C3W3_Assignment Exercise 4 Invalid Confusion Matrix

[Mahnoor_Malik]

I am getting an invalid confusion matrix in Exercise 4. I am not sure where I am going wrong

[Deepti_Prasad]

can you post screenshot of your invalid matrix value without posting any codes that you wrote.

[Mahnoor_Malik]

[Deepti_Prasad]

you seem to have edited the codes. don’t add your own codes.

You were suppose to only write codes between ###START AND END CODE HERE### for the instructions already given.

you initiated the below part of code

accuracy = 0

for j in range(batch_size):
    d = math.reduce_sum(v1[j]*v2[j])
    res = d > threshold 
    accuracy += tf.cast(y_test[j] == res, tf.float64)
    if(y_test[j] == res):
        y_pred.append(1)
    else:
        y_pred.append(0)
        
    
accuracy = accuracy / batch_size

then at the end

@Mahnoor_Malik

hello = y_test[:batch_size]

This is not the way codes works. Get a fresh copy, then implement the steps as per each instructions given.

The below thread should help you resolve your issue

NLP Course 3 Week 3: Confusion matrix

Hi @Taisiya_Kopytova You somehow mixed up two code lines Check if d>threshold to make predictions Here you were only suppose to check if cosine similarity is greater than threshold but you ended up adding the accuracy part of the code to this code line. Also do not hard code the path by introducing res = d > threshold (I KNOW THE HINT FROM PREVIOUS CELLS TELLS YOU TO DO SO. INSTEAD OF y_test == res, you only need to use tf.cast(d>threshold with the datatype). Next your accuracy code is…

Regards
DP

[Deepti_Prasad]

@Mahnoor_Malik

the correction you did is still not correct for cosine similarity and accuracy codes

for accuracy codes correction refer the previous comment link in the same thread.

For cosine similarity codes correction, refer the below thread comment

C3W3 Triplet Loss value is wrong

Hello @skinx.learning you have incorrect codes for use tf.linalg.matmul to take the dot product of the two batches. Don’t forget to transpose the second argument using transpose_b=True YOU HAVE USED MARGIN, NOT REQUIRED. ALSO THE TWO BATCHES, need to be mentioned as v2, v1 and not v1, v2 2.subtract the diagonal from scores. You can do this by creating a diagonal matrix with the values of all positive examples using tf.linalg.diag FOR THIS CODE LINE YOU ARE SUPPOSE TO SUBSTRACT tf.linglg.…