# GRADED FUNCTION: masked$\_$accuracy

def masked$\_$accuracy$($y$\_$true, y$\_$pred$)$:

$"""$

Calculate masked accuracy for predicted labels.

Parameters:

y$\_$true $($tensor$)$: True labels.

y$\_$pred $($tensor$)$: Predicted logits$.$

Returns:

accuracy $($tensor$)$: Masked accuracy.

$"""$

### START CODE HERE ###

# Calculate the loss for each item in the batch.

# You must always cast the tensors to the same type in order to use them in training. Since you will make divisions, it is safe to use tf$.$float$32$ data type.

y$\_$true $=$ tf$.$cast$($y$\_$true, tf$.$float$32)$

# Create the mask, i$.$e$.,$ the values that will be ignored

mask $=$ None

mask $=$ tf$.$cast$($mask$,$ tf$.$float$32)$

# Perform argmax to get the predicted values

y$\_$pred$\_$class $=$ None

y$\_$pred$\_$class $=$ tf$.$cast$($y$\_$pred$\_$class, tf$.$float$32)$

# Compare the true values with the predicted ones

matches$\_$true$\_$pred $=$ tf$.$equal$($None$,$ None$)$

matches$\_$true$\_$pred $=$ tf$.$cast$($matches$\_$true$\_$pred $,$ tf$.$float$32)$

# Multiply the acc tensor with the masks

matches$\_$true$\_$pred $*=$ None

# Compute masked accuracy $($quotient between the total matches and the total valid values, i$.$e$.,$ the amount of non$-$masked values$)$

masked$\_$acc $=$ None$/$None

### END CODE HERE ###

return masked$\_$acc