TensorBase: Minimalistic TensorFlow Framework

TensorBase provides a set of Python classes that abstract the typical functions involved in a machine learning application and facilitate the creation of new applications from data management and to model implementation. TensorBase differs from other TensorFlow-compatible APIs such as Keras and PrettyTensor in several ways:

1. Has a simpler structure but demands more knowledge of TensorFlow syntax
2. Facilitates the creation of new layers and functions in networks and training
3. Addresses data and metrics (e.g. loss, accuracy) management

Contents

The TensorBase packages contains 3 Classes in base.py and 1 Class in data.py.

Base:

• Model: a parent class that defines the general structure of TensorFlow models and manages metrics.
• Layers: a parent class that iteratively creates connected and convolutional networks.
• Data: a parent class for batch generation.

Data:

• MNIST: a child class that generates batchs for the MNIST dataset.