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([[ 309,   24,   19,  367,  126],
        [  24,  248,  335,   11,   36],
        [ 307,  161,   36,  515,   14],
        [ 359, 5885,  265,   18,    2],
        [  62,   10,   10,   14,    0],
        [4504,    2,    2,    2,    0],
        [  14,    0,    0,    0,    0],
        [   2,    0,    0,    0,    0]])
lengths: tensor([8, 6, 6, 6, 4])
target_variable: tensor([[  11,    7, 1972,    9,   11],
        [ 136, 2769, 3000,  677,  246],
        [  26,   14,   14,   14,  135],
        [ 229,    2,    2,    2,  616],
        [  14,    0,    0,    0,   24],
        [   2,    0,    0,    0,  365],
        [   0,    0,    0,    0,  126],
        [   0,    0,    0,    0,   36],
        [   0,    0,    0,    0,   14],
        [   0,    0,    0,    0,    2]])
mask: tensor([[ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True, False, False, False,  True],
        [ True, False, False, False,  True],
        [False, False, False, False,  True],
        [False, False, False, False,  True],
        [False, False, False, False,  True],
        [False, False, False, False,  True]])
max_target_len: 10

Define Models

Seq2Seq Model

The brains of our chatbot is a sequence-to-sequence (seq2seq) model. The goal of a seq2seq model is to take a variable-length sequence as an input, and return a variable-length sequence as an output using a fixed-sized model.

Sutskever et al. discovered that by using two separate recurrent neural nets together, we can accomplish this task. One RNN acts as an encoder, which encodes a variable length input sequence to a fixed-length context vector. In theory, this context vector (the final hidden layer of the RNN) will contain semantic information about the query sentence that is input to the bot. The second RNN is a decoder, which takes an input word and the context vector, and returns a guess for the next word in the sequence and a hidden state to use in the next iteration.

Image source: https://jeddy92.github.io/JEddy92.github.io/ts_seq2seq_intro/

Encoder

The encoder RNN iterates through the input sentence one token (e.g. word) at a time, at each time step outputting an “output” vector and a “hidden state” vector. The hidden state vector is then passed to the next time step, while the output vector is recorded. The encoder transforms the context it saw at each point in the sequence into a set of points in a high-dimensional space, which the decoder will use to generate a meaningful output for the given task.

At the heart of our encoder is a multi-layered Gated Recurrent Unit, invented by Cho et al. in 2014. We will use a bidirectional variant of the GRU, meaning that there are essentially two independent RNNs: one that is fed the input sequence in normal sequential order, and one that is fed the input sequence in reverse order. The outputs of each network are summed at each time step. Using a bidirectional GRU will give us the advantage of encoding both past and future contexts.

Bidirectional RNN:

Image source: https://colah.github.io/posts/2015-09-NN-Types-FP/

Note that an embedding layer is used to encode our word indices in an arbitrarily sized feature space. For our models, this layer will map each word to a feature space of size hidden_size. When trained, these values should encode semantic similarity between similar meaning words.

Finally, if passing a padded batch of sequences to an RNN module, we must pack and unpack padding around the RNN pass using nn.utils.rnn.pack_padded_sequence and nn.utils.rnn.pad_packed_sequence respectively.

Computation Graph:

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: 8.9804
Iteration: 2; Percent complete: 0.1%; Average loss: 8.8717
Iteration: 3; Percent complete: 0.1%; Average loss: 8.7083
Iteration: 4; Percent complete: 0.1%; Average loss: 8.3785
Iteration: 5; Percent complete: 0.1%; Average loss: 7.9744
Iteration: 6; Percent complete: 0.1%; Average loss: 7.3375
Iteration: 7; Percent complete: 0.2%; Average loss: 6.9743
Iteration: 8; Percent complete: 0.2%; Average loss: 6.8626
Iteration: 9; Percent complete: 0.2%; Average loss: 6.9651
Iteration: 10; Percent complete: 0.2%; Average loss: 6.4687
Iteration: 11; Percent complete: 0.3%; Average loss: 6.1786
Iteration: 12; Percent complete: 0.3%; Average loss: 5.6283
Iteration: 13; Percent complete: 0.3%; Average loss: 5.4415
Iteration: 14; Percent complete: 0.4%; Average loss: 5.4196
Iteration: 15; Percent complete: 0.4%; Average loss: 5.5100
Iteration: 16; Percent complete: 0.4%; Average loss: 5.3191
Iteration: 17; Percent complete: 0.4%; Average loss: 5.1024
Iteration: 18; Percent complete: 0.4%; Average loss: 5.0015
Iteration: 19; Percent complete: 0.5%; Average loss: 5.0807
Iteration: 20; Percent complete: 0.5%; Average loss: 5.0892
Iteration: 21; Percent complete: 0.5%; Average loss: 4.8727
Iteration: 22; Percent complete: 0.5%; Average loss: 5.0382
Iteration: 23; Percent complete: 0.6%; Average loss: 4.7017
Iteration: 24; Percent complete: 0.6%; Average loss: 4.8930
Iteration: 25; Percent complete: 0.6%; Average loss: 4.8484
Iteration: 26; Percent complete: 0.7%; Average loss: 4.5515
Iteration: 27; Percent complete: 0.7%; Average loss: 4.7980
Iteration: 28; Percent complete: 0.7%; Average loss: 4.9207
Iteration: 29; Percent complete: 0.7%; Average loss: 4.9634
Iteration: 30; Percent complete: 0.8%; Average loss: 4.6631
Iteration: 31; Percent complete: 0.8%; Average loss: 4.7510
Iteration: 32; Percent complete: 0.8%; Average loss: 4.9960
Iteration: 33; Percent complete: 0.8%; Average loss: 4.9773
Iteration: 34; Percent complete: 0.9%; Average loss: 4.7217
Iteration: 35; Percent complete: 0.9%; Average loss: 4.6367
Iteration: 36; Percent complete: 0.9%; Average loss: 4.5783
Iteration: 37; Percent complete: 0.9%; Average loss: 5.0432
Iteration: 38; Percent complete: 0.9%; Average loss: 4.7287
Iteration: 39; Percent complete: 1.0%; Average loss: 4.6310
Iteration: 40; Percent complete: 1.0%; Average loss: 4.7094
Iteration: 41; Percent complete: 1.0%; Average loss: 4.8378
Iteration: 42; Percent complete: 1.1%; Average loss: 4.4592
Iteration: 43; Percent complete: 1.1%; Average loss: 4.6016
Iteration: 44; Percent complete: 1.1%; Average loss: 4.6033
Iteration: 45; Percent complete: 1.1%; Average loss: 4.6968
Iteration: 46; Percent complete: 1.1%; Average loss: 4.7309
Iteration: 47; Percent complete: 1.2%; Average loss: 4.5417
Iteration: 48; Percent complete: 1.2%; Average loss: 4.6083
Iteration: 49; Percent complete: 1.2%; Average loss: 4.8707
Iteration: 50; Percent complete: 1.2%; Average loss: 4.5033
Iteration: 51; Percent complete: 1.3%; Average loss: 4.4708
Iteration: 52; Percent complete: 1.3%; Average loss: 4.7082
Iteration: 53; Percent complete: 1.3%; Average loss: 4.7672
Iteration: 54; Percent complete: 1.4%; Average loss: 4.5781
Iteration: 55; Percent complete: 1.4%; Average loss: 4.8964
Iteration: 56; Percent complete: 1.4%; Average loss: 4.3547
Iteration: 57; Percent complete: 1.4%; Average loss: 4.5911
Iteration: 58; Percent complete: 1.5%; Average loss: 4.6584
Iteration: 59; Percent complete: 1.5%; Average loss: 4.6124
Iteration: 60; Percent complete: 1.5%; Average loss: 4.6664
Iteration: 61; Percent complete: 1.5%; Average loss: 4.7345
Iteration: 62; Percent complete: 1.6%; Average loss: 4.1784
Iteration: 63; Percent complete: 1.6%; Average loss: 4.5878
Iteration: 64; Percent complete: 1.6%; Average loss: 4.4142
Iteration: 65; Percent complete: 1.6%; Average loss: 4.6603
Iteration: 66; Percent complete: 1.7%; Average loss: 4.3939
Iteration: 67; Percent complete: 1.7%; Average loss: 4.5713
Iteration: 68; Percent complete: 1.7%; Average loss: 4.4705
Iteration: 69; Percent complete: 1.7%; Average loss: 4.4783
Iteration: 70; Percent complete: 1.8%; Average loss: 4.4970
Iteration: 71; Percent complete: 1.8%; Average loss: 4.5793
Iteration: 72; Percent complete: 1.8%; Average loss: 4.3472
Iteration: 73; Percent complete: 1.8%; Average loss: 4.4572
Iteration: 74; Percent complete: 1.8%; Average loss: 4.4957
Iteration: 75; Percent complete: 1.9%; Average loss: 4.3120
Iteration: 76; Percent complete: 1.9%; Average loss: 4.4254
Iteration: 77; Percent complete: 1.9%; Average loss: 4.4196
Iteration: 78; Percent complete: 1.9%; Average loss: 4.5147
Iteration: 79; Percent complete: 2.0%; Average loss: 4.4432
Iteration: 80; Percent complete: 2.0%; Average loss: 4.5130
Iteration: 81; Percent complete: 2.0%; Average loss: 4.4369
Iteration: 82; Percent complete: 2.1%; Average loss: 4.5060
Iteration: 83; Percent complete: 2.1%; Average loss: 4.5399
Iteration: 84; Percent complete: 2.1%; Average loss: 4.4416
Iteration: 85; Percent complete: 2.1%; Average loss: 4.4139
Iteration: 86; Percent complete: 2.1%; Average loss: 4.5503
Iteration: 87; Percent complete: 2.2%; Average loss: 4.6024
Iteration: 88; Percent complete: 2.2%; Average loss: 4.4271
Iteration: 89; Percent complete: 2.2%; Average loss: 4.4216
Iteration: 90; Percent complete: 2.2%; Average loss: 4.5591
Iteration: 91; Percent complete: 2.3%; Average loss: 4.5309
Iteration: 92; Percent complete: 2.3%; Average loss: 4.4456
Iteration: 93; Percent complete: 2.3%; Average loss: 4.4790
Iteration: 94; Percent complete: 2.4%; Average loss: 4.2885
Iteration: 95; Percent complete: 2.4%; Average loss: 4.2647
Iteration: 96; Percent complete: 2.4%; Average loss: 4.5661
Iteration: 97; Percent complete: 2.4%; Average loss: 4.4013
Iteration: 98; Percent complete: 2.5%; Average loss: 4.4747
Iteration: 99; Percent complete: 2.5%; Average loss: 4.6262
Iteration: 100; Percent complete: 2.5%; Average loss: 4.4568
Iteration: 101; Percent complete: 2.5%; Average loss: 4.5310
Iteration: 102; Percent complete: 2.5%; Average loss: 4.1164
Iteration: 103; Percent complete: 2.6%; Average loss: 4.6262
Iteration: 104; Percent complete: 2.6%; Average loss: 4.3686
Iteration: 105; Percent complete: 2.6%; Average loss: 4.2504
Iteration: 106; Percent complete: 2.6%; Average loss: 4.5109
Iteration: 107; Percent complete: 2.7%; Average loss: 4.3880
Iteration: 108; Percent complete: 2.7%; Average loss: 4.4170
Iteration: 109; Percent complete: 2.7%; Average loss: 4.3785
Iteration: 110; Percent complete: 2.8%; Average loss: 4.3995
Iteration: 111; Percent complete: 2.8%; Average loss: 4.1595
Iteration: 112; Percent complete: 2.8%; Average loss: 4.1427
Iteration: 113; Percent complete: 2.8%; Average loss: 4.2824
Iteration: 114; Percent complete: 2.9%; Average loss: 4.3863
Iteration: 115; Percent complete: 2.9%; Average loss: 4.0192
Iteration: 116; Percent complete: 2.9%; Average loss: 4.2555
Iteration: 117; Percent complete: 2.9%; Average loss: 4.2164
Iteration: 118; Percent complete: 2.9%; Average loss: 4.4913
Iteration: 119; Percent complete: 3.0%; Average loss: 4.4328
Iteration: 120; Percent complete: 3.0%; Average loss: 4.3377
Iteration: 121; Percent complete: 3.0%; Average loss: 4.3721
Iteration: 122; Percent complete: 3.0%; Average loss: 4.0882
Iteration: 123; Percent complete: 3.1%; Average loss: 4.4139
Iteration: 124; Percent complete: 3.1%; Average loss: 4.1755
Iteration: 125; Percent complete: 3.1%; Average loss: 4.3791
Iteration: 126; Percent complete: 3.1%; Average loss: 4.2792
Iteration: 127; Percent complete: 3.2%; Average loss: 4.1866
Iteration: 128; Percent complete: 3.2%; Average loss: 4.3706
Iteration: 129; Percent complete: 3.2%; Average loss: 4.0035
Iteration: 130; Percent complete: 3.2%; Average loss: 4.1886
Iteration: 131; Percent complete: 3.3%; Average loss: 4.0292
Iteration: 132; Percent complete: 3.3%; Average loss: 4.5429
Iteration: 133; Percent complete: 3.3%; Average loss: 4.6343
Iteration: 134; Percent complete: 3.4%; Average loss: 4.0559
Iteration: 135; Percent complete: 3.4%; Average loss: 4.2611
Iteration: 136; Percent complete: 3.4%; Average loss: 4.1630
Iteration: 137; Percent complete: 3.4%; Average loss: 4.3465
Iteration: 138; Percent complete: 3.5%; Average loss: 4.2855
Iteration: 139; Percent complete: 3.5%; Average loss: 4.0825
Iteration: 140; Percent complete: 3.5%; Average loss: 4.1678
Iteration: 141; Percent complete: 3.5%; Average loss: 4.1006
Iteration: 142; Percent complete: 3.5%; Average loss: 4.1270
Iteration: 143; Percent complete: 3.6%; Average loss: 4.1990
Iteration: 144; Percent complete: 3.6%; Average loss: 4.4138
Iteration: 145; Percent complete: 3.6%; Average loss: 4.1235
Iteration: 146; Percent complete: 3.6%; Average loss: 4.0703
Iteration: 147; Percent complete: 3.7%; Average loss: 4.2999
Iteration: 148; Percent complete: 3.7%; Average loss: 4.1891
Iteration: 149; Percent complete: 3.7%; Average loss: 4.2805
Iteration: 150; Percent complete: 3.8%; Average loss: 4.0869
Iteration: 151; Percent complete: 3.8%; Average loss: 4.0448
Iteration: 152; Percent complete: 3.8%; Average loss: 4.0488
Iteration: 153; Percent complete: 3.8%; Average loss: 4.0184
Iteration: 154; Percent complete: 3.9%; Average loss: 3.9969
Iteration: 155; Percent complete: 3.9%; Average loss: 3.9231
Iteration: 156; Percent complete: 3.9%; Average loss: 3.9971
Iteration: 157; Percent complete: 3.9%; Average loss: 4.3173
Iteration: 158; Percent complete: 4.0%; Average loss: 4.1249
Iteration: 159; Percent complete: 4.0%; Average loss: 3.9381
Iteration: 160; Percent complete: 4.0%; Average loss: 3.9039
Iteration: 161; Percent complete: 4.0%; Average loss: 4.0158
Iteration: 162; Percent complete: 4.0%; Average loss: 4.3632
Iteration: 163; Percent complete: 4.1%; Average loss: 4.2245
Iteration: 164; Percent complete: 4.1%; Average loss: 4.3241
Iteration: 165; Percent complete: 4.1%; Average loss: 4.2306
Iteration: 166; Percent complete: 4.2%; Average loss: 4.2477
Iteration: 167; Percent complete: 4.2%; Average loss: 4.2271
Iteration: 168; Percent complete: 4.2%; Average loss: 3.8736
Iteration: 169; Percent complete: 4.2%; Average loss: 4.1053
Iteration: 170; Percent complete: 4.2%; Average loss: 4.1593
Iteration: 171; Percent complete: 4.3%; Average loss: 3.8501
Iteration: 172; Percent complete: 4.3%; Average loss: 3.8626
Iteration: 173; Percent complete: 4.3%; Average loss: 4.1886
Iteration: 174; Percent complete: 4.3%; Average loss: 3.7782
Iteration: 175; Percent complete: 4.4%; Average loss: 4.1126
Iteration: 176; Percent complete: 4.4%; Average loss: 4.1253
Iteration: 177; Percent complete: 4.4%; Average loss: 4.1633
Iteration: 178; Percent complete: 4.5%; Average loss: 4.2091
Iteration: 179; Percent complete: 4.5%; Average loss: 4.1982
Iteration: 180; Percent complete: 4.5%; Average loss: 4.2224
Iteration: 181; Percent complete: 4.5%; Average loss: 4.2223
Iteration: 182; Percent complete: 4.5%; Average loss: 3.8300
Iteration: 183; Percent complete: 4.6%; Average loss: 4.3198
Iteration: 184; Percent complete: 4.6%; Average loss: 4.1169
Iteration: 185; Percent complete: 4.6%; Average loss: 4.0680
Iteration: 186; Percent complete: 4.7%; Average loss: 4.2373
Iteration: 187; Percent complete: 4.7%; Average loss: 4.1479
Iteration: 188; Percent complete: 4.7%; Average loss: 4.0325
Iteration: 189; Percent complete: 4.7%; Average loss: 3.9353
Iteration: 190; Percent complete: 4.8%; Average loss: 4.2695
Iteration: 191; Percent complete: 4.8%; Average loss: 4.3329
Iteration: 192; Percent complete: 4.8%; Average loss: 3.9937
Iteration: 193; Percent complete: 4.8%; Average loss: 3.8723
Iteration: 194; Percent complete: 4.9%; Average loss: 3.9040
Iteration: 195; Percent complete: 4.9%; Average loss: 4.5081
Iteration: 196; Percent complete: 4.9%; Average loss: 4.1452
Iteration: 197; Percent complete: 4.9%; Average loss: 4.1457
Iteration: 198; Percent complete: 5.0%; Average loss: 3.9752
Iteration: 199; Percent complete: 5.0%; Average loss: 4.0246
Iteration: 200; Percent complete: 5.0%; Average loss: 4.0931
Iteration: 201; Percent complete: 5.0%; Average loss: 3.8113
Iteration: 202; Percent complete: 5.1%; Average loss: 4.3050
Iteration: 203; Percent complete: 5.1%; Average loss: 4.1511
Iteration: 204; Percent complete: 5.1%; Average loss: 3.8196
Iteration: 205; Percent complete: 5.1%; Average loss: 4.2424
Iteration: 206; Percent complete: 5.1%; Average loss: 4.0768
Iteration: 207; Percent complete: 5.2%; Average loss: 3.8944
Iteration: 208; Percent complete: 5.2%; Average loss: 4.0182
Iteration: 209; Percent complete: 5.2%; Average loss: 4.0465
Iteration: 210; Percent complete: 5.2%; Average loss: 4.0832
Iteration: 211; Percent complete: 5.3%; Average loss: 3.8325
Iteration: 212; Percent complete: 5.3%; Average loss: 4.2264
Iteration: 213; Percent complete: 5.3%; Average loss: 4.1008
Iteration: 214; Percent complete: 5.3%; Average loss: 3.8022
Iteration: 215; Percent complete: 5.4%; Average loss: 4.1038
Iteration: 216; Percent complete: 5.4%; Average loss: 4.0229
Iteration: 217; Percent complete: 5.4%; Average loss: 3.8580
Iteration: 218; Percent complete: 5.5%; Average loss: 4.0012
Iteration: 219; Percent complete: 5.5%; Average loss: 4.0585
Iteration: 220; Percent complete: 5.5%; Average loss: 3.9315
Iteration: 221; Percent complete: 5.5%; Average loss: 4.1826
Iteration: 222; Percent complete: 5.5%; Average loss: 3.9846
Iteration: 223; Percent complete: 5.6%; Average loss: 4.1227
Iteration: 224; Percent complete: 5.6%; Average loss: 4.2038
Iteration: 225; Percent complete: 5.6%; Average loss: 3.9262
Iteration: 226; Percent complete: 5.7%; Average loss: 3.8581
Iteration: 227; Percent complete: 5.7%; Average loss: 3.9456
Iteration: 228; Percent complete: 5.7%; Average loss: 4.1248
Iteration: 229; Percent complete: 5.7%; Average loss: 3.9157
Iteration: 230; Percent complete: 5.8%; Average loss: 3.9990
Iteration: 231; Percent complete: 5.8%; Average loss: 3.9931
Iteration: 232; Percent complete: 5.8%; Average loss: 3.9508
Iteration: 233; Percent complete: 5.8%; Average loss: 4.2869
Iteration: 234; Percent complete: 5.9%; Average loss: 4.1936
Iteration: 235; Percent complete: 5.9%; Average loss: 4.0091
Iteration: 236; Percent complete: 5.9%; Average loss: 4.1270
Iteration: 237; Percent complete: 5.9%; Average loss: 4.0670
Iteration: 238; Percent complete: 5.9%; Average loss: 4.2485
Iteration: 239; Percent complete: 6.0%; Average loss: 4.0908
Iteration: 240; Percent complete: 6.0%; Average loss: 3.9212
Iteration: 241; Percent complete: 6.0%; Average loss: 3.9646
Iteration: 242; Percent complete: 6.0%; Average loss: 3.8836
Iteration: 243; Percent complete: 6.1%; Average loss: 3.8898
Iteration: 244; Percent complete: 6.1%; Average loss: 4.0836
Iteration: 245; Percent complete: 6.1%; Average loss: 3.7967
Iteration: 246; Percent complete: 6.2%; Average loss: 3.9658
Iteration: 247; Percent complete: 6.2%; Average loss: 3.9167
Iteration: 248; Percent complete: 6.2%; Average loss: 4.0736
Iteration: 249; Percent complete: 6.2%; Average loss: 4.0676
Iteration: 250; Percent complete: 6.2%; Average loss: 4.0645
Iteration: 251; Percent complete: 6.3%; Average loss: 4.0842
Iteration: 252; Percent complete: 6.3%; Average loss: 3.9416
Iteration: 253; Percent complete: 6.3%; Average loss: 4.0686
Iteration: 254; Percent complete: 6.3%; Average loss: 4.0759
Iteration: 255; Percent complete: 6.4%; Average loss: 4.1302
Iteration: 256; Percent complete: 6.4%; Average loss: 3.9759
Iteration: 257; Percent complete: 6.4%; Average loss: 3.8994
Iteration: 258; Percent complete: 6.5%; Average loss: 3.8732
Iteration: 259; Percent complete: 6.5%; Average loss: 3.7959
Iteration: 260; Percent complete: 6.5%; Average loss: 3.6886
Iteration: 261; Percent complete: 6.5%; Average loss: 3.8835
Iteration: 262; Percent complete: 6.6%; Average loss: 3.9098
Iteration: 263; Percent complete: 6.6%; Average loss: 3.9616
Iteration: 264; Percent complete: 6.6%; Average loss: 3.7837
Iteration: 265; Percent complete: 6.6%; Average loss: 4.0380
Iteration: 266; Percent complete: 6.7%; Average loss: 4.0447
Iteration: 267; Percent complete: 6.7%; Average loss: 4.1256
Iteration: 268; Percent complete: 6.7%; Average loss: 4.0430
Iteration: 269; Percent complete: 6.7%; Average loss: 4.0220
Iteration: 270; Percent complete: 6.8%; Average loss: 3.9825
Iteration: 271; Percent complete: 6.8%; Average loss: 3.9246
Iteration: 272; Percent complete: 6.8%; Average loss: 3.8946
Iteration: 273; Percent complete: 6.8%; Average loss: 4.1068
Iteration: 274; Percent complete: 6.9%; Average loss: 4.0824
Iteration: 275; Percent complete: 6.9%; Average loss: 3.8610
Iteration: 276; Percent complete: 6.9%; Average loss: 3.8844
Iteration: 277; Percent complete: 6.9%; Average loss: 4.1431
Iteration: 278; Percent complete: 7.0%; Average loss: 3.9630
Iteration: 279; Percent complete: 7.0%; Average loss: 3.9289
Iteration: 280; Percent complete: 7.0%; Average loss: 3.9142
Iteration: 281; Percent complete: 7.0%; Average loss: 4.0134
Iteration: 282; Percent complete: 7.0%; Average loss: 3.7467
Iteration: 283; Percent complete: 7.1%; Average loss: 4.0478
Iteration: 284; Percent complete: 7.1%; Average loss: 3.8879
Iteration: 285; Percent complete: 7.1%; Average loss: 3.9241
Iteration: 286; Percent complete: 7.1%; Average loss: 4.0760
Iteration: 287; Percent complete: 7.2%; Average loss: 4.0118
Iteration: 288; Percent complete: 7.2%; Average loss: 4.0116
Iteration: 289; Percent complete: 7.2%; Average loss: 4.0213
Iteration: 290; Percent complete: 7.2%; Average loss: 3.6692
Iteration: 291; Percent complete: 7.3%; Average loss: 3.8096
Iteration: 292; Percent complete: 7.3%; Average loss: 3.8243
Iteration: 293; Percent complete: 7.3%; Average loss: 4.0010
Iteration: 294; Percent complete: 7.3%; Average loss: 4.0556
Iteration: 295; Percent complete: 7.4%; Average loss: 3.9658
Iteration: 296; Percent complete: 7.4%; Average loss: 3.6973
Iteration: 297; Percent complete: 7.4%; Average loss: 3.9335
Iteration: 298; Percent complete: 7.4%; Average loss: 3.9703
Iteration: 299; Percent complete: 7.5%; Average loss: 3.7144
Iteration: 300; Percent complete: 7.5%; Average loss: 3.7816
Iteration: 301; Percent complete: 7.5%; Average loss: 3.9084
Iteration: 302; Percent complete: 7.5%; Average loss: 3.9076
Iteration: 303; Percent complete: 7.6%; Average loss: 3.8556
Iteration: 304; Percent complete: 7.6%; Average loss: 3.9105
Iteration: 305; Percent complete: 7.6%; Average loss: 3.6553
Iteration: 306; Percent complete: 7.6%; Average loss: 3.8137
Iteration: 307; Percent complete: 7.7%; Average loss: 4.0709
Iteration: 308; Percent complete: 7.7%; Average loss: 3.9965
Iteration: 309; Percent complete: 7.7%; Average loss: 4.0810
Iteration: 310; Percent complete: 7.8%; Average loss: 3.7427
Iteration: 311; Percent complete: 7.8%; Average loss: 4.1848
Iteration: 312; Percent complete: 7.8%; Average loss: 3.6239
Iteration: 313; Percent complete: 7.8%; Average loss: 3.9073
Iteration: 314; Percent complete: 7.8%; Average loss: 4.0207
Iteration: 315; Percent complete: 7.9%; Average loss: 3.9335
Iteration: 316; Percent complete: 7.9%; Average loss: 3.8539
Iteration: 317; Percent complete: 7.9%; Average loss: 4.0479
Iteration: 318; Percent complete: 8.0%; Average loss: 3.7473
Iteration: 319; Percent complete: 8.0%; Average loss: 3.8879
Iteration: 320; Percent complete: 8.0%; Average loss: 3.7266
Iteration: 321; Percent complete: 8.0%; Average loss: 4.0895
Iteration: 322; Percent complete: 8.1%; Average loss: 3.7014
Iteration: 323; Percent complete: 8.1%; Average loss: 3.9917
Iteration: 324; Percent complete: 8.1%; Average loss: 3.8918
Iteration: 325; Percent complete: 8.1%; Average loss: 4.0046
Iteration: 326; Percent complete: 8.2%; Average loss: 3.6914
Iteration: 327; Percent complete: 8.2%; Average loss: 3.8151
Iteration: 328; Percent complete: 8.2%; Average loss: 3.8439
Iteration: 329; Percent complete: 8.2%; Average loss: 3.8619
Iteration: 330; Percent complete: 8.2%; Average loss: 4.0738
Iteration: 331; Percent complete: 8.3%; Average loss: 3.5199
Iteration: 332; Percent complete: 8.3%; Average loss: 3.8680
Iteration: 333; Percent complete: 8.3%; Average loss: 3.8252
Iteration: 334; Percent complete: 8.3%; Average loss: 3.9485
Iteration: 335; Percent complete: 8.4%; Average loss: 3.9332
Iteration: 336; Percent complete: 8.4%; Average loss: 3.8990
Iteration: 337; Percent complete: 8.4%; Average loss: 3.8352
Iteration: 338; Percent complete: 8.5%; Average loss: 3.7719
Iteration: 339; Percent complete: 8.5%; Average loss: 3.9614
Iteration: 340; Percent complete: 8.5%; Average loss: 4.0607
Iteration: 341; Percent complete: 8.5%; Average loss: 3.8685
Iteration: 342; Percent complete: 8.6%; Average loss: 3.8259
Iteration: 343; Percent complete: 8.6%; Average loss: 3.6846
Iteration: 344; Percent complete: 8.6%; Average loss: 3.8533
Iteration: 345; Percent complete: 8.6%; Average loss: 3.6480
Iteration: 346; Percent complete: 8.6%; Average loss: 3.9331
Iteration: 347; Percent complete: 8.7%; Average loss: 3.8283
Iteration: 348; Percent complete: 8.7%; Average loss: 3.5554
Iteration: 349; Percent complete: 8.7%; Average loss: 3.8331
Iteration: 350; Percent complete: 8.8%; Average loss: 3.7942
Iteration: 351; Percent complete: 8.8%; Average loss: 3.8578
Iteration: 352; Percent complete: 8.8%; Average loss: 3.6328
Iteration: 353; Percent complete: 8.8%; Average loss: 3.7404
Iteration: 354; Percent complete: 8.8%; Average loss: 3.7929
Iteration: 355; Percent complete: 8.9%; Average loss: 4.0611
Iteration: 356; Percent complete: 8.9%; Average loss: 3.9536
Iteration: 357; Percent complete: 8.9%; Average loss: 3.7304
Iteration: 358; Percent complete: 8.9%; Average loss: 4.0475
Iteration: 359; Percent complete: 9.0%; Average loss: 3.9113
Iteration: 360; Percent complete: 9.0%; Average loss: 3.7845
Iteration: 361; Percent complete: 9.0%; Average loss: 3.7609
Iteration: 362; Percent complete: 9.0%; Average loss: 3.6383
Iteration: 363; Percent complete: 9.1%; Average loss: 3.8705
Iteration: 364; Percent complete: 9.1%; Average loss: 3.9738
Iteration: 365; Percent complete: 9.1%; Average loss: 3.8551
Iteration: 366; Percent complete: 9.2%; Average loss: 3.7353
Iteration: 367; Percent complete: 9.2%; Average loss: 3.8068
Iteration: 368; Percent complete: 9.2%; Average loss: 3.9472
Iteration: 369; Percent complete: 9.2%; Average loss: 3.9000
Iteration: 370; Percent complete: 9.2%; Average loss: 3.7101
Iteration: 371; Percent complete: 9.3%; Average loss: 4.0104
Iteration: 372; Percent complete: 9.3%; Average loss: 3.8283
Iteration: 373; Percent complete: 9.3%; Average loss: 3.9422
Iteration: 374; Percent complete: 9.3%; Average loss: 3.8732
Iteration: 375; Percent complete: 9.4%; Average loss: 3.7733
Iteration: 376; Percent complete: 9.4%; Average loss: 3.9770
Iteration: 377; Percent complete: 9.4%; Average loss: 3.8814
Iteration: 378; Percent complete: 9.4%; Average loss: 3.8281
Iteration: 379; Percent complete: 9.5%; Average loss: 3.7491
Iteration: 380; Percent complete: 9.5%; Average loss: 3.8600
Iteration: 381; Percent complete: 9.5%; Average loss: 3.6178
Iteration: 382; Percent complete: 9.6%; Average loss: 3.9109
Iteration: 383; Percent complete: 9.6%; Average loss: 3.9145
Iteration: 384; Percent complete: 9.6%; Average loss: 3.9043
Iteration: 385; Percent complete: 9.6%; Average loss: 3.9704
Iteration: 386; Percent complete: 9.7%; Average loss: 3.7779
Iteration: 387; Percent complete: 9.7%; Average loss: 3.6683
Iteration: 388; Percent complete: 9.7%; Average loss: 3.8876
Iteration: 389; Percent complete: 9.7%; Average loss: 3.7301
Iteration: 390; Percent complete: 9.8%; Average loss: 3.5375
Iteration: 391; Percent complete: 9.8%; Average loss: 3.9281
Iteration: 392; Percent complete: 9.8%; Average loss: 3.5915
Iteration: 393; Percent complete: 9.8%; Average loss: 3.7275
Iteration: 394; Percent complete: 9.8%; Average loss: 3.7753
Iteration: 395; Percent complete: 9.9%; Average loss: 4.0404
Iteration: 396; Percent complete: 9.9%; Average loss: 4.0709
Iteration: 397; Percent complete: 9.9%; Average loss: 3.8524
Iteration: 398; Percent complete: 10.0%; Average loss: 3.7760
Iteration: 399; Percent complete: 10.0%; Average loss: 3.5630
Iteration: 400; Percent complete: 10.0%; Average loss: 3.9183
Iteration: 401; Percent complete: 10.0%; Average loss: 3.7916
Iteration: 402; Percent complete: 10.1%; Average loss: 3.8684
Iteration: 403; Percent complete: 10.1%; Average loss: 3.7385
Iteration: 404; Percent complete: 10.1%; Average loss: 3.5709
Iteration: 405; Percent complete: 10.1%; Average loss: 3.8274
Iteration: 406; Percent complete: 10.2%; Average loss: 3.4251
Iteration: 407; Percent complete: 10.2%; Average loss: 3.8399
Iteration: 408; Percent complete: 10.2%; Average loss: 3.6720
Iteration: 409; Percent complete: 10.2%; Average loss: 3.8603
Iteration: 410; Percent complete: 10.2%; Average loss: 3.7502
Iteration: 411; Percent complete: 10.3%; Average loss: 3.8133
Iteration: 412; Percent complete: 10.3%; Average loss: 4.0819
Iteration: 413; Percent complete: 10.3%; Average loss: 3.6652
Iteration: 414; Percent complete: 10.3%; Average loss: 3.5247
Iteration: 415; Percent complete: 10.4%; Average loss: 3.7576
Iteration: 416; Percent complete: 10.4%; Average loss: 3.8482
Iteration: 417; Percent complete: 10.4%; Average loss: 3.8112
Iteration: 418; Percent complete: 10.4%; Average loss: 3.6418
Iteration: 419; Percent complete: 10.5%; Average loss: 3.7366
Iteration: 420; Percent complete: 10.5%; Average loss: 3.7874
Iteration: 421; Percent complete: 10.5%; Average loss: 4.0097
Iteration: 422; Percent complete: 10.5%; Average loss: 3.7232
Iteration: 423; Percent complete: 10.6%; Average loss: 3.7543
Iteration: 424; Percent complete: 10.6%; Average loss: 3.6714
Iteration: 425; Percent complete: 10.6%; Average loss: 3.9514
Iteration: 426; Percent complete: 10.7%; Average loss: 3.9062
Iteration: 427; Percent complete: 10.7%; Average loss: 3.7616
Iteration: 428; Percent complete: 10.7%; Average loss: 3.6520
Iteration: 429; Percent complete: 10.7%; Average loss: 3.5106
Iteration: 430; Percent complete: 10.8%; Average loss: 3.6093
Iteration: 431; Percent complete: 10.8%; Average loss: 4.0270
Iteration: 432; Percent complete: 10.8%; Average loss: 3.9608
Iteration: 433; Percent complete: 10.8%; Average loss: 3.6653
Iteration: 434; Percent complete: 10.8%; Average loss: 3.6923
Iteration: 435; Percent complete: 10.9%; Average loss: 3.4942
Iteration: 436; Percent complete: 10.9%; Average loss: 3.9411
Iteration: 437; Percent complete: 10.9%; Average loss: 3.7922
Iteration: 438; Percent complete: 10.9%; Average loss: 3.5938
Iteration: 439; Percent complete: 11.0%; Average loss: 3.7268
Iteration: 440; Percent complete: 11.0%; Average loss: 3.8041
Iteration: 441; Percent complete: 11.0%; Average loss: 3.9406
Iteration: 442; Percent complete: 11.1%; Average loss: 3.4313
Iteration: 443; Percent complete: 11.1%; Average loss: 3.8708
Iteration: 444; Percent complete: 11.1%; Average loss: 3.4944
Iteration: 445; Percent complete: 11.1%; Average loss: 3.9618
Iteration: 446; Percent complete: 11.2%; Average loss: 3.7855
Iteration: 447; Percent complete: 11.2%; Average loss: 3.6660
Iteration: 448; Percent complete: 11.2%; Average loss: 3.5323
Iteration: 449; Percent complete: 11.2%; Average loss: 3.7032
Iteration: 450; Percent complete: 11.2%; Average loss: 3.8365
Iteration: 451; Percent complete: 11.3%; Average loss: 3.6624
Iteration: 452; Percent complete: 11.3%; Average loss: 3.9011
Iteration: 453; Percent complete: 11.3%; Average loss: 3.6965
Iteration: 454; Percent complete: 11.3%; Average loss: 3.7926
Iteration: 455; Percent complete: 11.4%; Average loss: 3.7572
Iteration: 456; Percent complete: 11.4%; Average loss: 3.6774
Iteration: 457; Percent complete: 11.4%; Average loss: 3.5911
Iteration: 458; Percent complete: 11.5%; Average loss: 3.7508
Iteration: 459; Percent complete: 11.5%; Average loss: 3.6580
Iteration: 460; Percent complete: 11.5%; Average loss: 3.8324
Iteration: 461; Percent complete: 11.5%; Average loss: 3.6156
Iteration: 462; Percent complete: 11.6%; Average loss: 3.9524
Iteration: 463; Percent complete: 11.6%; Average loss: 3.6070
Iteration: 464; Percent complete: 11.6%; Average loss: 3.5128
Iteration: 465; Percent complete: 11.6%; Average loss: 3.9189
Iteration: 466; Percent complete: 11.7%; Average loss: 3.7263
Iteration: 467; Percent complete: 11.7%; Average loss: 3.7465
Iteration: 468; Percent complete: 11.7%; Average loss: 3.7298
Iteration: 469; Percent complete: 11.7%; Average loss: 3.9198
Iteration: 470; Percent complete: 11.8%; Average loss: 3.8446
Iteration: 471; Percent complete: 11.8%; Average loss: 3.7936
Iteration: 472; Percent complete: 11.8%; Average loss: 3.7681
Iteration: 473; Percent complete: 11.8%; Average loss: 3.6142
Iteration: 474; Percent complete: 11.8%; Average loss: 3.7962
Iteration: 475; Percent complete: 11.9%; Average loss: 3.7485
Iteration: 476; Percent complete: 11.9%; Average loss: 3.7218
Iteration: 477; Percent complete: 11.9%; Average loss: 4.0081
Iteration: 478; Percent complete: 11.9%; Average loss: 3.7612
Iteration: 479; Percent complete: 12.0%; Average loss: 3.7164
Iteration: 480; Percent complete: 12.0%; Average loss: 3.6964
Iteration: 481; Percent complete: 12.0%; Average loss: 3.5667
Iteration: 482; Percent complete: 12.0%; Average loss: 3.9956
Iteration: 483; Percent complete: 12.1%; Average loss: 3.7020
Iteration: 484; Percent complete: 12.1%; Average loss: 3.8236
Iteration: 485; Percent complete: 12.1%; Average loss: 3.8412
Iteration: 486; Percent complete: 12.2%; Average loss: 3.7506
Iteration: 487; Percent complete: 12.2%; Average loss: 3.5135
Iteration: 488; Percent complete: 12.2%; Average loss: 3.9417
Iteration: 489; Percent complete: 12.2%; Average loss: 3.9263
Iteration: 490; Percent complete: 12.2%; Average loss: 3.7305
Iteration: 491; Percent complete: 12.3%; Average loss: 3.9895
Iteration: 492; Percent complete: 12.3%; Average loss: 3.7671
Iteration: 493; Percent complete: 12.3%; Average loss: 3.6893
Iteration: 494; Percent complete: 12.3%; Average loss: 3.7007
Iteration: 495; Percent complete: 12.4%; Average loss: 4.0718
Iteration: 496; Percent complete: 12.4%; Average loss: 3.5929
Iteration: 497; Percent complete: 12.4%; Average loss: 3.4819
Iteration: 498; Percent complete: 12.4%; Average loss: 3.7023
Iteration: 499; Percent complete: 12.5%; Average loss: 3.8437
Iteration: 500; Percent complete: 12.5%; Average loss: 3.6665
Iteration: 501; Percent complete: 12.5%; Average loss: 3.6782
Iteration: 502; Percent complete: 12.6%; Average loss: 3.6783
Iteration: 503; Percent complete: 12.6%; Average loss: 3.8473
Iteration: 504; Percent complete: 12.6%; Average loss: 3.6292
Iteration: 505; Percent complete: 12.6%; Average loss: 3.5676
Iteration: 506; Percent complete: 12.7%; Average loss: 3.5012
Iteration: 507; Percent complete: 12.7%; Average loss: 3.6115
Iteration: 508; Percent complete: 12.7%; Average loss: 3.9578
Iteration: 509; Percent complete: 12.7%; Average loss: 3.8267
Iteration: 510; Percent complete: 12.8%; Average loss: 3.8188
Iteration: 511; Percent complete: 12.8%; Average loss: 3.6703
Iteration: 512; Percent complete: 12.8%; Average loss: 3.6300
Iteration: 513; Percent complete: 12.8%; Average loss: 3.3329
Iteration: 514; Percent complete: 12.8%; Average loss: 3.7981
Iteration: 515; Percent complete: 12.9%; Average loss: 3.5960
Iteration: 516; Percent complete: 12.9%; Average loss: 3.7260
Iteration: 517; Percent complete: 12.9%; Average loss: 3.4987
Iteration: 518; Percent complete: 13.0%; Average loss: 3.7415
Iteration: 519; Percent complete: 13.0%; Average loss: 3.7479
Iteration: 520; Percent complete: 13.0%; Average loss: 3.8449
Iteration: 521; Percent complete: 13.0%; Average loss: 3.7547
Iteration: 522; Percent complete: 13.1%; Average loss: 3.5648
Iteration: 523; Percent complete: 13.1%; Average loss: 3.9053
Iteration: 524; Percent complete: 13.1%; Average loss: 3.8305
Iteration: 525; Percent complete: 13.1%; Average loss: 3.2981
Iteration: 526; Percent complete: 13.2%; Average loss: 3.9746
Iteration: 527; Percent complete: 13.2%; Average loss: 3.7308
Iteration: 528; Percent complete: 13.2%; Average loss: 3.7575
Iteration: 529; Percent complete: 13.2%; Average loss: 3.7473
Iteration: 530; Percent complete: 13.2%; Average loss: 3.6064
Iteration: 531; Percent complete: 13.3%; Average loss: 3.6962
Iteration: 532; Percent complete: 13.3%; Average loss: 3.7542
Iteration: 533; Percent complete: 13.3%; Average loss: 3.8670
Iteration: 534; Percent complete: 13.4%; Average loss: 3.6622
Iteration: 535; Percent complete: 13.4%; Average loss: 3.6248
Iteration: 536; Percent complete: 13.4%; Average loss: 3.9320
Iteration: 537; Percent complete: 13.4%; Average loss: 3.6912
Iteration: 538; Percent complete: 13.5%; Average loss: 3.9030
Iteration: 539; Percent complete: 13.5%; Average loss: 3.4477
Iteration: 540; Percent complete: 13.5%; Average loss: 3.6957
Iteration: 541; Percent complete: 13.5%; Average loss: 4.0149
Iteration: 542; Percent complete: 13.6%; Average loss: 3.9112
Iteration: 543; Percent complete: 13.6%; Average loss: 3.6924
Iteration: 544; Percent complete: 13.6%; Average loss: 3.4030
Iteration: 545; Percent complete: 13.6%; Average loss: 3.5663
Iteration: 546; Percent complete: 13.7%; Average loss: 3.7929
Iteration: 547; Percent complete: 13.7%; Average loss: 3.6028
Iteration: 548; Percent complete: 13.7%; Average loss: 3.7432
Iteration: 549; Percent complete: 13.7%; Average loss: 3.5663
Iteration: 550; Percent complete: 13.8%; Average loss: 3.5834
Iteration: 551; Percent complete: 13.8%; Average loss: 3.6430
Iteration: 552; Percent complete: 13.8%; Average loss: 3.7802
Iteration: 553; Percent complete: 13.8%; Average loss: 3.8535
Iteration: 554; Percent complete: 13.9%; Average loss: 3.7235
Iteration: 555; Percent complete: 13.9%; Average loss: 3.6626
Iteration: 556; Percent complete: 13.9%; Average loss: 3.7233
Iteration: 557; Percent complete: 13.9%; Average loss: 3.5885
Iteration: 558; Percent complete: 14.0%; Average loss: 3.7190
Iteration: 559; Percent complete: 14.0%; Average loss: 3.6101
Iteration: 560; Percent complete: 14.0%; Average loss: 3.5476
Iteration: 561; Percent complete: 14.0%; Average loss: 3.7796
Iteration: 562; Percent complete: 14.1%; Average loss: 3.9371
Iteration: 563; Percent complete: 14.1%; Average loss: 3.7248
Iteration: 564; Percent complete: 14.1%; Average loss: 3.6295
Iteration: 565; Percent complete: 14.1%; Average loss: 3.5698
Iteration: 566; Percent complete: 14.1%; Average loss: 3.7124
Iteration: 567; Percent complete: 14.2%; Average loss: 3.5366
Iteration: 568; Percent complete: 14.2%; Average loss: 3.7340
Iteration: 569; Percent complete: 14.2%; Average loss: 3.8545
Iteration: 570; Percent complete: 14.2%; Average loss: 3.2693
Iteration: 571; Percent complete: 14.3%; Average loss: 4.0251
Iteration: 572; Percent complete: 14.3%; Average loss: 3.6323
Iteration: 573; Percent complete: 14.3%; Average loss: 3.4765
Iteration: 574; Percent complete: 14.3%; Average loss: 3.7363
Iteration: 575; Percent complete: 14.4%; Average loss: 3.7516
Iteration: 576; Percent complete: 14.4%; Average loss: 3.8454
Iteration: 577; Percent complete: 14.4%; Average loss: 3.5189
Iteration: 578; Percent complete: 14.4%; Average loss: 3.5834
Iteration: 579; Percent complete: 14.5%; Average loss: 3.6692
Iteration: 580; Percent complete: 14.5%; Average loss: 3.5431
Iteration: 581; Percent complete: 14.5%; Average loss: 3.4492
Iteration: 582; Percent complete: 14.5%; Average loss: 3.5855
Iteration: 583; Percent complete: 14.6%; Average loss: 3.6698
Iteration: 584; Percent complete: 14.6%; Average loss: 3.9664
Iteration: 585; Percent complete: 14.6%; Average loss: 3.4800
Iteration: 586; Percent complete: 14.6%; Average loss: 3.7152
Iteration: 587; Percent complete: 14.7%; Average loss: 3.6399
Iteration: 588; Percent complete: 14.7%; Average loss: 3.8389
Iteration: 589; Percent complete: 14.7%; Average loss: 3.4276
Iteration: 590; Percent complete: 14.8%; Average loss: 3.3691
Iteration: 591; Percent complete: 14.8%; Average loss: 3.4730
Iteration: 592; Percent complete: 14.8%; Average loss: 3.4333
Iteration: 593; Percent complete: 14.8%; Average loss: 3.4523
Iteration: 594; Percent complete: 14.8%; Average loss: 3.7213
Iteration: 595; Percent complete: 14.9%; Average loss: 3.7220
Iteration: 596; Percent complete: 14.9%; Average loss: 3.5655
Iteration: 597; Percent complete: 14.9%; Average loss: 3.7710
Iteration: 598; Percent complete: 14.9%; Average loss: 3.6272
Iteration: 599; Percent complete: 15.0%; Average loss: 3.7118
Iteration: 600; Percent complete: 15.0%; Average loss: 3.4309
Iteration: 601; Percent complete: 15.0%; Average loss: 3.8842
Iteration: 602; Percent complete: 15.0%; Average loss: 3.8489
Iteration: 603; Percent complete: 15.1%; Average loss: 3.8543
Iteration: 604; Percent complete: 15.1%; Average loss: 3.6926
Iteration: 605; Percent complete: 15.1%; Average loss: 3.5728
Iteration: 606; Percent complete: 15.2%; Average loss: 3.6420
Iteration: 607; Percent complete: 15.2%; Average loss: 3.5311
Iteration: 608; Percent complete: 15.2%; Average loss: 3.8210
Iteration: 609; Percent complete: 15.2%; Average loss: 3.5913
Iteration: 610; Percent complete: 15.2%; Average loss: 3.5215
Iteration: 611; Percent complete: 15.3%; Average loss: 3.7680
Iteration: 612; Percent complete: 15.3%; Average loss: 3.8494
Iteration: 613; Percent complete: 15.3%; Average loss: 3.6369
Iteration: 614; Percent complete: 15.3%; Average loss: 3.6065
Iteration: 615; Percent complete: 15.4%; Average loss: 3.6639
Iteration: 616; Percent complete: 15.4%; Average loss: 3.7537
Iteration: 617; Percent complete: 15.4%; Average loss: 3.7416
Iteration: 618; Percent complete: 15.4%; Average loss: 3.6847
Iteration: 619; Percent complete: 15.5%; Average loss: 3.5444
Iteration: 620; Percent complete: 15.5%; Average loss: 3.7419
Iteration: 621; Percent complete: 15.5%; Average loss: 3.6621
Iteration: 622; Percent complete: 15.6%; Average loss: 3.5736
Iteration: 623; Percent complete: 15.6%; Average loss: 3.6076
Iteration: 624; Percent complete: 15.6%; Average loss: 3.5904
Iteration: 625; Percent complete: 15.6%; Average loss: 3.4777
Iteration: 626; Percent complete: 15.7%; Average loss: 3.9748
Iteration: 627; Percent complete: 15.7%; Average loss: 3.5856
Iteration: 628; Percent complete: 15.7%; Average loss: 3.6673
Iteration: 629; Percent complete: 15.7%; Average loss: 3.3362
Iteration: 630; Percent complete: 15.8%; Average loss: 4.0426
Iteration: 631; Percent complete: 15.8%; Average loss: 3.7649
Iteration: 632; Percent complete: 15.8%; Average loss: 3.5761
Iteration: 633; Percent complete: 15.8%; Average loss: 3.8791
Iteration: 634; Percent complete: 15.8%; Average loss: 3.7687
Iteration: 635; Percent complete: 15.9%; Average loss: 3.7756
Iteration: 636; Percent complete: 15.9%; Average loss: 3.4658
Iteration: 637; Percent complete: 15.9%; Average loss: 3.3938
Iteration: 638; Percent complete: 16.0%; Average loss: 3.8386
Iteration: 639; Percent complete: 16.0%; Average loss: 3.3948
Iteration: 640; Percent complete: 16.0%; Average loss: 3.6358
Iteration: 641; Percent complete: 16.0%; Average loss: 3.5601
Iteration: 642; Percent complete: 16.1%; Average loss: 3.5785
Iteration: 643; Percent complete: 16.1%; Average loss: 3.6977
Iteration: 644; Percent complete: 16.1%; Average loss: 3.6165
Iteration: 645; Percent complete: 16.1%; Average loss: 3.2330
Iteration: 646; Percent complete: 16.2%; Average loss: 3.5154
Iteration: 647; Percent complete: 16.2%; Average loss: 3.7350
Iteration: 648; Percent complete: 16.2%; Average loss: 3.4032
Iteration: 649; Percent complete: 16.2%; Average loss: 3.8342
Iteration: 650; Percent complete: 16.2%; Average loss: 3.5191
Iteration: 651; Percent complete: 16.3%; Average loss: 3.8871
Iteration: 652; Percent complete: 16.3%; Average loss: 3.8330
Iteration: 653; Percent complete: 16.3%; Average loss: 3.9438
Iteration: 654; Percent complete: 16.4%; Average loss: 3.5464
Iteration: 655; Percent complete: 16.4%; Average loss: 3.6797
Iteration: 656; Percent complete: 16.4%; Average loss: 3.6963
Iteration: 657; Percent complete: 16.4%; Average loss: 3.6067
Iteration: 658; Percent complete: 16.4%; Average loss: 3.6864
Iteration: 659; Percent complete: 16.5%; Average loss: 3.5673
Iteration: 660; Percent complete: 16.5%; Average loss: 3.6404
Iteration: 661; Percent complete: 16.5%; Average loss: 3.6416
Iteration: 662; Percent complete: 16.6%; Average loss: 3.5762
Iteration: 663; Percent complete: 16.6%; Average loss: 3.8568
Iteration: 664; Percent complete: 16.6%; Average loss: 3.7987
Iteration: 665; Percent complete: 16.6%; Average loss: 3.6765
Iteration: 666; Percent complete: 16.7%; Average loss: 3.4988
Iteration: 667; Percent complete: 16.7%; Average loss: 3.6834
Iteration: 668; Percent complete: 16.7%; Average loss: 3.6228
Iteration: 669; Percent complete: 16.7%; Average loss: 3.6336
Iteration: 670; Percent complete: 16.8%; Average loss: 3.5897
Iteration: 671; Percent complete: 16.8%; Average loss: 3.5159
Iteration: 672; Percent complete: 16.8%; Average loss: 3.6458
Iteration: 673; Percent complete: 16.8%; Average loss: 3.3294
Iteration: 674; Percent complete: 16.9%; Average loss: 3.4898
Iteration: 675; Percent complete: 16.9%; Average loss: 3.5226
Iteration: 676; Percent complete: 16.9%; Average loss: 3.8489
Iteration: 677; Percent complete: 16.9%; Average loss: 3.5708
Iteration: 678; Percent complete: 17.0%; Average loss: 3.3314
Iteration: 679; Percent complete: 17.0%; Average loss: 3.4196
Iteration: 680; Percent complete: 17.0%; Average loss: 3.8456
Iteration: 681; Percent complete: 17.0%; Average loss: 3.5670
Iteration: 682; Percent complete: 17.1%; Average loss: 3.6230
Iteration: 683; Percent complete: 17.1%; Average loss: 3.6704
Iteration: 684; Percent complete: 17.1%; Average loss: 3.5798
Iteration: 685; Percent complete: 17.1%; Average loss: 3.6674
Iteration: 686; Percent complete: 17.2%; Average loss: 3.4885
Iteration: 687; Percent complete: 17.2%; Average loss: 3.5999
Iteration: 688; Percent complete: 17.2%; Average loss: 3.6228
Iteration: 689; Percent complete: 17.2%; Average loss: 3.7611
Iteration: 690; Percent complete: 17.2%; Average loss: 4.0654
Iteration: 691; Percent complete: 17.3%; Average loss: 3.8477
Iteration: 692; Percent complete: 17.3%; Average loss: 3.4782
Iteration: 693; Percent complete: 17.3%; Average loss: 3.4856
Iteration: 694; Percent complete: 17.3%; Average loss: 3.8110
Iteration: 695; Percent complete: 17.4%; Average loss: 3.5521
Iteration: 696; Percent complete: 17.4%; Average loss: 3.4874
Iteration: 697; Percent complete: 17.4%; Average loss: 3.4517
Iteration: 698; Percent complete: 17.4%; Average loss: 3.7209
Iteration: 699; Percent complete: 17.5%; Average loss: 3.6009
Iteration: 700; Percent complete: 17.5%; Average loss: 3.5100
Iteration: 701; Percent complete: 17.5%; Average loss: 3.5668
Iteration: 702; Percent complete: 17.5%; Average loss: 3.6045
Iteration: 703; Percent complete: 17.6%; Average loss: 3.4890
Iteration: 704; Percent complete: 17.6%; Average loss: 3.7300
Iteration: 705; Percent complete: 17.6%; Average loss: 3.6978
Iteration: 706; Percent complete: 17.6%; Average loss: 3.5762
Iteration: 707; Percent complete: 17.7%; Average loss: 3.4846
Iteration: 708; Percent complete: 17.7%; Average loss: 3.5970
Iteration: 709; Percent complete: 17.7%; Average loss: 3.6337
Iteration: 710; Percent complete: 17.8%; Average loss: 3.5275
Iteration: 711; Percent complete: 17.8%; Average loss: 3.6997
Iteration: 712; Percent complete: 17.8%; Average loss: 3.5635
Iteration: 713; Percent complete: 17.8%; Average loss: 3.6442
Iteration: 714; Percent complete: 17.8%; Average loss: 3.6444
Iteration: 715; Percent complete: 17.9%; Average loss: 3.4178
Iteration: 716; Percent complete: 17.9%; Average loss: 3.4079
Iteration: 717; Percent complete: 17.9%; Average loss: 3.6767
Iteration: 718; Percent complete: 17.9%; Average loss: 3.6313
Iteration: 719; Percent complete: 18.0%; Average loss: 3.5367
Iteration: 720; Percent complete: 18.0%; Average loss: 3.4659
Iteration: 721; Percent complete: 18.0%; Average loss: 3.4588
Iteration: 722; Percent complete: 18.1%; Average loss: 3.7460
Iteration: 723; Percent complete: 18.1%; Average loss: 3.5390
Iteration: 724; Percent complete: 18.1%; Average loss: 3.4076
Iteration: 725; Percent complete: 18.1%; Average loss: 3.7167
Iteration: 726; Percent complete: 18.1%; Average loss: 3.7497
Iteration: 727; Percent complete: 18.2%; Average loss: 3.3903
Iteration: 728; Percent complete: 18.2%; Average loss: 3.4507
Iteration: 729; Percent complete: 18.2%; Average loss: 3.8481
Iteration: 730; Percent complete: 18.2%; Average loss: 3.6310
Iteration: 731; Percent complete: 18.3%; Average loss: 3.5475
Iteration: 732; Percent complete: 18.3%; Average loss: 3.3678
Iteration: 733; Percent complete: 18.3%; Average loss: 3.6111
Iteration: 734; Percent complete: 18.4%; Average loss: 3.7636
Iteration: 735; Percent complete: 18.4%; Average loss: 3.3011
Iteration: 736; Percent complete: 18.4%; Average loss: 3.6999
Iteration: 737; Percent complete: 18.4%; Average loss: 3.7518
Iteration: 738; Percent complete: 18.4%; Average loss: 3.4983
Iteration: 739; Percent complete: 18.5%; Average loss: 3.6750
Iteration: 740; Percent complete: 18.5%; Average loss: 3.6951
Iteration: 741; Percent complete: 18.5%; Average loss: 3.5717
Iteration: 742; Percent complete: 18.6%; Average loss: 3.5076
Iteration: 743; Percent complete: 18.6%; Average loss: 3.6934
Iteration: 744; Percent complete: 18.6%; Average loss: 3.6661
Iteration: 745; Percent complete: 18.6%; Average loss: 3.6333
Iteration: 746; Percent complete: 18.6%; Average loss: 3.7189
Iteration: 747; Percent complete: 18.7%; Average loss: 3.7627
Iteration: 748; Percent complete: 18.7%; Average loss: 3.3735
Iteration: 749; Percent complete: 18.7%; Average loss: 3.5800
Iteration: 750; Percent complete: 18.8%; Average loss: 3.7561
Iteration: 751; Percent complete: 18.8%; Average loss: 3.3881
Iteration: 752; Percent complete: 18.8%; Average loss: 3.4293
Iteration: 753; Percent complete: 18.8%; Average loss: 3.3324
Iteration: 754; Percent complete: 18.9%; Average loss: 3.4440
Iteration: 755; Percent complete: 18.9%; Average loss: 3.6824
Iteration: 756; Percent complete: 18.9%; Average loss: 3.5121
Iteration: 757; Percent complete: 18.9%; Average loss: 3.6918
Iteration: 758; Percent complete: 18.9%; Average loss: 3.4828
Iteration: 759; Percent complete: 19.0%; Average loss: 3.5821
Iteration: 760; Percent complete: 19.0%; Average loss: 3.6643
Iteration: 761; Percent complete: 19.0%; Average loss: 3.8446
Iteration: 762; Percent complete: 19.1%; Average loss: 3.7354
Iteration: 763; Percent complete: 19.1%; Average loss: 3.5284
Iteration: 764; Percent complete: 19.1%; Average loss: 3.6946
Iteration: 765; Percent complete: 19.1%; Average loss: 3.5303
Iteration: 766; Percent complete: 19.1%; Average loss: 3.5046
Iteration: 767; Percent complete: 19.2%; Average loss: 3.7645
Iteration: 768; Percent complete: 19.2%; Average loss: 3.8120
Iteration: 769; Percent complete: 19.2%; Average loss: 3.8638
Iteration: 770; Percent complete: 19.2%; Average loss: 3.6550
Iteration: 771; Percent complete: 19.3%; Average loss: 3.5480
Iteration: 772; Percent complete: 19.3%; Average loss: 3.5296
Iteration: 773; Percent complete: 19.3%; Average loss: 3.5724
Iteration: 774; Percent complete: 19.4%; Average loss: 3.4997
Iteration: 775; Percent complete: 19.4%; Average loss: 3.3628
Iteration: 776; Percent complete: 19.4%; Average loss: 3.3916
Iteration: 777; Percent complete: 19.4%; Average loss: 3.1951
Iteration: 778; Percent complete: 19.4%; Average loss: 3.3097
Iteration: 779; Percent complete: 19.5%; Average loss: 3.6797
Iteration: 780; Percent complete: 19.5%; Average loss: 3.4711
Iteration: 781; Percent complete: 19.5%; Average loss: 3.8295
Iteration: 782; Percent complete: 19.6%; Average loss: 3.5743
Iteration: 783; Percent complete: 19.6%; Average loss: 3.7240
Iteration: 784; Percent complete: 19.6%; Average loss: 3.3962
Iteration: 785; Percent complete: 19.6%; Average loss: 3.4170
Iteration: 786; Percent complete: 19.7%; Average loss: 3.4569
Iteration: 787; Percent complete: 19.7%; Average loss: 3.3167
Iteration: 788; Percent complete: 19.7%; Average loss: 3.3836
Iteration: 789; Percent complete: 19.7%; Average loss: 3.3965
Iteration: 790; Percent complete: 19.8%; Average loss: 3.3532
Iteration: 791; Percent complete: 19.8%; Average loss: 3.5288
Iteration: 792; Percent complete: 19.8%; Average loss: 3.2331
Iteration: 793; Percent complete: 19.8%; Average loss: 3.6064
Iteration: 794; Percent complete: 19.9%; Average loss: 3.5686
Iteration: 795; Percent complete: 19.9%; Average loss: 3.4337
Iteration: 796; Percent complete: 19.9%; Average loss: 3.5606
Iteration: 797; Percent complete: 19.9%; Average loss: 3.5450
Iteration: 798; Percent complete: 20.0%; Average loss: 3.5684
Iteration: 799; Percent complete: 20.0%; Average loss: 3.7279
Iteration: 800; Percent complete: 20.0%; Average loss: 3.6858
Iteration: 801; Percent complete: 20.0%; Average loss: 3.4532
Iteration: 802; Percent complete: 20.1%; Average loss: 3.4016
Iteration: 803; Percent complete: 20.1%; Average loss: 3.4715
Iteration: 804; Percent complete: 20.1%; Average loss: 3.4765
Iteration: 805; Percent complete: 20.1%; Average loss: 3.3451
Iteration: 806; Percent complete: 20.2%; Average loss: 3.4768
Iteration: 807; Percent complete: 20.2%; Average loss: 3.6459
Iteration: 808; Percent complete: 20.2%; Average loss: 3.5278
Iteration: 809; Percent complete: 20.2%; Average loss: 3.7710
Iteration: 810; Percent complete: 20.2%; Average loss: 3.6014
Iteration: 811; Percent complete: 20.3%; Average loss: 3.4434
Iteration: 812; Percent complete: 20.3%; Average loss: 3.4702
Iteration: 813; Percent complete: 20.3%; Average loss: 3.4396
Iteration: 814; Percent complete: 20.3%; Average loss: 3.5485
Iteration: 815; Percent complete: 20.4%; Average loss: 3.2903
Iteration: 816; Percent complete: 20.4%; Average loss: 3.3642
Iteration: 817; Percent complete: 20.4%; Average loss: 3.5938
Iteration: 818; Percent complete: 20.4%; Average loss: 3.4517
Iteration: 819; Percent complete: 20.5%; Average loss: 3.4407
Iteration: 820; Percent complete: 20.5%; Average loss: 3.8002
Iteration: 821; Percent complete: 20.5%; Average loss: 3.5033
Iteration: 822; Percent complete: 20.5%; Average loss: 3.7697
Iteration: 823; Percent complete: 20.6%; Average loss: 3.5246
Iteration: 824; Percent complete: 20.6%; Average loss: 3.5812
Iteration: 825; Percent complete: 20.6%; Average loss: 3.6470
Iteration: 826; Percent complete: 20.6%; Average loss: 3.4184
Iteration: 827; Percent complete: 20.7%; Average loss: 3.4979
Iteration: 828; Percent complete: 20.7%; Average loss: 3.4548
Iteration: 829; Percent complete: 20.7%; Average loss: 3.4031
Iteration: 830; Percent complete: 20.8%; Average loss: 3.3303
Iteration: 831; Percent complete: 20.8%; Average loss: 3.6864
Iteration: 832; Percent complete: 20.8%; Average loss: 3.6081
Iteration: 833; Percent complete: 20.8%; Average loss: 3.4276
Iteration: 834; Percent complete: 20.8%; Average loss: 3.1015
Iteration: 835; Percent complete: 20.9%; Average loss: 3.3465
Iteration: 836; Percent complete: 20.9%; Average loss: 3.5002
Iteration: 837; Percent complete: 20.9%; Average loss: 3.6641
Iteration: 838; Percent complete: 20.9%; Average loss: 3.5069
Iteration: 839; Percent complete: 21.0%; Average loss: 3.5482
Iteration: 840; Percent complete: 21.0%; Average loss: 3.5350
Iteration: 841; Percent complete: 21.0%; Average loss: 3.6343
Iteration: 842; Percent complete: 21.1%; Average loss: 3.1499
Iteration: 843; Percent complete: 21.1%; Average loss: 3.5890
Iteration: 844; Percent complete: 21.1%; Average loss: 3.6187
Iteration: 845; Percent complete: 21.1%; Average loss: 3.6471
Iteration: 846; Percent complete: 21.1%; Average loss: 3.3625
Iteration: 847; Percent complete: 21.2%; Average loss: 3.4716
Iteration: 848; Percent complete: 21.2%; Average loss: 3.4958
Iteration: 849; Percent complete: 21.2%; Average loss: 3.3698
Iteration: 850; Percent complete: 21.2%; Average loss: 3.6276
Iteration: 851; Percent complete: 21.3%; Average loss: 3.5099
Iteration: 852; Percent complete: 21.3%; Average loss: 3.5271
Iteration: 853; Percent complete: 21.3%; Average loss: 3.4744
Iteration: 854; Percent complete: 21.3%; Average loss: 3.5825
Iteration: 855; Percent complete: 21.4%; Average loss: 3.6596
Iteration: 856; Percent complete: 21.4%; Average loss: 3.5944
Iteration: 857; Percent complete: 21.4%; Average loss: 3.4551
Iteration: 858; Percent complete: 21.4%; Average loss: 3.6041
Iteration: 859; Percent complete: 21.5%; Average loss: 3.7434
Iteration: 860; Percent complete: 21.5%; Average loss: 3.6292
Iteration: 861; Percent complete: 21.5%; Average loss: 3.5316
Iteration: 862; Percent complete: 21.6%; Average loss: 3.4164
Iteration: 863; Percent complete: 21.6%; Average loss: 3.5146
Iteration: 864; Percent complete: 21.6%; Average loss: 3.3238
Iteration: 865; Percent complete: 21.6%; Average loss: 3.4043
Iteration: 866; Percent complete: 21.6%; Average loss: 3.4332
Iteration: 867; Percent complete: 21.7%; Average loss: 3.4373
Iteration: 868; Percent complete: 21.7%; Average loss: 3.6359
Iteration: 869; Percent complete: 21.7%; Average loss: 3.1778
Iteration: 870; Percent complete: 21.8%; Average loss: 3.7141
Iteration: 871; Percent complete: 21.8%; Average loss: 3.7307
Iteration: 872; Percent complete: 21.8%; Average loss: 3.2094
Iteration: 873; Percent complete: 21.8%; Average loss: 3.2042
Iteration: 874; Percent complete: 21.9%; Average loss: 3.5995
Iteration: 875; Percent complete: 21.9%; Average loss: 3.3242
Iteration: 876; Percent complete: 21.9%; Average loss: 3.6397
Iteration: 877; Percent complete: 21.9%; Average loss: 3.3999
Iteration: 878; Percent complete: 21.9%; Average loss: 3.3506
Iteration: 879; Percent complete: 22.0%; Average loss: 3.5552
Iteration: 880; Percent complete: 22.0%; Average loss: 3.6052
Iteration: 881; Percent complete: 22.0%; Average loss: 3.5618
Iteration: 882; Percent complete: 22.1%; Average loss: 3.4918
Iteration: 883; Percent complete: 22.1%; Average loss: 3.5149
Iteration: 884; Percent complete: 22.1%; Average loss: 3.6013
Iteration: 885; Percent complete: 22.1%; Average loss: 3.7999
Iteration: 886; Percent complete: 22.1%; Average loss: 3.5959
Iteration: 887; Percent complete: 22.2%; Average loss: 3.3911
Iteration: 888; Percent complete: 22.2%; Average loss: 3.3840
Iteration: 889; Percent complete: 22.2%; Average loss: 3.4449
Iteration: 890; Percent complete: 22.2%; Average loss: 3.3113
Iteration: 891; Percent complete: 22.3%; Average loss: 3.7211
Iteration: 892; Percent complete: 22.3%; Average loss: 3.3100
Iteration: 893; Percent complete: 22.3%; Average loss: 3.2223
Iteration: 894; Percent complete: 22.4%; Average loss: 3.6264
Iteration: 895; Percent complete: 22.4%; Average loss: 3.5071
Iteration: 896; Percent complete: 22.4%; Average loss: 3.6690
Iteration: 897; Percent complete: 22.4%; Average loss: 3.6193
Iteration: 898; Percent complete: 22.4%; Average loss: 3.3952
Iteration: 899; Percent complete: 22.5%; Average loss: 3.6394
Iteration: 900; Percent complete: 22.5%; Average loss: 3.3289
Iteration: 901; Percent complete: 22.5%; Average loss: 3.5082
Iteration: 902; Percent complete: 22.6%; Average loss: 3.4681
Iteration: 903; Percent complete: 22.6%; Average loss: 3.2371
Iteration: 904; Percent complete: 22.6%; Average loss: 3.4388
Iteration: 905; Percent complete: 22.6%; Average loss: 3.3782
Iteration: 906; Percent complete: 22.7%; Average loss: 3.3601
Iteration: 907; Percent complete: 22.7%; Average loss: 3.6514
Iteration: 908; Percent complete: 22.7%; Average loss: 3.4740
Iteration: 909; Percent complete: 22.7%; Average loss: 3.3399
Iteration: 910; Percent complete: 22.8%; Average loss: 3.5901
Iteration: 911; Percent complete: 22.8%; Average loss: 3.4474
Iteration: 912; Percent complete: 22.8%; Average loss: 3.6541
Iteration: 913; Percent complete: 22.8%; Average loss: 3.7709
Iteration: 914; Percent complete: 22.9%; Average loss: 3.6740
Iteration: 915; Percent complete: 22.9%; Average loss: 3.6234
Iteration: 916; Percent complete: 22.9%; Average loss: 3.4532
Iteration: 917; Percent complete: 22.9%; Average loss: 3.5495
Iteration: 918; Percent complete: 22.9%; Average loss: 3.3711
Iteration: 919; Percent complete: 23.0%; Average loss: 3.5084
Iteration: 920; Percent complete: 23.0%; Average loss: 3.3955
Iteration: 921; Percent complete: 23.0%; Average loss: 3.4099
Iteration: 922; Percent complete: 23.1%; Average loss: 3.5436
Iteration: 923; Percent complete: 23.1%; Average loss: 3.5771
Iteration: 924; Percent complete: 23.1%; Average loss: 3.5330
Iteration: 925; Percent complete: 23.1%; Average loss: 3.3862
Iteration: 926; Percent complete: 23.2%; Average loss: 3.6053
Iteration: 927; Percent complete: 23.2%; Average loss: 3.5330
Iteration: 928; Percent complete: 23.2%; Average loss: 3.7966
Iteration: 929; Percent complete: 23.2%; Average loss: 3.8235
Iteration: 930; Percent complete: 23.2%; Average loss: 3.7940
Iteration: 931; Percent complete: 23.3%; Average loss: 3.3589
Iteration: 932; Percent complete: 23.3%; Average loss: 3.2739
Iteration: 933; Percent complete: 23.3%; Average loss: 3.5915
Iteration: 934; Percent complete: 23.4%; Average loss: 3.6805
Iteration: 935; Percent complete: 23.4%; Average loss: 3.6119
Iteration: 936; Percent complete: 23.4%; Average loss: 3.4075
Iteration: 937; Percent complete: 23.4%; Average loss: 3.5209
Iteration: 938; Percent complete: 23.4%; Average loss: 3.2666
Iteration: 939; Percent complete: 23.5%; Average loss: 3.8034
Iteration: 940; Percent complete: 23.5%; Average loss: 3.5168
Iteration: 941; Percent complete: 23.5%; Average loss: 3.4733
Iteration: 942; Percent complete: 23.5%; Average loss: 3.3191
Iteration: 943; Percent complete: 23.6%; Average loss: 3.6749
Iteration: 944; Percent complete: 23.6%; Average loss: 3.7897
Iteration: 945; Percent complete: 23.6%; Average loss: 3.4522
Iteration: 946; Percent complete: 23.6%; Average loss: 3.5783
Iteration: 947; Percent complete: 23.7%; Average loss: 3.3084
Iteration: 948; Percent complete: 23.7%; Average loss: 3.4333
Iteration: 949; Percent complete: 23.7%; Average loss: 3.2799
Iteration: 950; Percent complete: 23.8%; Average loss: 3.2665
Iteration: 951; Percent complete: 23.8%; Average loss: 3.5377
Iteration: 952; Percent complete: 23.8%; Average loss: 3.6539
Iteration: 953; Percent complete: 23.8%; Average loss: 3.3805
Iteration: 954; Percent complete: 23.8%; Average loss: 3.3925
Iteration: 955; Percent complete: 23.9%; Average loss: 3.6792
Iteration: 956; Percent complete: 23.9%; Average loss: 3.1608
Iteration: 957; Percent complete: 23.9%; Average loss: 3.4949
Iteration: 958; Percent complete: 23.9%; Average loss: 3.4470
Iteration: 959; Percent complete: 24.0%; Average loss: 3.4784
Iteration: 960; Percent complete: 24.0%; Average loss: 3.6243
Iteration: 961; Percent complete: 24.0%; Average loss: 3.4568
Iteration: 962; Percent complete: 24.1%; Average loss: 3.5710
Iteration: 963; Percent complete: 24.1%; Average loss: 3.3422
Iteration: 964; Percent complete: 24.1%; Average loss: 3.5124
Iteration: 965; Percent complete: 24.1%; Average loss: 3.4488
Iteration: 966; Percent complete: 24.1%; Average loss: 3.3933
Iteration: 967; Percent complete: 24.2%; Average loss: 3.6385
Iteration: 968; Percent complete: 24.2%; Average loss: 3.3678
Iteration: 969; Percent complete: 24.2%; Average loss: 3.5761
Iteration: 970; Percent complete: 24.2%; Average loss: 3.8230
Iteration: 971; Percent complete: 24.3%; Average loss: 3.3214
Iteration: 972; Percent complete: 24.3%; Average loss: 3.4753
Iteration: 973; Percent complete: 24.3%; Average loss: 3.4417
Iteration: 974; Percent complete: 24.3%; Average loss: 3.3698
Iteration: 975; Percent complete: 24.4%; Average loss: 3.5764
Iteration: 976; Percent complete: 24.4%; Average loss: 3.2676
Iteration: 977; Percent complete: 24.4%; Average loss: 3.5116
Iteration: 978; Percent complete: 24.4%; Average loss: 3.4024
Iteration: 979; Percent complete: 24.5%; Average loss: 3.6679
Iteration: 980; Percent complete: 24.5%; Average loss: 3.5143
Iteration: 981; Percent complete: 24.5%; Average loss: 3.5991
Iteration: 982; Percent complete: 24.6%; Average loss: 3.6165
Iteration: 983; Percent complete: 24.6%; Average loss: 3.6701
Iteration: 984; Percent complete: 24.6%; Average loss: 3.7582
Iteration: 985; Percent complete: 24.6%; Average loss: 3.4506
Iteration: 986; Percent complete: 24.6%; Average loss: 3.2592
Iteration: 987; Percent complete: 24.7%; Average loss: 3.2782
Iteration: 988; Percent complete: 24.7%; Average loss: 3.7421
Iteration: 989; Percent complete: 24.7%; Average loss: 3.4173
Iteration: 990; Percent complete: 24.8%; Average loss: 3.8357
Iteration: 991; Percent complete: 24.8%; Average loss: 3.7715
Iteration: 992; Percent complete: 24.8%; Average loss: 3.4936
Iteration: 993; Percent complete: 24.8%; Average loss: 3.6208
Iteration: 994; Percent complete: 24.9%; Average loss: 3.6175
Iteration: 995; Percent complete: 24.9%; Average loss: 3.8336
Iteration: 996; Percent complete: 24.9%; Average loss: 3.3344
Iteration: 997; Percent complete: 24.9%; Average loss: 3.3267
Iteration: 998; Percent complete: 24.9%; Average loss: 3.2955
Iteration: 999; Percent complete: 25.0%; Average loss: 3.5119
Iteration: 1000; Percent complete: 25.0%; Average loss: 3.7411
Iteration: 1001; Percent complete: 25.0%; Average loss: 3.4957
Iteration: 1002; Percent complete: 25.1%; Average loss: 3.3913
Iteration: 1003; Percent complete: 25.1%; Average loss: 3.1787
Iteration: 1004; Percent complete: 25.1%; Average loss: 3.4879
Iteration: 1005; Percent complete: 25.1%; Average loss: 3.6342
Iteration: 1006; Percent complete: 25.1%; Average loss: 3.5598
Iteration: 1007; Percent complete: 25.2%; Average loss: 3.3751
Iteration: 1008; Percent complete: 25.2%; Average loss: 3.5929
Iteration: 1009; Percent complete: 25.2%; Average loss: 3.6787
Iteration: 1010; Percent complete: 25.2%; Average loss: 3.3750
Iteration: 1011; Percent complete: 25.3%; Average loss: 3.2108
Iteration: 1012; Percent complete: 25.3%; Average loss: 3.4642
Iteration: 1013; Percent complete: 25.3%; Average loss: 3.3777
Iteration: 1014; Percent complete: 25.4%; Average loss: 3.2585
Iteration: 1015; Percent complete: 25.4%; Average loss: 3.4932
Iteration: 1016; Percent complete: 25.4%; Average loss: 3.8645
Iteration: 1017; Percent complete: 25.4%; Average loss: 3.3963
Iteration: 1018; Percent complete: 25.4%; Average loss: 3.5279
Iteration: 1019; Percent complete: 25.5%; Average loss: 3.0712
Iteration: 1020; Percent complete: 25.5%; Average loss: 3.3258
Iteration: 1021; Percent complete: 25.5%; Average loss: 3.5269
Iteration: 1022; Percent complete: 25.6%; Average loss: 3.6190
Iteration: 1023; Percent complete: 25.6%; Average loss: 3.5345
Iteration: 1024; Percent complete: 25.6%; Average loss: 3.7125
Iteration: 1025; Percent complete: 25.6%; Average loss: 3.3977
Iteration: 1026; Percent complete: 25.7%; Average loss: 3.2658
Iteration: 1027; Percent complete: 25.7%; Average loss: 3.3251
Iteration: 1028; Percent complete: 25.7%; Average loss: 3.1992
Iteration: 1029; Percent complete: 25.7%; Average loss: 3.3234
Iteration: 1030; Percent complete: 25.8%; Average loss: 3.7090
Iteration: 1031; Percent complete: 25.8%; Average loss: 3.8459
Iteration: 1032; Percent complete: 25.8%; Average loss: 3.3310
Iteration: 1033; Percent complete: 25.8%; Average loss: 3.4242
Iteration: 1034; Percent complete: 25.9%; Average loss: 3.5773
Iteration: 1035; Percent complete: 25.9%; Average loss: 3.7682
Iteration: 1036; Percent complete: 25.9%; Average loss: 3.8167
Iteration: 1037; Percent complete: 25.9%; Average loss: 3.4806
Iteration: 1038; Percent complete: 25.9%; Average loss: 3.4311
Iteration: 1039; Percent complete: 26.0%; Average loss: 2.9949
Iteration: 1040; Percent complete: 26.0%; Average loss: 3.5245
Iteration: 1041; Percent complete: 26.0%; Average loss: 3.7622
Iteration: 1042; Percent complete: 26.1%; Average loss: 3.6206
Iteration: 1043; Percent complete: 26.1%; Average loss: 3.2106
Iteration: 1044; Percent complete: 26.1%; Average loss: 3.5104
Iteration: 1045; Percent complete: 26.1%; Average loss: 3.3193
Iteration: 1046; Percent complete: 26.2%; Average loss: 3.1911
Iteration: 1047; Percent complete: 26.2%; Average loss: 3.7166
Iteration: 1048; Percent complete: 26.2%; Average loss: 3.4253
Iteration: 1049; Percent complete: 26.2%; Average loss: 3.3250
Iteration: 1050; Percent complete: 26.2%; Average loss: 3.2198
Iteration: 1051; Percent complete: 26.3%; Average loss: 3.6681
Iteration: 1052; Percent complete: 26.3%; Average loss: 3.4451
Iteration: 1053; Percent complete: 26.3%; Average loss: 3.3535
Iteration: 1054; Percent complete: 26.4%; Average loss: 3.8832
Iteration: 1055; Percent complete: 26.4%; Average loss: 3.5142
Iteration: 1056; Percent complete: 26.4%; Average loss: 3.5144
Iteration: 1057; Percent complete: 26.4%; Average loss: 3.4455
Iteration: 1058; Percent complete: 26.5%; Average loss: 3.5620
Iteration: 1059; Percent complete: 26.5%; Average loss: 3.5903
Iteration: 1060; Percent complete: 26.5%; Average loss: 3.6736
Iteration: 1061; Percent complete: 26.5%; Average loss: 3.4282
Iteration: 1062; Percent complete: 26.6%; Average loss: 3.1764
Iteration: 1063; Percent complete: 26.6%; Average loss: 3.4135
Iteration: 1064; Percent complete: 26.6%; Average loss: 3.4421
Iteration: 1065; Percent complete: 26.6%; Average loss: 3.3423
Iteration: 1066; Percent complete: 26.7%; Average loss: 3.4852
Iteration: 1067; Percent complete: 26.7%; Average loss: 3.6785
Iteration: 1068; Percent complete: 26.7%; Average loss: 3.6281
Iteration: 1069; Percent complete: 26.7%; Average loss: 3.5574
Iteration: 1070; Percent complete: 26.8%; Average loss: 3.5585
Iteration: 1071; Percent complete: 26.8%; Average loss: 3.5160
Iteration: 1072; Percent complete: 26.8%; Average loss: 3.4119
Iteration: 1073; Percent complete: 26.8%; Average loss: 3.2212
Iteration: 1074; Percent complete: 26.9%; Average loss: 3.2496
Iteration: 1075; Percent complete: 26.9%; Average loss: 3.7476
Iteration: 1076; Percent complete: 26.9%; Average loss: 3.5836
Iteration: 1077; Percent complete: 26.9%; Average loss: 3.4964
Iteration: 1078; Percent complete: 27.0%; Average loss: 3.6534
Iteration: 1079; Percent complete: 27.0%; Average loss: 3.5691
Iteration: 1080; Percent complete: 27.0%; Average loss: 3.3053
Iteration: 1081; Percent complete: 27.0%; Average loss: 3.1955
Iteration: 1082; Percent complete: 27.1%; Average loss: 3.2103
Iteration: 1083; Percent complete: 27.1%; Average loss: 3.5917
Iteration: 1084; Percent complete: 27.1%; Average loss: 3.3143
Iteration: 1085; Percent complete: 27.1%; Average loss: 3.6017
Iteration: 1086; Percent complete: 27.2%; Average loss: 3.4492
Iteration: 1087; Percent complete: 27.2%; Average loss: 3.3057
Iteration: 1088; Percent complete: 27.2%; Average loss: 3.4750
Iteration: 1089; Percent complete: 27.2%; Average loss: 3.4831
Iteration: 1090; Percent complete: 27.3%; Average loss: 3.2845
Iteration: 1091; Percent complete: 27.3%; Average loss: 3.4747
Iteration: 1092; Percent complete: 27.3%; Average loss: 3.4082
Iteration: 1093; Percent complete: 27.3%; Average loss: 3.3102
Iteration: 1094; Percent complete: 27.4%; Average loss: 3.4263
Iteration: 1095; Percent complete: 27.4%; Average loss: 3.5606
Iteration: 1096; Percent complete: 27.4%; Average loss: 3.4028
Iteration: 1097; Percent complete: 27.4%; Average loss: 3.3764
Iteration: 1098; Percent complete: 27.5%; Average loss: 3.3067
Iteration: 1099; Percent complete: 27.5%; Average loss: 3.2703
Iteration: 1100; Percent complete: 27.5%; Average loss: 3.4811
Iteration: 1101; Percent complete: 27.5%; Average loss: 3.3367
Iteration: 1102; Percent complete: 27.6%; Average loss: 3.5478
Iteration: 1103; Percent complete: 27.6%; Average loss: 3.4883
Iteration: 1104; Percent complete: 27.6%; Average loss: 3.5832
Iteration: 1105; Percent complete: 27.6%; Average loss: 3.2801
Iteration: 1106; Percent complete: 27.7%; Average loss: 3.7013
Iteration: 1107; Percent complete: 27.7%; Average loss: 3.3986
Iteration: 1108; Percent complete: 27.7%; Average loss: 3.6612
Iteration: 1109; Percent complete: 27.7%; Average loss: 3.3844
Iteration: 1110; Percent complete: 27.8%; Average loss: 3.4861
Iteration: 1111; Percent complete: 27.8%; Average loss: 3.4261
Iteration: 1112; Percent complete: 27.8%; Average loss: 3.4388
Iteration: 1113; Percent complete: 27.8%; Average loss: 3.5744
Iteration: 1114; Percent complete: 27.9%; Average loss: 3.3267
Iteration: 1115; Percent complete: 27.9%; Average loss: 3.8739
Iteration: 1116; Percent complete: 27.9%; Average loss: 3.5119
Iteration: 1117; Percent complete: 27.9%; Average loss: 3.6997
Iteration: 1118; Percent complete: 28.0%; Average loss: 3.3892
Iteration: 1119; Percent complete: 28.0%; Average loss: 3.4197
Iteration: 1120; Percent complete: 28.0%; Average loss: 3.4894
Iteration: 1121; Percent complete: 28.0%; Average loss: 3.4009
Iteration: 1122; Percent complete: 28.1%; Average loss: 3.3962
Iteration: 1123; Percent complete: 28.1%; Average loss: 3.4045
Iteration: 1124; Percent complete: 28.1%; Average loss: 3.1511
Iteration: 1125; Percent complete: 28.1%; Average loss: 3.6858
Iteration: 1126; Percent complete: 28.1%; Average loss: 3.4030
Iteration: 1127; Percent complete: 28.2%; Average loss: 3.2558
Iteration: 1128; Percent complete: 28.2%; Average loss: 3.5156
Iteration: 1129; Percent complete: 28.2%; Average loss: 3.5870
Iteration: 1130; Percent complete: 28.2%; Average loss: 3.3570
Iteration: 1131; Percent complete: 28.3%; Average loss: 3.5198
Iteration: 1132; Percent complete: 28.3%; Average loss: 3.2925
Iteration: 1133; Percent complete: 28.3%; Average loss: 3.3671
Iteration: 1134; Percent complete: 28.3%; Average loss: 3.6057
Iteration: 1135; Percent complete: 28.4%; Average loss: 3.5616
Iteration: 1136; Percent complete: 28.4%; Average loss: 3.2754
Iteration: 1137; Percent complete: 28.4%; Average loss: 3.2687
Iteration: 1138; Percent complete: 28.4%; Average loss: 3.1942
Iteration: 1139; Percent complete: 28.5%; Average loss: 3.1419
Iteration: 1140; Percent complete: 28.5%; Average loss: 3.4309
Iteration: 1141; Percent complete: 28.5%; Average loss: 3.4690
Iteration: 1142; Percent complete: 28.5%; Average loss: 3.3285
Iteration: 1143; Percent complete: 28.6%; Average loss: 3.1663
Iteration: 1144; Percent complete: 28.6%; Average loss: 3.4605
Iteration: 1145; Percent complete: 28.6%; Average loss: 3.3385
Iteration: 1146; Percent complete: 28.6%; Average loss: 3.5181
Iteration: 1147; Percent complete: 28.7%; Average loss: 3.4097
Iteration: 1148; Percent complete: 28.7%; Average loss: 3.4997
Iteration: 1149; Percent complete: 28.7%; Average loss: 3.4533
Iteration: 1150; Percent complete: 28.7%; Average loss: 3.6962
Iteration: 1151; Percent complete: 28.8%; Average loss: 3.6041
Iteration: 1152; Percent complete: 28.8%; Average loss: 3.3918
Iteration: 1153; Percent complete: 28.8%; Average loss: 3.5822
Iteration: 1154; Percent complete: 28.8%; Average loss: 3.1658
Iteration: 1155; Percent complete: 28.9%; Average loss: 3.3135
Iteration: 1156; Percent complete: 28.9%; Average loss: 3.4376
Iteration: 1157; Percent complete: 28.9%; Average loss: 3.6531
Iteration: 1158; Percent complete: 28.9%; Average loss: 3.2714
Iteration: 1159; Percent complete: 29.0%; Average loss: 3.5139
Iteration: 1160; Percent complete: 29.0%; Average loss: 3.1365
Iteration: 1161; Percent complete: 29.0%; Average loss: 3.2758
Iteration: 1162; Percent complete: 29.0%; Average loss: 3.6631
Iteration: 1163; Percent complete: 29.1%; Average loss: 3.4347
Iteration: 1164; Percent complete: 29.1%; Average loss: 3.3252
Iteration: 1165; Percent complete: 29.1%; Average loss: 3.4920
Iteration: 1166; Percent complete: 29.1%; Average loss: 3.6543
Iteration: 1167; Percent complete: 29.2%; Average loss: 3.4599
Iteration: 1168; Percent complete: 29.2%; Average loss: 3.1004
Iteration: 1169; Percent complete: 29.2%; Average loss: 3.4610
Iteration: 1170; Percent complete: 29.2%; Average loss: 3.3352
Iteration: 1171; Percent complete: 29.3%; Average loss: 3.6357
Iteration: 1172; Percent complete: 29.3%; Average loss: 3.2506
Iteration: 1173; Percent complete: 29.3%; Average loss: 3.0915
Iteration: 1174; Percent complete: 29.3%; Average loss: 3.2930
Iteration: 1175; Percent complete: 29.4%; Average loss: 3.3571
Iteration: 1176; Percent complete: 29.4%; Average loss: 3.3075
Iteration: 1177; Percent complete: 29.4%; Average loss: 3.1429
Iteration: 1178; Percent complete: 29.4%; Average loss: 3.2921
Iteration: 1179; Percent complete: 29.5%; Average loss: 3.5770
Iteration: 1180; Percent complete: 29.5%; Average loss: 3.4541
Iteration: 1181; Percent complete: 29.5%; Average loss: 3.6257
Iteration: 1182; Percent complete: 29.5%; Average loss: 3.5367
Iteration: 1183; Percent complete: 29.6%; Average loss: 3.4526
Iteration: 1184; Percent complete: 29.6%; Average loss: 3.3684
Iteration: 1185; Percent complete: 29.6%; Average loss: 3.3177
Iteration: 1186; Percent complete: 29.6%; Average loss: 3.3572
Iteration: 1187; Percent complete: 29.7%; Average loss: 3.2749
Iteration: 1188; Percent complete: 29.7%; Average loss: 3.5700
Iteration: 1189; Percent complete: 29.7%; Average loss: 3.1353
Iteration: 1190; Percent complete: 29.8%; Average loss: 3.3774
Iteration: 1191; Percent complete: 29.8%; Average loss: 3.3060
Iteration: 1192; Percent complete: 29.8%; Average loss: 3.3019
Iteration: 1193; Percent complete: 29.8%; Average loss: 3.3283
Iteration: 1194; Percent complete: 29.8%; Average loss: 3.5068
Iteration: 1195; Percent complete: 29.9%; Average loss: 3.2961
Iteration: 1196; Percent complete: 29.9%; Average loss: 3.3701
Iteration: 1197; Percent complete: 29.9%; Average loss: 3.3592
Iteration: 1198; Percent complete: 29.9%; Average loss: 3.4466
Iteration: 1199; Percent complete: 30.0%; Average loss: 3.5887
Iteration: 1200; Percent complete: 30.0%; Average loss: 3.5044
Iteration: 1201; Percent complete: 30.0%; Average loss: 3.6329
Iteration: 1202; Percent complete: 30.0%; Average loss: 3.1526
Iteration: 1203; Percent complete: 30.1%; Average loss: 3.5237
Iteration: 1204; Percent complete: 30.1%; Average loss: 3.6137
Iteration: 1205; Percent complete: 30.1%; Average loss: 3.2581
Iteration: 1206; Percent complete: 30.1%; Average loss: 3.1208
Iteration: 1207; Percent complete: 30.2%; Average loss: 3.4544
Iteration: 1208; Percent complete: 30.2%; Average loss: 3.6561
Iteration: 1209; Percent complete: 30.2%; Average loss: 3.3502
Iteration: 1210; Percent complete: 30.2%; Average loss: 3.3853
Iteration: 1211; Percent complete: 30.3%; Average loss: 3.3035
Iteration: 1212; Percent complete: 30.3%; Average loss: 3.6980
Iteration: 1213; Percent complete: 30.3%; Average loss: 3.2366
Iteration: 1214; Percent complete: 30.3%; Average loss: 3.2931
Iteration: 1215; Percent complete: 30.4%; Average loss: 3.3931
Iteration: 1216; Percent complete: 30.4%; Average loss: 3.0987
Iteration: 1217; Percent complete: 30.4%; Average loss: 3.2553
Iteration: 1218; Percent complete: 30.4%; Average loss: 3.3109
Iteration: 1219; Percent complete: 30.5%; Average loss: 3.2473
Iteration: 1220; Percent complete: 30.5%; Average loss: 3.2212
Iteration: 1221; Percent complete: 30.5%; Average loss: 3.4105
Iteration: 1222; Percent complete: 30.6%; Average loss: 3.3591
Iteration: 1223; Percent complete: 30.6%; Average loss: 3.6090
Iteration: 1224; Percent complete: 30.6%; Average loss: 3.2864
Iteration: 1225; Percent complete: 30.6%; Average loss: 3.0718
Iteration: 1226; Percent complete: 30.6%; Average loss: 3.4573
Iteration: 1227; Percent complete: 30.7%; Average loss: 3.2783
Iteration: 1228; Percent complete: 30.7%; Average loss: 3.2063
Iteration: 1229; Percent complete: 30.7%; Average loss: 3.4757
Iteration: 1230; Percent complete: 30.8%; Average loss: 3.6916
Iteration: 1231; Percent complete: 30.8%; Average loss: 3.4661
Iteration: 1232; Percent complete: 30.8%; Average loss: 3.3856
Iteration: 1233; Percent complete: 30.8%; Average loss: 3.2998
Iteration: 1234; Percent complete: 30.9%; Average loss: 3.2277
Iteration: 1235; Percent complete: 30.9%; Average loss: 3.4847
Iteration: 1236; Percent complete: 30.9%; Average loss: 3.5311
Iteration: 1237; Percent complete: 30.9%; Average loss: 3.2967
Iteration: 1238; Percent complete: 30.9%; Average loss: 3.4866
Iteration: 1239; Percent complete: 31.0%; Average loss: 3.3990
Iteration: 1240; Percent complete: 31.0%; Average loss: 3.5213
Iteration: 1241; Percent complete: 31.0%; Average loss: 3.4288
Iteration: 1242; Percent complete: 31.1%; Average loss: 3.3238
Iteration: 1243; Percent complete: 31.1%; Average loss: 3.1417
Iteration: 1244; Percent complete: 31.1%; Average loss: 3.2664
Iteration: 1245; Percent complete: 31.1%; Average loss: 3.6448
Iteration: 1246; Percent complete: 31.1%; Average loss: 3.2351
Iteration: 1247; Percent complete: 31.2%; Average loss: 2.9252
Iteration: 1248; Percent complete: 31.2%; Average loss: 3.1485
Iteration: 1249; Percent complete: 31.2%; Average loss: 3.3490
Iteration: 1250; Percent complete: 31.2%; Average loss: 3.2643
Iteration: 1251; Percent complete: 31.3%; Average loss: 3.4198
Iteration: 1252; Percent complete: 31.3%; Average loss: 3.2963
Iteration: 1253; Percent complete: 31.3%; Average loss: 3.6795
Iteration: 1254; Percent complete: 31.4%; Average loss: 3.4060
Iteration: 1255; Percent complete: 31.4%; Average loss: 3.3720
Iteration: 1256; Percent complete: 31.4%; Average loss: 3.1421
Iteration: 1257; Percent complete: 31.4%; Average loss: 3.4953
Iteration: 1258; Percent complete: 31.4%; Average loss: 3.4564
Iteration: 1259; Percent complete: 31.5%; Average loss: 3.3242
Iteration: 1260; Percent complete: 31.5%; Average loss: 3.5624
Iteration: 1261; Percent complete: 31.5%; Average loss: 3.4763
Iteration: 1262; Percent complete: 31.6%; Average loss: 3.4757
Iteration: 1263; Percent complete: 31.6%; Average loss: 3.1866
Iteration: 1264; Percent complete: 31.6%; Average loss: 3.3920
Iteration: 1265; Percent complete: 31.6%; Average loss: 3.3879
Iteration: 1266; Percent complete: 31.6%; Average loss: 3.4393
Iteration: 1267; Percent complete: 31.7%; Average loss: 3.3273
Iteration: 1268; Percent complete: 31.7%; Average loss: 3.1266
Iteration: 1269; Percent complete: 31.7%; Average loss: 3.3853
Iteration: 1270; Percent complete: 31.8%; Average loss: 3.2371
Iteration: 1271; Percent complete: 31.8%; Average loss: 3.2223
Iteration: 1272; Percent complete: 31.8%; Average loss: 3.3427
Iteration: 1273; Percent complete: 31.8%; Average loss: 3.1904
Iteration: 1274; Percent complete: 31.9%; Average loss: 3.0946
Iteration: 1275; Percent complete: 31.9%; Average loss: 3.2490
Iteration: 1276; Percent complete: 31.9%; Average loss: 3.2243
Iteration: 1277; Percent complete: 31.9%; Average loss: 3.4177
Iteration: 1278; Percent complete: 31.9%; Average loss: 3.1555
Iteration: 1279; Percent complete: 32.0%; Average loss: 3.1928
Iteration: 1280; Percent complete: 32.0%; Average loss: 3.5236
Iteration: 1281; Percent complete: 32.0%; Average loss: 3.5670
Iteration: 1282; Percent complete: 32.0%; Average loss: 3.3204
Iteration: 1283; Percent complete: 32.1%; Average loss: 3.3619
Iteration: 1284; Percent complete: 32.1%; Average loss: 3.0553
Iteration: 1285; Percent complete: 32.1%; Average loss: 3.3543
Iteration: 1286; Percent complete: 32.1%; Average loss: 3.3386
Iteration: 1287; Percent complete: 32.2%; Average loss: 3.3753
Iteration: 1288; Percent complete: 32.2%; Average loss: 3.2223
Iteration: 1289; Percent complete: 32.2%; Average loss: 3.3297
Iteration: 1290; Percent complete: 32.2%; Average loss: 3.4134
Iteration: 1291; Percent complete: 32.3%; Average loss: 3.2983
Iteration: 1292; Percent complete: 32.3%; Average loss: 3.3084
Iteration: 1293; Percent complete: 32.3%; Average loss: 3.3127
Iteration: 1294; Percent complete: 32.4%; Average loss: 3.5907
Iteration: 1295; Percent complete: 32.4%; Average loss: 3.3342
Iteration: 1296; Percent complete: 32.4%; Average loss: 3.4462
Iteration: 1297; Percent complete: 32.4%; Average loss: 3.5829
Iteration: 1298; Percent complete: 32.5%; Average loss: 3.3548
Iteration: 1299; Percent complete: 32.5%; Average loss: 3.2090
Iteration: 1300; Percent complete: 32.5%; Average loss: 3.0340
Iteration: 1301; Percent complete: 32.5%; Average loss: 3.2730
Iteration: 1302; Percent complete: 32.6%; Average loss: 3.5036
Iteration: 1303; Percent complete: 32.6%; Average loss: 3.1672
Iteration: 1304; Percent complete: 32.6%; Average loss: 3.2996
Iteration: 1305; Percent complete: 32.6%; Average loss: 3.4720
Iteration: 1306; Percent complete: 32.6%; Average loss: 3.3279
Iteration: 1307; Percent complete: 32.7%; Average loss: 3.5493
Iteration: 1308; Percent complete: 32.7%; Average loss: 3.4788
Iteration: 1309; Percent complete: 32.7%; Average loss: 3.3193
Iteration: 1310; Percent complete: 32.8%; Average loss: 3.0794
Iteration: 1311; Percent complete: 32.8%; Average loss: 3.3606
Iteration: 1312; Percent complete: 32.8%; Average loss: 3.6510
Iteration: 1313; Percent complete: 32.8%; Average loss: 3.5097
Iteration: 1314; Percent complete: 32.9%; Average loss: 3.4267
Iteration: 1315; Percent complete: 32.9%; Average loss: 3.2089
Iteration: 1316; Percent complete: 32.9%; Average loss: 3.3505
Iteration: 1317; Percent complete: 32.9%; Average loss: 3.4299
Iteration: 1318; Percent complete: 33.0%; Average loss: 3.3797
Iteration: 1319; Percent complete: 33.0%; Average loss: 3.4244
Iteration: 1320; Percent complete: 33.0%; Average loss: 3.2695
Iteration: 1321; Percent complete: 33.0%; Average loss: 3.6639
Iteration: 1322; Percent complete: 33.1%; Average loss: 3.3237
Iteration: 1323; Percent complete: 33.1%; Average loss: 3.4144
Iteration: 1324; Percent complete: 33.1%; Average loss: 3.3165
Iteration: 1325; Percent complete: 33.1%; Average loss: 3.4302
Iteration: 1326; Percent complete: 33.1%; Average loss: 3.4031
Iteration: 1327; Percent complete: 33.2%; Average loss: 3.3361
Iteration: 1328; Percent complete: 33.2%; Average loss: 3.5205
Iteration: 1329; Percent complete: 33.2%; Average loss: 3.2536
Iteration: 1330; Percent complete: 33.2%; Average loss: 3.4227
Iteration: 1331; Percent complete: 33.3%; Average loss: 3.3289
Iteration: 1332; Percent complete: 33.3%; Average loss: 3.4022
Iteration: 1333; Percent complete: 33.3%; Average loss: 3.4184
Iteration: 1334; Percent complete: 33.4%; Average loss: 3.0132
Iteration: 1335; Percent complete: 33.4%; Average loss: 3.2904
Iteration: 1336; Percent complete: 33.4%; Average loss: 3.6270
Iteration: 1337; Percent complete: 33.4%; Average loss: 3.4823
Iteration: 1338; Percent complete: 33.5%; Average loss: 3.3622
Iteration: 1339; Percent complete: 33.5%; Average loss: 3.3783
Iteration: 1340; Percent complete: 33.5%; Average loss: 3.2092
Iteration: 1341; Percent complete: 33.5%; Average loss: 3.4536
Iteration: 1342; Percent complete: 33.6%; Average loss: 3.4307
Iteration: 1343; Percent complete: 33.6%; Average loss: 3.3361
Iteration: 1344; Percent complete: 33.6%; Average loss: 3.4251
Iteration: 1345; Percent complete: 33.6%; Average loss: 3.3837
Iteration: 1346; Percent complete: 33.7%; Average loss: 3.3184
Iteration: 1347; Percent complete: 33.7%; Average loss: 3.2319
Iteration: 1348; Percent complete: 33.7%; Average loss: 3.3072
Iteration: 1349; Percent complete: 33.7%; Average loss: 3.4485
Iteration: 1350; Percent complete: 33.8%; Average loss: 3.4846
Iteration: 1351; Percent complete: 33.8%; Average loss: 3.4366
Iteration: 1352; Percent complete: 33.8%; Average loss: 3.5334
Iteration: 1353; Percent complete: 33.8%; Average loss: 3.2468
Iteration: 1354; Percent complete: 33.9%; Average loss: 3.5856
Iteration: 1355; Percent complete: 33.9%; Average loss: 3.3322
Iteration: 1356; Percent complete: 33.9%; Average loss: 3.5208
Iteration: 1357; Percent complete: 33.9%; Average loss: 3.1113
Iteration: 1358; Percent complete: 34.0%; Average loss: 3.2471
Iteration: 1359; Percent complete: 34.0%; Average loss: 3.4438
Iteration: 1360; Percent complete: 34.0%; Average loss: 3.4433
Iteration: 1361; Percent complete: 34.0%; Average loss: 3.1687
Iteration: 1362; Percent complete: 34.1%; Average loss: 3.2885
Iteration: 1363; Percent complete: 34.1%; Average loss: 3.2918
Iteration: 1364; Percent complete: 34.1%; Average loss: 3.2477
Iteration: 1365; Percent complete: 34.1%; Average loss: 3.4302
Iteration: 1366; Percent complete: 34.2%; Average loss: 3.3863
Iteration: 1367; Percent complete: 34.2%; Average loss: 3.1618
Iteration: 1368; Percent complete: 34.2%; Average loss: 3.3852
Iteration: 1369; Percent complete: 34.2%; Average loss: 3.6004
Iteration: 1370; Percent complete: 34.2%; Average loss: 3.3545
Iteration: 1371; Percent complete: 34.3%; Average loss: 3.2226
Iteration: 1372; Percent complete: 34.3%; Average loss: 3.4951
Iteration: 1373; Percent complete: 34.3%; Average loss: 3.3600
Iteration: 1374; Percent complete: 34.4%; Average loss: 3.3234
Iteration: 1375; Percent complete: 34.4%; Average loss: 3.1715
Iteration: 1376; Percent complete: 34.4%; Average loss: 3.4418
Iteration: 1377; Percent complete: 34.4%; Average loss: 3.2090
Iteration: 1378; Percent complete: 34.4%; Average loss: 3.2226
Iteration: 1379; Percent complete: 34.5%; Average loss: 3.3500
Iteration: 1380; Percent complete: 34.5%; Average loss: 3.2066
Iteration: 1381; Percent complete: 34.5%; Average loss: 3.2772
Iteration: 1382; Percent complete: 34.5%; Average loss: 3.3086
Iteration: 1383; Percent complete: 34.6%; Average loss: 3.4350
Iteration: 1384; Percent complete: 34.6%; Average loss: 3.2263
Iteration: 1385; Percent complete: 34.6%; Average loss: 3.3896
Iteration: 1386; Percent complete: 34.6%; Average loss: 3.4181
Iteration: 1387; Percent complete: 34.7%; Average loss: 3.3020
Iteration: 1388; Percent complete: 34.7%; Average loss: 3.1789
Iteration: 1389; Percent complete: 34.7%; Average loss: 3.3083
Iteration: 1390; Percent complete: 34.8%; Average loss: 3.3570
Iteration: 1391; Percent complete: 34.8%; Average loss: 3.2579
Iteration: 1392; Percent complete: 34.8%; Average loss: 3.2375
Iteration: 1393; Percent complete: 34.8%; Average loss: 3.6818
Iteration: 1394; Percent complete: 34.8%; Average loss: 3.3103
Iteration: 1395; Percent complete: 34.9%; Average loss: 3.8244
Iteration: 1396; Percent complete: 34.9%; Average loss: 3.3387
Iteration: 1397; Percent complete: 34.9%; Average loss: 3.2398
Iteration: 1398; Percent complete: 34.9%; Average loss: 3.4220
Iteration: 1399; Percent complete: 35.0%; Average loss: 3.2531
Iteration: 1400; Percent complete: 35.0%; Average loss: 3.3444
Iteration: 1401; Percent complete: 35.0%; Average loss: 3.3055
Iteration: 1402; Percent complete: 35.0%; Average loss: 3.2320
Iteration: 1403; Percent complete: 35.1%; Average loss: 3.3051
Iteration: 1404; Percent complete: 35.1%; Average loss: 3.2260
Iteration: 1405; Percent complete: 35.1%; Average loss: 3.3825
Iteration: 1406; Percent complete: 35.1%; Average loss: 3.1521
Iteration: 1407; Percent complete: 35.2%; Average loss: 3.4490
Iteration: 1408; Percent complete: 35.2%; Average loss: 3.1689
Iteration: 1409; Percent complete: 35.2%; Average loss: 3.1880
Iteration: 1410; Percent complete: 35.2%; Average loss: 3.2352
Iteration: 1411; Percent complete: 35.3%; Average loss: 3.3932
Iteration: 1412; Percent complete: 35.3%; Average loss: 3.2474
Iteration: 1413; Percent complete: 35.3%; Average loss: 3.2780
Iteration: 1414; Percent complete: 35.4%; Average loss: 3.2018
Iteration: 1415; Percent complete: 35.4%; Average loss: 3.4152
Iteration: 1416; Percent complete: 35.4%; Average loss: 3.3357
Iteration: 1417; Percent complete: 35.4%; Average loss: 3.2674
Iteration: 1418; Percent complete: 35.4%; Average loss: 3.3193
Iteration: 1419; Percent complete: 35.5%; Average loss: 3.1612
Iteration: 1420; Percent complete: 35.5%; Average loss: 3.4848
Iteration: 1421; Percent complete: 35.5%; Average loss: 3.2684
Iteration: 1422; Percent complete: 35.5%; Average loss: 3.1660
Iteration: 1423; Percent complete: 35.6%; Average loss: 3.3488
Iteration: 1424; Percent complete: 35.6%; Average loss: 3.2345
Iteration: 1425; Percent complete: 35.6%; Average loss: 3.0420
Iteration: 1426; Percent complete: 35.6%; Average loss: 3.0530
Iteration: 1427; Percent complete: 35.7%; Average loss: 3.4695
Iteration: 1428; Percent complete: 35.7%; Average loss: 3.2085
Iteration: 1429; Percent complete: 35.7%; Average loss: 3.2140
Iteration: 1430; Percent complete: 35.8%; Average loss: 3.4325
Iteration: 1431; Percent complete: 35.8%; Average loss: 3.3320
Iteration: 1432; Percent complete: 35.8%; Average loss: 3.3539
Iteration: 1433; Percent complete: 35.8%; Average loss: 3.2462
Iteration: 1434; Percent complete: 35.9%; Average loss: 3.5987
Iteration: 1435; Percent complete: 35.9%; Average loss: 3.2724
Iteration: 1436; Percent complete: 35.9%; Average loss: 3.3965
Iteration: 1437; Percent complete: 35.9%; Average loss: 3.3873
Iteration: 1438; Percent complete: 35.9%; Average loss: 3.4977
Iteration: 1439; Percent complete: 36.0%; Average loss: 3.2959
Iteration: 1440; Percent complete: 36.0%; Average loss: 3.4074
Iteration: 1441; Percent complete: 36.0%; Average loss: 3.3856
Iteration: 1442; Percent complete: 36.0%; Average loss: 3.3782
Iteration: 1443; Percent complete: 36.1%; Average loss: 3.5460
Iteration: 1444; Percent complete: 36.1%; Average loss: 3.2147
Iteration: 1445; Percent complete: 36.1%; Average loss: 3.4232
Iteration: 1446; Percent complete: 36.1%; Average loss: 3.1334
Iteration: 1447; Percent complete: 36.2%; Average loss: 3.5784
Iteration: 1448; Percent complete: 36.2%; Average loss: 3.3251
Iteration: 1449; Percent complete: 36.2%; Average loss: 3.2476
Iteration: 1450; Percent complete: 36.2%; Average loss: 3.5389
Iteration: 1451; Percent complete: 36.3%; Average loss: 3.1918
Iteration: 1452; Percent complete: 36.3%; Average loss: 3.2404
Iteration: 1453; Percent complete: 36.3%; Average loss: 3.3234
Iteration: 1454; Percent complete: 36.4%; Average loss: 3.6821
Iteration: 1455; Percent complete: 36.4%; Average loss: 3.3404
Iteration: 1456; Percent complete: 36.4%; Average loss: 3.2240
Iteration: 1457; Percent complete: 36.4%; Average loss: 3.4189
Iteration: 1458; Percent complete: 36.4%; Average loss: 3.0857
Iteration: 1459; Percent complete: 36.5%; Average loss: 3.5678
Iteration: 1460; Percent complete: 36.5%; Average loss: 3.4560
Iteration: 1461; Percent complete: 36.5%; Average loss: 3.1687
Iteration: 1462; Percent complete: 36.5%; Average loss: 3.3300
Iteration: 1463; Percent complete: 36.6%; Average loss: 3.3211
Iteration: 1464; Percent complete: 36.6%; Average loss: 3.1738
Iteration: 1465; Percent complete: 36.6%; Average loss: 3.4536
Iteration: 1466; Percent complete: 36.6%; Average loss: 3.3308
Iteration: 1467; Percent complete: 36.7%; Average loss: 3.0473
Iteration: 1468; Percent complete: 36.7%; Average loss: 3.1897
Iteration: 1469; Percent complete: 36.7%; Average loss: 3.4905
Iteration: 1470; Percent complete: 36.8%; Average loss: 3.2048
Iteration: 1471; Percent complete: 36.8%; Average loss: 3.4902
Iteration: 1472; Percent complete: 36.8%; Average loss: 3.3630
Iteration: 1473; Percent complete: 36.8%; Average loss: 3.2952
Iteration: 1474; Percent complete: 36.9%; Average loss: 3.1205
Iteration: 1475; Percent complete: 36.9%; Average loss: 3.3329
Iteration: 1476; Percent complete: 36.9%; Average loss: 3.2070
Iteration: 1477; Percent complete: 36.9%; Average loss: 3.1177
Iteration: 1478; Percent complete: 37.0%; Average loss: 3.5945
Iteration: 1479; Percent complete: 37.0%; Average loss: 3.1727
Iteration: 1480; Percent complete: 37.0%; Average loss: 3.3145
Iteration: 1481; Percent complete: 37.0%; Average loss: 3.3927
Iteration: 1482; Percent complete: 37.0%; Average loss: 3.2771
Iteration: 1483; Percent complete: 37.1%; Average loss: 3.3994
Iteration: 1484; Percent complete: 37.1%; Average loss: 3.0471
Iteration: 1485; Percent complete: 37.1%; Average loss: 3.4152
Iteration: 1486; Percent complete: 37.1%; Average loss: 3.4313
Iteration: 1487; Percent complete: 37.2%; Average loss: 3.4234
Iteration: 1488; Percent complete: 37.2%; Average loss: 3.1956
Iteration: 1489; Percent complete: 37.2%; Average loss: 3.3053
Iteration: 1490; Percent complete: 37.2%; Average loss: 3.3607
Iteration: 1491; Percent complete: 37.3%; Average loss: 3.1713
Iteration: 1492; Percent complete: 37.3%; Average loss: 3.3760
Iteration: 1493; Percent complete: 37.3%; Average loss: 3.3159
Iteration: 1494; Percent complete: 37.4%; Average loss: 3.2669
Iteration: 1495; Percent complete: 37.4%; Average loss: 3.5618
Iteration: 1496; Percent complete: 37.4%; Average loss: 3.5767
Iteration: 1497; Percent complete: 37.4%; Average loss: 3.4208
Iteration: 1498; Percent complete: 37.5%; Average loss: 3.3554
Iteration: 1499; Percent complete: 37.5%; Average loss: 3.1335
Iteration: 1500; Percent complete: 37.5%; Average loss: 3.2998
Iteration: 1501; Percent complete: 37.5%; Average loss: 3.2594
Iteration: 1502; Percent complete: 37.5%; Average loss: 3.2278
Iteration: 1503; Percent complete: 37.6%; Average loss: 3.1204
Iteration: 1504; Percent complete: 37.6%; Average loss: 3.1774
Iteration: 1505; Percent complete: 37.6%; Average loss: 3.4023
Iteration: 1506; Percent complete: 37.6%; Average loss: 3.2349
Iteration: 1507; Percent complete: 37.7%; Average loss: 3.1769
Iteration: 1508; Percent complete: 37.7%; Average loss: 3.2618
Iteration: 1509; Percent complete: 37.7%; Average loss: 3.2603
Iteration: 1510; Percent complete: 37.8%; Average loss: 3.3089
Iteration: 1511; Percent complete: 37.8%; Average loss: 3.1424
Iteration: 1512; Percent complete: 37.8%; Average loss: 3.2700
Iteration: 1513; Percent complete: 37.8%; Average loss: 3.4770
Iteration: 1514; Percent complete: 37.9%; Average loss: 3.3403
Iteration: 1515; Percent complete: 37.9%; Average loss: 3.1255
Iteration: 1516; Percent complete: 37.9%; Average loss: 3.2890
Iteration: 1517; Percent complete: 37.9%; Average loss: 3.0862
Iteration: 1518; Percent complete: 38.0%; Average loss: 3.0826
Iteration: 1519; Percent complete: 38.0%; Average loss: 3.2346
Iteration: 1520; Percent complete: 38.0%; Average loss: 3.3657
Iteration: 1521; Percent complete: 38.0%; Average loss: 3.1613
Iteration: 1522; Percent complete: 38.0%; Average loss: 3.2311
Iteration: 1523; Percent complete: 38.1%; Average loss: 3.3112
Iteration: 1524; Percent complete: 38.1%; Average loss: 3.4497
Iteration: 1525; Percent complete: 38.1%; Average loss: 3.3651
Iteration: 1526; Percent complete: 38.1%; Average loss: 3.1021
Iteration: 1527; Percent complete: 38.2%; Average loss: 3.3100
Iteration: 1528; Percent complete: 38.2%; Average loss: 3.6046
Iteration: 1529; Percent complete: 38.2%; Average loss: 3.1668
Iteration: 1530; Percent complete: 38.2%; Average loss: 3.3604
Iteration: 1531; Percent complete: 38.3%; Average loss: 3.4421
Iteration: 1532; Percent complete: 38.3%; Average loss: 3.3337
Iteration: 1533; Percent complete: 38.3%; Average loss: 3.1888
Iteration: 1534; Percent complete: 38.4%; Average loss: 3.0608
Iteration: 1535; Percent complete: 38.4%; Average loss: 3.0074
Iteration: 1536; Percent complete: 38.4%; Average loss: 3.4744
Iteration: 1537; Percent complete: 38.4%; Average loss: 3.1311
Iteration: 1538; Percent complete: 38.5%; Average loss: 3.1951
Iteration: 1539; Percent complete: 38.5%; Average loss: 3.6005
Iteration: 1540; Percent complete: 38.5%; Average loss: 3.4495
Iteration: 1541; Percent complete: 38.5%; Average loss: 3.2817
Iteration: 1542; Percent complete: 38.6%; Average loss: 3.4792
Iteration: 1543; Percent complete: 38.6%; Average loss: 3.1801
Iteration: 1544; Percent complete: 38.6%; Average loss: 3.2368
Iteration: 1545; Percent complete: 38.6%; Average loss: 3.2045
Iteration: 1546; Percent complete: 38.6%; Average loss: 3.3466
Iteration: 1547; Percent complete: 38.7%; Average loss: 3.1552
Iteration: 1548; Percent complete: 38.7%; Average loss: 3.4776
Iteration: 1549; Percent complete: 38.7%; Average loss: 3.4365
Iteration: 1550; Percent complete: 38.8%; Average loss: 3.3199
Iteration: 1551; Percent complete: 38.8%; Average loss: 3.2400
Iteration: 1552; Percent complete: 38.8%; Average loss: 3.0189
Iteration: 1553; Percent complete: 38.8%; Average loss: 3.3202
Iteration: 1554; Percent complete: 38.9%; Average loss: 3.3265
Iteration: 1555; Percent complete: 38.9%; Average loss: 3.4394
Iteration: 1556; Percent complete: 38.9%; Average loss: 3.1401
Iteration: 1557; Percent complete: 38.9%; Average loss: 3.1997
Iteration: 1558; Percent complete: 39.0%; Average loss: 3.1003
Iteration: 1559; Percent complete: 39.0%; Average loss: 3.3467
Iteration: 1560; Percent complete: 39.0%; Average loss: 3.0637
Iteration: 1561; Percent complete: 39.0%; Average loss: 3.3361
Iteration: 1562; Percent complete: 39.1%; Average loss: 3.0920
Iteration: 1563; Percent complete: 39.1%; Average loss: 3.3638
Iteration: 1564; Percent complete: 39.1%; Average loss: 3.5433
Iteration: 1565; Percent complete: 39.1%; Average loss: 3.1851
Iteration: 1566; Percent complete: 39.1%; Average loss: 3.0456
Iteration: 1567; Percent complete: 39.2%; Average loss: 3.4130
Iteration: 1568; Percent complete: 39.2%; Average loss: 3.4168
Iteration: 1569; Percent complete: 39.2%; Average loss: 3.1092
Iteration: 1570; Percent complete: 39.2%; Average loss: 3.1257
Iteration: 1571; Percent complete: 39.3%; Average loss: 2.9933
Iteration: 1572; Percent complete: 39.3%; Average loss: 3.4936
Iteration: 1573; Percent complete: 39.3%; Average loss: 3.1374
Iteration: 1574; Percent complete: 39.4%; Average loss: 3.1721
Iteration: 1575; Percent complete: 39.4%; Average loss: 3.1511
Iteration: 1576; Percent complete: 39.4%; Average loss: 3.1835
Iteration: 1577; Percent complete: 39.4%; Average loss: 3.0654
Iteration: 1578; Percent complete: 39.5%; Average loss: 3.4479
Iteration: 1579; Percent complete: 39.5%; Average loss: 3.3181
Iteration: 1580; Percent complete: 39.5%; Average loss: 3.2265
Iteration: 1581; Percent complete: 39.5%; Average loss: 3.1973
Iteration: 1582; Percent complete: 39.6%; Average loss: 3.6077
Iteration: 1583; Percent complete: 39.6%; Average loss: 3.0557
Iteration: 1584; Percent complete: 39.6%; Average loss: 3.3712
Iteration: 1585; Percent complete: 39.6%; Average loss: 3.2969
Iteration: 1586; Percent complete: 39.6%; Average loss: 3.1002
Iteration: 1587; Percent complete: 39.7%; Average loss: 3.3461
Iteration: 1588; Percent complete: 39.7%; Average loss: 3.2373
Iteration: 1589; Percent complete: 39.7%; Average loss: 3.2876
Iteration: 1590; Percent complete: 39.8%; Average loss: 3.1841
Iteration: 1591; Percent complete: 39.8%; Average loss: 3.3931
Iteration: 1592; Percent complete: 39.8%; Average loss: 3.2268
Iteration: 1593; Percent complete: 39.8%; Average loss: 3.1772
Iteration: 1594; Percent complete: 39.9%; Average loss: 3.0897
Iteration: 1595; Percent complete: 39.9%; Average loss: 3.3600
Iteration: 1596; Percent complete: 39.9%; Average loss: 3.2966
Iteration: 1597; Percent complete: 39.9%; Average loss: 3.1314
Iteration: 1598; Percent complete: 40.0%; Average loss: 3.2905
Iteration: 1599; Percent complete: 40.0%; Average loss: 3.4097
Iteration: 1600; Percent complete: 40.0%; Average loss: 3.2801
Iteration: 1601; Percent complete: 40.0%; Average loss: 3.2636
Iteration: 1602; Percent complete: 40.1%; Average loss: 3.2011
Iteration: 1603; Percent complete: 40.1%; Average loss: 3.4186
Iteration: 1604; Percent complete: 40.1%; Average loss: 3.2218
Iteration: 1605; Percent complete: 40.1%; Average loss: 3.3684
Iteration: 1606; Percent complete: 40.2%; Average loss: 3.2787
Iteration: 1607; Percent complete: 40.2%; Average loss: 3.2429
Iteration: 1608; Percent complete: 40.2%; Average loss: 3.3062
Iteration: 1609; Percent complete: 40.2%; Average loss: 3.2409
Iteration: 1610; Percent complete: 40.2%; Average loss: 3.4204
Iteration: 1611; Percent complete: 40.3%; Average loss: 3.2962
Iteration: 1612; Percent complete: 40.3%; Average loss: 3.5057
Iteration: 1613; Percent complete: 40.3%; Average loss: 3.1588
Iteration: 1614; Percent complete: 40.4%; Average loss: 3.2882
Iteration: 1615; Percent complete: 40.4%; Average loss: 3.2236
Iteration: 1616; Percent complete: 40.4%; Average loss: 3.1351
Iteration: 1617; Percent complete: 40.4%; Average loss: 3.5196
Iteration: 1618; Percent complete: 40.5%; Average loss: 3.0105
Iteration: 1619; Percent complete: 40.5%; Average loss: 3.1725
Iteration: 1620; Percent complete: 40.5%; Average loss: 3.1237
Iteration: 1621; Percent complete: 40.5%; Average loss: 3.2524
Iteration: 1622; Percent complete: 40.6%; Average loss: 3.3481
Iteration: 1623; Percent complete: 40.6%; Average loss: 3.3605
Iteration: 1624; Percent complete: 40.6%; Average loss: 3.2390
Iteration: 1625; Percent complete: 40.6%; Average loss: 3.2061
Iteration: 1626; Percent complete: 40.6%; Average loss: 3.2607
Iteration: 1627; Percent complete: 40.7%; Average loss: 2.9506
Iteration: 1628; Percent complete: 40.7%; Average loss: 3.3265
Iteration: 1629; Percent complete: 40.7%; Average loss: 3.2707
Iteration: 1630; Percent complete: 40.8%; Average loss: 3.4848
Iteration: 1631; Percent complete: 40.8%; Average loss: 3.3846
Iteration: 1632; Percent complete: 40.8%; Average loss: 3.1869
Iteration: 1633; Percent complete: 40.8%; Average loss: 3.2466
Iteration: 1634; Percent complete: 40.8%; Average loss: 3.4036
Iteration: 1635; Percent complete: 40.9%; Average loss: 3.2374
Iteration: 1636; Percent complete: 40.9%; Average loss: 3.2709
Iteration: 1637; Percent complete: 40.9%; Average loss: 3.3222
Iteration: 1638; Percent complete: 40.9%; Average loss: 3.2923
Iteration: 1639; Percent complete: 41.0%; Average loss: 3.1128
Iteration: 1640; Percent complete: 41.0%; Average loss: 3.4013
Iteration: 1641; Percent complete: 41.0%; Average loss: 3.3180
Iteration: 1642; Percent complete: 41.0%; Average loss: 3.3193
Iteration: 1643; Percent complete: 41.1%; Average loss: 3.3543
Iteration: 1644; Percent complete: 41.1%; Average loss: 3.4336
Iteration: 1645; Percent complete: 41.1%; Average loss: 3.1785
Iteration: 1646; Percent complete: 41.1%; Average loss: 3.2765
Iteration: 1647; Percent complete: 41.2%; Average loss: 3.4537
Iteration: 1648; Percent complete: 41.2%; Average loss: 3.0994
Iteration: 1649; Percent complete: 41.2%; Average loss: 3.0587
Iteration: 1650; Percent complete: 41.2%; Average loss: 3.1975
Iteration: 1651; Percent complete: 41.3%; Average loss: 3.2555
Iteration: 1652; Percent complete: 41.3%; Average loss: 3.3474
Iteration: 1653; Percent complete: 41.3%; Average loss: 3.2429
Iteration: 1654; Percent complete: 41.3%; Average loss: 3.0717
Iteration: 1655; Percent complete: 41.4%; Average loss: 3.3271
Iteration: 1656; Percent complete: 41.4%; Average loss: 3.2107
Iteration: 1657; Percent complete: 41.4%; Average loss: 3.2566
Iteration: 1658; Percent complete: 41.4%; Average loss: 3.0806
Iteration: 1659; Percent complete: 41.5%; Average loss: 3.4141
Iteration: 1660; Percent complete: 41.5%; Average loss: 3.4012
Iteration: 1661; Percent complete: 41.5%; Average loss: 3.4238
Iteration: 1662; Percent complete: 41.5%; Average loss: 3.3482
Iteration: 1663; Percent complete: 41.6%; Average loss: 3.2432
Iteration: 1664; Percent complete: 41.6%; Average loss: 3.3457
Iteration: 1665; Percent complete: 41.6%; Average loss: 3.3187
Iteration: 1666; Percent complete: 41.6%; Average loss: 3.1118
Iteration: 1667; Percent complete: 41.7%; Average loss: 3.0407
Iteration: 1668; Percent complete: 41.7%; Average loss: 3.0891
Iteration: 1669; Percent complete: 41.7%; Average loss: 3.2661
Iteration: 1670; Percent complete: 41.8%; Average loss: 3.2217
Iteration: 1671; Percent complete: 41.8%; Average loss: 3.3883
Iteration: 1672; Percent complete: 41.8%; Average loss: 3.1661
Iteration: 1673; Percent complete: 41.8%; Average loss: 3.1444
Iteration: 1674; Percent complete: 41.9%; Average loss: 3.0069
Iteration: 1675; Percent complete: 41.9%; Average loss: 3.2796
Iteration: 1676; Percent complete: 41.9%; Average loss: 3.2607
Iteration: 1677; Percent complete: 41.9%; Average loss: 3.0089
Iteration: 1678; Percent complete: 41.9%; Average loss: 3.2531
Iteration: 1679; Percent complete: 42.0%; Average loss: 2.9115
Iteration: 1680; Percent complete: 42.0%; Average loss: 3.2272
Iteration: 1681; Percent complete: 42.0%; Average loss: 3.1636
Iteration: 1682; Percent complete: 42.0%; Average loss: 3.4856
Iteration: 1683; Percent complete: 42.1%; Average loss: 3.1268
Iteration: 1684; Percent complete: 42.1%; Average loss: 3.1611
Iteration: 1685; Percent complete: 42.1%; Average loss: 3.4349
Iteration: 1686; Percent complete: 42.1%; Average loss: 3.0199
Iteration: 1687; Percent complete: 42.2%; Average loss: 3.2007
Iteration: 1688; Percent complete: 42.2%; Average loss: 3.3814
Iteration: 1689; Percent complete: 42.2%; Average loss: 3.1976
Iteration: 1690; Percent complete: 42.2%; Average loss: 3.3523
Iteration: 1691; Percent complete: 42.3%; Average loss: 3.3292
Iteration: 1692; Percent complete: 42.3%; Average loss: 3.2663
Iteration: 1693; Percent complete: 42.3%; Average loss: 3.5034
Iteration: 1694; Percent complete: 42.4%; Average loss: 3.4523
Iteration: 1695; Percent complete: 42.4%; Average loss: 3.3606
Iteration: 1696; Percent complete: 42.4%; Average loss: 3.3570
Iteration: 1697; Percent complete: 42.4%; Average loss: 3.2789
Iteration: 1698; Percent complete: 42.4%; Average loss: 3.1511
Iteration: 1699; Percent complete: 42.5%; Average loss: 3.2741
Iteration: 1700; Percent complete: 42.5%; Average loss: 3.2206
Iteration: 1701; Percent complete: 42.5%; Average loss: 3.0260
Iteration: 1702; Percent complete: 42.5%; Average loss: 3.3740
Iteration: 1703; Percent complete: 42.6%; Average loss: 3.0892
Iteration: 1704; Percent complete: 42.6%; Average loss: 3.3394
Iteration: 1705; Percent complete: 42.6%; Average loss: 3.1916
Iteration: 1706; Percent complete: 42.6%; Average loss: 3.4672
Iteration: 1707; Percent complete: 42.7%; Average loss: 3.3063
Iteration: 1708; Percent complete: 42.7%; Average loss: 3.3932
Iteration: 1709; Percent complete: 42.7%; Average loss: 3.3084
Iteration: 1710; Percent complete: 42.8%; Average loss: 3.4246
Iteration: 1711; Percent complete: 42.8%; Average loss: 3.2073
Iteration: 1712; Percent complete: 42.8%; Average loss: 3.1804
Iteration: 1713; Percent complete: 42.8%; Average loss: 2.9660
Iteration: 1714; Percent complete: 42.9%; Average loss: 3.1041
Iteration: 1715; Percent complete: 42.9%; Average loss: 3.2394
Iteration: 1716; Percent complete: 42.9%; Average loss: 3.2930
Iteration: 1717; Percent complete: 42.9%; Average loss: 3.3356
Iteration: 1718; Percent complete: 43.0%; Average loss: 3.0262
Iteration: 1719; Percent complete: 43.0%; Average loss: 3.4595
Iteration: 1720; Percent complete: 43.0%; Average loss: 3.1915
Iteration: 1721; Percent complete: 43.0%; Average loss: 3.2449
Iteration: 1722; Percent complete: 43.0%; Average loss: 3.2350
Iteration: 1723; Percent complete: 43.1%; Average loss: 3.0799
Iteration: 1724; Percent complete: 43.1%; Average loss: 3.0757
Iteration: 1725; Percent complete: 43.1%; Average loss: 3.5285
Iteration: 1726; Percent complete: 43.1%; Average loss: 3.1056
Iteration: 1727; Percent complete: 43.2%; Average loss: 3.4714
Iteration: 1728; Percent complete: 43.2%; Average loss: 3.0427
Iteration: 1729; Percent complete: 43.2%; Average loss: 3.0728
Iteration: 1730; Percent complete: 43.2%; Average loss: 3.2721
Iteration: 1731; Percent complete: 43.3%; Average loss: 3.2489
Iteration: 1732; Percent complete: 43.3%; Average loss: 3.3046
Iteration: 1733; Percent complete: 43.3%; Average loss: 3.2720
Iteration: 1734; Percent complete: 43.4%; Average loss: 3.1978
Iteration: 1735; Percent complete: 43.4%; Average loss: 3.1066
Iteration: 1736; Percent complete: 43.4%; Average loss: 3.3144
Iteration: 1737; Percent complete: 43.4%; Average loss: 3.2959
Iteration: 1738; Percent complete: 43.5%; Average loss: 3.1527
Iteration: 1739; Percent complete: 43.5%; Average loss: 3.3102
Iteration: 1740; Percent complete: 43.5%; Average loss: 2.9994
Iteration: 1741; Percent complete: 43.5%; Average loss: 3.2184
Iteration: 1742; Percent complete: 43.5%; Average loss: 3.2109
Iteration: 1743; Percent complete: 43.6%; Average loss: 3.0822
Iteration: 1744; Percent complete: 43.6%; Average loss: 3.5305
Iteration: 1745; Percent complete: 43.6%; Average loss: 3.4264
Iteration: 1746; Percent complete: 43.6%; Average loss: 3.1640
Iteration: 1747; Percent complete: 43.7%; Average loss: 3.1037
Iteration: 1748; Percent complete: 43.7%; Average loss: 3.1221
Iteration: 1749; Percent complete: 43.7%; Average loss: 3.1014
Iteration: 1750; Percent complete: 43.8%; Average loss: 3.3038
Iteration: 1751; Percent complete: 43.8%; Average loss: 2.9611
Iteration: 1752; Percent complete: 43.8%; Average loss: 3.3274
Iteration: 1753; Percent complete: 43.8%; Average loss: 3.2371
Iteration: 1754; Percent complete: 43.9%; Average loss: 3.1965
Iteration: 1755; Percent complete: 43.9%; Average loss: 3.1556
Iteration: 1756; Percent complete: 43.9%; Average loss: 3.1311
Iteration: 1757; Percent complete: 43.9%; Average loss: 3.0604
Iteration: 1758; Percent complete: 44.0%; Average loss: 3.1351
Iteration: 1759; Percent complete: 44.0%; Average loss: 3.3634
Iteration: 1760; Percent complete: 44.0%; Average loss: 3.1669
Iteration: 1761; Percent complete: 44.0%; Average loss: 3.0989
Iteration: 1762; Percent complete: 44.0%; Average loss: 3.0670
Iteration: 1763; Percent complete: 44.1%; Average loss: 3.3394
Iteration: 1764; Percent complete: 44.1%; Average loss: 3.0615
Iteration: 1765; Percent complete: 44.1%; Average loss: 3.2100
Iteration: 1766; Percent complete: 44.1%; Average loss: 3.6635
Iteration: 1767; Percent complete: 44.2%; Average loss: 3.2904
Iteration: 1768; Percent complete: 44.2%; Average loss: 3.2304
Iteration: 1769; Percent complete: 44.2%; Average loss: 3.1182
Iteration: 1770; Percent complete: 44.2%; Average loss: 2.8891
Iteration: 1771; Percent complete: 44.3%; Average loss: 3.0373
Iteration: 1772; Percent complete: 44.3%; Average loss: 3.0767
Iteration: 1773; Percent complete: 44.3%; Average loss: 3.3402
Iteration: 1774; Percent complete: 44.4%; Average loss: 3.0858
Iteration: 1775; Percent complete: 44.4%; Average loss: 3.1150
Iteration: 1776; Percent complete: 44.4%; Average loss: 3.1159
Iteration: 1777; Percent complete: 44.4%; Average loss: 3.2159
Iteration: 1778; Percent complete: 44.5%; Average loss: 3.3856
Iteration: 1779; Percent complete: 44.5%; Average loss: 3.5000
Iteration: 1780; Percent complete: 44.5%; Average loss: 3.1748
Iteration: 1781; Percent complete: 44.5%; Average loss: 2.9891
Iteration: 1782; Percent complete: 44.5%; Average loss: 3.0872
Iteration: 1783; Percent complete: 44.6%; Average loss: 2.9774
Iteration: 1784; Percent complete: 44.6%; Average loss: 3.2096
Iteration: 1785; Percent complete: 44.6%; Average loss: 3.0767
Iteration: 1786; Percent complete: 44.6%; Average loss: 3.0435
Iteration: 1787; Percent complete: 44.7%; Average loss: 3.3796
Iteration: 1788; Percent complete: 44.7%; Average loss: 2.9469
Iteration: 1789; Percent complete: 44.7%; Average loss: 3.1287
Iteration: 1790; Percent complete: 44.8%; Average loss: 3.1043
Iteration: 1791; Percent complete: 44.8%; Average loss: 2.8580
Iteration: 1792; Percent complete: 44.8%; Average loss: 3.1688
Iteration: 1793; Percent complete: 44.8%; Average loss: 2.9942
Iteration: 1794; Percent complete: 44.9%; Average loss: 3.2009
Iteration: 1795; Percent complete: 44.9%; Average loss: 3.0783
Iteration: 1796; Percent complete: 44.9%; Average loss: 3.1181
Iteration: 1797; Percent complete: 44.9%; Average loss: 2.7707
Iteration: 1798; Percent complete: 45.0%; Average loss: 3.3109
Iteration: 1799; Percent complete: 45.0%; Average loss: 3.1873
Iteration: 1800; Percent complete: 45.0%; Average loss: 3.1167
Iteration: 1801; Percent complete: 45.0%; Average loss: 3.1307
Iteration: 1802; Percent complete: 45.1%; Average loss: 3.0936
Iteration: 1803; Percent complete: 45.1%; Average loss: 2.9570
Iteration: 1804; Percent complete: 45.1%; Average loss: 3.2153
Iteration: 1805; Percent complete: 45.1%; Average loss: 3.1444
Iteration: 1806; Percent complete: 45.1%; Average loss: 3.2062
Iteration: 1807; Percent complete: 45.2%; Average loss: 3.1099
Iteration: 1808; Percent complete: 45.2%; Average loss: 3.0108
Iteration: 1809; Percent complete: 45.2%; Average loss: 3.2731
Iteration: 1810; Percent complete: 45.2%; Average loss: 3.3533
Iteration: 1811; Percent complete: 45.3%; Average loss: 3.1725
Iteration: 1812; Percent complete: 45.3%; Average loss: 2.6731
Iteration: 1813; Percent complete: 45.3%; Average loss: 3.2565
Iteration: 1814; Percent complete: 45.4%; Average loss: 3.2962
Iteration: 1815; Percent complete: 45.4%; Average loss: 3.3709
Iteration: 1816; Percent complete: 45.4%; Average loss: 3.2352
Iteration: 1817; Percent complete: 45.4%; Average loss: 3.1798
Iteration: 1818; Percent complete: 45.5%; Average loss: 3.0693
Iteration: 1819; Percent complete: 45.5%; Average loss: 3.3571
Iteration: 1820; Percent complete: 45.5%; Average loss: 3.4347
Iteration: 1821; Percent complete: 45.5%; Average loss: 3.1069
Iteration: 1822; Percent complete: 45.6%; Average loss: 2.8326
Iteration: 1823; Percent complete: 45.6%; Average loss: 3.0093
Iteration: 1824; Percent complete: 45.6%; Average loss: 3.1750
Iteration: 1825; Percent complete: 45.6%; Average loss: 3.1205
Iteration: 1826; Percent complete: 45.6%; Average loss: 3.2167
Iteration: 1827; Percent complete: 45.7%; Average loss: 3.0203
Iteration: 1828; Percent complete: 45.7%; Average loss: 3.1681
Iteration: 1829; Percent complete: 45.7%; Average loss: 3.1693
Iteration: 1830; Percent complete: 45.8%; Average loss: 3.1061
Iteration: 1831; Percent complete: 45.8%; Average loss: 3.4102
Iteration: 1832; Percent complete: 45.8%; Average loss: 3.5314
Iteration: 1833; Percent complete: 45.8%; Average loss: 3.0148
Iteration: 1834; Percent complete: 45.9%; Average loss: 3.1079
Iteration: 1835; Percent complete: 45.9%; Average loss: 3.1807
Iteration: 1836; Percent complete: 45.9%; Average loss: 2.9965
Iteration: 1837; Percent complete: 45.9%; Average loss: 3.1442
Iteration: 1838; Percent complete: 46.0%; Average loss: 2.9102
Iteration: 1839; Percent complete: 46.0%; Average loss: 3.3083
Iteration: 1840; Percent complete: 46.0%; Average loss: 3.2802
Iteration: 1841; Percent complete: 46.0%; Average loss: 3.2606
Iteration: 1842; Percent complete: 46.1%; Average loss: 3.1325
Iteration: 1843; Percent complete: 46.1%; Average loss: 3.2895
Iteration: 1844; Percent complete: 46.1%; Average loss: 3.2625
Iteration: 1845; Percent complete: 46.1%; Average loss: 3.1281
Iteration: 1846; Percent complete: 46.2%; Average loss: 3.3718
Iteration: 1847; Percent complete: 46.2%; Average loss: 3.2513
Iteration: 1848; Percent complete: 46.2%; Average loss: 3.1303
Iteration: 1849; Percent complete: 46.2%; Average loss: 3.3394
Iteration: 1850; Percent complete: 46.2%; Average loss: 3.1228
Iteration: 1851; Percent complete: 46.3%; Average loss: 3.2032
Iteration: 1852; Percent complete: 46.3%; Average loss: 3.3397
Iteration: 1853; Percent complete: 46.3%; Average loss: 3.2749
Iteration: 1854; Percent complete: 46.4%; Average loss: 3.2616
Iteration: 1855; Percent complete: 46.4%; Average loss: 3.1155
Iteration: 1856; Percent complete: 46.4%; Average loss: 3.2044
Iteration: 1857; Percent complete: 46.4%; Average loss: 3.0380
Iteration: 1858; Percent complete: 46.5%; Average loss: 3.0755
Iteration: 1859; Percent complete: 46.5%; Average loss: 3.1404
Iteration: 1860; Percent complete: 46.5%; Average loss: 3.2544
Iteration: 1861; Percent complete: 46.5%; Average loss: 3.1671
Iteration: 1862; Percent complete: 46.6%; Average loss: 3.4034
Iteration: 1863; Percent complete: 46.6%; Average loss: 3.4523
Iteration: 1864; Percent complete: 46.6%; Average loss: 3.2122
Iteration: 1865; Percent complete: 46.6%; Average loss: 3.2701
Iteration: 1866; Percent complete: 46.7%; Average loss: 3.1068
Iteration: 1867; Percent complete: 46.7%; Average loss: 3.0615
Iteration: 1868; Percent complete: 46.7%; Average loss: 3.2581
Iteration: 1869; Percent complete: 46.7%; Average loss: 3.3630
Iteration: 1870; Percent complete: 46.8%; Average loss: 3.2016
Iteration: 1871; Percent complete: 46.8%; Average loss: 3.4480
Iteration: 1872; Percent complete: 46.8%; Average loss: 3.1186
Iteration: 1873; Percent complete: 46.8%; Average loss: 2.9049
Iteration: 1874; Percent complete: 46.9%; Average loss: 2.8769
Iteration: 1875; Percent complete: 46.9%; Average loss: 3.5073
Iteration: 1876; Percent complete: 46.9%; Average loss: 3.0492
Iteration: 1877; Percent complete: 46.9%; Average loss: 3.1506
Iteration: 1878; Percent complete: 46.9%; Average loss: 3.0661
Iteration: 1879; Percent complete: 47.0%; Average loss: 3.1073
Iteration: 1880; Percent complete: 47.0%; Average loss: 3.2124
Iteration: 1881; Percent complete: 47.0%; Average loss: 3.2317
Iteration: 1882; Percent complete: 47.0%; Average loss: 2.9747
Iteration: 1883; Percent complete: 47.1%; Average loss: 3.1350
Iteration: 1884; Percent complete: 47.1%; Average loss: 3.1872
Iteration: 1885; Percent complete: 47.1%; Average loss: 3.1432
Iteration: 1886; Percent complete: 47.1%; Average loss: 3.2042
Iteration: 1887; Percent complete: 47.2%; Average loss: 3.2769
Iteration: 1888; Percent complete: 47.2%; Average loss: 3.0315
Iteration: 1889; Percent complete: 47.2%; Average loss: 2.9530
Iteration: 1890; Percent complete: 47.2%; Average loss: 3.1371
Iteration: 1891; Percent complete: 47.3%; Average loss: 3.1349
Iteration: 1892; Percent complete: 47.3%; Average loss: 3.1081
Iteration: 1893; Percent complete: 47.3%; Average loss: 2.9368
Iteration: 1894; Percent complete: 47.3%; Average loss: 3.2221
Iteration: 1895; Percent complete: 47.4%; Average loss: 3.0387
Iteration: 1896; Percent complete: 47.4%; Average loss: 3.0621
Iteration: 1897; Percent complete: 47.4%; Average loss: 3.1231
Iteration: 1898; Percent complete: 47.4%; Average loss: 3.3701
Iteration: 1899; Percent complete: 47.5%; Average loss: 3.4552
Iteration: 1900; Percent complete: 47.5%; Average loss: 3.0921
Iteration: 1901; Percent complete: 47.5%; Average loss: 3.1304
Iteration: 1902; Percent complete: 47.5%; Average loss: 3.5051
Iteration: 1903; Percent complete: 47.6%; Average loss: 3.2004
Iteration: 1904; Percent complete: 47.6%; Average loss: 3.2658
Iteration: 1905; Percent complete: 47.6%; Average loss: 3.1216
Iteration: 1906; Percent complete: 47.6%; Average loss: 3.2818
Iteration: 1907; Percent complete: 47.7%; Average loss: 3.3320
Iteration: 1908; Percent complete: 47.7%; Average loss: 3.0128
Iteration: 1909; Percent complete: 47.7%; Average loss: 2.9837
Iteration: 1910; Percent complete: 47.8%; Average loss: 3.2705
Iteration: 1911; Percent complete: 47.8%; Average loss: 3.1044
Iteration: 1912; Percent complete: 47.8%; Average loss: 3.2838
Iteration: 1913; Percent complete: 47.8%; Average loss: 3.1759
Iteration: 1914; Percent complete: 47.9%; Average loss: 3.1238
Iteration: 1915; Percent complete: 47.9%; Average loss: 3.2032
Iteration: 1916; Percent complete: 47.9%; Average loss: 3.2740
Iteration: 1917; Percent complete: 47.9%; Average loss: 3.1764
Iteration: 1918; Percent complete: 47.9%; Average loss: 3.2610
Iteration: 1919; Percent complete: 48.0%; Average loss: 3.1314
Iteration: 1920; Percent complete: 48.0%; Average loss: 3.0870
Iteration: 1921; Percent complete: 48.0%; Average loss: 3.1057
Iteration: 1922; Percent complete: 48.0%; Average loss: 3.1551
Iteration: 1923; Percent complete: 48.1%; Average loss: 2.9449
Iteration: 1924; Percent complete: 48.1%; Average loss: 3.4542
Iteration: 1925; Percent complete: 48.1%; Average loss: 3.0532
Iteration: 1926; Percent complete: 48.1%; Average loss: 3.2411
Iteration: 1927; Percent complete: 48.2%; Average loss: 3.2984
Iteration: 1928; Percent complete: 48.2%; Average loss: 3.0520
Iteration: 1929; Percent complete: 48.2%; Average loss: 3.0019
Iteration: 1930; Percent complete: 48.2%; Average loss: 3.0925
Iteration: 1931; Percent complete: 48.3%; Average loss: 3.3131
Iteration: 1932; Percent complete: 48.3%; Average loss: 3.1368
Iteration: 1933; Percent complete: 48.3%; Average loss: 2.9813
Iteration: 1934; Percent complete: 48.4%; Average loss: 2.8052
Iteration: 1935; Percent complete: 48.4%; Average loss: 3.0593
Iteration: 1936; Percent complete: 48.4%; Average loss: 3.0323
Iteration: 1937; Percent complete: 48.4%; Average loss: 3.0842
Iteration: 1938; Percent complete: 48.4%; Average loss: 3.1903
Iteration: 1939; Percent complete: 48.5%; Average loss: 3.3978
Iteration: 1940; Percent complete: 48.5%; Average loss: 3.2037
Iteration: 1941; Percent complete: 48.5%; Average loss: 3.2716
Iteration: 1942; Percent complete: 48.5%; Average loss: 3.0377
Iteration: 1943; Percent complete: 48.6%; Average loss: 3.1857
Iteration: 1944; Percent complete: 48.6%; Average loss: 2.9999
Iteration: 1945; Percent complete: 48.6%; Average loss: 3.2685
Iteration: 1946; Percent complete: 48.6%; Average loss: 3.1870
Iteration: 1947; Percent complete: 48.7%; Average loss: 3.1812
Iteration: 1948; Percent complete: 48.7%; Average loss: 3.2978
Iteration: 1949; Percent complete: 48.7%; Average loss: 2.8776
Iteration: 1950; Percent complete: 48.8%; Average loss: 3.3331
Iteration: 1951; Percent complete: 48.8%; Average loss: 3.2357
Iteration: 1952; Percent complete: 48.8%; Average loss: 3.1254
Iteration: 1953; Percent complete: 48.8%; Average loss: 2.9266
Iteration: 1954; Percent complete: 48.9%; Average loss: 3.3655
Iteration: 1955; Percent complete: 48.9%; Average loss: 3.1643
Iteration: 1956; Percent complete: 48.9%; Average loss: 3.4198
Iteration: 1957; Percent complete: 48.9%; Average loss: 3.3497
Iteration: 1958; Percent complete: 48.9%; Average loss: 2.9710
Iteration: 1959; Percent complete: 49.0%; Average loss: 3.3387
Iteration: 1960; Percent complete: 49.0%; Average loss: 3.3115
Iteration: 1961; Percent complete: 49.0%; Average loss: 2.9215
Iteration: 1962; Percent complete: 49.0%; Average loss: 3.1864
Iteration: 1963; Percent complete: 49.1%; Average loss: 3.1052
Iteration: 1964; Percent complete: 49.1%; Average loss: 3.2961
Iteration: 1965; Percent complete: 49.1%; Average loss: 3.0306
Iteration: 1966; Percent complete: 49.1%; Average loss: 3.1906
Iteration: 1967; Percent complete: 49.2%; Average loss: 3.2441
Iteration: 1968; Percent complete: 49.2%; Average loss: 3.0302
Iteration: 1969; Percent complete: 49.2%; Average loss: 3.1572
Iteration: 1970; Percent complete: 49.2%; Average loss: 3.1836
Iteration: 1971; Percent complete: 49.3%; Average loss: 3.1700
Iteration: 1972; Percent complete: 49.3%; Average loss: 3.1940
Iteration: 1973; Percent complete: 49.3%; Average loss: 3.1372
Iteration: 1974; Percent complete: 49.4%; Average loss: 3.2444
Iteration: 1975; Percent complete: 49.4%; Average loss: 3.1362
Iteration: 1976; Percent complete: 49.4%; Average loss: 3.2937
Iteration: 1977; Percent complete: 49.4%; Average loss: 3.0668
Iteration: 1978; Percent complete: 49.5%; Average loss: 3.0285
Iteration: 1979; Percent complete: 49.5%; Average loss: 2.9799
Iteration: 1980; Percent complete: 49.5%; Average loss: 3.3265
Iteration: 1981; Percent complete: 49.5%; Average loss: 3.1292
Iteration: 1982; Percent complete: 49.5%; Average loss: 3.0039
Iteration: 1983; Percent complete: 49.6%; Average loss: 3.0469
Iteration: 1984; Percent complete: 49.6%; Average loss: 3.2177
Iteration: 1985; Percent complete: 49.6%; Average loss: 2.9470
Iteration: 1986; Percent complete: 49.6%; Average loss: 3.1943
Iteration: 1987; Percent complete: 49.7%; Average loss: 3.1259
Iteration: 1988; Percent complete: 49.7%; Average loss: 3.2046
Iteration: 1989; Percent complete: 49.7%; Average loss: 3.0100
Iteration: 1990; Percent complete: 49.8%; Average loss: 2.9844
Iteration: 1991; Percent complete: 49.8%; Average loss: 3.0272
Iteration: 1992; Percent complete: 49.8%; Average loss: 3.2341
Iteration: 1993; Percent complete: 49.8%; Average loss: 3.1862
Iteration: 1994; Percent complete: 49.9%; Average loss: 3.0180
Iteration: 1995; Percent complete: 49.9%; Average loss: 3.1518
Iteration: 1996; Percent complete: 49.9%; Average loss: 3.2095
Iteration: 1997; Percent complete: 49.9%; Average loss: 3.1278
Iteration: 1998; Percent complete: 50.0%; Average loss: 3.0618
Iteration: 1999; Percent complete: 50.0%; Average loss: 2.9200
Iteration: 2000; Percent complete: 50.0%; Average loss: 3.2826
Iteration: 2001; Percent complete: 50.0%; Average loss: 3.1723
Iteration: 2002; Percent complete: 50.0%; Average loss: 2.9967
Iteration: 2003; Percent complete: 50.1%; Average loss: 3.4152
Iteration: 2004; Percent complete: 50.1%; Average loss: 3.3439
Iteration: 2005; Percent complete: 50.1%; Average loss: 3.1489
Iteration: 2006; Percent complete: 50.1%; Average loss: 3.2483
Iteration: 2007; Percent complete: 50.2%; Average loss: 3.3597
Iteration: 2008; Percent complete: 50.2%; Average loss: 3.4421
Iteration: 2009; Percent complete: 50.2%; Average loss: 3.0052
Iteration: 2010; Percent complete: 50.2%; Average loss: 3.0821
Iteration: 2011; Percent complete: 50.3%; Average loss: 3.3400
Iteration: 2012; Percent complete: 50.3%; Average loss: 3.1931
Iteration: 2013; Percent complete: 50.3%; Average loss: 3.0503
Iteration: 2014; Percent complete: 50.3%; Average loss: 2.9684
Iteration: 2015; Percent complete: 50.4%; Average loss: 3.2563
Iteration: 2016; Percent complete: 50.4%; Average loss: 3.1535
Iteration: 2017; Percent complete: 50.4%; Average loss: 2.9349
Iteration: 2018; Percent complete: 50.4%; Average loss: 3.3191
Iteration: 2019; Percent complete: 50.5%; Average loss: 3.2717
Iteration: 2020; Percent complete: 50.5%; Average loss: 3.2774
Iteration: 2021; Percent complete: 50.5%; Average loss: 3.1277
Iteration: 2022; Percent complete: 50.5%; Average loss: 3.0603
Iteration: 2023; Percent complete: 50.6%; Average loss: 3.2726
Iteration: 2024; Percent complete: 50.6%; Average loss: 3.0101
Iteration: 2025; Percent complete: 50.6%; Average loss: 2.8503
Iteration: 2026; Percent complete: 50.6%; Average loss: 3.3581
Iteration: 2027; Percent complete: 50.7%; Average loss: 3.0548
Iteration: 2028; Percent complete: 50.7%; Average loss: 3.0824
Iteration: 2029; Percent complete: 50.7%; Average loss: 3.1800
Iteration: 2030; Percent complete: 50.7%; Average loss: 3.1590
Iteration: 2031; Percent complete: 50.8%; Average loss: 3.0727
Iteration: 2032; Percent complete: 50.8%; Average loss: 2.9735
Iteration: 2033; Percent complete: 50.8%; Average loss: 2.9314
Iteration: 2034; Percent complete: 50.8%; Average loss: 3.3108
Iteration: 2035; Percent complete: 50.9%; Average loss: 3.2888
Iteration: 2036; Percent complete: 50.9%; Average loss: 3.3655
Iteration: 2037; Percent complete: 50.9%; Average loss: 2.9595
Iteration: 2038; Percent complete: 50.9%; Average loss: 2.9535
Iteration: 2039; Percent complete: 51.0%; Average loss: 3.3507
Iteration: 2040; Percent complete: 51.0%; Average loss: 2.9435
Iteration: 2041; Percent complete: 51.0%; Average loss: 2.9961
Iteration: 2042; Percent complete: 51.0%; Average loss: 3.5145
Iteration: 2043; Percent complete: 51.1%; Average loss: 3.1060
Iteration: 2044; Percent complete: 51.1%; Average loss: 3.1524
Iteration: 2045; Percent complete: 51.1%; Average loss: 2.9769
Iteration: 2046; Percent complete: 51.1%; Average loss: 3.1051
Iteration: 2047; Percent complete: 51.2%; Average loss: 3.0188
Iteration: 2048; Percent complete: 51.2%; Average loss: 3.0100
Iteration: 2049; Percent complete: 51.2%; Average loss: 3.2041
Iteration: 2050; Percent complete: 51.2%; Average loss: 2.8299
Iteration: 2051; Percent complete: 51.3%; Average loss: 3.1406
Iteration: 2052; Percent complete: 51.3%; Average loss: 3.2067
Iteration: 2053; Percent complete: 51.3%; Average loss: 3.1959
Iteration: 2054; Percent complete: 51.3%; Average loss: 3.2279
Iteration: 2055; Percent complete: 51.4%; Average loss: 3.2058
Iteration: 2056; Percent complete: 51.4%; Average loss: 2.8985
Iteration: 2057; Percent complete: 51.4%; Average loss: 3.3068
Iteration: 2058; Percent complete: 51.4%; Average loss: 3.1016
Iteration: 2059; Percent complete: 51.5%; Average loss: 2.9940
Iteration: 2060; Percent complete: 51.5%; Average loss: 2.9294
Iteration: 2061; Percent complete: 51.5%; Average loss: 2.9893
Iteration: 2062; Percent complete: 51.5%; Average loss: 3.0186
Iteration: 2063; Percent complete: 51.6%; Average loss: 3.0857
Iteration: 2064; Percent complete: 51.6%; Average loss: 2.9937
Iteration: 2065; Percent complete: 51.6%; Average loss: 2.9971
Iteration: 2066; Percent complete: 51.6%; Average loss: 3.4322
Iteration: 2067; Percent complete: 51.7%; Average loss: 3.0963
Iteration: 2068; Percent complete: 51.7%; Average loss: 3.1402
Iteration: 2069; Percent complete: 51.7%; Average loss: 3.2181
Iteration: 2070; Percent complete: 51.7%; Average loss: 3.0979
Iteration: 2071; Percent complete: 51.8%; Average loss: 3.1844
Iteration: 2072; Percent complete: 51.8%; Average loss: 3.0433
Iteration: 2073; Percent complete: 51.8%; Average loss: 3.0207
Iteration: 2074; Percent complete: 51.8%; Average loss: 3.2643
Iteration: 2075; Percent complete: 51.9%; Average loss: 3.0549
Iteration: 2076; Percent complete: 51.9%; Average loss: 3.6431
Iteration: 2077; Percent complete: 51.9%; Average loss: 3.1493
Iteration: 2078; Percent complete: 51.9%; Average loss: 2.9794
Iteration: 2079; Percent complete: 52.0%; Average loss: 3.1461
Iteration: 2080; Percent complete: 52.0%; Average loss: 3.2732
Iteration: 2081; Percent complete: 52.0%; Average loss: 3.0968
Iteration: 2082; Percent complete: 52.0%; Average loss: 3.0930
Iteration: 2083; Percent complete: 52.1%; Average loss: 3.1367
Iteration: 2084; Percent complete: 52.1%; Average loss: 3.2442
Iteration: 2085; Percent complete: 52.1%; Average loss: 3.0508
Iteration: 2086; Percent complete: 52.1%; Average loss: 3.2784
Iteration: 2087; Percent complete: 52.2%; Average loss: 2.9687
Iteration: 2088; Percent complete: 52.2%; Average loss: 3.0276
Iteration: 2089; Percent complete: 52.2%; Average loss: 3.3037
Iteration: 2090; Percent complete: 52.2%; Average loss: 3.0717
Iteration: 2091; Percent complete: 52.3%; Average loss: 3.1621
Iteration: 2092; Percent complete: 52.3%; Average loss: 3.1201
Iteration: 2093; Percent complete: 52.3%; Average loss: 3.2305
Iteration: 2094; Percent complete: 52.3%; Average loss: 3.1190
Iteration: 2095; Percent complete: 52.4%; Average loss: 2.9541
Iteration: 2096; Percent complete: 52.4%; Average loss: 3.4491
Iteration: 2097; Percent complete: 52.4%; Average loss: 3.2346
Iteration: 2098; Percent complete: 52.4%; Average loss: 3.0223
Iteration: 2099; Percent complete: 52.5%; Average loss: 3.2546
Iteration: 2100; Percent complete: 52.5%; Average loss: 2.8373
Iteration: 2101; Percent complete: 52.5%; Average loss: 3.0697
Iteration: 2102; Percent complete: 52.5%; Average loss: 3.0024
Iteration: 2103; Percent complete: 52.6%; Average loss: 3.1367
Iteration: 2104; Percent complete: 52.6%; Average loss: 3.1817
Iteration: 2105; Percent complete: 52.6%; Average loss: 2.9102
Iteration: 2106; Percent complete: 52.6%; Average loss: 3.0016
Iteration: 2107; Percent complete: 52.7%; Average loss: 3.0831
Iteration: 2108; Percent complete: 52.7%; Average loss: 3.1969
Iteration: 2109; Percent complete: 52.7%; Average loss: 3.0611
Iteration: 2110; Percent complete: 52.8%; Average loss: 3.1417
Iteration: 2111; Percent complete: 52.8%; Average loss: 3.0798
Iteration: 2112; Percent complete: 52.8%; Average loss: 3.1424
Iteration: 2113; Percent complete: 52.8%; Average loss: 3.1892
Iteration: 2114; Percent complete: 52.8%; Average loss: 3.0780
Iteration: 2115; Percent complete: 52.9%; Average loss: 3.1213
Iteration: 2116; Percent complete: 52.9%; Average loss: 3.0179
Iteration: 2117; Percent complete: 52.9%; Average loss: 3.3585
Iteration: 2118; Percent complete: 52.9%; Average loss: 3.1016
Iteration: 2119; Percent complete: 53.0%; Average loss: 2.9413
Iteration: 2120; Percent complete: 53.0%; Average loss: 3.0732
Iteration: 2121; Percent complete: 53.0%; Average loss: 3.0978
Iteration: 2122; Percent complete: 53.0%; Average loss: 3.1403
Iteration: 2123; Percent complete: 53.1%; Average loss: 3.2504
Iteration: 2124; Percent complete: 53.1%; Average loss: 3.1047
Iteration: 2125; Percent complete: 53.1%; Average loss: 3.2671
Iteration: 2126; Percent complete: 53.1%; Average loss: 2.8781
Iteration: 2127; Percent complete: 53.2%; Average loss: 3.1664
Iteration: 2128; Percent complete: 53.2%; Average loss: 3.0475
Iteration: 2129; Percent complete: 53.2%; Average loss: 3.1100
Iteration: 2130; Percent complete: 53.2%; Average loss: 3.1586
Iteration: 2131; Percent complete: 53.3%; Average loss: 3.0107
Iteration: 2132; Percent complete: 53.3%; Average loss: 3.1553
Iteration: 2133; Percent complete: 53.3%; Average loss: 2.8756
Iteration: 2134; Percent complete: 53.3%; Average loss: 2.9447
Iteration: 2135; Percent complete: 53.4%; Average loss: 2.9897
Iteration: 2136; Percent complete: 53.4%; Average loss: 3.0076
Iteration: 2137; Percent complete: 53.4%; Average loss: 3.0081
Iteration: 2138; Percent complete: 53.4%; Average loss: 3.2410
Iteration: 2139; Percent complete: 53.5%; Average loss: 3.0940
Iteration: 2140; Percent complete: 53.5%; Average loss: 3.3867
Iteration: 2141; Percent complete: 53.5%; Average loss: 3.0923
Iteration: 2142; Percent complete: 53.5%; Average loss: 3.1463
Iteration: 2143; Percent complete: 53.6%; Average loss: 3.0446
Iteration: 2144; Percent complete: 53.6%; Average loss: 3.3660
Iteration: 2145; Percent complete: 53.6%; Average loss: 3.2037
Iteration: 2146; Percent complete: 53.6%; Average loss: 3.2882
Iteration: 2147; Percent complete: 53.7%; Average loss: 2.9894
Iteration: 2148; Percent complete: 53.7%; Average loss: 2.7934
Iteration: 2149; Percent complete: 53.7%; Average loss: 3.0596
Iteration: 2150; Percent complete: 53.8%; Average loss: 2.9371
Iteration: 2151; Percent complete: 53.8%; Average loss: 3.0868
Iteration: 2152; Percent complete: 53.8%; Average loss: 3.0257
Iteration: 2153; Percent complete: 53.8%; Average loss: 3.1314
Iteration: 2154; Percent complete: 53.8%; Average loss: 3.1807
Iteration: 2155; Percent complete: 53.9%; Average loss: 3.0059
Iteration: 2156; Percent complete: 53.9%; Average loss: 2.9807
Iteration: 2157; Percent complete: 53.9%; Average loss: 3.0847
Iteration: 2158; Percent complete: 53.9%; Average loss: 3.1528
Iteration: 2159; Percent complete: 54.0%; Average loss: 3.1675
Iteration: 2160; Percent complete: 54.0%; Average loss: 3.0031
Iteration: 2161; Percent complete: 54.0%; Average loss: 3.0843
Iteration: 2162; Percent complete: 54.0%; Average loss: 3.0496
Iteration: 2163; Percent complete: 54.1%; Average loss: 3.2150
Iteration: 2164; Percent complete: 54.1%; Average loss: 3.2141
Iteration: 2165; Percent complete: 54.1%; Average loss: 3.1765
Iteration: 2166; Percent complete: 54.1%; Average loss: 3.1540
Iteration: 2167; Percent complete: 54.2%; Average loss: 2.9682
Iteration: 2168; Percent complete: 54.2%; Average loss: 3.1274
Iteration: 2169; Percent complete: 54.2%; Average loss: 2.9545
Iteration: 2170; Percent complete: 54.2%; Average loss: 3.1378
Iteration: 2171; Percent complete: 54.3%; Average loss: 3.0568
Iteration: 2172; Percent complete: 54.3%; Average loss: 2.8517
Iteration: 2173; Percent complete: 54.3%; Average loss: 2.9465
Iteration: 2174; Percent complete: 54.4%; Average loss: 3.0685
Iteration: 2175; Percent complete: 54.4%; Average loss: 3.0721
Iteration: 2176; Percent complete: 54.4%; Average loss: 3.1000
Iteration: 2177; Percent complete: 54.4%; Average loss: 3.1096
Iteration: 2178; Percent complete: 54.4%; Average loss: 3.0900
Iteration: 2179; Percent complete: 54.5%; Average loss: 3.2225
Iteration: 2180; Percent complete: 54.5%; Average loss: 3.0093
Iteration: 2181; Percent complete: 54.5%; Average loss: 3.2013
Iteration: 2182; Percent complete: 54.5%; Average loss: 3.0139
Iteration: 2183; Percent complete: 54.6%; Average loss: 3.1101
Iteration: 2184; Percent complete: 54.6%; Average loss: 2.9309
Iteration: 2185; Percent complete: 54.6%; Average loss: 3.3145
Iteration: 2186; Percent complete: 54.6%; Average loss: 3.3039
Iteration: 2187; Percent complete: 54.7%; Average loss: 3.3634
Iteration: 2188; Percent complete: 54.7%; Average loss: 2.9580
Iteration: 2189; Percent complete: 54.7%; Average loss: 3.2581
Iteration: 2190; Percent complete: 54.8%; Average loss: 3.1769
Iteration: 2191; Percent complete: 54.8%; Average loss: 3.2563
Iteration: 2192; Percent complete: 54.8%; Average loss: 2.9643
Iteration: 2193; Percent complete: 54.8%; Average loss: 3.2710
Iteration: 2194; Percent complete: 54.9%; Average loss: 3.1839
Iteration: 2195; Percent complete: 54.9%; Average loss: 3.1302
Iteration: 2196; Percent complete: 54.9%; Average loss: 3.1113
Iteration: 2197; Percent complete: 54.9%; Average loss: 2.8725
Iteration: 2198; Percent complete: 54.9%; Average loss: 2.9456
Iteration: 2199; Percent complete: 55.0%; Average loss: 2.8819
Iteration: 2200; Percent complete: 55.0%; Average loss: 2.9080
Iteration: 2201; Percent complete: 55.0%; Average loss: 2.8810
Iteration: 2202; Percent complete: 55.0%; Average loss: 3.1544
Iteration: 2203; Percent complete: 55.1%; Average loss: 2.9252
Iteration: 2204; Percent complete: 55.1%; Average loss: 3.2606
Iteration: 2205; Percent complete: 55.1%; Average loss: 3.1103
Iteration: 2206; Percent complete: 55.1%; Average loss: 3.0408
Iteration: 2207; Percent complete: 55.2%; Average loss: 3.1399
Iteration: 2208; Percent complete: 55.2%; Average loss: 2.8803
Iteration: 2209; Percent complete: 55.2%; Average loss: 3.0521
Iteration: 2210; Percent complete: 55.2%; Average loss: 2.9926
Iteration: 2211; Percent complete: 55.3%; Average loss: 3.2633
Iteration: 2212; Percent complete: 55.3%; Average loss: 3.0581
Iteration: 2213; Percent complete: 55.3%; Average loss: 3.0941
Iteration: 2214; Percent complete: 55.4%; Average loss: 3.0692
Iteration: 2215; Percent complete: 55.4%; Average loss: 2.8865
Iteration: 2216; Percent complete: 55.4%; Average loss: 3.1265
Iteration: 2217; Percent complete: 55.4%; Average loss: 2.9586
Iteration: 2218; Percent complete: 55.5%; Average loss: 3.3199
Iteration: 2219; Percent complete: 55.5%; Average loss: 3.1129
Iteration: 2220; Percent complete: 55.5%; Average loss: 2.9803
Iteration: 2221; Percent complete: 55.5%; Average loss: 3.0194
Iteration: 2222; Percent complete: 55.5%; Average loss: 2.8297
Iteration: 2223; Percent complete: 55.6%; Average loss: 3.2438
Iteration: 2224; Percent complete: 55.6%; Average loss: 2.9949
Iteration: 2225; Percent complete: 55.6%; Average loss: 3.0497
Iteration: 2226; Percent complete: 55.6%; Average loss: 2.9577
Iteration: 2227; Percent complete: 55.7%; Average loss: 2.8911
Iteration: 2228; Percent complete: 55.7%; Average loss: 2.8810
Iteration: 2229; Percent complete: 55.7%; Average loss: 2.9864
Iteration: 2230; Percent complete: 55.8%; Average loss: 3.0245
Iteration: 2231; Percent complete: 55.8%; Average loss: 3.0023
Iteration: 2232; Percent complete: 55.8%; Average loss: 2.9677
Iteration: 2233; Percent complete: 55.8%; Average loss: 3.1198
Iteration: 2234; Percent complete: 55.9%; Average loss: 2.9266
Iteration: 2235; Percent complete: 55.9%; Average loss: 2.7813
Iteration: 2236; Percent complete: 55.9%; Average loss: 3.0031
Iteration: 2237; Percent complete: 55.9%; Average loss: 3.0300
Iteration: 2238; Percent complete: 56.0%; Average loss: 3.1078
Iteration: 2239; Percent complete: 56.0%; Average loss: 3.2516
Iteration: 2240; Percent complete: 56.0%; Average loss: 3.1909
Iteration: 2241; Percent complete: 56.0%; Average loss: 3.1699
Iteration: 2242; Percent complete: 56.0%; Average loss: 3.0723
Iteration: 2243; Percent complete: 56.1%; Average loss: 2.9873
Iteration: 2244; Percent complete: 56.1%; Average loss: 3.2495
Iteration: 2245; Percent complete: 56.1%; Average loss: 3.2514
Iteration: 2246; Percent complete: 56.1%; Average loss: 3.0429
Iteration: 2247; Percent complete: 56.2%; Average loss: 3.0910
Iteration: 2248; Percent complete: 56.2%; Average loss: 3.2104
Iteration: 2249; Percent complete: 56.2%; Average loss: 3.0996
Iteration: 2250; Percent complete: 56.2%; Average loss: 3.0434
Iteration: 2251; Percent complete: 56.3%; Average loss: 2.8580
Iteration: 2252; Percent complete: 56.3%; Average loss: 2.9465
Iteration: 2253; Percent complete: 56.3%; Average loss: 3.0576
Iteration: 2254; Percent complete: 56.4%; Average loss: 3.0827
Iteration: 2255; Percent complete: 56.4%; Average loss: 2.9372
Iteration: 2256; Percent complete: 56.4%; Average loss: 3.1205
Iteration: 2257; Percent complete: 56.4%; Average loss: 3.1617
Iteration: 2258; Percent complete: 56.5%; Average loss: 3.0960
Iteration: 2259; Percent complete: 56.5%; Average loss: 3.1725
Iteration: 2260; Percent complete: 56.5%; Average loss: 3.3132
Iteration: 2261; Percent complete: 56.5%; Average loss: 2.8388
Iteration: 2262; Percent complete: 56.5%; Average loss: 3.3007
Iteration: 2263; Percent complete: 56.6%; Average loss: 3.3013
Iteration: 2264; Percent complete: 56.6%; Average loss: 3.0788
Iteration: 2265; Percent complete: 56.6%; Average loss: 3.1333
Iteration: 2266; Percent complete: 56.6%; Average loss: 2.8701
Iteration: 2267; Percent complete: 56.7%; Average loss: 3.1923
Iteration: 2268; Percent complete: 56.7%; Average loss: 2.9332
Iteration: 2269; Percent complete: 56.7%; Average loss: 3.3054
Iteration: 2270; Percent complete: 56.8%; Average loss: 2.9772
Iteration: 2271; Percent complete: 56.8%; Average loss: 3.3476
Iteration: 2272; Percent complete: 56.8%; Average loss: 3.0309
Iteration: 2273; Percent complete: 56.8%; Average loss: 2.8527
Iteration: 2274; Percent complete: 56.9%; Average loss: 3.1025
Iteration: 2275; Percent complete: 56.9%; Average loss: 3.0843
Iteration: 2276; Percent complete: 56.9%; Average loss: 3.1466
Iteration: 2277; Percent complete: 56.9%; Average loss: 3.0534
Iteration: 2278; Percent complete: 57.0%; Average loss: 3.0398
Iteration: 2279; Percent complete: 57.0%; Average loss: 3.0368
Iteration: 2280; Percent complete: 57.0%; Average loss: 2.9542
Iteration: 2281; Percent complete: 57.0%; Average loss: 3.2266
Iteration: 2282; Percent complete: 57.0%; Average loss: 2.9060
Iteration: 2283; Percent complete: 57.1%; Average loss: 3.3984
Iteration: 2284; Percent complete: 57.1%; Average loss: 2.9768
Iteration: 2285; Percent complete: 57.1%; Average loss: 2.9339
Iteration: 2286; Percent complete: 57.1%; Average loss: 3.1597
Iteration: 2287; Percent complete: 57.2%; Average loss: 3.1782
Iteration: 2288; Percent complete: 57.2%; Average loss: 2.8990
Iteration: 2289; Percent complete: 57.2%; Average loss: 3.0684
Iteration: 2290; Percent complete: 57.2%; Average loss: 3.1526
Iteration: 2291; Percent complete: 57.3%; Average loss: 3.1592
Iteration: 2292; Percent complete: 57.3%; Average loss: 2.9322
Iteration: 2293; Percent complete: 57.3%; Average loss: 2.8838
Iteration: 2294; Percent complete: 57.4%; Average loss: 3.0331
Iteration: 2295; Percent complete: 57.4%; Average loss: 3.3373
Iteration: 2296; Percent complete: 57.4%; Average loss: 3.1139
Iteration: 2297; Percent complete: 57.4%; Average loss: 3.2239
Iteration: 2298; Percent complete: 57.5%; Average loss: 3.0978
Iteration: 2299; Percent complete: 57.5%; Average loss: 2.8969
Iteration: 2300; Percent complete: 57.5%; Average loss: 3.0755
Iteration: 2301; Percent complete: 57.5%; Average loss: 3.2893
Iteration: 2302; Percent complete: 57.6%; Average loss: 3.4099
Iteration: 2303; Percent complete: 57.6%; Average loss: 3.2982
Iteration: 2304; Percent complete: 57.6%; Average loss: 3.1996
Iteration: 2305; Percent complete: 57.6%; Average loss: 3.1180
Iteration: 2306; Percent complete: 57.6%; Average loss: 2.9605
Iteration: 2307; Percent complete: 57.7%; Average loss: 3.0944
Iteration: 2308; Percent complete: 57.7%; Average loss: 3.1223
Iteration: 2309; Percent complete: 57.7%; Average loss: 3.2186
Iteration: 2310; Percent complete: 57.8%; Average loss: 3.0151
Iteration: 2311; Percent complete: 57.8%; Average loss: 3.4367
Iteration: 2312; Percent complete: 57.8%; Average loss: 2.9007
Iteration: 2313; Percent complete: 57.8%; Average loss: 3.0365
Iteration: 2314; Percent complete: 57.9%; Average loss: 2.9064
Iteration: 2315; Percent complete: 57.9%; Average loss: 2.9946
Iteration: 2316; Percent complete: 57.9%; Average loss: 3.0625
Iteration: 2317; Percent complete: 57.9%; Average loss: 2.9881
Iteration: 2318; Percent complete: 58.0%; Average loss: 2.9415
Iteration: 2319; Percent complete: 58.0%; Average loss: 3.3175
Iteration: 2320; Percent complete: 58.0%; Average loss: 3.2390
Iteration: 2321; Percent complete: 58.0%; Average loss: 2.9360
Iteration: 2322; Percent complete: 58.1%; Average loss: 3.1394
Iteration: 2323; Percent complete: 58.1%; Average loss: 3.2289
Iteration: 2324; Percent complete: 58.1%; Average loss: 3.2932
Iteration: 2325; Percent complete: 58.1%; Average loss: 2.7849
Iteration: 2326; Percent complete: 58.1%; Average loss: 3.2090
Iteration: 2327; Percent complete: 58.2%; Average loss: 3.0733
Iteration: 2328; Percent complete: 58.2%; Average loss: 2.9447
Iteration: 2329; Percent complete: 58.2%; Average loss: 2.8511
Iteration: 2330; Percent complete: 58.2%; Average loss: 3.1792
Iteration: 2331; Percent complete: 58.3%; Average loss: 3.1660
Iteration: 2332; Percent complete: 58.3%; Average loss: 2.9168
Iteration: 2333; Percent complete: 58.3%; Average loss: 3.0156
Iteration: 2334; Percent complete: 58.4%; Average loss: 2.9709
Iteration: 2335; Percent complete: 58.4%; Average loss: 3.0715
Iteration: 2336; Percent complete: 58.4%; Average loss: 3.1011
Iteration: 2337; Percent complete: 58.4%; Average loss: 2.8773
Iteration: 2338; Percent complete: 58.5%; Average loss: 3.1876
Iteration: 2339; Percent complete: 58.5%; Average loss: 3.0119
Iteration: 2340; Percent complete: 58.5%; Average loss: 3.1618
Iteration: 2341; Percent complete: 58.5%; Average loss: 3.1437
Iteration: 2342; Percent complete: 58.6%; Average loss: 3.0489
Iteration: 2343; Percent complete: 58.6%; Average loss: 2.9800
Iteration: 2344; Percent complete: 58.6%; Average loss: 3.1860
Iteration: 2345; Percent complete: 58.6%; Average loss: 3.1365
Iteration: 2346; Percent complete: 58.7%; Average loss: 3.3832
Iteration: 2347; Percent complete: 58.7%; Average loss: 2.9649
Iteration: 2348; Percent complete: 58.7%; Average loss: 3.3281
Iteration: 2349; Percent complete: 58.7%; Average loss: 3.2443
Iteration: 2350; Percent complete: 58.8%; Average loss: 3.1961
Iteration: 2351; Percent complete: 58.8%; Average loss: 3.1104
Iteration: 2352; Percent complete: 58.8%; Average loss: 3.0572
Iteration: 2353; Percent complete: 58.8%; Average loss: 2.9686
Iteration: 2354; Percent complete: 58.9%; Average loss: 2.8195
Iteration: 2355; Percent complete: 58.9%; Average loss: 3.2019
Iteration: 2356; Percent complete: 58.9%; Average loss: 3.1903
Iteration: 2357; Percent complete: 58.9%; Average loss: 3.1031
Iteration: 2358; Percent complete: 59.0%; Average loss: 3.1028
Iteration: 2359; Percent complete: 59.0%; Average loss: 3.3299
Iteration: 2360; Percent complete: 59.0%; Average loss: 3.0630
Iteration: 2361; Percent complete: 59.0%; Average loss: 3.2582
Iteration: 2362; Percent complete: 59.1%; Average loss: 3.0877
Iteration: 2363; Percent complete: 59.1%; Average loss: 2.9703
Iteration: 2364; Percent complete: 59.1%; Average loss: 3.0863
Iteration: 2365; Percent complete: 59.1%; Average loss: 3.0313
Iteration: 2366; Percent complete: 59.2%; Average loss: 3.2628
Iteration: 2367; Percent complete: 59.2%; Average loss: 2.9287
Iteration: 2368; Percent complete: 59.2%; Average loss: 3.0794
Iteration: 2369; Percent complete: 59.2%; Average loss: 2.8098
Iteration: 2370; Percent complete: 59.2%; Average loss: 3.0894
Iteration: 2371; Percent complete: 59.3%; Average loss: 3.1846
Iteration: 2372; Percent complete: 59.3%; Average loss: 3.0629
Iteration: 2373; Percent complete: 59.3%; Average loss: 2.8715
Iteration: 2374; Percent complete: 59.4%; Average loss: 3.1309
Iteration: 2375; Percent complete: 59.4%; Average loss: 3.2715
Iteration: 2376; Percent complete: 59.4%; Average loss: 3.0413
Iteration: 2377; Percent complete: 59.4%; Average loss: 2.8984
Iteration: 2378; Percent complete: 59.5%; Average loss: 2.7892
Iteration: 2379; Percent complete: 59.5%; Average loss: 2.8549
Iteration: 2380; Percent complete: 59.5%; Average loss: 3.0170
Iteration: 2381; Percent complete: 59.5%; Average loss: 3.1338
Iteration: 2382; Percent complete: 59.6%; Average loss: 3.0823
Iteration: 2383; Percent complete: 59.6%; Average loss: 2.8215
Iteration: 2384; Percent complete: 59.6%; Average loss: 3.1251
Iteration: 2385; Percent complete: 59.6%; Average loss: 3.4612
Iteration: 2386; Percent complete: 59.7%; Average loss: 2.8388
Iteration: 2387; Percent complete: 59.7%; Average loss: 3.1807
Iteration: 2388; Percent complete: 59.7%; Average loss: 3.2073
Iteration: 2389; Percent complete: 59.7%; Average loss: 2.9378
Iteration: 2390; Percent complete: 59.8%; Average loss: 3.0882
Iteration: 2391; Percent complete: 59.8%; Average loss: 3.1822
Iteration: 2392; Percent complete: 59.8%; Average loss: 2.8744
Iteration: 2393; Percent complete: 59.8%; Average loss: 2.9977
Iteration: 2394; Percent complete: 59.9%; Average loss: 3.0955
Iteration: 2395; Percent complete: 59.9%; Average loss: 2.9900
Iteration: 2396; Percent complete: 59.9%; Average loss: 3.1059
Iteration: 2397; Percent complete: 59.9%; Average loss: 3.2261
Iteration: 2398; Percent complete: 60.0%; Average loss: 2.9392
Iteration: 2399; Percent complete: 60.0%; Average loss: 2.8397
Iteration: 2400; Percent complete: 60.0%; Average loss: 3.0557
Iteration: 2401; Percent complete: 60.0%; Average loss: 2.8528
Iteration: 2402; Percent complete: 60.1%; Average loss: 3.2621
Iteration: 2403; Percent complete: 60.1%; Average loss: 2.7696
Iteration: 2404; Percent complete: 60.1%; Average loss: 3.0964
Iteration: 2405; Percent complete: 60.1%; Average loss: 3.1214
Iteration: 2406; Percent complete: 60.2%; Average loss: 3.0356
Iteration: 2407; Percent complete: 60.2%; Average loss: 2.9765
Iteration: 2408; Percent complete: 60.2%; Average loss: 2.9765
Iteration: 2409; Percent complete: 60.2%; Average loss: 2.8744
Iteration: 2410; Percent complete: 60.2%; Average loss: 3.0168
Iteration: 2411; Percent complete: 60.3%; Average loss: 2.9870
Iteration: 2412; Percent complete: 60.3%; Average loss: 3.0656
Iteration: 2413; Percent complete: 60.3%; Average loss: 2.9022
Iteration: 2414; Percent complete: 60.4%; Average loss: 3.1481
Iteration: 2415; Percent complete: 60.4%; Average loss: 3.0329
Iteration: 2416; Percent complete: 60.4%; Average loss: 2.7932
Iteration: 2417; Percent complete: 60.4%; Average loss: 2.9032
Iteration: 2418; Percent complete: 60.5%; Average loss: 3.1251
Iteration: 2419; Percent complete: 60.5%; Average loss: 2.6319
Iteration: 2420; Percent complete: 60.5%; Average loss: 2.9623
Iteration: 2421; Percent complete: 60.5%; Average loss: 2.9830
Iteration: 2422; Percent complete: 60.6%; Average loss: 3.1031
Iteration: 2423; Percent complete: 60.6%; Average loss: 3.0127
Iteration: 2424; Percent complete: 60.6%; Average loss: 2.9707
Iteration: 2425; Percent complete: 60.6%; Average loss: 2.8421
Iteration: 2426; Percent complete: 60.7%; Average loss: 3.1180
Iteration: 2427; Percent complete: 60.7%; Average loss: 3.2301
Iteration: 2428; Percent complete: 60.7%; Average loss: 3.1618
Iteration: 2429; Percent complete: 60.7%; Average loss: 3.3556
Iteration: 2430; Percent complete: 60.8%; Average loss: 2.7812
Iteration: 2431; Percent complete: 60.8%; Average loss: 3.1029
Iteration: 2432; Percent complete: 60.8%; Average loss: 3.0245
Iteration: 2433; Percent complete: 60.8%; Average loss: 3.0149
Iteration: 2434; Percent complete: 60.9%; Average loss: 3.1077
Iteration: 2435; Percent complete: 60.9%; Average loss: 2.8706
Iteration: 2436; Percent complete: 60.9%; Average loss: 3.0917
Iteration: 2437; Percent complete: 60.9%; Average loss: 3.0865
Iteration: 2438; Percent complete: 61.0%; Average loss: 3.3163
Iteration: 2439; Percent complete: 61.0%; Average loss: 3.2870
Iteration: 2440; Percent complete: 61.0%; Average loss: 2.9880
Iteration: 2441; Percent complete: 61.0%; Average loss: 3.1021
Iteration: 2442; Percent complete: 61.1%; Average loss: 2.9161
Iteration: 2443; Percent complete: 61.1%; Average loss: 2.9621
Iteration: 2444; Percent complete: 61.1%; Average loss: 2.7726
Iteration: 2445; Percent complete: 61.1%; Average loss: 3.1591
Iteration: 2446; Percent complete: 61.2%; Average loss: 2.7019
Iteration: 2447; Percent complete: 61.2%; Average loss: 3.0605
Iteration: 2448; Percent complete: 61.2%; Average loss: 3.0723
Iteration: 2449; Percent complete: 61.2%; Average loss: 3.1441
Iteration: 2450; Percent complete: 61.3%; Average loss: 3.2661
Iteration: 2451; Percent complete: 61.3%; Average loss: 2.9811
Iteration: 2452; Percent complete: 61.3%; Average loss: 3.0839
Iteration: 2453; Percent complete: 61.3%; Average loss: 2.9448
Iteration: 2454; Percent complete: 61.4%; Average loss: 3.2968
Iteration: 2455; Percent complete: 61.4%; Average loss: 2.7226
Iteration: 2456; Percent complete: 61.4%; Average loss: 3.1245
Iteration: 2457; Percent complete: 61.4%; Average loss: 3.0782
Iteration: 2458; Percent complete: 61.5%; Average loss: 2.8396
Iteration: 2459; Percent complete: 61.5%; Average loss: 3.1357
Iteration: 2460; Percent complete: 61.5%; Average loss: 2.8059
Iteration: 2461; Percent complete: 61.5%; Average loss: 2.9988
Iteration: 2462; Percent complete: 61.6%; Average loss: 3.0349
Iteration: 2463; Percent complete: 61.6%; Average loss: 2.9060
Iteration: 2464; Percent complete: 61.6%; Average loss: 3.0924
Iteration: 2465; Percent complete: 61.6%; Average loss: 2.9821
Iteration: 2466; Percent complete: 61.7%; Average loss: 3.0006
Iteration: 2467; Percent complete: 61.7%; Average loss: 3.1389
Iteration: 2468; Percent complete: 61.7%; Average loss: 3.1514
Iteration: 2469; Percent complete: 61.7%; Average loss: 3.0599
Iteration: 2470; Percent complete: 61.8%; Average loss: 3.1617
Iteration: 2471; Percent complete: 61.8%; Average loss: 3.2360
Iteration: 2472; Percent complete: 61.8%; Average loss: 3.0013
Iteration: 2473; Percent complete: 61.8%; Average loss: 2.9225
Iteration: 2474; Percent complete: 61.9%; Average loss: 3.0435
Iteration: 2475; Percent complete: 61.9%; Average loss: 2.9456
Iteration: 2476; Percent complete: 61.9%; Average loss: 2.9654
Iteration: 2477; Percent complete: 61.9%; Average loss: 3.1281
Iteration: 2478; Percent complete: 62.0%; Average loss: 3.0447
Iteration: 2479; Percent complete: 62.0%; Average loss: 3.0553
Iteration: 2480; Percent complete: 62.0%; Average loss: 3.0875
Iteration: 2481; Percent complete: 62.0%; Average loss: 3.0336
Iteration: 2482; Percent complete: 62.1%; Average loss: 3.0345
Iteration: 2483; Percent complete: 62.1%; Average loss: 2.9592
Iteration: 2484; Percent complete: 62.1%; Average loss: 2.7623
Iteration: 2485; Percent complete: 62.1%; Average loss: 2.9417
Iteration: 2486; Percent complete: 62.2%; Average loss: 2.8181
Iteration: 2487; Percent complete: 62.2%; Average loss: 3.0027
Iteration: 2488; Percent complete: 62.2%; Average loss: 3.0095
Iteration: 2489; Percent complete: 62.2%; Average loss: 2.9904
Iteration: 2490; Percent complete: 62.3%; Average loss: 3.1097
Iteration: 2491; Percent complete: 62.3%; Average loss: 3.0663
Iteration: 2492; Percent complete: 62.3%; Average loss: 2.7464
Iteration: 2493; Percent complete: 62.3%; Average loss: 3.0511
Iteration: 2494; Percent complete: 62.4%; Average loss: 2.8818
Iteration: 2495; Percent complete: 62.4%; Average loss: 3.0536
Iteration: 2496; Percent complete: 62.4%; Average loss: 3.0165
Iteration: 2497; Percent complete: 62.4%; Average loss: 3.0757
Iteration: 2498; Percent complete: 62.5%; Average loss: 3.0031
Iteration: 2499; Percent complete: 62.5%; Average loss: 2.7843
Iteration: 2500; Percent complete: 62.5%; Average loss: 2.9809
Iteration: 2501; Percent complete: 62.5%; Average loss: 3.1284
Iteration: 2502; Percent complete: 62.5%; Average loss: 3.0392
Iteration: 2503; Percent complete: 62.6%; Average loss: 2.9140
Iteration: 2504; Percent complete: 62.6%; Average loss: 3.0286
Iteration: 2505; Percent complete: 62.6%; Average loss: 2.6279
Iteration: 2506; Percent complete: 62.6%; Average loss: 2.9822
Iteration: 2507; Percent complete: 62.7%; Average loss: 3.1125
Iteration: 2508; Percent complete: 62.7%; Average loss: 2.9701
Iteration: 2509; Percent complete: 62.7%; Average loss: 3.2790
Iteration: 2510; Percent complete: 62.7%; Average loss: 2.8294
Iteration: 2511; Percent complete: 62.8%; Average loss: 3.1072
Iteration: 2512; Percent complete: 62.8%; Average loss: 2.8647
Iteration: 2513; Percent complete: 62.8%; Average loss: 3.2658
Iteration: 2514; Percent complete: 62.8%; Average loss: 2.9120
Iteration: 2515; Percent complete: 62.9%; Average loss: 3.1311
Iteration: 2516; Percent complete: 62.9%; Average loss: 3.0758
Iteration: 2517; Percent complete: 62.9%; Average loss: 2.9659
Iteration: 2518; Percent complete: 62.9%; Average loss: 3.0593
Iteration: 2519; Percent complete: 63.0%; Average loss: 2.9349
Iteration: 2520; Percent complete: 63.0%; Average loss: 3.2010
Iteration: 2521; Percent complete: 63.0%; Average loss: 2.9579
Iteration: 2522; Percent complete: 63.0%; Average loss: 2.9172
Iteration: 2523; Percent complete: 63.1%; Average loss: 2.8330
Iteration: 2524; Percent complete: 63.1%; Average loss: 3.2151
Iteration: 2525; Percent complete: 63.1%; Average loss: 2.9648
Iteration: 2526; Percent complete: 63.1%; Average loss: 3.1108
Iteration: 2527; Percent complete: 63.2%; Average loss: 3.1535
Iteration: 2528; Percent complete: 63.2%; Average loss: 3.0006
Iteration: 2529; Percent complete: 63.2%; Average loss: 3.0762
Iteration: 2530; Percent complete: 63.2%; Average loss: 3.0785
Iteration: 2531; Percent complete: 63.3%; Average loss: 2.8181
Iteration: 2532; Percent complete: 63.3%; Average loss: 2.8389
Iteration: 2533; Percent complete: 63.3%; Average loss: 2.9599
Iteration: 2534; Percent complete: 63.3%; Average loss: 2.8252
Iteration: 2535; Percent complete: 63.4%; Average loss: 2.9579
Iteration: 2536; Percent complete: 63.4%; Average loss: 2.9077
Iteration: 2537; Percent complete: 63.4%; Average loss: 2.8920
Iteration: 2538; Percent complete: 63.4%; Average loss: 3.0057
Iteration: 2539; Percent complete: 63.5%; Average loss: 3.0353
Iteration: 2540; Percent complete: 63.5%; Average loss: 3.1413
Iteration: 2541; Percent complete: 63.5%; Average loss: 2.9937
Iteration: 2542; Percent complete: 63.5%; Average loss: 3.0230
Iteration: 2543; Percent complete: 63.6%; Average loss: 2.8836
Iteration: 2544; Percent complete: 63.6%; Average loss: 3.0338
Iteration: 2545; Percent complete: 63.6%; Average loss: 3.1909
Iteration: 2546; Percent complete: 63.6%; Average loss: 3.0499
Iteration: 2547; Percent complete: 63.7%; Average loss: 3.0258
Iteration: 2548; Percent complete: 63.7%; Average loss: 3.1908
Iteration: 2549; Percent complete: 63.7%; Average loss: 3.0665
Iteration: 2550; Percent complete: 63.7%; Average loss: 2.9715
Iteration: 2551; Percent complete: 63.8%; Average loss: 2.9496
Iteration: 2552; Percent complete: 63.8%; Average loss: 3.1924
Iteration: 2553; Percent complete: 63.8%; Average loss: 2.8664
Iteration: 2554; Percent complete: 63.8%; Average loss: 3.0297
Iteration: 2555; Percent complete: 63.9%; Average loss: 3.0265
Iteration: 2556; Percent complete: 63.9%; Average loss: 3.2606
Iteration: 2557; Percent complete: 63.9%; Average loss: 2.8754
Iteration: 2558; Percent complete: 63.9%; Average loss: 2.9094
Iteration: 2559; Percent complete: 64.0%; Average loss: 2.9881
Iteration: 2560; Percent complete: 64.0%; Average loss: 3.0103
Iteration: 2561; Percent complete: 64.0%; Average loss: 2.9498
Iteration: 2562; Percent complete: 64.0%; Average loss: 3.1319
Iteration: 2563; Percent complete: 64.1%; Average loss: 3.0361
Iteration: 2564; Percent complete: 64.1%; Average loss: 2.9902
Iteration: 2565; Percent complete: 64.1%; Average loss: 2.9185
Iteration: 2566; Percent complete: 64.1%; Average loss: 2.8283
Iteration: 2567; Percent complete: 64.2%; Average loss: 3.3041
Iteration: 2568; Percent complete: 64.2%; Average loss: 3.1318
Iteration: 2569; Percent complete: 64.2%; Average loss: 2.9115
Iteration: 2570; Percent complete: 64.2%; Average loss: 3.1257
Iteration: 2571; Percent complete: 64.3%; Average loss: 3.0701
Iteration: 2572; Percent complete: 64.3%; Average loss: 2.9877
Iteration: 2573; Percent complete: 64.3%; Average loss: 2.9877
Iteration: 2574; Percent complete: 64.3%; Average loss: 2.9743
Iteration: 2575; Percent complete: 64.4%; Average loss: 3.1577
Iteration: 2576; Percent complete: 64.4%; Average loss: 2.9735
Iteration: 2577; Percent complete: 64.4%; Average loss: 3.1975
Iteration: 2578; Percent complete: 64.5%; Average loss: 3.1607
Iteration: 2579; Percent complete: 64.5%; Average loss: 3.0704
Iteration: 2580; Percent complete: 64.5%; Average loss: 2.9185
Iteration: 2581; Percent complete: 64.5%; Average loss: 3.2245
Iteration: 2582; Percent complete: 64.5%; Average loss: 2.7584
Iteration: 2583; Percent complete: 64.6%; Average loss: 2.8979
Iteration: 2584; Percent complete: 64.6%; Average loss: 3.1432
Iteration: 2585; Percent complete: 64.6%; Average loss: 3.0444
Iteration: 2586; Percent complete: 64.6%; Average loss: 3.0585
Iteration: 2587; Percent complete: 64.7%; Average loss: 3.2389
Iteration: 2588; Percent complete: 64.7%; Average loss: 3.0068
Iteration: 2589; Percent complete: 64.7%; Average loss: 3.0418
Iteration: 2590; Percent complete: 64.8%; Average loss: 2.8575
Iteration: 2591; Percent complete: 64.8%; Average loss: 2.7590
Iteration: 2592; Percent complete: 64.8%; Average loss: 3.0414
Iteration: 2593; Percent complete: 64.8%; Average loss: 2.9616
Iteration: 2594; Percent complete: 64.8%; Average loss: 3.0795
Iteration: 2595; Percent complete: 64.9%; Average loss: 3.0017
Iteration: 2596; Percent complete: 64.9%; Average loss: 3.0557
Iteration: 2597; Percent complete: 64.9%; Average loss: 3.0046
Iteration: 2598; Percent complete: 65.0%; Average loss: 3.1536
Iteration: 2599; Percent complete: 65.0%; Average loss: 2.6954
Iteration: 2600; Percent complete: 65.0%; Average loss: 3.0702
Iteration: 2601; Percent complete: 65.0%; Average loss: 2.8661
Iteration: 2602; Percent complete: 65.0%; Average loss: 3.0583
Iteration: 2603; Percent complete: 65.1%; Average loss: 3.0386
Iteration: 2604; Percent complete: 65.1%; Average loss: 3.0102
Iteration: 2605; Percent complete: 65.1%; Average loss: 3.0997
Iteration: 2606; Percent complete: 65.1%; Average loss: 2.8052
Iteration: 2607; Percent complete: 65.2%; Average loss: 3.1301
Iteration: 2608; Percent complete: 65.2%; Average loss: 3.1604
Iteration: 2609; Percent complete: 65.2%; Average loss: 3.0163
Iteration: 2610; Percent complete: 65.2%; Average loss: 3.1070
Iteration: 2611; Percent complete: 65.3%; Average loss: 3.0201
Iteration: 2612; Percent complete: 65.3%; Average loss: 2.8811
Iteration: 2613; Percent complete: 65.3%; Average loss: 3.0993
Iteration: 2614; Percent complete: 65.3%; Average loss: 2.9127
Iteration: 2615; Percent complete: 65.4%; Average loss: 2.8341
Iteration: 2616; Percent complete: 65.4%; Average loss: 2.8653
Iteration: 2617; Percent complete: 65.4%; Average loss: 2.8225
Iteration: 2618; Percent complete: 65.5%; Average loss: 3.0646
Iteration: 2619; Percent complete: 65.5%; Average loss: 2.9292
Iteration: 2620; Percent complete: 65.5%; Average loss: 2.9957
Iteration: 2621; Percent complete: 65.5%; Average loss: 2.9462
Iteration: 2622; Percent complete: 65.5%; Average loss: 2.7770
Iteration: 2623; Percent complete: 65.6%; Average loss: 3.0777
Iteration: 2624; Percent complete: 65.6%; Average loss: 2.9691
Iteration: 2625; Percent complete: 65.6%; Average loss: 2.7077
Iteration: 2626; Percent complete: 65.6%; Average loss: 3.1546
Iteration: 2627; Percent complete: 65.7%; Average loss: 2.5521
Iteration: 2628; Percent complete: 65.7%; Average loss: 2.9554
Iteration: 2629; Percent complete: 65.7%; Average loss: 2.8985
Iteration: 2630; Percent complete: 65.8%; Average loss: 3.0102
Iteration: 2631; Percent complete: 65.8%; Average loss: 2.9268
Iteration: 2632; Percent complete: 65.8%; Average loss: 2.6446
Iteration: 2633; Percent complete: 65.8%; Average loss: 2.8456
Iteration: 2634; Percent complete: 65.8%; Average loss: 2.8994
Iteration: 2635; Percent complete: 65.9%; Average loss: 2.9611
Iteration: 2636; Percent complete: 65.9%; Average loss: 2.9757
Iteration: 2637; Percent complete: 65.9%; Average loss: 3.0818
Iteration: 2638; Percent complete: 66.0%; Average loss: 2.7864
Iteration: 2639; Percent complete: 66.0%; Average loss: 3.2629
Iteration: 2640; Percent complete: 66.0%; Average loss: 3.0951
Iteration: 2641; Percent complete: 66.0%; Average loss: 3.0012
Iteration: 2642; Percent complete: 66.0%; Average loss: 2.8777
Iteration: 2643; Percent complete: 66.1%; Average loss: 2.9763
Iteration: 2644; Percent complete: 66.1%; Average loss: 3.2495
Iteration: 2645; Percent complete: 66.1%; Average loss: 2.9458
Iteration: 2646; Percent complete: 66.1%; Average loss: 2.9913
Iteration: 2647; Percent complete: 66.2%; Average loss: 2.7745
Iteration: 2648; Percent complete: 66.2%; Average loss: 2.8832
Iteration: 2649; Percent complete: 66.2%; Average loss: 3.0072
Iteration: 2650; Percent complete: 66.2%; Average loss: 3.1435
Iteration: 2651; Percent complete: 66.3%; Average loss: 2.9769
Iteration: 2652; Percent complete: 66.3%; Average loss: 2.8792
Iteration: 2653; Percent complete: 66.3%; Average loss: 3.1295
Iteration: 2654; Percent complete: 66.3%; Average loss: 2.7312
Iteration: 2655; Percent complete: 66.4%; Average loss: 2.9178
Iteration: 2656; Percent complete: 66.4%; Average loss: 3.0981
Iteration: 2657; Percent complete: 66.4%; Average loss: 2.9493
Iteration: 2658; Percent complete: 66.5%; Average loss: 2.9971
Iteration: 2659; Percent complete: 66.5%; Average loss: 3.0664
Iteration: 2660; Percent complete: 66.5%; Average loss: 2.8491
Iteration: 2661; Percent complete: 66.5%; Average loss: 2.7866
Iteration: 2662; Percent complete: 66.5%; Average loss: 2.9886
Iteration: 2663; Percent complete: 66.6%; Average loss: 3.0857
Iteration: 2664; Percent complete: 66.6%; Average loss: 2.8764
Iteration: 2665; Percent complete: 66.6%; Average loss: 3.0914
Iteration: 2666; Percent complete: 66.6%; Average loss: 2.6979
Iteration: 2667; Percent complete: 66.7%; Average loss: 2.8701
Iteration: 2668; Percent complete: 66.7%; Average loss: 3.0700
Iteration: 2669; Percent complete: 66.7%; Average loss: 2.9296
Iteration: 2670; Percent complete: 66.8%; Average loss: 2.9492
Iteration: 2671; Percent complete: 66.8%; Average loss: 2.9495
Iteration: 2672; Percent complete: 66.8%; Average loss: 3.0157
Iteration: 2673; Percent complete: 66.8%; Average loss: 2.7975
Iteration: 2674; Percent complete: 66.8%; Average loss: 3.0344
Iteration: 2675; Percent complete: 66.9%; Average loss: 2.8472
Iteration: 2676; Percent complete: 66.9%; Average loss: 3.1498
Iteration: 2677; Percent complete: 66.9%; Average loss: 2.8958
Iteration: 2678; Percent complete: 67.0%; Average loss: 3.1195
Iteration: 2679; Percent complete: 67.0%; Average loss: 2.8081
Iteration: 2680; Percent complete: 67.0%; Average loss: 2.7353
Iteration: 2681; Percent complete: 67.0%; Average loss: 3.0782
Iteration: 2682; Percent complete: 67.0%; Average loss: 3.0413
Iteration: 2683; Percent complete: 67.1%; Average loss: 3.0288
Iteration: 2684; Percent complete: 67.1%; Average loss: 2.9496
Iteration: 2685; Percent complete: 67.1%; Average loss: 3.0190
Iteration: 2686; Percent complete: 67.2%; Average loss: 3.0409
Iteration: 2687; Percent complete: 67.2%; Average loss: 3.0732
Iteration: 2688; Percent complete: 67.2%; Average loss: 3.0044
Iteration: 2689; Percent complete: 67.2%; Average loss: 2.8566
Iteration: 2690; Percent complete: 67.2%; Average loss: 3.1302
Iteration: 2691; Percent complete: 67.3%; Average loss: 3.0092
Iteration: 2692; Percent complete: 67.3%; Average loss: 2.9753
Iteration: 2693; Percent complete: 67.3%; Average loss: 2.8225
Iteration: 2694; Percent complete: 67.3%; Average loss: 2.9347
Iteration: 2695; Percent complete: 67.4%; Average loss: 2.9863
Iteration: 2696; Percent complete: 67.4%; Average loss: 2.8722
Iteration: 2697; Percent complete: 67.4%; Average loss: 3.1997
Iteration: 2698; Percent complete: 67.5%; Average loss: 2.8708
Iteration: 2699; Percent complete: 67.5%; Average loss: 2.9156
Iteration: 2700; Percent complete: 67.5%; Average loss: 2.9147
Iteration: 2701; Percent complete: 67.5%; Average loss: 2.7985
Iteration: 2702; Percent complete: 67.5%; Average loss: 2.8677
Iteration: 2703; Percent complete: 67.6%; Average loss: 3.0035
Iteration: 2704; Percent complete: 67.6%; Average loss: 3.1548
Iteration: 2705; Percent complete: 67.6%; Average loss: 3.2965
Iteration: 2706; Percent complete: 67.7%; Average loss: 2.8221
Iteration: 2707; Percent complete: 67.7%; Average loss: 2.8919
Iteration: 2708; Percent complete: 67.7%; Average loss: 3.0257
Iteration: 2709; Percent complete: 67.7%; Average loss: 2.8738
Iteration: 2710; Percent complete: 67.8%; Average loss: 2.7745
Iteration: 2711; Percent complete: 67.8%; Average loss: 2.8027
Iteration: 2712; Percent complete: 67.8%; Average loss: 3.1703
Iteration: 2713; Percent complete: 67.8%; Average loss: 3.0300
Iteration: 2714; Percent complete: 67.8%; Average loss: 3.1080
Iteration: 2715; Percent complete: 67.9%; Average loss: 2.9239
Iteration: 2716; Percent complete: 67.9%; Average loss: 3.1210
Iteration: 2717; Percent complete: 67.9%; Average loss: 2.9567
Iteration: 2718; Percent complete: 68.0%; Average loss: 3.0593
Iteration: 2719; Percent complete: 68.0%; Average loss: 2.9795
Iteration: 2720; Percent complete: 68.0%; Average loss: 3.2056
Iteration: 2721; Percent complete: 68.0%; Average loss: 3.1874
Iteration: 2722; Percent complete: 68.0%; Average loss: 2.7219
Iteration: 2723; Percent complete: 68.1%; Average loss: 2.9985
Iteration: 2724; Percent complete: 68.1%; Average loss: 2.8301
Iteration: 2725; Percent complete: 68.1%; Average loss: 2.8869
Iteration: 2726; Percent complete: 68.2%; Average loss: 2.9048
Iteration: 2727; Percent complete: 68.2%; Average loss: 2.9690
Iteration: 2728; Percent complete: 68.2%; Average loss: 2.9167
Iteration: 2729; Percent complete: 68.2%; Average loss: 3.0305
Iteration: 2730; Percent complete: 68.2%; Average loss: 2.7494
Iteration: 2731; Percent complete: 68.3%; Average loss: 2.9614
Iteration: 2732; Percent complete: 68.3%; Average loss: 3.0789
Iteration: 2733; Percent complete: 68.3%; Average loss: 2.8095
Iteration: 2734; Percent complete: 68.3%; Average loss: 2.8336
Iteration: 2735; Percent complete: 68.4%; Average loss: 2.9001
Iteration: 2736; Percent complete: 68.4%; Average loss: 2.9813
Iteration: 2737; Percent complete: 68.4%; Average loss: 2.9784
Iteration: 2738; Percent complete: 68.5%; Average loss: 2.7598
Iteration: 2739; Percent complete: 68.5%; Average loss: 2.9824
Iteration: 2740; Percent complete: 68.5%; Average loss: 2.8485
Iteration: 2741; Percent complete: 68.5%; Average loss: 3.0476
Iteration: 2742; Percent complete: 68.5%; Average loss: 3.1391
Iteration: 2743; Percent complete: 68.6%; Average loss: 3.0502
Iteration: 2744; Percent complete: 68.6%; Average loss: 2.9703
Iteration: 2745; Percent complete: 68.6%; Average loss: 2.9012
Iteration: 2746; Percent complete: 68.7%; Average loss: 2.7759
Iteration: 2747; Percent complete: 68.7%; Average loss: 2.9787
Iteration: 2748; Percent complete: 68.7%; Average loss: 2.9050
Iteration: 2749; Percent complete: 68.7%; Average loss: 3.0012
Iteration: 2750; Percent complete: 68.8%; Average loss: 2.8288
Iteration: 2751; Percent complete: 68.8%; Average loss: 2.9978
Iteration: 2752; Percent complete: 68.8%; Average loss: 2.8444
Iteration: 2753; Percent complete: 68.8%; Average loss: 2.8740
Iteration: 2754; Percent complete: 68.8%; Average loss: 3.0238
Iteration: 2755; Percent complete: 68.9%; Average loss: 3.1048
Iteration: 2756; Percent complete: 68.9%; Average loss: 2.8977
Iteration: 2757; Percent complete: 68.9%; Average loss: 2.9055
Iteration: 2758; Percent complete: 69.0%; Average loss: 3.0617
Iteration: 2759; Percent complete: 69.0%; Average loss: 3.1215
Iteration: 2760; Percent complete: 69.0%; Average loss: 2.8844
Iteration: 2761; Percent complete: 69.0%; Average loss: 2.7423
Iteration: 2762; Percent complete: 69.0%; Average loss: 2.9462
Iteration: 2763; Percent complete: 69.1%; Average loss: 2.9341
Iteration: 2764; Percent complete: 69.1%; Average loss: 2.9719
Iteration: 2765; Percent complete: 69.1%; Average loss: 2.9582
Iteration: 2766; Percent complete: 69.2%; Average loss: 2.9212
Iteration: 2767; Percent complete: 69.2%; Average loss: 2.7779
Iteration: 2768; Percent complete: 69.2%; Average loss: 2.8736
Iteration: 2769; Percent complete: 69.2%; Average loss: 2.8132
Iteration: 2770; Percent complete: 69.2%; Average loss: 3.0173
Iteration: 2771; Percent complete: 69.3%; Average loss: 3.1213
Iteration: 2772; Percent complete: 69.3%; Average loss: 2.8037
Iteration: 2773; Percent complete: 69.3%; Average loss: 2.8582
Iteration: 2774; Percent complete: 69.3%; Average loss: 3.0795
Iteration: 2775; Percent complete: 69.4%; Average loss: 2.9612
Iteration: 2776; Percent complete: 69.4%; Average loss: 2.9687
Iteration: 2777; Percent complete: 69.4%; Average loss: 2.8945
Iteration: 2778; Percent complete: 69.5%; Average loss: 3.0622
Iteration: 2779; Percent complete: 69.5%; Average loss: 3.0030
Iteration: 2780; Percent complete: 69.5%; Average loss: 3.0242
Iteration: 2781; Percent complete: 69.5%; Average loss: 3.0396
Iteration: 2782; Percent complete: 69.5%; Average loss: 3.0462
Iteration: 2783; Percent complete: 69.6%; Average loss: 2.9185
Iteration: 2784; Percent complete: 69.6%; Average loss: 3.1644
Iteration: 2785; Percent complete: 69.6%; Average loss: 2.8989
Iteration: 2786; Percent complete: 69.7%; Average loss: 2.7646
Iteration: 2787; Percent complete: 69.7%; Average loss: 2.9721
Iteration: 2788; Percent complete: 69.7%; Average loss: 2.9791
Iteration: 2789; Percent complete: 69.7%; Average loss: 2.7896
Iteration: 2790; Percent complete: 69.8%; Average loss: 2.9333
Iteration: 2791; Percent complete: 69.8%; Average loss: 2.9004
Iteration: 2792; Percent complete: 69.8%; Average loss: 3.1148
Iteration: 2793; Percent complete: 69.8%; Average loss: 2.8194
Iteration: 2794; Percent complete: 69.8%; Average loss: 2.6745
Iteration: 2795; Percent complete: 69.9%; Average loss: 2.9269
Iteration: 2796; Percent complete: 69.9%; Average loss: 2.9271
Iteration: 2797; Percent complete: 69.9%; Average loss: 2.9620
Iteration: 2798; Percent complete: 70.0%; Average loss: 3.0940
Iteration: 2799; Percent complete: 70.0%; Average loss: 3.0792
Iteration: 2800; Percent complete: 70.0%; Average loss: 3.2878
Iteration: 2801; Percent complete: 70.0%; Average loss: 2.9085
Iteration: 2802; Percent complete: 70.0%; Average loss: 3.0919
Iteration: 2803; Percent complete: 70.1%; Average loss: 3.3696
Iteration: 2804; Percent complete: 70.1%; Average loss: 2.9077
Iteration: 2805; Percent complete: 70.1%; Average loss: 2.7921
Iteration: 2806; Percent complete: 70.2%; Average loss: 2.8557
Iteration: 2807; Percent complete: 70.2%; Average loss: 2.8379
Iteration: 2808; Percent complete: 70.2%; Average loss: 2.9268
Iteration: 2809; Percent complete: 70.2%; Average loss: 2.9328
Iteration: 2810; Percent complete: 70.2%; Average loss: 2.8980
Iteration: 2811; Percent complete: 70.3%; Average loss: 2.9845
Iteration: 2812; Percent complete: 70.3%; Average loss: 2.9161
Iteration: 2813; Percent complete: 70.3%; Average loss: 2.9136
Iteration: 2814; Percent complete: 70.3%; Average loss: 2.9605
Iteration: 2815; Percent complete: 70.4%; Average loss: 2.5873
Iteration: 2816; Percent complete: 70.4%; Average loss: 2.9704
Iteration: 2817; Percent complete: 70.4%; Average loss: 3.0324
Iteration: 2818; Percent complete: 70.5%; Average loss: 2.7757
Iteration: 2819; Percent complete: 70.5%; Average loss: 2.9242
Iteration: 2820; Percent complete: 70.5%; Average loss: 2.9629
Iteration: 2821; Percent complete: 70.5%; Average loss: 3.0357
Iteration: 2822; Percent complete: 70.5%; Average loss: 2.9166
Iteration: 2823; Percent complete: 70.6%; Average loss: 2.9126
Iteration: 2824; Percent complete: 70.6%; Average loss: 2.9709
Iteration: 2825; Percent complete: 70.6%; Average loss: 3.0605
Iteration: 2826; Percent complete: 70.7%; Average loss: 3.0223
Iteration: 2827; Percent complete: 70.7%; Average loss: 2.9767
Iteration: 2828; Percent complete: 70.7%; Average loss: 2.8177
Iteration: 2829; Percent complete: 70.7%; Average loss: 2.9840
Iteration: 2830; Percent complete: 70.8%; Average loss: 3.0269
Iteration: 2831; Percent complete: 70.8%; Average loss: 3.2182
Iteration: 2832; Percent complete: 70.8%; Average loss: 3.0607
Iteration: 2833; Percent complete: 70.8%; Average loss: 2.8671
Iteration: 2834; Percent complete: 70.9%; Average loss: 3.0213
Iteration: 2835; Percent complete: 70.9%; Average loss: 2.8136
Iteration: 2836; Percent complete: 70.9%; Average loss: 2.8511
Iteration: 2837; Percent complete: 70.9%; Average loss: 2.9862
Iteration: 2838; Percent complete: 71.0%; Average loss: 3.2191
Iteration: 2839; Percent complete: 71.0%; Average loss: 2.9602
Iteration: 2840; Percent complete: 71.0%; Average loss: 2.9038
Iteration: 2841; Percent complete: 71.0%; Average loss: 2.8473
Iteration: 2842; Percent complete: 71.0%; Average loss: 2.7790
Iteration: 2843; Percent complete: 71.1%; Average loss: 2.9198
Iteration: 2844; Percent complete: 71.1%; Average loss: 2.9601
Iteration: 2845; Percent complete: 71.1%; Average loss: 2.6186
Iteration: 2846; Percent complete: 71.2%; Average loss: 2.8079
Iteration: 2847; Percent complete: 71.2%; Average loss: 3.0038
Iteration: 2848; Percent complete: 71.2%; Average loss: 3.1047
Iteration: 2849; Percent complete: 71.2%; Average loss: 3.1846
Iteration: 2850; Percent complete: 71.2%; Average loss: 2.9064
Iteration: 2851; Percent complete: 71.3%; Average loss: 3.0443
Iteration: 2852; Percent complete: 71.3%; Average loss: 2.7950
Iteration: 2853; Percent complete: 71.3%; Average loss: 2.9312
Iteration: 2854; Percent complete: 71.4%; Average loss: 2.9766
Iteration: 2855; Percent complete: 71.4%; Average loss: 3.0575
Iteration: 2856; Percent complete: 71.4%; Average loss: 3.1181
Iteration: 2857; Percent complete: 71.4%; Average loss: 2.9626
Iteration: 2858; Percent complete: 71.5%; Average loss: 3.0376
Iteration: 2859; Percent complete: 71.5%; Average loss: 2.7767
Iteration: 2860; Percent complete: 71.5%; Average loss: 2.9695
Iteration: 2861; Percent complete: 71.5%; Average loss: 3.1038
Iteration: 2862; Percent complete: 71.5%; Average loss: 2.7513
Iteration: 2863; Percent complete: 71.6%; Average loss: 2.7440
Iteration: 2864; Percent complete: 71.6%; Average loss: 2.9810
Iteration: 2865; Percent complete: 71.6%; Average loss: 2.7950
Iteration: 2866; Percent complete: 71.7%; Average loss: 3.0963
Iteration: 2867; Percent complete: 71.7%; Average loss: 2.8685
Iteration: 2868; Percent complete: 71.7%; Average loss: 2.9491
Iteration: 2869; Percent complete: 71.7%; Average loss: 3.0067
Iteration: 2870; Percent complete: 71.8%; Average loss: 2.7364
Iteration: 2871; Percent complete: 71.8%; Average loss: 2.6441
Iteration: 2872; Percent complete: 71.8%; Average loss: 2.9954
Iteration: 2873; Percent complete: 71.8%; Average loss: 2.7730
Iteration: 2874; Percent complete: 71.9%; Average loss: 2.9703
Iteration: 2875; Percent complete: 71.9%; Average loss: 3.0785
Iteration: 2876; Percent complete: 71.9%; Average loss: 2.9983
Iteration: 2877; Percent complete: 71.9%; Average loss: 3.0662
Iteration: 2878; Percent complete: 72.0%; Average loss: 3.0421
Iteration: 2879; Percent complete: 72.0%; Average loss: 2.7459
Iteration: 2880; Percent complete: 72.0%; Average loss: 3.0779
Iteration: 2881; Percent complete: 72.0%; Average loss: 3.1172
Iteration: 2882; Percent complete: 72.0%; Average loss: 3.0150
Iteration: 2883; Percent complete: 72.1%; Average loss: 2.9049
Iteration: 2884; Percent complete: 72.1%; Average loss: 2.7934
Iteration: 2885; Percent complete: 72.1%; Average loss: 2.7914
Iteration: 2886; Percent complete: 72.2%; Average loss: 3.0549
Iteration: 2887; Percent complete: 72.2%; Average loss: 2.8141
Iteration: 2888; Percent complete: 72.2%; Average loss: 2.8836
Iteration: 2889; Percent complete: 72.2%; Average loss: 3.0314
Iteration: 2890; Percent complete: 72.2%; Average loss: 3.1273
Iteration: 2891; Percent complete: 72.3%; Average loss: 2.9502
Iteration: 2892; Percent complete: 72.3%; Average loss: 2.8098
Iteration: 2893; Percent complete: 72.3%; Average loss: 3.0949
Iteration: 2894; Percent complete: 72.4%; Average loss: 3.1085
Iteration: 2895; Percent complete: 72.4%; Average loss: 2.8586
Iteration: 2896; Percent complete: 72.4%; Average loss: 2.6861
Iteration: 2897; Percent complete: 72.4%; Average loss: 2.8784
Iteration: 2898; Percent complete: 72.5%; Average loss: 3.2061
Iteration: 2899; Percent complete: 72.5%; Average loss: 2.7927
Iteration: 2900; Percent complete: 72.5%; Average loss: 2.8350
Iteration: 2901; Percent complete: 72.5%; Average loss: 2.8981
Iteration: 2902; Percent complete: 72.5%; Average loss: 2.8630
Iteration: 2903; Percent complete: 72.6%; Average loss: 3.1544
Iteration: 2904; Percent complete: 72.6%; Average loss: 2.8941
Iteration: 2905; Percent complete: 72.6%; Average loss: 2.9064
Iteration: 2906; Percent complete: 72.7%; Average loss: 2.9269
Iteration: 2907; Percent complete: 72.7%; Average loss: 2.8305
Iteration: 2908; Percent complete: 72.7%; Average loss: 2.8458
Iteration: 2909; Percent complete: 72.7%; Average loss: 2.8601
Iteration: 2910; Percent complete: 72.8%; Average loss: 2.9045
Iteration: 2911; Percent complete: 72.8%; Average loss: 3.0072
Iteration: 2912; Percent complete: 72.8%; Average loss: 2.6544
Iteration: 2913; Percent complete: 72.8%; Average loss: 2.9908
Iteration: 2914; Percent complete: 72.9%; Average loss: 3.0260
Iteration: 2915; Percent complete: 72.9%; Average loss: 2.8810
Iteration: 2916; Percent complete: 72.9%; Average loss: 3.1777
Iteration: 2917; Percent complete: 72.9%; Average loss: 2.9272
Iteration: 2918; Percent complete: 73.0%; Average loss: 2.7337
Iteration: 2919; Percent complete: 73.0%; Average loss: 2.8010
Iteration: 2920; Percent complete: 73.0%; Average loss: 2.8398
Iteration: 2921; Percent complete: 73.0%; Average loss: 3.0112
Iteration: 2922; Percent complete: 73.0%; Average loss: 3.0575
Iteration: 2923; Percent complete: 73.1%; Average loss: 2.9977
Iteration: 2924; Percent complete: 73.1%; Average loss: 2.8061
Iteration: 2925; Percent complete: 73.1%; Average loss: 3.1417
Iteration: 2926; Percent complete: 73.2%; Average loss: 2.8016
Iteration: 2927; Percent complete: 73.2%; Average loss: 3.0713
Iteration: 2928; Percent complete: 73.2%; Average loss: 2.9337
Iteration: 2929; Percent complete: 73.2%; Average loss: 2.7886
Iteration: 2930; Percent complete: 73.2%; Average loss: 2.7529
Iteration: 2931; Percent complete: 73.3%; Average loss: 2.9402
Iteration: 2932; Percent complete: 73.3%; Average loss: 2.8041
Iteration: 2933; Percent complete: 73.3%; Average loss: 3.0778
Iteration: 2934; Percent complete: 73.4%; Average loss: 2.7658
Iteration: 2935; Percent complete: 73.4%; Average loss: 2.7460
Iteration: 2936; Percent complete: 73.4%; Average loss: 2.8225
Iteration: 2937; Percent complete: 73.4%; Average loss: 3.0388
Iteration: 2938; Percent complete: 73.5%; Average loss: 2.8224
Iteration: 2939; Percent complete: 73.5%; Average loss: 2.9419
Iteration: 2940; Percent complete: 73.5%; Average loss: 3.0761
Iteration: 2941; Percent complete: 73.5%; Average loss: 2.7666
Iteration: 2942; Percent complete: 73.6%; Average loss: 2.7968
Iteration: 2943; Percent complete: 73.6%; Average loss: 3.0294
Iteration: 2944; Percent complete: 73.6%; Average loss: 3.0463
Iteration: 2945; Percent complete: 73.6%; Average loss: 2.9107
Iteration: 2946; Percent complete: 73.7%; Average loss: 2.9588
Iteration: 2947; Percent complete: 73.7%; Average loss: 3.0005
Iteration: 2948; Percent complete: 73.7%; Average loss: 2.7229
Iteration: 2949; Percent complete: 73.7%; Average loss: 2.8398
Iteration: 2950; Percent complete: 73.8%; Average loss: 2.7475
Iteration: 2951; Percent complete: 73.8%; Average loss: 2.7414
Iteration: 2952; Percent complete: 73.8%; Average loss: 2.9828
Iteration: 2953; Percent complete: 73.8%; Average loss: 3.0154
Iteration: 2954; Percent complete: 73.9%; Average loss: 2.9770
Iteration: 2955; Percent complete: 73.9%; Average loss: 2.5663
Iteration: 2956; Percent complete: 73.9%; Average loss: 3.0521
Iteration: 2957; Percent complete: 73.9%; Average loss: 2.6851
Iteration: 2958; Percent complete: 74.0%; Average loss: 2.5963
Iteration: 2959; Percent complete: 74.0%; Average loss: 2.6286
Iteration: 2960; Percent complete: 74.0%; Average loss: 2.7809
Iteration: 2961; Percent complete: 74.0%; Average loss: 2.9011
Iteration: 2962; Percent complete: 74.1%; Average loss: 2.8015
Iteration: 2963; Percent complete: 74.1%; Average loss: 2.9351
Iteration: 2964; Percent complete: 74.1%; Average loss: 3.0212
Iteration: 2965; Percent complete: 74.1%; Average loss: 3.0419
Iteration: 2966; Percent complete: 74.2%; Average loss: 2.8670
Iteration: 2967; Percent complete: 74.2%; Average loss: 2.6363
Iteration: 2968; Percent complete: 74.2%; Average loss: 2.9825
Iteration: 2969; Percent complete: 74.2%; Average loss: 2.8998
Iteration: 2970; Percent complete: 74.2%; Average loss: 2.6291
Iteration: 2971; Percent complete: 74.3%; Average loss: 3.1561
Iteration: 2972; Percent complete: 74.3%; Average loss: 2.7204
Iteration: 2973; Percent complete: 74.3%; Average loss: 2.5941
Iteration: 2974; Percent complete: 74.4%; Average loss: 2.8444
Iteration: 2975; Percent complete: 74.4%; Average loss: 2.8267
Iteration: 2976; Percent complete: 74.4%; Average loss: 2.9522
Iteration: 2977; Percent complete: 74.4%; Average loss: 2.8430
Iteration: 2978; Percent complete: 74.5%; Average loss: 3.0284
Iteration: 2979; Percent complete: 74.5%; Average loss: 3.0078
Iteration: 2980; Percent complete: 74.5%; Average loss: 2.8766
Iteration: 2981; Percent complete: 74.5%; Average loss: 2.7728
Iteration: 2982; Percent complete: 74.6%; Average loss: 2.8979
Iteration: 2983; Percent complete: 74.6%; Average loss: 2.9665
Iteration: 2984; Percent complete: 74.6%; Average loss: 2.8971
Iteration: 2985; Percent complete: 74.6%; Average loss: 2.6976
Iteration: 2986; Percent complete: 74.7%; Average loss: 2.8303
Iteration: 2987; Percent complete: 74.7%; Average loss: 2.9699
Iteration: 2988; Percent complete: 74.7%; Average loss: 2.8461
Iteration: 2989; Percent complete: 74.7%; Average loss: 2.7177
Iteration: 2990; Percent complete: 74.8%; Average loss: 2.8525
Iteration: 2991; Percent complete: 74.8%; Average loss: 2.8086
Iteration: 2992; Percent complete: 74.8%; Average loss: 3.0029
Iteration: 2993; Percent complete: 74.8%; Average loss: 2.7118
Iteration: 2994; Percent complete: 74.9%; Average loss: 2.7878
Iteration: 2995; Percent complete: 74.9%; Average loss: 2.7530
Iteration: 2996; Percent complete: 74.9%; Average loss: 2.8121
Iteration: 2997; Percent complete: 74.9%; Average loss: 2.8290
Iteration: 2998; Percent complete: 75.0%; Average loss: 3.0265
Iteration: 2999; Percent complete: 75.0%; Average loss: 2.7363
Iteration: 3000; Percent complete: 75.0%; Average loss: 2.7874
Iteration: 3001; Percent complete: 75.0%; Average loss: 2.8628
Iteration: 3002; Percent complete: 75.0%; Average loss: 2.8637
Iteration: 3003; Percent complete: 75.1%; Average loss: 3.1139
Iteration: 3004; Percent complete: 75.1%; Average loss: 2.9072
Iteration: 3005; Percent complete: 75.1%; Average loss: 3.3491
Iteration: 3006; Percent complete: 75.1%; Average loss: 2.9526
Iteration: 3007; Percent complete: 75.2%; Average loss: 2.7137
Iteration: 3008; Percent complete: 75.2%; Average loss: 2.7822
Iteration: 3009; Percent complete: 75.2%; Average loss: 2.7851
Iteration: 3010; Percent complete: 75.2%; Average loss: 2.7231
Iteration: 3011; Percent complete: 75.3%; Average loss: 2.9908
Iteration: 3012; Percent complete: 75.3%; Average loss: 2.5491
Iteration: 3013; Percent complete: 75.3%; Average loss: 2.8503
Iteration: 3014; Percent complete: 75.3%; Average loss: 2.5657
Iteration: 3015; Percent complete: 75.4%; Average loss: 3.0502
Iteration: 3016; Percent complete: 75.4%; Average loss: 2.6141
Iteration: 3017; Percent complete: 75.4%; Average loss: 2.7563
Iteration: 3018; Percent complete: 75.4%; Average loss: 2.9410
Iteration: 3019; Percent complete: 75.5%; Average loss: 2.8724
Iteration: 3020; Percent complete: 75.5%; Average loss: 3.0042
Iteration: 3021; Percent complete: 75.5%; Average loss: 2.6358
Iteration: 3022; Percent complete: 75.5%; Average loss: 2.8418
Iteration: 3023; Percent complete: 75.6%; Average loss: 2.5849
Iteration: 3024; Percent complete: 75.6%; Average loss: 2.7358
Iteration: 3025; Percent complete: 75.6%; Average loss: 3.1500
Iteration: 3026; Percent complete: 75.6%; Average loss: 2.6225
Iteration: 3027; Percent complete: 75.7%; Average loss: 2.8467
Iteration: 3028; Percent complete: 75.7%; Average loss: 2.9388
Iteration: 3029; Percent complete: 75.7%; Average loss: 2.9839
Iteration: 3030; Percent complete: 75.8%; Average loss: 3.1296
Iteration: 3031; Percent complete: 75.8%; Average loss: 2.9457
Iteration: 3032; Percent complete: 75.8%; Average loss: 2.9352
Iteration: 3033; Percent complete: 75.8%; Average loss: 2.8695
Iteration: 3034; Percent complete: 75.8%; Average loss: 2.8290
Iteration: 3035; Percent complete: 75.9%; Average loss: 3.0780
Iteration: 3036; Percent complete: 75.9%; Average loss: 2.9559
Iteration: 3037; Percent complete: 75.9%; Average loss: 2.6746
Iteration: 3038; Percent complete: 75.9%; Average loss: 2.9587
Iteration: 3039; Percent complete: 76.0%; Average loss: 2.7444
Iteration: 3040; Percent complete: 76.0%; Average loss: 2.4778
Iteration: 3041; Percent complete: 76.0%; Average loss: 2.7562
Iteration: 3042; Percent complete: 76.0%; Average loss: 2.7570
Iteration: 3043; Percent complete: 76.1%; Average loss: 2.7575
Iteration: 3044; Percent complete: 76.1%; Average loss: 2.8829
Iteration: 3045; Percent complete: 76.1%; Average loss: 2.7543
Iteration: 3046; Percent complete: 76.1%; Average loss: 3.1476
Iteration: 3047; Percent complete: 76.2%; Average loss: 2.8698
Iteration: 3048; Percent complete: 76.2%; Average loss: 3.0771
Iteration: 3049; Percent complete: 76.2%; Average loss: 2.9810
Iteration: 3050; Percent complete: 76.2%; Average loss: 2.7809
Iteration: 3051; Percent complete: 76.3%; Average loss: 2.9875
Iteration: 3052; Percent complete: 76.3%; Average loss: 2.6702
Iteration: 3053; Percent complete: 76.3%; Average loss: 2.6362
Iteration: 3054; Percent complete: 76.3%; Average loss: 2.8664
Iteration: 3055; Percent complete: 76.4%; Average loss: 2.8848
Iteration: 3056; Percent complete: 76.4%; Average loss: 2.8500
Iteration: 3057; Percent complete: 76.4%; Average loss: 2.9431
Iteration: 3058; Percent complete: 76.4%; Average loss: 3.1528
Iteration: 3059; Percent complete: 76.5%; Average loss: 2.8186
Iteration: 3060; Percent complete: 76.5%; Average loss: 3.0604
Iteration: 3061; Percent complete: 76.5%; Average loss: 2.8412
Iteration: 3062; Percent complete: 76.5%; Average loss: 3.1042
Iteration: 3063; Percent complete: 76.6%; Average loss: 2.7613
Iteration: 3064; Percent complete: 76.6%; Average loss: 2.8972
Iteration: 3065; Percent complete: 76.6%; Average loss: 2.9213
Iteration: 3066; Percent complete: 76.6%; Average loss: 2.9376
Iteration: 3067; Percent complete: 76.7%; Average loss: 2.7753
Iteration: 3068; Percent complete: 76.7%; Average loss: 2.8935
Iteration: 3069; Percent complete: 76.7%; Average loss: 2.8813
Iteration: 3070; Percent complete: 76.8%; Average loss: 2.7770
Iteration: 3071; Percent complete: 76.8%; Average loss: 2.8568
Iteration: 3072; Percent complete: 76.8%; Average loss: 2.8042
Iteration: 3073; Percent complete: 76.8%; Average loss: 2.9543
Iteration: 3074; Percent complete: 76.8%; Average loss: 2.8150
Iteration: 3075; Percent complete: 76.9%; Average loss: 2.8233
Iteration: 3076; Percent complete: 76.9%; Average loss: 2.8541
Iteration: 3077; Percent complete: 76.9%; Average loss: 2.8070
Iteration: 3078; Percent complete: 77.0%; Average loss: 2.8403
Iteration: 3079; Percent complete: 77.0%; Average loss: 3.1025
Iteration: 3080; Percent complete: 77.0%; Average loss: 2.8545
Iteration: 3081; Percent complete: 77.0%; Average loss: 2.9978
Iteration: 3082; Percent complete: 77.0%; Average loss: 2.5861
Iteration: 3083; Percent complete: 77.1%; Average loss: 2.8697
Iteration: 3084; Percent complete: 77.1%; Average loss: 3.0619
Iteration: 3085; Percent complete: 77.1%; Average loss: 2.7388
Iteration: 3086; Percent complete: 77.1%; Average loss: 2.9854
Iteration: 3087; Percent complete: 77.2%; Average loss: 2.6872
Iteration: 3088; Percent complete: 77.2%; Average loss: 2.7051
Iteration: 3089; Percent complete: 77.2%; Average loss: 2.9601
Iteration: 3090; Percent complete: 77.2%; Average loss: 2.8832
Iteration: 3091; Percent complete: 77.3%; Average loss: 2.9013
Iteration: 3092; Percent complete: 77.3%; Average loss: 2.7385
Iteration: 3093; Percent complete: 77.3%; Average loss: 2.8650
Iteration: 3094; Percent complete: 77.3%; Average loss: 2.8725
Iteration: 3095; Percent complete: 77.4%; Average loss: 2.7659
Iteration: 3096; Percent complete: 77.4%; Average loss: 3.1253
Iteration: 3097; Percent complete: 77.4%; Average loss: 2.7967
Iteration: 3098; Percent complete: 77.5%; Average loss: 2.8585
Iteration: 3099; Percent complete: 77.5%; Average loss: 2.8811
Iteration: 3100; Percent complete: 77.5%; Average loss: 2.8558
Iteration: 3101; Percent complete: 77.5%; Average loss: 2.8247
Iteration: 3102; Percent complete: 77.5%; Average loss: 2.9496
Iteration: 3103; Percent complete: 77.6%; Average loss: 2.7874
Iteration: 3104; Percent complete: 77.6%; Average loss: 2.6757
Iteration: 3105; Percent complete: 77.6%; Average loss: 2.7936
Iteration: 3106; Percent complete: 77.6%; Average loss: 2.9528
Iteration: 3107; Percent complete: 77.7%; Average loss: 2.7486
Iteration: 3108; Percent complete: 77.7%; Average loss: 2.9041
Iteration: 3109; Percent complete: 77.7%; Average loss: 2.8595
Iteration: 3110; Percent complete: 77.8%; Average loss: 2.9633
Iteration: 3111; Percent complete: 77.8%; Average loss: 2.4150
Iteration: 3112; Percent complete: 77.8%; Average loss: 2.9252
Iteration: 3113; Percent complete: 77.8%; Average loss: 2.7326
Iteration: 3114; Percent complete: 77.8%; Average loss: 2.8689
Iteration: 3115; Percent complete: 77.9%; Average loss: 2.7976
Iteration: 3116; Percent complete: 77.9%; Average loss: 2.7058
Iteration: 3117; Percent complete: 77.9%; Average loss: 2.9262
Iteration: 3118; Percent complete: 78.0%; Average loss: 2.8023
Iteration: 3119; Percent complete: 78.0%; Average loss: 2.6414
Iteration: 3120; Percent complete: 78.0%; Average loss: 2.7120
Iteration: 3121; Percent complete: 78.0%; Average loss: 2.8430
Iteration: 3122; Percent complete: 78.0%; Average loss: 2.9505
Iteration: 3123; Percent complete: 78.1%; Average loss: 3.0892
Iteration: 3124; Percent complete: 78.1%; Average loss: 2.6946
Iteration: 3125; Percent complete: 78.1%; Average loss: 2.7470
Iteration: 3126; Percent complete: 78.1%; Average loss: 2.6886
Iteration: 3127; Percent complete: 78.2%; Average loss: 2.9543
Iteration: 3128; Percent complete: 78.2%; Average loss: 2.6327
Iteration: 3129; Percent complete: 78.2%; Average loss: 2.6013
Iteration: 3130; Percent complete: 78.2%; Average loss: 2.9876
Iteration: 3131; Percent complete: 78.3%; Average loss: 2.8651
Iteration: 3132; Percent complete: 78.3%; Average loss: 2.8916
Iteration: 3133; Percent complete: 78.3%; Average loss: 2.9468
Iteration: 3134; Percent complete: 78.3%; Average loss: 3.0271
Iteration: 3135; Percent complete: 78.4%; Average loss: 3.0078
Iteration: 3136; Percent complete: 78.4%; Average loss: 2.9036
Iteration: 3137; Percent complete: 78.4%; Average loss: 2.6169
Iteration: 3138; Percent complete: 78.5%; Average loss: 2.5628
Iteration: 3139; Percent complete: 78.5%; Average loss: 2.8241
Iteration: 3140; Percent complete: 78.5%; Average loss: 2.8019
Iteration: 3141; Percent complete: 78.5%; Average loss: 2.7458
Iteration: 3142; Percent complete: 78.5%; Average loss: 2.8544
Iteration: 3143; Percent complete: 78.6%; Average loss: 3.0794
Iteration: 3144; Percent complete: 78.6%; Average loss: 2.6872
Iteration: 3145; Percent complete: 78.6%; Average loss: 2.7748
Iteration: 3146; Percent complete: 78.6%; Average loss: 2.9549
Iteration: 3147; Percent complete: 78.7%; Average loss: 2.7092
Iteration: 3148; Percent complete: 78.7%; Average loss: 2.9908
Iteration: 3149; Percent complete: 78.7%; Average loss: 2.7691
Iteration: 3150; Percent complete: 78.8%; Average loss: 2.7273
Iteration: 3151; Percent complete: 78.8%; Average loss: 2.8782
Iteration: 3152; Percent complete: 78.8%; Average loss: 2.8668
Iteration: 3153; Percent complete: 78.8%; Average loss: 2.7863
Iteration: 3154; Percent complete: 78.8%; Average loss: 2.8819
Iteration: 3155; Percent complete: 78.9%; Average loss: 2.7371
Iteration: 3156; Percent complete: 78.9%; Average loss: 2.9131
Iteration: 3157; Percent complete: 78.9%; Average loss: 2.6319
Iteration: 3158; Percent complete: 79.0%; Average loss: 2.9637
Iteration: 3159; Percent complete: 79.0%; Average loss: 2.8271
Iteration: 3160; Percent complete: 79.0%; Average loss: 2.7289
Iteration: 3161; Percent complete: 79.0%; Average loss: 2.9295
Iteration: 3162; Percent complete: 79.0%; Average loss: 2.7313
Iteration: 3163; Percent complete: 79.1%; Average loss: 2.7557
Iteration: 3164; Percent complete: 79.1%; Average loss: 3.1413
Iteration: 3165; Percent complete: 79.1%; Average loss: 2.6950
Iteration: 3166; Percent complete: 79.1%; Average loss: 2.9410
Iteration: 3167; Percent complete: 79.2%; Average loss: 2.6790
Iteration: 3168; Percent complete: 79.2%; Average loss: 2.7317
Iteration: 3169; Percent complete: 79.2%; Average loss: 2.7266
Iteration: 3170; Percent complete: 79.2%; Average loss: 2.8532
Iteration: 3171; Percent complete: 79.3%; Average loss: 2.9545
Iteration: 3172; Percent complete: 79.3%; Average loss: 2.9748
Iteration: 3173; Percent complete: 79.3%; Average loss: 2.8483
Iteration: 3174; Percent complete: 79.3%; Average loss: 2.7602
Iteration: 3175; Percent complete: 79.4%; Average loss: 2.8550
Iteration: 3176; Percent complete: 79.4%; Average loss: 2.5609
Iteration: 3177; Percent complete: 79.4%; Average loss: 2.5878
Iteration: 3178; Percent complete: 79.5%; Average loss: 2.8167
Iteration: 3179; Percent complete: 79.5%; Average loss: 2.6140
Iteration: 3180; Percent complete: 79.5%; Average loss: 2.6517
Iteration: 3181; Percent complete: 79.5%; Average loss: 2.6815
Iteration: 3182; Percent complete: 79.5%; Average loss: 2.8416
Iteration: 3183; Percent complete: 79.6%; Average loss: 2.9629
Iteration: 3184; Percent complete: 79.6%; Average loss: 2.7870
Iteration: 3185; Percent complete: 79.6%; Average loss: 2.9309
Iteration: 3186; Percent complete: 79.7%; Average loss: 2.8224
Iteration: 3187; Percent complete: 79.7%; Average loss: 2.6705
Iteration: 3188; Percent complete: 79.7%; Average loss: 2.9696
Iteration: 3189; Percent complete: 79.7%; Average loss: 2.6374
Iteration: 3190; Percent complete: 79.8%; Average loss: 2.8937
Iteration: 3191; Percent complete: 79.8%; Average loss: 2.9023
Iteration: 3192; Percent complete: 79.8%; Average loss: 2.7678
Iteration: 3193; Percent complete: 79.8%; Average loss: 2.9341
Iteration: 3194; Percent complete: 79.8%; Average loss: 2.9452
Iteration: 3195; Percent complete: 79.9%; Average loss: 2.6960
Iteration: 3196; Percent complete: 79.9%; Average loss: 2.8871
Iteration: 3197; Percent complete: 79.9%; Average loss: 2.9352
Iteration: 3198; Percent complete: 80.0%; Average loss: 2.7389
Iteration: 3199; Percent complete: 80.0%; Average loss: 2.7128
Iteration: 3200; Percent complete: 80.0%; Average loss: 3.0392
Iteration: 3201; Percent complete: 80.0%; Average loss: 2.9473
Iteration: 3202; Percent complete: 80.0%; Average loss: 2.7630
Iteration: 3203; Percent complete: 80.1%; Average loss: 2.7764
Iteration: 3204; Percent complete: 80.1%; Average loss: 2.7764
Iteration: 3205; Percent complete: 80.1%; Average loss: 2.7722
Iteration: 3206; Percent complete: 80.2%; Average loss: 2.6448
Iteration: 3207; Percent complete: 80.2%; Average loss: 2.7286
Iteration: 3208; Percent complete: 80.2%; Average loss: 2.6847
Iteration: 3209; Percent complete: 80.2%; Average loss: 2.6581
Iteration: 3210; Percent complete: 80.2%; Average loss: 2.8376
Iteration: 3211; Percent complete: 80.3%; Average loss: 2.9861
Iteration: 3212; Percent complete: 80.3%; Average loss: 2.9937
Iteration: 3213; Percent complete: 80.3%; Average loss: 2.8150
Iteration: 3214; Percent complete: 80.3%; Average loss: 2.7778
Iteration: 3215; Percent complete: 80.4%; Average loss: 2.7966
Iteration: 3216; Percent complete: 80.4%; Average loss: 2.7949
Iteration: 3217; Percent complete: 80.4%; Average loss: 2.7332
Iteration: 3218; Percent complete: 80.5%; Average loss: 2.6217
Iteration: 3219; Percent complete: 80.5%; Average loss: 2.7780
Iteration: 3220; Percent complete: 80.5%; Average loss: 2.8935
Iteration: 3221; Percent complete: 80.5%; Average loss: 2.8037
Iteration: 3222; Percent complete: 80.5%; Average loss: 2.5716
Iteration: 3223; Percent complete: 80.6%; Average loss: 2.8971
Iteration: 3224; Percent complete: 80.6%; Average loss: 2.5893
Iteration: 3225; Percent complete: 80.6%; Average loss: 2.9665
Iteration: 3226; Percent complete: 80.7%; Average loss: 2.7812
Iteration: 3227; Percent complete: 80.7%; Average loss: 2.6221
Iteration: 3228; Percent complete: 80.7%; Average loss: 2.6948
Iteration: 3229; Percent complete: 80.7%; Average loss: 2.6016
Iteration: 3230; Percent complete: 80.8%; Average loss: 2.8645
Iteration: 3231; Percent complete: 80.8%; Average loss: 2.8630
Iteration: 3232; Percent complete: 80.8%; Average loss: 2.8041
Iteration: 3233; Percent complete: 80.8%; Average loss: 2.9686
Iteration: 3234; Percent complete: 80.8%; Average loss: 2.8510
Iteration: 3235; Percent complete: 80.9%; Average loss: 2.7420
Iteration: 3236; Percent complete: 80.9%; Average loss: 2.7116
Iteration: 3237; Percent complete: 80.9%; Average loss: 2.7927
Iteration: 3238; Percent complete: 81.0%; Average loss: 2.7730
Iteration: 3239; Percent complete: 81.0%; Average loss: 2.8874
Iteration: 3240; Percent complete: 81.0%; Average loss: 2.7221
Iteration: 3241; Percent complete: 81.0%; Average loss: 2.9426
Iteration: 3242; Percent complete: 81.0%; Average loss: 3.1379
Iteration: 3243; Percent complete: 81.1%; Average loss: 2.6346
Iteration: 3244; Percent complete: 81.1%; Average loss: 3.0739
Iteration: 3245; Percent complete: 81.1%; Average loss: 3.0389
Iteration: 3246; Percent complete: 81.2%; Average loss: 2.7620
Iteration: 3247; Percent complete: 81.2%; Average loss: 2.7436
Iteration: 3248; Percent complete: 81.2%; Average loss: 2.6830
Iteration: 3249; Percent complete: 81.2%; Average loss: 2.8506
Iteration: 3250; Percent complete: 81.2%; Average loss: 2.5663
Iteration: 3251; Percent complete: 81.3%; Average loss: 2.6877
Iteration: 3252; Percent complete: 81.3%; Average loss: 2.7084
Iteration: 3253; Percent complete: 81.3%; Average loss: 2.5438
Iteration: 3254; Percent complete: 81.3%; Average loss: 2.7809
Iteration: 3255; Percent complete: 81.4%; Average loss: 2.6850
Iteration: 3256; Percent complete: 81.4%; Average loss: 2.7414
Iteration: 3257; Percent complete: 81.4%; Average loss: 2.7887
Iteration: 3258; Percent complete: 81.5%; Average loss: 2.8892
Iteration: 3259; Percent complete: 81.5%; Average loss: 2.7280
Iteration: 3260; Percent complete: 81.5%; Average loss: 2.9785
Iteration: 3261; Percent complete: 81.5%; Average loss: 2.7898
Iteration: 3262; Percent complete: 81.5%; Average loss: 2.9967
Iteration: 3263; Percent complete: 81.6%; Average loss: 2.8597
Iteration: 3264; Percent complete: 81.6%; Average loss: 2.7989
Iteration: 3265; Percent complete: 81.6%; Average loss: 2.7163
Iteration: 3266; Percent complete: 81.7%; Average loss: 2.8763
Iteration: 3267; Percent complete: 81.7%; Average loss: 2.7786
Iteration: 3268; Percent complete: 81.7%; Average loss: 2.7857
Iteration: 3269; Percent complete: 81.7%; Average loss: 2.7425
Iteration: 3270; Percent complete: 81.8%; Average loss: 3.0217
Iteration: 3271; Percent complete: 81.8%; Average loss: 2.6245
Iteration: 3272; Percent complete: 81.8%; Average loss: 2.7613
Iteration: 3273; Percent complete: 81.8%; Average loss: 2.8275
Iteration: 3274; Percent complete: 81.8%; Average loss: 2.8504
Iteration: 3275; Percent complete: 81.9%; Average loss: 2.7752
Iteration: 3276; Percent complete: 81.9%; Average loss: 2.9120
Iteration: 3277; Percent complete: 81.9%; Average loss: 3.0724
Iteration: 3278; Percent complete: 82.0%; Average loss: 2.7088
Iteration: 3279; Percent complete: 82.0%; Average loss: 2.5819
Iteration: 3280; Percent complete: 82.0%; Average loss: 2.7939
Iteration: 3281; Percent complete: 82.0%; Average loss: 2.6309
Iteration: 3282; Percent complete: 82.0%; Average loss: 2.7999
Iteration: 3283; Percent complete: 82.1%; Average loss: 2.7221
Iteration: 3284; Percent complete: 82.1%; Average loss: 2.8378
Iteration: 3285; Percent complete: 82.1%; Average loss: 2.8561
Iteration: 3286; Percent complete: 82.2%; Average loss: 2.9595
Iteration: 3287; Percent complete: 82.2%; Average loss: 2.8400
Iteration: 3288; Percent complete: 82.2%; Average loss: 2.7463
Iteration: 3289; Percent complete: 82.2%; Average loss: 2.7794
Iteration: 3290; Percent complete: 82.2%; Average loss: 2.9354
Iteration: 3291; Percent complete: 82.3%; Average loss: 2.9053
Iteration: 3292; Percent complete: 82.3%; Average loss: 2.6375
Iteration: 3293; Percent complete: 82.3%; Average loss: 2.6920
Iteration: 3294; Percent complete: 82.3%; Average loss: 2.8669
Iteration: 3295; Percent complete: 82.4%; Average loss: 2.6255
Iteration: 3296; Percent complete: 82.4%; Average loss: 2.6265
Iteration: 3297; Percent complete: 82.4%; Average loss: 2.6911
Iteration: 3298; Percent complete: 82.5%; Average loss: 2.9886
Iteration: 3299; Percent complete: 82.5%; Average loss: 2.7091
Iteration: 3300; Percent complete: 82.5%; Average loss: 2.7714
Iteration: 3301; Percent complete: 82.5%; Average loss: 2.7640
Iteration: 3302; Percent complete: 82.5%; Average loss: 2.9939
Iteration: 3303; Percent complete: 82.6%; Average loss: 3.0744
Iteration: 3304; Percent complete: 82.6%; Average loss: 2.8065
Iteration: 3305; Percent complete: 82.6%; Average loss: 2.7979
Iteration: 3306; Percent complete: 82.7%; Average loss: 2.6739
Iteration: 3307; Percent complete: 82.7%; Average loss: 2.8120
Iteration: 3308; Percent complete: 82.7%; Average loss: 2.5489
Iteration: 3309; Percent complete: 82.7%; Average loss: 2.7776
Iteration: 3310; Percent complete: 82.8%; Average loss: 2.9369
Iteration: 3311; Percent complete: 82.8%; Average loss: 2.8036
Iteration: 3312; Percent complete: 82.8%; Average loss: 2.7491
Iteration: 3313; Percent complete: 82.8%; Average loss: 2.5943
Iteration: 3314; Percent complete: 82.8%; Average loss: 2.7889
Iteration: 3315; Percent complete: 82.9%; Average loss: 2.9794
Iteration: 3316; Percent complete: 82.9%; Average loss: 2.8050
Iteration: 3317; Percent complete: 82.9%; Average loss: 2.8544
Iteration: 3318; Percent complete: 83.0%; Average loss: 3.1033
Iteration: 3319; Percent complete: 83.0%; Average loss: 2.9128
Iteration: 3320; Percent complete: 83.0%; Average loss: 2.7250
Iteration: 3321; Percent complete: 83.0%; Average loss: 2.7384
Iteration: 3322; Percent complete: 83.0%; Average loss: 2.7610
Iteration: 3323; Percent complete: 83.1%; Average loss: 2.6516
Iteration: 3324; Percent complete: 83.1%; Average loss: 2.5759
Iteration: 3325; Percent complete: 83.1%; Average loss: 2.8277
Iteration: 3326; Percent complete: 83.2%; Average loss: 2.6599
Iteration: 3327; Percent complete: 83.2%; Average loss: 2.7738
Iteration: 3328; Percent complete: 83.2%; Average loss: 2.6193
Iteration: 3329; Percent complete: 83.2%; Average loss: 2.7914
Iteration: 3330; Percent complete: 83.2%; Average loss: 2.7833
Iteration: 3331; Percent complete: 83.3%; Average loss: 2.6587
Iteration: 3332; Percent complete: 83.3%; Average loss: 2.5463
Iteration: 3333; Percent complete: 83.3%; Average loss: 2.6638
Iteration: 3334; Percent complete: 83.4%; Average loss: 2.9579
Iteration: 3335; Percent complete: 83.4%; Average loss: 2.7120
Iteration: 3336; Percent complete: 83.4%; Average loss: 2.7567
Iteration: 3337; Percent complete: 83.4%; Average loss: 2.6608
Iteration: 3338; Percent complete: 83.5%; Average loss: 2.4325
Iteration: 3339; Percent complete: 83.5%; Average loss: 2.6778
Iteration: 3340; Percent complete: 83.5%; Average loss: 2.7624
Iteration: 3341; Percent complete: 83.5%; Average loss: 2.7549
Iteration: 3342; Percent complete: 83.5%; Average loss: 2.7864
Iteration: 3343; Percent complete: 83.6%; Average loss: 2.7721
Iteration: 3344; Percent complete: 83.6%; Average loss: 2.7572
Iteration: 3345; Percent complete: 83.6%; Average loss: 2.8731
Iteration: 3346; Percent complete: 83.7%; Average loss: 2.8693
Iteration: 3347; Percent complete: 83.7%; Average loss: 2.8884
Iteration: 3348; Percent complete: 83.7%; Average loss: 2.7442
Iteration: 3349; Percent complete: 83.7%; Average loss: 2.6442
Iteration: 3350; Percent complete: 83.8%; Average loss: 2.6670
Iteration: 3351; Percent complete: 83.8%; Average loss: 2.8376
Iteration: 3352; Percent complete: 83.8%; Average loss: 2.6801
Iteration: 3353; Percent complete: 83.8%; Average loss: 3.0138
Iteration: 3354; Percent complete: 83.9%; Average loss: 2.9538
Iteration: 3355; Percent complete: 83.9%; Average loss: 3.0340
Iteration: 3356; Percent complete: 83.9%; Average loss: 2.8542
Iteration: 3357; Percent complete: 83.9%; Average loss: 2.7626
Iteration: 3358; Percent complete: 84.0%; Average loss: 2.6757
Iteration: 3359; Percent complete: 84.0%; Average loss: 2.7266
Iteration: 3360; Percent complete: 84.0%; Average loss: 2.6795
Iteration: 3361; Percent complete: 84.0%; Average loss: 2.6479
Iteration: 3362; Percent complete: 84.0%; Average loss: 2.8751
Iteration: 3363; Percent complete: 84.1%; Average loss: 2.8919
Iteration: 3364; Percent complete: 84.1%; Average loss: 2.5891
Iteration: 3365; Percent complete: 84.1%; Average loss: 2.7679
Iteration: 3366; Percent complete: 84.2%; Average loss: 2.6103
Iteration: 3367; Percent complete: 84.2%; Average loss: 2.4247
Iteration: 3368; Percent complete: 84.2%; Average loss: 2.8617
Iteration: 3369; Percent complete: 84.2%; Average loss: 2.6647
Iteration: 3370; Percent complete: 84.2%; Average loss: 2.8874
Iteration: 3371; Percent complete: 84.3%; Average loss: 2.8869
Iteration: 3372; Percent complete: 84.3%; Average loss: 2.8207
Iteration: 3373; Percent complete: 84.3%; Average loss: 2.8333
Iteration: 3374; Percent complete: 84.4%; Average loss: 2.9725
Iteration: 3375; Percent complete: 84.4%; Average loss: 2.6585
Iteration: 3376; Percent complete: 84.4%; Average loss: 2.6805
Iteration: 3377; Percent complete: 84.4%; Average loss: 2.7903
Iteration: 3378; Percent complete: 84.5%; Average loss: 2.8967
Iteration: 3379; Percent complete: 84.5%; Average loss: 2.7827
Iteration: 3380; Percent complete: 84.5%; Average loss: 2.7268
Iteration: 3381; Percent complete: 84.5%; Average loss: 2.6961
Iteration: 3382; Percent complete: 84.5%; Average loss: 2.8050
Iteration: 3383; Percent complete: 84.6%; Average loss: 2.7361
Iteration: 3384; Percent complete: 84.6%; Average loss: 2.5695
Iteration: 3385; Percent complete: 84.6%; Average loss: 2.6682
Iteration: 3386; Percent complete: 84.7%; Average loss: 2.6901
Iteration: 3387; Percent complete: 84.7%; Average loss: 2.8878
Iteration: 3388; Percent complete: 84.7%; Average loss: 2.8893
Iteration: 3389; Percent complete: 84.7%; Average loss: 2.9552
Iteration: 3390; Percent complete: 84.8%; Average loss: 2.6240
Iteration: 3391; Percent complete: 84.8%; Average loss: 2.6137
Iteration: 3392; Percent complete: 84.8%; Average loss: 2.8172
Iteration: 3393; Percent complete: 84.8%; Average loss: 2.6398
Iteration: 3394; Percent complete: 84.9%; Average loss: 2.8958
Iteration: 3395; Percent complete: 84.9%; Average loss: 2.5506
Iteration: 3396; Percent complete: 84.9%; Average loss: 2.5666
Iteration: 3397; Percent complete: 84.9%; Average loss: 2.6966
Iteration: 3398; Percent complete: 85.0%; Average loss: 2.8584
Iteration: 3399; Percent complete: 85.0%; Average loss: 2.6484
Iteration: 3400; Percent complete: 85.0%; Average loss: 2.6659
Iteration: 3401; Percent complete: 85.0%; Average loss: 2.7997
Iteration: 3402; Percent complete: 85.0%; Average loss: 2.5551
Iteration: 3403; Percent complete: 85.1%; Average loss: 3.0014
Iteration: 3404; Percent complete: 85.1%; Average loss: 2.9175
Iteration: 3405; Percent complete: 85.1%; Average loss: 2.7356
Iteration: 3406; Percent complete: 85.2%; Average loss: 3.0235
Iteration: 3407; Percent complete: 85.2%; Average loss: 2.7753
Iteration: 3408; Percent complete: 85.2%; Average loss: 2.7515
Iteration: 3409; Percent complete: 85.2%; Average loss: 2.7262
Iteration: 3410; Percent complete: 85.2%; Average loss: 2.7280
Iteration: 3411; Percent complete: 85.3%; Average loss: 2.5476
Iteration: 3412; Percent complete: 85.3%; Average loss: 2.8674
Iteration: 3413; Percent complete: 85.3%; Average loss: 2.7078
Iteration: 3414; Percent complete: 85.4%; Average loss: 2.8010
Iteration: 3415; Percent complete: 85.4%; Average loss: 2.9046
Iteration: 3416; Percent complete: 85.4%; Average loss: 2.7911
Iteration: 3417; Percent complete: 85.4%; Average loss: 2.8237
Iteration: 3418; Percent complete: 85.5%; Average loss: 2.6711
Iteration: 3419; Percent complete: 85.5%; Average loss: 2.6441
Iteration: 3420; Percent complete: 85.5%; Average loss: 2.7571
Iteration: 3421; Percent complete: 85.5%; Average loss: 2.6625
Iteration: 3422; Percent complete: 85.5%; Average loss: 2.6011
Iteration: 3423; Percent complete: 85.6%; Average loss: 2.7535
Iteration: 3424; Percent complete: 85.6%; Average loss: 2.8468
Iteration: 3425; Percent complete: 85.6%; Average loss: 2.7596
Iteration: 3426; Percent complete: 85.7%; Average loss: 2.6327
Iteration: 3427; Percent complete: 85.7%; Average loss: 2.7403
Iteration: 3428; Percent complete: 85.7%; Average loss: 2.7036
Iteration: 3429; Percent complete: 85.7%; Average loss: 2.3660
Iteration: 3430; Percent complete: 85.8%; Average loss: 2.9494
Iteration: 3431; Percent complete: 85.8%; Average loss: 2.8229
Iteration: 3432; Percent complete: 85.8%; Average loss: 2.9394
Iteration: 3433; Percent complete: 85.8%; Average loss: 2.6673
Iteration: 3434; Percent complete: 85.9%; Average loss: 2.6304
Iteration: 3435; Percent complete: 85.9%; Average loss: 2.5139
Iteration: 3436; Percent complete: 85.9%; Average loss: 2.5614
Iteration: 3437; Percent complete: 85.9%; Average loss: 2.8271
Iteration: 3438; Percent complete: 86.0%; Average loss: 2.9774
Iteration: 3439; Percent complete: 86.0%; Average loss: 2.7586
Iteration: 3440; Percent complete: 86.0%; Average loss: 2.8562
Iteration: 3441; Percent complete: 86.0%; Average loss: 2.6091
Iteration: 3442; Percent complete: 86.1%; Average loss: 2.7443
Iteration: 3443; Percent complete: 86.1%; Average loss: 2.6874
Iteration: 3444; Percent complete: 86.1%; Average loss: 2.6897
Iteration: 3445; Percent complete: 86.1%; Average loss: 2.7308
Iteration: 3446; Percent complete: 86.2%; Average loss: 2.6582
Iteration: 3447; Percent complete: 86.2%; Average loss: 2.6130
Iteration: 3448; Percent complete: 86.2%; Average loss: 2.6140
Iteration: 3449; Percent complete: 86.2%; Average loss: 2.6609
Iteration: 3450; Percent complete: 86.2%; Average loss: 2.7809
Iteration: 3451; Percent complete: 86.3%; Average loss: 2.8366
Iteration: 3452; Percent complete: 86.3%; Average loss: 2.6990
Iteration: 3453; Percent complete: 86.3%; Average loss: 2.6245
Iteration: 3454; Percent complete: 86.4%; Average loss: 2.7236
Iteration: 3455; Percent complete: 86.4%; Average loss: 2.7094
Iteration: 3456; Percent complete: 86.4%; Average loss: 2.8015
Iteration: 3457; Percent complete: 86.4%; Average loss: 2.6374
Iteration: 3458; Percent complete: 86.5%; Average loss: 2.7204
Iteration: 3459; Percent complete: 86.5%; Average loss: 2.8283
Iteration: 3460; Percent complete: 86.5%; Average loss: 2.8526
Iteration: 3461; Percent complete: 86.5%; Average loss: 2.6795
Iteration: 3462; Percent complete: 86.6%; Average loss: 2.7674
Iteration: 3463; Percent complete: 86.6%; Average loss: 2.7573
Iteration: 3464; Percent complete: 86.6%; Average loss: 2.9804
Iteration: 3465; Percent complete: 86.6%; Average loss: 2.9407
Iteration: 3466; Percent complete: 86.7%; Average loss: 2.8433
Iteration: 3467; Percent complete: 86.7%; Average loss: 3.0304
Iteration: 3468; Percent complete: 86.7%; Average loss: 2.8882
Iteration: 3469; Percent complete: 86.7%; Average loss: 2.8139
Iteration: 3470; Percent complete: 86.8%; Average loss: 2.6361
Iteration: 3471; Percent complete: 86.8%; Average loss: 2.8270
Iteration: 3472; Percent complete: 86.8%; Average loss: 2.6175
Iteration: 3473; Percent complete: 86.8%; Average loss: 2.8045
Iteration: 3474; Percent complete: 86.9%; Average loss: 2.9265
Iteration: 3475; Percent complete: 86.9%; Average loss: 2.7495
Iteration: 3476; Percent complete: 86.9%; Average loss: 2.9711
Iteration: 3477; Percent complete: 86.9%; Average loss: 2.7120
Iteration: 3478; Percent complete: 87.0%; Average loss: 2.9512
Iteration: 3479; Percent complete: 87.0%; Average loss: 2.4328
Iteration: 3480; Percent complete: 87.0%; Average loss: 2.8066
Iteration: 3481; Percent complete: 87.0%; Average loss: 2.7747
Iteration: 3482; Percent complete: 87.1%; Average loss: 2.7196
Iteration: 3483; Percent complete: 87.1%; Average loss: 3.1317
Iteration: 3484; Percent complete: 87.1%; Average loss: 2.7251
Iteration: 3485; Percent complete: 87.1%; Average loss: 2.6350
Iteration: 3486; Percent complete: 87.2%; Average loss: 2.7266
Iteration: 3487; Percent complete: 87.2%; Average loss: 2.5727
Iteration: 3488; Percent complete: 87.2%; Average loss: 2.7558
Iteration: 3489; Percent complete: 87.2%; Average loss: 2.7875
Iteration: 3490; Percent complete: 87.2%; Average loss: 2.5964
Iteration: 3491; Percent complete: 87.3%; Average loss: 2.8597
Iteration: 3492; Percent complete: 87.3%; Average loss: 2.8960
Iteration: 3493; Percent complete: 87.3%; Average loss: 2.7586
Iteration: 3494; Percent complete: 87.4%; Average loss: 2.6897
Iteration: 3495; Percent complete: 87.4%; Average loss: 2.6215
Iteration: 3496; Percent complete: 87.4%; Average loss: 2.8029
Iteration: 3497; Percent complete: 87.4%; Average loss: 2.7420
Iteration: 3498; Percent complete: 87.5%; Average loss: 2.7462
Iteration: 3499; Percent complete: 87.5%; Average loss: 2.7106
Iteration: 3500; Percent complete: 87.5%; Average loss: 2.4106
Iteration: 3501; Percent complete: 87.5%; Average loss: 2.8756
Iteration: 3502; Percent complete: 87.5%; Average loss: 2.6889
Iteration: 3503; Percent complete: 87.6%; Average loss: 2.6356
Iteration: 3504; Percent complete: 87.6%; Average loss: 2.8084
Iteration: 3505; Percent complete: 87.6%; Average loss: 2.8551
Iteration: 3506; Percent complete: 87.6%; Average loss: 2.5201
Iteration: 3507; Percent complete: 87.7%; Average loss: 2.9324
Iteration: 3508; Percent complete: 87.7%; Average loss: 2.8894
Iteration: 3509; Percent complete: 87.7%; Average loss: 2.5673
Iteration: 3510; Percent complete: 87.8%; Average loss: 2.7942
Iteration: 3511; Percent complete: 87.8%; Average loss: 2.8799
Iteration: 3512; Percent complete: 87.8%; Average loss: 2.9388
Iteration: 3513; Percent complete: 87.8%; Average loss: 2.7074
Iteration: 3514; Percent complete: 87.8%; Average loss: 2.7974
Iteration: 3515; Percent complete: 87.9%; Average loss: 2.6383
Iteration: 3516; Percent complete: 87.9%; Average loss: 2.6496
Iteration: 3517; Percent complete: 87.9%; Average loss: 2.6124
Iteration: 3518; Percent complete: 87.9%; Average loss: 2.7511
Iteration: 3519; Percent complete: 88.0%; Average loss: 2.5520
Iteration: 3520; Percent complete: 88.0%; Average loss: 2.6993
Iteration: 3521; Percent complete: 88.0%; Average loss: 2.7166
Iteration: 3522; Percent complete: 88.0%; Average loss: 2.5598
Iteration: 3523; Percent complete: 88.1%; Average loss: 2.7794
Iteration: 3524; Percent complete: 88.1%; Average loss: 2.5177
Iteration: 3525; Percent complete: 88.1%; Average loss: 2.9623
Iteration: 3526; Percent complete: 88.1%; Average loss: 2.9012
Iteration: 3527; Percent complete: 88.2%; Average loss: 2.8991
Iteration: 3528; Percent complete: 88.2%; Average loss: 2.6748
Iteration: 3529; Percent complete: 88.2%; Average loss: 2.7369
Iteration: 3530; Percent complete: 88.2%; Average loss: 3.0640
Iteration: 3531; Percent complete: 88.3%; Average loss: 2.6764
Iteration: 3532; Percent complete: 88.3%; Average loss: 2.7756
Iteration: 3533; Percent complete: 88.3%; Average loss: 2.6319
Iteration: 3534; Percent complete: 88.3%; Average loss: 2.6662
Iteration: 3535; Percent complete: 88.4%; Average loss: 2.7107
Iteration: 3536; Percent complete: 88.4%; Average loss: 2.7167
Iteration: 3537; Percent complete: 88.4%; Average loss: 2.5116
Iteration: 3538; Percent complete: 88.4%; Average loss: 2.8476
Iteration: 3539; Percent complete: 88.5%; Average loss: 2.6705
Iteration: 3540; Percent complete: 88.5%; Average loss: 2.6609
Iteration: 3541; Percent complete: 88.5%; Average loss: 2.8090
Iteration: 3542; Percent complete: 88.5%; Average loss: 2.7324
Iteration: 3543; Percent complete: 88.6%; Average loss: 2.6771
Iteration: 3544; Percent complete: 88.6%; Average loss: 2.6387
Iteration: 3545; Percent complete: 88.6%; Average loss: 2.8650
Iteration: 3546; Percent complete: 88.6%; Average loss: 2.5561
Iteration: 3547; Percent complete: 88.7%; Average loss: 2.5561
Iteration: 3548; Percent complete: 88.7%; Average loss: 2.9712
Iteration: 3549; Percent complete: 88.7%; Average loss: 2.8024
Iteration: 3550; Percent complete: 88.8%; Average loss: 2.9264
Iteration: 3551; Percent complete: 88.8%; Average loss: 2.8319
Iteration: 3552; Percent complete: 88.8%; Average loss: 2.5961
Iteration: 3553; Percent complete: 88.8%; Average loss: 2.6943
Iteration: 3554; Percent complete: 88.8%; Average loss: 2.7598
Iteration: 3555; Percent complete: 88.9%; Average loss: 2.6927
Iteration: 3556; Percent complete: 88.9%; Average loss: 2.8715
Iteration: 3557; Percent complete: 88.9%; Average loss: 2.7682
Iteration: 3558; Percent complete: 88.9%; Average loss: 2.7241
Iteration: 3559; Percent complete: 89.0%; Average loss: 2.8001
Iteration: 3560; Percent complete: 89.0%; Average loss: 2.7883
Iteration: 3561; Percent complete: 89.0%; Average loss: 2.6005
Iteration: 3562; Percent complete: 89.0%; Average loss: 2.3866
Iteration: 3563; Percent complete: 89.1%; Average loss: 2.8527
Iteration: 3564; Percent complete: 89.1%; Average loss: 2.9017
Iteration: 3565; Percent complete: 89.1%; Average loss: 2.7516
Iteration: 3566; Percent complete: 89.1%; Average loss: 2.4963
Iteration: 3567; Percent complete: 89.2%; Average loss: 2.9681
Iteration: 3568; Percent complete: 89.2%; Average loss: 2.5940
Iteration: 3569; Percent complete: 89.2%; Average loss: 2.6622
Iteration: 3570; Percent complete: 89.2%; Average loss: 2.8949
Iteration: 3571; Percent complete: 89.3%; Average loss: 2.4675
Iteration: 3572; Percent complete: 89.3%; Average loss: 2.8213
Iteration: 3573; Percent complete: 89.3%; Average loss: 2.4939
Iteration: 3574; Percent complete: 89.3%; Average loss: 2.8551
Iteration: 3575; Percent complete: 89.4%; Average loss: 2.6465
Iteration: 3576; Percent complete: 89.4%; Average loss: 2.4560
Iteration: 3577; Percent complete: 89.4%; Average loss: 3.0132
Iteration: 3578; Percent complete: 89.5%; Average loss: 2.8187
Iteration: 3579; Percent complete: 89.5%; Average loss: 2.5613
Iteration: 3580; Percent complete: 89.5%; Average loss: 2.7846
Iteration: 3581; Percent complete: 89.5%; Average loss: 2.6966
Iteration: 3582; Percent complete: 89.5%; Average loss: 2.8437
Iteration: 3583; Percent complete: 89.6%; Average loss: 2.7150
Iteration: 3584; Percent complete: 89.6%; Average loss: 2.6321
Iteration: 3585; Percent complete: 89.6%; Average loss: 2.8321
Iteration: 3586; Percent complete: 89.6%; Average loss: 2.6822
Iteration: 3587; Percent complete: 89.7%; Average loss: 2.7544
Iteration: 3588; Percent complete: 89.7%; Average loss: 2.5927
Iteration: 3589; Percent complete: 89.7%; Average loss: 2.9562
Iteration: 3590; Percent complete: 89.8%; Average loss: 2.5695
Iteration: 3591; Percent complete: 89.8%; Average loss: 2.8636
Iteration: 3592; Percent complete: 89.8%; Average loss: 2.7301
Iteration: 3593; Percent complete: 89.8%; Average loss: 2.6033
Iteration: 3594; Percent complete: 89.8%; Average loss: 2.6314
Iteration: 3595; Percent complete: 89.9%; Average loss: 2.8008
Iteration: 3596; Percent complete: 89.9%; Average loss: 2.7193
Iteration: 3597; Percent complete: 89.9%; Average loss: 2.8288
Iteration: 3598; Percent complete: 90.0%; Average loss: 2.8511
Iteration: 3599; Percent complete: 90.0%; Average loss: 2.8628
Iteration: 3600; Percent complete: 90.0%; Average loss: 2.5357
Iteration: 3601; Percent complete: 90.0%; Average loss: 2.7837
Iteration: 3602; Percent complete: 90.0%; Average loss: 2.8161
Iteration: 3603; Percent complete: 90.1%; Average loss: 2.6808
Iteration: 3604; Percent complete: 90.1%; Average loss: 2.8293
Iteration: 3605; Percent complete: 90.1%; Average loss: 2.8600
Iteration: 3606; Percent complete: 90.1%; Average loss: 2.8121
Iteration: 3607; Percent complete: 90.2%; Average loss: 2.7465
Iteration: 3608; Percent complete: 90.2%; Average loss: 2.6361
Iteration: 3609; Percent complete: 90.2%; Average loss: 2.4563
Iteration: 3610; Percent complete: 90.2%; Average loss: 2.9350
Iteration: 3611; Percent complete: 90.3%; Average loss: 2.7618
Iteration: 3612; Percent complete: 90.3%; Average loss: 2.6174
Iteration: 3613; Percent complete: 90.3%; Average loss: 2.5816
Iteration: 3614; Percent complete: 90.3%; Average loss: 2.7100
Iteration: 3615; Percent complete: 90.4%; Average loss: 2.5583
Iteration: 3616; Percent complete: 90.4%; Average loss: 2.8720
Iteration: 3617; Percent complete: 90.4%; Average loss: 2.6251
Iteration: 3618; Percent complete: 90.5%; Average loss: 2.7617
Iteration: 3619; Percent complete: 90.5%; Average loss: 2.7484
Iteration: 3620; Percent complete: 90.5%; Average loss: 2.7846
Iteration: 3621; Percent complete: 90.5%; Average loss: 2.5472
Iteration: 3622; Percent complete: 90.5%; Average loss: 2.8269
Iteration: 3623; Percent complete: 90.6%; Average loss: 2.9334
Iteration: 3624; Percent complete: 90.6%; Average loss: 2.6522
Iteration: 3625; Percent complete: 90.6%; Average loss: 2.7960
Iteration: 3626; Percent complete: 90.6%; Average loss: 2.6610
Iteration: 3627; Percent complete: 90.7%; Average loss: 2.7364
Iteration: 3628; Percent complete: 90.7%; Average loss: 2.7532
Iteration: 3629; Percent complete: 90.7%; Average loss: 2.5389
Iteration: 3630; Percent complete: 90.8%; Average loss: 2.8808
Iteration: 3631; Percent complete: 90.8%; Average loss: 2.5860
Iteration: 3632; Percent complete: 90.8%; Average loss: 2.7303
Iteration: 3633; Percent complete: 90.8%; Average loss: 2.4578
Iteration: 3634; Percent complete: 90.8%; Average loss: 2.5835
Iteration: 3635; Percent complete: 90.9%; Average loss: 2.5172
Iteration: 3636; Percent complete: 90.9%; Average loss: 2.5603
Iteration: 3637; Percent complete: 90.9%; Average loss: 2.6519
Iteration: 3638; Percent complete: 91.0%; Average loss: 2.6355
Iteration: 3639; Percent complete: 91.0%; Average loss: 2.5423
Iteration: 3640; Percent complete: 91.0%; Average loss: 2.9458
Iteration: 3641; Percent complete: 91.0%; Average loss: 2.5448
Iteration: 3642; Percent complete: 91.0%; Average loss: 2.6137
Iteration: 3643; Percent complete: 91.1%; Average loss: 2.6805
Iteration: 3644; Percent complete: 91.1%; Average loss: 2.7998
Iteration: 3645; Percent complete: 91.1%; Average loss: 2.6137
Iteration: 3646; Percent complete: 91.1%; Average loss: 2.5421
Iteration: 3647; Percent complete: 91.2%; Average loss: 2.5911
Iteration: 3648; Percent complete: 91.2%; Average loss: 2.5033
Iteration: 3649; Percent complete: 91.2%; Average loss: 2.6350
Iteration: 3650; Percent complete: 91.2%; Average loss: 2.4519
Iteration: 3651; Percent complete: 91.3%; Average loss: 2.8481
Iteration: 3652; Percent complete: 91.3%; Average loss: 2.5510
Iteration: 3653; Percent complete: 91.3%; Average loss: 2.5921
Iteration: 3654; Percent complete: 91.3%; Average loss: 2.5257
Iteration: 3655; Percent complete: 91.4%; Average loss: 2.8396
Iteration: 3656; Percent complete: 91.4%; Average loss: 2.7878
Iteration: 3657; Percent complete: 91.4%; Average loss: 2.5679
Iteration: 3658; Percent complete: 91.5%; Average loss: 2.3857
Iteration: 3659; Percent complete: 91.5%; Average loss: 2.7803
Iteration: 3660; Percent complete: 91.5%; Average loss: 2.4518
Iteration: 3661; Percent complete: 91.5%; Average loss: 2.8724
Iteration: 3662; Percent complete: 91.5%; Average loss: 2.6524
Iteration: 3663; Percent complete: 91.6%; Average loss: 2.6082
Iteration: 3664; Percent complete: 91.6%; Average loss: 2.9034
Iteration: 3665; Percent complete: 91.6%; Average loss: 2.5820
Iteration: 3666; Percent complete: 91.6%; Average loss: 2.6437
Iteration: 3667; Percent complete: 91.7%; Average loss: 2.7192
Iteration: 3668; Percent complete: 91.7%; Average loss: 2.4674
Iteration: 3669; Percent complete: 91.7%; Average loss: 2.8522
Iteration: 3670; Percent complete: 91.8%; Average loss: 2.8496
Iteration: 3671; Percent complete: 91.8%; Average loss: 2.7777
Iteration: 3672; Percent complete: 91.8%; Average loss: 2.8813
Iteration: 3673; Percent complete: 91.8%; Average loss: 2.5282
Iteration: 3674; Percent complete: 91.8%; Average loss: 2.9189
Iteration: 3675; Percent complete: 91.9%; Average loss: 3.0343
Iteration: 3676; Percent complete: 91.9%; Average loss: 2.7120
Iteration: 3677; Percent complete: 91.9%; Average loss: 2.7706
Iteration: 3678; Percent complete: 92.0%; Average loss: 2.7302
Iteration: 3679; Percent complete: 92.0%; Average loss: 2.8402
Iteration: 3680; Percent complete: 92.0%; Average loss: 2.7713
Iteration: 3681; Percent complete: 92.0%; Average loss: 2.5729
Iteration: 3682; Percent complete: 92.0%; Average loss: 2.5616
Iteration: 3683; Percent complete: 92.1%; Average loss: 2.4756
Iteration: 3684; Percent complete: 92.1%; Average loss: 2.5138
Iteration: 3685; Percent complete: 92.1%; Average loss: 2.6719
Iteration: 3686; Percent complete: 92.2%; Average loss: 2.8030
Iteration: 3687; Percent complete: 92.2%; Average loss: 2.9051
Iteration: 3688; Percent complete: 92.2%; Average loss: 2.5874
Iteration: 3689; Percent complete: 92.2%; Average loss: 2.4586
Iteration: 3690; Percent complete: 92.2%; Average loss: 2.5912
Iteration: 3691; Percent complete: 92.3%; Average loss: 2.5287
Iteration: 3692; Percent complete: 92.3%; Average loss: 2.8202
Iteration: 3693; Percent complete: 92.3%; Average loss: 2.4341
Iteration: 3694; Percent complete: 92.3%; Average loss: 2.7280
Iteration: 3695; Percent complete: 92.4%; Average loss: 2.6192
Iteration: 3696; Percent complete: 92.4%; Average loss: 2.6591
Iteration: 3697; Percent complete: 92.4%; Average loss: 2.7176
Iteration: 3698; Percent complete: 92.5%; Average loss: 2.6439
Iteration: 3699; Percent complete: 92.5%; Average loss: 2.7583
Iteration: 3700; Percent complete: 92.5%; Average loss: 2.3595
Iteration: 3701; Percent complete: 92.5%; Average loss: 2.4573
Iteration: 3702; Percent complete: 92.5%; Average loss: 2.8127
Iteration: 3703; Percent complete: 92.6%; Average loss: 2.6985
Iteration: 3704; Percent complete: 92.6%; Average loss: 2.9043
Iteration: 3705; Percent complete: 92.6%; Average loss: 2.6912
Iteration: 3706; Percent complete: 92.7%; Average loss: 2.6697
Iteration: 3707; Percent complete: 92.7%; Average loss: 2.8993
Iteration: 3708; Percent complete: 92.7%; Average loss: 3.0796
Iteration: 3709; Percent complete: 92.7%; Average loss: 2.8368
Iteration: 3710; Percent complete: 92.8%; Average loss: 2.5991
Iteration: 3711; Percent complete: 92.8%; Average loss: 2.6144
Iteration: 3712; Percent complete: 92.8%; Average loss: 2.7234
Iteration: 3713; Percent complete: 92.8%; Average loss: 2.5567
Iteration: 3714; Percent complete: 92.8%; Average loss: 2.9877
Iteration: 3715; Percent complete: 92.9%; Average loss: 2.6322
Iteration: 3716; Percent complete: 92.9%; Average loss: 2.5817
Iteration: 3717; Percent complete: 92.9%; Average loss: 2.5786
Iteration: 3718; Percent complete: 93.0%; Average loss: 2.6243
Iteration: 3719; Percent complete: 93.0%; Average loss: 2.5811
Iteration: 3720; Percent complete: 93.0%; Average loss: 2.6805
Iteration: 3721; Percent complete: 93.0%; Average loss: 2.6374
Iteration: 3722; Percent complete: 93.0%; Average loss: 2.6141
Iteration: 3723; Percent complete: 93.1%; Average loss: 2.5570
Iteration: 3724; Percent complete: 93.1%; Average loss: 2.7114
Iteration: 3725; Percent complete: 93.1%; Average loss: 2.8280
Iteration: 3726; Percent complete: 93.2%; Average loss: 2.6449
Iteration: 3727; Percent complete: 93.2%; Average loss: 2.5340
Iteration: 3728; Percent complete: 93.2%; Average loss: 2.5211
Iteration: 3729; Percent complete: 93.2%; Average loss: 2.4405
Iteration: 3730; Percent complete: 93.2%; Average loss: 2.6575
Iteration: 3731; Percent complete: 93.3%; Average loss: 2.7031
Iteration: 3732; Percent complete: 93.3%; Average loss: 2.5876
Iteration: 3733; Percent complete: 93.3%; Average loss: 2.5729
Iteration: 3734; Percent complete: 93.3%; Average loss: 2.7141
Iteration: 3735; Percent complete: 93.4%; Average loss: 2.7428
Iteration: 3736; Percent complete: 93.4%; Average loss: 2.6453
Iteration: 3737; Percent complete: 93.4%; Average loss: 2.8060
Iteration: 3738; Percent complete: 93.5%; Average loss: 2.5407
Iteration: 3739; Percent complete: 93.5%; Average loss: 2.5668
Iteration: 3740; Percent complete: 93.5%; Average loss: 2.7640
Iteration: 3741; Percent complete: 93.5%; Average loss: 2.6761
Iteration: 3742; Percent complete: 93.5%; Average loss: 2.9748
Iteration: 3743; Percent complete: 93.6%; Average loss: 2.9465
Iteration: 3744; Percent complete: 93.6%; Average loss: 2.6344
Iteration: 3745; Percent complete: 93.6%; Average loss: 2.4820
Iteration: 3746; Percent complete: 93.7%; Average loss: 2.6403
Iteration: 3747; Percent complete: 93.7%; Average loss: 2.7458
Iteration: 3748; Percent complete: 93.7%; Average loss: 2.8170
Iteration: 3749; Percent complete: 93.7%; Average loss: 2.8501
Iteration: 3750; Percent complete: 93.8%; Average loss: 2.5901
Iteration: 3751; Percent complete: 93.8%; Average loss: 2.7100
Iteration: 3752; Percent complete: 93.8%; Average loss: 2.6430
Iteration: 3753; Percent complete: 93.8%; Average loss: 2.6700
Iteration: 3754; Percent complete: 93.8%; Average loss: 2.7505
Iteration: 3755; Percent complete: 93.9%; Average loss: 2.7111
Iteration: 3756; Percent complete: 93.9%; Average loss: 2.6216
Iteration: 3757; Percent complete: 93.9%; Average loss: 2.4438
Iteration: 3758; Percent complete: 94.0%; Average loss: 2.6226
Iteration: 3759; Percent complete: 94.0%; Average loss: 2.5225
Iteration: 3760; Percent complete: 94.0%; Average loss: 2.6998
Iteration: 3761; Percent complete: 94.0%; Average loss: 2.6331
Iteration: 3762; Percent complete: 94.0%; Average loss: 2.7513
Iteration: 3763; Percent complete: 94.1%; Average loss: 2.6427
Iteration: 3764; Percent complete: 94.1%; Average loss: 2.3247
Iteration: 3765; Percent complete: 94.1%; Average loss: 2.7107
Iteration: 3766; Percent complete: 94.2%; Average loss: 2.4763
Iteration: 3767; Percent complete: 94.2%; Average loss: 2.6791
Iteration: 3768; Percent complete: 94.2%; Average loss: 2.7608
Iteration: 3769; Percent complete: 94.2%; Average loss: 2.6065
Iteration: 3770; Percent complete: 94.2%; Average loss: 2.7388
Iteration: 3771; Percent complete: 94.3%; Average loss: 2.4436
Iteration: 3772; Percent complete: 94.3%; Average loss: 2.8479
Iteration: 3773; Percent complete: 94.3%; Average loss: 2.5525
Iteration: 3774; Percent complete: 94.3%; Average loss: 2.6550
Iteration: 3775; Percent complete: 94.4%; Average loss: 2.8180
Iteration: 3776; Percent complete: 94.4%; Average loss: 2.8931
Iteration: 3777; Percent complete: 94.4%; Average loss: 2.6434
Iteration: 3778; Percent complete: 94.5%; Average loss: 2.6885
Iteration: 3779; Percent complete: 94.5%; Average loss: 2.4929
Iteration: 3780; Percent complete: 94.5%; Average loss: 2.5429
Iteration: 3781; Percent complete: 94.5%; Average loss: 2.5254
Iteration: 3782; Percent complete: 94.5%; Average loss: 2.6022
Iteration: 3783; Percent complete: 94.6%; Average loss: 2.5372
Iteration: 3784; Percent complete: 94.6%; Average loss: 2.7404
Iteration: 3785; Percent complete: 94.6%; Average loss: 2.5811
Iteration: 3786; Percent complete: 94.7%; Average loss: 2.6028
Iteration: 3787; Percent complete: 94.7%; Average loss: 2.6400
Iteration: 3788; Percent complete: 94.7%; Average loss: 2.2352
Iteration: 3789; Percent complete: 94.7%; Average loss: 2.4222
Iteration: 3790; Percent complete: 94.8%; Average loss: 2.7211
Iteration: 3791; Percent complete: 94.8%; Average loss: 2.6606
Iteration: 3792; Percent complete: 94.8%; Average loss: 2.6131
Iteration: 3793; Percent complete: 94.8%; Average loss: 2.8333
Iteration: 3794; Percent complete: 94.8%; Average loss: 2.5483
Iteration: 3795; Percent complete: 94.9%; Average loss: 2.6507
Iteration: 3796; Percent complete: 94.9%; Average loss: 2.7025
Iteration: 3797; Percent complete: 94.9%; Average loss: 2.8368
Iteration: 3798; Percent complete: 95.0%; Average loss: 2.6881
Iteration: 3799; Percent complete: 95.0%; Average loss: 2.6908
Iteration: 3800; Percent complete: 95.0%; Average loss: 2.5174
Iteration: 3801; Percent complete: 95.0%; Average loss: 2.5521
Iteration: 3802; Percent complete: 95.0%; Average loss: 2.7834
Iteration: 3803; Percent complete: 95.1%; Average loss: 2.4710
Iteration: 3804; Percent complete: 95.1%; Average loss: 2.4700
Iteration: 3805; Percent complete: 95.1%; Average loss: 2.6481
Iteration: 3806; Percent complete: 95.2%; Average loss: 2.5587
Iteration: 3807; Percent complete: 95.2%; Average loss: 2.5971
Iteration: 3808; Percent complete: 95.2%; Average loss: 2.4877
Iteration: 3809; Percent complete: 95.2%; Average loss: 2.6849
Iteration: 3810; Percent complete: 95.2%; Average loss: 2.6562
Iteration: 3811; Percent complete: 95.3%; Average loss: 2.6142
Iteration: 3812; Percent complete: 95.3%; Average loss: 2.6244
Iteration: 3813; Percent complete: 95.3%; Average loss: 2.7159
Iteration: 3814; Percent complete: 95.3%; Average loss: 2.5535
Iteration: 3815; Percent complete: 95.4%; Average loss: 2.7417
Iteration: 3816; Percent complete: 95.4%; Average loss: 2.5024
Iteration: 3817; Percent complete: 95.4%; Average loss: 2.8449
Iteration: 3818; Percent complete: 95.5%; Average loss: 2.6291
Iteration: 3819; Percent complete: 95.5%; Average loss: 2.7233
Iteration: 3820; Percent complete: 95.5%; Average loss: 2.5484
Iteration: 3821; Percent complete: 95.5%; Average loss: 2.7086
Iteration: 3822; Percent complete: 95.5%; Average loss: 2.6275
Iteration: 3823; Percent complete: 95.6%; Average loss: 2.7866
Iteration: 3824; Percent complete: 95.6%; Average loss: 2.5929
Iteration: 3825; Percent complete: 95.6%; Average loss: 2.7131
Iteration: 3826; Percent complete: 95.7%; Average loss: 2.7211
Iteration: 3827; Percent complete: 95.7%; Average loss: 2.7954
Iteration: 3828; Percent complete: 95.7%; Average loss: 2.4552
Iteration: 3829; Percent complete: 95.7%; Average loss: 2.7267
Iteration: 3830; Percent complete: 95.8%; Average loss: 2.7296
Iteration: 3831; Percent complete: 95.8%; Average loss: 2.7206
Iteration: 3832; Percent complete: 95.8%; Average loss: 2.5930
Iteration: 3833; Percent complete: 95.8%; Average loss: 2.8695
Iteration: 3834; Percent complete: 95.9%; Average loss: 2.6178
Iteration: 3835; Percent complete: 95.9%; Average loss: 2.5950
Iteration: 3836; Percent complete: 95.9%; Average loss: 2.4848
Iteration: 3837; Percent complete: 95.9%; Average loss: 2.5974
Iteration: 3838; Percent complete: 96.0%; Average loss: 2.6416
Iteration: 3839; Percent complete: 96.0%; Average loss: 2.7277
Iteration: 3840; Percent complete: 96.0%; Average loss: 2.6777
Iteration: 3841; Percent complete: 96.0%; Average loss: 2.5463
Iteration: 3842; Percent complete: 96.0%; Average loss: 2.6009
Iteration: 3843; Percent complete: 96.1%; Average loss: 2.8307
Iteration: 3844; Percent complete: 96.1%; Average loss: 2.8057
Iteration: 3845; Percent complete: 96.1%; Average loss: 2.8054
Iteration: 3846; Percent complete: 96.2%; Average loss: 2.5637
Iteration: 3847; Percent complete: 96.2%; Average loss: 2.5876
Iteration: 3848; Percent complete: 96.2%; Average loss: 2.6425
Iteration: 3849; Percent complete: 96.2%; Average loss: 2.5392
Iteration: 3850; Percent complete: 96.2%; Average loss: 2.5688
Iteration: 3851; Percent complete: 96.3%; Average loss: 2.7100
Iteration: 3852; Percent complete: 96.3%; Average loss: 2.5362
Iteration: 3853; Percent complete: 96.3%; Average loss: 2.7745
Iteration: 3854; Percent complete: 96.4%; Average loss: 2.6098
Iteration: 3855; Percent complete: 96.4%; Average loss: 2.5448
Iteration: 3856; Percent complete: 96.4%; Average loss: 2.5916
Iteration: 3857; Percent complete: 96.4%; Average loss: 2.8076
Iteration: 3858; Percent complete: 96.5%; Average loss: 2.6626
Iteration: 3859; Percent complete: 96.5%; Average loss: 2.5496
Iteration: 3860; Percent complete: 96.5%; Average loss: 2.6865
Iteration: 3861; Percent complete: 96.5%; Average loss: 2.7610
Iteration: 3862; Percent complete: 96.5%; Average loss: 2.5324
Iteration: 3863; Percent complete: 96.6%; Average loss: 2.8183
Iteration: 3864; Percent complete: 96.6%; Average loss: 2.5172
Iteration: 3865; Percent complete: 96.6%; Average loss: 2.6996
Iteration: 3866; Percent complete: 96.7%; Average loss: 2.6379
Iteration: 3867; Percent complete: 96.7%; Average loss: 2.7287
Iteration: 3868; Percent complete: 96.7%; Average loss: 2.8853
Iteration: 3869; Percent complete: 96.7%; Average loss: 2.6859
Iteration: 3870; Percent complete: 96.8%; Average loss: 2.5432
Iteration: 3871; Percent complete: 96.8%; Average loss: 2.6105
Iteration: 3872; Percent complete: 96.8%; Average loss: 2.6457
Iteration: 3873; Percent complete: 96.8%; Average loss: 2.5818
Iteration: 3874; Percent complete: 96.9%; Average loss: 2.9453
Iteration: 3875; Percent complete: 96.9%; Average loss: 2.5106
Iteration: 3876; Percent complete: 96.9%; Average loss: 2.3963
Iteration: 3877; Percent complete: 96.9%; Average loss: 2.5394
Iteration: 3878; Percent complete: 97.0%; Average loss: 2.6493
Iteration: 3879; Percent complete: 97.0%; Average loss: 2.3047
Iteration: 3880; Percent complete: 97.0%; Average loss: 2.9488
Iteration: 3881; Percent complete: 97.0%; Average loss: 2.8201
Iteration: 3882; Percent complete: 97.0%; Average loss: 2.7367
Iteration: 3883; Percent complete: 97.1%; Average loss: 2.7933
Iteration: 3884; Percent complete: 97.1%; Average loss: 2.6527
Iteration: 3885; Percent complete: 97.1%; Average loss: 2.5586
Iteration: 3886; Percent complete: 97.2%; Average loss: 2.7476
Iteration: 3887; Percent complete: 97.2%; Average loss: 2.5383
Iteration: 3888; Percent complete: 97.2%; Average loss: 2.5487
Iteration: 3889; Percent complete: 97.2%; Average loss: 2.6958
Iteration: 3890; Percent complete: 97.2%; Average loss: 2.6808
Iteration: 3891; Percent complete: 97.3%; Average loss: 2.8181
Iteration: 3892; Percent complete: 97.3%; Average loss: 2.6276
Iteration: 3893; Percent complete: 97.3%; Average loss: 2.4633
Iteration: 3894; Percent complete: 97.4%; Average loss: 2.8256
Iteration: 3895; Percent complete: 97.4%; Average loss: 2.5855
Iteration: 3896; Percent complete: 97.4%; Average loss: 2.5801
Iteration: 3897; Percent complete: 97.4%; Average loss: 2.4412
Iteration: 3898; Percent complete: 97.5%; Average loss: 2.6083
Iteration: 3899; Percent complete: 97.5%; Average loss: 2.6586
Iteration: 3900; Percent complete: 97.5%; Average loss: 2.8238
Iteration: 3901; Percent complete: 97.5%; Average loss: 2.5401
Iteration: 3902; Percent complete: 97.5%; Average loss: 2.6290
Iteration: 3903; Percent complete: 97.6%; Average loss: 2.5330
Iteration: 3904; Percent complete: 97.6%; Average loss: 2.5631
Iteration: 3905; Percent complete: 97.6%; Average loss: 2.5453
Iteration: 3906; Percent complete: 97.7%; Average loss: 2.3632
Iteration: 3907; Percent complete: 97.7%; Average loss: 2.7421
Iteration: 3908; Percent complete: 97.7%; Average loss: 2.7137
Iteration: 3909; Percent complete: 97.7%; Average loss: 2.7649
Iteration: 3910; Percent complete: 97.8%; Average loss: 2.6380
Iteration: 3911; Percent complete: 97.8%; Average loss: 2.7658
Iteration: 3912; Percent complete: 97.8%; Average loss: 2.9755
Iteration: 3913; Percent complete: 97.8%; Average loss: 2.6747
Iteration: 3914; Percent complete: 97.9%; Average loss: 2.4604
Iteration: 3915; Percent complete: 97.9%; Average loss: 2.7501
Iteration: 3916; Percent complete: 97.9%; Average loss: 2.5045
Iteration: 3917; Percent complete: 97.9%; Average loss: 2.2622
Iteration: 3918; Percent complete: 98.0%; Average loss: 2.7363
Iteration: 3919; Percent complete: 98.0%; Average loss: 2.3152
Iteration: 3920; Percent complete: 98.0%; Average loss: 2.8148
Iteration: 3921; Percent complete: 98.0%; Average loss: 2.5456
Iteration: 3922; Percent complete: 98.0%; Average loss: 2.7048
Iteration: 3923; Percent complete: 98.1%; Average loss: 2.5100
Iteration: 3924; Percent complete: 98.1%; Average loss: 2.7527
Iteration: 3925; Percent complete: 98.1%; Average loss: 2.4070
Iteration: 3926; Percent complete: 98.2%; Average loss: 2.6717
Iteration: 3927; Percent complete: 98.2%; Average loss: 2.7420
Iteration: 3928; Percent complete: 98.2%; Average loss: 2.7455
Iteration: 3929; Percent complete: 98.2%; Average loss: 2.6694
Iteration: 3930; Percent complete: 98.2%; Average loss: 2.6665
Iteration: 3931; Percent complete: 98.3%; Average loss: 2.5683
Iteration: 3932; Percent complete: 98.3%; Average loss: 2.5238
Iteration: 3933; Percent complete: 98.3%; Average loss: 2.4975
Iteration: 3934; Percent complete: 98.4%; Average loss: 2.7916
Iteration: 3935; Percent complete: 98.4%; Average loss: 2.6174
Iteration: 3936; Percent complete: 98.4%; Average loss: 2.8025
Iteration: 3937; Percent complete: 98.4%; Average loss: 2.6781
Iteration: 3938; Percent complete: 98.5%; Average loss: 2.5178
Iteration: 3939; Percent complete: 98.5%; Average loss: 2.5371
Iteration: 3940; Percent complete: 98.5%; Average loss: 2.7459
Iteration: 3941; Percent complete: 98.5%; Average loss: 2.4583
Iteration: 3942; Percent complete: 98.6%; Average loss: 2.5124
Iteration: 3943; Percent complete: 98.6%; Average loss: 2.3879
Iteration: 3944; Percent complete: 98.6%; Average loss: 2.6638
Iteration: 3945; Percent complete: 98.6%; Average loss: 2.6487
Iteration: 3946; Percent complete: 98.7%; Average loss: 2.5780
Iteration: 3947; Percent complete: 98.7%; Average loss: 2.6594
Iteration: 3948; Percent complete: 98.7%; Average loss: 2.7672
Iteration: 3949; Percent complete: 98.7%; Average loss: 2.8295
Iteration: 3950; Percent complete: 98.8%; Average loss: 2.4493
Iteration: 3951; Percent complete: 98.8%; Average loss: 2.6322
Iteration: 3952; Percent complete: 98.8%; Average loss: 2.4470
Iteration: 3953; Percent complete: 98.8%; Average loss: 2.5083
Iteration: 3954; Percent complete: 98.9%; Average loss: 2.7176
Iteration: 3955; Percent complete: 98.9%; Average loss: 2.4447
Iteration: 3956; Percent complete: 98.9%; Average loss: 2.6418
Iteration: 3957; Percent complete: 98.9%; Average loss: 2.5721
Iteration: 3958; Percent complete: 99.0%; Average loss: 2.6396
Iteration: 3959; Percent complete: 99.0%; Average loss: 2.7145
Iteration: 3960; Percent complete: 99.0%; Average loss: 2.4144
Iteration: 3961; Percent complete: 99.0%; Average loss: 2.6055
Iteration: 3962; Percent complete: 99.1%; Average loss: 2.6095
Iteration: 3963; Percent complete: 99.1%; Average loss: 2.8131
Iteration: 3964; Percent complete: 99.1%; Average loss: 2.5450
Iteration: 3965; Percent complete: 99.1%; Average loss: 2.5757
Iteration: 3966; Percent complete: 99.2%; Average loss: 2.7001
Iteration: 3967; Percent complete: 99.2%; Average loss: 2.4922
Iteration: 3968; Percent complete: 99.2%; Average loss: 2.5924
Iteration: 3969; Percent complete: 99.2%; Average loss: 2.7176
Iteration: 3970; Percent complete: 99.2%; Average loss: 2.5759
Iteration: 3971; Percent complete: 99.3%; Average loss: 2.6936
Iteration: 3972; Percent complete: 99.3%; Average loss: 2.4951
Iteration: 3973; Percent complete: 99.3%; Average loss: 2.5385
Iteration: 3974; Percent complete: 99.4%; Average loss: 2.7494
Iteration: 3975; Percent complete: 99.4%; Average loss: 2.6882
Iteration: 3976; Percent complete: 99.4%; Average loss: 2.4114
Iteration: 3977; Percent complete: 99.4%; Average loss: 2.5631
Iteration: 3978; Percent complete: 99.5%; Average loss: 2.7770
Iteration: 3979; Percent complete: 99.5%; Average loss: 2.7936
Iteration: 3980; Percent complete: 99.5%; Average loss: 2.9674
Iteration: 3981; Percent complete: 99.5%; Average loss: 2.4417
Iteration: 3982; Percent complete: 99.6%; Average loss: 2.5805
Iteration: 3983; Percent complete: 99.6%; Average loss: 2.7029
Iteration: 3984; Percent complete: 99.6%; Average loss: 2.5725
Iteration: 3985; Percent complete: 99.6%; Average loss: 2.7559
Iteration: 3986; Percent complete: 99.7%; Average loss: 2.8026
Iteration: 3987; Percent complete: 99.7%; Average loss: 2.8482
Iteration: 3988; Percent complete: 99.7%; Average loss: 2.4746
Iteration: 3989; Percent complete: 99.7%; Average loss: 2.5284
Iteration: 3990; Percent complete: 99.8%; Average loss: 2.6967
Iteration: 3991; Percent complete: 99.8%; Average loss: 2.4750
Iteration: 3992; Percent complete: 99.8%; Average loss: 2.5310
Iteration: 3993; Percent complete: 99.8%; Average loss: 2.6383
Iteration: 3994; Percent complete: 99.9%; Average loss: 2.7120
Iteration: 3995; Percent complete: 99.9%; Average loss: 2.5332
Iteration: 3996; Percent complete: 99.9%; Average loss: 2.5464
Iteration: 3997; Percent complete: 99.9%; Average loss: 2.4915
Iteration: 3998; Percent complete: 100.0%; Average loss: 2.5045
Iteration: 3999; Percent complete: 100.0%; Average loss: 2.6834
Iteration: 4000; Percent complete: 100.0%; Average loss: 2.5355

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!