A Single Layer Perceptron for Regression: Part 6

 Error Resolved

The adjusted r-squared value was not working properly because the number of predictors passed to it in the epochs function was wrong. It was being passes the number of inputs instead of the number of predictors i.e. the number of rows instead of the number of columns.


New Function Added

  • add_bias:  This function adds the bias constant value to each row of the inputs.
    Parameters:  (1) inputs(numpy array (m,n)): All the predictors.
                         (2) bias_value(float) : The constant that the bias should be equal to. By default, its                                   value is 1.
                        

Next Step:

-  Extending this code so that multiple neurons/layers can be created.

Comments

Post a Comment

Popular posts from this blog

What Data Structure Do I use?

Hello Everyone! Creating a Neural Network from Scratch has been on my mind for the last few months. So, I have finally converted my thoughts to action! I am going to try to create neural networks from scratch, i.e., without using libraries like Tensorflow and PyTorch. While creating the basic roadmap, I came across the following: 1) What kind of data structure should I use for each neuron? After  a bit of looking around: The answer is numpy arrays.  Why Numpy arrays over Lists?  - Each item of a list is a pointer to a memory location that actually contains the value. (And this is why lists can store items of different data types) - Each item in a numpy array is stored in contiguous memory locations (they are written in C++). -This means that each time a list value is retrieved there are two visits to memory locations, but when a numpy array value is retrieved, there is only one memory location to go to. I hope to understand more about numpy arrays - the way they use memor...
Read more

A single layer Perceptron for Regression: Part 1

Image
 The next agenda: to create the code for a single layer perceptron. What is a single layer perceptron? A neural network with 1 layer that has a single neuron. This means: 1) Input/Predictor values enter this neuron. 2) Each input has some weight connected to it. 3) Inputs are multiplied to their respective weights and summed up. 4) This sum is passed through an activation function. 5) The activated value of the sum/net is the predicted/output value of the neuron. 6) The error is calculated and the weights are changed with the help of a backward pass. image created by Hridaya Annuncio on Kleki.com Functions created till now: initialize_weights : The initial random weights assigned. Parameters :  (1) row (string): a single row of predictor values. This is used to find the number of                            weights that have to be initialized.            activate : Based on the a...
Read more