How do you estimate the error in neural networks?
- In my code I used MSE for error calculations, not the (target-output) I just mentioned it as an example. So I can say that the total network error is the sum of the errors per epoch?
- Get a mean of your error. If you have n input units you need divide your square error by n.
How do you test for backpropagation?
In practice, if you want to know if your backpropagation is correct,
- pass a single example (x1) through your network and computes the output (o1).
- Then, add a small value to the input (h), pass through the network again, and computes the new output (o2).
- Subtract o2 – o1 and divide by h.
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.
How are neural networks used in the real world?
Neural networks are essentially self-optimizing functions that map inputs to the correct outputs. We can then place a new input into the function, where it will predict an output based on the function it created with the training data.
Can a neural network overfit the training data?
Neural networks are great at overfitting the training data, actually there is an experiment replacing CIFAR10 (image classification task) labels (y values) by random labels on the training dataset and the network fits the random labels resulting in almost zero loss.
How does a hand written neural network work?
Below is a very simple hand written neural network. This network takes randomly generated weights between -1 and 1, an “xor_network” function which defines the architecture (notice no sigmoid, hardlims, etc. only linear transformations of the mX+b form), and trains using standard backward propagation: