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.

This exercise is part of the course

Introduction to Deep Learning in Python

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Exercise instructions

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?

Hands-on interactive exercise

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

n_updates = 20
mse_hist = []

# Iterate over the number of updates
for i in range(n_updates):
    # Calculate the slope: slope
    slope = ____(____, ____, ____)
    
    # Update the weights: weights
    weights = ____ - ____ * ____
    
    # Calculate mse with new weights: mse
    mse = ____(____, ____, ____)
    
    # Append the mse to mse_hist
    ____

# Plot the mse history
plt.plot(mse_hist)
plt.xlabel('Iterations')
plt.ylabel('Mean Squared Error')
plt.show()

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