Why is it difficult to train RNN?
One of the simplest ways to explain why recurrent neural networks are hard to train is that they are not feedforward neural networks. In feedforward neural networks, signals only move one way. The signal moves from an input layer to various hidden layers, and forward, to the output layer of a system.
What is the problem with RNN?
However, RNNs suffer from the problem of vanishing gradients, which hampers learning of long data sequences. The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done.
How can I improve my RNN?
Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting. Increase the size of your model (either number of layers or the raw number of neurons per layer)
How do I stop Overfitting in RNN?
Dropout Layers can be an easy and effective way to prevent overfitting in your models. A dropout layer randomly drops some of the connections between layers. This helps to prevent overfitting, because if a connection is dropped, the network is forced to Luckily, with keras it’s really easy to add a dropout layer.
What’s the purpose of the cross entropy loss function?
The purpose of the loss function is to tell the model that some correction needs to be done in the learning process. In the context of sequence classification problem, to compare two probability distributions (true distribution and predicted distribution) we will use the cross-entropy loss function.
What is the loss function of a RNN?
The loss function is equal to the summation of the true probability and log of the predicted probability. For ‘m’ training samples, the total loss would be equal to the average of overall loss (Where c indicates the correct class or true class).
How is the output vector of a RNN influenced?
At the core, RNNs have a deceptively simple API: They accept an input vector x and give you an output vector y. However, crucially this output vector’s contents are influenced not only by the input you just fed in, but also on the entire history of inputs you’ve fed in in the past.