: 64.7%; Average loss: 3.0289
Iteration: 2589; Percent complete: 64.7%; Average loss: 2.9382
Iteration: 2590; Percent complete: 64.8%; Average loss: 2.9236
Iteration: 2591; Percent complete: 64.8%; Average loss: 2.9866
Iteration: 2592; Percent complete: 64.8%; Average loss: 2.9152
Iteration: 2593; Percent complete: 64.8%; Average loss: 3.1940
Iteration: 2594; Percent complete: 64.8%; Average loss: 3.0602
Iteration: 2595; Percent complete: 64.9%; Average loss: 3.1460
Iteration: 2596; Percent complete: 64.9%; Average loss: 3.0256
Iteration: 2597; Percent complete: 64.9%; Average loss: 2.8450
Iteration: 2598; Percent complete: 65.0%; Average loss: 2.8775
Iteration: 2599; Percent complete: 65.0%; Average loss: 2.8487
Iteration: 2600; Percent complete: 65.0%; Average loss: 2.9060
Iteration: 2601; Percent complete: 65.0%; Average loss: 3.1704
Iteration: 2602; Percent complete: 65.0%; Average loss: 3.0598
Iteration: 2603; Percent complete: 65.1%; Average loss: 2.7356
Iteration: 2604; Percent complete: 65.1%; Average loss: 3.0009
Iteration: 2605; Percent complete: 65.1%; Average loss: 3.0691
Iteration: 2606; Percent complete: 65.1%; Average loss: 2.9993
Iteration: 2607; Percent complete: 65.2%; Average loss: 3.0400
Iteration: 2608; Percent complete: 65.2%; Average loss: 3.1497
Iteration: 2609; Percent complete: 65.2%; Average loss: 2.9571
Iteration: 2610; Percent complete: 65.2%; Average loss: 2.7479
Iteration: 2611; Percent complete: 65.3%; Average loss: 3.0640
Iteration: 2612; Percent complete: 65.3%; Average loss: 3.1267
Iteration: 2613; Percent complete: 65.3%; Average loss: 3.0357
Iteration: 2614; Percent complete: 65.3%; Average loss: 2.8699
Iteration: 2615; Percent complete: 65.4%; Average loss: 2.9996
Iteration: 2616; Percent complete: 65.4%; Average loss: 3.0184
Iteration: 2617; Percent complete: 65.4%; Average loss: 2.9258
Iteration: 2618; Percent complete: 65.5%; Average loss: 2.9735
Iteration: 2619; Percent complete: 65.5%; Average loss: 3.0778
Iteration: 2620; Percent complete: 65.5%; Average loss: 3.1709
Iteration: 2621; Percent complete: 65.5%; Average loss: 2.7026
Iteration: 2622; Percent complete: 65.5%; Average loss: 2.7554
Iteration: 2623; Percent complete: 65.6%; Average loss: 2.9370
Iteration: 2624; Percent complete: 65.6%; Average loss: 2.8987
Iteration: 2625; Percent complete: 65.6%; Average loss: 2.9672
Iteration: 2626; Percent complete: 65.6%; Average loss: 2.9751
Iteration: 2627; Percent complete: 65.7%; Average loss: 2.8875
Iteration: 2628; Percent complete: 65.7%; Average loss: 3.1661
Iteration: 2629; Percent complete: 65.7%; Average loss: 2.9965
Iteration: 2630; Percent complete: 65.8%; Average loss: 2.9469
Iteration: 2631; Percent complete: 65.8%; Average loss: 2.8927
Iteration: 2632; Percent complete: 65.8%; Average loss: 3.0388
Iteration: 2633; Percent complete: 65.8%; Average loss: 3.0455
Iteration: 2634; Percent complete: 65.8%; Average loss: 3.0135
Iteration: 2635; Percent complete: 65.9%; Average loss: 3.1609
Iteration: 2636; Percent complete: 65.9%; Average loss: 3.0476
Iteration: 2637; Percent complete: 65.9%; Average loss: 3.0349
Iteration: 2638; Percent complete: 66.0%; Average loss: 2.7174
Iteration: 2639; Percent complete: 66.0%; Average loss: 3.0719
Iteration: 2640; Percent complete: 66.0%; Average loss: 2.9712
Iteration: 2641; Percent complete: 66.0%; Average loss: 3.1447
Iteration: 2642; Percent complete: 66.0%; Average loss: 2.7303
Iteration: 2643; Percent complete: 66.1%; Average loss: 2.9294
Iteration: 2644; Percent complete: 66.1%; Average loss: 2.8318
Iteration: 2645; Percent complete: 66.1%; Average loss: 2.8504
Iteration: 2646; Percent complete: 66.1%; Average loss: 3.0501
Iteration: 2647; Percent complete: 66.2%; Average loss: 2.9782
Iteration: 2648; Percent complete: 66.2%; Average loss: 2.7398
Iteration: 2649; Percent complete: 66.2%; Average loss: 2.9591
Iteration: 2650; Percent complete: 66.2%; Average loss: 2.9283
Iteration: 2651; Percent complete: 66.3%; Average loss: 2.9580
Iteration: 2652; Percent complete: 66.3%; Average loss: 3.0959
Iteration: 2653; Percent complete: 66.3%; Average loss: 2.9766
Iteration: 2654; Percent complete: 66.3%; Average loss: 2.9343
Iteration: 2655; Percent complete: 66.4%; Average loss: 2.8865
Iteration: 2656; Percent complete: 66.4%; Average loss: 2.8567
Iteration: 2657; Percent complete: 66.4%; Average loss: 2.9814
Iteration: 2658; Percent complete: 66.5%; Average loss: 3.1313
Iteration: 2659; Percent complete: 66.5%; Average loss: 3.0250
Iteration: 2660; Percent complete: 66.5%; Average loss: 3.0067
Iteration: 2661; Percent complete: 66.5%; Average loss: 2.9430
Iteration: 2662; Percent complete: 66.5%; Average loss: 2.8564
Iteration: 2663; Percent complete: 66.6%; Average loss: 2.9307
Iteration: 2664; Percent complete: 66.6%; Average loss: 3.0597
Iteration: 2665; Percent complete: 66.6%; Average loss: 3.0466
Iteration: 2666; Percent complete: 66.6%; Average loss: 3.0163
Iteration: 2667; Percent complete: 66.7%; Average loss: 3.0249
Iteration: 2668; Percent complete: 66.7%; Average loss: 2.8227
Iteration: 2669; Percent complete: 66.7%; Average loss: 2.9585
Iteration: 2670; Percent complete: 66.8%; Average loss: 2.8661
Iteration: 2671; Percent complete: 66.8%; Average loss: 3.0048
Iteration: 2672; Percent complete: 66.8%; Average loss: 3.0199
Iteration: 2673; Percent complete: 66.8%; Average loss: 2.8857
Iteration: 2674; Percent complete: 66.8%; Average loss: 2.6869
Iteration: 2675; Percent complete: 66.9%; Average loss: 3.0864
Iteration: 2676; Percent complete: 66.9%; Average loss: 3.0114
Iteration: 2677; Percent complete: 66.9%; Average loss: 2.7959
Iteration: 2678; Percent complete: 67.0%; Average loss: 3.1542
Iteration: 2679; Percent complete: 67.0%; Average loss: 2.9145
Iteration: 2680; Percent complete: 67.0%; Average loss: 2.9070
Iteration: 2681; Percent complete: 67.0%; Average loss: 3.0713
Iteration: 2682; Percent complete: 67.0%; Average loss: 3.0252
Iteration: 2683; Percent complete: 67.1%; Average loss: 2.9456
Iteration: 2684; Percent complete: 67.1%; Average loss: 2.8587
Iteration: 2685; Percent complete: 67.1%; Average loss: 2.7858
Iteration: 2686; Percent complete: 67.2%; Average loss: 2.9164
Iteration: 2687; Percent complete: 67.2%; Average loss: 2.9649
Iteration: 2688; Percent complete: 67.2%; Average loss: 2.8892
Iteration: 2689; Percent complete: 67.2%; Average loss: 2.9306
Iteration: 2690; Percent complete: 67.2%; Average loss: 2.9270
Iteration: 2691; Percent complete: 67.3%; Average loss: 2.8473
Iteration: 2692; Percent complete: 67.3%; Average loss: 2.9906
Iteration: 2693; Percent complete: 67.3%; Average loss: 2.6985
Iteration: 2694; Percent complete: 67.3%; Average loss: 2.9507
Iteration: 2695; Percent complete: 67.4%; Average loss: 2.8292
Iteration: 2696; Percent complete: 67.4%; Average loss: 2.9383
Iteration: 2697; Percent complete: 67.4%; Average loss: 2.9156
Iteration: 2698; Percent complete: 67.5%; Average loss: 3.1534
Iteration: 2699; Percent complete: 67.5%; Average loss: 2.9273
Iteration: 2700; Percent complete: 67.5%; Average loss: 2.9398
Iteration: 2701; Percent complete: 67.5%; Average loss: 2.8616
Iteration: 2702; Percent complete: 67.5%; Average loss: 3.0925
Iteration: 2703; Percent complete: 67.6%; Average loss: 2.8914
Iteration: 2704; Percent complete: 67.6%; Average loss: 2.9316
Iteration: 2705; Percent complete: 67.6%; Average loss: 3.0159
Iteration: 2706; Percent complete: 67.7%; Average loss: 3.0269
Iteration: 2707; Percent complete: 67.7%; Average loss: 2.9627
Iteration: 2708; Percent complete: 67.7%; Average loss: 2.6939
Iteration: 2709; Percent complete: 67.7%; Average loss: 3.1408
Iteration: 2710; Percent complete: 67.8%; Average loss: 2.7478
Iteration: 2711; Percent complete: 67.8%; Average loss: 3.0113
Iteration: 2712; Percent complete: 67.8%; Average loss: 3.0098
Iteration: 2713; Percent complete: 67.8%; Average loss: 2.4914
Iteration: 2714; Percent complete: 67.8%; Average loss: 2.9679
Iteration: 2715; Percent complete: 67.9%; Average loss: 2.8806
Iteration: 2716; Percent complete: 67.9%; Average loss: 2.7719
Iteration: 2717; Percent complete: 67.9%; Average loss: 2.8866
Iteration: 2718; Percent complete: 68.0%; Average loss: 2.9808
Iteration: 2719; Percent complete: 68.0%; Average loss: 2.9597
Iteration: 2720; Percent complete: 68.0%; Average loss: 2.9315
Iteration: 2721; Percent complete: 68.0%; Average loss: 2.7831
Iteration: 2722; Percent complete: 68.0%; Average loss: 2.9739
Iteration: 2723; Percent complete: 68.1%; Average loss: 2.9496
Iteration: 2724; Percent complete: 68.1%; Average loss: 2.9335
Iteration: 2725; Percent complete: 68.1%; Average loss: 2.9451
Iteration: 2726; Percent complete: 68.2%; Average loss: 2.8410
Iteration: 2727; Percent complete: 68.2%; Average loss: 2.8865
Iteration: 2728; Percent complete: 68.2%; Average loss: 2.8191
Iteration: 2729; Percent complete: 68.2%; Average loss: 2.6815
Iteration: 2730; Percent complete: 68.2%; Average loss: 2.9075
Iteration: 2731; Percent complete: 68.3%; Average loss: 2.6952
Iteration: 2732; Percent complete: 68.3%; Average loss: 2.7622
Iteration: 2733; Percent complete: 68.3%; Average loss: 3.0125
Iteration: 2734; Percent complete: 68.3%; Average loss: 2.8790
Iteration: 2735; Percent complete: 68.4%; Average loss: 3.0537
Iteration: 2736; Percent complete: 68.4%; Average loss: 2.8860
Iteration: 2737; Percent complete: 68.4%; Average loss: 2.9493
Iteration: 2738; Percent complete: 68.5%; Average loss: 2.8989
Iteration: 2739; Percent complete: 68.5%; Average loss: 2.9411
Iteration: 2740; Percent complete: 68.5%; Average loss: 3.0820
Iteration: 2741; Percent complete: 68.5%; Average loss: 2.9861
Iteration: 2742; Percent complete: 68.5%; Average loss: 2.8694
Iteration: 2743; Percent complete: 68.6%; Average loss: 3.1338
Iteration: 2744; Percent complete: 68.6%; Average loss: 2.8195
Iteration: 2745; Percent complete: 68.6%; Average loss: 3.0306
Iteration: 2746; Percent complete: 68.7%; Average loss: 2.8276
Iteration: 2747; Percent complete: 68.7%; Average loss: 2.9479
Iteration: 2748; Percent complete: 68.7%; Average loss: 2.8611
Iteration: 2749; Percent complete: 68.7%; Average loss: 2.9438
Iteration: 2750; Percent complete: 68.8%; Average loss: 2.8040
Iteration: 2751; Percent complete: 68.8%; Average loss: 3.0326
Iteration: 2752; Percent complete: 68.8%; Average loss: 2.7587
Iteration: 2753; Percent complete: 68.8%; Average loss: 2.8772
Iteration: 2754; Percent complete: 68.8%; Average loss: 2.9813
Iteration: 2755; Percent complete: 68.9%; Average loss: 2.9824
Iteration: 2756; Percent complete: 68.9%; Average loss: 2.9514
Iteration: 2757; Percent complete: 68.9%; Average loss: 2.9143
Iteration: 2758; Percent complete: 69.0%; Average loss: 2.9476
Iteration: 2759; Percent complete: 69.0%; Average loss: 3.0585
Iteration: 2760; Percent complete: 69.0%; Average loss: 2.9496
Iteration: 2761; Percent complete: 69.0%; Average loss: 2.9258
Iteration: 2762; Percent complete: 69.0%; Average loss: 2.8947
Iteration: 2763; Percent complete: 69.1%; Average loss: 2.6180
Iteration: 2764; Percent complete: 69.1%; Average loss: 2.9381
Iteration: 2765; Percent complete: 69.1%; Average loss: 2.8921
Iteration: 2766; Percent complete: 69.2%; Average loss: 3.0497
Iteration: 2767; Percent complete: 69.2%; Average loss: 2.8031
Iteration: 2768; Percent complete: 69.2%; Average loss: 2.8967
Iteration: 2769; Percent complete: 69.2%; Average loss: 3.0533
Iteration: 2770; Percent complete: 69.2%; Average loss: 3.1137
Iteration: 2771; Percent complete: 69.3%; Average loss: 2.8933
Iteration: 2772; Percent complete: 69.3%; Average loss: 2.9338
Iteration: 2773; Percent complete: 69.3%; Average loss: 2.9145
Iteration: 2774; Percent complete: 69.3%; Average loss: 2.9367
Iteration: 2775; Percent complete: 69.4%; Average loss: 3.1422
Iteration: 2776; Percent complete: 69.4%; Average loss: 2.9944
Iteration: 2777; Percent complete: 69.4%; Average loss: 2.9964
Iteration: 2778; Percent complete: 69.5%; Average loss: 2.7335
Iteration: 2779; Percent complete: 69.5%; Average loss: 2.6544
Iteration: 2780; Percent complete: 69.5%; Average loss: 2.7777
Iteration: 2781; Percent complete: 69.5%; Average loss: 2.7832
Iteration: 2782; Percent complete: 69.5%; Average loss: 2.9212
Iteration: 2783; Percent complete: 69.6%; Average loss: 2.7002
Iteration: 2784; Percent complete: 69.6%; Average loss: 3.2730
Iteration: 2785; Percent complete: 69.6%; Average loss: 3.1561
Iteration: 2786; Percent complete: 69.7%; Average loss: 2.8385
Iteration: 2787; Percent complete: 69.7%; Average loss: 2.8551
Iteration: 2788; Percent complete: 69.7%; Average loss: 3.0176
Iteration: 2789; Percent complete: 69.7%; Average loss: 2.7981
Iteration: 2790; Percent complete: 69.8%; Average loss: 2.9421
Iteration: 2791; Percent complete: 69.8%; Average loss: 2.7807
Iteration: 2792; Percent complete: 69.8%; Average loss: 2.9577
Iteration: 2793; Percent complete: 69.8%; Average loss: 2.6341
Iteration: 2794; Percent complete: 69.8%; Average loss: 2.9348
Iteration: 2795; Percent complete: 69.9%; Average loss: 3.0886
Iteration: 2796; Percent complete: 69.9%; Average loss: 2.8623
Iteration: 2797; Percent complete: 69.9%; Average loss: 2.8027
Iteration: 2798; Percent complete: 70.0%; Average loss: 2.9741
Iteration: 2799; Percent complete: 70.0%; Average loss: 2.7679
Iteration: 2800; Percent complete: 70.0%; Average loss: 2.9587
Iteration: 2801; Percent complete: 70.0%; Average loss: 2.9496
Iteration: 2802; Percent complete: 70.0%; Average loss: 2.8678
Iteration: 2803; Percent complete: 70.1%; Average loss: 2.8439
Iteration: 2804; Percent complete: 70.1%; Average loss: 2.7488
Iteration: 2805; Percent complete: 70.1%; Average loss: 2.9999
Iteration: 2806; Percent complete: 70.2%; Average loss: 3.1002
Iteration: 2807; Percent complete: 70.2%; Average loss: 3.0471
Iteration: 2808; Percent complete: 70.2%; Average loss: 3.0256
Iteration: 2809; Percent complete: 70.2%; Average loss: 2.9059
Iteration: 2810; Percent complete: 70.2%; Average loss: 3.1904
Iteration: 2811; Percent complete: 70.3%; Average loss: 2.8397
Iteration: 2812; Percent complete: 70.3%; Average loss: 2.8588
Iteration: 2813; Percent complete: 70.3%; Average loss: 2.9815
Iteration: 2814; Percent complete: 70.3%; Average loss: 2.9058
Iteration: 2815; Percent complete: 70.4%; Average loss: 2.8916
Iteration: 2816; Percent complete: 70.4%; Average loss: 3.0525
Iteration: 2817; Percent complete: 70.4%; Average loss: 2.7504
Iteration: 2818; Percent complete: 70.5%; Average loss: 2.9810
Iteration: 2819; Percent complete: 70.5%; Average loss: 2.9362
Iteration: 2820; Percent complete: 70.5%; Average loss: 2.8390
Iteration: 2821; Percent complete: 70.5%; Average loss: 2.7620
Iteration: 2822; Percent complete: 70.5%; Average loss: 3.1123
Iteration: 2823; Percent complete: 70.6%; Average loss: 3.1737
Iteration: 2824; Percent complete: 70.6%; Average loss: 3.0139
Iteration: 2825; Percent complete: 70.6%; Average loss: 2.8580
Iteration: 2826; Percent complete: 70.7%; Average loss: 2.9526
Iteration: 2827; Percent complete: 70.7%; Average loss: 2.8803
Iteration: 2828; Percent complete: 70.7%; Average loss: 3.0218
Iteration: 2829; Percent complete: 70.7%; Average loss: 2.9445
Iteration: 2830; Percent complete: 70.8%; Average loss: 2.5666
Iteration: 2831; Percent complete: 70.8%; Average loss: 3.0770
Iteration: 2832; Percent complete: 70.8%; Average loss: 2.9618
Iteration: 2833; Percent complete: 70.8%; Average loss: 2.8505
Iteration: 2834; Percent complete: 70.9%; Average loss: 2.6399
Iteration: 2835; Percent complete: 70.9%; Average loss: 2.8846
Iteration: 2836; Percent complete: 70.9%; Average loss: 3.1093
Iteration: 2837; Percent complete: 70.9%; Average loss: 3.0089
Iteration: 2838; Percent complete: 71.0%; Average loss: 3.1600
Iteration: 2839; Percent complete: 71.0%; Average loss: 2.9806
Iteration: 2840; Percent complete: 71.0%; Average loss: 3.0196
Iteration: 2841; Percent complete: 71.0%; Average loss: 3.0283
Iteration: 2842; Percent complete: 71.0%; Average loss: 2.8428
Iteration: 2843; Percent complete: 71.1%; Average loss: 2.7920
Iteration: 2844; Percent complete: 71.1%; Average loss: 2.8873
Iteration: 2845; Percent complete: 71.1%; Average loss: 3.2338
Iteration: 2846; Percent complete: 71.2%; Average loss: 2.9153
Iteration: 2847; Percent complete: 71.2%; Average loss: 3.0002
Iteration: 2848; Percent complete: 71.2%; Average loss: 2.8419
Iteration: 2849; Percent complete: 71.2%; Average loss: 2.7263
Iteration: 2850; Percent complete: 71.2%; Average loss: 2.9304
Iteration: 2851; Percent complete: 71.3%; Average loss: 2.9875
Iteration: 2852; Percent complete: 71.3%; Average loss: 2.9301
Iteration: 2853; Percent complete: 71.3%; Average loss: 3.1426
Iteration: 2854; Percent complete: 71.4%; Average loss: 2.6981
Iteration: 2855; Percent complete: 71.4%; Average loss: 2.9364
Iteration: 2856; Percent complete: 71.4%; Average loss: 2.9610
Iteration: 2857; Percent complete: 71.4%; Average loss: 2.9977
Iteration: 2858; Percent complete: 71.5%; Average loss: 3.0842
Iteration: 2859; Percent complete: 71.5%; Average loss: 3.0320
Iteration: 2860; Percent complete: 71.5%; Average loss: 3.1546
Iteration: 2861; Percent complete: 71.5%; Average loss: 2.8933
Iteration: 2862; Percent complete: 71.5%; Average loss: 2.9672
Iteration: 2863; Percent complete: 71.6%; Average loss: 3.0252
Iteration: 2864; Percent complete: 71.6%; Average loss: 2.9324
Iteration: 2865; Percent complete: 71.6%; Average loss: 2.8045
Iteration: 2866; Percent complete: 71.7%; Average loss: 2.9688
Iteration: 2867; Percent complete: 71.7%; Average loss: 3.0573
Iteration: 2868; Percent complete: 71.7%; Average loss: 2.8117
Iteration: 2869; Percent complete: 71.7%; Average loss: 2.9440
Iteration: 2870; Percent complete: 71.8%; Average loss: 2.8351
Iteration: 2871; Percent complete: 71.8%; Average loss: 2.8836
Iteration: 2872; Percent complete: 71.8%; Average loss: 2.8507
Iteration: 2873; Percent complete: 71.8%; Average loss: 2.7589
Iteration: 2874; Percent complete: 71.9%; Average loss: 2.9892
Iteration: 2875; Percent complete: 71.9%; Average loss: 2.9507
Iteration: 2876; Percent complete: 71.9%; Average loss: 2.8618
Iteration: 2877; Percent complete: 71.9%; Average loss: 2.8711
Iteration: 2878; Percent complete: 72.0%; Average loss: 2.7418
Iteration: 2879; Percent complete: 72.0%; Average loss: 2.7816
Iteration: 2880; Percent complete: 72.0%; Average loss: 2.8596
Iteration: 2881; Percent complete: 72.0%; Average loss: 2.8304
Iteration: 2882; Percent complete: 72.0%; Average loss: 3.0132
Iteration: 2883; Percent complete: 72.1%; Average loss: 2.8783
Iteration: 2884; Percent complete: 72.1%; Average loss: 2.6319
Iteration: 2885; Percent complete: 72.1%; Average loss: 2.7004
Iteration: 2886; Percent complete: 72.2%; Average loss: 3.1289
Iteration: 2887; Percent complete: 72.2%; Average loss: 2.9694
Iteration: 2888; Percent complete: 72.2%; Average loss: 2.8927
Iteration: 2889; Percent complete: 72.2%; Average loss: 3.0575
Iteration: 2890; Percent complete: 72.2%; Average loss: 3.0804
Iteration: 2891; Percent complete: 72.3%; Average loss: 3.0529
Iteration: 2892; Percent complete: 72.3%; Average loss: 3.2362
Iteration: 2893; Percent complete: 72.3%; Average loss: 2.5242
Iteration: 2894; Percent complete: 72.4%; Average loss: 3.0144
Iteration: 2895; Percent complete: 72.4%; Average loss: 2.7161
Iteration: 2896; Percent complete: 72.4%; Average loss: 2.6655
Iteration: 2897; Percent complete: 72.4%; Average loss: 2.8045
Iteration: 2898; Percent complete: 72.5%; Average loss: 2.9901
Iteration: 2899; Percent complete: 72.5%; Average loss: 2.8126
Iteration: 2900; Percent complete: 72.5%; Average loss: 3.0230
Iteration: 2901; Percent complete: 72.5%; Average loss: 2.7196
Iteration: 2902; Percent complete: 72.5%; Average loss: 2.8963
Iteration: 2903; Percent complete: 72.6%; Average loss: 3.0126
Iteration: 2904; Percent complete: 72.6%; Average loss: 2.9857
Iteration: 2905; Percent complete: 72.6%; Average loss: 2.6975
Iteration: 2906; Percent complete: 72.7%; Average loss: 3.0399
Iteration: 2907; Percent complete: 72.7%; Average loss: 3.0882
Iteration: 2908; Percent complete: 72.7%; Average loss: 2.8625
Iteration: 2909; Percent complete: 72.7%; Average loss: 2.7396
Iteration: 2910; Percent complete: 72.8%; Average loss: 2.9118
Iteration: 2911; Percent complete: 72.8%; Average loss: 2.8450
Iteration: 2912; Percent complete: 72.8%; Average loss: 2.8102
Iteration: 2913; Percent complete: 72.8%; Average loss: 2.7163
Iteration: 2914; Percent complete: 72.9%; Average loss: 2.7200
Iteration: 2915; Percent complete: 72.9%; Average loss: 2.9329
Iteration: 2916; Percent complete: 72.9%; Average loss: 2.7224
Iteration: 2917; Percent complete: 72.9%; Average loss: 2.7961
Iteration: 2918; Percent complete: 73.0%; Average loss: 3.0579
Iteration: 2919; Percent complete: 73.0%; Average loss: 2.8022
Iteration: 2920; Percent complete: 73.0%; Average loss: 3.1196
Iteration: 2921; Percent complete: 73.0%; Average loss: 2.9432
Iteration: 2922; Percent complete: 73.0%; Average loss: 2.7878
Iteration: 2923; Percent complete: 73.1%; Average loss: 3.0638
Iteration: 2924; Percent complete: 73.1%; Average loss: 2.6506
Iteration: 2925; Percent complete: 73.1%; Average loss: 2.7588
Iteration: 2926; Percent complete: 73.2%; Average loss: 3.1583
Iteration: 2927; Percent complete: 73.2%; Average loss: 2.8945
Iteration: 2928; Percent complete: 73.2%; Average loss: 3.1682
Iteration: 2929; Percent complete: 73.2%; Average loss: 2.8159
Iteration: 2930; Percent complete: 73.2%; Average loss: 2.8403
Iteration: 2931; Percent complete: 73.3%; Average loss: 3.0099
Iteration: 2932; Percent complete: 73.3%; Average loss: 2.9013
Iteration: 2933; Percent complete: 73.3%; Average loss: 2.6211
Iteration: 2934; Percent complete: 73.4%; Average loss: 2.8342
Iteration: 2935; Percent complete: 73.4%; Average loss: 2.9176
Iteration: 2936; Percent complete: 73.4%; Average loss: 2.9413
Iteration: 2937; Percent complete: 73.4%; Average loss: 2.8326
Iteration: 2938; Percent complete: 73.5%; Average loss: 2.7732
Iteration: 2939; Percent complete: 73.5%; Average loss: 2.7336
Iteration: 2940; Percent complete: 73.5%; Average loss: 3.0318
Iteration: 2941; Percent complete: 73.5%; Average loss: 2.8376
Iteration: 2942; Percent complete: 73.6%; Average loss: 2.8211
Iteration: 2943; Percent complete: 73.6%; Average loss: 2.8919
Iteration: 2944; Percent complete: 73.6%; Average loss: 2.7128
Iteration: 2945; Percent complete: 73.6%; Average loss: 2.9436
Iteration: 2946; Percent complete: 73.7%; Average loss: 2.7390
Iteration: 2947; Percent complete: 73.7%; Average loss: 2.9184
Iteration: 2948; Percent complete: 73.7%; Average loss: 2.8502
Iteration: 2949; Percent complete: 73.7%; Average loss: 2.7325
Iteration: 2950; Percent complete: 73.8%; Average loss: 2.9260
Iteration: 2951; Percent complete: 73.8%; Average loss: 2.9270
Iteration: 2952; Percent complete: 73.8%; Average loss: 2.9005
Iteration: 2953; Percent complete: 73.8%; Average loss: 2.7050
Iteration: 2954; Percent complete: 73.9%; Average loss: 2.8770
Iteration: 2955; Percent complete: 73.9%; Average loss: 2.7764
Iteration: 2956; Percent complete: 73.9%; Average loss: 2.8513
Iteration: 2957; Percent complete: 73.9%; Average loss: 3.1405
Iteration: 2958; Percent complete: 74.0%; Average loss: 2.7015
Iteration: 2959; Percent complete: 74.0%; Average loss: 2.7908
Iteration: 2960; Percent complete: 74.0%; Average loss: 2.8676
Iteration: 2961; Percent complete: 74.0%; Average loss: 3.0005
Iteration: 2962; Percent complete: 74.1%; Average loss: 3.0220
Iteration: 2963; Percent complete: 74.1%; Average loss: 2.6357
Iteration: 2964; Percent complete: 74.1%; Average loss: 2.9315
Iteration: 2965; Percent complete: 74.1%; Average loss: 3.0427
Iteration: 2966; Percent complete: 74.2%; Average loss: 3.0399
Iteration: 2967; Percent complete: 74.2%; Average loss: 3.0378
Iteration: 2968; Percent complete: 74.2%; Average loss: 2.7548
Iteration: 2969; Percent complete: 74.2%; Average loss: 3.0136
Iteration: 2970; Percent complete: 74.2%; Average loss: 2.8702
Iteration: 2971; Percent complete: 74.3%; Average loss: 3.0112
Iteration: 2972; Percent complete: 74.3%; Average loss: 2.7485
Iteration: 2973; Percent complete: 74.3%; Average loss: 2.6531
Iteration: 2974; Percent complete: 74.4%; Average loss: 3.0454
Iteration: 2975; Percent complete: 74.4%; Average loss: 2.7969
Iteration: 2976; Percent complete: 74.4%; Average loss: 3.0214
Iteration: 2977; Percent complete: 74.4%; Average loss: 3.1036
Iteration: 2978; Percent complete: 74.5%; Average loss: 2.9859
Iteration: 2979; Percent complete: 74.5%; Average loss: 2.7522
Iteration: 2980; Percent complete: 74.5%; Average loss: 2.8878
Iteration: 2981; Percent complete: 74.5%; Average loss: 2.9724
Iteration: 2982; Percent complete: 74.6%; Average loss: 2.8448
Iteration: 2983; Percent complete: 74.6%; Average loss: 2.9000
Iteration: 2984; Percent complete: 74.6%; Average loss: 3.0435
Iteration: 2985; Percent complete: 74.6%; Average loss: 2.9811
Iteration: 2986; Percent complete: 74.7%; Average loss: 2.6771
Iteration: 2987; Percent complete: 74.7%; Average loss: 2.9178
Iteration: 2988; Percent complete: 74.7%; Average loss: 2.9740
Iteration: 2989; Percent complete: 74.7%; Average loss: 2.8728
Iteration: 2990; Percent complete: 74.8%; Average loss: 3.0250
Iteration: 2991; Percent complete: 74.8%; Average loss: 2.8127
Iteration: 2992; Percent complete: 74.8%; Average loss: 2.8682
Iteration: 2993; Percent complete: 74.8%; Average loss: 2.8988
Iteration: 2994; Percent complete: 74.9%; Average loss: 2.7482
Iteration: 2995; Percent complete: 74.9%; Average loss: 3.1604
Iteration: 2996; Percent complete: 74.9%; Average loss: 2.8033
Iteration: 2997; Percent complete: 74.9%; Average loss: 2.7880
Iteration: 2998; Percent complete: 75.0%; Average loss: 2.7911
Iteration: 2999; Percent complete: 75.0%; Average loss: 2.6272
Iteration: 3000; Percent complete: 75.0%; Average loss: 2.9601
Iteration: 3001; Percent complete: 75.0%; Average loss: 2.6556
Iteration: 3002; Percent complete: 75.0%; Average loss: 3.0127
Iteration: 3003; Percent complete: 75.1%; Average loss: 3.0972
Iteration: 3004; Percent complete: 75.1%; Average loss: 2.9748
Iteration: 3005; Percent complete: 75.1%; Average loss: 3.0094
Iteration: 3006; Percent complete: 75.1%; Average loss: 2.7343
Iteration: 3007; Percent complete: 75.2%; Average loss: 2.7578
Iteration: 3008; Percent complete: 75.2%; Average loss: 2.8142
Iteration: 3009; Percent complete: 75.2%; Average loss: 2.9252
Iteration: 3010; Percent complete: 75.2%; Average loss: 2.9085
Iteration: 3011; Percent complete: 75.3%; Average loss: 2.6523
Iteration: 3012; Percent complete: 75.3%; Average loss: 2.8491
Iteration: 3013; Percent complete: 75.3%; Average loss: 2.8347
Iteration: 3014; Percent complete: 75.3%; Average loss: 2.8028
Iteration: 3015; Percent complete: 75.4%; Average loss: 2.7497
Iteration: 3016; Percent complete: 75.4%; Average loss: 3.0125
Iteration: 3017; Percent complete: 75.4%; Average loss: 2.7560
Iteration: 3018; Percent complete: 75.4%; Average loss: 2.8441
Iteration: 3019; Percent complete: 75.5%; Average loss: 2.8463
Iteration: 3020; Percent complete: 75.5%; Average loss: 2.7464
Iteration: 3021; Percent complete: 75.5%; Average loss: 2.8784
Iteration: 3022; Percent complete: 75.5%; Average loss: 2.7315
Iteration: 3023; Percent complete: 75.6%; Average loss: 2.8685
Iteration: 3024; Percent complete: 75.6%; Average loss: 3.0942
Iteration: 3025; Percent complete: 75.6%; Average loss: 2.9470
Iteration: 3026; Percent complete: 75.6%; Average loss: 2.9897
Iteration: 3027; Percent complete: 75.7%; Average loss: 3.1600
Iteration: 3028; Percent complete: 75.7%; Average loss: 2.8151
Iteration: 3029; Percent complete: 75.7%; Average loss: 2.9119
Iteration: 3030; Percent complete: 75.8%; Average loss: 2.9733
Iteration: 3031; Percent complete: 75.8%; Average loss: 3.0185
Iteration: 3032; Percent complete: 75.8%; Average loss: 2.6535
Iteration: 3033; Percent complete: 75.8%; Average loss: 2.9489
Iteration: 3034; Percent complete: 75.8%; Average loss: 3.2654
Iteration: 3035; Percent complete: 75.9%; Average loss: 2.8753
Iteration: 3036; Percent complete: 75.9%; Average loss: 2.6551
Iteration: 3037; Percent complete: 75.9%; Average loss: 2.7980
Iteration: 3038; Percent complete: 75.9%; Average loss: 2.9843
Iteration: 3039; Percent complete: 76.0%; Average loss: 2.8085
Iteration: 3040; Percent complete: 76.0%; Average loss: 3.2811
Iteration: 3041; Percent complete: 76.0%; Average loss: 2.9488
Iteration: 3042; Percent complete: 76.0%; Average loss: 2.9938
Iteration: 3043; Percent complete: 76.1%; Average loss: 2.6378
Iteration: 3044; Percent complete: 76.1%; Average loss: 2.8518
Iteration: 3045; Percent complete: 76.1%; Average loss: 2.6470
Iteration: 3046; Percent complete: 76.1%; Average loss: 2.7571
Iteration: 3047; Percent complete: 76.2%; Average loss: 2.6989
Iteration: 3048; Percent complete: 76.2%; Average loss: 2.9155
Iteration: 3049; Percent complete: 76.2%; Average loss: 2.8454
Iteration: 3050; Percent complete: 76.2%; Average loss: 2.7273
Iteration: 3051; Percent complete: 76.3%; Average loss: 2.5790
Iteration: 3052; Percent complete: 76.3%; Average loss: 2.9545
Iteration: 3053; Percent complete: 76.3%; Average loss: 2.7728
Iteration: 3054; Percent complete: 76.3%; Average loss: 3.1199
Iteration: 3055; Percent complete: 76.4%; Average loss: 2.9285
Iteration: 3056; Percent complete: 76.4%; Average loss: 2.9243
Iteration: 3057; Percent complete: 76.4%; Average loss: 3.0501
Iteration: 3058; Percent complete: 76.4%; Average loss: 2.8637
Iteration: 3059; Percent complete: 76.5%; Average loss: 3.0142
Iteration: 3060; Percent complete: 76.5%; Average loss: 2.8649
Iteration: 3061; Percent complete: 76.5%; Average loss: 2.7303
Iteration: 3062; Percent complete: 76.5%; Average loss: 2.6539
Iteration: 3063; Percent complete: 76.6%; Average loss: 2.6755
Iteration: 3064; Percent complete: 76.6%; Average loss: 3.0723
Iteration: 3065; Percent complete: 76.6%; Average loss: 2.8762
Iteration: 3066; Percent complete: 76.6%; Average loss: 2.8017
Iteration: 3067; Percent complete: 76.7%; Average loss: 2.8804
Iteration: 3068; Percent complete: 76.7%; Average loss: 2.9339
Iteration: 3069; Percent complete: 76.7%; Average loss: 2.7559
Iteration: 3070; Percent complete: 76.8%; Average loss: 3.1026
Iteration: 3071; Percent complete: 76.8%; Average loss: 2.8072
Iteration: 3072; Percent complete: 76.8%; Average loss: 2.7950
Iteration: 3073; Percent complete: 76.8%; Average loss: 2.8985
Iteration: 3074; Percent complete: 76.8%; Average loss: 3.0375
Iteration: 3075; Percent complete: 76.9%; Average loss: 2.8387
Iteration: 3076; Percent complete: 76.9%; Average loss: 2.6878
Iteration: 3077; Percent complete: 76.9%; Average loss: 2.9350
Iteration: 3078; Percent complete: 77.0%; Average loss: 3.0564
Iteration: 3079; Percent complete: 77.0%; Average loss: 2.8561
Iteration: 3080; Percent complete: 77.0%; Average loss: 2.7802
Iteration: 3081; Percent complete: 77.0%; Average loss: 2.9348
Iteration: 3082; Percent complete: 77.0%; Average loss: 2.7612
Iteration: 3083; Percent complete: 77.1%; Average loss: 3.0074
Iteration: 3084; Percent complete: 77.1%; Average loss: 3.0904
Iteration: 3085; Percent complete: 77.1%; Average loss: 2.7796
Iteration: 3086; Percent complete: 77.1%; Average loss: 2.6802
Iteration: 3087; Percent complete: 77.2%; Average loss: 3.0622
Iteration: 3088; Percent complete: 77.2%; Average loss: 2.8582
Iteration: 3089; Percent complete: 77.2%; Average loss: 2.9182
Iteration: 3090; Percent complete: 77.2%; Average loss: 3.1695
Iteration: 3091; Percent complete: 77.3%; Average loss: 3.0299
Iteration: 3092; Percent complete: 77.3%; Average loss: 2.5282
Iteration: 3093; Percent complete: 77.3%; Average loss: 3.1071
Iteration: 3094; Percent complete: 77.3%; Average loss: 2.8648
Iteration: 3095; Percent complete: 77.4%; Average loss: 2.8411
Iteration: 3096; Percent complete: 77.4%; Average loss: 3.0311
Iteration: 3097; Percent complete: 77.4%; Average loss: 2.8307
Iteration: 3098; Percent complete: 77.5%; Average loss: 2.4583
Iteration: 3099; Percent complete: 77.5%; Average loss: 2.8322
Iteration: 3100; Percent complete: 77.5%; Average loss: 2.8294
Iteration: 3101; Percent complete: 77.5%; Average loss: 3.0862
Iteration: 3102; Percent complete: 77.5%; Average loss: 2.9030
Iteration: 3103; Percent complete: 77.6%; Average loss: 2.8506
Iteration: 3104; Percent complete: 77.6%; Average loss: 2.8323
Iteration: 3105; Percent complete: 77.6%; Average loss: 2.9076
Iteration: 3106; Percent complete: 77.6%; Average loss: 2.8003
Iteration: 3107; Percent complete: 77.7%; Average loss: 2.8958
Iteration: 3108; Percent complete: 77.7%; Average loss: 3.0640
Iteration: 3109; Percent complete: 77.7%; Average loss: 2.8663
Iteration: 3110; Percent complete: 77.8%; Average loss: 2.6990
Iteration: 3111; Percent complete: 77.8%; Average loss: 2.9443
Iteration: 3112; Percent complete: 77.8%; Average loss: 2.9139
Iteration: 3113; Percent complete: 77.8%; Average loss: 2.8223
Iteration: 3114; Percent complete: 77.8%; Average loss: 2.6494
Iteration: 3115; Percent complete: 77.9%; Average loss: 2.9800
Iteration: 3116; Percent complete: 77.9%; Average loss: 3.0543
Iteration: 3117; Percent complete: 77.9%; Average loss: 2.9315
Iteration: 3118; Percent complete: 78.0%; Average loss: 2.7660
Iteration: 3119; Percent complete: 78.0%; Average loss: 3.1180
Iteration: 3120; Percent complete: 78.0%; Average loss: 2.7852
Iteration: 3121; Percent complete: 78.0%; Average loss: 2.9248
Iteration: 3122; Percent complete: 78.0%; Average loss: 2.7539
Iteration: 3123; Percent complete: 78.1%; Average loss: 2.9101
Iteration: 3124; Percent complete: 78.1%; Average loss: 2.5643
Iteration: 3125; Percent complete: 78.1%; Average loss: 2.7870
Iteration: 3126; Percent complete: 78.1%; Average loss: 2.8721
Iteration: 3127; Percent complete: 78.2%; Average loss: 2.8440
Iteration: 3128; Percent complete: 78.2%; Average loss: 2.5371
Iteration: 3129; Percent complete: 78.2%; Average loss: 2.7321
Iteration: 3130; Percent complete: 78.2%; Average loss: 2.6758
Iteration: 3131; Percent complete: 78.3%; Average loss: 2.8787
Iteration: 3132; Percent complete: 78.3%; Average loss: 2.6800
Iteration: 3133; Percent complete: 78.3%; Average loss: 2.7770
Iteration: 3134; Percent complete: 78.3%; Average loss: 2.8015
Iteration: 3135; Percent complete: 78.4%; Average loss: 2.7395
Iteration: 3136; Percent complete: 78.4%; Average loss: 2.5309
Iteration: 3137; Percent complete: 78.4%; Average loss: 3.2002
Iteration: 3138; Percent complete: 78.5%; Average loss: 3.0384
Iteration: 3139; Percent complete: 78.5%; Average loss: 3.0480
Iteration: 3140; Percent complete: 78.5%; Average loss: 3.0643
Iteration: 3141; Percent complete: 78.5%; Average loss: 3.0785
Iteration: 3142; Percent complete: 78.5%; Average loss: 2.6806
Iteration: 3143; Percent complete: 78.6%; Average loss: 2.8242
Iteration: 3144; Percent complete: 78.6%; Average loss: 2.7723
Iteration: 3145; Percent complete: 78.6%; Average loss: 2.4950
Iteration: 3146; Percent complete: 78.6%; Average loss: 2.6749
Iteration: 3147; Percent complete: 78.7%; Average loss: 3.0047
Iteration: 3148; Percent complete: 78.7%; Average loss: 2.8191
Iteration: 3149; Percent complete: 78.7%; Average loss: 2.8580
Iteration: 3150; Percent complete: 78.8%; Average loss: 2.6958
Iteration: 3151; Percent complete: 78.8%; Average loss: 2.7143
Iteration: 3152; Percent complete: 78.8%; Average loss: 2.8851
Iteration: 3153; Percent complete: 78.8%; Average loss: 2.5841
Iteration: 3154; Percent complete: 78.8%; Average loss: 2.7199
Iteration: 3155; Percent complete: 78.9%; Average loss: 2.8819
Iteration: 3156; Percent complete: 78.9%; Average loss: 2.6871
Iteration: 3157; Percent complete: 78.9%; Average loss: 2.8090
Iteration: 3158; Percent complete: 79.0%; Average loss: 3.0788
Iteration: 3159; Percent complete: 79.0%; Average loss: 3.1593
Iteration: 3160; Percent complete: 79.0%; Average loss: 2.5823
Iteration: 3161; Percent complete: 79.0%; Average loss: 2.9322
Iteration: 3162; Percent complete: 79.0%; Average loss: 2.8185
Iteration: 3163; Percent complete: 79.1%; Average loss: 2.9709
Iteration: 3164; Percent complete: 79.1%; Average loss: 2.9032
Iteration: 3165; Percent complete: 79.1%; Average loss: 2.9526
Iteration: 3166; Percent complete: 79.1%; Average loss: 2.7804
Iteration: 3167; Percent complete: 79.2%; Average loss: 3.0701
Iteration: 3168; Percent complete: 79.2%; Average loss: 2.7365
Iteration: 3169; Percent complete: 79.2%; Average loss: 3.0662
Iteration: 3170; Percent complete: 79.2%; Average loss: 2.6468
Iteration: 3171; Percent complete: 79.3%; Average loss: 2.8412
Iteration: 3172; Percent complete: 79.3%; Average loss: 2.9640
Iteration: 3173; Percent complete: 79.3%; Average loss: 2.9404
Iteration: 3174; Percent complete: 79.3%; Average loss: 2.8180
Iteration: 3175; Percent complete: 79.4%; Average loss: 2.9062
Iteration: 3176; Percent complete: 79.4%; Average loss: 2.7573
Iteration: 3177; Percent complete: 79.4%; Average loss: 2.8592
Iteration: 3178; Percent complete: 79.5%; Average loss: 3.0093
Iteration: 3179; Percent complete: 79.5%; Average loss: 2.9711
Iteration: 3180; Percent complete: 79.5%; Average loss: 3.0265
Iteration: 3181; Percent complete: 79.5%; Average loss: 2.8521
Iteration: 3182; Percent complete: 79.5%; Average loss: 3.0313
Iteration: 3183; Percent complete: 79.6%; Average loss: 2.7357
Iteration: 3184; Percent complete: 79.6%; Average loss: 2.7473
Iteration: 3185; Percent complete: 79.6%; Average loss: 2.8288
Iteration: 3186; Percent complete: 79.7%; Average loss: 2.8638
Iteration: 3187; Percent complete: 79.7%; Average loss: 2.7385
Iteration: 3188; Percent complete: 79.7%; Average loss: 2.7858
Iteration: 3189; Percent complete: 79.7%; Average loss: 2.7749
Iteration: 3190; Percent complete: 79.8%; Average loss: 2.9768
Iteration: 3191; Percent complete: 79.8%; Average loss: 2.7127
Iteration: 3192; Percent complete: 79.8%; Average loss: 2.7016
Iteration: 3193; Percent complete: 79.8%; Average loss: 3.0092
Iteration: 3194; Percent complete: 79.8%; Average loss: 2.5295
Iteration: 3195; Percent complete: 79.9%; Average loss: 3.0236
Iteration: 3196; Percent complete: 79.9%; Average loss: 2.5702
Iteration: 3197; Percent complete: 79.9%; Average loss: 2.9574
Iteration: 3198; Percent complete: 80.0%; Average loss: 2.7804
Iteration: 3199; Percent complete: 80.0%; Average loss: 2.8715
Iteration: 3200; Percent complete: 80.0%; Average loss: 2.7146
Iteration: 3201; Percent complete: 80.0%; Average loss: 3.0167
Iteration: 3202; Percent complete: 80.0%; Average loss: 2.6810
Iteration: 3203; Percent complete: 80.1%; Average loss: 2.9678
Iteration: 3204; Percent complete: 80.1%; Average loss: 2.6556
Iteration: 3205; Percent complete: 80.1%; Average loss: 2.8111
Iteration: 3206; Percent complete: 80.2%; Average loss: 2.8925
Iteration: 3207; Percent complete: 80.2%; Average loss: 2.8875
Iteration: 3208; Percent complete: 80.2%; Average loss: 2.8269
Iteration: 3209; Percent complete: 80.2%; Average loss: 2.7136
Iteration: 3210; Percent complete: 80.2%; Average loss: 2.8229
Iteration: 3211; Percent complete: 80.3%; Average loss: 2.8546
Iteration: 3212; Percent complete: 80.3%; Average loss: 3.1144
Iteration: 3213; Percent complete: 80.3%; Average loss: 2.5294
Iteration: 3214; Percent complete: 80.3%; Average loss: 2.8577
Iteration: 3215; Percent complete: 80.4%; Average loss: 2.7524
Iteration: 3216; Percent complete: 80.4%; Average loss: 2.5902
Iteration: 3217; Percent complete: 80.4%; Average loss: 2.8227
Iteration: 3218; Percent complete: 80.5%; Average loss: 2.9292
Iteration: 3219; Percent complete: 80.5%; Average loss: 2.8089
Iteration: 3220; Percent complete: 80.5%; Average loss: 2.6570
Iteration: 3221; Percent complete: 80.5%; Average loss: 2.7006
Iteration: 3222; Percent complete: 80.5%; Average loss: 2.6848
Iteration: 3223; Percent complete: 80.6%; Average loss: 2.8549
Iteration: 3224; Percent complete: 80.6%; Average loss: 2.7583
Iteration: 3225; Percent complete: 80.6%; Average loss: 2.9345
Iteration: 3226; Percent complete: 80.7%; Average loss: 2.7274
Iteration: 3227; Percent complete: 80.7%; Average loss: 2.7166
Iteration: 3228; Percent complete: 80.7%; Average loss: 2.8046
Iteration: 3229; Percent complete: 80.7%; Average loss: 2.9149
Iteration: 3230; Percent complete: 80.8%; Average loss: 2.7811
Iteration: 3231; Percent complete: 80.8%; Average loss: 2.5827
Iteration: 3232; Percent complete: 80.8%; Average loss: 2.8025
Iteration: 3233; Percent complete: 80.8%; Average loss: 2.9134
Iteration: 3234; Percent complete: 80.8%; Average loss: 2.8187
Iteration: 3235; Percent complete: 80.9%; Average loss: 2.8172
Iteration: 3236; Percent complete: 80.9%; Average loss: 2.8296
Iteration: 3237; Percent complete: 80.9%; Average loss: 2.6361
Iteration: 3238; Percent complete: 81.0%; Average loss: 2.9185
Iteration: 3239; Percent complete: 81.0%; Average loss: 2.8722
Iteration: 3240; Percent complete: 81.0%; Average loss: 2.7225
Iteration: 3241; Percent complete: 81.0%; Average loss: 2.7110
Iteration: 3242; Percent complete: 81.0%; Average loss: 2.8889
Iteration: 3243; Percent complete: 81.1%; Average loss: 2.3834
Iteration: 3244; Percent complete: 81.1%; Average loss: 2.9473
Iteration: 3245; Percent complete: 81.1%; Average loss: 2.6771
Iteration: 3246; Percent complete: 81.2%; Average loss: 2.7627
Iteration: 3247; Percent complete: 81.2%; Average loss: 2.7322
Iteration: 3248; Percent complete: 81.2%; Average loss: 2.9019
Iteration: 3249; Percent complete: 81.2%; Average loss: 2.7369
Iteration: 3250; Percent complete: 81.2%; Average loss: 2.8979
Iteration: 3251; Percent complete: 81.3%; Average loss: 2.8638
Iteration: 3252; Percent complete: 81.3%; Average loss: 2.9474
Iteration: 3253; Percent complete: 81.3%; Average loss: 2.8772
Iteration: 3254; Percent complete: 81.3%; Average loss: 2.7529
Iteration: 3255; Percent complete: 81.4%; Average loss: 2.7207
Iteration: 3256; Percent complete: 81.4%; Average loss: 2.9723
Iteration: 3257; Percent complete: 81.4%; Average loss: 2.8854
Iteration: 3258; Percent complete: 81.5%; Average loss: 2.9126
Iteration: 3259; Percent complete: 81.5%; Average loss: 3.0117
Iteration: 3260; Percent complete: 81.5%; Average loss: 2.7746
Iteration: 3261; Percent complete: 81.5%; Average loss: 2.9485
Iteration: 3262; Percent complete: 81.5%; Average loss: 2.5907
Iteration: 3263; Percent complete: 81.6%; Average loss: 2.8069
Iteration: 3264; Percent complete: 81.6%; Average loss: 2.7739
Iteration: 3265; Percent complete: 81.6%; Average loss: 2.8362
Iteration: 3266; Percent complete: 81.7%; Average loss: 3.0487
Iteration: 3267; Percent complete: 81.7%; Average loss: 3.0647
Iteration: 3268; Percent complete: 81.7%; Average loss: 2.7080
Iteration: 3269; Percent complete: 81.7%; Average loss: 2.9707
Iteration: 3270; Percent complete: 81.8%; Average loss: 2.6695
Iteration: 3271; Percent complete: 81.8%; Average loss: 2.7006
Iteration: 3272; Percent complete: 81.8%; Average loss: 2.7012
Iteration: 3273; Percent complete: 81.8%; Average loss: 2.7705
Iteration: 3274; Percent complete: 81.8%; Average loss: 2.8401
Iteration: 3275; Percent complete: 81.9%; Average loss: 2.7043
Iteration: 3276; Percent complete: 81.9%; Average loss: 2.9484
Iteration: 3277; Percent complete: 81.9%; Average loss: 2.9015
Iteration: 3278; Percent complete: 82.0%; Average loss: 2.8486
Iteration: 3279; Percent complete: 82.0%; Average loss: 2.7513
Iteration: 3280; Percent complete: 82.0%; Average loss: 2.5356
Iteration: 3281; Percent complete: 82.0%; Average loss: 2.9108
Iteration: 3282; Percent complete: 82.0%; Average loss: 2.7147
Iteration: 3283; Percent complete: 82.1%; Average loss: 2.6250
Iteration: 3284; Percent complete: 82.1%; Average loss: 2.7618
Iteration: 3285; Percent complete: 82.1%; Average loss: 2.8808
Iteration: 3286; Percent complete: 82.2%; Average loss: 2.6688
Iteration: 3287; Percent complete: 82.2%; Average loss: 2.6427
Iteration: 3288; Percent complete: 82.2%; Average loss: 2.7989
Iteration: 3289; Percent complete: 82.2%; Average loss: 2.7878
Iteration: 3290; Percent complete: 82.2%; Average loss: 2.9208
Iteration: 3291; Percent complete: 82.3%; Average loss: 2.9223
Iteration: 3292; Percent complete: 82.3%; Average loss: 2.8866
Iteration: 3293; Percent complete: 82.3%; Average loss: 2.9912
Iteration: 3294; Percent complete: 82.3%; Average loss: 2.8155
Iteration: 3295; Percent complete: 82.4%; Average loss: 2.7107
Iteration: 3296; Percent complete: 82.4%; Average loss: 2.9162
Iteration: 3297; Percent complete: 82.4%; Average loss: 2.7180
Iteration: 3298; Percent complete: 82.5%; Average loss: 2.6764
Iteration: 3299; Percent complete: 82.5%; Average loss: 2.7571
Iteration: 3300; Percent complete: 82.5%; Average loss: 2.7019
Iteration: 3301; Percent complete: 82.5%; Average loss: 2.6605
Iteration: 3302; Percent complete: 82.5%; Average loss: 2.8219
Iteration: 3303; Percent complete: 82.6%; Average loss: 2.8302
Iteration: 3304; Percent complete: 82.6%; Average loss: 2.5855
Iteration: 3305; Percent complete: 82.6%; Average loss: 2.7792
Iteration: 3306; Percent complete: 82.7%; Average loss: 2.8160
Iteration: 3307; Percent complete: 82.7%; Average loss: 2.7993
Iteration: 3308; Percent complete: 82.7%; Average loss: 2.8725
Iteration: 3309; Percent complete: 82.7%; Average loss: 2.6592
Iteration: 3310; Percent complete: 82.8%; Average loss: 2.8647
Iteration: 3311; Percent complete: 82.8%; Average loss: 2.8162
Iteration: 3312; Percent complete: 82.8%; Average loss: 2.6967
Iteration: 3313; Percent complete: 82.8%; Average loss: 2.7440
Iteration: 3314; Percent complete: 82.8%; Average loss: 2.6337
Iteration: 3315; Percent complete: 82.9%; Average loss: 2.8714
Iteration: 3316; Percent complete: 82.9%; Average loss: 2.5979
Iteration: 3317; Percent complete: 82.9%; Average loss: 2.7065
Iteration: 3318; Percent complete: 83.0%; Average loss: 2.5607
Iteration: 3319; Percent complete: 83.0%; Average loss: 3.0176
Iteration: 3320; Percent complete: 83.0%; Average loss: 2.9031
Iteration: 3321; Percent complete: 83.0%; Average loss: 2.5929
Iteration: 3322; Percent complete: 83.0%; Average loss: 2.5685
Iteration: 3323; Percent complete: 83.1%; Average loss: 2.7940
Iteration: 3324; Percent complete: 83.1%; Average loss: 2.6443
Iteration: 3325; Percent complete: 83.1%; Average loss: 2.6254
Iteration: 3326; Percent complete: 83.2%; Average loss: 2.6263
Iteration: 3327; Percent complete: 83.2%; Average loss: 3.0584
Iteration: 3328; Percent complete: 83.2%; Average loss: 2.6995
Iteration: 3329; Percent complete: 83.2%; Average loss: 2.7961
Iteration: 3330; Percent complete: 83.2%; Average loss: 2.9261
Iteration: 3331; Percent complete: 83.3%; Average loss: 2.4486
Iteration: 3332; Percent complete: 83.3%; Average loss: 2.5399
Iteration: 3333; Percent complete: 83.3%; Average loss: 2.7419
Iteration: 3334; Percent complete: 83.4%; Average loss: 2.5140
Iteration: 3335; Percent complete: 83.4%; Average loss: 3.0206
Iteration: 3336; Percent complete: 83.4%; Average loss: 2.8453
Iteration: 3337; Percent complete: 83.4%; Average loss: 2.8442
Iteration: 3338; Percent complete: 83.5%; Average loss: 2.8404
Iteration: 3339; Percent complete: 83.5%; Average loss: 2.8414
Iteration: 3340; Percent complete: 83.5%; Average loss: 2.7998
Iteration: 3341; Percent complete: 83.5%; Average loss: 2.7578
Iteration: 3342; Percent complete: 83.5%; Average loss: 2.6140
Iteration: 3343; Percent complete: 83.6%; Average loss: 2.7648
Iteration: 3344; Percent complete: 83.6%; Average loss: 2.5919
Iteration: 3345; Percent complete: 83.6%; Average loss: 3.0902
Iteration: 3346; Percent complete: 83.7%; Average loss: 2.8204
Iteration: 3347; Percent complete: 83.7%; Average loss: 2.8852
Iteration: 3348; Percent complete: 83.7%; Average loss: 2.6763
Iteration: 3349; Percent complete: 83.7%; Average loss: 2.8364
Iteration: 3350; Percent complete: 83.8%; Average loss: 2.8290
Iteration: 3351; Percent complete: 83.8%; Average loss: 2.8409
Iteration: 3352; Percent complete: 83.8%; Average loss: 3.0653
Iteration: 3353; Percent complete: 83.8%; Average loss: 3.0455
Iteration: 3354; Percent complete: 83.9%; Average loss: 2.7281
Iteration: 3355; Percent complete: 83.9%; Average loss: 2.5779
Iteration: 3356; Percent complete: 83.9%; Average loss: 2.6730
Iteration: 3357; Percent complete: 83.9%; Average loss: 2.7025
Iteration: 3358; Percent complete: 84.0%; Average loss: 2.8621
Iteration: 3359; Percent complete: 84.0%; Average loss: 2.7087
Iteration: 3360; Percent complete: 84.0%; Average loss: 2.7876
Iteration: 3361; Percent complete: 84.0%; Average loss: 2.7421
Iteration: 3362; Percent complete: 84.0%; Average loss: 2.9062
Iteration: 3363; Percent complete: 84.1%; Average loss: 2.7734
Iteration: 3364; Percent complete: 84.1%; Average loss: 2.7476
Iteration: 3365; Percent complete: 84.1%; Average loss: 2.8771
Iteration: 3366; Percent complete: 84.2%; Average loss: 2.8222
Iteration: 3367; Percent complete: 84.2%; Average loss: 2.7587
Iteration: 3368; Percent complete: 84.2%; Average loss: 2.6802
Iteration: 3369; Percent complete: 84.2%; Average loss: 2.7563
Iteration: 3370; Percent complete: 84.2%; Average loss: 2.7718
Iteration: 3371; Percent complete: 84.3%; Average loss: 2.6926
Iteration: 3372; Percent complete: 84.3%; Average loss: 2.7182
Iteration: 3373; Percent complete: 84.3%; Average loss: 2.5092
Iteration: 3374; Percent complete: 84.4%; Average loss: 2.7795
Iteration: 3375; Percent complete: 84.4%; Average loss: 2.8692
Iteration: 3376; Percent complete: 84.4%; Average loss: 2.8549
Iteration: 3377; Percent complete: 84.4%; Average loss: 2.7511
Iteration: 3378; Percent complete: 84.5%; Average loss: 2.7865
Iteration: 3379; Percent complete: 84.5%; Average loss: 2.8066
Iteration: 3380; Percent complete: 84.5%; Average loss: 2.7380
Iteration: 3381; Percent complete: 84.5%; Average loss: 2.9993
Iteration: 3382; Percent complete: 84.5%; Average loss: 2.6813
Iteration: 3383; Percent complete: 84.6%; Average loss: 2.7749
Iteration: 3384; Percent complete: 84.6%; Average loss: 2.8297
Iteration: 3385; Percent complete: 84.6%; Average loss: 2.7246
Iteration: 3386; Percent complete: 84.7%; Average loss: 2.8061
Iteration: 3387; Percent complete: 84.7%; Average loss: 2.8398
Iteration: 3388; Percent complete: 84.7%; Average loss: 2.6973
Iteration: 3389; Percent complete: 84.7%; Average loss: 2.8352
Iteration: 3390; Percent complete: 84.8%; Average loss: 2.6511
Iteration: 3391; Percent complete: 84.8%; Average loss: 2.8647
Iteration: 3392; Percent complete: 84.8%; Average loss: 2.7843
Iteration: 3393; Percent complete: 84.8%; Average loss: 2.9211
Iteration: 3394; Percent complete: 84.9%; Average loss: 2.8091
Iteration: 3395; Percent complete: 84.9%; Average loss: 2.8227
Iteration: 3396; Percent complete: 84.9%; Average loss: 2.9613
Iteration: 3397; Percent complete: 84.9%; Average loss: 2.7412
Iteration: 3398; Percent complete: 85.0%; Average loss: 2.6124
Iteration: 3399; Percent complete: 85.0%; Average loss: 2.8451
Iteration: 3400; Percent complete: 85.0%; Average loss: 2.6786
Iteration: 3401; Percent complete: 85.0%; Average loss: 2.8652
Iteration: 3402; Percent complete: 85.0%; Average loss: 2.8653
Iteration: 3403; Percent complete: 85.1%; Average loss: 2.8426
Iteration: 3404; Percent complete: 85.1%; Average loss: 2.6559
Iteration: 3405; Percent complete: 85.1%; Average loss: 2.8745
Iteration: 3406; Percent complete: 85.2%; Average loss: 2.6443
Iteration: 3407; Percent complete: 85.2%; Average loss: 2.6432
Iteration: 3408; Percent complete: 85.2%; Average loss: 2.8751
Iteration: 3409; Percent complete: 85.2%; Average loss: 2.9246
Iteration: 3410; Percent complete: 85.2%; Average loss: 2.4424
Iteration: 3411; Percent complete: 85.3%; Average loss: 2.6176
Iteration: 3412; Percent complete: 85.3%; Average loss: 2.8025
Iteration: 3413; Percent complete: 85.3%; Average loss: 2.8524
Iteration: 3414; Percent complete: 85.4%; Average loss: 2.8129
Iteration: 3415; Percent complete: 85.4%; Average loss: 2.8046
Iteration: 3416; Percent complete: 85.4%; Average loss: 2.6146
Iteration: 3417; Percent complete: 85.4%; Average loss: 2.9467
Iteration: 3418; Percent complete: 85.5%; Average loss: 2.7540
Iteration: 3419; Percent complete: 85.5%; Average loss: 2.6867
Iteration: 3420; Percent complete: 85.5%; Average loss: 2.6052
Iteration: 3421; Percent complete: 85.5%; Average loss: 2.6041
Iteration: 3422; Percent complete: 85.5%; Average loss: 2.7975
Iteration: 3423; Percent complete: 85.6%; Average loss: 2.8711
Iteration: 3424; Percent complete: 85.6%; Average loss: 2.8273
Iteration: 3425; Percent complete: 85.6%; Average loss: 2.8191
Iteration: 3426; Percent complete: 85.7%; Average loss: 3.1267
Iteration: 3427; Percent complete: 85.7%; Average loss: 2.9264
Iteration: 3428; Percent complete: 85.7%; Average loss: 2.8077
Iteration: 3429; Percent complete: 85.7%; Average loss: 2.8573
Iteration: 3430; Percent complete: 85.8%; Average loss: 2.8715
Iteration: 3431; Percent complete: 85.8%; Average loss: 2.7884
Iteration: 3432; Percent complete: 85.8%; Average loss: 2.6664
Iteration: 3433; Percent complete: 85.8%; Average loss: 2.6419
Iteration: 3434; Percent complete: 85.9%; Average loss: 2.8450
Iteration: 3435; Percent complete: 85.9%; Average loss: 2.8067
Iteration: 3436; Percent complete: 85.9%; Average loss: 2.9128
Iteration: 3437; Percent complete: 85.9%; Average loss: 2.8647
Iteration: 3438; Percent complete: 86.0%; Average loss: 2.7619
Iteration: 3439; Percent complete: 86.0%; Average loss: 2.7635
Iteration: 3440; Percent complete: 86.0%; Average loss: 2.6246
Iteration: 3441; Percent complete: 86.0%; Average loss: 2.6633
Iteration: 3442; Percent complete: 86.1%; Average loss: 2.7855
Iteration: 3443; Percent complete: 86.1%; Average loss: 2.8352
Iteration: 3444; Percent complete: 86.1%; Average loss: 2.8012
Iteration: 3445; Percent complete: 86.1%; Average loss: 2.6463
Iteration: 3446; Percent complete: 86.2%; Average loss: 2.7985
Iteration: 3447; Percent complete: 86.2%; Average loss: 2.5745
Iteration: 3448; Percent complete: 86.2%; Average loss: 2.9825
Iteration: 3449; Percent complete: 86.2%; Average loss: 2.7367
Iteration: 3450; Percent complete: 86.2%; Average loss: 2.6894
Iteration: 3451; Percent complete: 86.3%; Average loss: 2.8076
Iteration: 3452; Percent complete: 86.3%; Average loss: 2.6973
Iteration: 3453; Percent complete: 86.3%; Average loss: 2.6371
Iteration: 3454; Percent complete: 86.4%; Average loss: 2.7748
Iteration: 3455; Percent complete: 86.4%; Average loss: 2.9006
Iteration: 3456; Percent complete: 86.4%; Average loss: 2.7601
Iteration: 3457; Percent complete: 86.4%; Average loss: 2.7687
Iteration: 3458; Percent complete: 86.5%; Average loss: 2.7220
Iteration: 3459; Percent complete: 86.5%; Average loss: 2.9085
Iteration: 3460; Percent complete: 86.5%; Average loss: 2.7823
Iteration: 3461; Percent complete: 86.5%; Average loss: 2.8516
Iteration: 3462; Percent complete: 86.6%; Average loss: 2.8599
Iteration: 3463; Percent complete: 86.6%; Average loss: 3.0311
Iteration: 3464; Percent complete: 86.6%; Average loss: 2.5816
Iteration: 3465; Percent complete: 86.6%; Average loss: 2.8811
Iteration: 3466; Percent complete: 86.7%; Average loss: 2.7994
Iteration: 3467; Percent complete: 86.7%; Average loss: 2.9269
Iteration: 3468; Percent complete: 86.7%; Average loss: 2.6908
Iteration: 3469; Percent complete: 86.7%; Average loss: 2.6840
Iteration: 3470; Percent complete: 86.8%; Average loss: 2.8177
Iteration: 3471; Percent complete: 86.8%; Average loss: 3.0107
Iteration: 3472; Percent complete: 86.8%; Average loss: 2.7395
Iteration: 3473; Percent complete: 86.8%; Average loss: 2.5941
Iteration: 3474; Percent complete: 86.9%; Average loss: 2.6997
Iteration: 3475; Percent complete: 86.9%; Average loss: 3.0362
Iteration: 3476; Percent complete: 86.9%; Average loss: 2.5243
Iteration: 3477; Percent complete: 86.9%; Average loss: 2.8462
Iteration: 3478; Percent complete: 87.0%; Average loss: 2.7741
Iteration: 3479; Percent complete: 87.0%; Average loss: 2.7482
Iteration: 3480; Percent complete: 87.0%; Average loss: 2.7780
Iteration: 3481; Percent complete: 87.0%; Average loss: 2.5962
Iteration: 3482; Percent complete: 87.1%; Average loss: 2.7127
Iteration: 3483; Percent complete: 87.1%; Average loss: 2.6129
Iteration: 3484; Percent complete: 87.1%; Average loss: 2.6505
Iteration: 3485; Percent complete: 87.1%; Average loss: 2.6352
Iteration: 3486; Percent complete: 87.2%; Average loss: 2.7888
Iteration: 3487; Percent complete: 87.2%; Average loss: 2.6330
Iteration: 3488; Percent complete: 87.2%; Average loss: 2.8199
Iteration: 3489; Percent complete: 87.2%; Average loss: 2.6015
Iteration: 3490; Percent complete: 87.2%; Average loss: 2.8773
Iteration: 3491; Percent complete: 87.3%; Average loss: 2.5144
Iteration: 3492; Percent complete: 87.3%; Average loss: 2.5924
Iteration: 3493; Percent complete: 87.3%; Average loss: 2.7685
Iteration: 3494; Percent complete: 87.4%; Average loss: 2.8521
Iteration: 3495; Percent complete: 87.4%; Average loss: 2.8001
Iteration: 3496; Percent complete: 87.4%; Average loss: 2.9714
Iteration: 3497; Percent complete: 87.4%; Average loss: 2.6393
Iteration: 3498; Percent complete: 87.5%; Average loss: 2.7500
Iteration: 3499; Percent complete: 87.5%; Average loss: 2.5040
Iteration: 3500; Percent complete: 87.5%; Average loss: 2.6991
Iteration: 3501; Percent complete: 87.5%; Average loss: 2.5187
Iteration: 3502; Percent complete: 87.5%; Average loss: 2.7178
Iteration: 3503; Percent complete: 87.6%; Average loss: 2.8990
Iteration: 3504; Percent complete: 87.6%; Average loss: 2.8955
Iteration: 3505; Percent complete: 87.6%; Average loss: 2.6631
Iteration: 3506; Percent complete: 87.6%; Average loss: 2.9430
Iteration: 3507; Percent complete: 87.7%; Average loss: 2.9559
Iteration: 3508; Percent complete: 87.7%; Average loss: 2.6502
Iteration: 3509; Percent complete: 87.7%; Average loss: 2.8102
Iteration: 3510; Percent complete: 87.8%; Average loss: 2.6708
Iteration: 3511; Percent complete: 87.8%; Average loss: 2.5766
Iteration: 3512; Percent complete: 87.8%; Average loss: 2.6637
Iteration: 3513; Percent complete: 87.8%; Average loss: 2.9480
Iteration: 3514; Percent complete: 87.8%; Average loss: 2.8318
Iteration: 3515; Percent complete: 87.9%; Average loss: 2.6473
Iteration: 3516; Percent complete: 87.9%; Average loss: 2.5527
Iteration: 3517; Percent complete: 87.9%; Average loss: 2.5528
Iteration: 3518; Percent complete: 87.9%; Average loss: 2.6941
Iteration: 3519; Percent complete: 88.0%; Average loss: 2.6132
Iteration: 3520; Percent complete: 88.0%; Average loss: 2.8789
Iteration: 3521; Percent complete: 88.0%; Average loss: 2.8192
Iteration: 3522; Percent complete: 88.0%; Average loss: 2.8415
Iteration: 3523; Percent complete: 88.1%; Average loss: 2.8936
Iteration: 3524; Percent complete: 88.1%; Average loss: 2.7310
Iteration: 3525; Percent complete: 88.1%; Average loss: 2.7405
Iteration: 3526; Percent complete: 88.1%; Average loss: 2.7754
Iteration: 3527; Percent complete: 88.2%; Average loss: 2.8815
Iteration: 3528; Percent complete: 88.2%; Average loss: 2.7046
Iteration: 3529; Percent complete: 88.2%; Average loss: 2.5677
Iteration: 3530; Percent complete: 88.2%; Average loss: 2.5997
Iteration: 3531; Percent complete: 88.3%; Average loss: 2.7910
Iteration: 3532; Percent complete: 88.3%; Average loss: 2.8723
Iteration: 3533; Percent complete: 88.3%; Average loss: 2.9184
Iteration: 3534; Percent complete: 88.3%; Average loss: 2.8924
Iteration: 3535; Percent complete: 88.4%; Average loss: 2.5797
Iteration: 3536; Percent complete: 88.4%; Average loss: 2.7573
Iteration: 3537; Percent complete: 88.4%; Average loss: 2.7350
Iteration: 3538; Percent complete: 88.4%; Average loss: 2.8763
Iteration: 3539; Percent complete: 88.5%; Average loss: 2.7327
Iteration: 3540; Percent complete: 88.5%; Average loss: 2.9383
Iteration: 3541; Percent complete: 88.5%; Average loss: 2.8163
Iteration: 3542; Percent complete: 88.5%; Average loss: 2.5969
Iteration: 3543; Percent complete: 88.6%; Average loss: 2.6002
Iteration: 3544; Percent complete: 88.6%; Average loss: 2.7393
Iteration: 3545; Percent complete: 88.6%; Average loss: 2.7894
Iteration: 3546; Percent complete: 88.6%; Average loss: 2.6051
Iteration: 3547; Percent complete: 88.7%; Average loss: 2.8170
Iteration: 3548; Percent complete: 88.7%; Average loss: 2.6093
Iteration: 3549; Percent complete: 88.7%; Average loss: 2.9866
Iteration: 3550; Percent complete: 88.8%; Average loss: 2.3550
Iteration: 3551; Percent complete: 88.8%; Average loss: 2.6216
Iteration: 3552; Percent complete: 88.8%; Average loss: 2.6669
Iteration: 3553; Percent complete: 88.8%; Average loss: 2.5147
Iteration: 3554; Percent complete: 88.8%; Average loss: 2.7496
Iteration: 3555; Percent complete: 88.9%; Average loss: 2.7423
Iteration: 3556; Percent complete: 88.9%; Average loss: 2.4204
Iteration: 3557; Percent complete: 88.9%; Average loss: 2.7348
Iteration: 3558; Percent complete: 88.9%; Average loss: 2.9583
Iteration: 3559; Percent complete: 89.0%; Average loss: 2.7608
Iteration: 3560; Percent complete: 89.0%; Average loss: 2.6953
Iteration: 3561; Percent complete: 89.0%; Average loss: 2.6286
Iteration: 3562; Percent complete: 89.0%; Average loss: 2.8012
Iteration: 3563; Percent complete: 89.1%; Average loss: 2.5093
Iteration: 3564; Percent complete: 89.1%; Average loss: 2.6812
Iteration: 3565; Percent complete: 89.1%; Average loss: 2.4643
Iteration: 3566; Percent complete: 89.1%; Average loss: 2.9346
Iteration: 3567; Percent complete: 89.2%; Average loss: 2.3853
Iteration: 3568; Percent complete: 89.2%; Average loss: 2.6774
Iteration: 3569; Percent complete: 89.2%; Average loss: 2.6589
Iteration: 3570; Percent complete: 89.2%; Average loss: 2.7429
Iteration: 3571; Percent complete: 89.3%; Average loss: 2.6458
Iteration: 3572; Percent complete: 89.3%; Average loss: 2.4862
Iteration: 3573; Percent complete: 89.3%; Average loss: 2.7115
Iteration: 3574; Percent complete: 89.3%; Average loss: 2.6252
Iteration: 3575; Percent complete: 89.4%; Average loss: 2.7911
Iteration: 3576; Percent complete: 89.4%; Average loss: 2.6986
Iteration: 3577; Percent complete: 89.4%; Average loss: 2.5606
Iteration: 3578; Percent complete: 89.5%; Average loss: 2.6355
Iteration: 3579; Percent complete: 89.5%; Average loss: 2.7359
Iteration: 3580; Percent complete: 89.5%; Average loss: 2.7825
Iteration: 3581; Percent complete: 89.5%; Average loss: 2.5828
Iteration: 3582; Percent complete: 89.5%; Average loss: 2.8313
Iteration: 3583; Percent complete: 89.6%; Average loss: 2.5825
Iteration: 3584; Percent complete: 89.6%; Average loss: 2.7860
Iteration: 3585; Percent complete: 89.6%; Average loss: 2.8530
Iteration: 3586; Percent complete: 89.6%; Average loss: 2.6915
Iteration: 3587; Percent complete: 89.7%; Average loss: 2.8109
Iteration: 3588; Percent complete: 89.7%; Average loss: 2.8119
Iteration: 3589; Percent complete: 89.7%; Average loss: 2.5506
Iteration: 3590; Percent complete: 89.8%; Average loss: 2.7255
Iteration: 3591; Percent complete: 89.8%; Average loss: 2.6462
Iteration: 3592; Percent complete: 89.8%; Average loss: 2.5908
Iteration: 3593; Percent complete: 89.8%; Average loss: 2.6361
Iteration: 3594; Percent complete: 89.8%; Average loss: 2.6654
Iteration: 3595; Percent complete: 89.9%; Average loss: 2.8264
Iteration: 3596; Percent complete: 89.9%; Average loss: 2.8346
Iteration: 3597; Percent complete: 89.9%; Average loss: 2.7386
Iteration: 3598; Percent complete: 90.0%; Average loss: 2.7719
Iteration: 3599; Percent complete: 90.0%; Average loss: 2.6585
Iteration: 3600; Percent complete: 90.0%; Average loss: 2.5796
Iteration: 3601; Percent complete: 90.0%; Average loss: 2.9423
Iteration: 3602; Percent complete: 90.0%; Average loss: 2.5228
Iteration: 3603; Percent complete: 90.1%; Average loss: 2.6932
Iteration: 3604; Percent complete: 90.1%; Average loss: 2.5899
Iteration: 3605; Percent complete: 90.1%; Average loss: 2.6824
Iteration: 3606; Percent complete: 90.1%; Average loss: 2.7242
Iteration: 3607; Percent complete: 90.2%; Average loss: 2.8765
Iteration: 3608; Percent complete: 90.2%; Average loss: 2.6785
Iteration: 3609; Percent complete: 90.2%; Average loss: 2.5359
Iteration: 3610; Percent complete: 90.2%; Average loss: 2.7954
Iteration: 3611; Percent complete: 90.3%; Average loss: 2.6488
Iteration: 3612; Percent complete: 90.3%; Average loss: 2.6729
Iteration: 3613; Percent complete: 90.3%; Average loss: 2.7938
Iteration: 3614; Percent complete: 90.3%; Average loss: 2.8625
Iteration: 3615; Percent complete: 90.4%; Average loss: 2.8870
Iteration: 3616; Percent complete: 90.4%; Average loss: 2.7259
Iteration: 3617; Percent complete: 90.4%; Average loss: 2.7908
Iteration: 3618; Percent complete: 90.5%; Average loss: 2.5990
Iteration: 3619; Percent complete: 90.5%; Average loss: 2.9940
Iteration: 3620; Percent complete: 90.5%; Average loss: 2.7500
Iteration: 3621; Percent complete: 90.5%; Average loss: 2.7954
Iteration: 3622; Percent complete: 90.5%; Average loss: 2.6037
Iteration: 3623; Percent complete: 90.6%; Average loss: 3.0130
Iteration: 3624; Percent complete: 90.6%; Average loss: 2.9735
Iteration: 3625; Percent complete: 90.6%; Average loss: 2.6789
Iteration: 3626; Percent complete: 90.6%; Average loss: 2.6653
Iteration: 3627; Percent complete: 90.7%; Average loss: 2.8128
Iteration: 3628; Percent complete: 90.7%; Average loss: 2.5729
Iteration: 3629; Percent complete: 90.7%; Average loss: 2.6689
Iteration: 3630; Percent complete: 90.8%; Average loss: 2.7432
Iteration: 3631; Percent complete: 90.8%; Average loss: 2.5618
Iteration: 3632; Percent complete: 90.8%; Average loss: 2.8973
Iteration: 3633; Percent complete: 90.8%; Average loss: 2.9772
Iteration: 3634; Percent complete: 90.8%; Average loss: 2.8679
Iteration: 3635; Percent complete: 90.9%; Average loss: 2.7977
Iteration: 3636; Percent complete: 90.9%; Average loss: 2.5358
Iteration: 3637; Percent complete: 90.9%; Average loss: 2.6657
Iteration: 3638; Percent complete: 91.0%; Average loss: 2.6921
Iteration: 3639; Percent complete: 91.0%; Average loss: 2.9236
Iteration: 3640; Percent complete: 91.0%; Average loss: 2.5970
Iteration: 3641; Percent complete: 91.0%; Average loss: 2.8534
Iteration: 3642; Percent complete: 91.0%; Average loss: 2.8913
Iteration: 3643; Percent complete: 91.1%; Average loss: 2.5553
Iteration: 3644; Percent complete: 91.1%; Average loss: 2.7166
Iteration: 3645; Percent complete: 91.1%; Average loss: 2.5128
Iteration: 3646; Percent complete: 91.1%; Average loss: 2.8539
Iteration: 3647; Percent complete: 91.2%; Average loss: 2.4903
Iteration: 3648; Percent complete: 91.2%; Average loss: 2.4067
Iteration: 3649; Percent complete: 91.2%; Average loss: 2.5888
Iteration: 3650; Percent complete: 91.2%; Average loss: 2.4089
Iteration: 3651; Percent complete: 91.3%; Average loss: 2.8213
Iteration: 3652; Percent complete: 91.3%; Average loss: 2.8238
Iteration: 3653; Percent complete: 91.3%; Average loss: 2.7158
Iteration: 3654; Percent complete: 91.3%; Average loss: 2.7699
Iteration: 3655; Percent complete: 91.4%; Average loss: 2.7794
Iteration: 3656; Percent complete: 91.4%; Average loss: 2.4976
Iteration: 3657; Percent complete: 91.4%; Average loss: 2.5840
Iteration: 3658; Percent complete: 91.5%; Average loss: 2.7062
Iteration: 3659; Percent complete: 91.5%; Average loss: 2.7829
Iteration: 3660; Percent complete: 91.5%; Average loss: 2.6666
Iteration: 3661; Percent complete: 91.5%; Average loss: 2.7322
Iteration: 3662; Percent complete: 91.5%; Average loss: 2.6302
Iteration: 3663; Percent complete: 91.6%; Average loss: 2.5992
Iteration: 3664; Percent complete: 91.6%; Average loss: 2.4473
Iteration: 3665; Percent complete: 91.6%; Average loss: 2.6767
Iteration: 3666; Percent complete: 91.6%; Average loss: 2.8840
Iteration: 3667; Percent complete: 91.7%; Average loss: 2.8006
Iteration: 3668; Percent complete: 91.7%; Average loss: 2.9044
Iteration: 3669; Percent complete: 91.7%; Average loss: 2.5541
Iteration: 3670; Percent complete: 91.8%; Average loss: 2.8160
Iteration: 3671; Percent complete: 91.8%; Average loss: 2.5508
Iteration: 3672; Percent complete: 91.8%; Average loss: 2.7260
Iteration: 3673; Percent complete: 91.8%; Average loss: 2.4452
Iteration: 3674; Percent complete: 91.8%; Average loss: 2.6119
Iteration: 3675; Percent complete: 91.9%; Average loss: 2.6959
Iteration: 3676; Percent complete: 91.9%; Average loss: 2.8217
Iteration: 3677; Percent complete: 91.9%; Average loss: 2.9089
Iteration: 3678; Percent complete: 92.0%; Average loss: 2.5939
Iteration: 3679; Percent complete: 92.0%; Average loss: 2.5278
Iteration: 3680; Percent complete: 92.0%; Average loss: 2.7016
Iteration: 3681; Percent complete: 92.0%; Average loss: 2.7429
Iteration: 3682; Percent complete: 92.0%; Average loss: 2.6792
Iteration: 3683; Percent complete: 92.1%; Average loss: 2.5498
Iteration: 3684; Percent complete: 92.1%; Average loss: 2.4685
Iteration: 3685; Percent complete: 92.1%; Average loss: 2.7780
Iteration: 3686; Percent complete: 92.2%; Average loss: 2.6520
Iteration: 3687; Percent complete: 92.2%; Average loss: 2.6977
Iteration: 3688; Percent complete: 92.2%; Average loss: 2.5517
Iteration: 3689; Percent complete: 92.2%; Average loss: 2.6898
Iteration: 3690; Percent complete: 92.2%; Average loss: 2.4834
Iteration: 3691; Percent complete: 92.3%; Average loss: 2.9167
Iteration: 3692; Percent complete: 92.3%; Average loss: 2.5166
Iteration: 3693; Percent complete: 92.3%; Average loss: 2.7149
Iteration: 3694; Percent complete: 92.3%; Average loss: 2.5229
Iteration: 3695; Percent complete: 92.4%; Average loss: 2.5056
Iteration: 3696; Percent complete: 92.4%; Average loss: 2.7370
Iteration: 3697; Percent complete: 92.4%; Average loss: 2.8431
Iteration: 3698; Percent complete: 92.5%; Average loss: 2.7627
Iteration: 3699; Percent complete: 92.5%; Average loss: 2.6203
Iteration: 3700; Percent complete: 92.5%; Average loss: 2.8179
Iteration: 3701; Percent complete: 92.5%; Average loss: 3.0142
Iteration: 3702; Percent complete: 92.5%; Average loss: 2.8554
Iteration: 3703; Percent complete: 92.6%; Average loss: 2.7221
Iteration: 3704; Percent complete: 92.6%; Average loss: 2.7477
Iteration: 3705; Percent complete: 92.6%; Average loss: 2.7554
Iteration: 3706; Percent complete: 92.7%; Average loss: 2.6987
Iteration: 3707; Percent complete: 92.7%; Average loss: 2.7344
Iteration: 3708; Percent complete: 92.7%; Average loss: 2.6456
Iteration: 3709; Percent complete: 92.7%; Average loss: 2.8663
Iteration: 3710; Percent complete: 92.8%; Average loss: 2.5443
Iteration: 3711; Percent complete: 92.8%; Average loss: 2.6863
Iteration: 3712; Percent complete: 92.8%; Average loss: 2.7185
Iteration: 3713; Percent complete: 92.8%; Average loss: 2.6865
Iteration: 3714; Percent complete: 92.8%; Average loss: 2.4067
Iteration: 3715; Percent complete: 92.9%; Average loss: 2.6087
Iteration: 3716; Percent complete: 92.9%; Average loss: 2.7925
Iteration: 3717; Percent complete: 92.9%; Average loss: 2.6758
Iteration: 3718; Percent complete: 93.0%; Average loss: 2.7884
Iteration: 3719; Percent complete: 93.0%; Average loss: 2.6563
Iteration: 3720; Percent complete: 93.0%; Average loss: 2.7895
Iteration: 3721; Percent complete: 93.0%; Average loss: 2.7631
Iteration: 3722; Percent complete: 93.0%; Average loss: 2.7106
Iteration: 3723; Percent complete: 93.1%; Average loss: 2.4792
Iteration: 3724; Percent complete: 93.1%; Average loss: 2.4374
Iteration: 3725; Percent complete: 93.1%; Average loss: 2.8365
Iteration: 3726; Percent complete: 93.2%; Average loss: 2.6440
Iteration: 3727; Percent complete: 93.2%; Average loss: 2.9097
Iteration: 3728; Percent complete: 93.2%; Average loss: 2.6665
Iteration: 3729; Percent complete: 93.2%; Average loss: 2.7952
Iteration: 3730; Percent complete: 93.2%; Average loss: 3.1241
Iteration: 3731; Percent complete: 93.3%; Average loss: 2.5892
Iteration: 3732; Percent complete: 93.3%; Average loss: 2.4508
Iteration: 3733; Percent complete: 93.3%; Average loss: 2.6673
Iteration: 3734; Percent complete: 93.3%; Average loss: 2.8944
Iteration: 3735; Percent complete: 93.4%; Average loss: 2.6428
Iteration: 3736; Percent complete: 93.4%; Average loss: 2.5425
Iteration: 3737; Percent complete: 93.4%; Average loss: 2.7812
Iteration: 3738; Percent complete: 93.5%; Average loss: 2.7568
Iteration: 3739; Percent complete: 93.5%; Average loss: 2.6593
Iteration: 3740; Percent complete: 93.5%; Average loss: 2.7418
Iteration: 3741; Percent complete: 93.5%; Average loss: 2.8470
Iteration: 3742; Percent complete: 93.5%; Average loss: 2.6182
Iteration: 3743; Percent complete: 93.6%; Average loss: 2.7338
Iteration: 3744; Percent complete: 93.6%; Average loss: 2.4690
Iteration: 3745; Percent complete: 93.6%; Average loss: 2.7347
Iteration: 3746; Percent complete: 93.7%; Average loss: 2.6484
Iteration: 3747; Percent complete: 93.7%; Average loss: 2.5743
Iteration: 3748; Percent complete: 93.7%; Average loss: 2.4297
Iteration: 3749; Percent complete: 93.7%; Average loss: 2.6284
Iteration: 3750; Percent complete: 93.8%; Average loss: 2.7021
Iteration: 3751; Percent complete: 93.8%; Average loss: 2.5621
Iteration: 3752; Percent complete: 93.8%; Average loss: 2.5777
Iteration: 3753; Percent complete: 93.8%; Average loss: 2.8823
Iteration: 3754; Percent complete: 93.8%; Average loss: 2.7357
Iteration: 3755; Percent complete: 93.9%; Average loss: 2.8253
Iteration: 3756; Percent complete: 93.9%; Average loss: 2.4496
Iteration: 3757; Percent complete: 93.9%; Average loss: 2.5753
Iteration: 3758; Percent complete: 94.0%; Average loss: 2.8883
Iteration: 3759; Percent complete: 94.0%; Average loss: 2.8303
Iteration: 3760; Percent complete: 94.0%; Average loss: 2.5900
Iteration: 3761; Percent complete: 94.0%; Average loss: 2.5982
Iteration: 3762; Percent complete: 94.0%; Average loss: 2.5391
Iteration: 3763; Percent complete: 94.1%; Average loss: 2.5361
Iteration: 3764; Percent complete: 94.1%; Average loss: 2.6606
Iteration: 3765; Percent complete: 94.1%; Average loss: 2.6998
Iteration: 3766; Percent complete: 94.2%; Average loss: 2.7161
Iteration: 3767; Percent complete: 94.2%; Average loss: 2.3207
Iteration: 3768; Percent complete: 94.2%; Average loss: 2.7504
Iteration: 3769; Percent complete: 94.2%; Average loss: 2.5095
Iteration: 3770; Percent complete: 94.2%; Average loss: 2.5682
Iteration: 3771; Percent complete: 94.3%; Average loss: 2.6524
Iteration: 3772; Percent complete: 94.3%; Average loss: 2.4924
Iteration: 3773; Percent complete: 94.3%; Average loss: 2.4623
Iteration: 3774; Percent complete: 94.3%; Average loss: 2.5705
Iteration: 3775; Percent complete: 94.4%; Average loss: 2.4850
Iteration: 3776; Percent complete: 94.4%; Average loss: 2.5898
Iteration: 3777; Percent complete: 94.4%; Average loss: 2.5637
Iteration: 3778; Percent complete: 94.5%; Average loss: 2.8221
Iteration: 3779; Percent complete: 94.5%; Average loss: 2.6020
Iteration: 3780; Percent complete: 94.5%; Average loss: 2.8325
Iteration: 3781; Percent complete: 94.5%; Average loss: 2.5487
Iteration: 3782; Percent complete: 94.5%; Average loss: 2.6619
Iteration: 3783; Percent complete: 94.6%; Average loss: 2.5649
Iteration: 3784; Percent complete: 94.6%; Average loss: 2.4639
Iteration: 3785; Percent complete: 94.6%; Average loss: 2.6840
Iteration: 3786; Percent complete: 94.7%; Average loss: 2.7836
Iteration: 3787; Percent complete: 94.7%; Average loss: 2.6087
Iteration: 3788; Percent complete: 94.7%; Average loss: 2.7377
Iteration: 3789; Percent complete: 94.7%; Average loss: 2.7910
Iteration: 3790; Percent complete: 94.8%; Average loss: 2.7991
Iteration: 3791; Percent complete: 94.8%; Average loss: 2.6211
Iteration: 3792; Percent complete: 94.8%; Average loss: 2.7688
Iteration: 3793; Percent complete: 94.8%; Average loss: 2.8480
Iteration: 3794; Percent complete: 94.8%; Average loss: 2.4778
Iteration: 3795; Percent complete: 94.9%; Average loss: 2.7066
Iteration: 3796; Percent complete: 94.9%; Average loss: 2.5327
Iteration: 3797; Percent complete: 94.9%; Average loss: 2.3833
Iteration: 3798; Percent complete: 95.0%; Average loss: 2.8781
Iteration: 3799; Percent complete: 95.0%; Average loss: 2.7642
Iteration: 3800; Percent complete: 95.0%; Average loss: 2.5985
Iteration: 3801; Percent complete: 95.0%; Average loss: 2.5466
Iteration: 3802; Percent complete: 95.0%; Average loss: 2.6729
Iteration: 3803; Percent complete: 95.1%; Average loss: 2.6651
Iteration: 3804; Percent complete: 95.1%; Average loss: 2.7787
Iteration: 3805; Percent complete: 95.1%; Average loss: 2.7305
Iteration: 3806; Percent complete: 95.2%; Average loss: 2.6513
Iteration: 3807; Percent complete: 95.2%; Average loss: 2.5343
Iteration: 3808; Percent complete: 95.2%; Average loss: 2.5180
Iteration: 3809; Percent complete: 95.2%; Average loss: 2.4520
Iteration: 3810; Percent complete: 95.2%; Average loss: 2.5106
Iteration: 3811; Percent complete: 95.3%; Average loss: 2.7754
Iteration: 3812; Percent complete: 95.3%; Average loss: 2.6240
Iteration: 3813; Percent complete: 95.3%; Average loss: 3.0158
Iteration: 3814; Percent complete: 95.3%; Average loss: 2.7666
Iteration: 3815; Percent complete: 95.4%; Average loss: 2.9036
Iteration: 3816; Percent complete: 95.4%; Average loss: 2.6570
Iteration: 3817; Percent complete: 95.4%; Average loss: 2.8044
Iteration: 3818; Percent complete: 95.5%; Average loss: 2.6524
Iteration: 3819; Percent complete: 95.5%; Average loss: 2.5858
Iteration: 3820; Percent complete: 95.5%; Average loss: 2.6369
Iteration: 3821; Percent complete: 95.5%; Average loss: 2.6239
Iteration: 3822; Percent complete: 95.5%; Average loss: 2.6600
Iteration: 3823; Percent complete: 95.6%; Average loss: 2.7256
Iteration: 3824; Percent complete: 95.6%; Average loss: 2.6500
Iteration: 3825; Percent complete: 95.6%; Average loss: 2.7573
Iteration: 3826; Percent complete: 95.7%; Average loss: 2.7390
Iteration: 3827; Percent complete: 95.7%; Average loss: 2.6405
Iteration: 3828; Percent complete: 95.7%; Average loss: 2.6628
Iteration: 3829; Percent complete: 95.7%; Average loss: 2.5146
Iteration: 3830; Percent complete: 95.8%; Average loss: 2.5889
Iteration: 3831; Percent complete: 95.8%; Average loss: 2.6816
Iteration: 3832; Percent complete: 95.8%; Average loss: 2.6934
Iteration: 3833; Percent complete: 95.8%; Average loss: 2.7248
Iteration: 3834; Percent complete: 95.9%; Average loss: 2.6621
Iteration: 3835; Percent complete: 95.9%; Average loss: 2.6522
Iteration: 3836; Percent complete: 95.9%; Average loss: 2.7309
Iteration: 3837; Percent complete: 95.9%; Average loss: 2.5456
Iteration: 3838; Percent complete: 96.0%; Average loss: 2.6110
Iteration: 3839; Percent complete: 96.0%; Average loss: 2.6081
Iteration: 3840; Percent complete: 96.0%; Average loss: 2.7074
Iteration: 3841; Percent complete: 96.0%; Average loss: 2.8347
Iteration: 3842; Percent complete: 96.0%; Average loss: 2.4294
Iteration: 3843; Percent complete: 96.1%; Average loss: 2.5269
Iteration: 3844; Percent complete: 96.1%; Average loss: 2.4294
Iteration: 3845; Percent complete: 96.1%; Average loss: 2.6503
Iteration: 3846; Percent complete: 96.2%; Average loss: 2.5259
Iteration: 3847; Percent complete: 96.2%; Average loss: 3.0747
Iteration: 3848; Percent complete: 96.2%; Average loss: 2.5383
Iteration: 3849; Percent complete: 96.2%; Average loss: 2.9840
Iteration: 3850; Percent complete: 96.2%; Average loss: 2.6521
Iteration: 3851; Percent complete: 96.3%; Average loss: 2.8237
Iteration: 3852; Percent complete: 96.3%; Average loss: 2.8775
Iteration: 3853; Percent complete: 96.3%; Average loss: 2.6600
Iteration: 3854; Percent complete: 96.4%; Average loss: 2.8034
Iteration: 3855; Percent complete: 96.4%; Average loss: 2.5126
Iteration: 3856; Percent complete: 96.4%; Average loss: 2.5819
Iteration: 3857; Percent complete: 96.4%; Average loss: 2.5983
Iteration: 3858; Percent complete: 96.5%; Average loss: 2.8712
Iteration: 3859; Percent complete: 96.5%; Average loss: 2.4893
Iteration: 3860; Percent complete: 96.5%; Average loss: 2.4920
Iteration: 3861; Percent complete: 96.5%; Average loss: 2.6173
Iteration: 3862; Percent complete: 96.5%; Average loss: 2.5102
Iteration: 3863; Percent complete: 96.6%; Average loss: 2.8587
Iteration: 3864; Percent complete: 96.6%; Average loss: 2.8158
Iteration: 3865; Percent complete: 96.6%; Average loss: 2.6372
Iteration: 3866; Percent complete: 96.7%; Average loss: 2.6882
Iteration: 3867; Percent complete: 96.7%; Average loss: 2.5340
Iteration: 3868; Percent complete: 96.7%; Average loss: 2.6025
Iteration: 3869; Percent complete: 96.7%; Average loss: 2.6591
Iteration: 3870; Percent complete: 96.8%; Average loss: 2.6581
Iteration: 3871; Percent complete: 96.8%; Average loss: 2.7216
Iteration: 3872; Percent complete: 96.8%; Average loss: 2.5298
Iteration: 3873; Percent complete: 96.8%; Average loss: 2.7327
Iteration: 3874; Percent complete: 96.9%; Average loss: 2.3905
Iteration: 3875; Percent complete: 96.9%; Average loss: 2.5791
Iteration: 3876; Percent complete: 96.9%; Average loss: 2.6078
Iteration: 3877; Percent complete: 96.9%; Average loss: 2.5642
Iteration: 3878; Percent complete: 97.0%; Average loss: 2.4768
Iteration: 3879; Percent complete: 97.0%; Average loss: 2.7724
Iteration: 3880; Percent complete: 97.0%; Average loss: 2.5197
Iteration: 3881; Percent complete: 97.0%; Average loss: 2.7734
Iteration: 3882; Percent complete: 97.0%; Average loss: 2.7137
Iteration: 3883; Percent complete: 97.1%; Average loss: 2.8333
Iteration: 3884; Percent complete: 97.1%; Average loss: 2.7887
Iteration: 3885; Percent complete: 97.1%; Average loss: 2.4538
Iteration: 3886; Percent complete: 97.2%; Average loss: 2.6518
Iteration: 3887; Percent complete: 97.2%; Average loss: 2.5812
Iteration: 3888; Percent complete: 97.2%; Average loss: 2.8432
Iteration: 3889; Percent complete: 97.2%; Average loss: 2.5571
Iteration: 3890; Percent complete: 97.2%; Average loss: 2.7269
Iteration: 3891; Percent complete: 97.3%; Average loss: 2.6211
Iteration: 3892; Percent complete: 97.3%; Average loss: 2.6486
Iteration: 3893; Percent complete: 97.3%; Average loss: 2.7635
Iteration: 3894; Percent complete: 97.4%; Average loss: 2.6393
Iteration: 3895; Percent complete: 97.4%; Average loss: 2.7030
Iteration: 3896; Percent complete: 97.4%; Average loss: 2.6978
Iteration: 3897; Percent complete: 97.4%; Average loss: 2.7034
Iteration: 3898; Percent complete: 97.5%; Average loss: 2.7875
Iteration: 3899; Percent complete: 97.5%; Average loss: 2.4541
Iteration: 3900; Percent complete: 97.5%; Average loss: 2.5054
Iteration: 3901; Percent complete: 97.5%; Average loss: 2.4868
Iteration: 3902; Percent complete: 97.5%; Average loss: 2.6106
Iteration: 3903; Percent complete: 97.6%; Average loss: 2.6648
Iteration: 3904; Percent complete: 97.6%; Average loss: 2.6662
Iteration: 3905; Percent complete: 97.6%; Average loss: 2.5640
Iteration: 3906; Percent complete: 97.7%; Average loss: 2.7096
Iteration: 3907; Percent complete: 97.7%; Average loss: 2.5493
Iteration: 3908; Percent complete: 97.7%; Average loss: 2.7143
Iteration: 3909; Percent complete: 97.7%; Average loss: 2.5724
Iteration: 3910; Percent complete: 97.8%; Average loss: 2.5300
Iteration: 3911; Percent complete: 97.8%; Average loss: 2.4976
Iteration: 3912; Percent complete: 97.8%; Average loss: 2.4607
Iteration: 3913; Percent complete: 97.8%; Average loss: 2.7507
Iteration: 3914; Percent complete: 97.9%; Average loss: 2.7392
Iteration: 3915; Percent complete: 97.9%; Average loss: 2.6033
Iteration: 3916; Percent complete: 97.9%; Average loss: 2.5116
Iteration: 3917; Percent complete: 97.9%; Average loss: 2.4138
Iteration: 3918; Percent complete: 98.0%; Average loss: 2.5232
Iteration: 3919; Percent complete: 98.0%; Average loss: 2.6769
Iteration: 3920; Percent complete: 98.0%; Average loss: 2.5631
Iteration: 3921; Percent complete: 98.0%; Average loss: 2.5297
Iteration: 3922; Percent complete: 98.0%; Average loss: 2.5028
Iteration: 3923; Percent complete: 98.1%; Average loss: 2.6198
Iteration: 3924; Percent complete: 98.1%; Average loss: 2.6237
Iteration: 3925; Percent complete: 98.1%; Average loss: 2.7994
Iteration: 3926; Percent complete: 98.2%; Average loss: 2.5417
Iteration: 3927; Percent complete: 98.2%; Average loss: 2.6131
Iteration: 3928; Percent complete: 98.2%; Average loss: 2.7071
Iteration: 3929; Percent complete: 98.2%; Average loss: 2.6495
Iteration: 3930; Percent complete: 98.2%; Average loss: 2.6308
Iteration: 3931; Percent complete: 98.3%; Average loss: 2.5685
Iteration: 3932; Percent complete: 98.3%; Average loss: 2.3927
Iteration: 3933; Percent complete: 98.3%; Average loss: 2.5303
Iteration: 3934; Percent complete: 98.4%; Average loss: 2.5620
Iteration: 3935; Percent complete: 98.4%; Average loss: 2.2960
Iteration: 3936; Percent complete: 98.4%; Average loss: 2.7224
Iteration: 3937; Percent complete: 98.4%; Average loss: 2.6167
Iteration: 3938; Percent complete: 98.5%; Average loss: 2.3804
Iteration: 3939; Percent complete: 98.5%; Average loss: 2.6448
Iteration: 3940; Percent complete: 98.5%; Average loss: 2.6329
Iteration: 3941; Percent complete: 98.5%; Average loss: 2.6949
Iteration: 3942; Percent complete: 98.6%; Average loss: 2.8634
Iteration: 3943; Percent complete: 98.6%; Average loss: 2.8074
Iteration: 3944; Percent complete: 98.6%; Average loss: 2.6711
Iteration: 3945; Percent complete: 98.6%; Average loss: 2.5491
Iteration: 3946; Percent complete: 98.7%; Average loss: 2.4234
Iteration: 3947; Percent complete: 98.7%; Average loss: 2.5948
Iteration: 3948; Percent complete: 98.7%; Average loss: 2.6073
Iteration: 3949; Percent complete: 98.7%; Average loss: 2.8914
Iteration: 3950; Percent complete: 98.8%; Average loss: 2.8138
Iteration: 3951; Percent complete: 98.8%; Average loss: 2.7374
Iteration: 3952; Percent complete: 98.8%; Average loss: 2.6708
Iteration: 3953; Percent complete: 98.8%; Average loss: 2.5996
Iteration: 3954; Percent complete: 98.9%; Average loss: 2.5386
Iteration: 3955; Percent complete: 98.9%; Average loss: 2.6465
Iteration: 3956; Percent complete: 98.9%; Average loss: 2.4523
Iteration: 3957; Percent complete: 98.9%; Average loss: 2.4182
Iteration: 3958; Percent complete: 99.0%; Average loss: 2.5524
Iteration: 3959; Percent complete: 99.0%; Average loss: 2.6659
Iteration: 3960; Percent complete: 99.0%; Average loss: 2.4076
Iteration: 3961; Percent complete: 99.0%; Average loss: 2.5414
Iteration: 3962; Percent complete: 99.1%; Average loss: 2.7125
Iteration: 3963; Percent complete: 99.1%; Average loss: 2.7032
Iteration: 3964; Percent complete: 99.1%; Average loss: 2.6992
Iteration: 3965; Percent complete: 99.1%; Average loss: 2.5642
Iteration: 3966; Percent complete: 99.2%; Average loss: 2.6119
Iteration: 3967; Percent complete: 99.2%; Average loss: 2.5870
Iteration: 3968; Percent complete: 99.2%; Average loss: 2.6675
Iteration: 3969; Percent complete: 99.2%; Average loss: 2.6653
Iteration: 3970; Percent complete: 99.2%; Average loss: 2.4733
Iteration: 3971; Percent complete: 99.3%; Average loss: 2.4861
Iteration: 3972; Percent complete: 99.3%; Average loss: 2.6601
Iteration: 3973; Percent complete: 99.3%; Average loss: 2.6238
Iteration: 3974; Percent complete: 99.4%; Average loss: 2.6798
Iteration: 3975; Percent complete: 99.4%; Average loss: 2.6662
Iteration: 3976; Percent complete: 99.4%; Average loss: 2.5336
Iteration: 3977; Percent complete: 99.4%; Average loss: 2.5449
Iteration: 3978; Percent complete: 99.5%; Average loss: 2.7603
Iteration: 3979; Percent complete: 99.5%; Average loss: 2.6413
Iteration: 3980; Percent complete: 99.5%; Average loss: 2.3150
Iteration: 3981; Percent complete: 99.5%; Average loss: 2.7809
Iteration: 3982; Percent complete: 99.6%; Average loss: 2.5109
Iteration: 3983; Percent complete: 99.6%; Average loss: 2.6368
Iteration: 3984; Percent complete: 99.6%; Average loss: 2.7557
Iteration: 3985; Percent complete: 99.6%; Average loss: 2.5613
Iteration: 3986; Percent complete: 99.7%; Average loss: 2.6926
Iteration: 3987; Percent complete: 99.7%; Average loss: 2.4406
Iteration: 3988; Percent complete: 99.7%; Average loss: 2.4905
Iteration: 3989; Percent complete: 99.7%; Average loss: 2.5519
Iteration: 3990; Percent complete: 99.8%; Average loss: 2.5771
Iteration: 3991; Percent complete: 99.8%; Average loss: 2.5014
Iteration: 3992; Percent complete: 99.8%; Average loss: 2.4445
Iteration: 3993; Percent complete: 99.8%; Average loss: 2.6341
Iteration: 3994; Percent complete: 99.9%; Average loss: 2.3973
Iteration: 3995; Percent complete: 99.9%; Average loss: 2.6604
Iteration: 3996; Percent complete: 99.9%; Average loss: 2.6586
Iteration: 3997; Percent complete: 99.9%; Average loss: 2.5472
Iteration: 3998; Percent complete: 100.0%; Average loss: 2.6638
Iteration: 3999; Percent complete: 100.0%; Average loss: 2.6198
Iteration: 4000; Percent complete: 100.0%; Average loss: 2.7277

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!