Single Layer Network Python Modules (
These modules were built to explore the types of weight solutions learned for LS and NLS problems in different kinds of neural networks.
An example, to make this easy:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 import numpy as np from single_layer_nets import perceptron from single_layer_nets import autoassociator LS = dict( A = np.array([[0,0],[0,1]]), B = np.array([[1,0],[1,1]])) #train the perceptron for 1000 blocks on the LS problem net = perceptron([LS['A'], LS['B']]).train(1000) print(net.wts) print(net.output) # now, train the autoassociator net = autoassociator([LS['A'], LS['B']]).train(1000) print(net.wts) print(net.output)
I didn’t exactly out-do myself here. These classes don’t have methods for classification, and they don’t tell you how accurate the model was at each training block. I basically just use them to study weight solutions!