What is a multilayer perceptron (MLP) neural network?
An MLP is a type of feedforward artificial neural network with multiple layers, including an input layer, one or more hidden layers, and an output layer. Each layer is fully connected to the next. In this article, we will understand MultiLayer Perceptron Neural Network, an important concept of deep learning and neural networks.
MultiLayer Perceptron Neural Network is a Neural Network with multiple layers, and all its layers are connected. It uses a BackPropagation algorithm for training the model. Multilayer Perceptron is a class of Deep Learning, also known as MLP.
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Deep Learning is a buzzword among researchers due to the increasing dependency on digital data. However, a simple Artificial Neural Network is only the solution to some problems as it is limited to a linear function and cannot handle complex and massive data. To help in this situation, a Multilayer perceptron Neural Network can work with non-linear functions.
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In this article, we will learn about a multilayer perceptron neural network, with its advantages and disadvantages.
Table of Contents
What is Artificial Neural Network
An Artificial Neural Network is a training model with interconnected layers. ANN is the field of Artificial Intelligence in which a model is trained with training data, and it is expected that model will behave accurately with the actual data set.
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The concept of Artificial Neural Network got its ideal from the biological nervous system of the brain. In the human nervous system, there are neurons to receive and send signals; similarly, in the Neural Network, there are nodes and edges.
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Artificial Neural Network has three layers:
- Input Layer
- Hidden Layer
- Output Layer
There are nodes and edges in the Neural Network.
- A node is any object or individual.
- Edges are connectivity between two edges. These are directional and Nondirectional.
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What is a Multilayer Perceptron Neural Network?
A multilayer perceptron (MLP) Neural network belongs to the feedforward neural network. It is an Artificial Neural Network in which all nodes are interconnected with nodes of different layers.
Frank Rosenblatt first defined the word Perceptron in his perceptron program. Perceptron is a basic unit of an artificial neural network that defines the artificial neuron in the neural network. It is a supervised learning algorithm containing nodes’ values, activation functions, inputs, and weights to calculate the output.
The Multilayer Perceptron (MLP) Neural Network works only in the forward direction. All nodes are fully connected to the network. Each node passes its value to the coming node only in the forward direction. The MLP neural network uses a Backpropagation algorithm to increase the accuracy of the training model.
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Structure of MultiLayer Perceptron Neural Network
This network has three main layers that combine to form a complete Artificial Neural Network. These layers are as follows:
Input Layer
It is the initial or starting layer of the Multilayer perceptron. It takes input from the training data set and forwards it to the hidden layer. There are n input nodes in the input layer. The number of input nodes depends on the number of dataset features. Each input vector variable is distributed to each of the nodes of the hidden layer.
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Hidden Layer
It is the heart of all Artificial neural networks. This layer comprises all computations of the neural network. The edges of the hidden layer have weights multiplied by the node values. This layer uses the activation function.
There can be one or two hidden layers in the model.
Several hidden layer nodes should be accurate as few nodes in the hidden layer make the model unable to work efficiently with complex data. More nodes will result in an overfitting problem.
Output Layer
This layer gives the estimated output of the Neural Network. The number of nodes in the output layer depends on the type of problem. For a single targeted variable, use one node. N classification problem, ANN uses N nodes in the output layer.
Working of MultiLayer Perceptron Neural Network
- The input node represents the feature of the dataset.
- Each input node passes the vector input value to the hidden layer.
- In the hidden layer, each edge has some weight multiplied by the input variable. All the production values from the hidden nodes are summed together. To generate the output
- The activation function is used in the hidden layer to identify the active nodes.
- The output is passed to the output layer.
- Calculate the difference between predicted and actual output at the output layer.
- The model uses backpropagation after calculating the predicted output.
BackPropagation Algorithm
The backpropagation algorithm is used in a Multilayer perceptron neural network to increase the accuracy of the output by reducing the error in predicted output and actual output.
According to this algorithm,
- Calculate the error after calculating the output from the Multilayer perceptron neural network.
- This error is the difference between the output generated by the neural network and the actual output. The calculated error is fed back to the network, from the output layer to the hidden layer.
- Now, the output becomes the input to the network.
- The model reduces error by adjusting the weights in the hidden layer.
- Calculate the predicted output with adjusted weight and check the error. The process is recursively used till there is minimum or no error.
- This algorithm helps in increasing the accuracy of the neural network.
Difference Between Multilayer Perceptron Neural Network and Conventional Neural Network
MultiLayer Perceptron Neural Network | Convolutional Neural Network | |
Types of Input | It takes vector inputs. | It takes both vectors and matrices as input. |
Network Type | It is a fully connected Neural network | It is a spatially connected neural network. |
Focus Problem | It can deal with non-linear problems. | Can only deal with linear problems. |
Application | It is good for simple image classification. | It is mostly used for complex image classification. |
Advantages of MultiLayer Perceptron Neural Network
- MultiLayer Perceptron Neural Networks can easily work with non-linear problems.
- It can handle complex problems while dealing with large datasets.
- Developers use this model to deal with the fitness problem of Neural Networks.
- It has a higher accuracy rate and reduces prediction error by using backpropagation.
- After training the model, the Multilayer Perceptron Neural Network quickly predicts the output.
Disadvantages of MultiLayer Perceptron Neural Network
- This Neural Network consists of large computation, which sometimes increases the overall cost of the model.
- The model will perform well only when it is trained perfectly.
- Due to this model’s tight connections, the number of parameters and node redundancy increases.
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Conclusion
The conclusion is Multilayer Perceptron Neural Network is the most used deep learning concept. We can make use of it in various applications, like image classification, regression prediction problems, and the handling of non-linear problems. It has several nodes in its layer that increase computation complexity, resulting in more parameters.
The use of MLP, CNN, and RNN depends on the type of problem and the dataset.
Contributed By: Sonal Meenu Singh
FAQs
What is multilayer perceptron in neural network?
A multilayer perceptron in a neural network is a tightly connected neural network from the input to the output layer. It has 3 layers: an input layer, a hidden layer, and an output layer. There are various nodes in each layer, and all nodes are interconnected with each other.
Is multilayer perceptron the same as neural network?
A Multilayer perceptron is a neural network in which every node is connected to each layer's next node. It has the basic structure of a neural network. Multilayer perceptron uses backpropagation for increasing the model accuracy.
Is Multilayer Perceptron supervised or unsupervised?
MultiLayer Perceptron is a supervised learning neural network. It is a special case of multilayer neural networks. The multilayer perceptron is an achievement over the limitation of the neural network to work with non-linear problems.
What are the advantages of multi-layer Perceptron?
There are various advantages of multilayer perceptron and they are as follows: 1. It deals with complex problems. 2. It can handle nonlinear problems. 3. It has a higher accuracy than other Neural Networks. 4. It has a quick prediction rate.
What are the 3 different types of neural networks?
The three different Artificial Neural Networks are as follows. 1. Convolutional Neural Network 2. Recurrent Neural Network 3. Neural Network
Is Multilayer Perceptron a deep learning model?
Yes, Multilayer perceptron is a deep learning method and it is gaining popularity with the increasing use of deep learning. This network helps in dealing with complex computations and increases the prediction accuracy of the training model. Some of the applications of Multilayer Perceptron is image classification, text classification, and identifying vehicles in traffic.
How does multi-layer Perceptron work?
Multilayer perceptron has 3 layers; the input layer, the hidden layer, and the output layer. It takes vector input from the input layer. The input nodes will distribute the data set to the next connected nodes in the hidden layer. u202fIn the hidden layer, the weights on the edges are multiplied by the node values, and all the multiplications from different nodes are summed together. This sum is passed to the output layer to generate the output. The activation function is used in the hidden layer. A backpropagation algorithm is used on the output to reduce the error between the predicted and actual results.
What is difference between perceptron and Multilayer Perceptron?
A perceptron has only two layers: an input and an output layer. A multilayer perceptron has 3 basic layers: an input layer, a hidden layer, and an output layer.
What are the limitations of Multilayer Perceptron?
A Multilayer perceptron is a fully connected network that sometimes creates node redundancy and increases the number of parameters in the hidden layer.u202f This neural network is limited to simple image classification problems. Due to high computation, multilayer perceptron increases the cost of the model.
Why do we need hidden layers in multilayer perceptron?
The hidden layer in any Artificial neural network is the main computation layer. This layer trains the model by using different activation functions and algorithms. The hidden layer in the multilayer perceptron takes input from the input nodes and passes the output to the output node. It is a sandwich layer of input and output layers.
How does an MLP differ from a single-layer neural network?
Unlike single-layer networks, MLPs have one or more hidden layers between the input and output layers. This structure allows them to model complex relationships in data.
What is the significance of the backpropagation algorithm in MLPs?
Backpropagation is a key algorithm used for training MLPs. It involves adjusting the weights of the network by propagating the error from the output back through the layers to minimize prediction error.
What are some common applications of MLPs?
MLPs are used in various applications, including image and speech recognition, classification tasks, and regression problems.
Can MLPs solve non-linear problems?
Yes, the non-linear activation functions in MLPs enable them to solve complex, non-linear problems, which single-layer perceptrons cannot.
What challenges are associated with training MLPs?
Training MLPs can be challenging due to issues like vanishing gradients, overfitting, and the need to fine-tune hyperparameters like the number of layers and neurons.
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