---
tags:
- hojjatk/mnist-dataset
- handwriting-recognition
- classification
- deep-learning
metrics:
  accuracy: '0.98'
  precision: '0.98'
  recall: '0.98'
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 = 300
        batch_size = 64
        learning_rate = 0.001

        ## Model Architectue:
        ['(fc1): Linear(in_features=784, out_features=128, bias=True)', '(fc2): Linear(in_features=128, out_features=64, bias=True)', '(fc3): Linear(in_features=64, out_features=10, bias=True)', '(relu): ReLU()', '(dropout): Dropout(p=0.2, inplace=False)']
        
        ## Evaluation Results
        - Accuracy: 0.98
        - Precision: 0.98
        - Recall: 0.98