https://github.com/konynour/classification-using-mlp
This project involves building and training a deep learning model using TensorFlow and Keras to classify grayscale images of clothing items from the Fashion MNIST dataset. The goal is to correctly predict the type of clothing (e.g., T-shirt, dress, sneaker) from a 28x28 pixel image.
https://github.com/konynour/classification-using-mlp
classification cnn kares mlp
Last synced: about 2 months ago
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This project involves building and training a deep learning model using TensorFlow and Keras to classify grayscale images of clothing items from the Fashion MNIST dataset. The goal is to correctly predict the type of clothing (e.g., T-shirt, dress, sneaker) from a 28x28 pixel image.
- Host: GitHub
- URL: https://github.com/konynour/classification-using-mlp
- Owner: konynour
- Created: 2025-07-17T17:26:42.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-17T19:50:02.000Z (3 months ago)
- Last Synced: 2025-08-13T08:14:40.833Z (about 2 months ago)
- Topics: classification, cnn, kares, mlp
- Language: Jupyter Notebook
- Homepage:
- Size: 114 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 👕 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