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