Encoder-Decoder Model with Attention

In [35]:

    

from random import randint
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense, TimeDistributed, RepeatVector


    

    




Using TensorFlow backend.

In [2]:

    

def generate_sequence(length, n_unique):
    return [randint(0, n_unique - 1) for _ in range(length)]


    

In [13]:

    

def one_hot_encode(sequence, n_unique):
    encoding = list()
    for value in sequence:
        vector = [0 for _ in range(n_unique)]
        vector[value] = 1
        encoding.append(vector)
    return np.array(encoding)


    

In [14]:

    

def one_hot_decode(encoded_seq):
    return [np.argmax(vector) for vector in encoded_seq]


    

In [16]:

    

sequence = generate_sequence(5, 50)
print(sequence)
encoded = one_hot_encode(sequence, 50)
print(encoded.shape)
print(encoded)
decoded = one_hot_decode(encoded)
print(decoded)


    

    




[27, 22, 3, 5, 4]
(5, 50)
[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  0 0 0 0 0 0 0 0 0 0 0 0 0]]
[27, 22, 3, 5, 4]

In [28]:

    

def get_pair(n_in, n_out, n_unique):
    # n_in=5, n_out=2のとき
    # 入力系列と出力系列は同じ長さで0パディングする
    # [4, 8, 1, 2, 4] => [4, 8, 0, 0, 0]
    sequence_in = generate_sequence(n_in, n_unique)
    sequence_out = sequence_in[:n_out] + [0 for _ in range(n_in - n_out)]
    
    X = one_hot_encode(sequence_in, n_unique)
    y = one_hot_encode(sequence_out, n_unique)
    
    # (samples, time steps, features)
    X = X.reshape((1, X.shape[0], X.shape[1]))
    y = y.reshape((1, y.shape[0], y.shape[1]))
    
    return X, y


    

In [32]:

    

X, y = get_pair(5, 2, 50)
print(X.shape, y.shape)
print('X=%s, y=%s' % (one_hot_decode(X[0]), one_hot_decode(y[0])))


    

    




(1, 5, 50) (1, 5, 50)
X=[26, 42, 49, 24, 21], y=[26, 42, 0, 0, 0]

In [33]:

    

n_features = 50
n_timesteps_in = 5
n_timesteps_out = 2


    

In [36]:

    

model = Sequential()
# return_sequences=Falseなので最後の入力の出力のみ返す
model.add(LSTM(150, input_shape=(n_timesteps_in, n_features)))
model.add(RepeatVector(n_timesteps_in))  # 出力は入力系列と同じ系列長だけ出力する
model.add(LSTM(150, return_sequences=True))
model.add(TimeDistributed(Dense(n_features, activation='softmax')))
model.compile(loss='categorical_crossentropy', optimizer='adam')


    

In [37]:

    

model.summary()


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_1 (LSTM)                (None, 150)               120600    
_________________________________________________________________
repeat_vector_1 (RepeatVecto (None, 5, 150)            0         
_________________________________________________________________
lstm_2 (LSTM)                (None, 5, 150)            180600    
_________________________________________________________________
time_distributed_1 (TimeDist (None, 5, 50)             7550      
=================================================================
Total params: 308,750
Trainable params: 308,750
Non-trainable params: 0
_________________________________________________________________

In [38]:

    

for epoch in range(5000):
    X, y = get_pair(n_timesteps_in, n_timesteps_out, n_features)
    model.fit(X, y, epochs=1, verbose=2)


    

    




Epoch 1/1
0s - loss: 3.9090
Epoch 1/1
0s - loss: 3.9098
Epoch 1/1
0s - loss: 3.8977
Epoch 1/1
0s - loss: 3.8848
Epoch 1/1
0s - loss: 3.8890
Epoch 1/1
0s - loss: 3.8548
Epoch 1/1
0s - loss: 3.8532
Epoch 1/1
0s - loss: 3.8297
Epoch 1/1
0s - loss: 3.8393
Epoch 1/1
0s - loss: 3.8151
Epoch 1/1
0s - loss: 3.7643
Epoch 1/1
0s - loss: 3.7800
Epoch 1/1
0s - loss: 3.7405
Epoch 1/1
0s - loss: 3.7108
Epoch 1/1
0s - loss: 3.6442
Epoch 1/1
0s - loss: 3.6559
Epoch 1/1
0s - loss: 3.5473
Epoch 1/1
0s - loss: 3.4392
Epoch 1/1
0s - loss: 3.3717
Epoch 1/1
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Epoch 1/1
0s - loss: 3.1957
Epoch 1/1
0s - loss: 3.0078
Epoch 1/1
0s - loss: 2.5106
Epoch 1/1
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Epoch 1/1
0s - loss: 2.1107
Epoch 1/1
0s - loss: 1.8266
Epoch 1/1
0s - loss: 1.8224
Epoch 1/1
0s - loss: 1.7138
Epoch 1/1
0s - loss: 1.7005
Epoch 1/1
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Epoch 1/1
0s - loss: 1.5952
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.6035
Epoch 1/1
0s - loss: 1.5651
Epoch 1/1
0s - loss: 1.6528
Epoch 1/1
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Epoch 1/1
0s - loss: 1.6958
Epoch 1/1
0s - loss: 1.5437
Epoch 1/1
0s - loss: 1.6522
Epoch 1/1
0s - loss: 1.5715
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.6308
Epoch 1/1
0s - loss: 1.6743
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.6167
Epoch 1/1
0s - loss: 1.7857
Epoch 1/1
0s - loss: 1.7330
Epoch 1/1
0s - loss: 1.7092
Epoch 1/1
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Epoch 1/1
0s - loss: 1.8409
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.6838
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.6765
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.5931
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.5339
Epoch 1/1
0s - loss: 1.6420
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
0s - loss: 1.5516
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.5839
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 1.5915
Epoch 1/1
0s - loss: 1.6225
Epoch 1/1
0s - loss: 1.6087
Epoch 1/1
0s - loss: 1.5866
Epoch 1/1
0s - loss: 1.7037
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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0s - loss: 0.6616
Epoch 1/1
0s - loss: 1.0892
Epoch 1/1
0s - loss: 0.3624
Epoch 1/1
0s - loss: 0.6045
Epoch 1/1
0s - loss: 1.1877
Epoch 1/1
0s - loss: 0.3409
Epoch 1/1
0s - loss: 0.2820
Epoch 1/1
0s - loss: 0.6109
Epoch 1/1
0s - loss: 0.2676
Epoch 1/1
0s - loss: 0.3560
Epoch 1/1
0s - loss: 0.5798
Epoch 1/1
0s - loss: 1.0809
Epoch 1/1
0s - loss: 0.4256
Epoch 1/1
0s - loss: 0.4456
Epoch 1/1
0s - loss: 0.8976
Epoch 1/1
0s - loss: 0.3255
Epoch 1/1
0s - loss: 0.6168
Epoch 1/1
0s - loss: 1.0023
Epoch 1/1
0s - loss: 0.6727
Epoch 1/1
0s - loss: 0.4298
Epoch 1/1
0s - loss: 0.6283
Epoch 1/1
0s - loss: 0.6713
Epoch 1/1
0s - loss: 0.6962
Epoch 1/1
0s - loss: 0.7792
Epoch 1/1
0s - loss: 0.2003
Epoch 1/1
0s - loss: 0.4719
Epoch 1/1
0s - loss: 0.4678
Epoch 1/1
0s - loss: 0.6868
Epoch 1/1
0s - loss: 0.5852
Epoch 1/1
0s - loss: 0.6801
Epoch 1/1
0s - loss: 0.5748
Epoch 1/1
0s - loss: 0.3067
Epoch 1/1
0s - loss: 0.3722
Epoch 1/1
0s - loss: 0.7354
Epoch 1/1
0s - loss: 0.1844
Epoch 1/1
0s - loss: 0.4440
Epoch 1/1
0s - loss: 0.5743
Epoch 1/1
0s - loss: 0.6290
Epoch 1/1
0s - loss: 0.3699
Epoch 1/1
0s - loss: 0.3945
Epoch 1/1
0s - loss: 0.2940
Epoch 1/1
0s - loss: 0.6151
Epoch 1/1
0s - loss: 0.3058
Epoch 1/1
0s - loss: 0.2451
Epoch 1/1
0s - loss: 0.6210
Epoch 1/1
0s - loss: 0.6276
Epoch 1/1
0s - loss: 0.6738
Epoch 1/1
0s - loss: 0.7008
Epoch 1/1
0s - loss: 0.7513
Epoch 1/1
0s - loss: 0.4573
Epoch 1/1
0s - loss: 0.7293
Epoch 1/1
0s - loss: 0.7927
Epoch 1/1
0s - loss: 0.5100
Epoch 1/1
0s - loss: 0.2682
Epoch 1/1
0s - loss: 1.0033
Epoch 1/1
0s - loss: 1.0741
Epoch 1/1
0s - loss: 0.9140
Epoch 1/1
0s - loss: 0.5045
Epoch 1/1
0s - loss: 0.4331
Epoch 1/1
0s - loss: 0.5810
Epoch 1/1
0s - loss: 0.4626
Epoch 1/1
0s - loss: 0.6144
Epoch 1/1
0s - loss: 0.4379
Epoch 1/1
0s - loss: 0.2386
Epoch 1/1
0s - loss: 0.7476
Epoch 1/1
0s - loss: 0.5276
Epoch 1/1
0s - loss: 0.5084
Epoch 1/1
0s - loss: 0.7631
Epoch 1/1
0s - loss: 0.5552
Epoch 1/1
0s - loss: 0.1402
Epoch 1/1
0s - loss: 0.4445
Epoch 1/1
0s - loss: 0.4522
Epoch 1/1
0s - loss: 0.3620
Epoch 1/1
0s - loss: 0.5013
Epoch 1/1
0s - loss: 0.4225
Epoch 1/1
0s - loss: 0.2069
Epoch 1/1
0s - loss: 0.2323
Epoch 1/1
0s - loss: 0.5653
Epoch 1/1
0s - loss: 0.8128
Epoch 1/1
0s - loss: 0.5017
Epoch 1/1
0s - loss: 0.2326
Epoch 1/1
0s - loss: 0.2748
Epoch 1/1
0s - loss: 0.5650
Epoch 1/1
0s - loss: 0.5633
Epoch 1/1
0s - loss: 0.4277
Epoch 1/1
0s - loss: 0.3374
Epoch 1/1
0s - loss: 0.6602
Epoch 1/1
0s - loss: 0.3899

In [40]:

    

# evaluate LSTM
total, correct = 100, 0
for _ in range(total):
    X, y = get_pair(n_timesteps_in, n_timesteps_out, n_features)
    yhat = model.predict(X, verbose=0)
    if np.array_equal(one_hot_decode(y[0]), one_hot_decode(yhat[0])):
        correct += 1
print('Accuracy: %.2f%%' % (float(correct) / float(total) * 100.0))


    

    




Accuracy: 18.00%

In [41]:

    

for _ in range(10):
    X, y = get_pair(n_timesteps_in, n_timesteps_out, n_features)
    yhat = model.predict(X, verbose=0)
    print('Expected:', one_hot_decode(y[0]), 'Predicted:', one_hot_decode(yhat[0]))


    

    




Expected: [41, 46, 0, 0, 0] Predicted: [41, 41, 0, 0, 0]
Expected: [9, 48, 0, 0, 0] Predicted: [9, 26, 0, 0, 0]
Expected: [6, 1, 0, 0, 0] Predicted: [6, 0, 0, 0, 0]
Expected: [25, 32, 0, 0, 0] Predicted: [25, 32, 0, 0, 0]
Expected: [44, 2, 0, 0, 0] Predicted: [44, 2, 0, 0, 0]
Expected: [46, 31, 0, 0, 0] Predicted: [46, 1, 0, 0, 0]
Expected: [17, 7, 0, 0, 0] Predicted: [17, 14, 0, 0, 0]
Expected: [39, 42, 0, 0, 0] Predicted: [39, 39, 0, 0, 0]
Expected: [6, 24, 0, 0, 0] Predicted: [6, 0, 0, 0, 0]
Expected: [5, 40, 0, 0, 0] Predicted: [5, 5, 0, 0, 0]

In [42]:

    

from attention_decoder import AttentionDecoder


    

In [45]:

    

model = Sequential()
# Attentionを使うときは重み付けするため各ステップの出力も使う
model.add(LSTM(150, input_shape=(n_timesteps_in, n_features), return_sequences=True))
model.add(AttentionDecoder(150, n_features))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])


    

In [46]:

    

model.summary()


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_4 (LSTM)                (None, 5, 150)            120600    
_________________________________________________________________
AttentionDecoder (AttentionD (None, 5, 50)             243300    
=================================================================
Total params: 363,900
Trainable params: 363,900
Non-trainable params: 0
_________________________________________________________________

In [47]:

    

for epoch in range(5000):
    X, y = get_pair(n_timesteps_in, n_timesteps_out, n_features)
    model.fit(X, y, epochs=1, verbose=2)


    

    




Epoch 1/1
0s - loss: 3.9016 - acc: 0.0000e+00
Epoch 1/1
0s - loss: 3.8688 - acc: 0.6000
Epoch 1/1
0s - loss: 3.8750 - acc: 0.6000
Epoch 1/1
0s - loss: 3.8619 - acc: 0.4000
Epoch 1/1
0s - loss: 3.8246 - acc: 0.6000
Epoch 1/1
0s - loss: 3.8095 - acc: 0.6000
Epoch 1/1
0s - loss: 3.7962 - acc: 0.6000
Epoch 1/1
0s - loss: 3.7794 - acc: 0.6000
Epoch 1/1
0s - loss: 3.7577 - acc: 0.6000
Epoch 1/1
0s - loss: 3.7145 - acc: 0.6000
Epoch 1/1
0s - loss: 3.6517 - acc: 0.6000
Epoch 1/1
0s - loss: 3.6447 - acc: 0.6000
Epoch 1/1
0s - loss: 3.6159 - acc: 0.6000
Epoch 1/1
0s - loss: 3.6057 - acc: 0.6000
Epoch 1/1
0s - loss: 3.5661 - acc: 0.6000
Epoch 1/1
0s - loss: 3.4638 - acc: 0.6000
Epoch 1/1
0s - loss: 3.4736 - acc: 0.6000
Epoch 1/1
0s - loss: 3.4219 - acc: 0.6000
Epoch 1/1
0s - loss: 3.2853 - acc: 0.6000
Epoch 1/1
0s - loss: 3.1652 - acc: 0.6000
Epoch 1/1
0s - loss: 3.0485 - acc: 0.6000
Epoch 1/1
0s - loss: 3.0301 - acc: 0.6000
Epoch 1/1
0s - loss: 2.8677 - acc: 0.6000
Epoch 1/1
0s - loss: 2.5721 - acc: 0.6000
Epoch 1/1
0s - loss: 2.4066 - acc: 0.6000
Epoch 1/1
0s - loss: 2.4987 - acc: 0.6000
Epoch 1/1
0s - loss: 2.0872 - acc: 0.6000
Epoch 1/1
0s - loss: 1.9096 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7960 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5583 - acc: 0.8000
Epoch 1/1
0s - loss: 1.6340 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6703 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6803 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6298 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6811 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7381 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7146 - acc: 0.6000
Epoch 1/1
0s - loss: 1.8204 - acc: 0.6000
Epoch 1/1
0s - loss: 1.8151 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7699 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7831 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7469 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7883 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6561 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5116 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6951 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5067 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6551 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6437 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5457 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5990 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7363 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5503 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6907 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6622 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5685 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7034 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5128 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6885 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6562 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6539 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6650 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5913 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5187 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5607 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5722 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5815 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6461 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6854 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6372 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6921 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5928 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6241 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4625 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6286 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5796 - acc: 0.8000
Epoch 1/1
0s - loss: 1.6752 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5782 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6192 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5190 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4146 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5240 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4735 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5428 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4542 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4919 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7265 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5408 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4686 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7181 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4539 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7435 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5998 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5623 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5111 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6535 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4846 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5239 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6087 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4388 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5614 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5740 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5353 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7391 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6931 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6639 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5408 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4050 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5756 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5948 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6450 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5260 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6612 - acc: 0.6000
Epoch 1/1
0s - loss: 1.8188 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5854 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6279 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6323 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5863 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7058 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7222 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5881 - acc: 0.8000
Epoch 1/1
0s - loss: 1.6984 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6892 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6024 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4351 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5213 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4697 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4693 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5663 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4641 - acc: 0.6000
Epoch 1/1
0s - loss: 1.1698 - acc: 1.0000
Epoch 1/1
0s - loss: 1.6198 - acc: 0.6000
Epoch 1/1
0s - loss: 1.2549 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5731 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7308 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4928 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7453 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6748 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6682 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5865 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5416 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5892 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5944 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7721 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7028 - acc: 0.8000
Epoch 1/1
0s - loss: 1.6905 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5754 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5325 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5078 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5227 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5710 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5086 - acc: 0.8000
Epoch 1/1
0s - loss: 1.4396 - acc: 0.8000
Epoch 1/1
0s - loss: 1.7123 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6219 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7261 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4963 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5762 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6559 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6103 - acc: 0.8000
Epoch 1/1
0s - loss: 1.6441 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5549 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5280 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5035 - acc: 0.8000
Epoch 1/1
0s - loss: 1.4962 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6091 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6332 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6643 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6858 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6150 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5254 - acc: 0.8000
Epoch 1/1
0s - loss: 1.7100 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6283 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6211 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6312 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5352 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5823 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6902 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6276 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5683 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6110 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5458 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4915 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5463 - acc: 0.6000
Epoch 1/1
0s - loss: 1.1299 - acc: 1.0000
Epoch 1/1
0s - loss: 1.6817 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5597 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5790 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5742 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5405 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5141 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5872 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6507 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5329 - acc: 0.8000
Epoch 1/1
0s - loss: 1.6593 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6871 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6491 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4524 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6790 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5892 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5684 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5388 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4963 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5156 - acc: 0.6000
Epoch 1/1
0s - loss: 1.2179 - acc: 0.8000
Epoch 1/1
0s - loss: 1.4962 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6594 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5893 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5726 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5517 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6071 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5369 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6174 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5965 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5238 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5353 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5986 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6568 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7133 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5830 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5968 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5872 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5704 - acc: 0.6000
Epoch 1/1
0s - loss: 1.7108 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5335 - acc: 0.6000
Epoch 1/1
0s - loss: 1.6605 - acc: 0.6000
Epoch 1/1
0s - loss: 1.4819 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5771 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5872 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5147 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5943 - acc: 0.8000
Epoch 1/1
0s - loss: 1.4587 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5421 - acc: 0.8000
Epoch 1/1
0s - loss: 1.4852 - acc: 0.8000
Epoch 1/1
0s - loss: 1.5201 - acc: 0.6000
Epoch 1/1
0s - loss: 1.5260 - acc: 0.8000
Epoch 1/1
0s - loss: 1.4970 - acc: 0.8000
Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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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: 0.0606 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0060 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0090 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0115 - acc: 1.0000
Epoch 1/1
0s - loss: 0.5954 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0058 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0376 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0021 - acc: 1.0000
Epoch 1/1
0s - loss: 6.7801e-04 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0641 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0036 - acc: 1.0000
Epoch 1/1
0s - loss: 0.2053 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0012 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0128 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0073 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0061 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0073 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0018 - acc: 1.0000
Epoch 1/1
0s - loss: 0.3748 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0115 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0316 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0073 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0061 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0051 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0076 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0036 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0116 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0055 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0026 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0104 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0063 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0105 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0064 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0076 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0069 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1577 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0104 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0417 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0157 - acc: 1.0000
Epoch 1/1
0s - loss: 0.5238 - acc: 0.8000
Epoch 1/1
0s - loss: 0.6203 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0081 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0059 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0125 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0708 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0264 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0032 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0046 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0747 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0918 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0028 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0170 - acc: 1.0000
Epoch 1/1
0s - loss: 0.2483 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0033 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0604 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0218 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0078 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0233 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0284 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0054 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0030 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0271 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0309 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0243 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0088 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0092 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0027 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0158 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0018 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0036 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0431 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0104 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0045 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0118 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0047 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0095 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0254 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0251 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0037 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0071 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0075 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0060 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0252 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0111 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0122 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0137 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0146 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0250 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0609 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0023 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0069 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0219 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0021 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0228 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0041 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0047 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0877 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1055 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0145 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0444 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0040 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0203 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0841 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0943 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0081 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0265 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0065 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0050 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0201 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0281 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0015 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0029 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0462 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0148 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0240 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0030 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0047 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0160 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0227 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0089 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0270 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0038 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0063 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0212 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0080 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0107 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0076 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0423 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1383 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0029 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0055 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0174 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0188 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0025 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0041 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1703 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0120 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0039 - acc: 1.0000
Epoch 1/1
0s - loss: 0.4941 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0070 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0647 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0063 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1598 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0019 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0415 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0028 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1023 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0110 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0030 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1328 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0138 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0033 - acc: 1.0000
Epoch 1/1
0s - loss: 0.2731 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0179 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0137 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0214 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0717 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0081 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0211 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0046 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0578 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0089 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1124 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0106 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0149 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0029 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0172 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0052 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0225 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0301 - acc: 1.0000
Epoch 1/1
0s - loss: 0.2683 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0026 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0028 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0088 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0086 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0645 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1663 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0163 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0037 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0115 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0043 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1840 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0488 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0045 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0031 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0019 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0039 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0833 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0014 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0590 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0232 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0202 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0033 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0284 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0097 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0936 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0159 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0776 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0048 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0056 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0488 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0189 - acc: 1.0000
Epoch 1/1
0s - loss: 0.2351 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0050 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0131 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0601 - acc: 1.0000
Epoch 1/1
0s - loss: 0.3844 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0020 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0020 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0112 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0177 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0278 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0081 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0089 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0039 - acc: 1.0000
Epoch 1/1
0s - loss: 0.4008 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0072 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0021 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0096 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0126 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0012 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0236 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0592 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0030 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0053 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0047 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0091 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0129 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0079 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0119 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0116 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0036 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0028 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0319 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1879 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0047 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1975 - acc: 0.8000
Epoch 1/1
0s - loss: 0.1818 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0685 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0028 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0070 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0427 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0070 - acc: 1.0000
Epoch 1/1
0s - loss: 0.1210 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0270 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0276 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0080 - acc: 1.0000
Epoch 1/1
0s - loss: 0.4012 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0216 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0416 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0179 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0021 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0115 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0026 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0108 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0127 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0070 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0098 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0055 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0294 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0047 - acc: 1.0000
Epoch 1/1
0s - loss: 0.2272 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0162 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0430 - acc: 1.0000
Epoch 1/1
0s - loss: 0.3039 - acc: 0.8000
Epoch 1/1
0s - loss: 0.1305 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0109 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0085 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0074 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0044 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0134 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0145 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0052 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0031 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0098 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0079 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0213 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0139 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0121 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0020 - acc: 1.0000
Epoch 1/1
0s - loss: 0.2179 - acc: 0.8000
Epoch 1/1
0s - loss: 0.0042 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0166 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0011 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0038 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0034 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0150 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0024 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0305 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0175 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0824 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0670 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0024 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0110 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0416 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0050 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0061 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0022 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0109 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0065 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0251 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0072 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0051 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0022 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0052 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0011 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0349 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0200 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0019 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0119 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0763 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0037 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0045 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0427 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0050 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0034 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0254 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0090 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0508 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0036 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0152 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0157 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0047 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0013 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0140 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0241 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0035 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0091 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0063 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0055 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0211 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0032 - acc: 1.0000
Epoch 1/1
0s - loss: 0.0047 - acc: 1.0000

In [48]:

    

# evaluate LSTM
total, correct = 100, 0
for _ in range(total):
    X, y = get_pair(n_timesteps_in, n_timesteps_out, n_features)
    yhat = model.predict(X, verbose=0)
    if np.array_equal(one_hot_decode(y[0]), one_hot_decode(yhat[0])):
        correct += 1
print('Accuracy: %.2f%%' % (float(correct) / float(total) * 100.0))


    

    




Accuracy: 98.00%

In [49]:

    

for _ in range(10):
    X, y = get_pair(n_timesteps_in, n_timesteps_out, n_features)
    yhat = model.predict(X, verbose=0)
    print('Expected:', one_hot_decode(y[0]), 'Predicted:', one_hot_decode(yhat[0]))


    

    




Expected: [24, 33, 0, 0, 0] Predicted: [24, 33, 0, 0, 0]
Expected: [28, 44, 0, 0, 0] Predicted: [28, 44, 0, 0, 0]
Expected: [40, 23, 0, 0, 0] Predicted: [40, 23, 0, 0, 0]
Expected: [32, 47, 0, 0, 0] Predicted: [32, 47, 0, 0, 0]
Expected: [43, 27, 0, 0, 0] Predicted: [43, 27, 0, 0, 0]
Expected: [15, 34, 0, 0, 0] Predicted: [15, 34, 0, 0, 0]
Expected: [10, 16, 0, 0, 0] Predicted: [10, 16, 0, 0, 0]
Expected: [23, 28, 0, 0, 0] Predicted: [23, 28, 0, 0, 0]
Expected: [46, 47, 0, 0, 0] Predicted: [46, 47, 0, 0, 0]
Expected: [45, 35, 0, 0, 0] Predicted: [45, 35, 0, 0, 0]

In [50]:

    

def baseline_model(n_timesteps_in, n_features):
    model = Sequential()
    model.add(LSTM(150, input_shape=(n_timesteps_in, n_features)))
    model.add(RepeatVector(n_timesteps_in))
    model.add(LSTM(150, return_sequences=True))
    model.add(TimeDistributed(Dense(n_features, activation='softmax')))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
    return model


    

In [51]:

    

def attention_model(n_timesteps_in, n_features):
    model = Sequential()
    model.add(LSTM(150, input_shape=(n_timesteps_in, n_features)), return_sequences=True)
    model.add(AttentionDecoder(150, n_features))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
    return model


    

In [53]:

    

# train and evaluate a model, return accuracy
def train_evaluate_model(model, n_timesteps_in, n_timesteps_out, n_features):
    # train LSTM
    for epoch in range(5000):
        # generate new random sequence
        X, y = get_pair(n_timesteps_in, n_timesteps_out, n_features)
        # fit model for one epoch on this sequence
        model.fit(X, y, epochs=1, verbose=0)
    # evaluate LSTM
    total, correct = 100, 0
    for _ in range(total):
        X, y = get_pair(n_timesteps_in, n_timesteps_out, n_features)
        yhat = model.predict(X, verbose=0)
        if array_equal(one_hot_decode(y[0]), one_hot_decode(yhat[0])):
            correct += 1
    return float(correct) / float(total)*100.0


    

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