LSTM Autoencoder Keras variable batch size
I have a collection of sequences. I would like to encode them as vectors of length encoded_dim. To that end, I made an autoencoder using stacked LSTMs in Keras:
inputs = Input(shape=(None, input_dim))
encoded = LSTM(encoded_dim)(LSTM(latent_dim, return_sequences=True)(inputs))
decoded = RepeatVector(timesteps)(encoded)
decoded = Dense(8)(Dense(100)(LSTM(input_dim, return_sequences=True)(decoded)))
sequence_autoencoder = Model(inputs, decoded)
sequence_autoencoder.compile(optimizers.Adam(lr=0.01), loss='mean_squared_error')
sequence_autoencoder.fit(input, input, epochs=3000, batch_size=16, shuffle=False, callbacks=[...], validation_split=.2)
This works well when I restrict my attention to sequences of length timesteps. In general, however, my sequences are not the same length, so RepeatVector(timesteps) won't work as is. (This will also mess up batching, but I have had decent training with batch_size=1 so I'm not too worried about that for my particular use case.)
There is a fruitful conversation here involving variable length input sequences and batching, in the context of general seq2seq modeling. Unfortunately, there is no concern there for retrieving the fixed size encoding.
Any suggestions/code snippets are greatly appreciated.
lstm keras
asked Nov 7 '18 at 16:31
James McKeownJames McKeown
1044
migrated from stats.stackexchange.com Nov 11 '18 at 17:38
This question came from our site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.
add a comment |
I have a collection of sequences. I would like to encode them as vectors of length encoded_dim. To that end, I made an autoencoder using stacked LSTMs in Keras:
inputs = Input(shape=(None, input_dim))
encoded = LSTM(encoded_dim)(LSTM(latent_dim, return_sequences=True)(inputs))
decoded = RepeatVector(timesteps)(encoded)
decoded = Dense(8)(Dense(100)(LSTM(input_dim, return_sequences=True)(decoded)))
sequence_autoencoder = Model(inputs, decoded)
sequence_autoencoder.compile(optimizers.Adam(lr=0.01), loss='mean_squared_error')
sequence_autoencoder.fit(input, input, epochs=3000, batch_size=16, shuffle=False, callbacks=[...], validation_split=.2)
This works well when I restrict my attention to sequences of length timesteps. In general, however, my sequences are not the same length, so RepeatVector(timesteps) won't work as is. (This will also mess up batching, but I have had decent training with batch_size=1 so I'm not too worried about that for my particular use case.)
There is a fruitful conversation here involving variable length input sequences and batching, in the context of general seq2seq modeling. Unfortunately, there is no concern there for retrieving the fixed size encoding.
Any suggestions/code snippets are greatly appreciated.
lstm keras
asked Nov 7 '18 at 16:31
James McKeownJames McKeown
1044
migrated from stats.stackexchange.com Nov 11 '18 at 17:38
This question came from our site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.
add a comment |
I have a collection of sequences. I would like to encode them as vectors of length encoded_dim. To that end, I made an autoencoder using stacked LSTMs in Keras:
inputs = Input(shape=(None, input_dim))
encoded = LSTM(encoded_dim)(LSTM(latent_dim, return_sequences=True)(inputs))
decoded = RepeatVector(timesteps)(encoded)
decoded = Dense(8)(Dense(100)(LSTM(input_dim, return_sequences=True)(decoded)))
sequence_autoencoder = Model(inputs, decoded)
sequence_autoencoder.compile(optimizers.Adam(lr=0.01), loss='mean_squared_error')
sequence_autoencoder.fit(input, input, epochs=3000, batch_size=16, shuffle=False, callbacks=[...], validation_split=.2)
This works well when I restrict my attention to sequences of length timesteps. In general, however, my sequences are not the same length, so RepeatVector(timesteps) won't work as is. (This will also mess up batching, but I have had decent training with batch_size=1 so I'm not too worried about that for my particular use case.)
There is a fruitful conversation here involving variable length input sequences and batching, in the context of general seq2seq modeling. Unfortunately, there is no concern there for retrieving the fixed size encoding.
Any suggestions/code snippets are greatly appreciated.
lstm keras
asked Nov 7 '18 at 16:31
James McKeownJames McKeown
1044
I have a collection of sequences. I would like to encode them as vectors of length encoded_dim. To that end, I made an autoencoder using stacked LSTMs in Keras:
inputs = Input(shape=(None, input_dim))
encoded = LSTM(encoded_dim)(LSTM(latent_dim, return_sequences=True)(inputs))
decoded = RepeatVector(timesteps)(encoded)
decoded = Dense(8)(Dense(100)(LSTM(input_dim, return_sequences=True)(decoded)))
sequence_autoencoder = Model(inputs, decoded)
sequence_autoencoder.compile(optimizers.Adam(lr=0.01), loss='mean_squared_error')
sequence_autoencoder.fit(input, input, epochs=3000, batch_size=16, shuffle=False, callbacks=[...], validation_split=.2)
This works well when I restrict my attention to sequences of length timesteps. In general, however, my sequences are not the same length, so RepeatVector(timesteps) won't work as is. (This will also mess up batching, but I have had decent training with batch_size=1 so I'm not too worried about that for my particular use case.)
There is a fruitful conversation here involving variable length input sequences and batching, in the context of general seq2seq modeling. Unfortunately, there is no concern there for retrieving the fixed size encoding.
Any suggestions/code snippets are greatly appreciated.
lstm keras
lstm keras
asked Nov 7 '18 at 16:31
James McKeownJames McKeown
1044
asked Nov 7 '18 at 16:31
James McKeownJames McKeown
1044
asked Nov 7 '18 at 16:31
James McKeownJames McKeown
1044
asked Nov 7 '18 at 16:31
James McKeownJames McKeown
1044
asked Nov 7 '18 at 16:31
James McKeownJames McKeown
1044
1044
migrated from stats.stackexchange.com Nov 11 '18 at 17:38
This question came from our site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.
migrated from stats.stackexchange.com Nov 11 '18 at 17:38
This question came from our site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.
add a comment |
add a comment |
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