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1 results

  • 7 - Implementing Text Classification with Feed-Forward Networks

  • Mihai Surdeanu, University of Arizona, Marco Antonio Valenzuela-Escárcega, University of Arizona
  • Book:
    Deep Learning for Natural Language Processing
    Published online:
    01 February 2024
    Print publication:
    08 February 2024, pp 107-116

  • Summary

    In this chapter, we provide an implementation of the multilayer neural network described in Chapter 5, along with several of the best practices discussed in Chapter 6. Still keeping things fairly simple, our network will consist of two fully connected layers: a hidden layer and an output layer. Between these layers, we will include dropout and a nonlinearity. Further, we make use of two PyTorch classes: a Dataset and a DataLoader. The advantage of using these classes is that they make several things easy, including data shuffling and batching. Last, since the classifier’s architecture has become more complex, for optimization we transition from stochastic gradient descent to the Adam optimizer to take advantage of its additional features such as momentum and L2 regularization.