In [30]:
import numpy as np
import random
import pandas
from pandas import DataFrame
from keras.models import Sequential
from keras.layers import LSTM, Dense, TimeDistributed, RepeatVector
In [2]:
random.randint(0, 99)
Out[2]:
17
In [3]:
# generate a sequence of random numbers in [0, 99]
def generate_sequence(length=25):
return [random.randint(0, 99) for _ in range(length)]
In [4]:
# one hot encode sequence
def one_hot_encode(sequence, n_unique=100):
encoding = list()
for value in sequence:
vector = [0 for _ in range(n_unique)]
vector[value] = 1
encoding.append(vector)
return np.array(encoding)
In [5]:
a = one_hot_encode([1,2,3], 5)
a
Out[5]:
array([[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0]])
In [6]:
# decode a one hot encoded string
def one_hot_decode(encoded_seq):
return [np.argmax(vector) for vector in encoded_seq]
In [7]:
one_hot_decode(a)
Out[7]:
[1, 2, 3]
In [8]:
# convert encoded sequence to supervised learning
def to_supervised(sequence, n_in, n_out):
# create lag copies of the sequence
df = DataFrame(sequence)
df = pandas.concat([df.shift(n_in-i-1) for i in range(n_in)], axis=1)
# drop rows with missing values
df.dropna(inplace=True)
# specify columns for input and output pairs
values = df.values
width = sequence.shape[1]
X = values.reshape(len(values), n_in, width)
y = values[:, 0:(n_out*width)].reshape(len(values), n_out, width)
return X, y
In [9]:
sequence = generate_sequence()
print(sequence)
[61, 78, 89, 88, 59, 48, 61, 59, 35, 66, 71, 42, 59, 88, 15, 64, 77, 1, 95, 21, 28, 25, 78, 87, 57]
In [10]:
encoded = one_hot_encode(sequence)
In [11]:
X, y = to_supervised(encoded, 5, 3)
In [12]:
for i in range(len(X)):
print(one_hot_decode(X[i]), '=>', one_hot_decode(y[i]))
[61, 78, 89, 88, 59] => [61, 78, 89]
[78, 89, 88, 59, 48] => [78, 89, 88]
[89, 88, 59, 48, 61] => [89, 88, 59]
[88, 59, 48, 61, 59] => [88, 59, 48]
[59, 48, 61, 59, 35] => [59, 48, 61]
[48, 61, 59, 35, 66] => [48, 61, 59]
[61, 59, 35, 66, 71] => [61, 59, 35]
[59, 35, 66, 71, 42] => [59, 35, 66]
[35, 66, 71, 42, 59] => [35, 66, 71]
[66, 71, 42, 59, 88] => [66, 71, 42]
[71, 42, 59, 88, 15] => [71, 42, 59]
[42, 59, 88, 15, 64] => [42, 59, 88]
[59, 88, 15, 64, 77] => [59, 88, 15]
[88, 15, 64, 77, 1] => [88, 15, 64]
[15, 64, 77, 1, 95] => [15, 64, 77]
[64, 77, 1, 95, 21] => [64, 77, 1]
[77, 1, 95, 21, 28] => [77, 1, 95]
[1, 95, 21, 28, 25] => [1, 95, 21]
[95, 21, 28, 25, 78] => [95, 21, 28]
[21, 28, 25, 78, 87] => [21, 28, 25]
[28, 25, 78, 87, 57] => [28, 25, 78]
In [13]:
# prepare data for the LSTM
def get_data(n_in, n_out):
# generate random sequence
sequence = generate_sequence()
# one hot encode
encoded = one_hot_encode(sequence)
# convert to X,y pairs
X,y = to_supervised(encoded, n_in, n_out)
return X,y
In [14]:
model = Sequential()
# (samples, timesteps, features)
model.add(LSTM(20, batch_input_shape=(7, 5, 100), return_sequences=True, stateful=True))
model.add(TimeDistributed(Dense(100, activation='softmax')))
#model.add(Dense(100, activation='softmax'))
return_sequences=True なので各タイムステップにおいてベクトルを出力するstateful=True なのでバッチ間で独立ではなく、前のバッチの最終状態を次のバッチが引き継ぐstateful=True の場合は batch_input_shape の指定が必要TimeDistributed にすると各タイムステップの出力に対して Dense が適用される
In [15]:
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_1 (LSTM) (7, 5, 20) 9680
_________________________________________________________________
time_distributed_1 (TimeDist (7, 5, 100) 2100
=================================================================
Total params: 11,780
Trainable params: 11,780
Non-trainable params: 0
_________________________________________________________________
In [16]:
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
In [17]:
X,y = get_data(5, 5)
In [18]:
X.shape, y.shape
Out[18]:
((21, 5, 100), (21, 5, 100))
In [19]:
# train LSTM
# 各エポックで新規データの生成、内部状態のリセットをするために明示的にepochループが必要
# fit()ではepochs=1にしてバッチの学習のみ
for epoch in range(500):
# 各エポックで新規データを生成
X,y = get_data(5, 5)
# バッチ内では前のバッチの内部状態を引き継いで学習を進める
# fit model for one epoch on this sequence
model.fit(X, y, epochs=1, batch_size=7, verbose=2, shuffle=False)
# エポックの最後で内部状態をリセット
model.reset_states()
Epoch 1/1
0s - loss: 4.6051 - acc: 0.0476
Epoch 1/1
0s - loss: 4.6067 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 4.6084 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 4.6036 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 4.5999 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 4.6098 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 4.6050 - acc: 0.0095
Epoch 1/1
0s - loss: 4.5964 - acc: 0.0571
Epoch 1/1
0s - loss: 4.6011 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 4.6006 - acc: 0.0190
Epoch 1/1
0s - loss: 4.6014 - acc: 0.0667
Epoch 1/1
0s - loss: 4.5912 - acc: 0.0571
Epoch 1/1
0s - loss: 4.5945 - acc: 0.0190
Epoch 1/1
0s - loss: 4.6072 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 4.5972 - acc: 0.0095
Epoch 1/1
0s - loss: 4.5960 - acc: 0.0667
Epoch 1/1
0s - loss: 4.5985 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 4.5960 - acc: 0.0476
Epoch 1/1
0s - loss: 4.5827 - acc: 0.0762
Epoch 1/1
0s - loss: 4.5957 - acc: 0.0571
Epoch 1/1
0s - loss: 4.5908 - acc: 0.0095
Epoch 1/1
0s - loss: 4.5871 - acc: 0.0476
Epoch 1/1
0s - loss: 4.5913 - acc: 0.1238
Epoch 1/1
0s - loss: 4.5866 - acc: 0.1810
Epoch 1/1
0s - loss: 4.5901 - acc: 0.0286
Epoch 1/1
0s - loss: 4.5841 - acc: 0.0762
Epoch 1/1
0s - loss: 4.5792 - acc: 0.0952
Epoch 1/1
0s - loss: 4.5828 - acc: 0.0571
Epoch 1/1
0s - loss: 4.5823 - acc: 0.0571
Epoch 1/1
0s - loss: 4.5922 - acc: 0.0381
Epoch 1/1
0s - loss: 4.5648 - acc: 0.1143
Epoch 1/1
0s - loss: 4.5795 - acc: 0.1333
Epoch 1/1
0s - loss: 4.5863 - acc: 0.0571
Epoch 1/1
0s - loss: 4.5905 - acc: 0.0667
Epoch 1/1
0s - loss: 4.5824 - acc: 0.0952
Epoch 1/1
0s - loss: 4.5641 - acc: 0.1333
Epoch 1/1
0s - loss: 4.5728 - acc: 0.0952
Epoch 1/1
0s - loss: 4.5817 - acc: 0.0667
Epoch 1/1
0s - loss: 4.5865 - acc: 0.0190
Epoch 1/1
0s - loss: 4.5853 - acc: 0.0857
Epoch 1/1
0s - loss: 4.5859 - acc: 0.0762
Epoch 1/1
0s - loss: 4.5804 - acc: 0.0095
Epoch 1/1
0s - loss: 4.5715 - acc: 0.1048
Epoch 1/1
0s - loss: 4.5751 - acc: 0.1333
Epoch 1/1
0s - loss: 4.5797 - acc: 0.0857
Epoch 1/1
0s - loss: 4.5602 - acc: 0.1619
Epoch 1/1
0s - loss: 4.5692 - acc: 0.1619
Epoch 1/1
0s - loss: 4.5765 - acc: 0.0952
Epoch 1/1
0s - loss: 4.5737 - acc: 0.0476
Epoch 1/1
0s - loss: 4.5659 - acc: 0.1048
Epoch 1/1
0s - loss: 4.5751 - acc: 0.1905
Epoch 1/1
0s - loss: 4.5635 - acc: 0.1619
Epoch 1/1
0s - loss: 4.5633 - acc: 0.1619
Epoch 1/1
0s - loss: 4.5744 - acc: 0.0762
Epoch 1/1
0s - loss: 4.5722 - acc: 0.0667
Epoch 1/1
0s - loss: 4.5570 - acc: 0.2857
Epoch 1/1
0s - loss: 4.5716 - acc: 0.1048
Epoch 1/1
0s - loss: 4.5582 - acc: 0.1238
Epoch 1/1
0s - loss: 4.5617 - acc: 0.1143
Epoch 1/1
0s - loss: 4.5695 - acc: 0.1524
Epoch 1/1
0s - loss: 4.5674 - acc: 0.1714
Epoch 1/1
0s - loss: 4.5567 - acc: 0.1810
Epoch 1/1
0s - loss: 4.5479 - acc: 0.2952
Epoch 1/1
0s - loss: 4.5510 - acc: 0.1619
Epoch 1/1
0s - loss: 4.5359 - acc: 0.2571
Epoch 1/1
0s - loss: 4.5500 - acc: 0.1905
Epoch 1/1
0s - loss: 4.5431 - acc: 0.2190
Epoch 1/1
0s - loss: 4.5527 - acc: 0.1905
Epoch 1/1
0s - loss: 4.5418 - acc: 0.1429
Epoch 1/1
0s - loss: 4.5464 - acc: 0.1810
Epoch 1/1
0s - loss: 4.5515 - acc: 0.1429
Epoch 1/1
0s - loss: 4.5406 - acc: 0.1619
Epoch 1/1
0s - loss: 4.5406 - acc: 0.2857
Epoch 1/1
0s - loss: 4.5491 - acc: 0.1714
Epoch 1/1
0s - loss: 4.5422 - acc: 0.1333
Epoch 1/1
0s - loss: 4.5379 - acc: 0.2571
Epoch 1/1
0s - loss: 4.5260 - acc: 0.1905
Epoch 1/1
0s - loss: 4.5415 - acc: 0.2095
Epoch 1/1
0s - loss: 4.5451 - acc: 0.1524
Epoch 1/1
0s - loss: 4.5149 - acc: 0.3333
Epoch 1/1
0s - loss: 4.5376 - acc: 0.3048
Epoch 1/1
0s - loss: 4.5225 - acc: 0.3238
Epoch 1/1
0s - loss: 4.5307 - acc: 0.3048
Epoch 1/1
0s - loss: 4.5355 - acc: 0.2381
Epoch 1/1
0s - loss: 4.5390 - acc: 0.2667
Epoch 1/1
0s - loss: 4.5126 - acc: 0.3333
Epoch 1/1
0s - loss: 4.5203 - acc: 0.2762
Epoch 1/1
0s - loss: 4.5135 - acc: 0.2095
Epoch 1/1
0s - loss: 4.5160 - acc: 0.2762
Epoch 1/1
0s - loss: 4.5130 - acc: 0.3429
Epoch 1/1
0s - loss: 4.5178 - acc: 0.3048
Epoch 1/1
0s - loss: 4.5057 - acc: 0.3333
Epoch 1/1
0s - loss: 4.5359 - acc: 0.1810
Epoch 1/1
0s - loss: 4.5035 - acc: 0.2667
Epoch 1/1
0s - loss: 4.5131 - acc: 0.3143
Epoch 1/1
0s - loss: 4.5050 - acc: 0.3905
Epoch 1/1
0s - loss: 4.4793 - acc: 0.4000
Epoch 1/1
0s - loss: 4.5236 - acc: 0.3048
Epoch 1/1
0s - loss: 4.4710 - acc: 0.4381
Epoch 1/1
0s - loss: 4.5363 - acc: 0.2000
Epoch 1/1
0s - loss: 4.4872 - acc: 0.2381
Epoch 1/1
0s - loss: 4.4946 - acc: 0.2952
Epoch 1/1
0s - loss: 4.5053 - acc: 0.3524
Epoch 1/1
0s - loss: 4.4955 - acc: 0.2762
Epoch 1/1
0s - loss: 4.4980 - acc: 0.3143
Epoch 1/1
0s - loss: 4.4999 - acc: 0.2000
Epoch 1/1
0s - loss: 4.4726 - acc: 0.2952
Epoch 1/1
0s - loss: 4.5070 - acc: 0.2095
Epoch 1/1
0s - loss: 4.5022 - acc: 0.2190
Epoch 1/1
0s - loss: 4.4685 - acc: 0.2857
Epoch 1/1
0s - loss: 4.5044 - acc: 0.2095
Epoch 1/1
0s - loss: 4.4923 - acc: 0.2190
Epoch 1/1
0s - loss: 4.4771 - acc: 0.2381
Epoch 1/1
0s - loss: 4.4903 - acc: 0.1714
Epoch 1/1
0s - loss: 4.4761 - acc: 0.2571
Epoch 1/1
0s - loss: 4.4698 - acc: 0.2476
Epoch 1/1
0s - loss: 4.4370 - acc: 0.3714
Epoch 1/1
0s - loss: 4.4493 - acc: 0.3048
Epoch 1/1
0s - loss: 4.4688 - acc: 0.3905
Epoch 1/1
0s - loss: 4.4688 - acc: 0.3905
Epoch 1/1
0s - loss: 4.4316 - acc: 0.2571
Epoch 1/1
0s - loss: 4.4667 - acc: 0.2857
Epoch 1/1
0s - loss: 4.4645 - acc: 0.2286
Epoch 1/1
0s - loss: 4.4643 - acc: 0.2190
Epoch 1/1
0s - loss: 4.4153 - acc: 0.3524
Epoch 1/1
0s - loss: 4.4046 - acc: 0.4095
Epoch 1/1
0s - loss: 4.4317 - acc: 0.3143
Epoch 1/1
0s - loss: 4.4429 - acc: 0.2667
Epoch 1/1
0s - loss: 4.4267 - acc: 0.3714
Epoch 1/1
0s - loss: 4.5031 - acc: 0.2000
Epoch 1/1
0s - loss: 4.4323 - acc: 0.2667
Epoch 1/1
0s - loss: 4.3678 - acc: 0.3810
Epoch 1/1
0s - loss: 4.3731 - acc: 0.3238
Epoch 1/1
0s - loss: 4.3575 - acc: 0.4000
Epoch 1/1
0s - loss: 4.3931 - acc: 0.2667
Epoch 1/1
0s - loss: 4.2589 - acc: 0.4667
Epoch 1/1
0s - loss: 4.4021 - acc: 0.3619
Epoch 1/1
0s - loss: 4.3911 - acc: 0.4095
Epoch 1/1
0s - loss: 4.4315 - acc: 0.2476
Epoch 1/1
0s - loss: 4.3638 - acc: 0.3619
Epoch 1/1
0s - loss: 4.3495 - acc: 0.2571
Epoch 1/1
0s - loss: 4.4059 - acc: 0.1714
Epoch 1/1
0s - loss: 4.3411 - acc: 0.3714
Epoch 1/1
0s - loss: 4.3771 - acc: 0.2000
Epoch 1/1
0s - loss: 4.3101 - acc: 0.2857
Epoch 1/1
0s - loss: 4.3435 - acc: 0.3143
Epoch 1/1
0s - loss: 4.4221 - acc: 0.1905
Epoch 1/1
0s - loss: 4.3084 - acc: 0.2571
Epoch 1/1
0s - loss: 4.3031 - acc: 0.3429
Epoch 1/1
0s - loss: 4.3777 - acc: 0.2381
Epoch 1/1
0s - loss: 4.3240 - acc: 0.3143
Epoch 1/1
0s - loss: 4.2867 - acc: 0.4095
Epoch 1/1
0s - loss: 4.2396 - acc: 0.4381
Epoch 1/1
0s - loss: 4.2867 - acc: 0.2857
Epoch 1/1
0s - loss: 4.1876 - acc: 0.3429
Epoch 1/1
0s - loss: 4.2047 - acc: 0.3238
Epoch 1/1
0s - loss: 4.3183 - acc: 0.1429
Epoch 1/1
0s - loss: 4.3132 - acc: 0.2476
Epoch 1/1
0s - loss: 4.3008 - acc: 0.2000
Epoch 1/1
0s - loss: 4.2691 - acc: 0.1810
Epoch 1/1
0s - loss: 4.2569 - acc: 0.2857
Epoch 1/1
0s - loss: 4.1491 - acc: 0.4381
Epoch 1/1
0s - loss: 4.2503 - acc: 0.2571
Epoch 1/1
0s - loss: 4.1991 - acc: 0.3429
Epoch 1/1
0s - loss: 4.1211 - acc: 0.4286
Epoch 1/1
0s - loss: 4.2241 - acc: 0.3429
Epoch 1/1
0s - loss: 4.2495 - acc: 0.2762
Epoch 1/1
0s - loss: 4.1781 - acc: 0.3905
Epoch 1/1
0s - loss: 4.2431 - acc: 0.2762
Epoch 1/1
0s - loss: 4.2069 - acc: 0.2381
Epoch 1/1
0s - loss: 4.2020 - acc: 0.3143
Epoch 1/1
0s - loss: 4.1249 - acc: 0.3524
Epoch 1/1
0s - loss: 4.1499 - acc: 0.3714
Epoch 1/1
0s - loss: 4.1756 - acc: 0.2762
Epoch 1/1
0s - loss: 4.1642 - acc: 0.3048
Epoch 1/1
0s - loss: 4.0128 - acc: 0.4952
Epoch 1/1
0s - loss: 4.1186 - acc: 0.5619
Epoch 1/1
0s - loss: 4.0698 - acc: 0.3619
Epoch 1/1
0s - loss: 4.0955 - acc: 0.3333
Epoch 1/1
0s - loss: 4.0350 - acc: 0.5048
Epoch 1/1
0s - loss: 4.1213 - acc: 0.3714
Epoch 1/1
0s - loss: 4.0355 - acc: 0.2857
Epoch 1/1
0s - loss: 4.1676 - acc: 0.1619
Epoch 1/1
0s - loss: 4.1659 - acc: 0.3429
Epoch 1/1
0s - loss: 4.1489 - acc: 0.2381
Epoch 1/1
0s - loss: 4.0521 - acc: 0.4190
Epoch 1/1
0s - loss: 4.0353 - acc: 0.4476
Epoch 1/1
0s - loss: 3.9412 - acc: 0.6190
Epoch 1/1
0s - loss: 4.0000 - acc: 0.5524
Epoch 1/1
0s - loss: 4.0572 - acc: 0.4762
Epoch 1/1
0s - loss: 4.0114 - acc: 0.4476
Epoch 1/1
0s - loss: 4.0817 - acc: 0.3810
Epoch 1/1
0s - loss: 4.0628 - acc: 0.5429
Epoch 1/1
0s - loss: 3.8778 - acc: 0.5810
Epoch 1/1
0s - loss: 4.0559 - acc: 0.3905
Epoch 1/1
0s - loss: 3.9665 - acc: 0.3714
Epoch 1/1
0s - loss: 3.9693 - acc: 0.5714
Epoch 1/1
0s - loss: 3.9192 - acc: 0.5429
Epoch 1/1
0s - loss: 4.0327 - acc: 0.4190
Epoch 1/1
0s - loss: 3.8716 - acc: 0.6667
Epoch 1/1
0s - loss: 4.0738 - acc: 0.4286
Epoch 1/1
0s - loss: 4.0238 - acc: 0.4190
Epoch 1/1
0s - loss: 3.8602 - acc: 0.5333
Epoch 1/1
0s - loss: 3.9079 - acc: 0.6000
Epoch 1/1
0s - loss: 3.8274 - acc: 0.6762
Epoch 1/1
0s - loss: 3.8673 - acc: 0.6190
Epoch 1/1
0s - loss: 4.0154 - acc: 0.4667
Epoch 1/1
0s - loss: 3.8968 - acc: 0.6571
Epoch 1/1
0s - loss: 3.8531 - acc: 0.6762
Epoch 1/1
0s - loss: 3.7839 - acc: 0.6571
Epoch 1/1
0s - loss: 3.8674 - acc: 0.5905
Epoch 1/1
0s - loss: 3.7649 - acc: 0.6762
Epoch 1/1
0s - loss: 3.7625 - acc: 0.6857
Epoch 1/1
0s - loss: 3.8752 - acc: 0.5524
Epoch 1/1
0s - loss: 3.9389 - acc: 0.5048
Epoch 1/1
0s - loss: 3.8629 - acc: 0.6095
Epoch 1/1
0s - loss: 3.7637 - acc: 0.6476
Epoch 1/1
0s - loss: 3.7844 - acc: 0.6857
Epoch 1/1
0s - loss: 3.7842 - acc: 0.5714
Epoch 1/1
0s - loss: 3.8328 - acc: 0.6286
Epoch 1/1
0s - loss: 3.8422 - acc: 0.5238
Epoch 1/1
0s - loss: 3.9236 - acc: 0.4762
Epoch 1/1
0s - loss: 3.8938 - acc: 0.5048
Epoch 1/1
0s - loss: 3.7580 - acc: 0.6190
Epoch 1/1
0s - loss: 3.7216 - acc: 0.6667
Epoch 1/1
0s - loss: 3.7820 - acc: 0.6190
Epoch 1/1
0s - loss: 3.5762 - acc: 0.8286
Epoch 1/1
0s - loss: 3.6651 - acc: 0.8190
Epoch 1/1
0s - loss: 3.7634 - acc: 0.6190
Epoch 1/1
0s - loss: 3.6852 - acc: 0.7429
Epoch 1/1
0s - loss: 3.9270 - acc: 0.5238
Epoch 1/1
0s - loss: 3.6809 - acc: 0.6190
Epoch 1/1
0s - loss: 3.7621 - acc: 0.6571
Epoch 1/1
0s - loss: 3.7690 - acc: 0.5333
Epoch 1/1
0s - loss: 3.6637 - acc: 0.7333
Epoch 1/1
0s - loss: 3.6728 - acc: 0.6952
Epoch 1/1
0s - loss: 3.5897 - acc: 0.8286
Epoch 1/1
0s - loss: 3.6160 - acc: 0.8667
Epoch 1/1
0s - loss: 3.6476 - acc: 0.7905
Epoch 1/1
0s - loss: 3.6451 - acc: 0.7333
Epoch 1/1
0s - loss: 3.6097 - acc: 0.7333
Epoch 1/1
0s - loss: 3.6507 - acc: 0.7143
Epoch 1/1
0s - loss: 3.6401 - acc: 0.6286
Epoch 1/1
0s - loss: 3.6501 - acc: 0.7143
Epoch 1/1
0s - loss: 3.6763 - acc: 0.5714
Epoch 1/1
0s - loss: 3.6462 - acc: 0.8095
Epoch 1/1
0s - loss: 3.3959 - acc: 0.8095
Epoch 1/1
0s - loss: 3.6171 - acc: 0.7714
Epoch 1/1
0s - loss: 3.5504 - acc: 0.8095
Epoch 1/1
0s - loss: 3.5330 - acc: 0.7810
Epoch 1/1
0s - loss: 3.4469 - acc: 0.7524
Epoch 1/1
0s - loss: 3.4842 - acc: 0.7905
Epoch 1/1
0s - loss: 3.5857 - acc: 0.7048
Epoch 1/1
0s - loss: 3.6161 - acc: 0.7143
Epoch 1/1
0s - loss: 3.5414 - acc: 0.6571
Epoch 1/1
0s - loss: 3.6102 - acc: 0.6762
Epoch 1/1
0s - loss: 3.6353 - acc: 0.6571
Epoch 1/1
0s - loss: 3.5045 - acc: 0.8000
Epoch 1/1
0s - loss: 3.5979 - acc: 0.6000
Epoch 1/1
0s - loss: 3.4979 - acc: 0.8095
Epoch 1/1
0s - loss: 3.3385 - acc: 0.8286
Epoch 1/1
0s - loss: 3.4273 - acc: 0.7238
Epoch 1/1
0s - loss: 3.4870 - acc: 0.8381
Epoch 1/1
0s - loss: 3.4674 - acc: 0.8095
Epoch 1/1
0s - loss: 3.4833 - acc: 0.8000
Epoch 1/1
0s - loss: 3.5533 - acc: 0.7429
Epoch 1/1
0s - loss: 3.2912 - acc: 0.9048
Epoch 1/1
0s - loss: 3.3694 - acc: 0.8381
Epoch 1/1
0s - loss: 3.3909 - acc: 0.9048
Epoch 1/1
0s - loss: 3.5037 - acc: 0.6952
Epoch 1/1
0s - loss: 3.5330 - acc: 0.7333
Epoch 1/1
0s - loss: 3.5041 - acc: 0.7238
Epoch 1/1
0s - loss: 3.3054 - acc: 0.8571
Epoch 1/1
0s - loss: 3.3617 - acc: 0.8667
Epoch 1/1
0s - loss: 3.4740 - acc: 0.7524
Epoch 1/1
0s - loss: 3.4305 - acc: 0.8095
Epoch 1/1
0s - loss: 3.4153 - acc: 0.8381
Epoch 1/1
0s - loss: 3.2957 - acc: 0.9238
Epoch 1/1
0s - loss: 3.2843 - acc: 0.8952
Epoch 1/1
0s - loss: 3.3500 - acc: 0.8571
Epoch 1/1
0s - loss: 3.1901 - acc: 0.8476
Epoch 1/1
0s - loss: 3.2806 - acc: 0.8857
Epoch 1/1
0s - loss: 3.2187 - acc: 0.8762
Epoch 1/1
0s - loss: 3.2902 - acc: 0.7905
Epoch 1/1
0s - loss: 3.3103 - acc: 0.8762
Epoch 1/1
0s - loss: 3.2823 - acc: 0.7905
Epoch 1/1
0s - loss: 3.3293 - acc: 0.8667
Epoch 1/1
0s - loss: 3.2726 - acc: 0.7905
Epoch 1/1
0s - loss: 3.1874 - acc: 0.8952
Epoch 1/1
0s - loss: 3.2964 - acc: 0.7619
Epoch 1/1
0s - loss: 3.1774 - acc: 0.8952
Epoch 1/1
0s - loss: 3.3426 - acc: 0.7619
Epoch 1/1
0s - loss: 3.2911 - acc: 0.8667
Epoch 1/1
0s - loss: 3.1773 - acc: 0.9048
Epoch 1/1
0s - loss: 3.1938 - acc: 0.8286
Epoch 1/1
0s - loss: 3.0748 - acc: 0.9714
Epoch 1/1
0s - loss: 3.2196 - acc: 0.8381
Epoch 1/1
0s - loss: 3.1378 - acc: 0.8571
Epoch 1/1
0s - loss: 3.1842 - acc: 0.8286
Epoch 1/1
0s - loss: 3.0837 - acc: 0.8762
Epoch 1/1
0s - loss: 3.0403 - acc: 0.9714
Epoch 1/1
0s - loss: 3.1767 - acc: 0.7810
Epoch 1/1
0s - loss: 3.1174 - acc: 0.8667
Epoch 1/1
0s - loss: 3.0763 - acc: 0.8571
Epoch 1/1
0s - loss: 3.1151 - acc: 0.9143
Epoch 1/1
0s - loss: 3.0760 - acc: 0.9429
Epoch 1/1
0s - loss: 3.0639 - acc: 0.9810
Epoch 1/1
0s - loss: 3.1128 - acc: 0.9333
Epoch 1/1
0s - loss: 3.2281 - acc: 0.9238
Epoch 1/1
0s - loss: 3.1444 - acc: 0.9619
Epoch 1/1
0s - loss: 3.1006 - acc: 0.8476
Epoch 1/1
0s - loss: 3.0616 - acc: 0.8857
Epoch 1/1
0s - loss: 3.1336 - acc: 0.7905
Epoch 1/1
0s - loss: 2.8954 - acc: 0.9524
Epoch 1/1
0s - loss: 3.1309 - acc: 0.8286
Epoch 1/1
0s - loss: 2.9946 - acc: 0.8857
Epoch 1/1
0s - loss: 2.9196 - acc: 0.9333
Epoch 1/1
0s - loss: 2.8974 - acc: 0.9524
Epoch 1/1
0s - loss: 3.0223 - acc: 0.8571
Epoch 1/1
0s - loss: 2.9342 - acc: 0.9810
Epoch 1/1
0s - loss: 2.8997 - acc: 0.9524
Epoch 1/1
0s - loss: 2.9777 - acc: 0.9143
Epoch 1/1
0s - loss: 2.7881 - acc: 0.9810
Epoch 1/1
0s - loss: 2.9996 - acc: 0.8762
Epoch 1/1
0s - loss: 2.9999 - acc: 0.9048
Epoch 1/1
0s - loss: 2.9812 - acc: 0.9238
Epoch 1/1
0s - loss: 3.0749 - acc: 0.8762
Epoch 1/1
0s - loss: 2.9153 - acc: 0.9619
Epoch 1/1
0s - loss: 2.8941 - acc: 0.9714
Epoch 1/1
0s - loss: 2.8710 - acc: 0.9524
Epoch 1/1
0s - loss: 2.9205 - acc: 0.9524
Epoch 1/1
0s - loss: 2.8623 - acc: 0.9333
Epoch 1/1
0s - loss: 2.9528 - acc: 0.8857
Epoch 1/1
0s - loss: 2.9271 - acc: 0.8667
Epoch 1/1
0s - loss: 2.8671 - acc: 0.8381
Epoch 1/1
0s - loss: 2.8403 - acc: 0.9048
Epoch 1/1
0s - loss: 2.8323 - acc: 0.9524
Epoch 1/1
0s - loss: 2.7717 - acc: 0.9619
Epoch 1/1
0s - loss: 2.8919 - acc: 0.9429
Epoch 1/1
0s - loss: 2.7636 - acc: 0.9810
Epoch 1/1
0s - loss: 2.7925 - acc: 0.9048
Epoch 1/1
0s - loss: 3.0251 - acc: 0.7905
Epoch 1/1
0s - loss: 2.7595 - acc: 0.9429
Epoch 1/1
0s - loss: 2.7954 - acc: 0.9619
Epoch 1/1
0s - loss: 2.8873 - acc: 0.9619
Epoch 1/1
0s - loss: 2.8469 - acc: 0.9714
Epoch 1/1
0s - loss: 2.6487 - acc: 0.9810
Epoch 1/1
0s - loss: 2.7585 - acc: 0.8762
Epoch 1/1
0s - loss: 2.6885 - acc: 1.0000
Epoch 1/1
0s - loss: 2.8363 - acc: 0.8095
Epoch 1/1
0s - loss: 2.6022 - acc: 0.9810
Epoch 1/1
0s - loss: 2.6369 - acc: 0.9524
Epoch 1/1
0s - loss: 2.7778 - acc: 0.9619
Epoch 1/1
0s - loss: 2.8438 - acc: 0.8762
Epoch 1/1
0s - loss: 2.5690 - acc: 0.9810
Epoch 1/1
0s - loss: 2.5950 - acc: 0.9810
Epoch 1/1
0s - loss: 2.6057 - acc: 0.9714
Epoch 1/1
0s - loss: 2.7454 - acc: 0.9619
Epoch 1/1
0s - loss: 2.8331 - acc: 0.8571
Epoch 1/1
0s - loss: 2.6275 - acc: 0.9905
Epoch 1/1
0s - loss: 2.5736 - acc: 1.0000
Epoch 1/1
0s - loss: 2.6432 - acc: 0.9619
Epoch 1/1
0s - loss: 2.7908 - acc: 0.9524
Epoch 1/1
0s - loss: 2.7307 - acc: 0.9619
Epoch 1/1
0s - loss: 2.7145 - acc: 0.9333
Epoch 1/1
0s - loss: 2.5156 - acc: 0.9619
Epoch 1/1
0s - loss: 2.7896 - acc: 0.9619
Epoch 1/1
0s - loss: 2.4489 - acc: 0.9810
Epoch 1/1
0s - loss: 2.7718 - acc: 0.9524
Epoch 1/1
0s - loss: 2.8141 - acc: 0.9333
Epoch 1/1
0s - loss: 2.7379 - acc: 0.8857
Epoch 1/1
0s - loss: 2.4528 - acc: 1.0000
Epoch 1/1
0s - loss: 2.5433 - acc: 0.9429
Epoch 1/1
0s - loss: 2.4382 - acc: 0.9714
Epoch 1/1
0s - loss: 2.6107 - acc: 1.0000
Epoch 1/1
0s - loss: 2.2478 - acc: 1.0000
Epoch 1/1
0s - loss: 2.6051 - acc: 1.0000
Epoch 1/1
0s - loss: 2.3597 - acc: 1.0000
Epoch 1/1
0s - loss: 2.5807 - acc: 0.9905
Epoch 1/1
0s - loss: 2.5826 - acc: 1.0000
Epoch 1/1
0s - loss: 2.3293 - acc: 0.9905
Epoch 1/1
0s - loss: 2.5760 - acc: 0.9333
Epoch 1/1
0s - loss: 2.5123 - acc: 0.9714
Epoch 1/1
0s - loss: 2.6018 - acc: 0.9905
Epoch 1/1
0s - loss: 2.4218 - acc: 1.0000
Epoch 1/1
0s - loss: 2.3967 - acc: 1.0000
Epoch 1/1
0s - loss: 2.4148 - acc: 0.9429
Epoch 1/1
0s - loss: 2.3667 - acc: 1.0000
Epoch 1/1
0s - loss: 2.4461 - acc: 0.9714
Epoch 1/1
0s - loss: 2.5257 - acc: 0.9524
Epoch 1/1
0s - loss: 2.4121 - acc: 1.0000
Epoch 1/1
0s - loss: 2.4329 - acc: 0.9048
Epoch 1/1
0s - loss: 2.2741 - acc: 1.0000
Epoch 1/1
0s - loss: 2.3062 - acc: 1.0000
Epoch 1/1
0s - loss: 2.4695 - acc: 1.0000
Epoch 1/1
0s - loss: 2.4774 - acc: 0.9333
Epoch 1/1
0s - loss: 2.4871 - acc: 0.9810
Epoch 1/1
0s - loss: 2.3252 - acc: 1.0000
Epoch 1/1
0s - loss: 2.1293 - acc: 1.0000
Epoch 1/1
0s - loss: 2.3753 - acc: 1.0000
Epoch 1/1
0s - loss: 2.4328 - acc: 0.9714
Epoch 1/1
0s - loss: 2.3464 - acc: 1.0000
Epoch 1/1
0s - loss: 2.4524 - acc: 0.9524
Epoch 1/1
0s - loss: 2.4050 - acc: 0.9619
Epoch 1/1
0s - loss: 2.2978 - acc: 0.9905
Epoch 1/1
0s - loss: 2.5053 - acc: 0.9524
Epoch 1/1
0s - loss: 2.2653 - acc: 0.9905
Epoch 1/1
0s - loss: 2.2931 - acc: 1.0000
Epoch 1/1
0s - loss: 2.3981 - acc: 1.0000
Epoch 1/1
0s - loss: 2.3575 - acc: 0.9238
Epoch 1/1
0s - loss: 2.3438 - acc: 0.9429
Epoch 1/1
0s - loss: 2.1342 - acc: 0.9619
Epoch 1/1
0s - loss: 2.1598 - acc: 0.9714
Epoch 1/1
0s - loss: 2.4522 - acc: 0.9524
Epoch 1/1
0s - loss: 2.2487 - acc: 1.0000
Epoch 1/1
0s - loss: 2.0333 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9698 - acc: 1.0000
Epoch 1/1
0s - loss: 2.2631 - acc: 0.9524
Epoch 1/1
0s - loss: 2.2445 - acc: 1.0000
Epoch 1/1
0s - loss: 2.3094 - acc: 0.9333
Epoch 1/1
0s - loss: 2.1426 - acc: 0.9524
Epoch 1/1
0s - loss: 2.3325 - acc: 0.9524
Epoch 1/1
0s - loss: 2.0420 - acc: 1.0000
Epoch 1/1
0s - loss: 2.2914 - acc: 1.0000
Epoch 1/1
0s - loss: 2.1878 - acc: 0.9048
Epoch 1/1
0s - loss: 2.1455 - acc: 1.0000
Epoch 1/1
0s - loss: 2.0540 - acc: 1.0000
Epoch 1/1
0s - loss: 2.1296 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9402 - acc: 0.9619
Epoch 1/1
0s - loss: 2.0232 - acc: 1.0000
Epoch 1/1
0s - loss: 2.0272 - acc: 1.0000
Epoch 1/1
0s - loss: 2.1905 - acc: 0.8762
Epoch 1/1
0s - loss: 2.1073 - acc: 1.0000
Epoch 1/1
0s - loss: 2.1216 - acc: 0.9810
Epoch 1/1
0s - loss: 2.0654 - acc: 1.0000
Epoch 1/1
0s - loss: 2.1133 - acc: 1.0000
Epoch 1/1
0s - loss: 2.0291 - acc: 0.9524
Epoch 1/1
0s - loss: 2.0919 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9439 - acc: 0.9143
Epoch 1/1
0s - loss: 1.9532 - acc: 1.0000
Epoch 1/1
0s - loss: 2.0392 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9507 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8987 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9417 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9916 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8406 - acc: 0.9810
Epoch 1/1
0s - loss: 1.9577 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9590 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9977 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9922 - acc: 1.0000
Epoch 1/1
0s - loss: 2.0035 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8984 - acc: 0.9905
Epoch 1/1
0s - loss: 1.9608 - acc: 0.8667
Epoch 1/1
0s - loss: 2.0008 - acc: 0.9238
Epoch 1/1
0s - loss: 1.9739 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9312 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8412 - acc: 0.9524
Epoch 1/1
0s - loss: 1.8742 - acc: 0.9619
Epoch 1/1
0s - loss: 1.7004 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9169 - acc: 0.9619
Epoch 1/1
0s - loss: 1.9397 - acc: 0.9905
Epoch 1/1
0s - loss: 1.9424 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7338 - acc: 0.9524
Epoch 1/1
0s - loss: 1.9514 - acc: 0.9905
Epoch 1/1
0s - loss: 1.7261 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7397 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7193 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7855 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8726 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8942 - acc: 0.9619
Epoch 1/1
0s - loss: 1.8157 - acc: 0.9619
Epoch 1/1
0s - loss: 1.8906 - acc: 0.9524
Epoch 1/1
0s - loss: 1.8593 - acc: 1.0000
Epoch 1/1
0s - loss: 1.9050 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7575 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7686 - acc: 0.9619
Epoch 1/1
0s - loss: 1.7743 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8124 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7213 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7235 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7395 - acc: 1.0000
Epoch 1/1
0s - loss: 1.6603 - acc: 1.0000
Epoch 1/1
0s - loss: 1.6203 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8209 - acc: 0.9619
Epoch 1/1
0s - loss: 1.8318 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8342 - acc: 0.9714
Epoch 1/1
0s - loss: 1.9075 - acc: 0.9429
Epoch 1/1
0s - loss: 1.6172 - acc: 1.0000
Epoch 1/1
0s - loss: 1.6645 - acc: 1.0000
Epoch 1/1
0s - loss: 1.5305 - acc: 1.0000
Epoch 1/1
0s - loss: 1.8101 - acc: 0.9905
Epoch 1/1
0s - loss: 1.6792 - acc: 0.9810
Epoch 1/1
0s - loss: 1.8344 - acc: 0.9619
Epoch 1/1
0s - loss: 1.7919 - acc: 0.9048
Epoch 1/1
0s - loss: 1.5485 - acc: 1.0000
Epoch 1/1
0s - loss: 1.6476 - acc: 1.0000
Epoch 1/1
0s - loss: 1.7067 - acc: 0.9524
Epoch 1/1
0s - loss: 1.6512 - acc: 0.9619
Epoch 1/1
0s - loss: 1.5858 - acc: 1.0000
Epoch 1/1
0s - loss: 1.6401 - acc: 1.0000
In [20]:
# evaluate LSTM
X,y = get_data(5, 5)
yhat = model.predict(X, batch_size=7, verbose=0)
# decode all pairs
for i in range(len(X)):
print('Expected:', one_hot_decode(y[i]), 'Predicted', one_hot_decode(yhat[i]))
Expected: [15, 31, 20, 38, 61] Predicted [15, 31, 20, 38, 61]
Expected: [31, 20, 38, 61, 91] Predicted [31, 20, 38, 61, 91]
Expected: [20, 38, 61, 91, 93] Predicted [20, 38, 61, 91, 93]
Expected: [38, 61, 91, 93, 99] Predicted [38, 61, 91, 93, 99]
Expected: [61, 91, 93, 99, 83] Predicted [61, 91, 93, 99, 83]
Expected: [91, 93, 99, 83, 52] Predicted [91, 93, 99, 83, 52]
Expected: [93, 99, 83, 52, 30] Predicted [93, 99, 83, 52, 30]
Expected: [99, 83, 52, 30, 84] Predicted [99, 83, 52, 30, 84]
Expected: [83, 52, 30, 84, 66] Predicted [83, 52, 30, 84, 66]
Expected: [52, 30, 84, 66, 85] Predicted [52, 30, 84, 66, 85]
Expected: [30, 84, 66, 85, 46] Predicted [30, 84, 66, 85, 46]
Expected: [84, 66, 85, 46, 20] Predicted [84, 66, 85, 46, 20]
Expected: [66, 85, 46, 20, 71] Predicted [66, 85, 46, 20, 71]
Expected: [85, 46, 20, 71, 78] Predicted [85, 46, 20, 71, 78]
Expected: [46, 20, 71, 78, 55] Predicted [46, 20, 71, 78, 55]
Expected: [20, 71, 78, 55, 20] Predicted [20, 71, 78, 55, 20]
Expected: [71, 78, 55, 20, 2] Predicted [71, 78, 55, 20, 2]
Expected: [78, 55, 20, 2, 52] Predicted [78, 55, 20, 2, 52]
Expected: [55, 20, 2, 52, 25] Predicted [55, 20, 2, 52, 25]
Expected: [20, 2, 52, 25, 41] Predicted [20, 2, 52, 25, 41]
Expected: [2, 52, 25, 41, 84] Predicted [2, 52, 25, 41, 84]
In [41]:
model = Sequential()
# return_sequences=Falseなので最後の入力に対する出力のみ encoderの出力（fixed context vector）は (21, 150)
model.add(LSTM(150, batch_input_shape=(21, 5, 100), stateful=True))
# decoderの出力は (21, ?, 150) であってほしい ?はdecoderの系列長
# encoderの出力が繰り返される（context vectorが各ステップに入力される
# decoderの系列長は RepeatVector層を加える 2 timesteps
model.add(RepeatVector(2))
model.add(LSTM(150, return_sequences=True, stateful=True))
model.add(TimeDistributed(Dense(100, activation='softmax')))
In [42]:
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_18 (LSTM) (21, 150) 150600
_________________________________________________________________
repeat_vector_6 (RepeatVecto (21, 2, 150) 0
_________________________________________________________________
lstm_19 (LSTM) (21, 2, 150) 180600
_________________________________________________________________
time_distributed_2 (TimeDist (21, 2, 100) 15100
=================================================================
Total params: 346,300
Trainable params: 346,300
Non-trainable params: 0
_________________________________________________________________
In [44]:
model.compile(loss='categorical_crossentropy', optimizer='adam')
In [47]:
n_in = 5
n_out = 2
encoded_length = 100
batch_size = 21
model = Sequential()
model.add(LSTM(150, batch_input_shape=(batch_size, n_in, encoded_length), stateful=True))
model.add(RepeatVector(n_out))
model.add(LSTM(150, return_sequences=True, stateful=True))
model.add(TimeDistributed(Dense(encoded_length, activation='softmax')))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam')
for epoch in range(5000):
X, y = get_data(n_in, n_out)
model.fit(X, y, epochs=1, batch_size=batch_size, verbose=2, shuffle=False)
model.reset_states()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_24 (LSTM) (21, 150) 150600
_________________________________________________________________
repeat_vector_9 (RepeatVecto (21, 2, 150) 0
_________________________________________________________________
lstm_25 (LSTM) (21, 2, 150) 180600
_________________________________________________________________
time_distributed_5 (TimeDist (21, 2, 100) 15100
=================================================================
Total params: 346,300
Trainable params: 346,300
Non-trainable params: 0
_________________________________________________________________
Epoch 1/1
0s - loss: 4.6047
Epoch 1/1
0s - loss: 4.6053
Epoch 1/1
0s - loss: 4.6058
Epoch 1/1
0s - loss: 4.6031
Epoch 1/1
0s - loss: 4.6039
Epoch 1/1
0s - loss: 4.6044
Epoch 1/1
0s - loss: 4.6044
Epoch 1/1
0s - loss: 4.6027
Epoch 1/1
0s - loss: 4.6081
Epoch 1/1
0s - loss: 4.6050
Epoch 1/1
0s - loss: 4.6007
Epoch 1/1
0s - loss: 4.6060
Epoch 1/1
0s - loss: 4.6018
Epoch 1/1
0s - loss: 4.6044
Epoch 1/1
0s - loss: 4.5988
Epoch 1/1
0s - loss: 4.6039
Epoch 1/1
0s - loss: 4.6045
Epoch 1/1
0s - loss: 4.6058
Epoch 1/1
0s - loss: 4.6000
Epoch 1/1
0s - loss: 4.6053
Epoch 1/1
0s - loss: 4.6014
Epoch 1/1
0s - loss: 4.6030
Epoch 1/1
0s - loss: 4.6040
Epoch 1/1
0s - loss: 4.6089
Epoch 1/1
0s - loss: 4.5932
Epoch 1/1
0s - loss: 4.5998
Epoch 1/1
0s - loss: 4.6026
Epoch 1/1
0s - loss: 4.6024
Epoch 1/1
0s - loss: 4.6022
Epoch 1/1
0s - loss: 4.5968
Epoch 1/1
0s - loss: 4.5903
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
0s - loss: 0.0221
Epoch 1/1
0s - loss: 0.0424
Epoch 1/1
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Epoch 1/1
0s - loss: 0.0599
Epoch 1/1
0s - loss: 0.0304
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
0s - loss: 0.0403
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
0s - loss: 0.0334
Epoch 1/1
0s - loss: 0.0336
Epoch 1/1
0s - loss: 0.0334
Epoch 1/1
0s - loss: 0.0767
Epoch 1/1
0s - loss: 0.0412
Epoch 1/1
0s - loss: 0.0445
Epoch 1/1
0s - loss: 0.0367
Epoch 1/1
0s - loss: 0.0461
Epoch 1/1
0s - loss: 0.0254
Epoch 1/1
0s - loss: 0.0941
Epoch 1/1
0s - loss: 0.0464
Epoch 1/1
0s - loss: 0.0744
Epoch 1/1
0s - loss: 0.0685
Epoch 1/1
0s - loss: 0.0263
Epoch 1/1
0s - loss: 0.0209
Epoch 1/1
0s - loss: 0.0643
Epoch 1/1
0s - loss: 0.0246
Epoch 1/1
0s - loss: 0.0321
Epoch 1/1
0s - loss: 0.0171
Epoch 1/1
0s - loss: 0.0418
Epoch 1/1
0s - loss: 0.0473
Epoch 1/1
0s - loss: 0.0567
Epoch 1/1
0s - loss: 0.0423
Epoch 1/1
0s - loss: 0.0217
Epoch 1/1
0s - loss: 0.0293
Epoch 1/1
0s - loss: 0.0230
Epoch 1/1
0s - loss: 0.0382
Epoch 1/1
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Epoch 1/1
0s - loss: 0.0307
Epoch 1/1
0s - loss: 0.0549
Epoch 1/1
0s - loss: 0.0296
Epoch 1/1
0s - loss: 0.0176
Epoch 1/1
0s - loss: 0.0320
Epoch 1/1
0s - loss: 0.0283
Epoch 1/1
0s - loss: 0.0397
Epoch 1/1
0s - loss: 0.0803
Epoch 1/1
0s - loss: 0.0299
Epoch 1/1
0s - loss: 0.0457
Epoch 1/1
0s - loss: 0.0368
Epoch 1/1
0s - loss: 0.0433
Epoch 1/1
0s - loss: 0.0463
Epoch 1/1
0s - loss: 0.0470
Epoch 1/1
0s - loss: 0.0461
Epoch 1/1
0s - loss: 0.0373
Epoch 1/1
0s - loss: 0.0815
Epoch 1/1
0s - loss: 0.0236
Epoch 1/1
0s - loss: 0.0464
Epoch 1/1
0s - loss: 0.0382
Epoch 1/1
0s - loss: 0.0308
Epoch 1/1
0s - loss: 0.0241
Epoch 1/1
0s - loss: 0.0174
Epoch 1/1
0s - loss: 0.0372
Epoch 1/1
0s - loss: 0.0430
Epoch 1/1
0s - loss: 0.0520
Epoch 1/1
0s - loss: 0.0325
Epoch 1/1
0s - loss: 0.0274
Epoch 1/1
0s - loss: 0.0894
Epoch 1/1
0s - loss: 0.0613
Epoch 1/1
0s - loss: 0.0472
Epoch 1/1
0s - loss: 0.0426
Epoch 1/1
0s - loss: 0.0612
Epoch 1/1
0s - loss: 0.0356
Epoch 1/1
0s - loss: 0.0337
Epoch 1/1
0s - loss: 0.0307
Epoch 1/1
0s - loss: 0.0347
Epoch 1/1
0s - loss: 0.0252
Epoch 1/1
0s - loss: 0.0365
Epoch 1/1
0s - loss: 0.0414
Epoch 1/1
0s - loss: 0.0211
Epoch 1/1
0s - loss: 0.0434
Epoch 1/1
0s - loss: 0.0289
Epoch 1/1
0s - loss: 0.0190
Epoch 1/1
0s - loss: 0.0419
Epoch 1/1
0s - loss: 0.0334
Epoch 1/1
0s - loss: 0.0370
Epoch 1/1
0s - loss: 0.0653
Epoch 1/1
0s - loss: 0.0310
Epoch 1/1
0s - loss: 0.0305
Epoch 1/1
0s - loss: 0.0240
Epoch 1/1
0s - loss: 0.0423
Epoch 1/1
0s - loss: 0.0460
Epoch 1/1
0s - loss: 0.0705
Epoch 1/1
0s - loss: 0.0495
Epoch 1/1
0s - loss: 0.0224
Epoch 1/1
0s - loss: 0.0487
Epoch 1/1
0s - loss: 0.0301
Epoch 1/1
0s - loss: 0.0350
Epoch 1/1
0s - loss: 0.0307
Epoch 1/1
0s - loss: 0.0291
Epoch 1/1
0s - loss: 0.0413
Epoch 1/1
0s - loss: 0.0355
Epoch 1/1
0s - loss: 0.0823
Epoch 1/1
0s - loss: 0.0563
Epoch 1/1
0s - loss: 0.0189
Epoch 1/1
0s - loss: 0.0735
Epoch 1/1
0s - loss: 0.0446
Epoch 1/1
0s - loss: 0.0301
Epoch 1/1
0s - loss: 0.0299
In [48]:
# evaluate LSTM
X, y = get_data(n_in, n_out)
yhat = model.predict(X, batch_size=batch_size, verbose=0)
for i in range(len(X)):
print('expected:', one_hot_decode(y[i]), 'predicted:', one_hot_decode(yhat[i]))
expected: [53, 27] predicted: [53, 27]
expected: [27, 15] predicted: [27, 15]
expected: [15, 26] predicted: [15, 26]
expected: [26, 85] predicted: [26, 85]
expected: [85, 96] predicted: [85, 96]
expected: [96, 35] predicted: [96, 35]
expected: [35, 19] predicted: [35, 19]
expected: [19, 81] predicted: [19, 81]
expected: [81, 23] predicted: [81, 23]
expected: [23, 59] predicted: [23, 59]
expected: [59, 63] predicted: [59, 63]
expected: [63, 23] predicted: [63, 23]
expected: [23, 81] predicted: [23, 81]
expected: [81, 15] predicted: [81, 15]
expected: [15, 60] predicted: [15, 60]
expected: [60, 83] predicted: [60, 83]
expected: [83, 41] predicted: [83, 41]
expected: [41, 94] predicted: [41, 94]
expected: [94, 0] predicted: [94, 0]
expected: [0, 88] predicted: [0, 88]
expected: [88, 70] predicted: [88, 70]
In [ ]:
Content source: aidiary/notebooks
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