🧠 ANN Optimizer Comparison on CIFAR-10

This project compares the performance of different optimization algorithms on a simple Artificial Neural Network (ANN) using the CIFAR-10 dataset.

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📁 Files

• ANN(Different optimizers).py – Main script that:
  • Loads CIFAR-10 dataset
  • Trains the same ANN architecture using multiple optimizers
  • Visualizes validation loss and accuracy
  • Compares final test accuracy for each optimizer

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🧪 Optimizers Used

• Gradient Descent (GD)
• Stochastic Gradient Descent (SGD)
• Momentum
• RMSprop
• Adam

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📊 Evaluation

• Training and validation performed on 50% of CIFAR-10 dataset.
• Plots:
  • Validation Loss vs Epochs
  • Validation Accuracy vs Epochs
• Prints final test accuracy for each optimizer

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📦 Requirements

Install necessary packages using:

pip install tensorflow matplotlib seaborn scikit-learn

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▶️ How to Run

Simply run the script:

python "ANN(Different optimizers).py"

Make sure your environment has access to the internet to download the CIFAR-10 dataset if not cached locally.

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🎯 Objective

To observe and compare:

• Convergence behavior
• Generalization performance
• Speed of different optimizers on a classification task

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👤 Authors

Ishan Bansal
Deep Learning Explorer