Table of Contents
What is the loss function in RNN?
The loss function L internally computes y^ = softmax(o) and compares this to target y. The RNN has input to hidden connections parameterised by a weight matrix U, parameterised by a weight matrix W, and hidden to output connection parameterised by a weight matrix V.
How do Lstms train?
In order to train an LSTM Neural Network to generate text, we must first preprocess our text data so that it can be consumed by the network. In this case, since a Neural Network takes vectors as input, we need a way to convert the text into vectors.
How much training data is required for Lstm?
Concerning the LSTM, it has been shown that a data length of 9 years is required for the training procedure to reach acceptable performances and 12 years for more efficient prediction.
What is training Loss and Validation loss?
One of the most widely used metrics combinations is training loss + validation loss over time. The training loss indicates how well the model is fitting the training data, while the validation loss indicates how well the model fits new data.
How does RNN model work?
RNN converts the independent activations into dependent activations by providing the same weights and biases to all the layers, thus reducing the complexity of increasing parameters and memorizing each previous outputs by giving each output as input to the next hidden layer.
How Big Should training data be?
A general suggestion: Use 60-70\% for training and the rest for validation & testing.
What are LSTMs and how are they different from RNNs?
LSTMs are essentially improved versions of RNNs, capable of interpreting longer sequences of data. Let’s take a look at how RNNs and LSTMS are structured and how they enable the creation of sophisticated natural language processing systems. What are Feed-Forward Neural Networks?
Are RNNs capable of handling sequential data?
In this way, the context of the data (the previous inputs) is preserved as the network trains. The result of this architecture is that RNNs are capable fo handling sequential data. However, RNNs suffer from a couple of issues. RNNs suffer from the vanishing gradient and exploding gradient problems.
What are the features of an RNN?
RNNs are a special kind of neural networks that are designed to effectively deal with sequential data. This kind of data includes time series (a list of values of some parameters over a certain period of time) text documents, which can be seen as a sequence of words, or audio, which can be seen as a sequence of sound frequencies over time.
What are the problems of conventional RNNs?
This article talks about the problems of conventional RNNs, namely, the vanishing and exploding gradients and provides a convenient solution to these problems in the form of Long Short Term Memory (LSTM).