neuron_b = Neuron(eta, input_size, unit_step_b)
neuron_b.train(training_data, training_times)
neuron_c = Neuron(eta, input_size, unit_step_c)
neuron_c.train(training_data, training_times)
neurons = [neuron_a, neuron_b, neuron_c]
new_training_data = []
for x, label in training_data:
print("=")
unities = []
for neuron in neurons:
result = dot(x, neuron.weights)
unit = neuron.get_unit_step(x)
unities.append(unit)
print("{}: {} -> {}".format(x, result, unit))
new_training_data.append((array(unities), label))
neuron_d = Neuron(eta, input_size, unit_step_d)
neuron_d.train(new_training_data, training_times)
for x, label in new_training_data:
print("\n#####")
unit = neuron_d.get_unit_step(x)
unit = int(unit)
print("expected: {}, error: {}".format(neuron_d.expected, neuron_d.error))
print("{}: -> {}".format(x, unit))
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from numpy import array, dot
from neuron import Neuron
unit_step = lambda inputs, weights: 0 if dot(inputs, weights) < 0 else 1
training_data = [
(array([0, 0, 1]), 0),
(array([0, 1, 1]), 1),
(array([1, 0, 1]), 0),
(array([1, 1, 1]), 1),
]
eta = 0.2
input_size = 3
training_times = 100
neuron = Neuron(eta, input_size, unit_step)
neuron.train(training_data, training_times)
for x, _ in training_data:
unit = neuron.get_unit_step(x)
print("{}: -> {}".format(x, unit))
