In this two-post series is going to go through building neurones and neural networks in Python and by the end of the second post a python-based microbit neural network
- Post 1 (this one) A single neuron and a simple neuron network will be produced in Python are produced.
- Post 2 looks at producing a network of neurons, ie. neural network using the idea from post 1 but using a three microbits.; looking to solve the problem that a single neuron can't solve, making an Exclusive OR gate (XOR)
1. Overview and non-microbit neuron
1.1 Overview:
The characteristics of the system will be:
- Inputs are going to be binary
- Weighted sum is bias+W1*input1+w2*input2
- If weighted sum>=0 then the output is True (T on the LEDs) or '1'
- If weighted sum<0 then the output is False (F on the LEDs) or '0
1.2. Single Neuron (without the Microbit)
Lets start without the microbit and build a single neuron in Python and useful exercise in it's own right just to see the mechanism. A class Neuron is produced and all possible combination of a two-input binary system are feed in.
def __init__(self, input1, bias, w1, w2):
self.input1 = input1
self.bias = bias
self.w1=w1
self.w2=w2
def CalculateOutput (self):
output1 = 0
net = self.bias+self.input1[0]*self.w1+self.input1[1]*self.w2
if net >= 0:
output1 = 1
else:
output1 = 0
return output1
for x1 in range (2):
for x2 in range (2):
neuron1= Neuron([x1,x2],-1,1,1)
print("x1="+str(x1)+"x2= "+str(x2)+" Output= " +str(neuron1.CalculateOutput()))
The code above implements a simple single neuron and the weights -1,1,1 produce an OR gate and -2,1,1 produces an AND gate.
1.3 A Neural Network
We can extend the code above to produce a neural network, by feeding the outputs of one or more neurons as inputs to other neurones. The code below produces an Exclusive XOR - essentially for the two input case if the two inputs are different then the output is True. The same inputs go to two neurones but they have different weight (bias, W1 and W2) but the outputs from these neurones are the inputs to a third neurone. The code is shown below (the Neuron class hasn't changed):
class Neuron:def __init__(self, input1, bias, w1, w2):
self.input1 = input1
self.bias = bias
self.w1=w1
self.w2=w2
def CalculateOutput (self):
output1 = 0
net = self.bias+self.input1[0]*self.w1+self.input1[1]*self.w2
if net >= 0:
output1 = 1
else:
output1 = 0
return output1
for x1 in range (2):
for x2 in range (2):
neuron1= Neuron([x1,x2],-1,-1,1)
neuron2= Neuron([x1,x2],-1,1,-1)
neuron3= Neuron([neuron1.CalculateOutput(),neuron2.CalculateOutput()],-1,1,1)
print("x1="+str(x1)+"x2= "+str(x2)+" Output 1= "+str(neuron1.CalculateOutput())+" Output 2= "+str(neuron2.CalculateOutput())+" Output overall= "+str(neuron3.CalculateOutput()))
2. Where next - building a Physical Microbit Neural Network
The figure below shows the arrangement of the connections to be built; pin 2 is the output of each neuron. The two micro:bits/neurons on the left of the picture taking in the two same inputs; the output from these neurons are the two inputs to the output neuron on the right.
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| figure 1 |
The micro:bit objects used in Figure 1 were produced using the micro:bit Frtzing diagram available at https://github.com/microbit-foundation/dev-docs/issues/36 thanks to David Whale (@whalleygeek ) for this.
All opinions in this blog are the Author's and should not in any way be seen as reflecting the views of any organisation the Author has any association with. Twitter @scottturneruon
