Contents
How to identify error in a neural network?
In order to identify the kind of errors our model generates, we split the dev set into two parts — the eyeball set and the blackbox set. The eyeball set is the sample set that we actually evaluate. We can check these records manually, to guess the source of errors.
Where does the data for a neural network come from?
We may extract data from the net or surveys or various other sources. The basic inputs came from humans. And humans are error prone. Thus, we should acknowledge the fact that all our train/dev/test data has some mislabeled records. If our model is well built and trained properly, then it should be able to overcome such errors.
What does high variance mean in a neural network?
On the other hand, high variance means we are not training it enough. We need more data or we need much better processing on the available data. With this, we might be able to train a better model.
Which is the correct definition of error analysis?
Error analysis is the analysis of error. He he! You don’t have to tell me that. In fact, the whole of error analysis is just as intuitive. But, people tend to miss some points in real projects.
How does a neural network always have the same result?
To have always the same result, try to propose a fixed seed for the initial weights. Another option is that the neural network works with a random data division, taking randomly the order of input/target pairs to compute the error propagation phase.
What to do if your neural network is not working?
Try passing random numbers instead of actual data and see if the error behaves the same way. If it does, it’s a sure sign that your net is turning data into garbage at some point. Try debugging layer by layer /op by op/ and see where things go wrong.
When does a neural network need to generalize?
The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations.