Getting started with neural-network – Python Neuron class

import numpy as np #There is a lot of math in neurons, so use numpy to speed things up in python; in other languages, use an efficient array type for that language
import random      #Initial neuron weights should be random

class Neuron:

def __init__(self, nbr_inputs, weight_array = None):
    if (weight_array != None): #you might already have a trained neuron, and wish to recreate it by passing in a weight array h ere
        self.weight_array = weight_array
    else:                      #...but more often, you generate random, small numbers for the input weights.  DO NOT USE ALL ZEROES, or you increase the odds of getting stuck when learning
        self.weight_array = np.zeros(nbr_inputs+1)
        for el in range(nbr_inputs+1): #+1 to account for bias weight
            self.weight_array[el] = random.uniform((-2.4/nbr_inputs),(2.4/nbr_inputs))
    self.nbr_inputs = nbr_inputs

def neuron_output(self,input_array):
    input_array_with_bias = np.insert(input_array,0,-1)
    weighted_sum = np.dot(input_array_with_bias,self.weight_array)
    #Here we are using a hyperbolic tangent output; there are several output functions which could be used, with different max and min values and shapes
    self.output = 1.716 * np.tanh(0.67*weighted_sum)
    return self.output

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Getting started with neural-network – Python Neuron class - CodeDay