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keraslstmautoencoder
patti_jane
patti_jane

Reputation: 3861

Keras: LSTM Seq2Seq autoencoder input incompability error

I'm trying to run the Seq2Seq example here, https://blog.keras.io/building-autoencoders-in-keras.html

from keras.layers import Input, LSTM, RepeatVector
from keras.models import Model

inputs = Input(shape=(timesteps, input_dim))
encoded = LSTM(latent_dim)(inputs)

decoded = RepeatVector(timesteps)(encoded)
decoded = LSTM(input_dim, return_sequences=True)(decoded)

sequence_autoencoder = Model(inputs, decoded)
encoder = Model(inputs, encoded)

My input is categorical encoding, e.g. [1, 23, 6, 12, 4, 0, 0, 0], which has 25 categories and fixed length of 1000.

So, the updated version of the code looks like:

    MInput = Input(shape=(MAX_LEN, CATEGORY_NUMS))
    encode_seq = LSTM(32)(MInput)

    decode_seq = RepeatVector(MAX_LEN)(encode_seq)
    decode_seq = LSTM(CATEGORY_NUMS, return_sequences=True)(decode_seq)

    autoencoder = Model(MInput, decode_seq)
    encoder = Model(MInput, encode_seq)

However, I'm getting " Input 0 is incompatible with layer lstm_2: expected ndim=3, found ndim=2" error.

Adding return_sequences=True to the first LSTM layer or removing the RepeatVector all give incompability error.

I'm not sure how else I have to prepare my input.

Thanks!

Upvotes: 0

Views: 373

Answers (1)

Ajjo
Ajjo

Reputation: 244

Your input X and output Y should be of the shape (batch_size,timesteps,input_dim). Try to print their shape and compare it with the model summary output shape.

Upvotes: 1

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