Backpropagation Artificial Neural Network
(Tema 2 IA | Iulius Curt 343 C3)
Python (with Numpy) implementation of Artificial Neural Network with Backpropagation
- Sigmoid function
- Variable number of hidden layers / neurons on each layer
- Error plot
test_*.py file can be run to test some different data set / function.
> python test_xor.py
To tweak parameters of disable plot, edit the file.
Test files (associated test data sets found in
test_xor.py- Simplest, learns XOR function from data set file
test_simple.py- Learns an easy quadratic function of one parameter
test_concrete.py- Uses the data set at ics.uci.edu
test_mpg.py- Relatively good results on MPG data set
test_bodyfat.py- Pretty poor results on BodyFat data set
The weights of the inputs the neurons on each hidden layer and on the output layer are stored in matrices (2D numpy arrays) and computation is done with matrices and vectors operations.