Closed bundelesneha05 closed 6 years ago
I am also facing same problem. I am using same database and annotation as in the documentation. Not getting how to proceed for getting results as updated by @shreyaspadhy in the documentation ? Please reply Thanks in advance!!
i am getting " No such file or directory: 'data.npz'". what data.npz file contents ?
@mrudangpandya , have you trained the model by using tensorflow or theano? about your question in result.py file I think, data.npz might contain the numpy files of test and predicted images.
My humble request to the @shreyaspadhy and @codedecde, please let us answered about our doubt.
Try visualising the train data once ? Because if you are getting a dice coefficient of 0.92, that means the model is training, and the predicted mask and the gold mask have a significant overlap. I can take a better look at it after the 7th of May.
Hi, unfortunately right now our LUNA_Unet.py doesn't save the results as data.npz by default. I believe I had saved some results by manually modifying the LUNA_Unet.py code to save results into a data.npz file. Here is what I suggest -
In LUNA_UNet.py, after calling model.fit, call model.predict() and save the results as a Python dictionary data
, where it has the following keys imgs_test_X
(The original images as numpy arrays), imgs_test_Y
(the gold label masks), imgs_test_Pred
(the output from UNet).
Then you could just use Results_Plot.py (the code for plotting such a dictionary is commented out later, you can just uncomment it).
ok thank you
I will do this changes..
Thank you
On Wed, Apr 25, 2018 at 7:09 AM, Shreyas Padhy notifications@github.com wrote:
Hi, unfortunately right now our LUNA_Unet.py doesn't save the results as data.npz by default. I believe I had saved some results by manually modifying the LUNA_Unet.py code to save results into a data.npz file. Here is what I suggest -
In LUNA_UNet.py, after calling model.fit, call model.evaluate() and save the results as a Python dictionary data, where it has the following keys imgs_test_X (The original images as numpy arrays), imgs_test_Y(the gold label masks), imgs_test_Pred (the output from UNet).
Then you could just use Results_Plot.py (the code for plotting such a dictionary is commented out later, you can just uncomment it).
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I trained the given U-net model on 1459 samples of LUNA16 database with dice coefficient 0.92 with tensorflow(1.3.0) as well as theano(1.0.1) backend. But it gives me output as below,
It is not same as the results given in this documentation. How should I solve this problem??? Thanks in advance!