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: https://github.com/ywk991112/pytorch-chatbot

2. Sean Robertson’s practical-pytorch seq2seq-translation example: https://github.com/spro/practical-pytorch/tree/master/seq2seq-translation

3. FloydHub Cornell Movie Corpus preprocessing code: https://github.com/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([[ 548,   85,  263, 1039,   19],
        [ 194,   17,   19,    6,   10],
        [  72,   52,   10,    2,    2],
        [  31, 1285,    2,    0,    0],
        [  90,   14,    0,    0,    0],
        [ 665,    2,    0,    0,    0],
        [1968,    0,    0,    0,    0],
        [  14,    0,    0,    0,    0],
        [   2,    0,    0,    0,    0]])
lengths: tensor([9, 6, 4, 3, 3])
target_variable: tensor([[  85, 2988,   11,   50,   11],
        [  17,    2,  315,  113,  200],
        [ 665,    0,   24,  236,  161],
        [1968,    0,    5, 2710, 6916],
        [  14,    0,  481,  427,   14],
        [   2,    0,  483,    6,   90],
        [   0,    0,    6,    2,    8],
        [   0,    0,    2,    0,   66],
        [   0,    0,    0,    0,   10],
        [   0,    0,    0,    0,    2]])
mask: tensor([[ True,  True,  True,  True,  True],
        [ True,  True,  True,  True,  True],
        [ True, False,  True,  True,  True],
        [ True, False,  True,  True,  True],
        [ True, False,  True,  True,  True],
        [ True, False,  True,  True,  True],
        [False, False,  True,  True,  True],
        [False, False,  True, 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: 8.9704
Iteration: 2; Percent complete: 0.1%; Average loss: 8.8671
Iteration: 3; Percent complete: 0.1%; Average loss: 8.6768
Iteration: 4; Percent complete: 0.1%; Average loss: 8.4199
Iteration: 5; Percent complete: 0.1%; Average loss: 8.0110
Iteration: 6; Percent complete: 0.1%; Average loss: 7.4537
Iteration: 7; Percent complete: 0.2%; Average loss: 7.0055
Iteration: 8; Percent complete: 0.2%; Average loss: 6.6431
Iteration: 9; Percent complete: 0.2%; Average loss: 6.7206
Iteration: 10; Percent complete: 0.2%; Average loss: 6.6618
Iteration: 11; Percent complete: 0.3%; Average loss: 6.4450
Iteration: 12; Percent complete: 0.3%; Average loss: 6.1163
Iteration: 13; Percent complete: 0.3%; Average loss: 5.6654
Iteration: 14; Percent complete: 0.4%; Average loss: 5.5323
Iteration: 15; Percent complete: 0.4%; Average loss: 5.4948
Iteration: 16; Percent complete: 0.4%; Average loss: 5.4722
Iteration: 17; Percent complete: 0.4%; Average loss: 5.1427
Iteration: 18; Percent complete: 0.4%; Average loss: 5.1090
Iteration: 19; Percent complete: 0.5%; Average loss: 4.9118
Iteration: 20; Percent complete: 0.5%; Average loss: 4.7229
Iteration: 21; Percent complete: 0.5%; Average loss: 5.2390
Iteration: 22; Percent complete: 0.5%; Average loss: 4.9776
Iteration: 23; Percent complete: 0.6%; Average loss: 4.7201
Iteration: 24; Percent complete: 0.6%; Average loss: 4.9339
Iteration: 25; Percent complete: 0.6%; Average loss: 4.9955
Iteration: 26; Percent complete: 0.7%; Average loss: 4.8923
Iteration: 27; Percent complete: 0.7%; Average loss: 4.8548
Iteration: 28; Percent complete: 0.7%; Average loss: 5.0822
Iteration: 29; Percent complete: 0.7%; Average loss: 4.8083
Iteration: 30; Percent complete: 0.8%; Average loss: 4.7067
Iteration: 31; Percent complete: 0.8%; Average loss: 4.9446
Iteration: 32; Percent complete: 0.8%; Average loss: 4.4444
Iteration: 33; Percent complete: 0.8%; Average loss: 4.8128
Iteration: 34; Percent complete: 0.9%; Average loss: 4.6266
Iteration: 35; Percent complete: 0.9%; Average loss: 4.6959
Iteration: 36; Percent complete: 0.9%; Average loss: 4.8906
Iteration: 37; Percent complete: 0.9%; Average loss: 4.5194
Iteration: 38; Percent complete: 0.9%; Average loss: 4.7065
Iteration: 39; Percent complete: 1.0%; Average loss: 4.6675
Iteration: 40; Percent complete: 1.0%; Average loss: 4.8456
Iteration: 41; Percent complete: 1.0%; Average loss: 4.7139
Iteration: 42; Percent complete: 1.1%; Average loss: 4.6165
Iteration: 43; Percent complete: 1.1%; Average loss: 4.7441
Iteration: 44; Percent complete: 1.1%; Average loss: 4.8071
Iteration: 45; Percent complete: 1.1%; Average loss: 4.4757
Iteration: 46; Percent complete: 1.1%; Average loss: 4.5113
Iteration: 47; Percent complete: 1.2%; Average loss: 4.6507
Iteration: 48; Percent complete: 1.2%; Average loss: 4.7492
Iteration: 49; Percent complete: 1.2%; Average loss: 4.6172
Iteration: 50; Percent complete: 1.2%; Average loss: 4.7632
Iteration: 51; Percent complete: 1.3%; Average loss: 4.5460
Iteration: 52; Percent complete: 1.3%; Average loss: 4.7421
Iteration: 53; Percent complete: 1.3%; Average loss: 4.6045
Iteration: 54; Percent complete: 1.4%; Average loss: 4.7649
Iteration: 55; Percent complete: 1.4%; Average loss: 4.4047
Iteration: 56; Percent complete: 1.4%; Average loss: 4.5473
Iteration: 57; Percent complete: 1.4%; Average loss: 4.6619
Iteration: 58; Percent complete: 1.5%; Average loss: 4.6435
Iteration: 59; Percent complete: 1.5%; Average loss: 4.5729
Iteration: 60; Percent complete: 1.5%; Average loss: 4.6254
Iteration: 61; Percent complete: 1.5%; Average loss: 4.9412
Iteration: 62; Percent complete: 1.6%; Average loss: 4.5141
Iteration: 63; Percent complete: 1.6%; Average loss: 4.5487
Iteration: 64; Percent complete: 1.6%; Average loss: 4.6649
Iteration: 65; Percent complete: 1.6%; Average loss: 4.7150
Iteration: 66; Percent complete: 1.7%; Average loss: 4.6970
Iteration: 67; Percent complete: 1.7%; Average loss: 4.3352
Iteration: 68; Percent complete: 1.7%; Average loss: 4.4159
Iteration: 69; Percent complete: 1.7%; Average loss: 4.2993
Iteration: 70; Percent complete: 1.8%; Average loss: 4.4877
Iteration: 71; Percent complete: 1.8%; Average loss: 4.5236
Iteration: 72; Percent complete: 1.8%; Average loss: 4.3956
Iteration: 73; Percent complete: 1.8%; Average loss: 4.6789
Iteration: 74; Percent complete: 1.8%; Average loss: 4.4705
Iteration: 75; Percent complete: 1.9%; Average loss: 4.5618
Iteration: 76; Percent complete: 1.9%; Average loss: 4.5014
Iteration: 77; Percent complete: 1.9%; Average loss: 4.4915
Iteration: 78; Percent complete: 1.9%; Average loss: 4.4629
Iteration: 79; Percent complete: 2.0%; Average loss: 4.6837
Iteration: 80; Percent complete: 2.0%; Average loss: 4.4794
Iteration: 81; Percent complete: 2.0%; Average loss: 4.4823
Iteration: 82; Percent complete: 2.1%; Average loss: 4.5257
Iteration: 83; Percent complete: 2.1%; Average loss: 4.4444
Iteration: 84; Percent complete: 2.1%; Average loss: 4.6552
Iteration: 85; Percent complete: 2.1%; Average loss: 4.6960
Iteration: 86; Percent complete: 2.1%; Average loss: 4.6120
Iteration: 87; Percent complete: 2.2%; Average loss: 4.5285
Iteration: 88; Percent complete: 2.2%; Average loss: 4.4356
Iteration: 89; Percent complete: 2.2%; Average loss: 4.5010
Iteration: 90; Percent complete: 2.2%; Average loss: 4.5482
Iteration: 91; Percent complete: 2.3%; Average loss: 4.5374
Iteration: 92; Percent complete: 2.3%; Average loss: 4.6459
Iteration: 93; Percent complete: 2.3%; Average loss: 4.5926
Iteration: 94; Percent complete: 2.4%; Average loss: 4.5591
Iteration: 95; Percent complete: 2.4%; Average loss: 4.4979
Iteration: 96; Percent complete: 2.4%; Average loss: 4.3677
Iteration: 97; Percent complete: 2.4%; Average loss: 4.3652
Iteration: 98; Percent complete: 2.5%; Average loss: 4.5678
Iteration: 99; Percent complete: 2.5%; Average loss: 4.3390
Iteration: 100; Percent complete: 2.5%; Average loss: 4.3142
Iteration: 101; Percent complete: 2.5%; Average loss: 4.5803
Iteration: 102; Percent complete: 2.5%; Average loss: 4.4644
Iteration: 103; Percent complete: 2.6%; Average loss: 4.3204
Iteration: 104; Percent complete: 2.6%; Average loss: 4.5008
Iteration: 105; Percent complete: 2.6%; Average loss: 4.2047
Iteration: 106; Percent complete: 2.6%; Average loss: 4.3192
Iteration: 107; Percent complete: 2.7%; Average loss: 4.2505
Iteration: 108; Percent complete: 2.7%; Average loss: 4.2145
Iteration: 109; Percent complete: 2.7%; Average loss: 4.2711
Iteration: 110; Percent complete: 2.8%; Average loss: 4.4841
Iteration: 111; Percent complete: 2.8%; Average loss: 4.4286
Iteration: 112; Percent complete: 2.8%; Average loss: 4.5559
Iteration: 113; Percent complete: 2.8%; Average loss: 4.3549
Iteration: 114; Percent complete: 2.9%; Average loss: 4.1151
Iteration: 115; Percent complete: 2.9%; Average loss: 4.6528
Iteration: 116; Percent complete: 2.9%; Average loss: 4.3890
Iteration: 117; Percent complete: 2.9%; Average loss: 4.3282
Iteration: 118; Percent complete: 2.9%; Average loss: 4.3240
Iteration: 119; Percent complete: 3.0%; Average loss: 4.1374
Iteration: 120; Percent complete: 3.0%; Average loss: 4.2191
Iteration: 121; Percent complete: 3.0%; Average loss: 4.1713
Iteration: 122; Percent complete: 3.0%; Average loss: 4.1040
Iteration: 123; Percent complete: 3.1%; Average loss: 4.3144
Iteration: 124; Percent complete: 3.1%; Average loss: 4.1958
Iteration: 125; Percent complete: 3.1%; Average loss: 4.3940
Iteration: 126; Percent complete: 3.1%; Average loss: 4.5501
Iteration: 127; Percent complete: 3.2%; Average loss: 4.3437
Iteration: 128; Percent complete: 3.2%; Average loss: 4.3379
Iteration: 129; Percent complete: 3.2%; Average loss: 4.3620
Iteration: 130; Percent complete: 3.2%; Average loss: 4.1245
Iteration: 131; Percent complete: 3.3%; Average loss: 4.3705
Iteration: 132; Percent complete: 3.3%; Average loss: 4.3436
Iteration: 133; Percent complete: 3.3%; Average loss: 4.2592
Iteration: 134; Percent complete: 3.4%; Average loss: 4.2656
Iteration: 135; Percent complete: 3.4%; Average loss: 4.4873
Iteration: 136; Percent complete: 3.4%; Average loss: 4.3810
Iteration: 137; Percent complete: 3.4%; Average loss: 4.2409
Iteration: 138; Percent complete: 3.5%; Average loss: 4.2955
Iteration: 139; Percent complete: 3.5%; Average loss: 4.2307
Iteration: 140; Percent complete: 3.5%; Average loss: 4.1155
Iteration: 141; Percent complete: 3.5%; Average loss: 4.3326
Iteration: 142; Percent complete: 3.5%; Average loss: 4.4098
Iteration: 143; Percent complete: 3.6%; Average loss: 4.4392
Iteration: 144; Percent complete: 3.6%; Average loss: 4.4101
Iteration: 145; Percent complete: 3.6%; Average loss: 4.0944
Iteration: 146; Percent complete: 3.6%; Average loss: 4.3974
Iteration: 147; Percent complete: 3.7%; Average loss: 4.1817
Iteration: 148; Percent complete: 3.7%; Average loss: 4.0843
Iteration: 149; Percent complete: 3.7%; Average loss: 4.2849
Iteration: 150; Percent complete: 3.8%; Average loss: 4.3050
Iteration: 151; Percent complete: 3.8%; Average loss: 4.3699
Iteration: 152; Percent complete: 3.8%; Average loss: 4.4016
Iteration: 153; Percent complete: 3.8%; Average loss: 4.2631
Iteration: 154; Percent complete: 3.9%; Average loss: 4.3375
Iteration: 155; Percent complete: 3.9%; Average loss: 4.4753
Iteration: 156; Percent complete: 3.9%; Average loss: 4.5254
Iteration: 157; Percent complete: 3.9%; Average loss: 4.1578
Iteration: 158; Percent complete: 4.0%; Average loss: 4.3229
Iteration: 159; Percent complete: 4.0%; Average loss: 4.2718
Iteration: 160; Percent complete: 4.0%; Average loss: 4.3185
Iteration: 161; Percent complete: 4.0%; Average loss: 4.0021
Iteration: 162; Percent complete: 4.0%; Average loss: 4.2956
Iteration: 163; Percent complete: 4.1%; Average loss: 4.2821
Iteration: 164; Percent complete: 4.1%; Average loss: 4.3642
Iteration: 165; Percent complete: 4.1%; Average loss: 4.0692
Iteration: 166; Percent complete: 4.2%; Average loss: 4.1309
Iteration: 167; Percent complete: 4.2%; Average loss: 4.2125
Iteration: 168; Percent complete: 4.2%; Average loss: 3.9850
Iteration: 169; Percent complete: 4.2%; Average loss: 4.0454
Iteration: 170; Percent complete: 4.2%; Average loss: 3.9599
Iteration: 171; Percent complete: 4.3%; Average loss: 4.0916
Iteration: 172; Percent complete: 4.3%; Average loss: 4.1259
Iteration: 173; Percent complete: 4.3%; Average loss: 4.0554
Iteration: 174; Percent complete: 4.3%; Average loss: 4.2267
Iteration: 175; Percent complete: 4.4%; Average loss: 4.0038
Iteration: 176; Percent complete: 4.4%; Average loss: 4.1127
Iteration: 177; Percent complete: 4.4%; Average loss: 4.0803
Iteration: 178; Percent complete: 4.5%; Average loss: 4.1157
Iteration: 179; Percent complete: 4.5%; Average loss: 4.2424
Iteration: 180; Percent complete: 4.5%; Average loss: 4.2455
Iteration: 181; Percent complete: 4.5%; Average loss: 4.1437
Iteration: 182; Percent complete: 4.5%; Average loss: 4.0876
Iteration: 183; Percent complete: 4.6%; Average loss: 4.0197
Iteration: 184; Percent complete: 4.6%; Average loss: 4.1795
Iteration: 185; Percent complete: 4.6%; Average loss: 4.2484
Iteration: 186; Percent complete: 4.7%; Average loss: 4.2244
Iteration: 187; Percent complete: 4.7%; Average loss: 3.9968
Iteration: 188; Percent complete: 4.7%; Average loss: 4.3000
Iteration: 189; Percent complete: 4.7%; Average loss: 3.9502
Iteration: 190; Percent complete: 4.8%; Average loss: 4.0620
Iteration: 191; Percent complete: 4.8%; Average loss: 4.2994
Iteration: 192; Percent complete: 4.8%; Average loss: 4.1256
Iteration: 193; Percent complete: 4.8%; Average loss: 4.1423
Iteration: 194; Percent complete: 4.9%; Average loss: 4.2052
Iteration: 195; Percent complete: 4.9%; Average loss: 4.1118
Iteration: 196; Percent complete: 4.9%; Average loss: 4.0308
Iteration: 197; Percent complete: 4.9%; Average loss: 4.1532
Iteration: 198; Percent complete: 5.0%; Average loss: 4.0354
Iteration: 199; Percent complete: 5.0%; Average loss: 4.1440
Iteration: 200; Percent complete: 5.0%; Average loss: 4.2116
Iteration: 201; Percent complete: 5.0%; Average loss: 4.1703
Iteration: 202; Percent complete: 5.1%; Average loss: 4.0179
Iteration: 203; Percent complete: 5.1%; Average loss: 4.1550
Iteration: 204; Percent complete: 5.1%; Average loss: 4.3762
Iteration: 205; Percent complete: 5.1%; Average loss: 4.3184
Iteration: 206; Percent complete: 5.1%; Average loss: 4.1087
Iteration: 207; Percent complete: 5.2%; Average loss: 3.9642
Iteration: 208; Percent complete: 5.2%; Average loss: 4.0980
Iteration: 209; Percent complete: 5.2%; Average loss: 3.8423
Iteration: 210; Percent complete: 5.2%; Average loss: 4.1209
Iteration: 211; Percent complete: 5.3%; Average loss: 4.0579
Iteration: 212; Percent complete: 5.3%; Average loss: 3.9812
Iteration: 213; Percent complete: 5.3%; Average loss: 3.9262
Iteration: 214; Percent complete: 5.3%; Average loss: 3.9530
Iteration: 215; Percent complete: 5.4%; Average loss: 4.0881
Iteration: 216; Percent complete: 5.4%; Average loss: 4.1149
Iteration: 217; Percent complete: 5.4%; Average loss: 4.0740
Iteration: 218; Percent complete: 5.5%; Average loss: 4.1236
Iteration: 219; Percent complete: 5.5%; Average loss: 4.0013
Iteration: 220; Percent complete: 5.5%; Average loss: 3.9788
Iteration: 221; Percent complete: 5.5%; Average loss: 3.9147
Iteration: 222; Percent complete: 5.5%; Average loss: 4.2083
Iteration: 223; Percent complete: 5.6%; Average loss: 3.9805
Iteration: 224; Percent complete: 5.6%; Average loss: 3.9086
Iteration: 225; Percent complete: 5.6%; Average loss: 3.9307
Iteration: 226; Percent complete: 5.7%; Average loss: 3.8752
Iteration: 227; Percent complete: 5.7%; Average loss: 4.1297
Iteration: 228; Percent complete: 5.7%; Average loss: 3.9767
Iteration: 229; Percent complete: 5.7%; Average loss: 4.2022
Iteration: 230; Percent complete: 5.8%; Average loss: 4.1691
Iteration: 231; Percent complete: 5.8%; Average loss: 4.0440
Iteration: 232; Percent complete: 5.8%; Average loss: 3.9078
Iteration: 233; Percent complete: 5.8%; Average loss: 4.1625
Iteration: 234; Percent complete: 5.9%; Average loss: 3.8999
Iteration: 235; Percent complete: 5.9%; Average loss: 3.8724
Iteration: 236; Percent complete: 5.9%; Average loss: 3.9642
Iteration: 237; Percent complete: 5.9%; Average loss: 4.0710
Iteration: 238; Percent complete: 5.9%; Average loss: 3.8572
Iteration: 239; Percent complete: 6.0%; Average loss: 4.1229
Iteration: 240; Percent complete: 6.0%; Average loss: 4.1615
Iteration: 241; Percent complete: 6.0%; Average loss: 4.2325
Iteration: 242; Percent complete: 6.0%; Average loss: 4.3948
Iteration: 243; Percent complete: 6.1%; Average loss: 4.0489
Iteration: 244; Percent complete: 6.1%; Average loss: 4.1219
Iteration: 245; Percent complete: 6.1%; Average loss: 4.2320
Iteration: 246; Percent complete: 6.2%; Average loss: 4.1568
Iteration: 247; Percent complete: 6.2%; Average loss: 4.0239
Iteration: 248; Percent complete: 6.2%; Average loss: 3.9842
Iteration: 249; Percent complete: 6.2%; Average loss: 4.0791
Iteration: 250; Percent complete: 6.2%; Average loss: 3.7617
Iteration: 251; Percent complete: 6.3%; Average loss: 3.7984
Iteration: 252; Percent complete: 6.3%; Average loss: 4.3171
Iteration: 253; Percent complete: 6.3%; Average loss: 4.0799
Iteration: 254; Percent complete: 6.3%; Average loss: 3.6960
Iteration: 255; Percent complete: 6.4%; Average loss: 4.0349
Iteration: 256; Percent complete: 6.4%; Average loss: 3.9466
Iteration: 257; Percent complete: 6.4%; Average loss: 4.1170
Iteration: 258; Percent complete: 6.5%; Average loss: 4.1373
Iteration: 259; Percent complete: 6.5%; Average loss: 3.9178
Iteration: 260; Percent complete: 6.5%; Average loss: 3.9506
Iteration: 261; Percent complete: 6.5%; Average loss: 3.8936
Iteration: 262; Percent complete: 6.6%; Average loss: 4.1594
Iteration: 263; Percent complete: 6.6%; Average loss: 4.1562
Iteration: 264; Percent complete: 6.6%; Average loss: 4.2157
Iteration: 265; Percent complete: 6.6%; Average loss: 3.7991
Iteration: 266; Percent complete: 6.7%; Average loss: 4.0422
Iteration: 267; Percent complete: 6.7%; Average loss: 3.8738
Iteration: 268; Percent complete: 6.7%; Average loss: 3.8054
Iteration: 269; Percent complete: 6.7%; Average loss: 4.0534
Iteration: 270; Percent complete: 6.8%; Average loss: 3.8889
Iteration: 271; Percent complete: 6.8%; Average loss: 3.9087
Iteration: 272; Percent complete: 6.8%; Average loss: 3.9806
Iteration: 273; Percent complete: 6.8%; Average loss: 3.9611
Iteration: 274; Percent complete: 6.9%; Average loss: 4.0253
Iteration: 275; Percent complete: 6.9%; Average loss: 4.0020
Iteration: 276; Percent complete: 6.9%; Average loss: 4.1010
Iteration: 277; Percent complete: 6.9%; Average loss: 3.8650
Iteration: 278; Percent complete: 7.0%; Average loss: 4.0273
Iteration: 279; Percent complete: 7.0%; Average loss: 3.9417
Iteration: 280; Percent complete: 7.0%; Average loss: 3.9445
Iteration: 281; Percent complete: 7.0%; Average loss: 3.9641
Iteration: 282; Percent complete: 7.0%; Average loss: 4.0309
Iteration: 283; Percent complete: 7.1%; Average loss: 4.1432
Iteration: 284; Percent complete: 7.1%; Average loss: 3.7567
Iteration: 285; Percent complete: 7.1%; Average loss: 3.9334
Iteration: 286; Percent complete: 7.1%; Average loss: 3.8888
Iteration: 287; Percent complete: 7.2%; Average loss: 3.9314
Iteration: 288; Percent complete: 7.2%; Average loss: 3.7190
Iteration: 289; Percent complete: 7.2%; Average loss: 3.9291
Iteration: 290; Percent complete: 7.2%; Average loss: 3.9568
Iteration: 291; Percent complete: 7.3%; Average loss: 3.9203
Iteration: 292; Percent complete: 7.3%; Average loss: 4.0310
Iteration: 293; Percent complete: 7.3%; Average loss: 4.1448
Iteration: 294; Percent complete: 7.3%; Average loss: 3.8707
Iteration: 295; Percent complete: 7.4%; Average loss: 3.9919
Iteration: 296; Percent complete: 7.4%; Average loss: 4.0086
Iteration: 297; Percent complete: 7.4%; Average loss: 4.0808
Iteration: 298; Percent complete: 7.4%; Average loss: 4.1169
Iteration: 299; Percent complete: 7.5%; Average loss: 3.9764
Iteration: 300; Percent complete: 7.5%; Average loss: 3.8269
Iteration: 301; Percent complete: 7.5%; Average loss: 3.7695
Iteration: 302; Percent complete: 7.5%; Average loss: 3.9691
Iteration: 303; Percent complete: 7.6%; Average loss: 3.9165
Iteration: 304; Percent complete: 7.6%; Average loss: 3.6940
Iteration: 305; Percent complete: 7.6%; Average loss: 3.9851
Iteration: 306; Percent complete: 7.6%; Average loss: 3.7992
Iteration: 307; Percent complete: 7.7%; Average loss: 3.8012
Iteration: 308; Percent complete: 7.7%; Average loss: 4.1032
Iteration: 309; Percent complete: 7.7%; Average loss: 3.4650
Iteration: 310; Percent complete: 7.8%; Average loss: 3.8736
Iteration: 311; Percent complete: 7.8%; Average loss: 3.8746
Iteration: 312; Percent complete: 7.8%; Average loss: 3.9344
Iteration: 313; Percent complete: 7.8%; Average loss: 3.8903
Iteration: 314; Percent complete: 7.8%; Average loss: 3.6714
Iteration: 315; Percent complete: 7.9%; Average loss: 3.6338
Iteration: 316; Percent complete: 7.9%; Average loss: 3.9969
Iteration: 317; Percent complete: 7.9%; Average loss: 3.9784
Iteration: 318; Percent complete: 8.0%; Average loss: 3.7331
Iteration: 319; Percent complete: 8.0%; Average loss: 3.7064
Iteration: 320; Percent complete: 8.0%; Average loss: 3.9063
Iteration: 321; Percent complete: 8.0%; Average loss: 3.7417
Iteration: 322; Percent complete: 8.1%; Average loss: 3.9564
Iteration: 323; Percent complete: 8.1%; Average loss: 3.9479
Iteration: 324; Percent complete: 8.1%; Average loss: 3.7687
Iteration: 325; Percent complete: 8.1%; Average loss: 3.7173
Iteration: 326; Percent complete: 8.2%; Average loss: 4.0269
Iteration: 327; Percent complete: 8.2%; Average loss: 3.9861
Iteration: 328; Percent complete: 8.2%; Average loss: 3.7103
Iteration: 329; Percent complete: 8.2%; Average loss: 4.0372
Iteration: 330; Percent complete: 8.2%; Average loss: 3.6477
Iteration: 331; Percent complete: 8.3%; Average loss: 4.1493
Iteration: 332; Percent complete: 8.3%; Average loss: 3.7268
Iteration: 333; Percent complete: 8.3%; Average loss: 4.1671
Iteration: 334; Percent complete: 8.3%; Average loss: 3.9245
Iteration: 335; Percent complete: 8.4%; Average loss: 4.0406
Iteration: 336; Percent complete: 8.4%; Average loss: 4.3016
Iteration: 337; Percent complete: 8.4%; Average loss: 3.7910
Iteration: 338; Percent complete: 8.5%; Average loss: 4.1136
Iteration: 339; Percent complete: 8.5%; Average loss: 4.0123
Iteration: 340; Percent complete: 8.5%; Average loss: 3.8182
Iteration: 341; Percent complete: 8.5%; Average loss: 3.8411
Iteration: 342; Percent complete: 8.6%; Average loss: 3.8882
Iteration: 343; Percent complete: 8.6%; Average loss: 3.9008
Iteration: 344; Percent complete: 8.6%; Average loss: 3.8511
Iteration: 345; Percent complete: 8.6%; Average loss: 4.0312
Iteration: 346; Percent complete: 8.6%; Average loss: 4.2929
Iteration: 347; Percent complete: 8.7%; Average loss: 3.8944
Iteration: 348; Percent complete: 8.7%; Average loss: 4.0511
Iteration: 349; Percent complete: 8.7%; Average loss: 3.7314
Iteration: 350; Percent complete: 8.8%; Average loss: 3.7776
Iteration: 351; Percent complete: 8.8%; Average loss: 3.8225
Iteration: 352; Percent complete: 8.8%; Average loss: 3.7061
Iteration: 353; Percent complete: 8.8%; Average loss: 4.0138
Iteration: 354; Percent complete: 8.8%; Average loss: 3.6913
Iteration: 355; Percent complete: 8.9%; Average loss: 4.0473
Iteration: 356; Percent complete: 8.9%; Average loss: 3.6603
Iteration: 357; Percent complete: 8.9%; Average loss: 3.9371
Iteration: 358; Percent complete: 8.9%; Average loss: 3.7398
Iteration: 359; Percent complete: 9.0%; Average loss: 3.7831
Iteration: 360; Percent complete: 9.0%; Average loss: 3.8455
Iteration: 361; Percent complete: 9.0%; Average loss: 3.6412
Iteration: 362; Percent complete: 9.0%; Average loss: 3.8288
Iteration: 363; Percent complete: 9.1%; Average loss: 3.6475
Iteration: 364; Percent complete: 9.1%; Average loss: 3.7188
Iteration: 365; Percent complete: 9.1%; Average loss: 4.0038
Iteration: 366; Percent complete: 9.2%; Average loss: 3.7555
Iteration: 367; Percent complete: 9.2%; Average loss: 4.1847
Iteration: 368; Percent complete: 9.2%; Average loss: 3.6996
Iteration: 369; Percent complete: 9.2%; Average loss: 3.8573
Iteration: 370; Percent complete: 9.2%; Average loss: 3.9269
Iteration: 371; Percent complete: 9.3%; Average loss: 3.7394
Iteration: 372; Percent complete: 9.3%; Average loss: 3.9339
Iteration: 373; Percent complete: 9.3%; Average loss: 3.8400
Iteration: 374; Percent complete: 9.3%; Average loss: 3.8587
Iteration: 375; Percent complete: 9.4%; Average loss: 3.9168
Iteration: 376; Percent complete: 9.4%; Average loss: 3.9075
Iteration: 377; Percent complete: 9.4%; Average loss: 3.8820
Iteration: 378; Percent complete: 9.4%; Average loss: 3.7986
Iteration: 379; Percent complete: 9.5%; Average loss: 3.9220
Iteration: 380; Percent complete: 9.5%; Average loss: 3.8079
Iteration: 381; Percent complete: 9.5%; Average loss: 3.9485
Iteration: 382; Percent complete: 9.6%; Average loss: 4.0143
Iteration: 383; Percent complete: 9.6%; Average loss: 3.7737
Iteration: 384; Percent complete: 9.6%; Average loss: 3.9552
Iteration: 385; Percent complete: 9.6%; Average loss: 3.7451
Iteration: 386; Percent complete: 9.7%; Average loss: 3.5846
Iteration: 387; Percent complete: 9.7%; Average loss: 3.9638
Iteration: 388; Percent complete: 9.7%; Average loss: 3.9143
Iteration: 389; Percent complete: 9.7%; Average loss: 3.5612
Iteration: 390; Percent complete: 9.8%; Average loss: 3.7266
Iteration: 391; Percent complete: 9.8%; Average loss: 3.9704
Iteration: 392; Percent complete: 9.8%; Average loss: 3.8144
Iteration: 393; Percent complete: 9.8%; Average loss: 4.0175
Iteration: 394; Percent complete: 9.8%; Average loss: 4.1265
Iteration: 395; Percent complete: 9.9%; Average loss: 3.6017
Iteration: 396; Percent complete: 9.9%; Average loss: 3.7121
Iteration: 397; Percent complete: 9.9%; Average loss: 3.6263
Iteration: 398; Percent complete: 10.0%; Average loss: 3.5452
Iteration: 399; Percent complete: 10.0%; Average loss: 3.9833
Iteration: 400; Percent complete: 10.0%; Average loss: 3.7769
Iteration: 401; Percent complete: 10.0%; Average loss: 4.0057
Iteration: 402; Percent complete: 10.1%; Average loss: 3.8610
Iteration: 403; Percent complete: 10.1%; Average loss: 4.1444
Iteration: 404; Percent complete: 10.1%; Average loss: 3.7696
Iteration: 405; Percent complete: 10.1%; Average loss: 3.8444
Iteration: 406; Percent complete: 10.2%; Average loss: 3.5835
Iteration: 407; Percent complete: 10.2%; Average loss: 3.4870
Iteration: 408; Percent complete: 10.2%; Average loss: 3.4517
Iteration: 409; Percent complete: 10.2%; Average loss: 3.7453
Iteration: 410; Percent complete: 10.2%; Average loss: 3.5665
Iteration: 411; Percent complete: 10.3%; Average loss: 4.1583
Iteration: 412; Percent complete: 10.3%; Average loss: 3.9026
Iteration: 413; Percent complete: 10.3%; Average loss: 3.8268
Iteration: 414; Percent complete: 10.3%; Average loss: 3.8979
Iteration: 415; Percent complete: 10.4%; Average loss: 3.7576
Iteration: 416; Percent complete: 10.4%; Average loss: 4.0265
Iteration: 417; Percent complete: 10.4%; Average loss: 3.9088
Iteration: 418; Percent complete: 10.4%; Average loss: 3.7774
Iteration: 419; Percent complete: 10.5%; Average loss: 3.9081
Iteration: 420; Percent complete: 10.5%; Average loss: 3.7127
Iteration: 421; Percent complete: 10.5%; Average loss: 3.9124
Iteration: 422; Percent complete: 10.5%; Average loss: 3.7618
Iteration: 423; Percent complete: 10.6%; Average loss: 3.9017
Iteration: 424; Percent complete: 10.6%; Average loss: 3.8827
Iteration: 425; Percent complete: 10.6%; Average loss: 3.7684
Iteration: 426; Percent complete: 10.7%; Average loss: 3.8210
Iteration: 427; Percent complete: 10.7%; Average loss: 3.9055
Iteration: 428; Percent complete: 10.7%; Average loss: 3.9227
Iteration: 429; Percent complete: 10.7%; Average loss: 3.8779
Iteration: 430; Percent complete: 10.8%; Average loss: 3.8084
Iteration: 431; Percent complete: 10.8%; Average loss: 3.9110
Iteration: 432; Percent complete: 10.8%; Average loss: 3.8912
Iteration: 433; Percent complete: 10.8%; Average loss: 3.6807
Iteration: 434; Percent complete: 10.8%; Average loss: 4.0063
Iteration: 435; Percent complete: 10.9%; Average loss: 3.5809
Iteration: 436; Percent complete: 10.9%; Average loss: 3.5752
Iteration: 437; Percent complete: 10.9%; Average loss: 4.1420
Iteration: 438; Percent complete: 10.9%; Average loss: 3.7601
Iteration: 439; Percent complete: 11.0%; Average loss: 3.8477
Iteration: 440; Percent complete: 11.0%; Average loss: 3.9307
Iteration: 441; Percent complete: 11.0%; Average loss: 3.8711
Iteration: 442; Percent complete: 11.1%; Average loss: 3.8997
Iteration: 443; Percent complete: 11.1%; Average loss: 3.8756
Iteration: 444; Percent complete: 11.1%; Average loss: 3.8431
Iteration: 445; Percent complete: 11.1%; Average loss: 3.8335
Iteration: 446; Percent complete: 11.2%; Average loss: 3.7645
Iteration: 447; Percent complete: 11.2%; Average loss: 3.8407
Iteration: 448; Percent complete: 11.2%; Average loss: 3.7421
Iteration: 449; Percent complete: 11.2%; Average loss: 3.8768
Iteration: 450; Percent complete: 11.2%; Average loss: 3.7266
Iteration: 451; Percent complete: 11.3%; Average loss: 3.7614
Iteration: 452; Percent complete: 11.3%; Average loss: 3.7532
Iteration: 453; Percent complete: 11.3%; Average loss: 3.3980
Iteration: 454; Percent complete: 11.3%; Average loss: 3.7967
Iteration: 455; Percent complete: 11.4%; Average loss: 3.8462
Iteration: 456; Percent complete: 11.4%; Average loss: 3.5492
Iteration: 457; Percent complete: 11.4%; Average loss: 4.1391
Iteration: 458; Percent complete: 11.5%; Average loss: 3.6322
Iteration: 459; Percent complete: 11.5%; Average loss: 3.9169
Iteration: 460; Percent complete: 11.5%; Average loss: 3.9475
Iteration: 461; Percent complete: 11.5%; Average loss: 3.6120
Iteration: 462; Percent complete: 11.6%; Average loss: 3.8590
Iteration: 463; Percent complete: 11.6%; Average loss: 3.5741
Iteration: 464; Percent complete: 11.6%; Average loss: 3.6807
Iteration: 465; Percent complete: 11.6%; Average loss: 3.4341
Iteration: 466; Percent complete: 11.7%; Average loss: 3.7336
Iteration: 467; Percent complete: 11.7%; Average loss: 3.8747
Iteration: 468; Percent complete: 11.7%; Average loss: 3.7523
Iteration: 469; Percent complete: 11.7%; Average loss: 3.7198
Iteration: 470; Percent complete: 11.8%; Average loss: 3.6956
Iteration: 471; Percent complete: 11.8%; Average loss: 3.5630
Iteration: 472; Percent complete: 11.8%; Average loss: 3.7939
Iteration: 473; Percent complete: 11.8%; Average loss: 3.9456
Iteration: 474; Percent complete: 11.8%; Average loss: 3.5089
Iteration: 475; Percent complete: 11.9%; Average loss: 3.7439
Iteration: 476; Percent complete: 11.9%; Average loss: 3.8375
Iteration: 477; Percent complete: 11.9%; Average loss: 3.9882
Iteration: 478; Percent complete: 11.9%; Average loss: 3.7753
Iteration: 479; Percent complete: 12.0%; Average loss: 4.1118
Iteration: 480; Percent complete: 12.0%; Average loss: 3.9155
Iteration: 481; Percent complete: 12.0%; Average loss: 3.9243
Iteration: 482; Percent complete: 12.0%; Average loss: 3.7919
Iteration: 483; Percent complete: 12.1%; Average loss: 3.7437
Iteration: 484; Percent complete: 12.1%; Average loss: 3.6924
Iteration: 485; Percent complete: 12.1%; Average loss: 3.7867
Iteration: 486; Percent complete: 12.2%; Average loss: 3.9379
Iteration: 487; Percent complete: 12.2%; Average loss: 3.9134
Iteration: 488; Percent complete: 12.2%; Average loss: 3.5880
Iteration: 489; Percent complete: 12.2%; Average loss: 3.6612
Iteration: 490; Percent complete: 12.2%; Average loss: 3.7027
Iteration: 491; Percent complete: 12.3%; Average loss: 3.5473
Iteration: 492; Percent complete: 12.3%; Average loss: 3.8013
Iteration: 493; Percent complete: 12.3%; Average loss: 3.5981
Iteration: 494; Percent complete: 12.3%; Average loss: 3.6904
Iteration: 495; Percent complete: 12.4%; Average loss: 4.0215
Iteration: 496; Percent complete: 12.4%; Average loss: 3.8247
Iteration: 497; Percent complete: 12.4%; Average loss: 3.7866
Iteration: 498; Percent complete: 12.4%; Average loss: 3.6588
Iteration: 499; Percent complete: 12.5%; Average loss: 3.7022
Iteration: 500; Percent complete: 12.5%; Average loss: 3.8017
Iteration: 501; Percent complete: 12.5%; Average loss: 3.8490
Iteration: 502; Percent complete: 12.6%; Average loss: 3.7694
Iteration: 503; Percent complete: 12.6%; Average loss: 3.7393
Iteration: 504; Percent complete: 12.6%; Average loss: 3.9207
Iteration: 505; Percent complete: 12.6%; Average loss: 3.4431
Iteration: 506; Percent complete: 12.7%; Average loss: 3.7039
Iteration: 507; Percent complete: 12.7%; Average loss: 3.8196
Iteration: 508; Percent complete: 12.7%; Average loss: 3.7159
Iteration: 509; Percent complete: 12.7%; Average loss: 3.7129
Iteration: 510; Percent complete: 12.8%; Average loss: 3.5839
Iteration: 511; Percent complete: 12.8%; Average loss: 3.7209
Iteration: 512; Percent complete: 12.8%; Average loss: 3.6261
Iteration: 513; Percent complete: 12.8%; Average loss: 3.6417
Iteration: 514; Percent complete: 12.8%; Average loss: 3.5148
Iteration: 515; Percent complete: 12.9%; Average loss: 3.7245
Iteration: 516; Percent complete: 12.9%; Average loss: 3.7615
Iteration: 517; Percent complete: 12.9%; Average loss: 3.8002
Iteration: 518; Percent complete: 13.0%; Average loss: 3.8131
Iteration: 519; Percent complete: 13.0%; Average loss: 3.5665
Iteration: 520; Percent complete: 13.0%; Average loss: 3.6054
Iteration: 521; Percent complete: 13.0%; Average loss: 3.6118
Iteration: 522; Percent complete: 13.1%; Average loss: 3.6873
Iteration: 523; Percent complete: 13.1%; Average loss: 3.6474
Iteration: 524; Percent complete: 13.1%; Average loss: 3.4862
Iteration: 525; Percent complete: 13.1%; Average loss: 3.6026
Iteration: 526; Percent complete: 13.2%; Average loss: 3.7032
Iteration: 527; Percent complete: 13.2%; Average loss: 3.6784
Iteration: 528; Percent complete: 13.2%; Average loss: 3.7540
Iteration: 529; Percent complete: 13.2%; Average loss: 3.9213
Iteration: 530; Percent complete: 13.2%; Average loss: 3.4780
Iteration: 531; Percent complete: 13.3%; Average loss: 4.0193
Iteration: 532; Percent complete: 13.3%; Average loss: 3.2921
Iteration: 533; Percent complete: 13.3%; Average loss: 3.6112
Iteration: 534; Percent complete: 13.4%; Average loss: 3.4936
Iteration: 535; Percent complete: 13.4%; Average loss: 3.6412
Iteration: 536; Percent complete: 13.4%; Average loss: 3.6254
Iteration: 537; Percent complete: 13.4%; Average loss: 3.7137
Iteration: 538; Percent complete: 13.5%; Average loss: 3.8404
Iteration: 539; Percent complete: 13.5%; Average loss: 3.6282
Iteration: 540; Percent complete: 13.5%; Average loss: 3.5841
Iteration: 541; Percent complete: 13.5%; Average loss: 4.0694
Iteration: 542; Percent complete: 13.6%; Average loss: 3.8043
Iteration: 543; Percent complete: 13.6%; Average loss: 3.7490
Iteration: 544; Percent complete: 13.6%; Average loss: 3.8697
Iteration: 545; Percent complete: 13.6%; Average loss: 3.5123
Iteration: 546; Percent complete: 13.7%; Average loss: 3.5501
Iteration: 547; Percent complete: 13.7%; Average loss: 3.7433
Iteration: 548; Percent complete: 13.7%; Average loss: 3.6774
Iteration: 549; Percent complete: 13.7%; Average loss: 3.7882
Iteration: 550; Percent complete: 13.8%; Average loss: 3.7937
Iteration: 551; Percent complete: 13.8%; Average loss: 3.6714
Iteration: 552; Percent complete: 13.8%; Average loss: 3.6880
Iteration: 553; Percent complete: 13.8%; Average loss: 3.6599
Iteration: 554; Percent complete: 13.9%; Average loss: 3.9461
Iteration: 555; Percent complete: 13.9%; Average loss: 3.6812
Iteration: 556; Percent complete: 13.9%; Average loss: 4.0028
Iteration: 557; Percent complete: 13.9%; Average loss: 3.6815
Iteration: 558; Percent complete: 14.0%; Average loss: 3.4747
Iteration: 559; Percent complete: 14.0%; Average loss: 3.6860
Iteration: 560; Percent complete: 14.0%; Average loss: 3.3598
Iteration: 561; Percent complete: 14.0%; Average loss: 3.6056
Iteration: 562; Percent complete: 14.1%; Average loss: 3.7674
Iteration: 563; Percent complete: 14.1%; Average loss: 3.5654
Iteration: 564; Percent complete: 14.1%; Average loss: 3.6717
Iteration: 565; Percent complete: 14.1%; Average loss: 3.7891
Iteration: 566; Percent complete: 14.1%; Average loss: 3.6940
Iteration: 567; Percent complete: 14.2%; Average loss: 3.4778
Iteration: 568; Percent complete: 14.2%; Average loss: 3.5763
Iteration: 569; Percent complete: 14.2%; Average loss: 3.6262
Iteration: 570; Percent complete: 14.2%; Average loss: 3.6476
Iteration: 571; Percent complete: 14.3%; Average loss: 3.6809
Iteration: 572; Percent complete: 14.3%; Average loss: 3.6894
Iteration: 573; Percent complete: 14.3%; Average loss: 3.7424
Iteration: 574; Percent complete: 14.3%; Average loss: 3.7531
Iteration: 575; Percent complete: 14.4%; Average loss: 3.5131
Iteration: 576; Percent complete: 14.4%; Average loss: 3.5241
Iteration: 577; Percent complete: 14.4%; Average loss: 3.6978
Iteration: 578; Percent complete: 14.4%; Average loss: 3.5852
Iteration: 579; Percent complete: 14.5%; Average loss: 3.7920
Iteration: 580; Percent complete: 14.5%; Average loss: 4.0107
Iteration: 581; Percent complete: 14.5%; Average loss: 3.2258
Iteration: 582; Percent complete: 14.5%; Average loss: 3.7403
Iteration: 583; Percent complete: 14.6%; Average loss: 3.7202
Iteration: 584; Percent complete: 14.6%; Average loss: 3.7155
Iteration: 585; Percent complete: 14.6%; Average loss: 3.9296
Iteration: 586; Percent complete: 14.6%; Average loss: 3.6145
Iteration: 587; Percent complete: 14.7%; Average loss: 3.6834
Iteration: 588; Percent complete: 14.7%; Average loss: 3.9720
Iteration: 589; Percent complete: 14.7%; Average loss: 3.7331
Iteration: 590; Percent complete: 14.8%; Average loss: 3.6822
Iteration: 591; Percent complete: 14.8%; Average loss: 3.6028
Iteration: 592; Percent complete: 14.8%; Average loss: 3.8937
Iteration: 593; Percent complete: 14.8%; Average loss: 3.8451
Iteration: 594; Percent complete: 14.8%; Average loss: 3.6517
Iteration: 595; Percent complete: 14.9%; Average loss: 3.5835
Iteration: 596; Percent complete: 14.9%; Average loss: 3.6539
Iteration: 597; Percent complete: 14.9%; Average loss: 3.6924
Iteration: 598; Percent complete: 14.9%; Average loss: 3.7384
Iteration: 599; Percent complete: 15.0%; Average loss: 3.4174
Iteration: 600; Percent complete: 15.0%; Average loss: 3.6857
Iteration: 601; Percent complete: 15.0%; Average loss: 3.8530
Iteration: 602; Percent complete: 15.0%; Average loss: 3.5916
Iteration: 603; Percent complete: 15.1%; Average loss: 3.5898
Iteration: 604; Percent complete: 15.1%; Average loss: 3.4862
Iteration: 605; Percent complete: 15.1%; Average loss: 3.6301
Iteration: 606; Percent complete: 15.2%; Average loss: 3.7887
Iteration: 607; Percent complete: 15.2%; Average loss: 3.7812
Iteration: 608; Percent complete: 15.2%; Average loss: 3.8963
Iteration: 609; Percent complete: 15.2%; Average loss: 3.5561
Iteration: 610; Percent complete: 15.2%; Average loss: 3.7051
Iteration: 611; Percent complete: 15.3%; Average loss: 3.8039
Iteration: 612; Percent complete: 15.3%; Average loss: 3.8430
Iteration: 613; Percent complete: 15.3%; Average loss: 3.5788
Iteration: 614; Percent complete: 15.3%; Average loss: 3.5582
Iteration: 615; Percent complete: 15.4%; Average loss: 3.6634
Iteration: 616; Percent complete: 15.4%; Average loss: 3.4541
Iteration: 617; Percent complete: 15.4%; Average loss: 3.7356
Iteration: 618; Percent complete: 15.4%; Average loss: 3.7908
Iteration: 619; Percent complete: 15.5%; Average loss: 3.9407
Iteration: 620; Percent complete: 15.5%; Average loss: 3.6205
Iteration: 621; Percent complete: 15.5%; Average loss: 3.7313
Iteration: 622; Percent complete: 15.6%; Average loss: 3.5195
Iteration: 623; Percent complete: 15.6%; Average loss: 3.6804
Iteration: 624; Percent complete: 15.6%; Average loss: 3.6811
Iteration: 625; Percent complete: 15.6%; Average loss: 3.8084
Iteration: 626; Percent complete: 15.7%; Average loss: 3.7110
Iteration: 627; Percent complete: 15.7%; Average loss: 3.6256
Iteration: 628; Percent complete: 15.7%; Average loss: 3.6006
Iteration: 629; Percent complete: 15.7%; Average loss: 3.3233
Iteration: 630; Percent complete: 15.8%; Average loss: 3.4729
Iteration: 631; Percent complete: 15.8%; Average loss: 3.5706
Iteration: 632; Percent complete: 15.8%; Average loss: 3.5820
Iteration: 633; Percent complete: 15.8%; Average loss: 3.6099
Iteration: 634; Percent complete: 15.8%; Average loss: 3.5988
Iteration: 635; Percent complete: 15.9%; Average loss: 3.8763
Iteration: 636; Percent complete: 15.9%; Average loss: 3.6251
Iteration: 637; Percent complete: 15.9%; Average loss: 3.9073
Iteration: 638; Percent complete: 16.0%; Average loss: 3.7290
Iteration: 639; Percent complete: 16.0%; Average loss: 3.6053
Iteration: 640; Percent complete: 16.0%; Average loss: 3.6599
Iteration: 641; Percent complete: 16.0%; Average loss: 3.6829
Iteration: 642; Percent complete: 16.1%; Average loss: 3.6063
Iteration: 643; Percent complete: 16.1%; Average loss: 3.7637
Iteration: 644; Percent complete: 16.1%; Average loss: 3.6244
Iteration: 645; Percent complete: 16.1%; Average loss: 3.7969
Iteration: 646; Percent complete: 16.2%; Average loss: 3.7261
Iteration: 647; Percent complete: 16.2%; Average loss: 3.7253
Iteration: 648; Percent complete: 16.2%; Average loss: 3.6483
Iteration: 649; Percent complete: 16.2%; Average loss: 3.5431
Iteration: 650; Percent complete: 16.2%; Average loss: 3.6309
Iteration: 651; Percent complete: 16.3%; Average loss: 3.5844
Iteration: 652; Percent complete: 16.3%; Average loss: 3.5357
Iteration: 653; Percent complete: 16.3%; Average loss: 3.6432
Iteration: 654; Percent complete: 16.4%; Average loss: 3.6316
Iteration: 655; Percent complete: 16.4%; Average loss: 3.7903
Iteration: 656; Percent complete: 16.4%; Average loss: 3.6511
Iteration: 657; Percent complete: 16.4%; Average loss: 3.6795
Iteration: 658; Percent complete: 16.4%; Average loss: 3.5725
Iteration: 659; Percent complete: 16.5%; Average loss: 3.6026
Iteration: 660; Percent complete: 16.5%; Average loss: 3.6443
Iteration: 661; Percent complete: 16.5%; Average loss: 3.5122
Iteration: 662; Percent complete: 16.6%; Average loss: 3.6222
Iteration: 663; Percent complete: 16.6%; Average loss: 3.5060
Iteration: 664; Percent complete: 16.6%; Average loss: 3.7948
Iteration: 665; Percent complete: 16.6%; Average loss: 3.7188
Iteration: 666; Percent complete: 16.7%; Average loss: 3.4872
Iteration: 667; Percent complete: 16.7%; Average loss: 3.6273
Iteration: 668; Percent complete: 16.7%; Average loss: 3.3964
Iteration: 669; Percent complete: 16.7%; Average loss: 3.6513
Iteration: 670; Percent complete: 16.8%; Average loss: 3.5432
Iteration: 671; Percent complete: 16.8%; Average loss: 3.3892
Iteration: 672; Percent complete: 16.8%; Average loss: 3.6087
Iteration: 673; Percent complete: 16.8%; Average loss: 3.6379
Iteration: 674; Percent complete: 16.9%; Average loss: 3.6976
Iteration: 675; Percent complete: 16.9%; Average loss: 3.5497
Iteration: 676; Percent complete: 16.9%; Average loss: 3.4136
Iteration: 677; Percent complete: 16.9%; Average loss: 3.8614
Iteration: 678; Percent complete: 17.0%; Average loss: 3.5942
Iteration: 679; Percent complete: 17.0%; Average loss: 3.6214
Iteration: 680; Percent complete: 17.0%; Average loss: 3.5602
Iteration: 681; Percent complete: 17.0%; Average loss: 3.6437
Iteration: 682; Percent complete: 17.1%; Average loss: 3.4950
Iteration: 683; Percent complete: 17.1%; Average loss: 3.5470
Iteration: 684; Percent complete: 17.1%; Average loss: 3.7204
Iteration: 685; Percent complete: 17.1%; Average loss: 3.6632
Iteration: 686; Percent complete: 17.2%; Average loss: 3.5193
Iteration: 687; Percent complete: 17.2%; Average loss: 3.7661
Iteration: 688; Percent complete: 17.2%; Average loss: 3.8441
Iteration: 689; Percent complete: 17.2%; Average loss: 3.3925
Iteration: 690; Percent complete: 17.2%; Average loss: 3.4274
Iteration: 691; Percent complete: 17.3%; Average loss: 3.4960
Iteration: 692; Percent complete: 17.3%; Average loss: 3.6239
Iteration: 693; Percent complete: 17.3%; Average loss: 3.6244
Iteration: 694; Percent complete: 17.3%; Average loss: 3.5956
Iteration: 695; Percent complete: 17.4%; Average loss: 3.7885
Iteration: 696; Percent complete: 17.4%; Average loss: 3.6677
Iteration: 697; Percent complete: 17.4%; Average loss: 3.7260
Iteration: 698; Percent complete: 17.4%; Average loss: 3.3382
Iteration: 699; Percent complete: 17.5%; Average loss: 3.7732
Iteration: 700; Percent complete: 17.5%; Average loss: 3.7600
Iteration: 701; Percent complete: 17.5%; Average loss: 3.6746
Iteration: 702; Percent complete: 17.5%; Average loss: 3.9437
Iteration: 703; Percent complete: 17.6%; Average loss: 3.6071
Iteration: 704; Percent complete: 17.6%; Average loss: 3.4211
Iteration: 705; Percent complete: 17.6%; Average loss: 3.8925
Iteration: 706; Percent complete: 17.6%; Average loss: 3.6866
Iteration: 707; Percent complete: 17.7%; Average loss: 3.5948
Iteration: 708; Percent complete: 17.7%; Average loss: 3.1707
Iteration: 709; Percent complete: 17.7%; Average loss: 3.6017
Iteration: 710; Percent complete: 17.8%; Average loss: 3.6131
Iteration: 711; Percent complete: 17.8%; Average loss: 3.5875
Iteration: 712; Percent complete: 17.8%; Average loss: 3.8285
Iteration: 713; Percent complete: 17.8%; Average loss: 3.7275
Iteration: 714; Percent complete: 17.8%; Average loss: 3.6154
Iteration: 715; Percent complete: 17.9%; Average loss: 3.4699
Iteration: 716; Percent complete: 17.9%; Average loss: 3.6044
Iteration: 717; Percent complete: 17.9%; Average loss: 3.7107
Iteration: 718; Percent complete: 17.9%; Average loss: 3.5545
Iteration: 719; Percent complete: 18.0%; Average loss: 3.7190
Iteration: 720; Percent complete: 18.0%; Average loss: 3.8578
Iteration: 721; Percent complete: 18.0%; Average loss: 3.3258
Iteration: 722; Percent complete: 18.1%; Average loss: 3.7954
Iteration: 723; Percent complete: 18.1%; Average loss: 3.7849
Iteration: 724; Percent complete: 18.1%; Average loss: 3.7376
Iteration: 725; Percent complete: 18.1%; Average loss: 3.6054
Iteration: 726; Percent complete: 18.1%; Average loss: 3.7947
Iteration: 727; Percent complete: 18.2%; Average loss: 3.3944
Iteration: 728; Percent complete: 18.2%; Average loss: 3.7075
Iteration: 729; Percent complete: 18.2%; Average loss: 3.5541
Iteration: 730; Percent complete: 18.2%; Average loss: 3.4794
Iteration: 731; Percent complete: 18.3%; Average loss: 3.7558
Iteration: 732; Percent complete: 18.3%; Average loss: 3.5871
Iteration: 733; Percent complete: 18.3%; Average loss: 3.5364
Iteration: 734; Percent complete: 18.4%; Average loss: 3.5050
Iteration: 735; Percent complete: 18.4%; Average loss: 3.3617
Iteration: 736; Percent complete: 18.4%; Average loss: 3.4420
Iteration: 737; Percent complete: 18.4%; Average loss: 3.6345
Iteration: 738; Percent complete: 18.4%; Average loss: 3.4750
Iteration: 739; Percent complete: 18.5%; Average loss: 3.7527
Iteration: 740; Percent complete: 18.5%; Average loss: 3.8219
Iteration: 741; Percent complete: 18.5%; Average loss: 3.5920
Iteration: 742; Percent complete: 18.6%; Average loss: 3.6022
Iteration: 743; Percent complete: 18.6%; Average loss: 3.5705
Iteration: 744; Percent complete: 18.6%; Average loss: 3.5895
Iteration: 745; Percent complete: 18.6%; Average loss: 3.7842
Iteration: 746; Percent complete: 18.6%; Average loss: 3.7346
Iteration: 747; Percent complete: 18.7%; Average loss: 3.6587
Iteration: 748; Percent complete: 18.7%; Average loss: 3.6510
Iteration: 749; Percent complete: 18.7%; Average loss: 3.4940
Iteration: 750; Percent complete: 18.8%; Average loss: 3.7971
Iteration: 751; Percent complete: 18.8%; Average loss: 3.3854
Iteration: 752; Percent complete: 18.8%; Average loss: 3.4851
Iteration: 753; Percent complete: 18.8%; Average loss: 3.5760
Iteration: 754; Percent complete: 18.9%; Average loss: 3.4601
Iteration: 755; Percent complete: 18.9%; Average loss: 3.6509
Iteration: 756; Percent complete: 18.9%; Average loss: 3.6418
Iteration: 757; Percent complete: 18.9%; Average loss: 3.5499
Iteration: 758; Percent complete: 18.9%; Average loss: 3.7391
Iteration: 759; Percent complete: 19.0%; Average loss: 3.5021
Iteration: 760; Percent complete: 19.0%; Average loss: 3.6705
Iteration: 761; Percent complete: 19.0%; Average loss: 3.5498
Iteration: 762; Percent complete: 19.1%; Average loss: 3.3675
Iteration: 763; Percent complete: 19.1%; Average loss: 3.6580
Iteration: 764; Percent complete: 19.1%; Average loss: 3.8208
Iteration: 765; Percent complete: 19.1%; Average loss: 3.6864
Iteration: 766; Percent complete: 19.1%; Average loss: 3.6901
Iteration: 767; Percent complete: 19.2%; Average loss: 3.5551
Iteration: 768; Percent complete: 19.2%; Average loss: 3.4805
Iteration: 769; Percent complete: 19.2%; Average loss: 3.4258
Iteration: 770; Percent complete: 19.2%; Average loss: 3.5817
Iteration: 771; Percent complete: 19.3%; Average loss: 3.5337
Iteration: 772; Percent complete: 19.3%; Average loss: 3.5156
Iteration: 773; Percent complete: 19.3%; Average loss: 3.3954
Iteration: 774; Percent complete: 19.4%; Average loss: 3.3437
Iteration: 775; Percent complete: 19.4%; Average loss: 3.4436
Iteration: 776; Percent complete: 19.4%; Average loss: 3.4947
Iteration: 777; Percent complete: 19.4%; Average loss: 3.7629
Iteration: 778; Percent complete: 19.4%; Average loss: 3.6842
Iteration: 779; Percent complete: 19.5%; Average loss: 3.6066
Iteration: 780; Percent complete: 19.5%; Average loss: 3.7006
Iteration: 781; Percent complete: 19.5%; Average loss: 3.1620
Iteration: 782; Percent complete: 19.6%; Average loss: 3.8540
Iteration: 783; Percent complete: 19.6%; Average loss: 3.6739
Iteration: 784; Percent complete: 19.6%; Average loss: 3.8248
Iteration: 785; Percent complete: 19.6%; Average loss: 3.6437
Iteration: 786; Percent complete: 19.7%; Average loss: 3.7490
Iteration: 787; Percent complete: 19.7%; Average loss: 3.5532
Iteration: 788; Percent complete: 19.7%; Average loss: 3.5393
Iteration: 789; Percent complete: 19.7%; Average loss: 3.6580
Iteration: 790; Percent complete: 19.8%; Average loss: 3.4726
Iteration: 791; Percent complete: 19.8%; Average loss: 3.7198
Iteration: 792; Percent complete: 19.8%; Average loss: 3.6723
Iteration: 793; Percent complete: 19.8%; Average loss: 3.4345
Iteration: 794; Percent complete: 19.9%; Average loss: 3.8800
Iteration: 795; Percent complete: 19.9%; Average loss: 3.4500
Iteration: 796; Percent complete: 19.9%; Average loss: 3.6293
Iteration: 797; Percent complete: 19.9%; Average loss: 3.7327
Iteration: 798; Percent complete: 20.0%; Average loss: 3.3422
Iteration: 799; Percent complete: 20.0%; Average loss: 3.5294
Iteration: 800; Percent complete: 20.0%; Average loss: 3.2828
Iteration: 801; Percent complete: 20.0%; Average loss: 3.9580
Iteration: 802; Percent complete: 20.1%; Average loss: 3.4662
Iteration: 803; Percent complete: 20.1%; Average loss: 3.6840
Iteration: 804; Percent complete: 20.1%; Average loss: 3.7289
Iteration: 805; Percent complete: 20.1%; Average loss: 3.5034
Iteration: 806; Percent complete: 20.2%; Average loss: 3.4484
Iteration: 807; Percent complete: 20.2%; Average loss: 3.5823
Iteration: 808; Percent complete: 20.2%; Average loss: 3.6455
Iteration: 809; Percent complete: 20.2%; Average loss: 3.6580
Iteration: 810; Percent complete: 20.2%; Average loss: 3.4931
Iteration: 811; Percent complete: 20.3%; Average loss: 3.4657
Iteration: 812; Percent complete: 20.3%; Average loss: 3.3977
Iteration: 813; Percent complete: 20.3%; Average loss: 3.8369
Iteration: 814; Percent complete: 20.3%; Average loss: 3.5371
Iteration: 815; Percent complete: 20.4%; Average loss: 3.3095
Iteration: 816; Percent complete: 20.4%; Average loss: 3.6643
Iteration: 817; Percent complete: 20.4%; Average loss: 3.4656
Iteration: 818; Percent complete: 20.4%; Average loss: 3.2897
Iteration: 819; Percent complete: 20.5%; Average loss: 3.6718
Iteration: 820; Percent complete: 20.5%; Average loss: 3.4152
Iteration: 821; Percent complete: 20.5%; Average loss: 3.5493
Iteration: 822; Percent complete: 20.5%; Average loss: 3.6092
Iteration: 823; Percent complete: 20.6%; Average loss: 3.4603
Iteration: 824; Percent complete: 20.6%; Average loss: 3.5946
Iteration: 825; Percent complete: 20.6%; Average loss: 3.6421
Iteration: 826; Percent complete: 20.6%; Average loss: 3.4562
Iteration: 827; Percent complete: 20.7%; Average loss: 3.4256
Iteration: 828; Percent complete: 20.7%; Average loss: 3.5902
Iteration: 829; Percent complete: 20.7%; Average loss: 3.4837
Iteration: 830; Percent complete: 20.8%; Average loss: 3.4563
Iteration: 831; Percent complete: 20.8%; Average loss: 3.3830
Iteration: 832; Percent complete: 20.8%; Average loss: 3.7474
Iteration: 833; Percent complete: 20.8%; Average loss: 3.5399
Iteration: 834; Percent complete: 20.8%; Average loss: 3.2127
Iteration: 835; Percent complete: 20.9%; Average loss: 3.5807
Iteration: 836; Percent complete: 20.9%; Average loss: 3.4429
Iteration: 837; Percent complete: 20.9%; Average loss: 3.8224
Iteration: 838; Percent complete: 20.9%; Average loss: 3.4988
Iteration: 839; Percent complete: 21.0%; Average loss: 3.3572
Iteration: 840; Percent complete: 21.0%; Average loss: 3.8493
Iteration: 841; Percent complete: 21.0%; Average loss: 3.7116
Iteration: 842; Percent complete: 21.1%; Average loss: 3.7445
Iteration: 843; Percent complete: 21.1%; Average loss: 3.6322
Iteration: 844; Percent complete: 21.1%; Average loss: 3.4628
Iteration: 845; Percent complete: 21.1%; Average loss: 3.3856
Iteration: 846; Percent complete: 21.1%; Average loss: 3.3861
Iteration: 847; Percent complete: 21.2%; Average loss: 3.2883
Iteration: 848; Percent complete: 21.2%; Average loss: 3.7849
Iteration: 849; Percent complete: 21.2%; Average loss: 3.4410
Iteration: 850; Percent complete: 21.2%; Average loss: 3.3838
Iteration: 851; Percent complete: 21.3%; Average loss: 3.8528
Iteration: 852; Percent complete: 21.3%; Average loss: 3.8653
Iteration: 853; Percent complete: 21.3%; Average loss: 3.7388
Iteration: 854; Percent complete: 21.3%; Average loss: 3.5283
Iteration: 855; Percent complete: 21.4%; Average loss: 3.5798
Iteration: 856; Percent complete: 21.4%; Average loss: 3.7112
Iteration: 857; Percent complete: 21.4%; Average loss: 3.7753
Iteration: 858; Percent complete: 21.4%; Average loss: 3.5066
Iteration: 859; Percent complete: 21.5%; Average loss: 3.7413
Iteration: 860; Percent complete: 21.5%; Average loss: 3.5619
Iteration: 861; Percent complete: 21.5%; Average loss: 3.6815
Iteration: 862; Percent complete: 21.6%; Average loss: 3.6953
Iteration: 863; Percent complete: 21.6%; Average loss: 3.7213
Iteration: 864; Percent complete: 21.6%; Average loss: 3.6637
Iteration: 865; Percent complete: 21.6%; Average loss: 3.3364
Iteration: 866; Percent complete: 21.6%; Average loss: 3.6678
Iteration: 867; Percent complete: 21.7%; Average loss: 3.3649
Iteration: 868; Percent complete: 21.7%; Average loss: 3.4100
Iteration: 869; Percent complete: 21.7%; Average loss: 3.5240
Iteration: 870; Percent complete: 21.8%; Average loss: 3.3737
Iteration: 871; Percent complete: 21.8%; Average loss: 3.5834
Iteration: 872; Percent complete: 21.8%; Average loss: 3.4763
Iteration: 873; Percent complete: 21.8%; Average loss: 3.7275
Iteration: 874; Percent complete: 21.9%; Average loss: 3.7861
Iteration: 875; Percent complete: 21.9%; Average loss: 3.4293
Iteration: 876; Percent complete: 21.9%; Average loss: 3.5678
Iteration: 877; Percent complete: 21.9%; Average loss: 3.1617
Iteration: 878; Percent complete: 21.9%; Average loss: 3.7957
Iteration: 879; Percent complete: 22.0%; Average loss: 3.2877
Iteration: 880; Percent complete: 22.0%; Average loss: 3.2859
Iteration: 881; Percent complete: 22.0%; Average loss: 3.3597
Iteration: 882; Percent complete: 22.1%; Average loss: 3.6673
Iteration: 883; Percent complete: 22.1%; Average loss: 3.7081
Iteration: 884; Percent complete: 22.1%; Average loss: 3.5601
Iteration: 885; Percent complete: 22.1%; Average loss: 3.2930
Iteration: 886; Percent complete: 22.1%; Average loss: 3.5662
Iteration: 887; Percent complete: 22.2%; Average loss: 3.5046
Iteration: 888; Percent complete: 22.2%; Average loss: 3.6583
Iteration: 889; Percent complete: 22.2%; Average loss: 3.3681
Iteration: 890; Percent complete: 22.2%; Average loss: 3.6155
Iteration: 891; Percent complete: 22.3%; Average loss: 3.5400
Iteration: 892; Percent complete: 22.3%; Average loss: 3.8954
Iteration: 893; Percent complete: 22.3%; Average loss: 3.5586
Iteration: 894; Percent complete: 22.4%; Average loss: 3.6381
Iteration: 895; Percent complete: 22.4%; Average loss: 3.5985
Iteration: 896; Percent complete: 22.4%; Average loss: 3.0655
Iteration: 897; Percent complete: 22.4%; Average loss: 3.3840
Iteration: 898; Percent complete: 22.4%; Average loss: 3.3833
Iteration: 899; Percent complete: 22.5%; Average loss: 3.4565
Iteration: 900; Percent complete: 22.5%; Average loss: 3.4839
Iteration: 901; Percent complete: 22.5%; Average loss: 3.1607
Iteration: 902; Percent complete: 22.6%; Average loss: 3.7424
Iteration: 903; Percent complete: 22.6%; Average loss: 3.4755
Iteration: 904; Percent complete: 22.6%; Average loss: 3.5781
Iteration: 905; Percent complete: 22.6%; Average loss: 3.6480
Iteration: 906; Percent complete: 22.7%; Average loss: 3.4352
Iteration: 907; Percent complete: 22.7%; Average loss: 3.4596
Iteration: 908; Percent complete: 22.7%; Average loss: 3.6167
Iteration: 909; Percent complete: 22.7%; Average loss: 3.6201
Iteration: 910; Percent complete: 22.8%; Average loss: 3.4055
Iteration: 911; Percent complete: 22.8%; Average loss: 3.5910
Iteration: 912; Percent complete: 22.8%; Average loss: 3.5228
Iteration: 913; Percent complete: 22.8%; Average loss: 3.2380
Iteration: 914; Percent complete: 22.9%; Average loss: 3.5240
Iteration: 915; Percent complete: 22.9%; Average loss: 3.8632
Iteration: 916; Percent complete: 22.9%; Average loss: 3.4644
Iteration: 917; Percent complete: 22.9%; Average loss: 3.2945
Iteration: 918; Percent complete: 22.9%; Average loss: 3.5407
Iteration: 919; Percent complete: 23.0%; Average loss: 3.6468
Iteration: 920; Percent complete: 23.0%; Average loss: 3.4387
Iteration: 921; Percent complete: 23.0%; Average loss: 3.5381
Iteration: 922; Percent complete: 23.1%; Average loss: 3.8532
Iteration: 923; Percent complete: 23.1%; Average loss: 3.6414
Iteration: 924; Percent complete: 23.1%; Average loss: 3.5190
Iteration: 925; Percent complete: 23.1%; Average loss: 3.6133
Iteration: 926; Percent complete: 23.2%; Average loss: 3.5863
Iteration: 927; Percent complete: 23.2%; Average loss: 3.6298
Iteration: 928; Percent complete: 23.2%; Average loss: 3.4234
Iteration: 929; Percent complete: 23.2%; Average loss: 3.7202
Iteration: 930; Percent complete: 23.2%; Average loss: 3.6155
Iteration: 931; Percent complete: 23.3%; Average loss: 3.1913
Iteration: 932; Percent complete: 23.3%; Average loss: 3.4500
Iteration: 933; Percent complete: 23.3%; Average loss: 3.3604
Iteration: 934; Percent complete: 23.4%; Average loss: 3.5269
Iteration: 935; Percent complete: 23.4%; Average loss: 3.3572
Iteration: 936; Percent complete: 23.4%; Average loss: 3.4987
Iteration: 937; Percent complete: 23.4%; Average loss: 3.3483
Iteration: 938; Percent complete: 23.4%; Average loss: 3.6337
Iteration: 939; Percent complete: 23.5%; Average loss: 3.5011
Iteration: 940; Percent complete: 23.5%; Average loss: 3.2226
Iteration: 941; Percent complete: 23.5%; Average loss: 3.6859
Iteration: 942; Percent complete: 23.5%; Average loss: 3.3431
Iteration: 943; Percent complete: 23.6%; Average loss: 3.3687
Iteration: 944; Percent complete: 23.6%; Average loss: 3.7036
Iteration: 945; Percent complete: 23.6%; Average loss: 3.3109
Iteration: 946; Percent complete: 23.6%; Average loss: 3.3692
Iteration: 947; Percent complete: 23.7%; Average loss: 3.6215
Iteration: 948; Percent complete: 23.7%; Average loss: 3.4880
Iteration: 949; Percent complete: 23.7%; Average loss: 3.6913
Iteration: 950; Percent complete: 23.8%; Average loss: 3.7403
Iteration: 951; Percent complete: 23.8%; Average loss: 3.5489
Iteration: 952; Percent complete: 23.8%; Average loss: 3.5221
Iteration: 953; Percent complete: 23.8%; Average loss: 3.3459
Iteration: 954; Percent complete: 23.8%; Average loss: 3.3805
Iteration: 955; Percent complete: 23.9%; Average loss: 3.4670
Iteration: 956; Percent complete: 23.9%; Average loss: 3.5178
Iteration: 957; Percent complete: 23.9%; Average loss: 3.3732
Iteration: 958; Percent complete: 23.9%; Average loss: 3.7298
Iteration: 959; Percent complete: 24.0%; Average loss: 3.6148
Iteration: 960; Percent complete: 24.0%; Average loss: 3.2556
Iteration: 961; Percent complete: 24.0%; Average loss: 3.2813
Iteration: 962; Percent complete: 24.1%; Average loss: 3.3720
Iteration: 963; Percent complete: 24.1%; Average loss: 3.5256
Iteration: 964; Percent complete: 24.1%; Average loss: 3.3819
Iteration: 965; Percent complete: 24.1%; Average loss: 3.5652
Iteration: 966; Percent complete: 24.1%; Average loss: 3.4469
Iteration: 967; Percent complete: 24.2%; Average loss: 3.4526
Iteration: 968; Percent complete: 24.2%; Average loss: 3.2798
Iteration: 969; Percent complete: 24.2%; Average loss: 3.5736
Iteration: 970; Percent complete: 24.2%; Average loss: 3.2868
Iteration: 971; Percent complete: 24.3%; Average loss: 3.6283
Iteration: 972; Percent complete: 24.3%; Average loss: 3.5065
Iteration: 973; Percent complete: 24.3%; Average loss: 3.3966
Iteration: 974; Percent complete: 24.3%; Average loss: 3.4076
Iteration: 975; Percent complete: 24.4%; Average loss: 3.5097
Iteration: 976; Percent complete: 24.4%; Average loss: 3.5514
Iteration: 977; Percent complete: 24.4%; Average loss: 3.7423
Iteration: 978; Percent complete: 24.4%; Average loss: 3.2244
Iteration: 979; Percent complete: 24.5%; Average loss: 3.2915
Iteration: 980; Percent complete: 24.5%; Average loss: 3.4380
Iteration: 981; Percent complete: 24.5%; Average loss: 3.4950
Iteration: 982; Percent complete: 24.6%; Average loss: 3.2570
Iteration: 983; Percent complete: 24.6%; Average loss: 3.5270
Iteration: 984; Percent complete: 24.6%; Average loss: 3.5497
Iteration: 985; Percent complete: 24.6%; Average loss: 3.7274
Iteration: 986; Percent complete: 24.6%; Average loss: 3.5083
Iteration: 987; Percent complete: 24.7%; Average loss: 3.4442
Iteration: 988; Percent complete: 24.7%; Average loss: 3.5886
Iteration: 989; Percent complete: 24.7%; Average loss: 3.3997
Iteration: 990; Percent complete: 24.8%; Average loss: 3.5562
Iteration: 991; Percent complete: 24.8%; Average loss: 3.2652
Iteration: 992; Percent complete: 24.8%; Average loss: 3.6568
Iteration: 993; Percent complete: 24.8%; Average loss: 3.4330
Iteration: 994; Percent complete: 24.9%; Average loss: 3.5095
Iteration: 995; Percent complete: 24.9%; Average loss: 3.5099
Iteration: 996; Percent complete: 24.9%; Average loss: 3.3709
Iteration: 997; Percent complete: 24.9%; Average loss: 3.5676
Iteration: 998; Percent complete: 24.9%; Average loss: 3.2408
Iteration: 999; Percent complete: 25.0%; Average loss: 3.6586
Iteration: 1000; Percent complete: 25.0%; Average loss: 3.3936
Iteration: 1001; Percent complete: 25.0%; Average loss: 3.2864
Iteration: 1002; Percent complete: 25.1%; Average loss: 3.2779
Iteration: 1003; Percent complete: 25.1%; Average loss: 3.5109
Iteration: 1004; Percent complete: 25.1%; Average loss: 3.3354
Iteration: 1005; Percent complete: 25.1%; Average loss: 3.4123
Iteration: 1006; Percent complete: 25.1%; Average loss: 3.6599
Iteration: 1007; Percent complete: 25.2%; Average loss: 3.3027
Iteration: 1008; Percent complete: 25.2%; Average loss: 3.5119
Iteration: 1009; Percent complete: 25.2%; Average loss: 3.4299
Iteration: 1010; Percent complete: 25.2%; Average loss: 3.4322
Iteration: 1011; Percent complete: 25.3%; Average loss: 3.8439
Iteration: 1012; Percent complete: 25.3%; Average loss: 3.3742
Iteration: 1013; Percent complete: 25.3%; Average loss: 3.7020
Iteration: 1014; Percent complete: 25.4%; Average loss: 3.1914
Iteration: 1015; Percent complete: 25.4%; Average loss: 3.4073
Iteration: 1016; Percent complete: 25.4%; Average loss: 3.6239
Iteration: 1017; Percent complete: 25.4%; Average loss: 3.5796
Iteration: 1018; Percent complete: 25.4%; Average loss: 3.0500
Iteration: 1019; Percent complete: 25.5%; Average loss: 3.3665
Iteration: 1020; Percent complete: 25.5%; Average loss: 3.8039
Iteration: 1021; Percent complete: 25.5%; Average loss: 3.2599
Iteration: 1022; Percent complete: 25.6%; Average loss: 3.6830
Iteration: 1023; Percent complete: 25.6%; Average loss: 3.4629
Iteration: 1024; Percent complete: 25.6%; Average loss: 3.4117
Iteration: 1025; Percent complete: 25.6%; Average loss: 3.6035
Iteration: 1026; Percent complete: 25.7%; Average loss: 3.5745
Iteration: 1027; Percent complete: 25.7%; Average loss: 3.7056
Iteration: 1028; Percent complete: 25.7%; Average loss: 3.5675
Iteration: 1029; Percent complete: 25.7%; Average loss: 3.4966
Iteration: 1030; Percent complete: 25.8%; Average loss: 3.7155
Iteration: 1031; Percent complete: 25.8%; Average loss: 3.4847
Iteration: 1032; Percent complete: 25.8%; Average loss: 3.3343
Iteration: 1033; Percent complete: 25.8%; Average loss: 3.6629
Iteration: 1034; Percent complete: 25.9%; Average loss: 3.2198
Iteration: 1035; Percent complete: 25.9%; Average loss: 3.3006
Iteration: 1036; Percent complete: 25.9%; Average loss: 3.3581
Iteration: 1037; Percent complete: 25.9%; Average loss: 3.2022
Iteration: 1038; Percent complete: 25.9%; Average loss: 3.6423
Iteration: 1039; Percent complete: 26.0%; Average loss: 3.3272
Iteration: 1040; Percent complete: 26.0%; Average loss: 3.6163
Iteration: 1041; Percent complete: 26.0%; Average loss: 3.1086
Iteration: 1042; Percent complete: 26.1%; Average loss: 3.6207
Iteration: 1043; Percent complete: 26.1%; Average loss: 3.5836
Iteration: 1044; Percent complete: 26.1%; Average loss: 3.1654
Iteration: 1045; Percent complete: 26.1%; Average loss: 3.5122
Iteration: 1046; Percent complete: 26.2%; Average loss: 3.3827
Iteration: 1047; Percent complete: 26.2%; Average loss: 3.4884
Iteration: 1048; Percent complete: 26.2%; Average loss: 3.4618
Iteration: 1049; Percent complete: 26.2%; Average loss: 3.5638
Iteration: 1050; Percent complete: 26.2%; Average loss: 3.4715
Iteration: 1051; Percent complete: 26.3%; Average loss: 3.7932
Iteration: 1052; Percent complete: 26.3%; Average loss: 3.4432
Iteration: 1053; Percent complete: 26.3%; Average loss: 3.7160
Iteration: 1054; Percent complete: 26.4%; Average loss: 3.3608
Iteration: 1055; Percent complete: 26.4%; Average loss: 3.5365
Iteration: 1056; Percent complete: 26.4%; Average loss: 3.5423
Iteration: 1057; Percent complete: 26.4%; Average loss: 3.4045
Iteration: 1058; Percent complete: 26.5%; Average loss: 3.3700
Iteration: 1059; Percent complete: 26.5%; Average loss: 3.5241
Iteration: 1060; Percent complete: 26.5%; Average loss: 3.5459
Iteration: 1061; Percent complete: 26.5%; Average loss: 3.1078
Iteration: 1062; Percent complete: 26.6%; Average loss: 3.6714
Iteration: 1063; Percent complete: 26.6%; Average loss: 3.5602
Iteration: 1064; Percent complete: 26.6%; Average loss: 3.5982
Iteration: 1065; Percent complete: 26.6%; Average loss: 3.6712
Iteration: 1066; Percent complete: 26.7%; Average loss: 3.4754
Iteration: 1067; Percent complete: 26.7%; Average loss: 3.3842
Iteration: 1068; Percent complete: 26.7%; Average loss: 3.4531
Iteration: 1069; Percent complete: 26.7%; Average loss: 3.1465
Iteration: 1070; Percent complete: 26.8%; Average loss: 3.2013
Iteration: 1071; Percent complete: 26.8%; Average loss: 3.4552
Iteration: 1072; Percent complete: 26.8%; Average loss: 3.1945
Iteration: 1073; Percent complete: 26.8%; Average loss: 3.4524
Iteration: 1074; Percent complete: 26.9%; Average loss: 3.3841
Iteration: 1075; Percent complete: 26.9%; Average loss: 3.5571
Iteration: 1076; Percent complete: 26.9%; Average loss: 3.3854
Iteration: 1077; Percent complete: 26.9%; Average loss: 3.4716
Iteration: 1078; Percent complete: 27.0%; Average loss: 3.3937
Iteration: 1079; Percent complete: 27.0%; Average loss: 3.3360
Iteration: 1080; Percent complete: 27.0%; Average loss: 3.3346
Iteration: 1081; Percent complete: 27.0%; Average loss: 3.7278
Iteration: 1082; Percent complete: 27.1%; Average loss: 3.2653
Iteration: 1083; Percent complete: 27.1%; Average loss: 3.4911
Iteration: 1084; Percent complete: 27.1%; Average loss: 3.4693
Iteration: 1085; Percent complete: 27.1%; Average loss: 3.6437
Iteration: 1086; Percent complete: 27.2%; Average loss: 3.2948
Iteration: 1087; Percent complete: 27.2%; Average loss: 3.3042
Iteration: 1088; Percent complete: 27.2%; Average loss: 3.1756
Iteration: 1089; Percent complete: 27.2%; Average loss: 3.7309
Iteration: 1090; Percent complete: 27.3%; Average loss: 3.3378
Iteration: 1091; Percent complete: 27.3%; Average loss: 3.5041
Iteration: 1092; Percent complete: 27.3%; Average loss: 3.4330
Iteration: 1093; Percent complete: 27.3%; Average loss: 3.5660
Iteration: 1094; Percent complete: 27.4%; Average loss: 3.3520
Iteration: 1095; Percent complete: 27.4%; Average loss: 3.3538
Iteration: 1096; Percent complete: 27.4%; Average loss: 3.4662
Iteration: 1097; Percent complete: 27.4%; Average loss: 3.2417
Iteration: 1098; Percent complete: 27.5%; Average loss: 3.4294
Iteration: 1099; Percent complete: 27.5%; Average loss: 3.3220
Iteration: 1100; Percent complete: 27.5%; Average loss: 3.4301
Iteration: 1101; Percent complete: 27.5%; Average loss: 3.6212
Iteration: 1102; Percent complete: 27.6%; Average loss: 3.5144
Iteration: 1103; Percent complete: 27.6%; Average loss: 3.5178
Iteration: 1104; Percent complete: 27.6%; Average loss: 3.5101
Iteration: 1105; Percent complete: 27.6%; Average loss: 3.5122
Iteration: 1106; Percent complete: 27.7%; Average loss: 3.3756
Iteration: 1107; Percent complete: 27.7%; Average loss: 3.4816
Iteration: 1108; Percent complete: 27.7%; Average loss: 3.6804
Iteration: 1109; Percent complete: 27.7%; Average loss: 3.5505
Iteration: 1110; Percent complete: 27.8%; Average loss: 3.4508
Iteration: 1111; Percent complete: 27.8%; Average loss: 3.4145
Iteration: 1112; Percent complete: 27.8%; Average loss: 3.3827
Iteration: 1113; Percent complete: 27.8%; Average loss: 3.2503
Iteration: 1114; Percent complete: 27.9%; Average loss: 3.3979
Iteration: 1115; Percent complete: 27.9%; Average loss: 3.4379
Iteration: 1116; Percent complete: 27.9%; Average loss: 3.6791
Iteration: 1117; Percent complete: 27.9%; Average loss: 3.3026
Iteration: 1118; Percent complete: 28.0%; Average loss: 3.4163
Iteration: 1119; Percent complete: 28.0%; Average loss: 3.2663
Iteration: 1120; Percent complete: 28.0%; Average loss: 3.4508
Iteration: 1121; Percent complete: 28.0%; Average loss: 3.5783
Iteration: 1122; Percent complete: 28.1%; Average loss: 3.6908
Iteration: 1123; Percent complete: 28.1%; Average loss: 3.5858
Iteration: 1124; Percent complete: 28.1%; Average loss: 3.5589
Iteration: 1125; Percent complete: 28.1%; Average loss: 3.3208
Iteration: 1126; Percent complete: 28.1%; Average loss: 3.3910
Iteration: 1127; Percent complete: 28.2%; Average loss: 3.6270
Iteration: 1128; Percent complete: 28.2%; Average loss: 3.2581
Iteration: 1129; Percent complete: 28.2%; Average loss: 3.3469
Iteration: 1130; Percent complete: 28.2%; Average loss: 3.4846
Iteration: 1131; Percent complete: 28.3%; Average loss: 3.3080
Iteration: 1132; Percent complete: 28.3%; Average loss: 3.3256
Iteration: 1133; Percent complete: 28.3%; Average loss: 3.1515
Iteration: 1134; Percent complete: 28.3%; Average loss: 3.3686
Iteration: 1135; Percent complete: 28.4%; Average loss: 3.1972
Iteration: 1136; Percent complete: 28.4%; Average loss: 3.5356
Iteration: 1137; Percent complete: 28.4%; Average loss: 3.4067
Iteration: 1138; Percent complete: 28.4%; Average loss: 3.6166
Iteration: 1139; Percent complete: 28.5%; Average loss: 3.2578
Iteration: 1140; Percent complete: 28.5%; Average loss: 3.5210
Iteration: 1141; Percent complete: 28.5%; Average loss: 3.4450
Iteration: 1142; Percent complete: 28.5%; Average loss: 3.1445
Iteration: 1143; Percent complete: 28.6%; Average loss: 3.2837
Iteration: 1144; Percent complete: 28.6%; Average loss: 3.3469
Iteration: 1145; Percent complete: 28.6%; Average loss: 3.4506
Iteration: 1146; Percent complete: 28.6%; Average loss: 3.5164
Iteration: 1147; Percent complete: 28.7%; Average loss: 3.2072
Iteration: 1148; Percent complete: 28.7%; Average loss: 3.4603
Iteration: 1149; Percent complete: 28.7%; Average loss: 3.4572
Iteration: 1150; Percent complete: 28.7%; Average loss: 3.5628
Iteration: 1151; Percent complete: 28.8%; Average loss: 3.4954
Iteration: 1152; Percent complete: 28.8%; Average loss: 3.2770
Iteration: 1153; Percent complete: 28.8%; Average loss: 3.5288
Iteration: 1154; Percent complete: 28.8%; Average loss: 3.0041
Iteration: 1155; Percent complete: 28.9%; Average loss: 3.4603
Iteration: 1156; Percent complete: 28.9%; Average loss: 3.2826
Iteration: 1157; Percent complete: 28.9%; Average loss: 3.5012
Iteration: 1158; Percent complete: 28.9%; Average loss: 3.2903
Iteration: 1159; Percent complete: 29.0%; Average loss: 3.1897
Iteration: 1160; Percent complete: 29.0%; Average loss: 3.5158
Iteration: 1161; Percent complete: 29.0%; Average loss: 3.3174
Iteration: 1162; Percent complete: 29.0%; Average loss: 3.3011
Iteration: 1163; Percent complete: 29.1%; Average loss: 3.2525
Iteration: 1164; Percent complete: 29.1%; Average loss: 3.7205
Iteration: 1165; Percent complete: 29.1%; Average loss: 3.4856
Iteration: 1166; Percent complete: 29.1%; Average loss: 3.5471
Iteration: 1167; Percent complete: 29.2%; Average loss: 3.5107
Iteration: 1168; Percent complete: 29.2%; Average loss: 3.3969
Iteration: 1169; Percent complete: 29.2%; Average loss: 3.2780
Iteration: 1170; Percent complete: 29.2%; Average loss: 3.2592
Iteration: 1171; Percent complete: 29.3%; Average loss: 3.4977
Iteration: 1172; Percent complete: 29.3%; Average loss: 3.1338
Iteration: 1173; Percent complete: 29.3%; Average loss: 3.4368
Iteration: 1174; Percent complete: 29.3%; Average loss: 3.3588
Iteration: 1175; Percent complete: 29.4%; Average loss: 3.3605
Iteration: 1176; Percent complete: 29.4%; Average loss: 3.2742
Iteration: 1177; Percent complete: 29.4%; Average loss: 3.2089
Iteration: 1178; Percent complete: 29.4%; Average loss: 3.2205
Iteration: 1179; Percent complete: 29.5%; Average loss: 3.2838
Iteration: 1180; Percent complete: 29.5%; Average loss: 3.3041
Iteration: 1181; Percent complete: 29.5%; Average loss: 3.6119
Iteration: 1182; Percent complete: 29.5%; Average loss: 3.2533
Iteration: 1183; Percent complete: 29.6%; Average loss: 3.2095
Iteration: 1184; Percent complete: 29.6%; Average loss: 3.3372
Iteration: 1185; Percent complete: 29.6%; Average loss: 3.6325
Iteration: 1186; Percent complete: 29.6%; Average loss: 3.2280
Iteration: 1187; Percent complete: 29.7%; Average loss: 3.2646
Iteration: 1188; Percent complete: 29.7%; Average loss: 3.2465
Iteration: 1189; Percent complete: 29.7%; Average loss: 3.2016
Iteration: 1190; Percent complete: 29.8%; Average loss: 3.4527
Iteration: 1191; Percent complete: 29.8%; Average loss: 3.3579
Iteration: 1192; Percent complete: 29.8%; Average loss: 3.2487
Iteration: 1193; Percent complete: 29.8%; Average loss: 3.4047
Iteration: 1194; Percent complete: 29.8%; Average loss: 3.1350
Iteration: 1195; Percent complete: 29.9%; Average loss: 3.3915
Iteration: 1196; Percent complete: 29.9%; Average loss: 3.4759
Iteration: 1197; Percent complete: 29.9%; Average loss: 3.1454
Iteration: 1198; Percent complete: 29.9%; Average loss: 3.5905
Iteration: 1199; Percent complete: 30.0%; Average loss: 3.8146
Iteration: 1200; Percent complete: 30.0%; Average loss: 3.1934
Iteration: 1201; Percent complete: 30.0%; Average loss: 3.6238
Iteration: 1202; Percent complete: 30.0%; Average loss: 3.2941
Iteration: 1203; Percent complete: 30.1%; Average loss: 3.5792
Iteration: 1204; Percent complete: 30.1%; Average loss: 3.6334
Iteration: 1205; Percent complete: 30.1%; Average loss: 3.2459
Iteration: 1206; Percent complete: 30.1%; Average loss: 3.2090
Iteration: 1207; Percent complete: 30.2%; Average loss: 3.2504
Iteration: 1208; Percent complete: 30.2%; Average loss: 3.3011
Iteration: 1209; Percent complete: 30.2%; Average loss: 3.4079
Iteration: 1210; Percent complete: 30.2%; Average loss: 3.2777
Iteration: 1211; Percent complete: 30.3%; Average loss: 3.3692
Iteration: 1212; Percent complete: 30.3%; Average loss: 3.3758
Iteration: 1213; Percent complete: 30.3%; Average loss: 3.3625
Iteration: 1214; Percent complete: 30.3%; Average loss: 3.3600
Iteration: 1215; Percent complete: 30.4%; Average loss: 3.5852
Iteration: 1216; Percent complete: 30.4%; Average loss: 3.4328
Iteration: 1217; Percent complete: 30.4%; Average loss: 3.5499
Iteration: 1218; Percent complete: 30.4%; Average loss: 3.4492
Iteration: 1219; Percent complete: 30.5%; Average loss: 3.1505
Iteration: 1220; Percent complete: 30.5%; Average loss: 3.3711
Iteration: 1221; Percent complete: 30.5%; Average loss: 3.1822
Iteration: 1222; Percent complete: 30.6%; Average loss: 3.5435
Iteration: 1223; Percent complete: 30.6%; Average loss: 3.4559
Iteration: 1224; Percent complete: 30.6%; Average loss: 3.4306
Iteration: 1225; Percent complete: 30.6%; Average loss: 3.5875
Iteration: 1226; Percent complete: 30.6%; Average loss: 3.2147
Iteration: 1227; Percent complete: 30.7%; Average loss: 3.3600
Iteration: 1228; Percent complete: 30.7%; Average loss: 3.2068
Iteration: 1229; Percent complete: 30.7%; Average loss: 3.5392
Iteration: 1230; Percent complete: 30.8%; Average loss: 3.3670
Iteration: 1231; Percent complete: 30.8%; Average loss: 3.4822
Iteration: 1232; Percent complete: 30.8%; Average loss: 3.3671
Iteration: 1233; Percent complete: 30.8%; Average loss: 3.4550
Iteration: 1234; Percent complete: 30.9%; Average loss: 3.2264
Iteration: 1235; Percent complete: 30.9%; Average loss: 3.2618
Iteration: 1236; Percent complete: 30.9%; Average loss: 3.3874
Iteration: 1237; Percent complete: 30.9%; Average loss: 3.2637
Iteration: 1238; Percent complete: 30.9%; Average loss: 3.6191
Iteration: 1239; Percent complete: 31.0%; Average loss: 3.4331
Iteration: 1240; Percent complete: 31.0%; Average loss: 3.5802
Iteration: 1241; Percent complete: 31.0%; Average loss: 3.6445
Iteration: 1242; Percent complete: 31.1%; Average loss: 3.8035
Iteration: 1243; Percent complete: 31.1%; Average loss: 3.2885
Iteration: 1244; Percent complete: 31.1%; Average loss: 3.5373
Iteration: 1245; Percent complete: 31.1%; Average loss: 3.3548
Iteration: 1246; Percent complete: 31.1%; Average loss: 3.4249
Iteration: 1247; Percent complete: 31.2%; Average loss: 3.4429
Iteration: 1248; Percent complete: 31.2%; Average loss: 3.3298
Iteration: 1249; Percent complete: 31.2%; Average loss: 3.4089
Iteration: 1250; Percent complete: 31.2%; Average loss: 3.3813
Iteration: 1251; Percent complete: 31.3%; Average loss: 3.4858
Iteration: 1252; Percent complete: 31.3%; Average loss: 3.2965
Iteration: 1253; Percent complete: 31.3%; Average loss: 3.2942
Iteration: 1254; Percent complete: 31.4%; Average loss: 3.3035
Iteration: 1255; Percent complete: 31.4%; Average loss: 3.2904
Iteration: 1256; Percent complete: 31.4%; Average loss: 3.3630
Iteration: 1257; Percent complete: 31.4%; Average loss: 3.2439
Iteration: 1258; Percent complete: 31.4%; Average loss: 3.5293
Iteration: 1259; Percent complete: 31.5%; Average loss: 3.5711
Iteration: 1260; Percent complete: 31.5%; Average loss: 3.3346
Iteration: 1261; Percent complete: 31.5%; Average loss: 3.4054
Iteration: 1262; Percent complete: 31.6%; Average loss: 3.3372
Iteration: 1263; Percent complete: 31.6%; Average loss: 3.2347
Iteration: 1264; Percent complete: 31.6%; Average loss: 3.4966
Iteration: 1265; Percent complete: 31.6%; Average loss: 3.4159
Iteration: 1266; Percent complete: 31.6%; Average loss: 3.4054
Iteration: 1267; Percent complete: 31.7%; Average loss: 3.5149
Iteration: 1268; Percent complete: 31.7%; Average loss: 3.2358
Iteration: 1269; Percent complete: 31.7%; Average loss: 3.2441
Iteration: 1270; Percent complete: 31.8%; Average loss: 3.1764
Iteration: 1271; Percent complete: 31.8%; Average loss: 3.2660
Iteration: 1272; Percent complete: 31.8%; Average loss: 3.2415
Iteration: 1273; Percent complete: 31.8%; Average loss: 3.2136
Iteration: 1274; Percent complete: 31.9%; Average loss: 3.4512
Iteration: 1275; Percent complete: 31.9%; Average loss: 3.2419
Iteration: 1276; Percent complete: 31.9%; Average loss: 3.3651
Iteration: 1277; Percent complete: 31.9%; Average loss: 3.4549
Iteration: 1278; Percent complete: 31.9%; Average loss: 3.3927
Iteration: 1279; Percent complete: 32.0%; Average loss: 3.3105
Iteration: 1280; Percent complete: 32.0%; Average loss: 3.2966
Iteration: 1281; Percent complete: 32.0%; Average loss: 3.4735
Iteration: 1282; Percent complete: 32.0%; Average loss: 3.3976
Iteration: 1283; Percent complete: 32.1%; Average loss: 3.2433
Iteration: 1284; Percent complete: 32.1%; Average loss: 3.3263
Iteration: 1285; Percent complete: 32.1%; Average loss: 3.2474
Iteration: 1286; Percent complete: 32.1%; Average loss: 3.2292
Iteration: 1287; Percent complete: 32.2%; Average loss: 3.3387
Iteration: 1288; Percent complete: 32.2%; Average loss: 3.4348
Iteration: 1289; Percent complete: 32.2%; Average loss: 3.2328
Iteration: 1290; Percent complete: 32.2%; Average loss: 3.3966
Iteration: 1291; Percent complete: 32.3%; Average loss: 3.3014
Iteration: 1292; Percent complete: 32.3%; Average loss: 3.4182
Iteration: 1293; Percent complete: 32.3%; Average loss: 3.1971
Iteration: 1294; Percent complete: 32.4%; Average loss: 3.3165
Iteration: 1295; Percent complete: 32.4%; Average loss: 3.1216
Iteration: 1296; Percent complete: 32.4%; Average loss: 3.3298
Iteration: 1297; Percent complete: 32.4%; Average loss: 3.5367
Iteration: 1298; Percent complete: 32.5%; Average loss: 3.3405
Iteration: 1299; Percent complete: 32.5%; Average loss: 3.4136
Iteration: 1300; Percent complete: 32.5%; Average loss: 3.4287
Iteration: 1301; Percent complete: 32.5%; Average loss: 3.3154
Iteration: 1302; Percent complete: 32.6%; Average loss: 3.4547
Iteration: 1303; Percent complete: 32.6%; Average loss: 3.6128
Iteration: 1304; Percent complete: 32.6%; Average loss: 3.4448
Iteration: 1305; Percent complete: 32.6%; Average loss: 3.3009
Iteration: 1306; Percent complete: 32.6%; Average loss: 3.3409
Iteration: 1307; Percent complete: 32.7%; Average loss: 3.3156
Iteration: 1308; Percent complete: 32.7%; Average loss: 3.4101
Iteration: 1309; Percent complete: 32.7%; Average loss: 3.2351
Iteration: 1310; Percent complete: 32.8%; Average loss: 3.7539
Iteration: 1311; Percent complete: 32.8%; Average loss: 3.2010
Iteration: 1312; Percent complete: 32.8%; Average loss: 3.4119
Iteration: 1313; Percent complete: 32.8%; Average loss: 3.2720
Iteration: 1314; Percent complete: 32.9%; Average loss: 3.2007
Iteration: 1315; Percent complete: 32.9%; Average loss: 3.3410
Iteration: 1316; Percent complete: 32.9%; Average loss: 3.2916
Iteration: 1317; Percent complete: 32.9%; Average loss: 3.1990
Iteration: 1318; Percent complete: 33.0%; Average loss: 3.2930
Iteration: 1319; Percent complete: 33.0%; Average loss: 3.5049
Iteration: 1320; Percent complete: 33.0%; Average loss: 3.3727
Iteration: 1321; Percent complete: 33.0%; Average loss: 3.3608
Iteration: 1322; Percent complete: 33.1%; Average loss: 3.0229
Iteration: 1323; Percent complete: 33.1%; Average loss: 3.4739
Iteration: 1324; Percent complete: 33.1%; Average loss: 3.1972
Iteration: 1325; Percent complete: 33.1%; Average loss: 3.5135
Iteration: 1326; Percent complete: 33.1%; Average loss: 3.1802
Iteration: 1327; Percent complete: 33.2%; Average loss: 3.4017
Iteration: 1328; Percent complete: 33.2%; Average loss: 3.4096
Iteration: 1329; Percent complete: 33.2%; Average loss: 3.2671
Iteration: 1330; Percent complete: 33.2%; Average loss: 3.2196
Iteration: 1331; Percent complete: 33.3%; Average loss: 3.1733
Iteration: 1332; Percent complete: 33.3%; Average loss: 3.5727
Iteration: 1333; Percent complete: 33.3%; Average loss: 3.2606
Iteration: 1334; Percent complete: 33.4%; Average loss: 3.4343
Iteration: 1335; Percent complete: 33.4%; Average loss: 3.3628
Iteration: 1336; Percent complete: 33.4%; Average loss: 3.3039
Iteration: 1337; Percent complete: 33.4%; Average loss: 3.2307
Iteration: 1338; Percent complete: 33.5%; Average loss: 3.2701
Iteration: 1339; Percent complete: 33.5%; Average loss: 3.3209
Iteration: 1340; Percent complete: 33.5%; Average loss: 3.3484
Iteration: 1341; Percent complete: 33.5%; Average loss: 3.3614
Iteration: 1342; Percent complete: 33.6%; Average loss: 3.4608
Iteration: 1343; Percent complete: 33.6%; Average loss: 3.3114
Iteration: 1344; Percent complete: 33.6%; Average loss: 3.4657
Iteration: 1345; Percent complete: 33.6%; Average loss: 3.2105
Iteration: 1346; Percent complete: 33.7%; Average loss: 3.2114
Iteration: 1347; Percent complete: 33.7%; Average loss: 3.1198
Iteration: 1348; Percent complete: 33.7%; Average loss: 3.5615
Iteration: 1349; Percent complete: 33.7%; Average loss: 3.4624
Iteration: 1350; Percent complete: 33.8%; Average loss: 3.3542
Iteration: 1351; Percent complete: 33.8%; Average loss: 3.3128
Iteration: 1352; Percent complete: 33.8%; Average loss: 3.2290
Iteration: 1353; Percent complete: 33.8%; Average loss: 3.3577
Iteration: 1354; Percent complete: 33.9%; Average loss: 3.2111
Iteration: 1355; Percent complete: 33.9%; Average loss: 3.5148
Iteration: 1356; Percent complete: 33.9%; Average loss: 3.4618
Iteration: 1357; Percent complete: 33.9%; Average loss: 3.1035
Iteration: 1358; Percent complete: 34.0%; Average loss: 3.4106
Iteration: 1359; Percent complete: 34.0%; Average loss: 3.6494
Iteration: 1360; Percent complete: 34.0%; Average loss: 3.5214
Iteration: 1361; Percent complete: 34.0%; Average loss: 3.3720
Iteration: 1362; Percent complete: 34.1%; Average loss: 3.4681
Iteration: 1363; Percent complete: 34.1%; Average loss: 3.3981
Iteration: 1364; Percent complete: 34.1%; Average loss: 3.1470
Iteration: 1365; Percent complete: 34.1%; Average loss: 3.4965
Iteration: 1366; Percent complete: 34.2%; Average loss: 3.3664
Iteration: 1367; Percent complete: 34.2%; Average loss: 3.4439
Iteration: 1368; Percent complete: 34.2%; Average loss: 3.1696
Iteration: 1369; Percent complete: 34.2%; Average loss: 3.2709
Iteration: 1370; Percent complete: 34.2%; Average loss: 3.3580
Iteration: 1371; Percent complete: 34.3%; Average loss: 3.3732
Iteration: 1372; Percent complete: 34.3%; Average loss: 3.2170
Iteration: 1373; Percent complete: 34.3%; Average loss: 3.2977
Iteration: 1374; Percent complete: 34.4%; Average loss: 3.4573
Iteration: 1375; Percent complete: 34.4%; Average loss: 3.1414
Iteration: 1376; Percent complete: 34.4%; Average loss: 3.4013
Iteration: 1377; Percent complete: 34.4%; Average loss: 3.4441
Iteration: 1378; Percent complete: 34.4%; Average loss: 3.5419
Iteration: 1379; Percent complete: 34.5%; Average loss: 3.5661
Iteration: 1380; Percent complete: 34.5%; Average loss: 3.3709
Iteration: 1381; Percent complete: 34.5%; Average loss: 3.5983
Iteration: 1382; Percent complete: 34.5%; Average loss: 3.4151
Iteration: 1383; Percent complete: 34.6%; Average loss: 3.3245
Iteration: 1384; Percent complete: 34.6%; Average loss: 3.5131
Iteration: 1385; Percent complete: 34.6%; Average loss: 3.1993
Iteration: 1386; Percent complete: 34.6%; Average loss: 3.3212
Iteration: 1387; Percent complete: 34.7%; Average loss: 3.4102
Iteration: 1388; Percent complete: 34.7%; Average loss: 3.4935
Iteration: 1389; Percent complete: 34.7%; Average loss: 3.2194
Iteration: 1390; Percent complete: 34.8%; Average loss: 3.3066
Iteration: 1391; Percent complete: 34.8%; Average loss: 3.3470
Iteration: 1392; Percent complete: 34.8%; Average loss: 3.2928
Iteration: 1393; Percent complete: 34.8%; Average loss: 3.4258
Iteration: 1394; Percent complete: 34.8%; Average loss: 3.6002
Iteration: 1395; Percent complete: 34.9%; Average loss: 3.4496
Iteration: 1396; Percent complete: 34.9%; Average loss: 3.7649
Iteration: 1397; Percent complete: 34.9%; Average loss: 3.5167
Iteration: 1398; Percent complete: 34.9%; Average loss: 3.1137
Iteration: 1399; Percent complete: 35.0%; Average loss: 2.9716
Iteration: 1400; Percent complete: 35.0%; Average loss: 3.3688
Iteration: 1401; Percent complete: 35.0%; Average loss: 3.4713
Iteration: 1402; Percent complete: 35.0%; Average loss: 3.3351
Iteration: 1403; Percent complete: 35.1%; Average loss: 3.3001
Iteration: 1404; Percent complete: 35.1%; Average loss: 3.2151
Iteration: 1405; Percent complete: 35.1%; Average loss: 3.4151
Iteration: 1406; Percent complete: 35.1%; Average loss: 3.0322
Iteration: 1407; Percent complete: 35.2%; Average loss: 3.3074
Iteration: 1408; Percent complete: 35.2%; Average loss: 3.2134
Iteration: 1409; Percent complete: 35.2%; Average loss: 3.5200
Iteration: 1410; Percent complete: 35.2%; Average loss: 3.6157
Iteration: 1411; Percent complete: 35.3%; Average loss: 3.4914
Iteration: 1412; Percent complete: 35.3%; Average loss: 3.4468
Iteration: 1413; Percent complete: 35.3%; Average loss: 3.4958
Iteration: 1414; Percent complete: 35.4%; Average loss: 3.4292
Iteration: 1415; Percent complete: 35.4%; Average loss: 3.3980
Iteration: 1416; Percent complete: 35.4%; Average loss: 3.3650
Iteration: 1417; Percent complete: 35.4%; Average loss: 3.4301
Iteration: 1418; Percent complete: 35.4%; Average loss: 3.1636
Iteration: 1419; Percent complete: 35.5%; Average loss: 3.3708
Iteration: 1420; Percent complete: 35.5%; Average loss: 3.0765
Iteration: 1421; Percent complete: 35.5%; Average loss: 3.4803
Iteration: 1422; Percent complete: 35.5%; Average loss: 3.1259
Iteration: 1423; Percent complete: 35.6%; Average loss: 3.3338
Iteration: 1424; Percent complete: 35.6%; Average loss: 3.1234
Iteration: 1425; Percent complete: 35.6%; Average loss: 3.3568
Iteration: 1426; Percent complete: 35.6%; Average loss: 3.6060
Iteration: 1427; Percent complete: 35.7%; Average loss: 3.3494
Iteration: 1428; Percent complete: 35.7%; Average loss: 2.9652
Iteration: 1429; Percent complete: 35.7%; Average loss: 3.3730
Iteration: 1430; Percent complete: 35.8%; Average loss: 3.3371
Iteration: 1431; Percent complete: 35.8%; Average loss: 3.6911
Iteration: 1432; Percent complete: 35.8%; Average loss: 3.3250
Iteration: 1433; Percent complete: 35.8%; Average loss: 3.2810
Iteration: 1434; Percent complete: 35.9%; Average loss: 3.3625
Iteration: 1435; Percent complete: 35.9%; Average loss: 3.3053
Iteration: 1436; Percent complete: 35.9%; Average loss: 3.4668
Iteration: 1437; Percent complete: 35.9%; Average loss: 3.2839
Iteration: 1438; Percent complete: 35.9%; Average loss: 3.2497
Iteration: 1439; Percent complete: 36.0%; Average loss: 3.3317
Iteration: 1440; Percent complete: 36.0%; Average loss: 3.1112
Iteration: 1441; Percent complete: 36.0%; Average loss: 3.4222
Iteration: 1442; Percent complete: 36.0%; Average loss: 3.2350
Iteration: 1443; Percent complete: 36.1%; Average loss: 3.2279
Iteration: 1444; Percent complete: 36.1%; Average loss: 3.2647
Iteration: 1445; Percent complete: 36.1%; Average loss: 3.4369
Iteration: 1446; Percent complete: 36.1%; Average loss: 3.2549
Iteration: 1447; Percent complete: 36.2%; Average loss: 3.4957
Iteration: 1448; Percent complete: 36.2%; Average loss: 3.4014
Iteration: 1449; Percent complete: 36.2%; Average loss: 3.3906
Iteration: 1450; Percent complete: 36.2%; Average loss: 3.1759
Iteration: 1451; Percent complete: 36.3%; Average loss: 3.3601
Iteration: 1452; Percent complete: 36.3%; Average loss: 3.1123
Iteration: 1453; Percent complete: 36.3%; Average loss: 3.0919
Iteration: 1454; Percent complete: 36.4%; Average loss: 3.6565
Iteration: 1455; Percent complete: 36.4%; Average loss: 3.4116
Iteration: 1456; Percent complete: 36.4%; Average loss: 3.2208
Iteration: 1457; Percent complete: 36.4%; Average loss: 3.3754
Iteration: 1458; Percent complete: 36.4%; Average loss: 3.4720
Iteration: 1459; Percent complete: 36.5%; Average loss: 3.5277
Iteration: 1460; Percent complete: 36.5%; Average loss: 3.2019
Iteration: 1461; Percent complete: 36.5%; Average loss: 3.3734
Iteration: 1462; Percent complete: 36.5%; Average loss: 3.2169
Iteration: 1463; Percent complete: 36.6%; Average loss: 3.3898
Iteration: 1464; Percent complete: 36.6%; Average loss: 3.4924
Iteration: 1465; Percent complete: 36.6%; Average loss: 3.2951
Iteration: 1466; Percent complete: 36.6%; Average loss: 3.2391
Iteration: 1467; Percent complete: 36.7%; Average loss: 3.2366
Iteration: 1468; Percent complete: 36.7%; Average loss: 3.2393
Iteration: 1469; Percent complete: 36.7%; Average loss: 3.1754
Iteration: 1470; Percent complete: 36.8%; Average loss: 3.5277
Iteration: 1471; Percent complete: 36.8%; Average loss: 3.2879
Iteration: 1472; Percent complete: 36.8%; Average loss: 3.2111
Iteration: 1473; Percent complete: 36.8%; Average loss: 3.2543
Iteration: 1474; Percent complete: 36.9%; Average loss: 3.3487
Iteration: 1475; Percent complete: 36.9%; Average loss: 2.9374
Iteration: 1476; Percent complete: 36.9%; Average loss: 3.5757
Iteration: 1477; Percent complete: 36.9%; Average loss: 3.2686
Iteration: 1478; Percent complete: 37.0%; Average loss: 3.3845
Iteration: 1479; Percent complete: 37.0%; Average loss: 3.2989
Iteration: 1480; Percent complete: 37.0%; Average loss: 3.2342
Iteration: 1481; Percent complete: 37.0%; Average loss: 3.3878
Iteration: 1482; Percent complete: 37.0%; Average loss: 3.0819
Iteration: 1483; Percent complete: 37.1%; Average loss: 3.2770
Iteration: 1484; Percent complete: 37.1%; Average loss: 3.3720
Iteration: 1485; Percent complete: 37.1%; Average loss: 3.1600
Iteration: 1486; Percent complete: 37.1%; Average loss: 3.2346
Iteration: 1487; Percent complete: 37.2%; Average loss: 3.2105
Iteration: 1488; Percent complete: 37.2%; Average loss: 3.3998
Iteration: 1489; Percent complete: 37.2%; Average loss: 3.4737
Iteration: 1490; Percent complete: 37.2%; Average loss: 3.1342
Iteration: 1491; Percent complete: 37.3%; Average loss: 3.1781
Iteration: 1492; Percent complete: 37.3%; Average loss: 3.2095
Iteration: 1493; Percent complete: 37.3%; Average loss: 3.2379
Iteration: 1494; Percent complete: 37.4%; Average loss: 3.0561
Iteration: 1495; Percent complete: 37.4%; Average loss: 3.1891
Iteration: 1496; Percent complete: 37.4%; Average loss: 3.1969
Iteration: 1497; Percent complete: 37.4%; Average loss: 3.3423
Iteration: 1498; Percent complete: 37.5%; Average loss: 3.4565
Iteration: 1499; Percent complete: 37.5%; Average loss: 3.6055
Iteration: 1500; Percent complete: 37.5%; Average loss: 3.2413
Iteration: 1501; Percent complete: 37.5%; Average loss: 3.1494
Iteration: 1502; Percent complete: 37.5%; Average loss: 3.2870
Iteration: 1503; Percent complete: 37.6%; Average loss: 3.1524
Iteration: 1504; Percent complete: 37.6%; Average loss: 3.4515
Iteration: 1505; Percent complete: 37.6%; Average loss: 3.3693
Iteration: 1506; Percent complete: 37.6%; Average loss: 3.2884
Iteration: 1507; Percent complete: 37.7%; Average loss: 3.4815
Iteration: 1508; Percent complete: 37.7%; Average loss: 3.3706
Iteration: 1509; Percent complete: 37.7%; Average loss: 3.3787
Iteration: 1510; Percent complete: 37.8%; Average loss: 3.3093
Iteration: 1511; Percent complete: 37.8%; Average loss: 3.2722
Iteration: 1512; Percent complete: 37.8%; Average loss: 3.2665
Iteration: 1513; Percent complete: 37.8%; Average loss: 3.0372
Iteration: 1514; Percent complete: 37.9%; Average loss: 3.3351
Iteration: 1515; Percent complete: 37.9%; Average loss: 3.3905
Iteration: 1516; Percent complete: 37.9%; Average loss: 3.4209
Iteration: 1517; Percent complete: 37.9%; Average loss: 3.3459
Iteration: 1518; Percent complete: 38.0%; Average loss: 3.3028
Iteration: 1519; Percent complete: 38.0%; Average loss: 3.3632
Iteration: 1520; Percent complete: 38.0%; Average loss: 3.0883
Iteration: 1521; Percent complete: 38.0%; Average loss: 3.3974
Iteration: 1522; Percent complete: 38.0%; Average loss: 3.3941
Iteration: 1523; Percent complete: 38.1%; Average loss: 3.2684
Iteration: 1524; Percent complete: 38.1%; Average loss: 3.2850
Iteration: 1525; Percent complete: 38.1%; Average loss: 3.3399
Iteration: 1526; Percent complete: 38.1%; Average loss: 3.1412
Iteration: 1527; Percent complete: 38.2%; Average loss: 3.2170
Iteration: 1528; Percent complete: 38.2%; Average loss: 3.1375
Iteration: 1529; Percent complete: 38.2%; Average loss: 3.1134
Iteration: 1530; Percent complete: 38.2%; Average loss: 3.3041
Iteration: 1531; Percent complete: 38.3%; Average loss: 3.5313
Iteration: 1532; Percent complete: 38.3%; Average loss: 3.1776
Iteration: 1533; Percent complete: 38.3%; Average loss: 3.2753
Iteration: 1534; Percent complete: 38.4%; Average loss: 3.3486
Iteration: 1535; Percent complete: 38.4%; Average loss: 3.1600
Iteration: 1536; Percent complete: 38.4%; Average loss: 3.2848
Iteration: 1537; Percent complete: 38.4%; Average loss: 3.1280
Iteration: 1538; Percent complete: 38.5%; Average loss: 3.2152
Iteration: 1539; Percent complete: 38.5%; Average loss: 3.2251
Iteration: 1540; Percent complete: 38.5%; Average loss: 3.4207
Iteration: 1541; Percent complete: 38.5%; Average loss: 3.1055
Iteration: 1542; Percent complete: 38.6%; Average loss: 3.1269
Iteration: 1543; Percent complete: 38.6%; Average loss: 3.5790
Iteration: 1544; Percent complete: 38.6%; Average loss: 3.3031
Iteration: 1545; Percent complete: 38.6%; Average loss: 3.1829
Iteration: 1546; Percent complete: 38.6%; Average loss: 3.4760
Iteration: 1547; Percent complete: 38.7%; Average loss: 3.5214
Iteration: 1548; Percent complete: 38.7%; Average loss: 3.2943
Iteration: 1549; Percent complete: 38.7%; Average loss: 2.9429
Iteration: 1550; Percent complete: 38.8%; Average loss: 3.2326
Iteration: 1551; Percent complete: 38.8%; Average loss: 3.2846
Iteration: 1552; Percent complete: 38.8%; Average loss: 3.2351
Iteration: 1553; Percent complete: 38.8%; Average loss: 3.1476
Iteration: 1554; Percent complete: 38.9%; Average loss: 3.4459
Iteration: 1555; Percent complete: 38.9%; Average loss: 3.1591
Iteration: 1556; Percent complete: 38.9%; Average loss: 3.3158
Iteration: 1557; Percent complete: 38.9%; Average loss: 3.1624
Iteration: 1558; Percent complete: 39.0%; Average loss: 3.3570
Iteration: 1559; Percent complete: 39.0%; Average loss: 3.1758
Iteration: 1560; Percent complete: 39.0%; Average loss: 3.2351
Iteration: 1561; Percent complete: 39.0%; Average loss: 3.0643
Iteration: 1562; Percent complete: 39.1%; Average loss: 3.2724
Iteration: 1563; Percent complete: 39.1%; Average loss: 3.2607
Iteration: 1564; Percent complete: 39.1%; Average loss: 3.3004
Iteration: 1565; Percent complete: 39.1%; Average loss: 3.5349
Iteration: 1566; Percent complete: 39.1%; Average loss: 3.5898
Iteration: 1567; Percent complete: 39.2%; Average loss: 3.2988
Iteration: 1568; Percent complete: 39.2%; Average loss: 3.2428
Iteration: 1569; Percent complete: 39.2%; Average loss: 3.2050
Iteration: 1570; Percent complete: 39.2%; Average loss: 2.9935
Iteration: 1571; Percent complete: 39.3%; Average loss: 3.3041
Iteration: 1572; Percent complete: 39.3%; Average loss: 3.3298
Iteration: 1573; Percent complete: 39.3%; Average loss: 3.1704
Iteration: 1574; Percent complete: 39.4%; Average loss: 3.2943
Iteration: 1575; Percent complete: 39.4%; Average loss: 3.2587
Iteration: 1576; Percent complete: 39.4%; Average loss: 3.1988
Iteration: 1577; Percent complete: 39.4%; Average loss: 3.3392
Iteration: 1578; Percent complete: 39.5%; Average loss: 3.2635
Iteration: 1579; Percent complete: 39.5%; Average loss: 3.3343
Iteration: 1580; Percent complete: 39.5%; Average loss: 3.2470
Iteration: 1581; Percent complete: 39.5%; Average loss: 3.3121
Iteration: 1582; Percent complete: 39.6%; Average loss: 3.1117
Iteration: 1583; Percent complete: 39.6%; Average loss: 3.5089
Iteration: 1584; Percent complete: 39.6%; Average loss: 3.0381
Iteration: 1585; Percent complete: 39.6%; Average loss: 3.3528
Iteration: 1586; Percent complete: 39.6%; Average loss: 3.1402
Iteration: 1587; Percent complete: 39.7%; Average loss: 3.0550
Iteration: 1588; Percent complete: 39.7%; Average loss: 3.3781
Iteration: 1589; Percent complete: 39.7%; Average loss: 3.0792
Iteration: 1590; Percent complete: 39.8%; Average loss: 3.2294
Iteration: 1591; Percent complete: 39.8%; Average loss: 3.2581
Iteration: 1592; Percent complete: 39.8%; Average loss: 3.3259
Iteration: 1593; Percent complete: 39.8%; Average loss: 3.2975
Iteration: 1594; Percent complete: 39.9%; Average loss: 3.0590
Iteration: 1595; Percent complete: 39.9%; Average loss: 3.1842
Iteration: 1596; Percent complete: 39.9%; Average loss: 3.3722
Iteration: 1597; Percent complete: 39.9%; Average loss: 3.4152
Iteration: 1598; Percent complete: 40.0%; Average loss: 3.4299
Iteration: 1599; Percent complete: 40.0%; Average loss: 3.0616
Iteration: 1600; Percent complete: 40.0%; Average loss: 2.9650
Iteration: 1601; Percent complete: 40.0%; Average loss: 3.3154
Iteration: 1602; Percent complete: 40.1%; Average loss: 3.2373
Iteration: 1603; Percent complete: 40.1%; Average loss: 3.2197
Iteration: 1604; Percent complete: 40.1%; Average loss: 3.1857
Iteration: 1605; Percent complete: 40.1%; Average loss: 3.3279
Iteration: 1606; Percent complete: 40.2%; Average loss: 3.3547
Iteration: 1607; Percent complete: 40.2%; Average loss: 3.0428
Iteration: 1608; Percent complete: 40.2%; Average loss: 3.3225
Iteration: 1609; Percent complete: 40.2%; Average loss: 3.4043
Iteration: 1610; Percent complete: 40.2%; Average loss: 3.3801
Iteration: 1611; Percent complete: 40.3%; Average loss: 3.1130
Iteration: 1612; Percent complete: 40.3%; Average loss: 3.2816
Iteration: 1613; Percent complete: 40.3%; Average loss: 3.3775
Iteration: 1614; Percent complete: 40.4%; Average loss: 3.0578
Iteration: 1615; Percent complete: 40.4%; Average loss: 3.3334
Iteration: 1616; Percent complete: 40.4%; Average loss: 3.2504
Iteration: 1617; Percent complete: 40.4%; Average loss: 3.1099
Iteration: 1618; Percent complete: 40.5%; Average loss: 3.2939
Iteration: 1619; Percent complete: 40.5%; Average loss: 3.1419
Iteration: 1620; Percent complete: 40.5%; Average loss: 3.2842
Iteration: 1621; Percent complete: 40.5%; Average loss: 3.2314
Iteration: 1622; Percent complete: 40.6%; Average loss: 3.5355
Iteration: 1623; Percent complete: 40.6%; Average loss: 3.4256
Iteration: 1624; Percent complete: 40.6%; Average loss: 3.2595
Iteration: 1625; Percent complete: 40.6%; Average loss: 3.1930
Iteration: 1626; Percent complete: 40.6%; Average loss: 3.3319
Iteration: 1627; Percent complete: 40.7%; Average loss: 3.2281
Iteration: 1628; Percent complete: 40.7%; Average loss: 3.1842
Iteration: 1629; Percent complete: 40.7%; Average loss: 3.3123
Iteration: 1630; Percent complete: 40.8%; Average loss: 3.0581
Iteration: 1631; Percent complete: 40.8%; Average loss: 3.5009
Iteration: 1632; Percent complete: 40.8%; Average loss: 3.0899
Iteration: 1633; Percent complete: 40.8%; Average loss: 3.1313
Iteration: 1634; Percent complete: 40.8%; Average loss: 3.1836
Iteration: 1635; Percent complete: 40.9%; Average loss: 3.2037
Iteration: 1636; Percent complete: 40.9%; Average loss: 3.0219
Iteration: 1637; Percent complete: 40.9%; Average loss: 3.2028
Iteration: 1638; Percent complete: 40.9%; Average loss: 3.1774
Iteration: 1639; Percent complete: 41.0%; Average loss: 3.2921
Iteration: 1640; Percent complete: 41.0%; Average loss: 3.2317
Iteration: 1641; Percent complete: 41.0%; Average loss: 3.3690
Iteration: 1642; Percent complete: 41.0%; Average loss: 3.4333
Iteration: 1643; Percent complete: 41.1%; Average loss: 3.4072
Iteration: 1644; Percent complete: 41.1%; Average loss: 3.4340
Iteration: 1645; Percent complete: 41.1%; Average loss: 3.0662
Iteration: 1646; Percent complete: 41.1%; Average loss: 3.3539
Iteration: 1647; Percent complete: 41.2%; Average loss: 3.1343
Iteration: 1648; Percent complete: 41.2%; Average loss: 3.0521
Iteration: 1649; Percent complete: 41.2%; Average loss: 3.2430
Iteration: 1650; Percent complete: 41.2%; Average loss: 3.4412
Iteration: 1651; Percent complete: 41.3%; Average loss: 3.1493
Iteration: 1652; Percent complete: 41.3%; Average loss: 3.2137
Iteration: 1653; Percent complete: 41.3%; Average loss: 3.2360
Iteration: 1654; Percent complete: 41.3%; Average loss: 3.3719
Iteration: 1655; Percent complete: 41.4%; Average loss: 3.2161
Iteration: 1656; Percent complete: 41.4%; Average loss: 3.4487
Iteration: 1657; Percent complete: 41.4%; Average loss: 3.3394
Iteration: 1658; Percent complete: 41.4%; Average loss: 3.2235
Iteration: 1659; Percent complete: 41.5%; Average loss: 3.1531
Iteration: 1660; Percent complete: 41.5%; Average loss: 3.3397
Iteration: 1661; Percent complete: 41.5%; Average loss: 3.1778
Iteration: 1662; Percent complete: 41.5%; Average loss: 3.5693
Iteration: 1663; Percent complete: 41.6%; Average loss: 3.1740
Iteration: 1664; Percent complete: 41.6%; Average loss: 2.9752
Iteration: 1665; Percent complete: 41.6%; Average loss: 3.3503
Iteration: 1666; Percent complete: 41.6%; Average loss: 3.1040
Iteration: 1667; Percent complete: 41.7%; Average loss: 3.2612
Iteration: 1668; Percent complete: 41.7%; Average loss: 3.1368
Iteration: 1669; Percent complete: 41.7%; Average loss: 3.2879
Iteration: 1670; Percent complete: 41.8%; Average loss: 3.5909
Iteration: 1671; Percent complete: 41.8%; Average loss: 3.4392
Iteration: 1672; Percent complete: 41.8%; Average loss: 3.1383
Iteration: 1673; Percent complete: 41.8%; Average loss: 3.2949
Iteration: 1674; Percent complete: 41.9%; Average loss: 2.9716
Iteration: 1675; Percent complete: 41.9%; Average loss: 3.0159
Iteration: 1676; Percent complete: 41.9%; Average loss: 2.9874
Iteration: 1677; Percent complete: 41.9%; Average loss: 3.4102
Iteration: 1678; Percent complete: 41.9%; Average loss: 3.1547
Iteration: 1679; Percent complete: 42.0%; Average loss: 3.0776
Iteration: 1680; Percent complete: 42.0%; Average loss: 3.1747
Iteration: 1681; Percent complete: 42.0%; Average loss: 3.0111
Iteration: 1682; Percent complete: 42.0%; Average loss: 3.3211
Iteration: 1683; Percent complete: 42.1%; Average loss: 3.0845
Iteration: 1684; Percent complete: 42.1%; Average loss: 3.1133
Iteration: 1685; Percent complete: 42.1%; Average loss: 3.3720
Iteration: 1686; Percent complete: 42.1%; Average loss: 3.3433
Iteration: 1687; Percent complete: 42.2%; Average loss: 3.4081
Iteration: 1688; Percent complete: 42.2%; Average loss: 3.1623
Iteration: 1689; Percent complete: 42.2%; Average loss: 3.1531
Iteration: 1690; Percent complete: 42.2%; Average loss: 3.1928
Iteration: 1691; Percent complete: 42.3%; Average loss: 3.1126
Iteration: 1692; Percent complete: 42.3%; Average loss: 3.3182
Iteration: 1693; Percent complete: 42.3%; Average loss: 3.3151
Iteration: 1694; Percent complete: 42.4%; Average loss: 3.3181
Iteration: 1695; Percent complete: 42.4%; Average loss: 3.4739
Iteration: 1696; Percent complete: 42.4%; Average loss: 3.1711
Iteration: 1697; Percent complete: 42.4%; Average loss: 3.3312
Iteration: 1698; Percent complete: 42.4%; Average loss: 3.1675
Iteration: 1699; Percent complete: 42.5%; Average loss: 3.2677
Iteration: 1700; Percent complete: 42.5%; Average loss: 3.6022
Iteration: 1701; Percent complete: 42.5%; Average loss: 3.3818
Iteration: 1702; Percent complete: 42.5%; Average loss: 3.4886
Iteration: 1703; Percent complete: 42.6%; Average loss: 3.1004
Iteration: 1704; Percent complete: 42.6%; Average loss: 2.9794
Iteration: 1705; Percent complete: 42.6%; Average loss: 3.2853
Iteration: 1706; Percent complete: 42.6%; Average loss: 3.1315
Iteration: 1707; Percent complete: 42.7%; Average loss: 3.2898
Iteration: 1708; Percent complete: 42.7%; Average loss: 3.4011
Iteration: 1709; Percent complete: 42.7%; Average loss: 3.3077
Iteration: 1710; Percent complete: 42.8%; Average loss: 3.3527
Iteration: 1711; Percent complete: 42.8%; Average loss: 3.3126
Iteration: 1712; Percent complete: 42.8%; Average loss: 3.2018
Iteration: 1713; Percent complete: 42.8%; Average loss: 3.2469
Iteration: 1714; Percent complete: 42.9%; Average loss: 3.2612
Iteration: 1715; Percent complete: 42.9%; Average loss: 3.5329
Iteration: 1716; Percent complete: 42.9%; Average loss: 3.3114
Iteration: 1717; Percent complete: 42.9%; Average loss: 3.1676
Iteration: 1718; Percent complete: 43.0%; Average loss: 3.4037
Iteration: 1719; Percent complete: 43.0%; Average loss: 3.2571
Iteration: 1720; Percent complete: 43.0%; Average loss: 3.2367
Iteration: 1721; Percent complete: 43.0%; Average loss: 3.1936
Iteration: 1722; Percent complete: 43.0%; Average loss: 3.4505
Iteration: 1723; Percent complete: 43.1%; Average loss: 2.9461
Iteration: 1724; Percent complete: 43.1%; Average loss: 3.3269
Iteration: 1725; Percent complete: 43.1%; Average loss: 3.1522
Iteration: 1726; Percent complete: 43.1%; Average loss: 3.1799
Iteration: 1727; Percent complete: 43.2%; Average loss: 3.1851
Iteration: 1728; Percent complete: 43.2%; Average loss: 3.2475
Iteration: 1729; Percent complete: 43.2%; Average loss: 3.2272
Iteration: 1730; Percent complete: 43.2%; Average loss: 3.0935
Iteration: 1731; Percent complete: 43.3%; Average loss: 3.4776
Iteration: 1732; Percent complete: 43.3%; Average loss: 3.3978
Iteration: 1733; Percent complete: 43.3%; Average loss: 3.2150
Iteration: 1734; Percent complete: 43.4%; Average loss: 3.1977
Iteration: 1735; Percent complete: 43.4%; Average loss: 3.1387
Iteration: 1736; Percent complete: 43.4%; Average loss: 3.2857
Iteration: 1737; Percent complete: 43.4%; Average loss: 2.9901
Iteration: 1738; Percent complete: 43.5%; Average loss: 2.9670
Iteration: 1739; Percent complete: 43.5%; Average loss: 3.2303
Iteration: 1740; Percent complete: 43.5%; Average loss: 3.1562
Iteration: 1741; Percent complete: 43.5%; Average loss: 3.3572
Iteration: 1742; Percent complete: 43.5%; Average loss: 3.3637
Iteration: 1743; Percent complete: 43.6%; Average loss: 3.4182
Iteration: 1744; Percent complete: 43.6%; Average loss: 3.1072
Iteration: 1745; Percent complete: 43.6%; Average loss: 3.3079
Iteration: 1746; Percent complete: 43.6%; Average loss: 3.3759
Iteration: 1747; Percent complete: 43.7%; Average loss: 3.2216
Iteration: 1748; Percent complete: 43.7%; Average loss: 3.1875
Iteration: 1749; Percent complete: 43.7%; Average loss: 3.0882
Iteration: 1750; Percent complete: 43.8%; Average loss: 3.1844
Iteration: 1751; Percent complete: 43.8%; Average loss: 3.0920
Iteration: 1752; Percent complete: 43.8%; Average loss: 3.4352
Iteration: 1753; Percent complete: 43.8%; Average loss: 2.8526
Iteration: 1754; Percent complete: 43.9%; Average loss: 3.1606
Iteration: 1755; Percent complete: 43.9%; Average loss: 3.1977
Iteration: 1756; Percent complete: 43.9%; Average loss: 3.4654
Iteration: 1757; Percent complete: 43.9%; Average loss: 3.2220
Iteration: 1758; Percent complete: 44.0%; Average loss: 3.1802
Iteration: 1759; Percent complete: 44.0%; Average loss: 3.3679
Iteration: 1760; Percent complete: 44.0%; Average loss: 3.1913
Iteration: 1761; Percent complete: 44.0%; Average loss: 3.3399
Iteration: 1762; Percent complete: 44.0%; Average loss: 3.2543
Iteration: 1763; Percent complete: 44.1%; Average loss: 3.1828
Iteration: 1764; Percent complete: 44.1%; Average loss: 2.9484
Iteration: 1765; Percent complete: 44.1%; Average loss: 3.0304
Iteration: 1766; Percent complete: 44.1%; Average loss: 3.4780
Iteration: 1767; Percent complete: 44.2%; Average loss: 3.3803
Iteration: 1768; Percent complete: 44.2%; Average loss: 3.2229
Iteration: 1769; Percent complete: 44.2%; Average loss: 3.0650
Iteration: 1770; Percent complete: 44.2%; Average loss: 3.0195
Iteration: 1771; Percent complete: 44.3%; Average loss: 3.2112
Iteration: 1772; Percent complete: 44.3%; Average loss: 3.1751
Iteration: 1773; Percent complete: 44.3%; Average loss: 3.1901
Iteration: 1774; Percent complete: 44.4%; Average loss: 3.4821
Iteration: 1775; Percent complete: 44.4%; Average loss: 3.2612
Iteration: 1776; Percent complete: 44.4%; Average loss: 3.3132
Iteration: 1777; Percent complete: 44.4%; Average loss: 3.3909
Iteration: 1778; Percent complete: 44.5%; Average loss: 3.1326
Iteration: 1779; Percent complete: 44.5%; Average loss: 3.1800
Iteration: 1780; Percent complete: 44.5%; Average loss: 3.1728
Iteration: 1781; Percent complete: 44.5%; Average loss: 3.0712
Iteration: 1782; Percent complete: 44.5%; Average loss: 3.5713
Iteration: 1783; Percent complete: 44.6%; Average loss: 3.2431
Iteration: 1784; Percent complete: 44.6%; Average loss: 3.2914
Iteration: 1785; Percent complete: 44.6%; Average loss: 2.9292
Iteration: 1786; Percent complete: 44.6%; Average loss: 3.4301
Iteration: 1787; Percent complete: 44.7%; Average loss: 3.1557
Iteration: 1788; Percent complete: 44.7%; Average loss: 3.0609
Iteration: 1789; Percent complete: 44.7%; Average loss: 3.0964
Iteration: 1790; Percent complete: 44.8%; Average loss: 3.0450
Iteration: 1791; Percent complete: 44.8%; Average loss: 3.1646
Iteration: 1792; Percent complete: 44.8%; Average loss: 3.0397
Iteration: 1793; Percent complete: 44.8%; Average loss: 2.9785
Iteration: 1794; Percent complete: 44.9%; Average loss: 3.2845
Iteration: 1795; Percent complete: 44.9%; Average loss: 3.0244
Iteration: 1796; Percent complete: 44.9%; Average loss: 3.0891
Iteration: 1797; Percent complete: 44.9%; Average loss: 3.2005
Iteration: 1798; Percent complete: 45.0%; Average loss: 3.4376
Iteration: 1799; Percent complete: 45.0%; Average loss: 3.1597
Iteration: 1800; Percent complete: 45.0%; Average loss: 3.2261
Iteration: 1801; Percent complete: 45.0%; Average loss: 3.0646
Iteration: 1802; Percent complete: 45.1%; Average loss: 3.2419
Iteration: 1803; Percent complete: 45.1%; Average loss: 3.0025
Iteration: 1804; Percent complete: 45.1%; Average loss: 3.1921
Iteration: 1805; Percent complete: 45.1%; Average loss: 3.2863
Iteration: 1806; Percent complete: 45.1%; Average loss: 3.0127
Iteration: 1807; Percent complete: 45.2%; Average loss: 3.3262
Iteration: 1808; Percent complete: 45.2%; Average loss: 2.9367
Iteration: 1809; Percent complete: 45.2%; Average loss: 3.1833
Iteration: 1810; Percent complete: 45.2%; Average loss: 3.4213
Iteration: 1811; Percent complete: 45.3%; Average loss: 3.2577
Iteration: 1812; Percent complete: 45.3%; Average loss: 3.0980
Iteration: 1813; Percent complete: 45.3%; Average loss: 3.4417
Iteration: 1814; Percent complete: 45.4%; Average loss: 2.8769
Iteration: 1815; Percent complete: 45.4%; Average loss: 3.0068
Iteration: 1816; Percent complete: 45.4%; Average loss: 3.2267
Iteration: 1817; Percent complete: 45.4%; Average loss: 3.4309
Iteration: 1818; Percent complete: 45.5%; Average loss: 3.2755
Iteration: 1819; Percent complete: 45.5%; Average loss: 3.2626
Iteration: 1820; Percent complete: 45.5%; Average loss: 2.8909
Iteration: 1821; Percent complete: 45.5%; Average loss: 3.1460
Iteration: 1822; Percent complete: 45.6%; Average loss: 3.0938
Iteration: 1823; Percent complete: 45.6%; Average loss: 3.3699
Iteration: 1824; Percent complete: 45.6%; Average loss: 3.2802
Iteration: 1825; Percent complete: 45.6%; Average loss: 3.2113
Iteration: 1826; Percent complete: 45.6%; Average loss: 3.4028
Iteration: 1827; Percent complete: 45.7%; Average loss: 3.1328
Iteration: 1828; Percent complete: 45.7%; Average loss: 3.2184
Iteration: 1829; Percent complete: 45.7%; Average loss: 2.9819
Iteration: 1830; Percent complete: 45.8%; Average loss: 3.2021
Iteration: 1831; Percent complete: 45.8%; Average loss: 3.4259
Iteration: 1832; Percent complete: 45.8%; Average loss: 3.1730
Iteration: 1833; Percent complete: 45.8%; Average loss: 3.3570
Iteration: 1834; Percent complete: 45.9%; Average loss: 3.2927
Iteration: 1835; Percent complete: 45.9%; Average loss: 3.1921
Iteration: 1836; Percent complete: 45.9%; Average loss: 3.1755
Iteration: 1837; Percent complete: 45.9%; Average loss: 3.1817
Iteration: 1838; Percent complete: 46.0%; Average loss: 3.1510
Iteration: 1839; Percent complete: 46.0%; Average loss: 3.3143
Iteration: 1840; Percent complete: 46.0%; Average loss: 3.1453
Iteration: 1841; Percent complete: 46.0%; Average loss: 3.0835
Iteration: 1842; Percent complete: 46.1%; Average loss: 3.3388
Iteration: 1843; Percent complete: 46.1%; Average loss: 3.2216
Iteration: 1844; Percent complete: 46.1%; Average loss: 3.4390
Iteration: 1845; Percent complete: 46.1%; Average loss: 3.1436
Iteration: 1846; Percent complete: 46.2%; Average loss: 2.9749
Iteration: 1847; Percent complete: 46.2%; Average loss: 3.4019
Iteration: 1848; Percent complete: 46.2%; Average loss: 3.1109
Iteration: 1849; Percent complete: 46.2%; Average loss: 3.1789
Iteration: 1850; Percent complete: 46.2%; Average loss: 3.1260
Iteration: 1851; Percent complete: 46.3%; Average loss: 3.1954
Iteration: 1852; Percent complete: 46.3%; Average loss: 2.9983
Iteration: 1853; Percent complete: 46.3%; Average loss: 3.2231
Iteration: 1854; Percent complete: 46.4%; Average loss: 3.1070
Iteration: 1855; Percent complete: 46.4%; Average loss: 3.2035
Iteration: 1856; Percent complete: 46.4%; Average loss: 3.5948
Iteration: 1857; Percent complete: 46.4%; Average loss: 3.1312
Iteration: 1858; Percent complete: 46.5%; Average loss: 3.0659
Iteration: 1859; Percent complete: 46.5%; Average loss: 3.2287
Iteration: 1860; Percent complete: 46.5%; Average loss: 3.3958
Iteration: 1861; Percent complete: 46.5%; Average loss: 3.2282
Iteration: 1862; Percent complete: 46.6%; Average loss: 3.2407
Iteration: 1863; Percent complete: 46.6%; Average loss: 3.1563
Iteration: 1864; Percent complete: 46.6%; Average loss: 2.9224
Iteration: 1865; Percent complete: 46.6%; Average loss: 3.1286
Iteration: 1866; Percent complete: 46.7%; Average loss: 3.1783
Iteration: 1867; Percent complete: 46.7%; Average loss: 3.2740
Iteration: 1868; Percent complete: 46.7%; Average loss: 3.1427
Iteration: 1869; Percent complete: 46.7%; Average loss: 3.1556
Iteration: 1870; Percent complete: 46.8%; Average loss: 3.2507
Iteration: 1871; Percent complete: 46.8%; Average loss: 3.2599
Iteration: 1872; Percent complete: 46.8%; Average loss: 3.2777
Iteration: 1873; Percent complete: 46.8%; Average loss: 3.0038
Iteration: 1874; Percent complete: 46.9%; Average loss: 3.1476
Iteration: 1875; Percent complete: 46.9%; Average loss: 3.0075
Iteration: 1876; Percent complete: 46.9%; Average loss: 3.4332
Iteration: 1877; Percent complete: 46.9%; Average loss: 3.1037
Iteration: 1878; Percent complete: 46.9%; Average loss: 3.2616
Iteration: 1879; Percent complete: 47.0%; Average loss: 3.1928
Iteration: 1880; Percent complete: 47.0%; Average loss: 3.2031
Iteration: 1881; Percent complete: 47.0%; Average loss: 3.2177
Iteration: 1882; Percent complete: 47.0%; Average loss: 3.1936
Iteration: 1883; Percent complete: 47.1%; Average loss: 3.2713
Iteration: 1884; Percent complete: 47.1%; Average loss: 3.0639
Iteration: 1885; Percent complete: 47.1%; Average loss: 3.4085
Iteration: 1886; Percent complete: 47.1%; Average loss: 3.0772
Iteration: 1887; Percent complete: 47.2%; Average loss: 3.2531
Iteration: 1888; Percent complete: 47.2%; Average loss: 3.0796
Iteration: 1889; Percent complete: 47.2%; Average loss: 3.1323
Iteration: 1890; Percent complete: 47.2%; Average loss: 3.0498
Iteration: 1891; Percent complete: 47.3%; Average loss: 3.4438
Iteration: 1892; Percent complete: 47.3%; Average loss: 3.1163
Iteration: 1893; Percent complete: 47.3%; Average loss: 3.1669
Iteration: 1894; Percent complete: 47.3%; Average loss: 3.1511
Iteration: 1895; Percent complete: 47.4%; Average loss: 3.1109
Iteration: 1896; Percent complete: 47.4%; Average loss: 3.0506
Iteration: 1897; Percent complete: 47.4%; Average loss: 3.3849
Iteration: 1898; Percent complete: 47.4%; Average loss: 3.1999
Iteration: 1899; Percent complete: 47.5%; Average loss: 3.1272
Iteration: 1900; Percent complete: 47.5%; Average loss: 3.2323
Iteration: 1901; Percent complete: 47.5%; Average loss: 3.2548
Iteration: 1902; Percent complete: 47.5%; Average loss: 3.2473
Iteration: 1903; Percent complete: 47.6%; Average loss: 3.1894
Iteration: 1904; Percent complete: 47.6%; Average loss: 3.2136
Iteration: 1905; Percent complete: 47.6%; Average loss: 3.1108
Iteration: 1906; Percent complete: 47.6%; Average loss: 3.1442
Iteration: 1907; Percent complete: 47.7%; Average loss: 3.5546
Iteration: 1908; Percent complete: 47.7%; Average loss: 3.0813
Iteration: 1909; Percent complete: 47.7%; Average loss: 3.3263
Iteration: 1910; Percent complete: 47.8%; Average loss: 3.2961
Iteration: 1911; Percent complete: 47.8%; Average loss: 3.3372
Iteration: 1912; Percent complete: 47.8%; Average loss: 3.1898
Iteration: 1913; Percent complete: 47.8%; Average loss: 3.1357
Iteration: 1914; Percent complete: 47.9%; Average loss: 3.2023
Iteration: 1915; Percent complete: 47.9%; Average loss: 3.0335
Iteration: 1916; Percent complete: 47.9%; Average loss: 3.2033
Iteration: 1917; Percent complete: 47.9%; Average loss: 3.2518
Iteration: 1918; Percent complete: 47.9%; Average loss: 3.2890
Iteration: 1919; Percent complete: 48.0%; Average loss: 3.1049
Iteration: 1920; Percent complete: 48.0%; Average loss: 3.2165
Iteration: 1921; Percent complete: 48.0%; Average loss: 3.2735
Iteration: 1922; Percent complete: 48.0%; Average loss: 3.3019
Iteration: 1923; Percent complete: 48.1%; Average loss: 3.2604
Iteration: 1924; Percent complete: 48.1%; Average loss: 2.9859
Iteration: 1925; Percent complete: 48.1%; Average loss: 3.2623
Iteration: 1926; Percent complete: 48.1%; Average loss: 3.3339
Iteration: 1927; Percent complete: 48.2%; Average loss: 3.0616
Iteration: 1928; Percent complete: 48.2%; Average loss: 3.2025
Iteration: 1929; Percent complete: 48.2%; Average loss: 2.9762
Iteration: 1930; Percent complete: 48.2%; Average loss: 3.3155
Iteration: 1931; Percent complete: 48.3%; Average loss: 2.9465
Iteration: 1932; Percent complete: 48.3%; Average loss: 3.1726
Iteration: 1933; Percent complete: 48.3%; Average loss: 3.2069
Iteration: 1934; Percent complete: 48.4%; Average loss: 3.1415
Iteration: 1935; Percent complete: 48.4%; Average loss: 3.2241
Iteration: 1936; Percent complete: 48.4%; Average loss: 3.1880
Iteration: 1937; Percent complete: 48.4%; Average loss: 3.0278
Iteration: 1938; Percent complete: 48.4%; Average loss: 3.2680
Iteration: 1939; Percent complete: 48.5%; Average loss: 3.1673
Iteration: 1940; Percent complete: 48.5%; Average loss: 3.0594
Iteration: 1941; Percent complete: 48.5%; Average loss: 2.9710
Iteration: 1942; Percent complete: 48.5%; Average loss: 3.3155
Iteration: 1943; Percent complete: 48.6%; Average loss: 3.1229
Iteration: 1944; Percent complete: 48.6%; Average loss: 3.1815
Iteration: 1945; Percent complete: 48.6%; Average loss: 3.4047
Iteration: 1946; Percent complete: 48.6%; Average loss: 3.0071
Iteration: 1947; Percent complete: 48.7%; Average loss: 3.1432
Iteration: 1948; Percent complete: 48.7%; Average loss: 3.1211
Iteration: 1949; Percent complete: 48.7%; Average loss: 3.2403
Iteration: 1950; Percent complete: 48.8%; Average loss: 3.1338
Iteration: 1951; Percent complete: 48.8%; Average loss: 2.9842
Iteration: 1952; Percent complete: 48.8%; Average loss: 2.9207
Iteration: 1953; Percent complete: 48.8%; Average loss: 3.1783
Iteration: 1954; Percent complete: 48.9%; Average loss: 3.0873
Iteration: 1955; Percent complete: 48.9%; Average loss: 3.2638
Iteration: 1956; Percent complete: 48.9%; Average loss: 3.4060
Iteration: 1957; Percent complete: 48.9%; Average loss: 3.1254
Iteration: 1958; Percent complete: 48.9%; Average loss: 3.3718
Iteration: 1959; Percent complete: 49.0%; Average loss: 2.8695
Iteration: 1960; Percent complete: 49.0%; Average loss: 3.3188
Iteration: 1961; Percent complete: 49.0%; Average loss: 3.2560
Iteration: 1962; Percent complete: 49.0%; Average loss: 3.1445
Iteration: 1963; Percent complete: 49.1%; Average loss: 3.2966
Iteration: 1964; Percent complete: 49.1%; Average loss: 3.0318
Iteration: 1965; Percent complete: 49.1%; Average loss: 3.2192
Iteration: 1966; Percent complete: 49.1%; Average loss: 3.3171
Iteration: 1967; Percent complete: 49.2%; Average loss: 3.1554
Iteration: 1968; Percent complete: 49.2%; Average loss: 3.1896
Iteration: 1969; Percent complete: 49.2%; Average loss: 2.7903
Iteration: 1970; Percent complete: 49.2%; Average loss: 3.0497
Iteration: 1971; Percent complete: 49.3%; Average loss: 3.0363
Iteration: 1972; Percent complete: 49.3%; Average loss: 3.0076
Iteration: 1973; Percent complete: 49.3%; Average loss: 3.1156
Iteration: 1974; Percent complete: 49.4%; Average loss: 3.1276
Iteration: 1975; Percent complete: 49.4%; Average loss: 3.0122
Iteration: 1976; Percent complete: 49.4%; Average loss: 3.2234
Iteration: 1977; Percent complete: 49.4%; Average loss: 3.3284
Iteration: 1978; Percent complete: 49.5%; Average loss: 3.1310
Iteration: 1979; Percent complete: 49.5%; Average loss: 3.1558
Iteration: 1980; Percent complete: 49.5%; Average loss: 3.2235
Iteration: 1981; Percent complete: 49.5%; Average loss: 3.1652
Iteration: 1982; Percent complete: 49.5%; Average loss: 3.1977
Iteration: 1983; Percent complete: 49.6%; Average loss: 3.1319
Iteration: 1984; Percent complete: 49.6%; Average loss: 2.9419
Iteration: 1985; Percent complete: 49.6%; Average loss: 2.8794
Iteration: 1986; Percent complete: 49.6%; Average loss: 3.1125
Iteration: 1987; Percent complete: 49.7%; Average loss: 3.1478
Iteration: 1988; Percent complete: 49.7%; Average loss: 3.1596
Iteration: 1989; Percent complete: 49.7%; Average loss: 3.0774
Iteration: 1990; Percent complete: 49.8%; Average loss: 3.1515
Iteration: 1991; Percent complete: 49.8%; Average loss: 3.0557
Iteration: 1992; Percent complete: 49.8%; Average loss: 2.8403
Iteration: 1993; Percent complete: 49.8%; Average loss: 3.1488
Iteration: 1994; Percent complete: 49.9%; Average loss: 3.1136
Iteration: 1995; Percent complete: 49.9%; Average loss: 3.2972
Iteration: 1996; Percent complete: 49.9%; Average loss: 3.1774
Iteration: 1997; Percent complete: 49.9%; Average loss: 2.9311
Iteration: 1998; Percent complete: 50.0%; Average loss: 3.0637
Iteration: 1999; Percent complete: 50.0%; Average loss: 3.4164
Iteration: 2000; Percent complete: 50.0%; Average loss: 3.3449
Iteration: 2001; Percent complete: 50.0%; Average loss: 3.2091
Iteration: 2002; Percent complete: 50.0%; Average loss: 3.1878
Iteration: 2003; Percent complete: 50.1%; Average loss: 3.3220
Iteration: 2004; Percent complete: 50.1%; Average loss: 3.1895
Iteration: 2005; Percent complete: 50.1%; Average loss: 3.1084
Iteration: 2006; Percent complete: 50.1%; Average loss: 2.9593
Iteration: 2007; Percent complete: 50.2%; Average loss: 3.3550
Iteration: 2008; Percent complete: 50.2%; Average loss: 2.9407
Iteration: 2009; Percent complete: 50.2%; Average loss: 2.9476
Iteration: 2010; Percent complete: 50.2%; Average loss: 3.0459
Iteration: 2011; Percent complete: 50.3%; Average loss: 3.2829
Iteration: 2012; Percent complete: 50.3%; Average loss: 3.1351
Iteration: 2013; Percent complete: 50.3%; Average loss: 3.1625
Iteration: 2014; Percent complete: 50.3%; Average loss: 3.1426
Iteration: 2015; Percent complete: 50.4%; Average loss: 3.0435
Iteration: 2016; Percent complete: 50.4%; Average loss: 3.5404
Iteration: 2017; Percent complete: 50.4%; Average loss: 3.2330
Iteration: 2018; Percent complete: 50.4%; Average loss: 3.2363
Iteration: 2019; Percent complete: 50.5%; Average loss: 3.1424
Iteration: 2020; Percent complete: 50.5%; Average loss: 3.2770
Iteration: 2021; Percent complete: 50.5%; Average loss: 3.0917
Iteration: 2022; Percent complete: 50.5%; Average loss: 3.0189
Iteration: 2023; Percent complete: 50.6%; Average loss: 3.1549
Iteration: 2024; Percent complete: 50.6%; Average loss: 3.3198
Iteration: 2025; Percent complete: 50.6%; Average loss: 2.9616
Iteration: 2026; Percent complete: 50.6%; Average loss: 3.1103
Iteration: 2027; Percent complete: 50.7%; Average loss: 3.0625
Iteration: 2028; Percent complete: 50.7%; Average loss: 3.2753
Iteration: 2029; Percent complete: 50.7%; Average loss: 3.0339
Iteration: 2030; Percent complete: 50.7%; Average loss: 3.3022
Iteration: 2031; Percent complete: 50.8%; Average loss: 2.9467
Iteration: 2032; Percent complete: 50.8%; Average loss: 2.8596
Iteration: 2033; Percent complete: 50.8%; Average loss: 3.1870
Iteration: 2034; Percent complete: 50.8%; Average loss: 3.4065
Iteration: 2035; Percent complete: 50.9%; Average loss: 3.0014
Iteration: 2036; Percent complete: 50.9%; Average loss: 3.3181
Iteration: 2037; Percent complete: 50.9%; Average loss: 3.1640
Iteration: 2038; Percent complete: 50.9%; Average loss: 3.1565
Iteration: 2039; Percent complete: 51.0%; Average loss: 3.2860
Iteration: 2040; Percent complete: 51.0%; Average loss: 3.2608
Iteration: 2041; Percent complete: 51.0%; Average loss: 3.3184
Iteration: 2042; Percent complete: 51.0%; Average loss: 3.1462
Iteration: 2043; Percent complete: 51.1%; Average loss: 3.0021
Iteration: 2044; Percent complete: 51.1%; Average loss: 3.1248
Iteration: 2045; Percent complete: 51.1%; Average loss: 3.1759
Iteration: 2046; Percent complete: 51.1%; Average loss: 3.0223
Iteration: 2047; Percent complete: 51.2%; Average loss: 3.1947
Iteration: 2048; Percent complete: 51.2%; Average loss: 3.2759
Iteration: 2049; Percent complete: 51.2%; Average loss: 3.2474
Iteration: 2050; Percent complete: 51.2%; Average loss: 3.1641
Iteration: 2051; Percent complete: 51.3%; Average loss: 3.0559
Iteration: 2052; Percent complete: 51.3%; Average loss: 2.8773
Iteration: 2053; Percent complete: 51.3%; Average loss: 3.1851
Iteration: 2054; Percent complete: 51.3%; Average loss: 3.1802
Iteration: 2055; Percent complete: 51.4%; Average loss: 2.8028
Iteration: 2056; Percent complete: 51.4%; Average loss: 3.2633
Iteration: 2057; Percent complete: 51.4%; Average loss: 3.4706
Iteration: 2058; Percent complete: 51.4%; Average loss: 3.0906
Iteration: 2059; Percent complete: 51.5%; Average loss: 2.9677
Iteration: 2060; Percent complete: 51.5%; Average loss: 3.3633
Iteration: 2061; Percent complete: 51.5%; Average loss: 3.0611
Iteration: 2062; Percent complete: 51.5%; Average loss: 3.1042
Iteration: 2063; Percent complete: 51.6%; Average loss: 3.0517
Iteration: 2064; Percent complete: 51.6%; Average loss: 3.2330
Iteration: 2065; Percent complete: 51.6%; Average loss: 3.0676
Iteration: 2066; Percent complete: 51.6%; Average loss: 2.9970
Iteration: 2067; Percent complete: 51.7%; Average loss: 2.8657
Iteration: 2068; Percent complete: 51.7%; Average loss: 3.1151
Iteration: 2069; Percent complete: 51.7%; Average loss: 3.3693
Iteration: 2070; Percent complete: 51.7%; Average loss: 3.0649
Iteration: 2071; Percent complete: 51.8%; Average loss: 3.1496
Iteration: 2072; Percent complete: 51.8%; Average loss: 2.9975
Iteration: 2073; Percent complete: 51.8%; Average loss: 3.1307
Iteration: 2074; Percent complete: 51.8%; Average loss: 3.1512
Iteration: 2075; Percent complete: 51.9%; Average loss: 3.2542
Iteration: 2076; Percent complete: 51.9%; Average loss: 3.2858
Iteration: 2077; Percent complete: 51.9%; Average loss: 3.4025
Iteration: 2078; Percent complete: 51.9%; Average loss: 3.1684
Iteration: 2079; Percent complete: 52.0%; Average loss: 3.0222
Iteration: 2080; Percent complete: 52.0%; Average loss: 3.0552
Iteration: 2081; Percent complete: 52.0%; Average loss: 3.0248
Iteration: 2082; Percent complete: 52.0%; Average loss: 3.0366
Iteration: 2083; Percent complete: 52.1%; Average loss: 3.2171
Iteration: 2084; Percent complete: 52.1%; Average loss: 3.1955
Iteration: 2085; Percent complete: 52.1%; Average loss: 3.1354
Iteration: 2086; Percent complete: 52.1%; Average loss: 3.0433
Iteration: 2087; Percent complete: 52.2%; Average loss: 2.9573
Iteration: 2088; Percent complete: 52.2%; Average loss: 3.0130
Iteration: 2089; Percent complete: 52.2%; Average loss: 3.1238
Iteration: 2090; Percent complete: 52.2%; Average loss: 3.0454
Iteration: 2091; Percent complete: 52.3%; Average loss: 2.8282
Iteration: 2092; Percent complete: 52.3%; Average loss: 3.2264
Iteration: 2093; Percent complete: 52.3%; Average loss: 2.9300
Iteration: 2094; Percent complete: 52.3%; Average loss: 3.1534
Iteration: 2095; Percent complete: 52.4%; Average loss: 3.1134
Iteration: 2096; Percent complete: 52.4%; Average loss: 3.3225
Iteration: 2097; Percent complete: 52.4%; Average loss: 3.4299
Iteration: 2098; Percent complete: 52.4%; Average loss: 3.2703
Iteration: 2099; Percent complete: 52.5%; Average loss: 3.3142
Iteration: 2100; Percent complete: 52.5%; Average loss: 3.1294
Iteration: 2101; Percent complete: 52.5%; Average loss: 3.1689
Iteration: 2102; Percent complete: 52.5%; Average loss: 3.0754
Iteration: 2103; Percent complete: 52.6%; Average loss: 2.9028
Iteration: 2104; Percent complete: 52.6%; Average loss: 3.0344
Iteration: 2105; Percent complete: 52.6%; Average loss: 3.0153
Iteration: 2106; Percent complete: 52.6%; Average loss: 3.0461
Iteration: 2107; Percent complete: 52.7%; Average loss: 3.3634
Iteration: 2108; Percent complete: 52.7%; Average loss: 3.2529
Iteration: 2109; Percent complete: 52.7%; Average loss: 3.0767
Iteration: 2110; Percent complete: 52.8%; Average loss: 2.9847
Iteration: 2111; Percent complete: 52.8%; Average loss: 2.9518
Iteration: 2112; Percent complete: 52.8%; Average loss: 3.1823
Iteration: 2113; Percent complete: 52.8%; Average loss: 2.9628
Iteration: 2114; Percent complete: 52.8%; Average loss: 2.8477
Iteration: 2115; Percent complete: 52.9%; Average loss: 3.2334
Iteration: 2116; Percent complete: 52.9%; Average loss: 3.2040
Iteration: 2117; Percent complete: 52.9%; Average loss: 3.3216
Iteration: 2118; Percent complete: 52.9%; Average loss: 2.8862
Iteration: 2119; Percent complete: 53.0%; Average loss: 3.0461
Iteration: 2120; Percent complete: 53.0%; Average loss: 3.0875
Iteration: 2121; Percent complete: 53.0%; Average loss: 3.2472
Iteration: 2122; Percent complete: 53.0%; Average loss: 3.4213
Iteration: 2123; Percent complete: 53.1%; Average loss: 3.2051
Iteration: 2124; Percent complete: 53.1%; Average loss: 3.2681
Iteration: 2125; Percent complete: 53.1%; Average loss: 3.3422
Iteration: 2126; Percent complete: 53.1%; Average loss: 3.0198
Iteration: 2127; Percent complete: 53.2%; Average loss: 2.8829
Iteration: 2128; Percent complete: 53.2%; Average loss: 3.2167
Iteration: 2129; Percent complete: 53.2%; Average loss: 3.0881
Iteration: 2130; Percent complete: 53.2%; Average loss: 3.1950
Iteration: 2131; Percent complete: 53.3%; Average loss: 3.2829
Iteration: 2132; Percent complete: 53.3%; Average loss: 3.0724
Iteration: 2133; Percent complete: 53.3%; Average loss: 3.1525
Iteration: 2134; Percent complete: 53.3%; Average loss: 3.1254
Iteration: 2135; Percent complete: 53.4%; Average loss: 3.2913
Iteration: 2136; Percent complete: 53.4%; Average loss: 2.9130
Iteration: 2137; Percent complete: 53.4%; Average loss: 2.9632
Iteration: 2138; Percent complete: 53.4%; Average loss: 3.1473
Iteration: 2139; Percent complete: 53.5%; Average loss: 3.3625
Iteration: 2140; Percent complete: 53.5%; Average loss: 3.0379
Iteration: 2141; Percent complete: 53.5%; Average loss: 3.3238
Iteration: 2142; Percent complete: 53.5%; Average loss: 2.9901
Iteration: 2143; Percent complete: 53.6%; Average loss: 3.1828
Iteration: 2144; Percent complete: 53.6%; Average loss: 3.3070
Iteration: 2145; Percent complete: 53.6%; Average loss: 3.0682
Iteration: 2146; Percent complete: 53.6%; Average loss: 3.1023
Iteration: 2147; Percent complete: 53.7%; Average loss: 3.0162
Iteration: 2148; Percent complete: 53.7%; Average loss: 3.3155
Iteration: 2149; Percent complete: 53.7%; Average loss: 3.0366
Iteration: 2150; Percent complete: 53.8%; Average loss: 3.1773
Iteration: 2151; Percent complete: 53.8%; Average loss: 3.1927
Iteration: 2152; Percent complete: 53.8%; Average loss: 3.0831
Iteration: 2153; Percent complete: 53.8%; Average loss: 3.2545
Iteration: 2154; Percent complete: 53.8%; Average loss: 3.1660
Iteration: 2155; Percent complete: 53.9%; Average loss: 3.0146
Iteration: 2156; Percent complete: 53.9%; Average loss: 2.9458
Iteration: 2157; Percent complete: 53.9%; Average loss: 3.2217
Iteration: 2158; Percent complete: 53.9%; Average loss: 3.1143
Iteration: 2159; Percent complete: 54.0%; Average loss: 3.0303
Iteration: 2160; Percent complete: 54.0%; Average loss: 3.2767
Iteration: 2161; Percent complete: 54.0%; Average loss: 2.7907
Iteration: 2162; Percent complete: 54.0%; Average loss: 3.0018
Iteration: 2163; Percent complete: 54.1%; Average loss: 3.2125
Iteration: 2164; Percent complete: 54.1%; Average loss: 3.0148
Iteration: 2165; Percent complete: 54.1%; Average loss: 3.2857
Iteration: 2166; Percent complete: 54.1%; Average loss: 3.1463
Iteration: 2167; Percent complete: 54.2%; Average loss: 3.1498
Iteration: 2168; Percent complete: 54.2%; Average loss: 2.9880
Iteration: 2169; Percent complete: 54.2%; Average loss: 3.0132
Iteration: 2170; Percent complete: 54.2%; Average loss: 2.7891
Iteration: 2171; Percent complete: 54.3%; Average loss: 3.1613
Iteration: 2172; Percent complete: 54.3%; Average loss: 3.2517
Iteration: 2173; Percent complete: 54.3%; Average loss: 3.2534
Iteration: 2174; Percent complete: 54.4%; Average loss: 3.0428
Iteration: 2175; Percent complete: 54.4%; Average loss: 3.1142
Iteration: 2176; Percent complete: 54.4%; Average loss: 2.8342
Iteration: 2177; Percent complete: 54.4%; Average loss: 3.0155
Iteration: 2178; Percent complete: 54.4%; Average loss: 3.1925
Iteration: 2179; Percent complete: 54.5%; Average loss: 3.4074
Iteration: 2180; Percent complete: 54.5%; Average loss: 2.9775
Iteration: 2181; Percent complete: 54.5%; Average loss: 3.0613
Iteration: 2182; Percent complete: 54.5%; Average loss: 3.0383
Iteration: 2183; Percent complete: 54.6%; Average loss: 3.0275
Iteration: 2184; Percent complete: 54.6%; Average loss: 3.2458
Iteration: 2185; Percent complete: 54.6%; Average loss: 2.9875
Iteration: 2186; Percent complete: 54.6%; Average loss: 3.1384
Iteration: 2187; Percent complete: 54.7%; Average loss: 3.1881
Iteration: 2188; Percent complete: 54.7%; Average loss: 3.1435
Iteration: 2189; Percent complete: 54.7%; Average loss: 3.1538
Iteration: 2190; Percent complete: 54.8%; Average loss: 3.1693
Iteration: 2191; Percent complete: 54.8%; Average loss: 3.2849
Iteration: 2192; Percent complete: 54.8%; Average loss: 3.1911
Iteration: 2193; Percent complete: 54.8%; Average loss: 2.8615
Iteration: 2194; Percent complete: 54.9%; Average loss: 3.0444
Iteration: 2195; Percent complete: 54.9%; Average loss: 3.0433
Iteration: 2196; Percent complete: 54.9%; Average loss: 3.1929
Iteration: 2197; Percent complete: 54.9%; Average loss: 3.1503
Iteration: 2198; Percent complete: 54.9%; Average loss: 3.2558
Iteration: 2199; Percent complete: 55.0%; Average loss: 3.2079
Iteration: 2200; Percent complete: 55.0%; Average loss: 3.0966
Iteration: 2201; Percent complete: 55.0%; Average loss: 3.1258
Iteration: 2202; Percent complete: 55.0%; Average loss: 3.1007
Iteration: 2203; Percent complete: 55.1%; Average loss: 2.9517
Iteration: 2204; Percent complete: 55.1%; Average loss: 2.9814
Iteration: 2205; Percent complete: 55.1%; Average loss: 3.1855
Iteration: 2206; Percent complete: 55.1%; Average loss: 2.9968
Iteration: 2207; Percent complete: 55.2%; Average loss: 3.2074
Iteration: 2208; Percent complete: 55.2%; Average loss: 3.0744
Iteration: 2209; Percent complete: 55.2%; Average loss: 3.1088
Iteration: 2210; Percent complete: 55.2%; Average loss: 2.8786
Iteration: 2211; Percent complete: 55.3%; Average loss: 3.3234
Iteration: 2212; Percent complete: 55.3%; Average loss: 3.1113
Iteration: 2213; Percent complete: 55.3%; Average loss: 3.0024
Iteration: 2214; Percent complete: 55.4%; Average loss: 3.1920
Iteration: 2215; Percent complete: 55.4%; Average loss: 3.1190
Iteration: 2216; Percent complete: 55.4%; Average loss: 3.0583
Iteration: 2217; Percent complete: 55.4%; Average loss: 2.9317
Iteration: 2218; Percent complete: 55.5%; Average loss: 3.0268
Iteration: 2219; Percent complete: 55.5%; Average loss: 3.1580
Iteration: 2220; Percent complete: 55.5%; Average loss: 3.2684
Iteration: 2221; Percent complete: 55.5%; Average loss: 2.9525
Iteration: 2222; Percent complete: 55.5%; Average loss: 3.2188
Iteration: 2223; Percent complete: 55.6%; Average loss: 3.0512
Iteration: 2224; Percent complete: 55.6%; Average loss: 3.0246
Iteration: 2225; Percent complete: 55.6%; Average loss: 3.1880
Iteration: 2226; Percent complete: 55.6%; Average loss: 3.1996
Iteration: 2227; Percent complete: 55.7%; Average loss: 2.9534
Iteration: 2228; Percent complete: 55.7%; Average loss: 3.0649
Iteration: 2229; Percent complete: 55.7%; Average loss: 3.1868
Iteration: 2230; Percent complete: 55.8%; Average loss: 3.1592
Iteration: 2231; Percent complete: 55.8%; Average loss: 3.2252
Iteration: 2232; Percent complete: 55.8%; Average loss: 3.0320
Iteration: 2233; Percent complete: 55.8%; Average loss: 3.2229
Iteration: 2234; Percent complete: 55.9%; Average loss: 3.1830
Iteration: 2235; Percent complete: 55.9%; Average loss: 3.1651
Iteration: 2236; Percent complete: 55.9%; Average loss: 3.0450
Iteration: 2237; Percent complete: 55.9%; Average loss: 2.9827
Iteration: 2238; Percent complete: 56.0%; Average loss: 3.1233
Iteration: 2239; Percent complete: 56.0%; Average loss: 3.0277
Iteration: 2240; Percent complete: 56.0%; Average loss: 3.0499
Iteration: 2241; Percent complete: 56.0%; Average loss: 3.2023
Iteration: 2242; Percent complete: 56.0%; Average loss: 2.9847
Iteration: 2243; Percent complete: 56.1%; Average loss: 3.1495
Iteration: 2244; Percent complete: 56.1%; Average loss: 2.9849
Iteration: 2245; Percent complete: 56.1%; Average loss: 3.2820
Iteration: 2246; Percent complete: 56.1%; Average loss: 3.1462
Iteration: 2247; Percent complete: 56.2%; Average loss: 2.8477
Iteration: 2248; Percent complete: 56.2%; Average loss: 3.3838
Iteration: 2249; Percent complete: 56.2%; Average loss: 3.4016
Iteration: 2250; Percent complete: 56.2%; Average loss: 3.2390
Iteration: 2251; Percent complete: 56.3%; Average loss: 3.3976
Iteration: 2252; Percent complete: 56.3%; Average loss: 3.1041
Iteration: 2253; Percent complete: 56.3%; Average loss: 3.2108
Iteration: 2254; Percent complete: 56.4%; Average loss: 3.2221
Iteration: 2255; Percent complete: 56.4%; Average loss: 2.9620
Iteration: 2256; Percent complete: 56.4%; Average loss: 3.2489
Iteration: 2257; Percent complete: 56.4%; Average loss: 3.0251
Iteration: 2258; Percent complete: 56.5%; Average loss: 3.2066
Iteration: 2259; Percent complete: 56.5%; Average loss: 3.1808
Iteration: 2260; Percent complete: 56.5%; Average loss: 2.8471
Iteration: 2261; Percent complete: 56.5%; Average loss: 3.1737
Iteration: 2262; Percent complete: 56.5%; Average loss: 2.9006
Iteration: 2263; Percent complete: 56.6%; Average loss: 3.0721
Iteration: 2264; Percent complete: 56.6%; Average loss: 3.2289
Iteration: 2265; Percent complete: 56.6%; Average loss: 3.0610
Iteration: 2266; Percent complete: 56.6%; Average loss: 3.0858
Iteration: 2267; Percent complete: 56.7%; Average loss: 3.1696
Iteration: 2268; Percent complete: 56.7%; Average loss: 3.0695
Iteration: 2269; Percent complete: 56.7%; Average loss: 3.1522
Iteration: 2270; Percent complete: 56.8%; Average loss: 2.9145
Iteration: 2271; Percent complete: 56.8%; Average loss: 2.8281
Iteration: 2272; Percent complete: 56.8%; Average loss: 3.1938
Iteration: 2273; Percent complete: 56.8%; Average loss: 3.3140
Iteration: 2274; Percent complete: 56.9%; Average loss: 3.0471
Iteration: 2275; Percent complete: 56.9%; Average loss: 3.0451
Iteration: 2276; Percent complete: 56.9%; Average loss: 3.2490
Iteration: 2277; Percent complete: 56.9%; Average loss: 2.9320
Iteration: 2278; Percent complete: 57.0%; Average loss: 3.0034
Iteration: 2279; Percent complete: 57.0%; Average loss: 2.9369
Iteration: 2280; Percent complete: 57.0%; Average loss: 3.1441
Iteration: 2281; Percent complete: 57.0%; Average loss: 2.9701
Iteration: 2282; Percent complete: 57.0%; Average loss: 3.0010
Iteration: 2283; Percent complete: 57.1%; Average loss: 3.1172
Iteration: 2284; Percent complete: 57.1%; Average loss: 3.0994
Iteration: 2285; Percent complete: 57.1%; Average loss: 2.9782
Iteration: 2286; Percent complete: 57.1%; Average loss: 2.9229
Iteration: 2287; Percent complete: 57.2%; Average loss: 3.1306
Iteration: 2288; Percent complete: 57.2%; Average loss: 3.0865
Iteration: 2289; Percent complete: 57.2%; Average loss: 2.9333
Iteration: 2290; Percent complete: 57.2%; Average loss: 2.9802
Iteration: 2291; Percent complete: 57.3%; Average loss: 2.8457
Iteration: 2292; Percent complete: 57.3%; Average loss: 3.1197
Iteration: 2293; Percent complete: 57.3%; Average loss: 3.3394
Iteration: 2294; Percent complete: 57.4%; Average loss: 2.8895
Iteration: 2295; Percent complete: 57.4%; Average loss: 3.1446
Iteration: 2296; Percent complete: 57.4%; Average loss: 3.1594
Iteration: 2297; Percent complete: 57.4%; Average loss: 3.2501
Iteration: 2298; Percent complete: 57.5%; Average loss: 2.9903
Iteration: 2299; Percent complete: 57.5%; Average loss: 2.6471
Iteration: 2300; Percent complete: 57.5%; Average loss: 2.7821
Iteration: 2301; Percent complete: 57.5%; Average loss: 3.0724
Iteration: 2302; Percent complete: 57.6%; Average loss: 2.9996
Iteration: 2303; Percent complete: 57.6%; Average loss: 3.1562
Iteration: 2304; Percent complete: 57.6%; Average loss: 3.0213
Iteration: 2305; Percent complete: 57.6%; Average loss: 3.1170
Iteration: 2306; Percent complete: 57.6%; Average loss: 3.0237
Iteration: 2307; Percent complete: 57.7%; Average loss: 3.1098
Iteration: 2308; Percent complete: 57.7%; Average loss: 2.8979
Iteration: 2309; Percent complete: 57.7%; Average loss: 2.8422
Iteration: 2310; Percent complete: 57.8%; Average loss: 2.9927
Iteration: 2311; Percent complete: 57.8%; Average loss: 2.9508
Iteration: 2312; Percent complete: 57.8%; Average loss: 3.1423
Iteration: 2313; Percent complete: 57.8%; Average loss: 3.0279
Iteration: 2314; Percent complete: 57.9%; Average loss: 3.0124
Iteration: 2315; Percent complete: 57.9%; Average loss: 3.0356
Iteration: 2316; Percent complete: 57.9%; Average loss: 3.1235
Iteration: 2317; Percent complete: 57.9%; Average loss: 3.2937
Iteration: 2318; Percent complete: 58.0%; Average loss: 3.0002
Iteration: 2319; Percent complete: 58.0%; Average loss: 3.2072
Iteration: 2320; Percent complete: 58.0%; Average loss: 3.0335
Iteration: 2321; Percent complete: 58.0%; Average loss: 3.0905
Iteration: 2322; Percent complete: 58.1%; Average loss: 3.0313
Iteration: 2323; Percent complete: 58.1%; Average loss: 3.0913
Iteration: 2324; Percent complete: 58.1%; Average loss: 3.0139
Iteration: 2325; Percent complete: 58.1%; Average loss: 3.0788
Iteration: 2326; Percent complete: 58.1%; Average loss: 2.9846
Iteration: 2327; Percent complete: 58.2%; Average loss: 3.1655
Iteration: 2328; Percent complete: 58.2%; Average loss: 3.0646
Iteration: 2329; Percent complete: 58.2%; Average loss: 3.1032
Iteration: 2330; Percent complete: 58.2%; Average loss: 3.1251
Iteration: 2331; Percent complete: 58.3%; Average loss: 2.9676
Iteration: 2332; Percent complete: 58.3%; Average loss: 2.9036
Iteration: 2333; Percent complete: 58.3%; Average loss: 3.2219
Iteration: 2334; Percent complete: 58.4%; Average loss: 3.1744
Iteration: 2335; Percent complete: 58.4%; Average loss: 3.1196
Iteration: 2336; Percent complete: 58.4%; Average loss: 2.9210
Iteration: 2337; Percent complete: 58.4%; Average loss: 3.1639
Iteration: 2338; Percent complete: 58.5%; Average loss: 3.1710
Iteration: 2339; Percent complete: 58.5%; Average loss: 2.8770
Iteration: 2340; Percent complete: 58.5%; Average loss: 2.9842
Iteration: 2341; Percent complete: 58.5%; Average loss: 3.3893
Iteration: 2342; Percent complete: 58.6%; Average loss: 2.8790
Iteration: 2343; Percent complete: 58.6%; Average loss: 3.0222
Iteration: 2344; Percent complete: 58.6%; Average loss: 3.2415
Iteration: 2345; Percent complete: 58.6%; Average loss: 3.0423
Iteration: 2346; Percent complete: 58.7%; Average loss: 3.0372
Iteration: 2347; Percent complete: 58.7%; Average loss: 3.2080
Iteration: 2348; Percent complete: 58.7%; Average loss: 3.1730
Iteration: 2349; Percent complete: 58.7%; Average loss: 3.1273
Iteration: 2350; Percent complete: 58.8%; Average loss: 3.0801
Iteration: 2351; Percent complete: 58.8%; Average loss: 2.8482
Iteration: 2352; Percent complete: 58.8%; Average loss: 2.9451
Iteration: 2353; Percent complete: 58.8%; Average loss: 3.0540
Iteration: 2354; Percent complete: 58.9%; Average loss: 2.9454
Iteration: 2355; Percent complete: 58.9%; Average loss: 3.1784
Iteration: 2356; Percent complete: 58.9%; Average loss: 2.9898
Iteration: 2357; Percent complete: 58.9%; Average loss: 3.2816
Iteration: 2358; Percent complete: 59.0%; Average loss: 3.1457
Iteration: 2359; Percent complete: 59.0%; Average loss: 3.0188
Iteration: 2360; Percent complete: 59.0%; Average loss: 3.3108
Iteration: 2361; Percent complete: 59.0%; Average loss: 2.9373
Iteration: 2362; Percent complete: 59.1%; Average loss: 3.1021
Iteration: 2363; Percent complete: 59.1%; Average loss: 3.3034
Iteration: 2364; Percent complete: 59.1%; Average loss: 3.2164
Iteration: 2365; Percent complete: 59.1%; Average loss: 3.1217
Iteration: 2366; Percent complete: 59.2%; Average loss: 3.0498
Iteration: 2367; Percent complete: 59.2%; Average loss: 3.3042
Iteration: 2368; Percent complete: 59.2%; Average loss: 3.1464
Iteration: 2369; Percent complete: 59.2%; Average loss: 3.0624
Iteration: 2370; Percent complete: 59.2%; Average loss: 3.1735
Iteration: 2371; Percent complete: 59.3%; Average loss: 3.1815
Iteration: 2372; Percent complete: 59.3%; Average loss: 3.0410
Iteration: 2373; Percent complete: 59.3%; Average loss: 3.0747
Iteration: 2374; Percent complete: 59.4%; Average loss: 2.9682
Iteration: 2375; Percent complete: 59.4%; Average loss: 2.8135
Iteration: 2376; Percent complete: 59.4%; Average loss: 2.8282
Iteration: 2377; Percent complete: 59.4%; Average loss: 2.7138
Iteration: 2378; Percent complete: 59.5%; Average loss: 2.6431
Iteration: 2379; Percent complete: 59.5%; Average loss: 3.0876
Iteration: 2380; Percent complete: 59.5%; Average loss: 2.9187
Iteration: 2381; Percent complete: 59.5%; Average loss: 3.4977
Iteration: 2382; Percent complete: 59.6%; Average loss: 3.2027
Iteration: 2383; Percent complete: 59.6%; Average loss: 3.1231
Iteration: 2384; Percent complete: 59.6%; Average loss: 3.2292
Iteration: 2385; Percent complete: 59.6%; Average loss: 3.1234
Iteration: 2386; Percent complete: 59.7%; Average loss: 2.9903
Iteration: 2387; Percent complete: 59.7%; Average loss: 3.0832
Iteration: 2388; Percent complete: 59.7%; Average loss: 2.9448
Iteration: 2389; Percent complete: 59.7%; Average loss: 2.9340
Iteration: 2390; Percent complete: 59.8%; Average loss: 3.1281
Iteration: 2391; Percent complete: 59.8%; Average loss: 3.3221
Iteration: 2392; Percent complete: 59.8%; Average loss: 2.9880
Iteration: 2393; Percent complete: 59.8%; Average loss: 2.9375
Iteration: 2394; Percent complete: 59.9%; Average loss: 3.0300
Iteration: 2395; Percent complete: 59.9%; Average loss: 3.1509
Iteration: 2396; Percent complete: 59.9%; Average loss: 3.1321
Iteration: 2397; Percent complete: 59.9%; Average loss: 3.0276
Iteration: 2398; Percent complete: 60.0%; Average loss: 3.0314
Iteration: 2399; Percent complete: 60.0%; Average loss: 2.9849
Iteration: 2400; Percent complete: 60.0%; Average loss: 3.0601
Iteration: 2401; Percent complete: 60.0%; Average loss: 3.2046
Iteration: 2402; Percent complete: 60.1%; Average loss: 2.8203
Iteration: 2403; Percent complete: 60.1%; Average loss: 3.0894
Iteration: 2404; Percent complete: 60.1%; Average loss: 2.9945
Iteration: 2405; Percent complete: 60.1%; Average loss: 3.1851
Iteration: 2406; Percent complete: 60.2%; Average loss: 2.8074
Iteration: 2407; Percent complete: 60.2%; Average loss: 2.7951
Iteration: 2408; Percent complete: 60.2%; Average loss: 3.0590
Iteration: 2409; Percent complete: 60.2%; Average loss: 3.0229
Iteration: 2410; Percent complete: 60.2%; Average loss: 3.1995
Iteration: 2411; Percent complete: 60.3%; Average loss: 3.0874
Iteration: 2412; Percent complete: 60.3%; Average loss: 3.2854
Iteration: 2413; Percent complete: 60.3%; Average loss: 2.9206
Iteration: 2414; Percent complete: 60.4%; Average loss: 2.7029
Iteration: 2415; Percent complete: 60.4%; Average loss: 3.1272
Iteration: 2416; Percent complete: 60.4%; Average loss: 3.1305
Iteration: 2417; Percent complete: 60.4%; Average loss: 2.9597
Iteration: 2418; Percent complete: 60.5%; Average loss: 3.1706
Iteration: 2419; Percent complete: 60.5%; Average loss: 2.6910
Iteration: 2420; Percent complete: 60.5%; Average loss: 3.1875
Iteration: 2421; Percent complete: 60.5%; Average loss: 3.0744
Iteration: 2422; Percent complete: 60.6%; Average loss: 3.0492
Iteration: 2423; Percent complete: 60.6%; Average loss: 3.1136
Iteration: 2424; Percent complete: 60.6%; Average loss: 3.1871
Iteration: 2425; Percent complete: 60.6%; Average loss: 2.9691
Iteration: 2426; Percent complete: 60.7%; Average loss: 2.7085
Iteration: 2427; Percent complete: 60.7%; Average loss: 2.9368
Iteration: 2428; Percent complete: 60.7%; Average loss: 2.9826
Iteration: 2429; Percent complete: 60.7%; Average loss: 2.8333
Iteration: 2430; Percent complete: 60.8%; Average loss: 2.9107
Iteration: 2431; Percent complete: 60.8%; Average loss: 3.3617
Iteration: 2432; Percent complete: 60.8%; Average loss: 2.9281
Iteration: 2433; Percent complete: 60.8%; Average loss: 3.1071
Iteration: 2434; Percent complete: 60.9%; Average loss: 3.1271
Iteration: 2435; Percent complete: 60.9%; Average loss: 2.9168
Iteration: 2436; Percent complete: 60.9%; Average loss: 3.0699
Iteration: 2437; Percent complete: 60.9%; Average loss: 3.1236
Iteration: 2438; Percent complete: 61.0%; Average loss: 2.9654
Iteration: 2439; Percent complete: 61.0%; Average loss: 3.0196
Iteration: 2440; Percent complete: 61.0%; Average loss: 3.0238
Iteration: 2441; Percent complete: 61.0%; Average loss: 2.7834
Iteration: 2442; Percent complete: 61.1%; Average loss: 3.1053
Iteration: 2443; Percent complete: 61.1%; Average loss: 2.8769
Iteration: 2444; Percent complete: 61.1%; Average loss: 3.1617
Iteration: 2445; Percent complete: 61.1%; Average loss: 2.7952
Iteration: 2446; Percent complete: 61.2%; Average loss: 2.7477
Iteration: 2447; Percent complete: 61.2%; Average loss: 3.1193
Iteration: 2448; Percent complete: 61.2%; Average loss: 3.0883
Iteration: 2449; Percent complete: 61.2%; Average loss: 3.0594
Iteration: 2450; Percent complete: 61.3%; Average loss: 3.0217
Iteration: 2451; Percent complete: 61.3%; Average loss: 3.1395
Iteration: 2452; Percent complete: 61.3%; Average loss: 2.9407
Iteration: 2453; Percent complete: 61.3%; Average loss: 3.1839
Iteration: 2454; Percent complete: 61.4%; Average loss: 3.0120
Iteration: 2455; Percent complete: 61.4%; Average loss: 3.1134
Iteration: 2456; Percent complete: 61.4%; Average loss: 2.9157
Iteration: 2457; Percent complete: 61.4%; Average loss: 3.0503
Iteration: 2458; Percent complete: 61.5%; Average loss: 3.0720
Iteration: 2459; Percent complete: 61.5%; Average loss: 3.1043
Iteration: 2460; Percent complete: 61.5%; Average loss: 2.9199
Iteration: 2461; Percent complete: 61.5%; Average loss: 2.9548
Iteration: 2462; Percent complete: 61.6%; Average loss: 3.2279
Iteration: 2463; Percent complete: 61.6%; Average loss: 2.9808
Iteration: 2464; Percent complete: 61.6%; Average loss: 2.9373
Iteration: 2465; Percent complete: 61.6%; Average loss: 2.9817
Iteration: 2466; Percent complete: 61.7%; Average loss: 3.0079
Iteration: 2467; Percent complete: 61.7%; Average loss: 3.4270
Iteration: 2468; Percent complete: 61.7%; Average loss: 2.8293
Iteration: 2469; Percent complete: 61.7%; Average loss: 2.9240
Iteration: 2470; Percent complete: 61.8%; Average loss: 2.8256
Iteration: 2471; Percent complete: 61.8%; Average loss: 2.9230
Iteration: 2472; Percent complete: 61.8%; Average loss: 3.1007
Iteration: 2473; Percent complete: 61.8%; Average loss: 3.1203
Iteration: 2474; Percent complete: 61.9%; Average loss: 3.2430
Iteration: 2475; Percent complete: 61.9%; Average loss: 3.1032
Iteration: 2476; Percent complete: 61.9%; Average loss: 2.9133
Iteration: 2477; Percent complete: 61.9%; Average loss: 2.9763
Iteration: 2478; Percent complete: 62.0%; Average loss: 3.1400
Iteration: 2479; Percent complete: 62.0%; Average loss: 2.9327
Iteration: 2480; Percent complete: 62.0%; Average loss: 2.9369
Iteration: 2481; Percent complete: 62.0%; Average loss: 3.1312
Iteration: 2482; Percent complete: 62.1%; Average loss: 3.0986
Iteration: 2483; Percent complete: 62.1%; Average loss: 3.0902
Iteration: 2484; Percent complete: 62.1%; Average loss: 3.1066
Iteration: 2485; Percent complete: 62.1%; Average loss: 3.2640
Iteration: 2486; Percent complete: 62.2%; Average loss: 3.0598
Iteration: 2487; Percent complete: 62.2%; Average loss: 3.0109
Iteration: 2488; Percent complete: 62.2%; Average loss: 3.0633
Iteration: 2489; Percent complete: 62.2%; Average loss: 2.7462
Iteration: 2490; Percent complete: 62.3%; Average loss: 3.2006
Iteration: 2491; Percent complete: 62.3%; Average loss: 3.1553
Iteration: 2492; Percent complete: 62.3%; Average loss: 2.8536
Iteration: 2493; Percent complete: 62.3%; Average loss: 2.9336
Iteration: 2494; Percent complete: 62.4%; Average loss: 3.0693
Iteration: 2495; Percent complete: 62.4%; Average loss: 2.9411
Iteration: 2496; Percent complete: 62.4%; Average loss: 2.8331
Iteration: 2497; Percent complete: 62.4%; Average loss: 3.0143
Iteration: 2498; Percent complete: 62.5%; Average loss: 2.7708
Iteration: 2499; Percent complete: 62.5%; Average loss: 2.9884
Iteration: 2500; Percent complete: 62.5%; Average loss: 3.0550
Iteration: 2501; Percent complete: 62.5%; Average loss: 3.1898
Iteration: 2502; Percent complete: 62.5%; Average loss: 2.9544
Iteration: 2503; Percent complete: 62.6%; Average loss: 3.1545
Iteration: 2504; Percent complete: 62.6%; Average loss: 2.8037
Iteration: 2505; Percent complete: 62.6%; Average loss: 3.1336
Iteration: 2506; Percent complete: 62.6%; Average loss: 3.1768
Iteration: 2507; Percent complete: 62.7%; Average loss: 2.9267
Iteration: 2508; Percent complete: 62.7%; Average loss: 2.9139
Iteration: 2509; Percent complete: 62.7%; Average loss: 3.0536
Iteration: 2510; Percent complete: 62.7%; Average loss: 2.7747
Iteration: 2511; Percent complete: 62.8%; Average loss: 3.1432
Iteration: 2512; Percent complete: 62.8%; Average loss: 2.9960
Iteration: 2513; Percent complete: 62.8%; Average loss: 2.8101
Iteration: 2514; Percent complete: 62.8%; Average loss: 3.2452
Iteration: 2515; Percent complete: 62.9%; Average loss: 2.9063
Iteration: 2516; Percent complete: 62.9%; Average loss: 3.3004
Iteration: 2517; Percent complete: 62.9%; Average loss: 3.3857
Iteration: 2518; Percent complete: 62.9%; Average loss: 2.9813
Iteration: 2519; Percent complete: 63.0%; Average loss: 3.1772
Iteration: 2520; Percent complete: 63.0%; Average loss: 2.9734
Iteration: 2521; Percent complete: 63.0%; Average loss: 2.9760
Iteration: 2522; Percent complete: 63.0%; Average loss: 2.9511
Iteration: 2523; Percent complete: 63.1%; Average loss: 2.8677
Iteration: 2524; Percent complete: 63.1%; Average loss: 3.2164
Iteration: 2525; Percent complete: 63.1%; Average loss: 2.8761
Iteration: 2526; Percent complete: 63.1%; Average loss: 3.1084
Iteration: 2527; Percent complete: 63.2%; Average loss: 2.8939
Iteration: 2528; Percent complete: 63.2%; Average loss: 3.0182
Iteration: 2529; Percent complete: 63.2%; Average loss: 2.9145
Iteration: 2530; Percent complete: 63.2%; Average loss: 3.0319
Iteration: 2531; Percent complete: 63.3%; Average loss: 2.9333
Iteration: 2532; Percent complete: 63.3%; Average loss: 3.0159
Iteration: 2533; Percent complete: 63.3%; Average loss: 3.0386
Iteration: 2534; Percent complete: 63.3%; Average loss: 2.8865
Iteration: 2535; Percent complete: 63.4%; Average loss: 2.9201
Iteration: 2536; Percent complete: 63.4%; Average loss: 2.9212
Iteration: 2537; Percent complete: 63.4%; Average loss: 3.0067
Iteration: 2538; Percent complete: 63.4%; Average loss: 2.8179
Iteration: 2539; Percent complete: 63.5%; Average loss: 3.0353
Iteration: 2540; Percent complete: 63.5%; Average loss: 2.9696
Iteration: 2541; Percent complete: 63.5%; Average loss: 2.8310
Iteration: 2542; Percent complete: 63.5%; Average loss: 3.1100
Iteration: 2543; Percent complete: 63.6%; Average loss: 2.6760
Iteration: 2544; Percent complete: 63.6%; Average loss: 2.9465
Iteration: 2545; Percent complete: 63.6%; Average loss: 3.0228
Iteration: 2546; Percent complete: 63.6%; Average loss: 3.0074
Iteration: 2547; Percent complete: 63.7%; Average loss: 2.8680
Iteration: 2548; Percent complete: 63.7%; Average loss: 2.9953
Iteration: 2549; Percent complete: 63.7%; Average loss: 3.1769
Iteration: 2550; Percent complete: 63.7%; Average loss: 2.7351
Iteration: 2551; Percent complete: 63.8%; Average loss: 2.8743
Iteration: 2552; Percent complete: 63.8%; Average loss: 3.1318
Iteration: 2553; Percent complete: 63.8%; Average loss: 3.1384
Iteration: 2554; Percent complete: 63.8%; Average loss: 2.9738
Iteration: 2555; Percent complete: 63.9%; Average loss: 3.2312
Iteration: 2556; Percent complete: 63.9%; Average loss: 2.8841
Iteration: 2557; Percent complete: 63.9%; Average loss: 2.8316
Iteration: 2558; Percent complete: 63.9%; Average loss: 3.2919
Iteration: 2559; Percent complete: 64.0%; Average loss: 2.9802
Iteration: 2560; Percent complete: 64.0%; Average loss: 3.0962
Iteration: 2561; Percent complete: 64.0%; Average loss: 3.1891
Iteration: 2562; Percent complete: 64.0%; Average loss: 3.1707
Iteration: 2563; Percent complete: 64.1%; Average loss: 3.0269
Iteration: 2564; Percent complete: 64.1%; Average loss: 2.8785
Iteration: 2565; Percent complete: 64.1%; Average loss: 3.0106
Iteration: 2566; Percent complete: 64.1%; Average loss: 2.8117
Iteration: 2567; Percent complete: 64.2%; Average loss: 3.1083
Iteration: 2568; Percent complete: 64.2%; Average loss: 3.2472
Iteration: 2569; Percent complete: 64.2%; Average loss: 2.9007
Iteration: 2570; Percent complete: 64.2%; Average loss: 3.2395
Iteration: 2571; Percent complete: 64.3%; Average loss: 2.8834
Iteration: 2572; Percent complete: 64.3%; Average loss: 3.0937
Iteration: 2573; Percent complete: 64.3%; Average loss: 2.9924
Iteration: 2574; Percent complete: 64.3%; Average loss: 2.9939
Iteration: 2575; Percent complete: 64.4%; Average loss: 2.7581
Iteration: 2576; Percent complete: 64.4%; Average loss: 3.0733
Iteration: 2577; Percent complete: 64.4%; Average loss: 3.0158
Iteration: 2578; Percent complete: 64.5%; Average loss: 3.1969
Iteration: 2579; Percent complete: 64.5%; Average loss: 3.1605
Iteration: 2580; Percent complete: 64.5%; Average loss: 2.8206
Iteration: 2581; Percent complete: 64.5%; Average loss: 2.9505
Iteration: 2582; Percent complete: 64.5%; Average loss: 2.6678
Iteration: 2583; Percent complete: 64.6%; Average loss: 2.8331
Iteration: 2584; Percent complete: 64.6%; Average loss: 3.0050
Iteration: 2585; Percent complete: 64.6%; Average loss: 2.9403
Iteration: 2586; Percent complete: 64.6%; Average loss: 3.0374
Iteration: 2587; Percent complete: 64.7%; Average loss: 3.2284
Iteration: 2588; Percent complete: 64.7%; Average loss: 2.7426
Iteration: 2589; Percent complete: 64.7%; Average loss: 3.0325
Iteration: 2590; Percent complete: 64.8%; Average loss: 2.9830
Iteration: 2591; Percent complete: 64.8%; Average loss: 2.8414
Iteration: 2592; Percent complete: 64.8%; Average loss: 3.1248
Iteration: 2593; Percent complete: 64.8%; Average loss: 3.0754
Iteration: 2594; Percent complete: 64.8%; Average loss: 3.0840
Iteration: 2595; Percent complete: 64.9%; Average loss: 2.9402
Iteration: 2596; Percent complete: 64.9%; Average loss: 3.0081
Iteration: 2597; Percent complete: 64.9%; Average loss: 3.1010
Iteration: 2598; Percent complete: 65.0%; Average loss: 3.0490
Iteration: 2599; Percent complete: 65.0%; Average loss: 3.0362
Iteration: 2600; Percent complete: 65.0%; Average loss: 2.9641
Iteration: 2601; Percent complete: 65.0%; Average loss: 3.0250
Iteration: 2602; Percent complete: 65.0%; Average loss: 2.9081
Iteration: 2603; Percent complete: 65.1%; Average loss: 3.0905
Iteration: 2604; Percent complete: 65.1%; Average loss: 3.0546
Iteration: 2605; Percent complete: 65.1%; Average loss: 3.0561
Iteration: 2606; Percent complete: 65.1%; Average loss: 3.0840
Iteration: 2607; Percent complete: 65.2%; Average loss: 2.9048
Iteration: 2608; Percent complete: 65.2%; Average loss: 2.9537
Iteration: 2609; Percent complete: 65.2%; Average loss: 3.0914
Iteration: 2610; Percent complete: 65.2%; Average loss: 2.8667
Iteration: 2611; Percent complete: 65.3%; Average loss: 2.9952
Iteration: 2612; Percent complete: 65.3%; Average loss: 2.9291
Iteration: 2613; Percent complete: 65.3%; Average loss: 3.0559
Iteration: 2614; Percent complete: 65.3%; Average loss: 3.0074
Iteration: 2615; Percent complete: 65.4%; Average loss: 2.9904
Iteration: 2616; Percent complete: 65.4%; Average loss: 2.9377
Iteration: 2617; Percent complete: 65.4%; Average loss: 2.9312
Iteration: 2618; Percent complete: 65.5%; Average loss: 2.8576
Iteration: 2619; Percent complete: 65.5%; Average loss: 2.9705
Iteration: 2620; Percent complete: 65.5%; Average loss: 2.9893
Iteration: 2621; Percent complete: 65.5%; Average loss: 2.9568
Iteration: 2622; Percent complete: 65.5%; Average loss: 3.1368
Iteration: 2623; Percent complete: 65.6%; Average loss: 3.0101
Iteration: 2624; Percent complete: 65.6%; Average loss: 2.9536
Iteration: 2625; Percent complete: 65.6%; Average loss: 3.1506
Iteration: 2626; Percent complete: 65.6%; Average loss: 2.9522
Iteration: 2627; Percent complete: 65.7%; Average loss: 3.0158
Iteration: 2628; Percent complete: 65.7%; Average loss: 3.0082
Iteration: 2629; Percent complete: 65.7%; Average loss: 3.0567
Iteration: 2630; Percent complete: 65.8%; Average loss: 3.1703
Iteration: 2631; Percent complete: 65.8%; Average loss: 3.0862
Iteration: 2632; Percent complete: 65.8%; Average loss: 3.0610
Iteration: 2633; Percent complete: 65.8%; Average loss: 3.0456
Iteration: 2634; Percent complete: 65.8%; Average loss: 2.9295
Iteration: 2635; Percent complete: 65.9%; Average loss: 3.1546
Iteration: 2636; Percent complete: 65.9%; Average loss: 2.8478
Iteration: 2637; Percent complete: 65.9%; Average loss: 3.0158
Iteration: 2638; Percent complete: 66.0%; Average loss: 3.2072
Iteration: 2639; Percent complete: 66.0%; Average loss: 2.7638
Iteration: 2640; Percent complete: 66.0%; Average loss: 3.1043
Iteration: 2641; Percent complete: 66.0%; Average loss: 3.0175
Iteration: 2642; Percent complete: 66.0%; Average loss: 3.1418
Iteration: 2643; Percent complete: 66.1%; Average loss: 3.0459
Iteration: 2644; Percent complete: 66.1%; Average loss: 3.0648
Iteration: 2645; Percent complete: 66.1%; Average loss: 3.0399
Iteration: 2646; Percent complete: 66.1%; Average loss: 3.0381
Iteration: 2647; Percent complete: 66.2%; Average loss: 2.9647
Iteration: 2648; Percent complete: 66.2%; Average loss: 2.7820
Iteration: 2649; Percent complete: 66.2%; Average loss: 2.9895
Iteration: 2650; Percent complete: 66.2%; Average loss: 3.0432
Iteration: 2651; Percent complete: 66.3%; Average loss: 2.9458
Iteration: 2652; Percent complete: 66.3%; Average loss: 2.7946
Iteration: 2653; Percent complete: 66.3%; Average loss: 2.9338
Iteration: 2654; Percent complete: 66.3%; Average loss: 2.8772
Iteration: 2655; Percent complete: 66.4%; Average loss: 2.8378
Iteration: 2656; Percent complete: 66.4%; Average loss: 3.1017
Iteration: 2657; Percent complete: 66.4%; Average loss: 3.0262
Iteration: 2658; Percent complete: 66.5%; Average loss: 3.1022
Iteration: 2659; Percent complete: 66.5%; Average loss: 2.8451
Iteration: 2660; Percent complete: 66.5%; Average loss: 2.9094
Iteration: 2661; Percent complete: 66.5%; Average loss: 2.6810
Iteration: 2662; Percent complete: 66.5%; Average loss: 3.2544
Iteration: 2663; Percent complete: 66.6%; Average loss: 3.0245
Iteration: 2664; Percent complete: 66.6%; Average loss: 2.9630
Iteration: 2665; Percent complete: 66.6%; Average loss: 2.9529
Iteration: 2666; Percent complete: 66.6%; Average loss: 3.1057
Iteration: 2667; Percent complete: 66.7%; Average loss: 3.1162
Iteration: 2668; Percent complete: 66.7%; Average loss: 2.8138
Iteration: 2669; Percent complete: 66.7%; Average loss: 3.0148
Iteration: 2670; Percent complete: 66.8%; Average loss: 2.9907
Iteration: 2671; Percent complete: 66.8%; Average loss: 2.7818
Iteration: 2672; Percent complete: 66.8%; Average loss: 3.1508
Iteration: 2673; Percent complete: 66.8%; Average loss: 2.8648
Iteration: 2674; Percent complete: 66.8%; Average loss: 2.9278
Iteration: 2675; Percent complete: 66.9%; Average loss: 2.9611
Iteration: 2676; Percent complete: 66.9%; Average loss: 2.8874
Iteration: 2677; Percent complete: 66.9%; Average loss: 3.1401
Iteration: 2678; Percent complete: 67.0%; Average loss: 2.9859
Iteration: 2679; Percent complete: 67.0%; Average loss: 3.2900
Iteration: 2680; Percent complete: 67.0%; Average loss: 3.0320
Iteration: 2681; Percent complete: 67.0%; Average loss: 3.0933
Iteration: 2682; Percent complete: 67.0%; Average loss: 2.9063
Iteration: 2683; Percent complete: 67.1%; Average loss: 2.8588
Iteration: 2684; Percent complete: 67.1%; Average loss: 2.8143
Iteration: 2685; Percent complete: 67.1%; Average loss: 3.0832
Iteration: 2686; Percent complete: 67.2%; Average loss: 3.0245
Iteration: 2687; Percent complete: 67.2%; Average loss: 2.9788
Iteration: 2688; Percent complete: 67.2%; Average loss: 3.1367
Iteration: 2689; Percent complete: 67.2%; Average loss: 3.1785
Iteration: 2690; Percent complete: 67.2%; Average loss: 2.9115
Iteration: 2691; Percent complete: 67.3%; Average loss: 2.8679
Iteration: 2692; Percent complete: 67.3%; Average loss: 2.9618
Iteration: 2693; Percent complete: 67.3%; Average loss: 2.5484
Iteration: 2694; Percent complete: 67.3%; Average loss: 3.0610
Iteration: 2695; Percent complete: 67.4%; Average loss: 3.0110
Iteration: 2696; Percent complete: 67.4%; Average loss: 2.7090
Iteration: 2697; Percent complete: 67.4%; Average loss: 3.1969
Iteration: 2698; Percent complete: 67.5%; Average loss: 3.1480
Iteration: 2699; Percent complete: 67.5%; Average loss: 3.1134
Iteration: 2700; Percent complete: 67.5%; Average loss: 3.0981
Iteration: 2701; Percent complete: 67.5%; Average loss: 2.7326
Iteration: 2702; Percent complete: 67.5%; Average loss: 3.0388
Iteration: 2703; Percent complete: 67.6%; Average loss: 2.9149
Iteration: 2704; Percent complete: 67.6%; Average loss: 2.9835
Iteration: 2705; Percent complete: 67.6%; Average loss: 2.9847
Iteration: 2706; Percent complete: 67.7%; Average loss: 2.9669
Iteration: 2707; Percent complete: 67.7%; Average loss: 3.0043
Iteration: 2708; Percent complete: 67.7%; Average loss: 2.8814
Iteration: 2709; Percent complete: 67.7%; Average loss: 3.0936
Iteration: 2710; Percent complete: 67.8%; Average loss: 3.0544
Iteration: 2711; Percent complete: 67.8%; Average loss: 3.2634
Iteration: 2712; Percent complete: 67.8%; Average loss: 3.1185
Iteration: 2713; Percent complete: 67.8%; Average loss: 2.8903
Iteration: 2714; Percent complete: 67.8%; Average loss: 3.2994
Iteration: 2715; Percent complete: 67.9%; Average loss: 2.9549
Iteration: 2716; Percent complete: 67.9%; Average loss: 2.9276
Iteration: 2717; Percent complete: 67.9%; Average loss: 2.8790
Iteration: 2718; Percent complete: 68.0%; Average loss: 2.9551
Iteration: 2719; Percent complete: 68.0%; Average loss: 2.8893
Iteration: 2720; Percent complete: 68.0%; Average loss: 3.1735
Iteration: 2721; Percent complete: 68.0%; Average loss: 2.9598
Iteration: 2722; Percent complete: 68.0%; Average loss: 2.9205
Iteration: 2723; Percent complete: 68.1%; Average loss: 2.8055
Iteration: 2724; Percent complete: 68.1%; Average loss: 3.1466
Iteration: 2725; Percent complete: 68.1%; Average loss: 2.7861
Iteration: 2726; Percent complete: 68.2%; Average loss: 3.1231
Iteration: 2727; Percent complete: 68.2%; Average loss: 3.0640
Iteration: 2728; Percent complete: 68.2%; Average loss: 2.8930
Iteration: 2729; Percent complete: 68.2%; Average loss: 3.3010
Iteration: 2730; Percent complete: 68.2%; Average loss: 2.9948
Iteration: 2731; Percent complete: 68.3%; Average loss: 2.9235
Iteration: 2732; Percent complete: 68.3%; Average loss: 2.9344
Iteration: 2733; Percent complete: 68.3%; Average loss: 2.8285
Iteration: 2734; Percent complete: 68.3%; Average loss: 3.1163
Iteration: 2735; Percent complete: 68.4%; Average loss: 2.9944
Iteration: 2736; Percent complete: 68.4%; Average loss: 2.8903
Iteration: 2737; Percent complete: 68.4%; Average loss: 2.8805
Iteration: 2738; Percent complete: 68.5%; Average loss: 2.8578
Iteration: 2739; Percent complete: 68.5%; Average loss: 2.7851
Iteration: 2740; Percent complete: 68.5%; Average loss: 2.9533
Iteration: 2741; Percent complete: 68.5%; Average loss: 3.1014
Iteration: 2742; Percent complete: 68.5%; Average loss: 3.0439
Iteration: 2743; Percent complete: 68.6%; Average loss: 2.9398
Iteration: 2744; Percent complete: 68.6%; Average loss: 3.0965
Iteration: 2745; Percent complete: 68.6%; Average loss: 2.9440
Iteration: 2746; Percent complete: 68.7%; Average loss: 2.9454
Iteration: 2747; Percent complete: 68.7%; Average loss: 2.9029
Iteration: 2748; Percent complete: 68.7%; Average loss: 3.0947
Iteration: 2749; Percent complete: 68.7%; Average loss: 2.9209
Iteration: 2750; Percent complete: 68.8%; Average loss: 3.0425
Iteration: 2751; Percent complete: 68.8%; Average loss: 2.7861
Iteration: 2752; Percent complete: 68.8%; Average loss: 3.0321
Iteration: 2753; Percent complete: 68.8%; Average loss: 2.7492
Iteration: 2754; Percent complete: 68.8%; Average loss: 3.0897
Iteration: 2755; Percent complete: 68.9%; Average loss: 2.8720
Iteration: 2756; Percent complete: 68.9%; Average loss: 2.7231
Iteration: 2757; Percent complete: 68.9%; Average loss: 3.2353
Iteration: 2758; Percent complete: 69.0%; Average loss: 2.9655
Iteration: 2759; Percent complete: 69.0%; Average loss: 3.0345
Iteration: 2760; Percent complete: 69.0%; Average loss: 2.7203
Iteration: 2761; Percent complete: 69.0%; Average loss: 3.1143
Iteration: 2762; Percent complete: 69.0%; Average loss: 3.1011
Iteration: 2763; Percent complete: 69.1%; Average loss: 2.9907
Iteration: 2764; Percent complete: 69.1%; Average loss: 2.7748
Iteration: 2765; Percent complete: 69.1%; Average loss: 3.2526
Iteration: 2766; Percent complete: 69.2%; Average loss: 2.9787
Iteration: 2767; Percent complete: 69.2%; Average loss: 3.0110
Iteration: 2768; Percent complete: 69.2%; Average loss: 3.0034
Iteration: 2769; Percent complete: 69.2%; Average loss: 2.7986
Iteration: 2770; Percent complete: 69.2%; Average loss: 2.7809
Iteration: 2771; Percent complete: 69.3%; Average loss: 3.0409
Iteration: 2772; Percent complete: 69.3%; Average loss: 3.0188
Iteration: 2773; Percent complete: 69.3%; Average loss: 2.7672
Iteration: 2774; Percent complete: 69.3%; Average loss: 3.0286
Iteration: 2775; Percent complete: 69.4%; Average loss: 3.0080
Iteration: 2776; Percent complete: 69.4%; Average loss: 2.9187
Iteration: 2777; Percent complete: 69.4%; Average loss: 2.8698
Iteration: 2778; Percent complete: 69.5%; Average loss: 2.8751
Iteration: 2779; Percent complete: 69.5%; Average loss: 2.9934
Iteration: 2780; Percent complete: 69.5%; Average loss: 2.9176
Iteration: 2781; Percent complete: 69.5%; Average loss: 2.8803
Iteration: 2782; Percent complete: 69.5%; Average loss: 2.9739
Iteration: 2783; Percent complete: 69.6%; Average loss: 2.7981
Iteration: 2784; Percent complete: 69.6%; Average loss: 2.9353
Iteration: 2785; Percent complete: 69.6%; Average loss: 2.8458
Iteration: 2786; Percent complete: 69.7%; Average loss: 2.6712
Iteration: 2787; Percent complete: 69.7%; Average loss: 3.0125
Iteration: 2788; Percent complete: 69.7%; Average loss: 2.9901
Iteration: 2789; Percent complete: 69.7%; Average loss: 3.0059
Iteration: 2790; Percent complete: 69.8%; Average loss: 3.0230
Iteration: 2791; Percent complete: 69.8%; Average loss: 2.9434
Iteration: 2792; Percent complete: 69.8%; Average loss: 2.9122
Iteration: 2793; Percent complete: 69.8%; Average loss: 2.8791
Iteration: 2794; Percent complete: 69.8%; Average loss: 2.8799
Iteration: 2795; Percent complete: 69.9%; Average loss: 3.0309
Iteration: 2796; Percent complete: 69.9%; Average loss: 2.9807
Iteration: 2797; Percent complete: 69.9%; Average loss: 3.0646
Iteration: 2798; Percent complete: 70.0%; Average loss: 3.1184
Iteration: 2799; Percent complete: 70.0%; Average loss: 2.9993
Iteration: 2800; Percent complete: 70.0%; Average loss: 3.0017
Iteration: 2801; Percent complete: 70.0%; Average loss: 2.7744
Iteration: 2802; Percent complete: 70.0%; Average loss: 2.7272
Iteration: 2803; Percent complete: 70.1%; Average loss: 3.2213
Iteration: 2804; Percent complete: 70.1%; Average loss: 2.8466
Iteration: 2805; Percent complete: 70.1%; Average loss: 3.0284
Iteration: 2806; Percent complete: 70.2%; Average loss: 2.9217
Iteration: 2807; Percent complete: 70.2%; Average loss: 2.9379
Iteration: 2808; Percent complete: 70.2%; Average loss: 2.6799
Iteration: 2809; Percent complete: 70.2%; Average loss: 2.7115
Iteration: 2810; Percent complete: 70.2%; Average loss: 3.0497
Iteration: 2811; Percent complete: 70.3%; Average loss: 3.0913
Iteration: 2812; Percent complete: 70.3%; Average loss: 3.0578
Iteration: 2813; Percent complete: 70.3%; Average loss: 3.0115
Iteration: 2814; Percent complete: 70.3%; Average loss: 3.0171
Iteration: 2815; Percent complete: 70.4%; Average loss: 2.9813
Iteration: 2816; Percent complete: 70.4%; Average loss: 2.9361
Iteration: 2817; Percent complete: 70.4%; Average loss: 2.7588
Iteration: 2818; Percent complete: 70.5%; Average loss: 2.8435
Iteration: 2819; Percent complete: 70.5%; Average loss: 2.9311
Iteration: 2820; Percent complete: 70.5%; Average loss: 2.7964
Iteration: 2821; Percent complete: 70.5%; Average loss: 3.0313
Iteration: 2822; Percent complete: 70.5%; Average loss: 2.9906
Iteration: 2823; Percent complete: 70.6%; Average loss: 3.0871
Iteration: 2824; Percent complete: 70.6%; Average loss: 2.9080
Iteration: 2825; Percent complete: 70.6%; Average loss: 3.1458
Iteration: 2826; Percent complete: 70.7%; Average loss: 2.8112
Iteration: 2827; Percent complete: 70.7%; Average loss: 2.7962
Iteration: 2828; Percent complete: 70.7%; Average loss: 3.0141
Iteration: 2829; Percent complete: 70.7%; Average loss: 2.5300
Iteration: 2830; Percent complete: 70.8%; Average loss: 2.7267
Iteration: 2831; Percent complete: 70.8%; Average loss: 2.9382
Iteration: 2832; Percent complete: 70.8%; Average loss: 2.9859
Iteration: 2833; Percent complete: 70.8%; Average loss: 2.9296
Iteration: 2834; Percent complete: 70.9%; Average loss: 2.8583
Iteration: 2835; Percent complete: 70.9%; Average loss: 3.0230
Iteration: 2836; Percent complete: 70.9%; Average loss: 3.1472
Iteration: 2837; Percent complete: 70.9%; Average loss: 2.8987
Iteration: 2838; Percent complete: 71.0%; Average loss: 2.9405
Iteration: 2839; Percent complete: 71.0%; Average loss: 2.8534
Iteration: 2840; Percent complete: 71.0%; Average loss: 2.8237
Iteration: 2841; Percent complete: 71.0%; Average loss: 2.9311
Iteration: 2842; Percent complete: 71.0%; Average loss: 2.9050
Iteration: 2843; Percent complete: 71.1%; Average loss: 2.8647
Iteration: 2844; Percent complete: 71.1%; Average loss: 2.8617
Iteration: 2845; Percent complete: 71.1%; Average loss: 2.9904
Iteration: 2846; Percent complete: 71.2%; Average loss: 2.9958
Iteration: 2847; Percent complete: 71.2%; Average loss: 3.0512
Iteration: 2848; Percent complete: 71.2%; Average loss: 3.0354
Iteration: 2849; Percent complete: 71.2%; Average loss: 2.9570
Iteration: 2850; Percent complete: 71.2%; Average loss: 3.1612
Iteration: 2851; Percent complete: 71.3%; Average loss: 2.7494
Iteration: 2852; Percent complete: 71.3%; Average loss: 3.0207
Iteration: 2853; Percent complete: 71.3%; Average loss: 3.1466
Iteration: 2854; Percent complete: 71.4%; Average loss: 2.9081
Iteration: 2855; Percent complete: 71.4%; Average loss: 2.8165
Iteration: 2856; Percent complete: 71.4%; Average loss: 2.7789
Iteration: 2857; Percent complete: 71.4%; Average loss: 2.8820
Iteration: 2858; Percent complete: 71.5%; Average loss: 2.9535
Iteration: 2859; Percent complete: 71.5%; Average loss: 2.9457
Iteration: 2860; Percent complete: 71.5%; Average loss: 3.0322
Iteration: 2861; Percent complete: 71.5%; Average loss: 2.7876
Iteration: 2862; Percent complete: 71.5%; Average loss: 2.9340
Iteration: 2863; Percent complete: 71.6%; Average loss: 2.9540
Iteration: 2864; Percent complete: 71.6%; Average loss: 3.1573
Iteration: 2865; Percent complete: 71.6%; Average loss: 2.9491
Iteration: 2866; Percent complete: 71.7%; Average loss: 2.5472
Iteration: 2867; Percent complete: 71.7%; Average loss: 2.9106
Iteration: 2868; Percent complete: 71.7%; Average loss: 2.7551
Iteration: 2869; Percent complete: 71.7%; Average loss: 2.9934
Iteration: 2870; Percent complete: 71.8%; Average loss: 2.8058
Iteration: 2871; Percent complete: 71.8%; Average loss: 2.9447
Iteration: 2872; Percent complete: 71.8%; Average loss: 2.9683
Iteration: 2873; Percent complete: 71.8%; Average loss: 2.8828
Iteration: 2874; Percent complete: 71.9%; Average loss: 2.8078
Iteration: 2875; Percent complete: 71.9%; Average loss: 3.2306
Iteration: 2876; Percent complete: 71.9%; Average loss: 2.9728
Iteration: 2877; Percent complete: 71.9%; Average loss: 3.0936
Iteration: 2878; Percent complete: 72.0%; Average loss: 3.1120
Iteration: 2879; Percent complete: 72.0%; Average loss: 2.7908
Iteration: 2880; Percent complete: 72.0%; Average loss: 2.9592
Iteration: 2881; Percent complete: 72.0%; Average loss: 3.0926
Iteration: 2882; Percent complete: 72.0%; Average loss: 2.7727
Iteration: 2883; Percent complete: 72.1%; Average loss: 2.8824
Iteration: 2884; Percent complete: 72.1%; Average loss: 2.8084
Iteration: 2885; Percent complete: 72.1%; Average loss: 2.8209
Iteration: 2886; Percent complete: 72.2%; Average loss: 2.9874
Iteration: 2887; Percent complete: 72.2%; Average loss: 2.9966
Iteration: 2888; Percent complete: 72.2%; Average loss: 2.9295
Iteration: 2889; Percent complete: 72.2%; Average loss: 2.7552
Iteration: 2890; Percent complete: 72.2%; Average loss: 2.8905
Iteration: 2891; Percent complete: 72.3%; Average loss: 3.0178
Iteration: 2892; Percent complete: 72.3%; Average loss: 2.9301
Iteration: 2893; Percent complete: 72.3%; Average loss: 2.9203
Iteration: 2894; Percent complete: 72.4%; Average loss: 2.9334
Iteration: 2895; Percent complete: 72.4%; Average loss: 2.7128
Iteration: 2896; Percent complete: 72.4%; Average loss: 2.7717
Iteration: 2897; Percent complete: 72.4%; Average loss: 2.8134
Iteration: 2898; Percent complete: 72.5%; Average loss: 2.8933
Iteration: 2899; Percent complete: 72.5%; Average loss: 3.0931
Iteration: 2900; Percent complete: 72.5%; Average loss: 3.0640
Iteration: 2901; Percent complete: 72.5%; Average loss: 2.8986
Iteration: 2902; Percent complete: 72.5%; Average loss: 2.8880
Iteration: 2903; Percent complete: 72.6%; Average loss: 2.8469
Iteration: 2904; Percent complete: 72.6%; Average loss: 2.8817
Iteration: 2905; Percent complete: 72.6%; Average loss: 2.9361
Iteration: 2906; Percent complete: 72.7%; Average loss: 2.8371
Iteration: 2907; Percent complete: 72.7%; Average loss: 2.5726
Iteration: 2908; Percent complete: 72.7%; Average loss: 2.8407
Iteration: 2909; Percent complete: 72.7%; Average loss: 3.0128
Iteration: 2910; Percent complete: 72.8%; Average loss: 2.8817
Iteration: 2911; Percent complete: 72.8%; Average loss: 3.0376
Iteration: 2912; Percent complete: 72.8%; Average loss: 2.8033
Iteration: 2913; Percent complete: 72.8%; Average loss: 3.0271
Iteration: 2914; Percent complete: 72.9%; Average loss: 2.7751
Iteration: 2915; Percent complete: 72.9%; Average loss: 2.9459
Iteration: 2916; Percent complete: 72.9%; Average loss: 2.8759
Iteration: 2917; Percent complete: 72.9%; Average loss: 2.6832
Iteration: 2918; Percent complete: 73.0%; Average loss: 2.9540
Iteration: 2919; Percent complete: 73.0%; Average loss: 2.8776
Iteration: 2920; Percent complete: 73.0%; Average loss: 3.0436
Iteration: 2921; Percent complete: 73.0%; Average loss: 3.0811
Iteration: 2922; Percent complete: 73.0%; Average loss: 2.9290
Iteration: 2923; Percent complete: 73.1%; Average loss: 2.8776
Iteration: 2924; Percent complete: 73.1%; Average loss: 2.9916
Iteration: 2925; Percent complete: 73.1%; Average loss: 2.8317
Iteration: 2926; Percent complete: 73.2%; Average loss: 3.0514
Iteration: 2927; Percent complete: 73.2%; Average loss: 2.7147
Iteration: 2928; Percent complete: 73.2%; Average loss: 2.5777
Iteration: 2929; Percent complete: 73.2%; Average loss: 2.9248
Iteration: 2930; Percent complete: 73.2%; Average loss: 2.8995
Iteration: 2931; Percent complete: 73.3%; Average loss: 2.8503
Iteration: 2932; Percent complete: 73.3%; Average loss: 2.6986
Iteration: 2933; Percent complete: 73.3%; Average loss: 2.8494
Iteration: 2934; Percent complete: 73.4%; Average loss: 2.7816
Iteration: 2935; Percent complete: 73.4%; Average loss: 2.8960
Iteration: 2936; Percent complete: 73.4%; Average loss: 3.0512
Iteration: 2937; Percent complete: 73.4%; Average loss: 3.0632
Iteration: 2938; Percent complete: 73.5%; Average loss: 2.9026
Iteration: 2939; Percent complete: 73.5%; Average loss: 2.7875
Iteration: 2940; Percent complete: 73.5%; Average loss: 2.8825
Iteration: 2941; Percent complete: 73.5%; Average loss: 2.9505
Iteration: 2942; Percent complete: 73.6%; Average loss: 2.9525
Iteration: 2943; Percent complete: 73.6%; Average loss: 3.0956
Iteration: 2944; Percent complete: 73.6%; Average loss: 3.0059
Iteration: 2945; Percent complete: 73.6%; Average loss: 3.0007
Iteration: 2946; Percent complete: 73.7%; Average loss: 2.7670
Iteration: 2947; Percent complete: 73.7%; Average loss: 2.7642
Iteration: 2948; Percent complete: 73.7%; Average loss: 2.7365
Iteration: 2949; Percent complete: 73.7%; Average loss: 3.1075
Iteration: 2950; Percent complete: 73.8%; Average loss: 3.0186
Iteration: 2951; Percent complete: 73.8%; Average loss: 2.8008
Iteration: 2952; Percent complete: 73.8%; Average loss: 3.0274
Iteration: 2953; Percent complete: 73.8%; Average loss: 2.7997
Iteration: 2954; Percent complete: 73.9%; Average loss: 3.1963
Iteration: 2955; Percent complete: 73.9%; Average loss: 2.8819
Iteration: 2956; Percent complete: 73.9%; Average loss: 2.9397
Iteration: 2957; Percent complete: 73.9%; Average loss: 2.9758
Iteration: 2958; Percent complete: 74.0%; Average loss: 2.9328
Iteration: 2959; Percent complete: 74.0%; Average loss: 2.8078
Iteration: 2960; Percent complete: 74.0%; Average loss: 2.8192
Iteration: 2961; Percent complete: 74.0%; Average loss: 2.8084
Iteration: 2962; Percent complete: 74.1%; Average loss: 2.8409
Iteration: 2963; Percent complete: 74.1%; Average loss: 3.0084
Iteration: 2964; Percent complete: 74.1%; Average loss: 2.5673
Iteration: 2965; Percent complete: 74.1%; Average loss: 2.9088
Iteration: 2966; Percent complete: 74.2%; Average loss: 2.6437
Iteration: 2967; Percent complete: 74.2%; Average loss: 2.8565
Iteration: 2968; Percent complete: 74.2%; Average loss: 2.9086
Iteration: 2969; Percent complete: 74.2%; Average loss: 2.8194
Iteration: 2970; Percent complete: 74.2%; Average loss: 2.6754
Iteration: 2971; Percent complete: 74.3%; Average loss: 2.7315
Iteration: 2972; Percent complete: 74.3%; Average loss: 2.8775
Iteration: 2973; Percent complete: 74.3%; Average loss: 3.0794
Iteration: 2974; Percent complete: 74.4%; Average loss: 2.6972
Iteration: 2975; Percent complete: 74.4%; Average loss: 2.8058
Iteration: 2976; Percent complete: 74.4%; Average loss: 2.7932
Iteration: 2977; Percent complete: 74.4%; Average loss: 2.9181
Iteration: 2978; Percent complete: 74.5%; Average loss: 2.8541
Iteration: 2979; Percent complete: 74.5%; Average loss: 2.9998
Iteration: 2980; Percent complete: 74.5%; Average loss: 2.8910
Iteration: 2981; Percent complete: 74.5%; Average loss: 2.8405
Iteration: 2982; Percent complete: 74.6%; Average loss: 2.8808
Iteration: 2983; Percent complete: 74.6%; Average loss: 3.0889
Iteration: 2984; Percent complete: 74.6%; Average loss: 3.0336
Iteration: 2985; Percent complete: 74.6%; Average loss: 3.1317
Iteration: 2986; Percent complete: 74.7%; Average loss: 2.8900
Iteration: 2987; Percent complete: 74.7%; Average loss: 3.1773
Iteration: 2988; Percent complete: 74.7%; Average loss: 2.8046
Iteration: 2989; Percent complete: 74.7%; Average loss: 2.6094
Iteration: 2990; Percent complete: 74.8%; Average loss: 2.7869
Iteration: 2991; Percent complete: 74.8%; Average loss: 2.8432
Iteration: 2992; Percent complete: 74.8%; Average loss: 2.7649
Iteration: 2993; Percent complete: 74.8%; Average loss: 2.9115
Iteration: 2994; Percent complete: 74.9%; Average loss: 2.9994
Iteration: 2995; Percent complete: 74.9%; Average loss: 2.8070
Iteration: 2996; Percent complete: 74.9%; Average loss: 2.7367
Iteration: 2997; Percent complete: 74.9%; Average loss: 2.6713
Iteration: 2998; Percent complete: 75.0%; Average loss: 2.5859
Iteration: 2999; Percent complete: 75.0%; Average loss: 2.8226
Iteration: 3000; Percent complete: 75.0%; Average loss: 2.7015
Iteration: 3001; Percent complete: 75.0%; Average loss: 2.7381
Iteration: 3002; Percent complete: 75.0%; Average loss: 2.9083
Iteration: 3003; Percent complete: 75.1%; Average loss: 2.8188
Iteration: 3004; Percent complete: 75.1%; Average loss: 2.7801
Iteration: 3005; Percent complete: 75.1%; Average loss: 2.9781
Iteration: 3006; Percent complete: 75.1%; Average loss: 3.0231
Iteration: 3007; Percent complete: 75.2%; Average loss: 2.9962
Iteration: 3008; Percent complete: 75.2%; Average loss: 2.8886
Iteration: 3009; Percent complete: 75.2%; Average loss: 2.5240
Iteration: 3010; Percent complete: 75.2%; Average loss: 2.7736
Iteration: 3011; Percent complete: 75.3%; Average loss: 2.7957
Iteration: 3012; Percent complete: 75.3%; Average loss: 3.0843
Iteration: 3013; Percent complete: 75.3%; Average loss: 2.8968
Iteration: 3014; Percent complete: 75.3%; Average loss: 2.9551
Iteration: 3015; Percent complete: 75.4%; Average loss: 2.7721
Iteration: 3016; Percent complete: 75.4%; Average loss: 2.8766
Iteration: 3017; Percent complete: 75.4%; Average loss: 2.6821
Iteration: 3018; Percent complete: 75.4%; Average loss: 2.6843
Iteration: 3019; Percent complete: 75.5%; Average loss: 2.8190
Iteration: 3020; Percent complete: 75.5%; Average loss: 2.8098
Iteration: 3021; Percent complete: 75.5%; Average loss: 3.1656
Iteration: 3022; Percent complete: 75.5%; Average loss: 2.8041
Iteration: 3023; Percent complete: 75.6%; Average loss: 2.9432
Iteration: 3024; Percent complete: 75.6%; Average loss: 2.9830
Iteration: 3025; Percent complete: 75.6%; Average loss: 3.0560
Iteration: 3026; Percent complete: 75.6%; Average loss: 2.9874
Iteration: 3027; Percent complete: 75.7%; Average loss: 2.6105
Iteration: 3028; Percent complete: 75.7%; Average loss: 2.8284
Iteration: 3029; Percent complete: 75.7%; Average loss: 2.9814
Iteration: 3030; Percent complete: 75.8%; Average loss: 2.7916
Iteration: 3031; Percent complete: 75.8%; Average loss: 2.7196
Iteration: 3032; Percent complete: 75.8%; Average loss: 2.7273
Iteration: 3033; Percent complete: 75.8%; Average loss: 2.6429
Iteration: 3034; Percent complete: 75.8%; Average loss: 2.7255
Iteration: 3035; Percent complete: 75.9%; Average loss: 2.7606
Iteration: 3036; Percent complete: 75.9%; Average loss: 2.7995
Iteration: 3037; Percent complete: 75.9%; Average loss: 2.9565
Iteration: 3038; Percent complete: 75.9%; Average loss: 2.8126
Iteration: 3039; Percent complete: 76.0%; Average loss: 2.7887
Iteration: 3040; Percent complete: 76.0%; Average loss: 2.9927
Iteration: 3041; Percent complete: 76.0%; Average loss: 2.6409
Iteration: 3042; Percent complete: 76.0%; Average loss: 2.9580
Iteration: 3043; Percent complete: 76.1%; Average loss: 3.0461
Iteration: 3044; Percent complete: 76.1%; Average loss: 2.9646
Iteration: 3045; Percent complete: 76.1%; Average loss: 2.9959
Iteration: 3046; Percent complete: 76.1%; Average loss: 3.1347
Iteration: 3047; Percent complete: 76.2%; Average loss: 2.9165
Iteration: 3048; Percent complete: 76.2%; Average loss: 2.7096
Iteration: 3049; Percent complete: 76.2%; Average loss: 2.9094
Iteration: 3050; Percent complete: 76.2%; Average loss: 2.8496
Iteration: 3051; Percent complete: 76.3%; Average loss: 2.9305
Iteration: 3052; Percent complete: 76.3%; Average loss: 2.8628
Iteration: 3053; Percent complete: 76.3%; Average loss: 3.0088
Iteration: 3054; Percent complete: 76.3%; Average loss: 3.1082
Iteration: 3055; Percent complete: 76.4%; Average loss: 2.9901
Iteration: 3056; Percent complete: 76.4%; Average loss: 2.8606
Iteration: 3057; Percent complete: 76.4%; Average loss: 2.7178
Iteration: 3058; Percent complete: 76.4%; Average loss: 2.8787
Iteration: 3059; Percent complete: 76.5%; Average loss: 2.8201
Iteration: 3060; Percent complete: 76.5%; Average loss: 2.8443
Iteration: 3061; Percent complete: 76.5%; Average loss: 2.8044
Iteration: 3062; Percent complete: 76.5%; Average loss: 2.8128
Iteration: 3063; Percent complete: 76.6%; Average loss: 2.8299
Iteration: 3064; Percent complete: 76.6%; Average loss: 3.0912
Iteration: 3065; Percent complete: 76.6%; Average loss: 2.8984
Iteration: 3066; Percent complete: 76.6%; Average loss: 2.7299
Iteration: 3067; Percent complete: 76.7%; Average loss: 2.6317
Iteration: 3068; Percent complete: 76.7%; Average loss: 2.8912
Iteration: 3069; Percent complete: 76.7%; Average loss: 2.7194
Iteration: 3070; Percent complete: 76.8%; Average loss: 2.7780
Iteration: 3071; Percent complete: 76.8%; Average loss: 2.7974
Iteration: 3072; Percent complete: 76.8%; Average loss: 2.8069
Iteration: 3073; Percent complete: 76.8%; Average loss: 2.7449
Iteration: 3074; Percent complete: 76.8%; Average loss: 2.9873
Iteration: 3075; Percent complete: 76.9%; Average loss: 2.9732
Iteration: 3076; Percent complete: 76.9%; Average loss: 2.8369
Iteration: 3077; Percent complete: 76.9%; Average loss: 2.9258
Iteration: 3078; Percent complete: 77.0%; Average loss: 2.7291
Iteration: 3079; Percent complete: 77.0%; Average loss: 2.8829
Iteration: 3080; Percent complete: 77.0%; Average loss: 2.8475
Iteration: 3081; Percent complete: 77.0%; Average loss: 2.8776
Iteration: 3082; Percent complete: 77.0%; Average loss: 2.6453
Iteration: 3083; Percent complete: 77.1%; Average loss: 2.8990
Iteration: 3084; Percent complete: 77.1%; Average loss: 2.7734
Iteration: 3085; Percent complete: 77.1%; Average loss: 3.0384
Iteration: 3086; Percent complete: 77.1%; Average loss: 2.7759
Iteration: 3087; Percent complete: 77.2%; Average loss: 3.0528
Iteration: 3088; Percent complete: 77.2%; Average loss: 2.8965
Iteration: 3089; Percent complete: 77.2%; Average loss: 3.0499
Iteration: 3090; Percent complete: 77.2%; Average loss: 2.9636
Iteration: 3091; Percent complete: 77.3%; Average loss: 2.9518
Iteration: 3092; Percent complete: 77.3%; Average loss: 2.9190
Iteration: 3093; Percent complete: 77.3%; Average loss: 2.8937
Iteration: 3094; Percent complete: 77.3%; Average loss: 2.7226
Iteration: 3095; Percent complete: 77.4%; Average loss: 2.7756
Iteration: 3096; Percent complete: 77.4%; Average loss: 2.9230
Iteration: 3097; Percent complete: 77.4%; Average loss: 3.2125
Iteration: 3098; Percent complete: 77.5%; Average loss: 2.7824
Iteration: 3099; Percent complete: 77.5%; Average loss: 2.8364
Iteration: 3100; Percent complete: 77.5%; Average loss: 3.0364
Iteration: 3101; Percent complete: 77.5%; Average loss: 2.6541
Iteration: 3102; Percent complete: 77.5%; Average loss: 2.8709
Iteration: 3103; Percent complete: 77.6%; Average loss: 2.8732
Iteration: 3104; Percent complete: 77.6%; Average loss: 2.8881
Iteration: 3105; Percent complete: 77.6%; Average loss: 3.0576
Iteration: 3106; Percent complete: 77.6%; Average loss: 2.8582
Iteration: 3107; Percent complete: 77.7%; Average loss: 2.7329
Iteration: 3108; Percent complete: 77.7%; Average loss: 2.7915
Iteration: 3109; Percent complete: 77.7%; Average loss: 2.9426
Iteration: 3110; Percent complete: 77.8%; Average loss: 2.8483
Iteration: 3111; Percent complete: 77.8%; Average loss: 2.9074
Iteration: 3112; Percent complete: 77.8%; Average loss: 2.8087
Iteration: 3113; Percent complete: 77.8%; Average loss: 2.7729
Iteration: 3114; Percent complete: 77.8%; Average loss: 2.8307
Iteration: 3115; Percent complete: 77.9%; Average loss: 2.9422
Iteration: 3116; Percent complete: 77.9%; Average loss: 2.7535
Iteration: 3117; Percent complete: 77.9%; Average loss: 2.6841
Iteration: 3118; Percent complete: 78.0%; Average loss: 2.8045
Iteration: 3119; Percent complete: 78.0%; Average loss: 2.7181
Iteration: 3120; Percent complete: 78.0%; Average loss: 2.8795
Iteration: 3121; Percent complete: 78.0%; Average loss: 2.9614
Iteration: 3122; Percent complete: 78.0%; Average loss: 2.8599
Iteration: 3123; Percent complete: 78.1%; Average loss: 2.8303
Iteration: 3124; Percent complete: 78.1%; Average loss: 2.5143
Iteration: 3125; Percent complete: 78.1%; Average loss: 2.8496
Iteration: 3126; Percent complete: 78.1%; Average loss: 2.7492
Iteration: 3127; Percent complete: 78.2%; Average loss: 2.8098
Iteration: 3128; Percent complete: 78.2%; Average loss: 2.9344
Iteration: 3129; Percent complete: 78.2%; Average loss: 2.7881
Iteration: 3130; Percent complete: 78.2%; Average loss: 2.8747
Iteration: 3131; Percent complete: 78.3%; Average loss: 3.1136
Iteration: 3132; Percent complete: 78.3%; Average loss: 2.7921
Iteration: 3133; Percent complete: 78.3%; Average loss: 2.6711
Iteration: 3134; Percent complete: 78.3%; Average loss: 2.9630
Iteration: 3135; Percent complete: 78.4%; Average loss: 2.6673
Iteration: 3136; Percent complete: 78.4%; Average loss: 2.7652
Iteration: 3137; Percent complete: 78.4%; Average loss: 2.5886
Iteration: 3138; Percent complete: 78.5%; Average loss: 2.7128
Iteration: 3139; Percent complete: 78.5%; Average loss: 2.8483
Iteration: 3140; Percent complete: 78.5%; Average loss: 2.5342
Iteration: 3141; Percent complete: 78.5%; Average loss: 3.0166
Iteration: 3142; Percent complete: 78.5%; Average loss: 2.6609
Iteration: 3143; Percent complete: 78.6%; Average loss: 3.1262
Iteration: 3144; Percent complete: 78.6%; Average loss: 2.6434
Iteration: 3145; Percent complete: 78.6%; Average loss: 2.9157
Iteration: 3146; Percent complete: 78.6%; Average loss: 2.7461
Iteration: 3147; Percent complete: 78.7%; Average loss: 2.8439
Iteration: 3148; Percent complete: 78.7%; Average loss: 2.7295
Iteration: 3149; Percent complete: 78.7%; Average loss: 2.9227
Iteration: 3150; Percent complete: 78.8%; Average loss: 2.7177
Iteration: 3151; Percent complete: 78.8%; Average loss: 2.8466
Iteration: 3152; Percent complete: 78.8%; Average loss: 2.6176
Iteration: 3153; Percent complete: 78.8%; Average loss: 2.8701
Iteration: 3154; Percent complete: 78.8%; Average loss: 2.8263
Iteration: 3155; Percent complete: 78.9%; Average loss: 2.8062
Iteration: 3156; Percent complete: 78.9%; Average loss: 2.9029
Iteration: 3157; Percent complete: 78.9%; Average loss: 2.8406
Iteration: 3158; Percent complete: 79.0%; Average loss: 2.6828
Iteration: 3159; Percent complete: 79.0%; Average loss: 2.7595
Iteration: 3160; Percent complete: 79.0%; Average loss: 2.7032
Iteration: 3161; Percent complete: 79.0%; Average loss: 3.0736
Iteration: 3162; Percent complete: 79.0%; Average loss: 2.8425
Iteration: 3163; Percent complete: 79.1%; Average loss: 2.7719
Iteration: 3164; Percent complete: 79.1%; Average loss: 2.6604
Iteration: 3165; Percent complete: 79.1%; Average loss: 2.9528
Iteration: 3166; Percent complete: 79.1%; Average loss: 3.0029
Iteration: 3167; Percent complete: 79.2%; Average loss: 2.7034
Iteration: 3168; Percent complete: 79.2%; Average loss: 2.9442
Iteration: 3169; Percent complete: 79.2%; Average loss: 2.9680
Iteration: 3170; Percent complete: 79.2%; Average loss: 2.5501
Iteration: 3171; Percent complete: 79.3%; Average loss: 3.0925
Iteration: 3172; Percent complete: 79.3%; Average loss: 2.8903
Iteration: 3173; Percent complete: 79.3%; Average loss: 2.6854
Iteration: 3174; Percent complete: 79.3%; Average loss: 2.8035
Iteration: 3175; Percent complete: 79.4%; Average loss: 2.8836
Iteration: 3176; Percent complete: 79.4%; Average loss: 2.7647
Iteration: 3177; Percent complete: 79.4%; Average loss: 2.7107
Iteration: 3178; Percent complete: 79.5%; Average loss: 2.9629
Iteration: 3179; Percent complete: 79.5%; Average loss: 2.7905
Iteration: 3180; Percent complete: 79.5%; Average loss: 2.5411
Iteration: 3181; Percent complete: 79.5%; Average loss: 2.7613
Iteration: 3182; Percent complete: 79.5%; Average loss: 2.7970
Iteration: 3183; Percent complete: 79.6%; Average loss: 2.8214
Iteration: 3184; Percent complete: 79.6%; Average loss: 2.6981
Iteration: 3185; Percent complete: 79.6%; Average loss: 2.9052
Iteration: 3186; Percent complete: 79.7%; Average loss: 2.8526
Iteration: 3187; Percent complete: 79.7%; Average loss: 2.9277
Iteration: 3188; Percent complete: 79.7%; Average loss: 2.7859
Iteration: 3189; Percent complete: 79.7%; Average loss: 3.0111
Iteration: 3190; Percent complete: 79.8%; Average loss: 2.9348
Iteration: 3191; Percent complete: 79.8%; Average loss: 2.6491
Iteration: 3192; Percent complete: 79.8%; Average loss: 2.6707
Iteration: 3193; Percent complete: 79.8%; Average loss: 2.9718
Iteration: 3194; Percent complete: 79.8%; Average loss: 2.9811
Iteration: 3195; Percent complete: 79.9%; Average loss: 2.9847
Iteration: 3196; Percent complete: 79.9%; Average loss: 2.8395
Iteration: 3197; Percent complete: 79.9%; Average loss: 2.6672
Iteration: 3198; Percent complete: 80.0%; Average loss: 2.9240
Iteration: 3199; Percent complete: 80.0%; Average loss: 2.7829
Iteration: 3200; Percent complete: 80.0%; Average loss: 2.5758
Iteration: 3201; Percent complete: 80.0%; Average loss: 2.8378
Iteration: 3202; Percent complete: 80.0%; Average loss: 2.7907
Iteration: 3203; Percent complete: 80.1%; Average loss: 2.6896
Iteration: 3204; Percent complete: 80.1%; Average loss: 2.5914
Iteration: 3205; Percent complete: 80.1%; Average loss: 2.7791
Iteration: 3206; Percent complete: 80.2%; Average loss: 2.8500
Iteration: 3207; Percent complete: 80.2%; Average loss: 2.8785
Iteration: 3208; Percent complete: 80.2%; Average loss: 2.8863
Iteration: 3209; Percent complete: 80.2%; Average loss: 2.7474
Iteration: 3210; Percent complete: 80.2%; Average loss: 2.8443
Iteration: 3211; Percent complete: 80.3%; Average loss: 3.0470
Iteration: 3212; Percent complete: 80.3%; Average loss: 2.9429
Iteration: 3213; Percent complete: 80.3%; Average loss: 2.9490
Iteration: 3214; Percent complete: 80.3%; Average loss: 2.6076
Iteration: 3215; Percent complete: 80.4%; Average loss: 2.9077
Iteration: 3216; Percent complete: 80.4%; Average loss: 2.8056
Iteration: 3217; Percent complete: 80.4%; Average loss: 2.9016
Iteration: 3218; Percent complete: 80.5%; Average loss: 3.1293
Iteration: 3219; Percent complete: 80.5%; Average loss: 2.9415
Iteration: 3220; Percent complete: 80.5%; Average loss: 2.7890
Iteration: 3221; Percent complete: 80.5%; Average loss: 2.8775
Iteration: 3222; Percent complete: 80.5%; Average loss: 2.8837
Iteration: 3223; Percent complete: 80.6%; Average loss: 2.9138
Iteration: 3224; Percent complete: 80.6%; Average loss: 2.8397
Iteration: 3225; Percent complete: 80.6%; Average loss: 2.6286
Iteration: 3226; Percent complete: 80.7%; Average loss: 2.7653
Iteration: 3227; Percent complete: 80.7%; Average loss: 2.7447
Iteration: 3228; Percent complete: 80.7%; Average loss: 2.9817
Iteration: 3229; Percent complete: 80.7%; Average loss: 2.7029
Iteration: 3230; Percent complete: 80.8%; Average loss: 3.0454
Iteration: 3231; Percent complete: 80.8%; Average loss: 2.7350
Iteration: 3232; Percent complete: 80.8%; Average loss: 2.8496
Iteration: 3233; Percent complete: 80.8%; Average loss: 2.7439
Iteration: 3234; Percent complete: 80.8%; Average loss: 2.8982
Iteration: 3235; Percent complete: 80.9%; Average loss: 2.8579
Iteration: 3236; Percent complete: 80.9%; Average loss: 2.9720
Iteration: 3237; Percent complete: 80.9%; Average loss: 2.6787
Iteration: 3238; Percent complete: 81.0%; Average loss: 2.8996
Iteration: 3239; Percent complete: 81.0%; Average loss: 2.8800
Iteration: 3240; Percent complete: 81.0%; Average loss: 2.9137
Iteration: 3241; Percent complete: 81.0%; Average loss: 2.8923
Iteration: 3242; Percent complete: 81.0%; Average loss: 2.7395
Iteration: 3243; Percent complete: 81.1%; Average loss: 2.8752
Iteration: 3244; Percent complete: 81.1%; Average loss: 2.7054
Iteration: 3245; Percent complete: 81.1%; Average loss: 2.6123
Iteration: 3246; Percent complete: 81.2%; Average loss: 2.8562
Iteration: 3247; Percent complete: 81.2%; Average loss: 2.8653
Iteration: 3248; Percent complete: 81.2%; Average loss: 2.6831
Iteration: 3249; Percent complete: 81.2%; Average loss: 2.6068
Iteration: 3250; Percent complete: 81.2%; Average loss: 2.7756
Iteration: 3251; Percent complete: 81.3%; Average loss: 2.9318
Iteration: 3252; Percent complete: 81.3%; Average loss: 2.7721
Iteration: 3253; Percent complete: 81.3%; Average loss: 2.8475
Iteration: 3254; Percent complete: 81.3%; Average loss: 2.7224
Iteration: 3255; Percent complete: 81.4%; Average loss: 2.9036
Iteration: 3256; Percent complete: 81.4%; Average loss: 2.9034
Iteration: 3257; Percent complete: 81.4%; Average loss: 2.7053
Iteration: 3258; Percent complete: 81.5%; Average loss: 2.7347
Iteration: 3259; Percent complete: 81.5%; Average loss: 2.7952
Iteration: 3260; Percent complete: 81.5%; Average loss: 2.6298
Iteration: 3261; Percent complete: 81.5%; Average loss: 2.8326
Iteration: 3262; Percent complete: 81.5%; Average loss: 2.6009
Iteration: 3263; Percent complete: 81.6%; Average loss: 2.7008
Iteration: 3264; Percent complete: 81.6%; Average loss: 2.7886
Iteration: 3265; Percent complete: 81.6%; Average loss: 2.7786
Iteration: 3266; Percent complete: 81.7%; Average loss: 2.9574
Iteration: 3267; Percent complete: 81.7%; Average loss: 2.9114
Iteration: 3268; Percent complete: 81.7%; Average loss: 2.8342
Iteration: 3269; Percent complete: 81.7%; Average loss: 2.8008
Iteration: 3270; Percent complete: 81.8%; Average loss: 2.4973
Iteration: 3271; Percent complete: 81.8%; Average loss: 2.9349
Iteration: 3272; Percent complete: 81.8%; Average loss: 2.6986
Iteration: 3273; Percent complete: 81.8%; Average loss: 2.9578
Iteration: 3274; Percent complete: 81.8%; Average loss: 3.0188
Iteration: 3275; Percent complete: 81.9%; Average loss: 3.0854
Iteration: 3276; Percent complete: 81.9%; Average loss: 2.8046
Iteration: 3277; Percent complete: 81.9%; Average loss: 3.0229
Iteration: 3278; Percent complete: 82.0%; Average loss: 2.6934
Iteration: 3279; Percent complete: 82.0%; Average loss: 2.8060
Iteration: 3280; Percent complete: 82.0%; Average loss: 2.8491
Iteration: 3281; Percent complete: 82.0%; Average loss: 2.8540
Iteration: 3282; Percent complete: 82.0%; Average loss: 2.8653
Iteration: 3283; Percent complete: 82.1%; Average loss: 2.7400
Iteration: 3284; Percent complete: 82.1%; Average loss: 2.7432
Iteration: 3285; Percent complete: 82.1%; Average loss: 2.7992
Iteration: 3286; Percent complete: 82.2%; Average loss: 2.6936
Iteration: 3287; Percent complete: 82.2%; Average loss: 2.8680
Iteration: 3288; Percent complete: 82.2%; Average loss: 2.7779
Iteration: 3289; Percent complete: 82.2%; Average loss: 2.7876
Iteration: 3290; Percent complete: 82.2%; Average loss: 2.5646
Iteration: 3291; Percent complete: 82.3%; Average loss: 2.5977
Iteration: 3292; Percent complete: 82.3%; Average loss: 2.8811
Iteration: 3293; Percent complete: 82.3%; Average loss: 3.0521
Iteration: 3294; Percent complete: 82.3%; Average loss: 2.7118
Iteration: 3295; Percent complete: 82.4%; Average loss: 2.6727
Iteration: 3296; Percent complete: 82.4%; Average loss: 2.8336
Iteration: 3297; Percent complete: 82.4%; Average loss: 2.8010
Iteration: 3298; Percent complete: 82.5%; Average loss: 2.8640
Iteration: 3299; Percent complete: 82.5%; Average loss: 2.7904
Iteration: 3300; Percent complete: 82.5%; Average loss: 2.7996
Iteration: 3301; Percent complete: 82.5%; Average loss: 2.8121
Iteration: 3302; Percent complete: 82.5%; Average loss: 2.8190
Iteration: 3303; Percent complete: 82.6%; Average loss: 2.9048
Iteration: 3304; Percent complete: 82.6%; Average loss: 2.6760
Iteration: 3305; Percent complete: 82.6%; Average loss: 2.8694
Iteration: 3306; Percent complete: 82.7%; Average loss: 2.9507
Iteration: 3307; Percent complete: 82.7%; Average loss: 3.0878
Iteration: 3308; Percent complete: 82.7%; Average loss: 2.9591
Iteration: 3309; Percent complete: 82.7%; Average loss: 2.7150
Iteration: 3310; Percent complete: 82.8%; Average loss: 2.6661
Iteration: 3311; Percent complete: 82.8%; Average loss: 2.7951
Iteration: 3312; Percent complete: 82.8%; Average loss: 2.8075
Iteration: 3313; Percent complete: 82.8%; Average loss: 2.7214
Iteration: 3314; Percent complete: 82.8%; Average loss: 2.9837
Iteration: 3315; Percent complete: 82.9%; Average loss: 2.8049
Iteration: 3316; Percent complete: 82.9%; Average loss: 3.0565
Iteration: 3317; Percent complete: 82.9%; Average loss: 2.7845
Iteration: 3318; Percent complete: 83.0%; Average loss: 2.7213
Iteration: 3319; Percent complete: 83.0%; Average loss: 2.8859
Iteration: 3320; Percent complete: 83.0%; Average loss: 2.6855
Iteration: 3321; Percent complete: 83.0%; Average loss: 2.7984
Iteration: 3322; Percent complete: 83.0%; Average loss: 2.8549
Iteration: 3323; Percent complete: 83.1%; Average loss: 2.9299
Iteration: 3324; Percent complete: 83.1%; Average loss: 2.8721
Iteration: 3325; Percent complete: 83.1%; Average loss: 2.9187
Iteration: 3326; Percent complete: 83.2%; Average loss: 2.8096
Iteration: 3327; Percent complete: 83.2%; Average loss: 2.9007
Iteration: 3328; Percent complete: 83.2%; Average loss: 2.4951
Iteration: 3329; Percent complete: 83.2%; Average loss: 2.7645
Iteration: 3330; Percent complete: 83.2%; Average loss: 2.8197
Iteration: 3331; Percent complete: 83.3%; Average loss: 2.6859
Iteration: 3332; Percent complete: 83.3%; Average loss: 2.9113
Iteration: 3333; Percent complete: 83.3%; Average loss: 2.5327
Iteration: 3334; Percent complete: 83.4%; Average loss: 2.7743
Iteration: 3335; Percent complete: 83.4%; Average loss: 2.6401
Iteration: 3336; Percent complete: 83.4%; Average loss: 2.8656
Iteration: 3337; Percent complete: 83.4%; Average loss: 2.6648
Iteration: 3338; Percent complete: 83.5%; Average loss: 2.8390
Iteration: 3339; Percent complete: 83.5%; Average loss: 2.7087
Iteration: 3340; Percent complete: 83.5%; Average loss: 2.7824
Iteration: 3341; Percent complete: 83.5%; Average loss: 2.8340
Iteration: 3342; Percent complete: 83.5%; Average loss: 2.8370
Iteration: 3343; Percent complete: 83.6%; Average loss: 2.9061
Iteration: 3344; Percent complete: 83.6%; Average loss: 2.6537
Iteration: 3345; Percent complete: 83.6%; Average loss: 2.8716
Iteration: 3346; Percent complete: 83.7%; Average loss: 2.8466
Iteration: 3347; Percent complete: 83.7%; Average loss: 2.6369
Iteration: 3348; Percent complete: 83.7%; Average loss: 2.6907
Iteration: 3349; Percent complete: 83.7%; Average loss: 2.7831
Iteration: 3350; Percent complete: 83.8%; Average loss: 2.8018
Iteration: 3351; Percent complete: 83.8%; Average loss: 2.7300
Iteration: 3352; Percent complete: 83.8%; Average loss: 2.7388
Iteration: 3353; Percent complete: 83.8%; Average loss: 2.8060
Iteration: 3354; Percent complete: 83.9%; Average loss: 2.6991
Iteration: 3355; Percent complete: 83.9%; Average loss: 2.7959
Iteration: 3356; Percent complete: 83.9%; Average loss: 2.7168
Iteration: 3357; Percent complete: 83.9%; Average loss: 2.9023
Iteration: 3358; Percent complete: 84.0%; Average loss: 2.8898
Iteration: 3359; Percent complete: 84.0%; Average loss: 2.6899
Iteration: 3360; Percent complete: 84.0%; Average loss: 2.9423
Iteration: 3361; Percent complete: 84.0%; Average loss: 2.8779
Iteration: 3362; Percent complete: 84.0%; Average loss: 2.7500
Iteration: 3363; Percent complete: 84.1%; Average loss: 2.9028
Iteration: 3364; Percent complete: 84.1%; Average loss: 2.9418
Iteration: 3365; Percent complete: 84.1%; Average loss: 2.6072
Iteration: 3366; Percent complete: 84.2%; Average loss: 2.6664
Iteration: 3367; Percent complete: 84.2%; Average loss: 3.0393
Iteration: 3368; Percent complete: 84.2%; Average loss: 2.7581
Iteration: 3369; Percent complete: 84.2%; Average loss: 2.7120
Iteration: 3370; Percent complete: 84.2%; Average loss: 2.6593
Iteration: 3371; Percent complete: 84.3%; Average loss: 2.7252
Iteration: 3372; Percent complete: 84.3%; Average loss: 3.0031
Iteration: 3373; Percent complete: 84.3%; Average loss: 2.6864
Iteration: 3374; Percent complete: 84.4%; Average loss: 2.7822
Iteration: 3375; Percent complete: 84.4%; Average loss: 2.7775
Iteration: 3376; Percent complete: 84.4%; Average loss: 2.8099
Iteration: 3377; Percent complete: 84.4%; Average loss: 2.8603
Iteration: 3378; Percent complete: 84.5%; Average loss: 2.7001
Iteration: 3379; Percent complete: 84.5%; Average loss: 2.6630
Iteration: 3380; Percent complete: 84.5%; Average loss: 2.8863
Iteration: 3381; Percent complete: 84.5%; Average loss: 2.6848
Iteration: 3382; Percent complete: 84.5%; Average loss: 2.7692
Iteration: 3383; Percent complete: 84.6%; Average loss: 2.9070
Iteration: 3384; Percent complete: 84.6%; Average loss: 2.8902
Iteration: 3385; Percent complete: 84.6%; Average loss: 2.9759
Iteration: 3386; Percent complete: 84.7%; Average loss: 2.8296
Iteration: 3387; Percent complete: 84.7%; Average loss: 2.8128
Iteration: 3388; Percent complete: 84.7%; Average loss: 2.9867
Iteration: 3389; Percent complete: 84.7%; Average loss: 2.8470
Iteration: 3390; Percent complete: 84.8%; Average loss: 2.8451
Iteration: 3391; Percent complete: 84.8%; Average loss: 2.6616
Iteration: 3392; Percent complete: 84.8%; Average loss: 2.7320
Iteration: 3393; Percent complete: 84.8%; Average loss: 2.7360
Iteration: 3394; Percent complete: 84.9%; Average loss: 2.7162
Iteration: 3395; Percent complete: 84.9%; Average loss: 2.7399
Iteration: 3396; Percent complete: 84.9%; Average loss: 2.6773
Iteration: 3397; Percent complete: 84.9%; Average loss: 2.6111
Iteration: 3398; Percent complete: 85.0%; Average loss: 2.6918
Iteration: 3399; Percent complete: 85.0%; Average loss: 2.7702
Iteration: 3400; Percent complete: 85.0%; Average loss: 2.8396
Iteration: 3401; Percent complete: 85.0%; Average loss: 2.8535
Iteration: 3402; Percent complete: 85.0%; Average loss: 2.7050
Iteration: 3403; Percent complete: 85.1%; Average loss: 2.7138
Iteration: 3404; Percent complete: 85.1%; Average loss: 2.7090
Iteration: 3405; Percent complete: 85.1%; Average loss: 2.8534
Iteration: 3406; Percent complete: 85.2%; Average loss: 2.7944
Iteration: 3407; Percent complete: 85.2%; Average loss: 2.8110
Iteration: 3408; Percent complete: 85.2%; Average loss: 2.8237
Iteration: 3409; Percent complete: 85.2%; Average loss: 2.9402
Iteration: 3410; Percent complete: 85.2%; Average loss: 2.9345
Iteration: 3411; Percent complete: 85.3%; Average loss: 2.7583
Iteration: 3412; Percent complete: 85.3%; Average loss: 2.8171
Iteration: 3413; Percent complete: 85.3%; Average loss: 3.0574
Iteration: 3414; Percent complete: 85.4%; Average loss: 2.9432
Iteration: 3415; Percent complete: 85.4%; Average loss: 2.6386
Iteration: 3416; Percent complete: 85.4%; Average loss: 2.7394
Iteration: 3417; Percent complete: 85.4%; Average loss: 2.8590
Iteration: 3418; Percent complete: 85.5%; Average loss: 2.7929
Iteration: 3419; Percent complete: 85.5%; Average loss: 3.0000
Iteration: 3420; Percent complete: 85.5%; Average loss: 2.8041
Iteration: 3421; Percent complete: 85.5%; Average loss: 2.7366
Iteration: 3422; Percent complete: 85.5%; Average loss: 2.7594
Iteration: 3423; Percent complete: 85.6%; Average loss: 2.7603
Iteration: 3424; Percent complete: 85.6%; Average loss: 2.7849
Iteration: 3425; Percent complete: 85.6%; Average loss: 2.8633
Iteration: 3426; Percent complete: 85.7%; Average loss: 2.7833
Iteration: 3427; Percent complete: 85.7%; Average loss: 2.8152
Iteration: 3428; Percent complete: 85.7%; Average loss: 2.9443
Iteration: 3429; Percent complete: 85.7%; Average loss: 2.7252
Iteration: 3430; Percent complete: 85.8%; Average loss: 2.5968
Iteration: 3431; Percent complete: 85.8%; Average loss: 2.7371
Iteration: 3432; Percent complete: 85.8%; Average loss: 2.7987
Iteration: 3433; Percent complete: 85.8%; Average loss: 2.6005
Iteration: 3434; Percent complete: 85.9%; Average loss: 2.8446
Iteration: 3435; Percent complete: 85.9%; Average loss: 2.9360
Iteration: 3436; Percent complete: 85.9%; Average loss: 2.6226
Iteration: 3437; Percent complete: 85.9%; Average loss: 2.7985
Iteration: 3438; Percent complete: 86.0%; Average loss: 2.8098
Iteration: 3439; Percent complete: 86.0%; Average loss: 2.6956
Iteration: 3440; Percent complete: 86.0%; Average loss: 2.6189
Iteration: 3441; Percent complete: 86.0%; Average loss: 2.8078
Iteration: 3442; Percent complete: 86.1%; Average loss: 2.8021
Iteration: 3443; Percent complete: 86.1%; Average loss: 2.8996
Iteration: 3444; Percent complete: 86.1%; Average loss: 2.9768
Iteration: 3445; Percent complete: 86.1%; Average loss: 2.9697
Iteration: 3446; Percent complete: 86.2%; Average loss: 2.9334
Iteration: 3447; Percent complete: 86.2%; Average loss: 2.8252
Iteration: 3448; Percent complete: 86.2%; Average loss: 2.9165
Iteration: 3449; Percent complete: 86.2%; Average loss: 2.7634
Iteration: 3450; Percent complete: 86.2%; Average loss: 2.8676
Iteration: 3451; Percent complete: 86.3%; Average loss: 2.8987
Iteration: 3452; Percent complete: 86.3%; Average loss: 2.7857
Iteration: 3453; Percent complete: 86.3%; Average loss: 2.5855
Iteration: 3454; Percent complete: 86.4%; Average loss: 2.7502
Iteration: 3455; Percent complete: 86.4%; Average loss: 2.8128
Iteration: 3456; Percent complete: 86.4%; Average loss: 2.7114
Iteration: 3457; Percent complete: 86.4%; Average loss: 2.9193
Iteration: 3458; Percent complete: 86.5%; Average loss: 2.5986
Iteration: 3459; Percent complete: 86.5%; Average loss: 2.7304
Iteration: 3460; Percent complete: 86.5%; Average loss: 2.7926
Iteration: 3461; Percent complete: 86.5%; Average loss: 2.7443
Iteration: 3462; Percent complete: 86.6%; Average loss: 2.7466
Iteration: 3463; Percent complete: 86.6%; Average loss: 2.7157
Iteration: 3464; Percent complete: 86.6%; Average loss: 2.7471
Iteration: 3465; Percent complete: 86.6%; Average loss: 2.8782
Iteration: 3466; Percent complete: 86.7%; Average loss: 2.9089
Iteration: 3467; Percent complete: 86.7%; Average loss: 2.7873
Iteration: 3468; Percent complete: 86.7%; Average loss: 3.0712
Iteration: 3469; Percent complete: 86.7%; Average loss: 2.9830
Iteration: 3470; Percent complete: 86.8%; Average loss: 3.1447
Iteration: 3471; Percent complete: 86.8%; Average loss: 2.8681
Iteration: 3472; Percent complete: 86.8%; Average loss: 2.8156
Iteration: 3473; Percent complete: 86.8%; Average loss: 2.7335
Iteration: 3474; Percent complete: 86.9%; Average loss: 2.8411
Iteration: 3475; Percent complete: 86.9%; Average loss: 2.5699
Iteration: 3476; Percent complete: 86.9%; Average loss: 2.8121
Iteration: 3477; Percent complete: 86.9%; Average loss: 2.6531
Iteration: 3478; Percent complete: 87.0%; Average loss: 2.6688
Iteration: 3479; Percent complete: 87.0%; Average loss: 2.6856
Iteration: 3480; Percent complete: 87.0%; Average loss: 2.8735
Iteration: 3481; Percent complete: 87.0%; Average loss: 3.0121
Iteration: 3482; Percent complete: 87.1%; Average loss: 2.8546
Iteration: 3483; Percent complete: 87.1%; Average loss: 2.8210
Iteration: 3484; Percent complete: 87.1%; Average loss: 2.7726
Iteration: 3485; Percent complete: 87.1%; Average loss: 2.7433
Iteration: 3486; Percent complete: 87.2%; Average loss: 2.9695
Iteration: 3487; Percent complete: 87.2%; Average loss: 2.8499
Iteration: 3488; Percent complete: 87.2%; Average loss: 2.7985
Iteration: 3489; Percent complete: 87.2%; Average loss: 2.6708
Iteration: 3490; Percent complete: 87.2%; Average loss: 2.7644
Iteration: 3491; Percent complete: 87.3%; Average loss: 2.7567
Iteration: 3492; Percent complete: 87.3%; Average loss: 2.6622
Iteration: 3493; Percent complete: 87.3%; Average loss: 2.9266
Iteration: 3494; Percent complete: 87.4%; Average loss: 2.9957
Iteration: 3495; Percent complete: 87.4%; Average loss: 2.7435
Iteration: 3496; Percent complete: 87.4%; Average loss: 2.7441
Iteration: 3497; Percent complete: 87.4%; Average loss: 2.6561
Iteration: 3498; Percent complete: 87.5%; Average loss: 2.8212
Iteration: 3499; Percent complete: 87.5%; Average loss: 2.9747
Iteration: 3500; Percent complete: 87.5%; Average loss: 2.6306
Iteration: 3501; Percent complete: 87.5%; Average loss: 2.6897
Iteration: 3502; Percent complete: 87.5%; Average loss: 2.9930
Iteration: 3503; Percent complete: 87.6%; Average loss: 2.4928
Iteration: 3504; Percent complete: 87.6%; Average loss: 2.9394
Iteration: 3505; Percent complete: 87.6%; Average loss: 2.7608
Iteration: 3506; Percent complete: 87.6%; Average loss: 2.8189
Iteration: 3507; Percent complete: 87.7%; Average loss: 2.5632
Iteration: 3508; Percent complete: 87.7%; Average loss: 2.3914
Iteration: 3509; Percent complete: 87.7%; Average loss: 2.8129
Iteration: 3510; Percent complete: 87.8%; Average loss: 2.7437
Iteration: 3511; Percent complete: 87.8%; Average loss: 2.6396
Iteration: 3512; Percent complete: 87.8%; Average loss: 2.6960
Iteration: 3513; Percent complete: 87.8%; Average loss: 2.6431
Iteration: 3514; Percent complete: 87.8%; Average loss: 2.7438
Iteration: 3515; Percent complete: 87.9%; Average loss: 2.7650
Iteration: 3516; Percent complete: 87.9%; Average loss: 2.8489
Iteration: 3517; Percent complete: 87.9%; Average loss: 2.8048
Iteration: 3518; Percent complete: 87.9%; Average loss: 2.6382
Iteration: 3519; Percent complete: 88.0%; Average loss: 2.7106
Iteration: 3520; Percent complete: 88.0%; Average loss: 2.7532
Iteration: 3521; Percent complete: 88.0%; Average loss: 2.8773
Iteration: 3522; Percent complete: 88.0%; Average loss: 2.6214
Iteration: 3523; Percent complete: 88.1%; Average loss: 2.9623
Iteration: 3524; Percent complete: 88.1%; Average loss: 2.8545
Iteration: 3525; Percent complete: 88.1%; Average loss: 2.6469
Iteration: 3526; Percent complete: 88.1%; Average loss: 2.6496
Iteration: 3527; Percent complete: 88.2%; Average loss: 2.9146
Iteration: 3528; Percent complete: 88.2%; Average loss: 2.8040
Iteration: 3529; Percent complete: 88.2%; Average loss: 2.7248
Iteration: 3530; Percent complete: 88.2%; Average loss: 2.6174
Iteration: 3531; Percent complete: 88.3%; Average loss: 2.6690
Iteration: 3532; Percent complete: 88.3%; Average loss: 2.9340
Iteration: 3533; Percent complete: 88.3%; Average loss: 2.9081
Iteration: 3534; Percent complete: 88.3%; Average loss: 2.8212
Iteration: 3535; Percent complete: 88.4%; Average loss: 2.7180
Iteration: 3536; Percent complete: 88.4%; Average loss: 2.7864
Iteration: 3537; Percent complete: 88.4%; Average loss: 2.5421
Iteration: 3538; Percent complete: 88.4%; Average loss: 2.9584
Iteration: 3539; Percent complete: 88.5%; Average loss: 2.9505
Iteration: 3540; Percent complete: 88.5%; Average loss: 2.8636
Iteration: 3541; Percent complete: 88.5%; Average loss: 2.4983
Iteration: 3542; Percent complete: 88.5%; Average loss: 2.6536
Iteration: 3543; Percent complete: 88.6%; Average loss: 2.8321
Iteration: 3544; Percent complete: 88.6%; Average loss: 2.6845
Iteration: 3545; Percent complete: 88.6%; Average loss: 2.9160
Iteration: 3546; Percent complete: 88.6%; Average loss: 2.7193
Iteration: 3547; Percent complete: 88.7%; Average loss: 2.7708
Iteration: 3548; Percent complete: 88.7%; Average loss: 2.6204
Iteration: 3549; Percent complete: 88.7%; Average loss: 2.7453
Iteration: 3550; Percent complete: 88.8%; Average loss: 2.8724
Iteration: 3551; Percent complete: 88.8%; Average loss: 2.8948
Iteration: 3552; Percent complete: 88.8%; Average loss: 2.6542
Iteration: 3553; Percent complete: 88.8%; Average loss: 2.7645
Iteration: 3554; Percent complete: 88.8%; Average loss: 2.8977
Iteration: 3555; Percent complete: 88.9%; Average loss: 2.6976
Iteration: 3556; Percent complete: 88.9%; Average loss: 2.6252
Iteration: 3557; Percent complete: 88.9%; Average loss: 2.5686
Iteration: 3558; Percent complete: 88.9%; Average loss: 2.5936
Iteration: 3559; Percent complete: 89.0%; Average loss: 2.8581
Iteration: 3560; Percent complete: 89.0%; Average loss: 2.7652
Iteration: 3561; Percent complete: 89.0%; Average loss: 2.8526
Iteration: 3562; Percent complete: 89.0%; Average loss: 2.8399
Iteration: 3563; Percent complete: 89.1%; Average loss: 3.0280
Iteration: 3564; Percent complete: 89.1%; Average loss: 2.6629
Iteration: 3565; Percent complete: 89.1%; Average loss: 2.7024
Iteration: 3566; Percent complete: 89.1%; Average loss: 2.5661
Iteration: 3567; Percent complete: 89.2%; Average loss: 2.7361
Iteration: 3568; Percent complete: 89.2%; Average loss: 2.7340
Iteration: 3569; Percent complete: 89.2%; Average loss: 2.7647
Iteration: 3570; Percent complete: 89.2%; Average loss: 2.7026
Iteration: 3571; Percent complete: 89.3%; Average loss: 2.8382
Iteration: 3572; Percent complete: 89.3%; Average loss: 2.6888
Iteration: 3573; Percent complete: 89.3%; Average loss: 2.9134
Iteration: 3574; Percent complete: 89.3%; Average loss: 2.7725
Iteration: 3575; Percent complete: 89.4%; Average loss: 2.8587
Iteration: 3576; Percent complete: 89.4%; Average loss: 2.9696
Iteration: 3577; Percent complete: 89.4%; Average loss: 2.6814
Iteration: 3578; Percent complete: 89.5%; Average loss: 2.6030
Iteration: 3579; Percent complete: 89.5%; Average loss: 2.6086
Iteration: 3580; Percent complete: 89.5%; Average loss: 2.7749
Iteration: 3581; Percent complete: 89.5%; Average loss: 2.4658
Iteration: 3582; Percent complete: 89.5%; Average loss: 2.8719
Iteration: 3583; Percent complete: 89.6%; Average loss: 2.6730
Iteration: 3584; Percent complete: 89.6%; Average loss: 2.4361
Iteration: 3585; Percent complete: 89.6%; Average loss: 2.8661
Iteration: 3586; Percent complete: 89.6%; Average loss: 2.8227
Iteration: 3587; Percent complete: 89.7%; Average loss: 2.8581
Iteration: 3588; Percent complete: 89.7%; Average loss: 2.9594
Iteration: 3589; Percent complete: 89.7%; Average loss: 2.7334
Iteration: 3590; Percent complete: 89.8%; Average loss: 2.8063
Iteration: 3591; Percent complete: 89.8%; Average loss: 2.7614
Iteration: 3592; Percent complete: 89.8%; Average loss: 2.7090
Iteration: 3593; Percent complete: 89.8%; Average loss: 2.7390
Iteration: 3594; Percent complete: 89.8%; Average loss: 2.6367
Iteration: 3595; Percent complete: 89.9%; Average loss: 2.6839
Iteration: 3596; Percent complete: 89.9%; Average loss: 2.7119
Iteration: 3597; Percent complete: 89.9%; Average loss: 2.8336
Iteration: 3598; Percent complete: 90.0%; Average loss: 2.6887
Iteration: 3599; Percent complete: 90.0%; Average loss: 2.5744
Iteration: 3600; Percent complete: 90.0%; Average loss: 2.7279
Iteration: 3601; Percent complete: 90.0%; Average loss: 2.7207
Iteration: 3602; Percent complete: 90.0%; Average loss: 2.6168
Iteration: 3603; Percent complete: 90.1%; Average loss: 2.5361
Iteration: 3604; Percent complete: 90.1%; Average loss: 2.6810
Iteration: 3605; Percent complete: 90.1%; Average loss: 2.7804
Iteration: 3606; Percent complete: 90.1%; Average loss: 2.7062
Iteration: 3607; Percent complete: 90.2%; Average loss: 2.8544
Iteration: 3608; Percent complete: 90.2%; Average loss: 2.7324
Iteration: 3609; Percent complete: 90.2%; Average loss: 2.7743
Iteration: 3610; Percent complete: 90.2%; Average loss: 2.6867
Iteration: 3611; Percent complete: 90.3%; Average loss: 2.7623
Iteration: 3612; Percent complete: 90.3%; Average loss: 2.6221
Iteration: 3613; Percent complete: 90.3%; Average loss: 2.7232
Iteration: 3614; Percent complete: 90.3%; Average loss: 2.6532
Iteration: 3615; Percent complete: 90.4%; Average loss: 2.9619
Iteration: 3616; Percent complete: 90.4%; Average loss: 2.5885
Iteration: 3617; Percent complete: 90.4%; Average loss: 2.8118
Iteration: 3618; Percent complete: 90.5%; Average loss: 2.7366
Iteration: 3619; Percent complete: 90.5%; Average loss: 2.7856
Iteration: 3620; Percent complete: 90.5%; Average loss: 2.7822
Iteration: 3621; Percent complete: 90.5%; Average loss: 2.6219
Iteration: 3622; Percent complete: 90.5%; Average loss: 2.5797
Iteration: 3623; Percent complete: 90.6%; Average loss: 2.8435
Iteration: 3624; Percent complete: 90.6%; Average loss: 2.8636
Iteration: 3625; Percent complete: 90.6%; Average loss: 2.8145
Iteration: 3626; Percent complete: 90.6%; Average loss: 2.9551
Iteration: 3627; Percent complete: 90.7%; Average loss: 2.5813
Iteration: 3628; Percent complete: 90.7%; Average loss: 2.6774
Iteration: 3629; Percent complete: 90.7%; Average loss: 2.9589
Iteration: 3630; Percent complete: 90.8%; Average loss: 2.6116
Iteration: 3631; Percent complete: 90.8%; Average loss: 2.5359
Iteration: 3632; Percent complete: 90.8%; Average loss: 2.7382
Iteration: 3633; Percent complete: 90.8%; Average loss: 2.5899
Iteration: 3634; Percent complete: 90.8%; Average loss: 2.7957
Iteration: 3635; Percent complete: 90.9%; Average loss: 2.8031
Iteration: 3636; Percent complete: 90.9%; Average loss: 2.6214
Iteration: 3637; Percent complete: 90.9%; Average loss: 2.5080
Iteration: 3638; Percent complete: 91.0%; Average loss: 2.7490
Iteration: 3639; Percent complete: 91.0%; Average loss: 2.6711
Iteration: 3640; Percent complete: 91.0%; Average loss: 2.6483
Iteration: 3641; Percent complete: 91.0%; Average loss: 2.8609
Iteration: 3642; Percent complete: 91.0%; Average loss: 2.7783
Iteration: 3643; Percent complete: 91.1%; Average loss: 2.7440
Iteration: 3644; Percent complete: 91.1%; Average loss: 2.7503
Iteration: 3645; Percent complete: 91.1%; Average loss: 2.9032
Iteration: 3646; Percent complete: 91.1%; Average loss: 2.6494
Iteration: 3647; Percent complete: 91.2%; Average loss: 2.7913
Iteration: 3648; Percent complete: 91.2%; Average loss: 2.8735
Iteration: 3649; Percent complete: 91.2%; Average loss: 2.6247
Iteration: 3650; Percent complete: 91.2%; Average loss: 2.5476
Iteration: 3651; Percent complete: 91.3%; Average loss: 2.5372
Iteration: 3652; Percent complete: 91.3%; Average loss: 2.5868
Iteration: 3653; Percent complete: 91.3%; Average loss: 2.7408
Iteration: 3654; Percent complete: 91.3%; Average loss: 2.6851
Iteration: 3655; Percent complete: 91.4%; Average loss: 2.7793
Iteration: 3656; Percent complete: 91.4%; Average loss: 2.7387
Iteration: 3657; Percent complete: 91.4%; Average loss: 2.7531
Iteration: 3658; Percent complete: 91.5%; Average loss: 2.8462
Iteration: 3659; Percent complete: 91.5%; Average loss: 2.6826
Iteration: 3660; Percent complete: 91.5%; Average loss: 2.6932
Iteration: 3661; Percent complete: 91.5%; Average loss: 2.4818
Iteration: 3662; Percent complete: 91.5%; Average loss: 2.6430
Iteration: 3663; Percent complete: 91.6%; Average loss: 2.7239
Iteration: 3664; Percent complete: 91.6%; Average loss: 2.5783
Iteration: 3665; Percent complete: 91.6%; Average loss: 2.5632
Iteration: 3666; Percent complete: 91.6%; Average loss: 2.8238
Iteration: 3667; Percent complete: 91.7%; Average loss: 2.6724
Iteration: 3668; Percent complete: 91.7%; Average loss: 2.8135
Iteration: 3669; Percent complete: 91.7%; Average loss: 2.7615
Iteration: 3670; Percent complete: 91.8%; Average loss: 2.6780
Iteration: 3671; Percent complete: 91.8%; Average loss: 2.6961
Iteration: 3672; Percent complete: 91.8%; Average loss: 2.7506
Iteration: 3673; Percent complete: 91.8%; Average loss: 2.8905
Iteration: 3674; Percent complete: 91.8%; Average loss: 2.5252
Iteration: 3675; Percent complete: 91.9%; Average loss: 2.7408
Iteration: 3676; Percent complete: 91.9%; Average loss: 2.7017
Iteration: 3677; Percent complete: 91.9%; Average loss: 2.6646
Iteration: 3678; Percent complete: 92.0%; Average loss: 2.7414
Iteration: 3679; Percent complete: 92.0%; Average loss: 2.6131
Iteration: 3680; Percent complete: 92.0%; Average loss: 2.7890
Iteration: 3681; Percent complete: 92.0%; Average loss: 2.5013
Iteration: 3682; Percent complete: 92.0%; Average loss: 2.8845
Iteration: 3683; Percent complete: 92.1%; Average loss: 2.6289
Iteration: 3684; Percent complete: 92.1%; Average loss: 2.7545
Iteration: 3685; Percent complete: 92.1%; Average loss: 2.6002
Iteration: 3686; Percent complete: 92.2%; Average loss: 2.7216
Iteration: 3687; Percent complete: 92.2%; Average loss: 2.5897
Iteration: 3688; Percent complete: 92.2%; Average loss: 2.6185
Iteration: 3689; Percent complete: 92.2%; Average loss: 2.6042
Iteration: 3690; Percent complete: 92.2%; Average loss: 2.9549
Iteration: 3691; Percent complete: 92.3%; Average loss: 2.5672
Iteration: 3692; Percent complete: 92.3%; Average loss: 2.7234
Iteration: 3693; Percent complete: 92.3%; Average loss: 2.8367
Iteration: 3694; Percent complete: 92.3%; Average loss: 2.7839
Iteration: 3695; Percent complete: 92.4%; Average loss: 2.8787
Iteration: 3696; Percent complete: 92.4%; Average loss: 2.9251
Iteration: 3697; Percent complete: 92.4%; Average loss: 2.7878
Iteration: 3698; Percent complete: 92.5%; Average loss: 2.7590
Iteration: 3699; Percent complete: 92.5%; Average loss: 2.7305
Iteration: 3700; Percent complete: 92.5%; Average loss: 2.7345
Iteration: 3701; Percent complete: 92.5%; Average loss: 2.5779
Iteration: 3702; Percent complete: 92.5%; Average loss: 2.6720
Iteration: 3703; Percent complete: 92.6%; Average loss: 2.6504
Iteration: 3704; Percent complete: 92.6%; Average loss: 2.4811
Iteration: 3705; Percent complete: 92.6%; Average loss: 2.6759
Iteration: 3706; Percent complete: 92.7%; Average loss: 2.7830
Iteration: 3707; Percent complete: 92.7%; Average loss: 2.8455
Iteration: 3708; Percent complete: 92.7%; Average loss: 2.7594
Iteration: 3709; Percent complete: 92.7%; Average loss: 2.7801
Iteration: 3710; Percent complete: 92.8%; Average loss: 2.7298
Iteration: 3711; Percent complete: 92.8%; Average loss: 2.6713
Iteration: 3712; Percent complete: 92.8%; Average loss: 2.6425
Iteration: 3713; Percent complete: 92.8%; Average loss: 2.7265
Iteration: 3714; Percent complete: 92.8%; Average loss: 2.6891
Iteration: 3715; Percent complete: 92.9%; Average loss: 2.7673
Iteration: 3716; Percent complete: 92.9%; Average loss: 2.5287
Iteration: 3717; Percent complete: 92.9%; Average loss: 2.9608
Iteration: 3718; Percent complete: 93.0%; Average loss: 2.5571
Iteration: 3719; Percent complete: 93.0%; Average loss: 2.7509
Iteration: 3720; Percent complete: 93.0%; Average loss: 2.7401
Iteration: 3721; Percent complete: 93.0%; Average loss: 2.9276
Iteration: 3722; Percent complete: 93.0%; Average loss: 2.9907
Iteration: 3723; Percent complete: 93.1%; Average loss: 2.6709
Iteration: 3724; Percent complete: 93.1%; Average loss: 2.6459
Iteration: 3725; Percent complete: 93.1%; Average loss: 2.6998
Iteration: 3726; Percent complete: 93.2%; Average loss: 2.7315
Iteration: 3727; Percent complete: 93.2%; Average loss: 2.8342
Iteration: 3728; Percent complete: 93.2%; Average loss: 2.5323
Iteration: 3729; Percent complete: 93.2%; Average loss: 2.6368
Iteration: 3730; Percent complete: 93.2%; Average loss: 2.5527
Iteration: 3731; Percent complete: 93.3%; Average loss: 2.7653
Iteration: 3732; Percent complete: 93.3%; Average loss: 2.6072
Iteration: 3733; Percent complete: 93.3%; Average loss: 2.4693
Iteration: 3734; Percent complete: 93.3%; Average loss: 3.2092
Iteration: 3735; Percent complete: 93.4%; Average loss: 2.6538
Iteration: 3736; Percent complete: 93.4%; Average loss: 2.7344
Iteration: 3737; Percent complete: 93.4%; Average loss: 2.7355
Iteration: 3738; Percent complete: 93.5%; Average loss: 2.6596
Iteration: 3739; Percent complete: 93.5%; Average loss: 2.8833
Iteration: 3740; Percent complete: 93.5%; Average loss: 2.7251
Iteration: 3741; Percent complete: 93.5%; Average loss: 2.7829
Iteration: 3742; Percent complete: 93.5%; Average loss: 2.7874
Iteration: 3743; Percent complete: 93.6%; Average loss: 2.6209
Iteration: 3744; Percent complete: 93.6%; Average loss: 2.6082
Iteration: 3745; Percent complete: 93.6%; Average loss: 2.4700
Iteration: 3746; Percent complete: 93.7%; Average loss: 2.4787
Iteration: 3747; Percent complete: 93.7%; Average loss: 2.6138
Iteration: 3748; Percent complete: 93.7%; Average loss: 2.6726
Iteration: 3749; Percent complete: 93.7%; Average loss: 2.6973
Iteration: 3750; Percent complete: 93.8%; Average loss: 2.9015
Iteration: 3751; Percent complete: 93.8%; Average loss: 2.6879
Iteration: 3752; Percent complete: 93.8%; Average loss: 2.7937
Iteration: 3753; Percent complete: 93.8%; Average loss: 2.5955
Iteration: 3754; Percent complete: 93.8%; Average loss: 2.6046
Iteration: 3755; Percent complete: 93.9%; Average loss: 2.5641
Iteration: 3756; Percent complete: 93.9%; Average loss: 2.5444
Iteration: 3757; Percent complete: 93.9%; Average loss: 2.7266
Iteration: 3758; Percent complete: 94.0%; Average loss: 2.4721
Iteration: 3759; Percent complete: 94.0%; Average loss: 2.6493
Iteration: 3760; Percent complete: 94.0%; Average loss: 2.9188
Iteration: 3761; Percent complete: 94.0%; Average loss: 2.6443
Iteration: 3762; Percent complete: 94.0%; Average loss: 2.8269
Iteration: 3763; Percent complete: 94.1%; Average loss: 2.9932
Iteration: 3764; Percent complete: 94.1%; Average loss: 2.6971
Iteration: 3765; Percent complete: 94.1%; Average loss: 2.6243
Iteration: 3766; Percent complete: 94.2%; Average loss: 2.6718
Iteration: 3767; Percent complete: 94.2%; Average loss: 2.8389
Iteration: 3768; Percent complete: 94.2%; Average loss: 2.6181
Iteration: 3769; Percent complete: 94.2%; Average loss: 2.2924
Iteration: 3770; Percent complete: 94.2%; Average loss: 2.4585
Iteration: 3771; Percent complete: 94.3%; Average loss: 2.6802
Iteration: 3772; Percent complete: 94.3%; Average loss: 2.6403
Iteration: 3773; Percent complete: 94.3%; Average loss: 2.6940
Iteration: 3774; Percent complete: 94.3%; Average loss: 2.7285
Iteration: 3775; Percent complete: 94.4%; Average loss: 2.6590
Iteration: 3776; Percent complete: 94.4%; Average loss: 2.5290
Iteration: 3777; Percent complete: 94.4%; Average loss: 2.6306
Iteration: 3778; Percent complete: 94.5%; Average loss: 2.7206
Iteration: 3779; Percent complete: 94.5%; Average loss: 2.7037
Iteration: 3780; Percent complete: 94.5%; Average loss: 2.6779
Iteration: 3781; Percent complete: 94.5%; Average loss: 2.6446
Iteration: 3782; Percent complete: 94.5%; Average loss: 2.6295
Iteration: 3783; Percent complete: 94.6%; Average loss: 2.5160
Iteration: 3784; Percent complete: 94.6%; Average loss: 2.4997
Iteration: 3785; Percent complete: 94.6%; Average loss: 2.6398
Iteration: 3786; Percent complete: 94.7%; Average loss: 2.5803
Iteration: 3787; Percent complete: 94.7%; Average loss: 2.3986
Iteration: 3788; Percent complete: 94.7%; Average loss: 2.6227
Iteration: 3789; Percent complete: 94.7%; Average loss: 2.7448
Iteration: 3790; Percent complete: 94.8%; Average loss: 2.6158
Iteration: 3791; Percent complete: 94.8%; Average loss: 2.7214
Iteration: 3792; Percent complete: 94.8%; Average loss: 2.8112
Iteration: 3793; Percent complete: 94.8%; Average loss: 2.8495
Iteration: 3794; Percent complete: 94.8%; Average loss: 2.8590
Iteration: 3795; Percent complete: 94.9%; Average loss: 2.7705
Iteration: 3796; Percent complete: 94.9%; Average loss: 2.6971
Iteration: 3797; Percent complete: 94.9%; Average loss: 2.8654
Iteration: 3798; Percent complete: 95.0%; Average loss: 2.7905
Iteration: 3799; Percent complete: 95.0%; Average loss: 2.7129
Iteration: 3800; Percent complete: 95.0%; Average loss: 2.7397
Iteration: 3801; Percent complete: 95.0%; Average loss: 2.5521
Iteration: 3802; Percent complete: 95.0%; Average loss: 2.6687
Iteration: 3803; Percent complete: 95.1%; Average loss: 2.8763
Iteration: 3804; Percent complete: 95.1%; Average loss: 2.8763
Iteration: 3805; Percent complete: 95.1%; Average loss: 2.6101
Iteration: 3806; Percent complete: 95.2%; Average loss: 2.7743
Iteration: 3807; Percent complete: 95.2%; Average loss: 2.7068
Iteration: 3808; Percent complete: 95.2%; Average loss: 2.4739
Iteration: 3809; Percent complete: 95.2%; Average loss: 2.4672
Iteration: 3810; Percent complete: 95.2%; Average loss: 2.7493
Iteration: 3811; Percent complete: 95.3%; Average loss: 2.5640
Iteration: 3812; Percent complete: 95.3%; Average loss: 2.8090
Iteration: 3813; Percent complete: 95.3%; Average loss: 2.6004
Iteration: 3814; Percent complete: 95.3%; Average loss: 2.8656
Iteration: 3815; Percent complete: 95.4%; Average loss: 2.7499
Iteration: 3816; Percent complete: 95.4%; Average loss: 2.5686
Iteration: 3817; Percent complete: 95.4%; Average loss: 2.7282
Iteration: 3818; Percent complete: 95.5%; Average loss: 2.4773
Iteration: 3819; Percent complete: 95.5%; Average loss: 2.7534
Iteration: 3820; Percent complete: 95.5%; Average loss: 2.6012
Iteration: 3821; Percent complete: 95.5%; Average loss: 2.6699
Iteration: 3822; Percent complete: 95.5%; Average loss: 2.5426
Iteration: 3823; Percent complete: 95.6%; Average loss: 2.4024
Iteration: 3824; Percent complete: 95.6%; Average loss: 2.4588
Iteration: 3825; Percent complete: 95.6%; Average loss: 2.6100
Iteration: 3826; Percent complete: 95.7%; Average loss: 2.4231
Iteration: 3827; Percent complete: 95.7%; Average loss: 2.7917
Iteration: 3828; Percent complete: 95.7%; Average loss: 2.4656
Iteration: 3829; Percent complete: 95.7%; Average loss: 2.7959
Iteration: 3830; Percent complete: 95.8%; Average loss: 3.0282
Iteration: 3831; Percent complete: 95.8%; Average loss: 2.6315
Iteration: 3832; Percent complete: 95.8%; Average loss: 2.6173
Iteration: 3833; Percent complete: 95.8%; Average loss: 2.3541
Iteration: 3834; Percent complete: 95.9%; Average loss: 2.6280
Iteration: 3835; Percent complete: 95.9%; Average loss: 2.9225
Iteration: 3836; Percent complete: 95.9%; Average loss: 2.5806
Iteration: 3837; Percent complete: 95.9%; Average loss: 2.6118
Iteration: 3838; Percent complete: 96.0%; Average loss: 2.6940
Iteration: 3839; Percent complete: 96.0%; Average loss: 2.7506
Iteration: 3840; Percent complete: 96.0%; Average loss: 2.6647
Iteration: 3841; Percent complete: 96.0%; Average loss: 2.7197
Iteration: 3842; Percent complete: 96.0%; Average loss: 2.5456
Iteration: 3843; Percent complete: 96.1%; Average loss: 2.6856
Iteration: 3844; Percent complete: 96.1%; Average loss: 2.5394
Iteration: 3845; Percent complete: 96.1%; Average loss: 2.5064
Iteration: 3846; Percent complete: 96.2%; Average loss: 2.6062
Iteration: 3847; Percent complete: 96.2%; Average loss: 2.7631
Iteration: 3848; Percent complete: 96.2%; Average loss: 2.6027
Iteration: 3849; Percent complete: 96.2%; Average loss: 2.5140
Iteration: 3850; Percent complete: 96.2%; Average loss: 2.7325
Iteration: 3851; Percent complete: 96.3%; Average loss: 3.0778
Iteration: 3852; Percent complete: 96.3%; Average loss: 2.5076
Iteration: 3853; Percent complete: 96.3%; Average loss: 2.5666
Iteration: 3854; Percent complete: 96.4%; Average loss: 2.5526
Iteration: 3855; Percent complete: 96.4%; Average loss: 2.7574
Iteration: 3856; Percent complete: 96.4%; Average loss: 2.6218
Iteration: 3857; Percent complete: 96.4%; Average loss: 2.9241
Iteration: 3858; Percent complete: 96.5%; Average loss: 2.6474
Iteration: 3859; Percent complete: 96.5%; Average loss: 2.5590
Iteration: 3860; Percent complete: 96.5%; Average loss: 2.6287
Iteration: 3861; Percent complete: 96.5%; Average loss: 2.6889
Iteration: 3862; Percent complete: 96.5%; Average loss: 2.6326
Iteration: 3863; Percent complete: 96.6%; Average loss: 2.6494
Iteration: 3864; Percent complete: 96.6%; Average loss: 2.6801
Iteration: 3865; Percent complete: 96.6%; Average loss: 2.4928
Iteration: 3866; Percent complete: 96.7%; Average loss: 2.8643
Iteration: 3867; Percent complete: 96.7%; Average loss: 2.5935
Iteration: 3868; Percent complete: 96.7%; Average loss: 2.4143
Iteration: 3869; Percent complete: 96.7%; Average loss: 2.7686
Iteration: 3870; Percent complete: 96.8%; Average loss: 2.6257
Iteration: 3871; Percent complete: 96.8%; Average loss: 2.7207
Iteration: 3872; Percent complete: 96.8%; Average loss: 2.5068
Iteration: 3873; Percent complete: 96.8%; Average loss: 2.7013
Iteration: 3874; Percent complete: 96.9%; Average loss: 2.5069
Iteration: 3875; Percent complete: 96.9%; Average loss: 2.6173
Iteration: 3876; Percent complete: 96.9%; Average loss: 2.8325
Iteration: 3877; Percent complete: 96.9%; Average loss: 2.4675
Iteration: 3878; Percent complete: 97.0%; Average loss: 2.6544
Iteration: 3879; Percent complete: 97.0%; Average loss: 2.5957
Iteration: 3880; Percent complete: 97.0%; Average loss: 2.5312
Iteration: 3881; Percent complete: 97.0%; Average loss: 2.5681
Iteration: 3882; Percent complete: 97.0%; Average loss: 2.6165
Iteration: 3883; Percent complete: 97.1%; Average loss: 2.6667
Iteration: 3884; Percent complete: 97.1%; Average loss: 2.5501
Iteration: 3885; Percent complete: 97.1%; Average loss: 2.6457
Iteration: 3886; Percent complete: 97.2%; Average loss: 2.4981
Iteration: 3887; Percent complete: 97.2%; Average loss: 2.7780
Iteration: 3888; Percent complete: 97.2%; Average loss: 2.3944
Iteration: 3889; Percent complete: 97.2%; Average loss: 2.6214
Iteration: 3890; Percent complete: 97.2%; Average loss: 2.7018
Iteration: 3891; Percent complete: 97.3%; Average loss: 2.7011
Iteration: 3892; Percent complete: 97.3%; Average loss: 2.5304
Iteration: 3893; Percent complete: 97.3%; Average loss: 2.6830
Iteration: 3894; Percent complete: 97.4%; Average loss: 2.8262
Iteration: 3895; Percent complete: 97.4%; Average loss: 2.5755
Iteration: 3896; Percent complete: 97.4%; Average loss: 2.7956
Iteration: 3897; Percent complete: 97.4%; Average loss: 2.9094
Iteration: 3898; Percent complete: 97.5%; Average loss: 2.3665
Iteration: 3899; Percent complete: 97.5%; Average loss: 2.9734
Iteration: 3900; Percent complete: 97.5%; Average loss: 2.6017
Iteration: 3901; Percent complete: 97.5%; Average loss: 2.4327
Iteration: 3902; Percent complete: 97.5%; Average loss: 2.6878
Iteration: 3903; Percent complete: 97.6%; Average loss: 2.6659
Iteration: 3904; Percent complete: 97.6%; Average loss: 2.4974
Iteration: 3905; Percent complete: 97.6%; Average loss: 2.6163
Iteration: 3906; Percent complete: 97.7%; Average loss: 2.7707
Iteration: 3907; Percent complete: 97.7%; Average loss: 2.8043
Iteration: 3908; Percent complete: 97.7%; Average loss: 2.6656
Iteration: 3909; Percent complete: 97.7%; Average loss: 2.8277
Iteration: 3910; Percent complete: 97.8%; Average loss: 2.6916
Iteration: 3911; Percent complete: 97.8%; Average loss: 2.4978
Iteration: 3912; Percent complete: 97.8%; Average loss: 2.6631
Iteration: 3913; Percent complete: 97.8%; Average loss: 2.6290
Iteration: 3914; Percent complete: 97.9%; Average loss: 2.6994
Iteration: 3915; Percent complete: 97.9%; Average loss: 2.7400
Iteration: 3916; Percent complete: 97.9%; Average loss: 2.7236
Iteration: 3917; Percent complete: 97.9%; Average loss: 2.6956
Iteration: 3918; Percent complete: 98.0%; Average loss: 2.5109
Iteration: 3919; Percent complete: 98.0%; Average loss: 3.0269
Iteration: 3920; Percent complete: 98.0%; Average loss: 2.6094
Iteration: 3921; Percent complete: 98.0%; Average loss: 2.6724
Iteration: 3922; Percent complete: 98.0%; Average loss: 2.6974
Iteration: 3923; Percent complete: 98.1%; Average loss: 2.5253
Iteration: 3924; Percent complete: 98.1%; Average loss: 2.7034
Iteration: 3925; Percent complete: 98.1%; Average loss: 2.6377
Iteration: 3926; Percent complete: 98.2%; Average loss: 2.5620
Iteration: 3927; Percent complete: 98.2%; Average loss: 2.6386
Iteration: 3928; Percent complete: 98.2%; Average loss: 2.6604
Iteration: 3929; Percent complete: 98.2%; Average loss: 2.6805
Iteration: 3930; Percent complete: 98.2%; Average loss: 2.6339
Iteration: 3931; Percent complete: 98.3%; Average loss: 2.5493
Iteration: 3932; Percent complete: 98.3%; Average loss: 2.5140
Iteration: 3933; Percent complete: 98.3%; Average loss: 2.5141
Iteration: 3934; Percent complete: 98.4%; Average loss: 2.5095
Iteration: 3935; Percent complete: 98.4%; Average loss: 2.6005
Iteration: 3936; Percent complete: 98.4%; Average loss: 2.6911
Iteration: 3937; Percent complete: 98.4%; Average loss: 2.5049
Iteration: 3938; Percent complete: 98.5%; Average loss: 2.7021
Iteration: 3939; Percent complete: 98.5%; Average loss: 2.6259
Iteration: 3940; Percent complete: 98.5%; Average loss: 2.2567
Iteration: 3941; Percent complete: 98.5%; Average loss: 2.6952
Iteration: 3942; Percent complete: 98.6%; Average loss: 2.6309
Iteration: 3943; Percent complete: 98.6%; Average loss: 2.4994
Iteration: 3944; Percent complete: 98.6%; Average loss: 2.6693
Iteration: 3945; Percent complete: 98.6%; Average loss: 2.5518
Iteration: 3946; Percent complete: 98.7%; Average loss: 2.4119
Iteration: 3947; Percent complete: 98.7%; Average loss: 2.6247
Iteration: 3948; Percent complete: 98.7%; Average loss: 2.7192
Iteration: 3949; Percent complete: 98.7%; Average loss: 2.7714
Iteration: 3950; Percent complete: 98.8%; Average loss: 2.6786
Iteration: 3951; Percent complete: 98.8%; Average loss: 2.4714
Iteration: 3952; Percent complete: 98.8%; Average loss: 2.5323
Iteration: 3953; Percent complete: 98.8%; Average loss: 2.4523
Iteration: 3954; Percent complete: 98.9%; Average loss: 2.8196
Iteration: 3955; Percent complete: 98.9%; Average loss: 2.6958
Iteration: 3956; Percent complete: 98.9%; Average loss: 2.7049
Iteration: 3957; Percent complete: 98.9%; Average loss: 2.6115
Iteration: 3958; Percent complete: 99.0%; Average loss: 2.5027
Iteration: 3959; Percent complete: 99.0%; Average loss: 2.4976
Iteration: 3960; Percent complete: 99.0%; Average loss: 2.3073
Iteration: 3961; Percent complete: 99.0%; Average loss: 2.5584
Iteration: 3962; Percent complete: 99.1%; Average loss: 2.5453
Iteration: 3963; Percent complete: 99.1%; Average loss: 2.7408
Iteration: 3964; Percent complete: 99.1%; Average loss: 2.5674
Iteration: 3965; Percent complete: 99.1%; Average loss: 2.8919
Iteration: 3966; Percent complete: 99.2%; Average loss: 2.5230
Iteration: 3967; Percent complete: 99.2%; Average loss: 2.6898
Iteration: 3968; Percent complete: 99.2%; Average loss: 2.7946
Iteration: 3969; Percent complete: 99.2%; Average loss: 2.7358
Iteration: 3970; Percent complete: 99.2%; Average loss: 2.4020
Iteration: 3971; Percent complete: 99.3%; Average loss: 2.6585
Iteration: 3972; Percent complete: 99.3%; Average loss: 2.5460
Iteration: 3973; Percent complete: 99.3%; Average loss: 2.6849
Iteration: 3974; Percent complete: 99.4%; Average loss: 2.7292
Iteration: 3975; Percent complete: 99.4%; Average loss: 2.6915
Iteration: 3976; Percent complete: 99.4%; Average loss: 2.4922
Iteration: 3977; Percent complete: 99.4%; Average loss: 2.7212
Iteration: 3978; Percent complete: 99.5%; Average loss: 2.7712
Iteration: 3979; Percent complete: 99.5%; Average loss: 2.6428
Iteration: 3980; Percent complete: 99.5%; Average loss: 2.6149
Iteration: 3981; Percent complete: 99.5%; Average loss: 2.4232
Iteration: 3982; Percent complete: 99.6%; Average loss: 2.6612
Iteration: 3983; Percent complete: 99.6%; Average loss: 2.7731
Iteration: 3984; Percent complete: 99.6%; Average loss: 2.7299
Iteration: 3985; Percent complete: 99.6%; Average loss: 2.4799
Iteration: 3986; Percent complete: 99.7%; Average loss: 2.6626
Iteration: 3987; Percent complete: 99.7%; Average loss: 2.5618
Iteration: 3988; Percent complete: 99.7%; Average loss: 2.7324
Iteration: 3989; Percent complete: 99.7%; Average loss: 2.5473
Iteration: 3990; Percent complete: 99.8%; Average loss: 2.5807
Iteration: 3991; Percent complete: 99.8%; Average loss: 2.5462
Iteration: 3992; Percent complete: 99.8%; Average loss: 2.4512
Iteration: 3993; Percent complete: 99.8%; Average loss: 2.5491
Iteration: 3994; Percent complete: 99.9%; Average loss: 3.0102
Iteration: 3995; Percent complete: 99.9%; Average loss: 2.5172
Iteration: 3996; Percent complete: 99.9%; Average loss: 2.6364
Iteration: 3997; Percent complete: 99.9%; Average loss: 2.5119
Iteration: 3998; Percent complete: 100.0%; Average loss: 2.5417
Iteration: 3999; Percent complete: 100.0%; Average loss: 2.8450
Iteration: 4000; Percent complete: 100.0%; Average loss: 2.7575

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!