1.

Question 1

One of the ways of declaring a loss function is to import its object. Is the following code correct for using a loss object?

True

False

Answer: False

2.

Question 2

It is possible to add parameters to the object call when using the loss object.

True

False

Answer: True

3.

Question 3

You learned that you can do hyperparameter tuning within custom-built loss functions by creating a wrapper function around the loss function with hyperparameters defined as its parameter. What is the purpose of creating a wrapper function around the original loss function?

No particular reason, it just looks neater this way.

The loss ( model.compile(…, loss = ) ) expects a function that is only a wrapper function to the loss function itself.

That’s one way of doing it. We can also do the same by passing y_true, y_pred and threshold as parameters to the loss function itself.

The loss ( model.compile(…, loss = ) ) expects a function with two parameters, y_true and y_pred, so it is not possible to pass a 3rd parameter (threshold) to the loss function itself. This can be achieved by creating a wrapper function around the original loss function.

Answer: The loss ( model.compile(…, loss = ) ) expects a function with two parameters, y_true and y_pred, so it is not possible to pass a 3rd parameter (threshold) to the loss function itself. This can be achieved by creating a wrapper function around the original loss function.

4.

Question 4

One other way of implementing a custom loss function is by creating a class with two function definitions, init and call.

Which of the following is correct?

We pass y_true and y_pred to the init function, the hyperparameter (threshold) to the call function.

We pass the hyperparameter (threshold) , y_true and y_pred to the call function, and the init function returns the call function.

We pass the hyperparameter (threshold) to the init function, y_true and y_pred to the call function.

We pass the hyperparameter (threshold) , y_true and y_pred to the init function, and the call function returns the init function.

Answer:We pass the hyperparameter (threshold) to the init function, y_true and y_pred to the call function.

5.

Question 5

The formula for the contrastive loss, the function that is used in the siamese network for calculating image similarity, is defined as following:

Check all that are true:

If the euclidean distance between the pair of images is low then it means the images are similar.

Y is the tensor of details about image similarities.

Ds are 1 if images are similar, 0 if they are not.

Margin is a constant that we use to enforce a maximum distance between the two images in order to consider them similar or different from one another.

Answer:

• Y is the tensor of details about image similarities.
• If the euclidean distance between the pair of images is low then it means the images are similar.