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https://github.com/gsteixeira/nnpylib
Yet another Neural Network Python library
https://github.com/gsteixeira/nnpylib
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Yet another Neural Network Python library
- Host: GitHub
- URL: https://github.com/gsteixeira/nnpylib
- Owner: gsteixeira
- License: gpl-3.0
- Created: 2021-10-07T00:42:34.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2021-12-24T05:57:36.000Z (almost 3 years ago)
- Last Synced: 2023-12-14T16:57:42.630Z (11 months ago)
- Language: Python
- Homepage:
- Size: 38.1 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NNPyLib - Yet another Neural Network Python library
NNpylib is a simplistic neural network library for python.
It aims to be simple as possible, to be easy to use and to allow anyone who wishes to understand neural networks can study it.
NNpylib is built in pure python, it doesn't use numpy, to make it clear as possible. It is done for **educational purposes**.
Yet it provides a working reliable neural network that can be used in your applications with minimal effort.
## Features:
- Support for multiple hidden layers.
- Configurable non linear function. Supports: **sigmoid**, **relu**, **leaky relu** and **tanh**
- Built in pure python.
- It is possible to persist the network state, so once trained you can store it and use without training again.
## Instalation
```shell
pip install nnpylib
```
## usage
Create a neural network.
```python
# set the parameters
size_of_input = 2 # how many neurons on the input layer.
size_of_output = 1 # how many on the output.
hidden_layers = [4,] # a list with the size of each of hidden layers.
# create the network
nn = NewNeuralNetwork(size_of_input, size_of_output, hidden_layers,
method="leaky_relu")
# train it..
how_many_times = 10000
nn.train(input_data, expected_output, how_many_times)
# Now make predictions
predicted = nn2.predict(foo)
```
Now you can save your network's state and reuse it later.
```python
from nnpylib.storage import dump_nn, load_nn
# dump the network state
data = dump_nn(nn)
# You can save that to a file or db.
# Now, create a new network from the saved data.
nn2 = load_nn(data)
# make predictions with that saved network
predicted = nn2.predict(foo)
```
Test it
```shell
# if you don't have pytest
pip install pytest
# test it
pytest
```
For more ideas, look at *samples* dir.