Build Low Code Automated Tensorflow explainable models in just 3 lines of code. Library created by: Hasan Rafiq - https://www.linkedin.com/in/sam04/
Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code.
To make Deep Learning on Tensorflow absolutely easy for the masses with its low code framework and also increase trust on ML models through What-IF model explainability.
Built on top of the powerful Tensorflow ecosystem tools like TFX , TF APIs and What-IF Tool , the library automatically does all the heavy lifting internally like EDA, schema discovery, feature engineering, HPT, model search etc. This empowers developers to focus only on building end user applications quickly without any knowledge of Tensorflow, ML or debugging. Built for handling large volume of data / BigData - using only TF scalable components. Moreover the models trained with auto-tensorflow can directly be deployed on any cloud like GCP / AWS / Azure.
pip install auto-tensorflow
pip install git+https://github.com/rafiqhasan/auto-tensorflow.git
from auto_tensorflow.tfa import TFAuto
tfa = TFAuto(train_data_path='/content/train_data/', test_data_path='/content/test_data/', path_root='/content/tfauto')
tfa.step_data_explore(viz=True) ##Viz=False for no visualization
tfa.step_model_build(label_column = 'price', model_type='REGRESSION', model_complexity=1)
tfa.step_model_whatif()
Method TFAuto
train_data_path
: Path where training data is storedtest_data_path
: Path where Test / Eval data is storedpath_root
: Directory for running TFAuto( Directory should NOT exist )Method step_data_explore
viz
: Is data visualization required ? - True or False( Default )Method step_model_build
label_column
: The feature to be used as Labelmodel_type
: Either of 'REGRESSION'( Default ), 'CLASSIFICATION'model_complexity
:
0
: Model with default hyper-parameters1
(Default): Model with automated hyper-parameter tuning2
: Complexity 1 + Advanced fine-tuning of Text layersThere are a few limitations in the initial release but we are working day and night to resolve these and add them as future features.
1.3.4 - 09/12/2022 - Release Notes
1.3.3 - 09/12/2022 - Release Notes
1.3.2 - 27/11/2021 - Release Notes
1.3.1 - 18/11/2021 - Release Notes
1.2.0 - 24/07/2021 - Release Notes
1.1.1 - 14/07/2021 - Release Notes
1.0.1 - 07/07/2021 - Release Notes