[MADLIB-1389] Transfer learning for multi-model

• Type: New Feature
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Fix Version/s: v1.17
• Component/s: Module: Neural Networks
• Assignee: Unassigned
• Reporter: Frank McQuillan
• Created: 28/Oct/19 21:55
• Updated: 18/Dec/19 23:32
• Resolved: 18/Dec/19 23:32

Description

Context

The transfer learning workflow for 1.17 will be the same as 1.16. It means user needs to update the model architecture table with the weights to be used for initialization. If they are NULL, then Keras default initialization will be used (random, or perhaps what is specified in the model architecture which I think there might be options for initialization in model architecture).

Story

I think the only bit that is missing currently is to check the model table to see if there are any weights there, and if there are, to use them for initialization.

Acceptance

1) Train a model with 4 MSTs and plot the loss/accuracy curves. Use 2 MSTs for one model architecture and 2 MSTs for a second model architecture. Perhaps use CIFAR-10 dataset.
2) Copy model weights over to the model architecture table for 2 of the models from step #1 (not all 4).
3) Train the same 4 models as #1 and plot the loss/accuracy curves. Check that the 2 transfer learning cases pick up from where they left off, and that the other 2 start from scratch (due to random initialization).