This repository contains the implementation of a neural network model for handwritten digit recognition. The model architecture includes three dense layers and is designed to classify handwritten digits from the MNIST dataset.
The architecture of the my_model
is as follows:
Layer | Output Shape | Parameters |
---|---|---|
Dense (25) | (None, 25) | 10,025 |
Dense (15) | (None, 15) | 390 |
Dense (10) | (None, 10) | 160 |
Total parameters: 10,575 (41.31 KB)
Trainable parameters: 10,575 (41.31 KB)
Non-trainable parameters: 0 (0.00 Byte)
The model is trained on the MNIST dataset, which consists of 60,000 training images and 10,000 testing images of handwritten digits from 0 to 9. Each image is 28x28 pixels.
To run this project, you need the following libraries:
- Python 3.x
- TensorFlow
- NumPy
- Matplotlib
This project is licensed under the MIT License.
The MNIST dataset is provided by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges. TensorFlow for providing the deep learning framework used in this project.