Welcome to the LSTM tutorial using TensorFlow! This guide will walk you through building a Long Short-Term Memory (LSTM) network for sequence prediction tasks. LSTM is a type of recurrent neural network (RNN) that excels at capturing temporal dependencies in data.
🚀 What is LSTM?
LSTM uses memory cells and gates (input, forget, output) to selectively retain or discard information. This makes it ideal for tasks like:
- Time series forecasting
- Sentiment analysis
- Language modeling
- Sequence-to-sequence tasks
neural_network_structure
🧩 Step-by-Step Implementation
Import Libraries
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense
Prepare Data
Use time-series datasets like stock prices or sensor data. Example:# Sample data: [x, y] pairs data = [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], ...]
Build Model
model = Sequential([ LSTM(50, input_shape=(None, 1)), Dense(1) ]) model.compile(optimizer='adam', loss='mse')
Train Model
model.fit(X_train, y_train, epochs=20, batch_size=32)
⚠️ Key Considerations
- Use
tf.keras
for simplicity - Adjust
units
parameter based on complexity - Monitor training with
model.summary()
- For advanced architectures, check our Sequence-to-Sequence guide
📚 Expand Your Knowledge
code_example
training_process