Accumulate predictions in output tensor for Google Cloud serving
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
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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
add a comment |
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
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 '18 at 10:33
constantinopolskayaconstantinopolskaya
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