87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
|
|
from auto_diff import auto_diff
|
|
import classes
|
|
|
|
def sigmoid(x: auto_diff.Node):
|
|
return 1 / (1 + (-x).exp())
|
|
|
|
def tanh(x: auto_diff.Node):
|
|
return (x.exp() - (-x).exp()) / (x.exp() + (-x).exp())
|
|
|
|
class SimpleNN:
|
|
def __init__(self):
|
|
#111 - 1 слой, 1 нейрон, 1 вес
|
|
# self.w111 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w112 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.b11 = auto_diff.Node(random.uniform(-1, 1))
|
|
|
|
# self.w121 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w122 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.b12 = auto_diff.Node(random.uniform(-1, 1))
|
|
|
|
# self.w131 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w132 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.b13 = auto_diff.Node(random.uniform(-1, 1))
|
|
|
|
# self.w141 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w142 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.b14 = auto_diff.Node(random.uniform(-1, 1))
|
|
|
|
|
|
|
|
# self.w211 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w212 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w213 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w214 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.b21 = auto_diff.Node(random.uniform(-1, 1))
|
|
|
|
# self.w221 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w222 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w223 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w224 = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.b22 = auto_diff.Node(random.uniform(-1, 1))
|
|
|
|
|
|
|
|
# self.w1_out = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.w2_out = auto_diff.Node(random.uniform(-1, 1))
|
|
# self.b_out = auto_diff.Node(random.uniform(-1, 1))
|
|
|
|
self.network = classes.NeuronNetwork(4, 1, 2)
|
|
|
|
self.lr = 0.1 # скорость обучения
|
|
|
|
def forward(self, x1, x2):
|
|
# прямой проход
|
|
|
|
# #скрытые слои
|
|
# self.h11 = tanh(self.w111*x1 + self.w112*x2 + self.b11)
|
|
# self.h12 = tanh(self.w121*x1 + self.w122*x2 + self.b12)
|
|
# self.h13 = tanh(self.w131*x1 + self.w132*x2 + self.b13)
|
|
# self.h14 = tanh(self.w141*x1 + self.w142*x2 + self.b14)
|
|
|
|
# self.h21 = tanh(self.w211*self.h11 + self.w212*self.h12 + self.w213*self.h13 + self.w214*self.h14 + self.b21)
|
|
# self.h22 = tanh(self.w221*self.h11 + self.w222*self.h12 + self.w223*self.h13 + self.w224*self.h14 + self.b22)
|
|
|
|
# #выходной слой
|
|
# self.a1 = sigmoid(self.w1_out*self.h21 + self.w2_out*self.h22 + self.b_out)
|
|
|
|
self.a1 = self.network.forward(sigmoid, x1, x2)
|
|
|
|
return self.a1
|
|
|
|
def backward(self, y):
|
|
# вычисляем ошибку
|
|
error = (self.a1 - y)**2 # dL/da2
|
|
|
|
auto_diff.backward(error)
|
|
|
|
self.network.update_weights(self.lr)
|
|
|
|
def train(self, dataset, answs, epochs=1000):
|
|
for _ in range(epochs):
|
|
for i in range(len(dataset)):
|
|
self.forward(*dataset[i])
|
|
self.backward(answs[i])
|
|
|