def setUp(self):
"""Prepares a Halfadder Neuron Network"""
# Layer 1 Neurons:
n1 = Neuron([12,12], Sigmoid().activate, bias=-18)
n2 = Neuron([-12,-12], Sigmoid().activate, bias=6)
n3 = Neuron([12,12], Sigmoid().activate, bias=-18)
# Layer 2 Neurons:
n4 = Neuron([-12,-12,0], Sigmoid().activate, bias=6)
n5 = Neuron([0,0,12], Sigmoid().activate, bias=-6)
# Layers
l1 = NeuronLayer([n1,n2,n3])
l2 = NeuronLayer([n4,n5])
self.ntwrk = NeuronNetwork([l1,l2])
class Neuron_INVERT(unittest.TestCase):
"""Tests the Neuron class by building a INVERT logic gate Neuron"""
def setUp(self):
"""Prepares a INVERT-type Neuron."""
self.INVERT_Neuron = Neuron([-12], Sigmoid().activate, bias=6)
def test_INVERT_high(self):
"""Tests a scenario in which a Neuron - designed to be an INVERT port - returns a output as close as possible to high (1)"""
outcome = self.INVERT_Neuron.activate([0])
self.assertAlmostEqual(outcome, 1, 2)
def test_INVERT_low(self):
"""Tests a scenario in which a Neuron - designed to be an INVERT port - returns a output as close as possible to low (0)"""
outcome = self.INVERT_Neuron.activate([1])
self.assertAlmostEqual(outcome, 0, 2)
class Neuron_NOR(unittest.TestCase):
"""Tests the Neuron class by building a NOR logic gate Neuron"""
def setUp(self):
"""Prepares a NOR-type Neuron"""
self.NOR_Neuron = Neuron([-12, -12, -12], Sigmoid().activate, bias=6)
def test_NOR_high(self):
"""Tests a scenario in which a Neuron - designed to be an NOR port - returns a output as close as possible to high (1)"""
outcome = self.NOR_Neuron.activate([0, 0, 0])
self.assertAlmostEqual(outcome, 1, 2)
def test_NOR_low(self):
"""Tests some scenarios in which a Neuron - designed to be an NOR port - returns a output as close as possible to low (0)"""
outcome = self.NOR_Neuron.activate([1, 0, 0])
self.assertAlmostEqual(outcome, 0, 2)
outcome = self.NOR_Neuron.activate([0, 1, 0])
self.assertAlmostEqual(outcome, 0, 2)
outcome = self.NOR_Neuron.activate([1, 1, 0])
self.assertAlmostEqual(outcome, 0, 2)
outcome = self.NOR_Neuron.activate([0, 0, 1])
self.assertAlmostEqual(outcome, 0, 2)
outcome = self.NOR_Neuron.activate([1, 0, 1])
self.assertAlmostEqual(outcome, 0, 2)
outcome = self.NOR_Neuron.activate([0, 1, 1])
self.assertAlmostEqual(outcome, 0, 2)
from classes.Neuron import Neuron
from classes.NeuronLayer import NeuronLayer
from classes.NeuronNetwork import NeuronNetwork
from classes.Activation import Sigmoid
# Layer 1
n1 = Neuron([0.2, -0.4], Sigmoid().activate)
n2 = Neuron([0.7, 0.1], Sigmoid().activate)
# Layer 2
n3 = Neuron([0.6, 0.9], Sigmoid().activate)
l1 = NeuronLayer([n1, n2])
l2 = NeuronLayer([n3])
ntwrk = NeuronNetwork([l1, l2], 1)
x = [[0, 0], [0, 1], [1, 0], [1, 1]]
y = [[0], [1], [1], [0]]
ntwrk.train(x, y, 40000, 0.001)
print(ntwrk.__str__())
print("MSE network:")
print(ntwrk.error(x, y))
print("Should be as close as possible to high (1)")
print("[0,1] gives:")
print(ntwrk.feed_forward([0, 1]))
print("[1,0] gives:")
print(ntwrk.feed_forward([1, 0]))
print("Should be as close as possible to low (0)")
from classes.Neuron import Neuron
from classes.NeuronLayer import NeuronLayer
from classes.NeuronNetwork import NeuronNetwork
from classes.Activation import Sigmoid
n1 = Neuron([0.0,0.1],Sigmoid().activate)
n2 = Neuron([0.2,0.3],Sigmoid().activate)
n3 = Neuron([0.4,0.5],Sigmoid().activate)
n4 = Neuron([0.6,0.7,0.8],Sigmoid().activate)
n5 = Neuron([0.9,1.0,1.1],Sigmoid().activate)
l1 = NeuronLayer([n1,n2,n3])
l2 = NeuronLayer([n4,n5])
ntwrk = NeuronNetwork([l1,l2],0.5)
x = [[0,0],[1,0],[0,1],[1,1]]
y = [[0,0],[1,0],[1,0],[0,1]]
ntwrk.train(x,y,80000,0.001)
print(ntwrk.__str__())
print("MSE network:")
print(ntwrk.error(x,y))
print("Output should be close to [0,0]")
print(ntwrk.feed_forward([0,0]))
print("Output should be close to [1,0]")
print(ntwrk.feed_forward([1,0]))
print(ntwrk.feed_forward([0,1]))
def setUp(self):
"""Prepares a OR-type Neuron"""
self.OR_Neuron = Neuron([12, 12], Sigmoid().activate, bias=-6)
def setUp(self):
"""Prepares a AND-type Neuron"""
self.AND_Neuron = Neuron([12, 12], Sigmoid().activate, bias=-18)
def setUp(self):
"""Prepares a NOR-type Neuron"""
self.NOR_Neuron = Neuron([-12, -12, -12], Sigmoid().activate, bias=6)
from classes.Activation import Sigmoid
from classes.Neuron import Neuron
AND_Neuron_wrong = Neuron([0.5, 0.5], Sigmoid().activate, bias= -1)
print("Should be high (1):")
outcome = AND_Neuron_wrong.activate([1, 1])
print("Input [1,1] gives {}".format(outcome))
print("")
print("Should be low (0):")
outcome = AND_Neuron_wrong.activate([1, 0])
print("Input [1,0] gives {}".format(outcome))
outcome = AND_Neuron_wrong.activate([0, 1])
print("Input [0,1] gives {}".format(outcome))
outcome = AND_Neuron_wrong.activate([0, 0])
print("Input [0,0] gives {}".format(outcome))
from classes.Activation import Sigmoid
from classes.Neuron import Neuron
INVERTER_Neuron_wrong = Neuron([-1],Sigmoid().activate)
print("Should be high (1):")
outcome = INVERTER_Neuron_wrong.activate([0])
print("Input [0] gives {}".format(outcome))
print("")
print("Should be low (0):")
outcome = INVERTER_Neuron_wrong.activate([1])
print("Input [1] gives {}".format(outcome))
def setUp(self):
"""Prepares a INVERT-type Neuron."""
self.INVERT_Neuron = Neuron([-12], Sigmoid().activate, bias=6)