How can you build a custom RNN architecture for text generation using Keras Sequential API

Question

Can you tell me how can you build a custom RNN architecture for text generation using Keras Sequential API?

Answer 1

You can build a custom RNN architecture for text generation using the Keras Sequential API by stacking layers such as Embedding, SimpleRNN (or LSTM/GRU), and a final Dense layer to predict the next character or word.

Here is the code snippet you can refer to:

In the above code, we are using the following:

• Embedding Layer:

  • Converts input integer tokens into dense vector representations.

• SimpleRNN Layer:

  • Handles sequential data by maintaining a hidden state across time steps.
  • Use return_sequences=True to output sequences for each time step.

• Dense Layer:

  • Outputs probabilities for each word or character in the vocabulary.

• Loss Function:

  • Use sparse_categorical_crossentropy for classification over the vocabulary.

Hence, this architecture is suitable for generating text by sampling from the output probabilities at each step during inference.