-
Notifications
You must be signed in to change notification settings - Fork 0
/
001_neuron.py
68 lines (39 loc) · 1.07 KB
/
001_neuron.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
# single neuron within a Neural Network (NN)
# receiving values from 3 neurons from previous layer
# in NN every neuron has unique connection
# to every single previous neuron
###
# let say there are 3 neurons that are feeding
# into this 1 neuron,
# and those neurons are outputting some values
# the outputs from those 3 neurons
# are the inputs of the neuron in next layer
###
#inputs
inputs = [1.0, 2.0, 3.0]
###
# every input also has
# unique weight associated with it
# since there are 3 inputs
# we'll have 3 weights
###
# weights
weights = [0.2, 0.8, -0.5]
# every unique neuron has a unique bias
bias = 2.0
###
# first step for a neuron is to compute the output
# sum(weight * input) + bias ,
# or
# x1*w1 + x2+w2 + ... + xN*wN + bias
###
# output of the neuron
# Neuron 1
output = (inputs[0] * weights[0] +
inputs[1] * weights[1] +
inputs[2] * weights[2] +
bias)
print("One neuroun 3 inputs")
print(output, "\n")
# this is later passed into activation function
# activationFunction(x1*w1 + x2+w2 + ... + xN*wN + bias)