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Why does my custom cosine similarity loss lead to NaNs when it is equivalent and largely identical to Keras’ implementation?

I need to implement CosineSimilarity myself because i need to work on the individual losses before calculating the batch-wide mean.

I do it like this:  

 

 

a_n = tf.math.l2_normalize(a, axis=-1) 

 

b_n = tf.math.l2_normalize(b, axis=-1) 

 

d = -tf.math.reduce_sum(a_n * b_n, axis=-1) 

 

# Above is _identical_ to Keras' implementation. 

 

return d, tf.math.reduce_mean(d) 

 

I already compared the output to Keras’ implementation by repeatedly printing

 print(tf.math.reduce_sum(tf.math.abs(my_loss - keras_loss))) 

However, even though this outputs straight zeros (and never any NaNs), i still encounter NaNs, while with Keras’ implementation i do not. I already tried a higher epsilon in the l2_normalize, or using multiply_no_nan, to no avail.

Update: This comment.

submitted by /u/tfhwchoice
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