Comments (5)
I am also trying to do the same. After inspecting the code, I figured out the following solution, to train with more than one data volumes.
Let's assume that we have two training volumes at hand.
- First, generate the coordinates for both volumes, but with different tags. i.e., while using 'build_coordinates.py' we could use "validation1" as the tag for volume 1 and "validation2" as the tag for volume 2.
eg. python build_coordinates.py --partition_volumes validation1:address_to_volume1_af.h5:af_file,validation2:address_to_volume2_af.h5:af_file --coordinate_output .. - Next, change the arguments corresponding to input volume and corresponding labels:
data_volumes
from
validation1:address_to_volume_1.h5:raw
to
validation1:address_to_volume_1.h5:raw,validation2:address_to_volume_2.h5:raw
Repeat the above step for
label_volumes
too
- Now run the training code as usual.
from ffn.
How to save the model according to the set conditions, not periodically.
I noticed that MonitoredSession is a very inefficient method.
from ffn.
I am also trying to do the same. After inspecting the code, I figured out the following solution, to train with more than one data volumes.
Let's assume that we have two training volumes at hand.
- First, generate the coordinates for both volumes, but with different tags. i.e., while using 'build_coordinates.py' we could use "validation1" as the tag for volume 1 and "validation2" as the tag for volume 2.
eg. python build_coordinates.py --partition_volumes validation1:address_to_volume1_af.h5:af_file,validation2:address_to_volume2_af.h5:af_file --coordinate_output ..- Next, change the arguments corresponding to input volume and corresponding labels:
data_volumes
from
validation1:address_to_volume_1.h5:raw
to
validation1:address_to_volume_1.h5:raw,validation2:address_to_volume_2.h5:rawRepeat the above step for
label_volumes
too
- Now run the training code as usual.
Hello, I'm confused that this program has no validation set.
from ffn.
I am also trying to do the same. After inspecting the code, I figured out the following solution, to train with more than one data volumes.
Let's assume that we have two training volumes at hand.
- First, generate the coordinates for both volumes, but with different tags. i.e., while using 'build_coordinates.py' we could use "validation1" as the tag for volume 1 and "validation2" as the tag for volume 2.
eg. python build_coordinates.py --partition_volumes validation1:address_to_volume1_af.h5:af_file,validation2:address_to_volume2_af.h5:af_file --coordinate_output ..- Next, change the arguments corresponding to input volume and corresponding labels:
data_volumes
from
validation1:address_to_volume_1.h5:raw
to
validation1:address_to_volume_1.h5:raw,validation2:address_to_volume_2.h5:rawRepeat the above step for
label_volumes
too
- Now run the training code as usual.
Hello, I'm confused that this program has no verification set.
I didn't understand. You meant: "No setup/data to check monitor the improvement in segmentation?". If yes, I agree. I would also like to figure out a way to save the network weights based on some sort of quantitative evaluation. I haven't explored that direction yet. Let me know if you figure out a way to do it. Thanks.
from ffn.
I am also trying to do the same. After inspecting the code, I figured out the following solution, to train with more than one data volumes.
Let's assume that we have two training volumes at hand.
- First, generate the coordinates for both volumes, but with different tags. i.e., while using 'build_coordinates.py' we could use "validation1" as the tag for volume 1 and "validation2" as the tag for volume 2.
eg. python build_coordinates.py --partition_volumes validation1:address_to_volume1_af.h5:af_file,validation2:address_to_volume2_af.h5:af_file --coordinate_output ..- Next, change the arguments corresponding to input volume and corresponding labels:
data_volumes
from
validation1:address_to_volume_1.h5:raw
to
validation1:address_to_volume_1.h5:raw,validation2:address_to_volume_2.h5:rawRepeat the above step for
label_volumes
too
- Now run the training code as usual.
Hello, I'm confused that this program has no verification set.
I didn't understand. You meant: "No setup/data to check monitor the improvement in segmentation?". If yes, I agree. I would also like to figure out a way to save the network weights based on some sort of quantitative evaluation. I haven't explored that direction yet. Let me know if you figure out a way to do it. Thanks.
I don't understand enough about FFN training process, which is different from what I know about segmentation tasks.
"MonitoredTrainingSession" is different from Session.You can try "Supervisor".
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Related Issues (19)
- Issue with build_coordinates.py HOT 9
- Undefined names: 'sampling' and '_required' HOT 3
- Training model does not use GPU
- syntax error in _required definition
- What about consensus and agglomeration steps? HOT 1
- Train error,maybe something wrong in bounding_box.py HOT 1
- AxisError with numpy 1.18
- TensorFlow record files are corrupted
- Parameter Optimisation Recommendations
- Combine multiple sess.run into one ?
- Multi-GPU utilization HOT 4
- Share trained weights of SNEMI3D dataset?
- Qustion about blank areas in inference labels HOT 1
- Realignment and Irregular section substitution HOT 4
- TypeError: string indices must be integers HOT 1
- Evaluation the segmentation HOT 1
- build_coordinates generating TFRecords so slowly
- ffn_inference_colab_demo issue HOT 1
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