Seq2Seq Architecture based DL chatbot


This is a Proof-of-Concept Project on Seq2Seq Neural Architecture.

  • Trained on Movie-lens dataset having conversations between different people by building a Seq2Seq neural architecture.
  • It is trained on 100 epochs with a batch size of 64, and number of layers =3. The encoding and decoding embedding size are of 512 each.
  • Dropout regularization used to remove any kind of overfitting and the losses are optimized using Adam optimizer.
  • All the sentences in a batch, whether they are questions/ answers must have the same length. Hence ‘PAD’ tokens used.