Training an LSTM Classifier
Fit a gated model on sequences.
Training an LSTM Classifier is a free NLP Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
From Theory to Practice
Time to build something. You will wire an LSTM classifier that reads a sequence of words and predicts a single label. 🛠️
Text Becomes Integers
First each word maps to an integer id, so a sentence turns into a list of numbers your model can tokenize and process.
ids = [vocab[w] for w in tokens]Pad to Equal Length
LSTMs need uniform batches, so you pad short sequences with zeros and truncate long ones to a fixed length.
X = pad_sequences(ids, maxlen=200)The Embedding Layer
An embedding layer turns each integer id into a dense learnable vector, giving the LSTM rich word meaning instead of raw numbers.
Embedding(input_dim=10000, output_dim=128)Add the LSTM Layer
Next comes the LSTM layer. It reads the embedded sequence step by step and outputs a summary of the whole text.
model.add(LSTM(64))The Output Layer
A final dense layer with sigmoid maps the LSTM summary to a probability, perfect for binary classification like positive or negative.
model.add(Dense(1, activation='sigmoid'))Compile the Model
You compile with a loss and optimizer. Binary cross-entropy and Adam are a reliable starting pair for two-class text.
model.compile(loss='binary_crossentropy', optimizer='adam')Fit on Your Data
Calling fit runs training: the model reads batches, compares predictions to labels, and adjusts its weights to reduce loss.
model.fit(X_train, y_train, epochs=3, batch_size=32)Watch for Overfitting
If training accuracy climbs but validation drops, you are overfitting. Add dropout or stop training earlier to fix it.
model.add(LSTM(64, dropout=0.2))Evaluate and Predict
After training, score the model on held-out data with evaluate, then call predict to label brand-new text.
model.evaluate(X_test, y_test)The Whole Pipeline
So the full pipeline is tokenize, pad, embed, run the LSTM, then classify. Each piece feeds cleanly into the next.
Quick Check
Recall the model layout you just built.
Recap
You built an LSTM classifier: tokenize, pad, embed, run the LSTM, and output a label. Add dropout to guard against overfitting. ✅
Frequently asked questions
Is the “Training an LSTM Classifier” lesson free?
Yes — the full text of “Training an LSTM Classifier” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Training an LSTM Classifier”?
Fit a gated model on sequences. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Training an LSTM Classifier” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Gates That Control Memory
- GRU: A Leaner Alternative
- Training an LSTM Classifier
- Bidirectional and Stacked Layers