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How To Use Back Propagation Algorithm In Python

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The backpropagation algorithm is generally used in data science to perform data mining. This also finds its use in machine learning, where it can make accurate predictions of data and can measure derivatives easily. 

Many industries are using backpropagation to locate errors in their observations and to minimize the overall error in their network. To use this method in programming, you need to understand the underlying principle and what sites that you need to follow to apply backpropagation in your network. Let’s start by understanding the definition of it. 

What Is Back Propagation?

Introduced in the 1970s, backpropagation has been used to improve the accuracy of mathematical tools. This method is used to calculate the gradient descent with weights. In backpropagation, the desired output is set into the system, which is then matched with the obtained value to check the accuracy of the mathematical tool. Backpropagation has become very significant and has found its use in various fields. Some of the uses of backpropagation are:

  • Backpropagation is used in artificial intelligence.
  • Optical character recognition ( OCR) uses it to obtain necessary insights. 
  • Used in natural language processing 
  • Backpropagation is used in image processing to maintain accuracy. 

Phases Of Backpropagation

A reliable MCA degree will teach you that backpropagation algorithm can be broadly classified into two types: 

Forward Pass

In this phase of backpropagation, inputs are passed through the network to obtain the output prediction. The forward pass phase is also referred to as the propagation phase. In this phase, the inputs are propagated into the network using dot products and activations until it reaches the output layer.

Backward Pass

This phase is also called the weight update phase. In this phase, the final gradient is calculated and is used recursively to update the weights in the network using the chain rule. 

Kinds Of Backpropagation Networks

Generally, there are two types of backpropagation networks. Let’s discuss both of them briefly. 

1.      Status Propagation

It is the backpropagation network type that intends to map for static output by creating a mapping of static input. Static backpropagation helps to solve static category issues, such as optical character recognition ( OCR ).

2.     Recurrent Backpropagation

This backpropagation network is utilized in fixed point learning. This method continues till it achieves a fixed value. This way, errors are identified easily, and then they are propagated backwards. The only software which can execute the recurrent backpropagation is the NeuroSolutions. 

The only difference between both static and recurrent backpropagation is that static backpropagation gives immediate and precise mapping, whereas recurrent back propagation delays the mapping.

How To Use Back Propagation Algorithm In Python

To execute the backpropagation algorithm, you need to follow 6 key steps:

1.     Initialize Network

To start backpropagation, you need to lay down a network of neurons, where every neuron will be maintaining a particular weight. A network consists of several layers, which you can add by yourself. These layers can be broadly classified as:

  • Input layers
  • Output layers

These layers are organized in the form of arrays that contain dictionaries, and the whole combination of these layers with dictionaries and different nodes is known as a network.  You can initialize a network using the following code sequence. 

2.     Forward Propagate 

To calculate the output of a given network, we use forward propagation. This method is executed by the following steps to obtain a value that we will be required to correct. This method can be subdivided into three sections 

  • Neuron Activation
  • Forward Propagation
  • Neuron Transfer

All these three can be executed in Python using the following code.

Neuron Activation

Neuron Transfer

Forward Propagation 

3.     Back Propagation

This method is used to rectify the error in the data that you have obtained using forward propagation. The error in the expected output is given back into the system to rectify it. This process is based on calculus and has two different parts. The coding involved in these two steps are-

Transfer Derivative

In this step, we need to calculate the slope of the neuron. 

Error Backpropagation 

This step is responsible for carrying the error back into the network in order to rectify it. This error signal is sent with the use of the following code. 

The accumulated signal of the error is sent by using the following code :

4.      Train Network  

The network is instructed with the use of random probability distribution or the pattern that can be easily analyzed statistically but cannot be foreseen specifically gradient descent. 

This includes numerous iterations or new versions of exposing a training dataset to the system, and for each layer of data forward propagating the Python inputs, updating the network weights, and back-propagating the errors. 

Further, the train network is divided into two sections. 

1. Update weights 

Soon after the error is detected, it needs to be updated on the network by using the following code :

2. Train Network

This method is used to find the occurrence of the errors and replace them with suitable entries. It can be done as follows :

5.     Predictions

The output obtained using propagation can be used to construct a different class used to predict the class value using a larger probability. This can be obtained using the following code. 

6.     Wheat Seeds Dataset

This method is used to apply backpropagation on a wheat seed dataset. dataset_minmax() and normalize_dataset()  functions are used in this step, which is accompanied by the back_propagation() method that can be used to apply the backpropagation algorithm. Let’s have a look at its code. 

Benefits Of Back-propagation

There are several advantages of using backpropagation. 

  • It is an adaptable method, as there is no need for any preliminary knowledge of networks.  
  • Backpropagation has no defined limits to tune other than the input numbers. 
  • It is easy, fast, and simple to program.  
  • It is considered the standard method, which usually works well in programming and data science. 
  • No special explanation about the features of the Functions is needed to be learned.

Application Of Backpropagation

The uses of backpropagation are:

  • The neural Python networks are given sufficient training to express each letter of a term and a sentence. 
  • This method is widely used in speech recognition.
  • It is also helpful in the face and character recognition field.

Disadvantages Of Backpropagation

  • The basic performance of backpropagation on any particular issue is all dependent on the data input. 
  • The algorithm of the backpropagation in data mining can be susceptible to noisy ones.  
  • The mini-batch approach for backpropagation will not be effective, and thus you must use the approach that is based on the matrix.

Conclusion

Python is an effective way to reduce errors in any type of network. It can be seen as a way to reinstate the loss back into the network. It can be used effectively to determine the lost weight  of each node within the network and rectify the same using propagation. 

You can learn how to do it by taking an MCA degree. Backpropagation, as we learnt,  can be employed in numerous fields, such as AI, data mining, and data networking. You can do that in 6 simple steps that were mentioned above: initialize Network, Forward Propagate, Back Propagation, Train Network, Predictions, and White Seeds Dataset. 

If you also want to learn backpropagation from certified trainers, do check out the amazing range of courses at Great Learning. 

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