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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
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