---
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