Metadata-Version: 2.1
Name: amrnet
Version: 0.1.1
Summary: Deep Learning library
Author: Amr Hegazy
Author-email: amr.hegazy128@gmail.com
License: MIT
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy

# AmrNet: Deep Learning Library



AmrNet is a lightweight deep learning library designed for simplicity and ease of use. It provides a set of basic tools to help you quickly build and train neural networks.



<!-- ## Features



- **Simple API:** Easily define, train, and evaluate neural networks with just a few lines of code.

- **Modularity:** Construct networks using pre-built layers or create custom layers for more flexibility.

- **Optimizers:** Choose from a variety of gradient descent optimizers to train your models.

- **Example Usage:** Check out the `examples` directory for code snippets and sample projects. -->



## Installation



```bash

pip install amrnet==0.1.0

```



## Implemented Features



- [Tensors](./amrnet/tensor.py)

- [Layers](./amrnet/layers.py) :



    1. Linear

    2. ReLU

    3. Tanh

    4. LeakyReLU

    <br></br>

- [Neural Networks](./amrnet/nn.py)

- [Loss Functions](./amrnet/loss.py) :



    1. MSE

    2. TSE

    3. MAE

    4. LogCosh

    <br></br>

- [Optimizers](./amrnet/optim.py) :



    1. SGD

    <br></br>

- [Data Utilities](./amrnet/data.py)

- [Training Utilities](./amrnet/train.py)







## Usage



### Creating a Model



```python

from amrnet.nn import NeuralNet

from amrnet.layers import Linear, Tanh,  ReLU



net = NeuralNet([

    Linear(input_size, hidden_size),

    Tanh(),

    Linear(hidden_size, output_size)

])

```



### Training the Model



```python

from amrnet.train import train



train(net, inputs, targets, num_epochs, data_iterator, loss, optimizer)

```



### Predicting



```python

predicted = net.forward(x)

```



## Examples



Check out the [examples](./examples/) directory for a variety of different projects using AmrNet.



## License



[MIT](LICENSE)





## TODO

- [ ] Add more layers

- [ ] Add more optimizers

- [ ] Add more loss functions

- [ ] Add more data utilities

- [ ] Add more training utilities

- [ ] Add more examples

- [ ] Add more tests

- [ ] Add more documentation





