Comments (3)
Unfortunately, tensorflow-directml doesn't support DT_DOUBLE
. If double precision is not a hard requirement for your training scenario, you could use DT_FLOAT
to work around it.
from tensorflow-directml.
Thanks for the tip! Well, double precision is not necessary.
After having a close looking over the traceback, I noticed that the function that uses float64 comes from tensorflow.keras.metrics.MeanIoU.update_state().
in my team callback:
IoU=MeanIoU(num_classes=2,dtype='float32') IoU.update_state(self.Y_val,self.Y_predict_val) # This line uses DT_DOUBLE med_Iou=IoU.result().numpy()
I tryied to turn it into float32, without success. So I opened the file "C:\Users\USER\anaconda3\envs\tf2-directml\Lib\site-packages\tensorflow_core\python\keras\metrics.py" and saw 'dtype=dtypes.float64'.
I changed it to 32, however new error appeared as follow on print:
I will be glade if the is a way to use update_state() with directml
from tensorflow-directml.
SOLVED:
You will need to edit 'site-packages\tensorflow_core\python\keras\metrics.py' . As said by PatriceVignola early, directml doesn't support DT_DOUBLE, so you will need to change dtype arguments of self.total_cm and current_cm as follow (from dtype.float64 to dtype.float32):
After this my code runned flawless.
from tensorflow-directml.
Related Issues (20)
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from tensorflow-directml.