---
datasets:
- ylecun/mnist
---
# MNIST CNN Classifier

A simple CNN for handwritten digit classification, trained on the MNIST dataset. 

# Model Details
- Architecture: 2 conv layers and 2 fully connected layers
- Accuracy: 99.4% on test set
- Pytorch

# Usage
```python
import torch
from torch import nn

#Define the architecture
class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 10)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = x.view(-1, 64 * 7 * 7)
        x = self.relu(self.fc1(x))
        return self.fc2(x)

#Load model
model = CNN()
model.load_state_dict(torch.load("mnist_cnn.pth"))
model.eval()
```

# Showcase
![image](https://cdn-uploads.huggingface.co/production/uploads/6945d6be622680b0eee91373/vNpvjnEHBU8KGbA3Wr7RB.png)