Accumulate predictions in output tensor for Google Cloud serving
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I'm new to TF and trying to deploy Quick, Draw! model on Google Cloud.
It uses custom Estimator and takes 1 tfexample and returns classification tensor of shape (8).
One of the requirements for models to be deployed is that input and output tensors should support batching, e.g. their outer dimensions should be None.
Doing so for input tensor is easy since we have serving_input_receiver_fn under control, as for output tensor I'm confused because whether I change output tensor I also need to refactor whole train/evaluate operation. Are there any shortcuts to accomplish that?
python tensorflow google-cloud-ml tensorflow-estimator
asked Nov 20 at 10:33
constantinopolskaya
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0
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I'm new to TF and trying to deploy Quick, Draw! model on Google Cloud.
It uses custom Estimator and takes 1 tfexample and returns classification tensor of shape (8).
One of the requirements for models to be deployed is that input and output tensors should support batching, e.g. their outer dimensions should be None.
Doing so for input tensor is easy since we have serving_input_receiver_fn under control, as for output tensor I'm confused because whether I change output tensor I also need to refactor whole train/evaluate operation. Are there any shortcuts to accomplish that?
python tensorflow google-cloud-ml tensorflow-estimator
asked Nov 20 at 10:33
constantinopolskaya
155
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I'm new to TF and trying to deploy Quick, Draw! model on Google Cloud.
It uses custom Estimator and takes 1 tfexample and returns classification tensor of shape (8).
One of the requirements for models to be deployed is that input and output tensors should support batching, e.g. their outer dimensions should be None.
Doing so for input tensor is easy since we have serving_input_receiver_fn under control, as for output tensor I'm confused because whether I change output tensor I also need to refactor whole train/evaluate operation. Are there any shortcuts to accomplish that?
python tensorflow google-cloud-ml tensorflow-estimator
asked Nov 20 at 10:33
constantinopolskaya
155
I'm new to TF and trying to deploy Quick, Draw! model on Google Cloud.
It uses custom Estimator and takes 1 tfexample and returns classification tensor of shape (8).
One of the requirements for models to be deployed is that input and output tensors should support batching, e.g. their outer dimensions should be None.
Doing so for input tensor is easy since we have serving_input_receiver_fn under control, as for output tensor I'm confused because whether I change output tensor I also need to refactor whole train/evaluate operation. Are there any shortcuts to accomplish that?
python tensorflow google-cloud-ml tensorflow-estimator
python tensorflow google-cloud-ml tensorflow-estimator
asked Nov 20 at 10:33
constantinopolskaya
155
asked Nov 20 at 10:33
constantinopolskaya
155
asked Nov 20 at 10:33
constantinopolskaya
155
asked Nov 20 at 10:33
constantinopolskaya
155
asked Nov 20 at 10:33
constantinopolskaya
155
155
add a comment |
add a comment |
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