Generic selectors
Exact matches only
Search in title
Search in content
Search in posts
Search in pages
wb_sunny

Building a Single Perceptron Neural Network

Building A Single Perceptron Neural Network

Welcome. Today, we are going to cover how to build a basic single perceptron neural network.

I want to make this the first of a series of articles where we delve deep into everything – CNNs, transfer learning, etc. so be sure to bookmark the site and keep checking it. Also, this will include some math, so strap in.

What is Perceptron?

It is the most fundamental unit of a neural network (NN). In a complex NN, all data pass through several of these, often at the same time and performing different roles. But we’ll get to that later.

For now, what is a perceptron?

In supervised learning, a perceptron is a form of a linear classifier. So, any dataset that can be divided by a single straight line can be classified using a perceptron, i.e., any dataset that looks like this:

Linearly Separable Dataset Examples
Linearly Separable Dataset Examples

The last one cannot be considered linearly separable, because it is not a single line that can approximately separate the database.

Perceptron
Perceptron

Building A Single Perceptron Neural Network

Let’s move on to building our first single perceptron neural network today. For this, we’ll begin with creating the data.

1. Create our dataset

First, we need our data set, which in our case will a 2D array. Open up your code editors, Jupyter notebook, or Google Colab.

import pandas as pd
import numpy as np
import random

Let’s make our data. I consider a 20*20 plane in this example to keep our results small and concise.

#Dataset
df = pd.DataFrame()
df['x'] = [random.randint(1,20) for x in range(10)]
df['y'] = [random.randint(1,20) for x in range(10)]
df.head()

Now, we need to label these. So we’ll filter out based on a line (I considered y=x). So every point below the line is y<x and every point above the line is mean y>x.

label=[]
for i in range(df.shape[0]):
  if df.iloc[i,0] < df.iloc[i,1]:
    label.append(1)
  else:
    label.append(-1)

df['label'] = label
df
Dataset With Labels For Neural Nets Perceptron Model
Dataset With Labels For Neural Nets Perceptron Model

2. Initialize weights for the values

Now we can initialize the weights. We can’t use zero value, so we’ll just go with a random uniform distribution for weights:

#weights and bias
weights = [np.round(random.uniform(-0.99,0.99),2) for i in range(2)]

Then we multiply the weights with the input data points and sum:

w = weights.copy()
X = [[df.iloc[i,0],df.iloc[i,1]] for i in range(df.shape[0])]
wx = [X[i][0]*w[0]+X[i][1]*w[1] for i in range(df.shape[0])]

Now, we have the sum of weights and inputs for each point.

So what the procedure is, is that we’ll plug in all these values one at a time into the activation function, and then based on output we’ll modify the weights.

3. Creating the Activation Function

Now we come to the activation function. The perceptron treats the sum and gives us a label, which we compare with the original label and determine if it is correct. If it is incorrect, the error is found and the weights are adjusted so that our solution *moves* in the direction of the original.

We’ll be using the signum function:

If wx <=0 , then output is 0. Else, the output is 1.

for i in range(df.shape[0]):
  if wx[i]<=0:
    pred = 0
  else:
    pred = 1

Remember, we only have two inputs: x and y. Not the whole dataframe. So, we will expand this activation function to take in one datapoint at a time, and then finding the error and then adjusting the error:

for i in range(df.shape[0]):
  if wx[i]<=0:
    pred = -1
  else:
    pred = 1
  if pred != df['label'][i] :
    err = df['label'][i] - pred
    w[0] = w[0] + err
    w[1] = w[1] + err

This works perfectly now. To clearly see the outputs, we’ll put in a bunch of print statements:

for i in range(df.shape[0]):
  print('wx : ',wx[i])
  if wx[i]<=0:
    pred = -1
  else:
    pred = 1
  print('label=',df['label'][i])
  print('pred = ',pred)
  if pred != df['label'][i] :
    err = df['label'][i] - pred
    print('err',err)
    print('before', w[0],w[1])
    w[0] = w[0] + err
    w[1] = w[1] + err
    print('after',w[0],w[1])
  else:
    print('w_i', w[0],w[1])

And now if we run this:

Updating Weights Single Perceptron Neural Network
Updating Weights Single Perceptron Neural Network

A simple print formatting statement gives us the final weights.

Final Weights Single Perceptron Neural Network
Final Weights Single Perceptron Neural Network

4. Testing our model on another database

Similar to how we did the train-test-split, we’ll use a different database for our testing here.

#Test Dataset
new_df = pd.DataFrame()
new_df['x'] = [random.randint(1,20) for x in range(100)]
new_df['y'] = [random.randint(1,20) for x in range(100)]
new_df.head()

Then we generate the labels based on y=x line, and the sum of weights*inputs:

label_text = []
for i in range(new_df.shape[0]):
  if new_df.iloc[i,0] < new_df.iloc[i,1]:
    label_text.append(1)
  else:
    label_text.append(-1)
new_wX = w[0]*new_df['x']+w[1]*new_df['y']

Here is mine:

New Weight Sums Neural Net
New Weight Sums Neural Net

So, now the moment of truth, we apply our activation function, and then we can compare the given labels with the predicted labels:

new_df['given label'] = label_text
pred_label_text = []

for i in range(new_df.shape[0]):
  if new_wX[i]>=0:
    pred_label_text.append(-1)
  else:
    pred_label_text.append(1)

new_df['predicted labels'] = pred_label_text
Predicted Labels For New Dataset
Predicted Labels For New Dataset

As you can see, we did pretty well for ourselves 🙂

Conclusion

Congratulations on completing this tutorial. I hope this gave you a lot of insight into a “perceptron”. Stay in touch with us to read our future tutorials.