jsxlei
commented
4 years ago See the note on the README page: If come across the nan loss, try another random seed filter peaks with harsher threshold, e.g. -x 0.04 or 0.06 filter low quality cells, e.g. --min_peaks 400 or 600 change the initial learning rate, e.g. --lr 0.0002
this error is not caused by your input data containing nan values, but due to the exploded gradient caused by some outlier samples in training process, filtering some low quality cells or rare peaks usually solve this issue.
Hi, I encountered the following error when running SCALE. I have checked my data matrix. The maximum value is 35, the minimum is 0 and there is no na value. Just wondering how to fix the problem? Thanks!
Traceback (most recent call last): File "/home-4/zji4@jhu.edu/scratch/software/scale/SCALE/SCALE.py", line 134, in
pred = model.predict(testloader, device)
File "/scratch/users/zji4@jhu.edu/software/scale/SCALE/scale/model.py", line 97, in predict
pred = kmeans.fit_predict(feature)
File "/home-4/zji4@jhu.edu/.local/lib/python3.7/site-packages/sklearn/cluster/kmeans.py", line 998, in fit_predict
return self.fit(X, sample_weight=sampleweight).labels
File "/home-4/zji4@jhu.edu/.local/lib/python3.7/site-packages/sklearn/cluster/kmeans.py", line 972, in fit
return_n_iter=True)
File "/home-4/zji4@jhu.edu/.local/lib/python3.7/site-packages/sklearn/cluster/kmeans.py", line 312, in k_means
order=order, copy=copy_x)
File "/home-4/zji4@jhu.edu/.local/lib/python3.7/site-packages/sklearn/utils/validation.py", line 542, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/home-4/zji4@jhu.edu/.local/lib/python3.7/site-packages/sklearn/utils/validation.py", line 56, in _assert_all_finite
raise ValueError(msg_err.format(type_err, X.dtype))
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').