Somshubra Majumdar

Have you tried training it with the auxiliary loss for both the mobile and large models ? It is a strong regularizer which is required for training NASNet-A Model from...

I don't really have an answer then. It's either a problem with the regularizer strength, or with the auxiliary branch not working, or perhaps the model is too large so...

@Agent007 A 87 Million parameter model for a dataset of 6600 images. I would say that is cause for concern for any large model, not just NASNet. Use the NASNet...

I was reading through the paper again. They used a *lots* of tricks to get such high performance. Just skimming through a few : - Use Cut-out regularization for Cifar...

H_i is the output of your current cell. H_i-1 is the input to your current cell. H_i-2 is the input to the cell prior to your current cell.

@alkari I'd suggest holding off on using the Keras version of NASNet for fine-tuning purposes for the moment. There have been mumrioke independent reports that suggest the weight loading mechanism...

Usually all layers will the last convolutions layer are frozen and then you would add a new classifier to the end. I dunno how, but it's learning something. I'm guessing...

Yes. Though I haven't tested the auxiliary branches yet, but the weights have been ported. If they still don't work, it's gonna be a problem.

You must have used an odd input shape here. Can you provide the full script with all variables ?

Weird. I don't get this error. I an using TF with Channs last data format. I'm guessing you are having the same, so I don't understand the cause.