Automatic and unsupervised detached subgraph detection in deep learning programs

Abstract

Detecting an anomaly in deep learning programming can include receiving a deep learning program with neural network. A pre-trained machine learning model can be run with the deep learning program with neural network as input. The pre-trained machine learning model detects whether the neural network includes a detaching subgraph. Responsive to detecting that the neural network includes a detaching subgraph, a location of the deep learning program causing the detaching subgraph can be output. The neural network of the deep learning program can be run in training mode and weight gradients associated with training of the neural network can be monitored. Based on the monitoring, occurrence of one or more detaching subgraphs can be detected. Responsive to detecting a detaching subgraph, the detaching subgraph can be output. A suggestion to correct the deep learning program can also be output.