semvec/pypi-packages huggingface dataset:
quicknn 1.0.9
An implementation of Feedforward Neural Networks for quick applications.


# QuickNN

The *quicknn* is a Tensorflow-based package that aims to simplify the application of the feedforward neural networks in classification and regression problems.
The main features of the *quicknn* package are:

* internally management of the categorical variables with one-hot-encoding(OHE) method batch-wise, just you have to feed it with pandas object;
* internally management of the validation of the data while training;
* possibility to stop the training phase, change some parameters and then resume the training from where it had remained;
* allows easy visualization of the learning curves using [Tensorboard](https://www.tensorflow.org/guide/summaries_and_tensorboard);

## Example

```python
from quicknn import QuickNN
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

X, y = load_boston(return_X_y=True)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.25)

qnn = QuickNN(list_neurons=[100, 200, 1])
qnn.fit(X_train, y_train, n_epochs=10) ## In IPython session you can stop-change-resume the training.
qnn.fit(X_train, y_train, n_epochs=20) ## Just increasing the n_epochs.
qnn.fit(X_train, y_train, n_epochs=30,
        learning_rate=0.01) ## You can change e.g., the learning_rate param while training
y_pred = qnn.predict(X_val)

```

## Installing
The dependencies are showed in [requirements.txt](requirements.txt), which can be installed with the command:
```bash
$ pip install -r requirements.txt
```
Then the library can easily downloaded through pip:
```bash
$ pip install quicknn
```

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.

## References
* [IPython](https://ipython.org/)
* [Tensor](https://www.tensorflow.org/)
* [pandas](https://pandas.pydata.org/)
* [scikit-learn](http://scikit-learn.org/stable/)
* [path.py](https://github.com/jaraco/path.py)