**Registrations Closing Soon** for **DevOps Certification Training** by CloudxLab | Registrations Closing in

- Home
- Assessment

Let's implement huber loss. Huber loss is less sensitive to outliers in data than mean squared error.

Below is the formula of huber loss.

**Note:**

Huber loss is defined as:

*error*, if^{2}/2*error < delta*(ie, if it is a small error)*delta * ( |error| - delta/2)*, otherwise (*|error|*means the absolute value*error*)

In this exercise, we consider

*delta=1*.Thus, the

`huber_fn`

is defined as:*error*, if^{2}/2*error < 1*(ie, if it is a small error).*|error| - 0.5*, otherwise

`tf.abs(x)`

returns the positive value(absolute value) of`x`

.`tf.square(x)`

returns the squared value of`x`

.`tf.where(bool_array, x, y)`

returns the elements where condition is True in`bool_array`

(multiplexing`x`

and`y`

).In simpler terms,

`tf.where`

will choose an output shape from the shapes of condition,`x`

, and`y`

that all three shapes are broadcastable to.The condition tensor acts as a mask that chooses whether the corresponding element/row in the output should be taken from x (if the element in the condition is

`True`

) or from`y`

(if it is`False`

).For example, upon executing the following,

`tf.where([True, False, False, True], [1,2,3,4], [100,200,300,400])`

the output would be :

`<tf.Tensor: shape=(4,), dtype=int32, numpy=array([ 1, 200, 300, 4], dtype=int32)>`

Define the

`huber_fn`

, the Huber Loss function, and pass the`y_true, y_pred`

as input arguments to the function. We do this as follows:Calculate

`error`

which is`y_true - y_pred`

If

`tf.abs(error) < 1`

, then`is_small_error`

is`True`

. Else,`is_small_error`

is`False`

.Define

`squared_loss`

as`tf.square(error) / 2`

.Define

`linear_loss`

as`tf.abs(error) - 0.5`

.Use

`tf.where`

and pass`is_small_error, squared_loss, linear_loss`

as input arguments to it, to choose either the`squared_loss`

value or the`linear_loss`

value based on if the`is_small_error`

condition is`True`

or`False`

.Thus, return the huber loss for each prediction.

So use the following code to do the same:

`def huber_fn(y_true, y_pred): error = y_true - y_pred is_small_error = tf.abs(error) < 1 squared_loss = tf.square(error) / 2 linear_loss = tf.abs(error) - 0.5 return tf.where(is_small_error, squared_loss, linear_loss)`

XP

Checking Please wait.

Success

Error

No hints are availble for this assesment

Answer is not availble for this assesment

**Note - **Having trouble with the assessment engine? Follow the steps listed
here

## Loading comments...