Comments (2)
Can you clarify what you mean by multi-output? This could mean two different things:
- If you mean your output vector is of length 100, this will be taken care of by the conversion.
- Your output has multiple branches
See here for details on the difference and how FKB handles them
For ensembles first you must convert the keras files to Fortran format using h5_to_txt
. Then you put the names of those txt files in a single file which is then provided to the ensemble. This way it knows which networks you want in the ensemble.
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Hello,
in my case is (1), the output vector has length 100. So, in this case ensembles are not necessary.
Thank you.
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Related Issues (19)
- dense layer with no biases can't be converted HOT 1
- Unsupported layer found! Skipping... HOT 4
- corrupted size vs. prev_size HOT 2
- Incompatibilities with Tensorflow 2
- Wrong derivative of leaky-relu activation HOT 1
- Typo "usable" not "usuable" in README HOT 1
- Performance enhancements (batched predictions using GEMM) HOT 1
- Keras saved mode is not .h5 file HOT 2
- No "tanh" activation in "convert_weights.py"
- add topics HOT 1
- FKB with threading turned on
- Should linear_prime return 1 instead of x? HOT 1
- Recognize InputLayer HOT 1
- Undefined TBP (train()) used in src/tests/example_simple.F90 and example_mnist.F90 HOT 1
- Possible typo (filename) in the header comment of test_training.F90 HOT 1
- Redundant evaluation of db in dense_backward() in mod_dense_layer.F90 ? HOT 1
- "Unable to open object (object 'dense_1' doesn't exist)" HOT 2
- how can we put the value from one subroutine to another subroutine, containing a function? HOT 3
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