👕 Fashion MNIST Classification with Neural Networks

This project demonstrates a multi-class image classification model trained on the Fashion MNIST dataset using TensorFlow and Keras. The goal is to classify grayscale images of clothing items into 10 categories (e.g., T-shirt, trouser, sneaker, etc.).

🧠 Model Overview

• Input: 28x28 grayscale image
• Model Type: Multi-Layer Perceptron (MLP)
• Architecture:
  • Input Layer (Flatten)
  • Hidden Layer 1: Dense (128 units, ReLU)
  • Hidden Layer 2: Dense (128 units, ReLU)
  • Output Layer: Dense (10 units, Softmax)

🧪 Loss Function & Optimizer

• Loss: CategoricalCrossentropy (for one-hot encoded labels)
• Optimizer: RMSprop
• Metrics: Accuracy

📊 Training Results

• Training performed over 21 epochs
• Accuracy and loss are tracked for both training and validation sets
• Results are visualized using Matplotlib for performance monitoring

📈 Sample Output Plots

• Training vs. Validation Loss
• Training vs. Validation Accuracy

🗂 Dataset

• Fashion MNIST dataset (loaded via tensorflow.keras.datasets)
• Contains 70,000 images:
  • 60,000 training samples
  • 10,000 test samples
• Each image is 28x28 pixels, labeled with one of 10 fashion categories