Comments (5)
We don't have an explicit test mode at the moment in this example code release. You could one by using the existing code to load stored models (from the --restore
option) and by modifying make_model
in chem_tensorflow.py
to expose the computed values as a graph op accessible from the outside (i.e., by writing it to self.ops
).
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For chem_tensorflow_dense.py
we have a short example of what code for a test mode would look like after loading a stored model with --restore
.
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@51alg
Thank you for your response. However, it is not very clear for me. Using --restore is for continuing training. Did you modify your "short example" which has a link in your reply? Is it the same file as chem_tensorflow_dense.py ? Thanks a lot
from gated-graph-neural-network-samples.
I've added a command line argument --evaluate
to chem_tensorflow_dense.py
to make this easier.
Example usage:
$ python chem_tensorflow_dense.py
(produces ./[hash]_model_best.pickle)
$ python chem_tensorflow_dense.py --restore ./[hash]_model_best.pickle --evaluate
from gated-graph-neural-network-samples.
Thank you @51alg
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Related Issues (14)
- Where can we got the pytorch version? HOT 3
- UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. HOT 1
- Do you use GPU for training? HOT 2
- Can I use float rather than int in the graph? HOT 3
- Unable to wget the sample data HOT 2
- Dense for small graph? HOT 1
- Chemical accuracy values HOT 2
- Please explain comments like ' # [b x v x h]' HOT 4
- where are the gates during propagation HOT 5
- reported loss HOT 1
- Explaining data in .json files HOT 2
- It seems strange to set the lowest value of valid accuracy as the best result. HOT 2
- tying layers HOT 4
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