[Question] about nasnet-a used as subnetwork in adanet

[tianxh1212]

@cweill
hi,Weill, I saw an article on the Google ai blog：https://ai.googleblog.com/2018/10/introducing-adanet-fast-and-flexible.html The article mentions using nasnet-a as subnetwork after 8 adanet iteration and get the error-rate of 2.3% on cifar-10 and with fewer parameters at the same time. I would like to ask two questions. 1.do you use the entire nasnet-a architecture network（for exapmle, N=6 and F=32） as subnetwork, or use the normal cell as subnetwork or something else. 2. how do the subnetworks combine to each other.

Thanks !

[cweill]

@tianxh1212 : With the following code and settings you should be able to get the same results: https://github.com/tensorflow/adanet/blob/master/adanet/examples/nasnet.py#L181

• We used the N=6, F=32 in the config. A single subnetwork with those settings should have 3.3M parameters. Also I think we disabled use_aux_head.
• We used the Estimator force_grow=True setting.
• We used the SCALAR mixture weights, use_bias=False, max_iteration_steps=1000000 and all the other Estimator settings were at their defaults. So we simply took the average of the subnetworks' outputs at each iteration. We also ran for 10 iterations.

[Smallyolk2024]

@cweill hi,Weill, I got some errors, like this: ValueError：'generator' yielded an element of shape (1, ) where an element of shape () was expected Note that:

• the input data that I used is CIFAR-10 dataset, which was preprocessed and defined using input_fn like that in adanet/adanet/examples/tutorials/customizing_adanet.ipynb. For example: dataset = tf.data.Dataset.from_generator(generator(x_train, y_train), (tf.float32, tf.int32), ((32, 32, 3), ()))
• What I used is tensorflow 1.12, python3.6

Solution：I resolve the above question by setting:

• dataset = tf.data.Dataset.from_generator(generator(x_train, y_train), (tf.float32, tf.int32), ((32, 32, 3), 1))

[cweill]

@SmallyolkLiu: Have a look at our research code that uses NASNet in AdaNet. It shows you how you can get it working on Google Cloud MLE.

[tl-yang]

Hi @cweill . Thanks for all the great work. Did you apply any data augmentation? In your research code, I see that you apply basic augmentation (flip + crop) to input images. Did you do the same to the performance reported in the blog? Thanks!

[cweill]

@tl-yang: Please see the details in our recent paper: https://arxiv.org/abs/1903.06236