Health checks for training issues in deep learning models
This library implements a suite of health checks for training deep learning models. These methods provide feedback during training that can help machine learning developers quickly identify and correct common training issues like disconnected layers, unstable parameters, exploding gradients, vanishing gradients, zero loss, saturated neurons, dead neurons, etc.
- Houssem Ben Braiek, Foutse Khomh, TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs
- Mohammad Wardat, Breno Dantas Cruz, Wei Le, Hridesh Rajan DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs. DeepDiagnosis is on Github.