hypoxie/training-primitive-neural-network
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Нейросеть с 5 нейронами

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HypoxiE
2025-08-18 23:29:53 +07:00
parent 6b16020a93
commit a2511a5a7e
1 changed files with 70 additions and 39 deletions
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109
neuro_defs.py
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@@ -1,4 +1,5 @@
import random
from auto_diff import auto_diff from auto_diff import auto_diff
import classes import classes
@@ -11,62 +12,58 @@ def tanh(x: auto_diff.Node):
class SimpleNN: class SimpleNN:
def __init__(self): def __init__(self):
#111 - 1 слой, 1 нейрон, 1 вес #111 - 1 слой, 1 нейрон, 1 вес
# self.w111 = auto_diff.Node(random.uniform(-1, 1)) self.w111 = auto_diff.Node(random.uniform(-1, 1))
# self.w112 = 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.b11 = auto_diff.Node(random.uniform(-1, 1))
# self.w121 = 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.w122 = auto_diff.Node(random.uniform(-1, 1))
# self.b12 = 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.w131 = auto_diff.Node(random.uniform(-1, 1))
# self.w132 = 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.b13 = auto_diff.Node(random.uniform(-1, 1))
# self.w141 = 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.w142 = auto_diff.Node(random.uniform(-1, 1))
# self.b14 = 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.w211 = auto_diff.Node(random.uniform(-1, 1))
# self.w212 = 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.w213 = auto_diff.Node(random.uniform(-1, 1))
# self.w214 = 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.b21 = auto_diff.Node(random.uniform(-1, 1))
# self.w221 = 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.w222 = auto_diff.Node(random.uniform(-1, 1))
# self.w223 = 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.w224 = auto_diff.Node(random.uniform(-1, 1))
# self.b22 = 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.w1_out = auto_diff.Node(random.uniform(-1, 1))
# self.w2_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.b_out = auto_diff.Node(random.uniform(-1, 1))
self.network = classes.NeuronNetwork(4, 1, 2)
self.lr = 0.1 # скорость обучения self.lr = 0.1 # скорость обучения
def forward(self, x1, x2): def forward(self, x1, x2):
# прямой проход # прямой проход
# #скрытые слои #скрытые слои
# self.h11 = tanh(self.w111*x1 + self.w112*x2 + self.b11) self.h11 = tanh(self.w111*x1 + self.w112*x2 + self.b11)
# self.h12 = tanh(self.w121*x1 + self.w122*x2 + self.b12) self.h12 = tanh(self.w121*x1 + self.w122*x2 + self.b12)
# self.h13 = tanh(self.w131*x1 + self.w132*x2 + self.b13) self.h13 = tanh(self.w131*x1 + self.w132*x2 + self.b13)
# self.h14 = tanh(self.w141*x1 + self.w142*x2 + self.b14) 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.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) self.h22 = tanh(self.w221*self.h11 + self.w222*self.h12 + self.w223*self.h13 + self.w224*self.h14 + self.b22)
# #выходной слой #выходной слой
# self.a1 = sigmoid(self.w1_out*self.h21 + self.w2_out*self.h22 + self.b_out) 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 return self.a1
@@ -76,7 +73,41 @@ class SimpleNN:
auto_diff.backward(error) auto_diff.backward(error)
self.network.update_weights(self.lr) self.w111 = auto_diff.update_weights(self.w111, self.lr)
self.w112 = auto_diff.update_weights(self.w112, self.lr)
self.b11 = auto_diff.update_weights(self.b11, self.lr)
self.w121 = auto_diff.update_weights(self.w121, self.lr)
self.w122 = auto_diff.update_weights(self.w122, self.lr)
self.b12 = auto_diff.update_weights(self.b12, self.lr)
self.w131 = auto_diff.update_weights(self.w131, self.lr)
self.w132 = auto_diff.update_weights(self.w132, self.lr)
self.b13 = auto_diff.update_weights(self.b13, self.lr)
self.w141 = auto_diff.update_weights(self.w141, self.lr)
self.w142 = auto_diff.update_weights(self.w142, self.lr)
self.b14 = auto_diff.update_weights(self.b14, self.lr)
self.w211 = auto_diff.update_weights(self.w211, self.lr)
self.w212 = auto_diff.update_weights(self.w212, self.lr)
self.w213 = auto_diff.update_weights(self.w213, self.lr)
self.w214 = auto_diff.update_weights(self.w214, self.lr)
self.b21 = auto_diff.update_weights(self.b21, self.lr)
self.w221 = auto_diff.update_weights(self.w221, self.lr)
self.w222 = auto_diff.update_weights(self.w222, self.lr)
self.w223 = auto_diff.update_weights(self.w223, self.lr)
self.w224 = auto_diff.update_weights(self.w224, self.lr)
self.b22 = auto_diff.update_weights(self.b22, self.lr)
self.w1_out = auto_diff.update_weights(self.w1_out, self.lr)
self.w2_out = auto_diff.update_weights(self.w2_out, self.lr)
self.b_out = auto_diff.update_weights(self.b_out, self.lr)
def train(self, dataset, answs, epochs=1000): def train(self, dataset, answs, epochs=1000):
for _ in range(epochs): for _ in range(epochs):