# How smaller does the input data get reduced in a LSTM autoencoder

## Question

1. In a LSTM autoencder, how smaller does my input data(59 features) get reduced in a latent vector, which is usually located in the middle between an encoder and a decoder?

2. Why did the author increase the feature number from 5 to 16 in the middle of encoding stage. This question is described in more detail below after the picture of LSTM autoencoder structure.

My questions are based on the article LSTM Autoencoder for Extreme Rare Event Classification in Keras. You can take a look at the codes from this github repository. Please refer to these resources to get to know my questions better.

## Details of the Question

1. My Autoencoder model is as follows:
lstm_autoencoder = Sequential()

# Encoder

# Decoder

1. The shape of the input data is X_train_y0_scaled.shape = (11692,5,59). It means that we have 11692 batches. Each batch is comprised of 5 rows and 59 columns, and since the data is time series data, it means that 59 features are gathered for each of 5 days.
2. However in the LSTM autoencoder, it is not clear to me how long the 59 feature-long vector got reduced to. The layer lstm_18 is only 1-node long while the repeat vector is (5,1) long. Does that mean that 59-feature long vector got reduced to 1-node long vector?