charanhu/mnist_model

Model Card: MNIST Classification

Overview

• Model Name: MNIST Classification Model
• Author: [Charana H U]
• Date: [03/01/2023]

Model Information

• Architecture: Feedforward Neural Network
• Input Size: 784
• Hidden Size: 100
• Output Size (Number of Classes): 10
• Loss Function: CrossEntropyLoss
• Optimizer: Adam
• Learning Rate: 0.001
• Training Batch Size: 100
• Number of Epochs: 2

Data

• Training Dataset: MNIST
• Testing Dataset: MNIST

Training

• Training Device: GPU (if available)

Evaluation

• Testing Accuracy: [96%]

Usage - Loading

• Dependencies: PyTorch, torchvision, matplotlib
• How to Load the Model:

  from torchvision import transforms
  from your_model_module import NeuralNet
  
  model = NeuralNet(input_size=784, hidden_size=100, num_classes=10)
  model.load_state_dict(torch.load("model/mnist_model.pt"))
  model.eval()
  

Usage - Testing

• How to Make Predictions:

  Assuming you have an image tensor named 'image'

  image = image.reshape(-1, 28*28)
  output = model(image)
  _, prediction = torch.max(output, 1)
  

Limitations and Future Work

• Limitations:

  • The model is relatively simple and may not perform well on more complex datasets.
  • Limited training data augmentation was applied.

• Future Work:

  • Experiment with different network architectures.
  • Explore advanced data augmentation techniques.
  • Fine-tune hyperparameters for better performance.

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