Calculating slopes

You're now going to practice calculating slopes. When plotting the mean-squared error loss function against predictions, the slope is 2 * x * (xb-y), or 2 * input_data * error. Note that x and b may have multiple numbers (x is a vector for each data point, and b is a vector). In this case, the output will also be a vector, which is exactly what you want.

You're ready to write the code to calculate this slope while using a single data point. You'll use pre-defined weights called weights as well as data for a single point called input_data. The actual value of the target you want to predict is stored in target.

This exercise is part of the course

Introduction to Deep Learning in Python

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Exercise instructions

Calculate the predictions, preds, by multiplying weights by the input_data and computing their sum.
Calculate the error, which is preds minus target. Notice that this error corresponds to xb-y in the gradient expression.
Calculate the slope of the loss function with respect to the prediction. To do this, you need to take the product of input_data and error and multiply that by 2.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Calculate the predictions: preds
preds = ____

# Calculate the error: error
error = ____ - ____

# Calculate the slope: slope
slope = ____ * ____ * ____

# Print the slope
print(slope)

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