Hello.

I am trying to create a custom metric for my sequential model, where it takes two probability distribuitions, y_pred and y_true, and for every [1] in y_true, it sums the value from y_pred in the same index, and returns it normalized.

ex. y_true: [ [0,0,1], [1,0,0] ]

y_pred: [ [0.2,0.2,0.6] , [0.3,0.4,0.3] ]

returned value: 0.45 ((0.6 + 0.3) / 2)

So I created a function, and passed it as the metric for the model. It did not work, as I could not convert the tensors to numpy, because the function was being converted into a graph function.

Then I wrapped it in a tf.py_function(), which is wrapped in a lambda, and passed it. Now

it seems to be retrieving the correct values, but I do not know how to use the slice() function correctly.

Before, I was getting an error, which told me that it was expecting a certain size for slice, and it was too big.

I changed it, so now slice works. But now it tells me there are multiple values inside the sliced tensor, so it cannot retrieve the true value.

In a 3-D tensor, with shape [1,3,7], when slicing to get a single value from it, what is the correct size for it?

Currently my function looks like this:

def forgivingAccuracy(y_true, y_pred):

```
"""y_pred and y_true are both 2D vectors in a categorical format, with shape [3,7]
Both represents 3 mood scores, that ranges from 1 to 7, but
y_pred is a probability distribuition.
returns the sum of probabilities guessed correctly, normalized.
e.g. 3 is the correct score, and it predicts with 0.3 certainty, 0.3 is added.
then the sum is divided to normalize."""
correct = float(0)
total = 3
row = 0
"""Loops through all scores, adds together the chances predicted correctly.
e.g. Correct is "Good", and AI predicted it with 60% certainty,
0.6 is added to the "correct" variable.
Then, it is divided by the total probability (1 * 3)."""
for mood in y_true:
column = 0
for score in mood:
#Ugly code, but what it does is slice the tensor to get index [row][column]
#And then access the content
#Here is the bug. What is the correct size for this to return a single float?
target = tf.slice(y_pred,begin=[0,row,column],size=[0,0,1])
print("target is:")
tf.print(target)
if score == float(1) and score.size > 0:
correct = correct + score.eval()
row = row + 1
print("row is: %x" %row)
return correct / total
```

Thank you in advance. If there is any other information I need to post, tell me and I will add it as soon as possible.