Classification Model using Artificial Neural Networks (ANN)

In the machine learning terminology, classification model using artificial neural networks refers to a predictive modelling problem where the input data is classified as one of the predefined labelled classes.For example, predicting Yes or No, True or False falls in the category of Binary Classification as the number of outputs are limited to two labels.

Similarly, output having multiple classes, like classifying different age groups, is called a multiple-class classification problem. Classification problems are one of the most commonly used or defined types of ML problems that can be used in various use cases. Various Machine Learning models can be used for classification problems.

Ranging from Bagging to Boosting techniques, although ML is more than capable of handling classification use cases, Artificial Intelligence-driven neural networks come into play when we have a high number of output classes and sufficient data to enhance model performance. Going forward, we’ll look at how we can implement a Classification Model using Neural Networks on Keras (Python). 

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Neural Networks

Neural networks are loosely representative of the human brain's learning. An Artificial Neural Network consists of Neurons, which in turn are responsible for creating layers. These neurons, much like biological neurons, process and transmit information, enabling the network to learn and adapt. These Neurons are also known as tuned parameters.

The output from each layer is passed on to the next layer. There are different nonlinear activation functions for each layer, which help in the learning process and the output of each layer. The output layer is also known as the terminal neurons. The application of CNNs in various domains showcases how these networks efficiently process complex data, particularly in visual recognition tasks.

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The weights associated with the neurons and which are responsible for the overall predictions are updated on each epoch. The learning rate is optimised using various optimisers. Each Neural Network is provided with a cost function which is minimised as the learning continues. The best weights are then used on which the cost function is giving the best results.

Read: TensorFlow Object Detection Tutorial For Beginners

Classification Problem

For this article, we will be using Keras to build the Neural Network. Keras can be directly imported in python using the following commands.

import tensorflow as tf

from tensorflow import keras

from keras.models import Sequential

from keras.layers import Dense

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Dataset and Target variable

We will be using Diabetes dataset which will be having the following features:

Input Variables (X):

• Pregnancies: Number of times pregnant
• Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
• BloodPressure: Diastolic blood pressure (mm Hg)
• SkinThickness: Triceps skin fold thickness (mm)
• Insulin: 2-Hour serum insulin (mu U/ml)
• BMI: Body mass index (weight in kg/(height in m)^2)
• DiabetesPedigreeFunction: Diabetes pedigree function
• Age: Age (years)

Output Variables (y):

Outcome: Class variable (0 or 1) [Patient is having Diabetes or not]

# load the dataset

df= loadtxt(‘pima-indians-diabetes.csv’, delimiter=’,’)

# Split data into X (input) and Y (output)

X = dataset[:,0:8]

y = dataset[:,8]

Define Keras Model

We can start building the classification model using artificial neural networks using sequential models. This top down approach helps build a Neural net architecture and play with the shape and layers. The first layer will have the number of features which can be fixed using input_dim. We will set it to 8 in this condition.

Creating Neural Networks is not a very easy process. There are many trials and errors that take place before a good model is built. We will build a Fully Connected network structure using the Dense class in keras. The Neuron counts as the first argument to be provided to the dense layer.

The activation function can be set using the activation argument. We will use the Rectified Linear Unit as the activation function in this case. There are other options like Sigmoid or TanH, but RELU is a very generalised and a better option.

# define the keras model

model = Sequential()

model.add(Dense(12, input_dim=8, activation=’relu’))

model.add(Dense(8, activation=’relu’))

model.add(Dense(1, activation=’sigmoid’))

Compile Keras Model

Compiling the model is the next step after model definition when creating a classification model using artificial neural networks. Tensorflow is utilized for model compilation, a crucial phase where parameters are defined for the model’s training and predictions. The process can leverage CPU/GPU or distributed memories in the background for efficient computation.

We have to specify a loss function which is used to evaluate weights for the different layers. The optimiser adjusts the learning rate and goes through various sets of weights. Binary Cross Entropy is chosen as the loss function due to its efficacy in classification models using artificial neural networks. For the optimizer, ADAM, known for its efficient stochastic gradient descent properties, is selected.

It is very popularly used for tuning. Finally, because it is a classification problem, we will collect and report the classification accuracy, defined via the metrics argument. We will use accuracy in this case.

# compile the keras model

model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

Model fit and Evaluation

Fitting the model is essentially known as model training. After Compiling the model, the model is ready to efficiently go over the data and train itself. The fit() function from Keras can be used for the process of model training. The two main parameters used before model training are:

1. Epochs: One pass through the whole dataset.
2. Batch Size: Weights are updated at each batch size. Epochs consist of equally distributed batches of data.

# fit the keras model on the dataset

model.fit(X, y, epochs=150, batch_size=10)

A GPU or a CPU is used in this process. The training can be a very long process depending on the epochs, batch size and most importantly the size of Data.

We can also evaluate the model on the training dataset using the evaluate() function. The data can be divided into training and testing sets and testing X and Y can be used for model evaluation. 

For each input and output pair, this will produce a forecast and gather scores, including the average loss and any measurements we have installed, such as precision. 

Also Read: Neural Network Model Introduction

A list of two values will be returned by the evaluate() function. The first will be the model loss on the dataset and the second will be the model’s accuracy on the dataset. We are only interested in the accuracy of the report, so we will disregard the importance of the loss.

# evaluate the keras model

_, accuracy = model.evaluate(Xtest, ytest)

print(‘Accuracy: %.2f’ % (accuracy*100))

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Conclusion

The journey through creating a classification model using artificial neural networks (ANN) underscores the transformative power of this technology in tackling classification problems. ANN’s ability to learn from complex datasets and make accurate predictions has proven to be a game-changer in data science and machine learning. 

This exploration has highlighted not only the theoretical aspects but also the practical applications of neural networks in classifying data with precision and efficiency. Inspired by the structure and functionality of a biological neuron, ANN adapts and scales efficiently, making it an indispensable tool for professionals looking to harness the potential of artificial intelligence in solving real-world problems. 

Whether you’re a novice stepping into the world of AI or a seasoned expert refining your skills, the journey of mastering Classification Models using Artificial Neural Networks promises a rewarding blend of challenges and breakthroughs, paving the way for future innovations in the domain. 

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