Hi, I intend to use a multi layer perceptron network trained with backpropagation (one hidden layer, inputs served as 8x8 bit matrices containing the B/W pixels from the image). The following questions arise: which type of learning should I use: batch or on-line? how could I estimate the right number of nodes in the hidden layer? I in...
Can anyone recommend a website or give me a brief of how backpropagation is implemented in a NN? I understand the basic concept, but I'm unsure of how to go about writing the code. Many of sources I've found simply show equations without giving any explanation of why they're doing it, and the variable names make it difficult to find out...
Backpropagation calculates dW (weight delta) per weight per pattern, so it's straightforward how to modify weights when doing stochastic training. How do I use it for batch training, though? Simply accumluate dW over the entire training set and then apply the modfication, or is there more to it? ...
When designing a feed forward neural network with multiple outputs, is there a conceptual difference (other than computational efficency) between having a single network with multiple outputs, and having multiple networks, each having a single output? Although output neurons in the same network don't affect each other "on the fly", they...
Update: a better formulation of the issue. I'm trying to understand the backpropagation algorithm with an XOR neural network as an example. For this case there are 2 input neurons + 1 bias, 2 neurons in the hidden layer + 1 bias, and 1 output neuron. A B A XOR B 1 1 -1 1 -1 1 -1 1 1 -1 -1 -1 I'm using the...
I am trying to write a program for weather forecasting using backpropagation. I am a beginner in this field. I have historical data with different parameters like temperature, humidity, wind speed, rainfall etc. I am confused about how to provide this data to the input layer. Is each input node to be given the whole of the data for a g...
Hello Guys, First I want to say that I'm really new to neural networks and I don't understand it very good ;) I've made my first C# implementation of the backpropagation neural network. I've tested it using XOR and it looks it work. Now I would like change my implementation to use resilient backpropagation (Rprop - http://en.wikipedia...
I am creating a tool for predicting the time and cost of software projects based on past data. The tool uses a neural network to do this and so far, the results are promising, but I think I can do a lot more optimisation just by changing the properties of the network. There don't seem to be any rules or even many best-practices when it c...
I'm working on a feed-forward backpropagation network in C++ but cannot seem to make it work properly. The network I'm basing mine on is using the cross-entropy error function. However, I'm not very familiar with it and even though I'm trying to look it up I'm still not sure. Sometimes it seems easy, sometimes difficult. The network will...
Hello everyone. I am trying to write a simple neural network that can come up with weights to for, say, the y=x function. Here's my code: http://codepad.org/rPdZ7fOz As you can see, the error level never really goes down much. I tried changing the momentum and learning rate but it did not help much. Is my number of input, hidden and ou...
Hi, I'm trying to add to the code for a single layer neural network which takes a bitmap as input and has 26 outputs for the likelihood of each letter in the alphabet. The first question I have is regarding the single hidden layer that is being added. Am I correct in thinking that the hidden layer will have it's own set of output value...