High-level APIs in deep learning frameworks provide built-in implementations of standard recurrent neural networks (RNNs). Utilizing these libraries prevents the redundant effort of reimplementing standard architectures from scratch. Furthermore, these framework-provided implementations are highly optimized at a low level, resulting in significant computational performance gains and faster execution times compared to custom from-scratch implementations, while achieving comparable perplexity.

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A vanilla Recurrent Neural Network (RNN) module can be implemented concisely by wrapping high-level deep learning APIs, such as nn.RNN in PyTorch or tf.keras.layers.SimpleRNN in TensorFlow. The custom module initializes the built-in recurrent layer and defines a forward pass that accepts an input sequence along with an optional initial hidden state. The built-in layer efficiently processes the sequence to compute and return both the output sequence and the final updated hidden state.

Concise RNN Module Implementation

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When defining Recurrent Neural Networks (RNNs) using MXNet's high-level APIs, the hidden state is explicitly initialized by invoking the begin_state method. This method generates a list containing the initial hidden state for each example in the minibatch, structured with a shape of (number of hidden layers, batch size, number of hidden units). For advanced architectures like Long Short-Term Memory (LSTM) networks, this list will also encompass additional cell state information.

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