Classification Model Using Artificial Neural Networks (ANN) with Keras
Updated on Jul 07, 2025 | 14 min read | 15.92K+ views
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Updated on Jul 07, 2025 | 14 min read | 15.92K+ views
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Did you know? Keras 3 now includes the Keras Sig library, which reduces training time by 55% and boosts performance by up to 10 times. It achieves this by efficiently computing path signatures on GPUs. This makes the process of building Classification Models with Artificial Neural Networks faster and more efficient. |
Building a classification model using Artificial Neural Networks (ANNs) with Keras is an optimized process that utilizes the simplicity and flexibility of Keras to design and train powerful models for various applications. Keras, built on top of TensorFlow, enables you to quickly develop ANN-based models for classification tasks, offering scalability, efficiency, and ease of use.
This blog guides you through creating a classification model using artificial neural networks with Keras. We cover installation, data preprocessing, and practical applications!
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Keras, a high-level neural networks API, is perfect for quickly building powerful classification model using Artificial Neural Networks (ANNs). It simplifies the process of creating, training, and evaluating deep learning models. By using Keras, you can use its simplicity and flexibility to develop efficient models for a variety of practical applications, from healthcare to finance, retail, and beyond.
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To get started on building your Classification Model, the first step is installing the necessary libraries, ensuring your environment is ready for the next stages.
The first step is to ensure that Keras and TensorFlow are installed in your environment. TensorFlow, the backbone of Keras, makes the deployment of Classification Model Using Artificial Neural Networks efficient and scalable, enabling applications in diverse fields.
To install Keras and TensorFlow, use the following command:
pip install tensorflow
TensorFlow includes Keras, so no need for a separate installation.
Practical Example: In India's healthcare sector, TensorFlow and Keras are being used to build ANN-based classification models that predict diseases like diabetes and heart conditions by analyzing patient data. These models have helped improve diagnostic accuracy and speed up the process of detection.
Also Read: Keras vs. PyTorch: Difference Between Keras & PyTorch
Once Keras and TensorFlow are set up, you can start building your model by importing the necessary libraries. Here’s the basic setup:
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
Practical Example: In India’s agricultural sector, ANNs are being used to classify crops based on image data (like leaf patterns). By using the right libraries and preprocessing techniques, farmers can identify disease outbreaks early and take corrective measures, leading to better yield prediction and crop management.
Also Read: Clustering vs Classification
Data preprocessing is the foundation of building an effective Classification Model Using Artificial Neural Networks. Clean and well-prepared data directly impacts the performance of your model.
Load Data: Load your dataset into the environment:
dataset = pd.read_csv('your_data.csv')
X = dataset.iloc[:, :-1].values # Features
y = dataset.iloc[:, -1].values # Target labels
Splitting the Data: Split your dataset into training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Normalization: Normalize your features so they all fall within the same range:
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
One-Hot Encoding for Labels: For multi-class classification, one-hot encoding is essential:
y_train = to_categorical(y_train, num_classes=3)
y_test = to_categorical(y_test, num_classes=3)
Note: The num_classes=3 in this example is just for illustration. In real scenarios, you should replace this with len(np.unique(y)) to dynamically adjust the number of classes based on your dataset.
Practical Example: In the retail industry, ANN-based classification models are widely used to predict customer behavior. For example, a model might classify customers based on purchasing patterns. Preprocessing like one-hot encoding ensures that features like "product category" are transformed into a usable format for the model.
With the data prepared, it's time to define the architecture of your Classification Model Using Artificial Neural Networks. In Keras, this is done using a Sequential model, where layers are added one after another.
Initialize the Model:
model = Sequential()
Input Layer: The input layer receives the features. For example, if you are classifying images, the number of neurons would correspond to the number of pixels in each image:
model.add(Dense(units=64, activation='relu', input_dim=X_train.shape[1]))
Hidden Layers: You can add one or more hidden layers with appropriate activation functions:
model.add(Dense(units=64, activation='relu'))
Output Layer: For multi-class classification, you will typically use the softmax activation function, while for binary classification, you would use sigmoid:
model.add(Dense(units=3, activation='softmax')) # For a multi-class classification task with 3 classes
Practical Example: In India, ANN-based models are used in fraud detection systems. These systems classify transaction data as either "fraudulent" or "legitimate." The architecture might involve several hidden layers to capture complex patterns in financial transactions, helping banks minimize fraud-related losses.
Once the model structure is ready, compile it by specifying the optimizer, loss function, and evaluation metrics:
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
Practical Example: In healthcare, models like these are used to classify medical images such as X-rays. The loss function is designed to penalize incorrect classifications, while the optimizer fine-tunes the model to ensure more accurate diagnoses over time.
With everything set up, it’s time to train your model:
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
Practical Example: In agriculture, training Classification Model Using Artificial Neural Networks helps predict crop diseases. The model learns to classify healthy or diseased crops by analyzing past images of leaves. More epochs ensure that the model generalizes well to new images, improving accuracy.
After training, you should evaluate how well your model performs on unseen data:
loss, accuracy = model.evaluate(X_test, y_test)
print(f'Loss: {loss}, Accuracy: {accuracy}')
Practical Example: For a Classification Model Using Artificial Neural Networks in autonomous driving, the evaluation metrics might include not just accuracy but also recall and precision, as false positives (e.g., misclassifying a pedestrian as a non-threat) could be dangerous.
Finally, you can use the trained model to make predictions:
predictions = model.predict(X_test)
For multi-class classification, you can extract the class with the highest probability:
predicted_classes = np.argmax(predictions, axis=1)
Practical Example: In e-commerce, companies use ANNs to predict the next product a customer might buy based on their previous purchase behavior. After training the model, predictions are made for individual users to offer personalized product recommendations.
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Also Read: Deep Learning Models 2025: Types, Uses & Trends
Now that you've built your classification model, it's essential to focus on enhancements and advanced techniques that will help fine-tune its performance and scalability.
Once you've built the initial Classification Model Using Artificial Neural Networks, enhancing its performance and generalization capabilities is the next critical step. The challenges of overfitting, high computational costs, and underutilizing available data can all hinder the model's success.
Let’s explore the key techniques and advanced architectures that can optimize your model for better performance.
Overfitting occurs when your model is too focused on the training data, making it perform poorly on unseen data. Regularization techniques can help prevent this by balancing data fitting and generalization.
Dropout
Dropout is a powerful regularization technique that randomly disables neurons during training, preventing the model from becoming overly reliant on any single neuron. This encourages the model to learn a broader set of features and helps reduce overfitting in Classification Model using Artificial Neural Networks.
The dropout rate typically ranges from 20-50%, meaning that a random subset of neurons is ignored during each training iteration. This is particularly effective in deep networks, where the sheer number of parameters often leads to overfitting.
L1/L2 Regularization
L1 and L2 regularization are two techniques that add penalty terms to the loss function. These penalties constrain the complexity of the model by encouraging smaller weights.
Regularization via L1/L2 helps in training a Classification Model Using Artificial Neural Networks that generalizes well, ensuring the model’s accuracy and robustness.
Once you've understood basic ANN architectures, you can enhance your Classification Model Using Artificial Neural Networks by exploring advanced neural network architectures. These architectures are designed to tackle more complex data structures and problems.
Let’s explore some of the most effective architectures that elevate the performance of neural networks.
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs) are designed specifically for image and video recognition tasks, excelling at processing high-dimensional data like images. By applying convolutional filters, CNNs detect patterns such as edges, textures, and shapes, making them ideal for object detection, facial recognition, and image segmentation.
In medical imaging, CNNs are used to identify anomalies like tumors in X-rays and MRIs, enhancing diagnostic accuracy and reducing human error. Their power lies in capturing features at multiple levels, from basic edges to complex shapes, all while reducing computational costs through shared weights.
Recurrent Neural Networks (RNNs)
Recurrent Neural Networks (RNNs) are ideal for sequential data like time series, text, or speech, as they capture dependencies between elements in a sequence by maintaining "memory" of previous inputs.
In Classification Models Using Artificial Neural Networks, RNNs are key for tasks like speech recognition, language modeling, and time series forecasting. In NLP, they handle tasks such as sentiment analysis, next-word prediction, and named entity recognition. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) excel at capturing long-range dependencies in data.
Training a neural network from scratch can be time-consuming and resource-intensive, but transfer learning offers an efficient solution. By using pre-trained models, you can utilize knowledge from large datasets and apply it to new, related tasks.
When building a Classification Model Using Artificial Neural Networks, models like VGG, ResNet, or BERT can be fine-tuned to suit your specific problem. This is particularly valuable in areas with limited labeled data or computing resources.
Transfer learning has proven especially useful in image recognition and NLP, enabling AI applications in sectors like retail, healthcare, and finance in India.
Ensemble Methods are a powerful technique for improving the performance of Classification Models Using Artificial Neural Networks by combining multiple models. The idea is simple: instead of relying on a single model, you combine the predictions of several models to improve accuracy, robustness, and generalization.
The most common ensemble methods include:
Also Read: 9 Key Types of Artificial Neural Networks for ML Engineers
Having explored key techniques to enhance your model's performance, it's now time to look ahead at the emerging trends that will shape the future of Classification Models using Artificial Neural Networks.
As the field of machine learning continues to advance, the evolution of Classification Models Using Artificial Neural Networks (ANNs) brings with it promising new technologies and methodologies. These developments not only improve the efficiency and effectiveness of models but also expand their applications across diverse industries.
Let’s explore the future trends that will shape the development and deployment of Classification Models Using Artificial Neural Networks.
ANNs are often seen as "black boxes," making it difficult to understand how decisions are made, especially important in sectors like healthcare, finance, and law.
What Is XAI?
Why It Matters:
Running machine learning models locally on devices instead of relying on cloud systems.
Benefits of Edge Computing:
Hardware Advances:
Training and tuning complex models is resource-intensive, requiring significant expertise.
What Is AutoML?
Why It’s Important:
The future of Classification Models Using Artificial Neural Networks lies in their integration with emerging technologies like IoT, blockchain, and quantum computing. This convergence will enable more powerful, intelligent, and secure systems.
IoT & ANN Integration:
Blockchain & ANN Integration:
Quantum Computing & ANN Integration:
Also Read: 16 Neural Network Project Ideas For Beginners [2025]
As new trends emerge in Classification Models, upGrad’s specialized courses will equip you with the skills to become an expert and drive innovation in the field.
Building Classification Models with Artificial Neural Networks and Keras allows you to solve practical problems across diverse industries. For optimal results, focus on data preprocessing, normalize features and one-hot encode labels.
Start with a simple architecture, then experiment with adding layers, applying dropout for regularization, and tuning hyperparameters to enhance model performance.
Classification models can be challenging. However, with hands-on projects and expert insights, upGrad helps you refine your skills. We also help bridge knowledge gaps, accelerating your career in machine learning.
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Reference:
https://arxiv.org/abs/2501.08455
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