How to predict a Test Sample? about edgeml HOT 5 CLOSED

Hi @affot, to obtain the predictions you should first obtain a reference to the prediction operation by

   op = protoNNObj.getPredictionsOp() # or 
   op = bonsaiObj.getPredictions()

Once you have this, use your text data in the input X placeholder and evaluate this operation to obtain the predictions:

predictions = sess.run(op, feed_dict ={ X: X_test}) 

from edgeml.

Hello @metastableB

thanks a lot for your fast help here!! :-)
For protoNN it works. In the example I can handover for example the first 20 samples of the test set with:
predictions = sess.run(op, feed_dict ={ X: x_test[0:20]}) and it returns me an array with 20 predicted classes.

With the BonsaiObject I still have a problem. When I do the same here I get an error:

"InvalidArgumentError: You must feed a value for placeholder tensor 'sigmaI' with dtype float [[{{node sigmaI}}]]"
and
"InvalidArgumentError: You must feed a value for placeholder tensor 'sigmaI' with dtype float [[node sigmaI (defined at C:\Users\CK\anaconda3_neu\envs\BT36\lib\site-packages\tensorflow_core\python\framework\ops.py:1748) ]]"

I allready tried predictions = sess.run(op, feed_dict ={ X: x_test[0:20], sigmaI: 1.0}) but here I get
"NameError: name 'sigmaI' is not defined" and don't find a solution.
Do you know where is the problem?

from edgeml.

You will need a reference to the sigmaI placeholder in the computation graph. You could try,

sigmaI = bonsaiTrainer.sigmaI
predictions = sess.run(op, feed_dict ={ X: x_test[0:20], sigmaI: 1.0})

See the declaration of sigmaI here.

Also for inference, you should set sigmaI to some large value ( 1e10 ) instead of 1.0.

from edgeml.

Thanks for your fast help here!! It works.

from edgeml.

Closing this for now, since the query is answered.

from edgeml.