https://github.com/rossheat/scorch

The scorch deep learning library
https://github.com/rossheat/scorch

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The scorch deep learning library

• Host: GitHub
• URL: https://github.com/rossheat/scorch
• Owner: rossheat
• License: mit
• Created: 2023-11-14T21:37:42.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-17T20:38:18.000Z (about 1 year ago)
• Last Synced: 2023-11-17T21:36:42.800Z (about 1 year ago)
• Language: Python
• Size: 9.77 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
I made this library to gain a better understanding of how backpropagation is used to train a neural network.

## Features

- **Backpropagation:** Scorch supports the backpropagation algorithm, enabling models to learn from their training data.

- **Neural Network Foundation Blocks:** Define a neural network with core components like Linear, ReLU, and Flatten.

- **Loss Functions:** Implement loss functions such as CrossEntropyLoss and MSELoss.

- **Optimizers:** Use gradient descent optimization with the provided SGD optimizer.

- **Data Handling:** Load and batch datasets effectively for model training with Dataset and DataLoader.

  

### Examples

Scorch Neural Network definition mimics PyTorch, as shown in the side-by-side comparison:

#### PyTorch

```python

import torch

import torch.nn as nn

class PyTorchNetwork(nn.Module): 

    def __init__(self):

        super().__init__()

        self.flatten = nn.Flatten()

        self.linear_relu_stack = nn.Sequential(

            nn.Linear(28*28, 512),

            nn.ReLU(),

            nn.Linear(512, 512),

            nn.ReLU(),

            nn.Linear(512, 10)

        )

    def forward(self, x):

        x = self.flatten(x)

        return self.linear_relu_stack(x)

model = PyTorchNetwork()

loss_fn = nn.CrossEntropyLoss()

optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

```

#### Scorch

```python

import scorch

import scorch.nn as nn

class ScorchNetwork(nn.Module): 

    def __init__(self):

        super().__init__()

        self.flatten = nn.Flatten()

        self.stack = nn.Sequential(

            nn.Linear(28*28, 512),

            nn.ReLU(),

            nn.Linear(512, 512),

            nn.ReLU(),

            nn.Linear(512, 10)

        )

    

    def forward(self, x):

        x = self.flatten(x)

        return self.stack(x)

model = ScorchNetwork()

loss_fn = nn.CrossEntropyLoss()

optimizer = scorch.optim.SGD(model.parameters(), lr=1e-3)

```

Please find training examples in the `examples` directory.

## Installation

Clone the repository and install:

```bash

git clone https://github.com/rossheat/scorch.git

cd scorch

python setup.py install

python examples/diabetes.py

```

## License

Scorch is MIT licensed, as found in the LICENSE file.

## Acknowledgments

Scorch is inspired by PyTorch and is intended strictly for educational purposes, honoring the original work of the creators and contributors of PyTorch.