Do we need more data?

It's time to check whether the digits dataset model you built benefits from more training examples!

In order to keep code to a minimum, various things are already initialized and ready to use:

The model you just built.
X_train,y_train,X_test, and y_test.
The initial_weights of your model, saved after using model.get_weights().
A pre-defined list of training sizes: training_sizes.
A pre-defined early stopping callback monitoring loss: early_stop.
Two empty lists to store the evaluation results: train_accs and test_accs.

Train your model on the different training sizes and evaluate the results on X_test. End by plotting the results with plot_results().

The full code for this exercise can be found on the slides!

Get a fraction of the training data determined by the size we are currently evaluating in the loop.
Set the model weights to the initial_weights with set_weights() and train your model on the fraction of training data using early_stop as a callback.
Evaluate and store the accuracy for the training fraction and the test set.
Call plot_results() passing in the training and test accuracies for each training size.