Neural Network Algorithms — Learn How To Train ANN
Now that we know the basics, applications, and the various models associated with the Artificial Neural Network, it is now time to learn about the algorithms that are associated with ANN. These algorithms are used to train the Artificial Neural Networks. These algorithms are beneficial for effective training of the ANN models for accurate outputs. So, let’s head straight to the first algorithm, The Gradient Descent Algorithm.
Neural Network Algorithms
1. Gradient Descent Algorithm
Gradient Descent algorithm is an optimizing algorithm that is used to train the Neural Network models. It is one of the most common approaches in training the models since it is based on the principle of minimizing errors between the actual output and the output that we expect. Furthermore, Gradient Descent is also used for training Machine Learning Models.
As discussed earlier, Gradient Descent is an optimizing model. In a Mathematical sense, it is a function that will tend to minimize or maximize an objective function f(x), for which x acts as a parameter. Similarly, for Neural Networks, the term optimization means that it will tend to minimize the cost function for which the model’s parameter will act as a parameter.
The major objective of this algorithm is to attain a minimum value for the convex function using iterative operations on the parameter updates. Once we perform optimization on these models, they become a really powerful tool for Machine Learning algorithms like regression and classification.
Gradient Descent was originally coined by Cauchy in 1874. It is also known by the name steepest descent. The main focus of this algorithm is to minimize the given function (mostly cost function). In order to do so, it performs the following two iterative steps:
1. Compute the gradient: In this step, we try to find the slope of the given function by looking for the first-order derivative of the function at the given point.
2. Move-in the opposite direction of the gradient: We move in the opposite direction from the slope since the slope increases from the current point by the magnitude of (α * gradient) at that point. Here, α is the learning rate.
This process can be stated mathematically as
pn+1 = pn – αΔf(pn)
The value of the iteration of the algorithm depends on the value of the learning rate. If the learning rate is smaller, then the gradient graph tends to converge, leading to an increase in the minimum point. On the other hand, if the learning rate is too high, then the algorithm may not converge at all to attain an optimum point. The results of this algorithm have a great impact on the performance of the Multilayer Perceptron Network.
Now that we know about the Gradient Descent algorithm, let’s move ahead with the next algorithm- The Evolutionary Algorithm.
2. Evolutionary Algorithm
Evolutionary Algorithms are effective when we have to deal with problems that are otherwise difficult to solve in the given polynomial time. They are a heuristic-based approach that is analogical to the “Survival of the Fittest” principle in Biology. When we use these algorithms on their own, they are beneficial for combinatorial problems.
The basic layout of this algorithm is quite simple since it deals with the basic 4 step model comprising of initialization, selection, genetic operators, and termination. These steps are combinedly used to attain modularization of implementation of this algorithm.
As we said earlier, the algorithm tends to follow the survival of the fittest approach. Putting it in simple words, the fitter elements of an algorithm are allowed to contribute to the algorithm whereas the unfit ones are discarded. We can use an abstract function to find out the fittest algorithms.
In the context of these algorithms, recombination is also an operator. We apply it to two or more entities of the model which are typically known as parents. Then the result is one more of the new entities formed which are typically known as children. In order to get a new entity, we apply mutation to the existing entity. Based on the fittest measure threshold, we can now place the set of these new entities that we attain using iterative recombination and mutation.
The two basic elements while implementing Evolutionary Algorithms in Artificial Neural Networks are:
- Variation Operators i.e., recombination and mutation operation
- Selection process i.e., choosing the fittest entities from the newly formed lot
The common features of Evolutionary Models are:
- They are population-based
- They perform recombination and mutation to create new entities
- Instead of random selection, it chooses the fittest entities only
Evolutionary Algorithms exist in various formats. Depending on the details and problems that we encounter, these formats of the algorithms are used.
1. Genetic Algorithm: It deals with the problems dealing with optimization. It reaches a solution using the natural evolutionary process.
2. Genetic Programming: Genetic Programming tends to provide a solution using computer programs. It measures the accuracy of the program that we use to solve the computational problem.
3. Evolutionary Programming: We predominantly use this format of the Evolutionary Algorithm in a simulated environment for the development of AI models.
4. Evolution Strategy: This algorithm is used for optimizing the cost function of the problem. It is based on the principle of adaptation and evolution.
Neuroevolution: We use this algorithm for training neural networks that we use in Neuroevolution. By can use it in an accurate way by specifying the structure and connection weights of genomes.
3. Genetic Algorithm
Like Evolutionary Algorithms, Genetic Algorithms are adaptive heuristic search algorithms that are a part of the Evolutionary Algorithm variants. It was developed in the 1970s by John Holland and his group. This algorithm is based on the principle of natural selection and genetics. They are intelligent exploitation of random search we provide with historical data to attain better performance results in the solution space. They are predominantly used to attain high accuracy results for optimization problems and search problems.
Genetic Algorithms are highly inspired by the natural selection process, i.e., the entities that are able to adapt to the changes in the program are able to survive in the network. Each generation of these entities comprises a population of individual entities and each entity represents a point in the search space. Each entity that is present in the search space is having its representation in the form of character/integer/float/bits. These entities are analogous to chromosomes.
Following is the foundation of the Genetic Algorithm based on the analogy of the chromosomes:
- Each individual in the search space competes in the space for resources and mates.
- Those individuals who emerge as successful individuals get to recombine and form new entities.
- Characteristics of these fitter individuals are then passed to the next generation to attain better results with the help of recombination and mutation.
Thus, each successive generation attains more accurate results as compared to the previous ones.
Once we create the initial generation in any Genetic Algorithm, the algorithm iterates over the following basic steps:
- Randomly initializing the population p
- Determining fitness of the population
- Until we attain convergence, repeat the following steps:
1) Selecting parents from the population
2) Crossover and generation of new population
3) Performing mutation on the new population
4) Calculating the fitness of the new population.
To elaborate the process in a rather brief way, let us look at the description below:
Step 1: Rules of initializing the population- We first define the constraints which will help us to choose the random variables for the process of initializing the population.
Step 2: Choosing the best rule- By best rule, we mean the rule which will improve the functionality of the new generations. Selecting such rules which will increase the functions of the new generation is a crucial step of the process.
Step 3: Generation of new Rules- We first wait for step 2 of the algorithm to execute completely. After this, when the initialization of the new generation takes place, we tend to look for new rules according to the functions of the new generation in order to attain better results for the further generations.
The algorithms come to a halt when the results satisfy one of the two conditions
- The algorithm reaches a specified number of iterations.
- Starting from the generation having rank n, rules of generations having ranks n, n-1, n-2 become identical.
Conclusion
Thus, we have reached the end of this article. Hope that this article from PythonGeeks was able to solve your queries relating to the Algorithms used to train the Artificial Neural Network models. In the coming times, it will be evident that the training of the Neural Network models in an accurate environment will be of great benefit to us. These algorithms are a great tool to overcome the mathematical, computational, and engineering problems that we face as programming concepts.