The Short Version

A from-scratch LSTM text generation model trained on Moby Dick, built as a first exploration of sequence modeling with Keras. The pipeline covers spaCy tokenization, sliding-window sequence construction, Keras Tokenizer vocabulary encoding, a three-layer stacked LSTM with Dropout, early stopping, and a generate() inference function that autoregressively predicts one word at a time from a seed phrase. The generated text is honestly poor — and the notebook says so directly — but the architecture and pipeline are correct.

Problem

Can a deep LSTM learn the statistical patterns of Melville’s prose well enough to generate plausible text continuations? And what does it take to build a complete next-word generation pipeline from raw text to model to inference — from scratch?

Approach

Data — Moby Dick loaded as a raw string. To fit within GPU RAM, only the first 25% of the text was used (str_txt.split()[:int(len * 0.25)]). An explicit note invites readers to remove the slice if they have more resources.

Tokenization — spaCy en_core_web_sm with parser, tagger, and NER disabled (only the tokenizer component needed). Tokens were lowercased and punctuation/whitespace characters filtered out. Disabling unused components is explicitly explained in the notebook — a good practice that reduces processing time significantly on long texts.

Sequence construction — A sliding window of train_len = 21 tokens (20 context + 1 target) was applied across the full token list, producing one training sequence per token position. Each sequence’s first 20 tokens become X; the 21st becomes y.

Vocabulary encoding — Keras Tokenizer fit on all sequences, converting words to integer indices. Target y was one-hot encoded with to_categorical across the full vocabulary (voc_size + 1 classes).

Architecture — A Keras Sequential model:

Embedding(voc_size+1, seq_len, input_length=seq_len)
→ LSTM(seq_len×4, return_sequences=True) → Dropout(0.1)
→ LSTM(seq_len×9, return_sequences=True) → Dropout(0.2)
→ LSTM(seq_len×2)                        → Dropout(0.05)
→ Dense(voc_size+1, activation='softmax')

The three-stage LSTM funnel (4× → 9× → 2× sequence length units) progressively distills the sequence representation before the classification head. return_sequences=True on the first two LSTM layers passes the full hidden state sequence to the next layer.

Training — categorical_crossentropy, Adam, batch size 64, up to 1,000 epochs with EarlyStopping(monitor='loss', patience=5, min_delta=0.001, restore_best_weights=True). Both the model (.h5) and tokenizer (.pkl) were saved after training.

Inference — A generate(seed_txt, num_gen_wrds) function autoregressively generates text: encode the seed → pad to seq_len → forward pass → argmax → decode index to word → append to input → repeat. Each predicted word becomes part of the next input context.

Result

The model trained to convergence under early stopping. Generated text on two seed phrases — one from the novel itself, one a personal introduction — produced grammatically weak but structurally plausible word sequences. The notebook closes honestly: “Of course, you can see how bad the generated text is, but that would be a lot better if we could train the model on more data.”

Quality limitations are attributable to three factors: only 25% of the novel was used for training; Moby Dick’s archaic 19th-century vocabulary is a harder distribution to model; and a greedy argmax decoding strategy (vs. temperature sampling) produces repetitive outputs.

What I Learned

The sliding-window sequence construction is the conceptual foundation of all sequence-to-sequence learning — the idea that “context window → next token” is the universal training signal for language models, from this LSTM up to modern transformers. Building it manually with a list comprehension before reaching for any abstraction made that foundation concrete. The return_sequences=True flag is the LSTM detail that most confuses beginners: the first two layers need it to pass the full hidden state sequence forward; the final LSTM layer doesn’t, because only the last hidden state feeds the classification head. The honest quality assessment at the end is also worth keeping — the architecture is correct, the data is just insufficient. Knowing why the output is poor (training size, decoding strategy, domain vocabulary) is more useful than pretending the results were better than they were.

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