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31 lines
1.0 KiB
Python
31 lines
1.0 KiB
Python
import math
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import random
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class Neuron:
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def __init__(self, isize: int) -> None:
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self.isize = isize
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self.weight = [random.uniform(0, 1) for _ in range(self.isize)]
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self.bias = random.uniform(0, 1)
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def forward(self, inputs: list) -> float:
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assert len(inputs) == self.isize, "error: incorrect inputs number"
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total = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias
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return self.sigmoid(total)
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def sigmoid(self, x: float) -> float:
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return 1/(1 + math.exp(-x))
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# target needs to be between 0 and 1
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def train(self, inputs: list, target: float, learning_rate: float = 0.1):
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z = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias
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output = self.sigmoid(z)
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error = output - target
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d_sigmoid = output * (1 - output)
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dz = error * d_sigmoid
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for i in range(self.isize):
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self.weight[i] -= learning_rate * dz * inputs[i]
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self.bias -= learning_rate * dz
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