log_loss from sklearn gives nan, while tensorflow.losses.log_loss works

Question

I have a binary classification problem. I am using the log_loss from tensorflow.losses.log_loss.

To check, I use sklearn.metrics.log_loss. Most of the times, the two functions give the same result (only difference in dtype). In some instance, the sklearn function returns NaN while tf.losses.log_loss returns a correct value.

data is here: https://pastebin.com/BvDgDnVT

code:

import sklearn.metrics
import tensorflow as tf
y_true = [... see pastebin link]
y_pred = [... see pastebin link]
loss_sk = sklearn.metrics.log_loss(y_true, y_pred, labels=[0, 1]) # -> returns NaN
with tf.Session() as sess:
    loss_tf = tf.losses.log_loss(y_true, y_pred).eval(session=sess) # -> returns 0.0549

There seems to be some log(0) happening, but why does tensorflow not have this problem?

Answer 1

Changing the dtype of both arrays to a 64-bit float fixes it

dtype=np.float64

for example adding y_pred = y_pred.astype(np.float64)

Answer 2

Another way of fixing the issue is to provide eps=1e-7 to log_loss, which is a more appropriate epsilon for float32 and is what tensorflow's using. Scikit however uses 1e-15 as a default (expecting float64).