quicknn 1.0.7

An implementation of Feedforward Neural Networks for quick applications.

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 of the metrics provided.
• Inner management of the validation set in the training phase.

Example

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)

qdnn = QuickNN(list_neurons=[100, 200, 1])
qnn.fit(X_train, y_train, n_epochs=10) ## If you run this in an IPython session you can stop-change-resume the training
y_pred = qnn.predict(X_val)

score = mean_squared_error(y_val, y_pred)

Installing

The dependencies are showed in requirements.txt, which can be installed with the command:

pip install -r requirements.txt

Then the library can easily downloaded through pip:

$ pip install quicknn

License

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