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# Model Card: MNIST Classification
## Overview
- **Model Name:** MNIST Classification Model
- **Author:** [Charana H U]
- **Date:** [03/01/2023]
## Model Information
- **Architecture:** Feedforward Neural Network
- **Input Size:** 784
- **Hidden Size:** 100
- **Output Size (Number of Classes):** 10
- **Loss Function:** CrossEntropyLoss
- **Optimizer:** Adam
- **Learning Rate:** 0.001
- **Training Batch Size:** 100
- **Number of Epochs:** 2
## Data
- **Training Dataset:** MNIST
- **Testing Dataset:** MNIST
## Training
- **Training Device:** GPU (if available)
## Evaluation
- **Testing Accuracy:** [96%]
## Usage - Loading
- **Dependencies:** PyTorch, torchvision, matplotlib
- **How to Load the Model:**
```python
from torchvision import transforms
from your_model_module import NeuralNet
model = NeuralNet(input_size=784, hidden_size=100, num_classes=10)
model.load_state_dict(torch.load("model/mnist_model.pt"))
model.eval()
```
## Usage - Testing
- **How to Make Predictions:**
Assuming you have an image tensor named 'image'
```python
image = image.reshape(-1, 28*28)
output = model(image)
_, prediction = torch.max(output, 1)
```
## Limitations and Future Work
- **Limitations:**
- The model is relatively simple and may not perform well on more complex datasets.
- Limited training data augmentation was applied.
- **Future Work:**
- Experiment with different network architectures.
- Explore advanced data augmentation techniques.
- Fine-tune hyperparameters for better performance.
Feel free to customize this template based on your specific model and use case. Additionally, consider adding visualizations, examples, and any other information that would help users understand and use your model effectively. |