Blog

Multilayer Perceptron (MLP) classifier is an important and powerful neural network model that has been used in machine learning for the classification of data and solving complex problems. It is a form of supervised learning model using neural networks for mapping input data to output data labels. It is the first model to use neurons and works on the model of how human brains work and mimics the core building block of the brain.

The first discovery in the neural networks field was perceptron which was initially intended for image recognition and is named for its human-type function of recognizing images. After this, the perceptron were used for the binary classification model using the activation function and picking an initial set of weights at random. However, due to the limitation of perceptron having only one neuron, and being unable to be implemented to non-linear data, Multilayer Perceptron Classifier in Python was developed to tackle this limitation where the mapping between the inputs and outputs is non-linear. This blog focuses on discussing Multilayer Perceptron (MLP) classifier in detail so that data scientists can implement it in their projects and learn the depths of the MLP classifier. The below sections will help us understand MLP and are designed to gain all the necessary information required.

Introduction to Multilayer Perceptron Classifier in Python

A Multilayer Perceptron (MLP) classifier is a feedforward neural network model consisting of multiple neuron layers in which each neuron is internally connected to every other neuron in the next layer forming a structure of an internally connected knowledge network. There are three layers in the MLP classifier, the first layer is the input layer, the second layer consists of one or more hidden layers and the last layer is the output layer. This section briefly introduces MLP classifier as we dive deep into the working of this algorithm and understand the role of Artificial neural networks in MLP classifier.

A. Brief overview of Artificial Neural Networks (ANNs)

Artificial neural networks (ANNs) are a subset of ML algorithms that have been inspired by the biological network in the human brain and recognizes patterns in data to make predictions. The neuron is the basic building block of ANN that receives input, processes them, and generates an output signal which is organized into layers as input-output and hidden layers. Backpropagation is used for training ANN where output gets compared to expected output and adjustment of weights and biases are done for minimizing the error. The process gets repeated until satisfactory performance is obtained.

The key benefit of ANN is its ability to learn from huge amounts of data and make accurate predictions even with noisy data. ANN is computationally expensive and can be used for a wide range of tasks that includes image recognition, speech recognition, prediction, and NLP.

B. Introduce Multilayer Perceptron (MLP) Classifier

A multilayer perceptron classifier is a form of neural network model inspired by ANN that is used for multiple classification tasks in machine learning. The MLP classifier provides similar benefits as the ANN and is computationally less expensive than ANN. MLP classifier is a feedforward neural network model consisting of multiple layers of neurons being connected to every neuron of the next layer. MLP classifier uses a non-linear activation function for weights and biases such as the rectified Linear Unit (ReLU) or the sigmoid function, that transforms the input data before being passed through the network.

The model learns complex relations among the input data and output labels through the activation function which adds non-linearity to the network. It is trained using a supervised learning approach in which the network is fed a set of input data and corresponding labels. The network adjusts its weights and biases for minimizing the difference between predicted output and actual output which is done through the backpropagation algorithm by calculating ingredients of the loss function concerning biases and weights of the network to adjust it accordingly.

There are several factors on which the performance of the MLP classifier depends like the number of neurons in each layer, hidden layers, choice of activation function, and the optimization algorithm for training. The hyperparameters need to be tuned carefully for obtaining the performance of test data. MLP classifiers have been widely used for multiple applications like image classification, speech recognition, and NLP. It is very effective in many tasks and is used as a baseline model in solving multiple machine-learning experiments.

C. Importance of MLP in machine learning and deep learning

MLP can be used as an important tool in machine learning and deep learning due to its ability to handle complex non-linear relationships between the input and output. There are several reasons why it is important for application in machine learning  tasks and some of them are discussed here:

1. Deep learning: The multi-layer classifier is the basic building block of several deep learning models such as CNN, RNN, etc., and the models created through the MLP classifier have achieved several state-of-the-art results in several tasks such as speech recognition and image recognition.
2. Optimization: MLP classifier can be trained through multiple optimization algorithms like stochastic gradient descent that optimizes its network parameters for minimizing a loss function. This enables the network to learn from large datasets and generalize the unseen data.
3. Scalability: MLP can learn more complex relations in data through the addition of more layers and neurons and can be scaled to handle complex datasets such as in NLP and computer vision.
4. Flexibility: MLP is very flexible to work with both regression and classification which makes it a versatile tool in machine learning and enables it to handle input data of multiple features.
5. Non-linearity: MLP inhibits non-linear activation function for learning and modeling complex relationships of input data which helps in solving problems having non-linear decision boundaries.

These features of the MLP classifier make it an important tool in deep learning and machine learning which can be used successfully in several real-world applications for continuing to be an active research area.

Understanding Multilayer Perceptron (MLP)

MLP classifier is a type of neural network model consisting of multiple layers o interconnected neurons that transfer information within each other to generate the prediction. MLP consists of three layers of neurons, weights, biases, and activation functions for the non-linearity of the network.

A. Structure of an MLP

MLP classifier consists of three layers of neurons that are interconnected with each other and are used for transferring information from one layer to another. They are the input layer, the hidden layer, and the output layer:

1. Input layer– It is the first layer of a neural network that receives the input data and the number of neurons in the input layer is decided through the number of features in the input data.
2. Hidden layers– The hidden layer consists of layers between the input and output layers and contains neurons capable of transforming input into resourceful information for the output layer. The neurons in the hidden layer are present in multiple layers and not just in layers like the input and output layers as it helps us process the data and make the final prediction and each neuron of hidden layers is connected to every neuron of the next and previous layers.
3. Output layer– The output layer produces the output of the network and is the final layer where the number of neurons in the layer is represented through the number of classes in the classification problem or the presence of several output features in the regression problem.

B. Activation functions

The activation function is a key component for a neural network as it introduces non-linearity to the network and enables them to learn complex relationships between input and output. It is used for determining the output based on input received by the neuron. The activation function takes in the weighted sum of input and adds bias to it while applying non-linear transformation for producing the output of the neuron. the non-linear transformation of the activation function allows the network to model a non-linear relationship between inputs and outputs.

The activation function also helps in resolving the vanishing gradient problem which occurs when the gradient of the loss function becomes very small concerning weights making it difficult for us to update weights during training. Some of the activation functions are discussed here which helps us resolve and prevent these issues.

1. Sigmoid function

It is a commonly used activation function in MLP which is a smooth, S-shaped curve mapping input values between 0 and 1. It is represented by the formula:

f(x) = 1 / (1 + exp(-x))

Here, x is the input value and f(x) is the output value to the neuron.

The sigmoid function provides an output that is always positive and lies between 0 and 1, making the output interpretable as a probability and is particularly used in binary classification problems. The sigmoid function is differentiable, meaning that the gradient descent, necessary for updating the weights during backpropagation for training of MLP classifier can be calculated. The issue with the sigmoid function is that the gradient becomes very small for large positive or negative values which leads to a vanishing gradient problem. Moreover, when we deal with large datasets, the sigmoid function becomes computationally expensive to work with.

2. Hyperbolic tangent (tanh)

The Hyperbolic tangent (tanh) function is a common activation function in the MLP classifier that’s similar to the sigmoid function but maps input values in a range between -1 and 1. The formula for Hyperbolic tangent (tanh) can be given as:

f(x) = (exp(x) – exp(-x)) / (exp(x) + exp(-x))

here, x is the input value and f(x) is the output value of the neuron.

the Hyperbolic tangent (tanh) function produces an output that is both positive and negative lying between -1 and 1 that can be used for several classification problems. The hyperbolic tangent (tanh) function is differentiable therefore it can be used for updating weights during the backpropagation algorithm for training the MLP classifier. If the gradient of the Hyperbolic tangent (tanh) function becomes very small for large input values it will lead to a vanishing gradient problem.

3. Rectified Linear Unit (ReLU)

Rectified Linear Unit (ReLU) function is commonly used as an activation function for several neural networks and is a simple function that returns 0 for negative input and non-negative value it returns the input itself. The formula for Rectified Linear Unit (ReLU) can be given as:

f(x) = max(0, x)

here, x is the input value and f(x) is the output value of the neuron.

The Rectified Linear Unit (ReLU) function is computationally efficient making it suitable to work with large datasets having multiple layers. Moreover, ReLU does not suffer from the vanishing gradient problem like the other two functions. The gradient of the ReLU function is either 0 or 1 making it easier to calculate and update weights during the backpropagation algorithm. ReLU differs from the ‘dying ReLU’ problem which is a problem where some neurons become inactive and stop producing outputs as their input values are consistently negative. This problem is usually mitigated using the variants of the ReLU function such as Exponential ReLU or Leaky ReLU that allows small non-zero outputs for negative input values.

C. Forward propagation and backpropagation

Forward propagation and backpropagation are the two important processes for training an MLP classifier and are used for adjusting the weights of the network based on input data and desired output.

In Forward propagation, the input data is fed into the input layer of MLP and then the data is processed through hidden layers of MLP where weighted inputs are passed through the activation function for producing output values. The output values from the final hidden layer are then passed through another activation function for producing the final output of MLP. The output of MLP is compared with the desired output and the error between these two is calculated.

In Backpropagation, the error calculated in forward propagation is propagated back through MLP for adjustment of weights of the network. First, the error for the output layer is calculated and weight adjustments for the layer are computed based on that error. The error is propagated back through hidden layers of MLP with the error of each layer calculated as a combination of errors from layers above it. Then the weight adjustments of each layer are computed based on the error for that layer. This process gets repeated for training examples in the dataset until the weights of MLP have been adjusted to minimizing of the overall error.

Backpropagation is usually implemented through a variant of gradient descent where weights are adjusted in the direction of the negative gradient of the error function. This allows MLP to improve the performance based on the training dataset through adjustment of weights of the network through errors made during the forward pass.

D. Weight initialization strategies

Initialization of weights is an important aspect of training the neural networks which determines the strength and direction of connections between neurons and has a significant impact on the performance of the network. Some of the common weight initialization strategies are:

1. Random initialization: It is the simplest strategy for weight initialization where the weights of the network are initialized with random values drawn from uniform or normal distribution. This strategy can lead to poor convergence or slow training concerned with deep networks.
2. Xavier initialization: It is also called Glorot initialization and is used for weight initialization where the idea is to scale the initial weights for ensuring that the variance of outputs and inputs is the same. This helps prevent vanishing and exploding gradient problems from occurring during training.
3. Uniform initialization: It is similar to random initialization where weights are drawn from uniform distribution instead of normal distribution which leads to the prevention of large initial weights leading to slow convergence.
4. Orthogonal initialization: This strategy involves initializing the weights of the network with an orthogonal matrix and helps prevents weights from collapsing to low-dimensional subspace during training and also prevents from leading to poor performance.

Thus, the choice of using weight initialization strategies is very efficient and has a significant impact on the performance of neural networks. Optimal results can be achieved through careful experimentation and consideration of different strategies.

E. Loss functions and optimization algorithms

Loss functions and optimization algorithms are important concepts in MLP classifiers where the loss function is used for measuring the difference between predicted output and actual output while the optimization algorithms are used for adjusting the weights during training so that the loss function can be minimized. This section introduces some of the loss functions and optimization algorithms that can be used in neural network modeling.

One of the primary goals of training MLP is minimizing the loss function that is achieved through the adjustment of weights of the network through backpropagation. Some of the commonly used loss functions for classification problems in MLP are:

1. Mean squared error (MSE) loss: It is a common loss function that measures an average of squared differences between predicted and true outputs and it penalizes the network more heavily for larger errors.
2. Cross-entropy loss: It measures the distance between the predicted and true probability distribution of the labels and penalizes heavily for predictions that are further from the true distribution.
3. Hinge loss: it is commonly used for classification problems that measure the maximum difference between predicted output and true output and penalize incorrect predictions. It is useful with sparse data.

Choosing appropriate loss functions depends upon the specific problem and desired outcomes. For classification problems, cross-entropy is preferred more as it produces better results. However, the choice also depends upon the activation function for the final layer of MLP.

The optimization algorithm is very necessary for loss function and choosing an optimization algorithm depends on the problem and desired output. For MLP, Adam is preferred as it converges faster and is more reliable than other optimization algorithms. Some of the commonly used optimization algorithms that can be used for MLP are:

1. Gradient descent: It involves calculating the gradient of the loss function to weights and updating the weights in the direction of a negative gradient. This process repeats until the loss function reaches a minimum.
2. Stochastic gradient descent (SGD): A variation of gradient descent that involves updating weights after a batch of samples instead of the entire dataset, resulting in faster convergence but also leading to noisy updates and slow converges in some cases.
3. Momentum: An extension of gradient descent that involves the addition of momentum term to weight updates. This momentum tern allows the optimizer to move in the same direction as previous updates which helps accelerate convergence.
4. Adaptive moment estimation (Adam): An optimization algorithm that combines the advantages of SGD and momentum with an adaptation of the learning rate for weights based on gradients. It is often preferred for MLP’s as it tends to converge faster and more reliably than other algorithms.
5. Limited memory BFGS (L-BFGS): it is a quasi-Newton algorithm that approximates the Hessian matrix of loss function using limited memory and makes a good choice for problems having a large number of weights.

Thus, the loss function and optimization algorithms play a major role in the development of the MLP classifiers model and help improve its performance making it more reliable.

Implementation of Multilayer Perceptron Classifier in Python

Problem description

Identifying network intrusions is an essential component of cybersecurity. Analysis of network traffic data is required to find suspicious or malicious activity that can point to a security problem. An artificial neural network known as a multilayer perceptron (MLP) may be taught to categorize network traffic as normal or anomalous based on parameters such as duration, protocol type, service, flag, etc. Once trained, the MLP classifier may identify possible breaches and serve as a powerful tool for managing network security. To improve and have confidence in the model, it is essential to comprehend the performance of the classifier, including its recall, accuracy, and the type of misclassifications.

Dataset Collection

https://www.kaggle.com/datasets/sampadab17/network-intrusion-detection

Implementation

Conclusion

This article introduces a Multilayer Perceptron Classifier in Python in detail and also provides information about its occurrence from ANN and neural networks. We also discussed the importance of MLP classifiers in the real world and how they can be useful for machine learning and deep learning problems. The MLP classifier is structured in three layers, an input layer, a hidden layer, and an output layer that helps process the data and make predictions.

The activation functions used in MLP classifies, the sigmoid function, ReLU function, and tanh function are discussed in detail which provides a great deal of information and its use in MLP classifier. We also discussed how Forward propagation and backpropagation can be useful in MLP and discussed the weight initialization strategies in MLP. Finally, we discussed loss functions and optimization algorithms in MLP which helped us decide the beneficial function and algorithms that can be implemented in MLP classifier. Thus, this article provides a deep dive into mastering MLP classifiers and is very beneficial to data scientists around the world.

If you like the article and would like to support me, make sure to:

• 👏 Like for this article and subscribe to our newsletter
• 📰 View more content on my DataSpoof website
• 🔔 Follow Me: LinkedIn| Youtube | Instagram | Twitter