Making multiple updates to weights

You're now going to make multiple updates so you can dramatically improve your model weights, and see how the predictions improve with each update.

To keep your code clean, there is a pre-loaded get_slope() function that takes input_data, target, and weights as arguments. There is also a get_mse() function that takes the same arguments. The input_data, target, and weights have been pre-loaded.

This network does not have any hidden layers, and it goes directly from the input (with 3 nodes) to an output node. Note that weights is a single array.

We have also pre-loaded matplotlib.pyplot, and the error history will be plotted after you have done your gradient descent steps.

Using a for loop to iteratively update weights:
- Calculate the slope using the get_slope() function.
- Update the weights using a learning rate of 0.01.
- Calculate the mean squared error (mse) with the updated weights using the get_mse() function.
- Append mse to mse_hist.
Hit 'Submit Answer' to visualize mse_hist. What trend do you notice?

Exercise

Making multiple updates to weights

Instructions