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LSTM Changing Batch Size for Training and Testing

  • When using built-in method of keras, the batch size limits the number of samples to be shown to the network before a weight update can be performed. Specifically, the batch size used when fitting your model controls how many predictions you must make at a time.
  • This will become an error when the number of predictions is lower than the batch size. For example, you may get the best results with a large batch size, but are required to make predictions for one observation at a time on something like a time series or sequence problem.
  • So, it will be better to have different batch size for training and testing.




In [31]:



    


from tensorflow import set_random_seed
set_random_seed(410)

from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential

import pandas as pd



    











In [32]:



    


# Generate data
## create sequence
length = 10
sequence = [i/float(length) for i in range(length)]
print sequence

## create X/y pairs
df = pd.DataFrame(sequence)
df = pd.concat([df, df.shift(1)], axis=1)  # add second column which is the first column shift 1 period
df.dropna(inplace=True)
df



    


















    






[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]










    
Out[32]:











  
    

      
      0
      0
    

  
  
    

      1
      0.1
      0.0
    

    

      2
      0.2
      0.1
    

    

      3
      0.3
      0.2
    

    

      4
      0.4
      0.3
    

    

      5
      0.5
      0.4
    

    

      6
      0.6
      0.5
    

    

      7
      0.7
      0.6
    

    

      8
      0.8
      0.7
    

    

      9
      0.9
      0.8
    

  




















In [33]:



    


## convert to LSTM friendly format
values = df.values
X, y = values[:, 0], values[:, 1]
X = X.reshape(len(X), 1, 1)  # LSTM needs (number of records, timesteps, number of features)
print(X.shape, y.shape)



    


















    






((9, 1, 1), (9,))

3 Solutions

  • If we just want to predict 1 step, there are 3 solutions:
    • Solution 1 - batch size = 1
      • Both training and testing are using batch_size=1.
      • This can have the effect of faster learning, but also adds instability to the learning process as the weights widely vary with each batch.
    • Solution 2 - batch size = n
      • Make all the predictions at once in a batch.
      • But later you need to use all predictions made at once, or only keep the first prediction and discard the rest.
    • Solution 3 - different batch size for training & testing
      • The better solution
      • majorly is to copy the weights from the fit network and to create a new network with the pre-trained weights.




In [40]:



    


# Solution 3
## Model 1 - 3 batches for training

### configure network
n_batch = 3
n_epoch = 1000
n_neurons = 10

### design network
model = Sequential()
model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
### fit network
for i in range(n_epoch):
    model.fit(X, y, epochs=1, batch_size=n_batch, verbose=1, shuffle=False)
    model.reset_states() # manually set internal state update after epoch, otherwise state will reset after each batch



    


















    






Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
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Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0039
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0038
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0037
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0036
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0035
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0034
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0033
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0032
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0031
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0030
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0029
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0028
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0027
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0026
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0025
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0024
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0023
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0022
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0021
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0020
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0019
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0018
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 987us/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0017
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0016
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0015
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0014
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0013
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0012
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0011
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 0.0010
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9940e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9865e-04
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 9.9792e-04
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 9.9721e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9651e-04
Epoch 1/1
9/9 [==============================] - 0s 2ms/step - loss: 9.9582e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9514e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9446e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9380e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9313e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9246e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9180e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9116e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.9053e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.8990e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.8928e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.8867e-04
Epoch 1/1
9/9 [==============================] - 0s 1ms/step - loss: 9.8802e-04

















In [41]:



    


## Model 2 - 1 batch for prediction

### re-define the batch size
n_batch = 1

### re-define model
new_model = Sequential()
new_model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), stateful=True))
new_model.add(Dense(1))

### copy weights
old_weights = model.get_weights()
new_model.set_weights(old_weights)

### compile model
new_model.compile(loss='mean_squared_error', optimizer='adam')



    











In [42]:



    


# Use original data to check the prediction (NOT suggested)
for i in range(len(X)):
    testX, testy = X[i], y[i]
    testX = testX.reshape(1, 1, 1)
    yhat = new_model.predict(testX, batch_size=n_batch)
    print('>Expected=%.1f, Predicted=%.1f' % (testy, yhat))



    


















    






>Expected=0.0, Predicted=0.1
>Expected=0.1, Predicted=0.2
>Expected=0.2, Predicted=0.4
>Expected=0.3, Predicted=0.7
>Expected=0.4, Predicted=0.8
>Expected=0.5, Predicted=0.9
>Expected=0.6, Predicted=0.9
>Expected=0.7, Predicted=0.9
>Expected=0.8, Predicted=0.9

















In [43]:



    


# Try a new set of data but has the same values as original data
length = 5
sequence = [i/10.0 for i in range(length)]
print sequence

## create X/y pairs
df = pd.DataFrame(sequence)
df = pd.concat([df, df.shift(1)], axis=1)  # add second column which is the first column shift 1 period
df.dropna(inplace=True)
df



    


















    






[0.0, 0.1, 0.2, 0.3, 0.4]










    
Out[43]:











  
    

      
      0
      0
    

  
  
    

      1
      0.1
      0.0
    

    

      2
      0.2
      0.1
    

    

      3
      0.3
      0.2
    

    

      4
      0.4
      0.3
    

  




















In [44]:



    


values = df.values
X, y = values[:, 0], values[:, 1]
X = X.reshape(len(X), 1, 1)  # LSTM needs (number of records, timesteps, number of features)
print(X.shape, y.shape)



    


















    






((4, 1, 1), (4,))

















In [45]:



    


# predict
for i in range(len(X)):
    testX, testy = X[i], y[i]
    testX = testX.reshape(1, 1, 1)
    yhat = new_model.predict(testX, batch_size=n_batch)
    print('>Expected=%.1f, Predicted=%.1f' % (testy, yhat))



    


















    






>Expected=0.0, Predicted=0.9
>Expected=0.1, Predicted=0.9
>Expected=0.2, Predicted=0.9
>Expected=0.3, Predicted=0.9

















In [46]:



    


# Try a new set of data but has the different values as original data
length = 20
sequence = [i/10.0 for i in range(10, length)]
print sequence

## create X/y pairs
df = pd.DataFrame(sequence)
df = pd.concat([df, df.shift(1)], axis=1)  # add second column which is the first column shift 1 period
df.dropna(inplace=True)
df



    


















    






[1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]










    
Out[46]:











  
    

      
      0
      0
    

  
  
    

      1
      1.1
      1.0
    

    

      2
      1.2
      1.1
    

    

      3
      1.3
      1.2
    

    

      4
      1.4
      1.3
    

    

      5
      1.5
      1.4
    

    

      6
      1.6
      1.5
    

    

      7
      1.7
      1.6
    

    

      8
      1.8
      1.7
    

    

      9
      1.9
      1.8
    

  




















In [47]:



    


values = df.values
X, y = values[:, 0], values[:, 1]
X = X.reshape(len(X), 1, 1)  # LSTM needs (number of records, timesteps, number of features)
print(X.shape, y.shape)



    


















    






((9, 1, 1), (9,))

















In [48]:



    


# predict
for i in range(len(X)):
    testX, testy = X[i], y[i]
    testX = testX.reshape(1, 1, 1)
    yhat = new_model.predict(testX, batch_size=n_batch)
    print('>Expected=%.1f, Predicted=%.1f' % (testy, yhat))



    


















    






>Expected=1.0, Predicted=1.0
>Expected=1.1, Predicted=1.0
>Expected=1.2, Predicted=1.0
>Expected=1.3, Predicted=1.0
>Expected=1.4, Predicted=1.0
>Expected=1.5, Predicted=1.0
>Expected=1.6, Predicted=1.0
>Expected=1.7, Predicted=1.0
>Expected=1.8, Predicted=1.0

Summary

  • I know, wit epoch=1000, what's shown on github is very long... But 1000 epoch brough better accuracy, although even when using the original data to predict, it dind't look good, I tried different tensorflow seed, the seed will also make a difference. But anyway, 1000 epoch got better prediction...
  • Later I tried new datasets to predict
    • When I was using a subset of original data to predict, it predicts results didn't appear in this new dataset
    • I tried a dataset that its values didn't not appear in the original data, and it cannot predict those values
  • So, having a dataset large enough to train is the rule of thumb. Also, the training data better contains the testing cases.

Content source: hanhanwu/Hanhan_Data_Science_Practice

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