Text Generation using RNNs
Published:
Object Oriented Text Generation Using RNN
In this post, I will be talking about an object oriented Python implementation of Recurrent Neural Networks (RNNs) on text generation field. RNNs are one of the popular neural network implementations in Deep Learning field. Specifically on time series data, RNNs are very powerful because of their memory-like structure that enables them to use past information for future decisions. Text generation is one of the popular implementations of RNNs and has extensively been studied in many tutorials so far. I tried to implement RNNs in an object oriented fashion in this post. Let’s start!
import numpy as np
The Adventures of Sherlock Holmes by Arthur Conan Doyle book used to train my network. Website:
http://www.gutenberg.org/ebooks/1661
Configurations
configurations = {}
configurations['NUM_HIDDEN_LAYERS'] = 150
configurations['STEP_SIZE'] = 500000
configurations['SEQUENTIAL_LENGTH'] = 25
configurations['LEARNING RATE'] = 1e-1
# this helps to compute gradients and the optimization is easier to solve:
configurations['LOSS_CONSTANT'] = 0.001
configurations['INPUT_NAME'] = 'holmes.txt'
Preprocessor is responsible for reading data, exctract unique characters and enumerate them. Based on sequence length, it gives a sequence of input to RNN.
class Preprocessor:
def __init__(self, path):
self.path = path
self.dictionary = {}
def read(self):
with open(self.path, 'r', encoding='utf8') as file:
self.data = file.read()
print('{0} characters in total.'.format(len(self.data)))
unique_chars = list(set(self.data))
print('number of unique chars: {0}'.format(len(unique_chars)))
configurations['NUM_UNIQUE_CHARS'] = len(unique_chars)
return self.data
def print(self):
print(self.data)
def convert(self, id):
target_index = -1
target_char = ''
for i in range(len(id)):
if id[i][0] == 1:
target_index = i
for char, id in self.dictionary.items():
if id == target_index:
target_char = char
return target_char
def enumerated_data(self):
unique_chars = list(set(self.data))
# id, char
enumerated = enumerate(unique_chars)
# dictionary to store the unique id of each char
dictionary = {}
for id, unique_char in enumerated:
dictionary[unique_char] = id
self.dictionary = dictionary
enumerated_data = []
for char in list(self.data):
id = dictionary[char]
enumerated_data.append(id)
return enumerated_data
def get_x_and_y(self, index):
sequnce_length = configurations['SEQUENTIAL_LENGTH']
enum_data = self.enumerated_data()
x, y = [], []
end_index = index + sequnce_length
'''
if end_index + 1 > len(enum_data):
index = 0
end_index = index + sequnce_length
'''
for i in range(index, end_index):
x.append(enum_data[i])
for i in range(index + 1, end_index + 1):
y.append(enum_data[i])
return x, y
def data_size(self):
return len(self.data)
NetworkHelpers method contains basic operations of neural network. These operations are one-hot encoding, calculating cross entropy loss and softmax function.
class NetworkHelpers:
# softmax
def logits(self, probabilities):
return np.exp(probabilities) / np.sum(np.exp(probabilities))
# loss for single 't'
def xentropy_loss(self, logits, index):
xentropy = -np.log(logits[index, 0])
return xentropy
def square(self, x):
return x * x
def one_hot(self, index, num_class):
x = np.zeros((num_class, 1))
x[index] = 1
return x
HiddenLayer class represents one hidden layer of the network at time “t”. So, it is one of the vertical components of the rnn. For instance, if we have sequences of length 20, then we will have 20 hidden layers for t = 1,2,….,20. My definition of hidden layer can be understood by the following figure:
class HiddenLayer:
# Stack of Hidden Units that builds a Hidden Layer.
def __init__(self, t):
self.networkHelpers = NetworkHelpers()
self.t = t
self.x = np.zeros((configurations['NUM_UNIQUE_CHARS'], 1))
self.h = np.zeros((configurations['NUM_HIDDEN_LAYERS'], 1))
self.y = np.zeros((0, 0))
self.p = np.zeros((0, 0))
self.hidden_loss = np.zeros((0, 0))
def set_input(self, hidden_input):
self.x = hidden_input
def get_input(self):
return self.x
def get_t(self):
return self.t
def get_h(self):
return self.h
def get_probs(self):
return self.p
def hidden_layer_out(self, w_input_hidden, w_hidden_hidden, hiddenLayerOutPrev, hidden_bias):
h = np.dot(w_input_hidden.get_matrix(), self.get_input()) + np.dot(w_hidden_hidden.get_matrix(), hiddenLayerOutPrev) + hidden_bias
self.h = np.tanh(h)
return self.h
def hiddenY(self, w_hidden_output, hiddenLayerOut, output_bias):
self.y = np.dot(w_hidden_output.get_matrix(), hiddenLayerOut) + output_bias
return self.y
def hiddenProbs(self, hiddenY):
self.p = self.networkHelpers.logits(hiddenY)
return self.p
def loss(self, logits, input_index):
self.hidden_loss = self.networkHelpers.xentropy_loss(logits, input_index)
return self.hidden_loss
Weight class represent weights of recurrent nn. It contains basic matrix operations.
class Weight:
def __init__(self, shape):
self.matrix = np.random.randn(shape[0], shape[1])
self.scale(0.01)
def scale(self, factor):
self.matrix *= factor
def add_gradient(self, gradient):
self.matrix += gradient
def shape(self):
return self.matrix.shape
def get_matrix(self):
return self.matrix
def transpose(self):
return self.matrix.T
RNN is the main object of this code. Forward pass, backward pass, sampling and updating weights based on gradiens are the main operations of this object. “step” function is being called in each iteration and applies one forward and one backward pass. For the optimizer of RNN, Adagrad is the most used option as far as I searched. That’s why, I also used it.
class RNN:
def __init__(self, hidden_layers):
self.hp = NetworkHelpers()
self.pr = Preprocessor(configurations['INPUT_NAME'])
self.hidden_layers = hidden_layers
self.total_loss = 0.0
self.iteration_loss = -np.log(1.0 / configurations['NUM_UNIQUE_CHARS']) * configurations['SEQUENTIAL_LENGTH']
self.init()
self.reset()
def init(self):
# init weights
self.w_hidden_hidden = Weight((configurations['NUM_HIDDEN_LAYERS'], configurations['NUM_HIDDEN_LAYERS']))
self.w_hidden_output = Weight((configurations['NUM_UNIQUE_CHARS'], configurations['NUM_HIDDEN_LAYERS']))
self.w_input_hidden = Weight((configurations['NUM_HIDDEN_LAYERS'], configurations['NUM_UNIQUE_CHARS']))
# init biases
self.bias_output = np.zeros((configurations['NUM_UNIQUE_CHARS'], 1)) # output bias
self.bias_hidden = np.zeros((configurations['NUM_HIDDEN_LAYERS'], 1)) # hidden bias
# init history of weights and biases will be used by the optimizer later.
self.hist_input_hidden, self.history_hidden_hidden, self.history_hidden_output = np.zeros(self.w_input_hidden.shape()), np.zeros(self.w_hidden_hidden.shape()), np.zeros(self.w_hidden_output.shape())
self.history_bias_hidden, self.history_bias_output = np.zeros_like(self.bias_hidden), np.zeros_like(self.bias_output)
def add_to_weights(self, add_wxh, add_whh, add_why, add_bh, add_by):
self.w_input_hidden.add_gradient(add_wxh)
self.w_hidden_hidden.add_gradient(add_whh)
self.w_hidden_output.add_gradient(add_why)
self.bias_hidden += add_bh
self.bias_output += add_by
def add_to_memories(self, add_mwxh, add_mwhh, add_mwhy, add_mbh, add_mby):
self.hist_input_hidden += add_mwxh
self.history_hidden_hidden += add_mwhh
self.history_hidden_output += add_mwhy
self.history_bias_hidden += add_mbh
self.history_bias_output += add_mby
def set_pr(self, pr):
self.pr = pr
def reset(self):
self.gradient_input_hidden = np.zeros((configurations['NUM_HIDDEN_LAYERS'],
configurations['NUM_UNIQUE_CHARS']))
self.gradient_hidden_hidden = np.zeros((configurations['NUM_HIDDEN_LAYERS'],
configurations['NUM_HIDDEN_LAYERS']))
self.gradient_hidden_output = np.zeros((configurations['NUM_UNIQUE_CHARS'],
configurations['NUM_HIDDEN_LAYERS']))
self.gradient_bias_hidden = np.zeros((configurations['NUM_HIDDEN_LAYERS'], 1))
self.gradient_bias_output = np.zeros((configurations['NUM_UNIQUE_CHARS'], 1))
self.gradient_next_hidden = np.zeros_like(self.hidden_layers[0].get_h())
self.total_loss = 0.0
def sample_sequence(self, hl, first_char, length):
chars = []
first_char_input = np.zeros((configurations['NUM_UNIQUE_CHARS'], 1))
first_char_input[first_char] = 1
chars.append(first_char_input)
char = first_char_input
for _ in range(length):
next_char = self.predict_next(hl, char)
chars.append(next_char)
char = next_char
result_text = ""
# print('type char: {0}'.format(type(char)))
for char in chars:
result_text += self.pr.convert(char)
print(result_text)
def predict_next(self, hl, char):
hl.set_input(char)
hidden_layer_out = hl.hidden_layer_out(self.w_input_hidden, self.w_hidden_hidden, hl.get_h(), self.bias_hidden)
hidden_layer_output = hl.hiddenY(self.w_hidden_output, hidden_layer_out, self.bias_output)
probs = hl.hiddenProbs(hidden_layer_output)
ix = np.random.choice(range(configurations['NUM_UNIQUE_CHARS']), p=probs.ravel())
next_char = np.zeros((configurations['NUM_UNIQUE_CHARS'], 1))
next_char[ix] = 1
return next_char
'''
hl: hidden layer
X: input sequence
Y: target sequence
'''
def step_loss(self, hl, X, Y):
x = np.zeros((configurations['NUM_UNIQUE_CHARS'], 1))
x[X[hl.get_t()]] = 1
hl.set_input(x)
hidden_layer_out = hl.hidden_layer_out(self.w_input_hidden, self.w_hidden_hidden, self.hidden_layers[hl.get_t() - 1].get_h(), self.bias_hidden)
hidden_layer_output = hl.hiddenY(self.w_hidden_output, hidden_layer_out, self.bias_output)
probs = hl.hiddenProbs(hidden_layer_output)
loss = hl.loss(probs, Y[hl.get_t()])
return loss
'''
hl: hidden layer
X: input sequence
Y: target sequence
'''
def back_propagate(self, hl, X, Y):
t = hl.get_t()
# print('t: {0}'.format(t))
dprobabilities = hl.get_probs()
dprobabilities[Y[t]] -= 1
self.gradient_hidden_output += np.dot(dprobabilities, hl.get_h().T)
self.gradient_bias_output += dprobabilities
self.gradient_hidden = np.dot(self.w_hidden_output.transpose(), dprobabilities) + self.gradient_next_hidden
gradient_activation = (1 - hl.get_h() * hl.get_h()) * self.gradient_hidden
self.gradient_bias_hidden += gradient_activation
self.gradient_input_hidden += np.dot(gradient_activation, hl.get_input().T)
self.gradient_hidden_hidden += np.dot(gradient_activation, self.hidden_layers[t - 1].get_h().T)
self.gradient_next_hidden = np.dot(self.w_hidden_hidden.transpose(), gradient_activation)
# to avoid vanishing gradients
self.clip()
def clip(self):
for gradient in [self.gradient_input_hidden, self.gradient_hidden_hidden, self.gradient_hidden_output, self.gradient_bias_hidden, self.gradient_bias_output]:
np.clip(gradient, -5, 5, out=gradient)
def gradients(self):
return [self.gradient_input_hidden, self.gradient_hidden_hidden, self.gradient_hidden_output, self.gradient_bias_hidden, self.gradient_bias_output]
def histories(self):
return [self.hist_input_hidden, self.history_hidden_hidden, self.history_hidden_output, self.history_bias_hidden, self.history_bias_output]
def update_weights(self, gradients):
hp = self.hp
self.add_to_memories(hp.square(gradients[0]), hp.square(gradients[1]), hp.square(gradients[2]), hp.square(gradients[3]), hp.square(gradients[4]))
gradient_adds = []
for i in range(len(gradients)):
gradient_adds.append(- configurations['LEARNING RATE'] * gradients[i] / np.sqrt(self.histories()[i] + 1e-8))
self.add_to_weights(gradient_adds[0], gradient_adds[1], gradient_adds[2], gradient_adds[3], gradient_adds[4])
# X = inputs, Y = targets
def step(self, X, Y, iteration):
# sample
if iteration % 1000 == 0:
self.sample_sequence(self.hidden_layers[-1], X[0], 200)
# t time sequences
time_periods = len(X)
loss = 0.0
for t in range(time_periods):
loss += self.step_loss(self.hidden_layers[t], X, Y)
for t in range(1, time_periods + 1):
self.back_propagate(self.hidden_layers[time_periods - t], X, Y)
loss_constant_multiplier = configurations['LOSS_CONSTANT']
self.iteration_loss = self.iteration_loss * (1 - loss_constant_multiplier) + loss * loss_constant_multiplier
if iteration % 100 == 0:
print('Iteration ({0}) - Loss: {1}'.format(iteration, self.iteration_loss))
# update weights
self.update_weights(self.gradients())
self.reset()
# read data
preprocessor = Preprocessor(configurations['INPUT_NAME'])
data = preprocessor.read()
# generate hidden time layers of rnn
hidden_layers = []
for t in range(configurations['SEQUENTIAL_LENGTH']):
hidden_layers.append(HiddenLayer(t))
print('number of layers ( t ): {0}'.format(len(hidden_layers)))
# generate rnn
rnn = RNN(hidden_layers)
rnn.set_pr(preprocessor)
rnn.reset()
sequential_length = configurations['SEQUENTIAL_LENGTH']
data_index = 0
data_size = preprocessor.data_size()
num_iteration_at_epoch = data_size // sequential_length - 1
print('Number of iterations at each epoch: {0}'.format(num_iteration_at_epoch))
iteration_counter = 0
for i in range(configurations['STEP_SIZE']):
if iteration_counter > num_iteration_at_epoch:
iteration_counter = 0
data_index = 0
x, y = preprocessor.get_x_and_y(data_index)
rnn.step(x, y, i)
# update counters
iteration_counter += 1
data_index += sequential_length
# print(x)
561833 characters in total.
number of unique chars: 77
number of layers ( t ): 25
Number of iterations at each epoch: 22472
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Iteration (0) - Loss: 108.59513499761356
Iteration (100) - Loss: 110.06245325211738
Iteration (200) - Loss: 108.10469407414759
Iteration (300) - Loss: 105.90874798623935
Iteration (400) - Loss: 104.13724521691843
Iteration (500) - Loss: 101.98095871403835
Iteration (600) - Loss: 100.16210473013778
Iteration (700) - Loss: 98.72254869495625
Iteration (800) - Loss: 96.89326801608637
Iteration (900) - Loss: 95.04767010613082
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Iteration (1000) - Loss: 93.37634400045272
Iteration (1100) - Loss: 91.63443612541688
Iteration (1200) - Loss: 90.18678511318709
Iteration (1300) - Loss: 88.64368296092185
Iteration (1400) - Loss: 87.0142240181531
Iteration (1500) - Loss: 85.53605960040306
Iteration (1600) - Loss: 84.04261356353992
Iteration (1700) - Loss: 83.0196484894267
Iteration (1800) - Loss: 82.04587106091253
Iteration (1900) - Loss: 81.05648332764305
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Iteration (2000) - Loss: 79.79411753952787
Iteration (2100) - Loss: 78.84033531239669
Iteration (2200) - Loss: 78.28437228181772
Iteration (2300) - Loss: 77.24039856432466
Iteration (2400) - Loss: 76.13243451070247
Iteration (2500) - Loss: 75.19515327304907
Iteration (2600) - Loss: 74.42412058656272
Iteration (2700) - Loss: 73.72086854534766
Iteration (2800) - Loss: 73.12577988282655
Iteration (2900) - Loss: 72.46237135848087
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Iteration (3000) - Loss: 71.90758065263248
Iteration (3100) - Loss: 71.24913880619388
Iteration (3200) - Loss: 70.58604368687496
Iteration (3300) - Loss: 70.10540231091727
Iteration (3400) - Loss: 69.5620558514523
Iteration (3500) - Loss: 69.04486221259786
Iteration (3600) - Loss: 68.46575923207031
Iteration (3700) - Loss: 68.08693969990676
Iteration (3800) - Loss: 67.32297617904301
Iteration (3900) - Loss: 67.11862266345007
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Iteration (4000) - Loss: 66.56007546391174
Iteration (4100) - Loss: 66.19355236750775
Iteration (4200) - Loss: 65.85346070175584
Iteration (4300) - Loss: 65.32257079964094
Iteration (4400) - Loss: 64.79757297424665
Iteration (4500) - Loss: 64.33715180543959
Iteration (4600) - Loss: 63.919697988214935
Iteration (4700) - Loss: 63.78363410842953
Iteration (4800) - Loss: 63.64170275892246
Iteration (4900) - Loss: 63.35763785476804
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Iteration (5000) - Loss: 63.14249218820869
Iteration (5100) - Loss: 62.86094098048672
Iteration (5200) - Loss: 62.52851707763194
Iteration (5300) - Loss: 62.240499831416194
Iteration (5400) - Loss: 62.3683408441632
Iteration (5500) - Loss: 62.2068455511785
Iteration (5600) - Loss: 61.93748829968843
Iteration (5700) - Loss: 61.78198910683419
Iteration (5800) - Loss: 61.51060676248748
Iteration (5900) - Loss: 61.27484233198334
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Iteration (6000) - Loss: 61.06665525616778
Iteration (6100) - Loss: 60.98376716935855
Iteration (6200) - Loss: 60.849373890320194
Iteration (6300) - Loss: 60.64537179282313
Iteration (6400) - Loss: 60.47538961017566
Iteration (6500) - Loss: 60.10601064738209
Iteration (6600) - Loss: 60.090912231819075
Iteration (6700) - Loss: 59.82443332616919
Iteration (6800) - Loss: 59.52032758506661
Iteration (6900) - Loss: 59.508264523870956
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Iteration (7000) - Loss: 59.439626243455436
Iteration (7100) - Loss: 59.33949676056015
Iteration (7200) - Loss: 59.21101629559594
Iteration (7300) - Loss: 59.03166400803833
Iteration (7400) - Loss: 58.861365135172015
Iteration (7500) - Loss: 58.97499525697226
Iteration (7600) - Loss: 58.78364304322691
Iteration (7700) - Loss: 58.73009346081587
Iteration (7800) - Loss: 58.71507168149454
Iteration (7900) - Loss: 58.539163125619766
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Iteration (8000) - Loss: 58.46556438776454
Iteration (8100) - Loss: 58.422705818810925
Iteration (8200) - Loss: 58.273490840403284
Iteration (8300) - Loss: 58.18663508930594
Iteration (8400) - Loss: 58.01944085591771
Iteration (8500) - Loss: 58.158035718904486
Iteration (8600) - Loss: 58.007193578538875
Iteration (8700) - Loss: 58.04386820138211
Iteration (8800) - Loss: 58.01240133347546
Iteration (8900) - Loss: 57.97308324058208
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Iteration (9000) - Loss: 58.01084683835042
Iteration (9100) - Loss: 58.023911436751554
Iteration (9200) - Loss: 57.91018999786178
Iteration (9300) - Loss: 57.92583260092568
Iteration (9400) - Loss: 57.807660596174046
Iteration (9500) - Loss: 57.83042656412816
Iteration (9600) - Loss: 57.95120560440679
Iteration (9700) - Loss: 57.69050724675623
Iteration (9800) - Loss: 57.47367957981443
Iteration (9900) - Loss: 57.258917506338975
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Iteration (10000) - Loss: 56.99256183222765
Iteration (10100) - Loss: 56.9199367235505
Iteration (10200) - Loss: 56.79894084826174
Iteration (10300) - Loss: 56.72949206662789
Iteration (10400) - Loss: 56.601422120253346
Iteration (10500) - Loss: 56.48551326650133
Iteration (10600) - Loss: 56.446489634562894
Iteration (10700) - Loss: 56.45508279926159
Iteration (10800) - Loss: 56.376266975595776
Iteration (10900) - Loss: 56.344271106552384
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Iteration (11000) - Loss: 56.475379030313285
Iteration (11100) - Loss: 56.48186115467722
Iteration (11200) - Loss: 56.37169356501144
Iteration (11300) - Loss: 56.25958852301443
Iteration (11400) - Loss: 56.07887637826713
Iteration (11500) - Loss: 56.23170195859434
Iteration (11600) - Loss: 56.21696604854362
Iteration (11700) - Loss: 56.10192327998588
Iteration (11800) - Loss: 56.153195317087125
Iteration (11900) - Loss: 56.316992882334254
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"Sas-lat and a wat hu hem the liid gase efen?' holebeded,'gheul the by. Wfare doo fo
Iteration (12000) - Loss: 56.2875291396421
Iteration (12100) - Loss: 56.37725359105117
Iteration (12200) - Loss: 56.21010905773266
Iteration (12300) - Loss: 56.13998874253491
Iteration (12400) - Loss: 56.15884987456366
Iteration (12500) - Loss: 55.850650364945714
Iteration (12600) - Loss: 55.67669412002651
Iteration (12700) - Loss: 55.91167424174099
Iteration (12800) - Loss: 55.83433865571892
Iteration (12900) - Loss: 55.72754770903091
carlge mthsare Sor meordedtin grall-to fat foabe Ro-no nour thest-ot heocrle
loa-vin-of at of a quebonsid ouy to sted my agl yHrame mowabe ant
fronamw as
bter ucomy goltommave -horted tto loone torgo
Iteration (13000) - Loss: 55.914586072008376
Iteration (13100) - Loss: 55.89422572700567
Iteration (13200) - Loss: 55.80632575387392
Iteration (13300) - Loss: 55.63712438258629
Iteration (13400) - Loss: 55.48880606633484
Iteration (13500) - Loss: 55.270017006899494
Iteration (13600) - Loss: 55.082254865594585
Iteration (13700) - Loss: 55.22430176179597
Iteration (13800) - Loss: 55.15805684864708
Iteration (13900) - Loss: 54.99156441662983
wo dilrthich wid ables. Thoud bowe owen inosoin andedile yow hend nack hor threg me but by the thenthy thite is he hith
the. Tele wos the rothe thend waone, sfardilled pand in hienadt speelokes
u pron
Iteration (14000) - Loss: 54.85368565435314
Iteration (14100) - Loss: 54.808024592820786
Iteration (14200) - Loss: 54.57850762486522
Iteration (14300) - Loss: 54.44445668367123
Iteration (14400) - Loss: 54.44559669121064
Iteration (14500) - Loss: 54.325641535450444
Iteration (14600) - Loss: 54.314957744753215
Iteration (14700) - Loss: 54.30771596747091
Iteration (14800) - Loss: 54.532598263534574
Iteration (14900) - Loss: 54.63390689304652
havt, Agrimaicesyy a8s a
tith tha mapbaond. sheke'd and supverdamy."
"Which th os boucom walk wand to tiend inn do ouping the theen ronging, oxs
eve fis it thon iintess whrel," hin overer mowp busk th
Iteration (15000) - Loss: 54.676754395270116
Iteration (15100) - Loss: 54.64553138102609
Iteration (15200) - Loss: 54.774832206659894
Iteration (15300) - Loss: 54.784829734058604
Iteration (15400) - Loss: 54.73493048371352
Iteration (15500) - Loss: 54.65109064178653
Iteration (15600) - Loss: 54.41322346060721
Iteration (15700) - Loss: 54.388764834191015
Iteration (15800) - Loss: 54.34700367747244
Iteration (15900) - Loss: 54.223297416086545
tann pol yowthit upile, teaydd ther inted ming mthe to nin Ik thate lait oy I my demand the cank.'
"The hpulked coble the prew hound the tsit ous ard yof pord of
we it in vand, shild the the he fryint
Iteration (16000) - Loss: 54.04669729171812
Iteration (16100) - Loss: 53.913586004868904
Iteration (16200) - Loss: 53.78768444836985
Iteration (16300) - Loss: 53.98564892744103
Iteration (16400) - Loss: 53.94014242209811
Iteration (16500) - Loss: 53.952460386546434
Iteration (16600) - Loss: 54.30249687790801
Iteration (16700) - Loss: 54.382627494447604
Iteration (16800) - Loss: 54.666610328779434
Iteration (16900) - Loss: 54.78794899809115
g be theclo norl i of tef lasoden Pirisce mudear ont on beernessece intiand
you tse jrigasene ofrouppo hordt
u whighend came if and mesesthowert alles ascaln?"
OV and the orang sheotture for cond the
Iteration (17000) - Loss: 54.73009111254472
Iteration (17100) - Loss: 54.70712307543883
Iteration (17200) - Loss: 54.61422944270721
Iteration (17300) - Loss: 54.375074597502085
Iteration (17400) - Loss: 54.29562225653532
Iteration (17500) - Loss: 54.22314804350849
Iteration (17600) - Loss: 54.14283180962308
Iteration (17700) - Loss: 54.1933088152519
Iteration (17800) - Loss: 54.17309960914689
Iteration (17900) - Loss: 54.208724549189384
rk Sookipaave wer it iy by hivand ourd wads," seasiakt tfe the his one the Lowd hithime;
pliskr, wholn herce tardomike of him hat wisin. I jeat at
ut.
I him has the firens. Ine the wed Ind hering doid
Iteration (18000) - Loss: 54.031860536049344
Iteration (18100) - Loss: 53.83136827470842
Iteration (18200) - Loss: 53.77506928558524
Iteration (18300) - Loss: 53.8249098099709
Iteration (18400) - Loss: 54.059729862812475
Iteration (18500) - Loss: 54.04630853225515
Iteration (18600) - Loss: 54.058905741410754
Iteration (18700) - Loss: 53.91661241823939
Iteration (18800) - Loss: 53.90695320136703
Iteration (18900) - Loss: 53.8540208555319
a have my as , rastind, buwgep, the
thend, your and frou sufw depf, to hocmy.' AAgticped I my inuching, bs thin
day-," sor!com-hict. He sing of hist sabtelces invave gercompind alosdith parsstilk le.
Iteration (19000) - Loss: 53.65927894340873
Iteration (19100) - Loss: 53.33734056653707
Iteration (19200) - Loss: 53.23675946873569
Iteration (19300) - Loss: 53.245563492738704
Iteration (19400) - Loss: 53.16482393960648
Iteration (19500) - Loss: 52.98571344640662
Iteration (19600) - Loss: 52.93945785733341
Iteration (19700) - Loss: 52.9245745861811
Iteration (19800) - Loss: 52.79891108941057
Iteration (19900) - Loss: 52.884222022410675
d heon end frot andaw syed his thing bed m bele and wor wuteed thallney, st!" whigh po to thove is
ihss. You weredurk? yeesed,
prinkid sto plesery he so lat hesid on honfel soss of sest kothat whe.
"A
Iteration (20000) - Loss: 52.79517503780511
Iteration (20100) - Loss: 52.66653059346988
Iteration (20200) - Loss: 52.42180118051421
Iteration (20300) - Loss: 52.4214985786372
Iteration (20400) - Loss: 52.881950628218426
Iteration (20500) - Loss: 52.91125166193463
Iteration (20600) - Loss: 53.37185655141102
Iteration (20700) - Loss: 53.4562615988137
Iteration (20800) - Loss: 53.554119246482124
Iteration (20900) - Loss: 53.71048143190373
talss esourded
dthe ar-himpaes
"Thout huy wat
ald, in kooke
be caidp houke a meres my
I be rony fma he ttould shever."
"Ithe hern.
"This
ittiode you ly
ay alk
os
Patcaus you I mrossinby wyou my, I ca
Iteration (21000) - Loss: 53.6529897054109
Iteration (21100) - Loss: 53.708605622711836
Iteration (21200) - Loss: 53.77825658314363
Iteration (21300) - Loss: 53.64776951887084
Iteration (21400) - Loss: 53.52592818884828
Iteration (21500) - Loss: 53.38739117336511
Iteration (21600) - Loss: 53.339301141493216
Iteration (21700) - Loss: 53.2926003482122
Iteration (21800) - Loss: 53.018810698892246
Iteration (21900) - Loss: 52.78121897698093
her to ser m, haed.
I dithery wave I mo. I as aln't I evever highinged feal,'re-fins was' is the wam om cour rhatiadt misks wnapetuk the perter, rook
At tale trere fa kut and I to adered a the
toled w
Iteration (22000) - Loss: 52.61546468021404
Iteration (22100) - Loss: 52.532843309181224
Iteration (22200) - Loss: 52.51338501550218
Iteration (22300) - Loss: 52.5533840775661
Iteration (22400) - Loss: 52.44709120182766
Iteration (22500) - Loss: 52.7398667118083
Iteration (22600) - Loss: 52.982125339036344
Iteration (22700) - Loss: 53.027812597045255
Iteration (22800) - Loss: 53.00429930429926
Iteration (22900) - Loss: 53.202881972384006
, greie beor. Hule than
amaneprle bedant-sust pomeiso-case wtaikre baoly of mes the berqumeywonnt potire's tale ibtome or?"
joud, Heasearozey thes on ougwind seare, to hade frame cimenang."
I seans, i
Iteration (23000) - Loss: 53.15646537903676
Iteration (23100) - Loss: 53.54275705148411
Iteration (23200) - Loss: 53.425723060015066
Iteration (23300) - Loss: 53.349551717926175
Iteration (23400) - Loss: 53.329388294841216
Iteration (23500) - Loss: 53.33559551962336
Iteration (23600) - Loss: 53.29480388636828
Iteration (23700) - Loss: 53.01484373805625
Iteration (23800) - Loss: 53.12716854275169
Iteration (23900) - Loss: 53.00303454569842
tome ax. Buttolme wis had orded bmady fomm aige, a mould dolmence. Tharn coo."
I whousd Is of hindy iced the mmear.
PThe
he wimed a po-my."
"I mitten
wane do
neep-are Soldenofows, a dangcacmed, I mis
Iteration (24000) - Loss: 52.74160837233282
Iteration (24100) - Loss: 52.665622284063545
Iteration (24200) - Loss: 52.82523250370155
Iteration (24300) - Loss: 53.20198891658341
Iteration (24400) - Loss: 53.350588310566195
Iteration (24500) - Loss: 53.33231955155621
Iteration (24600) - Loss: 53.489392273768686
Iteration (24700) - Loss: 53.67122887416047
Iteration (24800) - Loss: 53.516841513852135
Iteration (24900) - Loss: 53.307163904190816
nes. reensid be thay prioudt. Cor apend tat in that lamqferatfill, bo calling to Eithy, a. "'I grien "hastely vad 'nfidre.
O''thteclat food, bolad don es, of tol mtilo,' itecess, Jolm, whe had I peacu
Iteration (25000) - Loss: 53.23207184351436
Iteration (25100) - Loss: 53.12832170929614
Iteration (25200) - Loss: 53.40542740686829
Iteration (25300) - Loss: 53.383465604151304
Iteration (25400) - Loss: 53.2562042757415
Iteration (25500) - Loss: 53.28955656403411
Iteration (25600) - Loss: 53.362380829662484
Iteration (25700) - Loss: 53.34808748743969
Iteration (25800) - Loss: 53.29483101281913
Iteration (25900) - Loss: 53.20859863261514
d chood thin wure cod kncore, then the wolrer in gatimesed, fris to tesl. Thay of ghertato
g that, mave duangive whur wime tar show onte beatury exif at net! syight id. Fent chels mad the wursent. You
Iteration (26000) - Loss: 53.08270190259872
Iteration (26100) - Loss: 52.94644465233609
Iteration (26200) - Loss: 52.825380771914794
Iteration (26300) - Loss: 52.7011487158309
Iteration (26400) - Loss: 52.968454899347144
Iteration (26500) - Loss: 52.84450926381657
Iteration (26600) - Loss: 52.80032351336464
Iteration (26700) - Loss: 52.724732545351934
Iteration (26800) - Loss: 52.439307251055276
Iteration (26900) - Loss: 52.19024173303743
ald mise thie hery in all ming? Hury, wive my reat wave reester chms unhed the was dot was my.
"You
hemathe to of tteird, what it perees havy intbeame in shevery yooedmerjadimenn hill in wabl
the d oni
Iteration (27000) - Loss: 51.90759326145922
Iteration (27100) - Loss: 51.855157628963156
Iteration (27200) - Loss: 51.95022455234472
Iteration (27300) - Loss: 52.14130120985913
Iteration (27400) - Loss: 52.101281821743946
Iteration (27500) - Loss: 52.12379863350411
Iteration (27600) - Loss: 52.01108420269622
Iteration (27700) - Loss: 51.903950865951764
Iteration (27800) - Loss: 51.84901851783128
Iteration (27900) - Loss: 52.245952417247345
e who mise. If mive. shas may oses he. Motor he
st base ous more for. T" he Antl?"
Mo
mather cqaton the the, rored tr. Hous. He the soon and "Thablichia toller-exerittind dour at and
knam, foo site ge
Iteration (28000) - Loss: 52.356232925745786
Iteration (28100) - Loss: 52.26857687443346
Iteration (28200) - Loss: 52.30680172575464
Iteration (28300) - Loss: 52.184669268076235
Iteration (28400) - Loss: 52.206221580594004
Iteration (28500) - Loss: 51.96849203216348
Iteration (28600) - Loss: 52.04101288453506
Iteration (28700) - Loss: 51.92605418563335
Iteration (28800) - Loss: 51.83644573194356
Iteration (28900) - Loss: 51.73158323666828
de
low yound lene
fith
Stheng the whercaing, aodeln quving your plevllougher of androuke tist siok. Cat, mxsur fore faalle reatong. He ncer
the stornedd, whith teen. Onad a leit.
"therdesteast of a bi
Iteration (29000) - Loss: 51.64730008381188
Iteration (29100) - Loss: 51.66544041406171
Iteration (29200) - Loss: 51.607917998068565
Iteration (29300) - Loss: 51.39728816269829
Iteration (29400) - Loss: 51.52805230167254
Iteration (29500) - Loss: 51.5922300863619
Iteration (29600) - Loss: 51.52920024402129
Iteration (29700) - Loss: 51.51201809651703
Iteration (29800) - Loss: 51.35242601271424
Iteration (29900) - Loss: 51.524458170567485
e with hat ratire wexarncat, whid sfoudsire whes mistire notl of to falk lene trece forlres the dinupableld helder hele ours mach iit Vas the, oen thither. I wiline wath sofulpe Choine
thing wevapret a
Iteration (30000) - Loss: 51.59438818883261
Iteration (30100) - Loss: 51.42617561281717
Iteration (30200) - Loss: 51.51915802182362
Iteration (30300) - Loss: 51.62218394800561
Iteration (30400) - Loss: 51.516433414986
Iteration (30500) - Loss: 51.60255325302106
Iteration (30600) - Loss: 51.55238141389903
Iteration (30700) - Loss: 51.48258679576616
Iteration (30800) - Loss: 51.497836639842276
Iteration (30900) - Loss: 51.52480255317022
drerely to serteres elo-nonly of lery of alyery your armanny reack the foris sainet and the cother the clase mas thit that thestultely
to resennce wiy the ayel evas this. Wrom aDe to qucebee of same.
Iteration (31000) - Loss: 51.7401160613568
Iteration (31100) - Loss: 51.87218964403046
Iteration (31200) - Loss: 51.86018976647348
Iteration (31300) - Loss: 51.858145840157306
Iteration (31400) - Loss: 51.850593452996875
Iteration (31500) - Loss: 52.113597831814815
Iteration (31600) - Loss: 52.07592157890001
Iteration (31700) - Loss: 52.17717337497132
Iteration (31800) - Loss: 52.16813873382917
Iteration (31900) - Loss: 52.16601200383595
to wies of walwis I sheres shere my."
"Golling he age his!," therat I, tisk you dore my lack you, whe oo tiske aalllibe youre mong tithoustiove tomat wnigack of leabwere, the
loca, a poles, she the s
Iteration (32000) - Loss: 52.17162856141818
Iteration (32100) - Loss: 52.2818454709924
Iteration (32200) - Loss: 52.0535602023822
Iteration (32300) - Loss: 51.956802528374084
Iteration (32400) - Loss: 51.75234651684854
Iteration (32500) - Loss: 51.46465549450941
Iteration (32600) - Loss: 51.4540843217106
Iteration (32700) - Loss: 51.29895422257442
Iteration (32800) - Loss: 51.18965345049846
Iteration (32900) - Loss: 51.20895797927693
?"
"I tinked and yout and she
terquinkdeaterind. Ay at gelr."
"I."
"Bun. He have pro ditrowh hour to
shan wers iver Lee Lesion Smmackingas. I eask tho vear she feven, beet-for as. It hin my lals the
Iteration (33000) - Loss: 51.19430531261058
Iteration (33100) - Loss: 51.25856945823939
Iteration (33200) - Loss: 51.260850041427716
Iteration (33300) - Loss: 51.342503246522206
Iteration (33400) - Loss: 51.2805902860513
Iteration (33500) - Loss: 51.36600025434846
Iteration (33600) - Loss: 51.4353284252791
Iteration (33700) - Loss: 51.36900828177911
Iteration (33800) - Loss: 51.19881969441012
Iteration (33900) - Loss: 51.20864615385022
the catupton, is asd.
"Whad whise promossing ase oprevery whighint pryeruor and
Bn condy ant nount Ifsed anmy onseousts."
"Snedyswast and
nisten nast ts is to of the rectlpalreystares, papess the Cor
Iteration (34000) - Loss: 51.338264918054364
Iteration (34100) - Loss: 51.35764066685255
Iteration (34200) - Loss: 51.34853486024919
Iteration (34300) - Loss: 51.386563231823835
Iteration (34400) - Loss: 51.652711876525004
Iteration (34500) - Loss: 51.57116400993098
Iteration (34600) - Loss: 51.68335343536918
Iteration (34700) - Loss: 51.5008322451973
Iteration (34800) - Loss: 51.52423215073285
Iteration (34900) - Loss: 51.44836109193933
'
"Ot pesterey,
waw the rest noe I and suigrou wad tto you gr
whigl.
"'R't it
', the weske, opr; I ky'sle why copmex and that the scrakes owarkhy of ant for Sthavoede wher ih
the foo fact, 'hat in ow
Iteration (35000) - Loss: 51.21348815066355
Iteration (35100) - Loss: 51.07266012333748
Iteration (35200) - Loss: 51.356359787957565
Iteration (35300) - Loss: 51.2725784609351
Iteration (35400) - Loss: 51.24739728626246
Iteration (35500) - Loss: 51.57229725963554
Iteration (35600) - Loss: 51.576432067735446
Iteration (35700) - Loss: 51.5173073835747
Iteration (35800) - Loss: 51.34651635464582
Iteration (35900) - Loss: 51.23265037339154
reas."
"
the the with comeste and Whithe my ase to ralt-tore sithtichincb,
mhave eh with
hvirse himnAakern-not waster so
be licoods is gad to her aromy as, wimy fownes wabl ut as the doghte-soy,"
Yene
Iteration (36000) - Loss: 50.992564701334246
Iteration (36100) - Loss: 51.000634810367295
Iteration (36200) - Loss: 51.13714499860396
Iteration (36300) - Loss: 50.998952014366
Iteration (36400) - Loss: 50.927041661708785
Iteration (36500) - Loss: 50.80977387408794
Iteration (36600) - Loss: 50.796628534628695
Iteration (36700) - Loss: 50.633425862091464
Iteration (36800) - Loss: 50.462882884913554
Iteration (36900) - Loss: 50.372469154708455
lail sid, bustlyle the."
"Im, deeching, wers and that ding you dthert whanow of thenalmd,
a subing lo thiealded I bech shistiep then as it a sely fas he, sith at emabss firsim in a that gthe perthent
Iteration (37000) - Loss: 50.32728311804418
Iteration (37100) - Loss: 50.48949299607886
Iteration (37200) - Loss: 50.36081653768325
Iteration (37300) - Loss: 50.72282091382568
Iteration (37400) - Loss: 50.821352255133434
Iteration (37500) - Loss: 50.731863724771166
Iteration (37600) - Loss: 50.78363232661019
Iteration (37700) - Loss: 50.872909218526715
Iteration (37800) - Loss: 50.82060787786644
Iteration (37900) - Loss: 50.76962604903388
es," Crame.'
"'Thoued at prove; chescely, buangent, It howed He thach it.
"'Heur, wnouco ment."
"'Thes, mn to seighaf' was is tindsteed-derin iver nowness out he sery po itwerlen weacte. I thongs th
Iteration (38000) - Loss: 50.70343597615312
Iteration (38100) - Loss: 50.49866127283658
Iteration (38200) - Loss: 50.57409463814959
Iteration (38300) - Loss: 50.55604120426858
Iteration (38400) - Loss: 50.43693381610727
Iteration (38500) - Loss: 50.34468347932976
Iteration (38600) - Loss: 50.28051447549611
Iteration (38700) - Loss: 50.16380054546846
Iteration (38800) - Loss: 50.33269631355611
Iteration (38900) - Loss: 50.329430341536636
oo
sad ough and the glick and tors, my the my. Wherines of
and
a sut mo
tone the mance waghind of vane frige vach verllin, momenps opsins I has a mure anthary afaods of had mich enowmewwer the sill, y
Iteration (39000) - Loss: 50.36559547979955
Iteration (39100) - Loss: 50.71771732427915
Iteration (39200) - Loss: 50.94537388575984
Iteration (39300) - Loss: 51.23199099672592
Iteration (39400) - Loss: 51.36337704649981
Iteration (39500) - Loss: 51.15463725556048
Iteration (39600) - Loss: 51.18912255842495
Iteration (39700) - Loss: 51.04184881833608
Iteration (39800) - Loss: 50.87578255219682
Iteration (39900) - Loss: 50.81323062932975
nce, ofer, Ir ableet, I, the prew gad, lase ace dipm, a gabrusams of the a hould laim
sowalaanve to mave, of that a bet Protgeis."
"Wive a be son dive a chotlime your Stice."
"Youl? Ay in and it I a
Iteration (40000) - Loss: 50.808004152002354
Iteration (40100) - Loss: 50.71533073819218
Iteration (40200) - Loss: 50.87406669988159
Iteration (40300) - Loss: 50.78567424610703
Iteration (40400) - Loss: 50.81210159209594
Iteration (40500) - Loss: 50.63707759220693
Iteration (40600) - Loss: 50.47392833129973
Iteration (40700) - Loss: 50.489505302857445
Iteration (40800) - Loss: 50.669299193863594
Iteration (40900) - Loss: 50.63090663391429
exsuly im ve you reemy mtery-me sittildt with
reer spabl hored are he son Stod the
ufing ficticing Hoak beppey ack to he lamed the pasinven fare
an
han an frich to y
your Dremured m. And me. Serted fr
Iteration (41000) - Loss: 50.689703993117476
Iteration (41100) - Loss: 50.64253223552052
Iteration (41200) - Loss: 50.60847339527681
Iteration (41300) - Loss: 50.57366960388829
Iteration (41400) - Loss: 50.42472572705323
Iteration (41500) - Loss: 50.22994262368175
Iteration (41600) - Loss: 49.92415927781082
Iteration (41700) - Loss: 49.883795133561044
Iteration (41800) - Loss: 49.97199575218179
Iteration (41900) - Loss: 49.89105017150947
obys
ut uponet, hime thared
whiwsed in to dake? Herice fesed mea, to souttat, as who to yeel fare om
a could byot you sist my of aferyedsendssiog konittMy ew
the shaw se ifo fistelics intomrishy
of the
Iteration (42000) - Loss: 49.7245592000836
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. Burrs ang ostat and
sef and oh stors foour so smave
bleenct for
hea for a beed aokoar
blon by."
he poled to my im and be twe hersaclerofouvappinigalmave a as
litustentle ther, whicht he hard," oly a
Iteration (43000) - Loss: 49.89551144338542
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Iteration (43900) - Loss: 50.45388132584069
m
for."
"The care decher and
herselty to ableand to gele wel ale coun on lady fo ent dow dengemence, and. that courlded onasdigher, at en. Whice ann guncly
me fulues
youad ablen, oves, you dalre conce
Iteration (44000) - Loss: 50.403882063213274
Iteration (44100) - Loss: 50.28751045577519
Iteration (44200) - Loss: 50.23019377360325
Iteration (44300) - Loss: 50.00950923806504
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Iteration (44700) - Loss: 49.665589475293025
Iteration (44800) - Loss: 49.65023803298565
Iteration (44900) - Loss: 49.49723782352959
ter. The aocdsallons in then mas ser non, ceied
enen boquins. Fraret comlele rupilpee vir veant indont were it who smating, chan
enprow my. Hould whicred oney in
to face fare whi hisV fare cosen lins
Iteration (45000) - Loss: 49.88981976176248
Iteration (45100) - Loss: 50.111612432559234
Iteration (45200) - Loss: 50.166818540375125
Iteration (45300) - Loss: 50.361875207585584
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Iteration (45700) - Loss: 50.74979731788285
Iteration (45800) - Loss: 50.654263383259156
Iteration (45900) - Loss: 50.63899329207185
ng wfos woughird, duap.r and was I Treattice not. Waw fur
out-uremeh it the deisestle juch a woulnshich, 0ertir
there thiu
on. Tht when, herder. If and fored ther of namacht mfar
that ferbant."
"It er
Iteration (46000) - Loss: 50.64086588288461
Iteration (46100) - Loss: 50.53552472935556
Iteration (46200) - Loss: 50.4953056998234
Iteration (46300) - Loss: 50.47454908816654
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Iteration (46800) - Loss: 50.577215599295975
Iteration (46900) - Loss: 50.67221127856746
nound folk uppolvet, en
hitttaprave Yis the starmen in, and made tu neal a heam?"
nancass betiomvirill in that Erlow ot me us there or
neno-ed, mut in Mr and ramate cecans
abe, id lade, ate, and con
c
Iteration (47000) - Loss: 50.74776241074684
Iteration (47100) - Loss: 51.04634502671359
Iteration (47200) - Loss: 51.056546067676024
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Iteration (47700) - Loss: 50.761399382770854
Iteration (47800) - Loss: 50.66158699870781
Iteration (47900) - Loss: 50.801986957216776
rt by at saod whew thenelabed the korg the gosters.
'"
Weile
mantt tecr noke be"thiring a
cors melt fugensed was I conencrean for from helsomphimed
seish bro thound reraRt ter. Shat dodn.
"We?'
"A
tha
Iteration (48000) - Loss: 50.698722248655606
Iteration (48100) - Loss: 50.73699223828946
Iteration (48200) - Loss: 50.75640276020176
Iteration (48300) - Loss: 50.72922279713683
Iteration (48400) - Loss: 50.66714685058542
Iteration (48500) - Loss: 50.50397454318632
Iteration (48600) - Loss: 50.42121107179345
Iteration (48700) - Loss: 50.20338563305448
Iteration (48800) - Loss: 50.40390808383845
Iteration (48900) - Loss: 50.438656388658316
d a rast lighime
very upoted folr mem in pufed that thear ave, at trarting cole tur
shoush maar, ne has wand hat the reming tapilled was and val beraesid ofropke were ame but to come, quiting to of all
Iteration (49000) - Loss: 50.3651159884344
Iteration (49100) - Loss: 50.209544931522665
Iteration (49200) - Loss: 50.20718457794563
Iteration (49300) - Loss: 49.910458984254134
Iteration (49400) - Loss: 49.67740721917596
Iteration (49500) - Loss: 49.32989131082971
Iteration (49600) - Loss: 49.37417822503153
Iteration (49700) - Loss: 49.486820385579286
Iteration (49800) - Loss: 49.718657354715404
Iteration (49900) - Loss: 49.68555156781515
a an muss has, spanthike this. Whered of ghark, suenatto.
"'D. Hombliognlatuant to seake heariy to dow," orsin anjily to dano, Make in posmatcrice in to has myogk, a lare mitimim, whittice hard he fel
Iteration (50000) - Loss: 49.654385140125285
Iteration (50100) - Loss: 49.63164699778145
Iteration (50200) - Loss: 49.52927497311267
Iteration (50300) - Loss: 49.801342207171686
Iteration (50400) - Loss: 49.78732577206835
Iteration (50500) - Loss: 50.0204988189154
Iteration (50600) - Loss: 49.95974973538918
Iteration (50700) - Loss: 49.88632210443436
Iteration (50800) - Loss: 49.794464669210925
Iteration (50900) - Loss: 49.707347801084595
nd sura nollamut you samm
buted. I
heasiouge. I on his?" geped you wat the twawly of a ca shoung his plong guth uppos is ef
ous of he the Cuwan and in'tlen a nast in our sint.
I Aray, the for
-hallt n
Iteration (51000) - Loss: 49.63306508539978
Iteration (51100) - Loss: 49.66467254834791
Iteration (51200) - Loss: 49.54676022879063
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Iteration (51700) - Loss: 49.1828906770929
Iteration (51800) - Loss: 49.13356322811921
Iteration (51900) - Loss: 49.24082732493824
ntenon stwen the
cangesteccance vounneswros copron the kmved of cor bod a cheit hor At fal. It seen?
But anf gither'
"I tas soar hmise. The clesthas in nequytiof sution the con.
"You "Burd?"
"Whaid
Iteration (52000) - Loss: 49.25399854290805
Iteration (52100) - Loss: 49.195651594503595
Iteration (52200) - Loss: 49.167096853073176
Iteration (52300) - Loss: 48.99985671668226
Iteration (52400) - Loss: 49.183629445932446
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Iteration (52700) - Loss: 49.180313142748204
Iteration (52800) - Loss: 49.3588739353878
Iteration (52900) - Loss: 49.23288009843304
h-now he bad he the mad.'
"'0' to bychy parn, and obeaft."
oy ovind. He't be pwopriount man'.'
"Ked.'
Hiss, inked te sme,
how. he to. I Foint frove to that on anding ammytall. Ban my bush gmins, yo
Iteration (53000) - Loss: 49.30304709389319
Iteration (53100) - Loss: 49.31790012659409
Iteration (53200) - Loss: 49.4426320668252
Iteration (53300) - Loss: 49.22879132646775
Iteration (53400) - Loss: 49.48587699382146
Iteration (53500) - Loss: 49.505599420327634
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Iteration (53700) - Loss: 49.762861272928795
Iteration (53800) - Loss: 49.688997537433394
Iteration (53900) - Loss: 49.76072548772453
I and he to mus, unfion, acise be.
"It te or iivl elee bon have Holmouar--"I luch I to holl we to a deruped Snomed gellice for its is of the retur usme's
he
beagh
therisingh, Lerppiled in noy soud mor
Iteration (54000) - Loss: 50.2149038817802
Iteration (54100) - Loss: 50.13637746090953
Iteration (54200) - Loss: 50.284684115698866
Iteration (54300) - Loss: 50.20024001548645
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Iteration (54600) - Loss: 50.27504721405085
Iteration (54700) - Loss: 50.013668222426425
Iteration (54800) - Loss: 49.979570586604275
Iteration (54900) - Loss: 49.68883473476743
iending well howid with hilk as arocher alsser, altalt and comventideshigaed opo the. Ig poure that
This cearnadode-siot, I herure at for the
hackecestent of Mr the dowriant thot. H wwin I shige ccened
Iteration (55000) - Loss: 49.53448677523627
Iteration (55100) - Loss: 49.45831756419176
Iteration (55200) - Loss: 49.42591276452698
Iteration (55300) - Loss: 49.17640944618555
Iteration (55400) - Loss: 49.11676694950877
Iteration (55500) - Loss: 49.0834408629562
Iteration (55600) - Loss: 49.25572981877956
Iteration (55700) - Loss: 49.22888754347925
Iteration (55800) - Loss: 49.2795479904639
Iteration (55900) - Loss: 49.16032948955418
out arders will my thighed hingioy my mir,
which wairet wolreliblt be thuther-on't yound bome, nown allelthy," slingonved to
lopred that, mad heall."
"On,
whe with whitt is I havseds glotuln usore asa
Iteration (56000) - Loss: 49.329393364040925
Iteration (56100) - Loss: 49.4576108998352
Iteration (56200) - Loss: 49.353082081333156
Iteration (56300) - Loss: 49.15693655278042
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Iteration (56500) - Loss: 49.3852481973135
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Iteration (56800) - Loss: 49.58594563566737
Iteration (56900) - Loss: 49.771361943044035
lkeld sice that the supokonf?"
Orent mankesfin-y misigeytion all aicurg it squateld blon notstarf stliride have ray rould lidl roon shore the lenily, for hourk, you limeyicl,"
It whom ally holked th
Iteration (57000) - Loss: 49.62388578098728
Iteration (57100) - Loss: 49.73078568104353
Iteration (57200) - Loss: 49.655891342972986
Iteration (57300) - Loss: 49.6139234751015
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Iteration (57600) - Loss: 49.39319661046995
Iteration (57700) - Loss: 49.35226094054962
Iteration (57800) - Loss: 49.28442457548982
Iteration (57900) - Loss: 49.4056003595044
h and weredonf, the our dremust asled anderls alf ay and raupfore abilowed linged stapply that to thit
hathert rovses, assey I an Dotcay. Af and hase noong't
mes condap."
"Yapest takeichint whife. As
Iteration (58000) - Loss: 49.67134411603404
Iteration (58100) - Loss: 49.61509434710926
Iteration (58200) - Loss: 49.58136803705094
Iteration (58300) - Loss: 49.35125622793324
Iteration (58400) - Loss: 49.26362732218362
Iteration (58500) - Loss: 49.073546421775085
Iteration (58600) - Loss: 49.09865573795132
Iteration (58700) - Loss: 49.16465748030172
Iteration (58800) - Loss: 49.09019658895063
Iteration (58900) - Loss: 48.96993222667094
bam. "Yeatep mofed wald pomled bpark in roiss wafter cavenel. How encon of homablel hattismat buse
lets batablge am ane a bist wo vy beem,
souly and used
down no, wind benidardet cuse the stire of be
Iteration (59000) - Loss: 48.94582609576522
Iteration (59100) - Loss: 48.80970655396174
Iteration (59200) - Loss: 48.679454257105995
Iteration (59300) - Loss: 48.58352850903875
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Iteration (59700) - Loss: 48.564435539011896
Iteration (59800) - Loss: 49.00821859571491
Iteration (59900) - Loss: 49.1253871741648
e we would I gruth. She verytelcly to of the for my of no
mops ip to the to has Iated cofed it I be who tull the stom hos live the form comy of you to sutey. I glam intery reid you dase was him coust b
Iteration (60000) - Loss: 48.96268671244042
Iteration (60100) - Loss: 49.14795565456595
Iteration (60200) - Loss: 49.11747856174245
Iteration (60300) - Loss: 49.01992232623799
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Iteration (60500) - Loss: 48.829100281050174
Iteration (60600) - Loss: 48.7567887682506
Iteration (60700) - Loss: 48.805907585407326
Iteration (60800) - Loss: 48.79985819785196
Iteration (60900) - Loss: 48.68589832224739
d alair hand the to coined mit to at bedad imperocl, and who merecy of de wroky a
light whad thlor the my renly pess mocrone a
srot deapbed the mabdeds, samely why wabt. I mundet, wews no goome the ay
Iteration (61000) - Loss: 48.62549294855689
Iteration (61100) - Loss: 48.559429944602385
Iteration (61200) - Loss: 48.58423885073003
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Iteration (61400) - Loss: 48.5861635158914
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Iteration (61600) - Loss: 48.94989429750652
Iteration (61700) - Loss: 49.21860895335466
Iteration (61800) - Loss: 49.552175653560546
Iteration (61900) - Loss: 49.61846432657916
row docest, a phaled pance is he this wisced conder's fered
of sise solkong be, rad out bt I to
fore thas Luloul hatt a basive, te that Seve a
guped. Thing, brest betuvinat working lothasr."
"
here Go
Iteration (62000) - Loss: 49.512013679569904
Iteration (62100) - Loss: 49.4305409846865
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Iteration (62900) - Loss: 49.07335325873881
alring and the sed Mr. I had he ardedaald hack a foraghtwe had of enolother, I you but way in him!
"Nod do, theard andy wadgely of he have Stance rowed
a gas has
sork is the aman was lmanbnow a made ma
Iteration (63000) - Loss: 48.90201226878264
Iteration (63100) - Loss: 48.79641238364262
Iteration (63200) - Loss: 48.7548625488673
Iteration (63300) - Loss: 49.02473217885412
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Iteration (63800) - Loss: 48.82539526906072
Iteration (63900) - Loss: 48.61493240642203
e alld yeven's osse lyink! I wold 'I sare the dore. "Dits I sthous -than sish a ceald
up and is and fo'd hive but no
mise that I dose, beane, 'You dely up estald is locn that of the whall in of and sha
Iteration (64000) - Loss: 48.41810753361704
Iteration (64100) - Loss: 48.233717890248855
Iteration (64200) - Loss: 48.27872875215145
Iteration (64300) - Loss: 48.31177250803774
Iteration (64400) - Loss: 48.23399294682449
Iteration (64500) - Loss: 48.06433573138856
Iteration (64600) - Loss: 47.92114427179121
Iteration (64700) - Loss: 47.90653688205386
Iteration (64800) - Loss: 47.87354835745169
Iteration (64900) - Loss: 47.843603491074774
nt, Ally, onrad a
konderreno
clowhn pher her him."
"I his hand in to a urssing anterss
whimt, in the rinten of the wowing hongcention, and cratt Bear it and but ,
Stentcen the handrare me. Gonmally in
Iteration (65000) - Loss: 47.776289361290054
Iteration (65100) - Loss: 47.62005689875859
Iteration (65200) - Loss: 47.487447131748304
Iteration (65300) - Loss: 47.80239156335897
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Iteration (65600) - Loss: 48.54852604450821
Iteration (65700) - Loss: 48.7644269024008
Iteration (65800) - Loss: 48.708033675118905
Iteration (65900) - Loss: 48.93856209687655
ied of ever shins mistly
know you derelieght whire."
A"'sucgolded," seilainly geptweremed encettion rreach were it the lewurely fabked was fordly, buther turqust devick watt. Af saflee maach! Is howin
Iteration (66000) - Loss: 48.82578377767026
Iteration (66100) - Loss: 49.023148265374736
Iteration (66200) - Loss: 48.933833032869366
Iteration (66300) - Loss: 48.873817496963476
Iteration (66400) - Loss: 48.78042218804136
Iteration (66500) - Loss: 48.727860286987664
Iteration (66600) - Loss: 48.68748404015018
Iteration (66700) - Loss: 48.48664436172627
Iteration (66800) - Loss: 48.29972505273421
Iteration (66900) - Loss: 48.14311167146016
now his lough
I cot in bres fard end sundowe unde with a -and save imlour old acting pape lat was have I darey.'
"My my heard fitselteld frung out like I her alle raig. A it her. And his il de intlish
Iteration (67000) - Loss: 47.89379205954537
Iteration (67100) - Loss: 47.920401007161
Iteration (67200) - Loss: 47.897088532484595
Iteration (67300) - Loss: 47.92026608148208
Iteration (67400) - Loss: 47.88528413781537
Iteration (67500) - Loss: 48.358751265208205
Iteration (67600) - Loss: 48.482789951227986
Iteration (67700) - Loss: 48.50994558680255
Iteration (67800) - Loss: 48.823207996623886
Iteration (67900) - Loss: 48.84105322445506
ice eaghy in the en, me," dorpenougher ae." O"I distur derecand Holm
that anco me as and shis was alened and gothome was to oxnamame intterloter morand that put yam
demough, take the mast ofe Horres yo
Iteration (68000) - Loss: 48.91464709631816
Iteration (68100) - Loss: 49.28173839562848
Iteration (68200) - Loss: 49.15973122385029
Iteration (68300) - Loss: 49.08677252992183
Iteration (68400) - Loss: 49.11081866372582
Iteration (68500) - Loss: 49.20930485646631
Iteration (68600) - Loss: 49.00858950959461
Iteration (68700) - Loss: 49.04126893950443
Iteration (68800) - Loss: 48.918668032360785
Iteration (68900) - Loss: 48.80458375219232
utthich there upold suphened fremousted. Then treand haar tho pilutim the
sas men
the that he
rastures him wrooselad
you Smalpin! dows yeed
Amed a chard awters., 'fooumep into cut as
ferpied of sharl,
Iteration (69000) - Loss: 48.64189579269604
Iteration (69100) - Loss: 48.533693005681066
Iteration (69200) - Loss: 48.72698307856201
Iteration (69300) - Loss: 49.11435242741846
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Iteration (69500) - Loss: 49.148301712972916
Iteration (69600) - Loss: 49.5585089683427
Iteration (69700) - Loss: 49.410167136399664
Iteration (69800) - Loss: 49.19329038504735
Iteration (69900) - Loss: 49.10475285989411
Jorrive
tho puplowis astonow the has she ent carnouly should of the bpars's.'
"Thay in in
reanvers?"
"Yeh
the sille ovore thay, was of shalrne
'so food crimay, te ans of the so the bange you stisince
Iteration (70000) - Loss: 48.93578377504278
Iteration (70100) - Loss: 48.85185634088296
Iteration (70200) - Loss: 49.159065517485764
Iteration (70300) - Loss: 48.999564528732456
Iteration (70400) - Loss: 49.17844948476004
Iteration (70500) - Loss: 49.25972916346241
Iteration (70600) - Loss: 49.21864797529367
Iteration (70700) - Loss: 49.23980765070139
Iteration (70800) - Loss: 49.201287926941184
Iteration (70900) - Loss: 49.1222084815926
as thin I heary of lomeet, e
thened and mryow
saig, uster tround the
have sthersf-o stoed busene
that with us deart as athiempide setwear fforness out he
phoandt. It some, he whund laomes dowen where M
Iteration (71000) - Loss: 48.96139977362781
Iteration (71100) - Loss: 48.82861702040266
Iteration (71200) - Loss: 48.656712043838915
Iteration (71300) - Loss: 48.93386223109971
Iteration (71400) - Loss: 48.885751899213
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Iteration (71700) - Loss: 48.573733157515505
Iteration (71800) - Loss: 48.234695607100576
Iteration (71900) - Loss: 48.02192869264067
pratesk," seive hi
hert, be ta and Op nodres; saigened of uvinds, a end tome, nime be dide fo. He, Fand, this saiy cablich ancerd wave fris fat-evened. The whiak whith rom a
wive tulle have the haim,
Iteration (72000) - Loss: 47.78645438488739
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uctciwpelly gly
cerme for then wey hownike and the sceking to argher wam sis Masech hisgel.
"We foodtow of wevesstaptor,
in an of the toore thresed m. "Your
ofid yeghopentense curesery-egupionson thon
Iteration (73000) - Loss: 48.4927558544285
Iteration (73100) - Loss: 48.50149341527242
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Iteration (73900) - Loss: 47.80061983694155
s fall a
colse chitess, wak On the wrilof
te thas
ofroughe busideist
ro wurtibed, In the acstatith Panger
seet, and frompst thair that a fo then is of stent. Itwe he, is of to
the Courting of the Rus a
Iteration (74000) - Loss: 47.825963779605495
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Iteration (74900) - Loss: 47.98094465530934
of the by in exowned lother ufsen your
entwook ought and oS know."
Holmes weelank, folpwece defleeing pibour ane oncate kundly and a cane you lacke
muesser dusss.
"Go myich do Is wnep and theod sitt
Iteration (75000) - Loss: 47.97751827829914
Iteration (75100) - Loss: 47.887912365491
Iteration (75200) - Loss: 47.99834433175219
Iteration (75300) - Loss: 48.07319440571257
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Iteration (75600) - Loss: 48.05548027060594
Iteration (75700) - Loss: 48.06457958329479
Iteration (75800) - Loss: 48.020495272471734
Iteration (75900) - Loss: 48.30928374618632
mens expe; sime I mabutco it it sus arrottals
onet that is exseardes overy whate to lick berlit
crardencreaste of a muron onchay to nas the esdornas teare upoul filled herely clatireasines un: the sth
Iteration (76000) - Loss: 48.23403100973168
Iteration (76100) - Loss: 48.47732287228619
Iteration (76200) - Loss: 48.57066764244934
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Iteration (76700) - Loss: 49.12608276176005
Iteration (76800) - Loss: 49.02114077838232
Iteration (76900) - Loss: 49.1550255001444
er whll whing the gunged to that he maged of cearadesk a very linges niviee bote to soge a soutl that cot whit, theanto mo stwerrete sif the have a maghters my. I
sive mad liging dinghhy hind. I refe,
Iteration (77000) - Loss: 49.149884063668566
Iteration (77100) - Loss: 49.038333963848956
Iteration (77200) - Loss: 48.839346726028765
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Iteration (77800) - Loss: 48.00921424452907
Iteration (77900) - Loss: 48.00567416531327
whep.
Hown aselycedf.r
youll had Marlly concur one tithar inting the
extecs, the gaice."
"Ben Head bilrep rie'
"Herled ramed, ay. Whit a stion and Hollich it ar claint my the hat but of-cos the polcr
Iteration (78000) - Loss: 47.972474212328706
Iteration (78100) - Loss: 48.036928419587774
Iteration (78200) - Loss: 48.10435468959001
Iteration (78300) - Loss: 48.08640717547363
Iteration (78400) - Loss: 48.113423857862934
Iteration (78500) - Loss: 48.1830918860825
Iteration (78600) - Loss: 48.24713946558697
Iteration (78700) - Loss: 48.14058866450957
Iteration (78800) - Loss: 47.952282459168494
Iteration (78900) - Loss: 48.13178925242765
ded beared roulds amseres
putter a rirke yof grom me wribe
the hiss that alver, was the cat wrouw.
"E
Attocend paukentt. This inton reisely is you trapser.
"Whimether phanco blase my alpering mist,
Iteration (79000) - Loss: 48.22541393082083
Iteration (79100) - Loss: 48.24754447688881
Iteration (79200) - Loss: 48.269183037107716
Iteration (79300) - Loss: 48.46079262178903
Iteration (79400) - Loss: 48.556833381485305
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Iteration (79800) - Loss: 48.45913513250916
Iteration (79900) - Loss: 48.16905022391446
to. Yit dowes our arid I raint
non swintion wer."
"But the megenep't trice inshow' conge not that this named sich wagked
ghere?'
You havate! It
beed. Mllyt And wiggane of the sardy
my teess, I was ex
Iteration (80000) - Loss: 47.92052134153264
Iteration (80100) - Loss: 48.215673298852366
Iteration (80200) - Loss: 48.16983799917533
Iteration (80300) - Loss: 48.08946294998183
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Iteration (80700) - Loss: 48.39875414106207
Iteration (80800) - Loss: 48.25482009737537
Iteration (80900) - Loss: 48.07079627726726
is
out but with that osterly to of Mr. An, whating. He veran," sticling cordeit bus, fathes, ordict acaliascougtsire throe Bare full exined it it neaghin, us, I hig utoncto-wiinlyst's ,
Afmer."
"That
Iteration (81000) - Loss: 47.941815410683404
Iteration (81100) - Loss: 47.95392661551517
Iteration (81200) - Loss: 47.98641434846696
Iteration (81300) - Loss: 47.89082596320636
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Iteration (81700) - Loss: 47.453525182208665
Iteration (81800) - Loss: 47.43505982993685
Iteration (81900) - Loss: 47.40324913939813
t is in of murded I the rempllding who capelathen her forter all the staoses, "You disten fandafked, sher Nor we are the dienps pasd in on the takens
, bectres, and the setter."
He to wat youn her see
Iteration (82000) - Loss: 47.42719007724012
Iteration (82100) - Loss: 47.52581059997929
Iteration (82200) - Loss: 47.598711952670406
Iteration (82300) - Loss: 47.74928093423792
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Iteration (82800) - Loss: 47.745802318719065
Iteration (82900) - Loss: 47.692353653888965
e no
gandelver mew. The brive-cut which drom her the sart was onre all mad come then down in as in tis gondlod, pratted it to fithe, por that? I comeenther of with he wo
loo saif of the ocke, oto is is
Iteration (83000) - Loss: 47.5632259908679
Iteration (83100) - Loss: 47.57304672264192
Iteration (83200) - Loss: 47.61188541387925
Iteration (83300) - Loss: 47.58149391461268
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Iteration (83800) - Loss: 47.40827976346881
Iteration (83900) - Loss: 47.46804755806074
hall, arrew I a ut over arterall, sfoctilt as to you ciloughinains for cam a cerpon the shad, and
sore which nom aball ans of he beam of swat whe Brabling me a down is
were tryoune in pench that indorn
Iteration (84000) - Loss: 47.75740826000093
Iteration (84100) - Loss: 47.84688012899865
Iteration (84200) - Loss: 48.2083956031004
Iteration (84300) - Loss: 48.37101175134641
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Iteration (84700) - Loss: 48.00737368087296
Iteration (84800) - Loss: 47.97137056157116
Iteration (84900) - Loss: 47.84869902440071
the forgeatally is to yeming
nood the capaly on phad-all jotefarg. I mardent of St flomes
fatting natgeced, forayked cinoid is us and of a net. Eutad the
has weres "the lady. He camperson feainad a nl
Iteration (85000) - Loss: 47.81639055396493
Iteration (85100) - Loss: 47.90382393053668
Iteration (85200) - Loss: 47.9260483215898
Iteration (85300) - Loss: 47.90695858210599
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Iteration (85600) - Loss: 47.51307800183339
Iteration (85700) - Loss: 47.57920403472166
Iteration (85800) - Loss: 47.78873667489575
Iteration (85900) - Loss: 47.75371446495202
f efat 'Vlu steacutaly tor watles oo fet fedimasao,
bring. sxat in
bley fate dise tricked Shtyicuchy you dates
with bage than Whi duster that of homed oncal exaont a farss afe loid a peaen-to.
oreard
Iteration (86000) - Loss: 47.754103345612265
Iteration (86100) - Loss: 47.64028092027491
Iteration (86200) - Loss: 47.668383219885584
Iteration (86300) - Loss: 47.702293883458886
Iteration (86400) - Loss: 47.43768145222836
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Iteration (86600) - Loss: 47.221740966268506
Iteration (86700) - Loss: 47.18620231587025
Iteration (86800) - Loss: 47.20617167500559
Iteration (86900) - Loss: 47.03360468833726
upor?"
"'Ere chase? "My the drewtlor mank the gakesper-Mrokated net which that liensed it buch forich
rade. And be infers an trough could a canss ovave, ha blaien ingining in enited tof of gake rithe
Iteration (87000) - Loss: 46.957615761112834
Iteration (87100) - Loss: 46.85990770448187
Iteration (87200) - Loss: 46.73623570803197
Iteration (87300) - Loss: 46.79071795914517
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Iteration (87700) - Loss: 46.501578021750014
Iteration (87800) - Loss: 46.83644129098901
Iteration (87900) - Loss: 46.94810036624741
n encures to the kin, 2ther dling a prito, to the nes
afpenced I al reen and feess of a larver, aw
or offers. rut we call for Mrime to gomsan us ence to ne afsure.
"froums thoughly, breald in of his t
Iteration (88000) - Loss: 47.358885228411886
Iteration (88100) - Loss: 47.53155197408615
Iteration (88200) - Loss: 47.631327167054145
Iteration (88300) - Loss: 47.63780555499834
Iteration (88400) - Loss: 47.71464175087423
Iteration (88500) - Loss: 47.70352092849419
Iteration (88600) - Loss: 47.8610592895189
Iteration (88700) - Loss: 47.73230809655342
Iteration (88800) - Loss: 47.687373579639704
Iteration (88900) - Loss: 47.637005106047184
'Lepiry cafl
enchive exore will
iiplelcey have for
my a verucheld frackinid in in the mad and slampeding carfel, this cut boughinard to my me withen for I mays, "hat hee, dinthor, I han
sil froyd whoan
Iteration (89000) - Loss: 47.61950380115901
Iteration (89100) - Loss: 47.632501415471054
Iteration (89200) - Loss: 47.30833362131499
Iteration (89300) - Loss: 47.1699494512136
Iteration (89400) - Loss: 47.02663119208522
Iteration (89500) - Loss: 46.9050553195392
Iteration (89600) - Loss: 46.95326503455927
Iteration (89700) - Loss: 46.91325166655104
Iteration (89800) - Loss: 46.882493938304656
Iteration (89900) - Loss: 47.10583114133891
mome old thet of think, the
sflens in Hollrent
inlerd, which gase or peritineath
preme hare alloughther and been dock me, my apon ineeds; gasly there prieted gure intyess treaplect has stace his rerpa
Iteration (90000) - Loss: 47.39470558167736
Iteration (90100) - Loss: 47.46520688243089
Iteration (90200) - Loss: 47.42748376538343
Iteration (90300) - Loss: 47.76766970211753
Iteration (90400) - Loss: 47.74628653368707
Iteration (90500) - Loss: 47.99193084917899
Iteration (90600) - Loss: 48.18431327894739
Iteration (90700) - Loss: 48.06634539571846
Iteration (90800) - Loss: 48.0829939957634
Iteration (90900) - Loss: 48.17222386338953
ere a mon I sthesfwo a know honle ierted froping't there thomly. Thoult, and it whet to onh vemoling gut Goon moring with in wavendly sapesurt of Thin were up peming! glrash to howed the sain catongo't
Iteration (91000) - Loss: 48.11855333016273
Iteration (91100) - Loss: 47.93633759457585
Iteration (91200) - Loss: 48.02475426514572
Iteration (91300) - Loss: 47.89604019969489
Iteration (91400) - Loss: 47.70081405024911
Iteration (91500) - Loss: 47.544671657773044
Iteration (91600) - Loss: 47.5113235847405
Iteration (91700) - Loss: 47.97218135703456
Iteration (91800) - Loss: 48.050063064164235
Iteration (91900) - Loss: 47.978524400604876
ou trioking, and Aider apon which so Bast of nersfe it insind mat, and esstoret, withiny that up hove that it ill he ane beack tomer
I bering nos, that stunged frokm Secintl."
"At intihe
persshed frop
Iteration (92000) - Loss: 48.12466167643354
Iteration (92100) - Loss: 48.455042008569194
Iteration (92200) - Loss: 48.386321294991625
Iteration (92300) - Loss: 48.155910806816244
Iteration (92400) - Loss: 48.068078029153284
Iteration (92500) - Loss: 47.96197521627819
Iteration (92600) - Loss: 48.15952610842545
Iteration (92700) - Loss: 48.05342434409617
Iteration (92800) - Loss: 47.89056038305656
Iteration (92900) - Loss: 48.020917736376475
up courdremed noubulved was loin I saal him mad areloked remald ention, at rught aisutot noom and Lofk mithy
alching make
pommat
which."
"Frailt forled in his coudy a maid it to strock wnot metlown f
Iteration (93000) - Loss: 48.22343543987889
Iteration (93100) - Loss: 48.17390530962866
Iteration (93200) - Loss: 48.18204337357366
Iteration (93300) - Loss: 48.07087133868091
Iteration (93400) - Loss: 48.04579584605202
Iteration (93500) - Loss: 47.895679759864045
Iteration (93600) - Loss: 47.87267023088185
Iteration (93700) - Loss: 47.58868675785502
Iteration (93800) - Loss: 47.971718294859265
Iteration (93900) - Loss: 47.790792798350694
u cafe to the mashed then have hee exphe fall end mutuf I
clighing was chary oun here to bed,
bound Cload whink chaas's
outh as upor whock Whene fromed
I smever hive which te horo, "Ohise, the
have. Ho
Iteration (94000) - Loss: 47.886154768851114
Iteration (94100) - Loss: 47.772396459118845
Iteration (94200) - Loss: 47.574902251808446
Iteration (94300) - Loss: 47.26038573596718
Iteration (94400) - Loss: 46.96007182428559
Iteration (94500) - Loss: 46.85791380224713
Iteration (94600) - Loss: 47.033442177568325
Iteration (94700) - Loss: 47.27975017866287
Iteration (94800) - Loss: 47.240391127186065
Iteration (94900) - Loss: 47.23536947236847
re po paratershted tatcingell noverserated.
"What band, Mave to fed an," saice ofority, buse inceed."
"'I ham, and fonderly triugher osher ha-chake marly laddistamann of he dor to werre,
subpedet, ro
Iteration (95000) - Loss: 47.132743630762604
Iteration (95100) - Loss: 47.17685923918712
Iteration (95200) - Loss: 47.035169472414054
Iteration (95300) - Loss: 47.37227004207218
Iteration (95400) - Loss: 47.50369365365578
Iteration (95500) - Loss: 47.462911985008006
Iteration (95600) - Loss: 47.52637292946112
Iteration (95700) - Loss: 47.39522566307821
Iteration (95800) - Loss: 47.37154756023225
Iteration (95900) - Loss: 47.19154341329283
aturioh?"
""I real of the blon I muy to mackished Bant houlo. Holmestore oos?"
"Will masto's all frimel. I veeson ser. The
rase myontous to bmathint I wnimestiog anvery than up in the a
kille miy. Ee
Iteration (96000) - Loss: 47.326464133150594
Iteration (96100) - Loss: 47.15448674726534
Iteration (96200) - Loss: 46.95814211932578
Iteration (96300) - Loss: 46.95574504646959
Iteration (96400) - Loss: 46.83511194142509
Iteration (96500) - Loss: 46.87515504864421
Iteration (96600) - Loss: 46.88781199634118
Iteration (96700) - Loss: 46.80152342868821
Iteration (96800) - Loss: 46.82874694654319
Iteration (96900) - Loss: 46.853974276837135
oured mugl of the ang one
rean os louble."
""You stherang lonkep anly shou?" It mit had pofing at the his with his litted riss
y, lidt stillany of an the wore buenille to I here fourt wistioufs gear, a
Iteration (97000) - Loss: 46.95525824398567
Iteration (97100) - Loss: 46.882448822637144
Iteration (97200) - Loss: 46.79015982457628
Iteration (97300) - Loss: 46.90341506252832
Iteration (97400) - Loss: 46.993662716115224
Iteration (97500) - Loss: 46.95676854791471
Iteration (97600) - Loss: 46.9612790438574
Iteration (97700) - Loss: 47.079120004203105
Iteration (97800) - Loss: 47.02107573976713
Iteration (97900) - Loss: 47.05673223486153
keer at of ghank in upre ro his nard, you have, and it
his dacked greaken antabpeverlysield the him the
spoker his om. If everying interes'd Heret. "I vince, that
is mose be tof nox inat. It his lost
l
Iteration (98000) - Loss: 47.116822875921045
Iteration (98100) - Loss: 47.05877246297264
Iteration (98200) - Loss: 47.08335293758114
Iteration (98300) - Loss: 47.10076120968032
Iteration (98400) - Loss: 47.4078195131841
Iteration (98500) - Loss: 47.432150252159914
Iteration (98600) - Loss: 47.63429089743154
Iteration (98700) - Loss: 47.650553446002014
Iteration (98800) - Loss: 47.76197806260025
Iteration (98900) - Loss: 48.021853839917526
. This bat at. It fere than nepod!"
hoss-sinich
the meind what you abmom
lark be
handtindce fropok to pobest fao,
what down underall, bet shim beswucing, St pharo."
"Goubon in con indow beved
Undard,
Iteration (99000) - Loss: 48.08606143983557
Iteration (99100) - Loss: 48.174007046608395
Iteration (99200) - Loss: 48.244362362787804
Iteration (99300) - Loss: 48.15809762497233
Iteration (99400) - Loss: 48.2062786424727
Iteration (99500) - Loss: 48.30392027298974
Iteration (99600) - Loss: 48.116690166703556
Iteration (99700) - Loss: 47.96798859845185
Iteration (99800) - Loss: 47.707151222071644
Iteration (99900) - Loss: 47.42529030868038
n," the
lity loosined ponly an,
facaly Has elflan drieg non
as housh
rome arsor tapmn made to-nokeats my sed hom dous no had dearcher math a qut.
"700nor, was
of
saintice, Sncaf-never hid frowavead ar
Iteration (100000) - Loss: 47.49174871935582
Iteration (100100) - Loss: 47.34047628460603
Iteration (100200) - Loss: 47.229952461789196
Iteration (100300) - Loss: 47.16665987538763
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Iteration (100600) - Loss: 47.163907568731574
Iteration (100700) - Loss: 47.23807067136488
Iteration (100800) - Loss: 47.2112485809383
Iteration (100900) - Loss: 47.28823790280271
soor mation.
"And
now hass out sthort had oponen pongering and of kfothist rivagever."
"I was wideriss to deind afcome reamed."
f of you helad the arcile-waseart, lino thations theaksed cruse fat it
h
Iteration (101000) - Loss: 47.3759923391704
Iteration (101100) - Loss: 47.373469113475664
Iteration (101200) - Loss: 47.23873521193258
Iteration (101300) - Loss: 47.14181414816672
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Iteration (101700) - Loss: 47.45819445674102
Iteration (101800) - Loss: 47.666162512224425
Iteration (101900) - Loss: 47.64536983148905
of put
geasing.
FAre fourt allerel,
and in the keingengach tave as's the markou!" the murneved you onnt, the havt be in thewef, that and that Sterinid ?"Which now think."
"What your in we frops al hi
Iteration (102000) - Loss: 47.776150878666535
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nd a supilese to
rerettirising-was aghorss the mouke afpered, afk we bring overs of
they higrat andeewn bane is him uponed with fy and, and the man woulder arloger al sme be lingantllw roosentrles, poo
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s of
basing well ma ald is slienf was said his to you whiet. To the enew."
'"'"I knor turnew, whation mist be abelled beparr," said I had my the co-ctang the quich the listher. You that hime blaonsed o
Iteration (104000) - Loss: 46.996156735672876
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"Aotergach seangos coublested sufe to think the arele of farl of
the fir! lake is of she ostabler thied dill Holmes, ar' roughterer.
" he promare to ane as -op shere I cried widous roond indeaes hea
Iteration (105000) - Loss: 47.02640892794436
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r his whepurd for herer. It ois bored if he has had whing mistled
was laint, "nes, bubbso bbersers, and my abunt.
"For is youfhad remard in the was be, besftersher. And that ded Snament a blow, wend,
Iteration (106000) - Loss: 46.65508606128085
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laaning on in at downyss inay and which ace, and tixtejorengtisery."
"Him upon my docken,."
I carghen siid abpeens, and a very. Then I ather atiof."
"The valr and
praping bring. Ang "gomution houff
Iteration (107000) - Loss: 47.58110641723742
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horher, trimbend-by be justined the en,
Lose
or to gas in putalowring becafist ocest oo there? I dritle her befrighidoun. Not chist up firm which Stico, enter sime I Froall. Limp roo list to hy capjnep
Iteration (108000) - Loss: 46.849993530869455
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d anible to all sum. I awyed
wolnan the cweved showhy, but I for in tere mad have
lact facn opreasibuld bryock mamy I what his ane-out an the ste candiontide, that
as rever was blangeread ona callee pr
Iteration (109000) - Loss: 46.3845010563414
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d
am ofe He had arsibe. No thottermone blabone shounthed lifed resalles, sberwarsfmopejure boughane
yetther you canfor
twere dosmat I there
one it swooen, ssentsibgaples
know whore, you sork? It ofed t
Iteration (110000) - Loss: 45.904151704454655
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," notle you momenfpectainy abourded am that what be in ou the smagned found, requed quich sfowads a
tomend swoted be that is be.'
"'Holl?"
"And
he. "Sytom I itsing them I staings perceatorowe
the lo
Iteration (111000) - Loss: 46.92037561107965
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ch ally. Thes, longhior. Theyed to sell who af a worters, out werrestly that so the it
untaluls susting, will, and there will-camightsed cains. Burds ever
loobleend. "Ber ard of and hurturle seighthy n
Iteration (112000) - Loss: 46.047081027379264
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oughist it
srecgiout me and grealdevivoring op
this dight
as as was handing jucl by in Friestilt
om of
said iy sactfeen.-"You moon.
"There he toun my and even mims I dofe hein, into sode yourdensuved
Iteration (113000) - Loss: 47.471157674278906
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. There faared up in the. "he have the lissibage,
bether geturh the
rither rem be beetionelak which we wrapter, I cowmer to the stomes and an
hill neeting
sher wyerter al
whee hatkeitgel. My cheature c
Iteration (114000) - Loss: 46.81885167251374
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t wel gurmen age, wmoy 18t to the mess it
how
of I was of is Tatide the lont."
'And of I memanvide," thell count, and at home driens in
thimemoot Helter and, buther beforets, nowes tait the knested th
Iteration (115000) - Loss: 47.14188583622128
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my
I you my at he thoneln which wave the ban your the younks mat fouldalst any not antarm opeds of I hocrused was astore maf then clearn agary yand. Jack here that for a ose wassed," sonk they to had c
Iteration (116000) - Loss: 47.1102501607772
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Ny heowsed slise hid Mralvelmm bade; the bear shaple, and fall opevined ofed have that non able the can spiow upon, and has a hered, myter
hoursed
I hat It one rematestingast into."
"Yous thigkebre o
Iteration (117000) - Loss: 46.028233125773944
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onvergarstereby in than safsucour act the mood use maidong of and traid oft koin. Cocen deet an a
qappeen then the was and fion, and efle."
"There his the carrounce the veriched scopt mond,
wrred. She
Iteration (118000) - Loss: 46.70100127811656
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ply oo to quith sase buem, and at his
salrusk from knored, willed was sho.
Hormet?"
Horweder twas osfither her a so to gress. Het san hat this not-cealine well, wno
waid on the last as; somrolenwelf.'
Iteration (119000) - Loss: 46.095751114371645
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to come see. "I in pritelffer."
"No, and, aid from you or
the sirton to you colpers At
husht sill."
E'Clattaly shous forming was fack," the coubler have sticl his we had wore a
bad-ars me mubation i
Iteration (120000) - Loss: 46.19692262712558
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is gettered."
"This did my ind wills, of Ky obfopniked. Nown you thincer to that intear-a corncfed and-bidat. Fulk that the
shay?"
"He the sained. Wastive desire lidivery-Kbeal befevers it exsoned to
Iteration (121000) - Loss: 46.962426838099105
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rouked coiman at cCfone and who campelesed latine," simencriched a coms. Youns. Well the horel, 4n howeningy.
"TherningCals, his obs:
smo antion. Wet howed, no
foresfion guC braw reess, HolmeI
lipere
Iteration (122000) - Loss: 47.64484077108369
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ow
stery, is his had say exapin, me when, and inthosghed we emple ih." Hould it of thecs of the ground of Leoccaled of high! Ieper had a hanjommer it. CUs sonted at can, mur seener
she trough
me sthowe
Iteration (123000) - Loss: 46.58442648832375
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in beption be; Bhan Pomnous wasser, of the may hisk as a betucured bome, is the silk who he have he workdyst tiviredss in a troid and your iy it. Hetherpel," 000 casjeul, emprearixtland you diry.
But
Iteration (124000) - Loss: 46.6998383938108
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e reames, I
was a caugake-. As. Heard.
"Aglt forbsalfe. Ostaings
mined in jut then head an astary of the atf and and at foones tornes, 1he bave tork you, if is seash, and thracan.
THolilatiley for to
Iteration (125000) - Loss: 46.47838234394237
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amard-beinge my reefs, in
nealls offore upralmed mut
is there yould frove, beanie and for bupl forms to fastion some peaks of so a carf. I had verod os is a sthastly whith enverf.r
the serearding his
Iteration (126000) - Loss: 46.50713256824544
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epiattaler hap the stlest of down and with a
stry there, Proces. That acrid?"
"Whay," hey a slasioy torching one
moomury
so then, ofone hightt with to doncal
the soor,
and mide.
"Shmat bitare
dore in
Iteration (127000) - Loss: 46.16325567856788
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keron
ancerr which lastle crase owe naghen which he shere, drabvaasing the. "It in that that wighern. The aming that mistlystlery hler frofite whate is silk-carery to of a fwruck, and street quith here
Iteration (128000) - Loss: 46.02353088855665
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have sture that of ladselves, he, where to bucl to yourdarcher see is mo lasatiot, were
ex to huther:s the motteety and grimention rantave a fithong
upon
companily. There defose this by out was a pryo
Iteration (129000) - Loss: 46.316410273627035
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pcas, indice."
"He enfist one the derectine
Holme. "What yeid--gotrest, Pad. Tho.
Save rourreather he all to past ate indist twithinidy fresh rothing, my nargherind shom, pasels stird of coust intelve
Iteration (130000) - Loss: 46.401117963995226
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caint, mishor I but of say is thourdperesher enowend to your pally
thast he a kount.'
"'But of
o'd eass obes to miaked very
and morector of I semay that in the should he.
I hound is comakiny's of Dar
Iteration (131000) - Loss: 46.26307449495622
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re to ress."
"I ders the
bapk rough, excreat eicual never if the peask.'
"At?"
"The. As theel, and in that wouvier ay he wen of poant ve proult no nown
owind
nom searly tingo's hope, and is thone
an
Iteration (132000) - Loss: 45.46214024211665
Iteration (132100) - Loss: 45.4279344104374
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Iteration (132800) - Loss: 45.452020408881346
Iteration (132900) - Loss: 45.93754386786861
al nottates: He be in are
swough, rean to wrount makle. I ghats that and fithone
susiched ind maketsile that was stemponsod. She your droly Street wisegly I word-for the say fult to the sise when -pown
Iteration (133000) - Loss: 45.89548050930033
Iteration (133100) - Loss: 46.05316551444582
Iteration (133200) - Loss: 46.05048352899134
Iteration (133300) - Loss: 46.27549599353443
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Iteration (133800) - Loss: 46.25034772722802
Iteration (133900) - Loss: 46.186060802412214
to
the con prows-sate as upuas and in any
slell.
"'00 Clumber unsaik
which we temend."
"The paidontringe hourd byed lighture the siled sopoled founsture a ther wring of at ournes all my is in, he fa
Iteration (134000) - Loss: 46.08573502490278
Iteration (134100) - Loss: 46.07144405282827
Iteration (134200) - Loss: 45.840939005840305
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Iteration (134800) - Loss: 45.45939411022533
Iteration (134900) - Loss: 45.861964079455895
, we well is with iever has sprait mo Sas and
the and hom thinked a wush? It ingak sut it to prilon been
exfoir I was nan
awe, sece of a croze jon's at a her with a mast some, his to headed a
forcamori
Iteration (135000) - Loss: 46.06174647920017
Iteration (135100) - Loss: 46.05674252625465
Iteration (135200) - Loss: 46.38501492024099
Iteration (135300) - Loss: 46.43234369732589
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Iteration (135700) - Loss: 46.809279228658774
Iteration (135800) - Loss: 46.81571792097993
Iteration (135900) - Loss: 46.850774195678945
ver it
of I ligkeratnes
was you sover of what and refinitin refaome."
"'I droupled would the soentibrieg the laence and my sutcer
eirffors un the thinver," Thankosion bllat yach better threapparyent w
Iteration (136000) - Loss: 46.73003946916089
Iteration (136100) - Loss: 46.66430223498779
Iteration (136200) - Loss: 46.617020917295434
Iteration (136300) - Loss: 46.46559456652575
Iteration (136400) - Loss: 46.32112545257593
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Iteration (136600) - Loss: 46.299163659442904
Iteration (136700) - Loss: 46.53357713636519
Iteration (136800) - Loss: 46.479641091439596
Iteration (136900) - Loss: 46.58863644427671
littom pake, ruszing uf?"
"Nom. Iglys face the quitine thaved
here the chaping at wiun is, ant it a
pech couruals
prantuy.
UThilk to ut girryine, to the nomplonied over, but his
not and
luan."
"4Ya
Iteration (137000) - Loss: 47.00799294628415
Iteration (137100) - Loss: 46.93794270830885
Iteration (137200) - Loss: 46.720350042604544
Iteration (137300) - Loss: 46.62915122330583
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Iteration (137800) - Loss: 46.62324007203879
Iteration (137900) - Loss: 46.66102261293277
e takene exconven ham shand. Womer."
"There of ir
eners,
which just
jus, in gelly heesedfext for gevering
on that is and opr for a Storned. I
"'Dad
with I came on mith
his lace and hosen to the unced
Iteration (138000) - Loss: 46.6798814634038
Iteration (138100) - Loss: 46.74638040863548
Iteration (138200) - Loss: 46.72977754085499
Iteration (138300) - Loss: 46.61298689083392
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Iteration (138700) - Loss: 46.50875192670831
Iteration (138800) - Loss: 46.5947579818829
Iteration (138900) - Loss: 46.55410569851677
Here to trlowe, my urog the repioking,
an detam, than "Vhorbreagenerly Con
his whres, what he save had primine cut in
ubsers. What what I'nd is, and onfeigase his hainy your
Lortsing cuthy a whomenchin
Iteration (139000) - Loss: 46.39681134953753
Iteration (139100) - Loss: 46.32877499227452
Iteration (139200) - Loss: 46.00264391969049
Iteration (139300) - Loss: 45.78353360796022
Iteration (139400) - Loss: 45.53602090175095
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Iteration (139700) - Loss: 45.897325627028096
Iteration (139800) - Loss: 45.87828187012274
Iteration (139900) - Loss: 45.85782979764148
af some site come me upensed, bufter
soame
he very."
"At is no
quames of that you has no Binhirid to, which recay.
He which to naw it the raor, ver
drimanttle the, that lias, avatiled pugner, Bmant
wf
Iteration (140000) - Loss: 45.81852355647091
Iteration (140100) - Loss: 45.735079246678325
Iteration (140200) - Loss: 45.98996558162874
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Iteration (140700) - Loss: 45.970766649520186
Iteration (140800) - Loss: 45.92706337092967
Iteration (140900) - Loss: 45.84915290244407
Mall it, municame which,' y fore as Closk?"
"He neventine lids. I to smoant of thiint
jungoo aining the sritted and the evenched youn eather
upon in to his deel goury, Whur
ut wish of
than his very to
Iteration (141000) - Loss: 45.927867151444005
Iteration (141100) - Loss: 45.741064131992914
Iteration (141200) - Loss: 45.59809850503201
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Iteration (141500) - Loss: 45.60751128508333
Iteration (141600) - Loss: 45.60937092927101
Iteration (141700) - Loss: 45.52203681570995
Iteration (141800) - Loss: 45.635551031175375
Iteration (141900) - Loss: 45.69103166217423
o
bachen--oblet anters to to you sbee srap the is the maided aAk, all glancuirised opat feing let, not I dresed the. The upotion has the there reash even Holmes
with Holto ring-pet."
"Mo gluztealy you
Iteration (142000) - Loss: 45.532986203351676
Iteration (142100) - Loss: 45.601702428680355
Iteration (142200) - Loss: 45.45552768996391
Iteration (142300) - Loss: 45.698124595545536
Iteration (142400) - Loss: 45.735906116194556
Iteration (142500) - Loss: 45.62026933958379
Iteration (142600) - Loss: 45.73591829263834
Iteration (142700) - Loss: 45.87109221623938
Iteration (142800) - Loss: 45.793290165617016
Iteration (142900) - Loss: 45.8635180396556
tter the of my out in he fornan--oo hadd foring the contif
me
to fuer. Ol'ss. Ke. is
niving had '8rsaftur is.'
LoothinKy ang onninigae. That I hooke and have and oveals, got
upons
that orame, and resc
Iteration (143000) - Loss: 45.94031706429923
Iteration (143100) - Loss: 45.997892619565086
Iteration (143200) - Loss: 45.853297641420625
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Iteration (143900) - Loss: 46.94276503030927
to mong indiatth a por to
muse the fand, as the Pake it
you arever, was lice
liggess her of a cosets rid him then heme toon abpere. It smor to
comn coid,' thim Lome, this. But Lade whion, are two for
Iteration (144000) - Loss: 46.93136626118831
Iteration (144100) - Loss: 47.14859221202238
Iteration (144200) - Loss: 47.04586420433853
Iteration (144300) - Loss: 47.08038674121458
Iteration (144400) - Loss: 47.12131026141947
Iteration (144500) - Loss: 47.08739720222778
Iteration (144600) - Loss: 46.82798626417135
Iteration (144700) - Loss: 46.79767926198807
Iteration (144800) - Loss: 46.45606644358379
Iteration (144900) - Loss: 46.33972671547166
had
emiatrive astought. And a do aspefan the
pasing of then the lrow at I ""he Basting a slat on I Nayden for he the creing the
fin theard had aAdaoken out the sten own. shis wat agree of com bemore m
Iteration (145000) - Loss: 46.23780192508329
Iteration (145100) - Loss: 46.19088860922695
Iteration (145200) - Loss: 45.924411901910695
Iteration (145300) - Loss: 45.934300108602855
Iteration (145400) - Loss: 45.88227166595763
Iteration (145500) - Loss: 46.09340388871163
Iteration (145600) - Loss: 46.10348569532254
Iteration (145700) - Loss: 46.13393096825408
Iteration (145800) - Loss: 46.04552192955192
Iteration (145900) - Loss: 46.077014654270215
dmen! Heren inding-Clente refich ane tnon a desreened your to comed my shyientiries, ubloirt himnes he, which at of you abtets upto gripperoonlbought."
"Nindregtrittisgate Ofinghint tastuy tase that t
Iteration (146000) - Loss: 46.20699373287593
Iteration (146100) - Loss: 46.14314160771512
Iteration (146200) - Loss: 45.95373531806879
Iteration (146300) - Loss: 46.05083168226802
Iteration (146400) - Loss: 46.24641318483555
Iteration (146500) - Loss: 46.31171059621342
Iteration (146600) - Loss: 46.27945595915343
Iteration (146700) - Loss: 46.3707651640719
Iteration (146800) - Loss: 46.60319227709758
Iteration (146900) - Loss: 46.470573270333325
y, there of gout
furnog?"
"Now now then of acauld ang iplatay the handoble fally to go rangelvecrat, from, wished wak esirauysombl off
are chood, with the larder. I who doctition
onler fataw in
that
b
Iteration (147000) - Loss: 46.59059379531216
Iteration (147100) - Loss: 46.443007421330094
Iteration (147200) - Loss: 46.42603853727244
Iteration (147300) - Loss: 46.27715410926582
Iteration (147400) - Loss: 45.9988520421198
Iteration (147500) - Loss: 46.16971394446386
Iteration (147600) - Loss: 46.092621946168556
Iteration (147700) - Loss: 46.020699391662106
Iteration (147800) - Loss: 46.200986916906594
Iteration (147900) - Loss: 46.476465748997136
sts.
"Oh, and aty?' ston the bald bach marvelby the lost in
I jemoring-de' I how couts Brestingly some comitie.'
"Were matse of of had night noke il
jusshrong
That anpy looke?"
OfE's in sid paceve
l
Iteration (148000) - Loss: 46.438409419552826
Iteration (148100) - Loss: 46.38394447823979
Iteration (148200) - Loss: 46.214212361441135
Iteration (148300) - Loss: 46.12116647288767
Iteration (148400) - Loss: 45.930900425284335
Iteration (148500) - Loss: 45.99402804344713
Iteration (148600) - Loss: 46.066337260990274
Iteration (148700) - Loss: 45.94592318427575
Iteration (148800) - Loss: 45.853533404980055
Iteration (148900) - Loss: 45.816398725744214
is doan seisther the nle?"
Jannenty now."
"That my sedid ser.
Holmes that him."
"I
must for himen?s do nown in into the statyonal.
I hel?"
"Vave this corniven of cornived so that this and
which eas
Iteration (149000) - Loss: 45.659429595599164
Iteration (149100) - Loss: 45.52038075695798
Iteration (149200) - Loss: 45.4611975889568
Iteration (149300) - Loss: 45.442082900958816
Iteration (149400) - Loss: 45.483818077182846
Iteration (149500) - Loss: 45.640915043320426
Iteration (149600) - Loss: 45.63986653774637
Iteration (149700) - Loss: 45.843002449143846
Iteration (149800) - Loss: 45.99616454145838
Iteration (149900) - Loss: 45.7976401517981
gty. Oh. I have crieg
I was sontible criel-up tond, bomin,
I primonlyong os sacked busing wers of pack of kich urled ask iy
proulon wass tood for this. Have baster with bust bead you
smaave thin, whess
Iteration (150000) - Loss: 45.89945063155764
Iteration (150100) - Loss: 45.90484475750991
Iteration (150200) - Loss: 45.75034986992244
Iteration (150300) - Loss: 45.72649362579531
Iteration (150400) - Loss: 45.546377274329934
Iteration (150500) - Loss: 45.563913725541816
Iteration (150600) - Loss: 45.610267510118504
Iteration (150700) - Loss: 45.656663172830626
Iteration (150800) - Loss: 45.534102924933464
Iteration (150900) - Loss: 45.48917447240092
he chiouln be upqe in ferosh of all on a knor, and which at how ar.
"A mefpresending St in a coto lo gled suk than veryt prentine pufgelt, and Yound as then I le the unkes had thit solfard
havainfed wh
Iteration (151000) - Loss: 45.46698435273174
Iteration (151100) - Loss: 45.54583140788283
Iteration (151200) - Loss: 45.552398229896646
Iteration (151300) - Loss: 45.47231990805676
Iteration (151400) - Loss: 45.78945021212243
Iteration (151500) - Loss: 45.75304050360407
Iteration (151600) - Loss: 46.079835577628394
Iteration (151700) - Loss: 46.44921258498659
Iteration (151800) - Loss: 46.494063054100025
Iteration (151900) - Loss: 46.478755371315664
d sinity," Cans unticky is fron pint the had was and but. Oh thrat a metion."
"Welpsly
be altaok to he wres, rut
a reding, and that he duccon-bagats wisking swad no Leht is exenge his fale a cerkount,
Iteration (152000) - Loss: 46.38766795759968
Iteration (152100) - Loss: 46.20783006498807
Iteration (152200) - Loss: 46.16353866727002
Iteration (152300) - Loss: 45.949662140639006
Iteration (152400) - Loss: 45.99127092277372
Iteration (152500) - Loss: 46.04308843052594
Iteration (152600) - Loss: 46.03033534536093
Iteration (152700) - Loss: 45.97455100501913
Iteration (152800) - Loss: 45.94857562879584
Iteration (152900) - Loss: 45.77491833950735
ech the ame, be for expheral for cane, and been werven asseedone that Hordes in
the hill-bring tollet all a dised of we had ne thick any colernooly to bectatist bectrientaght be for pinging fore the is
Iteration (153000) - Loss: 45.6361451057006
Iteration (153100) - Loss: 45.66163211419188
Iteration (153200) - Loss: 45.83938692501053
Iteration (153300) - Loss: 45.71625479354743
Iteration (153400) - Loss: 45.778693786887985
Iteration (153500) - Loss: 45.7417707274144
Iteration (153600) - Loss: 45.709798382800464
Iteration (153700) - Loss: 45.67750093129016
Iteration (153800) - Loss: 45.453739685170696
Iteration (153900) - Loss: 45.31113552259079
s it. I corlough indedicn
strichalling lest to swert hind now fornemed as of I veen stult.f
"'Quyt-for sutur to that undealp!' 'Holmet sastion-'sleen for withould hell oas you hourdeny a
very bands war
Iteration (154000) - Loss: 45.1989669364128
Iteration (154100) - Loss: 45.24161138280131
Iteration (154200) - Loss: 45.32228286438256
Iteration (154300) - Loss: 45.16819521815991
Iteration (154400) - Loss: 45.01612810490384
Iteration (154500) - Loss: 44.853485369281636
Iteration (154600) - Loss: 44.83799199897837
Iteration (154700) - Loss: 44.83694829278588
Iteration (154800) - Loss: 44.8080486637996
Iteration (154900) - Loss: 44.75302778812545
I attered than
clidiemand real. Halry
down,
wtoudcher, and the had, wisle to loor."
"Ohe lital yohard not man therr wollow up an ander shay
been might Stens had to tweekh and the supsed, and his noce
Iteration (155000) - Loss: 44.66805797238315
Iteration (155100) - Loss: 44.611529543736836
Iteration (155200) - Loss: 44.8832434775433
Iteration (155300) - Loss: 45.06091068500282
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Iteration (155500) - Loss: 45.43552546936506
Iteration (155600) - Loss: 45.598676932177405
Iteration (155700) - Loss: 45.58450091511634
Iteration (155800) - Loss: 45.62411070447952
Iteration (155900) - Loss: 45.59956152665445
y toakes whith to aline the upon not of the rowered one excrive no is of the quspione to the
knous tis of the fruet you a nept to my from somples, besiover man you haid a for Premince-Asoneed?" Im Eced
Iteration (156000) - Loss: 45.78319344705723
Iteration (156100) - Loss: 45.75632758748301
Iteration (156200) - Loss: 45.715131835562325
Iteration (156300) - Loss: 45.66809801239533
Iteration (156400) - Loss: 45.59233954469305
Iteration (156500) - Loss: 45.58451128183761
Iteration (156600) - Loss: 45.38045708678513
Iteration (156700) - Loss: 45.20953837473035
Iteration (156800) - Loss: 45.11327946084969
Iteration (156900) - Loss: 44.87672119066855
y we mough.
'I had inneg of the very, your ug until to he reilon all back she yelimand up a fa siokle of we. loceming, buch I cack whec he put he
may his sten of thing thoulony lite than a confe. Agra
Iteration (157000) - Loss: 44.91334632364385
Iteration (157100) - Loss: 44.87436288835823
Iteration (157200) - Loss: 44.886117218636606
Iteration (157300) - Loss: 44.947888707819395
Iteration (157400) - Loss: 45.41863486266289
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Iteration (157600) - Loss: 45.5103663348848
Iteration (157700) - Loss: 45.89461362105233
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to besifiling we qainodebred inthived, caindelly timack-eate twom how ligasuy, put my inlicbent sumanded of the plong up. When macins."
"Weal," somroubso resing in of tomess."
"This of the bacryven b
Iteration (158000) - Loss: 46.42262272667364
Iteration (158100) - Loss: 46.27228777401368
Iteration (158200) - Loss: 46.2738608049675
Iteration (158300) - Loss: 46.307326216043656
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Iteration (158800) - Loss: 46.02171323954509
Iteration (158900) - Loss: 45.814832832199876
iour and riunds oo
lound was shoughly fids."
"I I drleg. Acmocojy
been prompan, to hive
cous!
Sherle?"
"Bo mirring a pechone all concinging
owe flook. Suf bettock wurted hil. She
is there fach sight
Iteration (159000) - Loss: 45.74612717433545
Iteration (159100) - Loss: 45.99266955773931
Iteration (159200) - Loss: 46.023407348298804
Iteration (159300) - Loss: 45.96561411520722
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Iteration (159600) - Loss: 46.34809269021808
Iteration (159700) - Loss: 46.203776699219915
Iteration (159800) - Loss: 46.10915951675728
Iteration (159900) - Loss: 45.93850234195501
itay puspoid of the quith mmaven sautthonbasing tursiouts geem
have a evered and os the
panet youre feal."
Stherul contly affonely baard, g ot heapediwantilg tolwer, with "sher unt inkesly wordingeven
Iteration (160000) - Loss: 45.96094474541169
Iteration (160100) - Loss: 45.978098151540735
Iteration (160200) - Loss: 45.838431355073716
Iteration (160300) - Loss: 46.05646481120671
Iteration (160400) - Loss: 46.24389331929218
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Iteration (160600) - Loss: 46.205903255741134
Iteration (160700) - Loss: 46.14999357361926
Iteration (160800) - Loss: 46.13516826699124
Iteration (160900) - Loss: 46.0250484598185
obur licning it howge up yeo, besurtled besexs oa greary upon. You come thoubuarn consead, I maykate-seeptering,
mearttibed forf."
Sut
colmouve brswigh.
"Verusted sherefuly is,
whrapine, to plite. "I
Iteration (161000) - Loss: 45.947144758243155
Iteration (161100) - Loss: 45.75790982179536
Iteration (161200) - Loss: 46.064571387407845
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le whighelter."
"Oh, Mive the to
vemary clutcling it was me wher, ace dran," I had ofme me. The frimads fale fall
allouge. The
was swear and aboutled anttchan over ingenemesay that not
draw like befer
Iteration (162000) - Loss: 45.1460671229997
Iteration (162100) - Loss: 45.401951599633634
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for that he carpion;
Stuge. For to Boy. She to the caing one to hell wincel. The sawis into list to the. "Pep ones his abkesting beens but can pent peat it ace, I had thair you alrelgenwort that wrigh
Iteration (163000) - Loss: 45.65006984582009
Iteration (163100) - Loss: 45.55717064469919
Iteration (163200) - Loss: 45.47617181676043
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glist rosed beent
whach owe, night the and they then out with bemage neenwely mury, ond slony the daid, he whow no kedver not man in, Cottle would that the had show werk is conotionseded knewver bemard
Iteration (164000) - Loss: 45.123054524568914
Iteration (164100) - Loss: 45.035034052043315
Iteration (164200) - Loss: 44.97543979326945
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e your come and time mi have wheterpon,
and with was nould which I knentions forthas and of will.
It his creaymed. The amnarly to miner thas indan come the Roriloned, Mr. "He paked of ey seas cane cra
Iteration (165000) - Loss: 45.29692787591413
Iteration (165100) - Loss: 45.430496659063834
Iteration (165200) - Loss: 45.47765400224728
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"thim, an) the pricioss bryss intunttiser youn of my rlunce be a comes, bber. To his shad in the served
bumathae ut mave pasesich. I thandsic1sure the willleded merain, and comserve to he havl the edl
Iteration (166000) - Loss: 46.04634524092199
Iteration (166100) - Loss: 46.079980789296755
Iteration (166200) - Loss: 46.08853777436678
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ret in
this Nuss reaps ale fit, we deal the yeet the where lattle the rean where cellock be a paid herse ser. Holmes that occted she was a storn, when his deen edeite scanding houmey in mituls larn, Lo
Iteration (167000) - Loss: 46.59673784993148
Iteration (167100) - Loss: 46.46886175146297
Iteration (167200) - Loss: 46.23948632367628
Iteration (167300) - Loss: 45.95195855081958
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Iteration (167900) - Loss: 45.54978355417235
dode, and the ittide, whockeen with in to," surl--his sinked agontred wajkan pire whothercamar comes!" I sobing the waft in the ssingly have do rightrearseathing."
cA, and the matt am aver.'
"Gelbave
Iteration (168000) - Loss: 45.56073134761066
Iteration (168100) - Loss: 45.60354871104894
Iteration (168200) - Loss: 45.614872840982734
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Iteration (168800) - Loss: 45.723215343501266
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"This site
out him!" Hy
mints wattle was startort, wurtiy He coutse for a mad the praw, howesh aamovily it inding notnes upear of a comsping toching, and pran theod, we coustrried hax
the haz
proite
Iteration (169000) - Loss: 45.809804637255176
Iteration (169100) - Loss: 45.81157398690926
Iteration (169200) - Loss: 46.04364424101307
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to the sumine midt go dad with ransher at cee pital said-timed were thotter which, bot three, so aich for anty, -he ssoratale. I have have to dising come gloctplt your, and me it iclon dim?"
4Sere
of
Iteration (170000) - Loss: 45.727842114221346
Iteration (170100) - Loss: 45.65786871911778
Iteration (170200) - Loss: 45.63814485432639
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Iteration (170900) - Loss: 45.52038018396883
ow and been his then," surt of the plaid winden be was a de-nied shen,
concled, but are in gaived a teceneringent sod herf-a bin, butur Holmes, Jome be his strent?"
Mreest, and the burn deevose inded.
Iteration (171000) - Loss: 45.528936357873896
Iteration (171100) - Loss: 45.56893515157913
Iteration (171200) - Loss: 45.43816058304182
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Iteration (171700) - Loss: 45.003853748073944
Iteration (171800) - Loss: 44.98628617164954
Iteration (171900) - Loss: 45.05741231101998
and to triikopergmater wam pure ha. Thomator I wourding 'Dappe-dear, was so
that the polent wor, arrasssice
bany. I anich primes
ins of 3f condudy,' hat mencilbe, as meaple. Thincard-silence for ous o
Iteration (172000) - Loss: 45.117530886683895
Iteration (172100) - Loss: 45.15650265802279
Iteration (172200) - Loss: 45.2858984702117
Iteration (172300) - Loss: 45.41147115098954
Iteration (172400) - Loss: 45.28426258373472
Iteration (172500) - Loss: 45.39461124140921
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Iteration (172700) - Loss: 45.274436690649026
Iteration (172800) - Loss: 45.205249352355324
Iteration (172900) - Loss: 45.08058974658945
hen us a
cenatle abmes, think,
and blatimppines avlow, and was of and in to in
the was the what out."
"No, one mat
reen is beh. This the co, but het to abdory, thedes time a seston efprapelf; a ceet t
Iteration (173000) - Loss: 45.14978692933893
Iteration (173100) - Loss: 45.20299235091609
Iteration (173200) - Loss: 45.22196711362358
Iteration (173300) - Loss: 45.090595636604924
Iteration (173400) - Loss: 45.033463538702065
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Iteration (173700) - Loss: 45.04305612020178
Iteration (173800) - Loss: 45.120702357523946
Iteration (173900) - Loss: 45.3768188754582
hat Rad thought sumy the
sher Holmes aster sever, and Cood hemmemed has buce tolsines in latttasy a have alpould and proRly enired cave, which himuE have her up a cre docked on and.
"Nondersiok ave fa
Iteration (174000) - Loss: 45.362253583460955
Iteration (174100) - Loss: 45.7891446961176
Iteration (174200) - Loss: 45.97493259578223
Iteration (174300) - Loss: 45.98980321026109
Iteration (174400) - Loss: 45.94176804417591
Iteration (174500) - Loss: 45.87934693702793
Iteration (174600) - Loss: 45.623474936847785
Iteration (174700) - Loss: 45.582941081908665
Iteration (174800) - Loss: 45.418396562240346
Iteration (174900) - Loss: 45.43982837837318
ot?"
"Ye?"
"Yesstro potnep wheres case ond in the on and surs whine hould up wars. Nown my has prear sis so thre of the
beardnerr or six ttHer.' Had head the jut inseld anas had," fathe right, not-we
Iteration (175000) - Loss: 45.50707585409081
Iteration (175100) - Loss: 45.5396343524673
Iteration (175200) - Loss: 45.52881035448272
Iteration (175300) - Loss: 45.38156636404957
Iteration (175400) - Loss: 45.158988563982476
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Iteration (175900) - Loss: 45.276191742003796
ou therly a brotlivie. Dut of you must bucat.
"Noscers to as surd is the face, you sout teary, to could in in he otedf to lom the os mesy to
so rety case it
capthis It is as do aw stoidente
by poren
Iteration (176000) - Loss: 45.24259344557699
Iteration (176100) - Loss: 45.25401899218719
Iteration (176200) - Loss: 45.277803929322936
Iteration (176300) - Loss: 45.03770277859883
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his thas that comply by that I ged mish a ve untar a lut me agass, ald tuble preet of tyosetire the, which swed him who
doint.
"I sed in han Dup a glam lise binly, and you or
wits I staming it for the
Iteration (177000) - Loss: 44.47222870182285
Iteration (177100) - Loss: 44.37322731927541
Iteration (177200) - Loss: 44.408876006312006
Iteration (177300) - Loss: 44.38175527596172
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Iteration (177900) - Loss: 44.81311720092743
to thick
who olimange this whoce ancing a froarys rade'r ey her which
my wat sausting
holmess shather lied brimentel. Jould stoud the dywancy hill ound, and lone unceop, my wilk tcany I
dimang, and si
Iteration (178000) - Loss: 44.95576464308034
Iteration (178100) - Loss: 45.04328019991097
Iteration (178200) - Loss: 45.13412955647382
Iteration (178300) - Loss: 45.085616835087926
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Iteration (178900) - Loss: 45.159241024090534
and me a lard opemend shere have a corved or Yever nervegal leal coul ga
for lase nees. Iters seak concemeve an underingtronycet."
"'Oh a wood we lain. I malding I has Efpance."
"I asteas buinttie,
Iteration (179000) - Loss: 45.1623986981246
Iteration (179100) - Loss: 44.91654804397825
Iteration (179200) - Loss: 44.779317346126966
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brits not enie rubiled," suvessed face, very druadingaterans would shooning he dourorttear, is man had upor., which the kecthositan mire not
exseenout
only suel abloniot here was prominaldathy here Mrs
Iteration (180000) - Loss: 45.16411257726586
Iteration (180100) - Loss: 45.09628945146273
Iteration (180200) - Loss: 45.46682707933247
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Iteration (180800) - Loss: 45.92570338119846
Iteration (180900) - Loss: 45.91796552420747
ou wound a raking which
a rightationy
of the pot its. Ictibred a was sireving, fering when piany?"
"But of a my of futtresen one chilt lust it to was any of
the ten to seen, I verye explele Spraine wa
Iteration (181000) - Loss: 45.660113495022706
Iteration (181100) - Loss: 45.79714826334962
Iteration (181200) - Loss: 45.68323562333679
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Iteration (181900) - Loss: 45.67773668439198
antissiday
at limate-b'gase, who bustr count
crele used nom that dowe bath the ading as sis?"
"Boing man all ertion, do very is Holmess is thent. A a bontche
than sineL."
"Jolmes, from in less to
me
Iteration (182000) - Loss: 45.975571372406044
Iteration (182100) - Loss: 45.8611776971486
Iteration (182200) - Loss: 45.664920319691525
Iteration (182300) - Loss: 45.55096250899181
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Iteration (182800) - Loss: 45.48145630406117
Iteration (182900) - Loss: 45.75159098456336
onags to sury! Therrack that the haid holr-sa, which Holmes not at Cearion two
poibred at pharle it brourtacoraintion dralent
reef."
un uptardmed some that me, shere as suppeemound
reppious sain of ho
Iteration (183000) - Loss: 45.73680313905583
Iteration (183100) - Loss: 45.700050143378185
Iteration (183200) - Loss: 45.65429294829181
Iteration (183300) - Loss: 45.58920661851759
Iteration (183400) - Loss: 45.50306731682736
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Iteration (183700) - Loss: 45.65378532279595
Iteration (183800) - Loss: 45.52802458326003
Iteration (183900) - Loss: 45.5374973088054
nd whoches unversenfulnmang Mr minuatagliquy finghalres, wrevway of prosost, and lome at diverrate," sump in, wave have of itone a
very
but lowned. "Whath, my wearry a gucs, you we kemperss a ling to m
Iteration (184000) - Loss: 45.42569389672096
Iteration (184100) - Loss: 45.24958461560274
Iteration (184200) - Loss: 44.925561978044215
Iteration (184300) - Loss: 44.6387331066978
Iteration (184400) - Loss: 44.559178153308174
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Iteration (184600) - Loss: 44.99442832906411
Iteration (184700) - Loss: 44.942435254990656
Iteration (184800) - Loss: 44.97728248751648
Iteration (184900) - Loss: 44.92117557823295
rom
Mrnom now, and when -neat
but of sam eppenteres, to the man relide res to her oroulnserwave me wam a jo come lones behark-nanders. At for he veander. Thes pould the man is broutes my promayfatted f
Iteration (185000) - Loss: 44.90146080632732
Iteration (185100) - Loss: 44.78670337159154
Iteration (185200) - Loss: 45.13282839297622
Iteration (185300) - Loss: 45.24041852403072
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Iteration (185600) - Loss: 45.11774382054186
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Iteration (185800) - Loss: 44.90936449070327
Iteration (185900) - Loss: 45.07370919924839
bagitid ay astace nea injos. Hom to in and atreculticacuing."
"You droker allel. It man,
youre. O"'
How
wad
injumbely
would it!" net him in his
any. "I was mopenit to khich, it will them up of the fy
Iteration (186000) - Loss: 44.873648808850554
Iteration (186100) - Loss: 44.695325160056264
Iteration (186200) - Loss: 44.61804204772858
Iteration (186300) - Loss: 44.57382865560478
Iteration (186400) - Loss: 44.62536377664876
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Iteration (186600) - Loss: 44.58372795983486
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Iteration (186900) - Loss: 44.74566646978105
eall lyak allis tepion ax latwoked you lose my."
"I wa. I with wit
not and cuit boe." I made time is with toctly littiyeensure was had lmalred hought in fust, and doed as jan's
we. I conemply pards."
Iteration (187000) - Loss: 44.70173828852461
Iteration (187100) - Loss: 44.64833415661089
Iteration (187200) - Loss: 44.75510010488699
Iteration (187300) - Loss: 44.85947690293245
Iteration (187400) - Loss: 44.788795931385174
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Iteration (187600) - Loss: 44.99516700467736
Iteration (187700) - Loss: 44.91865980663351
Iteration (187800) - Loss: 45.007749926407506
Iteration (187900) - Loss: 45.03882106649496
ast?'
"Wisked.
It,
aith,"'s onced it domberlice, nown on to the
can of I, that had boted to your ohe it injopmswruch out livzoo. I cappopes samly
lay to that it."
"'Kanth onf lesten upon to cave made
Iteration (188000) - Loss: 44.988517648503766
Iteration (188100) - Loss: 45.0446372758462
Iteration (188200) - Loss: 45.05485607814512
Iteration (188300) - Loss: 45.38357642883553
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Iteration (188700) - Loss: 45.732112224492724
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Iteration (188900) - Loss: 46.093823437276534
sh has who shee of the brink, theor,
shall increck windess band.
"Yell to from
reianked what forry mothing nes, but a pone he domen, leck for I saught,
Wased fas antter. In of that. We stamjooded the
Iteration (189000) - Loss: 46.26185392353935
Iteration (189100) - Loss: 46.31365326583996
Iteration (189200) - Loss: 46.23945818798044
Iteration (189300) - Loss: 46.23519568598213
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! Nother with twis beation."
Sil, the rearteran, "8o about in
fropse to-coiver you has it lave in ee livess. Helpo merers in in Paving he hove
seatshor tralace
thone an in stond him to thin. His
sumar
Iteration (190000) - Loss: 45.33481127168219
Iteration (190100) - Loss: 45.18191985959455
Iteration (190200) - Loss: 45.12477991281557
Iteration (190300) - Loss: 45.13366137384442
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Iteration (190800) - Loss: 45.17551564555723
Iteration (190900) - Loss: 45.32450688104552
it mearse the ploker's punsing then of the cers at, and you las the
sathill mest. Lindell tood the
rackeary sepperppel, there of than theever Howe, a care than?"
"But the cears doute my dopcut and sa
Iteration (191000) - Loss: 45.332307035304844
Iteration (191100) - Loss: 45.194629614911584
Iteration (191200) - Loss: 45.16094393851031
Iteration (191300) - Loss: 45.38577824764992
Iteration (191400) - Loss: 45.390584577470825
Iteration (191500) - Loss: 45.44604062472055
Iteration (191600) - Loss: 45.484144582605985
Iteration (191700) - Loss: 45.7055913653599
Iteration (191800) - Loss: 45.683216125963774
Iteration (191900) - Loss: 45.796577473933844
h Mrssed
Bot at the I his being!" H
Los now to
derackavionsult. Attensor Snow relimatly shair. Notle"'s
hat About, End., hom. Haw have Men
withly dry interes, so now not two! Hothough insome, Rustokees
Iteration (192000) - Loss: 45.63752507908214
Iteration (192100) - Loss: 45.66707501096874
Iteration (192200) - Loss: 45.54693451063913
Iteration (192300) - Loss: 45.40495955381238
Iteration (192400) - Loss: 45.13133014940612
Iteration (192500) - Loss: 45.304243949050424
Iteration (192600) - Loss: 45.22199717844316
Iteration (192700) - Loss: 45.232342933996826
Iteration (192800) - Loss: 45.5929086468817
Iteration (192900) - Loss: 45.625010153259716
soom oully, stoped having is of a allan with the heising him. And a verull. Aldare a strest
Caming
whace bused gat one perisling uplouldang.
"'Yous Hep underwel
heard youth, my seiller ardon.'T we shac
Iteration (193000) - Loss: 45.55856464170206
Iteration (193100) - Loss: 45.46549959425564
Iteration (193200) - Loss: 45.41685558228412
Iteration (193300) - Loss: 45.15183592874859
Iteration (193400) - Loss: 45.12763331301429
Iteration (193500) - Loss: 45.165298495944555
Iteration (193600) - Loss: 45.163911601866104
Iteration (193700) - Loss: 45.03314370542498
Iteration (193800) - Loss: 44.96146719777729
Iteration (193900) - Loss: 44.9546601469966
drimps only as she luching every popine
sirfe sone gried."
"Texcent in the was questely fill, to his deen was him even. I prime onss."
"Do ams urseding. The gomar his cwing oo Abpeitter yoo some war
Iteration (194000) - Loss: 44.76377384345176
Iteration (194100) - Loss: 44.58663431174761
Iteration (194200) - Loss: 44.619665960646145
Iteration (194300) - Loss: 44.58382929528162
Iteration (194400) - Loss: 44.829693095776186
Iteration (194500) - Loss: 44.75666721918619
Iteration (194600) - Loss: 44.93232524041658
Iteration (194700) - Loss: 45.10811544409544
Iteration (194800) - Loss: 45.081419954304195
Iteration (194900) - Loss: 44.99693149216273
. He spare."
"Well brave to
my hereancule at of ecfilocts--poftorely I sholemayy promarsceart that of."
Where is frofl, which trestostion that I dust o6
thock to slint squred anceen
sad the wide, wer
Iteration (195000) - Loss: 45.05903369746784
Iteration (195100) - Loss: 45.041995863879336
Iteration (195200) - Loss: 44.912701950670055
Iteration (195300) - Loss: 44.79170805751132
Iteration (195400) - Loss: 44.6829791559615
Iteration (195500) - Loss: 44.76843803352811
Iteration (195600) - Loss: 44.77391214918711
Iteration (195700) - Loss: 44.75681151654922
Iteration (195800) - Loss: 44.71619627494328
Iteration (195900) - Loss: 44.65423379540027
ather bettatter. I swarry afkting cotonch, I seen refadenvence on to ufon mast it as it, had an a nurtine. The difien of deise intered
that bemarken frookent no
mooully some."
"He worting fallen
you d
Iteration (196000) - Loss: 44.5305050549396
Iteration (196100) - Loss: 44.7305535431757
Iteration (196200) - Loss: 44.741040665327205
Iteration (196300) - Loss: 44.79691184388285
Iteration (196400) - Loss: 45.0920274826902
Iteration (196500) - Loss: 45.08744943328766
Iteration (196600) - Loss: 45.52809488079228
Iteration (196700) - Loss: 45.59365389414359
Iteration (196800) - Loss: 45.59246665734828
Iteration (196900) - Loss: 45.62770480257481
sert an har like of a flove were orly over oide the mong tare to were becired table be trush will to had the bell from. Hak a veride that in at 1assidiend (hon form am it is I sectis.r
had fach bevofe,
Iteration (197000) - Loss: 45.55304848813133
Iteration (197100) - Loss: 45.35539868349939
Iteration (197200) - Loss: 45.275443133791555
Iteration (197300) - Loss: 45.23345208176359
Iteration (197400) - Loss: 45.15811085934485
Iteration (197500) - Loss: 45.17242109173701
Iteration (197600) - Loss: 45.19417912562136
Iteration (197700) - Loss: 45.17015050507194
Iteration (197800) - Loss: 45.01726342954705
Iteration (197900) - Loss: 44.820738918632735
the
loom, quelunny simple in of smild he has nepse sishelich, whed bosin pent which me to
the cain in a prableayice, kiry intorn at helf cearon, ardisf, ant Inly.
Nonent, aw ied
have busssnewlew, and
Iteration (198000) - Loss: 44.78551158454727
Iteration (198100) - Loss: 44.89308844115425
Iteration (198200) - Loss: 44.904041625627535
Iteration (198300) - Loss: 44.94795886348303
Iteration (198400) - Loss: 44.93659305636592
Iteration (198500) - Loss: 44.863957719268996
Iteration (198600) - Loss: 44.87112706070336
Iteration (198700) - Loss: 44.763595986794
Iteration (198800) - Loss: 44.65140654475476
Iteration (198900) - Loss: 44.42507084151497
"
"Iy my paling incinge wordgugey of the doody you.'
"Gmray I my &ron so foondfined dingalds of at he mapone you hat . untelt bul.
He had no wide /hat your corsperes was antased hourice, a hact would
Iteration (199000) - Loss: 44.411433364089284
Iteration (199100) - Loss: 44.505850223697934
Iteration (199200) - Loss: 44.50733784589577
Iteration (199300) - Loss: 44.25210695938051
Iteration (199400) - Loss: 44.16911028008303
Iteration (199500) - Loss: 44.11060039507005
Iteration (199600) - Loss: 44.015158006726594
Iteration (199700) - Loss: 43.9444777518999
Iteration (199800) - Loss: 44.006704714496934
Iteration (199900) - Loss: 43.96852631167524
the scowes pow mesire go laid haid hef youl swortile that with hure which of had that Her he very dishe; havio, ma than the danghen besingwant-oresed the tink
was are conemen as
whlwown whethaurione a
Iteration (200000) - Loss: 43.7779163664087
Iteration (200100) - Loss: 43.872279183082796
Iteration (200200) - Loss: 44.17465021105508
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-8-fb71e54c11b9> in <module>()
29 data_index = 0
30
---> 31 x, y = preprocessor.get_x_and_y(data_index)
32 rnn.step(x, y, i)
33 # update counters
<ipython-input-3-0003e2a5912b> in get_x_and_y(self, index)
45 def get_x_and_y(self, index):
46 sequnce_length = configurations['SEQUENTIAL_LENGTH']
---> 47 enum_data = self.enumerated_data()
48 x, y = [], []
49 end_index = index + sequnce_length
<ipython-input-3-0003e2a5912b> in enumerated_data(self)
40 for char in list(self.data):
41 id = dictionary[char]
---> 42 enumerated_data.append(id)
43 return enumerated_data
44
KeyboardInterrupt:
IMPORTANT NOTE: The reason behind the “KeyboardInterrupt” error will be explained below!
Sampling Sequence
Here are some example generated texts:
x, y = preprocessor.get_x_and_y(0)
rnn.sample_sequence(rnn.hidden_layers[-1], x[0], 200)
A lighed a marner upon of
quine which do is have as lead you mad fard."
"We him flit come lieth
ythouse, me
he. You haid is me
it siett as howed in the
dich
to cloot her: If might a prees, fick a lat
rnn.sample_sequence(rnn.hidden_layers[-1], x[0], 200)
And
Holmes It one you breaniged me the carimy with a goien evidver sourciig, Ir."
"Yes, frarrity offed of segr. As she his bey, shome
will woave whered pattion, then anthrirging dirn.
"I
the plooking
rnn.sample_sequence(rnn.hidden_layers[-1], x[10], 200)
I may could ome deaeted shoo sived Bake it In
sove a from hanw that that coal a plone
streckece."
"It lyanly in it two
larf-sece to his sur the chalse to enow every pelarselad thin was ihence fall far
rnn.sample_sequence(rnn.hidden_layers[-1], x[0], 200)
As, your, whorg his flust for it it I
addarse.'
"Iy, which was I Holmess in the more strips hied firls very who tos with the rooeted has neAred.
Will of for and, for she with neg licnised we. I am sa
rnn.sample_sequence(rnn.hidden_layers[-1], x[0], 200)
A could the macken coled infed the senceetile, I this
for might I have the forwelreced copour wam to bedise,' said him therissed at to
dore, Whet and to home, colmank that a upow masinet tinds bitter s
rnn.sample_sequence(rnn.hidden_layers[-1], x[0], 200)
Accouring to be ofraist
om mith extell it whom?
"'You have what, the enverge of Rupl to it or up. That have a viesal upon she like had note, If loode that at
an for and couft you wis, ber that my his
Comment: My code works slower than some library implementations. I think it is because of its object-oriented architecture. But when the iteration increases, the quality of the generated texts are significantly increases. I planned to iterate 500K time to achieve a reasonable result. However, the code works slowly and I needed to submit the code before 23:59. So, I needed to stop iteration at 200K. That is why Keybord Interruption Error occured above.
So, I believe that if I did not stop iterations, the quality of words will be much more than this. But still, most of the words are understandable.