Metadata-Version: 2.1
Name: quicknn
Version: 1.0.8
Summary: An implementation of Feedforward Neural Networks for quick applications.
Home-page: https://gitlab.com/deeplego/quicknn
Author: Lorenzo Palloni
Author-email: palloni.lorenzo@gmail.com
License: UNKNOWN
Description: 
        # QuickNN
        
        An implementation of Feedforward Neural Networks for quick applications.
        
        * The training phase can be stopped, some parameters on fit method can be changed and then the training can be resumed
        with the same weights of the last interruption.
        * If feed with pandas objects it can handle categorical variables with one-hot-encoding(OHE) method batch-wise as well
        as continuous variables.
        * Easy visualization in [Tensorboard](https://www.tensorflow.org/guide/summaries_and_tensorboard) of the metrics provided.
        * Inner management of the validation set in the training phase.
        
        ## Example
        
        ```python
        from quicknn import QuickNN
        from sklearn.datasets import load_boston
        from sklearn.model_selection import train_test_split
        from sklearn.metrics import mean_squared_error
        
        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, learning_rate=0.01) ## you can change e.g., the learning_rate param while training
        y_pred = qnn.predict(X_val)
        
        score = mean_squared_error(y_val, y_pred)
        
        ```
        
        ## 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.
        
        ## Reference
        * [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)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
