AlexNet is a deep convolutional neural network used for image classification. It consists of multiple convolutional and fully connected layers designed to extract features and perform classification efficiently. It's features are:
ReLU activation enables faster training and better gradient flow. Dropout reduces overfitting in fully connected layers. Data augmentation helps in improving model generalization on image data. Architecture 5 convolutional layers with max pooling after the 1st, 2nd, and 5th layers. Overlapping max pooling (3×3 filter, stride 2) improves performance. 2 fully connected layers with dropout for regularization. Softmax layer for final classification output. 👁 Image Implementation 1. Importing Libraries Import libraries like
2. Loading and Preprocessing CIFAR-10 Dataset CIFAR-10 contains 60,000 32×32 RGB images across 10 classes. Pixel values are scaled to [0, 1]. Labels are one-hot encoded for softmax classification. 3. Defining the AlexNet Model (Adjusted for CIFAR-10) Adapted for CIFAR-10: Handles 32×32 images with 10 output classes. Reduced FC layers: Prevents overfitting on small datasets. Uses ReLU, Dropout, BatchNorm and softmax in the final layer. 4. Compiling the Model Using adam optimize r and categorical_crossentropy for multi-class classification.
5. Training the Model Train for 15 epochs, with 20% validation split. You can increase epochs for better accuracy. Output:
👁 Screenshot-2025-07-03-120714 Training 6. Evaluating the Model Evaluates the trained model on test data to measure accuracy and performance.
Output:
Test Accuracy: 0.7387
7. Plotting Training & Validation Accuracy Plots training and validation accuracy to visualize model performance over epochs.
Output:
👁 AlexNet AlexNet on CIFAR-10 Advantages Uses ReLU activation for faster training compared to traditional tanh/sigmoid. Applies dropout to reduce overfitting during training. Utilizes GPU-based parallel computation for faster processing. Uses overlapping max pooling to improve generalization and performance. Disadvantages Has a large number of parameters, making it memory-intensive. Requires high computational resources for training. Lacks modular and automated architecture design. Tends to overfit on small datasets. Does not include modern architectural improvements. Applications Used for image classification of objects in images. Acts as a feature extractor for transfer learning tasks. Serves as a backbone for object detection models. Applied in medical imaging for detecting abnormalities. Used in facial recognition and emotion detection systems. Helps in identifying objects in autonomous driving systems. 
