Data Science, Machine Learning, Artificial Intelligence, Technology, Fintech.
model.compile(optimizer='adam', loss='mean_squared_error') # Train the model # history = model.fit(X_train, y_train, epochs=100, batch_size=25, validation_split=0.2) This custom layer computes a weighted sum of the input sequence, allowing the model to pay more attention to certain time steps.