Exercise

# Custom loss function

Up to now, we've used the mean squared error as a loss function. This works fine, but with stock price prediction it can be useful to implement a custom loss function. A custom loss function can help improve our model's performance in specific ways we choose. For example, we're going to create a custom loss function with a large penalty for predicting price movements in the wrong direction. This will help our net learn to at least predict price movements in the correct direction.

To do this, we need to write a function that takes arguments of `(y_true, y_predicted)`

. We'll also use functionality from the backend `keras`

(using `tensorflow`

) to find cases where the true value and prediction don't match signs, then penalize those cases.

Instructions

**100 XP**

- Set the arguments of the
`sign_penalty()`

function to be`y_true`

and`y_pred`

. - Multiply the squared error (
`tf.square(y_true - y_pred)`

) by`penalty`

when the signs of`y_true`

and`y_pred`

are different. - Return the average of the
`loss`

variable from the function -- this is the mean squared error (with our penalty for opposite signs of actual vs predictions).