Comments (2)
Hi @chepyle ,
When using a fingerprint representation, the use_fp
keyword in architecture specification should be making its way to main.core.build_model()
and main.data.get_data_full()
, which will use a fixed FP input instead of the learned FP. I see that the problem is coming from train_model()
, because this feeds the three input arrays in (regardless of use_fp
- this was a late change to the code after running the baseline models).
I've created a branch chepyle
and modified main.core.train_model()
to add two dummy inputs to the model. That way, when training or testing, these inputs will be accepted but ignored. Because these will be disconnected from the computational graph, you will have to pass on_unused_input='warn'
to Theano when running the script.
An alternate solution would be to change all of the calls to .fit
or .predict
to be inside conditionals that look at use_fp
and pass only the first element of the inputs if using a fixed FP representation.
Let me know if this solves the issue; if so, I will merge it into the master branch.
-Connor
from conv_qsar_fast.
Thanks Connor, the model does run now for with the fingerprint data sets. However, the disconnected graph causes problems with save_model
similar to this:
keras-team/keras#2790
Jake
from conv_qsar_fast.
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from conv_qsar_fast.