Zolisa/pytorch-mnist-tutorial

Simple PyTorch Neural Network for MNIST

This model is a basic feed-forward neural network trained on the MNIST dataset as part of a PyTorch tutorial.

Model Architecture

The model consists of:

1. 1.Input Layer: 784 neurons (28x28 flattened images).
2. 2.Hidden Layer: 128 neurons with ReLU activation.
3. 3.Output Layer: 10 neurons (one for each digit from 0-9).

Training Details

• —Dataset: MNIST (60,000 training images, 10,000 test images)
• —Epochs: 5 (by default)
• —Optimizer: Adam (lr=0.001)
• —Loss Function: CrossEntropyLoss

Usage

To load this model in your PyTorch project:

import torch
from simple_nn import SimpleNN

# 1. Initialize the model architecture
model = SimpleNN()

# 2. Load the state dictionary
model.load_state_dict(torch.load("model.pth"))
model.eval()

Dataset Information

The MNIST dataset consists of 28x28 grayscale images of the 10 digits. It is a classic dataset for image classification tasks.