Usage

This guide demonstrates how to use the Hashtron Network Implementation.

Installation

Install the package using pip:

pip install hashtron

Basic Usage

1. Import the necessary modules:

   from hashtron.net.feedforward.net import Net
   from hashtron.layer.majpool2d.layer import MajPool2DLayer
   from hashtron.datasets.mnist.mnist import MNISTDataset
   import urllib.request
   import tempfile
   import os
   

2. Create and configure the network:

   # Specify network size
   fanout1 = 3
   fanout2 = 5
   fanout3 = 3
   fanout4 = 5
   
   # Create a Hashtron network
   tron = Net.new()
   tron.new_layer(fanout1 * fanout2 * fanout3 * fanout4, 0, 1 << fanout4)
   tron.new_combiner(MajPool2DLayer(fanout1 * fanout2 * fanout4, 1, fanout3, 1, fanout4, 1, 1))
   tron.new_layer(fanout1 * fanout2, 0, 1 << fanout2)
   tron.new_combiner(MajPool2DLayer(fanout2, 1, fanout1, 1, fanout2, 1, 1))
   tron.new_layer(1, 0)
   

3. Load the model weights:

   filename = os.path.expanduser('~/classifier/cmd/train_mnist/output.77.json.t.zlib')
   if os.path.exists(filename):
       ok = tron.io.read_zlib_weights_from_file(filename)
   else:
       # Load online model weights
       with urllib.request.urlopen('https://www.hashtron.cloud/dl/mnist/output.77.json.t.zlib') as f:
           with tempfile.NamedTemporaryFile(delete=True) as dst_file:
               data = f.read()
               dst_file.write(data)
               ok = tron.io.read_zlib_weights_from_file(dst_file.name)
   
   if not ok:
       raise Exception('Model weights were not loaded')
   

4. Test the network on the MNIST dataset:

   for i in range(2):
       # Load offline or online MNIST dataset
       try:
           dataset = MNISTDataset(True, i == 0)
       except FileNotFoundError:
           dataset = MNISTDataset(True, i == 0, MNISTDataset.download())
   
       correct = 0
       for sample in dataset:
           pred = tron.network.infer(sample) & 1
           actual = sample.output() & 1
           if pred == actual:
               correct += 1
       print(100 * correct // len(dataset), '% on', len(dataset), 'MNIST samples')
   

Contributing

To contribute to this project:

1. Open an issue.

2. Fork the repository.

3. Implement the required changes (avoid adding large files to the repository; host datasets or demo models externally).

4. Add tests as needed.

5. Install the package in development mode:

   pip install -e .
   

6. Run all test cases:

   pip install pytest
   python3 -m pytest
   

7. Submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.