Contents
Why does LSTM not suffer from vanishing gradient problem?
LSTM was invented specifically to avoid the vanishing gradient problem. It is supposed to do that with the Constant Error Carousel (CEC), which on the diagram below (from Greff et al.) correspond to the loop around cell.
How vanishing gradient problem can be solved in LSTM?
LSTMs solve the problem using a unique additive gradient structure that includes direct access to the forget gate’s activations, enabling the network to encourage desired behaviour from the error gradient using frequent gates update on every time step of the learning process.
Why is the recursive gradient equal to 1 in LSTM?
In the original LSTM formulation in 1997, the recursive gradient actually was equal to 1. The reason for this is because, in order to enforce this constant error flow, the gradient calculation was truncated so as not to flow back to the input or candidate gates.
Why are LSTMs Stop Your gradients from vanishing?
LSTMs: The Gentle Giants On their surface, LSTMs (and related architectures such as GRUs) seems like wonky, overly complex contraptions. Indeed, at first it Why LSTMs Stop Your Gradients From Vanishing: A View from the Backwards Pass | weberna’s blog
How does LSTM solve the basic RNN problem?
This solves the problem in the basic RNN that every time step applies an affine transformation and nonlinearity, meaning that the longer the time distance between the input and output, the smaller the error gets. Thanks for contributing an answer to Cross Validated!
How does LSTM reduce the magnitude of the error?
Again, when the input gate opens, the error exits through the input gate, activation function, and affine transformation, reducing the magnitude of the error. Thus the error is reduced when it is backpropagated through an LSTM layer, but only when it enters and exits the CEC.