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---
tags:
- hojjatk/mnist-dataset
- handwriting-recognition
- classification
- deep-learning
metrics:
accuracy: '0.96'
precision: '0.96'
recall: '0.96'
dataset:
name: hojjatk/mnist-dataset
type: image
license: mit
downloads:
count: 0
---
# Handwriting Recognition Model
This is a trained model for handwriting recognition using **hojjatk/mnist-dataset** dataset.
## Usage
```python
model = torch.load("mnsit_digit_nn")
model.eval()
```
## Training Param:
epochs = 1
batch_size = 64
learning_rate = 0.001
## Model Architectue:
['(conv1): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))', '(conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))', '(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)', '(fc1): Linear(in_features=1568, out_features=128, bias=True)', '(fc2): Linear(in_features=128, out_features=10, bias=True)']
## Evaluation Results
- Accuracy: 0.96
- Precision: 0.96
- Recall: 0.96
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