[Question] about nasnet-a used as subnetwork in adanet about adanet HOT 5 OPEN

@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.

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@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))

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@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.

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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!

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

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