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Single Layer Network Python Modules (Download.zip)

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:

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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!