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Implementing LSTM based sequence to sequence autoencoder
See original GitHub issueI’m working on reconstructing a 10 timesteps sequence of 32 features.
Here is my Keras model
inputs = Input(shape=(timesteps, dimension))
encoded = LSTM(8)(inputs)
decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(dimension, return_sequences=True)(decoded)
sequence_autoencoder = Model(inputs, decoded)
sequence_autoencoder.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 10, 32) 0
_________________________________________________________________
lstm_1 (LSTM) (None, 8) 1312
_________________________________________________________________
repeat_vector_1 (RepeatVecto (None, 10, 8) 0
_________________________________________________________________
lstm_2 (LSTM) (None, 10, 32) 5248
=================================================================
Total params: 6,560
Trainable params: 6,560
Non-trainable params: 0
My dataset is a pyspark dataframe. Each row fave a features column as a wrapped array (10, 32). I guess I need to have wrapped arrays in input and output. Does elephas support this?
Issue Analytics
- State:
- Created 5 years ago
- Comments:6 (1 by maintainers)
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This is currently not supported - I can look into it if it’s something that would benefit a lot of users? I would definitely want some input and assistance, as I do not know what the best way to implement this is.
Moved this issue to the new fork: https://github.com/danielenricocahall/elephas/issues/10. Closing this for now but still on the radar!