Recurrent Neural Networks for Sequence Modeling
Character-level generation learns the relationship between a fixed-length character context and the character that follows it.
One Character at a Time
Character-level generation begins with a narrow question: given the characters seen so far, which character should come next? The model does not generate an entire word or sentence in one operation. It predicts one character, appends that character to the available text, and uses the enlarged text to make the next prediction.
For example_origin="generated", imagine a model that has received the context "the ". Its task is not to produce a complete sentence. Its immediate task is to assign probabilities to possible next characters. After one character is selected, that new character becomes part of the context used for the following prediction.
Overlapping Training Windows
Training text is divided into fixed-length character contexts. Each context is paired with the character immediately following it. The contexts overlap: after one window is shifted forward, most of its characters can remain in the next window. This creates the central training relationship: x supplies the context, while y identifies the next character the model should learn to predict.
Three Overlapping Examples
Use the character sequence "abcdef" and a fixed context length of three.
First window: The input context is "abc" and its target is the following character, "d".
Second window: Shift the window forward by one character. The input becomes "bcd" and the target becomes "e".
Third window: Shift forward again. The input becomes "cde" and the target becomes "f".
The text yields the aligned pairs ("abc", "d"), ("bcd", "e"), and ("cde", "f").
The input array x is three-dimensional. Its dimensions correspond to the number of sequences, the sequence length maxlen, and the number of unique characters. The target array y contains the one-hot-encoded character that follows each input sequence.
LSTM to Softmax
The model combines an LSTM with a Dense softmax classifier. The LSTM receives the character context, and the Dense softmax layer produces the model's output distribution for the next character. The output size of the Dense classifier matches the character vocabulary, so the distribution has a position for each unique character the model can predict.
The Dense softmax output is not itself the final generated character. It is a probability distribution over the character vocabulary. Generation still needs a sampling step to convert that distribution into one selected character.
For example_origin="generated", if the vocabulary contains the characters a, b, c, and a space, the Dense softmax output has one probability position for each of those characters. The selected character is then obtained from that distribution rather than from a whole-word prediction.
The Generation Loop
After training, generation starts with a seed snippet. The routine uses the seed as context, predicts a next-character distribution, adjusts that distribution with a temperature, and samples one character. The sampled character is appended to the generated text. The oldest character is then removed from the fixed-length context, leaving a new window that contains the newest available characters. This repeats for every character the routine generates.
Tracing Two Generation Steps
Assume a fixed-length context and a seed snippet. Trace the operations without choosing any particular probability values.
Start: The seed snippet is the current context.
Predict: The model produces a distribution over the character vocabulary for the next character.
Sample and append: Temperature is applied to the distribution, one character is sampled, and that character is appended to the generated text.
Shift: The oldest character is removed from the fixed-length context so the context contains the newest window.
Repeat: The updated context is passed through the same predict, adjust, sample, append, and shift process again.
Generation is autoregressive: every sampled character becomes part of the context for the next prediction.
Temperature and Variety
Temperature changes the probability distribution used for sampling. It does not create a separate model and does not retrain the LSTM or Dense classifier. Instead, different temperatures provide alternative ways to sample from the same model predictions. The source compares 0.2, 0.5, 1.0, and 1.2 while training progresses.
| Sampling distribution | Character choices | What to compare |
|---|---|---|
| More concentrated | Less varied | Whether the output follows the strongest learned choices |
| More varied | More varied | Whether the output explores alternatives while remaining related to the model's distribution |
Debugging the Pipeline
A generated passage can look wrong for more than one reason. Separate the pipeline into data preparation, model construction, and sampling. This separation turns a vague symptom such as bad text into a more specific investigation.
Pairing an input sequence with the wrong target character.
The model is asked to learn a next-character relationship that does not match the text.
Fix:
Check that each target is the character immediately following its input sequence.Using an output layer that does not represent the full character vocabulary.
Some possible next characters cannot be represented by the output distribution.
Fix:
Check that the Dense softmax output size matches the character vocabulary.Stopping after obtaining predicted probabilities.
Generation requires a sampled character, not only a distribution.
Fix:
Check the conversion from predicted probabilities to a sampled index and then back to a character.Expecting temperature to repair incorrect training data.
Temperature changes sampling from the model predictions; it does not correct a faulty data contract.
Fix:
Inspect data preparation and model construction before treating temperature as the issue.
Corpus and Learned Style
The training corpus affects what the model learns. The source implementation uses a large text file containing translated writings by Nietzsche, and lowercasing is performed before training preparation. Consequently, the model learns patterns associated with that writing rather than a generic version of English.
When evaluating generated text, ask what the model was trained to imitate. A passage may be consistent with the training corpus even when it does not resemble general-purpose English.
A generated passage looks incorrect. Classify the first check you would perform: data preparation, model construction, or sampling. Explain which specific relationship or conversion you would inspect.
Hints
- Start with the context-target alignment.
- Then consider whether every unique character has an output position.
- Finally inspect the conversion from probabilities to an index and then to a character.
Summary
- Character-level generation learns which character follows a fixed-length character context.
- Overlapping input sequences form x, while the following one-hot-encoded characters form y.
- An LSTM processes the context, and a vocabulary-sized Dense softmax classifier produces next-character probabilities.
- Generation is autoregressive: sample one character, append it, remove the oldest context character, and repeat.
- Temperature changes how the same model distribution is sampled; debugging should distinguish data preparation, model construction, and sampling.
Key Takeaways
- The training task is to map a fixed-length character context to the character that follows it.
- Overlapping windows preserve many related context-target pairs from a continuous text.
- The LSTM and Dense softmax classifier produce a distribution over the character vocabulary.
- The generation loop repeatedly samples one character and feeds the updated context into the next prediction.
- Temperature changes sampling variety, while pipeline checks help identify whether a failure comes from data, the model, or sampling.