seq2seq

Took the example code for an LSTM seq2seq model from here (https://keras.io/examples/nlp/lstm_seq2seq/). Added two seq2seq models: One has 2 LSTM cells. The other has 2 LSTM layers with 2 LSTM cells per layer.

Setting up the environment

To run the scripts, create and activate a virtual environment by running the following command:

python3 -m venv venv;
. ./venv/bin/actgivate;
pip install -r requirements.txt;

Training the models

To train the plain seq2seq model (no layers or cells), run the following command:

python3 ./scripts/seq2seq_plain.py

This script saves the trained model to 's2s' folder.

To train the seq2seq model with 2 cells (no layers), run the following command:

python3 ./scripts/seq2seq_cells.py

This script saves the trained model to 's2s-cells' folder.

To train the seq2seq model with 2 layers and 2 cells per layer, run the following command:

python3 ./scripts/seq2seq_layers.py

This script saves the trained model to 's2s-layers' folder.

Predicting with the models

To predict by loading the saved plain seq2seq model (no layers or cells), run the following command:

python3 ./scripts/seq2seq_plain.py predict

To predict by loading the seq2seq model with 2 cells (no layers), run the following command:

python3 ./scripts/seq2seq_cells.py predict

To predict by loading the seq2seq model with 2 layers and 2 cells per layer, run the following command:

python3 ./scripts/seq2seq_layers.py predict