To demonstrate the baseline behavior of a recurrent neural network (RNN) language model prior to optimization, we can invoke its `predict` method using randomly initialized weights. Providing a short prefix string such as 'it has' results in a repetitive and nonsensical sequence of characters, illustrating that the model lacks the linguistic representations necessary to generate coherent text:

```python
model.predict('it has', 20, data.vocab)
```

Output:
```text
'it hasoadd dd dd dd dd dd '
```

Claude

A complete RNN-based language model can be built concisely by inheriting from a foundational base class, such as RNNLMScratch, which manages the overarching training loop and sequence generation. In this high-level implementation, the primary requirement is defining a separate fully connected output layer (e.g., nn.LazyLinear in PyTorch or tf.keras.layers.Dense in TensorFlow) sized to the vocabulary. During the forward pass, this linear layer projects the intermediate hidden states generated by the high-level RNN module into unnormalized probability distributions (logits) across the vocabulary to predict subsequent tokens.

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