Sometimes, a different choice of hyper$-$parameter can provide better results. In machine learning, we perform a line$-$search over a hyper$-$parameter by selecting a range of values for one particular hyper$-$parameter and retraining the model to see if better results are achievable. For this problem, pick five different values of your own for learning rate, and retrain the models using gradient descent from part a$)$ for $10$ epochs. Compare the training and testing error you achieve with these values by reporting the final values. You may wish to do additional plotting with seaborn and matplotlib to summarize your results.

$[1]$

$0$s# PUT YOUR CODE FOR SIMPLE LINE SEARCH HERE

# YOU ONLY NEED TO TEST FIVE VALUES OF LEARNING RATE