def __init__(self):
self.layer_1st = LayerNetwork(2)
self.layer_2nd = LayerNetwork(5)
self.layer_3rd = LayerNetwork(5)
self.layer_last = LayerNetwork(2)
self.layer_2nd.random_weight(2, self.layer_1st.num_nut)
self.layer_3rd.random_weight(2, self.layer_2nd.num_nut)
self.layer_last.random_weight(2, self.layer_3rd.num_nut)
def __init__(self):
self.layer_1st = LayerNetwork(2)
self.layer_2nd = LayerNetwork(5)
self.layer_3rd = LayerNetwork(5)
self.layer_last = LayerNetwork(2)
self.layer_2nd.random_weight(2, self.layer_1st.num_nut)
self.layer_3rd.random_weight(2, self.layer_2nd.num_nut)
self.layer_last.random_weight(2, self.layer_3rd.num_nut)
class XorNeuralNetwork():
'''
xor function using neural network
'''
input = [[0, 0], [0, 1], [1, 0], [1, 1]]
output = [0, 1, 1, 0]
def __init__(self):
self.layer_1st = LayerNetwork(2)
self.layer_2nd = LayerNetwork(5)
self.layer_3rd = LayerNetwork(5)
self.layer_last = LayerNetwork(2)
self.layer_2nd.random_weight(2, self.layer_1st.num_nut)
self.layer_3rd.random_weight(2, self.layer_2nd.num_nut)
self.layer_last.random_weight(2, self.layer_3rd.num_nut)
def train(self, round):
for _ in range(round):
case_match = 0
for i in range(len(self.input)):
# propagation
self.layer_1st.set_output_layer_first(self.input[i])
self.layer_2nd.propagation(self.layer_1st)
self.layer_3rd.propagation(self.layer_2nd)
self.layer_last.propagation(self.layer_3rd)
#check rate
if self.layer_last.node_expected() == self.output[i]:
case_match += 1
# back_propagation
expected_output = [0] * self.layer_last.num_nut
expected_output[self.output[i]] = 1
self.layer_last.set_expected_output(expected_output)
self.layer_3rd.back_propagation(self.layer_2nd,
self.layer_last)
self.layer_2nd.back_propagation(self.layer_1st, self.layer_3rd)
# train
self.layer_2nd.train(self.layer_1st)
self.layer_3rd.train(self.layer_2nd)
self.layer_last.train(self.layer_3rd)
# print case_match*100.0/len(self.input)
def test(self):
for i in range(len(self.input)):
self.layer_1st.set_output_layer_first(self.input[i])
self.layer_2nd.propagation(self.layer_1st)
self.layer_3rd.propagation(self.layer_2nd)
self.layer_last.propagation(self.layer_3rd)
print 0 if (
self.layer_last.output[0] > self.layer_last.output[1]) else 1
class XorNeuralNetwork():
'''
xor function using neural network
'''
input = [[0, 0],
[0, 1],
[1, 0],
[1, 1]]
output = [0, 1, 1, 0]
def __init__(self):
self.layer_1st = LayerNetwork(2)
self.layer_2nd = LayerNetwork(5)
self.layer_3rd = LayerNetwork(5)
self.layer_last = LayerNetwork(2)
self.layer_2nd.random_weight(2, self.layer_1st.num_nut)
self.layer_3rd.random_weight(2, self.layer_2nd.num_nut)
self.layer_last.random_weight(2, self.layer_3rd.num_nut)
def train(self, round):
for _ in range(round):
case_match = 0
for i in range(len(self.input)):
# propagation
self.layer_1st.set_output_layer_first(self.input[i])
self.layer_2nd.propagation(self.layer_1st)
self.layer_3rd.propagation(self.layer_2nd)
self.layer_last.propagation(self.layer_3rd)
#check rate
if self.layer_last.node_expected() == self.output[i]:
case_match += 1
# back_propagation
expected_output = [0]* self.layer_last.num_nut
expected_output[self.output[i]] = 1
self.layer_last.set_expected_output(expected_output)
self.layer_3rd.back_propagation(self.layer_2nd, self.layer_last)
self.layer_2nd.back_propagation(self.layer_1st, self.layer_3rd)
# train
self.layer_2nd.train(self.layer_1st)
self.layer_3rd.train(self.layer_2nd)
self.layer_last.train(self.layer_3rd)
# print case_match*100.0/len(self.input)
def test(self):
for i in range(len(self.input)):
self.layer_1st.set_output_layer_first(self.input[i])
self.layer_2nd.propagation(self.layer_1st)
self.layer_3rd.propagation(self.layer_2nd)
self.layer_last.propagation(self.layer_3rd)
print 0 if (self.layer_last.output[0] > self.layer_last.output[1]) else 1
def create_layers(self, layer_nodes=None):
if layer_nodes:
self.layer = [
LayerNetwork(i, self.using_sigmod) for i in layer_nodes
]