# GRADED FUNCTION: classify

def classify$($test$\_$Q$1,$ test$\_$Q$2,$ y$\_$test, threshold, model, batch$\_$size$=64,$ verbose$=$True$)$:

$"""$Function to test the accuracy of the model.

Args:

test$\_$Q$1($numpy$.$ndarray$)$: Array of Q$1$ questions. Each element of the array would be a string.

test$\_$Q$2($numpy$.$ndarray$)$: Array of Q$2$ questions. Each element of the array would be a string.

y$\_$test $($numpy$.$ndarray$)$: Array of actual target.

threshold $($float$)$: Desired threshold

model $($tensorflow$.$Keras.Model$)$: The Siamese model.

batch$\_$size $($int$,$ optional$)$: Size of the batches. Defaults to $64.$

Returns:

float: Accuracy of the model

numpy.array: confusion matrix

$"""$

y$\_$pred $=[]$

test$\_$gen $=$ tf$.$data.Dataset.from$\_$tensor$\_$slices$((($test$\_$Q$1,$ test$\_$Q$2),$None$)).$batch$($batch$\_$size$=$batch$\_$size$)$

### START CODE HERE ###

pred $=$ model.predict$($test$\_$gen$)$

$\_,$ n$\_$feat $=$ pred.shape

v$1=$ None

v$2=$ None

# Compute the cosine similarity. Using $`$tf$.$math.reduce$\_$sum$`.$

# Don't forget to use the appropriate axis argument.

d $=$ None

# Check if d$>$threshold to make predictions

y$\_$pred $=$ tf$.$cast$(($d $>$ threshold$),$ tf$.$float$64)$

# take the average of correct predictions to get the accuracy

accuracy $=$ tf$.$math.reduce$\_$mean$($y$\_$pred$)$

# compute the confusion matrix using $`$tf$.$math.confusion$\_$matrix$`$

cm $=$ tf$.$math.confusion$\_$matrix$($accuracy$)$

### END CODE HERE ###

return accuracy, cm